diff --git "a/1751.jsonl" "b/1751.jsonl"
new file mode 100644--- /dev/null
+++ "b/1751.jsonl"
@@ -0,0 +1,716 @@
+{"seq_id":"391292686","text":"# -*- coding:UTF-8 -*-\n\n'''\nCreated on 2013-9-7\n\n@author: cfh\n'''\nimport win32api\nimport win32file\nimport win32pipe\nimport winerror\nimport pywintypes\nimport time\nimport creditutils.base_util as base\n\nimport inspect\n\ndef func_flag():\n    pass\n\nglobal __modpath__\n__modpath__ = base.module_path(func_flag)\n\n\ndef get_file_line():\n    return (__modpath__, inspect.currentframe().f_back.f_lineno)\n\n# 命名管道读取实现    \nclass ReadClient(object):\n    MS_TO_SEC = 0.001\n    WAIT_TIMEOUT = 2000\n    BUFFER_SIZE = 16384\n    PIPE_PATH_FORMAT = '\\\\\\\\{0}\\\\pipe\\\\{1}'\n    PIPE_NAME = 'USB_DEVICE_MONITOR_WRITE_SERVER_201309122124'\n    PIPE_PATH = PIPE_PATH_FORMAT.format('.', PIPE_NAME)\n    MSG_HEAD_FLAG = '!PIPE_MSG_HEAD!'\n    \n    def __init__(self, pipePath=PIPE_PATH):\n        self.pipePath = pipePath\n        self.hPipe = None\n        self.receiver = None\n        \n    def Connect(self):\n        isFirstConnect = True\n        while True:\n            try:\n                self.hPipe = win32file.CreateFile(self.pipePath, \n                                             win32file.GENERIC_READ, \n                                             0, \n                                             None, \n                                             win32file.OPEN_EXISTING,\n                                             0,\n                                             None)\n                \n                infoFormat = 'The named pipe {0} is connected.'\n                info = infoFormat.format(self.pipePath)\n                win32api.OutputDebugString(info)\n                \n                return self.hPipe\n            \n            except pywintypes.error as e:\n                infoFormat = 'Unable to open named pipe {0}, w/err: {1}'\n                info = infoFormat.format(self.pipePath, e.args[2]);\n                win32api.OutputDebugString(info)\n                \n                if isFirstConnect:\n                    isFirstConnect = False\n                \n                    # 如果提示没有找到文件，则等待一定时间之后再尝试打开\n                    if e.args[0] == winerror.ERROR_FILE_NOT_FOUND:\n                        time.sleep(ReadClient.WAIT_TIMEOUT*ReadClient.MS_TO_SEC)\n                        continue\n    \n                    elif e.args[0] == winerror.ERROR_PIPE_BUSY:\n                        # All pipes instances are busy, so wait for some seconds\n                        try:\n                            win32pipe.WaitNamedPipe(self.pipePath, ReadClient.WAIT_TIMEOUT)\n                            continue\n                        except pywintypes.error as waitError:\n#                             if waitError.args[0] == winerror.ERROR_SEM_TIMEOUT:\n                            infoFormat = '{0} {1} failed, w/err: {2}'\n                            info = infoFormat.format(waitError.args[1], self.pipePath, waitError.args[2])\n                            win32api.OutputDebugString(info)\n                            \n                            return None\n                                \n                    else:   # Exit if an error other than ERROR_FILE_NOT_FOUND or ERROR_PIPE_BUSY occurs\n                        return None\n                else:\n                    return None\n                \n    def Receive(self):\n        # Receive data from server\n        \n        bufObject = win32file.AllocateReadBuffer(ReadClient.BUFFER_SIZE)\n        \n        while True:\n            try:\n                (readRtn, data) = win32file.ReadFile(self.hPipe, bufObject)\n                if readRtn != 0 and readRtn != winerror.ERROR_MORE_DATA:\n                    showFormat = 'ReadFile failed, w/err: {0} {1}: {2}'\n                    showInfo = showFormat.format(win32api.FormatMessage(win32api.GetLastError()), *get_file_line())\n                    win32api.OutputDebugString(showInfo)\n                    break\n                \n                data_list = data.split(ReadClient.MSG_HEAD_FLAG)\n                if self.receiver:\n                    for item in data_list:\n                        if len(item) > 0:\n                            self.receiver(item)\n                \n            except pywintypes.error as e:\n                showFormat = '{0} failed, w/err: {1} {2}: {3}'\n                showInfo = showFormat.format(e.args[1], e.args[2], *get_file_line())\n                win32api.OutputDebugString(showInfo)\n                break\n            \n    def SetReceiver(self, receiver):\n        self.receiver = receiver\n    \n    def Close(self):\n        if self.hPipe:\n            self.hPipe.Close()\n            self.hPipe = None\n\nclass Consumer:\n    TYPE_FLAG = 'CHANGE_TYPE'       # 设备状态变化标识\n    TYPE_ARRIVAL_FLAG = 'ARRIVAL'       # 设备插入\n    TYPE_REMOVE_FLAG = 'REMOVE'     # 设备拔出\n    TYPE_HEARTBEAT_FLAG = 'HEARTBEAT'       # 心跳\n    PIPE_MSG_COLON_ALIAS = '&!colon!&'      # \":\"替换符\n    PIPE_MSG_COMMA_ALIAS = '&!comma!&'      # \",\"替换符\n    \n    VID_FLAG = 'VID'\n    PID_FLAG = 'PID'\n    SN_FLAG = 'SN'\n    NAME_FLAG = 'FRIENDLYNAME'\n    MFG_FLAG = 'MFG'        # 厂商信息\n    DESC_FLAG = 'DEVICEDESC'\n    \n    \n    TYPE_ARRIVAL = 1\n    TYPE_REMOVE = 2\n    TYPE_HEARTBEAT = 3\n    \n    TYPE_MAP = {\n        TYPE_ARRIVAL_FLAG: TYPE_ARRIVAL,\n        TYPE_REMOVE_FLAG: TYPE_REMOVE,\n        TYPE_HEARTBEAT_FLAG: TYPE_HEARTBEAT\n    }\n    \n    HEARTBEAT_SHOW_INTERVAL = 60\n    \n    def __init__(self):\n        self.heartbeatCnt = 0\n        self.receiver = None\n    \n    def _replace_seperator_alias_in_data(self, data):\n        return data.replace(Consumer.PIPE_MSG_COLON_ALIAS, ':').replace(Consumer.PIPE_MSG_COMMA_ALIAS, ',')\n    \n    def Process(self, item):\n        infoDict = {}\n        \n        infos = item.split(',')\n        for subItem in infos:\n            if len(subItem) > 0:\n                infoItems = subItem.split(':')\n                key = self._replace_seperator_alias_in_data(infoItems[0].strip())\n                val = self._replace_seperator_alias_in_data(infoItems[1].strip())\n                infoDict[key] = val\n        \n        self._ShowDebugInfo(infoDict)\n        \n        try:\n            msgType = Consumer.TYPE_MAP[infoDict[Consumer.TYPE_FLAG]]\n            if msgType != Consumer.TYPE_HEARTBEAT:\n                if self.receiver:\n                    self.receiver(infoDict)\n                    \n        except KeyError as e:\n            win32api.OutputDebugString(str(e))\n            return\n        \n    def _ShowDebugInfo(self, item):\n        try:\n            msgType = Consumer.TYPE_MAP[item[Consumer.TYPE_FLAG]]\n        except KeyError as e:\n            win32api.OutputDebugString(str(e))\n            return\n        \n        showFormat = 'Receives Message: \"{0}\"'\n        if msgType == Consumer.TYPE_HEARTBEAT:\n            self.heartbeatCnt += 1\n            if self.heartbeatCnt % Consumer.HEARTBEAT_SHOW_INTERVAL == 0:\n                showInfo = showFormat.format(item)\n            else:\n                showInfo = None\n        else:\n            showInfo = showFormat.format(item)\n\n        if showInfo:            \n            win32api.OutputDebugString(showInfo)\n    \n    def SetReceiver(self, receiver):\n        self.receiver = receiver\n\nBUFFER_SIZE = 65536\ndef CreateServer():\n    p = win32pipe.CreateNamedPipe('\\\\\\\\.\\\\pipe\\\\test_pipe',\n        win32pipe.PIPE_ACCESS_DUPLEX,\n        win32pipe.PIPE_TYPE_MESSAGE | win32pipe.PIPE_WAIT,\n        1, 65536, 65536,300, None)\n    \n    bufObject = win32file.AllocateReadBuffer(BUFFER_SIZE)\n    data = 'Hello Pipe' \n    for i in range(len(data)):\n        bufObject[i] = ord(data[i])\n        \n    win32pipe.ConnectNamedPipe(p, None)\n    \n    \n    win32file.WriteFile(p, bufObject[0:len(data)])\n    \ndef CreateClient():\n    fileHandle = win32file.CreateFile('\\\\\\\\.\\\\pipe\\\\test_pipe',\n                              win32file.GENERIC_READ | win32file.GENERIC_WRITE,\n                              0, None,\n                              win32file.OPEN_EXISTING,\n                              0, None)\n    data = win32file.ReadFile(fileHandle, 4096)\n    win32file.CloseHandle(fileHandle)\n    print(data)","sub_path":"src/creditutils/pipe_util.py","file_name":"pipe_util.py","file_ext":"py","file_size_in_byte":8059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"143009674","text":"n1 = int(input('Insira o 1ª número: '))\r\nn2 = int(input('Insira o 2ª número: '))\r\nprint('-' * 36)\r\nprint('Qual opção você deseja?')\r\nprint('-' * 36)\r\nopcao = 0\r\nwhile opcao != 5:\r\n    print('[1]\\33[35msomar\\33[m\\n[2]\\33[35mmultiplicar\\33[m\\n[3]\\33[35mmaior\\33[m'\r\n              '\\n[4]\\33[35mnovos números\\33[m\\n[5]\\33[35msair do programa\\33[m')\r\n    opcao = int(input('Digite a opção desejada: '))\r\n    if opcao == 1:\r\n        print('A soma de {} e {} é igual a {}'.format(n1, n2, (n1+n2)))\r\n    elif opcao == 2:\r\n        print('A multiplicação de {} e {} é igual a {}.'.format(n1, n2, (n1 * n2)))\r\n    elif opcao == 3:\r\n        if n1 > n2:\r\n            print('O maior número é {}.'.format(n1))\r\n        else:\r\n            print('O maior número é {}.'.format(n2))\r\n    elif opcao == 4:\r\n        n1 = int(input('Insira um novo número: '))\r\n        n2 = int(input('Insira um novo número: '))\r\n    else:\r\n        print('\\nVoce saiu do programa! Até logo!!')\r\n","sub_path":"Ex0059/ex0059.py","file_name":"ex0059.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"300164197","text":"def binarySearch (arr, l, r, x):\n    if r >= l:\n        mid = l + (r - l) // 2\n        if arr[mid] == x:\n            return mid\n        elif arr[mid] > x:\n            return binarySearch(arr, l, mid-1, x)\n        else:\n            return binarySearch(arr, mid + 1, r, x)\n    else:\n        return -1\n\ndef partition(arr,low,high):\n    i = (low-1)\n    pivot = arr[high]\n  \n    for j in range(low , high):\n        if   arr[j] < pivot:\n            i = i+1\n            arr[i],arr[j] = arr[j],arr[i]\n  \n    arr[i+1],arr[high] = arr[high],arr[i+1]\n    return (i+1)\n    \ndef quickSort(arr,low,high):\n    if low < high:\n        pi = partition(arr,low,high)\n        quickSort(arr, low, pi-1)\n        quickSort(arr, pi+1, high)\n\nn,q = list(map(int,input().split()))\narr = input().split()\nquickSort(arr,0,n-1)\nprint(' '.join(arr))\n\nfor i in range(q):\n\tx = input()\n\tres = binarySearch(arr, 0, n-1, x)\n\tprint(res)","sub_path":"ene-jun-2020/juan flores/examen2.py","file_name":"examen2.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"69385652","text":"\"\"\"\nThis module contains the class for Simple Genetic Algorithm strategy\n\"\"\"\n\nimport os\nimport pickle\nimport numpy as np\nimport random\nfrom deap import base, creator, tools\nfrom dowel import logger\n\nfrom varro.algo.strategies.strategy import Strategy\n\n\nclass StrategySGA(Strategy):\n    def __init__(self, **kwargs):\n        if \"name\" not in kwargs.keys():\n            kwargs[\"name\"] = \"sga\"\n        super().__init__(**kwargs)\n\n    @staticmethod\n    def init_fitness_and_inds():\n        logger.start_timer()\n        \"\"\"Initializes the fitness and definition of individuals\"\"\"\n\n        class Fitness(base.Fitness):\n            def __init__(self):\n                super().__init__()\n                self.__fitness_score = None\n\n            @property\n            def fitness_score(self):\n                return self.values[0]\n\n            @fitness_score.setter\n            def fitness_score(self, fitness_score):\n                self.__fitness_score = fitness_score\n                if fitness_score:\n                    # WARNING:\n                    # Setting values breaks alot of things:\n                    # self.__fitness_score is reset to None\n                    # after setting values, so you should only\n                    # set values after all the scores you require are set\n                    self.values = (fitness_score,)\n\n            @fitness_score.deleter\n            def fitness_score(self):\n                if hasattr(self, '__fitness_score'):\n                    del self.__fitness_score\n\n            def delValues(self):\n                super().delValues()\n                if hasattr(self, '__fitness_score'):\n                    del self.__fitness_score\n\n        creator.create(\"FitnessMin\", Fitness, weights=(-1.0,)) # Just Fitness\n        creator.create(\"Individual\", np.ndarray, fitness=creator.FitnessMin)\n\n        logger.stop_timer('SGA.PY Initializing fitness and individuals')\n        logger.start_timer()\n\n\n    def init_toolbox(self):\n        \"\"\"Initializes the toolbox according to strategy\"\"\"\n        # Define specific Fitness and Individual for SGA\n        self.init_fitness_and_inds()\n\n        # Configure the rest of the toolbox that is independent\n        # of which evolutionary strategy\n        super().config_toolbox()\n\n\n\n    def load_es_vars(self):\n        \"\"\"Loads the evolutionary strategy variables from checkpoint given after\n        creating the fitness and individual templates for DEAP evolution or initializes them\n        \"\"\"\n\n        logger.start_timer()\n\n        if self.ckpt:\n            # A file name has been given, then load the data from the file\n            # Load data from pickle file\n            with open(self.ckpt, \"rb\") as cp_file:\n                cp = pickle.load(cp_file)\n\n            self.rndstate = random.seed(cp[\"rndstate\"])\n            self.pop = cp[\"pop\"]\n            self.curr_gen = int(cp[\"curr_gen\"])\n            self.halloffame = cp[\"halloffame\"]\n            self.logbook = cp[\"logbook\"]\n\n        else:\n            # Start a new evolution\n            self.rndstate = random.seed(100) # Set seed\n            self.pop = self.toolbox.population(n=self.popsize)\n            self.curr_gen = 0\n            self.halloffame = tools.HallOfFame(maxsize=int(self.halloffamesize*self.popsize), similar=np.array_equal)\n            self.logbook = tools.Logbook()\n\n        self.paretofront = None\n        logger.stop_timer('SGA.PY Loading ES Vars')\n\n\n    def save_ckpt(self):\n        \"\"\"Saves information necessary to resume algorithm after stopping\"\"\"\n\n        logger.start_timer()\n\n        # Fill the dictionary using the dict(key=value[, ...]) constructor\n        cp = dict(pop=self.pop,\n                  strategy=self.name,\n                  curr_gen=self.curr_gen,\n                  halloffame=self.halloffame,\n                  paretofront=self.paretofront,\n                  logbook=self.logbook,\n                  rndstate=self.rndstate)\n\n        with open(os.path.join(self.ckpt_dir, '{0:09d}.pkl'.format(self.curr_gen)), \"wb\") as cp_file:\n            pickle.dump(cp, cp_file)\n\n        logger.stop_timer('SGA.PY Saving checkpoint')\n\n\n    def compute_fitness(self, pop):\n        \"\"\"Calculates the fitness scores for the entire Population\n\n        Args:\n            pop (list): An iterable of Individual(np.ndarrays) that represent the individuals\n\n        Returns:\n            Number of individuals with invalid fitness scores we updated\n        \"\"\"\n\n        logger.start_timer()\n\n        # Evaluate the individuals with an invalid fitness or if we are at the start\n        # of the evolutionary algo, AKA curr_gen == 0\n        # (These are the individuals that have not been evaluated before -\n        # individuals at the start of the evolutionary algorithm - or those\n        # that have been mutated / the offspring after crossover with fitness deleted)\n        invalid_inds = [ind for ind in pop if not ind.fitness.valid or self.curr_gen == 0]\n\n        # Get fitness score for each individual with\n        # invalid fitness score in population\n        for ind in invalid_inds:\n\n            # Load Weights into model using individual\n            self.model.load_parameters(ind)\n\n            # Calculate the Fitness score of the individual\n            ind.fitness.fitness_score = self.fitness_score()\n\n        logger.stop_timer('SGA.PY Computing fitness')\n\n        return len(invalid_inds)\n\n\n    def evaluate(self, pop):\n\n        logger.start_timer()\n\n        \"\"\"Evaluates an entire population on a dataset on the neural net / fpga\n        architecture specified by the model, and calculates the fitness scores for\n        each individual, sorting the entire population by fitness scores in-place\n\n        Args:\n            pop (list): An iterable of np.ndarrays that represent the individuals\n\n        Returns:\n            Average fitness score of population\n\n        \"\"\"\n        # Re-generates the training set for the problem (if possible) to prevent overfitting\n        self.problem.reset_train_set()\n\n        logger.stop_timer('SGA.PY Regenerate the training set')\n        logger.start_timer()\n\n        # Compute all fitness for population\n        num_invalid_inds = self.compute_fitness(pop)\n        logger.start_timer()\n        logger.stop_timer('SGA.PY Computing all fitness for population')\n        logger.start_timer()\n\n        # The population is entirely replaced by the\n        # evaluated offspring\n        self.pop[:] = pop\n        logger.stop_timer('SGA.PY Replace population with evaluated offspring')\n        logger.start_timer()\n\n        # Update population statistics\n        self.halloffame.update(self.pop)\n        # record = self.stats.compile(self.pop)\n        # self.logbook.record(gen=self.curr_gen, evals=num_invalid_inds, **record)\n        logger.stop_timer('SGA.PY Updating population statistics')\n\n        return np.mean([ind.fitness.fitness_score for ind in pop])\n\n\n    def generate_offspring(self):\n\n        \"\"\"Generates new offspring using a combination of the selection methods\n        specified to choose fittest individuals and custom preference\n\n        Returns:\n            A Tuple of (Non-alterable offspring, Alterable offspring)\n\n        \"\"\"\n        # Keep the elite individuals for next generation\n        # without mutation or cross over\n        elite_num = int(self.elitesize*self.popsize)\n        elite = self.toolbox.select_elite(self.pop, k=elite_num)\n\n        # Clone the selected individuals\n        non_alterable_elite_offspring = list(map(self.toolbox.clone, elite))\n\n        # Choose the rest of the individuals\n        # to be altered\n        random_inds = self.toolbox.select(self.pop, k=self.popsize-2*elite_num)\n        alterable_offspring = list(map(self.toolbox.clone, random_inds)) + list(map(self.toolbox.clone, elite))\n\n        return non_alterable_elite_offspring, alterable_offspring\n","sub_path":"varro/algo/strategies/sga.py","file_name":"sga.py","file_ext":"py","file_size_in_byte":7817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"647706673","text":"from ask_sdk_core.handler_input import HandlerInput\n\nBASE_URL = \"<audio src='https://animal-sound-clips.s3.amazonaws.com/\"\n\nANIMAL_SOUNDS = {\n    \"bat\": \"bat_01.mp3\",\n    \"dog\": \"dog_01.mp3\",\n    \"hedgehog\": \"hedgehog_01.mp3\",\n    \"whale\": \"humpback-whale_01.mp3\",\n    \"leopard\": \"leopard_01.mp3\",\n    \"lion\": \"lion_01.mp3\",\n    \"owl\": \"owl_01.mp3\",\n    \"pig\": \"pig_01.mp3\",\n    \"raccoon\": \"raccoon_01.mp3\",\n    \"robin\": \"robin_01.mp3\",\n    \"turkey\": \"turkey_01.mp3\",\n}\n\nEND_URL = \"'/>\"\n\n\nclass AnimalSound:\n    def __init__(self, handler_input: HandlerInput):\n        slots = handler_input.request_envelope.request.intent.slots\n        animal_slot = slots[\"animal\"]\n        animal = animal_slot.value\n        self.sound = f\"{BASE_URL}{ANIMAL_SOUNDS[animal]}{END_URL}\"\n","sub_path":"lambda/py/animal_sounds.py","file_name":"animal_sounds.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"66557586","text":"#!/usr/bin/env python\n\nfrom __future__ import unicode_literals\n\nimport gzip\nimport logging\nimport os\nimport sys\nimport time\n\nfrom os.path import abspath, splitext, join as pjoin\n\nfrom six import BytesIO\n\nimport boto\n\nLOG = logging.getLogger(__name__)\n\nGZIP_CONTENT_TYPES = {\n    '.html': 'text/html',\n    '.css': 'text/css',\n    '.js': 'application/javascript',\n}\n\n\ndef main(html_directory):\n    logging.basicConfig(level=logging.INFO)\n\n    upload_to_s3(html_directory)\n\n\ndef usage():\n    sys.stderr.write('Usage: {} <local directory>\\n'.format(sys.argv[0]))\n    return 2\n\n\ndef upload_to_s3(local_directory):\n    if not os.path.isdir(local_directory):\n        raise RuntimeError('Not a directory: {}'.format(local_directory))\n\n    s3_bucket = get_bucket()\n\n    s3_subdir = get_s3_subdirectory()\n\n    for filename, key_name in list_files_and_keys(local_directory, s3_subdir):\n        logging.info('Uploading {} => {}'.format(filename, key_name))\n        upload_file(s3_bucket, filename, key_name)\n        time.sleep(2)\n    return\n\n\ndef get_s3_subdirectory():\n    subdir = os.environ.get('AWS_BUCKET_SUBDIRECTORY', '')\n    assert not subdir.startswith('/')\n    assert not subdir.endswith('/')\n    return subdir\n\n\ndef list_files_and_keys(directory, s3_subdirectory):\n    full_directory = abspath(directory)\n\n    for dir_name, dirnames, filenames in os.walk(directory):\n        for filename in filenames:\n            full_fn = abspath(pjoin(dir_name, filename))\n            key_name = '{}/{}'.format(s3_subdirectory,\n                                      full_fn[len(full_directory) + 1:])\n            yield (full_fn, key_name)\n\n\ndef get_bucket():\n    AWS_ACCESS_KEY_ID = os.environ['AWS_ACCESS_KEY_ID']\n    AWS_SECRET_ACCESS_KEY = os.environ['AWS_SECRET_ACCESS_KEY']\n    logging.debug('Access key ID: `{}`'.format(AWS_ACCESS_KEY_ID))\n\n    conn = boto.connect_s3(AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)\n\n    AWS_BUCKET_NAME = os.environ['AWS_BUCKET_NAME']\n    logging.debug('Bucket name: `{}`'.format(AWS_BUCKET_NAME))\n\n    logging.info('Connected: {}'.format(conn))\n    bucket = conn.get_bucket(AWS_BUCKET_NAME)\n    bucket_location = bucket.get_location()\n\n    # Workaround, see\n    # https://github.com/boto/boto/issues/2207#issuecomment-60682869\n\n    if bucket_location:\n        conn = boto.s3.connect_to_region(bucket_location)\n        bucket = conn.get_bucket(AWS_BUCKET_NAME)\n\n    return bucket\n\n\ndef upload_file(bucket, filename, key_name):\n    key = bucket.new_key(key_name)\n    extension = splitext(filename)[1]\n\n    if extension in GZIP_CONTENT_TYPES:\n        upload_gzipped(key, filename, key_name, GZIP_CONTENT_TYPES[extension])\n    else:\n        upload_unmodified(key, filename, key_name)\n\n\ndef upload_unmodified(key, filename, key_name):\n    key.set_contents_from_filename(\n        filename,\n        replace=True,\n        policy='public-read')\n\n\ndef upload_gzipped(key, filename, key_name, content_type):\n    key.content_type = content_type\n\n    with open(filename, 'rb') as f:\n        logging.info('Compressing {} file {}'.format(content_type, key_name))\n        gzipped_content = gzip_compress(f.read())\n        logging.info(type(gzipped_content))\n\n    key.set_contents_from_string(\n        gzipped_content,\n        headers={'Content-Encoding': 'gzip'},\n        replace=True,\n        policy='public-read')\n\n\ndef gzip_compress(string):\n    out = BytesIO()\n    with gzip.GzipFile(fileobj=out, mode='w') as f:\n        f.write(string)\n    return out.getvalue()\n\n\nif __name__ == '__main__':\n    if len(sys.argv) < 2:\n        sys.exit(usage())\n    sys.exit(main(sys.argv[1]))\n","sub_path":"generate_dashboard/upload_to_s3.py","file_name":"upload_to_s3.py","file_ext":"py","file_size_in_byte":3584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"426491278","text":"from __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\nimport tensorflow as tf\r\nimport math\r\n\r\nfrom models.model import Net\r\nfrom constants import *\r\n\r\n\r\n\r\nclass Model(Net):\r\n    def __init__(self,model_param, model_def):\r\n        \"\"\"\r\n        common params: a params dict\r\n        model_params   : a params dict\r\n        \"\"\"\r\n\r\n        self.name = model_param['model_type']\r\n\r\n        # Define all the model variables....\r\n        self.batch_norm = model_def['batch_norm']\r\n        self.drop_out =  model_def['drop_out']\r\n        self.reg_type = model_def['reg_type']\r\n        self.reg_strength = model_def['reg_strength']\r\n        self.layers = model_def['layers']\r\n        \r\n        self.drop_out_rate = model_def['drop_out_rate']\r\n        self.residual = model_def['residual']\r\n        \r\n\r\n\r\n    def build(self, features, categories):\r\n        # build graph!\r\n        self.inference(features,categories)\r\n        self.loss()\r\n        print('build model complete!')\r\n        \r\n\r\n    def inference(self, features,categories):\r\n        \r\n        # Define our input data placeholders\r\n        # we have both scalar quantities and categorical data \r\n        # we turn the categorical data into one-hot representation\r\n        # and concatenate all the features together.\r\n        self.x = tf.placeholder(tf.float32,[None,features])\r\n\r\n        \r\n        \r\n        \r\n        \r\n        # create a categorical input and transform into one hot representation.\r\n        self.y = tf.placeholder(tf.int64,[None])\r\n        self.y_onehot = tf.one_hot(self.y,categories)\r\n        \r\n \r\n        # grab batch size  -> this is not needed in this model.\r\n        # but is often useful to have on hand.\r\n        self.batch_size = tf.shape(self.x)[0]\r\n\r\n\r\n        \r\n        # this propogates whether the model is training or being\r\n        # used for inference for methods like dropout and batchnorm.\r\n        self.is_training = tf.placeholder(tf.bool, name='is_training')\r\n\r\n\r\n        # define the fc model....\r\n        h = self.x\r\n        for i in range(len(self.layers) - 1):\r\n            var_scope = ('hidden%d' % i)\r\n            with tf.variable_scope(var_scope):\r\n                h = self.linear(\r\n                    h,\r\n                    output_dim=self.layers[i],\r\n                    is_training=self.is_training,\r\n                    reg_def = self.reg_type,\r\n                    do_batch_norm=self.batch_norm,\r\n                    init_deviation = 1.0 / math.sqrt(float(h.shape[1].value)),\r\n                    reg=self.reg_strength,\r\n                    do_drop_out = self.drop_out,\r\n                    drop_out_rate = self.drop_out_rate,\r\n                    residual = self.residual\r\n                )\r\n  \r\n        # now compute scores from the last hidden layer.\r\n        self.scores = self.linear(\r\n            h,\r\n            output_dim=categories,\r\n            is_training=self.is_training,\r\n            init_deviation = 1.0 / math.sqrt(float(categories))\r\n        )\r\n\r\n\r\n\r\n\r\n\r\n    def loss(self):\r\n\r\n    \r\n      # we will use soft-max loss\r\n      self.scores = tf.nn.softmax(self.scores)\r\n\r\n    \r\n      # get the cross entropy loss\r\n      L_class = -tf.reduce_mean(self.y_onehot * tf.log(self.scores + EPS),1)\r\n      L_class = tf.reduce_mean(L_class)\r\n      \r\n      # get predictions\r\n      self.correct_prediction = tf.equal(tf.argmax(self.scores, 1), self.y)\r\n      # %% And now we can look at the mean of our network's correct guesses\r\n      self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, \"float\"))\r\n    \r\n      \r\n      # grab our regularization loss\r\n      L_reg = sum( tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))\r\n\r\n      self.loss = L_class + L_reg\r\n\r\n\r\n      # collect logging variables\r\n      tf.summary.scalar(\"Reg_Loss\", L_reg)\r\n      tf.summary.scalar(\"Class_Loss\", L_class)\r\n      tf.summary.scalar(\"Loss\", self.loss)\r\n      tf.summary.scalar(\"accuracy\", self.accuracy)\r\n\r\n\r\n\r\n","sub_path":"src/models/fc.py","file_name":"fc.py","file_ext":"py","file_size_in_byte":3980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"70027485","text":"import glob\nimport numpy as np\nfrom netCDF4 import Dataset\nimport os\nimport pickle\nimport shutil\nfrom scipy.interpolate import griddata\nfrom datetime import datetime,timedelta\n\n#models = ['ACCESS-CM2','ACCESS-ESM1-5','AWI-ESM-1-1-LR','BCC-CSM2-MR','BCC-ESM1','CAMS-CSM1-0','CanESM5','CESM2','CESM2-WACCM','CESM2-WACCM-FV2','CNRM-CM6-1','CNRM-CM6-1-HR','CNRM-ESM2-1','E3SM-1-0','EC-Earth3','EC-Earth3-Veg','FGOALS-g3','FIO-ESM-2-0','GFDL-CM4','GFDL-ESM4','GISS-E2-1-G','GISS-E2-1-G-CC','GISS-E2-1-H','HadGEM3-GC31-LL','HadGEM3-GC31-MM','INM-CM4-8','INM-CM5-0','IPSL-CM6A-LR','MIROC-ES2L','MIROC6','MPI-ESM-1-2-HAM','MPI-ESM1-2-HR','MPI-ESM1-2-LR','MRI-ESM2-0','NESM3','NorCPM1','NorESM2-LM','NorESM2-MM','SAM0-UNICON','UKESM1-0-LL']\n\n#PARAMETER SETUP\nmodel = input('Please specify which model you would like to analyse. E.g., HadGEM3-GC31-LL:\\n')\nyrange = input('Please specify time window to perform analysis. E.g., 1850-2100:\\n')\ny1 = int(yrange.split('-')[0]) ; y2 = int(yrange.split('-')[1])\nif y2 > 2014:\n    ssp = input('Please specify scenario to include observations beyond 2014. E.g., ssp585:\\n')\nelse:\n    ssp = False\nres = input('Please specify frequency. E.g., mon or day:\\n')\nvariables = input('Please specify which variables. E.g., siconc,sithick:\\n')\nvariables = variables.split(',')\nnvars = len(variables)\nbase = '/badc/cmip6/data/CMIP6/'\n\ndef make_npstere_grid(boundinglat,lon_0,grid_res=25e3):\n    import pyproj as proj\n    p = proj.Proj('+proj=stere +R=6370997.0 +units=m +lon_0='+str(float(lon_0))+' +lat_ts=90.0 +lat_0=90.0',\\\n                  preserve_units=True)\n    llcrnrlon = lon_0 - 45\n    urcrnrlon = lon_0 + 135\n    y_ = p(lon_0,boundinglat)[1]\n    llcrnrlat = p(np.sqrt(2.)*y_,0.,inverse=True)[1]\n    urcrnrlat = llcrnrlat\n    llcrnrx,llcrnry = p(llcrnrlon,llcrnrlat)\n    p = proj.Proj('+proj=stere +R=6370997.0 +units=m +lon_0='+str(float(lon_0))+' +lat_ts=90.0 +lat_0=90.0 +x_0='\\\n                  +str(-llcrnrx)+' +y_0='+str(-llcrnry), preserve_units=True)\n    urcrnrx,urcrnry = p(urcrnrlon,urcrnrlat)\n\n    nx = int(urcrnrx/grid_res)+1\n    ny = int(urcrnry/grid_res)+1\n    dx = urcrnrx/(nx-1)\n    dy = urcrnry/(ny-1)\n\n    x = dx*np.indices((ny,nx),np.float32)[1,:,:]\n    y = dy*np.indices((ny,nx),np.float32)[0,:,:]\n    lon,lat = p(x,y,inverse=True)\n    return lon,lat,x,y,p\n\ndef common(lists):\n    return sorted(list(set.intersection(*map(set, lists))))\n\ndef get_common_ripfs(var):\n    histf = sorted(glob.glob(base+'CMIP/*/'+model+'/historical/*/SI'+res+'/'+var+'/gn/latest/*.nc'))\n    if len(histf) == 0:\n        os.system('python cmip6_downloader.py --variable_id='+var+' --frequency='+res+' --experiment_id=historical --source_id='+model)\n        histf = sorted(glob.glob('./data/s_'+model+'_e_historical_f_'+res+'_v_'+var+'/*'))\n        hist_ripfs = [key.split('/')[-1].split('_')[4] for key in histf]\n    else:\n        hist_ripfs = [key.split('/')[9] for key in histf]\n    if ssp:\n        sspf = sorted(glob.glob(base+'ScenarioMIP/*/'+model+'/'+ssp+'/*/SI'+res+'/'+var+'/gn/latest/*.nc'))\n        if len(sspf) == 0:\n            os.system('python cmip6_downloader.py --variable_id='+var+' --frequency='+res+' --experiment_id='+ssp+' --source_id='+model)\n            sspf = sorted(glob.glob('./data/s_'+model+'_e_'+ssp+'_f_'+res+'_v_'+var+'/*'))\n            ssp_ripfs = [key.split('/')[-1].split('_')[4] for key in sspf]\n        else:\n            ssp_ripfs = [key.split('/')[9] for key in sspf]\n        return common([hist_ripfs,ssp_ripfs])\n    else:\n        return hist_ripfs\n\ndef trim_files(files):\n    if res == 'mon':\n        scale = 100\n    elif res == 'day':\n        scale = 10000\n    yrange = [key.split('/')[-1].split('_')[-1].split('.')[0].split('-') for key in files]\n    ymins = [round(int(a[0])/scale) for a in yrange]\n    ymaxs = [round(int(a[1])/scale) for a in yrange]\n    dist_y1 = np.array([y-y1 for y in ymins])\n    dist_y2 = np.array([y-y2 for y in ymaxs])\n    min_dist = max([d for d in dist_y1 if d<=0])\n    max_dist = min([d for d in dist_y2  if d>=0])\n    f1 = np.array(yrange)[np.where(dist_y1==min_dist)]\n    f2 = np.array(yrange)[np.where(dist_y2==max_dist)]\n    f1 = str(f1[0][0])+'-'+str(f1[0][1])\n    f2 = str(f2[0][0])+'-'+str(f2[0][1])\n    yrange_str = np.array([key.split('/')[-1].split('_')[-1].split('.')[0] for key in files])\n    lID = np.where(yrange_str==f1)[0][0]\n    uID = np.where(yrange_str==f2)[0][0]\n    return list(np.array(files)[lID:uID+1])\n\ndef read(files,ripf,var):\n    read = Dataset(files[0])\n    nc = [] ; times = []\n    for file in files:\n        nc.append(np.array(Dataset(file)[var]))\n        times.append(np.array(Dataset(file)['time']))\n    times = np.concatenate(times,0)\n    data = np.concatenate(nc,0)\n    data[data>100] = np.nan\n    data[data<0] = np.nan\n    if (var == 'siconc') & (np.nanmax(data)>1):\n        data = data/100\n    try:\n        try:\n            try:\n                lat = np.array(read['lat'])\n                lon = np.array(read['lon'])\n            except IndexError as e1:\n                lat = np.array(read['latitude'])\n                lon = np.array(read['longitude'])\n        except IndexError as e2:\n            lat = np.array(read['nav_lat'])\n            lon = np.array(read['nav_lon'])\n    except IndexError as e3:\n        lat = np.array(read['nav_latitude'])\n        lon = np.array(read['nav_longitude'])\n    if (lat.ndim == 1) & (lon.ndim == 1):\n        lon,lat = np.meshgrid(lon,lat)\n    x,y = m(lon,lat)\n    NH = np.where(lat>=40)\n    yrange = [key.split('/')[-1].split('_')[-1].split('.')[0].split('-') for key in files]\n    years = [int(a) for b in yrange for a in b]\n    dT,dX,dY = data.shape\n    if res == 'mon':\n        ymin = round(np.min(years)/100) ; ymax = round(np.max(years)/100)\n        data = data.transpose(1,2,0).reshape(dX,dY,12,ymax-ymin+1,order='F')\n        data = data[:,:,:,y1-ymin:y2-ymin+1].reshape(dX,dY,(y2-y1+1)*12,order='F')\n    elif res == 'day':\n        t0 = datetime.strptime(\"1/1/1850\", \"%m/%d/%Y\")\n        tplus = np.array([str(t0 + timedelta(days=t)).split(' ')[0] for t in times])\n        startID = np.where(tplus==str(y1)+'-01-01')[0][0]\n        endID = np.where(tplus==str(y2)+'-12-31')[0][0]\n        data = data[startID:endID+1,:,:].transpose(1,2,0)\n    dT = data.shape[2]\n    regrid = np.zeros((dXR,dYR,dT))*np.nan\n    for t in range(dT):\n        regrid[:,:,t] = griddata((x[NH],y[NH]),data[:,:,t][NH],(xr,yr),'nearest')\n    return regrid\n  \nlonr,latr,xr,yr,m = make_npstere_grid(50,360,50e3)\n\ndata = {}\n#GET COMMON REALISATIONS FOR ALL VARIABLES, HISTORICAL AND FUTURE\ncommon_internal = []\nfor variable in range(nvars):\n    common_internal.append(get_common_ripfs(variables[variable]))\ncheck_data = [len(n) for n in common_internal]\nif 0 in check_data:\n    which_data = np.where(np.array(check_data)==0)\n    missing_var = np.array(variables)[which_data][0]\n    print('Unable to find any consistent data files that span the range '+str(y1)+'-'+str(y2)+' for '+missing_var+'. Please try an alternative range, e.g., dates prior to or after 2014, or try a different model')\nelse:\n    all_common = common(common_internal)\n    if len(all_common) == 0:\n        print('No common realisations found between selected variables')\n        if (os.path.exists('./logs.txt')) & (os.path.exists('./data')) & (len(glob.glob('./s_*.txt'))>0):\n            shutil.rmtree('./data',ignore_errors=True)\n            os.remove('./logs.txt')\n            [os.remove(f) for f in glob.glob('./s_*.txt')]\n    else:\n        #READ AND PROCESS DATA\n        for ripf in all_common:\n            for variable in range(nvars):\n                print('Reading '+model+' '+ripf+' '+variables[variable]+' data...')\n                histf = sorted(glob.glob(base+'CMIP/*/'+model+'/historical/'+ripf+'/SI'+res+'/'+variables[variable]+'/gn/latest/*.nc'))\n                if len(histf) == 0:\n                    histf = sorted(glob.glob('./data/s_'+model+'_e_historical_f_'+res+'_v_'+variables[variable]+'/*'+ripf+'*'))\n                if ssp:\n                    sspf = sorted(glob.glob(base+'ScenarioMIP/*/'+model+'/'+ssp+'/'+ripf+'/SI'+res+'/'+variables[variable]+'/gn/latest/*.nc'))\n                    if len(sspf) == 0:\n                        sspf = sorted(glob.glob('./data/s_'+model+'_e_'+ssp+'_f_'+res+'_v_'+variables[variable]+'/*'+ripf+'*'))\n                    files = trim_files(histf+sspf)\n                else:\n                    files = trim_files(histf)\n                data[model+'_'+variables[variable]+'_'+ripf] = read(files,ripf,variables[variable])\n\n        clean = 'y'#input('Loading complete. Remove downloaded files on local disk to save space? y or n:\\n')\n        if clean == 'y':\n            if (os.path.exists('./logs.txt')) & (os.path.exists('./data')) & (len(glob.glob('./s_*.txt'))>0):\n                shutil.rmtree('./data',ignore_errors=True)\n                os.remove('./logs.txt')\n                [os.remove(f) for f in glob.glob('./s_*.txt')]\n\nif nvars == 1:\n    if ssp:\n        fname = './'+model+'_'+variables[0]+'_'+res+'_'+ssp+'_'+str(y1)+'-'+str(y2)+'.pkl'\n    else:\n        fname = './'+model+'_'+variables[0]+'_'+res+'_'+str(y1)+'-'+str(y2)+'.pkl'\nelse:\n    if ssp:\n        fname = './'+model+'_multivars_'+res+'_'+ssp+'_'+str(y1)+'-'+str(y2)+'.pkl'\n    else:\n        fname = './'+model+'_multivars_'+res+'_'+str(y1)+'-'+str(y2)+'.pkl'\nf = open(fname,'wb')\npickle.dump(data,f)\nf.close()\n    \n\n\n\n\n","sub_path":"cmip6_download_and_extract.py","file_name":"cmip6_download_and_extract.py","file_ext":"py","file_size_in_byte":9361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"550418980","text":"import awsutils as awsutils\nimport plugininfo\nfrom logger.logger import get_logger\nfrom dimensionhandler import Dimensions\n\nclass MetricDataStatistic(object):\n    \"\"\"\n    The MetricDataStatistic object encapsulates the information sent with putMetricData.\n    \n    Keyword arguments:\n    namespace -- the default name space for CloudWatch metric (default defined by NAMESPACE)\n    metric_name -- the metric identifier (default '')\n    timestamp -- the time stamp in AWS format (default current date-time)\n    value -- the raw metric value (default None)\n    statistics -- the MetricDataStatistic.Statistics object used to aggregate raw values (default None)\n    \"\"\"\n    NAMESPACE = plugininfo.NAMESPACE\n    \n    def __init__(self, metric_name='', unit=\"\", dimensions={}, statistic_values=None,\n                 timestamp=None, namespace=NAMESPACE):\n        \"\"\" Constructor \"\"\"\n        self.namespace = namespace\n        self.metric_name = metric_name\n        self.unit = unit\n        self.dimensions = dimensions\n        self.statistics = statistic_values\n        if timestamp:\n            self.timestamp = timestamp\n        else:\n            self.timestamp = awsutils.get_aws_timestamp() \n        \n    def add_value(self, value):\n        if not self.statistics:\n            self.statistics = self.Statistics(value) \n        else:\n            self.statistics._add_value(value)\n        \n    class Statistics:\n        \"\"\"\n        The Statistics object encapsulates the aggregated metric values used by MetricDataStatistic.\n        \n        Keyword arguments:\n        min -- the minimum aggregated value (default None)\n        max -- the maximum aggregated value (default None)\n        sum -- the sum of all aggregated values (default None)\n        avg -- the average of all aggregated values (default None)\n        sample_count -- the count of aggregated values (default 0)\n        \"\"\"\n        \n        def __init__(self, value):\n            \"\"\" Constructor \"\"\"\n            self.min = value\n            self.max = value\n            self.sum = value\n            self.sample_count = 1\n        \n        def _add_value(self, value):\n            \"\"\" \n            Add new value and recalculate the statistics\n            \n            Keyword arguments:\n            value -- new value to be included in the statistics\n            \"\"\"\n            if value > self.max:\n                self.max = value\n            if value < self.min:\n                self.min = value\n            self.sum += value\n            self.sample_count += 1\n\n\nclass MetricDataBuilder(object):\n    \"\"\"\n    The metric data builder is responsible for translating Collectd value list objects\n    to CloudWatch MetricData.\n    \n    Keyword arguments:\n    config_helper -- The ConfigHelper object with configuration loaded\n    vl -- The Collectd ValueList object with metric information\n    \"\"\"\n    \n    def __init__(self, config_helper, vl):\n        self.config = config_helper\n        self.vl = vl\n        \n    def build(self):\n        \"\"\" Builds metric data object with name and dimensions but without value or statistics \"\"\"\n        return MetricDataStatistic(metric_name=self._build_metric_name(), unit=self.vl.type_instance, dimensions=self._build_metric_dimensions())\n        \n    def _build_metric_name(self): \n        \"\"\"\n        Creates single string metric name from the Collectd ValueList naming format by flattening the\n        multilevel structure into the string in the following format: \"plugin.type.type_instance\".\n        The not required name parts (type_instance) will be skipped if empty.\n        \"\"\"\n        name_builder = [str(self.vl.plugin)]\n        name_builder.append(str(self.vl.type))\n        if self.vl.type_instance:\n            name_builder.append(str(self.vl.type_instance))\n        return \".\".join(name_builder)\n    \n    def _build_metric_dimensions(self):\n        if self.config.DIMENSION_CONFIG_PATH != None:\n            d = Dimensions(self.config, self.vl)\n            dimensions = d.get_dimensions()\n        else:\n            dimensions = {\n                \"Host\" : self._get_host_dimension(),\n                \"PluginInstance\" : self._get_plugin_instance_dimension()\n                }\n        return dimensions\n\n    def _get_plugin_instance_dimension(self):\n        if self.vl.plugin_instance:\n            return self.vl.plugin_instance\n        return \"NONE\"\n\n    def _get_host_dimension(self):\n        if self.config.host:\n            return self.config.host\n        return self.vl.host\n\n\n","sub_path":"src/cloudwatch/modules/metricdata.py","file_name":"metricdata.py","file_ext":"py","file_size_in_byte":4486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"70024648","text":"import asyncio\nimport unittest\nfrom unittest import TestCase, IsolatedAsyncioTestCase\nfrom kubesat.testing import FakeNatsHandler, FakeLogger\nfrom clock_service import send_timestep\nimport time\n\nclass Tests(IsolatedAsyncioTestCase):\n\n    async def test_send_timestep(self):\n        \"\"\"\n        Test timesteps\n        \"\"\"\n        loop = asyncio.get_running_loop()\n        shared_storage = {\n        \"start_time\": \"2020-07-15T00:00:00.000000\",\n        \"time_step\": 50.0\n        }\n        nats = FakeNatsHandler(\"clock\", \"0.0.0.0\", \"4222\", loop=loop, user=\"a\", password=\"b\")\n        await nats.connect()\n        logger = FakeLogger()\n        \n        await send_timestep(nats, shared_storage, logger)\n\n        self.assertEqual(len(nats._dict[\"simulation.timestep\"]), 1)\n        self.assertEqual(nats._dict[\"simulation.timestep\"][0].data[\"time\"], \"2020-07-15T00:00:00.000000\")\n        self.assertEqual(shared_storage[\"start_time\"], \"2020-07-15T00:00:50.000000\")\n        \n","sub_path":"simulation/clock/test_clock.py","file_name":"test_clock.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"537410142","text":"# coding: utf-8\n\nimport MeCab\nfrom gensim.corpora.dictionary import Dictionary\nfrom gensim.models import LdaModel\nfrom collections import defaultdict\n\n# MeCabオブジェクトの生成\nmt = MeCab.Tagger()\n\n# トピック数の設定\nNUM_TOPICS = 3\n\nif __name__ == \"__main__\":\n    # トレーニングデータの読み込み\n    # train_texts は二次元のリスト\n    # テキストデータを一件ずつ分かち書き（名詞、動詞、形容詞に限定）して train_texts に格納するだけ\n    train_texts = []\n    with open('./train.txt', 'r', encoding='utf-8') as f:\n        for line in f:\n            text = []\n            print(mt.parse(line))\n            node = mt.parseToNode(line.strip())\n            while node:\n                fields = node.feature.split(\",\")\n                if fields[0] == u'名詞' or fields[0] == u'動詞' or fields[0] == u'形容詞':\n                    text.append(node.surface)\n                node = node.next\n            train_texts.append(text)\n\n    # モデル作成\n    dictionary = Dictionary(train_texts)\n    corpus = [dictionary.doc2bow(text) for text in train_texts]\n    lda = LdaModel(corpus=corpus, num_topics=NUM_TOPICS, id2word=dictionary)\n\n    # テストデータ読み込み\n    # test_texts は train_texts と同じフォーマット\n    test_texts = []\n    raw_test_texts = []\n    with open('./test.txt', 'r') as f:\n        for line in f:\n            text = []\n            raw_test_texts.append(line.strip())\n            node = mt.parseToNode(line.strip())\n            while node:\n                fields = node.feature.split(\",\")\n                if fields[0] == '名詞' or fields[0] == '動詞' or fields[0] == '形容詞':\n                    text.append(node.surface)\n                node = node.next\n            test_texts.append(text)\n\n    # テストデータをモデルに掛ける\n    score_by_topic = defaultdict(int)\n    test_corpus = [dictionary.doc2bow(text) for text in test_texts]\n\n    # クラスタリング結果を出力\n    for unseen_doc, raw_train_text in zip(test_corpus, raw_test_texts):\n        print(raw_train_text, end='\\t')\n        for topic, score in lda[unseen_doc]:\n            score_by_topic[int(topic)] = float(score)\n        for i in range(NUM_TOPICS):\n            print('{:.2f}'.format(score_by_topic[i]), end='\\t')\n        print()\n","sub_path":"machineleaning/source/topic.py","file_name":"topic.py","file_ext":"py","file_size_in_byte":2340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"552493563","text":"# Based on https://stackoverflow.com/a/3041990\ndef query_yes_no(question, default='yes'):\n    '''\n    Ask a yes/no question via input() and return the user's answer.\n\n    :param str question: Prompt given to the user.\n    :param str default: The assumed answer if th user just hits **Enter**. It\n        must be ``'yes'`` (the default if no default is given), ``'no'``, or\n        ``None`` (meaning an answer is required from the user).\n    :returns: ``True`` if we ended up with a 'yes' answer, otherwise\n        ``False``.\n    '''\n    valid = {'yes': True, 'y': True, 'ye': True, 'no': False, 'n': False}\n    if default is None:\n        prompt = ' [y/n] '\n    elif default == 'yes':\n        prompt = ' [Y/n] '\n    elif default == 'no':\n        prompt = ' [y/N] '\n    else:\n        raise ValueError('invalid default answer: \"%s\"' % default)\n    prompt = question + prompt\n    first_loop = True\n    while True:\n        choice = input(prompt).lower()\n        if default is not None and choice == '':\n            return valid[default]\n        elif choice in valid:\n            return valid[choice]\n        elif first_loop:\n            prompt = 'Please respond with \"yes\" or \"no\" (or y or n).\\n' +\\\n                prompt\n            first_loop = False\n","sub_path":"sbws/util/userquery.py","file_name":"userquery.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"122913679","text":"import sys\n\n\n\"\"\"\nFirst set of squares\n[(0, 0), (1, 0), (2, 0), (0, 1), (1, 1), (2, 1), (0, 2), (1, 2), (2, 2)]\n[(0, 3), (1, 3), (2, 3), (0, 4), (1, 4), (2, 4), (0, 5), (1, 5), (2, 5)]\n[(0, 6), (1, 6), (2, 6), (0, 7), (1, 7), (2, 7), (0, 8), (1, 8), (2, 8)]\n\n\"\"\"\nSQUARE_ONE = [(x, y) for y in range(3) for x in range(3)]\nSQUARE_TWO = [(x, y) for y in range(3, 6) for x in range(3)]\nSQUARE_THREE = [(x, y) for y in range(6, 9) for x in range(3)]\n\n\"\"\"\nSecond set of squares\n[(3, 0), (4, 0), (5, 0), (3, 1), (4, 1), (5, 1), (3, 2), (4, 2), (5, 2)]\n[(3, 3), (4, 3), (5, 3), (3, 4), (4, 4), (5, 4), (3, 5), (4, 5), (5, 5)]\n[(3, 6), (4, 6), (5, 6), (3, 7), (4, 7), (5, 7), (3, 8), (4, 8), (5, 8)]\n\"\"\"\nSQUARE_FOUR = [(x, y) for y in range(3) for x in range(3, 6)]\nSQUARE_FIVE = [(x, y) for y in range(3, 6) for x in range(3, 6)]\nSQUARE_SIX = [(x, y) for y in range(6, 9) for x in range(3, 6)]\n\n\"\"\"\nThird set of squares\n[(6, 0), (7, 0), (8, 0), (6, 1), (7, 1), (8, 1), (6, 2), (7, 2), (8, 2)]\n[(6, 3), (7, 3), (8, 3), (6, 4), (7, 4), (8, 4), (6, 5), (7, 5), (8, 5)]\n[(6, 6), (7, 6), (8, 6), (6, 7), (7, 7), (8, 7), (6, 8), (7, 8), (8, 8)]\n\"\"\"\nSQUARE_SEVEN = [(x, y) for y in range(3) for x in range(6, 9)]\nSQUARE_EIGHT = [(x, y) for y in range(3, 6) for x in range(6, 9)]\nSQUARE_NINE = [(x, y) for y in range(6, 9) for x in range(6, 9)]\n\nALL_SQUARES = [SQUARE_ONE, SQUARE_TWO, SQUARE_THREE, SQUARE_FOUR,\n               SQUARE_FIVE, SQUARE_SIX, SQUARE_SEVEN, SQUARE_EIGHT,\n               SQUARE_NINE]\n\n\nclass Cell(object):\n    \"\"\"A unit in the grid that is aware of it's position and neighbors.\"\"\"\n\n    def __init__(self, x, y, grid):\n        self.x = x\n        self.y = y\n        self.grid = grid\n\n        # row that the cell belongs too\n        self.row = [element for element in self.grid[y] if element != \"-\"]\n\n        # column that the cell belongs too\n        self.col = self.load_col()\n\n        # square that the cell belongs too\n        self.square = self.load_square()\n\n        # the union of possible values between the row, col, and square\n        self.possible_values = self.find_possible_values()\n\n    def load_col(self):\n        return [row[self.x] for row in self.grid if row[self.x] != \"-\"]\n\n    def load_square(self):\n        for square in ALL_SQUARES:\n            if (self.x, self.y) in square:\n                # load a square or sub grid (3x3 cells)\n                return [self.grid[pos[1]][pos[0]] for pos in square\n                        if self.grid[pos[1]][pos[0]] != \"-\"]\n\n    def find_possible_values(self):\n        \"\"\"Transform all values into a single set\"\"\"\n        numbers_found = set(self.row)\n        numbers_found = numbers_found.union(self.col)\n        numbers_found = numbers_found.union(self.square)\n        return [str(n) for n in range(1, 10) if str(n) not in numbers_found]\n\n\nclass Grid(object):\n    \"\"\"The main class that contains the sudoku puzzle.\"\"\"\n\n    def __init__(self):\n        self.grid = []\n        self.missing_cells = []\n        self.previous_cell_count = 0\n\n    def loadfile(self, file):\n        \"\"\"Reads in data from a file.\"\"\"\n        with open(file, 'r') as f:\n            data = f.readlines()\n\n        for line in data:\n            line = line.rstrip(\"\\n\")\n            row = line.split(',')\n            self.grid.append(row)\n\n    def find_missing_cells(self):\n        \"\"\"Find and store the missing cells.\"\"\"\n        missing_cells = []\n        for y, row in enumerate(self.grid):\n            for x, element in enumerate(row):\n                if element == \"-\":\n                    c = Cell(x=x, y=y, grid=self.grid)\n                    missing_cells.append(c)\n        # update missing cells\n        self.missing_cells = missing_cells\n\n    def update_grid(self):\n        \"\"\"Replace missing cells in the grid when there is 1 possible value.\"\"\"\n        for c in self.missing_cells:\n            if len(c.possible_values) == 1:\n                # update the grid\n                self.grid[c.y][c.x] = c.possible_values[0]\n\n    def print_grid(self):\n        \"\"\"Dumps on the contents of the grid.\"\"\"\n        for row in self.grid:\n            print(row)\n        print(\"*\" * 60)\n\n\nif __name__ == '__main__':\n    try:\n        file_in = sys.argv[1]\n        g = Grid()\n        g.loadfile(file_in)\n        g.find_missing_cells()\n\n        run = 1\n        while len(g.missing_cells) != 0:\n\n            g.find_missing_cells()\n            if g.previous_cell_count == len(g.missing_cells):\n                raise Exception(\"Algorithm can't solve this type of puzzle.\")\n\n            if len(g.missing_cells) != 0:\n                # print problem\n                print(\"Grid: %s\" % run)\n                g.print_grid()\n\n                g.update_grid()\n                g.previous_cell_count = len(g.missing_cells)\n\n                # grid solution\n                print(\"Solution: %s\" % run)\n                g.print_grid()\n                run += 1\n\n    except Exception as e:\n        print(\"Usage: solve.sh --file <file_path>\")\n\n","sub_path":"grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":4950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"81851331","text":"from PySide import QtCore, QtGui\nfrom YouTubeProvider import Video\nimport requests\n\nclass Ui_MainWindow(object):\n\n    def setupUi(self, MainWindow):\n        sizePolicy = QtGui.QSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)\n        sizePolicy.setHorizontalStretch(1)\n        sizePolicy.setVerticalStretch(1)\n        ### Main Window\n        MainWindow.setMinimumSize(QtCore.QSize(900, 650))\n        self.MW = MainWindow\n\n        ### Central Widget\n        self.centralwidget = QtGui.QWidget(MainWindow)\n        self.centralwidget.setSizePolicy(sizePolicy)\n\n        ### Main tabWidget\n        self.tabWidget = QtGui.QTabWidget(self.centralwidget)\n        self.tabWidget.setGeometry(MainWindow.geometry())\n\n        ### Central Widget\n        self.centralwidget = QtGui.QWidget(MainWindow)\n        self.centralLayout = QtGui.QHBoxLayout()\n        self.centralwidget.setLayout(self.centralLayout)\n        self.tabWidget = QtGui.QTabWidget(self.centralwidget)\n        self.centralLayout.addWidget(self.tabWidget)\n\n        ######### Tab 0\n        self.tab = QtGui.QWidget(self.tabWidget)\n        self.tab0Layout = BorderLayout()\n        self.tab.setLayout(self.tab0Layout)\n        self.tabWidget.addTab(self.tab, \"\")\n\n        ##Tab 0 right side\n        self.tab0RightWidget = QtGui.QWidget(self.tab)\n        self.tab0RightLayout = QtGui.QVBoxLayout(self.tab0RightWidget)\n\n        self.tab0RightScrollArea = QtGui.QScrollArea(self.tab0RightWidget)\n        self.tab0RightLayout.addWidget(self.tab0RightScrollArea)\n        self.tab0Layout.addWidget(self.tab0RightWidget, BorderLayout.Center)\n\n        self.tab0RightContainerWidget = QtGui.QWidget(self.tab0RightScrollArea)\n        self.tab0RightContainerWidget.setLayout(QtGui.QVBoxLayout(self.tab0RightContainerWidget))\n\n        self.tab0RightScrollArea.setWidget(self.tab0RightContainerWidget)\n        self.tab0RightScrollArea.setWidgetResizable(True)\n\n        ###Tab 0 Playlist Management\n        self.tab0LeftWidget = QtGui.QWidget(self.tab)\n        self.tab0Layout.addWidget(self.tab0LeftWidget, BorderLayout.West)\n        self.tab0LeftLayout = QtGui.QVBoxLayout(self.tab0LeftWidget)\n\n        self.tab0YourPlaylistsLabel = QtGui.QLabel(self.tab0LeftWidget)\n        self.tab0LeftLayout.addWidget(self.tab0YourPlaylistsLabel)\n        self.tab0UserPlaylistsList = QtGui.QListWidget(self.tab0LeftWidget)\n        self.tab0LeftLayout.addWidget(self.tab0UserPlaylistsList)\n        self.tab0LoadPlaylistBtn = QtGui.QPushButton(self.tab0LeftWidget)\n        self.tab0LeftLayout.addWidget(self.tab0LoadPlaylistBtn)\n\n        self.tab0BtnHLayout = QtGui.QHBoxLayout()\n        self.tab0BtnHLayout.setSpacing(3)\n        self.tab0AddPlaylistBtn = QtGui.QPushButton(self.tab0LeftWidget)\n        self.tab0BtnHLayout.addWidget(self.tab0AddPlaylistBtn)\n        self.tab0DeletePlaylistBtn = QtGui.QPushButton(self.tab0LeftWidget)\n        self.tab0BtnHLayout.addWidget(self.tab0DeletePlaylistBtn)\n\n        self.tab0LeftLayout.addLayout(self.tab0BtnHLayout)\n\n        #####Tab 1\n        self.tab1 = QtGui.QWidget(self.tabWidget)\n        self.tab1Layout = BorderLayout()\n        self.tab1.setLayout(self.tab1Layout)\n        self.tabWidget.addTab(self.tab1, \"\")\n\n        #Tab 1 right side\n        self.tab1RightWidget = QtGui.QWidget(self.tab1)\n        self.tab1RightLayout = QtGui.QVBoxLayout(self.tab1RightWidget)\n\n        self.tab1RightScrollArea = QtGui.QScrollArea(self.tab1RightWidget)\n        self.tab1RightLayout.addWidget(self.tab1RightScrollArea)\n        self.tab1Layout.addWidget(self.tab1RightWidget, BorderLayout.Center)\n\n        self.tab1RightContainerWidget = QtGui.QWidget(self.tab1RightScrollArea)\n        self.tab1RightContainerWidget.setLayout(QtGui.QVBoxLayout(self.tab1RightContainerWidget))\n\n        self.tab1RightScrollArea.setWidget(self.tab1RightContainerWidget)\n        self.tab1RightScrollArea.setWidgetResizable(True)\n\n        ####Tab 1 Playlist Management + YT search\n        self.tab1LeftWidget = QtGui.QWidget(self.tab1)\n        self.tab1Layout.addWidget(self.tab1LeftWidget, BorderLayout.West)\n        self.tab1LeftLayout = QtGui.QVBoxLayout(self.tab1LeftWidget)\n\n        self.tab1YourPlaylistsLabel = QtGui.QLabel(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1YourPlaylistsLabel)\n        self.tab1UserPlaylistsList = QtGui.QListWidget(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1UserPlaylistsList)\n\n        self.tab1AddPlaylistBtn = QtGui.QPushButton(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1AddPlaylistBtn)\n\n        self.tab1FoundAnimesLabel = QtGui.QLabel(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1FoundAnimesLabel)\n\n        self.tab1AnimesList = QtGui.QListWidget(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1AnimesList)\n\n        self.tab1BtnHLayout = QtGui.QHBoxLayout()\n        self.tab1BtnHLayout.setSpacing(3)\n        self.tab1AddAnimeBtn = QtGui.QPushButton(self.tab1LeftWidget)\n        self.tab1BtnHLayout.addWidget(self.tab1AddAnimeBtn)\n        self.tab1DeleteAnimeBtn = QtGui.QPushButton(self.tab1LeftWidget)\n        self.tab1BtnHLayout.addWidget(self.tab1DeleteAnimeBtn)\n        self.tab1LeftLayout.addLayout(self.tab1BtnHLayout)\n\n        self.tab1InfoLabel0 = QtGui.QLabel(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1InfoLabel0)\n\n        self.tab1InfoLabel1 = QtGui.QLabel(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1InfoLabel1)\n\n        self.tab1AddTextLabel = QtGui.QLineEdit(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1AddTextLabel)\n\n        self.tab1SearchYTBtn = QtGui.QPushButton(self.tab1LeftWidget)\n        self.tab1LeftLayout.addWidget(self.tab1SearchYTBtn)\n\n        MainWindow.setCentralWidget(self.centralwidget)\n        ###Menu bar/Status bar\n        self.menubar = QtGui.QMenuBar(MainWindow)\n        self.menuFile = QtGui.QMenu(self.menubar)\n        self.menuAbout = QtGui.QMenu(self.menubar)\n        MainWindow.setMenuBar(self.menubar)\n\n        self.statusbar = QtGui.QStatusBar(MainWindow)\n        MainWindow.setStatusBar(self.statusbar)\n\n        self.actionOpen_HTML = QtGui.QAction(MainWindow)\n        self.actionExit = QtGui.QAction(MainWindow)\n        self.actionAbout = QtGui.QAction(MainWindow)\n        self.actionLog_In = QtGui.QAction(MainWindow)\n\n        self.menuFile.addAction(self.actionLog_In)\n        self.menuFile.addAction(self.actionOpen_HTML)\n        self.menuFile.addSeparator()\n        self.menuFile.addAction(self.actionExit)\n        self.menuAbout.addAction(self.actionAbout)\n        self.menubar.addAction(self.menuFile.menuAction())\n        self.menubar.addAction(self.menuAbout.menuAction())\n\n        self.retranslateUi(MainWindow)\n        #self.tabWidget.setCurrentIndex(1)\n        QtCore.QObject.connect(self.actionExit, QtCore.SIGNAL(\"triggered()\"), MainWindow.close)\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n    def retranslateUi(self, MainWindow):\n        MainWindow.setWindowTitle(QtGui.QApplication.translate(\"MainWindow\", \"AnimeSTProvider\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab0YourPlaylistsLabel.setText(QtGui.QApplication.translate(\"MainWindow\", \"Your playlists:\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab0LoadPlaylistBtn.setText(QtGui.QApplication.translate(\"MainWindow\", \"Load playlist\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab0AddPlaylistBtn.setText(QtGui.QApplication.translate(\"MainWindow\", \"+\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab0DeletePlaylistBtn.setText(QtGui.QApplication.translate(\"MainWindow\", \"-\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab), QtGui.QApplication.translate(\"MainWindow\", \"Playlist management\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1YourPlaylistsLabel.setText(QtGui.QApplication.translate(\"MainWindow\", \"Your playlists:\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1AddPlaylistBtn.setText(QtGui.QApplication.translate(\"MainWindow\", \"+\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1FoundAnimesLabel.setText(QtGui.QApplication.translate(\"MainWindow\", \"Found animes:\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1AddAnimeBtn.setToolTip(QtGui.QApplication.translate(\"MainWindow\", \"Add anime to this list\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1AddAnimeBtn.setText(QtGui.QApplication.translate(\"MainWindow\", \"+\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1DeleteAnimeBtn.setToolTip(QtGui.QApplication.translate(\"MainWindow\", \"Delete anime from this list\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1DeleteAnimeBtn.setText(QtGui.QApplication.translate(\"MainWindow\", \"-\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1InfoLabel0.setText(QtGui.QApplication.translate(\"MainWindow\", \"Text to add at the end of anime title:\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1InfoLabel1.setText(QtGui.QApplication.translate(\"MainWindow\", \"(like OP, ED or OST)\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tab1SearchYTBtn.setText(QtGui.QApplication.translate(\"MainWindow\", \"Search YouTube\", None, QtGui.QApplication.UnicodeUTF8))\n        self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab1), QtGui.QApplication.translate(\"MainWindow\", \"Anime ST Search\", None, QtGui.QApplication.UnicodeUTF8))\n        self.menuFile.setTitle(QtGui.QApplication.translate(\"MainWindow\", \"File\", None, QtGui.QApplication.UnicodeUTF8))\n        self.menuAbout.setTitle(QtGui.QApplication.translate(\"MainWindow\", \"About\", None, QtGui.QApplication.UnicodeUTF8))\n        self.actionOpen_HTML.setText(QtGui.QApplication.translate(\"MainWindow\", \"Open HTML\", None, QtGui.QApplication.UnicodeUTF8))\n        self.actionExit.setText(QtGui.QApplication.translate(\"MainWindow\", \"Exit\", None, QtGui.QApplication.UnicodeUTF8))\n        self.actionAbout.setText(QtGui.QApplication.translate(\"MainWindow\", \"About AnimeSTProvider\", None, QtGui.QApplication.UnicodeUTF8))\n        self.actionLog_In.setText(QtGui.QApplication.translate(\"MainWindow\", \"Log in\", None, QtGui.QApplication.UnicodeUTF8))\n\nclass VideoItem(QtGui.QWidget):\n    def __init__(self, parent = None, video = None):\n        QtGui.QWidget.__init__(self, parent)\n        self.videoID = video.id\n        self.videoURL = video.videoId\n        self.thumbW = 112\n        self.thumbH = 63\n        self.createWidget()\n        self.setContent(video)\n\n\n    def createWidget(self):\n        self.layout = QtGui.QHBoxLayout(self)\n\n        self.videoView = QtGui.QGraphicsView(self)\n        self.videoView.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n        self.videoView.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n        self.videoView.setMaximumSize(self.thumbW, self.thumbH) #16:9\n        self.layout.addWidget(self.videoView)\n\n        self.label = QtGui.QLabel(self)\n        self.layout.addWidget(self.label)\n\n        self.btn = QtGui.QPushButton(self)\n        self.layout.addWidget(self.btn)\n\n        self.previewBtn = QtGui.QPushButton(self, \"Preview\")\n        self.layout.addWidget(self.previewBtn)\n\n    def setContent(self, video):\n        self.setLabel(video.title)\n        self.setThumbnail(video.thumbUrl)\n\n    def setLabel(self, text):\n        self.label.setText(text)\n\n    def setThumbnail(self, thumb_url):\n        data = requests.get(thumb_url).content\n        thumbImg = QtGui.QImage()\n        thumbImg.loadFromData(data)\n        thumbPix = QtGui.QPixmap()\n        thumbPix.convertFromImage(thumbImg.scaled(self.thumbW, self.thumbH))\n        self.videoScene = QtGui.QGraphicsScene()\n        self.videoScene.addPixmap(thumbPix)\n        self.videoView.setScene(self.videoScene)\n\nclass BorderLayout(QtGui.QLayout):\n    West, North, South, East, Center = range(5)\n    MinimumSize, SizeHint = range(2)\n\n    def __init__(self, parent=None, margin=0, spacing=-1):\n        super(BorderLayout, self).__init__(parent)\n\n        self.setContentsMargins(margin, margin, margin, margin)\n        self.setSpacing(spacing)\n        self.list = []\n\n    def __del__(self):\n        l = self.takeAt(0)\n        while l:\n            l = self.takeAt(0)\n\n    def addItem(self, item):\n        self.add(item, BorderLayout.West)\n\n    def addWidget(self, widget, position):\n        self.add(QtGui.QWidgetItem(widget), position)\n\n    def expandingDirections(self):\n        return QtCore.Qt.Horizontal | QtCore.Qt.Vertical\n\n    def hasHeightForWidth(self):\n        return False\n\n    def count(self):\n        return len(self.list)\n\n    def itemAt(self, index):\n        if index < len(self.list):\n            return self.list[index].item\n\n        return None\n\n    def minimumSize(self):\n        return self.calculateSize(BorderLayout.MinimumSize)\n\n    def setGeometry(self, rect):\n        center = None\n        eastWidth = 0\n        westWidth = 0\n        northHeight = 0\n        southHeight = 0\n        centerHeight = 0\n\n        super(BorderLayout, self).setGeometry(rect)\n\n        for wrapper in self.list:\n            item = wrapper.item\n            position = wrapper.position\n\n            if position == BorderLayout.North:\n                item.setGeometry(QtCore.QRect(rect.x(), northHeight,\n                        rect.width(), item.sizeHint().height()))\n\n                northHeight += item.geometry().height() + self.spacing()\n\n            elif position == BorderLayout.South:\n                item.setGeometry(QtCore.QRect(item.geometry().x(),\n                        item.geometry().y(), rect.width(),\n                        item.sizeHint().height()))\n\n                southHeight += item.geometry().height() + self.spacing()\n\n                item.setGeometry(QtCore.QRect(rect.x(),\n                        rect.y() + rect.height() - southHeight + self.spacing(),\n                        item.geometry().width(), item.geometry().height()))\n\n            elif position == BorderLayout.Center:\n                center = wrapper\n\n        centerHeight = rect.height() - northHeight - southHeight\n\n        for wrapper in self.list:\n            item = wrapper.item\n            position = wrapper.position\n\n            if position == BorderLayout.West:\n                item.setGeometry(QtCore.QRect(rect.x() + westWidth,\n                        northHeight, item.sizeHint().width(), centerHeight))\n\n                westWidth += item.geometry().width() + self.spacing()\n\n            elif position == BorderLayout.East:\n                item.setGeometry(QtCore.QRect(item.geometry().x(),\n                        item.geometry().y(), item.sizeHint().width(),\n                        centerHeight))\n\n                eastWidth += item.geometry().width() + self.spacing()\n\n                item.setGeometry(QtCore.QRect(rect.x() + rect.width() - eastWidth + self.spacing(),\n                        northHeight, item.geometry().width(),\n                        item.geometry().height()))\n\n        if center:\n            center.item.setGeometry(QtCore.QRect(westWidth, northHeight,\n                    rect.width() - eastWidth - westWidth, centerHeight))\n\n    def sizeHint(self):\n        return self.calculateSize(BorderLayout.SizeHint)\n\n    def takeAt(self, index):\n        if index >= 0 and index < len(self.list):\n            layoutStruct = self.list.pop(index)\n            return layoutStruct.item\n\n        return None\n\n    def add(self, item, position):\n        self.list.append(ItemWrapper(item, position))\n\n    def calculateSize(self, sizeType):\n        totalSize = QtCore.QSize()\n\n        for wrapper in self.list:\n            position = wrapper.position\n            itemSize = QtCore.QSize()\n\n            if sizeType == BorderLayout.MinimumSize:\n                itemSize = wrapper.item.minimumSize()\n            else: # sizeType == BorderLayout.SizeHint\n                itemSize = wrapper.item.sizeHint()\n\n            if position in (BorderLayout.North, BorderLayout.South, BorderLayout.Center):\n                totalSize.setHeight(totalSize.height() + itemSize.height())\n\n            if position in (BorderLayout.West, BorderLayout.East, BorderLayout.Center):\n                totalSize.setWidth(totalSize.width() + itemSize.width())\n\n        return totalSize\n\nclass ItemWrapper(object):\n    def __init__(self, i, p):\n        self.item = i\n        self.position = p\n","sub_path":"AnimeSTProviderUI.py","file_name":"AnimeSTProviderUI.py","file_ext":"py","file_size_in_byte":16319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"574225287","text":"\n\nfrom xai.brain.wordbase.nouns._sailboat import _SAILBOAT\n\n#calss header\nclass _SAILBOATS(_SAILBOAT, ):\n\tdef __init__(self,): \n\t\t_SAILBOAT.__init__(self)\n\t\tself.name = \"SAILBOATS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"sailboat\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_sailboats.py","file_name":"_sailboats.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"236140005","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# =============================================================================\n# @file BenderTools/Fill.py\n#\n# Decorate the algorithm with proper methods to fill n-tuples \n#  \n#  This file is a part of \n#  <a href=\"http://cern.ch/lhcb-comp/Analysis/Bender/index.html\">Bender project</a>\n#  <b>``Python-based Interactive Environment for Smart and Friendly Physics Analysis''</b>\n#\n#  The package has been designed with the kind help from\n#  Pere MATO and Andrey TSAREGORODTSEV. \n#  And it is based on the \n#  <a href=\"http://cern.ch/lhcb-comp/Analysis/LoKi/index.html\">LoKi project:</a>\n#  <b>``C++ ToolKit for Smart and Friendly Physics Analysis''</b>\n#\n#  By usage of this code one clearly states the disagreement \n#  with the smear campaign of Dr.O.Callot et al.: \n#  ``No Vanya's lines are allowed in LHCb/Gaudi software''\n#\n# @author Vanya BELYAEV Ivan.Belyaev@cern.ch\n# @date   2011-06-21\n#\n#                   $Revision$\n# Last modification $Date$\n#                by $Author$\n# =============================================================================\n\"\"\"Decorate the algorithm with proper methods to fill n-tuples\n\noooooooooo.                              .o8                     \n`888'   `Y8b                            \\\"888                     \n 888     888  .ooooo.  ooo. .oo.    .oooo888   .ooooo.  oooo d8b \n 888oooo888' d88' `88b `888P\\\"Y88b  d88' `888  d88' `88b `888\\\"\\\"8P \n 888    `88b 888ooo888  888   888  888   888  888ooo888  888     \n 888    .88P 888    .o  888   888  888   888  888    .o  888     \no888bood8P'  `Y8bod8P' o888o o888o `Y8bod88P\\\" `Y8bod8P' d888b    \n                                                                 \nThis file is a part of BENDER project:\n\n  ``Python-based Interactive Environment for Smart and Friendly Physics Analysis''\n\nThe project has been designed with the kind help from Pere MATO and Andrey TSAREGORODTSEV. \n\nAnd it is based on the LoKi project:\n \n   ``C++ ToolKit for Smart and Friendly Physics Analysis''\n\nBy usage of this code one clearly states the disagreement with the smear campaign of Dr.O.Callot et al.:\n\n   ``No Vanya's lines are allowed in LHCb/Gaudi software''\n\n\"\"\"\n# =============================================================================\n__version__ = \"$Revision$\"\n__author__  = \"Vanya BELYAEV Ivan.Belyaev@cern.ch\"\n__date__    = \"2011-06-07\"\n# =============================================================================\n__all__= (\n    'treatPions'    , ## add information about pions \n    'treatKaons'    , ## add information about kaons \n    'treatProtons'  , ## add information about protons\n    'treatPhotons'  , ## add information about photons\n    'treatMuons'    , ## add information about muons \n    'treatTracks'   , ## add information about the tracks\n    'treatKine'     , ## add detailed information for particle ,\n    'fillMasses'    , ## masses of sub-combinations\n    'addRecSummary' , ## add rec-summary information\n    'addGecInfo'    , ## add some GEC-info \n    )\n# ==============================================================================\nfrom   LoKiCore.basic                import cpp,LoKi \nfrom   LoKiPhys.decorators           import *\nfrom   GaudiKernel.SystemOfUnits     import GeV, MeV, mm \n# =============================================================================\n# logging \n# =============================================================================\nfrom Bender.Logger import getLogger \nif '__main__' == __name__ : logger = getLogger ( 'BenderTools.Fill' )\nelse                      : logger = getLogger ( __name__ )\n# ==============================================================================\nSUCCESS                 = cpp.StatusCode( cpp.StatusCode.SUCCESS , True )\nFAILURE                 = cpp.StatusCode( cpp.StatusCode.FAILURE , True )\nv_doubles               = cpp.std.vector('double')\n# ==============================================================================\n## constants: jets? \n#max_muons               =  30 ## maximal number of     muons in the decay tree \n#max_gamma               =  60 ## maximal number of    gammas in the decay tree \n#max_digamma             =  30 ## maximal number of di-gammas in the decay tree\n#max_tracks              = 250 ## maximal number of    tracks in the decay tree\n#max_protons             = 120 ## maximal number of   protons in the decay tree \n#max_kaons               = 120 ## maximal number of     kaons in the decay tree \n#max_pions               = 250 ## maximal number of     pions in the decay tree \n# ==============================================================================\nmax_muons               =  10 ## maximal number of     muons in the decay tree \nmax_gamma               =  10 ## maximal number of    gammas in the decay tree \nmax_digamma             =  10 ## maximal number of di-gammas in the decay tree\nmax_tracks              =  10 ## maximal number of    tracks in the decay tree\nmax_protons             =  10 ## maximal number of   protons in the decay tree \nmax_kaons               =  10 ## maximal number of     kaons in the decay tree \nmax_pions               =  10 ## maximal number of     pions in the decay tree \n# ==============================================================================\n## initialize te internal machinery\ndef fill_initialize ( self ) :\n    \"\"\"\n    Initialize the internal machinery \n    \"\"\"\n    self._pions           = LoKi.Child.Selector ( 'pi+'   == ABSID )\n    self._kaons           = LoKi.Child.Selector ( 'K+'    == ABSID )\n    self._protons         = LoKi.Child.Selector ( 'p+'    == ABSID )\n    self._muons           = LoKi.Child.Selector ( 'mu+'   == ABSID )\n    self._gamma           = LoKi.Child.Selector ( 'gamma' == ID    )\n    self._digamma         = LoKi.Child.Selector ( DECTREE ( ' ( pi0 | eta ) -> <gamma> <gamma>' ) ) \n    ##self._digamma         = LoKi.Child.Selector ( '( pi0 | eta ) -> <gamma> <gamma>' ) \n    self._pi0             = LoKi.Child.Selector ( 'pi0'   == ID    )\n    self._tracks          = LoKi.Child.Selector (       HASTRACK   )\n    self._basic           = LoKi.Child.Selector (        ISBASIC   )\n    #\n    self._ctau            = BPVLTIME (    ) * c_light\n    self._ctau_9          = BPVLTIME (  9 ) * c_light\n    self._ctau_25         = BPVLTIME ( 25 ) * c_light\n    self._lv01            = LV01  \n    self._vchi2           = VFASPF       ( VCHI2     )\n    self._vchi2ndf        = VFASPF       ( VCHI2PDOF )\n    self._dtfchi2         = DTF_CHI2NDOF ( True      )\n    self._ipchi2          = BPVIPCHI2    ()\n    self._dls             = LoKi.Particles.DecayLengthSignificanceDV () \n    #\n    self._min_dll_K       = MINTREE ( 'K+'  == ABSID   , PIDK  - PIDpi ) \n    self._min_dll_Pi      = MINTREE ( 'pi+' == ABSID   , PIDpi - PIDK  ) \n    self._min_dll_PK      = MINTREE ( 'p+'  == ABSID   , PIDp  - PIDK  ) \n    self._min_dll_Ppi     = MINTREE ( 'p+'  == ABSID   , PIDp  - PIDpi ) \n    self._min_dll_Mu      = MINTREE ( 'mu+' == ABSID   , PIDmu - PIDpi )\n    #\n    self._min_annpid_K    = MINTREE ( 'K+'  == ABSID     , PROBNNk      ) \n    self._min_annpid_Pi   = MINTREE ( 'pi+' == ABSID     , PROBNNpi     ) \n    self._min_annpid_Mu   = MINTREE ( 'mu+' == ABSID     , PROBNNmu     ) \n    self._min_annpid_E    = MINTREE ( 'e+'  == ABSID     , PROBNNe      )\n    self._min_annpid_P    = MINTREE ( 'p+'  == ABSID     , PROBNNp      )\n    \n    from LoKiCore.functions import switch \n    self._has_rich        = switch  ( HASRICH , 1 , 0 ) \n    #\n    self._min_Pt          = MINTREE ( ISBASIC & HASTRACK , PT  ) / GeV \n    self._min_Eta         = MINTREE ( ISBASIC & HASTRACK , ETA ) \n    self._max_Eta         = MAXTREE ( ISBASIC & HASTRACK , ETA )\n    #\n    self._min_CL_gamma    = MINTREE ( 'gamma' == ID , CL ) \n    self._min_Et_gamma    = MINTREE ( 'gamma' == ID , PT ) / GeV \n    self._min_E_gamma     = MINTREE ( 'gamma' == ID ,  E ) / GeV \n    #\n    self._maxTrChi2       = MAXTREE ( ISBASIC & HASTRACK , TRCHI2DOF    )\n    self._maxTrGhost      = MAXTREE ( ISBASIC & HASTRACK , TRGHOSTPROB  )\n    self._minTrKL         = MINTREE ( ISBASIC & HASTRACK , CLONEDIST    ) \n    self._minTrIPchi2     = MINTREE ( ISBASIC & HASTRACK , BPVIPCHI2()  )\n    self._max_anngh_track = MAXTREE ( ISBASIC & HASTRACK , PROBNNghost  )\n    #\n    ##\n    self._EtC             = PINFO   ( 55001 , -100 * GeV ) \n    self._PtC             = PINFO   ( 55002 , -100 * GeV )  \n    self._maxEtC          = MAXTREE ( 'mu+' == ABSID , self._EtC ) \n    self._maxPtC          = MAXTREE ( 'mu+' == ABSID , self._PtC )\n    ## \n    self._EcalE           = PPINFO  ( LHCb.ProtoParticle.CaloEcalE , -100 * GeV ) \n    self._HcalE           = PPINFO  ( LHCb.ProtoParticle.CaloHcalE , -100 * GeV )  \n    self._maxEcalE        = MAXTREE ( 'mu+' == ABSID , self._EcalE )\n    self._maxHcalE        = MAXTREE ( 'mu+' == ABSID , self._HcalE )\n    #\n    ## gamma-ID\n    #\n    # ProtoParticle.h: \n    # PhotonID = 380, // Combined PDF for photonID - OBSOLETE DLL-based neutralID (neutral)\n    # IsPhoton = 381, // Gamma/Pi0 separation variable (neutral)\n    # IsNotE = 382,   // MLP-based neutralID - anti-electron ID (neutral)\n    # IsNotH = 383,   // MLP-based neutralID - anti-hadron ID   (neutral)\n    self._IsNotE          = PPINFO  ( LHCb.ProtoParticle.IsNotE    , -1.e+6 ) \n    self._IsNotH          = PPINFO  ( LHCb.ProtoParticle.IsNotH    , -1.e+6 ) \n    self._IsPhoton        = PPINFO  ( LHCb.ProtoParticle.IsPhoton  , -1.e+6 ) \n    self._PhotonID        = PPINFO  ( LHCb.ProtoParticle.PhotonID  , -1.e+6 )\n    # \n    self._min_IsNotE_gamma   = MINTREE ( 'gamma' == ID , self._IsNotE   ) \n    self._min_IsNotH_gamma   = MINTREE ( 'gamma' == ID , self._IsNotH   ) \n    self._min_IsPhoton_gamma = MINTREE ( 'gamma' == ID , self._IsPhoton ) \n    self._min_PhotonID_gamma = MINTREE ( 'gamma' == ID , self._PhotonID ) \n    #\n    self._cellID          = LoKi.Photons.cellID  ## get the seed for the given photon\n    #\n    self._delta_m2        = LoKi.PhysKinematics.deltaM2\n    self._masses          = {} \n    #\n    self.fill_initialized = True \n    return SUCCESS \n\n# ==============================================================================\n## finalize \ndef fill_finalize   ( self ) :\n    \"\"\"\n    Finalie the internal machinery \n    \"\"\"\n    if not self.fill_initialized : return SUCCESS\n    if     self.fill_finalized   : return SUCCESS\n    \n    del self._pions           # = None \n    del self._kaons           # = None \n    del self._protons         # = None \n    del self._muons           # = None \n    del self._gamma           # = None \n    del self._digamma         # = None \n    del self._pi0             # = None \n    del self._tracks          # = None \n    del self._basic           # = None \n    #\n    del self._ctau            # = None \n    del self._ctau_9          # = None \n    del self._ctau_25         # = None \n    del self._lv01            # = None \n    del self._vchi2           # = None \n    del self._vchi2ndf        # = None \n    del self._dtfchi2         # = None \n    del self._ipchi2          # = None \n    del self._dls             # = None \n    #\n    del self._min_dll_K       # = None \n    del self._min_dll_Pi      # = None \n    del self._min_dll_PK      # = None \n    del self._min_dll_Ppi     # = None \n    del self._min_dll_Mu      # = None\n    #\n    del self._min_annpid_K    # = None \n    del self._min_annpid_Pi   # = None \n    del self._min_annpid_Mu   # = None \n    del self._min_annpid_E    # = None \n    del self._min_annpid_P    # = None \n\n    del self._has_rich        # = None\n    \n    del self._min_Pt          # = None \n    del self._min_Eta         # = None \n    del self._max_Eta         # = None\n    #\n    del self._min_E_gamma     # = None \n    del self._min_Et_gamma    # = None \n    del self._min_CL_gamma    # = None \n    #\n    del self._maxTrChi2       # = None \n    del self._maxTrGhost      # = None \n    del self._minTrKL         # = None \n    del self._minTrIPchi2     # = None \n    del self._max_anngh_track # = None \n    #\n    del self._EtC             # = None \n    del self._PtC             # = None \n    del self._maxEtC          # = None \n    del self._maxPtC          # = None \n    ## \n    del self._EcalE           # = None\n    del self._HcalE           # = None \n    del self._maxEcalE        # = None \n    del self._maxHcalE        # = None \n    #\n    del self._IsNotE          \n    del self._IsNotH          \n    del self._IsPhoton        \n    del self._PhotonID        \n    #\n    del self._min_IsNotE_gamma   \n    del self._min_IsNotH_gamma   \n    del self._min_IsPhoton_gamma \n    del self._min_PhotonID_gamma \n    #\n    del self._cellID\n    # \n    del self._delta_m2        # = None\n    del self._masses          # = None \n    #\n    self.fill_finalized = True\n    #\n    return SUCCESS \n\n# =============================================================================\n## add pion information into n-tuple\n#  @param tup   n-tuple\n#  @param p     the particle \ndef treatPions ( self         ,\n                 tup          ,\n                 p            ,\n                 suffix  = '' ) :\n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ##    \n    if hasattr ( p , 'particle' ) : p = p.particle() \n    #\n    ## get all pions from the decay tree\n    #\n    good  = LHCb.Particle.ConstVector()\n    p.children ( self._pions , good ) \n    #\n    _cnt  = self.counter ( '#pion' + suffix )\n    _cnt += good.size()\n    #\n    sc = tup.column_float ( 'mindll_piK' + suffix , self._min_dll_Pi    ( p ) ) \n    sc = tup.column_float ( 'minann_pi'  + suffix , self._min_annpid_Pi ( p ) ) \n    #\n    tup.fArrayP (\n        'p_pion'       + suffix , P   / GeV      ,  \n        'pt_pion'      + suffix , PT  / GeV      , \n        'eta_pion'     + suffix , ETA            , \n        'hasRich_pion' + suffix , self._has_rich ,  \n        LHCb.Particle.Range ( good )             ,\n        'n_pion'       + suffix , max_pions      )\n    return tup.fArrayP (\n        'pid_pion'     + suffix , PIDpi - PIDK   ,  \n        'ann_pion'     + suffix , PROBNNpi       ,  \n        'ann_pion_P'   + suffix , PROBNNK        ,  \n        'ann_pion_K'   + suffix , PROBNNp        ,  \n        LHCb.Particle.Range ( good )             , \n        'n_pion'       + suffix , max_pions      )\n\n# ==============================================================================\n## add kaon information into n-tuple\n#  @param tup   n-tuple\n#  @param p     the particle \ndef treatKaons ( self         ,\n                 tup          ,\n                 p            ,\n                 suffix  = '' ) :\n\n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ## \n    if hasattr ( p , 'particle' ) : p = p.particle() \n    #\n    ## get all kaons from the decay tree\n    #\n    good  = LHCb.Particle.ConstVector()\n    p.children ( self._kaons , good ) \n    #\n    _cnt  = self.counter ( '#kaon' + suffix )\n    _cnt += good.size()\n    #\n    sc = tup.column_float ( 'mindll_K' + suffix , self._min_dll_K     ( p ) ) \n    sc = tup.column_float ( 'minann_K' + suffix , self._min_annpid_K  ( p ) ) \n    #\n    tup.fArrayP (\n        'p_kaon'       + suffix , P   / GeV      , \n        'pt_kaon'      + suffix , PT  / GeV      , \n        'eta_kaon'     + suffix , ETA            ,  \n        'hasRich_kaon' + suffix , self._has_rich ,  \n        LHCb.Particle.Range ( good )             ,\n        'n_kaon'       + suffix , max_kaons      )\n    return tup.fArrayP (\n        'pid_kaon'     + suffix , PIDK - PIDpi   ,  \n        'ann_kaon'     + suffix , PROBNNk        ,\n        'ann_kaon_PI'  + suffix , PROBNNpi       ,  \n        'ann_kaon_P'   + suffix , PROBNNp        ,  \n        LHCb.Particle. Range ( good )            ,\n        'n_kaon'       + suffix , max_kaons      )\n\n# ==============================================================================\n## add proton information into n-tuple\n#  @param tup   n-tuple\n#  @param p     the particle \ndef treatProtons ( self         ,\n                   tup          ,\n                   p            ,\n                   suffix  = '' ) :\n\n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ## \n    if hasattr ( p , 'particle' ) : p = p.particle() \n    #\n    ## get all protons from the decay tree\n    #\n    good   = LHCb.Particle.ConstVector()\n    p.children ( self._protons , good ) \n    #\n    _cnt  = self.counter ( '#proton' + suffix )\n    _cnt += good.size()\n    #\n    sc = tup.column_float ( 'mindll_pK'  + suffix , self._min_dll_PK   ( p ) ) \n    sc = tup.column_float ( 'mindll_ppi' + suffix , self._min_dll_Ppi  ( p ) ) \n    sc = tup.column_float ( 'minann_P'   + suffix , self._min_annpid_P ( p ) ) \n    #\n    tup.fArrayP (\n        'p_proton'       + suffix , P   / GeV      , \n        'pt_proton'      + suffix , PT  / GeV      , \n        'eta_proton'     + suffix , ETA            , \n        'ann_proton'     + suffix , PROBNNp        ,\n        LHCb.Particle.Range ( good )               ,\n        'n_proton'       + suffix , max_protons    )\n    tup.fArrayP (\n        'hasRich_proton' + suffix , self._has_rich ,  \n        LHCb.Particle.Range ( good )               ,\n        'n_proton'       + suffix , 10             )\n    return tup.fArrayP (\n        'pid_proton_pi'  + suffix , PIDp - PIDpi   ,\n        'pid_proton_K'   + suffix , PIDp - PIDK    ,\n        'ann_proton_PI'  + suffix , PROBNNpi       ,\n        'ann_proton_K'   + suffix , PROBNNk        ,\n        LHCb.Particle.Range ( good )               ,\n        'n_proton'       + suffix , max_protons    )\n\n# ==============================================================================\n## add photon information into n-tuple\n#  @param tup   n-tuple\n#  @param p     the particle \ndef treatPhotons ( self         ,\n                   tup          ,\n                   p            ,\n                   suffix  = '' ) :\n\n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ## \n    if hasattr ( p , 'particle' ) : p = p.particle() \n    #\n    ## get all protons form decay tree\n    #\n    good   = LHCb.Particle.ConstVector()\n    p.children ( self._gamma , good ) \n    #\n    _cnt  = self.counter  ( '#gamma' + suffix )\n    _cnt += good.size()\n    #\n    sc = tup.column_float ( 'minE_gamma'        + suffix , self._min_E_gamma         ( p ) ) \n    sc = tup.column_float ( 'minEt_gamma'       + suffix , self._min_Et_gamma        ( p ) ) \n    sc = tup.column_float ( 'minIsNotE_gamma'   + suffix , self._min_IsNotE_gamma    ( p ) ) \n    sc = tup.column_float ( 'minIsNotH_gamma'   + suffix , self._min_IsNotH_gamma    ( p ) )\n    sc = tup.column_float ( 'minPhotonID_gamma' + suffix , self._min_PhotonID_gamma  ( p ) )\n    sc = tup.column_float ( 'minIsPhoton_gamma' + suffix , self._min_IsPhoton_gamma  ( p ) )\n    #\n    tup.fArrayP (\n        'e_photon'        + suffix       , P   / GeV , \n        'et_photon'       + suffix       , PT  / GeV , \n        'CL_photon'       + suffix       , CL        , \n        LHCb.Particle.Range ( good )     ,\n        'n_photon'        + suffix       , max_gamma )\n    \n    tup.fArrayP (\n        'IsNotE_photon'   + suffix       , self._IsNotE   , \n        'IsNotH_photon'   + suffix       , self._IsNotH   , \n        'IsPhoton_photon' + suffix       , self._IsPhoton , \n        'PhotonID_photon' + suffix       , self._PhotonID , \n        LHCb.Particle.Range ( good ) ,\n        'n_photon'        + suffix       , max_gamma      )\n\n    ## put into n-tuple the full cell seed and the calo area index \n    v_seed = v_doubles()\n    v_area = v_doubles()\n    for g in good :\n        seed = self._cellID ( g ) \n        v_seed.push_back ( seed.all  () ) ## get all bits \n        v_area.push_back ( seed.area () ) ## get only thr area\n\n    tup.farray ( 'seed_gamma' + suffix ,\n                 v_seed                , \n                 'n_gamma'    + suffix , max_gamma )\n    \n    tup.farray ( 'area_gamma' + suffix ,\n                 v_area                , \n                 'n_gamma'    + suffix , max_gamma )\n        \n    return tup.fArrayP (\n        'eta_photon'      + suffix       , ETA       , \n        'tx_photon'       + suffix       , PX / PZ   , \n        'ty_photon'       + suffix       , PY / PZ   , \n        LHCb.Particle.Range ( good ) ,\n        'n_photon'        + suffix       , max_gamma )\n\n\n# ==============================================================================\n## add di-gamma information into n-tuple\n#  @param tup   n-tuple\n#  @param p     the particle \ndef treatDiGamma ( self         ,\n                   tup          ,\n                   p            ,\n                   suffix  = '' ) :\n    \n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ##\n    if hasattr ( p , 'particle' ) : p = p.particle() \n    #\n    ## get all protons form decay tree\n    #\n    good   = LHCb.Particle.ConstVector()\n    p.children ( self._digamma , good ) \n    #\n    _cnt  = self.counter ( '#digamma' + suffix )\n    _cnt += good.size()\n    #\n    tup.fArrayP (\n        'e_digamma'          + suffix       , P   / GeV   , \n        'et_digamma'         + suffix       , PT  / GeV   , \n        'ID_digamma'         + suffix       , ID          ,\n        LHCb.Particle.Range ( good ) ,\n        'n_digamma'          + suffix       , max_digamma )\n\n    return tup.fArrayP (\n        'm_digamma'          + suffix       , M   / GeV   , \n        'lv01_digamma'       + suffix       , LV01        ,\n        LHCb.Particle.Range ( good ) ,\n        'n_digamma'          + suffix       , max_digamma )\n\n# ==============================================================================\n## add muon information into n-tuple\n#  @param tup   n-tuple\n#  @param p     the particle \ndef treatMuons ( self         ,\n                 tup          ,\n                 p            ,\n                 suffix  = '' ) :\n    \n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ## \n    if hasattr ( p , 'particle' ) : p = p.particle() \n    #\n    ## get all muons from decay tree\n    #\n    good    = LHCb.Particle.ConstVector()\n    p.children ( self._muons , good ) \n    #\n    _cnt  = self.counter ( '#muon' + suffix )\n    _cnt += good.size()\n    #\n    sc = tup.column_float ( 'mindll_mu'   + suffix , self._min_dll_Mu    ( p ) ) \n    sc = tup.column_float ( 'minann_mu'   + suffix , self._min_annpid_Mu ( p ) ) \n    #\n    sc = tup.fArrayP   (\n        'p_mu'       + suffix , P   / GeV     ,  \n        'pt_mu'      + suffix , PT  / GeV     , \n        'eta_mu'     + suffix , ETA           , \n        LHCb.Particle.Range ( good )          ,\n        'n_muon'     + suffix ,  max_muons    )\n    return tup.fArrayP (\n        'eEcal_mu'   + suffix , self._EcalE   ,\n        'eHcal_mu'   + suffix , self._HcalE   ,\n        'pid_mu'     + suffix , PIDmu - PIDpi ,  \n        'ann_mu'     + suffix , PROBNNmu      ,  \n        LHCb.Particle.Range ( good )          ,\n        'n_muon'     + suffix , max_muons     )\n\n# ==============================================================================\n## add tracks information into n-tuple\n#  @param tup   n-tuple\n#  @param p     the particle \ndef treatTracks ( self         ,\n                  tup          ,\n                  p            ,\n                  suffix  = '' ) :\n    \n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ## \n    if hasattr ( p , 'particle' ) : p = p.particle() \n    #\n    ## get all tracks from the decay tree\n    #\n    from math import sqrt, acos \n    #\n    #\n    good   = LHCb.Particle.ConstVector()\n    p.children ( self._tracks , good ) \n    #\n    _cnt  = self.counter ( '#track' + suffix )\n    _cnt += good.size()\n    #\n    ## calcualate the minimal mass variable \n    m2min = 2.0\n    for i in range ( 0 , good.size() ) :\n        p_i = good[i]\n        q_i = Q ( p_i )\n        \n        for j in range ( i + 1 , good.size() ) :\n            p_j = good [j]\n            q_j = Q ( p_j )\n            \n            if q_i * q_j < 0 : continue\n            \n            m2    = self._delta_m2 ( p_i , p_j )\n            m2min = min ( m2min , m2 )\n            \n    sc = tup.column_float ( 'm2min_track'     + suffix , m2min                       )    \n    sc = tup.column_float ( 'minPt_track'     + suffix , self._min_Pt          ( p ) )\n    sc = tup.column_float ( 'minEta_track'    + suffix , self._min_Eta         ( p ) )\n    sc = tup.column_float ( 'maxEta_track'    + suffix , self._max_Eta         ( p ) )\n    sc = tup.column_float ( 'maxChi2_track'   + suffix , self._maxTrChi2       ( p ) )\n    sc = tup.column_float ( 'maxTrGh_track'   + suffix , self._maxTrGhost      ( p ) )\n    sc = tup.column_float ( 'minKL_track'     + suffix , self._minTrKL         ( p ) )\n    sc = tup.column_float ( 'maxAnnGh_track'  + suffix , self._max_anngh_track ( p ) )\n    \n    tup.fArrayP (\n        'p_track'     + suffix , P   / GeV   , \n        'pt_track'    + suffix , PT  / GeV   , \n        'eta_track'   + suffix , ETA         , \n        'phi_track'   + suffix , PHI         ,                        \n        LHCb.Particle.Range ( good )         , \n        'n_track'     + suffix , max_tracks  )\n    \n    return tup.fArrayP (\n        'chi2_track'  + suffix , TRCHI2DOF   ,\n        'PChi2_track' + suffix , TRPCHI2     , \n        'ann_track'   + suffix , PROBNNghost ,                        \n        'trgh_track'  + suffix , TRGHOSTPROB ,                        \n        LHCb.Particle.Range ( good )         , \n        'n_track'     + suffix , max_tracks  )\n\n# =============================================================================\n## add basic kinematical information into N-tuple\ndef treatKine ( self          ,\n                tup           ,\n                p             ,\n                suffix        ,\n                good   = None ) :\n    \"\"\"\n    Add basic kinematical information into N-tuple\n    \"\"\"\n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ##\n    if hasattr ( p , 'particle' ) : p = p.particle() \n    ##\n    tup.column_int   ( 'pid'   + suffix , int ( ID      ( p ) )     )\n    tup.column_float ( 'pt'    + suffix ,       PT      ( p ) / GeV )\n    tup.column_float ( 'm'     + suffix ,        M      ( p ) / GeV )\n    tup.column_float ( 'eta'   + suffix ,      ETA      ( p )       )\n    tup.column_float ( 'phi'   + suffix ,      PHI      ( p )       )\n    #\n    ## 4-vector\n    #\n    tup.column       ( 'p4'    + suffix , p.momentum() / GeV  )\n    tup.column_float ( 'c2ip'  + suffix , self._ipchi2  ( p ) )\n    ##\n    if not p.isBasicParticle() :\n        \n        tup.column_float ( 'y'     + suffix , Y          ( p ) )\n        tup.column_float ( 'lv01'  + suffix , self._lv01 ( p ) )\n        \n        if p.endVertex() :\n            \n            tup.column_float ( 'ctau'    + suffix , self._ctau     ( p ) )\n            tup.column_float ( 'ctau9'   + suffix , self._ctau_9   ( p ) )\n            tup.column_float ( 'ctau25'  + suffix , self._ctau_25  ( p ) )\n            tup.column_float ( 'dtf'     + suffix , self._dtfchi2  ( p ) )\n            tup.column_float ( 'dls'     + suffix , self._dls      ( p ) )\n            tup.column_float ( 'vchi2'   + suffix , self._vchi2    ( p ) )\n            tup.column_float ( 'vchi2ndf'+ suffix , self._vchi2ndf ( p ) )\n        \n    ##\n    ok = True \n    if good : ok = good ( p )\n    \n    tup.column_bool ( 'good' + suffix ,  ok )\n        \n    return SUCCESS\n\n# =============================================================================\n## fill information about masses\n#\n#  @code\n#\n#  tup = ...\n#    \n#  b   = ...\n#\n#  ## var-names will be m12, m23, ...\n#  self.fillMasses ( tup , b )  ## no constraints are applies \n#    \n#  ## var-names will be m12c, m23c, ...\n#  ## last two parameters are propagated to Decay Tree Fitter\n#  self.fillMasses ( tup , b , 'c' , True , 'J/psi(1S)')  ## with constraints\n#\n#  @endcode\n#\ndef fillMasses ( self        ,\n                 tup         ,\n                 b           ,\n                 suffix      ,\n                 *args       ) :\n    \"\"\"\n    Fill information about mass of all sub-combinations \n    \n    tup = ...\n    \n    b   = ...\n    \n    ## var-names will be m12, m23, ...\n    self.fillMasses ( tup , b )  ## no constraints are applies \n    \n    ## var-names will be m12c, m23c, ...\n    ## last parameters are propagated to Decay Tree Fitter\n    self.fillMasses ( tup , b , 'c' , True , 'J/psi(1S)')  ## with constraints\n    \n    \"\"\"\n    #\n    ## initialize it\n    #\n    if not self.fill_initialized : fill_initialize ( self )\n    ##\n    if not self._masses.has_key (suffix) : self._masses[suffix] = {}\n        \n    nc = b.nChildren()\n        \n    _ms = self._masses[suffix]\n    \n    nc = b.nChildren()\n\n    if not _ms :\n\n        ##\n        for i in range ( 1 , nc + 1 )  :\n            \n            bi = b(i)\n            if bi and not bi.isBasicParticle() : \n                k1 = \"m%d\" % i \n                m1 = MASS  ( i )\n                if args : m1 = DTF_FUN ( m1 , *args )\n                _ms [ k1 + suffix ]  =   m1\n\n            ##\n            for j in range ( i + 1 , nc + 1 ) :\n                \n                k2 = \"m%d%d\" % ( i , j )\n                m2 =   MASS    ( i , j )\n                if args : m2 = DTF_FUN ( m2 , *args )\n                _ms [ k2 + suffix ]  =   m2\n\n                ##\n                for k in range ( j + 1 , nc + 1 ) :\n                    \n                    k3 = \"m%d%d%d\" % ( i , j , k )\n                    m3 =     MASS    ( i , j , k )\n                    if args : m3 = DTF_FUN ( m3 , *args )\n                    _ms [ k3 + suffix ]  =   m3\n\n                    ## \n                    for l in range ( k + 1 , nc + 1 ) :\n                        \n                        k4 = \"m%d%d%d%d\" % ( i , j , k , l )\n                        m4 =       MASS    ( i , j , k , l )\n                        if args : m4 = DTF_FUN ( m4 , *args )\n                        _ms [ k4 + suffix ]  =   m4\n                        \n                        ## \n                        for m in range ( l + 1 , nc + 1 ) :\n                            \n                            k5 = \"m%d%d%d%d%d\" % ( i , j , k , l , m )\n                            \n                            _ind5 = LoKi.Particles.InvariantMass.Indices ()\n                            \n                            _ind5 . push_back ( i )\n                            _ind5 . push_back ( j )\n                            _ind5 . push_back ( k )\n                            _ind5 . push_back ( l )\n                            _ind5 . push_back ( m )\n                            \n                            m5 = MASS     ( _ind5 )\n                            if args : m5 = DTF_FUN ( m5 , *args )\n                            _ms [ k5 + suffix ]  =   m5\n                            \n                            for n in range ( m + 1 , nc + 1 ) :\n                                \n                                k6 = \"m%d%d%d%d%d%d\" % ( i , j , k , l , m , n )\n                                \n                                _ind6 = LoKi.Particles.InvariantMass.Indices ()\n                                \n                                _ind6 . push_back ( i )\n                                _ind6 . push_back ( j )\n                                _ind6 . push_back ( k )\n                                _ind6 . push_back ( l )\n                                _ind6 . push_back ( m )\n                                _ind6 . push_back ( n )\n                                \n                                m6 = MASS     ( _ind6 )\n                                if args : m6 = DTF_FUN ( m6 , *args )\n                                _ms [ k6 + suffix ]  =   m6\n                                \n                                for o in range ( n + 1 , nc + 1 ) :\n                                    \n                                    k7 = \"m%d%d%d%d%d%d%d\" % ( i , j , k , l , m , n , o )\n                                \n                                    _ind7 = LoKi.Particles.InvariantMass.Indices ()\n                                    \n                                    _ind7 . push_back ( i )\n                                    _ind7 . push_back ( j )\n                                    _ind7 . push_back ( k )\n                                    _ind7 . push_back ( l )\n                                    _ind7 . push_back ( m )\n                                    _ind7 . push_back ( n )\n                                    _ind7 . push_back ( o )\n                                    \n                                    m7 = MASS     ( _ind7 )\n                                    if args : m7 = DTF_FUN ( m7 , *args )\n                                    _ms [ k7 + suffix ]  =   m7\n\n                                \n    ## finally fill n-tuple \n    for k in _ms :\n        tup.column_float ( k , _ms [ k ] ( b ) / GeV )\n\n    return SUCCESS\n\n# =============================================================================\n## add some event summary information\n#  @see LHCb::RecSummary\ndef addRecSummary ( self  ,\n                    tup   ,\n                    data  ) :\n    \"\"\"\n    Add event summary information\n    \"\"\"\n    #\n    nPV     = int ( data ( LHCb.RecSummary.nPVs               ) ) \n    nLong   = int ( data ( LHCb.RecSummary.nLongTracks        ) )\n    nDown   = int ( data ( LHCb.RecSummary.nDownstreamTracks  ) ) \n    nUp     = int ( data ( LHCb.RecSummary.nUpstreamTracks    ) ) \n    nVelo   = int ( data ( LHCb.RecSummary.nVeloTracks        ) ) \n    nTT     = int ( data ( LHCb.RecSummary.nTTracks           ) ) \n    nBack   = int ( data ( LHCb.RecSummary.nBackTracks        ) ) \n    nTracks = int ( data ( LHCb.RecSummary.nTracks            ) ) \n    #\n    hRich1  = int ( data ( LHCb.RecSummary.nRich1Hits         ) ) \n    hRich2  = int ( data ( LHCb.RecSummary.nRich2Hits         ) )\n    hVelo   = int ( data ( LHCb.RecSummary.nVeloClusters      ) ) \n    hIT     = int ( data ( LHCb.RecSummary.nITClusters        ) ) \n    hTT     = int ( data ( LHCb.RecSummary.nTTClusters        ) ) \n    hOT     = int ( data ( LHCb.RecSummary.nOTClusters        ) ) \n    hSPD    = int ( data ( LHCb.RecSummary.nSPDhits           ) ) \n    \n    tup.column ( 'nPV'       , nPV      , 0 ,    30 )\n    tup.column ( 'nLong'     , nLong    , 0 ,  1000 ) \n    tup.column ( 'nDown'     , nDown    , 0 ,   500 ) \n    tup.column ( 'nUp'       , nUp      , 0 ,   500 ) \n    tup.column ( 'nVelo'     , nVelo    , 0 ,  1000 ) \n    tup.column ( 'nTT'       , nTT      , 0 ,   500 ) \n    tup.column ( 'nBack'     , nBack    , 0 ,   200 ) \n    tup.column ( 'nTracks'   , nTracks  , 0 ,  2000 ) \n    \n    tup.column ( 'hSPD'      , hSPD     , 0 ,  2000 ) \n    tup.column ( 'hRich1'    , hRich1   , 0 , 30000 ) \n    tup.column ( 'hRich2'    , hRich2   , 0 , 30000 ) \n    tup.column ( 'hVelo'     , hVelo    , 0 , 60000 ) \n    tup.column ( 'hIT'       , hIT      , 0 , 30000 ) \n    tup.column ( 'hTT'       , hTT      , 0 , 30000 ) \n    tup.column ( 'hOT'       , hOT      , 0 , 30000 ) \n    \n    return SUCCESS\n\n# =============================================================================\n## add some event summary information \ndef addGecInfo  ( self  ,\n                  tup   ,\n                  data  ) :\n    \"\"\"\n    Add global event counters \n    \"\"\"\n    for key in data :\n        \n        v = int ( data[key] )\n        \n        tup.column_int ( key + '_gec', v )\n        \n    return SUCCESS\n\n\n# =============================================================================\n## decorate \nLoKi.Algo.treatPions       =  treatPions\nLoKi.Algo.treatKaons       =  treatKaons\nLoKi.Algo.treatProtons     =  treatProtons\nLoKi.Algo.treatPhotons     =  treatPhotons\nLoKi.Algo.treatGammas      =  treatPhotons ## ditto \nLoKi.Algo.treatMuons       =  treatMuons\nLoKi.Algo.treatTracks      =  treatTracks\nLoKi.Algo.treatKine        =  treatKine\n\nLoKi.Algo.treatDiGamma     =  treatDiGamma\nLoKi.Algo.treatDiGammas    =  treatDiGamma ## ditto \nLoKi.Algo.treatDiPhotons   =  treatDiGamma ## ditto \n\nLoKi.Algo.fillMasses       =  fillMasses\n\nLoKi.Algo.addRecSummary    =  addRecSummary \nLoKi.Algo.addGecInfo       =  addGecInfo \n\nLoKi.Algo.fill_initialize  = fill_initialize\nLoKi.Algo.fill_finalize    = fill_finalize\n\n## class attribute \nLoKi.Algo.fill_initialized = False ## class attribute \nLoKi.Algo.fill_finalized   = False ## class attribute \n\n# =============================================================================\n## insert \"fill\" finalization into the global finalization\ndef decorateFill ( ALGO ) :\n    \"Insert ``fill'' finalization into the global finalization\"\n    if hasattr ( ALGO , '_prev_fill_finalize_' ) : return      ## SKIP IT!\n    # =========================================================================\n    ## updated finalization \n    def _algo_fill_finalize_ ( algorithm ) :\n        \"\"\"Finalize ``specific'' stuff related BenderTools.Fill module\n        and then continue with the base-class finalization\n        \"\"\"\n        if algorithm.fill_initialized :\n            algorithm.Print( 'Finalize \"Fill\"-machinery' , SUCCESS , 2 )\n            algorithm.fill_finalize  ()\n        return algorithm._prev_fill_finalize_()\n    # =====================================================================\n    ALGO._prev_fill_finalize_ = ALGO.finalize \n    ALGO.finalize             = _algo_fill_finalize_ \n\n# =============================================================================\nif '__main__' == __name__ :\n    \n    logger.info ( 80*'*') \n    logger.info (  __doc__ ) \n    logger.info ( ' Author  : %s' %        __author__    )\n    logger.info ( ' Version : %s' %        __version__   ) \n    logger.info ( ' Date    : %s' %        __date__      ) \n    logger.info ( ' Symbols : %s' % list ( __all__     ) ) \n    logger.info ( 80*'*') \n\n# ==============================================================================\n# The END \n# ==============================================================================\n","sub_path":"Bender/Phys/BenderTools/python/BenderTools/Fill.py","file_name":"Fill.py","file_ext":"py","file_size_in_byte":37994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"136691089","text":"while True:\n    try:\n        sentence = input().strip()\n        string = ''\n        for s in sentence:\n            to_append = s if s.isalpha() else ' '\n            string += to_append\n        word_list = string.split()\n        print(' '.join(reversed(word_list)))\n    except:\n        break","sub_path":"huawei/字符串单词倒排.py","file_name":"字符串单词倒排.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"451430177","text":"# -*- coding: utf-8 -*-\n#from scrapy import log\n#from scrapy.http import Request\nfrom scrapy.contrib.spiders import CrawlSpider, Rule\nfrom scrapy.contrib.linkextractors import LinkExtractor\n\nfrom media_app_crawler.items import Article\nfrom media_app_crawler.spiders import takeFirst\n\nfrom datetime import datetime\nfrom dateutil.parser import parse\nfrom urlparse import urlparse\n\n\nclass GameDedPostSpider(CrawlSpider):\n    name = \"game_ded_post\"\n    allowed_domains = [\"www.game-ded.com\"]\n    start_urls = (\n        'http://www.game-ded.com/category/gamethai',\n        'http://www.game-ded.com/category/game-reviews',\n        'http://www.game-ded.com/category/upcoming-game',\n        'http://www.game-ded.com/category/thai-gameonline',\n        'http://www.game-ded.com/category/gameweb',\n        'http://www.game-ded.com/category/gamemobile',\n        'http://www.game-ded.com/category/listgameonline'\n    )\n\n    rules = (\n        Rule(LinkExtractor(allow='category/([^/]+)/.*',\n                           restrict_xpaths='//*[@class=\"page-numbers\"]')),\n        #     callback='parse_news_list'),\n        Rule(LinkExtractor(allow='([^/]+)/[^/]+/?',\n                           restrict_xpaths='//*[@class=\"entry-content\"]'),\n             callback='parse_article')\n    )\n\n    def parse_article(self, response):\n        title = self.parse_title(response)\n        publish_time = self.parse_publish_time(response)\n\n        author = self.parse_title(response)\n\n        raw_html = self.parse_article_content(response)\n\n        tags = self.parse_tags(response)\n\n        return Article(url=self.parse_page_url(response),\n                       site=self.allowed_domains[0],\n                       title=title,\n                       image=self.parse_image(response),\n                       author=author,\n                       publish_time=publish_time,\n                       content=raw_html,\n                       categories=self.parse_categories(response),\n                       article_type='normal',\n                       tags=tags,\n                       video_url=None,\n                       lang='th',\n                       platform='2')\n\n    def parse_image(self, response):\n        return takeFirst(self.meta(response, 'og:image'))\n\n    def parse_author(self, response):\n        a = takeFirst(self.meta(response, 'article:author'))\n        if a:\n            a = urlparse(a).path.split('/')[-1]\n        return a\n\n    def parse_publish_time(self, response):\n        t = takeFirst(self.meta(response, 'article:modified_time'))\n        if not t:\n            publish_time = datetime.now()\n        else:\n            try:\n                publish_time = parse(t)\n            except:\n                publish_time = datetime.now()\n\n        return int(publish_time.strftime('%s'))\n\n    def parse_article_content(self, response):\n        children = response.css('#primary-left '\n                                '> div.single-post-container '\n                                '> div.post > *')\n        #print len(children)\n        f = False\n        contents = []\n        for e in children:\n            #print f, e\n            if f:\n                contents.append(e.extract())\n\n            if takeFirst(e.xpath('@class').extract()) == 'entry-meta':\n                f = True\n            if 'fb-social-plugin' in takeFirst(e.xpath('@class').extract()):\n                f = False\n\n        if len(contents) > 0:\n            contents = contents[:-1]\n        return ''.join(contents)\n\n    def parse_tags(self, response):\n        return None\n\n    def parse_categories(self, response):\n        return [urlparse(response.url).path.split('/')[1]]\n\n    def parse_title(self, response):\n        title = takeFirst(self.meta(response, 'og:title'))\n        if not title:\n            title = takeFirst(response.xpath(\n                '/html/head/title/text()').extract())\n        return title\n\n    def parse_banner(self, response):\n        return takeFirst(response.xpath(\n            '//*[@id=\"omc-inner-placeholder/img/@src').extract())\n\n    def parse_page_url(self, response):\n        #return urlparse(response.url).path\n        return response.url\n\n    def meta(self, response, key):\n        return response.xpath(\n            '//meta[@property=\"'+key+'\"]/@content').extract()\n","sub_path":"media_app_crawler/spiders/game_ded_post.py","file_name":"game_ded_post.py","file_ext":"py","file_size_in_byte":4257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"180806438","text":"from django.core.management.base import BaseCommand\nfrom organisation.models import DepartmentUser, CostCentre, Location\nfrom organisation.utils import get_azure_users_json, update_deptuser_from_azure\n\n\nclass Command(BaseCommand):\n    help = 'Checks user accounts from Azure AD and creates/updates linked DepartmentUser objects'\n\n    def add_arguments(self, parser):\n        parser.add_argument(\n            '--container',\n            action='store',\n            dest='container',\n            required=True,\n            help='Azure container name'\n        )\n        parser.add_argument(\n            '--json_path',\n            action='store',\n            dest='json_path',\n            required=True,\n            help='JSON output file path'\n        )\n\n    def handle(self, *args, **options):\n        self.stdout.write(self.style.SUCCESS('Comparing Department Users to Azure AD user accounts'))\n        azure_users = get_azure_users_json(container=options['container'], azure_json_path=options['json_path'])\n\n        for az in azure_users:\n            if not DepartmentUser.objects.filter(azure_guid=az['ObjectId']).exists():\n                if az['Mail'] and az['AssignedLicenses']:  # Azure object has an email and is licensed; proceed.\n\n                    # A department user with matching email may already exist in IT Assets with a different azure_guid.\n                    # If so, return a warning and skip that user.\n                    if DepartmentUser.objects.filter(email__istartswith=az['Mail'], azure_guid__isnull=False).exists():\n                        existing_user = DepartmentUser.objects.filter(email__istartswith=az['Mail']).first()\n                        self.stdout.write(\n                            self.style.NOTICE(\n                                'Skipped {}: email exists and already associated with Azure ObjectId {} (this ObjectId is {})'.format(az['Mail'], existing_user.azure_guid, az['ObjectId'])\n                            )\n                        )\n                        continue\n\n                    # A department user with matching email may already exist in IT Assets with no azure_guid.\n                    # If so, simply associate the Azure AD ObjectId with that user.\n                    if DepartmentUser.objects.filter(email__istartswith=az['Mail'], azure_guid__isnull=True).exists():\n                        existing_user = DepartmentUser.objects.filter(email__istartswith=az['Mail']).first()\n                        existing_user.azure_guid = az['ObjectId']\n                        existing_user.save()\n                        self.stdout.write(\n                            self.style.WARNING('Updated existing user {} with Azure ObjectId {}'.format(az['Mail'], az['ObjectId']))\n                        )\n                        continue\n\n                    if az['Manager'] and DepartmentUser.objects.filter(azure_guid=az['Manager']['ObjectId']).exists():\n                        manager = DepartmentUser.objects.get(azure_guid=az['Manager']['ObjectId'])\n                    else:\n                        manager = None\n\n                    if az['CompanyName'] and CostCentre.objects.filter(code=az['CompanyName']).exists():\n                        cost_centre = CostCentre.objects.get(code=az['CompanyName'])\n                    else:\n                        cost_centre = None\n\n                    if az['PhysicalDeliveryOfficeName'] and Location.objects.filter(name=az['PhysicalDeliveryOfficeName']).exists():\n                        location = Location.objects.get(name=az['PhysicalDeliveryOfficeName'])\n                    else:\n                        location = None\n\n                    new_user = DepartmentUser.objects.create(\n                        azure_guid=az['ObjectId'],\n                        active=az['AccountEnabled'],\n                        email=az['Mail'],\n                        name=az['DisplayName'],\n                        given_name=az['GivenName'],\n                        surname=az['Surname'],\n                        title=az['JobTitle'],\n                        telephone=az['TelephoneNumber'],\n                        mobile_phone=az['Mobile'],\n                        manager=manager,\n                        cost_centre=cost_centre,\n                        location=location,\n                        mail_nickname=az['MailNickName'],\n                        dir_sync_enabled=az['DirSyncEnabled'],\n                    )\n                    update_deptuser_from_azure(az, new_user)  # Easier way to set some fields.\n                    self.stdout.write(self.style.SUCCESS('Created {}'.format(new_user.email)))\n            else:\n                if az['Mail']:  # Azure object has an email; proceed.\n                    # Update the existing DepartmentUser object fields with values from Azure.\n                    user = DepartmentUser.objects.get(azure_guid=az['ObjectId'])\n                    try:\n                        update_deptuser_from_azure(az, user)\n                    except:\n                        self.stdout.write(\n                            self.style.NOTICE(\n                                'Error during sync of {} with Azure ObjectId {}'.format(user.email, az['ObjectId'])\n                            )\n                        )\n\n        self.stdout.write(self.style.SUCCESS('Completed'))\n","sub_path":"organisation/management/commands/check_azure_accounts.py","file_name":"check_azure_accounts.py","file_ext":"py","file_size_in_byte":5305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"455034285","text":"\"\"\"\nDjango settings for contactDB project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.7/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.7/ref/settings/\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\n\nfrom contactDB import config as CF\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = ')g(@v6q+jd1=q@wh##=mqg46a%5g1-q(%gv1z9ln8jvzr#pe8-'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = CF.debug\n\nTEMPLATE_DEBUG = DEBUG\n\nALLOWED_HOSTS = CF.allowed_hosts\n\n\n# Application definition\n\nINSTALLED_APPS = (\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'contact',\n    'globalz',\n    'project',\n    'mailist',\n)\n\nMIDDLEWARE_CLASSES = (\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'contactDB.urls'\n\nWSGI_APPLICATION = 'contactDB.wsgi.application'\n\n\n\n\n# Database\n# https://docs.djangoproject.com/en/1.7/ref/settings/#databases\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.mysql',\n        'NAME': CF.db_database,\n        'USER' : CF.db_user,\n        'PASSWORD' : CF.db_password,\n        'HOST' : CF.db_host,\n    }\n}\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.7/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.7/howto/static-files/\n\nSTATIC_URL = '/static/'\nSTATIC_ROOT = CF.static_root\n","sub_path":"contactDB/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"603328729","text":"# coding=utf-8\nimport sys\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\nif __name__ == \"__main__\":\n    out = open('/home/littlebei/program/python/pycharm/HotWords/data/dump/result/shishi.txt', 'w')\n    inputFile = '/home/littlebei/program/python/pycharm/HotWords/data/dump/shishi.txt'\n    for line in open(inputFile, 'r').readlines():\n        words = line.strip().strip('\\n').split('\\t')\n        if len(words) == 3:\n            mid = words[0]\n            title = words[1]\n            summary = ' '\n            domain = ' '\n            uid = ' '\n            user_name = ' '\n            level = ' '\n            url = ' '\n            content = words[2]\n            extend = ' '\n            user_type = ' '\n            out.write(mid + '\\t' + title + '\\t' + summary + '\\t' + domain + '\\t' + uid + '\\t' +\n                      user_name + '\\t' + level + '\\t' + url + '\\t' + content + '\\t' +\n                      extend + '\\t' + user_type + '\\n')\n    out.close()\n","sub_path":"com/sina/dealdump/deal_extract_field.py","file_name":"deal_extract_field.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"629861299","text":"import csv\nimport datetime\nimport logging\nimport os\nimport json\nimport uuid\nimport traceback\nfrom django_mongokit import connection\nfrom celery import task\nfrom dvutils.utils import get_dates, queue_wrapper, mail_tracking, \\\n     push_fe_data_to_kinesis\nimport requests\n\nfrom django.core.urlresolvers import reverse\nfrom django.conf import settings\nfrom settings import DOMAIN, DEF_DOMAIN, ADMIN_TOKEN, DEF_TOKEN\nfrom settings import CT_LINK, CT_DISPATCH_INFO_URL\nfrom dispatch.app_settings import DEF_DISPATCH_URL\nfrom django.contrib.auth.models import User\nfrom package.app_settings import LMAP_STREAMS, DEFAULT_DISPATCH_KEYS\nfrom dvutils.replica_set import MongoConnection\nfrom deliverycenters.cache import get_center_from_cache\n\nfrom dispatch import app_settings as dispatch_settings\nfrom dvutils.emailer import CustomEmailHandler\nfrom dvutils.replica_set import MongoConnection\n\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef create_eodcoll(day, cn, dwbns):\n    \"\"\"\n    creates an eod collection, if it does not exist\n    for a given day and center.\n    Adds list of packages for each center\n    \"\"\"\n    from dispatch.models import EODCollection\n    from deliverycenters.cache import get_lmap_enable_status\n    if cn in get_lmap_enable_status():\n        return False\n    if not connection.EODCollection.one(\n            {'day': day, 'cn': cn}):\n        e = EODCollection()\n        e.uid = u'{}'.format(uuid.uuid4().int >> 96)\n        e.day = day\n        e.cn = cn\n        e.dwbns = dwbns\n\n        e = connection.EODCollection(e)\n        e.save(u'tech.admin')\n        return e\n    return False\n\n\n@task\ndef dv_eod_cash(delta=1):\n\n    # delta = number of days.\n    # should not be greater than 6\n    # day for EODCollection will be the prev day\n    now = datetime.datetime.now()\n    now = now.replace(hour=0, minute=0, second=0, microsecond=0)\n    try:\n        if delta > 6:\n            raise ValueError\n        day = now + datetime.timedelta(days=-delta)\n    except (TypeError, ValueError) as e:\n        day = now + datetime.timedelta(days=-1)\n        LOGGER.exception(\"Delta should less than 7 and numeric, {}\".format(e))\n    # get all the dispatches by center created for the specific day\n    # stick to regular dispatches only\n    # inserts number of dispatches and dispatch numbers\n    dispatches = connection.Dispatch.collection.group(\n        key=[\"cn\"],\n        condition={'dpd': {'$gte': day, '$lt': now}, 't': u''},\n        initial={\"c\": 0, \"dwbns\": []},\n        reduce=\"function(obj, prev) {prev.c++; prev.dwbns.push(obj.dwbn) }\")\n\n    for d in dispatches:\n        if d['c'] > 0:\n            e = create_eodcoll(day, d['cn'], d['dwbns'])\n            if e:\n                LOGGER.debug(\n                    'Created EODCollection %s (%s, %s)' % (\n                        e.wbn, e.day, e.cn))\n\n\n@task(name='New Vehicle', queue=queue_wrapper('dispatch.tasks.new_vehicle'))\ndef new_vehicle(dwbn):\n    # accepts dwbn and creates new if complete for the same parameters\n    old = connection.Dispatch.find_one({'dwbn': dwbn})\n    if not old:\n        return 'No such dwbn: {} found'.format(dwbn)\n\n    if not (old.t == ''):\n        return 'invalid type for {0}: {1}'.format(dwbn, old.t)\n\n    if not (old.ds == u'Complete'):\n        return 'invalid status for {0}: {1}'.format(dwbn, old.ds)\n\n    if not old.get('vid', None):\n        return 'dwbn {} vid is none'.format(dwbn)\n\n    from backend.models import Vehicle\n    v = Vehicle.active.filter(id=old.get('vid'))\n    if v.count() == 0:\n        return 'Active vehicle with id {} not found. Completing dispatch {}'\\\n            .format(old.get('vid'), dwbn)\n    elif v.count() == 1:\n        return new_fe(v[0])\n    else:\n        return 'Error occured'\n\n\n@task(name='New FE', queue=queue_wrapper('dispatch.tasks.new_fe'))\ndef new_fe(v, fe_id=None, dispatch_type=u'', fe_uid=None,\n           non_veh=None, center=None, dispatch_mode=u'Regular'):\n    \"\"\"\n    create dispatch whenever new vehicle is added\n    \"\"\"\n    # check if the dispatch creation is happening for\n    # non_veh vehicle\n    if non_veh in ['Footer', 'Cycle']:\n        # the incoming vehicle is\n        # a non vehicle type dispatch\n        creation_status, dsp_id, msg = create_free_dispatch(\n            fe_id, non_veh, center, dispatch_type=dispatch_type)\n\n        # get the data from the\n        return creation_status, dsp_id, msg\n\n    # Cyclic import\n    from deliverycenters.cache import get_lmap_enable_status\n    # if vehicle is not active make no more dispatces for that vehicle\n    if not v.is_active:\n        error = 'Vehicle {} has expired, \\\n            hence its dispatches can\\'t be made'.format(v.id)\n        LOGGER.info(error)\n        return False, error, None\n\n    # For LMAP enabled centers don't create new FEs\n    if v.center.__unicode__() in get_lmap_enable_status():\n        error = 'Cannot Create dispatches for LMAP enabled centers'\n        return False, error, None\n\n    # check if user is tagged to vehicle and user is still active\n    # if not active dont create any more dispatches\n    # else if user is not attached to vehicle work in legacy  mode\n    try:\n\n        if fe_id:\n            fe_user = User.objects.get(pk=fe_id)\n        else:\n            fe_user = v.user\n\n        if not fe_user.is_active and not fe_user.userprofile.employee_id:\n            LOGGER.info('Vehicle FE {} is not active'.format(fe_user.username))\n            return False, 'Vehicle FE {} is not active'.format(fe_user.username), None\n    except:\n        fe_user = None\n\n    t = dispatch_type\n    exists = connection.Dispatch.find_one(\n        {'cn': v.center.__unicode__(),\n         'vid': v.id,\n         't': t,\n         'ds': {'$in': [u'WIP', u'Dispatched']}\n         })\n\n    if exists:\n        return False, 'vehicle {0} already exists in' \\\n                      ' WIP/Dispatched Dispatch'.format(\n                          v.registration_number), None\n\n    new = connection.Dispatch()\n    # if user is attached to vehicle\n    # update his information in dispatch\n    if fe_user:\n        new['dn'] = u'{}'.format(fe_user.username)\n        new['fu'] = {}\n        new['fu']['username'] = fe_user.username\n        new['fu']['user_id'] = fe_user.id\n        new['fu']['emp_id'] = fe_user.userprofile.employee_id\n        new['fu']['emp_type'] = fe_user.userprofile.user_type\n    else:\n        new['dn'] = u'{}'.format(v.driver_name)\n\n    new['vid'] = v.id\n    new['vn'] = u'{}'.format(v.registration_number)\n    new['vt'] = u'{}'.format(v.vehicle_type)\n    new['cn'] = v.center.__unicode__()\n    new['cnid'] = v.center.code\n    new['rs'] = {'cod': 0.0}\n    new['ds'] = u'WIP'\n    new['cd'] = datetime.datetime.now()\n    new['t'] = t\n    new['md'] = dispatch_mode\n    new['u'] = u'tech.admin'\n    new['adhoc_dispatch'] = False\n    new['company_vehicle'] = True\n    new.save(safe=True)\n\n    # add the fe unique id to the\n    # dispatch\n    if fe_uid:\n        new['fe_uid'] = fe_uid\n\n    return True, 'new dispatch created {}'.format(new.dwbn), str(new.get('_id'))\n\n\n@task(queue=queue_wrapper('dispatch.tasks.vehicle_dispatch'))\ndef vehicle_dispatch():\n    from backend.models import Vehicle\n    # set of  active vehicle ids\n    va_ids = set(Vehicle.objects.filter(\n        end_date__gte=datetime.datetime.now(),\n        use_type=u'lastmile').values_list('id', flat=True))\n    # set of vehivles ids in dispatch\n    vd_ids = set(connection.Dispatch.find({'t': '',\n                                           'ds': {\n                                                '$in': [u'WIP', u'Dispatched']}\n                                           }).distinct('vid'))\n    diff = va_ids - vd_ids\n    if diff:\n        for vid in diff:\n            v = Vehicle.objects.get(id=vid)\n            new_fe(v)\n\n\n@task(name=\"Dispatch Status Push\")\ndef status_push(wbns, cl, dt, func, dwbn):\n    \"\"\"\n    :param wbns: list of all wbns incoming done for\n    :param cl: fetch client urls w.r.t cl\n    :param dt: date time stamp for dispatch completion.\n    :param dwbn: dispatch number closed.\n    generally set by the inqueue_task decorator\n    :return: tuple list containing details w.r.t url\n    \"\"\"\n    from backend.models import ClientStatuspush\n    client_qs = ClientStatuspush.objects.filter(\n        client__name=u'{0:s}'.format(cl),\n        functionality=func)\n    out = []\n    subject = \"Status Push Failed\"\n    message = \"Functionality: {0.functionality} <br>\" \\\n              \"Client: {0.client} <br>\" \\\n              \"URL: {0.url} <br>\" \\\n              \"Error: {1}<br>\"\n    for client in client_qs:\n        headers = client.header\n        data = {\n            \"waybills\": wbns,\n            \"action\": \"Complete\",\n            \"dwbn\": dwbn,\n            \"date-time\": dt\n        }\n        data = json.dumps(data)\n        try:\n            result = client.api_push(data, headers)\n        except:\n            content = message.format(client,\n                                     traceback.format_exc())\n            mail_tracking(subject, content, html_message=content)\n        else:\n            if result.status_code != 200:\n                content = message.format(client,\n                                         result.status_code)\n                mail_tracking(subject, content, html_message=content)\n            out.append((result.url, result.ok, result.status_code))\n    return True\n\n\n@task()\ndef pickup_notification_receiver(instance):\n    \"\"\"\n    Signal recipient to send push notification to client when incoming\n    dispatch on wbns is completed for auto dispatches.\n    \"\"\"\n    from package.models import find_by_wbn\n    wbns = instance.get(\"wbn\", [])\n    dt = instance.get(\"cpd\", None)\n    dwbn = instance.get(\"dwbn\")\n\n    # Get client name from any waybill out of the lot\n    cl = None\n    if wbns:\n        some_pkg = find_by_wbn(wbns[0])\n        cl = some_pkg.get(\"cl\")\n    if not cl:\n        return False\n\n    func = \"Dispatch Completion Push\"\n    # send notification after 30 minutes\n    # status_push(wbns, cl, dt, func, dwbn)\n    status_push.apply_async(args=[wbns, cl, dt, func, dwbn],\n                            countdown=30*60)\n\n\n@task(queue=queue_wrapper('dispatch.tasks.send_dispatch_info'))\ndef send_dispatch_info(dwbn):\n    \"\"\"\n    Task for sending data to Control Tower\n    \"\"\"\n    url = reverse('dispatch_info', args=[dwbn])\n    try:\n        res = requests.get(\n            'https://'+DOMAIN+url,\n            headers={\n                'Authorization': 'Token {}'.format(ADMIN_TOKEN),\n                'Accept': 'application/json'}, verify=False)\n        if res.status_code == 200:\n            data = res.json()\n            try:\n                response = requests.post(\n                    'http://'+CT_LINK + CT_DISPATCH_INFO_URL,\n                    data=json.dumps(data), headers={'Authorization': 'Token {}'.format(\n                        DEF_TOKEN)})\n            except Exception as e:\n                LOGGER.warning(\n                    \"Control Tower dispatch request push failed:{}\".format(e))\n                return\n            else:\n                if response.status_code == 200:\n                    return \"success\"\n    except Exception as e:\n        LOGGER.warning(\n            \"Control Tower dispatch request push failed:{}\".format(e))\n    return None\n\n\n@task(name='Unexpected scan',\n      queue=queue_wrapper('dispatch.tasks.unexpected_scan_task'))\ndef unexpected_scan_task(data):\n    try:\n        requests.post(\n            'https://' + settings.APP_API_URL + '/api/p/uscan.json',\n            data=data, verify=False)\n    except Exception as e:\n        msg = \"Unexpected incoming failed. Package might be updated. \" \\\n              \"data is {0} and error is {1}\".format(data, e.message)\n        LOGGER.info(msg=msg)\n\n\ndef create_free_dispatch(fe_id, veh, center, dispatch_type=u'',\n                         dispatch_mode=u'Regular'):\n    \"\"\"\n    Util function for creating\n    dispatch\n    \"\"\"\n    try:\n        fe_user = User.objects.get(pk=int(fe_id))\n    except User.DoesNotExist:\n        return False, None, 'User not found'\n\n    new = connection.Dispatch()\n    # if user is attached to vehicle\n    # update his information in dispatch\n    new['dn'] = u'{}'.format(fe_user.username)\n    new['fu'] = {}\n    new['fu']['username'] = fe_user.username\n    new['fu']['user_id'] = fe_user.id\n    new['fu']['emp_id'] = fe_user.userprofile.employee_id\n    new['fu']['emp_type'] = fe_user.userprofile.user_type\n    new['vid'] = -1\n    new['vn'] = veh\n    new['vt'] = u'{}'.format(veh)\n    new['cn'] = center.__unicode__()\n    new['rs'] = {'cod': 0.0}\n    new['ds'] = u'WIP'\n    new['cd'] = datetime.datetime.now()\n    new['t'] = dispatch_type\n    new['md'] = dispatch_mode\n    new['u'] = u'tech.admin'\n    new['adhoc_dispatch'] = False\n    new['company_vehicle'] = True\n    new.save(safe=True)\n\n    return True, str(new.get('_id')), 'Dispatch created successfully'\n\n\ndef create_agent_dispatch(agent_id, fe_id, center, user):\n    \"\"\"\n    Util function for creating agent dispatch\n    \"\"\"\n    try:\n        fe_user = User.objects.get(pk=int(fe_id))\n    except User.DoesNotExist:\n        return False, None, 'User not found'\n\n    # check fe center is mapped at this center\n    if center not in fe_user.userprofile.center.all():\n        return False, None, \"FE Not at this center\"\n    # cyclic import\n    from backend.models import Agent\n    try:\n        agent = Agent.objects.get(pk=int(agent_id))\n    except User.DoesNotExist:\n        return False, None, 'Agent not found'\n\n    # check if agent is mapped to this center\n    if center not in agent.center.all():\n        return False, None, \"Agent Not Mapped to this center\"\n\n    dispatch_type = u''\n    dispatch_mode = u'Agent'\n    # check if there is already a dispatch for this agent\n    query_dict = {\n        't': dispatch_type,\n        'md': dispatch_mode,\n        'fu.name': u'{}'.format(agent.name),\n        'ds': u'WIP',\n        'cn': center.__unicode__()\n    }\n\n    existing_dsp = connection.Dispatch.find_one(query_dict)\n\n    if existing_dsp:\n        return False, None, 'Dispatch Already Exist for this agent {}'.format(\n            existing_dsp.get('dwbn'))\n    new = connection.Dispatch()\n    # if user is attached to vehicle\n    # update his information in dispatch\n    new['fu'] = {}\n    new['fu']['hf'] = u'{}'.format(fe_user.username)\n    new['fu']['name'] = u'{}'.format(agent.name)\n    new['fu']['rname'] = u'{}'.format(agent.registered_name)\n    new['fu']['agent_id'] = u'{}'.format(agent.pk)\n    new['fu']['oracle_id'] = u'{}'.format(agent.oracle_id)\n    new['fu']['type'] = u'{}'.format(agent.get_type_display())\n    new['fu']['address'] = u'{}'.format(agent.address)\n    new['fu']['locality'] = u'{}'.format(agent.locality)\n    new['fu']['pincode'] = u'{}'.format(agent.pincode)\n    new['fu']['city'] = u'{}'.format(agent.city)\n    new['fu']['phone'] = u'{}'.format(agent.phone)\n\n    new['vn'] = u'{}'.format(agent.name)\n    new['vt'] = u'{}'.format('Agent')\n    new['dn'] = u'{}'.format(fe_user.username)\n    new['cn'] = u'{}'.format(center.__unicode__())\n    new['rs'] = {u'cod': 0.0}\n    new['ds'] = u'WIP'\n    new['cd'] = datetime.datetime.now()\n    new['t'] = u''\n    new['md'] = dispatch_mode\n    new['u'] = user.username\n    new.save(safe=True)\n    return True, str(new.get('_id')), 'Dispatch created successfully',\n\n\n@task(name=\"EOD Collection Report\", queue=queue_wrapper('dispatch.tasks.eod_report'))\ndef eod_coll_report(**data):\n\n    '''today=datetime.datetime.now(), delta_days=1, get_link=True, **kwargs\n\n    Required arguments data should  be in given format\n    data = {\n            'span': defaults to D1\n            'date_filter': default will be deposit date\n            'center':[if report needed for certain centers],type list\n        }\n    '''\n    # cyclic import\n    from dispatch.utils import get_recon_csv_row\n    from package.utils import upload_file_to_s3\n\n    data_filter = {}\n    sd, ed = get_dates(data.get('span', 'D1'))\n    date_filter = data.get('date_filter', 'dpd')\n    center = data.get('center', [])\n    query = data.get('query')\n    collname = data.get('model', 'eodcollection')\n    labels = data.get('labels', [])\n    keys = data.get('keys', [])\n\n    if not sd and not ed:\n        return 'Invalid Span Specified'\n\n    data_filter = {date_filter: {'$gte': sd, '$lte': ed}}\n\n    if center:\n        data_filter.update({'cn': center})\n\n    db = MongoConnection().get_database()\n\n    dt_fmt = \"%Y-%m-%d\"\n\n    duration = u\"{}_{}\".format(sd.strftime(dt_fmt), ed.strftime(dt_fmt))\n\n    fname = open('/tmp/%s report for %s.csv' % (collname, duration), 'w')\n\n    if query:\n        eodc = db[collname].find(query)\n\n        with fname as csvfile:\n            fieldnames = labels\n            writer = csv.writer(csvfile, dialect='excel')\n            writer.writerow(fieldnames)\n            for result in eodc:\n                writer.writerow(get_any_csv_row(keys, result))\n    else:\n        eodc = db[collname].find(data_filter).sort(date_filter, -1)\n        labels = dispatch_settings.EOD_CSV_DUMP['label']\n        keys = dispatch_settings.EOD_CSV_DUMP['key']\n\n        with fname as csvfile:\n            fieldnames = labels\n            writer = csv.writer(csvfile, dialect='excel')\n            writer.writerow(fieldnames)\n            for result in eodc:\n                writer.writerow(get_recon_csv_row(keys, result))\n\n    csvfile.close()\n\n    url = upload_file_to_s3(csvfile.name, expires=259200)\n\n    os.remove(csvfile.name)\n\n    from_mail = settings.NO_REPLY\n    to_mail = settings.DEFAULT_EMAIL\n    emails = data.get('email', None)\n    if emails:\n        to_mail.extend(emails)\n    subject = '{0} Report - {1}'.format(collname, duration)\n\n    text_content = '{0} Report Ready for download \\n{1}'.format(collname, url)\n\n    msg = CustomEmailHandler(subject, text_content, from_mail, to_mail)\n    msg.send()\n\n\n\ndef get_any_csv_row(keys, eod_obj):\n    \"\"\"Prepares a record  for header field from EOD_CSV_DUMP\"\"\"\n    processed_row = list()\n    for key in keys:\n        row = eod_obj.get(key)\n        processed_row.append(unidecode(unicode(row)))\n    return processed_row\n\n\n@task(name=\"Push dispatch to FE app\",\n      queue=queue_wrapper('dispatch.tasks.push_dispatch_structure'))\ndef push_dispatch_structure(dwbn):\n    \"\"\"\n        Task for pushing dispatch data to kinesis for FE Delhivery App.\n        Called while dispatching a dispatch, only for FE ENABLED CENTERS\n        :param dwbn: Dwbn number of the dispatch\n        :return: None\n    \"\"\"\n    # Cyclic Import\n    from dispatch.models import Dispatch\n    from backend.models import Vehicle\n    from dispatch.utils import push_dispatched_package_details\n\n    # Get LMAP data push stream\n    push_url = LMAP_STREAMS.get('LMAP_DATA')\n    dispatch_stream = LMAP_STREAMS.get('DISPATCH_DETAILS')\n    employee_stream = LMAP_STREAMS.get('EMPLOYEE_DATA')\n\n    if not push_url or not dispatch_stream or not employee_stream:\n        LOGGER.error('No push url configured for LMAP database')\n        return\n\n    conn = MongoConnection().get_database()\n\n    # Get dispatch and type cast it\n    dsp = conn['dispatch'].find_one({'dwbn': dwbn})\n    dsp = Dispatch(dsp)\n\n    pkgs = conn['packages'].find({'wbn': {'$in': dsp.get('wbn')}})\n\n    # Intialize code to none\n    code = dsp.get('cnid')\n    if not code:\n        center = get_center_from_cache(dsp.get('cn'))\n        code = center.get('code')\n\n    try:\n        veh = Vehicle.objects.get(id=dsp.get('vid'))\n    except Vehicle.DoesNotExist:\n        if dsp.get('vid') != -1:\n            LOGGER.info('Vehicle missing in the dispatch {}'.format(dsp.get('dwbn')))\n            return\n\n    dispatch_data = dsp.serializer(fields=DEFAULT_DISPATCH_KEYS, encode_datetime=True)\n\n    # Push packages to kinesis\n    pkg_dict = push_dispatched_package_details(pkgs, dsp, code)\n\n    if not pkg_dict:\n        LOGGER.error('Could not push package data to kinesis')\n        return\n\n    assigned_emp_id = dsp.get('fu', {}).get('emp_id')\n\n    # If no employee assigned to dispatch, then return\n    if not assigned_emp_id:\n        LOGGER.info('No employee_id associated with dispatch {}'.format(dsp.get('dwbn')))\n        return\n\n    # Adding an extra key here\n    dispatch_data.update({'packages': pkg_dict})\n    dispatch_data.update({'assigned_empid': str(dsp.get('fu', {}).get('emp_id'))})\n\n    center_name_push_url = push_url + '{}/'.format(code)\n    dispatch_url = push_url + '{}/'.format(code) + dispatch_stream\n    employee_dispatch_url = employee_stream + '/{}/'.format(assigned_emp_id)\n\n    # push_center_name_to_firebase\n    if dsp.get('cn'):\n        push_fe_data_to_kinesis(center_name_push_url, 'name', dsp.get('cn'))\n\n    # push dispatch, assigned vehicle to employee and employee data to kinesis\n    push_fe_data_to_kinesis(dispatch_url, '{}'.format(dsp.dwbn), dispatch_data)\n    push_fe_data_to_kinesis(employee_dispatch_url, 'dispatch', {'id': dsp.get('dwbn'), 'status': 'approved'})\n    push_fe_data_to_kinesis(employee_dispatch_url, 'assigned_vehicleid', dsp.get('vid'))\n","sub_path":"dispatch/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":20886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"54386351","text":"#!/usr/bin/env python3\n\"\"\"\nFile Name       :   Problem071.py\nDate started    :   2012-10-06\nDate solved     :   2013-11-05\nRun time        :   0.00001 seconds\n\nConsider the fraction, n/d, where n and d are positive integers.  If n<d and\nHCF(n, d)=1, it is called a reduced proper fraction.\n\nIf we list the set of reduced proper fractions for d <= 8 in ascending order of\nsize, we get:\n    1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2,\n    4/7, 3/5, 5/8, 2/3, 5/7, 3/4, 4/5, 5/6, 6/7, 7/8\n\nIt can be seen that 2/5 is the fraction immediately to the left of 3/7.\n\nBy listing the set of reduced proper fractions for d <= 1,000,000 in ascending\norder of size, find the numerator of the fraction immediately to the left of\n3/7.\n\n\"\"\"\n\nimport project_euler\n\nPROBLEM_NUMBER = 71\nSOLVED = 1\n\n\ndef farey(n, asc=True):\n    \"\"\"Python function to print the nth Farey sequence, either ascending or\n    descending.\n\n    http://en.wikipedia.org/wiki/Farey_sequence\"\"\"\n    b, d = 1, 7\n    if asc:\n        a, c = 0, 1  # (*)\n    else:\n        a, c = 1, 3  # (*)\n    while (asc and c <= n) or (not asc and a > 0):\n        k = int((n + b) / d)\n        a, b, c, d = c, d, k * c - a, k * d - b\n        if (a, b) == (3, 7):\n            continue\n        return a\n\n\ndef problem071(input_=None):\n    return farey(10 ** 6, False)\n\n\ndef run():\n    print(project_euler.print_timing(problem071))\n\n\nif __name__ == \"__main__\":\n    run()\n\n","sub_path":"problems/Problem071.py","file_name":"Problem071.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"432907947","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Nov  9 22:52:04 2016\n\n@author: Sriharsha\n\"\"\"\n\ndef objective(x):\n    return entropy(np.dot(p[:,:,t],x),Pbar[:,theta])\n \ndef constr1(x):\n    x\n    \ndef constr2(x):\n    1-np.sum(x)    \n\nfrom glob import glob\nfrom scipy.optimize import fmin_cobyla\nfrom scipy.stats import entropy\nimport numpy as np\n\nframes = glob('./frames/*')\nframesTotal = np.size(frames)\n\nproposals = np.load('./5proposals.npy')#.round()\nproposals = proposals[:,0:4,:].astype(int)\nproposalsTotal = proposals.shape[0]\n\npAll = np.load('./pFeature.npy')\nqAll = np.load('./qFeature.npy')\n\n# remove frames with nan values in p features\np = np.delete(pAll,np.unique(np.where(np.isnan(pAll))[2]),2)\nq = np.delete(qAll,np.unique(np.where(np.isnan(pAll))[2]),2)\n\n# initialize model parameters\nP = 1/p.shape[2] * np.sum(p,1)\nc = np.ones((p.shape[2],1))\nPbar = np.dot(P,c)/np.sum(c)\nPbar = np.append(np.zeros(Pbar.shape),Pbar,axis=1)\nR = np.zeros((Pbar.shape[0],p.shape[2]))\nB = np.zeros((Pbar.shape[0],q.shape[2]))\n\ngammaInit = np.random.uniform(0,1,(1,proposalsTotal))\ngammaInit = gammaInit/gammaInit.sum()\ngamma=np.zeros((p.shape[2],gammaInit.shape[1]))\ntheta=0\n\n#while(np.linalg.norm(Pbar[:,theta-1]-Pbar[:,theta])!=0):\ntheta+=1\n    \nfor t in range(0):#p.shape[2]):\n    gamma[t,:] = fmin_cobyla(objective, gammaInit.transpose(), [constr1, constr2], \\\n        rhoend=1e-7)\n","sub_path":"getModelsRBP.py","file_name":"getModelsRBP.py","file_ext":"py","file_size_in_byte":1374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"38565996","text":"import requests\nimport threading\nimport json\nimport time\nimport datetime\nimport concurrent.futures\n\ndef download_images(json_file):\n\twith open(json_file, 'r') as photo_file:\n\t\tphotos = json.load(photo_file)\n\n\tfor i in photos['images']:\n\t\timages = requests.get(i['URL'])\n\t\twith open(f\"{i['name']}.jpeg\", 'wb') as uploads:\n\t\t\tuploads.write(images.content)\n\nstarting_time = datetime.datetime.now()# .strftime('%m/%d/%Y, %H:%M:%S')\n\nprint(f\"Starting Time: {starting_time.strftime('%m/%d/%Y, %H:%M:%S')}\")\n\nthreading_list = []\n\nx = threading.Thread(target=download_images, args=(\"images.json\",))\nx.start()\n\n\n\n# with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n#    executor.map(download_images, [\"images.json\"])\n    \n# print(f\"Code Execution Time: {(datetime.datetime.today() - starting_time).seconds}\")\ndownload_images('images.json')\n\nprint(f\"Code Execution Time: {(datetime.datetime.today() - starting_time).seconds}\")\n\n\n","sub_path":"25.01/Threading.py","file_name":"Threading.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"20995447","text":"#   My solution\n# -----------------------\n##def sum_timestamps(tstamps):\n##\n##    sum_minute = sum_second = sum_hour = 0\n##    for x in tstamps:\n##        if x.count(':') == 2:\n##            hour, minute, second = x.split(':')\n##        else:\n##            minute, second = x.split(':')\n##            hour = 0\n##            \n##        sum_hour += int(hour)\n##        sum_minute += int(minute)\n##        sum_second += int(second)\n##        \n##    sum_minute += int(sum_second / 60)\n##    sum_second = sum_second % 60\n##\n##    sum_hour += int(sum_minute / 60)\n##    sum_minute = sum_minute % 60\n##    \n##    return str(sum_hour) + \":\" + str(sum_minute).rjust(2,'0') + \":\" + str(sum_second).rjust(2,'0')\n\n\n#  Better solution\n# ----------------------\ndef parse_time(time_string):\n    sections = time_string.split(':')\n    try:\n        hours, minutes, seconds = sections\n    except ValueError:\n        hours = 0\n        minutes, seconds = sections\n    return int(hours)*3600 + int(minutes)*60 + int(seconds)\n\ndef format_time(seconds):\n    minutes, seconds = divmod(seconds, 60)\n    hours, minutes = divmod(minutes, 60)\n    if hours:\n        return f\"{hours}:{minutes:02d}:{seconds:02d}\"\n    else:\n        return f\"{minutes:01d}:{seconds:02d}\"\n    \n##def sum_timestamps(timestamps):\n##    total_time = 0\n##    for time in timestamps:\n##        total_time += parse_time(time)\n##    return format_time(total_time)\n\n# use generator expression\ndef sum_timestamps(timestamps):\n    total_time = sum(\n        parse_time(time)\n        for time in timestamps\n    )\n    return format_time(total_time)\n\n\n\ndef main():\n    time_stamps = ['1:05:32','0:03:37']\n    print(time_stamps)\n    print(sum_timestamps(time_stamps))\n    time_stamps = ['2:03:27','1:08:17','4:28:32']\n    print(time_stamps)\n    print(sum_timestamps(time_stamps))\n    time_stamps = ['4:03:27','2:08:17','1:28:32', '0:45:03']\n    print(time_stamps)\n    print(sum_timestamps(time_stamps))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Morsels/sum_timestamps/sum_timestamps.py","file_name":"sum_timestamps.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"545342590","text":"import requests\nfrom newsapi import NewsApiClient\nimport json\n\nwith open('credentials.json', mode='r', encoding='utf-8') as f:\n    credentials = json.load(f)\n    ADDRESS = credentials[\"address\"]\n    API_KEY = credentials[\"api_key\"]\n\n\ndef news_articles():\n    news_api = NewsApiClient(api_key=API_KEY[8:])\n    headlines = news_api.get_everything(q='Ukraine',\n                                        from_param='2020-04-20',\n                                        to='2020-05-10',\n                                        page=1\n                                        )\n    with open('example_news_api.json', 'w', encoding='utf-8') as f:\n        json.dump(headlines, f, ensure_ascii=False, indent=4)\n\nnews_articles()","sub_path":"API Examples/newsapi_example.py","file_name":"newsapi_example.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"472400998","text":"import subprocess as sp\nimport os\nimport sys \nimport time\nimport random\n\ndef input_gen():\n    return \"GET  \" + \"http://google.com/\" + \"a\"*(random.randint(1, 100))\n    \ndef fuzz(inp, i):\n    global prog\n    log = \"log_\" + str(i)\n    p = sp.Popen(\"valgrind --leak-check=full --log-file={} {} -w 1234\".format(log, prog), shell=True)\n    sp.Popen(\"echo \\\"{}\\\" | nc 0 1234\".format(inp), shell=True, stdout=sp.PIPE)\n    time.sleep(1)\n    p.terminate()\n\nprog = sys.argv[1]\n\nfor i in xrange(100):\n    fuzz(input_gen(), i)\n","sub_path":"sdg/fuzz/fuzz.py","file_name":"fuzz.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"574494329","text":"''' \nIn this script we solve the 2-d wave equation over an L-shaped domain \nwith a RBF-FD scheme. The domain has free boundary conditions. Time \nintegration is done with the fourth-order Runge-Kutta method.\n'''\nimport numpy as np\nfrom rbf.fd import weight_matrix\nfrom rbf.nodes import menodes,neighbors\nfrom rbf.geometry import contains,simplex_outward_normals\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import ode\nfrom scipy.interpolate import griddata\nfrom scipy.sparse.linalg import spsolve\n\n# define the problem domain\nvert = np.array([[0.0,0.0],[2.0,0.0],[2.0,1.0],\n                 [1.0,1.0],[1.0,2.0],[0.0,2.0]])\nsmp = np.array([[0,1],[1,2],[2,3],[3,4],[4,5],[5,0]])\ntimes = np.linspace(0.0,2.0,5) # output times\nN = 50000 # total number of nodes\nnodes,smpid = menodes(N,vert,smp) # generate nodes\ninterior = np.nonzero(smpid==-1)[0].tolist() # identify boundary nodes\nboundary = np.nonzero(smpid>=0)[0].tolist() # identify boundary nodes\n# calculate surface normal vector for each boundary node\nnormals = simplex_outward_normals(vert,smp)[smpid[boundary]]\n# dx is the shortest distance between any two nodes\ndx = np.min(neighbors(nodes,2)[1][:,1])\n# add ghost nodes to greatly improve accuracy at the free surface\nnodes = np.vstack((nodes,nodes[boundary] + 0.5*dx*normals))\nghost = range(N,N+len(boundary)) # ghost node indices\n# create differentiation matrices for the interior and boundary nodes\nD = weight_matrix(nodes[interior+boundary],nodes,[(2,0),(0,2)],n=30)\ndD = weight_matrix(nodes[boundary],nodes,[(1,0),(0,1)],coeffs=normals.T,n=30)\n# create initial and boundary conditions\nr = np.sqrt((nodes[interior+boundary,0] - 0.5)**2 + \n            (nodes[interior+boundary,1] - 0.5)**2)\nu_init = 1.0/(1 + (r/0.05)**4) # initial u in the interior\ndudt_init = np.zeros(N) # initial velocity in the interior\nu_bnd = np.zeros(len(boundary)) # boundary conditions\n# Make state vector containing the initial displacements and velocities\nv = np.hstack([u_init,dudt_init])\n\ndef f(t,v):\n  ''' \n  Function used for time integration. This calculates the time \n  derivative of the current state vector. \n  '''\n  # make a vector of displacements which will includes ghost nodes\n  v = v.reshape((2,-1))\n  u = np.empty(len(nodes))\n  # interior and boundary nodes come from the state vector\n  u[interior+boundary] = v[0] \n  # solve for the ghost node values that satisfy the boundary \n  # conditions. NOTE: this can be made much more efficient.\n  u[ghost] = spsolve(dD[:,ghost],u_bnd - dD[:,interior+boundary].dot(v[0]))\n  # return time derivative of the state vector\n  return np.hstack([v[1],D.dot(u)])\n              \n# perform time integration with 'dopri5', which is Runge Kutta \nintegrator = ode(f).set_integrator('dopri5',nsteps=1000)\nintegrator.set_initial_value(v,times[0])\nsoln = []\nfor t in times[1:]:\n  # calculate state vector at time *t*\n  v = integrator.integrate(t).reshape((2,-1))\n  soln += [v[0]] # only save displacements\n\n# plot the results\nfig,axs = plt.subplots(2,2,figsize=(7,7))\nfor i,t in enumerate(times[1:]):\n  ax = axs.ravel()[i]\n  xg,yg = np.mgrid[0.0:2.0:200j,0:2.0:200j]\n  points = np.array([xg.ravel(),yg.ravel()]).T\n  # interpolate the solution onto a grid\n  ug = griddata(nodes[interior+boundary],soln[i],(xg,yg),method='linear')\n  # mask the points outside of the domain\n  ug.ravel()[~contains(points,vert,smp)] = np.nan \n  # plot the boudary\n  for s in smp: ax.plot(vert[s,0],vert[s,1],'k-')\n  ax.imshow(ug,extent=(0.0,2.0,0.0,2.0),origin='lower',\n            vmin=-0.2,vmax=0.2,cmap='seismic')\n  ax.set_aspect('equal')\n  ax.text(0.6,0.85,'time : %s\\nnodes : %s' % (t,N),\n          transform=ax.transAxes,fontsize=10)\n  ax.tick_params(labelsize=10)\n  ax.set_axis_bgcolor((0.8,0.8,0.8))\n  ax.set_xlim(-0.1,2.1);ax.set_ylim(-0.1,2.1)\n    \nplt.tight_layout()    \nplt.savefig('../figures/fd.f.png')\nplt.show()\n","sub_path":"prj_src/nn_examples/rbf_examples/RBF-master/docs/scripts/fd.f.py","file_name":"fd.f.py","file_ext":"py","file_size_in_byte":3845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"126248476","text":"import argparse\r\nimport json\r\nfrom collections import Counter\r\nfrom functools import partial\r\nfrom typing import List, Dict\r\nimport sys\r\nimport os\r\nimport logging\r\n\r\nimport numpy as np\r\nimport torch\r\nimport random\r\nfrom multiprocessing import Pool\r\nfrom tqdm import tqdm\r\nfrom transformers import PreTrainedTokenizer, AutoTokenizer, RobertaTokenizer, BertTokenizer\r\n\r\nsys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))\r\n\r\nfrom oss_utils import torch_save_to_oss, load_buffer_from_oss\r\n\r\nlogging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',\r\n                    datefmt='%m/%d/%Y %H:%M:%S',\r\n                    level=logging.INFO)\r\nlogger = logging.getLogger(f'fangxi.{__name__}')\r\n\r\n\"\"\"\r\nConverting like processor `wiki_pre_train_ent_baseline_mlm`.\r\n\r\nMask as BERT do.\r\n\"\"\"\r\n\r\n\r\ndef generate_pre_sent_id(example, offset):\r\n    question = example['question']\r\n    passage = example['passage']\r\n\r\n    true_id2rank = {\r\n        s['index']: idx for idx, s in enumerate(question + passage)\r\n    }\r\n\r\n    pre_idx = []\r\n    for _q_sent in question:\r\n        q_sent_id = _q_sent['index']\r\n        pre_sent_id = q_sent_id + offset\r\n        if pre_sent_id < 0 or pre_sent_id >= len(true_id2rank):\r\n            pre_idx.append(-1)\r\n        else:\r\n            pre_idx.append(true_id2rank[pre_sent_id])\r\n    return pre_idx\r\n\r\n\r\ndef get_sentence_spans(token_tuple, _start_offset, _sent_id_map, _sentence_spans):\r\n    ini_sent_ids, ini_tokens = zip(*token_tuple)\r\n    ini_sent_ids = Counter(ini_sent_ids)\r\n    sorted_ini_sent_ids = sorted(\r\n        ini_sent_ids.items(), key=lambda x: x[0])\r\n\r\n    _start_offset = _start_offset\r\n    for ini_s_id, s_len in sorted_ini_sent_ids:\r\n        _new_t_s = _start_offset\r\n        _new_t_e = _start_offset + s_len - 1\r\n        _start_offset = _new_t_e + 1\r\n        _sent_id_map[ini_s_id] = len(_sentence_spans)\r\n        _sentence_spans.append((_new_t_s, _new_t_e))\r\n\r\n    return ini_tokens\r\n\r\n\r\ndef process_example(example, max_seq_length, if_bpe, mask_ratio=0.15):\r\n    true_idx2ini_idx_map = {}\r\n\r\n    for sent_id, q_sent in enumerate(example['question']):\r\n        true_idx = q_sent['index']\r\n        true_idx2ini_idx_map[true_idx] = sent_id\r\n\r\n    q_sent_num = len(example['question'])\r\n\r\n    for sent_id, d_sent in enumerate(example['passage']):\r\n        true_idx = d_sent['index']\r\n        true_idx2ini_idx_map[true_idx] = sent_id + q_sent_num\r\n\r\n    all_sentences = example['question'] + example['passage']\r\n\r\n    true_sentences = []\r\n    for _index in range(len(all_sentences)):\r\n        true_sentences.append(all_sentences[true_idx2ini_idx_map[_index]])\r\n\r\n    pure_text = ' '.join([_sent['text'] for _sent in true_sentences])\r\n\r\n    test_input_ids = tokenizer.tokenize(pure_text)\r\n    test_noised_input_ids = tokenizer.tokenize(noised_text)\r\n\r\n    if not len(test_input_ids) == len(test_noised_input_ids):\r\n        # logger.warning(\"Consistency checking failed.\")\r\n        return None\r\n\r\n    tokenizer_outputs = tokenizer(pure_text, padding='max_length',\r\n                                  max_length=max_seq_length, truncation='longest_first')\r\n\r\n    input_ids = tokenizer_outputs['input_ids']\r\n    if 'token_type_ids' in tokenizer_outputs:\r\n        token_type_ids = tokenizer_outputs['token_type_ids']\r\n    else:\r\n        token_type_ids = [0]\r\n    attention_mask = tokenizer_outputs['attention_mask']\r\n\r\n    mask_token_num = int(len(input_ids) * mask_ratio)\r\n\r\n    \r\n\r\n    return {\r\n        \"input_ids\": input_ids,\r\n        \"token_type_ids\": token_type_ids,\r\n        \"attention_mask\": attention_mask,\r\n        \"labels\": pure_input_ids\r\n    }\r\n\r\n\r\ndef data2tensors(all_features: List[Dict]):\r\n    all_input_ids = torch.tensor([f[\"input_ids\"] for f in all_features], dtype=torch.long)\r\n    all_token_type_ids = torch.tensor([f[\"token_type_ids\"] for f in all_features], dtype=torch.long)\r\n    all_attention_mask = torch.tensor([f[\"attention_mask\"] for f in all_features], dtype=torch.long)\r\n    all_labels = torch.tensor([f[\"labels\"] for f in all_features], dtype=torch.long)\r\n\r\n    all_feature_index = torch.arange(all_input_ids.size(0), dtype=torch.long)\r\n\r\n    logger.info(f'input_ids size: {all_input_ids.size()}')\r\n    logger.info(f'token_type_ids size: {all_token_type_ids.size()}')\r\n    logger.info(f'attention_mask size: {all_attention_mask.size()}')\r\n    logger.info(f'labels size: {all_labels.size()}')\r\n\r\n    return all_input_ids, all_token_type_ids, all_attention_mask, all_labels, all_feature_index\r\n\r\n\r\ndef initializer(tokenizer_for_convert: PreTrainedTokenizer):\r\n    global tokenizer\r\n    tokenizer = tokenizer_for_convert\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    parser = argparse.ArgumentParser()\r\n    parser.add_argument(\"--input_file_list\", type=str, required=True)\r\n    parser.add_argument(\"--output_dir\", type=str, required=True)\r\n    parser.add_argument(\"--tokenizer_name\", type=str, required=True)\r\n    parser.add_argument(\"--oss_dir\", type=str, default=None)\r\n\r\n    parser.add_argument(\"--seed\", type=int, default=42)\r\n\r\n    args = parser.parse_args()\r\n\r\n    torch.manual_seed(args.seed)\r\n    np.random.seed(args.seed)\r\n    random.seed(args.seed)\r\n\r\n    MAX_SEQ_LENGTH = 512\r\n    IF_ADD_LM = True\r\n\r\n    cache_suffix = f'wiki_pre_train_mlm_{MAX_SEQ_LENGTH}_{IF_ADD_LM}'\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name)\r\n\r\n    if isinstance(tokenizer, RobertaTokenizer):\r\n        IF_BPE = True\r\n    elif isinstance(tokenizer, BertTokenizer):\r\n        IF_BPE = False\r\n    else:\r\n        raise RuntimeError(tokenizer.__class__.__name__)\r\n\r\n    # set_bucket_dir(args.output_dir)\r\n    print(cache_suffix, IF_BPE)\r\n    print(os.cpu_count())\r\n\r\n    input_file_list = json.load(open(args.input_file_list, \"r\"))\r\n    input_file_steps = {}\r\n    for input_file in input_file_list:\r\n\r\n        output_file_name = f'{input_file.split(\"/\")[-1]}_{cache_suffix}'\r\n\r\n        logger.info(f\"Reading data set from {input_file}...\")\r\n\r\n        try:\r\n            _cache_examples, _cache_tensors = torch.load(load_buffer_from_oss(os.path.join(args.oss_dir, output_file_name)))\r\n            logger.info(\"Find cache file from oss. Saving to volume...\")\r\n            input_file_steps[input_file] = _cache_tensors[0].size(0)\r\n            torch.save((_cache_examples, _cache_tensors), os.path.join(args.output_dir, output_file_name))\r\n            continue\r\n        except:\r\n            pass\r\n\r\n        examples = json.load(open(input_file, \"r\", encoding=\"utf-8\"))\r\n\r\n        unique_id = 100000\r\n\r\n        # features = []\r\n        # for example in tqdm(examples, total=len(examples)):\r\n        #     features.append(process_example(example, max_seq_length=MAX_SEQ_LENGTH, if_bpe=IF_BPE))\r\n\r\n        with Pool(os.cpu_count(), initializer=initializer, initargs=(tokenizer,)) as p:\r\n            annotate_ = partial(\r\n                process_example,\r\n                max_seq_length=MAX_SEQ_LENGTH,\r\n                if_bpe=IF_BPE\r\n            )\r\n            features = list(\r\n                tqdm(\r\n                    p.imap(annotate_, examples, chunksize=128),\r\n                    total=len(examples),\r\n                    desc=\"convert examples to features\",\r\n                    disable=False,\r\n                )\r\n            )\r\n\r\n        new_features = []\r\n        example_index = 0\r\n        for example_feature in tqdm(\r\n                features, total=len(features), desc=\"add example index and unique id\",\r\n                disable=False\r\n        ):\r\n            if example_feature is None:\r\n                continue\r\n            example_feature[\"example_index\"] = example_index\r\n            example_feature[\"unique_id\"] = unique_id\r\n            example_index += 1\r\n            unique_id += 1\r\n            new_features.append(example_feature)\r\n        features = new_features\r\n\r\n        logger.info(f\"Converting {len(features)} features.\")\r\n        logger.info(f\"Starting convert features to tensors.\")\r\n        all_tensors = data2tensors(features)\r\n\r\n        logger.info(f\"Saving to oss...\")\r\n\r\n        # torch_save_to_oss((examples, all_tensors), output_file_name)\r\n        torch.save((examples, all_tensors), os.path.join(args.output_dir, output_file_name))\r\n        # torch_save_to_oss((examples, all_tensors), os.path.join(args.oss_dir, output_file_name))\r\n\r\n        input_file_steps[input_file] = all_tensors[0].size(0)\r\n\r\n        del examples\r\n        del all_tensors\r\n\r\n        logger.info(\"Finished.\")\r\n\r\n    steps_file_name = args.input_file_list[:-5] + '-' + cache_suffix + '-steps.json'\r\n    with open(os.path.join(args.output_dir, steps_file_name), 'w') as f:\r\n        json.dump(input_file_steps, f, indent=2)\r\n","sub_path":"processors/wiki_convert_examples_baseline_mlm_ini.py","file_name":"wiki_convert_examples_baseline_mlm_ini.py","file_ext":"py","file_size_in_byte":8632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"342367372","text":"from django.shortcuts import render_to_response, render\nfrom django.core.mail import send_mail\nfrom django.http import HttpResponse\nfrom hollytea.forms import ContactForm\n#from django.contrib.flatpages.models import FlatPage\n#import datetime\n\ndef home_old(request):\n        \"\"\" Home Page  \"\"\"\n        return HttpResponse(\"<h1>Home Page</h1>\")\n\ndef home(request):\n        \"\"\" Simple home page \"\"\"\n        page_title = \" Hollytea.ru - Hollyest tea ever! \"\n        page_text = \" Hollytea.ru Page - Just simple text, nothing fancy\"\n        #posts = FlatPage.objects.all()\n        return render_to_response('home.html', locals())\n#def blog(request):\n\t#return \n\n\ndef Contact(request):\n\tif request.method == 'POST':\n\t\tform = ContactForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tcd = form.cleaned_data\n\t\t\t#send_mail(\n\t\t\t#\tcd['subject'],\n\t\t\t#\tcd['message'],\n\t\t\t#\tcd.get('email', 'noreplay@localhost'),\n\t\t\t#\t['maxya123@ya.ru'],\n\t\t\t#)\n\t\t\treturn HttpResponseRedirect('/contact/')\n\n\telse:\n\t\tform = ContactForm(\n\t\t\tinitial = {'subject': 'I love your site!', 'message': 'Your site rocks!'}\n\t\t)\n\treturn render(request,'contact_form.html', {'form': form})\n\n","sub_path":"hollytea/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"517162097","text":"import os\nimport glob\n\nimport markdown\nfrom jinja2 import Template\n\n# GLOBAL VARIABLES\ndocs_path = \"docs\"\nbuild_path = \"docs/build\"\ntemplate_file = \"scripts/notes/template.html\"\nmacros_file = \"scripts/notes/macros.tex\"\nmd = markdown.Markdown(extensions=[\"fenced_code\"])\n\n\ndef get_notes(docs_path):\n    note_files = []\n    for x in os.walk(docs_path):\n        for y in glob.glob(os.path.join(x[0], '*.html')):\n            note_files.append(y)\n    return sorted(note_files)\n\n\ndef escape_html(s):\n    return s.replace(\"\\\\\", \"&#92;\").replace(\"_\", \"&#95;\")\n\n\ndef unescape_html(s):\n    return s.replace(\"&amp;#92;\", \"\\\\\") .replace(\"&amp;#95;\", \"_\")\n\n\ndef render_note(docs_path, template_file, macros_file, note_file, sidebar):\n    # Macros\n    macros = open(macros_file, mode=\"r\", encoding=\"utf-8\").read()\n    macros = \"$$\" + macros + \"$$\"\n\n    # Convert note file to html\n    content = macros + \"\\n\"\n    content += open(note_file, mode=\"r\", encoding=\"utf-8\").read()\n\n    # Render template\n    template = Template(open(template_file).read())\n    html = template.render(sidebar=sidebar, content=content)\n\n    # Output rendered html\n    docs_path += \"/\" if docs_path[-1] != \"/\" else \"\"\n    fname = note_file.replace(docs_path, \"\")\n    print(fname)\n    output_path = os.path.join(build_path, fname)\n    output_file = open(output_path, \"w\")\n    output_file.write(html)\n    output_file.close()\n\n\nsidebar = open(os.path.join(docs_path, \"sidebar.md\")).read()\nsidebar = md.convert(escape_html(sidebar))\nsidebar = unescape_html(sidebar)\n\nnote_files = get_notes(docs_path)\nfor note_file in note_files:\n    print(\"processing [%s]\" % note_file)\n    # render_note(build_path, template_file, macros_file, note_file, sidebar)\n","sub_path":"scripts/notes/notes.py","file_name":"notes.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"385363969","text":"#!/usr/bin/env python\nimport time\n\nfrom mininet.net import Mininet\nfrom mininet.node import Controller, RemoteController, OVSController\nfrom mininet.node import CPULimitedHost, Host, Node\nfrom mininet.node import OVSKernelSwitch, UserSwitch\nfrom mininet.node import IVSSwitch\nfrom mininet.cli import CLI\nfrom mininet.log import setLogLevel, info\nfrom mininet.link import TCLink, Intf\nfrom subprocess import call\n\n\ndef myNetwork():\n    net = Mininet(topo=None,\n                  build=False,\n                  ipBase='10.0.0.0/8')\n\n    info('*** Adding controller\\n')\n    info('*** Add switches\\n')\n    r1 = net.addHost('r1', cls=Node, ip='0.0.0.0')\n    r1.cmd('sysctl -w net.ipv4.ip_forward=1')\n\n    info('*** Add hosts\\n')\n    h2 = net.addHost('h2', cls=Host, ip='10.0.0.2', defaultRoute=None)\n    h4 = net.addHost('h4', cls=Host, ip='10.0.0.4', defaultRoute=None)\n    h1 = net.addHost('h1', cls=Host, ip='10.0.0.1', defaultRoute=None)\n    h3 = net.addHost('h3', cls=Host, ip='10.0.0.3', defaultRoute=None)\n    h5 = net.addHost('h5', cls=Host, ip='10.0.0.6', defaultRoute=None)\n    h6 = net.addHost('h6', cls=Host, ip='10.0.0.3', defaultRoute=None)\n\n    info('*** Add links\\n')\n\n    net.addLink(r1, h1, bw=0.000001)\n    net.addLink(r1, h2, bw=0.000001)\n    net.addLink(r1, h3, bw=0.000001)\n    net.addLink(r1, h4, bw=0.000001)\n    net.addLink(r1, h5, bw=0.000001)\n    net.addLink(r1, h6, bw=0.000001)\n\n    info('*** Starting network\\n')\n    net.build()\n    info('*** Starting controllers\\n')\n    for controller in net.controllers:\n        controller.start()\n\n    info('*** Starting switches\\n')\n\n    info('*** Post configure switches and hosts\\n')\n    h1.cmd('ifconfig h1-eth0 192.168.10.1/24')\n    h2.cmd('ifconfig h2-eth0 192.168.20.1/24')\n    h3.cmd('ifconfig h3-eth0 192.168.30.1/24')\n    h4.cmd('ifconfig h4-eth0 192.168.40.1/24')\n    h5.cmd('ifconfig h5-eth0 192.168.50.1/24')\n    h6.cmd('ifconfig h6-eth0 192.168.60.1/24')\n\n    r1.cmd('ifconfig r1-eth0 192.168.10.2/24')\n    r1.cmd('ifconfig r1-eth1 192.168.20.2/24')\n    r1.cmd('ifconfig r1-eth2 192.168.30.2/24')\n    r1.cmd('ifconfig r1-eth3 192.168.40.2/24')\n    r1.cmd('ifconfig r1-eth4 192.168.50.2/24')\n    r1.cmd('ifconfig r1-eth5 192.168.60.2/24')\n\n    h1.cmd('route add default gw 192.168.10.2')\n    h2.cmd('route add default gw 192.168.20.2')\n    h3.cmd('route add default gw 192.168.30.2')\n    h4.cmd('route add default gw 192.168.40.2')\n    h5.cmd('route add default gw 192.168.50.2')\n    h6.cmd('route add default gw 192.168.60.2')\n\n    r1.cmd('python3 -u server.py > server.log &')\n    time.sleep(1)\n    h1.cmdPrint('python3 client.py -n h1 -i 192.168.10.2')\n    h2.cmd('python3 client.py -n h2 -i 192.168.20.2')\n    h3.cmd('python3 client.py -n h3 -i 192.168.30.2')\n    h4.cmd('python3 client.py -n h4 -i 192.168.40.2')\n    h5.cmd('python3 client.py -n h5 -i 192.168.50.2')\n    h6.cmd('python3 client.py -n h6 -i 192.168.60.2')\n\n    CLI(net)\n    net.stop()\n\n\nif __name__ == '__main__':\n    setLogLevel( 'info' )\n    myNetwork()\n\n","sub_path":"CS339-lab3/CS.py","file_name":"CS.py","file_ext":"py","file_size_in_byte":3002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"208591639","text":"#!/usr/bin/python\n\n# ================================================= #\n#    Distributed Control Systems Monitor Main        #\n# ================================================= #\n\n# used to parse files more easily\nfrom __future__ import with_statement\n\n# Numpy module\nimport numpy as np\n\n# for command-line arguments\nimport sys\n\n# to generate random numbers\nimport random\n\n# for communicate with pic\nimport serial\n\n# for recive data from pic\nimport array\n\n# for sleep\nimport time\n\n# to unpack the data that sends dsPIC\nimport struct\n\n# to print binary vlaues to ascii\nimport binascii\n\n# Qt4 bindings for core Qt functionalities (non-GUI)\nfrom PyQt4 import QtCore\n# Python Qt4 bindings for GUI objects\nfrom PyQt4 import QtGui\n\ntry:\n    _fromUtf8 = QtCore.QString.fromUtf8\nexcept AttributeError:\n    _fromUtf8 = lambda s: s\n\n# import the MainWindow widget from the converted .ui files\nfrom dcsmmainwindow import Ui_MainWindow\n\nINPUT_DATA_SIZE = 23 # 71\nID_PERCENT = 4\n\nclass DesignerMainWindow(QtGui.QMainWindow, Ui_MainWindow):\n    \"\"\"Customization for Qt Designer created window\"\"\"\n    graph_timer = QtCore.QTimer()\n    data_timer = QtCore.QTimer()\n    updated_data = 0\n    ser = serial.Serial('/dev/ttyUSB0', 115200, timeout=10)\n    # TODO : he iniciat el valor de time, nomes per veure una grafica bona\n    time = 0\n    \n    #values for the graphic\n    #xdata, ydata, ymax, ywarn, ymin, ycrit = [], [], [], [], [], []\n    \n\n    \n    def __init__(self, parent = None):\n        # initialization of the superclass\n        super(DesignerMainWindow, self).__init__(parent)\n        # setup the GUI --> function generated by pyuic4\n        self.setupUi(self)\n        \n        # connect the signals with the slots\n        QtCore.QObject.connect(self.pushButton_reload, QtCore.SIGNAL(\"clicked()\"), self.update_graph)\n        QtCore.QObject.connect(self.graph_timer, QtCore.SIGNAL(\"timeout()\"), self.update_graph)\n        QtCore.QObject.connect(self.data_timer, QtCore.SIGNAL(\"timeout()\"), self.calculate_data)\n        QtCore.QObject.connect(self.textBrowser, QtCore.SIGNAL(\"textChanged()\"), self.move_scroll_bar_down)\n        QtCore.QObject.connect(self.pushButton_monitor, QtCore.SIGNAL(\"toggled(bool)\"), self.toggled_monitor)\n        \n        # Changed Lenguage Signals\n        QtCore.QObject.connect(self.actionCatala, QtCore.SIGNAL(\"toggled(bool)\"), self.toggled_idiom)\n        QtCore.QObject.connect(self.actionEspanol, QtCore.SIGNAL(\"toggled(bool)\"), self.toggled_idiom)\n        QtCore.QObject.connect(self.actionEnglish, QtCore.SIGNAL(\"toggled(bool)\"), self.toggled_idiom)\n        \n        # Changed Device comunication\n        QtCore.QObject.connect(self.actionPC_Controlador, QtCore.SIGNAL(\"toggled(bool)\"), self.toggled_comunication)\n        QtCore.QObject.connect(self.actionPC_Monitor, QtCore.SIGNAL(\"toggled(bool)\"), self.toggled_comunication)\n        QtCore.QObject.connect(self.actionPC_Sensor_Actuador, QtCore.SIGNAL(\"toggled(bool)\"), self.toggled_comunication)\n        \n        # Changed saturation percent value\n        QtCore.QObject.connect(self.slider_saturation, QtCore.SIGNAL(_fromUtf8(\"valueChanged(int)\")), self.percent_changed)\n        \n        #QtCore.QObject.connect(self.mplactionOpen, QtCore.SIGNAL('triggered()'), self.select_file)\n        #QtCore.QObject.connect(self.mplactionQuit, QtCore.SIGNAL('triggered()'), QtGui.qApp, QtCore.SLOT(\"quit()\"))\n        \n        # clear data of the serial port\n        #self.ser.flushInput()\n        # start timer data calculate\n        #self.data_timer.start(20)\n        # start timer graphic refresh\n        #self.graph_timer.start(100)\n        \n\n        self.referenceLine, = self.mplWidget.canvas.ax.plot([], [], animated=True, lw=2, color='blue',      label=\"r\")\n        self.x0Line,        = self.mplWidget.canvas.ax.plot([], [], animated=True, lw=1, color='grey',      label=\"x0\")\n        self.x1Line,        = self.mplWidget.canvas.ax.plot([], [], animated=True, lw=1, color='orange',    label=\"x1\")\n        self.uLine,         = self.mplWidget.canvas.ax.plot([], [], animated=True, lw=1, color='red',       label=\"u\")\n        \n    \n    def generate_data(self):\n        \"\"\"Parse a text file to extract letters frequencies\"\"\"\n        #while self.pushButton_monitor.isChecked() == 1:\n        #for x in range(0,12):\n        while self.ser.inWaiting() > 100:\n            data = array.array('c')\n            data.append(self.ser.read(1))\n            while data[0] != chr(1):\n                data[0] = self.ser.read(1)\n        \n            while self.ser.inWaiting() < (INPUT_DATA_SIZE-1):\n                time.sleep(0.05);\n                \n            data = self.ser.read(INPUT_DATA_SIZE-1)\n    \n            t  = struct.unpack('I', data[3]+data[2]+data[1]+data[0])\n            r  = struct.unpack('f', data[4]+data[5]+data[6]+data[7])\n            x0 = struct.unpack('f', data[8]+data[9]+data[10]+data[11])\n            x1 = struct.unpack('f', data[12]+data[13]+data[14]+data[15])\n            u  = struct.unpack('f', data[16]+data[17]+data[18]+data[19])\n            \n            #TODO: he cambiat el valor de les coordenades de temps\n            self.time = t[0]*25e-9\n            #self.time = self.time+0.01\n            \n            aux_str  = \" t = \"+str(self.time)+\"\\t\"\n            aux_str += \" r = \"+str(r[0])+\"\\t\"\n            aux_str += \" u = \"+str(u[0])+\"\\t\"\n            aux_str += \" x0 = \"+str(x0[0])+\"\\t\"\n            aux_str += \" x1 = \"+str(x1[0])+\"\\n\"\n            \n            self.textBrowser.insertPlainText(aux_str)\n            \n            self.graf_t.append(self.time)\n            self.graf_r.append(r[0])\n            self.graf_x0.append(x0[0])\n            self.graf_x1.append(x1[0])\n            self.graf_u.append(u[0])\n            \n            self.referenceLine.set_data(self.graf_t, self.graf_r)\n            self.x0Line.set_data(self.graf_t, self.graf_x0)\n            self.x1Line.set_data(self.graf_t, self.graf_x1)\n            self.uLine.set_data(self.graf_t, self.graf_u)\n            \n            # reload number of samples lavel\n            self.label_samples_value.setText(str(self.graf_t.buffer_info()[1]))\n            # reload number of waiting chars in serial rx buffer\n            self.label_rx_buff_value.setText(str(self.ser.inWaiting()))\n            \n            \n            # \n            self.updated_data = 1\n        \n        \n    \n    def update_graph(self):\n        \"\"\"Updates the graph with new letters frequencies\"\"\"\n        \n        if self.pushButton_monitor.isChecked() == 0:\n            return\n        \n        if self.updated_data == 1 :\n            # clear the Axes\n            #self.mplWidget.canvas.ax.clear()\n            \n            # enable grid only on the Y axis\n            #self.mplWidget.canvas.ax.get_yaxis().grid(True)\n            self.mplWidget.canvas.ax.set_ylim(-1, 1)\n            self.mplWidget.canvas.ax.set_xlim(self.time-1.5, self.time)\n            \n            # force an image redraw\n            self.mplWidget.canvas.draw()\n            \n            try:\n                #Draw the lines\n                if self.checkBox_R.isChecked():\n                    self.mplWidget.canvas.ax.draw_artist(self.referenceLine)\n                if self.checkBox_x0.isChecked():\n                    self.mplWidget.canvas.ax.draw_artist(self.x0Line)\n                if self.checkBox_U.isChecked():\n                    self.mplWidget.canvas.ax.draw_artist(self.uLine)\n                if self.checkBox_x1.isChecked():\n                    self.mplWidget.canvas.ax.draw_artist(self.x1Line)\n            except AssertionError:\n                pass\n            try:\n                self.mplWidget.canvas.blit(self.mplWidget.canvas.ax.bbox)\n            except AttributeError:\n                pass\n                     \n            \n            self.updated_data = 0\n            \n            \n        # activate the periodical update graph\n        self.graph_timer.start(50)\n        \n    def calculate_data(self):\n        \"\"\"Updates the data\"\"\"\n        if self.pushButton_monitor.isChecked() == 0:\n            return\n        \n        if self.updated_data == 0 :\n            # get the letters frequencies\n            self.generate_data()\n            \n            self.updated_data = 1\n            \n        # activate the periodical update graph\n        self.data_timer.start(1)\n\n        \n    def update_percent(self):\n        \"\"\"Updates the value percentage of Bus Saturation\"\"\"\n        \n    def move_scroll_bar_down(self):\n        \"\"\"Moves down the scroll bar\"\"\"\n        scroll = self.textBrowser.verticalScrollBar()\n        scroll.setSliderPosition(scroll.maximum())\n        \n    def toggled_monitor(self, pushed):\n        \"\"\"Starts or Stops the graphics\"\"\"\n        if pushed:\n            self.textBrowser.insertPlainText(\"\\n\\t----NOVA MONITORITZACIO----\\n\")\n            # clear graph\n            self.clean_graph()\n            # clear data of the serial port\n            self.ser.flushInput()\n            # start timer data calculate\n            #self.data_timer.start(1)\n            self.calculate_data()\n            # start timer graphic refresh\n            #self.graph_timer.start(100)\n            self.update_graph()\n            \n    def clean_graph(self):\n        \"\"\" Clean all old graphs\"\"\"\n        #self.time = 0#\n        \n        # values of microcontroller\n        self.graf_t  = array.array('f')    # time value        Unsigned Long (4bytes)\n        self.graf_r  = array.array('f')    # reference value    float (4bytes) \n        self.graf_x0 = array.array('f')    # x0 value            float (4bytes) \n        self.graf_x1 = array.array('f')    # x1 value            float (4bytes)\n        self.graf_u  = array.array('f')    # u value            float (4bytes)\n        \n    def toggled_idiom(self):\n        \"\"\" Change the lenguage of the interface \"\"\"\n        if self.actionCatala.isChecked() and self.actionCatala.isEnabled():\n            self.actionCatala.setEnabled(0)\n            self.actionEspanol.setChecked(0)\n            self.actionEspanol.setEnabled(1)\n            self.actionEnglish.setChecked(0)\n            self.actionEnglish.setEnabled(1)\n        elif self.actionEspanol.isChecked() and self.actionEspanol.isEnabled():\n            self.actionEspanol.setEnabled(0)\n            self.actionCatala.setChecked(0)\n            self.actionCatala.setEnabled(1)\n            self.actionEnglish.setChecked(0)\n            self.actionEnglish.setEnabled(1)\n        elif self.actionEnglish.isChecked() and self.actionEnglish.isEnabled():\n            self.actionEnglish.setEnabled(0)\n            self.actionCatala.setChecked(0)\n            self.actionCatala.setEnabled(1)\n            self.actionEspanol.setChecked(0)\n            self.actionEspanol.setEnabled(1)\n            \n    def toggled_comunication(self):\n        \"\"\" Change the lenguage of the interface \"\"\"\n        if self.actionPC_Controlador.isChecked() and self.actionPC_Controlador.isEnabled():\n            self.actionPC_Controlador.setEnabled(0)\n            self.actionPC_Monitor.setChecked(0)\n            self.actionPC_Monitor.setEnabled(1)\n            self.actionPC_Sensor_Actuador.setChecked(0)\n            self.actionPC_Sensor_Actuador.setEnabled(1)\n        elif self.actionPC_Monitor.isChecked() and self.actionPC_Monitor.isEnabled():\n            self.actionPC_Monitor.setEnabled(0)\n            self.actionPC_Controlador.setChecked(0)\n            self.actionPC_Controlador.setEnabled(1)\n            self.actionPC_Sensor_Actuador.setChecked(0)\n            self.actionPC_Sensor_Actuador.setEnabled(1)\n        elif self.actionPC_Sensor_Actuador.isChecked() and self.actionPC_Sensor_Actuador.isEnabled():\n            self.actionPC_Sensor_Actuador.setEnabled(0)\n            self.actionPC_Controlador.setChecked(0)\n            self.actionPC_Controlador.setEnabled(1)\n            self.actionPC_Monitor.setChecked(0)\n            self.actionPC_Monitor.setEnabled(1)\n            \n    def percent_changed(self, num):\n        \"\"\"Change de label percent value and send this information to the Monitor Flex\"\"\"\n        self.label_percent_value.setNum(num)\n        word = struct.pack(\"BBBBBBBB\", ID_PERCENT,0,0,0,0,0,0,num)\n        \n        self.textBrowser.append(\"Sent : \"+binascii.hexlify(word))\n        \n        self.ser.write(word)\n        self.textBrowser.append(\"Reciv : \"+binascii.hexlify(self.ser.read(INPUT_DATA_SIZE)))\n          \n# create the GUI application\napp = QtGui.QApplication(sys.argv)\n\n# instantiate the main window\ndmw = DesignerMainWindow()\n\n# show it\ndmw.show()\n\n# start the Qt main loop execution, exiting from this script\n# with the same return code of Qt application\nsys.exit(app.exec_())\n","sub_path":"src/DCSMonitor/tags/version04/main_dcsm.py","file_name":"main_dcsm.py","file_ext":"py","file_size_in_byte":12576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"370642392","text":"from libs.Main_class_app import *\n\n\nlogging.basicConfig(#filename='log_filename.txt',\n                    format='[%(asctime)s] %(levelname)s - %(message)s',\n                    level=logging.INFO)\nlog = logging.getLogger('Expedia')\n\nparams_for_driver = {\n    'appium_port' : '4722',\n    # 'language'    : 'en',\n    # 'locale'      : 'en_US',\n    'app_file_apk': 'com.expedia.bookings_18.25.0_free-www.apkhere.com.apk',\n    'appPackage'  : 'com.expedia.bookings',\n    'appActivity' : '.launch.activity.PhoneLaunchActivity'\n}\n\ndriver = setUp(**params_for_driver)\n\n\nclass Expedia(MainAppClass):\n    def start_app_and_enter_to_start_page(self):\n        sleep(5)\n        self.driver.implicitly_wait(10)\n        sleep(5)\n        search_edit = self.driver.find_element_by_xpath(\"//*[@content-desc='Hotels Button']\")\n        search_edit.click()\n        log.info(\"Choose hotel place...\")\n        sleep(10)\n        pass\n","sub_path":"libs/Expedia.py","file_name":"Expedia.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"407629362","text":"import numpy as np\nfrom math import factorial\nimport matplotlib.pyplot as plt\nimport argparse\nimport random\n\n# Setting up argument parsing\nparser = argparse.ArgumentParser(description=\"Plot posterior distribtution of simulated coin flips\")\nparser.add_argument('--trials', help='sets number of trials')\nparser.add_argument('--size', help='number of samples for H')\nparser.add_argument('--filename', help='sets filename to save to')\nargs = parser.parse_args()\n\nnum_trials = int(args.trials)\nnum_samples = int(args.size)\nfilename = args.filename\n# Generating flips\nnum_heads = 0\nfor x in range(num_trials):\n    if random.choice([0, 1]) is 1:\n        num_heads += 1\ntest = np.array([num_heads])\ntest.savetxt('test.txt')\n# Plotting posterior using simulated data\nh = np.linspace(0, 1, num=num_samples) \nbinomial_factor = float(factorial(num_trials)) / (float(factorial(num_heads))*float(factorial(num_trials-num_heads)))\nposterior = binomial_factor*np.power(h, num_heads)*(np.ones(num_samples)\n        - np.power(h, num_trials-num_heads))\n\nplt.plot(h, posterior)\nplt.savefig(filename)\n","sub_path":"Archive/Ph21/hw4/coin_posterior.py","file_name":"coin_posterior.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"98449167","text":"from sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\nfrom keras.layers import LSTM\nfrom keras.layers import Dense\nfrom keras.models import Sequential\nfrom keras.models import load_model\nimport keras.backend as K\nfrom keras.callbacks import ModelCheckpoint\nimport numpy as np\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\n\nscaler = MinMaxScaler()\n\ndef splitTrainTest(data, train_period, test_period, multiVariate=False, gtruthName='SPI', period=1):\n    if not multiVariate:\n        train = data[[gtruthName]].loc[:train_period - 1]\n        test = data[[gtruthName]].loc[train_period:train_period + test_period - 1]\n        \n        sc_data = scaler.fit_transform(data)\n        sc_train = sc_data[:train_period]\n        sc_test = sc_data[train_period:train_period+test_period]\n        \n        return train, test, sc_train, sc_test\n        \n    else:\n        train = data.loc[:train_period - 1]\n        test = data.loc[train_period:train_period + test_period - 1]\n        \n        sc_data = scaler.fit_transform(data)\n        sc_train = sc_data[:train_period]\n        sc_test = sc_data[train_period:train_period+test_period]\n        \n        return train, test, sc_train, sc_test\n        \ndef makeSlidingWindows(origin_train, origin_test, train, test, lookBack=2, multiVariate=False, gtruthName='SPI'):\n\n    if not multiVariate:\n        train_df = pd.DataFrame(train[:,0], index=origin_train.index)\n        test_df = pd.DataFrame(test[:,0], index=origin_test.index)\n        train_df.columns = origin_train.columns\n        test_df.columns = origin_test.columns\n        column_list = list(train_df)\n        \n        for s in range(1, lookBack + 1):\n            tmp_train = train_df[column_list].shift(s)\n            tmp_test = test_df[column_list].shift(s)\n\n            tmp_train.columns = \"shift_\" + tmp_train.columns + \"_\" + str(s)\n            tmp_test.columns = \"shift_\" + tmp_test.columns + \"_\" + str(s)\n\n            train_df[tmp_train.columns] = train_df[column_list].shift(s)\n            test_df[tmp_test.columns] = test_df[column_list].shift(s)\n\n        return train_df, test_df\n\n    else:\n        train_df = pd.DataFrame(train, index=origin_train.index)\n        test_df = pd.DataFrame(test, index=origin_test.index)\n        train_df.columns = origin_train.columns\n        test_df.columns = origin_test.columns\n        column_list = list(train_df)\n\n        for s in range(1, lookBack + 1):\n            tmp_train = train_df[column_list].shift(s)\n            tmp_test = test_df[column_list].shift(s)\n\n            tmp_train.columns = \"shift_\" + tmp_train.columns + \"_\" + str(s)\n            tmp_test.columns = \"shift_\" + tmp_test.columns + \"_\" + str(s)\n\n            train_df[tmp_train.columns] = train_df[column_list].shift(s)\n            test_df[tmp_test.columns] = test_df[column_list].shift(s)\n\n        return train_df, test_df\n\ndef evaluate(pred, gtruth, n_features):\n    tmp_pred = np.zeros(shape=(len(pred),n_features))\n    tmp_gtruth = np.zeros(shape=(len(gtruth),n_features))\n    \n    tmp_pred[:,0] = pred[:,0]\n    tmp_gtruth[:,0] = gtruth[:,0]\n\n    MAE = mean_absolute_error(gtruth, pred)\n    print(\"test MAE\", MAE)\n    RMSE = np.sqrt(mean_squared_error(gtruth, pred))\n    print(\"test RMSE\", RMSE)\n    \n    return MAE, RMSE\n\n\ndef plot(pred, gtruth, n_features):\n    tmp_pred = np.zeros(shape=(len(pred),n_features))\n    tmp_gtruth = np.zeros(shape=(len(gtruth),n_features))\n    \n    tmp_pred[:,0] = pred[:,0]\n    tmp_gtruth[:,0] = gtruth[:,0]\n    \n    plt.plot(pred, 'g')\n    plt.plot(gtruth, 'r')\n    plt.show()\n\n\nif __name__ == \"__main__\":\n    lookBack = 4\n    multiVariate = True\n    n_features = 1\n    train_period1 = 128-lookBack\n    test_period1 = 12+lookBack\n    train_period2 = 32-lookBack\n    test_period2 = 12+lookBack\n    \n    epochs = 100\n    n_units = [20, 50, 100, 150, 200, 250, 300]\n    \n\n    df = pd.read_csv('./data/houseprice.csv')\n    data = df[['SPI', 'CPI', 'M2', 'MMI', 'CBD']]\n    column_list = list(data)\n\n    train1, test1, train1_sc, test1_sc = splitTrainTest(data=data,\n                                                        train_period=train_period1,\n                                                        test_period=test_period1,\n                                                        multiVariate=multiVariate,\n                                                        period=1)\n    train1_df, test1_df = makeSlidingWindows(origin_train=train1,\n                                             origin_test=test1,\n                                             train=train1_sc,\n                                             test=test1_sc,\n                                             lookBack=lookBack,\n                                             multiVariate=multiVariate)\n    \n    X_train1 = train1_df.dropna().drop('SPI', axis=1).values \n    X_test1 = test1_df.dropna().drop('SPI', axis=1).values\n\n    if not multiVariate:\n        n_features = 1\n\n    else:           \n        X_train1 = train1_df.dropna().drop(column_list, axis=1).values \n        X_test1 = test1_df.dropna().drop(column_list, axis=1).values\n        n_features = len(column_list)        \n        \n    Y_train1 = train1_df.dropna()[['SPI']].values\n    Y_test1 = test1_df.dropna()[['SPI']].values\n\n    X_train1 = X_train1.reshape(X_train1.shape[0], lookBack, n_features)\n    X_test1 = X_test1.reshape(X_test1.shape[0], lookBack, n_features)\n    \n    train2, test2, train2_sc, test2_sc = splitTrainTest(data=data,\n                                                        train_period=train_period2,\n                                                        test_period=test_period2,\n                                                        multiVariate=multiVariate,\n                                                        period=2)\n    train2_df, test2_df = makeSlidingWindows(origin_train=train2,\n                                             origin_test=test2,\n                                             train=train2_sc,\n                                             test=test2_sc,\n                                             lookBack=lookBack,\n                                             multiVariate=multiVariate)\n    \n    X_train2 = train2_df.dropna().drop('SPI', axis=1).values \n    X_test2 = test2_df.dropna().drop('SPI', axis=1).values\n\n    if not multiVariate:\n        n_features = 1\n\n    else:           \n        X_train2 = train2_df.dropna().drop(column_list, axis=1).values \n        X_test2 = test2_df.dropna().drop(column_list, axis=1).values\n        n_features = len(column_list)        \n        \n    Y_train2 = train2_df.dropna()[['SPI']].values\n    Y_test2 = test2_df.dropna()[['SPI']].values\n\n    X_train2 = X_train2.reshape(X_train2.shape[0], lookBack, n_features)\n    X_test2 = X_test2.reshape(X_test2.shape[0], lookBack, n_features)\n    \n    # For Checkpoint\n    # Period1\n    MODEL_SAVE_FOLDER_PATH = 'model/'\n    if not os.path.exists(MODEL_SAVE_FOLDER_PATH):\n      os.mkdir(MODEL_SAVE_FOLDER_PATH)\n\n    model_path = MODEL_SAVE_FOLDER_PATH + 'period1.hdf5'\n    cb_checkpoint = ModelCheckpoint(filepath=model_path, monitor='val_loss',\n                                    verbose=1, save_best_only=True)\n    '''\n    # Period1\n    period1_rmse = []\n    period1_mae = []\n    for units in n_units:\n        print(\"#######################\"+str(units)+\"#######################\")\n              \n        K.clear_session()\n        model = Sequential()\n        model.add(LSTM(units, activation='relu', input_shape=(lookBack, n_features)))\n        print(model.layers[-1].input_shape)\n        model.add(Dense(1))\n        model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse','mae'])\n        model.summary()\n        \n        history = model.fit(X_train1, Y_train1, epochs=epochs, batch_size=5, verbose=1)\n        model.save('model/model_period1_'+str(units)+'.hdf5')\n        acc = history.history['mae']\n        loss = history.history['loss']\n        \n        x_epochs = range(1, len(acc) + 1)\n    \n        plt.plot(x_epochs, acc, 'b', label='Training mae')\n        plt.title('Mean_Absolute_Error')\n        plt.legend()\n        plt.figure()\n    \n        plt.plot(x_epochs, loss, 'b', label='Training loss')\n        plt.title('Loss')\n        plt.legend()\n        plt.show()\n    \n        Y_pred = model.predict(X_test1)\n\n        mae, rmse = evaluate(Y_pred, Y_test1, n_features=n_features)\n        \n        period1_rmse.append(rmse)\n        period1_mae.append(mae)\n        \n    # period2\n    MODEL_SAVE_FOLDER_PATH = 'model/'\n    if not os.path.exists(MODEL_SAVE_FOLDER_PATH):\n      os.mkdir(MODEL_SAVE_FOLDER_PATH)\n\n    model_path = MODEL_SAVE_FOLDER_PATH + 'period2.hdf5'\n    cb_checkpoint = ModelCheckpoint(filepath=model_path, monitor='val_loss',\n                                    verbose=1, save_best_only=True)\n    \n    period2_rmse = []\n    period2_mae = []\n    for units in n_units:\n        print(\"#######################\"+str(units)+\"#######################\")\n              \n        K.clear_session()\n        model = Sequential()\n        model.add(LSTM(units, activation='relu', input_shape=(lookBack, n_features)))\n        print(model.layers[-1].input_shape)\n        model.add(Dense(1))\n        model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse','mae'])\n        model.summary()\n        \n        history = model.fit(X_train2, Y_train2, epochs=epochs, batch_size=5, verbose=1)\n        model.save('model/model_period2_'+str(units)+'.hdf5')\n        acc = history.history['mae']\n        loss = history.history['loss']\n        \n        x_epochs = range(1, len(acc) + 1)\n    \n        plt.plot(x_epochs, acc, 'b', label='Training mae')\n        plt.title('Mean_Absolute_Error')\n        plt.legend()\n        plt.figure()\n    \n        plt.plot(x_epochs, loss, 'b', label='Training loss')\n        plt.title('Loss')\n        plt.legend()\n        plt.show()\n        \n        Y_pred = model.predict(X_test2)\n        \n        mae, rmse = evaluate(Y_pred, Y_test2, n_features=n_features)\n        \n        period2_rmse.append(rmse)\n        period2_mae.append(mae)\n    '''\n    model1 = load_model('model/model_period1_100.hdf5')\n    model2 = load_model('model/model_period2_300.hdf5')\n    \n    Y1_pred = model1.predict(X_test1)\n    Y2_pred = model2.predict(X_test2)\n    gtruth = list(train1_sc[:,0])+list(test1_sc[:,0])\n    \n    x_coord = range(train_period1+test_period1)\n    \n    plt.plot(gtruth,'r')\n    plt.plot(x_coord[train_period1+lookBack:],Y1_pred,'g')\n    plt.plot(x_coord[train_period2+lookBack:train_period2+test_period2],Y2_pred,'g')\n    plt.show()","sub_path":"HousePricePrediction/LSTM.py","file_name":"LSTM.py","file_ext":"py","file_size_in_byte":10694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"67575220","text":"from tkinter import Tk, Text, Entry\n\n \nroot = Tk()\n \ne = Text(root)\ne.pack()\n \ne_1 = Entry(root)\ne_1.pack()\n \nroot.event_add('<<Paste>>', '<Control-Cyrillic_em>')\nroot.event_add(\"<<Copy>>\", \"<Control-ntilde>\")\n\ndef copy(event):\n    print('copy')\ndef paste(event):\n    print('paste')\ndef test(event):    \n    print('event.char:', event.char)\n    print('event.keycode:', event.keycode)\n    print('event.keysym:', event.keysym)\n    print('---')\n\nroot.bind('<Key>', test)\nroot.bind('<ntilde>',            copy)\nroot.bind('<igrave>',           paste)\nroot.bind('<Control-Cyrillic_em>', paste)\nroot.mainloop(\n)\n\nroot.mainloop()\n","sub_path":"media/file_to_question/pasting.py","file_name":"pasting.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"381415792","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2016 Red Hat, Inc\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport logging\nfrom lxml import etree\nfrom datetime import timedelta\n\nLOG = logging.getLogger(__name__)\n\n\ndef parse_testcase(root):\n    return {\n        'name': root.attrib.get('name', ''),\n        'classname': root.attrib.get('classname', ''),\n        'time': float(root.attrib.get('time', 0))\n    }\n\n\ndef parse_action(root):\n    return {\n        'message': root.get('message', ''),\n        'value': root.text,\n        'action': root.tag if root.tag not in ['system-out', 'system-err']\n        else 'passed',\n        'type': root.get('type', '')\n    }\n\n\ndef parse_testscases(root):\n    testscases = []\n    for testcase in root.findall('testcase'):\n        testcase_dict = {\n            'action': 'passed',\n            'message': '',\n            'type': '',\n            'value': ''\n        }\n        testcase_dict.update(parse_testcase(testcase))\n        if len(testcase) > 0:\n            action = parse_action(testcase[0])\n            testcase_dict.update(action)\n        testscases.append(testcase_dict)\n    return testscases\n\n\ndef parse_testssuites(root):\n    testssuites = []\n    for testsuite in root.findall('testsuite'):\n        testssuites.append(testsuite)\n    if not testssuites:\n        testssuites.append(root)\n    return testssuites\n\n\ndef junit2dict(string):\n    if not string:\n        return {}\n    results = {\n        'success': 0,\n        'errors': 0,\n        'failures': 0,\n        'skips': 0,\n        'total': 0,\n        'testscases': [],\n        'time': 0,\n    }\n    try:\n        root = etree.fromstring(string)\n        testssuites = parse_testssuites(root)\n\n        for testsuite in testssuites:\n            testscases = parse_testscases(testsuite)\n\n            test_duration = timedelta(seconds=0)\n            for testcase in testscases:\n                results['total'] += 1\n                test_duration += timedelta(seconds=float(testcase['time']))\n                if testcase['action'] == 'skipped':\n                    results['skips'] += 1\n                if testcase['action'] == 'error':\n                    results['errors'] += 1\n                if testcase['action'] == 'failure':\n                    results['failures'] += 1\n                results['testscases'].append(testcase)\n\n            results['success'] = (results['total'] -\n                                  results['failures'] -\n                                  results['errors'] -\n                                  results['skips'])\n            results['time'] += int(test_duration.total_seconds() * 1000)\n    except etree.XMLSyntaxError as e:\n        results['error'] = \"XMLSyntaxError: %s \" % str(e)\n        LOG.error('XMLSyntaxError %s' % str(e))\n    return results\n","sub_path":"dci/api/v1/transformations.py","file_name":"transformations.py","file_ext":"py","file_size_in_byte":3267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"245837796","text":"import functools\nimport sqlalchemy.orm.session\nfrom tornado.web import HTTPError\nfrom seplis.api import exceptions\nfrom seplis.api.connections import database\nfrom contextlib import contextmanager\n\ndef authenticated(level):\n    def decorator(method):\n        @functools.wraps(method)\n        def wrapper(self, *args, **kwargs):\n            if not self.current_user:\n                raise exceptions.Not_signed_in_exception()\n            if self.current_user.level < level:\n                raise exceptions.Restricted_access_exception(\n                    self.current_user.level, \n                    level\n                )\n            return method(self, *args, **kwargs)\n        return wrapper\n    return decorator\n\n\ndef auto_session(method):\n    @functools.wraps(method)\n    def wrapper(self, *args, **kwargs):\n        for arg in args:\n            if isinstance(arg, sqlalchemy.orm.session.Session):\n                kwargs['session'] = arg\n                args = list(args)\n                args.remove(arg)\n                args = tuple(args)\n                break        \n        if ('session' in kwargs) and (kwargs['session'] != None):\n            return method(self, *args, **kwargs)\n        else:\n            with new_session() as session:\n                kwargs['session'] = session\n                result = method(self, *args, **kwargs)\n                session.commit()\n                return result\n    return wrapper\n\ndef auto_pipe(method):\n    @functools.wraps(method)\n    def wrapper(self, *args, **kwargs):     \n        if ('pipe' in kwargs) and (kwargs['pipe'] != None):\n            return method(self, *args, **kwargs)\n        else:                \n            kwargs['pipe'] = database.redis.pipeline()\n            result = method(self, *args, **kwargs)\n            kwargs['pipe'].execute()\n            return result\n    return wrapper\n\n@contextmanager\ndef new_session():\n    '''\n    Creates a new session, remembers to close and rollsback\n    if the session fails.\n\n    Usage:\n    \n        with new_session() as session:\n            session.add(some_model())\n    '''\n    s = database.session()\n    try:\n        yield s\n    except:\n        s.rollback()\n        raise\n    finally:\n        s.close()","sub_path":"src/seplis/api/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"216318734","text":"import bin_tree_node\n\n\nclass BinTree:\n    root = None\n\n    def __init__(self):\n        pass\n\n    def insert(self, value):\n\n        if self.root is None:\n            self.root = bin_tree_node.BinTreeNode(value)\n            return self.root\n        else:\n            self.root.insert_node(value)\n\n    def search(self, value):\n        if self.root is not None:\n            found = self.root.search_node(value)\n            return found\n        else:\n            return None\n\n\ntree = BinTree()\ntree.insert(5)\ntree.insert(6)\ntree.insert(4)\n\nprint(tree.search(25))\n","sub_path":"bin_tree.py","file_name":"bin_tree.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"336877808","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import scrolledtext\nfrom tkinter import messagebox as mBox\nfrom tkinter import Menu\n\nventana = tk.Tk()\nventana.title (\"EDUCACIÓN FÍSICA\")\n\n#PREGUNTA 1\nlabel = ttk.Label (ventana, text = \"1) Ecriba cual es su deporte favorito. Justifique su respuesta\").grid(column=1, row=4)\nscroll_ancho=15\nscroll_alto=2\ncaja1 = scrolledtext.ScrolledText(ventana, width = scroll_ancho, height = scroll_alto, wrap=tk.WORD)\ncaja1.grid(column=1, row = 5)\n\n#PREGUNTA 2\nlabel = ttk.Label (ventana, text = \"2) Explique en que consiste el pivote en BasquetBall\").grid(column=1, row=6)\nscroll_ancho=15\nscroll_alto=2\ncaja2 = scrolledtext.ScrolledText(ventana, width = scroll_ancho, height = scroll_alto, wrap=tk.WORD)\ncaja2.grid(column=1, row = 7)\n\n#PREGUNTA 3\ndef est():\n\n    selector = opcion.get()\n\nlabel2= ttk.Label (ventana, text=\" 3) Deporte que se juega en equipo de 6  a 11\").grid (column=1, row=8)\n\nopcion = tk.StringVar()\nradio1 = tk.Radiobutton (ventana, text= \"Golf\", variable =opcion, value=\"Casado\", command = est)\nradio1.grid (column = 1,row = 9, sticky = tk.W)\nradio1.select()\n\nradio2 = tk.Radiobutton (ventana, text= \"Futbol\", variable =opcion, value=\"Soltero\", command = est)\nradio2.grid (column = 2,row = 9, sticky = tk.W)\n\nradio3 = tk.Radiobutton (ventana, text= \"Voleyball\", variable =opcion, value=\"viudo\", command = est)\nradio3.grid (column = 3,row = 9, sticky = tk.W)\n\n#PREGUNTA 4\ndef est1():\n\n    selector1 = opcion.get()\n\nlabel3= ttk.Label (ventana, text=\" 4) Seleccione lo que se debe hacer antes de practicar algun deporte.\").grid (column=1, row=10)\n\nopcion = tk.StringVar()\nradio1 = tk.Radiobutton (ventana, text= \"Comer\", variable =opcion, value=\"Casado\", command = est1)\nradio1.grid (column = 1,row = 11, sticky = tk.W)\nradio1.select()\n\nradio2 = tk.Radiobutton (ventana, text= \"Cantar\", variable =opcion, value=\"Soltero\", command = est1)\nradio2.grid (column = 2,row = 11, sticky = tk.W)\n\nradio3 = tk.Radiobutton (ventana, text= \"Calentar\", variable =opcion, value=\"viudo\", command = est1)\nradio3.grid (column = 3,row = 11, sticky = tk.W)\n\n#PREGUNTA 5\n\n#CALIFICAR\ndef cal():\n    mBox.showinfo(\"Examen Aprobado\")\n\nboton = ttk.Button (ventana, text=\"CALIFICAR\", command = cal)\nboton.grid(column=5, row=15)\n\n\nventana.mainloop()","sub_path":"Examen.py","file_name":"Examen.py","file_ext":"py","file_size_in_byte":2288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"355651396","text":"\n# coding: utf-8\n\n# # created on Feb 18 2018\n\n# In[1]:\n\n\nimport os\n\n\nfrom Utilities import get_time_str, decorated_os_rename, decorated_subprocess_check_output, get_current_firework_from_cal_loc\nfrom Execute_user_defined_cmd import Execute_user_defined_cmd\n\nfrom Submit_and_Kill_job import Job_management, kill_error_jobs\n\nfrom Error_checker import Write_and_read_error_tag\nfrom Error_checker import Vasp_Error_Saver\nfrom Error_checker import Queue_std_files\nfrom Error_checker import Vasp_Error_checker\n\n\n# In[ ]:\n\n\ndef update_job_status(cal_folder, workflow):\n    job_status_dict = check_calculations_status(cal_folder=cal_folder)\n    update_running_jobs_status(running_jobs_list=job_status_dict[\"running_folder_list\"], workflow=workflow)\n    job_status_dict = check_calculations_status(cal_folder=cal_folder)\n    kill_error_jobs(error_jobs=job_status_dict[\"error_folder_list\"], workflow=workflow)\n    job_status_dict = check_calculations_status(cal_folder=cal_folder)\n    update_killed_jobs_status(killed_jobs_list=job_status_dict[\"killed_folder_list\"], workflow=workflow)\n    update_sub_dir_cal_jobs_status(sub_dir_cal_jobs_list=job_status_dict[\"sub_dir_cal_folder_list\"], workflow=workflow)\n\n\n# In[2]:\n\n\ndef check_calculations_status_0(cal_folder):\n    \"\"\"\n    Check the status of all calculations under folder cal_folder \n    input argument:\n        - cal_folder (str): Under cal_folder, a sub-folder will be created where a set of DFT calculations defined by workflow will be made.\n                        Note that the absolute path should be provided.\n    return a dictionary having keys below:\n        - ready_folder_list (list): a list of absolute pathes where the calculations are ready.\n                                    Note that the pathes where instead file __prior_ready__ exists will be put at the beginning\n                                    of list read_folder_list.\n        - running_folder_list (list): a list of absolute pathes where the calculations are ongoing.\n        - done_folder_list (list): a list of absolute pathes where the calculations are done.\n        - error_folder_list (list): a list of absolute pathes where the calculations encounter errors.\n        - killed_folder_list (list): a list of absolute pathes where the calculation has been killed.\n        - manual_folder_list (list): a list of absolute pathes where the error can not be fixed automatically.\n        - vis_folder_list (list): a list of absolute pathes where the input files for calculations need to be prepared\n    \"\"\"\n    signal_file_list = [\"__manual__\", \"__test__\", \"__vis__\", \"__skipped__\", \"__ready__\", \"__prior_ready__\", \n                        \"__error__\", \"__running__\", \"__done__\", \"__killed__\"]\n    job_status_folder_list = [\"manual_folder_list\", \"test_folder_list\", \"vis_folder_list\", \"skipped_folder_list\", \n                              \"ready_folder_list\", \"prior_ready_folder_list\", \"error_folder_list\", \"running_folder_list\", \n                              \"done_folder_list\", \"killed_folder_list\", \"other_folder_list\"]\n    job_status_dict = {key: [] for key in job_status_folder_list}\n    if not os.path.isdir(cal_folder):\n        return job_status_dict\n        \n    mater_folder_list = os.listdir(cal_folder)\n    firework_folder_list = []\n    for mater_folder in mater_folder_list:\n        mater_folder_path = os.path.join(cal_folder, mater_folder)\n        #in case any file appears in cal_folder.\n        if not os.path.isdir(mater_folder_path):\n            continue\n        #ignore irrelevant files or folders\n        firework_name_list = [firework_name for firework_name in os.listdir(mater_folder_path) if firework_name.startswith(\"step\")]\n        firework_folder_list += [os.path.join(mater_folder_path, firework_name) for firework_name in firework_name_list]\n    \n    #categorize fireworks\n    for firework_folder in firework_folder_list:\n        firework_belongs_to_other = True\n        for signal_file_ind, signal_file in enumerate(signal_file_list):\n            if os.path.isfile(os.path.join(firework_folder, signal_file)):\n                job_status_dict[job_status_folder_list[signal_file_ind]].append(firework_folder)\n                firework_belongs_to_other = False\n                break\n        if firework_belongs_to_other:\n            job_status_dict[\"other_folder_list\"].append(firework_folder)\n       \n    return job_status_dict\n\n\n# In[2]:\n\n\ndef check_calculations_status(cal_folder):\n    \"\"\"\n    Check the status of all calculations under folder cal_folder \n    input argument:\n        - cal_folder (str): The absolute path to a directory under which the program creates a sub-directory for every to-be-calculated\n                            materials. In the sub-directory, a series of DFT calculations predefined will be carried out.\n                            This function checks the calculation status of all DFT calculations under the folder referenced by cal_folder/\n    return a dictionary having keys below:\n        - ready_folder_list (list): a list of absolute pathes where the calculations are ready.\n                                    Note that the pathes where instead file __prior_ready__ exists will be put at the beginning\n                                    of list read_folder_list.\n        - running_folder_list (list): a list of absolute pathes where the calculations are ongoing.\n        - done_folder_list (list): a list of absolute pathes where the calculations are done.\n        - error_folder_list (list): a list of absolute pathes where the calculations encounter errors.\n        - killed_folder_list (list): a list of absolute pathes where the calculation has been killed.\n        - manual_folder_list (list): a list of absolute pathes where the error can not be fixed automatically.\n        - vis_folder_list (list): a list of absolute pathes where the input files for calculations need to be prepared\n    \"\"\"\n    signal_file_list = [\"__manual__\", \"__test__\", \"__vis__\", \"__skipped__\", \"__ready__\", \"__prior_ready__\", \"__sub_dir_cal__\",\n                        \"__error__\", \"__running__\", \"__done__\", \"__killed__\"]\n    job_status_folder_list = [\"manual_folder_list\", \"test_folder_list\", \"vis_folder_list\", \"skipped_folder_list\", \n                              \"ready_folder_list\", \"prior_ready_folder_list\", \"sub_dir_cal_folder_list\", \"error_folder_list\", \"running_folder_list\", \n                              \"done_folder_list\", \"killed_folder_list\", \"other_folder_list\"]\n    job_status_dict = {key: [] for key in job_status_folder_list}\n    \n    jobs_in_str = decorated_subprocess_check_output(\"find %s -type f -name INCAR\" % cal_folder)[0]\n    job_list = []\n    for job in jobs_in_str.split(\"\\n\"):\n        job = job.strip()\n        if job and \"step\" in job and \"error_folder\" not in job:\n            job_list.append(os.path.split(job)[0])\n    \n    for job in job_list:\n        file_list = os.listdir(job)\n        file_belong_to_other = True\n        for signal_file, status_type in zip(signal_file_list, job_status_folder_list):\n            if signal_file in file_list:\n                job_status_dict[status_type].append(job)\n                file_belong_to_other = False\n                break\n        if file_belong_to_other:\n            job_status_dict[\"other_folder_list\"].append(job)\n    \n    return job_status_dict\n\n\n# In[3]:\n\n\ndef update_running_jobs_status(running_jobs_list, workflow):\n    \"\"\"\n    Update jobs's status. for the running jobs, if any errors are detected, change __running__ to __error__ and \n        the error type will be written into __error__.\n    input arguments:\n        - running_jobs_list (list): a list of absolute pathes of running jobs.\n        - workflow:  the output of func Parse_calculation_workflow.parse_calculation_workflow\n    \"\"\"\n    #Check_after_cal = [\"__electronic_divergence__\", \"__positive_energy__\", \"__ionic_divergence__\"]\n    #Check_on_the_fly = [\"__electronic_divergence__\", \"__positive_energy__\"]\n    \n    job_status_str = Job_management.check_jobs_in_queue_system(workflow=workflow, return_a_str=True)\n    #print()\n    #print(get_time_str())\n    #print(job_status_str)\n    #job_status_str = \"\"\n    #if job_status_list:\n    #    for i in range(1, len(job_status_list)):\n    #        job_status_str += job_status_list[i]\n    \n    for job_path in running_jobs_list:\n        \n        \n        find_error = False\n        if Queue_std_files(cal_loc=job_path, workflow=workflow).find_std_files() != [None, None]:\n            #for func Vasp_Error_checker, error_type=[\"after_cal\"] will automatically check errors after cal.\n            #If found, __running__ --> __error__ and the error info will be written into __error__ and return False\n            #If not found, return True\n            if Vasp_Error_checker(error_type=[\"after_cal\"], cal_loc=job_path, workflow=workflow):\n                cal_name = os.path.split(job_path)[-1]\n                with open(os.path.join(job_path, \"log.txt\"), \"a\") as f:\n                    f.write(\"{} INFO: Calculation successfully finishes at {}\\n\".format(get_time_str(), cal_name))\n                    f.write(\"\\t\\t\\t__running__ --> __done__\\n\")\n                    decorated_os_rename(loc=job_path, old_filename=\"__running__\", new_filename=\"__done__\")\n                    #os.rename(os.path.join(job_path, \"__running__\"), os.path.join(job_path, \"__done__\"))\n        else:\n            #for func Vasp_Error_checker, error_type=[\"on_the_fly\"] will automatically check errors on the fly.\n            #If found, __running__ --> __error__ and the error info will be written into __error__ and return False\n            #If not found, return True\n            Vasp_Error_checker(error_type=[\"on_the_fly\"], cal_loc=job_path, workflow=workflow)\n            \n        if os.path.isfile(os.path.join(job_path, \"__running__\")):\n            if Queue_std_files(cal_loc=job_path, workflow=workflow).find_std_files() != [None, None]:\n                continue\n                \n            \n                    \n                \n            queue_id = Job_management(cal_loc=job_path, workflow=workflow).find_queue_id()\n            #print(queue_id, queue_id in job_status_str)\n            if queue_id not in job_status_str:\n                if not os.path.isfile(os.path.join(job_path, \"__no_of_times_not_in_queue__\")):\n                    with open(os.path.join(job_path, \"__no_of_times_not_in_queue__\"), \"w\") as f:\n                        f.write(\"1\")\n                else:\n                    with open(os.path.join(job_path, \"__no_of_times_not_in_queue__\"), \"r\") as f:\n                        times = int(next(f).strip())\n                    if times <= 5:\n                        with open(os.path.join(job_path, \"__no_of_times_not_in_queue__\"), \"w\") as f:\n                            f.write(str(times+1))\n                        continue\n                \n                cal_name = os.path.split(job_path)[-1]\n                with open(os.path.join(job_path, \"log.txt\"), \"a\") as f:\n                    f.write(\"{} Queue Error: {}\\n\".format(get_time_str(), cal_name))\n                    f.write(\"\\t\\t\\tThe running job is not found in queue.\\n\")\n                    f.write(\"\\t\\t\\t__running__ --> __manual__\\n\")\n                    f.write(\"\\t\\t\\tCreate file __running_job_not_in_queue__.\\n\")\n                    open(os.path.join(job_path, \"__running_job_not_in_queue__\"), \"w\").close()\n                    decorated_os_rename(loc=job_path, old_filename=\"__running__\", new_filename=\"__manual__\")\n                    #os.rename(os.path.join(job_path, \"__running__\"), os.path.join(job_path, \"__manual__\"))                \n            else:\n                if os.path.isfile(os.path.join(job_path, \"__no_of_times_not_in_queue__\")):\n                    os.remove(os.path.join(job_path, \"__no_of_times_not_in_queue__\"))\n\n\n# In[4]:\n\n\ndef update_killed_jobs_status(killed_jobs_list, workflow, max_error_times=5):\n    \"\"\"\n    Update killed jobs's status. If the error in __killed__ can be fixed, fix it and __killed__ --> __ready__; \n        Ohterwise __killed__ --> __manual__\n    input arguments:\n        - killed_jobs_list (list): a list of absolute pathes of killed jobs.\n        - workflow:  the output of func Parse_calculation_workflow.parse_calculation_workflow\n        - max_error_times (int): the maximum error times. Beyond this value, __killed__ --> __manual__. Default: 5\n    \"\"\"\n    \n    #Error_type_dict = [\"__unfinished_OUTCAR__\", \"__electronic_divergence__\", \n    #                   \"__ionic_divergence__\", \"__positive_energy__\"]\n    \n    for killed_job in killed_jobs_list:\n        #The killed job won't be processed until the stdout & stderr files of the queue system appear.\n        if Queue_std_files(cal_loc=killed_job, workflow=workflow).find_std_files() == [None, None]:\n            continue\n        \n        error_type = Write_and_read_error_tag(killed_job).read_error_tag(\"__killed__\")\n        error_checker = Vasp_Error_checker(cal_loc=killed_job, error_type=error_type, workflow=workflow)\n        cal_name = os.path.split(killed_job)[-1]\n        if Vasp_Error_Saver(cal_loc=killed_job, workflow=workflow).find_error_times() >= max_error_times:\n            decorated_os_rename(loc=killed_job, old_filename=\"__killed__\", new_filename=\"__manual__\")\n            #os.rename(os.path.join(killed_job, \"__killed__\"), os.path.join(killed_job, \"__manual__\"))\n            with open(os.path.join(killed_job, \"log.txt\"), \"a\") as f:\n                f.write(\"{} Killed: {}\\n\".format(get_time_str(), cal_name))\n                f.write(\"\\t\\t\\tThe error times hit the max_error_times ({})\\n\".format(max_error_times))\n                f.write(\"\\t\\t\\t__killed__ -> __manual__\\n\")\n        elif error_checker.correct():\n            #Queue_std_files(cal_loc=killed_job, workflow=workflow).remove_std_files()\n            #to_be_removed = [\"OUTCAR\", \"OSZICAR\", workflow[0][\"vasp.out\"]]\n            #for file_ in to_be_removed:\n            #    if os.path.isfile(os.path.join(killed_job, file_)):\n            #        os.remove(os.path.join(killed_job, file_))\n                    \n            os.remove(os.path.join(killed_job, \"__killed__\"))\n            open(os.path.join(killed_job, \"__ready__\"), \"w\").close()\n            with open(os.path.join(killed_job, \"log.txt\"), \"a\") as f:\n                f.write(\"{} Killed: Successfully correct the error {} under {}\\n\".format(get_time_str(), error_type, cal_name))\n                f.write(\"\\t\\t\\t__killed__ --> __ready__\\n\")\n            \n        else:\n            decorated_os_rename(loc=killed_job, old_filename=\"__killed__\", new_filename=\"__manual__\")\n            #os.rename(os.path.join(killed_job, \"__killed__\"), os.path.join(killed_job, \"__manual__\"))\n            with open(os.path.join(killed_job, \"log.txt\"), \"a\") as f:\n                f.write(\"{} Killed: Fail to correct the error {} under {}\\n\".format(get_time_str(), error_type, cal_name))\n                f.write(\"\\t\\t\\t__killed__ --> __manual__\\n\")\n            \n    \n\n\n# In[4]:\n\n\ndef update_sub_dir_cal_jobs_status(sub_dir_cal_jobs_list, workflow):\n    \"\"\"\n    update the status of the sub-directoray calculations using the corresponding sub_dir_cal_cmd predefined in workflow.\n    Note that sub_dir_cal_cmd should be responsible for the job status switching from __sub_dir_cal__ to either __done__ or __manual__.\n    \"\"\"\n    \n    for sub_dir_cal_path in sub_dir_cal_jobs_list:\n        current_firework = get_current_firework_from_cal_loc(sub_dir_cal_path, workflow)\n        \n        Execute_user_defined_cmd(cal_loc=sub_dir_cal_path, user_defined_cmd_list=current_firework[\"sub_dir_cal_cmd\"],                                 where_to_execute=sub_dir_cal_path)\n    \n    \n\n    \n\n","sub_path":"HTC_lib/Check_and_update_calculation_status.py","file_name":"Check_and_update_calculation_status.py","file_ext":"py","file_size_in_byte":15628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"282086272","text":"# 기본값 매개변수(Default Argument Value)\n\n\n# 기본값 매개변수 cnt=1로 정의\ndef str(text, cnt=1):\n    for i in range(cnt):  # i=0~2까지 3번 loop가 돌아감      \n        print(text)\n        \n       \n       \n        \n#함수를 호출할때 2번째 매개변수를 생략하면 기본값 매개변수  cnt=1이 사용됨\nstr('안녕 하세요')           \nprint()\n\nstr('안녕 하세요', 3)\n","sub_path":"basic/ch15/function/def06.py","file_name":"def06.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"27581159","text":"# At present Python SciPy library supports integration, gradient optimization, special functions, \n# ordinary differential equation solvers, parallel programming tools and many more; in other words, \n# we can say that if something is there in general textbook of numerical computation, there are high chances you’ll\n# find it’s implementation in SciPy.\n# An interactive session with SciPy is basically a data-processing and system-prototyping environment just\n# like MATLAB, Octave, Scilab or R-lab.\n\n\nfrom numpy import poly1d\nfrom scipy import linalg\nimport numpy as np\nfrom scipy import integrate, special\nfrom scipy.fftpack import fft\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nfrom scipy.interpolate import interp1d\nfrom scipy.interpolate import griddata\nfrom scipy import interpolate\nfrom scipy.interpolate import Rbf, InterpolatedUnivariateSpline\n\n# We'll use some functions from numpy remember!!\n# Creating a simple polynomial object using coefficients\nsomePolynomial = poly1d([1,2,3])\n\n# Printing the result\n# Notice how easy it is to read the polynomial this way\nprint(somePolynomial)\n\n# Let's perform some manipulations\nprint(\"\\nSquaring the polynomial: \\n\")\nprint(somePolynomial* somePolynomial)\n\n#How about integration, we just have to call a function\n# We just have to pass a constant say 3\nprint(\"\\nIntegrating the polynomial: \\n\")\nprint(somePolynomial.integ(k=3))\n\n#We can also find derivatives in similar way\nprint(\"\\nFinding derivative of the polynomial: \\n\")\nprint(somePolynomial.deriv())\n\n# We can also solve the polynomial for some value, \n# let's try to solve it for 2\nprint(\"\\nSolving the polynomial for 2: \\n\")\nprint(somePolynomial(2))\n\n\n#Linear Algebra\nprint(\"Linear Algebra Using SciPy\")\n# We are trying to solve a linear algebra system which can be given as:\n#               1x + 2y =5\n#               3x + 4y =6\n\n# Create input array\nA= np.array([[1,2],[3,4]])\n\n# Solution Array\nB= np.array([[5],[6]])\n\n# Solve the linear algebra\nX= linalg.solve(A,B)\n\n# Print results\nprint(X)\n\n# Checking Results\nprint(\"\\n Checking results, following vector should be all zeros\")\nprint(A.dot(X)-B)\n\n#SciPy Integration\nprint(\"SciPy Integration\")\n# Using quad as we can see in list quad is used for simple integration\n# arg1: A lambda function which returns x squared for every x\n# We'll be integrating this function\n# arg2: lower limit\n# arg3: upper limit\nresult= integrate.quad(lambda x: x**2, 0,3)\nprint(result)\n\n#SciPy Fourier Transforms\nprint(\"SciPy Fourier Transforms\")\n#Number of sample points\nN = 600\n\n# sample spacing\nT = 1.0 / 800.0\nx = np.linspace(0.0, N*T, N)\ny = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)\nyf = fft(y)\nxf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n\n# matplotlib for plotting purposes\nplt.plot(xf, 2.0/N * np.abs(yf[0:N//2]))\nplt.grid()\nplt.show()\n\n#Bessel Function\n#Bessel functions are a family of solutions to Bessel’s differential equation with real or complex order alpha\ndef drumhead_height(n, k, distance, angle, t):\n    kth_zero = special.jn_zeros(n, k)[-1]\n    return np.cos(t) * np.cos(n*angle) * special.jn(n, distance*kth_zero)\ntheta = np.r_[0:2*np.pi:50j]\nradius = np.r_[0:1:50j]\nx = np.array([r * np.cos(theta) for r in radius])\ny = np.array([r * np.sin(theta) for r in radius])\nz = np.array([drumhead_height(1, 1, r, theta, 0.5) for r in radius])\n\nfig = plt.figure()\nax = Axes3D(fig)\nax.plot_surface(x, y, z, rstride=1, cstride=1, cmap=cm.jet)\nax.set_xlabel('X')\nax.set_ylabel('Y')\nax.set_zlabel('Z')\nplt.show()\n\n#interpolation\nx = np.linspace(0, 10, num=11, endpoint=True)\ny = np.cos(-x**2/9.0)\nf = interp1d(x, y)\nf2 = interp1d(x, y, kind='cubic')\n\nxnew = np.linspace(0, 10, num=41, endpoint=True)\n\nplt.plot(x, y, 'o', xnew, f(xnew), '-', xnew, f2(xnew), '--')\nplt.legend(['data', 'linear', 'cubic'], loc='best')\nplt.show()\n\n\nx = np.linspace(0, 10, num=11, endpoint=True)\ny = np.cos(-x**2/9.0)\nf1 = interp1d(x, y, kind='nearest')\nf2 = interp1d(x, y, kind='previous')\nf3 = interp1d(x, y, kind='next')\n\nxnew = np.linspace(0, 10, num=1001, endpoint=True)\nplt.plot(x, y, 'o')\nplt.plot(xnew, f1(xnew), '-', xnew, f2(xnew), '--', xnew, f3(xnew), ':')\nplt.legend(['data', 'nearest', 'previous', 'next'], loc='best')\nplt.show()\n\n#Multivariant data interpolation (grid data)\nprint(\"Multivariant data interpolation (grid data)\")\ndef func(x, y):\n    return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2\ngrid_x, grid_y = np.mgrid[0:1:100j, 0:1:200j]\npoints = np.random.rand(1000, 2)\nvalues = func(points[:,0], points[:,1])\ngrid_z0 = griddata(points, values, (grid_x, grid_y), method='nearest')\ngrid_z1 = griddata(points, values, (grid_x, grid_y), method='linear')\ngrid_z2 = griddata(points, values, (grid_x, grid_y), method='cubic')\nplt.subplot(221)\nplt.imshow(func(grid_x, grid_y).T, extent=(0,1,0,1), origin='lower')\nplt.plot(points[:,0], points[:,1], 'k.', ms=1)\nplt.title('Original')\nplt.subplot(222)\nplt.imshow(grid_z0.T, extent=(0,1,0,1), origin='lower')\nplt.title('Nearest')\nplt.subplot(223)\nplt.imshow(grid_z1.T, extent=(0,1,0,1), origin='lower')\nplt.title('Linear')\nplt.subplot(224)\nplt.imshow(grid_z2.T, extent=(0,1,0,1), origin='lower')\nplt.title('Cubic')\nplt.gcf().set_size_inches(6, 6)\nplt.show()\n\n\n#Spline interpolation\nprint(\"Spline interpolation\")\nprint(\"spline interpolation in 1-d:\")\nx = np.arange(0, 2*np.pi+np.pi/4, 2*np.pi/8)\ny = np.sin(x)\ntck = interpolate.splrep(x, y, s=0)\nxnew = np.arange(0, 2*np.pi, np.pi/50)\nynew = interpolate.splev(xnew, tck, der=0)\n\nplt.figure()\nplt.plot(x, y, 'x', xnew, ynew, xnew, np.sin(xnew), x, y, 'b')\nplt.legend(['Linear', 'Cubic Spline', 'True'])\nplt.axis([-0.05, 6.33, -1.05, 1.05])\nplt.title('Cubic-spline interpolation')\nplt.show()\n\nyder = interpolate.splev(xnew, tck, der=1)\nplt.figure()\nplt.plot(xnew, yder, xnew, np.cos(xnew),'--')\nplt.legend(['Cubic Spline', 'True'])\nplt.axis([-0.05, 6.33, -1.05, 1.05])\nplt.title('Derivative estimation from spline')\nplt.show()\n\ndef integ(x, tck, constant=-1):\n     x = np.atleast_1d(x)\n     out = np.zeros(x.shape, dtype=x.dtype)\n     for n in range(len(out)):\n         out[n] = interpolate.splint(0, x[n], tck)\n     out += constant\n     return out\nyint = integ(xnew, tck)\nplt.figure()\nplt.plot(xnew, yint, xnew, -np.cos(xnew), '--')\nplt.legend(['Cubic Spline', 'True'])\nplt.axis([-0.05, 6.33, -1.05, 1.05])\nplt.title('Integral estimation from spline')\nplt.show()\n\nx = np.linspace(-np.pi/4, 2.*np.pi + np.pi/4, 21)\ny = np.sin(x)\ntck = interpolate.splrep(x, y, s=0)\nprint(interpolate.sproot(tck))\n# print(array([0., 3.1416]))\n\nt = np.arange(0, 1.1, .1)\nx = np.sin(2*np.pi*t)\ny = np.cos(2*np.pi*t)\ntck, u = interpolate.splprep([x, y], s=0)\nunew = np.arange(0, 1.01, 0.01)\nout = interpolate.splev(unew, tck)\nplt.figure()\nplt.plot(x, y, 'x', out[0], out[1], np.sin(2*np.pi*unew), np.cos(2*np.pi*unew), x, y, 'b')\nplt.legend(['Linear', 'Cubic Spline', 'True'])\nplt.axis([-1.05, 1.05, -1.05, 1.05])\nplt.title('Spline of parametrically-defined curve')\nplt.show()\n\nprint(\"spline interpolation in 1-d object oriented:\")\nx = np.arange(0, 2*np.pi+np.pi/4, 2*np.pi/8)\ny = np.sin(x)\ns = interpolate.InterpolatedUnivariateSpline(x, y)\nxnew = np.arange(0, 2*np.pi, np.pi/50)\nynew = s(xnew)\n\nplt.figure()\nplt.plot(x, y, 'x', xnew, ynew, xnew, np.sin(xnew), x, y, 'b')\nplt.legend(['Linear', 'InterpolatedUnivariateSpline', 'True'])\nplt.axis([-0.05, 6.33, -1.05, 1.05])\nplt.title('InterpolatedUnivariateSpline')\nplt.show()\n\nt = [np.pi/2-.1, np.pi/2+.1, 3*np.pi/2-.1, 3*np.pi/2+.1]\ns = interpolate.LSQUnivariateSpline(x, y, t, k=2)\nynew = s(xnew)\n\nplt.figure()\nplt.plot(x, y, 'x', xnew, ynew, xnew, np.sin(xnew), x, y, 'b')\nplt.legend(['Linear', 'LSQUnivariateSpline', 'True'])\nplt.axis([-0.05, 6.33, -1.05, 1.05])\nplt.title('Spline with Specified Interior Knots')\nplt.show()\n\nprint(\"spline interpolation in 2-d procedural (bisplrep):\")\nx, y = np.mgrid[-1:1:20j, -1:1:20j]\nz = (x+y) * np.exp(-6.0*(x*x+y*y))\n\nplt.figure()\nplt.pcolor(x, y, z)\nplt.colorbar()\nplt.title(\"Sparsely sampled function.\")\nplt.show()\n\nxnew, ynew = np.mgrid[-1:1:70j, -1:1:70j]\ntck = interpolate.bisplrep(x, y, z, s=0)\nznew = interpolate.bisplev(xnew[:,0], ynew[0,:], tck)\n\nplt.figure()\nplt.pcolor(xnew, ynew, znew)\nplt.colorbar()\nplt.title(\"Interpolated function.\")\nplt.show()\n\nprint(\"spline interpolation in 2-d object oriented (bivariatespline):\")\n# setup data\nx = np.linspace(0, 10, 9)\ny = np.sin(x)\nxi = np.linspace(0, 10, 101)\n\n# use fitpack2 method\nius = InterpolatedUnivariateSpline(x, y)\nyi = ius(xi)\n\nplt.subplot(2, 1, 1)\nplt.plot(x, y, 'bo')\nplt.plot(xi, yi, 'g')\nplt.plot(xi, np.sin(xi), 'r')\nplt.title('Interpolation using univariate spline')\n\n# use RBF method\nrbf = Rbf(x, y)\nfi = rbf(xi)\n\nplt.subplot(2, 1, 2)\nplt.plot(x, y, 'bo')\nplt.plot(xi, fi, 'g')\nplt.plot(xi, np.sin(xi), 'r')\nplt.title('Interpolation using RBF - multiquadrics')\nplt.show()\n\n\n# 2-d tests - setup scattered data\nx = np.random.rand(100)*4.0-2.0\ny = np.random.rand(100)*4.0-2.0\nz = x*np.exp(-x**2-y**2)\nti = np.linspace(-2.0, 2.0, 100)\nXI, YI = np.meshgrid(ti, ti)\n\n# use RBF\nrbf = Rbf(x, y, z, epsilon=2)\nZI = rbf(XI, YI)\n\n# plot the result\nplt.subplot(1, 1, 1)\nplt.pcolor(XI, YI, ZI, cmap=cm.jet)\nplt.scatter(x, y, 100, z, cmap=cm.jet)\nplt.title('RBF interpolation - multiquadrics')\nplt.xlim(-2, 2)\nplt.ylim(-2, 2)\nplt.colorbar()","sub_path":"scipyprac.py","file_name":"scipyprac.py","file_ext":"py","file_size_in_byte":9289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"570914551","text":"import cv2\nimport mediapipe as mp\nimport time\n\ncap = cv2.VideoCapture('videos/vid4.mp4')\n\nmpPose=mp.solutions.pose\npose=mpPose.Pose()\nmpDraw=mp.solutions.drawing_utils\n\npTime=0\ncTime=0\n\nwhile (cap.isOpened()):\n\tsuccess, img=cap.read()\n\tif success:\n\t\timg=cv2.resize(img, (0,0), fx=.2,fy=.2,interpolation=cv2.INTER_AREA)\n\t\timgRGB=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\t\t\n\t\tresults=pose.process(imgRGB)\n\n\t\tif results.pose_landmarks:\n\t\t\tmpDraw.draw_landmarks(img,results.pose_landmarks,mpPose.POSE_CONNECTIONS)\n\t\t\tfor id, lm in enumerate(results.pose_landmarks.landmark):\n\t\t\t\th, w, c=img.shape\n\t\t\t\tcx, cy=int(lm.x*w), int(lm.y*h)\n\t\t\t\t\t\n\t\t\t\tif id==0:\n\t\t\t\t\tcv2.circle(img,(cx,cy),10,(255,250,0),cv2.FILLED)\n\n\n\t\tcTime=time.time()\n\t\tfps=1/(cTime-pTime)\n\t\tpTime=cTime\n\n\t\tcv2.imshow('image',img)\n\t\t\n\t\tk=cv2.waitKey(1)\n\t\tif k==27:\n\t\t\tbreak\n\telse:\n\t\tbreak\n\ncap.release()\n\n\n","sub_path":"position_tracking/position_tracking_1.py","file_name":"position_tracking_1.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"53101553","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"Simple read-only Thing 3 CLI.\"\"\"\n\nfrom __future__ import print_function\n\n__author__ = \"Alexander Willner\"\n__copyright__ = \"2020 Alexander Willner\"\n__credits__ = [\"Alexander Willner\"]\n__license__ = \"Apache License 2.0\"\n__version__ = \"2.1.2\"\n__maintainer__ = \"Alexander Willner\"\n__email__ = \"alex@willner.ws\"\n__status__ = \"Development\"\n\nimport sys\nimport argparse\nimport json\nimport csv\nimport webbrowser\nfrom things3.things3 import Things3\n\n\nclass Things3CLI():\n    \"\"\"Simple read-only Thing 3 CLI.\"\"\"\n\n    print_json = False\n    print_csv = False\n    things3 = None\n\n    def __init__(self, args, things):\n        self.print_json = args.json\n        self.print_csv = args.csv\n        self.things3 = things\n\n    def print_tasks(self, tasks):\n        \"\"\"Print a task.\"\"\"\n        if self.print_json:\n            print(json.dumps(self.things3.convert_tasks_to_model(tasks)))\n        elif self.print_csv:\n            fieldnames = ['uuid', 'title', 'context', 'context_uuid', 'due',\n                          'created', 'modified', 'started', 'stopped']\n            writer = csv.DictWriter(sys.stdout, fieldnames=fieldnames)\n            writer.writeheader()\n            writer.writerows(self.things3.convert_tasks_to_model(tasks))\n        else:\n            for task in tasks:\n                title = task[self.things3.I_TITLE]\n                context = task[self.things3.I_CONTEXT]\n                print(' - ', title, ' (', context, ')')\n\n    @classmethod\n    def print_unimplemented(cls):\n        \"\"\"Show warning that method is not yet implemented.\"\"\"\n        print(\"not implemented yet (see things.sh for a more complete CLI)\")\n\n\ndef get_parser():\n    \"\"\"Create command line argument parser\"\"\"\n    parser = argparse.ArgumentParser(\n        description='Simple read-only Thing 3 CLI.')\n\n    subparsers = parser.add_subparsers(help='',\n                                       metavar=\"command\",\n                                       required=True,\n                                       dest=\"command\")\n    subparsers.add_parser('inbox',\n                          help='Shows all inbox tasks')\n    subparsers.add_parser('today',\n                          help='Shows all todays tasks')\n    subparsers.add_parser('upcoming',\n                          help='Shows all upcoming tasks')\n    subparsers.add_parser('next',\n                          help='Shows all next tasks')\n    subparsers.add_parser('someday',\n                          help='Shows all someday tasks')\n    subparsers.add_parser('completed',\n                          help='Shows all completed tasks')\n    subparsers.add_parser('cancelled',\n                          help='Shows all cancelled tasks')\n    subparsers.add_parser('trashed',\n                          help='Shows all trashed tasks')\n    subparsers.add_parser('feedback',\n                          help='Give feedback')\n    subparsers.add_parser('all',\n                          help='Shows all tasks')\n    subparsers.add_parser('csv',\n                          help='Exports all tasks as CSV')\n    subparsers.add_parser('due',\n                          help='Shows all tasks with due dates')\n    subparsers.add_parser('headings',\n                          help='Shows all headings')\n    subparsers.add_parser('hours',\n                          help='Shows how many hours have been planned today')\n    subparsers.add_parser('ical',\n                          help='Shows all tasks ordered by due date as iCal')\n    subparsers.add_parser('logbook',\n                          help='Shows all tasks completed today')\n    subparsers.add_parser('mostClosed',\n                          help='Shows days on which most tasks were closed')\n    subparsers.add_parser('mostCancelled',\n                          help='Shows days on which most tasks were cancelled')\n    subparsers.add_parser('mostTrashed',\n                          help='Shows days on which most tasks were trashed')\n    subparsers.add_parser('mostCreated',\n                          help='Shows days on which most tasks were created')\n    subparsers.add_parser('mostTasks',\n                          help='Shows projects that have most tasks')\n    subparsers.add_parser('mostCharacters',\n                          help='Shows tasks that have most characters')\n    subparsers.add_parser('nextish',\n                          help='Shows all nextish tasks')\n    subparsers.add_parser('old',\n                          help='Shows all old tasks')\n    subparsers.add_parser('projects',\n                          help='Shows all projects')\n    subparsers.add_parser('repeating',\n                          help='Shows all repeating tasks')\n    subparsers.add_parser('schedule',\n                          help='Schedules an event using a template')\n    subparsers.add_parser('search',\n                          help='Searches for a specific task')\n    subparsers.add_parser('stat',\n                          help='Provides a number of statistics')\n    subparsers.add_parser('statcsv',\n                          help='Exports some statistics as CSV')\n    subparsers.add_parser('subtasks',\n                          help='Shows all subtasks')\n    subparsers.add_parser('tag',\n                          help='Shows all tasks with the waiting for tag')\n    subparsers.add_parser('tags',\n                          help='Shows all tags ordered by their usage')\n    subparsers.add_parser('waiting',\n                          help='Shows all tasks with the waiting for tag')\n\n    parser.add_argument(\"-j\", \"--json\",\n                        action=\"store_true\", default=False,\n                        help=\"output as JSON\", dest=\"json\")\n\n    parser.add_argument(\"-c\", \"--csv\",\n                        action=\"store_true\", default=False,\n                        help=\"output as CSV\", dest=\"csv\")\n\n    parser.add_argument(\n        \"--version\",\n        action=\"version\",\n        version=\"%(prog)s (version {version})\".format(version=__version__))\n\n    return parser\n\n\ndef main(args=None, things3=Things3()):\n    \"\"\" Main entry point of the app \"\"\"\n\n    if args is None:\n        main(get_parser().parse_args())\n    else:\n        things_cli = Things3CLI(args, things3)\n        command = args.command\n\n        if command in things3.functions:\n            func = things3.functions[command]\n            things_cli.print_tasks(func(things3))\n        elif command == \"csv\":\n            print(\"Deprecated: use --csv instead\")\n        elif command == \"feedback\":\n            webbrowser.open(\n                'https://github.com/AlexanderWillner/KanbanView/issues')\n        else:\n            Things3CLI.print_unimplemented()\n\n\nif __name__ == \"__main__\":\n    main(get_parser().parse_args())\n","sub_path":"things3/things3_cli.py","file_name":"things3_cli.py","file_ext":"py","file_size_in_byte":6685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"348606636","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar  2 11:02:11 2021\n\n@author: marlowcj\n\"\"\"\n\nimport os,sys\nimport traitlets\nfrom ipywidgets import widgets\nimport PyQt5.QtWidgets as Widgets\n\n\nif os.name == 'posix':\n    rootpathname = '/'\nelse:\n    rootpathname = os.path.splitdrive(sys.executable)[0]\nif os.path.isfile(os.path.realpath(__file__)):\n    RELATIVE_LIB_PATH = os.path.realpath(__file__)\n    while os.path.basename(RELATIVE_LIB_PATH) != 'src' and RELATIVE_LIB_PATH != rootpathname:\n        RELATIVE_LIB_PATH = os.path.dirname(RELATIVE_LIB_PATH)\n    if __name__ == '__main__':\n        sys.path.append(RELATIVE_LIB_PATH)\n        sys.path.pop(0)\nelse:\n    RELATIVE_LIB_PATH = os.path.dirname(sys.executable)\n\n    \nfrom HMI.utils import QtUtils\n\n\nclass SelectFileButton(widgets.Button):\n    \"\"\"A file widget that leverages Qt FileDialog.\"\"\"\n\n    def __init__(self,extensions=['.h5','.csv']):\n        super(SelectFileButton, self).__init__()\n        # Add the selected_files trait\n        self.add_traits(files=traitlets.traitlets.Unicode())\n        # Create the button.\n        self.description = \"Select File\"\n        self.icon = \"square-o\"\n        self.style.button_color = \"red\"\n        # Set on click behavior.\n        self.on_click(self.select_files)\n        self.make_extensions(extensions)\n    \n    def make_extensions(self,ext):\n        self.extensions = {}\n        for e in ext:\n            if e.startswith('.'):\n                key = e[1:]\n                value = [e]\n            else:\n                key = e\n                value = [f'.{e}']\n            self.extensions[key] = value\n\n    def select_files(self,b):\n        \"\"\"Generate instance of Qt FileDialog.\n\n        Parameters\n        ----------\n        b : obj:\n            An instance of ipywidgets.widgets.Button \n        \"\"\"\n        fname = QtUtils.openSingleFile(directory=os.getcwd(),extensions=self.extensions)\n        b.file = fname\n        \n        if fname:\n            b.description = \"File Selected\"\n            b.icon = \"check-square-o\"\n            b.style.button_color = \"lightgreen\"\n        else:\n            b.description = \"Select File\"\n            b.icon = \"square-o\"\n            b.style.button_color = \"red\"\n\nif __name__ == '__main__':\n    app = Widgets.QApplication([])","sub_path":"tools/src/jupyter_widgets/FileOpenButton.py","file_name":"FileOpenButton.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"132064699","text":"from flask import Flask, render_template, url_for, request, redirect, flash, session, g, abort\nfrom flask_sqlalchemy import SQLAlchemy\n\n# константи(config)\nDEBUG = True\nSECRET_KEY = 'development key'\nUSERNAME = 'admin'\nPASSWORD = 'default'\n\n\napp = Flask(__name__)\n# знаходить усі змінні у верхньому регістрі\napp.config.from_object(__name__)\n# загрузка визначень моїх констант з файлу конфігурації\n#app.config.from_envvar('FLASKR_SETTINGS', silent = True)\n\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///schema.sqlite3'\n\napp.secret_key = SECRET_KEY\n\ndb = SQLAlchemy(app)\n\nclass Entries(db.Model):\n\tid = db.Column('entries_id', db.Integer, primary_key=True)\n\tname = db.Column('name', db.String(20))\n\ttitle = db.Column('title', db.String(50))\n\ttext = db.Column('text', db.String(300))\n\tcomplete = db.Column('complete', db.Boolean)\n\t\n\tdef __init__(self, name, title, text, complete):\n\t\tself.name = name\n\t\tself.title = title\n\t\tself.text = text\n\t\tself.complete = complete\n\n\n# функції\n\n@app.route('/')\ndef show_entries():\n\tincomplete = Entries.query.filter_by(complete=False).all()\n\treturn(render_template('show_entries.html', incomplete = incomplete, entry_r = None))\n\n\n@app.route('/add', methods = ['POST'])\ndef add_entry():\n\tif not session.get('logged_in'):\n\t\tabort(401)\n\telse:\n\t\tredact = Entries.query.filter_by(title = request.form['title']).first()\n\t\tif redact:\n\t\t\tredact.name = request.form['name']\n\t\t\tredact.title = request.form['title']\n\t\t\tredact.text = request.form['text']\n\t\t\tdb.session.commit()\n\t\t\tflash('changes was successfully applied')\n\t\telse:\n\t\t\tentry = Entries(request.form['name'], request.form['title'], request.form['text'], False)\n\t\t\tdb.session.add(entry)\n\t\t\tdb.session.commit()\n\t\t\tflash('Record was successfully added')\n\n\treturn redirect(url_for('show_entries'))\n\n\n@app.route('/delete/<id>')\ndef del_entry(id):\n\tif not session.get('logged_in'):\n\t\tabort(401)\n\telse:\n\t\tentry = Entries.query.filter_by(id = int(id)).first()\n\t\tentry.complete = True\n\t\tdb.session.commit()\n\t\treturn redirect(url_for('show_entries'))\n\ndef delete():\n\tnotes = Entries.query.all()\n\tfor note in notes:\n\t\tif note.complete == True:\n\t\t\tdb.session.delete(note)\n\tprint(' * DB was cleared')\n\n\n@app.route('/redact/<id>', methods = ['POST', 'GET'])\ndef redact(id):\n\tif not session.get('logged_in'):\n\t\tabort(401)\n\telse:\n\t\tentry_r = Entries.query.filter_by(id = int(id)).first()\n\t\treturn (render_template('show_entries.html', entry_r = entry_r))\n\n\n\n\n@app.route('/login', methods = ['POST', 'GET'])\ndef login():\n\terror = None\n\tif request.method == 'POST':\n\t\tif request.form['username'] != app.config[\"USERNAME\"]:\n\t\t   error = \"Bad username\"\n\n\t\telif request.form['password'] != app.config[\"PASSWORD\"]:\n\t\t\t error = 'Bad password'\n\n\t\telse:\n\t\t\tsession[\"logged_in\"] = True\n\t\t\tflash('u were logged in')\n\t\t\treturn redirect(url_for('show_entries'))\n\n\treturn render_template(\"login.html\", error = error)\n\n\n@app.route('/logout')\ndef logout():\n\tsession.pop('logged_in', None)\n\tflash(\"logout succeded\")\n\treturn redirect(url_for('show_entries'))\n\n\n# очищення бази даних\ndelete()\n\nif __name__ == \"__main__\":\n\tdb.create_all()\n\tapp.run(debug = DEBUG)\n","sub_path":"lab3_4/flaskr/flaskr.py","file_name":"flaskr.py","file_ext":"py","file_size_in_byte":3285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"507276148","text":"# -*- coding:utf-8 -*-\nimport os\nimport pydat\nimport sys\nimport re\n\nclass PrepareEvaData(object):\n    \n    def __init__(self):\n        self.tag_file_io = open(\"data/dev\", \"w\")\n\n    def read(self, file_name, encoding=\"utf-8\"):\n        with open(file_name, \"rb\") as fopen:\n            return fopen.read().decode(\"gbk\")\n            # return fopen.read().decode(\"gb2312\").encode(\"utf-8\").decode(\"utf-8\")\n    \n    def make_dat(self, file_name):\n        self.pro_dat = pydat.Dat()\n        with open(file_name, encoding=\"gbk\") as fopen:\n            lines = fopen.readlines()\n            for line in lines:\n                try:\n                    entity, etype = line.split(\"|\")\n                    entity = entity.strip()\n                    etype = etype.strip()\n                except Exception:\n                    continue\n                if etype == \"PRO\":\n                    self.pro_dat.add_word(entity)\n            self.pro_dat.make()\n\n    def find_tag(self, entity_type, tags, news):\n        entities = []\n        if entity_type == \"PRO\":\n            entities = self.pro_dat.search(news).get(\"entities\", [])\n        # elif entity_type == \"PER\":\n        #     entities = self.per_dat.search(news).get(\"entities\", [])\n        # elif entity_type == \"ORG2\":\n        #     entities = self.org2_dat.search(news).get(\"entities\", [])\n        # elif entity_type == \"S-ORG\":\n        #     entities = self.s_org_dat.search(news).get(\"entities\", [])\n        #     entities = self.valid_check(entities, news)\n        return entities\n\n    def valid_check(self, entities, news):\n        valid_data = []\n        threshold = 10\n        for entity in entities:\n            word = entity.get(\"entity\")\n            stop = entity.get(\"end\")\n\n            if stop + threshold > len(news):\n                content = \"pass\"\n            else:\n                content = news[stop:stop+threshold]\n            if re.findall(r\"(有限[责任]*公司)\", content):\n                continue\n            valid_data.append(entity)\n        return valid_data\n\n    def tag_data(self, entities, entity_type, tags):\n        for entity in entities:\n            begin = entity.get(\"begin\")\n            end = entity.get(\"end\")\n            if tags[begin:end] == [\"O\"] * (end - begin):\n                tag = [\"I-{}\".format(entity_type)] * (end - begin)\n                tag[0] = \"B-{}\".format(entity_type)\n                tag[-1] = \"E-{}\".format(entity_type)\n                tags = tags[:begin] + tag + tags[end:]\n        return tags\n\n    def load_tag_data(self, file_path):\n        tag_files = []\n        files = os.listdir(file_path)\n        for file in files:\n            if \"entity\" in file:\n                continue\n            news_file = os.path.join(file_path, file)\n            tag_files.append(news_file)\n        return tag_files\n\n    def step(self, tag_file, found_tags=[\"ORG\", \"PER\", \"S-ORG\"]):\n        news_file = tag_file\n        print(news_file)\n        try:\n            news = self.read(news_file, \"gbk\")\n            print(news)\n        except Exception as error:\n            return\n        for sentence in news.split(u\"。\"):\n            sentence = re.sub(r\"\\s+\", \"\", sentence)\n            if not sentence:\n                continue\n            tags = [\"O\"] * len(sentence)\n            origin_tags = [\"O\"] * len(sentence)\n            for entity_type in found_tags:\n                entities = self.find_tag(entity_type, tags, sentence)\n                tags = self.tag_data(entities, entity_type, tags)\n                tagged_data = list(zip(sentence, tags))\n            if tags != origin_tags:\n                self.save(tagged_data)\n\n    def save(self, tag_data):\n        for i in tag_data:\n            self.tag_file_io.write(\"{} {}\\n\".format(\n                i[0], i[1]\n            ))\n        self.tag_file_io.write(\"。\\nend\\n\")\n\n    def main(self, tag_file_path):\n        # tag_file_path = \"tag_data/20180806\"\n        self.make_dat(tag_file_path + \"/entity.txt.txt\")\n\n        tag_files = self.load_tag_data(\n            tag_file_path\n        )\n        for tag_file in tag_files:\n            self.step(tag_file, [\"PRO\"])\n            # self.step(tag_file, [\"S-ORG\"])\n\nif __name__ == \"__main__\":\n    ped = PrepareEvaData()\n    assert len(sys.argv) >= 2, Exception(\"请输入待处理的文件夹名\")\n    ped.main(sys.argv[1])","sub_path":"model_evaluation/prepare_pro_data.py","file_name":"prepare_pro_data.py","file_ext":"py","file_size_in_byte":4299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"618870930","text":"import numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\n\ndef sk_model(X_train,y_train,X_test,y_test):\n    from sklearn.linear_model import LogisticRegression\n    constant = np.ones((len(X_train),1))\n    X_train = np.hstack((constant,X_train))\n    const = np.ones((len(X_test), 1))\n    X_test = np.hstack((const, X_test))\n    lr = LogisticRegression(tol=0.0003,max_iter=1000)\n    lr.fit(X_train,y_train)\n    y_pred_test = lr.predict(X_test)\n    #print(\"weights in sk\")\n    #print(lr.intercept_, lr.coef_)\n    from sklearn.metrics import precision_recall_fscore_support\n    output = precision_recall_fscore_support(y_test, y_pred_test, average='weighted')\n    return output\n\ndef my_model(X_train,y_train,X_test,y_test):\n    from logisticregression import LogisticRegression\n    constant = np.ones((len(X_train),1))\n    X_train = np.hstack((constant,X_train))\n    const = np.ones((len(X_test), 1))\n    X_test = np.hstack((const, X_test))\n    lr = LogisticRegression(0.0004,0.1,0.0003,1000)\n    iteration_array,allLosses = lr.fit(X_train,y_train)\n    plt.plot(iteration_array, allLosses)\n    plt.title('Loss for all iterations in a fold')\n    plt.xlabel('ITERATIONS')\n    plt.ylabel('LOSS')\n    plt.show()\n    y_pred_test = lr.predict(X_test)\n    from sklearn.metrics import precision_recall_fscore_support\n    output = precision_recall_fscore_support(y_test, y_pred_test, average='weighted')\n    return output\n\ndef main():\n    filename = 'spambase.csv'\n    dataset = pd.read_csv(filename,header=None)\n    X = dataset.iloc[:,:-1].values\n    y = dataset.iloc[:,-1].values\n\n    from sklearn.utils import shuffle\n    X,y = shuffle(X,y,random_state=13)\n    #kfold generation\n    from sklearn.model_selection import KFold\n    from sklearn.preprocessing import StandardScaler\n    counter = 1\n    kf = KFold(n_splits=10)\n    allAccuracy = []\n    allRecall = []\n    for train_index,test_index in kf.split(X):\n        print(\"Executing Fold \",counter)\n        X_train, X_test = X[train_index], X[test_index]\n        y_train, y_test = y[train_index], y[test_index]\n\n        sc_X = StandardScaler()\n        X_train = sc_X.fit_transform(X_train)\n        X_test = sc_X.transform(X_test)\n        #print(\"Executing SKLearn model\")\n        #sk_model(X_train,y_train,X_test,y_test)\n        print(\"Executing my model\")\n        output = my_model(X_train,y_train,X_test,y_test)\n        allAccuracy.append(output[0])\n        allRecall.append(output[1])\n        print(\"Precision: \",output[0])\n        print(\"Recall: \",output[1])\n        counter += 1\n    print(\"Average Accuracy across all folds\",np.mean(allAccuracy))\n    print(\"Standard deviation Accuracy across all folds\", np.std(allAccuracy))\n    print(\"Average Recall across all folds\",np.mean(allRecall))\n    print(\"Standard Deviation Accuracy across all folds\", np.std(allRecall))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"LogisticRegression/LogisticRegressionSpamBaseDataset.py","file_name":"LogisticRegressionSpamBaseDataset.py","file_ext":"py","file_size_in_byte":2872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"623291087","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\nfrom collections import deque\nclass Solution(object):\n    def zigzagLevelOrder(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: List[List[int]]\n        \"\"\"\n        \n        #direction = \"lr\" # \"rt\"\n        def helper(node, level, levDirMap, levQueMap):\n            \n            if not node:\n                return\n            \n            if level not in levDirMap:\n                if level % 2 == 0:\n                    levDirMap[level] = \"lr\"\n                else:\n                    levDirMap[level] = \"rl\"\n            \n            if level not in levQueMap:\n                queue = deque()\n                levQueMap[level] = queue\n            \n            if levDirMap[level] == \"lr\":\n                levQueMap[level].append(node.val)\n            else:\n                levQueMap[level].appendleft(node.val)\n            \n            helper(node.left, level+1, levDirMap, levQueMap)\n            helper(node.right, level+1, levDirMap, levQueMap)\n\n                \n        levDirMap = {} # level: diriction\n        levQueMap = {} # level: queue\n        \n        helper(root, 0, levDirMap, levQueMap)\n        \n        # print(levQueMap)\n        output = []\n        for key in levQueMap:\n            arr = []\n            for el in levQueMap[key]:\n                arr.append(el)\n            output.append(arr)\n            \n        return output","sub_path":"Trees and Graphs/Binary Tree Zigzag Level Order Traversal.py","file_name":"Binary Tree Zigzag Level Order Traversal.py","file_ext":"py","file_size_in_byte":1533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"433759038","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef read_data(filepath, d = ','):\n    \"\"\" returns an np.matrix of the data \"\"\"\n    return np.asmatrix(np.genfromtxt(filepath, delimiter = d, dtype = None))\n\n\ndef fitpoly(x, t, model_order):\n    \n    #### YOUR CODE HERE ####\n    w = None  ### Calculate w column vector (as an np.matrix)\n\n    myarray = np.ones((x.shape[0], 1))\n    #print(x.shape)\n    #print(myarray.shape)\n    #myarray = np.concatenate((myarray, x))\n\n    if(model_order > 1):\n        x = np.column_stack((myarray, x))\n\n         #clear our array\n        myarray = np.zeros((x.shape[0], 1))\n\n        mypow = 2;\n        for col in range(model_order -1):\n          myarray = np.power(x.T[1], mypow)\n          #print(myarray)\n          #rint(x.T[1])\n          mypow += 1\n          x = np.column_stack((x,myarray.T))\n\n    elif(model_order == 1):\n         x = np.column_stack((myarray, x))\n\n    elif(model_order == 0):\n        x = myarray           \n        \n    #np.insert(x,myarray, axis=0)\n    #x[:,:-1] = myarray\n\n    #print(x)\n\n    #m1 = ((X^t)X)\n    m1 = x.T.dot(x)\n\n    #print m1\n\n    #m2 = m1^-1 = ((X^t)X)^1\n\n    m2 = np.linalg.inv(m1)\n\n    #print m2\n\n    #m3 = m2.X^t = (((X^t)X)^1) X^t\n\n    m3 = m2.dot(x.T)\n\n    w = m3.dot(t)\n\n    #w = cloumn vector\n    return np.asmatrix(w).T\n\n\ndef setPolyMatrix(x, model_order):\n    myarray = np.ones((x.shape[0], 1))\n    #print(x.shape)\n    #print(myarray.shape)\n    #myarray = np.concatenate((myarray, x))\n\n    if(model_order > 1):\n        x = np.column_stack((myarray, x))\n\n         #clear our array\n        myarray = np.zeros((x.shape[0], 1))\n\n        mypow = 2;\n        for col in range(model_order -1):\n          myarray = np.power(x.T[1], mypow)\n          #print(myarray)\n          #rint(x.T[1])\n          mypow += 1\n          x = np.column_stack((x,myarray.T))\n\n    elif(model_order == 1):\n         x = np.column_stack((myarray, x))\n\n    elif(model_order == 0):\n        x = myarray   \n\n\n     #w = cloumn vector\n    return x       \n\n#END OF OUR FUNCTIONS    \n###############################################################    \n#filepath1 = '../data/test1.csv'   ## Problem 4\nfilepath1 = '../data/synthdata2014.csv'   ## Problem 4\n\n\n\n## Run a cross-validation over model orders\nmaxorder = 7\nData = read_data(filepath1, ',')\n\nX = Data[:, 0] # extract x (slice first column)\nt = Data[:, 1] # extract t (slice second column)\n#X = np.asmatrix(np.zeros(shape = (x.shape[0], maxorder + 1)))\n#testX = np.asmatrix(np.zeros(shape = (testx.shape[0], maxorder + 1)))\n\nK = 50 # K-fold CV\nN = Data.shape[0]\n\n#prints 3rd row and column zero\n#print Data[3, 0]\n\n\nindexArray = list(range(N))\nnp.random.seed(1)\nnp.random.shuffle(indexArray)\n\n#print indexArray\n\nelements_each_folder = N/K\n\ncv_loss = np.zeros((K, maxorder + 1))\ntrain_loss = np.zeros((K, maxorder + 1))\n\n#tests\n\nfor cur_order in range(maxorder +1):\n#for cur_order in range(3,4):\n\n    for k in range(K):\n        #reset our test matrices\n        testx = np.zeros(elements_each_folder)\n        testt = np.zeros(elements_each_folder)\n        trainx = np.zeros(N - elements_each_folder)\n        traint = np.zeros(N - elements_each_folder)\n\n        #print testx\n        #print testt, \"\\n\\n\"\n        #train sets\n        #only one fold ... up to now :((    \n        indextrain = 0\n        indextestx = 0\n        for i in range(N):\n            idxmin = k*elements_each_folder\n            idxmax = idxmin + elements_each_folder\n            #print \"min max \", idxmin, idxmax\n            indexArrayTmp = indexArray[idxmin:idxmax]\n            if i not in indexArrayTmp:\n                #print i, \" nao esta em indexArray  \"\n                #print indexArrayTmp\n                trainx[indextrain] = Data[i, 0]\n                traint[indextrain] = Data[i, 1]\n\n                indextrain += 1\n                #print \"i vale \", i\n            #make our test data    \n            else:\n                testx[indextestx] = Data[i, 0]\n                testt[indextestx] = Data[i, 1]\n                indextestx += 1\n\n\n        #print \"index train: \", indextrain, \"\\n\"\n        #print trainx.shape\n        #print testx.shape\n        #print \"k = \", k\n        w = fitpoly(trainx, traint, model_order=cur_order)\n\n\n        testx = setPolyMatrix(testx, model_order=cur_order)\n        #testt = setPolyMatrix(testt, model_order=cur_order)\n\n        testt = np.asmatrix(testt).T\n\n        fold_pred = testx * w\n\n       \n\n        trainx = setPolyMatrix(trainx, model_order=cur_order)\n        #print traint\n        traint = np.asmatrix(traint).T\n        train_pred = trainx * w\n        \n        #print fold_pred\n        if testt.shape[0] == 1:\n            cv_loss[k, cur_order] = np.power(fold_pred - testt, 2)\n        else:\n            cv_loss[k, cur_order] = np.mean(np.power(fold_pred - testt, 2))\n        train_loss[k, cur_order] = np.mean(np.power(train_pred - traint, 2))    \n       \n \n\n\nlog_cv_loss = np.log(cv_loss)\nlog_train_loss = np.log(train_loss)\n\n## Plot log scale loss results\nplt.figure(1);\nplt.title('Log-scale Loss')\n\nplt.subplot(131)\nplt.plot(np.arange(0, maxorder + 1), np.mean(log_cv_loss, 0), linewidth = 2)\nplt.xlabel('Model Order')\nplt.ylabel('Log Loss')\nplt.title('CV Loss')\nplt.pause(.1) # required on some systems so that rendering can happen\n\nplt.subplot(132)\nplt.plot(np.arange(0, maxorder + 1), np.mean(log_train_loss, 0), linewidth = 2)\nplt.xlabel('Model Order')\nplt.ylabel('Log Loss')\nplt.title('Train Loss')\nplt.pause(100) # required on some systems so that rendering can happen\n\n\n\n\n\n   \n\n#print indexArray\n\n\n","sub_path":"hw/hw2/ista421ML-Homework2-release/code/scripts/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":5516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"192679000","text":"from flask import Flask,request\napp = Flask(__name__)\n\n\n@app.route('/')\n@app.route('/hello',methods=['POST'])\ndef HelloWorld():\n\tif request.method == 'POST':\n\t\ttes = request.data\n\t\tprint (tes)\n\t\treturn tes\n\telse:\n\t\treturn \"Hello wdfckydg World\"\n\nif __name__ == '__main__':\n    app.debug = True\n    app.run(host='0.0.0.0', port=5000)","sub_path":"flask1.py","file_name":"flask1.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"403575954","text":"#!/bin/python3\n# PROBLEM STATEMENT: https://www.hackerrank.com/challenges/ctci-array-left-rotation/problem?h_l=interview&page=1&playlist_slugs%5B%5D=interview-preparation-kit&playlist_slugs%5B%5D=arrays\n\n# Given an array of values and a number d, rotate the elements of the array in anticlockwise direction, i.e.,\n# from right to left, d number of times.\n\nimport os\n\n\n# Complete the rotLeft function below.\ndef rotLeft(a, d):\n    final = [a[i + d] for i in range(len(a)) if i + d < len(a)]\n    [final.append(a[i]) for i in range(d)]\n    return final\n\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    nd = input().split()\n\n    n = int(nd[0])\n\n    d = int(nd[1])\n\n    a = list(map(int, input().rstrip().split()))\n\n    result = rotLeft(a, d)\n\n    fptr.write(' '.join(map(str, result)))\n    fptr.write('\\n')\n\n    fptr.close()\n","sub_path":"2D Array - DS/rotate_left/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"143814288","text":"'''给定一个字符串和一个整数 k，你需要对从字符串开头算起的每个 2k 个字符的前k个字符进行反转。如果剩余少于 k 个字符，则将剩余的所有全部反转。如果有小于 2k 但大于或等于 k 个字符，则反转前 k 个字符，并将剩余的字符保持原样。\n\n示例:\n\n输入: s = \"abcdefg\", k = 2\n输出: \"bacdfeg\"\n要求:\n\n该字符串只包含小写的英文字母。\n给定字符串的长度和 k 在[1, 10000]范围内。'''\n\n\nclass Solution:\n    def reverseStr(self, s, k):\n        \"\"\"\n        :type s: str\n        :type k: int\n        :rtype: str\n        \"\"\"\n        out=\"\"\n        times=len(s)//(2*k)+1\n        for time in range(times):\n            l=int(time*2*k)\n            out=out+s[l:l+k][::-1]+s[l+k:l+2*k]\n        return out\n\ns=''\nprint(s)\nk=3\nsolu=Solution()\nprint(solu.reverseStr(s,k))\n","sub_path":"Questions/541. 反转字符串 II.py","file_name":"541. 反转字符串 II.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"394915943","text":"\"\"\"\n当异常和错误不可避免时，最好的方式就是让我们知道程序到底是哪里出了错，或者是因为什么导致错误\n这样才能更好地让开发人员及时应对并解决。`loguru`集成了一个名为`better_exceptions` 的库\n不仅能够将异常和错误记录，并且还能对异常进行追溯\n\"\"\"\nimport sys\nimport os\nfrom loguru import logger\n\nlogger.add(\"./logs/my_log.log\", backtrace=True, diagnose=True)\n\n\ndef fun(a: float, b: float) -> float:\n    return a / b\n\n\ndef nested(c: float):\n    try:\n        fun(5, c)\n    except ZeroDivisionError:\n        logger.exception(\"What?!\")\n\n\nif __name__ == '__main__':\n    nested(0)\n","sub_path":"log_systems/loguru/loguru_examples/06_exception_track.py","file_name":"06_exception_track.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"368299343","text":"#!/usr/bin/env python\n#\n# -----------------------------------------------------------------------------\n# Copyright (c) 2017 The Regents of the University of California\n#\n# This file is part of kevlar (http://github.com/dib-lab/kevlar) and is\n# licensed under the MIT license: see LICENSE.\n# -----------------------------------------------------------------------------\n\nimport re\nimport sys\nimport khmer\nimport kevlar\n\n\nclass Variant(object):\n    \"\"\"Base class for handling variant calls and no-calls.\"\"\"\n\n    def __init__(self, seqid, pos, refr, alt, **kwargs):\n        \"\"\"\n        Constructor method.\n\n        The `pos` parameter expects the genomic position as a 0-based index.\n        Setting the `refr` or `alt` parameters to `.` will designate this\n        variant as a \"no call\".\n        \"\"\"\n        self._seqid = seqid\n        self._pos = pos\n        self._refr = refr\n        self._alt = alt\n        self.info = dict()\n        for key, value in kwargs.items():\n            self.info[key] = value\n\n    @property\n    def seqid(self):\n        return self._seqid\n\n    @property\n    def position(self):\n        return self._pos\n\n    @property\n    def vcf(self):\n        \"\"\"Print variant to VCF.\"\"\"\n        attrstr = '.'\n        if len(self.info) > 0:\n            kvpairs = list()\n            for key in sorted(self.info):\n                if key != 'QS':\n                    kvpairs.append(self.attribute(key, pair=True))\n            queryseq = self.attribute('QS', pair=True)\n            if queryseq:\n                kvpairs.append(queryseq)\n            attrstr = ';'.join(kvpairs)\n\n        filterstr = 'PASS' if self._refr != '.' else '.'\n        return '{:s}\\t{:d}\\t.\\t{:s}\\t{:s}\\t.\\t{:s}\\t{:s}'.format(\n            self._seqid, self._pos + 1, self._refr, self._alt, filterstr,\n            attrstr\n        )\n\n    @property\n    def cigar(self):\n        return self.attribute('CG')\n\n    @property\n    def window(self):\n        \"\"\"\n        Getter method for the variant window.\n\n        The \"variant window\" (abbreviated `VW` in VCF output) is the sequence\n        interval in the proband contig that encompasses all k-mers overlapping\n        the variant.\n\n        GCCTAGTTAGCTAACGTCCCGATCACTGTGTCACTGC\n                    .....A\n                     ....A.\n                      ...A..\n                       ..A...\n                        .A....\n                         A.....\n                         |        <-- position of variant\n                    [---------]   <-- variant window, interval (inclusive)\n                                      encompassing all 6-mers that overlap the\n                                      variant\n        \"\"\"\n        return self.attribute('VW')\n\n    @property\n    def refrwindow(self):\n        \"\"\"Similar to `window`, but encapsulating the reference allele.\"\"\"\n        return self.attribute('RW')\n\n    def attribute(self, key, pair=False):\n        if key not in self.info:\n            return None\n        value = self.info[key].replace(';', ':')\n        if pair:\n            keyvaluepair = '{:s}={:s}'.format(key, value)\n            return keyvaluepair\n        else:\n            return value\n\n    @property\n    def genotypes(self):\n        gt = self.attribute('GT')\n        if not gt:\n            return None\n        return tuple(gt.split(','))\n\n\nclass VariantSNV(Variant):\n    def __str__(self):\n        return '{:s}:{:d}:{:s}->{:s}'.format(self._seqid, self._pos,\n                                             self._refr, self._alt)\n\n\nclass VariantIndel(Variant):\n    def __str__(self):\n        \"\"\"\n        Return a string representation of this variant.\n\n        The reason that 1 is added to the variant position is to offset the\n        nucleotide shared by the reference and alternate alleles. This position\n        is still 0-based (as opposed to VCF's 1-based coordinate system) but\n        does not include the shared nucleotide.\n        \"\"\"\n        pos = self._pos + 1\n        if len(self._refr) > len(self._alt):\n            dellength = len(self._refr) - len(self._alt)\n            return '{:s}:{:d}:{:d}D'.format(self._seqid, pos, dellength)\n        else:\n            insertion = self._alt[1:]\n            return '{:s}:{:d}:I->{:s}'.format(self._seqid, pos, insertion)\n\n\ndef local_to_global(localcoord, subseqid):\n    match = re.search('(\\S+)_(\\d+)-(\\d+)', subseqid)\n    assert match, 'unable to parse subseqid {:s}'.format(subseqid)\n    seqid = match.group(1)\n    globaloffset = int(match.group(2))\n    globalcoord = globaloffset + localcoord\n    return seqid, globalcoord\n\n\ndef call_snv(target, query, offset, length, ksize):\n    t = target.sequence[offset:offset+length]\n    q = query.sequence[:length]\n    diffs = [(i, t[i], q[i]) for i in range(length) if t[i] != q[i]]\n    if len(diffs) == 0:\n        seqid, globalcoord = local_to_global(offset, target.name)\n        nocall = Variant(seqid, globalcoord, '.', '.', NC='perfectmatch',\n                         QN=query.name, QS=q)\n        return [nocall]\n\n    snvs = list()\n    for diff in diffs:\n        minpos = max(diff[0] - ksize + 1, 0)\n        maxpos = min(diff[0] + ksize, length)\n        window = q[minpos:maxpos]\n        refrwindow = t[minpos:maxpos]\n\n        numoverlappingkmers = len(window) - ksize + 1\n        kmers = [window[i:i+ksize] for i in range(numoverlappingkmers)]\n        refr = diff[1].upper()\n        alt = diff[2].upper()\n        localcoord = offset + diff[0]\n        seqid, globalcoord = local_to_global(localcoord, target.name)\n        snv = VariantSNV(seqid, globalcoord, refr, alt, VW=window,\n                         RW=refrwindow)\n        snvs.append(snv)\n    return snvs\n\n\ndef call_deletion(target, query, offset, ksize, leftmatch, indellength):\n    minpos = leftmatch - ksize + 1\n    maxpos = leftmatch + ksize - 1\n    window = query.sequence[minpos:maxpos]\n    minpos += offset\n    maxpos += offset + indellength\n    refrwindow = target.sequence[minpos:maxpos]\n\n    refr = target.sequence[offset+leftmatch-1:offset+leftmatch+indellength]\n    alt = refr[0]\n    assert len(refr) == indellength + 1\n    localcoord = offset + leftmatch\n    seqid, globalcoord = local_to_global(localcoord, target.name)\n    return [VariantIndel(seqid, globalcoord - 1, refr, alt, VW=window,\n                         RW=refrwindow)]\n\n\ndef call_insertion(target, query, offset, ksize, leftmatch, indellength):\n    minpos = leftmatch - ksize + 1\n    maxpos = leftmatch + ksize + indellength - 1\n    window = query.sequence[minpos:maxpos]\n    minpos += offset\n    maxpos += offset - indellength\n    refrwindow = target.sequence[minpos:maxpos]\n\n    insertion = query.sequence[leftmatch-1:leftmatch+indellength]\n    refr = insertion[0]\n    assert len(insertion) == indellength + 1\n    localcoord = offset + leftmatch\n    seqid, globalcoord = local_to_global(localcoord, target.name)\n    return [VariantIndel(seqid, globalcoord - 1, refr, insertion, VW=window,\n                         RW=refrwindow)]\n\n\ndef make_call(target, query, cigar, ksize):\n    snvmatch = re.search('^(\\d+)D(\\d+)M(\\d+)D$', cigar)\n    snvmatch2 = re.search('^(\\d+)D(\\d+)M(\\d+)D(\\d+)M$', cigar)\n    if snvmatch:\n        offset = int(snvmatch.group(1))\n        length = int(snvmatch.group(2))\n        return call_snv(target, query, offset, length, ksize)\n    elif snvmatch2 and int(snvmatch2.group(4)) <= 5:\n        offset = int(snvmatch2.group(1))\n        length = int(snvmatch2.group(2))\n        return call_snv(target, query, offset, length, ksize)\n\n    indelmatch = re.search('^(\\d+)D(\\d+)M(\\d+)([ID])(\\d+)M(\\d+)D$', cigar)\n    indelmatch2 = re.search('^(\\d+)D(\\d+)M(\\d+)([ID])(\\d+)M(\\d+)D(\\d+)M$',\n                            cigar)\n    if indelmatch:\n        offset = int(indelmatch.group(1))\n        leftmatch = int(indelmatch.group(2))\n        indellength = int(indelmatch.group(3))\n        indeltype = indelmatch.group(4)\n        callfunc = call_deletion if indeltype == 'D' else call_insertion\n        return callfunc(target, query, offset, ksize, leftmatch, indellength)\n    elif indelmatch2 and int(indelmatch2.group(7)) <= 5:\n        offset = int(indelmatch2.group(1))\n        leftmatch = int(indelmatch2.group(2))\n        indellength = int(indelmatch2.group(3))\n        indeltype = indelmatch2.group(4)\n        callfunc = call_deletion if indeltype == 'D' else call_insertion\n        return callfunc(target, query, offset, ksize, leftmatch, indellength)\n\n    seqid, globalcoord = local_to_global(0, target.name)\n    nocall = Variant(seqid, globalcoord, '.', '.', NC='inscrutablecigar',\n                     QN=query.name, QS=query.sequence, CG=cigar)\n    return [nocall]\n\n\ndef align_both_strands(targetseq, queryseq, match=1, mismatch=2, gapopen=5,\n                       gapextend=0):\n    cigar1, score1 = kevlar.align(targetseq, queryseq, match, mismatch,\n                                  gapopen, gapextend)\n    cigar2, score2 = kevlar.align(targetseq, kevlar.revcom(queryseq), match,\n                                  mismatch, gapopen, gapextend)\n\n    if score2 > score1:\n        cigar = cigar2\n        score = score2\n        strand = -1\n    else:\n        cigar = cigar1\n        score = score1\n        strand = 1\n    return cigar, score, strand\n\n\ndef call(targetlist, querylist, match=1, mismatch=2, gapopen=5, gapextend=0,\n         ksize=31):\n    \"\"\"\n    Wrap the `kevlar call` procedure as a generator function.\n\n    Input is the following.\n    - an iterable containing one or more target sequences from the reference\n      genome, stored as khmer or screed sequence records\n    - an iterable containing one or more contigs assembled by kevlar, stored as\n      khmer or screed sequence records\n    - alignment match score (integer)\n    - alignment mismatch penalty (integer)\n    - alignment gap open penalty (integer)\n    - alignment gap extension penalty (integer)\n\n    The function yields tuples of target sequence name, query sequence name,\n    and alignment CIGAR string\n    \"\"\"\n    for query in sorted(querylist, reverse=True, key=len):\n        bestcigar = None\n        bestscore = None\n        besttarget = None\n        bestorientation = None\n        for target in sorted(targetlist, key=lambda record: record.name):\n            cigar, score, strand = align_both_strands(\n                target.sequence, query.sequence, match, mismatch, gapopen,\n                gapextend\n            )\n            if bestscore is None or score > bestscore:\n                bestscore = score\n                bestcigar = cigar\n                besttarget = target\n                bestorientation = strand\n\n        if bestorientation == -1:\n            query.sequence = kevlar.revcom(query.sequence)\n        for varcall in make_call(besttarget, query, bestcigar, ksize):\n            yield varcall\n\n\ndef main(args):\n    outstream = kevlar.open(args.out, 'w')\n    qinstream = kevlar.parse_augmented_fastx(kevlar.open(args.queryseq, 'r'))\n    queryseqs = [record for record in qinstream]\n    targetseqs = [record for record in khmer.ReadParser(args.targetseq)]\n    caller = call(\n        targetseqs, queryseqs,\n        args.match, args.mismatch, args.open, args.extend,\n        args.ksize,\n    )\n    for varcall in caller:\n        print(varcall.vcf, file=outstream)\n","sub_path":"kevlar/call.py","file_name":"call.py","file_ext":"py","file_size_in_byte":11160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"136319500","text":"import pandas as pd\r\nimport numpy as np\r\nimport math\r\ndata=pd.read_excel('附件一：已结束项目任务数据.xls')\r\ntrd=pd.read_excel('附件二：会员信息数据.xlsx')\r\nx1=data.loc[data['任务号码'].values=='A0001','任务gps纬度'].values#第A0020任务的纬度\r\ny1=data.loc[data['任务号码'].values=='A0001','任务gps经度'].values\r\nx2=data.loc[data['任务号码'].values=='A0002','任务gps纬度'].values#第A0020任务的纬度\r\ny2=data.loc[data['任务号码'].values=='A0002','任务gps经度'].values#第A0020任务的经度\r\nb=trd.iloc[:,[1]].values\r\nz1=trd.iloc[:,0].values\r\nx4=[]#第g个会员的纬度\r\ny4=[]#第g个会员的经度\r\nfor g in range(len(b)):\r\n    q=str(b[g])\r\n    l=q.find(' ',0,len(q))\r\n    d1=float(q[2:l])\r\n    d2=float(q[l:len(q)-2])\r\n    x4.append(d1)\r\n    y4.append(d2)\r\nD1=np.zeros(len(x4))\r\nD2=np.zeros(len(x4))\r\nfor i in range(len(x4)):\r\n    d1=111.199*math.sqrt((x1-x4[i])**2+(y1-y4[i])**2*math.cos((x1+x4[i])*math.pi/180)**2)\r\n    D1[i]=d1\r\n    d2=111.199*math.sqrt((x2-x4[i])**2+(y2-y4[i])**2*math.cos((x2+x4[i])*math.pi/180)**2)\r\n    D2[i]=d2\r\ns_A0001=pd.Series(D1,index=z1)#会员之间的距离\r\ns_A0002=pd.Series(D2,index=z1)\r\nA0001_Bnum=len(D1[D1<=5])\r\nD3=s_A0002[s_A0002<=5]\r\nlist3=list(D3.index)\r\nA0001_Bavg=0\r\nfor j in range(len(list3)):\r\n    xy=trd.loc[trd['会员编号'].values==list3[j],'信誉值'].values\r\n    A0001_Bavg=A0001_Bavg+xy","sub_path":"编程技能考核+课程设计实训/编程技能考核/参考答案/答题程序/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"166110154","text":"from parsl import *\nimport time\nimport argparse\nfrom parsl.configs.local import localThreads as config\ndfk = DataFlowKernel(config=config)\n\n\n@App(\"python\", dfk)\ndef python_app():\n    import platform\n    return \"Hello from {0}\".format(platform.uname())\n\n\n@App(\"python\", dfk)\ndef python_noop():\n    return\n\n\n@App(\"bash\", dfk)\ndef bash_app(stdout=None, stderr=None):\n    return 'echo \"Hello from $(uname -a)\" ; sleep 2'\n\n\ndef test_python(count):\n    results = {}\n\n    print(\"Priming the system\")\n    items = []\n    for i in range(0, 100):\n        items.extend([python_app()])\n    print(\"Launched primer application\")\n    for i in items:\n        i.result()\n    print(\"Primer done\")\n\n    latency = []\n    rtt = []\n    for i in range(0, 100):\n        pre = time.time()\n        results[i] = python_noop()\n        post = time.time()\n        results[i].result()\n        final = time.time()\n        latency.append(final - post)\n        rtt.append(final - pre)\n\n    min_latency = min(latency) * 1000\n    max_latency = max(latency) * 1000\n    avg_latency = average(latency) * 1000\n    print(\"Latency   |   Min:{0:0.3}ms Max:{1:0.3}ms Average:{2:0.3}ms\".format(min_latency,\n                                                                               max_latency,\n                                                                               avg_latency))\n\n    min_rtt = min(rtt) * 1000\n    max_rtt = max(rtt) * 1000\n    avg_rtt = average(rtt) * 1000\n\n    print(\"Roundtrip |   Min:{0:0.3}ms Max:{1:0.3}ms Average:{2:0.3}ms\".format(min_rtt,\n                                                                               max_rtt,\n                                                                               avg_rtt))\n\n\ndef average(l):\n    return sum(l) / len(l)\n\n\ndef test_bash():\n    import os\n    fname = os.path.basename(__file__)\n\n    x = bash_app(stdout=\"{0}.out\".format(fname))\n    print(\"Waiting ....\")\n    print(x.result())\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-c\", \"--count\", default=\"10\",\n                        help=\"Count of apps to launch\")\n    parser.add_argument(\"-d\", \"--debug\", action='store_true',\n                        help=\"Count of apps to launch\")\n    args = parser.parse_args()\n\n    # if args.debug:\n    #     parsl.set_stream_logger()\n\n    test_python(int(args.count))\n    # test_bash()\n","sub_path":"parsl/tests/test_scaling/test_latency/test_localThreads_latency.py","file_name":"test_localThreads_latency.py","file_ext":"py","file_size_in_byte":2360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"335578911","text":"\"\"\"Some tests for utility functions in slack parsing utility.\"\"\"\nimport utils.slack_parse as util\nfrom app.model.user import User\nfrom app.model.team import Team\nfrom app.model.permissions import Permissions\n\n\ndef test_regularize_char_standard():\n    \"\"\"Test how this function reacts to normal operation.\"\"\"\n    assert util.regularize_char('a') == 'a'\n    assert util.regularize_char(' ') == ' '\n    assert util.regularize_char('\\'') == '\\''\n    assert util.regularize_char('‘') == '\\''\n    assert util.regularize_char('’') == '\\''\n    assert util.regularize_char('“') == '\"'\n    assert util.regularize_char('”') == '\"'\n\n\ndef test_escaped_id_conversion():\n    \"\"\"Test how this function reacts to normal operation.\"\"\"\n    CMDS = [\n        # Normal operation\n        ('/rocket user edit --member <@U1234|user> --name \"User\"',\n         '/rocket user edit --member U1234 --name \"User\"'),\n        # No users\n        ('/rocket user view',\n         '/rocket user view'),\n        # Multiple users\n        ('/rocket foo <@U1234|user> <@U4321|ruse> <@U3412|sure> -h',\n         '/rocket foo U1234 U4321 U3412 -h')\n    ]\n\n    for inp, expect in CMDS:\n        assert util.escaped_id_to_id(inp) == expect\n\n\ndef test_check_credentials_admin():\n    \"\"\"Test checking to see if user is admin.\"\"\"\n    user = User(\"USFAS689\")\n    user.permissions_level = Permissions.admin\n    assert util.check_permissions(user, None)\n\n\ndef test_check_credentials_not_admin():\n    \"\"\"Test checking to see if user is not admin.\"\"\"\n    user = User(\"USFAS689\")\n    user.permissions_level = Permissions.member\n    assert not util.check_permissions(user, None)\n\n\ndef test_check_credentials_lead():\n    \"\"\"Test checking to see if user is lead for certain team.\"\"\"\n    user = User(\"USFAS689\")\n    user.github_id = \"IDGithub\"\n    team = Team(\"brussels\", \"team\", \"id\")\n    team.add_member(user.github_id)\n    team.add_team_lead(user.github_id)\n    user.permissions_level = Permissions.team_lead\n    assert util.check_permissions(user, team)\n\n\ndef test_check_credentials_not_lead():\n    \"\"\"Test checking to see if user is lead for certain team.\"\"\"\n    user = User(\"USFAS689\")\n    user.github_id = \"IDGithub\"\n    team = Team(\"brussels\", \"team\", \"id\")\n    team.add_member(user.github_id)\n    user.permissions_level = Permissions.team_lead\n    assert not util.check_permissions(user, team)\n\n\ndef test_check_string_is_not_slack_id():\n    \"\"\"Test checking to see if a string is not a slack id.\"\"\"\n    string_to_test = \"ABCDEFG\"\n    assert not util.is_slack_id(string_to_test)\n\n\ndef test_check_string_is_slack_id():\n    \"\"\"Test checking to see if string is a slack id.\"\"\"\n    string_to_test = \"UFJ42EU67\"\n    assert util.is_slack_id(string_to_test)\n\n\ndef test_escape_email():\n    \"\"\"Test parsing escaped emails.\"\"\"\n    email = \"<mailto:email@a.com|email@a.com>\"\n    ret = util.escape_email(email)\n    assert ret == \"email@a.com\"\n","sub_path":"tests/utils/slack_parse_test.py","file_name":"slack_parse_test.py","file_ext":"py","file_size_in_byte":2885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"206933567","text":"#!/usr/bin/env python3\n\n\"\"\"\nMade by @nizhib\n\"\"\"\n\nimport base64\nimport io\nimport logging\nimport sys\nimport time\nfrom http import HTTPStatus\n\nimport numpy as np\nimport requests\nfrom flask import Flask, request, jsonify\nfrom imageio import imsave\nfrom PIL import Image\nfrom waitress import serve\n\nfrom api import Segmentator\n\nLOGGING_LEVEL = 'INFO'\nLOGGING_FORMAT = '[%(asctime)s] %(name)s:%(lineno)d: %(message)s'\n\nlogging.basicConfig(format=LOGGING_FORMAT, level=LOGGING_LEVEL)\n\nsegmentator = Segmentator()\n#  segmentator.load('resource/unet_resnext50.pth')\nsegmentator.load('resource/oc_densenet128.pth')\n\napp = Flask(__name__)\nlogger = logging.getLogger(__file__)\n\n\n@app.route('/segment', methods=['POST'])\ndef handle():\n    start = time.time()\n    status = HTTPStatus.OK\n    result = {'success': False}\n\n    try:\n        data = request.json\n        if 'image' in data:\n            blob = io.BytesIO(base64.b64decode(data['image']))\n            img = Image.open(blob).convert('RGB')\n        elif 'url' in data:\n            blob = io.BytesIO(requests.get(data['url']).content)\n            img = Image.open(blob).convert('RGB')\n        else:\n            raise ValueError(f'No image source found in request fields: {data.keys()}')\n\n        mask = segmentator.predict(img)\n        mask = (mask * 255).astype(np.uint8)\n\n        fmem = io.BytesIO()\n        imsave(fmem, mask, 'png')\n        fmem.seek(0)\n        mask64 = base64.b64encode(fmem.read()).decode('utf-8')\n\n        result['data'] = {'mask': mask64}\n        result['success'] = True\n    except Exception as e:\n        logger.exception(e)\n        result['message'] = str(e)\n        status = HTTPStatus.INTERNAL_SERVER_ERROR\n\n    result['total'] = time.time() - start\n\n    return jsonify(result), status\n\n\nif __name__ == '__main__':\n    if len(sys.argv) > 1:\n        port = int(sys.argv[1])\n    else:\n        port = 5000\n\n    serve(app, host='0.0.0.0', port=port)\n","sub_path":"backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"610960310","text":"#!/usr/bin/python\n# -*- coding: utf8 -*-\n\"\"\"\n.. moduleauthor:: Adrian Brasoveanu <adrian.brasoveanu@htwchur.ch>\n\"\"\"\nfrom __future__ import unicode_literals\nfrom builtins import object\nimport logging\nimport requests\nimport json\n\nfrom eWRT.ws.rest import MultiRESTClient\n\nfrom weblyzard_api.client import (WEBLYZARD_API_URL, WEBLYZARD_API_USER,\n                                  WEBLYZARD_API_PASS, OGER_API_URL)\nfrom weblyzard_api.xml_content import XMLContent\n\nlogger = logging.getLogger(__name__)\n\nDEFAULT_MAX_RETRY_DELAY = 15\nDEFAULT_MAX_RETRY_ATTEMPTS = 5\nDAYS_BACK_DEFAULT = 20\n\n# GET URL/{pubmed|pmd}/{txt|bioc|pxml|nxml|pxml.gz}/DOC_ID\n\n\nclass OgerClient(object):\n    \"\"\"\n    Provides access to the OntoGene Entity Recognition.\n\n    Currently we support the following types of requests: status, fetch and upload.\n\n    Requests to root are currently not supported since they would simply return an HTML page.\n\n    A fetch request will retrieve an existing document from a known source:   \n        GET <BASE_URL>/fetch/<SOURCE>/<OUTPUT_FORMAT>/<DOC_ID>\n        POST <BASE_URL>/fetch/<SOURCE>/<OUTPUT_FORMAT>/<DOC_ID>\n\n    An upload request is used to submit a text to be annotated.\n        POST <BASE_URL>/upload/<INPUT_FORMAT>/<OUTPUT_FORMAT> [/<DOC_ID>]\n\n    Accepted values:\n        SOURCE: pubmed, pmc\n        INPUT_FORMAT: txt, bioc, pxml, nxml, pxml.gz\n        OUTPUT_FORMAT: bioc, odin, odin_custom, tsv, xml\n\n    \"\"\"\n\n    ENTITY_TYPE = 'MedicalEntity'\n    DEFAULT_TIMEOUT_SEC = 10\n\n    # available endpoints\n    ANNOTATE_PATH = 'upload/txt/bioc_json'\n    STATUS_PATH = 'status'\n\n    def __init__(self, url=OGER_API_URL, service_timeout=None):\n        \"\"\"\n        :param url: URL of the OGER web service\n        :param timeout: optional timeout for service responses\n        \"\"\"\n        if isinstance(url, list):\n            raise Exception('Oger url cannot be an array')\n        if url.endswith('/'):\n            url = url[:-1]\n        self.url = url\n        self.service_timeout = service_timeout\n        if self.service_timeout is None:\n            self.service_timeout = self.DEFAULT_TIMEOUT_SEC\n\n    def status(self):\n        \"\"\"\n        :returns: the status of the OGER web service.\n        \"\"\"\n        url = '/'.join([self.url, self.STATUS_PATH])\n        return requests.get(url, timeout=self.service_timeout).json()\n\n    \"\"\"\n    def fetch_document(self, docid):\n\n        fetchpath = 'fetch/pubmed/pubtator/' + str(docid)\n        r = self.request(path=fetchpath)\n        \n        return r.json()\n    \"\"\"\n\n    def convert_result(self, result_dict):\n        \"\"\"\n        Convert a dict result as produced by the OGER annotation web service.\n        :param result_dict: a result from the OGER annotation service, as dict.\n        :returns: OGER document converted to Recognyze format.\n        \"\"\"\n        result = []\n        annotations = []\n        try:\n            if not 'documents' in result_dict or not len(result_dict['documents']):\n                return result\n\n            # this version fixes the offset error\n            for passage in result_dict['documents'][0]['passages']:\n\n                for rs in passage['annotations']:\n                    start = rs['locations'][0]['offset']\n                    end = rs['locations'][0]['offset'] + len(rs['text'])\n                    ditem = {\n                        \"key\": rs['infons']['native_id'],\n                        # \"resource\": rs['infons']['original_resource'],\n                        \"surfaceForm\": rs['text'],  # .encode(\"utf8\")\n                        \"start\": start,\n                        \"end\": end,\n                        \"confidence\": 1,\n                        \"preferred_name\": rs['infons']['preferred_form'],\n\n                        # formerly: rs['infons']['type']\n                        \"entity_type\": self.ENTITY_TYPE,\n#                         \"annotation_type\": self.ENTITY_TYPE\n                    }\n                    annotations.append(ditem)\n        except Exception as message:\n            logger.error(message)\n            raise Exception('Error: {}'.format(message))\n\n        return annotations\n\n    def annotate_text(self, docid, doctext):\n        \"\"\"\n        Annotate a document's text content with the OGER annotation service.\n        :param docid: the document's ID.\n        :param doctext: the document's content to be annotated.\n        :returns: OGER annotated document after uploading a text.\n        \"\"\"\n        url = '/'.join([self.url, self.ANNOTATE_PATH, docid])\n        r = requests.post(url=url, data=doctext.encode(\n            'utf-8'), timeout=self.service_timeout)\n        if r.status_code == 200:\n            return self.convert_result(json.loads(r.content.decode('utf-8')))\n        return []\n","sub_path":"src/python/weblyzard_api/client/ontogene/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"96634694","text":"# -*- coding: utf-8 -*-\n# MLToolkit (mltoolkit)\n\n__name__=\"mltk\"\n\n\"\"\"\nMLToolkit - a verstile helping library for machine learning\n===========================================================\n'MLToolkit' is a Python package providing a set of user-friendly functions to \nhelp building machine learning models in data science research or production \nfocused projects. It is compatible with and interoperate with popular data \nanalysis, manipulation and machine learning libraries Pandas, Sci-kit Learn, \nTensorflow, Statmodels, Catboost, XGboost, etc.\n\nMain Features\n-------------\n- Data Extraction (SQL, Flatfiles, Images, etc.)\n- Exploratory data analysis (statistical summary, univariate analysis, etc.)\n- Feature Extraction and Engineering\n- Model performance analysis, Explain Predictions and comparison between models\n- Cross Validation and Hyper parameter tuning\n- JSON input script for executing model building and scoring tasks.\n- Model Building UI\n- Auto ML (automated machine learning)\n- Model Deploymet and Serving via RESTful  API\n\nAuthor\n------\n- Sumudu Tennakoon\n\nLinks\n-----\nWebsite: http://sumudu.tennakoon.net/projects/MLToolkit\nGithub: https://mltoolkit.github.io/MLToolKit\n\nLicense\n-------\nApache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)\n\"\"\"\n\nfrom datetime import datetime\nimport gc\nimport traceback\nimport gc\nimport os\nimport copy\nfrom timeit import default_timer as timer\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport re\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom mltk.explore import *\nfrom mltk.matrics import *\nfrom mltk.deploy import *\nfrom mltk.etl import *\n\ndef train_validate_test_split(DataFrame, ratios=(0.6,0.2,0.2)):\n    \"\"\"    \n    Parameters\n    ----------\n    DataFrame : pandas.DataFrame\n        DataFrame\n    ratios : tuple\n        E.g. \n            (train, validate, test) = (0.6, 0.25, 0.15)  \n            (train, test) = (0.6, 0.4) ->  validate = test\n            \n    Returns\n    -------\n    TrainDataset : pandas.DataFrame\n    ValidateDataset : pandas.DataFrame\n    TestDataset : pandas.DataFrame\n    \"\"\"       \n    from sklearn.model_selection import train_test_split\n    N = len(DataFrame.index)\n    \n    if len(ratios)==3:        \n        train_size = ratios[0]/np.sum(ratios)  \n        test_size = ratios[2]/np.sum(ratios[1:3])        \n        TrainDataset, TestDataset = train_test_split(DataFrame, train_size=train_size, random_state=42)\n        ValidateDataset, TestDataset = train_test_split(TestDataset, test_size=test_size, random_state=42)        \n    elif len(ratios)==2:\n        train_size = ratios[0]/np.sum(ratios)\n        TrainDataset, TestDataset = train_test_split(DataFrame, train_size=train_size, random_state=42)\n        ValidateDataset = TestDataset\n        print('Validate = Test')\n    else:\n        print('ERROR in splitting train, validate, test')\n        return None, None, None\n        \n    n_train = len(TrainDataset.index)\n    n_validate = len(ValidateDataset.index)\n    n_test = len(TestDataset.index)\n    \n    print('Train Samples: {} [{:.1f}%]'.format(n_train, n_train/N*100))\n    print('Validate Samples: {} [{:.1f}%]'.format(n_validate, n_validate/N*100))\n    print('Test Samples: {} [{:.1f}%]'.format(n_test, n_test/N*100))        \n    \n    return TrainDataset, ValidateDataset, TestDataset\n\nclass MLModel():\n    def __init__(self, model_attributes, model_parameters, sample_attributes, model_variables, variable_setup, target_variable, score_parameters, model_interpretation, model_evaluation, model_object=None):\n        self.model_attributes = model_attributes\n        self.model_parameters = model_parameters\n        self.sample_attributes=sample_attributes\n        self.model_variables = model_variables\n        self.target_variable = target_variable\n        self.score_parameters = score_parameters\n        self.model_interpretation=model_interpretation\n        self.model_evaluation = model_evaluation\n        self.model_object = model_object \n        self.variable_setup = variable_setup\n    \n    def get_model_type(self):   \n        try:\n            return self.model_attributes['ModelType'] #regression/classification/clautering\n        except:\n            return None\n    \n    def set_model_object(self, model_object):\n        self.model_object = model_object     \n    \n    def get_model_object(self):\n        return self.model_object\n    \n    def get_model_algorithm(self):\n        return self.model_parameters['MLAlgorithm']\n        \n    def get_identifier_variables(self):\n        return self.sample_attributes['RecordIdentifiers']\n        \n    def get_target_variable(self):\n        return self.target_variable\n    \n    def get_model_variables(self):\n        return self.model_variables\n    \n    def get_variable_setup(self):\n        return self.variable_setup\n\n    def set_variable_setup(self, variable_setup):\n        self.variable_setup = variable_setup\n    \n    def get_model_name(self):\n        return self.model_attributes['ModelName']\n\n    def get_model_id(self):\n        return self.model_attributes['ModelID']\n        \n    def get_score_parameter(self, variable):\n        return self.score_parameters[variable]\n\n    def get_score_variable(self):\n        return self.score_parameters['ScoreVariable']\n\t\t\n    def get_score_label(self):\n        return self.score_parameters['ScoreLabel']\n\n    def get_score_edges(self):\n        return self.score_parameters['Edges']\n    \n    def get_predicted_label(self):\n        return self.score_parameters['PredictedLabel']\n\n    def get_predict_threshold(self):\n        return self.score_parameters['Threshold']\n        \n    def get_robustness_table(self):\n        return self.model_evaluation['RobustnessTable']\n\n    def get_roc_curve(self):\n        return self.model_evaluation['ROCCurve']\n\n    def get_precision_recall_curve(self):\n        return self.model_evaluation['PrecisionRecallCurve']\n    \n    def get_auc(self, curve='roc'):\n        try:\n            if curve=='roc':\n                return self.model_evaluation['ROC_AUC']\n            if curve=='prc':\n                return self.model_evaluation['PRC_AUC']\n        except:\n            return None\n        \n    def get_rmse(self):\n        try:\n            return self.model_evaluation['RMSE']\n        except:\n            return None\n\n    def get_r2(self):\n        try:\n            return self.model_evaluation['R2']\n        except:\n            return None\n        \n    def set_score_edges(self, edges):\n        self.score_parameters['Edges']=edges\n\n    def set_predict_threshold(self, threshold):\n        self.score_parameters['Threshold'] = threshold\n    \n    def get_model_data_stats(self):\n        return self.sample_attributes['ModelDataStats']\n    \n    def get_model_manifest(self, save=False, save_path=''):       \n        import json\n        model_manifest= {'model_attributes':self.model_attributes,\n                        'model_parameters':self.model_parameters,\n                        'score_parameters':self.score_parameters,\n                        'sample_attributes':self.sample_attributes,  \n                        'roc_auc':self.get_auc(curve='roc'),\n                        'prc_auc':self.get_auc(curve='prc'),\n                        'rmse':self.get_rmse(),\n                        'r2':self.get_r2(),\n                        'robustness_table':self.get_robustness_table().to_dict(orient='dict')\n                        }        \n        if save==True:\n            json_object = json.dumps(model_manifest)          \n            f = open(os.path.join(save_path, '{}_ModelManifest.json'.format(self.get_model_id())),'w')\n            f.write(json_object)\n            f.close() \n\n        return model_manifest\n    \n    def plot_eval_matrics(self, figure=0, comparison=False, fig_size=(8, 6), layout=(2,3), dpi=80):\n        import matplotlib.pyplot as plt\n        from matplotlib import colors #For custom color maps\n        \n        description = self.get_model_id()\n        model_type = self.get_model_type()\n\n        if model_type=='classification':\n            response_quantity_label = 'ResponseCount'\n            bucket_mean_label = 'BucketPrecision'\n            cum_bucket_mean_label = 'CumulativePrecision'\n        elif model_type=='regression':\n            response_quantity_label = 'ResponseAmount'\n            bucket_mean_label = 'BucketMeanAmount'\n            cum_bucket_mean_label = 'CumulativeMeanAmount'\n        else:\n            print('No Model Type provided')\n            return None\n        \n        plt.figure(figure, figsize=fig_size, dpi=dpi)\n\n        ax3 = plt.subplot(233)\n        RobustnessTable = self.get_robustness_table().copy()[:-1]\n        #plt.plot(thresholds, precision[:-1], 'o-', label='{} precision'.format(description))\n        x_column = 'CumulativeBucketFraction'\n        y_column = 'CumulativeResponseFraction'\n        plt.plot(list(RobustnessTable[x_column])+[0.0], list(RobustnessTable[y_column])+[0.0], marker=\".\", linestyle='-', label='{}'.format(description)) #, marker='o'\n        plt.legend()\n        plt.xlabel(x_column)\n        plt.ylabel(y_column)\n        plt.title('Bucket Fraction vs. Threshold Curve')\n        \n        max_x = max(RobustnessTable[x_column])*1.05\n        max_y = max(RobustnessTable[y_column])*1.05   \n\n        plt.plot([0, max(max_x, max_y)], [0, max(max_x, max_y)], color='black', linestyle='--')\n\n        plt.ylim([0.0, max_y])\n        plt.xlim([0.0, max_x])\n            \n        ax5 = plt.subplot(236)\n        x_column = 'Mean{}'.format(self.get_score_parameter('ScoreVariable'))\n        y_column = bucket_mean_label\n        size_column = 'ResponseFraction' \n\n        max_x = max(RobustnessTable[x_column])*1.05\n        max_y = max(RobustnessTable[y_column])*1.05  \n        \n        plt.plot([0, max(max_x, max_y, 1)], [0, max(max_x, max_y, 1)], color='black', linestyle='--')\n        plt.xlabel(x_column)\n        plt.ylabel(y_column)\n        plt.title('Model Charateristics \\n ResponseFraction ~ Marker size')\n        \n        plt.ylim([0.0, max_y])\n        plt.xlim([0.0, max_x])\n        \n        size_scale=2000 \n        size_offset=1      \t\t\n        \n        if comparison:\n            plt.scatter(RobustnessTable[x_column], RobustnessTable[y_column], s=size_offset+RobustnessTable[size_column].values*size_scale, label='{}'.format(description))\n            plt.legend()\n        else:\n            color_column = 'BucketFraction'\n            color_scale=100\n            #plt.plot(thresholds, precision[:-1], 'o-', label='{} precision'.format(description))\n            bounds = np.array([0.0, 0.3, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0, 25.0, 50.0])\n            norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n            RobustnessTable.sort_values(by=size_column, ascending=False, inplace=True)\n            plt.scatter(RobustnessTable[x_column], RobustnessTable[y_column], c=RobustnessTable[color_column].values*color_scale, s=size_offset+RobustnessTable[size_column].values*size_scale, cmap='nipy_spectral', norm=norm, marker='s')\n            cbar = plt.colorbar()\n            cbar.set_label(color_column)  \n        \n        #############################\n        if model_type=='regression':\n            pass\n        elif model_type=='classification':    \n            roc_auc = self.get_auc(curve='roc')\n            prc_auc = self.get_auc(curve='prc')\n            \n            plot_tile = {\n                    'ROC':231,\n                    'PvTH':232,\n                    'RFvCF':233,\n                    'PRC':234,               \n                    'RvTH':235,\n                    'PREDvACT': 236\n                    }\n            \n            ax0 = plt.subplot(231) \n            ROCCurve = self.get_roc_curve()\n            plt.plot(ROCCurve.FPR.values, ROCCurve.TPR.values, linestyle='-', label='{} (area = {:.2f}) '.format(description, roc_auc))\n            plt.plot([0, 1], [0, 1], color='black', linestyle='--')\n            plt.xlim([0.0, 1.0])\n            plt.ylim([0.0, 1.05])\n            plt.xlabel('False Positive Rate')\n            plt.ylabel('True Positive Rate')\n            plt.title('ROC')\n            plt.legend(loc=\"lower right\")\n            #plt.show()\n            \n            ax1 = plt.subplot(234) \n            PrecisionRecallCurve = self.get_precision_recall_curve()\n            plt.plot(PrecisionRecallCurve['Recall'].values, PrecisionRecallCurve['Precision'].values, linestyle='-', label='{} (auc = {:.2f}) '.format(description, prc_auc))\n            plt.legend()\n            plt.xlabel('Recall')\n            plt.ylabel('Precision')\n            plt.title('Precision-Recall Curve')\n            plt.ylim([0.0, 1.05])\n            plt.xlim([0.0, 1.0])\n            \n            ax2 = plt.subplot(232, sharey=ax1, sharex=ax3)\n            plt.plot(PrecisionRecallCurve['Threshold'].values, PrecisionRecallCurve['Precision'].values, linestyle='-', label='{}'.format(description))\n            #plt.plot(thresholds, recall[:-1], 'o-', label='{} recall'.format(description))\n            plt.legend()\n            plt.xlabel('Threshold')\n            plt.ylabel('Precision')\n            plt.title('Precision vs. Threshold Curve')\n            plt.ylim([0.0, 1.05])\n            plt.xlim([0.0, 1.0])\n            \n            ax4 = plt.subplot(235, sharex=ax2)\n            #plt.plot(thresholds, precision[:-1], 'o-', label='{} precision'.format(description))\n            plt.plot(PrecisionRecallCurve['Threshold'].values, PrecisionRecallCurve['Recall'].values, linestyle='-', label='{}'.format(description))\n            plt.legend()\n            plt.xlabel('Threshold')\n            plt.ylabel('Recall')\n            plt.title('Recall vs. Threshold Curve')\n            plt.ylim([0.0, 1.05])\n            plt.xlim([0.0, 1.0])\n        #######################\n        \n        plt.subplots_adjust(hspace=0.4)\n        #plt.show()\n\ndef load_tensorflow_model(file_path):\n    from tensorflow import keras \n    model = keras.models.load_model(file_path)\n    return model\n        \ndef load_object(file_name):\n    try:\n        import pickle\n        pickle_in = open(file_name,'rb')\n        print('Loading model from file {}'.format(file_name))\n        object = pickle.load(pickle_in)\n        pickle_in.close()\n        return object\n    except:\n        print('ERROR in loading model: {}'.format(traceback.format_exc()))\n        return None\n    \ndef save_object(object_to_save, file_name):\n    try:\n        import pickle\n        pickle_out = open(file_name,'wb')\n        print('Saving model to file {}'.format(file_name))\n        pickle.dump(object_to_save, pickle_out)\n        pickle_out.close()\n    except:\n        print('ERROR in saving model: {}'.format(traceback.format_exc()))\n\ndef save_model(Model, file_path):\n    \"\"\"\n    Parameters\n    ----------\n    Model : mltk.MLModel\n        MLModel Object\n    file_path : str, dafault ''\n        Path to file including the file name.    \n    \n    Returns\n    -------\n    None\n    \"\"\"\n    if Model.model_parameters['MLAlgorithm']=='NN':\n        Model.model_object.save(os.path.splitext(file_path)[0]+'.tfh5')\n        Model.set_model_object(None)       \n    save_object(Model, file_path)\n\ndef load_model(file_path):\n    \"\"\"\n    Parameters\n    ----------\n    file_path : str, dafault ''\n        Path to file including the file name.    \n    \n    Returns\n    -------\n    Model : mltk.MLModel\n        MLModel Object\n    \"\"\"\n    Model = load_object(file_path)\n    if Model.model_parameters['MLAlgorithm']=='NN':\n        model = load_tensorflow_model(os.path.splitext(file_path)[0]+'.tfh5')\n        Model.set_model_object(model)\n    return Model\n\ndef build_regression_model(x_train, y_train, model_variables, target_variable=None, model_parameters=None):\n    \"\"\"\n    Parameters\n    ----------\n    x_train : np.array\n    y_train : np.array\n    model_variables : list(str)\n    target_variable : str\n    model_parameters : dict\n    \n    Returns\n    -------\n    model : mltk.MLModel\n    output : dict\n    model_fit_time : float\n    \"\"\"\n    from timeit import default_timer as timer\n    \n    MLAlgorithm = model_parameters['MLAlgorithm']    \n    \n    if MLAlgorithm == 'LREG':\n        import statsmodels as sm\n        import statsmodels.discrete.discrete_model as sm\n        import statsmodels.api as smapi\n\n        maxiter=model_parameters['MaxIterations']    \n        model=smapi.OLS(y_train, x_train)\n\n        start_time = timer()  \n        model=model.fit(maxiter=maxiter)\n        model_fit_time = timer() - start_time\n\n        output = model.summary(xname=list(model_variables))\n    \n    elif MLAlgorithm == 'RFREG':\n        from sklearn.ensemble import RandomForestRegressor\n        from sklearn.datasets import make_regression\n\n        NTrees = model_parameters['NTrees']\n        MaxDepth = model_parameters['MaxDepth']\n        Processors = model_parameters['Processors']\n        MinSamplesToSplit = model_parameters['MinSamplesToSplit']\n    \n        try:\n            verbose = int(model_variables['Verbose'])\n        except:\n            verbose = 0\n               \n        model = RandomForestRegressor(max_depth=MaxDepth, n_jobs=Processors, verbose=verbose, min_samples_split=MinSamplesToSplit, n_estimators=NTrees)\n        \n        start_time = timer()      \n        model.fit(x_train, y_train)  \n        model_fit_time = timer() - start_time\n    \n        Importances = model.feature_importances_\n        stdev = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)    \n        output = pd.DataFrame({'Features':model_variables, 'Importances':Importances, 'stdev':stdev}).sort_values(by='Importances', ascending=False)       \n\n    return model, output, model_fit_time\n\t\ndef build_logit_model(x_train, y_train, model_variables, target_variable, model_parameters):\n    \"\"\"\n    Parameters\n    ----------\n    x_train : np.array\n    y_train : np.array\n    model_variables : list(str)\n    target_variable : str\n    model_parameters : dict\n    \n    Returns\n    -------\n    model : mltk.MLModel\n    output : dict\n    model_fit_time : float\n    \"\"\"\n    \n    import statsmodels as sm\n    import statsmodels.discrete.discrete_model as sm\n    import statsmodels.api as smapi\n    \n    maxiter=model_parameters['MaxIterations']    \n    model=smapi.Logit(y_train, x_train)\n    \n    startTime = timer()  \n    model=model.fit(maxiter=maxiter)\n    model_fit_time = timer() - startTime\n    \n    output = model.summary(xname=list(model_variables))\n        \n    return model, output, model_fit_time\n\n###############################################################################\ndef stack_nn_layers_tf(architecture, print_summary=False):\n    \"\"\"\n    Parameters\n    ----------\n    architecture : dict\n        list of layers\n    print_summary : bool, default False\n        prints model summary if True\n        \n    Returns\n    -------\n    model : keras.engine.sequential.Sequential\n        Keras Sequential model object\n    \"\"\"\n    \n    from tensorflow.keras.models import Sequential\n    from tensorflow.keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, MaxPooling1D, Flatten\n    from keras import regularizers\n\n    def set_default_param(config, param, default=None):    \n        if param not in config:\n            config.update({param:default})\n        else:\n            if param in ('kernel_regularizer', 'bias_regularizer', 'activity_regularizer'):\n                l1 = config[param]['l1'] if ('l1' in config) else 0.0\n                l2 = config[param]['l2'] if ('l2' in config) else 0.0 \n                config[param] = regularizers.l1_l2(l1=l1, l2=l2)\n        return config\n    \n    model = Sequential()\n    \n    for layer in architecture['layers']:\n        try:\n            layer['class_name'] = layer['type']\n        except:\n            pass\n        try:\n            layer['config']['name'] = layer['name']\n        except:\n            pass        \n        config = layer['config']\n        config = set_default_param(config, param='name', default=None)\n        config = set_default_param(config, param='strides', default=None)\n        config = set_default_param(config, param='padding', default='valid') \n        config = set_default_param(config, param='data_format', default=None) \n        config = set_default_param(config, param='kernel_regularizer', default=None) \n        config = set_default_param(config, param='bias_regularizer', default=None) \n        config = set_default_param(config, param='activity_regularizer', default=None)\n        \n        if layer['class_name'] in ('Conv1D', 'Conv2D', 'Conv3D') and config['strides']==None:\n            config['strides'] = 1 #Set to default value\n        \n        if layer['class_name']=='Dense':\n            if layer['position']=='input':\n                model.add(Dense(name=config['name'], units=config['units'], activation=config['activation'], input_shape=config['input_shape'], kernel_regularizer=config['kernel_regularizer'], activity_regularizer=config['activity_regularizer'], bias_regularizer=config['bias_regularizer']))\n            else:\n                model.add(Dense(name=config['name'], units=config['units'], activation=config['activation'], kernel_regularizer=config['kernel_regularizer'], activity_regularizer=config['activity_regularizer'], bias_regularizer=config['bias_regularizer']))\n        elif layer['class_name']=='Dropout': \n             model.add(Dropout(name=config['name'], rate=config['rate']))   \n        elif layer['class_name']=='Conv2D':\n            if layer['position']=='input':\n                model.add(Conv2D(name=config['name'], filters=config['filters'], kernel_size=config['kernel_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format'], activation=config['activation'], input_shape=config['input_shape'],  kernel_regularizer=config['kernel_regularizer'], activity_regularizer=config['activity_regularizer'], bias_regularizer=config['bias_regularizer'])) \n            elif layer['position']=='hidden':\n                model.add(Conv2D(name=config['name'], filters=config['filters'], kernel_size=config['kernel_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format'], activation=config['activation'], kernel_regularizer=config['kernel_regularizer'], activity_regularizer=config['activity_regularizer'], bias_regularizer=config['bias_regularizer']))\n        elif layer['class_name']=='MaxPooling2D':\n                model.add(MaxPooling2D(name=config['name'], pool_size=config['pool_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format']))\n        elif layer['class_name']=='Conv1D':\n            if layer['position']=='input':\n                model.add(Conv1D(name=config['name'], filters=config['filters'], kernel_size=config['kernel_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format'], activation=config['activation'], input_shape=config['input_shape'], kernel_regularizer=config['kernel_regularizer'], activity_regularizer=config['activity_regularizer'], bias_regularizer=config['bias_regularizer'])) \n            else:\n                model.add(Conv1D(name=config['name'], filters=config['filters'], kernel_size=config['kernel_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format'], activation=config['activation'], kernel_regularizer=config['kernel_regularizer'], activity_regularizer=config['activity_regularizer'], bias_regularizer=config['bias_regularizer']))\n        elif layer['class_name']=='MaxPooling1D':\n                model.add(MaxPooling1D(name=config['name'], pool_size=config['pool_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format']))\n        elif layer['class_name']=='Conv3D':\n            if layer['position']=='input':\n                model.add(Conv3D(name=config['name'], filters=config['filters'], kernel_size=config['kernel_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format'], activation=config['activation'], input_shape=config['input_shape'], kernel_regularizer=config['kernel_regularizer'], activity_regularizer=config['activity_regularizer'], bias_regularizer=config['bias_regularizer'])) \n            else:\n                model.add(Conv3D(name=config['name'], filters=config['filters'], kernel_size=config['kernel_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format'], activation=config['activation'], kernel_regularizer=config['kernel_regularizer'], activity_regularizer=config['activity_regularizer'], bias_regularizer=config['bias_regularizer']))\n        elif layer['class_name']=='MaxPooling3D':\n                model.add(MaxPooling3D(name=config['name'], pool_size=config['pool_size'], strides=config['strides'], padding=config['padding'], data_format=config['data_format']))\n        elif layer['class_name']=='Flatten':\n                model.add(Flatten(name=config['name'], data_format=config['data_format']))\n    \n    if print_summary:\n        print(model.summary())\n                \n    return model\n\ndef stack_nn_layers_tf_depreciate_in_0_2_0(architecture, print_summary=False):\n    from tensorflow.keras.models import Sequential\n    from tensorflow.keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, MaxPooling1D, Flatten\n    \n    model = Sequential()\n    \n    for key, layer in architecture.items():\n        if layer['type']=='Dense':\n            if layer['position']=='input':\n                model.add(Dense(units=layer['units'], activation=layer['activation'], input_shape=layer['input_shape']))\n            elif layer['position']=='hidden':\n                model.add(Dense(units=layer['units'], activation=layer['activation']))\n            elif layer['position']=='output':\n                model.add(Dense(units=layer['units'], activation=layer['activation'])) #, output_shape=layer['output_shape']\n        elif layer['type']=='Dropout': \n             model.add(Dropout(rate=layer['rate']))   \n        elif layer['type']=='Conv2D':\n            if layer['position']=='input':\n                model.add(Conv2D(filters=layer['filters'], kernel_size=layer['kernel_size'], strides=layer['strides'], padding=layer['padding'], data_format='channels_last', activation=layer['activation'], input_shape=layer['input_shape'])) #, strides=(2, 2)\n            elif layer['position']=='hidden':\n                model.add(Conv2D(filters=layer['filters'], kernel_size=layer['kernel_size'], strides=layer['strides'], padding=layer['padding'], data_format='channels_last', activation=layer['activation'])) #, strides=(2, 2)\n        elif layer['type']=='MaxPooling2D':\n                model.add(MaxPooling2D(pool_size=layer['pool_size'], strides=None, padding=layer['padding'], data_format='channels_last'))\n        elif layer['type']=='MaxPooling1D':\n                model.add(MaxPooling1D(pool_size=layer['pool_size'], strides=None, padding=layer['padding'], data_format='channels_last'))\n        elif layer['type']=='Flatten':\n                model.add(Flatten())\n    \n    if print_summary:\n        model.summary()\n                \n    return model\n\ndef stack_nn_layers(architecture, print_summary=False):\n    architecture_ = copy.deepcopy(architecture)\n    try:\n        return stack_nn_layers_tf(architecture_, print_summary=print_summary)\n    except:\n        return stack_nn_layers_tf_depreciate_in_0_2_0(architecture_, print_summary=print_summary)\n###############################################################################\n    \ndef build_nn_model(x_train, y_train, x_validate, y_validate, model_variables, target_variable, model_parameters):\n    \"\"\"\n    Parameters\n    ----------\n    x_train : np.array\n    y_train : np.array\n    x_validate : np.array\n    y_validate : np.array  \n    model_variables : list(str)\n    target_variable : str\n    model_parameters : dict\n    \n    Returns\n    -------\n    model : mltk.MLModel\n    output : dict\n    model_fit_time : float\n    \"\"\"\n    \n    import tensorflow as tf\n    import tensorflow.keras as keras\n    #from tensorflow.keras.models import Sequential\n    #from tensorflow.keras.layers import Dense, Dropout\n\n    #tf.enable_eager_execution()\n    \n    batch_size = model_parameters['BatchSize']\n    #input_shape = model_parameters['InputShape']\n    epochs = model_parameters['Epochs']   \n    metrics = model_parameters['EvalMatrics']    \n    num_classes = model_parameters['NumClasses']      \n    architecture = model_parameters['Architecture']\n    \n    y_train = keras.utils.to_categorical(y_train, num_classes)\n    y_validate = keras.utils.to_categorical(y_validate, num_classes)\n    \n    ###########################################################################    \n    print('Input shape:', x_train.shape)\n    model = stack_nn_layers(architecture)\n    model.summary()  \n    ###########################################################################\n    \n    model.compile(loss=keras.losses.categorical_crossentropy,\n                  optimizer=keras.optimizers.Adadelta(), #keras.optimizers.RMSprop(),\n                  metrics=metrics) #['accuracy']\n\n    # Create Tensorflow Session\n    sess = tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=0, inter_op_parallelism_threads=0, log_device_placement=True)) #device_count={'GPU': 0}, \n    keras.backend.set_session(sess)\n    \n    startTime = timer()\n    hist = model.fit(x_train, y_train,\n                        batch_size=batch_size,\n                        epochs=epochs,\n                        verbose=1,\n                        validation_data=(x_validate, y_validate))\n    model_fit_time = timer() - startTime\n    \n    output = pd.DataFrame(data = hist.history)\n    \n    score = model.evaluate(x_validate, y_validate, verbose=1)\n    print('Test loss:', score[0])\n    print('Test accuracy:', score[1])\n       \n    return model, output, model_fit_time\n    \n    \ndef build_rf_model(x_train, y_train, model_variables, target_variable, model_parameters):\n    \"\"\"\n    Parameters\n    ----------\n    x_train : np.array\n    y_train : np.array\n    model_variables : list(str)\n    target_variable : str\n    model_parameters : dict\n    \n    Returns\n    -------\n    model : mltk.MLModel\n    output : dict\n    model_fit_time : float\n    \"\"\"\n    from sklearn.ensemble import RandomForestClassifier\n    \n    try:\n        verbose = int(model_variables['Verbose'])\n    except:\n        verbose = 0\n        \n    NTrees = model_parameters['NTrees']\n    MaxDepth = model_parameters['MaxDepth']\n    Processors = model_parameters['Processors']\n    MinSamplesToSplit = model_parameters['MinSamplesToSplit']\n    model = RandomForestClassifier(n_estimators=NTrees, max_depth=MaxDepth, n_jobs=Processors, verbose=verbose, min_samples_split=MinSamplesToSplit)\n\n    startTime = timer()      \n    model.fit(x_train, y_train)  \n    model_fit_time = timer() - startTime\n    \n    Importances = model.feature_importances_\n    stdev = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)    \n    output = pd.DataFrame({'Features':model_variables, 'Importances':Importances, 'stdev':stdev}).sort_values(by='Importances', ascending=False)       \n        \n    return model, output, model_fit_time\n\ndef build_cbst_model(x_train, y_train, x_validate, y_validate, model_variables, target_variable, model_parameters):\n    \"\"\"\n    Parameters\n    ----------\n    x_train : np.array\n    y_train : np.array\n    model_variables : list(str)\n    target_variable : str\n    model_parameters : dict\n    \n    Returns\n    -------\n    model : mltk.MLModel\n    output : dict\n    model_fit_time : float\n    \"\"\"\n    \n    import catboost\n    from catboost import CatBoostClassifier, Pool\n    import numpy as np\n    \n    # Model Params   \n    num_trees = model_parameters['NTrees']\n    depth = model_parameters['MaxDepth']\n    learning_rate = model_parameters['LearningRate']   \n    imbalanced = model_parameters['Imbalanced']\n    loss_function = model_parameters['LossFunction']\n    eval_metric = model_parameters['EvalMatrics'] \n    task_type = model_parameters['TaskType']\n    thread_count = model_parameters['Processors'] \n    use_best_model = model_parameters['UseBestModel'] \n    verbose=int((num_trees+9)/10)\n\n    if imbalanced==True:\n        sample_size = len(y_train)\n        actual_positives = np.sum(y_train)\n        actual_negatives= sample_size-actual_positives\n        scale_pos_weight = (1.0)*actual_negatives/actual_positives\n    else:\n        scale_pos_weight=1.0\n    \n    model = CatBoostClassifier(num_trees=num_trees,\n                               depth=depth,                               \n                               learning_rate=learning_rate,\n                               loss_function=loss_function,\n                               eval_metric=eval_metric,\n                               scale_pos_weight=scale_pos_weight,\n                               verbose=True, \n                               task_type = task_type,\n                               thread_count=thread_count)\n    \n    startTime = timer()\n    model.fit(x_train, y_train, eval_set=(x_validate, y_validate), use_best_model=use_best_model, verbose=verbose)\n    model_fit_time = timer() - startTime\n\n    Importances = model.feature_importances_\n    #stdev = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)    \n    output = pd.DataFrame({'Features':model_variables, 'Importances':Importances}).sort_values(by='Importances', ascending=False)        #, 'stdev':stdev\n    \n    score = model.score(x_validate, y_validate)\n    print('Test accuracy:', score)\n       \n    return model, output, model_fit_time        \n\ndef build_xgbst_model(x_train, y_train, x_validate, y_validate, model_variables, target_variable, model_parameters):\n    \"\"\"\n    Parameters\n    ----------\n    x_train : np.array\n    y_train : np.array\n    model_variables : list(str)\n    target_variable : str\n    model_parameters : dict\n    \n    Returns\n    -------\n    model : mltk.MLModel\n    output : dict\n    model_fit_time : float\n    https://xgboost.readthedocs.io/en/latest/python/python_api.html\n    \"\"\"\n    \n    import xgboost\n    from xgboost import XGBClassifier\n    import numpy as np\n    \n    # Performance enhanced data structure\n    #data_dmatrix = xgboost.DMatrix(data=x_train, label=y_train)\n\n    # Model Params \n    num_trees = model_parameters['NTrees'] #n_estimators\n    depth = model_parameters['MaxDepth'] #max_depth\n    learning_rate = model_parameters['LearningRate'] \n    loss_function = model_parameters['LossFunction'] #objective\n    eval_metric = model_parameters['EvalMatrics'] #eval_metric\n    early_stopping_rounds = model_parameters['EarlyStopAttempts']\n    samples_per_tree = model_parameters['SamplesRatioPerTree'] #subsample\n    features_per_tree = model_parameters['FeaturesRatioPerTree'] #colsample_bytree\n    #gamma = model_parameters['Gamma'] \n    l1 = model_parameters['Regularization']['L1'] #alpha (L1 regularization on leaf weights)\n    l2 = model_parameters['Regularization']['L2'] #lambda (L2 regularization on leaf weights)\n    verbose = model_parameters['Verbose'] \n    \n    model = XGBClassifier(objective=loss_function, \n                          n_estimators=num_trees,\n                          max_depth=depth,                               \n                          learning_rate=learning_rate,\n                          eval_metric=eval_metric,\n                          verbose=verbose)    \n    #imbalanced = model_parameters['Imbalanced']\n    # \n    #task_type = model_parameters['TaskType']\n    #thread_count = model_parameters['Processors'] \n    #use_best_model = model_parameters['UseBestModel'] \n    #verbose=int((num_trees+9)/10)\n\n    #if imbalanced==True:\n    #    sample_size = len(y_train)\n    #    actual_positives = np.sum(y_train)\n    #    actual_negatives= sample_size-actual_positives\n    #    scale_pos_weight = (1.0)*actual_negatives/actual_positives\n    #else:\n    #    scale_pos_weight=1.0\n    \n    startTime = timer()\n    model.fit(x_train, y_train, eval_set=(x_validate, y_validate), early_stopping_rounds=early_stopping_rounds)\n    model_fit_time = timer() - startTime\n\n    Importances = model.feature_importances_\n    #stdev = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)    \n    output = pd.DataFrame({'Features':model_variables, 'Importances':Importances}).sort_values(by='Importances', ascending=False)        #, 'stdev':stdev\n    \n    score = model.score(x_validate, y_validate)\n    print('Test accuracy:', score)\n       \n    return model, output, model_fit_time\n\ndef build_lgbm_model(x_train, y_train, x_validate, y_validate, model_variables, target_variable, model_parameters):\n    \"\"\"\n    Parameters\n    ----------\n    x_train : np.array\n    y_train : np.array\n    model_variables : list(str)\n    target_variable : str\n    model_parameters : dict\n    \n    Returns\n    -------\n    model : mltk.MLModel\n    output : dict\n    model_fit_time : float\n    https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMModel.html#lightgbm.LGBMModel\n    \"\"\"\n    \n    import lightgbm \n    from lightgbm  import LGBMClassifier\n    import numpy as np\n\n    # Model Params \n    num_trees = model_parameters['NTrees'] #n_estimators\n    depth = model_parameters['MaxDepth'] #max_depth\n    learning_rate = model_parameters['LearningRate'] \n    loss_function = model_parameters['LossFunction'] #objective\n    eval_metric = model_parameters['EvalMatrics'] #eval_metric\n    early_stopping_rounds = model_parameters['EarlyStopAttempts']\n    samples_per_tree = model_parameters['SamplesRatioPerTree'] #subsample\n    features_per_tree = model_parameters['FeaturesRatioPerTree'] #colsample_bytree\n    l1 = model_parameters['Regularization']['L1'] #alpha (L1 regularization on leaf weights)\n    l2 = model_parameters['Regularization']['L2'] #lambda (L2 regularization on leaf weights)\n    thread_count = model_parameters['Processors']  #n_jobs \n    verbose = model_parameters['Verbose'] \n    \n    model = LGBMClassifier(objective=loss_function, \n                          n_estimators=num_trees,\n                          max_depth=depth,                               \n                          learning_rate=learning_rate,\n                          eval_metric=eval_metric,\n                          n_jobs=thread_count)    \n    #imbalanced = model_parameters['Imbalanced']\n    # \n    #task_type = model_parameters['TaskType']\n    \n    #use_best_model = model_parameters['UseBestModel'] \n    #verbose=int((num_trees+9)/10)\n\n    #if imbalanced==True:\n    #    sample_size = len(y_train)\n    #    actual_positives = np.sum(y_train)\n    #    actual_negatives= sample_size-actual_positives\n    #    scale_pos_weight = (1.0)*actual_negatives/actual_positives\n    #else:\n    #    scale_pos_weight=1.0\n    \n    startTime = timer()\n    model.fit(x_train, y_train, eval_set=(x_validate, y_validate), early_stopping_rounds=early_stopping_rounds, verbose=verbose)\n    model_fit_time = timer() - startTime\n\n    Importances = model.feature_importances_\n    #stdev = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)    \n    output = pd.DataFrame({'Features':model_variables, 'Importances':Importances}).sort_values(by='Importances', ascending=False)        #, 'stdev':stdev\n    \n    score = model.score(x_validate, y_validate)\n    print('Test accuracy:', score)\n       \n    return model, output, model_fit_time\n    \ndef model_dataset_to_array(Dataset, model_variables, target_variable, is_image_data=False):\n    \"\"\"\n    TO DO: Find a more robust solution for this\n    is_image_data : bool, defult False\n        Converting Pandas series to numpy array will loose the shape when the data is mulidimentional (image data)\n    \"\"\"\n    \n    if is_image_data:\n        x_data = np.asarray(list(Dataset[model_variables[0]].values), dtype ='float32') # Fails when model_varibles provided as a list\n    else:\n        x_data = Dataset[model_variables].values\n    \n    y_data = Dataset[target_variable].values\n    y_data_act = y_data\n    \n    return x_data, y_data, y_data_act\n    \n    \n    \ndef build_ml_model(TrainDataset, ValidateDataset, TestDataset, model_variables, target_variable, \n                 variable_setup, model_attributes, sample_attributes, model_parameters, score_parameters,\n                   return_model_object=False, show_results=False, show_plot=False):\n    \"\"\"\n    Parameters\n    ----------\n    TrainDataset : pandas.DataFrame\n    ValidateDataset : pandas.DataFrame\n    TestDataset : pandas.DataFrame\n    model_variables : dict\n    variable_setup : dict\n    target_variable : str \n    model_attributes : dict\n    sample_attributes : dict\n    model_parameters : dict\n    score_parameters : dict\n    return_model_object : bool, default False\n    show_results : bool, default False\n    show_plot : bool, default False\n    \n    Returns\n    -------\n    Model : mltk.MLModel\n        MLModel Object\n    \"\"\"\n    ###############################################################################\n    # Create deep copies of each dictionary\n    model_variables = copy.deepcopy(model_variables)\n    target_variable = copy.deepcopy(target_variable)\n    model_attributes = copy.deepcopy(model_attributes)\n    sample_attributes = copy.deepcopy(sample_attributes)\n    model_parameters = copy.deepcopy(model_parameters)\n    score_parameters = copy.deepcopy(score_parameters)\n    ###############################################################################\n    ml_algorithm = model_parameters['MLAlgorithm']\n    model_type = model_attributes['ModelType']\n    data_format = sample_attributes['DataFormat']\n    \n    if data_format=='image' or ml_algorithm=='CNN': # \"ml_algorithm=='CNN'\" WILL DEPRECIATE IN v0.2.0\n        is_image_data = True\n    else:\n        is_image_data = False\n    ###############################################################################\n    if model_type=='classification':\n        target_class = score_parameters['TargetClass']\n        class_labels_map = sample_attributes['ClassLabelsMap'] \n        model_parameters['NumClasses'] = len(class_labels_map.keys())\n    try:    \n        verbose = model_parameters['Verbose']\n    except:\n        verbose = True\n    ###############################################################################\n    \n    # TRAIN DATASET\n    x_train, y_train, y_train_act = model_dataset_to_array(TrainDataset, \n                                                           model_variables, \n                                                           target_variable, \n                                                           is_image_data=is_image_data)\n    \n    print('Train samples: {} loded...'.format(x_train.shape[0]))\n    ###############################################################################\n    # VALIDATE DATASET\n    x_validate, y_validate, y_validate_act = model_dataset_to_array(ValidateDataset, \n                                                                   model_variables, \n                                                                   target_variable, \n                                                                   is_image_data=is_image_data)\n    \n    print('Validate samples: {} loded...'.format(x_validate.shape[0]))\n    ###############################################################################\n    # TEST DATASET\n    x_test, y_test, y_test_act = model_dataset_to_array(TestDataset, \n                                                           model_variables, \n                                                           target_variable, \n                                                           is_image_data=is_image_data)\n    \n    print('Test samples: {} loded...'.format(x_test.shape[0]))\n    ###############################################################################\n    \n    model_interpretation={}\n\n    model_evaluation={}\n    \n    # ModelAttributes \n    import mltk\n    model_attributes['MLTKVersion'] = mltk.__version__\n    model_attributes['BuiltTime'] = datetime.now().strftime('%Y%m%d%H%M%S')\n    model_attributes['ModelID'] = model_attributes['ModelName'].replace(' ', '').upper()+model_parameters['MLAlgorithm'].replace(' ', '').upper()+model_attributes['BuiltTime']  \n    model_attributes['ModelFitTime']=-1\n\n    # SampleAttribues\n    RecordIdentifiers = sample_attributes['RecordIdentifiers']\n    sample_attributes['TrainSize'] = len(TrainDataset.index)\n    sample_attributes['ValidateSize'] = len(ValidateDataset.index)\n    sample_attributes['TestSize'] = len(TestDataset.index)\n\t#\n    sample_attributes['TrainValidateTestRatio'] = '{}'.format(np.round(np.array([sample_attributes['TrainSize'],sample_attributes['ValidateSize'],sample_attributes['TestSize']])/(sample_attributes['TrainSize']+sample_attributes['ValidateSize']+sample_attributes['TestSize']),2))\n    sample_attributes['TrainResponseRate'] = len(TrainDataset.loc[TrainDataset[target_variable]==1].index)/sample_attributes['TrainSize'] \n    sample_attributes['ValidateResponseRate'] = len(ValidateDataset.loc[ValidateDataset[target_variable]==1].index)/sample_attributes['ValidateSize']\n    sample_attributes['TestResponseRate'] = len(TestDataset.loc[TestDataset[target_variable]==1].index)/sample_attributes['TestSize'] \n\n    # ScoreParameters\n    edges = score_parameters['Edges']\n    threshold=score_parameters['Threshold']\n    quantiles = score_parameters['Quantiles']\n    quantile_label = score_parameters['QuantileLabel']\n    score_variable=score_parameters['ScoreVariable']\n    score_label=score_parameters['ScoreLabel']\n    predicted_label=score_parameters['PredictedLabel']\n    \n    ###############################################################################\n    print(model_attributes)\n    print(model_parameters)\n    print(sample_attributes['SampleDescription'])\n    print(score_parameters)\n    ###############################################################################\n    \n    # Model fit routine selection\n    if model_type == 'classification':\n        if ml_algorithm=='LGR':\n            ###############################################################################\n            import statsmodels\n            model_attributes['MLTool'] = 'statsmodels={}'.format(statsmodels.__version__) \n            # Logit Model\n            model, summary, model_fit_time = build_logit_model(x_train, y_train, model_variables, target_variable, model_parameters)\n            model_interpretation['ModelSummary'] = summary    \n\n    #        y_pred_prob = model.predict(x_validate)\n    #        ValidateDataset[score_variable]=y_pred_prob\n            \n            y_pred_prob = model.predict(x_test)\n            TestDataset[score_variable]=y_pred_prob\n            ###############################################################################    \n        elif ml_algorithm=='RF':\n            ###############################################################################\n            import sklearn\n            model_attributes['MLTool'] = 'sklearn={}'.format(sklearn.__version__) \n            # Random Forest Model\n            model, summary, model_fit_time= build_rf_model(x_train, y_train, model_variables, target_variable, model_parameters)\n            model_interpretation['ModelSummary'] = summary  \n            \n    #        y_pred_prob = model.predict(x_validate)\n    #        ValidateDataset[score_variable]=y_pred_prob\n            \n            y_pred_prob = model.predict_proba(x_test).T #[:,1] \n            TestDataset[score_variable]=y_pred_prob[1]\n            \n            for i in range(len(y_pred_prob)): # ADDED TO SUPPORT MULTI-CLASS CLASSIFICATION\n                print('{}_{}'.format(score_variable,i)) \n                TestDataset['{}_{}'.format(score_variable,i)] = y_pred_prob[i]\n            ###############################################################################\n            \n        elif ml_algorithm in ['NN', 'CNN']:\n            ###############################################################################\n            import tensorflow\n            import tensorflow.keras\n            model_attributes['MLTool'] = 'tensorflow={}; keras={}'.format(tensorflow.__version__, tensorflow.keras.__version__) \n            # Deep Feed Forward Model using TensorFlow\n            model, summary, model_fit_time= build_nn_model(x_train, y_train, x_validate, y_validate, model_variables, target_variable, model_parameters)\n            model_interpretation['ModelSummary'] = summary  \n            \n    #        y_pred_prob = model.predict(x_validate)\n    #        ValidateDataset[score_variable]=y_pred_prob\n            \n            y_pred_prob = model.predict(x_test, verbose=int(verbose), batch_size=model_parameters['BatchSize']).T #[:,1] \n            TestDataset[score_variable]=y_pred_prob[1]\n            \n            for i in range(len(y_pred_prob)): # ADDED TO SUPPORT MULTI-CLASS CLASSIFICATION\n                #print('{}_{}'.format(score_variable,i)) \n                TestDataset['{}_{}'.format(score_variable,i)] = y_pred_prob[i]\n                \n            ###############################################################################   \n        elif ml_algorithm=='CBST':\n            ###############################################################################\n            import catboost\n            model_attributes['MLTool'] = 'catboost={}'.format(catboost.__version__)  \n            # Cat Boost Model\n            model, summary, model_fit_time= build_cbst_model(x_train, y_train, x_validate, y_validate, model_variables, target_variable, model_parameters)\n            model_interpretation['ModelSummary'] = summary  \n            \n            y_pred_prob = model.predict_proba(x_test, verbose=verbose, thread_count=model_parameters['Processors']).T #[:,1]           \n            TestDataset[score_variable]=y_pred_prob[1]\n            \n            for i in range(len(y_pred_prob)): # ADDED TO SUPPORT MULTI-CLASS CLASSIFICATION\n                print('{}_{}'.format(score_variable,i)) \n                TestDataset['{}_{}'.format(score_variable,i)] = y_pred_prob[i]\n    \n            ###############################################################################\n        elif ml_algorithm=='XGBST':\n            ###############################################################################\n            import xgboost\n            model_attributes['MLTool'] = 'xgboost={}'.format(xgboost.__version__) \n            # Cat Boost Model\n            model, summary, model_fit_time= build_xgbst_model(x_train, y_train, x_validate, y_validate, model_variables, target_variable, model_parameters)\n            model_interpretation['ModelSummary'] = summary  \n            \n            y_pred_prob = model.predict_proba(x_test, verbose=True, thread_count=model_parameters['Processors']).T #[:,1] \n            TestDataset[score_variable]=y_pred_prob[1]\n            \n            for i in range(len(y_pred_prob)): # ADDED TO SUPPORT MULTI-CLASS CLASSIFICATION\n                print('{}_{}'.format(score_variable,i)) \n                TestDataset['{}_{}'.format(score_variable,i)] = y_pred_prob[i]\n                \n            ###############################################################################\n        elif ml_algorithm=='LGBM':\n            ###############################################################################\n            import lightgbm\n            model_attributes['MLTool'] = 'lightgbm={}'.format(lightgbm.__version__) \n            # Cat Boost Model\n            model, summary, model_fit_time= build_lgbm_model(x_train, y_train, x_validate, y_validate, model_variables, target_variable, model_parameters)\n            model_interpretation['ModelSummary'] = summary  \n            \n            y_pred_prob = model.predict_proba(x_test, verbose=int(verbose), thread_count=model_parameters['Processors']).T #[:,1] \n            TestDataset[score_variable]=y_pred_prob[1]\n            \n            for i in range(len(y_pred_prob)): # ADDED TO SUPPORT MULTI-CLASS CLASSIFICATION\n                print('{}_{}'.format(score_variable,i)) \n                TestDataset['{}_{}'.format(score_variable,i)] = y_pred_prob[i]\n            ###############################################################################\n        else:\n            raise Exception('No Classification MLAlgorithm is specified in the model_parameters!')\n        \n    elif model_type == 'regression':\n        if ml_algorithm=='LREG':\n            ###############################################################################\n            import statsmodels\n            model_attributes['MLTool'] = 'statsmodels={}'.format(statsmodels.__version__) \n            # Logit Model\n            model, summary, model_fit_time = build_regression_model(x_train, y_train, model_variables, target_variable, model_parameters)\n            model_interpretation['ModelSummary'] = summary    \n\n    #        y_pred_prob = model.predict(x_validate)\n    #        ValidateDataset[score_variable]=y_pred_prob\n            \n            y_pred = model.predict(x_test)\n            TestDataset[score_variable] = y_pred\n            ###############################################################################\n\n        elif ml_algorithm=='RFREG':\n            ###############################################################################\n            import sklearn\n            model_attributes['MLTool'] = 'sklearn={}'.format(sklearn.__version__) \n            # Random Forest Model\n            model, summary, model_fit_time= build_regression_model(x_train, y_train, model_variables, target_variable, model_parameters)\n            model_interpretation['ModelSummary'] = summary  \n            \n    #        y_pred_prob = model.predict(x_validate)\n    #        ValidateDataset[score_variable]=y_pred_prob\n            \n            y_pred = model.predict(x_test)\n            TestDataset[score_variable] = y_pred\n            ###############################################################################\n        else:\n            raise Exception('No Regression MLAlgorithm is specified in the model_parameters!')\n\n        \n\n    \n    model_attributes['ModelFitTime'] = model_fit_time\n    ############################################################################### \n    if ml_algorithm[-3:]=='REG':\n        model_evaluation['RobustnessTable'], model_evaluation['ROCCurve'], model_evaluation['PrecisionRecallCurve'], model_evaluation['ROC_AUC'], model_evaluation['PRC_AUC'] = model_performance_matrics(TestDataset, target_variable=target_variable, score_variable=score_variable, quantile_label=quantile_label,  quantiles=quantiles, problem_type='regression', show_plot=show_plot)  \n        model_evaluation['RMSE'], model_evaluation['R2'] = get_regression_errors(TestDataset, target_variable=target_variable, predicted_variable=score_variable)\n    else:\n        model_evaluation['RobustnessTable'], model_evaluation['ROCCurve'], model_evaluation['PrecisionRecallCurve'], model_evaluation['ROC_AUC'], model_evaluation['PRC_AUC'] = model_performance_matrics(TestDataset, target_variable=target_variable, score_variable=score_variable, quantile_label=quantile_label,  quantiles=quantiles, problem_type='classification', show_plot=show_plot)        \n\n    if show_results:\n        print(model_evaluation['RobustnessTable'])\n    ###############################################################################\n    \n    ###############################################################################\n    if return_model_object:\n        Model = MLModel(model_attributes, model_parameters, sample_attributes, model_variables, variable_setup, \n                        target_variable, score_parameters, model_interpretation, model_evaluation, model_object=model)\n    else:\n        Model = MLModel(model_attributes, model_parameters, sample_attributes, model_variables, variable_setup, \n                        target_variable, score_parameters, model_interpretation, model_evaluation, model_object=None)\n    ###############################################################################   \n    # Cleanup memeory\n    gc.collect()\n    \n    #return copy.deepcopy(Model)\n    return Model\n\ndef model_guages_to_row(Model):\n    guages = [{'Model':Model.get_model_id(),\n                'ROC_AUC':Model.get_auc(curve='roc'),\n                'PRC_AUC':Model.get_auc(curve='prc'),\n            }]\n    return pd.DataFrame(guages)\n    \ndef model_guages_comparison(Models):\n    model_guages = pd.DataFrame()\n    for i in range(len(Models)):\n        Model = Models[i]\n        model_guages = model_guages.append(model_guages_to_row(Model), ignore_index=True)\n    return model_guages\n\t\ndef confusion_matrix_comparison(DataFrame, Models, thresholds=0.5, score_variable=None, save_prediction=False, show_plot=False):\n    \"\"\"\n    Parameters\n    ----------\n    DataFrame : pandas.DataFrame\n        DataFrame\n    Models : \n    thresholds : int or list(int), default 0.5    \n    score_variable : str, optional, dafault None\n        Name of the variable where the score is based on.  If None, score variabel is assigned by the model      \n    save_prediction: bool, default False\n        Retains prediction in column named by Model ID\n    show_plot : bool, default False\n        plot results if True\n\n    Returns\n    -------\n    ConfusionMatrixComparison : pandas.DataFrame\n    \"\"\"\n    try:\n        if len(thresholds)>0:\n            thresholds = thresholds        \n    except:\n        thresholds = [thresholds]\n    \n    ConfusionMatrixComparison=pd.DataFrame()\n    \n    for i in range(len(Models)):\n        \n        Model = Models[i]\n        if score_variable==None:\n            score_variable = Model.get_score_variable()\n            \n        DataFrame=score_processed_dataset(DataFrame, Model, edges=None, score_label=None, fill_missing=0)\n        \n        for threshold in thresholds:            \n            model_id = Model.get_model_id()+'_[TH={}]'.format(threshold)    \n            target_variable = Model.get_target_variable()\n            predict_column = 'Predict'\n            DataFrame[predict_column] = np.where(DataFrame[score_variable]>threshold,1,0)\n            ConfusionMatrix=confusion_matrix(DataFrame, actual_variable=target_variable, predcted_variable=predict_column, labels=[0,1], sample_weight=None, totals=True)\n            ConfusionMatrixComparison=ConfusionMatrixComparison.append(confusion_matrix_to_row(ConfusionMatrix, ModelID=model_id),ignore_index=True) \n            if save_prediction:\n                pass\n            else:\n                DataFrame.drop(columns=[predict_column], inplace=True)\n                \n    if show_plot:\n        ConfusionMatrixComparison[['PPV', 'TPR', 'ACC', 'F1']].plot(kind='bar', subplots=True, figsize=(5,10), legend=False)\n    \n    return ConfusionMatrixComparison\n\n###############################################################################\n##[ JSON INTERFACE ]###########################################################\n###############################################################################\n    \ndef build_ml_model_task(DataFrame, model_setup_dict, variables_setup_dict, return_script=False):\n    \"\"\"\n    Parameters\n    ----------\n    DataFrame: pandas.DataFrame\n    model_setup_dict: json or dict\n    variables_setup_dict: json or dict\n    \n    Returns\n    -------\n    Model: mltk.MLModel\n        \n    \"\"\"    \n    import json\n    import pandas as pd\n    \n    if type(model_setup_dict)==dict:\n        pass\n    else:\n        try:\n            model_setup_dict = json.loads(model_setup_dict) \n        except:\n            print('ERROR in fitting model:{}\\n {}'.format(model_setup_dict, traceback.format_exc()))  \n\n    if type(variables_setup_dict)==dict:\n        pass\n    else:\n        try:\n            variables_setup_dict = json.loads(variables_setup_dict) \n        except:\n            print('ERROR in fitting model:{}\\n {}'.format(variables_setup_dict, traceback.format_exc()))  \n    \n    if type(DataFrame)==dict:\n        dict_keys = DataFrame.keys()\n        if ('TrainDataset' in dict_keys) and ('ValidateDataset' in dict_keys) and ('TestDataset' in dict_keys):\n            TrainDataset = DataFrame['TrainDataset']\n            ValidateDataset = DataFrame['ValidateDataset']\n            TestDataset = DataFrame['TestDataset']\n        else:\n            print('Data input error check if the input dictionary has keys: \"TrainDataset\",  \"ValidateDataset\", \"TestDataset\" ')\n    else:    \n        TrainDataset, ValidateDataset, TestDataset = train_validate_test_split(DataFrame, ratios=model_setup_dict['sample_split'])\n    \n    model_data_stats = data_description(TrainDataset)\n    model_setup_dict['sample_attributes']['ModelDataStats'] = model_data_stats\n    \n    TrainDataset, category_variables, binary_variables, target_variable, preprocess_variables_script = setup_variables_task(TrainDataset, variables_setup_dict, return_script=True)\n    TrainDataset, feature_variables, target_variable = to_one_hot_encode(TrainDataset, \n                                                                         category_variables=category_variables, \n                                                                         binary_variables=binary_variables, \n                                                                         target_variable=target_variable)\n    \n    ValidateDataset, category_variables_, binary_variables_, target_variable_ = setup_variables_task(ValidateDataset, variables_setup_dict)\n    ValidateDataset, feature_variables, target_variable = to_one_hot_encode(ValidateDataset, \n                                                                         category_variables=category_variables, \n                                                                         binary_variables=binary_variables, \n                                                                         target_variable=target_variable)\n    \n    TestDataset, category_variables_, binary_variables_, target_variable_  = setup_variables_task(TestDataset, variables_setup_dict)\n    TestDataset, feature_variables, target_variable = to_one_hot_encode(TestDataset, \n                                                                         category_variables=category_variables, \n                                                                         binary_variables=binary_variables, \n                                                                         target_variable=target_variable)\n        \n    Model = build_ml_model(TrainDataset, ValidateDataset, TestDataset, \n                                  model_variables=model_setup_dict['model_variables'],\n                                  variable_setup = variables_setup_dict,\n                                  target_variable=model_setup_dict['target_variable'],\n                                  model_attributes=model_setup_dict['model_attributes'], \n                                  sample_attributes=model_setup_dict['sample_attributes'], \n                                  model_parameters=model_setup_dict['model_parameters'], \n                                  score_parameters=model_setup_dict['score_parameters'], \n                                  return_model_object=model_setup_dict['model_outputs']['return_model_object'], \n                                  show_results=model_setup_dict['model_outputs']['show_results'], \n                                  show_plot=model_setup_dict['model_outputs']['show_plot']\n                                  )\n    \n\n    if return_script:\n        variables_setup_dict = {\n            \"setting\":\"score\",    \n            \"variables\": variables_setup_dict[\"variables\"],    \n            \"preprocess_tasks\": preprocess_variables_script    \n        }\n        return Model, variables_setup_dict\n    else:\n        return Model\n\ndef build_ml_model_from_data_task(DataFrame, model_building_setup_dict, return_script=False):\n    \"\"\"\n    Parameters\n    ----------\n    DataFrame: pandas.DataFrame\n    model_building_setup_dict: json or dict\n    \n    Returns\n    -------\n    Model: mltk.MLModel\n    \"\"\"   \n    \n    import json\n    \n    if type(model_building_setup_dict)==dict:\n        pass\n    else:\n        try:\n            model_building_setup_dict = json.loads(model_building_setup_dict) \n        except:\n            print('ERROR in fitting model:{}\\n {}'.format(model_building_setup_dict, traceback.format_exc())) \n            \n    model_setup_dict = model_building_setup_dict['model_setup_dict']\n    variables_setup_dict = model_building_setup_dict['variables_setup_dict']\n    \n    Model, variables_setup_dict = build_ml_model_task(DataFrame, model_setup_dict=model_setup_dict, variables_setup_dict=variables_setup_dict, return_script=True)\n    print(Model.model_attributes['ModelID'])\n    print('ROC AUC: ', Model.get_auc(curve='roc'))\n    print('PRC AUC: ', Model.get_auc(curve='prc'))\n    \n    if return_script:\n        return Model, variables_setup_dict\n    else:\n        return Model\n","sub_path":"MLToolkit/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":64646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"337574749","text":"f = open(\"input.txt\", \"r\").read().split(\"\\n\")\nw = open(\"output.txt\", \"w\")\n\nn = int(f[0])\nmas = f[1].split(\" \")\nline = ''\nk = 0\ntemplate = '{:.2f}'\n\nfor i in range(n):\n    if ((i+1) % 3 == 0) and float(mas[i]) > 0:\n        line = line + template.format(float(mas[i])) + ' '\n        k += 1\n        \n\nw.write(str(k) + ' ' + line)\nw.close()\n","sub_path":"0000-0999/#919.py","file_name":"#919.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"298160029","text":"from sys import exc_info\r\nfrom xlogging import Logger, LogLevel\r\nfrom xexcerr import *\r\nfrom cxtk import CanvasContainer\r\n\r\n# canvas values\r\nwidth = 976\r\nheight = 976\r\npad = 25.0\r\ntotal_W = width + 2*pad\r\ntotal_H = height + 2*pad\r\n\r\ncvc = CanvasContainer(total_W, total_H)\r\ncvc.create_border(int(pad))\r\n\r\n# dotsize\r\nsize_D = 20\r\n\r\n# letter size\r\nwidth_L = 95\r\nheight_L = 95\r\nwidth_M = int(width / width_L)\r\nheight_M = int(height / height_L)\r\n\r\nhist = [\"\"]\r\nletters = dict()\r\nletterBoxes = list()\r\nsettings = [0, \"],[\", \"],[\"]\r\n\r\nlogger = Logger(LogLevel.EXC)\r\n\r\ndef changeSetting( index, newval):\r\n    if index is 1:\r\n        intersect = set(str(newval)) & set(\"abcdefghijklmnopqrstuvwxyz,[]_^<>\")\r\n        if len(intersect) is not 0:\r\n            logger.log(LogLevel.EXC, \"Intersection of: \" + str(intersect))\r\n            raise IllegalCharacterException(\"Illegal seperator '\"+str(newval)+\"'.\", None)\r\n        else:\r\n            logger.log(LogLevel.MSG, \"Changing existing letters\")\r\n            for letter in letters.keys():\r\n                logger.log(LogLevel.DEBUG, \"Letter: \" + letter.upper())\r\n                logger.log(LogLevel.DEBUG, \"Old: \" + letters[letter])\r\n                letters[letter] = letters[letter].replace(settings[index],newval)\r\n                logger.log(LogLevel.DEBUG, \"New: \" + letters[letter])\r\n    elif index is 0:\r\n        logger.log(LogLevel.MSG, \"Setting position to index \" + str(newval))\r\n    settings[index] = newval\r\n\r\ndef incLetterCount():\r\n    settings[0] = settings[0] + 1\r\n\r\ndef fold(fnct, lst):\r\n    for elem in lst:\r\n        fnct(elem)\r\n\r\ndef definePath(name, strlst):\r\n    letters[str(name)] = strlst\r\n\r\ndef drawPath(strarg, name=None):\r\n    strlst = \"\"\r\n    if strarg in letters.keys():\r\n        strlst = letters[strarg]\r\n    else:\r\n        raise NoDefinitionException(\"No draw definition for given key: \" + str(strarg), None)\r\n    lst = list()\r\n    count = settings[0] % len(letterBoxes)\r\n    incLetterCount()\r\n    for s in strlst.split(settings[1]):\r\n        xadjust = 0.2 * width_L\r\n        yadjust = 0.2 * height_L\r\n        logger.log(LogLevel.DEBUG, \"Split: \" + str(s))\r\n        #s = s.split(\",\")\r\n        #logger.log(LogLevel.DEBUG, \"After: \" + str(s))\r\n        vp = list([0,0])\r\n        if \"left\" in s or \"l\" in s:\r\n            vp[0] = letterBoxes[count][0][0]#left\r\n        elif \"center\" in s or \"c\" in s:\r\n            vp[0] = letterBoxes[count][0][1]#center\r\n        else:\r\n            vp[0] = letterBoxes[count][0][2]#right\r\n        if \"<\" in s:\r\n            vp[0] -= xadjust\r\n        elif \">\" in s:\r\n            vp[0] += xadjust\r\n        if \"up\" in s or \"u\" in s:\r\n            vp[1] = letterBoxes[count][1][0]#up\r\n        elif \"mid\" in s or \"m\" in s:\r\n            vp[1] = letterBoxes[count][1][1]#mid\r\n        else:\r\n            vp[1] = letterBoxes[count][1][2]#down\r\n        if \"_\" in s:\r\n            vp[1] += yadjust\r\n        elif \"^\" in s:\r\n            vp[1] -= yadjust\r\n        lst.append(vp)\r\n    prv = -1\r\n    for elem in lst:\r\n        if prv == -1:\r\n            prv = elem\r\n        else:\r\n            cvc.create_line([prv[0],prv[1],elem[0],elem[1]], name)\r\n            prv = elem\r\n\r\ndef drawMany( strarg, numarg):\r\n    for _x in range(0,int(numarg)):\r\n        drawPath( strarg, name=str(settings[0] % len(letterBoxes)))\r\n\r\n# letter boxes\r\nfor row in range(0, height-int(pad+height_L), height_L + height_M):\r\n    row += pad\r\n    rowu = row + 0.0 * height_L\r\n    rowm = row + 0.5 * height_L\r\n    rowd = row + 1.0 * height_L\r\n    for col in range(0,width-int(pad+width_L), width_L + width_M):\r\n        col += pad\r\n        coll = col + 0.0 * width_L\r\n        colc = col + 0.5 * width_L\r\n        colr = col + 1.0 * width_L\r\n        letterBoxes.append([[coll,colc,colr],[rowu,rowm,rowd]])\r\nlogger.log(LogLevel.VERBOSE, \"count: \" + str(len(letterBoxes)))\r\n\r\n# relative y values\r\nup = 0.0 * height + pad\r\nmid = 0.5 * height + pad\r\ndown = 1.0 * height + pad\r\n# relative x values\r\nleft = 0.0 * width + pad\r\ncenter = 0.5 * width + pad\r\nright = 1.0 * width + pad\r\n\r\n# setting spot names as list\r\nspots = list()\r\nfor h in [\"up\",\"mid\",\"down\"]:\r\n    for w in [\"left\",\"center\",\"right\"]:\r\n        spots.append(h+w)\r\n\r\n# initializing dict\r\ndots = dict()\r\nfor spot in spots:\r\n    dots[spot] = list([0,0,0,0])\r\n    if \"up\" in spot or \"u\" in spot:\r\n        dots[spot][1] = up - size_D*0.5\r\n        dots[spot][3] = up + size_D*0.5\r\n    elif \"mid\" in spot or \"m\" in spot:\r\n        dots[spot][1] = mid - size_D*0.5\r\n        dots[spot][3] = mid + size_D*0.5\r\n    else:\r\n        dots[spot][1] = down - size_D*0.5\r\n        dots[spot][3] = down + size_D*0.5\r\n    if \"left\" in spot or \"l\" in spot:\r\n        dots[spot][0] = left - size_D*0.5\r\n        dots[spot][2] = left + size_D*0.5\r\n    elif \"center\" in spot or \"c\" in spot:\r\n        dots[spot][0] = center - size_D*0.5\r\n        dots[spot][2] = center + size_D*0.5\r\n    else:\r\n        dots[spot][0] = right - size_D*0.5\r\n        dots[spot][2] = right + size_D*0.5\r\n\r\ndss = \"#\"*14 +\" \"*5+ \"x1 |\" +\" \"*4+ \"y1 |\" +\" \"*4+ \"x2 |\" +\" \"*4+ \"y2 |\\n\"\r\nfor k in dots:\r\n    dss += str(k) + \":\" + \" \"*(12-len(str(k))) + \"| \"\r\n    for v in dots[k]:\r\n        dss += \" \"*(7-len(str(v))) + str(v) + \"|\"\r\n    dss += \"\\n\"\r\nlogger.log(LogLevel.VERBOSE, dss)\r\n\r\ncmds = dict()\r\ncmds[\"\"] = lambda: logger.log(LogLevel.MSG, \"Try !h\")\r\ncmds[\"!q\"] = lambda: logger.log(LogLevel.MSG, \"Exiting...\")\r\ncmds[\"!h\"] = lambda: logger.log(LogLevel.MSG, *(list(cmds.keys()))[1:],sep=\"\\n\", indent=3)\r\n\r\ncmds[\"hist\"] = lambda: logger.log(LogLevel.MSG, *hist[1:],sep=\"\\n\", indent=3)\r\ncmds[\"list\"] = lambda: logger.log(LogLevel.MSG, *cvc.getElements(),sep=\"\\n\", indent=3)\r\n\r\ncmds[\"dots\"] = lambda: fold(cvc.create_oval, dots.values() )\r\ncmds[\"draw\"] = lambda arg: drawPath(arg, name=str(settings[0] % len(letterBoxes)))\r\ncmds[\"drawmany\"] = lambda sarg, narg: drawMany( sarg, narg)\r\n\r\ncmds[\"log\"] = lambda narg: logger.level(int(narg))\r\n\r\ncmds[\"rem\"] = lambda arg: logger.log(LogLevel.MSG, \"Removed \" + str(arg)) if cvc.remove(arg) else logger.log(LogLevel.MSG, \"No such key\")\r\ncmds[\"remmany\"] = lambda arglist: logger.log(LogLevel.MSG, \"Removed \" + str(cvc.removeMany(str(arglist).split(\",\"))) )\r\ncmds[\"pos\"] = lambda arg: changeSetting( 0, int(arg) % len(letterBoxes))\r\n\r\ncmds[\"def\"] = lambda name, lst: definePath(name, lst)\r\ncmds[\"sep\"] = lambda sep: changeSetting(1,sep) if sep!='' else changeSetting(1,settings[2])\r\n\r\n# in code path def\r\naletter = \"[[left,down],[center,up],[right,down]]\"\r\nletters[\"a\"] = aletter\r\nletters[\"xc\"] = \"[u^l<],[u^l>],[u_l>],[u_l<],[u^l<],[u_l>],[u_l<],[u^l>],[u^r<],[u^r>],[u_r>],[u_r<],[u^r<],[u_r>],[u_r<],[u^r>],[d^r>],[d^r<],[d^r>],[d_r>],[d_r<],[d^r<],[d_r>],[d_r<],[d^r>],[d_r>],[d_l<],[d^l<],[d^l>],[d_l>],[d_l<],[d^l<],[d_l>],[d_l<],[d^l>],[d^l<],[u^l<]\"\r\nletters[\"x\"] = \"[ur],[dl],[mc],[ul],[dr]\"\r\nletters[\"box\"] = \"[ur],[ul],[dl],[dr],[ur]\"\r\n# function usage\r\ndefinePath(\"b\", \"[[up,left],[mid,right],[mid,left],[down,right],[down,left],[up,left]]\")\r\n\r\nwhile hist[len(hist)-1] != \"!q\" :\r\n    cvc.updateCanvas(True)\r\n    usrin = input(\"....> \").lower().strip()\r\n    hist.append(usrin)\r\n    cvc.updateCanvas(True)\r\n    try:\r\n        usrcmd = usrin.split(\" \")[0]\r\n        usrprm = usrin.split(\" \")[1:]\r\n        cmds[usrcmd](*usrprm)\r\n    except:\r\n        logger.log(LogLevel.ERR, exc_info()[:-1])\r\n        if usrcmd not in cmds.keys():\r\n            logger.log(LogLevel.EXC, \"Unknown command '\"+ str(usrin) +\"'.\")\r\n            logger.log(LogLevel.MSG, \"Type '!h' to list available commands.\")\r\ncvc.updateCanvas(False)","sub_path":"alphadraw.py","file_name":"alphadraw.py","file_ext":"py","file_size_in_byte":7504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"261340428","text":"import os\nimport requests\nfrom bs4 import BeautifulSoup\nfrom IPython.display import Image\nfrom PIL import Image as PILImage\n\n# 출처: https://rednooby.tistory.com/101 [개발자의 취미생활]\n\n# 전역변수\nimg_dir = './img'\nresult_img_dir = './result'\n\nresult_name = '좀비딸'\nurl = 'https://comic.naver.com/webtoon/detail.nhn?titleId=715772&no=1&weekday=thu'\nimg_unit = 20 # 이미지 병합 단위\n\nfile_list = []\n# ep_num = int(url.split('&')[1][3:]) # 회차\nep_start_num = 1\nep_last_num = 3\nep_num_list = list(range(ep_start_num, ep_last_num+1))\nep_url = url.split('&')[0] + '&' + 'no=' + str(ep_start_num) + '&' + url.split('&')[2]\n\n# img 디렉토리 만들것\ndef check_dir():\n    img_dir = './img'\n    if not os.path.exists(img_dir):\n        os.makedirs(img_dir)\n    # result 디렉토리 만들것\n    result_img_dir = './result'\n    if not os.path.exists(result_img_dir):\n        os.makedirs(result_img_dir)\n\n\n# 폴더 내 모든 파일 제거\ndef remove_all_files(filepath = img_dir):\n    if os.path.exists(filepath):\n        for file in os.scandir(filepath):\n            os.remove(file.path)\n        return 'Remove All Files'\n    else:\n        return 'Directory Not Found'\n\n# 파일 불러오기\ndef load_files():\n    file_list = os.listdir(img_dir + '/')\n    file_list.sort() # 이미지 이름 순서대로 정렬\n    return file_list\n\n### 이미지 받아오기 ###\ndef run_crawler(ep_url):\n    # html 파싱\n    # ep_url = 'https://comic.naver.com/webtoon/detail.nhn?titleId=662774&no=179&weekday=wed'\n    html = requests.get(ep_url).text\n    soup = BeautifulSoup(html, 'html.parser')\n    img_num = 0\n\n    # DOM에서 크롤링할 이미지 식별자 넣기\n    for tag in soup.select('.wt_viewer img'):\n        img_url = tag['src']\n        headers = {'Referer': ep_url}\n        img_data = requests.get(img_url, headers=headers).content # 해더 추가해서 리퀘스트 보내기\n        # 파일 넘버링\n        img_num = img_num + 1\n        img_name = img_dir + '/' + str(img_num).zfill(3) + '.jpg'\n\n        with open(img_name, 'wb') as f:\n            f.write(img_data)\n\n\n\n### 이미지 합치기 ###\n# Maximum supported image dimension is 65500 pixels :: JPG 이미지 한계임\n\n\ndef merge_img(file_list = file_list, img_unit = 20, quotient = 'none', remainder = 'none', ep_num = 0):\n    cnt = 0\n    # 차례대로 이미지를 저장할 리스트 생성\n    image_list = []\n    # 반복문이 돌때마다 값을 누적시킬 변수\n    full_width, full_height = 0, 0\n\n    # 나머지 파라미터를 전달받았다면 그걸 단위로 사용할 것\n    if remainder != 'none':\n        img_unit = remainder\n\n    for file in file_list[i * img_unit : i * img_unit + img_unit]: # 합칠 이미지 단위 만큼만\n\n        #지금 이미지를 im에 넣기\n        im = PILImage.open(img_dir + '/' + file)\n        #가로, 세로 size를 지금 들러온 im에 맞춘다\n        width, height = im.size\n        #작업을 마친 이미지를 미리선언한 list에 차례대로 저장\n        image_list.append(im)\n        #최대 가로길이 설정. 가로의 x축0, y축은 im.size에서 수정한 가로너비\n        full_width = max(full_width, width)\n        #세로길이는 이미지가 누적될수록 계속 길어지므로 길이를 기존길이에서 더함\n        full_height += height\n\n    # 캔버스 설정. 배경은 흰색 캔버스 설정을 위해서 최대 크기를 먼저 알아야함 jpg 압축 한계 때문에 높이 65500 넘지 말것\n    canvas = PILImage.new('RGB', (full_width, full_height), 'white')\n\n    output_height = 0\n\n    # 리스트에 있는 이미지를 캔버스에 덮어씌우기\n    for im in image_list: # 이미지 하나하나, final_image_list는 im을 모아놓은 리스트\n        # 현재 이미지의 사이즈를 가로세로 변수에 넣는다\n        width, height = im.size\n        # 캔버스에 현재 이미지를 붙여넣고(x축0, y축누적높이)\n        canvas.paste(im, (0, output_height))\n        #세로로 이미지를 붙이기 때문에 높이값을 누적시켜줘야 함\n        output_height += height\n\n\n    # 반복문을 돌면 저장!\n    canvas.save(result_img_dir + '/' + result_name + str(ep_num).zfill(3) + '_' + str(i+1).zfill(3) + '.jpg')\n\n\n### controller ###\n\n# 디렉토리 만들고\ncheck_dir()\n\nfor ep_num in ep_num_list:\n    # 디렉토리 먼저 비우고\n    remove_all_files()\n\n    # 주소 정하고\n    ep_url = url.split('&')[0] + '&' + 'no=' + str(ep_num) + '&' + url.split('&')[2]\n    print(ep_url)\n    print('크롤링 시작 ep:' + str(ep_num))\n    # 크롤링\n    run_crawler(ep_url)\n\n    # 크롤링 한 파일 불러오고\n    file_list = load_files()\n    # 이미지 총개수를 단위로 나누기\n    quotient, remainder = divmod(len(file_list), img_unit) # 나누기의 몫과 나머지\n\n    # 병합하기\n    for i in range(quotient + 1):\n\n        if i == (quotient): # 마지막 반복만\n            # remainder\n            merge_img(file_list = file_list, quotient = quotient, remainder = remainder, ep_num = ep_num)\n        else:\n            merge_img(file_list = file_list, quotient = quotient, ep_num = ep_num)\n","sub_path":"web_crawler_04.py","file_name":"web_crawler_04.py","file_ext":"py","file_size_in_byte":5163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"509776706","text":"from grass import script as gscript\nimport argparse\n\ndef main(npy_in,tif_out,tif_like):\n    \n    arr_in = np.load(npy_in)\n    locs = arr_in[:,:2].astype(np.int)\n\n    ## \"un-flatten\" to array shape of basin raster\n    # get correct shape and coordinates\n    with rs.drivers():\n        with rs.open(tif_like) as src:\n            profile = src.profile\n            w,h = (src.width, src.height)\n            aff = src.affine\n            ndv = src.get_nodatavals()\n            x,y = [i.astype(np.int) for i in (~aff * (locs[:,0],locs[:,1]))]\n\n    # construct 3d array\n    arr_out = np.ones((w,h,arr_in.shape[1]-2),dtype=np.float32)\n    arr_out[:] = ndv\n    arr_out[x,y,:] = arr_in[:,2:]\n    arr_out = np.swapaxes(arr_out,0,2)\n\n    # update output profile to get correct # of bands and compression mechanism\n    profile.update(\n        count = arr_out.shape[0],\n        compress = 'DEFLATE')\n\n\n    ## output Tiff file\n    with rs.drivers():\n        with rs.open(tif_out, 'w', **profile) as dst:\n            dst.write(arr_out)\n            \nif __name__=='__main__':\n    parser = argparse.ArgumentParser(description='Convert the results of a PySAL regression to a tiff file')\n    parser.add_argument('npy_in')\n    parser.add_argument('tif_out')\n    parser.add_argument('tif_like')\n\n    args=parser.parse_args()\n    main(args.npy_in,args.tif_out,args.tif_like)\n    ","sub_path":"scripts/viz_param_estimates.py","file_name":"viz_param_estimates.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"571941960","text":"\n\n\"\"\"\ninput : any string\noutput:\n  <span> <i><b>hello</b></i> </span> <\n\n\"\"\"\nfrom functools import wraps\n\ndef tagify(*args):\n    print(args)\n    def wrapper(original_func):\n        @wraps(original_func) # Recommended if something depends on original function attributes, metadata\n        def wrapped(string):\n            print(string)\n            res = original_func(string)\n            for tag in args:\n                print(tag)\n                start_tag = f'<{tag}>'\n                end_tag = f'</{tag}>'\n                res = start_tag + res + end_tag\n            print(\"ending call wrapped\")\n            return res\n        print(\"ending call wrapper\")\n        return wrapped\n    print(\"ending tagify call\")\n    return wrapper\n\n\n@tagify(\"b\", \"i\", \"span\", \"div\")\ndef text(string):\n    return string\n\n\n#print(tagify(\"b\", \"i\",\"span\", \"div\")(text)(\"hello\"))\n","sub_path":"Nikhil/pyarena/param_deco.py","file_name":"param_deco.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"143001617","text":" \nfrom socios.models import Socio, ActividadPorPeriodo\nfrom compras.models import Compra\n\ndef patrocinados(socio_uiid):\n    i = Socio.objects.filter(patrocinador_id=socio_uiid)\n    return i\n\n\ndef nivelesG(request,socio_uiid):\n    niveles = []\n    nivel0 = patrocinados(socio_uiid)\n    n0=[]\n    for i in range(len(nivel0)):\n        print(nivel0[i].socio_uiid)\n        Zer0=[]\n        Zer0.append(nivel0[i].socio_uiid)\n        Zer0.append(nivel0[i].first_name)\n        Zer0.append(1)\n        n0.append(Zer0)\n    niveles.append(n0)\n    \n    nivel1=[]\n    nivel2=[]\n    nivel3=[]\n    nivel4=[]\n    for k in range(len(nivel0)):\n        indices1 = nivel0[k].socio_uiid\n        level1 = patrocinados(indices1)\n        nivel1.append(level1)\n        for l in range(len(level1)):\n            level2 = patrocinados(level1[l].socio_uiid)\n            nivel2.append(level2)\n            for c in range(len(level2)):\n                level3 = patrocinados(level2[c].socio_uiid)\n                nivel3.append(level3)\n                for d in range(len(level3)):\n                    level4 = patrocinados(level3[d].socio_uiid)\n                    nivel4.append(level4)\n    n1 = levels(nivel1,2)\n    n2 = levels(nivel2,3)\n    n3 = levels(nivel3,4)\n    n4 = levels(nivel4,5)\n    niveles.append(n1)\n    niveles.append(n2)\n    niveles.append(n3)\n    niveles.append(n4)\n    print(niveles)\n    return niveles\n\n                \n\n\ndef levels(nivel,b): #no se retornan los querysets jeje, se les sacan los campos\n    nivelOne=[]\n    for i in range(len(nivel)):\n        for o in range(len(nivel[i])):\n            nivelUno=[]\n            nivelUno.append(nivel[i][o].socio_uiid)\n            nivelUno.append(nivel[i][o].first_name)\n            nivelUno.append(b)\n            nivelOne.append(nivelUno)\n    return nivelOne\n            \n    \n    \"\"\" for l in range(len(nivel1)):\n        indices2 = nivel1[l].socio_uiid\n        level2 = patrocinados(indices2)\n        nivel2.append(level2)\n    nivel3=[]\n    for m in range(len(nivel2)):\n        indices3 = nivel2[m].socio_uiid\n        level3 = patrocinados(indices3)\n        nivel3.append(level3)\n    nivel4=[]\n    for h in range(len(nivel3)):\n        indices4 = nivel3[h].socio_uiid\n        level4 = patrocinados(indices4)\n        nivel4.append(level4)\n    \n    niveles.append(nivel1)\n    niveles.append(nivel2)\n    niveles.append(nivel3)\n    niveles.append(nivel4)\n    print(niveles)                   \n    return niveles\"\"\"","sub_path":"redes/niveles.py","file_name":"niveles.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"368452214","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        ('emtool', '0005_outage_notify_groups'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='outage',\n            name='current_status',\n            field=models.ForeignKey(to='emtool.StatusCode', default=1),\n        ),\n    ]\n","sub_path":"emtool/migrations/0006_auto_20150718_2108.py","file_name":"0006_auto_20150718_2108.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"35526180","text":"from django.contrib.auth import login, logout\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework import status\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.authtoken.serializers import AuthTokenSerializer\nfrom rest_framework import permissions\nfrom rest_framework.response import Response\n\nfrom weatherinfo.helpers import validate_email, send_email\nfrom weatherinfo.models import CityWeatherDetails\nfrom django.core.serializers import serialize\nfrom .serializers import RegisterSerializer\n\nimport logging\nimport json\nimport pandas as pd\n\nlogging = logging.getLogger(__name__)\n\n\n# API to get Weather info\n@api_view(['GET'])\ndef get_weather_info(request):\n    try:\n        cityWeatherDetails = CityWeatherDetails.objects.all().order_by('-id')[:30]\n        # convert queryset to json data\n        json_data = json.loads(serialize('json', cityWeatherDetails))\n    except Exception as e:\n        logging.error(\"Exception {}\", e)\n        return Response(\"Exception {}\".format(e), status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n    return Response(json_data, status=status.HTTP_200_OK)\n\n\n# API to email Weather info\n@api_view(['POST'])\ndef email_weather_info(request):\n    try:\n        logging.info(\"email_weather_info: request data {}\".format(request.data))\n        emails = request.data[\"emails\"]\n        if emails:\n            # check emails if valid or not\n            valid_emails = validate_email(emails)\n            if valid_emails:\n                logging.info(\"email_weather_info: valid emails {}\".format(valid_emails))\n                cityWeatherDetails = CityWeatherDetails.objects.all().order_by('-id')[:10]\n                # json_data = CityWeatherDetailsSerializer(\"json\", list(cityWeatherDetails.values()), many=True)\n                # convert queryset to json data\n                json_data = json.loads(serialize('json', cityWeatherDetails))\n                pd.DataFrame(json_data).to_excel('output.xlsx', header=False, index=False)\n                # call send email method\n                send_email(valid_emails)\n            else:\n                return Response({\"msg\": \"no valid emails\"}, status=status.HTTP_400_BAD_REQUEST)\n    except Exception as e:\n        logging.error(\"email_weather_info: Exception {}\".format(e))\n        return Response(\"email_weather_info: Exception {}\".format(e), status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n    return Response(valid_emails, status=status.HTTP_200_OK)\n\n\n# Register API\n@api_view(['POST'])\n@permission_classes([permissions.AllowAny])\ndef Register(request):\n    serializer = RegisterSerializer(data=request.data)\n    serializer.is_valid(raise_exception=True)\n    user = serializer.save()\n    token, created = Token.objects.get_or_create(user=user)\n    data = {\n        \"token\": token.key\n    }\n    return Response(data=data, status=status.HTTP_201_CREATED)\n\n\n@api_view(['POST'])\n@permission_classes([permissions.AllowAny])\ndef Login(request):\n    serializer = AuthTokenSerializer(data=request.data)\n    serializer.is_valid(raise_exception=True)\n    user = serializer.validated_data['user']\n    login(request, user)\n    return Response(serializer.data, status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\n@permission_classes([permissions.AllowAny])\ndef Logout(request):\n        \"\"\"\n        Calls Django logout method; Does not work for UserTokenAuth.\n        \"\"\"\n        logout(request)\n        return Response({\"success\": \"Successfully logged out.\"}, status=status.HTTP_200_OK)\n\n\n\n\n","sub_path":"weatherinfo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"79662032","text":"import MapReduce\nimport sys\nimport re\n\n\"\"\"\nWord Count Example in the Simple Python MapReduce Framework\n\"\"\"\n\nmr = MapReduce.MapReduce()\n\n# =============================\n# Do not modify above this line\n\ndef mapper(record):\n    i = record[0]\n    j = record[1]\n    value = record[2]\n\n    for x in range (0,5):\n      mr.emit_intermediate((i,x),[ 'A' ,i , j , value])\n\n    for y in range (0,5):\n      mr.emit_intermediate((y,j),['B',i,j, value])       \n\ndef reducer(key, list_of_values):\n    a = [0,0,0,0,0]\n    b = [0,0,0,0,0]\n    sum = 0\n    \n    for l in list_of_values:\n      if (l[0] == 'A'):\n        a[l[2]] = l[3]\n      if (l[0] == 'B'):\n        b[l[1]] = l[3]\n\n    for i in range (0,5):\n      sum = sum + a[i]*b[i]\n\n    key = key + (sum,)\n    mr.emit((key))\n\n# Do not modify below this line\n# =============================\nif __name__ == '__main__':\n  inputdata = open(sys.argv[1])\n  mr.execute(inputdata, mapper, reducer)\n","sub_path":"shambhavi_punja_squared_one.py","file_name":"shambhavi_punja_squared_one.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"558239073","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: longshuicui\n@date  :   2021/1/28\n@function:\n347. Top K Frequent Elements (Medium)\nhttps://leetcode.com/problems/top-k-frequent-elements/\n题目描述\n    给定一个数组，求前 k 个最频繁的数字。\n输入输出样例\n    输入是一个数组和一个目标值 k。输出是一个长度为 k 的数组。\n    Input: nums = [1,1,1,1,2,2,3,4], k = 2\n    Output: [1,2]\n    在这个样例中，最频繁的两个数是 1 和 2。\n\n题解\n    桶排序。将相同的值放到同一个桶中，记录这个值出现的次数；然后对桶进行排序，\n\"\"\"\ndef topKFrequent(nums, k):\n    counts={}\n    max_count=0\n    for num in nums:\n        counts[num]=counts.get(num,0)+1\n        max_count=max(max_count, counts[num])\n    buckets=[[] for _ in range(max_count+1)] # 按照数量从小到大进行排序\n    for num in counts:\n        buckets[counts[num]].append(num)\n\n    ans=[]\n    for i in range(max_count,-1,-1):\n        if i>=0 and len(ans)<k:\n            for num in buckets[i]:\n                ans.append(num)\n                if len(ans)==k:\n                    break\n    return ans\n\n\n\nnums = [1,1,1,1,2,2,3,4]\nk = 3\nans=topKFrequent(nums,k)\nprint(ans)","sub_path":"04.排序/347.Top K Frequent Elements (Medium).py","file_name":"347.Top K Frequent Elements (Medium).py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"136288741","text":"#!/opt/python3.4/bin/python3.4\n\n#!/usr/bin/python3.4\n\n\"\"\"\nconvert Apple Mail VCF archive to CSV file for Mozilla Thunderbird (tbird)\ntbird cannot read phone numbers from Apple Mail VCF file\nhttp://wordpress.jmcgowan.com/wp/how-to-move-your-contacts-with-phone-numbers-from-apple-mail-to-mozilla-thunderbird/\n\"\"\"\n\nimport sys\nimport os.path  # os.path.isfile(fname)\nimport re # regular expressions\nimport phone  # my phone number validation module\n\nVERBOSE_FLAG = False  # debug trace flag\n\n# CSV file header generated by exporting contacts from Mozilla Thunderbird 52.6.0\nTBIRD_ADR_BOOK_HEADER = 'First Name,Last Name,Display Name,Nickname,Primary Email,Secondary Email,Screen Name,Work Phone,Home Phone,Fax Number,Pager Number,Mobile Number,Home Address,Home Address 2,Home City,Home State,Home ZipCode,Home Country,Work Address,Work Address 2,Work City,Work State,Work ZipCode,Work Country,Job Title,Department,Organization,Web Page 1,Web Page 2,Birth Year,Birth Month,Birth Day,Custom 1,Custom 2,Custom 3,Custom 4,Notes'  # was carriage return here\n\n# John Nada from John Carpenter's THEY LIVE\nDUMMY_CONTACT = 'John,Nada,John Nada,Nada,nada@nowhere.com,nada@cable54.com,NADA,999-555-1212,888-555-1234,777-555-6655,111-555-1234,111-555-9876,123 Main Street, Apt 13, Los Angeles, CA, 91210,USA,Work Address,Work Address 2,Work City,Work State,Work ZipCode,Work Country,Job Title,Department,Organization,Web Page 1,Web Page 2,Birth Year,Birth Month,Birth Day,Custom 1,Custom 2,Custom 3,Custom 4,Notes'\n\n# break into values\nFIELD_NAMES = TBIRD_ADR_BOOK_HEADER.split(',')\nFIELD_VALUES_START = DUMMY_CONTACT.split(',')\nfor index, value in enumerate(FIELD_VALUES_START):\n    FIELD_VALUES_START[index] = ''  # try single space\n\n# build dictionary to map from field name to index\nFIELD_INDEX = {}\nfor index, field_name in enumerate(FIELD_NAMES):\n    FIELD_INDEX[field_name] = index\n\nif VERBOSE_FLAG:\n    print(FIELD_INDEX)\n\n    print(TBIRD_ADR_BOOK_HEADER)\n    print(DUMMY_CONTACT)\n\n\nif len(sys.argv) < 2:\n    VCARD_FILE = 'jfm_contacts.vcf'\nelse:\n    VCARD_FILE = sys.argv[1]  # 0 is script name\n\n\ndef usage(cmd):\n    \"\"\" usage message \"\"\"\n    print(\"Usage: \", cmd, \"  [-license] [-o output_file.csv] \")\n    print(\"   -- generate Thunderbird Compliant CSV file with importable telephone numbers \")\n    print(\"   -- from Apple Mail generated .vcf (vcard) file\")\n    print(\"   -- (C) 2018 by John F. McGowan, Ph.D.\")\n    print(\"   \")\n    print(\"   -license -- print license terms\")\n    print(\" \")\n    print(\"In Mozilla Thunderbird 52.6.0, Tools | Import | Address Books | Text file(LDIF,csv,tab,txt) | choose output file from this program.\")\n    print(\" \")\n    print(\"Tested with Python 3.6.4 installed by/with Anaconda\")\n\ndef license_terms():\n    \"\"\" license terms \"\"\"\n    license_msg = \"\"\"Copyright 2018 John F. McGowan, Ph.D.\n\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n    \"\"\"\n    print(license_msg)\n\nif VCARD_FILE == \"--help\" or VCARD_FILE == \"-h\"\\\n   or VCARD_FILE == \"-help\" or VCARD_FILE == \"-?\":\n    usage(sys.argv[0])\n    sys.exit(0)\n\nif VCARD_FILE == \"--license\" or VCARD_FILE == \"-license\":\n    license_terms()\n    sys.exit(0)\n\nOUTPUT_FILENAME = 'tbird_imports.csv'\nOUTPUT_FLAG = False\nfor arg_index, argval in enumerate(sys.argv):\n    if OUTPUT_FLAG:\n        OUTPUT_FILENAME = argval\n        OUTPUT_FLAG = False\n    if argval == \"-o\":\n        OUTPUT_FLAG = True\n\n\n# write import file with one dummy contact (John Nada from THEY LIVE)\nOUTPUT_FILE = open(OUTPUT_FILENAME, 'w')\nOUTPUT_FILE.write(TBIRD_ADR_BOOK_HEADER)\nOUTPUT_FILE.write('\\n')\nOUTPUT_FILE.write(DUMMY_CONTACT)\nOUTPUT_FILE.write('\\n')\n\nCOMMA_DELIM = r\"\\,\"\n\nVCARD_COUNT = 0\nb_processing = False  # processing a vcard\n\n# check if input file exists\nif os.path.isfile(VCARD_FILE):\n    print(\"Reading vcard (vcf) file: \", VCARD_FILE)\nelse:\n    print(\"Input vcard (vcf) file \", VCARD_FILE, \" does not exist (missing)!\")\n    sys.exit(0)\n\ninput_file = open(VCARD_FILE)\nfor line in input_file:\n    values = line.split(':')\n    if values[0] == 'BEGIN':\n        if len(values) > 1:\n            if values[1] == 'VCARD\\n':\n                VCARD_COUNT = VCARD_COUNT + 1\n                b_processing = True\n                FIELD_VALUES = FIELD_VALUES_START.copy()\n\n    if b_processing:\n        tag = values[0]\n        if tag == 'END':  # reached end of vcard\n            if VERBOSE_FLAG:\n                print(\"END OF VCARD \", VCARD_COUNT)\n            b_processing = False\n            # process non-dummy contact\n            if FIELD_VALUES[FIELD_INDEX['Display Name']] != FIELD_VALUES_START[FIELD_INDEX['Display Name']]:\n                contact_record = ','.join(FIELD_VALUES)\n                if VERBOSE_FLAG:\n                    print(contact_record)\n                OUTPUT_FILE.write(contact_record)\n                OUTPUT_FILE.write('\\n')\n\n        # parse info for the contact\n        if tag == 'N':\n            contact_name = values[1].strip().replace(';', ' ')\n            if COMMA_DELIM in contact_name:\n                contact_name = contact_name.split(COMMA_DELIM)[0]\n            if isinstance(contact_name, str):\n                name_parts = contact_name.split()\n                if len(name_parts) > 1:\n                    contact_first_name = name_parts[1]\n                    contact_last_name = name_parts[0]\n                else:\n                    contact_first_name = ''\n                    contact_last_name = ''\n                FIELD_VALUES[FIELD_INDEX['First Name']] = contact_first_name\n                FIELD_VALUES[FIELD_INDEX['Last Name']] = contact_last_name\n\n        if tag == 'FN':  # FN (full name) is usually first_name last_name\n            contact_fullname = values[1].strip().replace(';', ' ')\n            if COMMA_DELIM in contact_fullname:\n                contact_fullname = contact_fullname.split(COMMA_DELIM)[0]\n            if isinstance(contact_fullname, str):\n                FIELD_VALUES[FIELD_INDEX['Display Name']] = contact_fullname\n                name_parts = contact_fullname.split()\n                contact_first_name = name_parts[0]\n                if len(name_parts) > 1:\n                    contact_last_name = name_parts[1]\n                else:\n                    contact_last_name = ''\n            else:\n                contact_first_name = ''\n                contact_last_name = ''\n            FIELD_VALUES[FIELD_INDEX['First Name']] = contact_first_name\n            FIELD_VALUES[FIELD_INDEX['Last Name']] = contact_last_name\n\n            #print(contact_fullname)\n\n        if tag == 'ORG':  # ORG (organization)\n            contact_org = values[1].strip().replace(';', ' ')\n            # Apple vcard uses semicolon as embedded delimiter\n            FIELD_VALUES[FIELD_INDEX['Organization']] = contact_org\n\n        if tag == 'NOTE': # NOTE (notes) in VCF\n            contact_notes = values[1].strip().replace(r'\\n', ' ')\n            FIELD_VALUES[FIELD_INDEX['Notes']] = 'NOTE: ' + contact_notes\n\n        if tag == 'TITLE':  # TITLE\n            contact_title = values[1].strip()\n            FIELD_VALUES[FIELD_INDEX['Job Title']] = 'TITLE: ' + contact_title\n\n        if tag.startswith('EMAIL'):  #process emails\n            contact_email = values[1].strip()\n            FIELD_VALUES[FIELD_INDEX['Primary Email']] = contact_email\n\n        if tag.startswith('TEL'):  # process phone numbers\n            contact_phone = values[1].strip()\n            # remove special characters and other noise\n            contact_phone = re.sub('[^A-Za-z0-9() -]+', ' ', contact_phone)\n            contact_phone = contact_phone.strip() # remove leading/trailing whitespace\n            if not phone.is_valid_phone(contact_phone):\n                print(\"WARNING: VCARD \", VCARD_COUNT, \" (\", contact_fullname, \") \", \\\n                      contact_phone, \" MAY NOT BE A VALID PHONE NUMBER\")\n\n            if \"HOME\" in tag:\n                FIELD_VALUES[FIELD_INDEX['Home Phone']] = contact_phone\n            elif \"WORK\" in tag:\n                FIELD_VALUES[FIELD_INDEX['Work Phone']] = contact_phone\n            elif \"MAIN\" in tag:\n                FIELD_VALUES[FIELD_INDEX['Work Phone']] = contact_phone\n            elif \"CELL\" in tag:\n                FIELD_VALUES[FIELD_INDEX['Mobile Number']] = contact_phone\n            elif \"OTHER\" in tag:\n                FIELD_VALUES[FIELD_INDEX['Custom 1']] = 'OTHER PHONE: ' + contact_phone\n            else:\n                FIELD_VALUES[FIELD_INDEX['Work Phone']] = contact_phone\n\n        if tag.startswith('ADR'):  # physical addresses\n            contact_address = values[1].strip().strip(';')\n            contact_address = contact_address.replace(r'\\n', ';')\n            if \"HOME\" in tag:\n                FIELD_VALUES[FIELD_INDEX['Home Address']] = contact_address\n            elif \"WORK\" in tag:\n                FIELD_VALUES[FIELD_INDEX['Work Address']] = contact_address\n            elif \"OTHER\" in tag:\n                FIELD_VALUES[FIELD_INDEX['Custom 2']] = 'OTHER ADDRESS: ' + contact_address\n            else:\n                FIELD_VALUES[FIELD_INDEX['Home Address']] = contact_address\n\n        # just ^URL;....:url\n        if tag.startswith('URL'):  # url\n            contact_url = values[1].strip()\n            index = FIELD_INDEX['Web Page 1']\n            if not FIELD_VALUES[index]:\n                FIELD_VALUES[index] = contact_url\n            else:\n                FIELD_VALUES[FIELD_INDEX['Web Page 2']] = contact_url\n\n        if tag.startswith('item1.URL'):  # item1.URL...:https:remaining_url\n            contact_url = ':'.join(values[1:])\n            contact_url = contact_url.strip()\n            if contact_url[:4] != 'http':\n                contact_url = 'http://' + contact_url\n\n            index = FIELD_INDEX['Web Page 1']\n            if not FIELD_VALUES[index]:\n                FIELD_VALUES[index] = contact_url\n            else:\n                FIELD_VALUES[FIELD_INDEX['Web Page 2']] = contact_url\n\n        if tag.startswith('item2.URL'):  # item2.URL...:https:remaining_url\n            contact_url = ':'.join(values[1:])\n            contact_url = contact_url.strip()\n            if contact_url[:4] != 'http':\n                contact_url = 'http://' + contact_url\n\n            index = FIELD_INDEX['Web Page 1']\n            if not FIELD_VALUES[index]:\n                FIELD_VALUES[index] = contact_url\n            else:\n                FIELD_VALUES[FIELD_INDEX['Web Page 2']] = contact_url\n\nprint(\"Processed \", VCARD_COUNT, \" vcards\")\nOUTPUT_FILE.close()\nprint(\"Wrote Thunderbird Compliant CSV file with phone numbers to: \", OUTPUT_FILENAME)\nprint('ALL DONE')\n","sub_path":"@TEST_LIB/convert_vcard_gmail_to_csv/test_01.py","file_name":"test_01.py","file_ext":"py","file_size_in_byte":11700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"442926318","text":"#!/usr/bin/python\n\n#LED1 - 18 - 24\n#LED2 - 16 - 23\n#LED3 - 15 - 22\n#LED4 - 40 - 21\n#LED5 - 38 - 20\n#LED6 - 35 - 19\n#LED7 - 12 - 18\n#LED8 - 11 - 17\n#LED9 - 36 - 16\n#LED10 - 33 - 13\n#LED11 (GREEN) - 31 - 6\n#LED12 (GREEN) - 32 - 12\n\n# sudo apt-get install python-gpiozero python3-gpiozero\n\nfrom time import sleep\nfrom pumpkinpi import PumpkinPi\nfrom gpiozero.tools import random_values\nfrom signal import pause\n\ndef pump_pulse(pumpkin):\n\tfor x in range(10):\n\t\tleds = pumpkin.leds\n\t\tfor led in leds:\n\t\t\tled.source_delay = 0.1\n\t\t\tled.source = random_values()\n\ndef main():\n\tpumpkin = PumpkinPi(pwm=True)\n\ttry:\n\t\tpump_pulse(pumpkin)\n\t\tsleep(0.3)\n\t\tpause()\n\texcept KeyboardInterrupt:\n\t\tpumpkin.off()\n\t\tpumpkin.close()\n\nif __name__ == \"__main__\":\n\tmain()","sub_path":"random_leds.py","file_name":"random_leds.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"594045217","text":"import requests\nfrom bs4 import BeautifulSoup\n\nbase_link = \"http://files.teamspeak-services.com/releases/client\"\n\n# Get versions\n\nr = requests.get(base_link)\nsoup = BeautifulSoup(r.text, 'html.parser')\ninvalid_versions = (\"../\", \"\")\n\nversions = [a.text for a in soup.findAll(\"a\") if not a.text in invalid_versions]\nprint(f\"Found versions: {versions}\")\n\nlatest = versions[-1]\nprint(f\"Selected latest version: {latest}\")\n\n# Download\n\nlink_win = f\"{base_link}/{latest}/TeamSpeak3-Client-win64-{latest}.exe\"\nlink_mac = f\"{base_link}/{latest}/TeamSpeak3-Client-macosx-{latest}.dmg\"\n\ndef download(link, dest):\n    r = requests.get(link)\n    open(dest, \"wb\").write(r.content)\n    print(f\"Downloaded {link} to {dest}\")\n\ndownload(link_win, \"dist/ts-win.exe\")\ndownload(link_mac, \"dist/ts-mac.dmg\")\n\nprint(\"Completed\")\n","sub_path":"update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"380556017","text":"#\n#   Copyright 2020 Logical Clocks AB\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n#\n\n\"\"\"\nExperiment module used for running asynchronous optimization tasks.\n\nThe programming model is that you wrap the code containing the model\ntraining inside a wrapper function.\nInside that wrapper function provide all imports and parts that make up your\nexperiment, see examples below. Whenever a function to run an experiment is\ninvoked it is also registered in the Experiments service along with the\nprovided information.\n\"\"\"\nimport os\nimport atexit\nimport time\n\nfrom hops import util as hopsutil\nfrom hops import hdfs as hopshdfs\nfrom hops.experiment_impl.util import experiment_utils\n\nfrom maggy import util, tensorboard\nfrom maggy.core import trialexecutor, experimentdriver\n\napp_id = None\nrunning = False\nrun_id = 1\nexperiment_json = None\n\n\ndef lagom(\n    map_fun,\n    name=\"no-name\",\n    experiment_type=\"optimization\",\n    searchspace=None,\n    optimizer=None,\n    direction=\"max\",\n    num_trials=1,\n    ablation_study=None,\n    ablator=None,\n    optimization_key=\"metric\",\n    hb_interval=1,\n    es_policy=\"median\",\n    es_interval=300,\n    es_min=10,\n    description=\"\",\n):\n    \"\"\"Launches a maggy experiment, which depending on `experiment_type` can\n    either be a hyperparameter optimization or an ablation study experiment.\n    Given a search space, objective and a model training procedure `map_fun`\n    (black-box function), an experiment is the whole process of finding the\n    best hyperparameter combination in the search space, optimizing the\n    black-box function. Currently maggy supports random search and a median\n    stopping rule.\n\n    **lagom** is a Swedish word meaning \"just the right amount\".\n\n    :param map_fun: User defined experiment containing the model training.\n    :type map_fun: function\n    :param name: A user defined experiment identifier.\n    :type name: str\n    :param experiment_type: Type of Maggy experiment, either 'optimization'\n        (default) or 'ablation'.\n    :type experiment_type: str\n    :param searchspace: A maggy Searchspace object from which samples are\n        drawn.\n    :type searchspace: Searchspace\n    :param optimizer: The optimizer is the part generating new trials.\n    :type optimizer: str, AbstractOptimizer\n    :param direction: If set to ‘max’ the highest value returned will\n        correspond to the best solution, if set to ‘min’ the opposite is true.\n    :type direction: str\n    :param num_trials: the number of trials to evaluate given the search space,\n        each containing a different hyperparameter combination\n    :type num_trials: int\n    :param ablation_study: Ablation study object. Can be None for optimization\n        experiment type.\n    :type ablation_study: AblationStudy\n    :param ablator: Ablator to use for experiment type 'ablation'.\n    :type ablator: str, AbstractAblator\n    :param optimization_key: Name of the metric to be optimized\n    :type optimization_key: str, optional\n    :param hb_interval: The heartbeat interval in seconds from trial executor\n        to experiment driver, defaults to 1\n    :type hb_interval: int, optional\n    :param es_policy: The earlystopping policy, defaults to 'median'\n    :type es_policy: str, optional\n    :param es_interval: Frequency interval in seconds to check currently\n        running trials for early stopping, defaults to 300\n    :type es_interval: int, optional\n    :param es_min: Minimum number of trials finalized before checking for\n        early stopping, defaults to 10\n    :type es_min: int, optional\n    :param description: A longer description of the experiment.\n    :type description: str, optional\n    :raises RuntimeError: An experiment is currently running.\n    :return: A dictionary indicating the best trial and best hyperparameter\n        combination with it's performance metric\n    :rtype: dict\n    \"\"\"\n    global running\n    if running:\n        raise RuntimeError(\"An experiment is currently running.\")\n\n    job_start = time.time()\n    sc = hopsutil._find_spark().sparkContext\n    exp_driver = None\n\n    try:\n        global app_id\n        global experiment_json\n        global run_id\n        app_id = str(sc.applicationId)\n\n        app_id, run_id = util._validate_ml_id(app_id, run_id)\n\n        # start run\n        running = True\n        experiment_utils._set_ml_id(app_id, run_id)\n\n        # create experiment dir\n        hopshdfs.mkdir(experiment_utils._get_logdir(app_id, run_id))\n\n        tensorboard._register(experiment_utils._get_logdir(app_id, run_id))\n\n        num_executors = util.num_executors(sc)\n\n        # start experiment driver\n        if experiment_type == \"optimization\":\n\n            assert num_trials > 0, \"number of trials should be greater \" + \"than zero\"\n            tensorboard._write_hparams_config(\n                experiment_utils._get_logdir(app_id, run_id), searchspace\n            )\n\n            if num_executors > num_trials:\n                num_executors = num_trials\n\n            exp_driver = experimentdriver.ExperimentDriver(\n                \"optimization\",\n                searchspace=searchspace,\n                optimizer=optimizer,\n                direction=direction,\n                num_trials=num_trials,\n                name=name,\n                num_executors=num_executors,\n                hb_interval=hb_interval,\n                es_policy=es_policy,\n                es_interval=es_interval,\n                es_min=es_min,\n                description=description,\n                log_dir=experiment_utils._get_logdir(app_id, run_id),\n            )\n\n            exp_function = exp_driver.optimizer.name()\n\n        elif experiment_type == \"ablation\":\n            exp_driver = experimentdriver.ExperimentDriver(\n                \"ablation\",\n                ablation_study=ablation_study,\n                ablator=ablator,\n                name=name,\n                num_executors=num_executors,\n                hb_interval=hb_interval,\n                description=description,\n                log_dir=experiment_utils._get_logdir(app_id, run_id),\n            )\n            # using exp_driver.num_executor since\n            # it has been set using ablator.get_number_of_trials()\n            # in experiment.py\n            if num_executors > exp_driver.num_executors:\n                num_executors = exp_driver.num_executors\n\n            exp_function = exp_driver.ablator.name()\n        else:\n            running = False\n            raise RuntimeError(\n                \"Unknown experiment_type:\"\n                \"should be either 'optimization' or 'ablation', \"\n                \"But it is '{0}'\".format(str(experiment_type))\n            )\n\n        nodeRDD = sc.parallelize(range(num_executors), num_executors)\n\n        # Do provenance after initializing exp_driver, because exp_driver does\n        # the type checks for optimizer and searchspace\n        sc.setJobGroup(os.environ[\"ML_ID\"], \"{0} | {1}\".format(name, exp_function))\n\n        experiment_json = experiment_utils._populate_experiment(\n            name,\n            exp_function,\n            \"MAGGY\",\n            exp_driver.searchspace.json(),\n            description,\n            app_id,\n            direction,\n            optimization_key,\n        )\n\n        experiment_json = experiment_utils._attach_experiment_xattr(\n            app_id, run_id, experiment_json, \"CREATE\"\n        )\n\n        util._log(\n            \"Started Maggy Experiment: {0}, {1}, run {2}\".format(name, app_id, run_id)\n        )\n\n        exp_driver.init(job_start)\n\n        server_addr = exp_driver.server_addr\n\n        # Force execution on executor, since GPU is located on executor\n        nodeRDD.foreachPartition(\n            trialexecutor._prepare_func(\n                app_id,\n                run_id,\n                experiment_type,\n                map_fun,\n                server_addr,\n                hb_interval,\n                exp_driver._secret,\n                optimization_key,\n                experiment_utils._get_logdir(app_id, run_id),\n            )\n        )\n        job_end = time.time()\n\n        result = exp_driver.finalize(job_end)\n        best_logdir = (\n            experiment_utils._get_logdir(app_id, run_id) + \"/\" + result[\"best_id\"]\n        )\n\n        util._finalize_experiment(\n            experiment_json,\n            float(result[\"best_val\"]),\n            app_id,\n            run_id,\n            \"FINISHED\",\n            exp_driver.duration,\n            experiment_utils._get_logdir(app_id, run_id),\n            best_logdir,\n            optimization_key,\n        )\n\n        util._log(\"Finished Experiment\")\n\n        return result\n\n    except:  # noqa: E722\n        _exception_handler(\n            experiment_utils._seconds_to_milliseconds(time.time() - job_start)\n        )\n        if exp_driver:\n            if exp_driver.exception:\n                raise exp_driver.exception\n        raise\n    finally:\n        # grace period to send last logs to sparkmagic\n        # sparkmagic hb poll intervall is 5 seconds, therefore wait 6 seconds\n        time.sleep(6)\n        # cleanup spark jobs\n        if running and exp_driver is not None:\n            exp_driver.stop()\n        run_id += 1\n        running = False\n        sc.setJobGroup(\"\", \"\")\n\n    return result\n\n\ndef _exception_handler(duration):\n    \"\"\"\n    Handles exceptions during execution of an experiment\n\n    :param duration: duration of the experiment until exception in milliseconds\n    :type duration: int\n    \"\"\"\n    try:\n        global running\n        global experiment_json\n        if running and experiment_json is not None:\n            experiment_json[\"state\"] = \"FAILED\"\n            experiment_json[\"duration\"] = duration\n            experiment_utils._attach_experiment_xattr(\n                app_id, run_id, experiment_json, \"REPLACE\"\n            )\n    except Exception as err:\n        util._log(err)\n\n\ndef _exit_handler():\n    \"\"\"\n    Handles jobs killed by the user.\n    \"\"\"\n    try:\n        global running\n        global experiment_json\n        if running and experiment_json is not None:\n            experiment_json[\"status\"] = \"KILLED\"\n            experiment_utils._attach_experiment_xattr(\n                app_id, run_id, experiment_json, \"REPLACE\"\n            )\n    except Exception as err:\n        util._log(err)\n\n\natexit.register(_exit_handler)\n","sub_path":"maggy/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":10809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"532359369","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-1\n\nfrom __future__ import division\nimport random\nimport math\nimport cProfile\nimport pstats\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pyprof2calltree import convert, visualize\n\nquantity_pro = [[1, 1 / 6], [2, 3 / 6], [3, 5 / 6], [4, 6 / 6]]\n\n\n# 获取全部需求发生日\ndef get_all_demand(whole_time=120):\n    all_demand = [0]\n    while all_demand[-1] < whole_time:\n        all_demand.append(all_demand[-1] + -math.log(random.random()))\n    return all_demand\n\n\n# 获取下一个盘点日\ndef next_check(current_time):\n    new_time = math.ceil(current_time)\n    return new_time if new_time > current_time else current_time + 1\n\n\n# 获取下一个到货日\ndef next_arrive(order_quantity):\n    return random.uniform(0.5, 1) if order_quantity > 0 else 0\n\n\n# 获取用户需求数量\ndef get_demand_quantity():\n    random_pro = random.uniform(0, 1)\n    return min([x[0] for x in quantity_pro if x[1] > random_pro])\n\n\n# 计算订货数量\ndef get_order_quantity(inventory_quantity, maximum_storage, minimum_storage):\n    return 0 if inventory_quantity > minimum_storage else maximum_storage - inventory_quantity\n\n\n# 判断该日是否为月初盘点日\ndef is_check_time(current_time):\n    return current_time == int(current_time)\n\n\n# 判断该日是否为到货日\ndef is_arrive_time(current_time, current_schedule):\n    if not current_schedule[-1]:\n        return False\n    else:\n        return current_schedule[-1][0] == current_time\n\n\n# 判断该日是否为需求发生日\ndef is_demand_time(current_time, current_schedule):\n    if not current_schedule[0]:\n        return False\n    else:\n        return current_schedule[0][0] == current_time\n\n\n# 计算订货成本\ndef get_order_cost(order_quantity, fixed_cost, unit_cost):\n    return fixed_cost + unit_cost * order_quantity if order_quantity > 0 else 0\n\n\n# 计算库存成本\ndef get_inventory_cost(inventory, inventory_cost, opportunity_cost):\n    return inventory * inventory_cost if inventory > 0 else -inventory * opportunity_cost\n\n\n# 获取未来发生时间列表中的最小值\ndef get_min_time(current_schedule):\n    key_node = []\n    # if len(current_schedule[0]) > 0:\n    #     key_node.append(current_schedule[0][0])\n    # if len(current_schedule[1]) > 0:\n    #     key_node.append(current_schedule[1][0])\n    for item in current_schedule:\n        key_node.append(item)\n    return min(key_node)\n\n\n# 删除过期的日期信息\ndef delete_old_schedule(current_schedule, current_time):\n    for item in current_schedule:\n        for i in item:\n            if i < current_time:\n                    item.remove(i)\n    return current_schedule\n\n\n# 模拟过程\ndef simulate(initial_store=60, whole_time=120):\n    time = 0  # 目前时间\n    store = initial_store  # 目前库存量\n    order_num = 0  # 订货量\n    schedule = [get_all_demand(whole_time=whole_time), [-1]]  # [demand, arrive]\n\n    statistics_indicator = []  # 统计指标\n\n    while time < whole_time:\n\n        order_price = 0  # 订货成本\n        demand_num = 0  # 需求数量\n\n        # print \"----------------\"\n        # print \"time %6.3f\" % time\n\n        # 判断是否是月初\n        if is_check_time(time):\n            # 计算订货数量\n            order_num = get_order_quantity(store, 80, 40)\n            # 如果存在订货\n            if order_num > 0:\n                # 计算订货开销\n                order_price = get_order_cost(order_num, 32, 4)\n                # 计算到货时间\n                arrive_time = next_arrive(order_num)\n                # 将到货时间加入序列\n                schedule[1].append(time + arrive_time)\n\n        # 判断是否是需求日\n        if is_demand_time(time, schedule):\n            # 得到需求数量 并 在库存中扣除\n            demand_num = get_demand_quantity()\n            store -= demand_num\n            # print \"Demand : %.3f\" % demand_num\n            # 移除需求时间序列的首位\n            schedule[0][:] = schedule[0][1:]\n\n        # 判断是否是到货日\n        if is_arrive_time(time, schedule):\n            # 增加库存\n            store += order_num\n            # 移除到货时间序列的首位\n            schedule[1][:] = schedule[1][1:]\n            # 情况订货量\n            order_num = 0\n\n        # 计算存储费用\n        store_cost = get_inventory_cost(store, 1, 5)\n        # print \"Store : %.3f\" % store\n\n        # 跟踪统计指标\n        statistics_indicator.append([time, demand_num, store, order_price, store_cost])\n\n        # 计算下个关键日 并且 时空跳跃\n        time = min(get_min_time(schedule), next_check(time))\n\n    return statistics_indicator\n\n\ndef show_plot(stat_data, whole_time):\n\n    stat_data = np.array(stat_data)\n\n    # 把图表分成两个子图\n    plt.figure(12)\n\n    ##############################\n    # 绘制第一个子图，在2*1布局的第1个 #\n    ##############################\n    plt.subplot(211)\n\n    # 设置横坐标和纵坐标\n    plt.title(u\"库存量&需求量随时间变化关系\", fontproperties='SimHei')\n    plt.xlabel(u\"时间/月\", fontproperties='SimHei')\n    plt.ylabel(u\"货物个数/件\", fontproperties='SimHei')\n\n    # 设置横轴和纵轴的区间\n    ax = plt.subplot(211)\n    ax.set_xlim([0, whole_time])\n    ax.set_ylim([0, 100])\n\n    # 标注线段的名称\n    line_label = [u\"Demand\", u\"Inventory\"]\n\n    # 绘制多个线段\n    for i in range(len(stat_data[1, :]) - 3):\n        plt.plot(stat_data[:, 0].tolist(), stat_data[:, i + 1], \".-\", label=line_label[i])\n\n    # 图表网格\n    plt.grid(True)\n\n    plt.legend(bbox_to_anchor=(1.0, 1), loc=1, borderaxespad=0.)\n\n    ##############################\n    # 绘制第二个子图，在2*1布局的第2个#\n    ##############################\n    plt.subplot(212)\n\n    # 设置横坐标和纵坐标\n    plt.title(u\"订货成本&存储成本随时间变化关系\", fontproperties='SimHei')\n    plt.xlabel(u\"时间/月\", fontproperties='SimHei')\n    plt.ylabel(u\"成本/元\", fontproperties='SimHei')\n\n    # 设置横轴和纵轴的区间\n    ax = plt.subplot(212)\n    ax.set_xlim([0, whole_time])\n    ax.set_ylim([0, 200])\n\n    # 标注线段的名称\n    line_label = [\"Order Cost\", \"Store Cost\"]\n\n    # 绘制多个线段\n    for i in range(len(stat_data[1, :]) - 3):\n        plt.plot(stat_data[:, 0].tolist(), stat_data[:, i +3], \".-\", label=line_label[i])\n\n    # 图表网格\n    plt.grid(True)\n\n    plt.legend(bbox_to_anchor=(1.0, 1), loc=1, borderaxespad=0.)\n\n    plt.show()\n\nprofiler = cProfile.Profile()\nprofiler.enable()\n\ndata = simulate(whole_time=50000)\n# show_plot(data, 120000)\n\nprofiler.create_stats()\nstats = pstats.Stats(profiler)\nvisualize(stats)\nstats.strip_dirs().sort_stats('cumulative').print_stats()\n\n","sub_path":"StorageManage/BaseInfo.py","file_name":"BaseInfo.py","file_ext":"py","file_size_in_byte":6761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"138430740","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import animation\n\n#f(x, y) = (x^2 + y^2)/2\n#vector field\nx_vect = np.linspace(-5, 5, 20)\ny_vect = np.linspace(-5, 5, 20)\nX_vect, Y_vect = np.meshgrid(x_vect, y_vect)\nU = X_vect\nV = Y_vect\ncolors = np.sqrt(U**2 + V**2)\nU_vect = U/colors\nV_vect = V/colors\n\nfig = plt.figure()\nax = plt.gca()\n\nax.quiver(X_vect, Y_vect, U_vect, V_vect, colors, scale_units = \"xy\", angles = \"xy\")\n\n#circle\ntheta = np.linspace(0, 2*np.pi,100)\nR = 2\nx_circle = [R*np.sin(thetai) for thetai in theta]\ny_circle = [R*np.cos(thetai) for thetai in theta]\nax.plot(x_circle, y_circle, \"r\")\n\n#animation scatter plots\nx_points = np.linspace(-5, 5, 20)\ny_points = np.linspace(-5, 5, 20)\nX_points, Y_points = np.meshgrid(x_points, y_points)\nX_pointsi, Y_pointsi = np.meshgrid(x_points, y_points)\n\npoints = ax.scatter([], [])\ndx = 0.01\ndy = 0.01\n\ndef init():\n\tglobal X_points, Y_points\n\tpoints.set_offsets([[None,None]]*X_points.shape[0]**2)\n\treturn points\n\ndef update(i):\n\tglobal X_points, Y_points, X_pointsi, Y_pointsi, dx, dy\n\tU_points = X_points\n\tV_points = Y_points\n\tX_points = X_points + U_points*dx\n\tY_points = Y_points + V_points*dy\n\tfor i in range(X_points.shape[0]):\n\t\tfor j in range(X_points.shape[1]):\n\t\t\tif X_points[i, j] < -7 or X_points[i, j] > 7 or Y_points[i, j] < -7 or Y_points[i, j] > 7:\n\t\t\t\tX_points[i, j] = X_pointsi[np.random.randint(-X_points.shape[0], X_points.shape[0]), np.random.randint(-X_points.shape[0], X_points.shape[0])]\n\t\t\t\tY_points[i, j] = Y_pointsi[np.random.randint(-X_points.shape[0], X_points.shape[0]), np.random.randint(-X_points.shape[0], X_points.shape[0])]\n\tx_coords = X_points.flatten()\n\ty_coords = Y_points.flatten()\n\tcoords = []\n\tfor j in range(len(x_coords)):\n\t\tcoords.append([x_coords[j], y_coords[j]])\n\tpoints.set_offsets(coords)\n\treturn points, X_points, Y_points\n\nax.set_title(\"$f(x, y) = (x^2 + y^2)/2$\")\nax.set_xlabel(\"$x$\")\nax.set_ylabel(\"$y$\")\n\nanim = animation.FuncAnimation(fig, update, init_func=init, frames=400, interval=10)\n\nplt.show()","sub_path":"divergence_example3.py","file_name":"divergence_example3.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"106240518","text":"alphabet = \"abcdeghijklmnopqrstuvwxyz\"\r\nprint(alphabet.find(\"g\"))\r\nprint(alphabet.find(\"i\"))\r\n\r\n\r\ndef translate(string):\r\n    newstring = \"\"\r\n    for element in string:\r\n        if element != \" \":\r\n            newstring.append(element)\r\n    for letter in newstring:\r\n        index_of_letter = alphabet.find(letter)\r\n        print(alphabet[index_of_letter + 2])\r\n\r\n\r\n# print(translate(input(\"Enter thing to translate: \")))\r\ndef sigma(func, start, end):\r\n    value = sum([(print(func)) for x in range(start, end)])\r\n    return value\r\n\r\n\r\nimport math\r\n# print(sigma(\"5x\", 1, 10))\r\nimport random\r\nprint(\"Python Challenges\")\r\n\r\n\r\ndef list_overlap(l1, l2):\r\n    l3 = []\r\n    for x in l1:\r\n        if x in l1 and x in l2:\r\n            l3.append(x)\r\n    return l3\r\n\r\n\r\nprint(list_overlap([1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]))\r\n\r\n\r\ndef cows_and_bulls():\r\n    secret_number = random.randint(1000, 9999)\r\n    guess = \"\"\r\n    cows = 0\r\n    bulls = 0\r\n    number_of_guesses = 0\r\n    print(\"Guess a number between and including 1000 and 9999. A cow is a correct digit in the correct place. A bull is anything else.\")\r\n    while guess != secret_number:\r\n        guess = int(input(\"Enter guess: \"))\r\n        guess_thousands = guess // 1000\r\n        guess_hundreds = (guess % 1000) // 100\r\n        guess_tens = ((guess % 1000) % 100) // 10\r\n        guess_ones = (((guess % 1000) % 100) % 10) // 1\r\n        thousands_digit_secret = secret_number // 1000\r\n        hundreds_secret = (secret_number % 1000) // 100\r\n        secret_tens = ((secret_number % 1000) % 100) // 10\r\n        secret_ones = (((secret_number % 1000) % 100) % 10) // 1\r\n        if thousands_digit_secret == guess_thousands:\r\n            cows += 1\r\n        else:\r\n            bulls += 1\r\n        if hundreds_secret == guess_hundreds:\r\n            cows += 1\r\n        else:\r\n            bulls += 1\r\n        if guess_tens == secret_tens:\r\n            cows += 1\r\n        else:\r\n            bulls += 1\r\n        if guess_ones == secret_ones:\r\n            cows += 1\r\n        else:\r\n            bulls += 1\r\n        number_of_guesses += 1\r\n        result = f\"You have {cows} cows and {bulls} bulls. You have guessed {number_of_guesses} times.\"\r\n        print(result)\r\n        bulls = 0\r\n        cows = 0\r\n    if guess == secret_number:\r\n        print(\"You guessed the secret number! You win!\")\r\n\r\n\r\nprint(cows_and_bulls())\r\n","sub_path":"Elephant.py","file_name":"Elephant.py","file_ext":"py","file_size_in_byte":2412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"134521066","text":"\"\"\"\n영화 추천 시스템 만들기\n\"\"\"\n\nimport pandas as pd\nfrom sklearn.metrics.pairwise import linear_kernel\nfrom scipy.io import mmwrite, mmread\nimport pickle\nfrom gensim.models import Word2Vec\n\n#데이터 불러오기\ndf_review_one_sentence = pd.read_csv('./crawling_data/movie_review_one_sentence_2015_2021.csv')\n\n\n# ls = ['겨울왕국', '라이온킹', '알라딘']\n# print(enumerate(ls))\n# for idx, i in enumerate(ls):\n#     if i == '라이온킹':\n#         print(idx)\n#\n# exit()\n\n#저장해놓은 tfidf, tfidf_matrix 불러오기\nTfidf_matrix = mmread('./models/tfidf_movie_review.mtx').tocsr()\nwith open('./models/tfidf.pickle', 'rb') as f:\n    Tfidf = pickle.load(f)\n\n# 추천 함수 생성\ndef getRcommendation(cosine_sim):\n    # movie_idx와 전체 martix의 cosine 유사도에 해당하는 값과 index list로 반환\n    #cosine_sime = shape: (1, len(Tfidf_matrix))라서 [[~~~~]] 이런 형식\n    simScore = list(enumerate(cosine_sim[-1]))  #코사인 유사도로 나온 영화들을 -1로 정렬시키고 인덱스를 주기, enumerate=인덱스가 붙는다\n    simScore = sorted(simScore, key=lambda x:x[-1], reverse=True)  #코사인 유사도 값이 큰 순으로 정렬이 된다\n    simScore = simScore[1:11]  #10개만 인덱싱하기, 0은 지정 영화 자체(movie_idx)라서 뺀 것\n    movieidx = [i[0] for i in simScore]  #10개의 영화 컴럼의 0번째인 인덱스만 가져와 movie_idx에 저장\n    recMovieList = df_review_one_sentence.iloc[movieidx]  #영화들의 해당 인덱스를 원문에서 찾아서 반환하기\n    return recMovieList\n\n#영화 제목이 기생충인 것만 찾아서 인덱싱해주기, 한 줄밖에 없지만 리스트로 오기 때문에 index[0] 해야 한다\nmovie_idx = df_review_one_sentence[df_review_one_sentence['titles'] == '라이온 킹 (The Lion King)'].index[0]\n\n#movie_idx = 127  #임의의 숫자를 지정해서 영화를 선택해도 된다(영화 제목이 아닌)\n#print(df_review_one_sentence.iloc[movie_idx, 0])  #movie_idx의 row에서 0번 컬럼이 title 이라서 이렇게 찍으면 영화 이름을 알 수 있다\n\n# #코사인 유사도 확인하기\n# cosine_sim = linear_kernel(Tfidf_matrix[movie_idx], Tfidf_matrix)  #점수 표에서 지정시킨 영화와 전체 영화를 비교해 유사한 영화를 선택\n# recommendation = getRcommendation(cosine_sim)  #유사한 영화 10개를 추천\n# print(recommendation)  #추천된 영화 확인\n\n#키워드를 입력했을 때 추천되는 영화 10건을 가져오기\nembedding_model = Word2Vec.load('./models/word2VecModel_2015_2021.model')\nkey_word = '아이언맨'\nsentence = [key_word] * 10\nif key_word in embedding_model.wv.index_to_key:  #모델에 키 단어가 있다면\n    sim_word = embedding_model.wv.most_similar(key_word, topn=10)  #유사단어 10개를 가져오기\n    labels = []\n    for label, _ in sim_word:\n        labels.append(label)  #10개 단어의 이름을 가져오기\n    print(labels)\n    # 가장 유사한 단어를 많이 저장 ex) [겨울 겨울 겨울 가을 가을 여름]\n    for i, word in enumerate(labels):\n        sentence +=[word] * (9-i)  #리스트에 들어간 순대로 하나씩 단어의 개수가 적어진 상태로 나온다\n\n# 유사한 단어를 조합한 문장\nsentence = ' '.join(sentence)  #출력 단어들을 한 줄로 합치기\nprint(sentence)\n\nsentence_vec = Tfidf.transform([sentence])  #단어를 tfidf에 합습시키기\ncosine_sim = linear_kernel(sentence_vec, Tfidf_matrix)  #점수표에서 해당 단어와 유사한 점수 선택\n\nrecommendataion = getRcommendation(cosine_sim)  #단어와 유사한 영화 추천\nprint(recommendataion['titles'])  #추천한 영화의 제목만 가져오기\n","sub_path":"movie_recommendation_system.py","file_name":"movie_recommendation_system.py","file_ext":"py","file_size_in_byte":3721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"218239219","text":"import pandas as pd\nimport numpy as np\nimport cmapPy.pandasGEXpress.GCToo as GCToo\nimport cmapPy.pandasGEXpress.concat as cg\nfrom cmapPy.pandasGEXpress.parse import parse\nimport cmapPy.pandasGEXpress.write_gctx as wg\nimport cmapPy.pandasGEXpress.subset_gctoo as sg\n# cmapPy = LINCS data 다루기위한 module\n# https://clue.io/cmapPy/pandasGEXpress.html 참고\n\n#파일 경로\nLINCS='/home/jojo9103/LINCS/LINCS/'\n# LINCS_Cell = Cell line 정보\n# LINCS_G = Gene들의 최신화 (duplication, withdrawn등)\n# GSE70138_Broad_LINCS_inst_info_2017-03-06.txt.gz = LINCS id 정보\nLINCS_G=pd.read_csv(f'{LINCS}/LINCS_Gene.txt',sep='\\t')\nCell_info=pd.read_csv(f'{LINCS}/LINCS_Cell.txt',sep='\\t')\ninst_info=pd.read_csv(f'{LINCS}/GSE70138_Broad_LINCS_inst_info_2017-03-06.txt.gz',sep='\\t',compression='gzip')\n\ninst_info1=inst_info[inst_info['cell_id'].isin(Cell_info['cell_id'])]\n\n# LINCS_data = gctx 핵심 데이터! 정리해야할 데이터 읽어오기\nLINCS_data=parse(f'{LINCS}GSE70138_Broad_LINCS_Level3_INF_mlr12k_n345976x12328_2017-03-06.gctx')\nLINCS_data=LINCS_data.data_df\n\n# dup_g = duplicated 유전자 확인\ndup_g=LINCS_G[LINCS_G['Symbol'].duplicated()]['Symbol']\n# for문 이용해서 duplicated gene 제거 (mean으로 합침)\nfor g in dup_g:\n    dup_g1=LINCS_G[LINCS_G['Symbol'].isin([g])]['pr_gene_id']\n    dup_mat=list(map(str,dup_g1.to_list()))\n    dup_m=LINCS_data.loc[dup_mat,].apply(np.mean,axis=1)\n    selet_dup_g=dup_m.index[dup_m!=max(dup_m)]\n    LINCS_data=LINCS_data.loc[~LINCS_data.index.isin(selet_dup_g)]\n\n# WithDrawn 제거\nWithDr=LINCS_G[LINCS_G['Entrez']=='Withdrawn']['pr_gene_id']\nfor g in WithDr:\n     LINCS_data=LINCS_data.loc[~LINCS_data.index.isin([str(g)])]\n\nLINCS_G.index=LINCS_G['pr_gene_id']\nLINCS_G1=LINCS_G.loc[map(int,LINCS_data.index)]\nLINCS_G1=LINCS_G1.loc[:,['pr_gene_id','Entrez','Symbol']]\n\n# inst_id_new_inst_id_20210125 = 34만개정도 되는 id에서 중복이 안되게 새로운 id를 만듬 (추후 과정에서 필요)\nnew_inst_id=pd.read_csv(f'{LINCS}/inst_id_new_inst_id_20210125.txt',sep='\\t')\n\n# 농도나, Cell line, 적용시간이 중복되는 경우 평균값으로 합침\nfor dg in set(new_inst_id['pert_iname']):\n    print(dg)\n    s_inst=new_inst_id[new_inst_id['pert_iname']==dg]\n    result_mat={}\n    for dose in set(s_inst['pert_dose']):\n        dose_inst=s_inst[s_inst['pert_dose']==dose]\n        for cl in set(dose_inst['cell_id']):\n                cl_inst=dose_inst[dose_inst['cell_id']==cl]\n                for hr in set(cl_inst['pert_time']):\n                    hr_inst1=cl_inst[cl_inst['pert_time']==hr]\n                    key_id=hr_inst1['id_1'].values[0]\n                    # DMSO = 농도가 없기때문에 column에 -666인 경우 제외하고 진행\n                    if dg=='DMSO':\n                        key_id1=key_id.split('-666_')\n                        key_id=''.join(key_id1)\n                    Ldata_sub=LINCS_data.loc[:,hr_inst1['inst_id']]\n                    Ldata_sub=Ldata_sub.apply(np.mean,1)\n                    result_mat[hr_inst1['id_1'].values[0]]=Ldata_sub\n    LINCS_data_mat=pd.DataFrame(result_mat)\n    dg=dg.split(' ')\n    dg='_'.join(dg)\n    dg=dg.split('(')\n    dg='_'.join(dg)\n    dg=dg.split(')')\n    dg='_'.join(dg)\n    dg=dg.split('/')\n    dg='_'.join(dg)\n    dg=dg.split(\"'\")\n    dg='_'.join(dg)\n    dg=dg.split('&')\n    dg='_'.join(dg)\n    LINCS_data_mat.index=Ldata_sub.index\n    LINCS_data_mat=pd.concat([LINCS_G1.reset_index(drop=True),LINCS_data_mat.reset_index(drop=True)],axis=1)\n    if dg=='DMSO':\n        LINCS_data_mat.columns=[''.join(i.split('-666_')) for i in LINCS_data_mat.columns]\n    # 각 약물에 대해서 파일로 저장하기\n    LINCS_data_mat.to_csv(f'./Data/{dg}_subset.txt.gz',compression='gzip',sep='\\t',index=False)\n    LINCS_data_mat=LINCS_data.loc[:,s_inst['inst_id']]\n    LINCS_data_mat.columns=s_inst['id_2']\n\n","sub_path":"remove_dup_big_1.py","file_name":"remove_dup_big_1.py","file_ext":"py","file_size_in_byte":3860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"15280696","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nA simple simulation environment with one Casu and one bee.\n\"\"\"\n\nfrom assisipy import sim\n\nimport random\nfrom math import pi, sin, cos\n\nif __name__ == '__main__':\n\n    simctrl = sim.Control()\n    \n    # Spawn the Bee and the Casu\n    simctrl.spawn('Casu','casu-001',(0,0,0))\n\n    random.seed()\n    r_max = 8\n    r_min = 2\n    r = random.uniform(r_min,r_max)\n    a = random.uniform(-pi,pi)\n    yaw = random.uniform(-pi,pi)\n    simctrl.spawn('Bee','bee-1',(r*cos(a),r*sin(a),yaw))\n\n\n\n","sub_path":"temp_demo/spawn_casu_and_bee.py","file_name":"spawn_casu_and_bee.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"193681142","text":"# -*- coding:utf-8 -*-\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\nclass Solution:\n    def EntryNodeOfLoop(self, pHead):\n        # write code here\n        if not pHead:\n            return pHead\n\n        head = ListNode(0)\n        head.next = pHead\n\n        fast = head\n        slow = head\n\n        # 循环\n        while True:\n            if fast and fast.next:\n                fast = fast.next.next  # 快指针一次2步\n                slow = slow.next  # 慢指针一次1步\n            else:\n                return None\n\n            if fast == slow:  # 快慢指针相遇时，len(快) = 2 * len(慢)\n                fast = head  # 快指针再从头走\n                break\n\n        while fast != slow:  # fast、slow相遇在环的入口\n            fast = fast.next\n            slow = slow.next\n\n        return fast\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"JZ/链表中环的入口结点.py","file_name":"链表中环的入口结点.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"556392347","text":"import numpy as np\nfrom scipy.sparse import spdiags\n\n\nclass Data(object):\n    '''\n    Read course files and return a data set object. Course pairs are splited into\n    3 folds, for training, validating and testing.\n\n    According to Section 2 in the paper, the number of courses is denoted with $n$\n    and dimension of universal concept space is denoted with $p$. We replace them\n    with `num_courses` and `num_dimensions` to improve readability.\n    '''\n\n    def __init__(self, opt):\n        '''\n        Each data object has 4 member variables:\n            1. `opt`: Configuration object\n            2. `X`: Bag-of-concepts representations of all courses, an array of size\n                    [num_courses, num_dimensions]\n            3. `chunks`: A length 3 dict, as 3-fold data set consisting of raw course\n                    pairs and labels\n            4. `T`: Course-level triplets created with raw course pairs, a list with\n                    3 numpy arrays as the train, validation and test set\n        '''\n        self.opt = opt\n\n        concepts, links = self._read_dataset()\n\n        self.X = self._normalize(concepts)\n\n        pairs, y = self._get_pairs(links, len(concepts))\n        self.chunks = self._split_pairs(pairs, y)\n        self.T = self._get_chunks(self.chunks)\n\n    def _read_dataset(self):\n        '''\n        Read .lsvm file into `X` and .link file into `links`\n\n        Each row of *.lsvm is the bag-of-words feature for a course\n        <course-id> <word>:<count> <word>:<count> ... <word>:<count>\n\n        Each row of *.link is a pair of courses with a prerequisite relationship\n        <prerequisite-course-id> <postrequisite-course-id>\n\n        Args:\n            `opt`: Configuration object\n\n        Return:\n            `concepts`: Length `num_courses` dict, every key is a feature id\n            `links`: Length [num_links, 2] list, all prerequisite and postrequisite\n                    links between courses\n        '''\n        # Read *.lsvm file into features of each course\n        lines = open(self.opt.course_file).readlines()\n        concepts = []\n        for line in lines:\n            line = line.split()\n            bag_of_words = {}\n            # The first number in every line of data is course id, skip it\n            for word in line[1:]:\n                idx = word.split(':')\n                bag_of_words[int(idx[0])] = int(idx[1])\n            concepts.append(bag_of_words)\n\n        # Read *.link files into course links\n        lines = open(self.opt.prereq_file).readlines()\n        links = []\n        for line in lines:\n            course = line.split()\n            links.append([int(course[1]), int(course[0])])\n\n        return concepts, links\n\n    def _normalize(self, concepts):\n        '''\n        Convert `concepts` into numpy array and normalize by courses.\n\n        Args:\n            `concepts`: Bags of concepts of all courses, a length `num_courses` dict.\n\n        Return:\n            `X`: Normalized numpy array form of all concepts\n        '''\n        # Count total number of features\n        num_courses = len(concepts)\n        num_dimensions = 0\n        for course in concepts:\n            n = max(course.keys())\n            if n > num_dimensions:\n                num_dimensions = n\n\n        # Convert list into numpy array\n        X = np.zeros((num_courses, num_dimensions))\n\n        for course_id, course in enumerate(concepts):\n            for feature in course:\n                X[course_id, feature - 1] = course[feature]\n\n        # Normalize X\n        # The matlab code uses `row_norm = max=(d,1)` and I do not understand\n        norm = np.sqrt(np.sum(X**2, axis=1, keepdims=True)).transpose()\n        diags = np.array([0])\n        temp = spdiags(norm, diags, num_courses, num_courses).toarray()\n        # We use np.linalg.lstsq as a substitute for matlab \\ operator\n        X = np.linalg.lstsq(temp, X)[0]\n\n        return X\n\n    def _get_pairs(self, links, num_courses):\n        '''\n        Generate all possible pairs of courses and add labels\n\n        Args:\n            `links`: Length `num_links` list\n            `n`: Number of concepts\n\n        Return:\n            `pairs`: Possible pairs between two courses, [num_pairs, 2] list\n            `y`: Label of pairs, [num_pairs, 1] list\n        '''\n\n        num_pairs = num_courses * (num_courses - 1)\n        pairs = [[]] * num_pairs\n        k = 0\n\n        for i in range(num_courses):\n            for j in range(num_courses):\n                if i != j:\n                    # It is tricky here, since Matlab's index starts from 1 whereas\n                    # Python from 0, we need to plus 1 to all indices\n                    pairs[k] = [i + 1, j + 1]\n                    k += 1\n\n        y = [0] * num_pairs\n        for i, r in enumerate(pairs):\n            if r in links:\n                y[i] = 1\n            else:\n                y[i] = -1\n\n        return pairs, y\n\n    def _split_pairs(self, pairs, y):\n        '''\n        Split the data into 3 folds for cross validation.\n\n        Args:\n            `pairs`: Outputs from `self.get_pairs`\n            `y`: Outputs from `self.get_pairs`\n\n        Return:\n            `chunk`: 3-fold data set, dict with following keys:\n                'pairs': Course pairs\n                'labels': ±1 labels\n                'num_pairs': Number of pairs\n        '''\n        pairs = np.array(pairs)\n        y = np.array(y)\n\n        num_pairs = len(pairs)\n        chunks = []\n        for k in range(3):\n            chunk = {}\n            index = np.arange(k, num_pairs, 3)\n\n            chunk['pairs'] = pairs[index]\n            chunk['labels'] = y[index]\n            chunk['num_pairs'] = index.shape[0]\n\n            chunks.append(chunk)\n\n        return chunks\n\n    def _generate_triplets(self, chunk):\n        '''\n        Generate all triplets based on pairs and their labels, where (i, j) are\n        positive samples and (i, k) are negative.\n\n        In the `pairs` array, each line is a course pair in the form of:\n        <prerequisite-course-id> <postrequisite-course-id>, we pick up the positive\n        and negative samples\n\n\n        Args:\n            `chunk`: A dict with course pairs and labels\n\n        Return：\n            `triplets`: Triplets (i, j, k) based on the pairs, a [num_pairs, 3]\n                        size array.\n        '''\n        Triplets = []\n        # Convert from list to numpy arrays\n        pairs = np.array(chunk['pairs'], dtype=int)\n        y = np.array(chunk['labels'], dtype=int)\n\n        # Pairs that with positive or negative labels\n        pos = pairs[y == 1, :]\n        neg = pairs[y == -1, :]\n\n        # For every course\n        for i in list(set(pairs[:, 0])):\n            # The postrequisite course of this course\n            pos_pool = pos[pos[:, 0] == i, 1]\n            # If no correspondence, pass\n            if len(pos_pool) == 0:\n                continue\n            # Negative samples of postrequisite courses\n            neg_pool = neg[neg[:, 0] == i, 1]\n\n            # Compose an array where each line is an (i, j, k) sample,\n            # representing (course, positive, negative)\n            a, b = np.meshgrid(pos_pool, neg_pool)\n            combo = np.array([a.flatten(), b.flatten()]).transpose()\n\n            triplet = np.append(\n                i * np.ones((combo.shape[0], 1), dtype=int), combo, axis=1)\n            Triplets.append(triplet)\n\n        Triplets = np.vstack(Triplets)\n\n        return Triplets\n\n    def _get_chunks(self, chunks):\n        '''\n        Make all chunks of course pairs into course triplets\n\n        Args:\n            `chunks`: A dict with course pairs and labels\n\n        Return:\n            `T`: A list of course triplet arrays\n        '''\n        T = []\n        for i in range(3):\n            triplets = self._generate_triplets(chunks[i])\n            T.append(triplets)\n\n        return T\n","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":7837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"18565604","text":"import matplotlib.pyplot as plt\n\nwith open(\"data.txt\") as newFile:\n    data = newFile.readlines()\n\nmath,phy,chem =0,0,0\nfor studentData in data:\n    temdata = studentData.split()\n    math +=int(temdata[0])\n    phy += int(temdata[1])\n    chem += int(temdata[2])\n\nlables ='Maths','Phys','Chem'\nsizes = [math,phy,chem]\ncolors = ['red','yellow','lightblue']\nexplode = (0.1,0,0)\nplt.pie(\n        sizes,\n        explode=explode,\n        labels=lables,\n        colors=colors,\n        autopct='%1.1f%%',\n        shadow=True,\n        startangle=140\n        )\nplt.axis('equal')\nplt.savefig(\"test.jpg\")\nplt.show()\n","sub_path":"Data Analytics/jits/sample/pie.py","file_name":"pie.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"601912897","text":"import os, sys, re, shutil, requests, bs4, subprocess, requests, math, time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\nfrom datetime import datetime\nfrom datetime import timedelta\nimport sqlite3\n\nDateSearch = re.compile(r'(\\d\\d\\d\\d)/(\\d\\d)/(\\d\\d)')\nf = open('last_date_of_stock_holder.log',\"r\", encoding=\"utf-8\")\nstart_date = f.readline()\nstart_date = start_date.replace('Last update of stock holder: ','')\nstart_date = start_date.replace('-','/')\nDate = DateSearch.search(start_date)\nStart_date = datetime(int(Date.group(1)), int(Date.group(2)), int(Date.group(3)))\nf.close()\n\nconn = sqlite3.connect('stock_list.db')\nc = conn.cursor()\n\npath = os.getcwd()\npath = path + '\\\\stock_db'\nos.chdir(path)\nprint(os.getcwd())\n\nchrome_path = \"C:\\chromedriver\\chromedriver.exe\"\nweb = webdriver.Chrome(chrome_path)\nweb.get('https://www.tdcc.com.tw/smWeb/QryStock.jsp')\ntime.sleep(10)\n\nkey_strings = ['400,001-600,000', '600,001-800,000', '800,001-1,000,000', '1,000,001以上']\nkey_string_total = '合　計'\nholder_percentage = [0, 0, 0, 0]\nprint(key_strings[0])\n#f= open('tmp.txt',\"w\", encoding=\"utf-8\")\n#f.write(web.page_source)\n#f.close()\n\ndelta_7day = timedelta(days = 7) #delta_1day\ndelta_1day = timedelta(days = 1) #delta_1day\nStart_date_ptr = Start_date\nEnd_date = datetime.today()\n\nwhile Start_date_ptr <= End_date:\n    date_str = str(Start_date_ptr.year) + Start_date_ptr.strftime(\"%m\") + Start_date_ptr.strftime(\"%d\")\n    print(date_str)\n    soup = bs4.BeautifulSoup(web.page_source, \"lxml\")\n    cursor = c.execute(\"SELECT stock_id, stock_name, start_date, stock_type, business  from stock_list\")\n    for current_entry in cursor:\n        if ((current_entry[3] == '上櫃') or (current_entry[3] == '上市')) and (current_entry[4] != 'ETF'):\n            db_name = current_entry[0] + '.db'\n            conn_stock = sqlite3.connect(db_name)\n            c_stock = conn_stock.cursor()\n            print(db_name)\n            date_tmp = current_entry[2]\n            date_tmp = date_tmp.replace('-','/')\n            Date = DateSearch.search(date_tmp)\n            date_record = datetime(int(Date.group(1)), int(Date.group(2)), int(Date.group(3)))\n            if Start_date_ptr <= date_record:\n                continue\n            cursor = c_stock.execute('SELECT max(date_ID) FROM stock_week') \n            max_id = cursor.fetchone()[0]\n            if max_id != None:\n                max_id = max_id.replace('-','/')\n                Date = DateSearch.search(max_id)\n                Start_date_cmp = datetime(int(Date.group(1)), int(Date.group(2)), int(Date.group(3)))\n                if  Start_date_ptr <= Start_date_cmp:\n                    continue\n                #else:\n                #    Start_date_ptr = Start_date_cmp + delta_7day\n            #if max_id == str(Start_date_ptr.year) + '-' + Start_date_ptr.strftime(\"%m\") + '-' + Start_date_ptr.strftime(\"%d\"):\n            #    continue\n            n = 0\n            while 1:\n                input1 = web.find_element_by_id('StockNo')\n                input1.clear()\n                if n == 0:\n                    input1.send_keys(current_entry[0])\n                else:\n                    input1.send_keys(current_entry[0] + 'O')\n                time.sleep(2)\n                s1 = Select(web.find_element_by_id('scaDates'))\n                s1.select_by_value(date_str)\n                time.sleep(2)\n                web.find_element_by_xpath(\"//input [@value='查詢']\").click()\n                time.sleep(2)\n                soup = bs4.BeautifulSoup(web.page_source, \"lxml\")\n                td = soup.find(string=key_strings[0])\n                if n == 0:\n                   n = 1\n                else:\n                   n = 0\n                if td != None:\n                    break\n            n = 0\n            for key_string in key_strings:\n                td = soup.find(string=key_string)\n                soup_temp = bs4.BeautifulSoup(str(td.parent.parent), \"lxml\")\n                tds = soup_temp.find_all('td')\n                #print(tds[4].getText().strip().replace(',',''))\n                holder_percentage[n] = float(tds[4].getText().strip().replace(',',''))\n                n = n + 1\n            holder_400 = holder_percentage[0] + holder_percentage[1] + holder_percentage[2] + holder_percentage[3]\n            holder_1000 = holder_percentage[3]\n            holder_400 = round(holder_400, 2)\n            holder_1000 = round(holder_1000, 2)\n            print(holder_400)\n            print(holder_1000)\n\n            td = soup.find(string=key_string_total)\n            soup_temp = bs4.BeautifulSoup(str(td.parent.parent), \"lxml\")\n            tds = soup_temp.find_all('td')\n            total_people = int(tds[2].getText().strip().replace(',',''))\n            print(total_people)\n            sql_cmd = 'INSERT or IGNORE INTO stock_week (date_ID, holder400_per, holder1000_per, total_people) VALUES (\\'' + str(Start_date_ptr.year) + '-' + \\\n                  Start_date_ptr.strftime(\"%m\") + '-' + Start_date_ptr.strftime(\"%d\") + '\\',' + str(holder_400) + ',' \\\n                                                                                              + str(holder_1000) + ',' \\\n                                                                                              + str(total_people) + ')'\n            print(sql_cmd)\n            c_stock.execute(sql_cmd)\n            conn_stock.commit()\n            conn_stock.close()\n    \n    while 1:\n        Start_date_ptr = Start_date_ptr + delta_1day\n        date_str = str(Start_date_ptr.year) + Start_date_ptr.strftime(\"%m\") + Start_date_ptr.strftime(\"%d\")\n        print(date_str)\n        option_tmp = soup.find('option', value=date_str)\n        if option_tmp != None:\n            break\n    path = os.getcwd()\n    path = path.replace('\\\\stock_db','')\n    os.chdir(path)\n    f = open('last_date_of_stock_holder.log',\"w\", encoding=\"utf-8\")\n    f.write('Last update of stock holder: ' + str(Start_date_ptr.year) + '-' + Start_date_ptr.strftime(\"%m\") + '-' + Start_date_ptr.strftime(\"%d\"))\n    f.close()\n    path = os.getcwd()\n    path = path + '\\\\stock_db'\n    os.chdir(path)\n\n\nweb.close()\nconn.close()\n\n","sub_path":"10_stock_holder.py","file_name":"10_stock_holder.py","file_ext":"py","file_size_in_byte":6192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"354499702","text":"# -*- coding: utf-8 -*-\n\"\"\"\nHow does capitalism end?\nA simulation of dissapation driven percolation in competitive networks, and how\nthe developed structures decay once the dissapative rescource runs out.\nPython 3.7\nBy Sam Migirditch and Thomas Varley\nsamigir@iu.edu\n\nTo Do:\n\n1) Define Rescource, Producer and Consumer classes\n2) Define base versions of functions for node spawning, prefferential\nattachment, rescource consumption, rescource output and node death.\n3) Define main loop.\n4) Get on NPR\n    \nChange Log:\n01/25 SVM: Created\n\"\"\"\n\n# Imports\nimport statistics as stats\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport networkx as nx\nimport numpy as np\nimport math as m\nimport os\nfrom datetime import datetime\n\n#Misc path stuff\npath = os.getcwd()\ntime = datetime.now()\ntarget_dir = \"run_\" + str(time.hour) + \":\" + str(time.minute) + \".\" + str(time.second) + \"/\"\nos.mkdir(path + \"/\" + target_dir)\n\n# Globals\n## Safety triggers\npopultion_limit = 10**5\ntime_limit = 10**2\n## Experimental parameters\n\n#rescource volume regeneration and metabolism controls\n\n# Regen and metabolism are seperated because we'll probably want to look at non\n#-linear metabolic/regen scaling effects at some future point. \nsource_initial_volume = 10**3\nproducer_initial_volume = 1\nsource_regen_ratio = 0\nproduce_regen_ratio = 0.0\nconsumer_regen_ratio = 0.0\nsource_metabolic_rate = 0.0\nproducer_metabolic_ratio = 0.1\n#consumer_metabolic_ratio = 0.1\nproducer_consumption_ratio = 0.1\nDEATH_LIMIT = 0.01\n\n#niche controls\nniche_creep_rate = 0.01 # rate of increase in niche dist. mean per # of nodes\n\n#Network Growth Controls\nconsumer_delay_time=100#How many time steps to wait until consumers appear\nproducer_spawn_ratio=0.01\nconsumer_spawn_ratio=10\nproducer_seed_number = 10\n\n#Plotting controls\nscale_factor = 200.0 # scales node volumes\nplot_frequency = 1\n\n#Global indicies and trakcers\nmax_niche_score = 0.0 \nniche_list = []\nkill_list = []\n\n# SVM 01/27: All node objects must be hashable for nice networkx features. \n\n### Useful Functions\ndef set_niche_score_2():\n    global max_niche_score\n    moment = []\n    total_volume = 0\n    for node_index in G.nodes:\n        role = G.node[node_index][\"role\"]\n        #if role != \"Producer\": continue\n        try:\n            niche_score = G.node[node_index][\"niche_score\"]\n            volume = G.node[node_index][\"volume\"]\n            moment.append( volume * niche_score )\n            total_volume += volume\n        except: continue\n    if len(moment)<2: return( niche_creep_rate,0,niche_creep_rate)\n    sd = stats.stdev( moment )\n    moment = stats.mean(moment)\n    lb = moment - (niche_creep_rate * sd)\n    lb = max(0, lb)\n    ub = moment + ( niche_creep_rate * sd)\n    ub = min( max_niche_score, ub)\n    return(moment,lb,ub)\n    \ndef set_niche_score(node_index):\n    global max_niche_score\n    niche_score = first_moment() + niche_creep_rate\n    #print(\"SETTING NICHE:\", niche_score)\n    update_niche_stats\n    niche_list.append(niche_score)\n    return( niche_score )\n    \ndef update_niche_list():\n    global niche_list, max_niche_score\n    niche_list = []\n    for node_index in G.nodes:\n        niche_score = G.node[node_index][\"niche_score\"]\n        niche_list.append(niche_score)\n        if (niche_score > max_niche_score):\n            max_niche_score = niche_score\n\n# For updating individual w/0 updating all\ndef update_niche_stats(node_index):\n    global max_niche_score, niche_list\n    niche_score = G.node[node_index][\"niche_score\"]\n    if niche_score > max_niche_score: max_niche_score = niche_score\n\n\ndef create_source(node_index):\n    #init node\n    G.add_node(node_index)\n    #set properties\n    G.nodes[node_index][\"node_index\"]=node_index\n    G.nodes[node_index]['role']=\"Source\"\n    G.nodes[node_index][\"volume\"]= source_initial_volume\n    G.nodes[node_index][\"niche_score\"]= 0\n    G.nodes[node_index][\"niche_lb\"]= 0\n    G.nodes[node_index][\"niche_ub\"]= 0\n    G.nodes[node_index][\"metabolic_ratio\"]= 0\n    G.nodes[node_index][\"consumption_ratio\"]= 0\n    G.nodes[node_index][\"regen_ratio\"]= 0.0\n    update_niche_list()\n\ndef create_producer(node_index):\n    #init node\n    G.add_node(node_index)\n    #set properties\n    G.nodes[node_index][\"node_index\"]=node_index\n    G.nodes[node_index][\"role\"]=\"Producer\"\n    G.nodes[node_index][\"volume\"]= producer_initial_volume \n    niche, lb, ub = set_niche_score_2()\n    G.nodes[node_index][\"niche_score\"]= niche\n    G.nodes[node_index][\"niche_ub\"]= ub\n    G.nodes[node_index][\"niche_lb\"]= 0.01*ub\n    G.nodes[node_index][\"metabolic_ratio\"]= producer_metabolic_ratio\n    G.nodes[node_index][\"consumption_ratio\"]= producer_consumption_ratio/(ub)\n    G.nodes[node_index][\"regen_ratio\"]= 0.0\n    #update trackers\n    update_niche_list()\n    #find targets\n    find_target( node_index )\n\ndef find_target( node_index ):\n    ub = G.node[node_index][\"niche_ub\"]\n    lb = G.node[node_index][\"niche_lb\"]\n    best_score = 0.0\n    best = -1\n    possible_targets = list(G.nodes())\n    np.random.shuffle(possible_targets)\n    for target in possible_targets:\n        # don't make parallel or self loops\n        blacklist = [node_index]\n        edges = G.out_edges(node_index)\n        blacklist.extend(edges)\n        if target not in blacklist:\n            target_niche_score = G.node[target][\"niche_score\"]\n            if ( lb<=target_niche_score<=ub ):\n                target_volume = G.node[target][\"volume\"]\n                target_degree = G.degree(target)\n                target_score = target_volume / max(1,target_degree)\n                if ( target_score > best_score ):\n                    best_score = target_score\n                    best = target\n    if (best_score > 0):\n        G.add_edge(node_index,best)\n    #else: print( \"EDGE:\",node_index,\" failed to find target\")\n                    \ndef kill_node( node_index ):\n    #global niche_array\n    #niche_score = G.node[node_index][\"niche_score\"]\n    G.remove_node( node_index )\n    #niche_array.remove(niche_score)\n\ndef remove_isolates():\n    global kill_list\n    isolates = nx.isolates(G)\n    kill_list.extend(isolates)\n    \ndef run_kill_list():\n    global kill_list\n    #remove duplicates\n    kill_list = list(set(kill_list))\n    for k in kill_list:\n        kill_node(k)\n    kill_list = []\n\ndef do_producer_step(node):\n    global kill_list\n    node_index = G.node[node][\"node_index\"]\n    #iche_score = G.node[node][\"niche_score\"]\n    \n    targets = [ t for t in G.neighbors(node) ]\n    target_number = len( targets )\n    \n    node_volume = G.node[node][\"volume\"]\n    node_quota = G.node[node][\"consumption_ratio\"] * node_volume\n    node_metabolism = G.node[node][\"metabolic_ratio\"] * node_volume\n        \n    \n    # Die if starved\n    if (node_volume<=DEATH_LIMIT):\n        #print( \"t:\",t,\"node:\", node,\"fate: 0 starved\\n\")\n        kill_list.append(node_index)\n        return()\n    \n    # Hunger\n    if (target_number>0):\n        per_capita_quota = node_volume / target_number\n        node_volume-=node_metabolism\n        intake = 0.0\n        for target in targets:\n            target_index = G.node[target][\"node_index\"]\n            target_volume = G.node[target][\"volume\"]\n            if (per_capita_quota >= target_volume):\n                intake += target_volume\n                kill_list.append( target_index )\n                #print( \"t:\",t,\"node:\", node,\"killed\",target,\"\\n\")\n            else:\n                intake += per_capita_quota\n                target_volume += -per_capita_quota\n                G.node[target][\"volume\"] = target_volume\n        # eat gathered rescource\n        G.node[node][\"volume\"] += intake\n        # serch for new targets if under quota\n        if (intake < node_quota):\n            find_target( node_index )\n    else: find_target(node_index)\n    G.node[node_index][\"volume\"] -= node_metabolism\n\n\ndef change_niche_score(node_index, new_score):\n    global max_niche_score, niche_list\n    #swap\n    old_score = G.node[node_index][\"niche_score\"]\n    G.node[node_index][\"niche_score\"] = new_score\n    #update stats\n    niche_list.remove(old_score)\n    niche_list.append(new_score)\n    if (new_score > max_niche_score):\n        max_niche_score = new_score\n    \n    \ndef plotter(target_dir, show = True):\n    pos=nx.spring_layout(G) # positions for all nodes\n    labels={}\n    max_volume = 0.0\n    for node in G.nodes:\n        role = G.node[node][\"role\"]\n        if(role==\"Source\"): continue\n        volume = G.node[node][\"volume\"]\n        if volume>max_volume:\n            max_volume = volume\n\n# Draw nodes\n    for node_index in G.nodes:\n        # Type-> color\n        role = G.node[node_index][\"role\"]\n        if (role==\"Source\"):\n            color = \"green\"\n        elif (role==\"Producer\"):\n            color = \"blue\"\n        elif (role==\"consumer\"):\n            color = \"red\"\n        else: \n            color = \"black\"\n        # volume -> size\n        volume = scale_factor*min(1,G.node[node_index][\"volume\"] / max_volume )\n        \n        #Draw node\n        nx.draw_networkx_nodes(G,pos,\n                               nodelist=[node_index],\n                               node_color= color,\n                               node_size=volume,\n                           alpha=0.8) \n        # draw edges\n        edges = G.out_edges(node_index)\n        nx.draw_networkx_edges(G,pos,\n                               edgelist=edges,\n                               width=1,\n                               alpha=0.5,\n                               edge_color=color)\n        \n        # some math labels\n        labels[node_index]=node_index\n        \n    nx.draw_networkx_labels(G,pos,labels,font_size=16)\n\n    plt.axis('off')\n    local_now = datetime.now()\n    #plt.savefig(target_dir + str(local_now) + \"_graph.png\") # save as png\n   \n    plt.show() # display\n    #nx.write_gexf(G, target_dir + str(local_now) + \"_graph.gexf\")\n    \n### Script   \n# Init the world\nG = nx.DiGraph()\n\n# Create source node & some seed producers\ncreate_source(0)\n\nfor t in range(1, producer_seed_number):\n    create_producer(t)\n    #force into source niche range\n    G.node[t][\"niche_lb\"]=0.0\n    G.node[t][\"niche_ub\"]=niche_creep_rate\n    change_niche_score(t,0.1*niche_creep_rate)\n\n### GRIND\n\nrun_condition=1\nt=0\nindex_max = producer_seed_number\n\nnum_producers = []\nnum_nodes = []\nsize_source = []\ntotal_volumes = []\nmin_niche_lb = []\nmax_niche_ub = []\n\nwhile (run_condition):\n\n    # Update State Variables\n    num_producers.append(len([x for x in G.nodes() if G.node[x][\"role\"] == \"Producer\"]))\n    num_nodes.append(len(G.nodes()))\n    if nx.degree(G)[0] != 0:\n        size_source.append(G.node[0][\"volume\"])\n        \n    total_volumes.append(np.sum([G.node[i][\"volume\"] for i in G.nodes() if i != 0]))\n    min_niche_lb.append(min([G.node[i][\"niche_lb\"] for i in G.nodes() if i >= 9]))\n    max_niche_ub.append(max([G.node[i][\"niche_ub\"] for i in G.nodes() if i >= 9]))\n    \n    t+=1\n    population = list( G.nodes() )\n    population_size = len(population)\n    \n    # run through and-listed run conditions\n    run_condition *= t<time_limit \n    run_condition *= population_size < popultion_limit\n    run_condition *= population_size > 2\n    #print( \"TIME:\", t,\"| POPULATION:\", population_size,\"|\",population)\n    \n    \n    update_list = list(G.nodes())\n    np.random.shuffle(update_list)\n    for node in update_list:\n        if(G.node[node][\"role\"] == \"Producer\"):\n            update_niche_stats(node)\n            do_producer_step(node)\n    \n    # Reap nodes\n    #remove_isolates()\n    run_kill_list()\n    #plot\n    if t%plot_frequency ==0:\n        plotter(target_dir)\n    # spawn Nodes\n    \n    #skip spawn if rescource has died\n    #if 0 not in G.nodes: continue\n    if nx.degree(G)[0] == 0: continue\n    \n    spawn_number = 2#m.ceil(population_size*producer_spawn_ratio)\n    for spawn in range(0,spawn_number):\n        index_max +=1\n        create_producer(index_max)\n        find_target(index_max)        \n\nsns.set(style = \"darkgrid\")\n\nplt.subplots()\nplt.plot(size_source)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Source Volume\")\nplt.title(\"Source Volume Over Time\")\n\nplt.subplots()\nplt.plot(min_niche_lb, label = \"Min Niche Score\")\nplt.plot(max_niche_ub, label = \"Max Niche Score\")\nplt.xlabel(\"Time\")\nplt.ylabel(\"Min/Max Niche Score\")\nplt.title(\"Niche Range\")\nplt.legend()\n\nplt.subplots()\nplt.plot(num_nodes)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Population\")\nplt.title(\"Population Over Time\")\n#clean up for print\n#remove_isolates()\n#run_kill_list()\n#plotter()\n    \n\n\n","sub_path":"dissapativeNets_model1.py","file_name":"dissapativeNets_model1.py","file_ext":"py","file_size_in_byte":12404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"145138987","text":"import numpy as np\nimport pandas as pd\n# Classifier used\nfrom sklearn.ensemble import RandomForestClassifier\nimport matplotlib.pyplot as plt\nfrom sklearn.manifold import TSNE\n# Best Feature selection\nfrom sklearn.feature_selection import RFE\n# Input Matrix\ndataframe_trn = pd.read_csv(\"../input/train.csv\")\ndataframe_tst = pd.read_csv(\"../input/test.csv\")\n\n#Target Vector\nlabels_trn = dataframe_trn[[\"target\",\"id\"]]\n\n#Dropping columns\ndataframe_trn = dataframe_trn.drop([\"target\",\"id\"],axis=1)\nids = pd.DataFrame(dataframe_tst[\"id\"].values,columns= [\"id\"])\ndataframe_tst = dataframe_tst.drop(\"id\",axis=1)\nheaders = dataframe_trn.columns.values\n# String to numeric Mapping\ndict_mapping = {\"Class_1\":1,\"Class_2\":2,\"Class_3\":3,\"Class_4\":4,\"Class_5\":5,\"Class_6\":6,\"Class_7\":7,\"Class_8\":8,\"Class_9\":9}\nlabels_trn_numeric = labels_trn[\"target\"].apply(lambda x: dict_mapping[x])\n# Reducing Dimensions\n#trn_embedded = TSNE(n_components=2).fit_transform(dataframe_trn)\n\n#Plotting the Dataset\n#plt.scatter(trn_embedded[:, 0], trn_embedded[:, 1],c=labels_trn_numeric)\n#plt.show()\nclf = RandomForestClassifier( n_estimators=500,n_jobs=4)\nrfe = RFE(clf, 85)\nfit = rfe.fit(dataframe_trn, labels_trn_numeric)\nfeatures = []\nfor i , j in zip(dataframe_trn.columns,fit.support_):\n    if j == True:\n        features.append(str(i))\narray = pd.DataFrame(fit.predict_proba(dataframe_tst),columns=[\"Class_1\",\"Class_2\",\"Class_3\",\"Class_4\",\"Class_5\",\"Class_6\",\"Class_7\",\"Class_8\",\"Class_9\"])\ncomplete_array = pd.concat([ids,array],axis=1)\ncomplete_array.to_csv(\"submission.csv\",sep=\",\",index=None)\n","sub_path":"kaggle/otto-group-product-classification-challenge/script_26.py","file_name":"script_26.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"80638350","text":"\"\"\"records by year\"\"\"\nimport datetime\n\nimport numpy as np\nimport psycopg2.extras\nimport pandas as pd\nfrom pyiem.network import Table as NetworkTable\nfrom pyiem.util import get_autoplot_context, get_dbconn\n\n\ndef get_description():\n    \"\"\" Return a dict describing how to call this plotter \"\"\"\n    desc = dict()\n    desc['data'] = True\n    desc['description'] = \"\"\"This chart plots the number of daily maximum\n    high temperatures and daily minimum low temperatures set by year.  Ties\n    are not included.\"\"\"\n    desc['arguments'] = [\n        dict(type='station', name='station', default='IA2203',\n             label='Select Station:', network='IACLIMATE')\n    ]\n    return desc\n\n\ndef plotter(fdict):\n    \"\"\" Go \"\"\"\n    import matplotlib\n    matplotlib.use('agg')\n    import matplotlib.pyplot as plt\n    pgconn = get_dbconn('coop')\n    cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n    ctx = get_autoplot_context(fdict, get_description())\n    station = ctx['station']\n\n    table = \"alldata_%s\" % (station[:2],)\n    nt = NetworkTable(\"%sCLIMATE\" % (station[:2],))\n    syear = max(1893, nt.sts[station]['archive_begin'].year)\n    eyear = datetime.datetime.now().year\n\n    cursor.execute(\"\"\"SELECT sday, year, high, low, day from \"\"\"+table+\"\"\"\n      where station = %s and sday != '0229'\n      and year >= %s ORDER by day ASC\"\"\", (station, syear))\n\n    hrecords = {}\n    hyears = [0]*(eyear - syear + 1)\n    lrecords = {}\n    lyears = [0]*(eyear - syear + 1)\n    expect = [0]*(eyear - syear + 1)\n\n    # hstraight = 0\n    for row in cursor:\n        sday = row[0]\n        year = row[1]\n        high = row[2]\n        low = row[3]\n        if year == syear:\n            hrecords[sday] = high\n            lrecords[sday] = low\n            continue\n        if high > hrecords[sday]:\n            hrecords[sday] = row['high']\n            hyears[year - syear] += 1\n            # hstraight += 1\n            # if hstraight > 3:\n            #    print hstraight, sday, row[4]\n        # else:\n        #     hstraight = 0\n        if low < lrecords[sday]:\n            lrecords[sday] = low\n            lyears[year - syear] += 1\n\n    for year in range(syear, eyear+1):\n        expect[year-syear] = 365.0/float(year-syear+1)\n\n    df = pd.DataFrame(dict(expected=pd.Series(expect),\n                           highs=pd.Series(hyears),\n                           lows=pd.Series(lyears),\n                           year=np.arange(syear, eyear+1)))\n\n    (fig, ax) = plt.subplots(2, 1, sharex=True, sharey=True)\n    rects = ax[0].bar(np.arange(syear, eyear+1)-0.5, hyears,\n                      facecolor='b', edgecolor='b')\n    for i in range(len(rects)):\n        if rects[i].get_height() > expect[i]:\n            rects[i].set_facecolor('r')\n            rects[i].set_edgecolor('r')\n    ax[0].plot(np.arange(syear, eyear+1), expect, color='black',\n               label=\"$365/n$\")\n    ax[0].set_ylim(0, 50)\n    ax[0].set_xlim(syear, eyear+1)\n    ax[0].grid(True)\n    ax[0].legend()\n    ax[0].set_ylabel(\"Records set per year\")\n    ax[0].set_title((\"[%s] %s\\nDaily Records Set Per Year\"\n                     \"\") % (station, nt.sts[station]['name']))\n    ax[0].text(eyear - 50, 22, \"Max High Temperature\")\n\n    rects = ax[1].bar(np.arange(syear, eyear+1)-0.5, lyears,\n                      facecolor='r', edgecolor='r')\n    for i in range(len(rects)):\n        if rects[i].get_height() > expect[i]:\n            rects[i].set_facecolor('b')\n            rects[i].set_edgecolor('b')\n    ax[1].plot(np.arange(syear, eyear+1), expect, color='black',\n               label=\"$365/n$\")\n    ax[1].grid(True)\n    ax[1].legend()\n    ax[1].set_ylabel(\"Records set per year\")\n    ax[1].text(eyear-50, 22, \"Min Low Temperature\")\n\n    return fig, df\n\n\nif __name__ == '__main__':\n    plotter(dict(station='IA6389', network='IACLIMATE'))\n","sub_path":"htdocs/plotting/auto/scripts/p63.py","file_name":"p63.py","file_ext":"py","file_size_in_byte":3806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"430413677","text":"# coding: utf-8\n\n# In[171]:\n\n# Ido Michael\nimport tensorflow as tf\nimport os, struct\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport ParsePowerEDFA\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import mean_absolute_error\nimport math\nimport sys\nimport configparser\nimport random\n\n\nprint(tf.__version__)\n# In case we need to average results of 5 different debug files and plot them on a graph.\n# ParsePowerEDFA.getTestFiles()\n\n# Average files by name and then write collected results into a csv file.\n# [testdb, tr2, tr4, tr6, tr8, tr1, mse_tr, mae_tr] = ParsePowerEDFA.averageResults(\"100%\")\n# ParsePowerEDFA.plot_to_matrix(tr2, tr4, tr6, tr8, tr1, mse_tr, mae_tr)\n\n# 20%\n# [testdb, val2, val4, val6, val8, val1] = ParsePowerEDFA.averageResults([\n#     \"./TestPar29.ini140-debug.log\",\n#     \"./TestPar29.ini84-debug.log\",\n#     \"./TestPar29.ini150-debug.log\"\n# ])\n\n# [testdb, val2, val4, val6, val8, val1] = ParsePowerEDFA.averageResults([\"./test/TestPar25.ini-smaller53-debug.log\", \"./test/TestPar25.ini-smaller103-debug.log\", \"./test/TestPar25.ini-smaller25-debug.log\", \"./test/TestPar25.ini-smaller37-debug.log\", \"./test/TestPar25.ini-smaller30-debug.log\"])\n# ParsePowerEDFA.plotGraph(val2, val4, val6, val8, val1)\n\n# file='Data/sameroute_3initial.txt'\n# file='Data/train_dataset_3to5initial_1new_2017-02-03-16-58-03.txt'\n# [counter,on_channels,input_power,new_channel,power_excursion]=ParseData.ParseDataFile(file)\n\n\nimport imp\n\nimp.reload(ParsePowerEDFA)\n\n# Set config file and parse it to the different variables.\nconfig = configparser.ConfigParser()\nconfig_file = sys.argv[1]\nconfig.read(config_file)\n\nconfiguration = \"DEFAULT\"\nin_format = config.get(configuration, 'input')\nout_format = config.get(configuration, 'output')\nnorm_in = config.get(configuration, 'normalize_in')\nnorm_out = config.get(configuration, 'normalize_out')\nnum_neurons = config.get(configuration, 'layers').replace(\"\\n\",\"\")\nnum_neurons = list(map(int, num_neurons.split(\",\")))\nlayers = int(len(num_neurons))\nact_functions = config.get(configuration, 'functions').replace(\"\\n\",\"\")\nact_functions = list(map(str, act_functions.split(\",\")))\nnum_iterations = int(config.get(configuration, 'iterations'))\nlearning_rate = float(config.get(configuration, 'epsilon'))\ndecrease_rate = float(config.get(configuration, 'decrease_rate'))\nout_channel = int(config.get(configuration, 'output_shape'))\nscale = int(config.get(configuration, 'scaler'))\nscale_in = int(config.get(configuration, 'scaleInput'))\nonly_on = config.get(configuration, 'onlyOn')\ntrain_percentage = float(config.get(configuration, 'trainPercent'))\ntest_percentage = float(config.get(configuration, 'testPercent'))\nisLoging = config.get(configuration, 'debugFile')\ndata_percentage = float(config.get(configuration, 'dataPercentage'))\nfile1 = config.get(configuration, 'dataFile')\nchan_num = int(config.get(configuration, 'activeChannel'))\n\n[counter, input_dict, output_dict] = ParsePowerEDFA.ParseDataFileSingleChannel(file1)\n\n# Set a single channel out of the dictionary\ninput_power = input_dict[chan_num]\noutput_power = output_dict[chan_num]\n\n# Save print outputs into a log file, always works with most updated file at a time.\nif(isLoging == \"on\"):\n    old_stdout = sys.stdout\n    try:\n        files = [f for f in os.listdir('.') if os.path.isfile(f)]\n        wantedLogFiles = [filename for filename in files if filename.startswith(config_file)]\n        wantedLogFiles = [file for file in wantedLogFiles if file.replace(config_file, \"\")]\n\n        # Randomize logs name for multiThreading\n        if len(wantedLogFiles) != 0:\n            numIdentifier = random.randint(2, 150)\n            log_file = open(config_file + str(numIdentifier) + \"-debug.log\", 'w')\n        else:\n            numIdentifier = random.randint(2, 150)\n            log_file = open(config_file + str(numIdentifier) + \"-debug.log\", 'w')\n    except:\n        # There was an exception\n        print(\"exception opening a file - log file\")\n        log_file = open(\"default.log\", 'w')\n\n    sys.stdout = log_file\n\n# In[180]:\n# print parsed config file values.\nprint(\"Config file: %s \" % str(config_file))\nprint(\"Channel number: %s \" % chan_num)\nprint(\"Input Parameter file: %s \" % str(file1))\nprint(\"Input Format: %s\" % str(in_format))\nprint(\"Output Format: %s\" % str(out_format))\nprint(\"Input Normalize: %s\" % str(norm_in))\nprint(\"Output Normalize: %s\" % str(norm_out))\nprint(\"Number of layers: %s\" % str(layers))\nprint(\"Number of neurons per layer: %s\" % str(num_neurons))\nprint(\"Activation functions for each layer: %s\" % str(act_functions))\nprint(\"Number of iterations of loop: %d\" % num_iterations)\nprint(\"Learning rate: %f\" % learning_rate)\nprint(\"Output Channel: %d\" % out_channel)\nprint(\"Scale: %d\" % scale)\nprint(\"Scale in: %d\" % scale_in)\nprint(\"On or All Channels: %s\" % str(only_on))\nprint(\"Training percentage: %f\" % train_percentage)\nprint(\"Testing percentage: %f\" % test_percentage)\nprint(\"Debug to external file: %s\" % str(isLoging))\nprint(\"Data percentage: %f\" % data_percentage)\nprint(\"Config file name: %s\" % str(file1))\nprint(\"Active channel number: %d\" % chan_num)\nprint(\"Those are the active channels:\")\nprint(output_dict.keys())\n\n\n\n# In[10]:\n\ndef find_max_values(data):\n    a, b = data.shape\n    x_max = [0] * b\n    for i in range(0, b):\n        x_max[i] = max([item[i] for item in data])\n    return x_max\n\n\n# In[11]:\n\ndef find_min_values(data):\n    a, b = data.shape\n    x_min = [0] * b\n    for i in range(0, b):\n        x_min[i] = min([item[i] for item in data])\n    return x_min\n\n\n# In[12]:\n\ndef find_mean(data):\n    a, b = data.shape\n    mean_data = [0] * b\n    for i in range(0, b):\n        mean_data[i] = np.mean([item[i] for item in data])\n    return mean_data\n\n\n# In[13]:\n\ndef normalize_input(values, min_values, max_values, normal_factor, scale):\n    temp = values.copy()\n    a, b = temp.shape\n    for i in range(0, a):\n        for j in range(0, b):\n            if (max_values[j] == min_values[j]):\n                temp[i][j] = 0\n            else:\n                temp[i][j] = (temp[i][j] - normal_factor[j]) / (max_values[j] - min_values[j]) * scale\n    return temp\n\n\n# In[14]:\n\ndef denormalize_input(values, min_values, max_values, normal_factor, out_format, scale):\n    temp = values.copy()\n    a, b = temp.shape\n    for i in range(0, a):\n        for j in range(0, b):\n            temp[i][j] = (temp[i][j] / scale) * (max_values[j] - min_values[j]) + normal_factor[j]\n    if out_format == 'dB':\n        return temp\n    if out_format == 'dec':\n        return dec_to_dB(temp)\n\n\n# Convert values from decimals to db (inputs are automatically converted to decimals).\ndef dec_to_dB(values):\n    temp = values.copy()\n    a, b = temp.shape\n    for i in range(0, a):\n        for j in range(0, b):\n            if temp[i][j] > 0:\n                temp[i][j] = 10 * math.log10(temp[i][j])\n            else:\n                temp[i][j] = -1000\n    return temp\n\n\n# In[16]:\n\ndef ErrorMethod(methodtype, vector_on, power_diff):\n    # does calculations on individual inputs, need to creat function that does it on all of the data\n    if methodtype == 'Absolute':\n        out = [sum(abs(power_diff[i, :])) / (sum((vector_on[i, :] != 0))) for i in range(vector_on.shape[0])]\n    if methodtype == 'Square':\n        out = [sum(np.square(power_diff[i, :])) / (sum((vector_on[i, :] != 0))) for i in range(vector_on.shape[0])]\n    if methodtype == 'Max':\n        out = [max(abs(power_diff[i, :])) for i in range(vector_on.shape[0])]\n    return out\n\n\n# Set input/outputs to correct measurment of db/decimals\nif in_format == 'dB':\n    in_power_unit = dec_to_dB(input_power)\nif in_format == 'dec':\n    in_power_unit = input_power\nif out_format == 'dB':\n    out_power_unit = dec_to_dB(output_power)\nif out_format == 'dec':\n    out_power_unit = output_power\n\n# In[125]:\n\n# print input array\nin_power_unit.shape\n\n# Shuffle data for randomness\nn = in_power_unit.shape[0]\nindices = np.arange(n)\nrandom.shuffle(indices)\nin_power_unit = in_power_unit[indices]\nout_power_unit = out_power_unit[indices]\n\ndef printInputs():\n    a, b = in_power_unit.shape\n    for i in range(0, a):\n        print(\"input\")\n        print(in_power_unit[i])\n        print(\"output\")\n        print(out_power_unit[i])\n\ndef get_channel(values, channel):\n    a = values.shape\n    out_power = [0] * a[0]\n    for i in range(0, a[0]):\n        out_power[i] = values[i][channel - 1]\n    out_power = np.asarray(out_power).astype(np.float32)\n    d = out_power.reshape((a, 1))\n    return d\n\n\n# only_on=1\nif only_on == 'on':\n    out_power_unit = out_power_unit[input_power[:, out_channel - 1] > 0]\n    in_power_unit = in_power_unit[input_power[:, out_channel - 1] > 0]\n\n# In[149]:\n\ndata_size =  math.floor(len(out_power_unit) * data_percentage)\nprint(\"Data size: %d\" % data_size)\n\n\n# In[131]:\n\n# out_power_channel=get_channel(out_power_unit,out_channel)\n\n\n# In[132]:\n\ndef create_weight(shape):\n    # creates and initializes a weight matrix of the specified size\n    return tf.Variable(tf.truncated_normal(shape, stddev=.1, dtype=tf.float32))\n\n\ndef create_bias(shape):\n    # creates and initializes a bias term of the specified size\n    return tf.Variable(tf.constant(.1, shape=shape, dtype=tf.float32))\n\n\ndef create_perm(data):\n    out = np.random.permuation(data.shape[0])\n    return out\n\n\n# In[133]:\n\ndef all_nn_computations(X, weights_0, biases_0, weights_1, biases_1):\n    Z_0 = tf.nn.relu(tf.matmul(X, weights_0) + biases_0)\n    logits = tf.matmul(Z_0, weights_1) + biases_1\n    return logits\n\n\n# In[134]:\n\ndef all_nn_computations2(X, weights, biases):\n    return tf.matmul(X, weights) + biases\n\n\n# In[135]:\n\ndef all_nn_computations3(X, weights_0, biases_0, weights_1, biases_1, weights_2, biases_2):\n    Z_0 = (tf.matmul(X, weights_0) + biases_0)\n    Z_1 = (tf.matmul(Z_0, weights_1) + biases_1)\n    logits = tf.matmul(Z_1, weights_2) + biases_2\n    return logits\n\n\n# In[136]:\n\ndef all_nn_computations5(X, weights_0, biases_0, weights_1, biases_1, weights_2, biases_2, weights_3, biases_3,\n                         weights_4, biases_4):\n    Z_0 = tf.nn.tanh(tf.matmul(X, weights_0) + biases_0)\n    Z_1 = tf.nn.tanh(tf.matmul(Z_0, weights_1) + biases_1)\n    Z_2 = tf.nn.tanh(tf.matmul(Z_1, weights_2) + biases_2)\n    Z_3 = (tf.matmul(Z_2, weights_3) + biases_3)\n    logits = tf.matmul(Z_3, weights_4) + biases_4\n\n    return logits\n\n\n# In[137]:\n\ndef all_nn_computations5relu(X, weights_0, biases_0, weights_1, biases_1, weights_2, biases_2, weights_3, biases_3,\n                             weights_4, biases_4):\n    Z_0 = tf.nn.relu(tf.matmul(X, weights_0) + biases_0)\n    Z_1 = tf.nn.relu(tf.matmul(Z_0, weights_1) + biases_1)\n    Z_2 = tf.nn.relu(tf.matmul(Z_1, weights_2) + biases_2)\n    Z_3 = tf.nn.relu(tf.matmul(Z_2, weights_3) + biases_3)\n    logits = tf.matmul(Z_3, weights_4) + biases_4\n\n    return logits\n\n\n\ncounter2 = np.arange(1, data_size + 1)\n\n# In[158]:\n\n# Number of observations in each group by size, split data to training, testing and validation.\nn_test_cases = math.floor(len(out_power_unit) * test_percentage) #72000*.1   #200\nn_training_cases = math.floor(data_size * train_percentage)  # 72000*.8 #500\nn_validation_cases = math.floor(data_size * (1 - test_percentage - train_percentage))\nn_subset_train = n_training_cases * 1\ntotal_data = n_test_cases + n_validation_cases + n_training_cases\n\ninput_power_train = in_power_unit[0: n_training_cases].astype(np.float32)\noutput_power_train = out_power_unit[0: n_training_cases].astype(np.float32)\n\n# input_power_train = in_power_unit[counter2[:] <= n_subset_train].astype(np.float32)\n# output_power_train = out_power_unit[counter2[:] <= n_subset_train].astype(np.float32)\n\ninput_power_test=in_power_unit[n_training_cases: n_training_cases+n_test_cases].astype(np.float32)\noutput_power_test=out_power_unit[n_training_cases: n_training_cases+n_test_cases].astype(np.float32)\n\n# input_power_test=in_power_unit[np.logical_and(counter2[:]>n_training_cases,counter2[:]<=n_training_cases+n_test_cases)].astype(np.float32)\n# output_power_test=out_power_unit[np.logical_and(counter2[:]>n_training_cases,counter2[:]<=n_training_cases+n_test_cases)].astype(np.float32)\n\ninput_power_val=in_power_unit[n_training_cases+n_test_cases:total_data].astype(np.float32)\noutput_power_val=out_power_unit[n_training_cases+n_test_cases:total_data].astype(np.float32)\n\n# input_power_val=in_power_unit[np.logical_and(counter2[:]>(n_training_cases+n_test_cases),counter2[:]<=data_size)].astype(np.float32)\n# output_power_val=out_power_unit[np.logical_and(counter2[:]>(n_training_cases+n_test_cases),counter2[:]<=data_size)].astype(np.float32)\n\n\nprint(\"Shape of X_train: \", input_power_train.shape, \"\\t Shape of Y_train: \", output_power_train.shape)\nprint(\"Shape of X_test: \", input_power_test.shape, \"\\t Shape of Y_test: \", output_power_test.shape)\nprint(\"Shape of X_val: \", input_power_val.shape, \"\\t Shape of Y_val: \", output_power_val.shape)\n\n# Collect different mean/max/min values for inputs\nmax_values_in = find_max_values(input_power_train)\nmin_values_in = find_min_values(input_power_train)\nmean_values_in = find_mean(input_power_train)\nif norm_in == 'no':\n    X_train = input_power_train\n    X_test = input_power_test\n    X_val = input_power_val\nelif norm_in == 'mu':\n    X_train = normalize_input(input_power_train, min_values_in, max_values_in, mean_values_in, scale_in)\n    X_test = normalize_input(input_power_test, min_values_in, max_values_in, mean_values_in, scale_in)\n    X_val = normalize_input(input_power_val, min_values_in, max_values_in, mean_values_in, scale_in)\nelif norm_in == 'min':\n    X_train = normalize_input(input_power_train, min_values_in, max_values_in, min_values_in, scale_in)\n    X_test = normalize_input(input_power_test, min_values_in, max_values_in, min_values_in, scale_in)\n    X_val = normalize_input(input_power_val, min_values_in, max_values_in, min_values_in, scale_in)\n\n# Collect different mean/max/min values for outputs\nmax_values_out = find_max_values(output_power_train)\nmin_values_out = find_min_values(output_power_train)\nmean_values_out = find_mean(output_power_train)\nif norm_out == 'no':\n    Y_train = output_power_train\n    Y_test = output_power_test\n    Y_val = output_power_val\nelif norm_out == 'mu':\n\n    Y_train = normalize_input(output_power_train, min_values_out, max_values_out, mean_values_out, scale)\n    Y_test = normalize_input(output_power_test, min_values_out, max_values_out, mean_values_out, scale)\n    Y_val = normalize_input(output_power_val, min_values_out, max_values_out, mean_values_out, scale)\nelif norm_out == 'min':\n    Y_train = normalize_input(output_power_train, min_values_out, max_values_out, min_values_out, scale)\n    Y_test = normalize_input(output_power_test, min_values_out, max_values_out, min_values_out, scale)\n    Y_val = normalize_input(output_power_val, min_values_out, max_values_out, min_values_out, scale)\n\n# Different measures for success\n# error for each individual\ndef accuracy(predictions, truth):\n    # Return % of correctly classified images\n    # return (100.0 * np.sum(np.argmax(predictions, axis=1) == np.squeeze(labels)) / predictions.shape[0])\n    return (np.sum((predictions - truth) ** 2)) / predictions.shape[0]\n\n\n# how many you get correct\ndef PredictionAccuracy(predictions, truth, set_num):\n    mi = int(min(set_num))\n    mx = int(max(set_num))\n    # print(type(mi))\n    correct = 0\n    for i in range(mi, mx + 1):\n        # print(i)\n        temp1 = truth[set_num == i]\n        np.where(temp1 == min(temp1))\n        temp2 = predictions[set_num == i]\n        mi_truth = np.where(temp1 == min(temp1))\n        mi_truth = np.array(mi_truth)\n        # print(temp1)\n        mi_predict = np.where(temp2 == min(temp2))\n        mi_predict = np.array(mi_predict)\n        # print(temp2)\n        # print(mi_truth)\n        # print(mi_predict)\n        if np.any(mi_truth[0] == mi_predict[0]):\n            correct = correct + 1\n    return 1.0 * correct / (mx - mi + 1)\n\n\ndef PredictionDifference(predictions, truth, set_num):\n    mi = int(min(set_num))\n    mx = int(max(set_num))\n    # print(type(mi))\n    error_total = 0\n    for i in range(mi, mx + 1):\n        # print(i)\n        temp1 = truth[set_num == i]\n        # np.where(temp1==min(temp1))\n        temp2 = predictions[set_num == i]\n        # mi_truth=np.where(temp1==min(temp1))\n        # mi_truth=np.array(mi_truth)\n        # print(temp1)\n        mi_predict = np.where(temp2 == min(temp2))\n        mi_predict = np.array(mi_predict)\n        min_value_actual = min(temp1)\n        min_value_predicted = temp1[mi_predict[0][0]]\n        err = min_value_predicted - min_value_actual\n        # print(temp2)\n        # print(mi_truth)\n        # print(mi_predict)\n        error_total = error_total + err\n    return 1.0 * error_total / (mx - mi + 1)\n\n\ndef PredictionEpsilonAccuracy(predictions, truth, set_num, epsilon):\n    # if channel chosen is within epsilon of the best channel, it is considered correct\n    mi = int(min(set_num))\n    mx = int(max(set_num))\n    # print(type(mi))\n    correct = 0\n    for i in range(mi, mx + 1):\n        # print(i)\n        temp1 = truth[set_num == i]\n        # np.where(temp1==min(temp1))\n        temp2 = predictions[set_num == i]\n        mi_predict = np.where(temp2 == min(temp2))\n        mi_predict = np.array(mi_predict)\n        min_value_actual = min(temp1)\n        min_value_predicted = temp1[mi_predict[0][0]]\n        err = min_value_predicted - min_value_actual\n        if err <= epsilon:\n            correct = correct + 1\n    return 1.0 * correct / (mx - mi + 1)\n\n\ndef plot_weights(weights):\n    plt.figure()\n    for j in range(num_classes):\n        # Create and choose subplot\n        ax = plt.subplot(1, num_classes, j + 1)\n        # Obtain the weights corresponding to class j\n        weights_j = weights[:, j]\n        # Reshape\n        weights_reshaped = np.reshape(weights_j, (28, 28))\n        # Plot\n        ax.imshow(weights_reshaped, cmap=plt.get_cmap('Greys'))\n        plt.axis('off')\n        plt.title('digit #' + str(j), fontsize=7.0)\n    plt.show()\n\n\n# In[65]:\n# if some entry has a big error between predicted and actual output.\ndef checkErrors(pred, true):\n    a, b = pred.shape\n    pred_vector = []\n    true_vector = []\n    index = []\n    for i in range(0, a):\n        for j in range(0, b):\n            if abs(pred[i][j] - true[i][j]) > 500:\n                index.append(i)\n                index.append(j)\n                pred_vector.append(float(pred[i][j]))\n                true_vector.append(float(true[i][j]))\n    return index, pred_vector, true_vector\n\n\n# In[66]:\n\ndef next_minibatch(X_, Y_, batch_size):\n    # Create a vector with batch_size random integers\n    perm = np.random.permutation(X_.shape[0])\n    perm = perm[:batch_size]\n    # Generate the minibatch\n    X_batch = X_[perm, :]\n    Y_batch = Y_[perm, :]\n    # Return the images and the labels\n    return X_batch, Y_batch\n\n\n# In[67]:\n\ndef Accuracy(pred, true, error):\n    a, b = pred.shape\n    count = 0\n    hit = 0\n    for i in range(0, a):\n            if true[i] != -1000:\n                count += 1\n                if abs(pred[i] - true[i]) < error:\n                    hit += 1\n\n    return hit / count * 1.0\n\ndef layer_calculations(X, weights, biases, act_function):\n    if act_function == 'tanh':\n        Z = tf.nn.tanh(tf.matmul(X, weights) + biases)\n    if act_function == 'linear':\n        Z = (tf.matmul(X, weights) + biases)\n    if act_function == 'relu':\n        Z = tf.nn.relu(tf.matmul(X, weights) + biases)\n    if act_function == 'sigmoid':\n        Z = tf.nn.sigmoid(tf.matmul(X, weights) + biases)\n    return Z\n\n\ndef all_nn_computationsloop(X, layers, weights, biases, activations):\n    Z = X\n    for i in range(0, layers):\n        Z = layer_calculations(Z, weights[i], biases[i], activations[i])\n\n    return Z\n\n\n\ndef print_data(data, limit):\n    a, b = data.shape\n    mi = min(a, limit)\n    for i in range(0, mi):\n        print(data[i])\n\n\n#  Create a Tensorflow graph for multinomial logistic regression\nnum_pixels = X_train.shape[1]\nprint(num_pixels)\n# num_classes = 90\nnum_classes = 1\nbatch_size = 60\nbeta = 0\ngraph_MLR = tf.Graph()\n\nwith graph_MLR.as_default():\n    # (a) Input data\n    #     Load the training, validation and test data into constants that are\n    #     attached to the graph\n    tf_train_data = tf.placeholder(tf.float32, shape=(batch_size, num_pixels))\n    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_classes))\n    tf_test_data = tf.constant(X_test)\n    tf_val_data = tf.constant(X_val)\n\n    # (b) Variables\n    #     Indicate the parameters that we need to infer\n    network_weights = {}\n    network_biases = {}\n    for i in range(0, layers):\n        if i == 0:\n            network_weights[0] = create_weight([num_pixels, num_neurons[i]])\n            network_biases[0] = create_bias([num_neurons[i]])\n        elif i < (layers - 1):\n            network_weights[i] = create_weight([num_neurons[i - 1], num_neurons[i]])\n            network_biases[i] = create_bias([num_neurons[i]])\n        else:\n            network_weights[i] = create_weight([num_neurons[i - 1], num_classes])\n            network_biases[i] = create_bias([num_classes])\n    # num_pixels = input layer, num_neurons = inner layers, Num_classes = output layer\n\n\n    # (c) Computations\n    #     Indicate the computations that we want to perform with the variables and data\n    train_logits = all_nn_computationsloop(tf_train_data, layers, network_weights, network_biases, act_functions)\n    # loss = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(train_logits, tf_train_labels) )\n\n\n    # Mean squared error\n    # cost = tf.reduce_sum(tf.pow(train_logits-tf_train_labels, 2))/(2*num_pixels)\n    # bias=tf.reduce_mean(tf.sub(tf.transpose(train_logits),tf_train_labels))\n    print(train_logits)\n    print(tf_train_labels)\n    # loss = tf.reduce_mean(tf.square(tf.sub(tf.transpose(train_logits),tf_train_labels)))\n    # loss = tf.reduce_mean(tf.mul(tf.sub((train_logits),tf_train_labels),(tf.sub((train_logits),tf_train_labels))))\n    loss = tf.reduce_mean(tf.square(tf.subtract((train_logits),\n                                                tf_train_labels)))\n    print(loss)\n    global_step = tf.Variable(0, trainable=False)\n    learning_rate = tf.train.inverse_time_decay(learning_rate, global_step,10000, decrease_rate, staircase=False)\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss,global_step=global_step)\n\n    train_prediction = (train_logits)\n    val_prediction = all_nn_computationsloop(tf_val_data, layers, network_weights, network_biases, act_functions)\n    test_prediction = all_nn_computationsloop(tf_test_data, layers, network_weights, network_biases, act_functions)\n\n# Run NN\nmax_iterations = num_iterations  # 100001#500001\n\nwith tf.Session(graph=graph_MLR) as session:\n    # 1. Initialize the weights and biases. This is a one-time operation\n    # tf.global_variables_initializer().run()\n    tf.global_variables_initializer().run()\n    # tf.initialize_all_variables().run()\n    print('Initialized')\n    tr_predict_hist = []\n    tr_score_hist = []\n    val_predict_hist = []\n    val_score_hist = []\n    val_error_hist = []\n    val_error_epsilon_pt1_hist = []\n    val_error_epsilon_pt2_hist = []\n    val_error_epsilon_pt3_hist = []\n    for step in range(max_iterations):\n        X_batch, Y_batch = next_minibatch(X_train, Y_train, batch_size)\n\n        # feed_dict = { tf_train_data   : X_batch,\n        #              tf_train_labels : np.squeeze(Y_batch)}\n        feed_dict = {tf_train_data: X_batch,\n                     tf_train_labels: Y_batch}\n        # Run the computations. We tell .run() that we want to run the optimizer,\n        # Craig: May want to run optimizer by itself most time, dont need to calculate train prediction every step\n        # print(\"boo\")\n        _, l, predictions, lr = session.run([optimizer, loss, train_prediction, learning_rate], feed_dict=feed_dict)\n\n        # Report every 10000 iterations\n        if (step % 200 == 0):\n            # Print the loss\n            # predictions=train_prediction.eval()\n\n            print('Loss at step %d: %f' % (step, l))\n            print(\"learning rate %d: %f\" % (step, lr))\n            # Obtain and print the accuracy on the training set\n            tr_predict = mean_squared_error(predictions, Y_batch)\n            tr_predict_hist = np.append(tr_predict_hist, tr_predict)\n\n            print('  +Training MSE: %f' % tr_predict)\n            print('  +Training MAE: %f' % mean_absolute_error(predictions, Y_batch))\n            if norm_in != 'no':\n                tr_pred_dB = denormalize_input(predictions, min_values_out, max_values_out, min_values_out, out_format,\n                                               scale)\n                Y_tr_dB = denormalize_input(Y_batch, min_values_out, max_values_out, min_values_out, out_format, scale)\n                print('  +Training MSE: %f' % mean_squared_error(tr_pred_dB, Y_tr_dB))\n                print('  +Training MAE: %f' % mean_absolute_error(tr_pred_dB, Y_tr_dB))\n            print('Predictions')\n            print_data(predictions, 20)\n            print('      ')\n            print('Batch')\n            print_data(Y_batch, 20)\n            print('      ')\n            if norm_in != 'no':\n                print('Tr Predictions dB')\n                print_data(tr_pred_dB, 20)\n                print('      ')\n                print('Tr_dB')\n                print_data(Y_tr_dB, 20)\n\n            # print('  +Training Prediction: %f' % tr_score)\n            # Obtain and print the accuracy on the validation set\n            val_pred_value = val_prediction.eval()\n            val_predict = mean_squared_error(val_pred_value, Y_val)\n            val_predict_hist = np.append(val_predict_hist, val_predict)\n\n            print('  +Validation MSE: %f' % mean_squared_error(val_prediction.eval(), Y_val))\n            print('  +Validation MAE: %f' % mean_absolute_error(val_prediction.eval(), Y_val))\n            if norm_out != 'no':\n                pred_dB = denormalize_input(val_prediction.eval(), min_values_out, max_values_out, min_values_out,\n                                            out_format, scale)\n                Y_Val_dB = denormalize_input(Y_val, min_values_out, max_values_out, min_values_out, out_format, scale)\n                print('  +Validation MSE: %f' % mean_squared_error(pred_dB, Y_Val_dB))\n                print('  +Validation MAE: %f' % mean_absolute_error(pred_dB, Y_Val_dB))\n\n            print('Val Predictions')\n            print_data(val_prediction.eval(), 20)\n            print('      ')\n            print('Val Data')\n            print_data(Y_val, 20)\n            print('      ')\n            if norm_out != 'no':\n                print('Predictions dB')\n                print_data(pred_dB, 20)\n                print('      ')\n                print('Val_dB')\n                print_data(Y_Val_dB, 20)\n                print('Validation: Percent of non zero Power predictions within .2,.4,.6 dB')\n                print(Accuracy(pred_dB,Y_Val_dB,.2))\n                print(Accuracy(pred_dB,Y_Val_dB,.4))\n                print(Accuracy(pred_dB,Y_Val_dB,.6))\n                print(Accuracy(pred_dB, Y_Val_dB, .8))\n                print(Accuracy(pred_dB, Y_Val_dB, 1))\n                print('Training: Percent of non zero Power predictions within .2,.4,.6 dB')\n                print(Accuracy(tr_pred_dB, Y_tr_dB, .2))\n                print(Accuracy(tr_pred_dB, Y_tr_dB, .4))\n                print(Accuracy(tr_pred_dB, Y_tr_dB, .6))\n                print(Accuracy(tr_pred_dB, Y_tr_dB, .8))\n                print(Accuracy(tr_pred_dB, Y_tr_dB, 1))\n\n                # There's no testing defined yet.\n                if step != 0:\n                    ind, pred_vector, true_vector = checkErrors(pred_dB, Y_Val_dB)\n                    print(\"Number of Test Cases 0 Power\")\n                    for i in range(0,len(Y_Val_dB[0])):\n                        print(int(sum(Y_Val_dB[i] == -1000)))\n                    print(\"Number of cases that were 0 power that we had predicted power\")\n                    print(len(ind)/2)\n                    print(\"Number of Test Cases that had non 0 power\")\n                    print(ind)\n                    # print(pred_vector)\n                    # print(true_vector)\n    # 3. Accuracy on the test set\n    test_pred = test_prediction.eval()\n    if norm_out != 'no':\n        test_pred_dB = denormalize_input(test_pred, min_values_out, max_values_out, min_values_out, out_format, scale)\n        Y_test_dB = denormalize_input(Y_test, min_values_out, max_values_out, min_values_out, out_format, scale)\n    print('Test Data Pred')\n    print_data(test_pred, 200)\n    print('   ')\n    print('Test Data')\n    print_data(Y_test, 200)\n    if norm_out != 'no':\n        print('Test Data Pred dB')\n        print_data(test_pred_dB, 200)\n        print('   ')\n        print('Test Data dB')\n        print_data(Y_test_dB, 200)\n    print('Test MSE: %f' % mean_squared_error(test_pred, Y_test))\n    print('Test MAE: %f' % mean_absolute_error(test_pred, Y_test))\n    if norm_out != 'no':\n        print('  +TEST MSE: %f' % mean_squared_error(test_pred_dB, Y_test_dB))\n        print('  +TEST MAE: %f' % mean_absolute_error(test_pred_dB, Y_test_dB))\n\n# Print accuracy vals for each size\nprint('Test: Percent of non zero Power predictions within .2,.4,.6 dB')\nprint(Accuracy(test_pred_dB, Y_test_dB, .2))\nprint(Accuracy(test_pred_dB, Y_test_dB, .4))\nprint(Accuracy(test_pred_dB, Y_test_dB, .6))\nprint(Accuracy(test_pred_dB, Y_test_dB, .8))\nprint(Accuracy(test_pred_dB, Y_test_dB, 1))\n\n# In[57]:\n\nprint(\"Large Test Errors\")\nind, pred_vector, true_vector = checkErrors(test_pred_dB, Y_test_dB)\nprint(ind)\nprint(pred_vector)\nprint(true_vector)\n\nprint(\"Training RMSE\")\nprint(tr_predict_hist)\n\n# In[110]:\n\nprint(\"validation RMSE\")\nprint(val_predict_hist)\n\n# Set the stdout back to console instead of debug file.\nif(isLoging == \"on\"):\n    sys.stdout = old_stdout\n    log_file.close()\n","sub_path":"PowerPredEDFA_w_input1Channelv4tf1.py","file_name":"PowerPredEDFA_w_input1Channelv4tf1.py","file_ext":"py","file_size_in_byte":29925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"130498529","text":"import datetime\nimport time\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\ndef timedTask():\n    print(datetime.datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S.%f\")[:-3])\n\nif __name__ == '__main__':\n    # 创建后台执行的schedulers\n    scheduler = BackgroundScheduler()\n\n    # 添加调度任务\n    scheduler.add_job(timedTask,'interval',seconds=2)\n\n    # 启动调度任务\n    scheduler.start()\n\n    while True:\n        print(time.time())\n        time.sleep(5)","sub_path":"007_常用模块/定时任务/APSchedulerDemo_简单示例.py","file_name":"APSchedulerDemo_简单示例.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"398542741","text":"# -*- coding: utf-8 -*-\nfrom django.conf import settings\nfrom src.utils import utils, prettytable\nfrom src.commands.default.muxcommand import MuxCommand\n\n\nclass CmdGive(MuxCommand):\n    \"\"\"\n    give away things\n\n    Usage:\n      give <inventory obj> = <target>\n\n    Gives an items from your inventory to another character,\n    placing it in their inventory.\n    \"\"\"\n    key = \"give\"\n    aliases = [\"put\"]\n    locks = \"cmd:all()\"\n\n    def func(self):\n        \"Implement give\"\n        from game.gamesrc.objects.character import Character\n\n        caller = self.caller\n        if not self.args:\n            caller.msg(\"Usage: give/put <inventory object> = <target>\")\n            return\n        \n        # Parse input\n        \n        \n        if self.rhs:\n            to_give_name = self.lhs\n            target_name = self.rhs\n        else:\n            to_give_name = None\n            target_name = None\n            if self.lhs:\n                to_give_name = self.lhs.split(' ', 1)[0]\n                if len(self.lhs.split(' ', 1)) > 1:\n                    target_name = self.lhs.split(' ', 1)[1]\n            \n            if not target_name:\n                return\n        \n        to_give = caller.search(to_give_name, location=caller)\n        target = caller.search(target_name)\n        if not (to_give and target):\n            return\n        if target == caller:\n            caller.msg(\"You keep %s to yourself.\" % to_give.name)\n            return\n        #if not to_give.location == caller:\n        #    caller.msg(\"You are not holding %s.\" % to_give.name)\n        #    return\n        # give object\n        \n        \n        if isinstance(target, Character):\n            if to_give.total_weight + target.contents_weight > target.max_contents_weight:\n                caller.msg(\"%s cannot carry %s.\" % (target.name, to_give.name))\n                return\n            \n            to_give.location = target\n            caller.msg(\"You give %s to %s.\" % (to_give.name, target.name))\n            target.msg(\"%s gives you %s.\" % (caller.name, to_give.name))\n            caller.location.msg_contents(\"%s gives %s to %s.\" % (caller.name, to_give.name, target.name), exclude=[caller, target])\n        else:\n            if not target.is_container:\n                caller.msg(\"That's not a container.\")\n                return\n            if to_give.total_weight + target.contents_weight > target.max_contents_weight:\n                caller.msg(\"It's full.\")\n                return\n\n            to_give.location = target\n            caller.msg(\"You put %s in %s.\" % (to_give.name, target.name))\n            target.msg(\"%s puts %s in you.\" % (caller.name, to_give.name))\n            caller.location.msg_contents(\"%s puts %s in %s.\" % (caller.name, to_give.name, target.name), exclude=[caller, target])\n","sub_path":"game/gamesrc/commands/give.py","file_name":"give.py","file_ext":"py","file_size_in_byte":2791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"127962761","text":"from scipy.misc import logsumexp\n\n\ndef normal(a, *, log=False):\n    if log:\n        return a - logsumexp(a)\n    return a / a.sum()\n\n\ndef conditional(a, axis, *, log=False):\n    try:\n        axis = tuple(axis)\n    except TypeError:\n        axis = (axis,)\n    finally:\n        axis = tuple(i for i in range(a.ndim) if i not in axis)\n    if log: return a - logsumexp(a, axis, keepdims=True)\n    return a / a.sum(axis, keepdims=True)\n\n\ndef marginal(a, axis, *, log=False):\n    if log:\n        return logsumexp(a, axis)\n    return a.sum(axis=axis)\n\n\nif __name__ == '__main__':\n    import numpy as np\n\n    a = np.arange(24).reshape((2, 3, 4))\n\n    print(a)\n\n    log = False\n\n    b = normal(a, log=log)\n    # b = np.exp(b)\n    print('normal')\n    print(b)\n    print(b.shape)\n    print(b.sum())\n    print()\n\n#     a = b\n\n    b = conditional(a, 0, log=log)\n    # b = np.exp(b)\n    print('conditional')\n    print(b)\n    print(b.shape)\n    print(b.sum(1))\n    print()\n\n    b = marginal(a, 0, log=log)\n    # b = np.exp(b)\n    print('marginal')\n    print(b)\n    print(b.shape)\n    print(b.sum())\n","sub_path":"src/pytk/probability.py","file_name":"probability.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"420820568","text":"# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n# Generated file, DO NOT EDIT\n# Changes may cause incorrect behavior and will be lost if the code is regenerated.\n# --------------------------------------------------------------------------------------------\n\n\nfrom msrest.serialization import Model\n\n\n\nclass BehaviorCreateModel(Model):\n    \"\"\"BehaviorCreateModel.\n\n    :param color: Color\n    :type color: str\n    :param inherits: Parent behavior id\n    :type inherits: str\n    :param name: Name of the behavior\n    :type name: str\n    \"\"\"\n\n    _attribute_map = {\n        'color': {'key': 'color', 'type': 'str'},\n        'inherits': {'key': 'inherits', 'type': 'str'},\n        'name': {'key': 'name', 'type': 'str'}\n    }\n\n    def __init__(self, color=None, inherits=None, name=None):\n        super(BehaviorCreateModel, self).__init__()\n        self.color = color\n        self.inherits = inherits\n        self.name = name\n\n\n\nclass BehaviorModel(Model):\n    \"\"\"BehaviorModel.\n\n    :param abstract: Is the behavior abstract (i.e. can not be associated with any work item type)\n    :type abstract: bool\n    :param color: Color\n    :type color: str\n    :param description: Description\n    :type description: str\n    :param id: Behavior Id\n    :type id: str\n    :param inherits: Parent behavior reference\n    :type inherits: :class:`WorkItemBehaviorReference <work-item-tracking.v4_1.models.WorkItemBehaviorReference>`\n    :param name: Behavior Name\n    :type name: str\n    :param overridden: Is the behavior overrides a behavior from system process\n    :type overridden: bool\n    :param rank: Rank\n    :type rank: int\n    :param url: Url of the behavior\n    :type url: str\n    \"\"\"\n\n    _attribute_map = {\n        'abstract': {'key': 'abstract', 'type': 'bool'},\n        'color': {'key': 'color', 'type': 'str'},\n        'description': {'key': 'description', 'type': 'str'},\n        'id': {'key': 'id', 'type': 'str'},\n        'inherits': {'key': 'inherits', 'type': 'WorkItemBehaviorReference'},\n        'name': {'key': 'name', 'type': 'str'},\n        'overridden': {'key': 'overridden', 'type': 'bool'},\n        'rank': {'key': 'rank', 'type': 'int'},\n        'url': {'key': 'url', 'type': 'str'}\n    }\n\n    def __init__(self, abstract=None, color=None, description=None, id=None, inherits=None, name=None, overridden=None, rank=None, url=None):\n        super(BehaviorModel, self).__init__()\n        self.abstract = abstract\n        self.color = color\n        self.description = description\n        self.id = id\n        self.inherits = inherits\n        self.name = name\n        self.overridden = overridden\n        self.rank = rank\n        self.url = url\n\n\n\nclass BehaviorReplaceModel(Model):\n    \"\"\"BehaviorReplaceModel.\n\n    :param color: Color\n    :type color: str\n    :param name: Behavior Name\n    :type name: str\n    \"\"\"\n\n    _attribute_map = {\n        'color': {'key': 'color', 'type': 'str'},\n        'name': {'key': 'name', 'type': 'str'}\n    }\n\n    def __init__(self, color=None, name=None):\n        super(BehaviorReplaceModel, self).__init__()\n        self.color = color\n        self.name = name\n\n\n\nclass Control(Model):\n    \"\"\"Control.\n\n    :param contribution: Contribution for the control.\n    :type contribution: :class:`WitContribution <work-item-tracking.v4_1.models.WitContribution>`\n    :param control_type: Type of the control.\n    :type control_type: str\n    :param height: Height of the control, for html controls.\n    :type height: int\n    :param id: The id for the layout node.\n    :type id: str\n    :param inherited: A value indicating whether this layout node has been inherited from a parent layout.  This is expected to only be only set by the combiner.\n    :type inherited: bool\n    :param is_contribution: A value indicating if the layout node is contribution or not.\n    :type is_contribution: bool\n    :param label: Label for the field\n    :type label: str\n    :param metadata: Inner text of the control.\n    :type metadata: str\n    :param order:\n    :type order: int\n    :param overridden: A value indicating whether this layout node has been overridden by a child layout.\n    :type overridden: bool\n    :param read_only: A value indicating if the control is readonly.\n    :type read_only: bool\n    :param visible: A value indicating if the control should be hidden or not.\n    :type visible: bool\n    :param watermark: Watermark text for the textbox.\n    :type watermark: str\n    \"\"\"\n\n    _attribute_map = {\n        'contribution': {'key': 'contribution', 'type': 'WitContribution'},\n        'control_type': {'key': 'controlType', 'type': 'str'},\n        'height': {'key': 'height', 'type': 'int'},\n        'id': {'key': 'id', 'type': 'str'},\n        'inherited': {'key': 'inherited', 'type': 'bool'},\n        'is_contribution': {'key': 'isContribution', 'type': 'bool'},\n        'label': {'key': 'label', 'type': 'str'},\n        'metadata': {'key': 'metadata', 'type': 'str'},\n        'order': {'key': 'order', 'type': 'int'},\n        'overridden': {'key': 'overridden', 'type': 'bool'},\n        'read_only': {'key': 'readOnly', 'type': 'bool'},\n        'visible': {'key': 'visible', 'type': 'bool'},\n        'watermark': {'key': 'watermark', 'type': 'str'}\n    }\n\n    def __init__(self, contribution=None, control_type=None, height=None, id=None, inherited=None, is_contribution=None, label=None, metadata=None, order=None, overridden=None, read_only=None, visible=None, watermark=None):\n        super(Control, self).__init__()\n        self.contribution = contribution\n        self.control_type = control_type\n        self.height = height\n        self.id = id\n        self.inherited = inherited\n        self.is_contribution = is_contribution\n        self.label = label\n        self.metadata = metadata\n        self.order = order\n        self.overridden = overridden\n        self.read_only = read_only\n        self.visible = visible\n        self.watermark = watermark\n\n\n\nclass Extension(Model):\n    \"\"\"Extension.\n\n    :param id:\n    :type id: str\n    \"\"\"\n\n    _attribute_map = {\n        'id': {'key': 'id', 'type': 'str'}\n    }\n\n    def __init__(self, id=None):\n        super(Extension, self).__init__()\n        self.id = id\n\n\n\nclass FieldModel(Model):\n    \"\"\"FieldModel.\n\n    :param description: Description about field\n    :type description: str\n    :param id: ID of the field\n    :type id: str\n    :param name: Name of the field\n    :type name: str\n    :param pick_list: Reference to picklist in this field\n    :type pick_list: :class:`PickListMetadataModel <work-item-tracking.v4_1.models.PickListMetadataModel>`\n    :param type: Type of field\n    :type type: object\n    :param url: Url to the field\n    :type url: str\n    \"\"\"\n\n    _attribute_map = {\n        'description': {'key': 'description', 'type': 'str'},\n        'id': {'key': 'id', 'type': 'str'},\n        'name': {'key': 'name', 'type': 'str'},\n        'pick_list': {'key': 'pickList', 'type': 'PickListMetadataModel'},\n        'type': {'key': 'type', 'type': 'object'},\n        'url': {'key': 'url', 'type': 'str'}\n    }\n\n    def __init__(self, description=None, id=None, name=None, pick_list=None, type=None, url=None):\n        super(FieldModel, self).__init__()\n        self.description = description\n        self.id = id\n        self.name = name\n        self.pick_list = pick_list\n        self.type = type\n        self.url = url\n\n\n\nclass FieldUpdate(Model):\n    \"\"\"FieldUpdate.\n\n    :param description:\n    :type description: str\n    :param id:\n    :type id: str\n    \"\"\"\n\n    _attribute_map = {\n        'description': {'key': 'description', 'type': 'str'},\n        'id': {'key': 'id', 'type': 'str'}\n    }\n\n    def __init__(self, description=None, id=None):\n        super(FieldUpdate, self).__init__()\n        self.description = description\n        self.id = id\n\n\n\nclass FormLayout(Model):\n    \"\"\"FormLayout.\n\n    :param extensions: Gets and sets extensions list\n    :type extensions: list of :class:`Extension <work-item-tracking.v4_1.models.Extension>`\n    :param pages: Top level tabs of the layout.\n    :type pages: list of :class:`Page <work-item-tracking.v4_1.models.Page>`\n    :param system_controls: Headers controls of the layout.\n    :type system_controls: list of :class:`Control <work-item-tracking.v4_1.models.Control>`\n    \"\"\"\n\n    _attribute_map = {\n        'extensions': {'key': 'extensions', 'type': '[Extension]'},\n        'pages': {'key': 'pages', 'type': '[Page]'},\n        'system_controls': {'key': 'systemControls', 'type': '[Control]'}\n    }\n\n    def __init__(self, extensions=None, pages=None, system_controls=None):\n        super(FormLayout, self).__init__()\n        self.extensions = extensions\n        self.pages = pages\n        self.system_controls = system_controls\n\n\n\nclass Group(Model):\n    \"\"\"Group.\n\n    :param contribution: Contribution for the group.\n    :type contribution: :class:`WitContribution <work-item-tracking.v4_1.models.WitContribution>`\n    :param controls: Controls to be put in the group.\n    :type controls: list of :class:`Control <work-item-tracking.v4_1.models.Control>`\n    :param height: The height for the contribution.\n    :type height: int\n    :param id: The id for the layout node.\n    :type id: str\n    :param inherited: A value indicating whether this layout node has been inherited from a parent layout.  This is expected to only be only set by the combiner.\n    :type inherited: bool\n    :param is_contribution: A value indicating if the layout node is contribution are not.\n    :type is_contribution: bool\n    :param label: Label for the group.\n    :type label: str\n    :param order: Order in which the group should appear in the section.\n    :type order: int\n    :param overridden: A value indicating whether this layout node has been overridden by a child layout.\n    :type overridden: bool\n    :param visible: A value indicating if the group should be hidden or not.\n    :type visible: bool\n    \"\"\"\n\n    _attribute_map = {\n        'contribution': {'key': 'contribution', 'type': 'WitContribution'},\n        'controls': {'key': 'controls', 'type': '[Control]'},\n        'height': {'key': 'height', 'type': 'int'},\n        'id': {'key': 'id', 'type': 'str'},\n        'inherited': {'key': 'inherited', 'type': 'bool'},\n        'is_contribution': {'key': 'isContribution', 'type': 'bool'},\n        'label': {'key': 'label', 'type': 'str'},\n        'order': {'key': 'order', 'type': 'int'},\n        'overridden': {'key': 'overridden', 'type': 'bool'},\n        'visible': {'key': 'visible', 'type': 'bool'}\n    }\n\n    def __init__(self, contribution=None, controls=None, height=None, id=None, inherited=None, is_contribution=None, label=None, order=None, overridden=None, visible=None):\n        super(Group, self).__init__()\n        self.contribution = contribution\n        self.controls = controls\n        self.height = height\n        self.id = id\n        self.inherited = inherited\n        self.is_contribution = is_contribution\n        self.label = label\n        self.order = order\n        self.overridden = overridden\n        self.visible = visible\n\n\n\nclass HideStateModel(Model):\n    \"\"\"HideStateModel.\n\n    :param hidden:\n    :type hidden: bool\n    \"\"\"\n\n    _attribute_map = {\n        'hidden': {'key': 'hidden', 'type': 'bool'}\n    }\n\n    def __init__(self, hidden=None):\n        super(HideStateModel, self).__init__()\n        self.hidden = hidden\n\n\n\nclass Page(Model):\n    \"\"\"Page.\n\n    :param contribution: Contribution for the page.\n    :type contribution: :class:`WitContribution <work-item-tracking.v4_1.models.WitContribution>`\n    :param id: The id for the layout node.\n    :type id: str\n    :param inherited: A value indicating whether this layout node has been inherited from a parent layout.  This is expected to only be only set by the combiner.\n    :type inherited: bool\n    :param is_contribution: A value indicating if the layout node is contribution are not.\n    :type is_contribution: bool\n    :param label: The label for the page.\n    :type label: str\n    :param locked: A value indicating whether any user operations are permitted on this page and the contents of this page\n    :type locked: bool\n    :param order: Order in which the page should appear in the layout.\n    :type order: int\n    :param overridden: A value indicating whether this layout node has been overridden by a child layout.\n    :type overridden: bool\n    :param page_type: The icon for the page.\n    :type page_type: object\n    :param sections: The sections of the page.\n    :type sections: list of :class:`Section <work-item-tracking.v4_1.models.Section>`\n    :param visible: A value indicating if the page should be hidden or not.\n    :type visible: bool\n    \"\"\"\n\n    _attribute_map = {\n        'contribution': {'key': 'contribution', 'type': 'WitContribution'},\n        'id': {'key': 'id', 'type': 'str'},\n        'inherited': {'key': 'inherited', 'type': 'bool'},\n        'is_contribution': {'key': 'isContribution', 'type': 'bool'},\n        'label': {'key': 'label', 'type': 'str'},\n        'locked': {'key': 'locked', 'type': 'bool'},\n        'order': {'key': 'order', 'type': 'int'},\n        'overridden': {'key': 'overridden', 'type': 'bool'},\n        'page_type': {'key': 'pageType', 'type': 'object'},\n        'sections': {'key': 'sections', 'type': '[Section]'},\n        'visible': {'key': 'visible', 'type': 'bool'}\n    }\n\n    def __init__(self, contribution=None, id=None, inherited=None, is_contribution=None, label=None, locked=None, order=None, overridden=None, page_type=None, sections=None, visible=None):\n        super(Page, self).__init__()\n        self.contribution = contribution\n        self.id = id\n        self.inherited = inherited\n        self.is_contribution = is_contribution\n        self.label = label\n        self.locked = locked\n        self.order = order\n        self.overridden = overridden\n        self.page_type = page_type\n        self.sections = sections\n        self.visible = visible\n\n\n\nclass PickListItemModel(Model):\n    \"\"\"PickListItemModel.\n\n    :param id:\n    :type id: str\n    :param value:\n    :type value: str\n    \"\"\"\n\n    _attribute_map = {\n        'id': {'key': 'id', 'type': 'str'},\n        'value': {'key': 'value', 'type': 'str'}\n    }\n\n    def __init__(self, id=None, value=None):\n        super(PickListItemModel, self).__init__()\n        self.id = id\n        self.value = value\n\n\n\nclass PickListMetadataModel(Model):\n    \"\"\"PickListMetadataModel.\n\n    :param id: ID of the picklist\n    :type id: str\n    :param is_suggested: Is input values by user only limited to suggested values\n    :type is_suggested: bool\n    :param name: Name of the picklist\n    :type name: str\n    :param type: Type of picklist\n    :type type: str\n    :param url: Url of the picklist\n    :type url: str\n    \"\"\"\n\n    _attribute_map = {\n        'id': {'key': 'id', 'type': 'str'},\n        'is_suggested': {'key': 'isSuggested', 'type': 'bool'},\n        'name': {'key': 'name', 'type': 'str'},\n        'type': {'key': 'type', 'type': 'str'},\n        'url': {'key': 'url', 'type': 'str'}\n    }\n\n    def __init__(self, id=None, is_suggested=None, name=None, type=None, url=None):\n        super(PickListMetadataModel, self).__init__()\n        self.id = id\n        self.is_suggested = is_suggested\n        self.name = name\n        self.type = type\n        self.url = url\n\n\n\nclass PickListModel(PickListMetadataModel):\n    \"\"\"PickListModel.\n\n    :param id: ID of the picklist\n    :type id: str\n    :param is_suggested: Is input values by user only limited to suggested values\n    :type is_suggested: bool\n    :param name: Name of the picklist\n    :type name: str\n    :param type: Type of picklist\n    :type type: str\n    :param url: Url of the picklist\n    :type url: str\n    :param items: A list of PicklistItemModel\n    :type items: list of :class:`PickListItemModel <work-item-tracking.v4_1.models.PickListItemModel>`\n    \"\"\"\n\n    _attribute_map = {\n        'id': {'key': 'id', 'type': 'str'},\n        'is_suggested': {'key': 'isSuggested', 'type': 'bool'},\n        'name': {'key': 'name', 'type': 'str'},\n        'type': {'key': 'type', 'type': 'str'},\n        'url': {'key': 'url', 'type': 'str'},\n        'items': {'key': 'items', 'type': '[PickListItemModel]'}\n    }\n\n    def __init__(self, id=None, is_suggested=None, name=None, type=None, url=None, items=None):\n        super(PickListModel, self).__init__(id=id, is_suggested=is_suggested, name=name, type=type, url=url)\n        self.items = items\n\n\n\nclass Section(Model):\n    \"\"\"Section.\n\n    :param groups:\n    :type groups: list of :class:`Group <work-item-tracking.v4_1.models.Group>`\n    :param id: The id for the layout node.\n    :type id: str\n    :param overridden: A value indicating whether this layout node has been overridden by a child layout.\n    :type overridden: bool\n    \"\"\"\n\n    _attribute_map = {\n        'groups': {'key': 'groups', 'type': '[Group]'},\n        'id': {'key': 'id', 'type': 'str'},\n        'overridden': {'key': 'overridden', 'type': 'bool'}\n    }\n\n    def __init__(self, groups=None, id=None, overridden=None):\n        super(Section, self).__init__()\n        self.groups = groups\n        self.id = id\n        self.overridden = overridden\n\n\n\nclass WitContribution(Model):\n    \"\"\"WitContribution.\n\n    :param contribution_id: The id for the contribution.\n    :type contribution_id: str\n    :param height: The height for the contribution.\n    :type height: int\n    :param inputs: A dictionary holding key value pairs for contribution inputs.\n    :type inputs: dict\n    :param show_on_deleted_work_item: A value indicating if the contribution should be show on deleted workItem.\n    :type show_on_deleted_work_item: bool\n    \"\"\"\n\n    _attribute_map = {\n        'contribution_id': {'key': 'contributionId', 'type': 'str'},\n        'height': {'key': 'height', 'type': 'int'},\n        'inputs': {'key': 'inputs', 'type': '{object}'},\n        'show_on_deleted_work_item': {'key': 'showOnDeletedWorkItem', 'type': 'bool'}\n    }\n\n    def __init__(self, contribution_id=None, height=None, inputs=None, show_on_deleted_work_item=None):\n        super(WitContribution, self).__init__()\n        self.contribution_id = contribution_id\n        self.height = height\n        self.inputs = inputs\n        self.show_on_deleted_work_item = show_on_deleted_work_item\n\n\n\nclass WorkItemBehaviorReference(Model):\n    \"\"\"WorkItemBehaviorReference.\n\n    :param id: The ID of the reference behavior\n    :type id: str\n    :param url: The url of the reference behavior\n    :type url: str\n    \"\"\"\n\n    _attribute_map = {\n        'id': {'key': 'id', 'type': 'str'},\n        'url': {'key': 'url', 'type': 'str'}\n    }\n\n    def __init__(self, id=None, url=None):\n        super(WorkItemBehaviorReference, self).__init__()\n        self.id = id\n        self.url = url\n\n\n\nclass WorkItemStateInputModel(Model):\n    \"\"\"WorkItemStateInputModel.\n\n    :param color: Color of the state\n    :type color: str\n    :param name: Name of the state\n    :type name: str\n    :param order: Order in which state should appear\n    :type order: int\n    :param state_category: Category of the state\n    :type state_category: str\n    \"\"\"\n\n    _attribute_map = {\n        'color': {'key': 'color', 'type': 'str'},\n        'name': {'key': 'name', 'type': 'str'},\n        'order': {'key': 'order', 'type': 'int'},\n        'state_category': {'key': 'stateCategory', 'type': 'str'}\n    }\n\n    def __init__(self, color=None, name=None, order=None, state_category=None):\n        super(WorkItemStateInputModel, self).__init__()\n        self.color = color\n        self.name = name\n        self.order = order\n        self.state_category = state_category\n\n\n\nclass WorkItemStateResultModel(Model):\n    \"\"\"WorkItemStateResultModel.\n\n    :param color: Color of the state\n    :type color: str\n    :param hidden: Is the state hidden\n    :type hidden: bool\n    :param id: The ID of the State\n    :type id: str\n    :param name: Name of the state\n    :type name: str\n    :param order: Order in which state should appear\n    :type order: int\n    :param state_category: Category of the state\n    :type state_category: str\n    :param url: Url of the state\n    :type url: str\n    \"\"\"\n\n    _attribute_map = {\n        'color': {'key': 'color', 'type': 'str'},\n        'hidden': {'key': 'hidden', 'type': 'bool'},\n        'id': {'key': 'id', 'type': 'str'},\n        'name': {'key': 'name', 'type': 'str'},\n        'order': {'key': 'order', 'type': 'int'},\n        'state_category': {'key': 'stateCategory', 'type': 'str'},\n        'url': {'key': 'url', 'type': 'str'}\n    }\n\n    def __init__(self, color=None, hidden=None, id=None, name=None, order=None, state_category=None, url=None):\n        super(WorkItemStateResultModel, self).__init__()\n        self.color = color\n        self.hidden = hidden\n        self.id = id\n        self.name = name\n        self.order = order\n        self.state_category = state_category\n        self.url = url\n\n\n\nclass WorkItemTypeBehavior(Model):\n    \"\"\"WorkItemTypeBehavior.\n\n    :param behavior:\n    :type behavior: :class:`WorkItemBehaviorReference <work-item-tracking.v4_1.models.WorkItemBehaviorReference>`\n    :param is_default:\n    :type is_default: bool\n    :param url:\n    :type url: str\n    \"\"\"\n\n    _attribute_map = {\n        'behavior': {'key': 'behavior', 'type': 'WorkItemBehaviorReference'},\n        'is_default': {'key': 'isDefault', 'type': 'bool'},\n        'url': {'key': 'url', 'type': 'str'}\n    }\n\n    def __init__(self, behavior=None, is_default=None, url=None):\n        super(WorkItemTypeBehavior, self).__init__()\n        self.behavior = behavior\n        self.is_default = is_default\n        self.url = url\n\n\n\nclass WorkItemTypeFieldModel(Model):\n    \"\"\"WorkItemTypeFieldModel.\n\n    :param allow_groups:\n    :type allow_groups: bool\n    :param default_value:\n    :type default_value: str\n    :param name:\n    :type name: str\n    :param pick_list:\n    :type pick_list: :class:`PickListMetadataModel <work-item-tracking.v4_1.models.PickListMetadataModel>`\n    :param read_only:\n    :type read_only: bool\n    :param reference_name:\n    :type reference_name: str\n    :param required:\n    :type required: bool\n    :param type:\n    :type type: object\n    :param url:\n    :type url: str\n    \"\"\"\n\n    _attribute_map = {\n        'allow_groups': {'key': 'allowGroups', 'type': 'bool'},\n        'default_value': {'key': 'defaultValue', 'type': 'str'},\n        'name': {'key': 'name', 'type': 'str'},\n        'pick_list': {'key': 'pickList', 'type': 'PickListMetadataModel'},\n        'read_only': {'key': 'readOnly', 'type': 'bool'},\n        'reference_name': {'key': 'referenceName', 'type': 'str'},\n        'required': {'key': 'required', 'type': 'bool'},\n        'type': {'key': 'type', 'type': 'object'},\n        'url': {'key': 'url', 'type': 'str'}\n    }\n\n    def __init__(self, allow_groups=None, default_value=None, name=None, pick_list=None, read_only=None, reference_name=None, required=None, type=None, url=None):\n        super(WorkItemTypeFieldModel, self).__init__()\n        self.allow_groups = allow_groups\n        self.default_value = default_value\n        self.name = name\n        self.pick_list = pick_list\n        self.read_only = read_only\n        self.reference_name = reference_name\n        self.required = required\n        self.type = type\n        self.url = url\n\n\n\nclass WorkItemTypeModel(Model):\n    \"\"\"WorkItemTypeModel.\n\n    :param behaviors: Behaviors of the work item type\n    :type behaviors: list of :class:`WorkItemTypeBehavior <work-item-tracking.v4_1.models.WorkItemTypeBehavior>`\n    :param class_: Class of the work item type\n    :type class_: object\n    :param color: Color of the work item type\n    :type color: str\n    :param description: Description of the work item type\n    :type description: str\n    :param icon: Icon of the work item type\n    :type icon: str\n    :param id: The ID of the work item type\n    :type id: str\n    :param inherits: Parent WIT Id/Internal ReferenceName that it inherits from\n    :type inherits: str\n    :param is_disabled: Is work item type disabled\n    :type is_disabled: bool\n    :param layout: Layout of the work item type\n    :type layout: :class:`FormLayout <work-item-tracking.v4_1.models.FormLayout>`\n    :param name: Name of the work item type\n    :type name: str\n    :param states: States of the work item type\n    :type states: list of :class:`WorkItemStateResultModel <work-item-tracking.v4_1.models.WorkItemStateResultModel>`\n    :param url: Url of the work item type\n    :type url: str\n    \"\"\"\n\n    _attribute_map = {\n        'behaviors': {'key': 'behaviors', 'type': '[WorkItemTypeBehavior]'},\n        'class_': {'key': 'class', 'type': 'object'},\n        'color': {'key': 'color', 'type': 'str'},\n        'description': {'key': 'description', 'type': 'str'},\n        'icon': {'key': 'icon', 'type': 'str'},\n        'id': {'key': 'id', 'type': 'str'},\n        'inherits': {'key': 'inherits', 'type': 'str'},\n        'is_disabled': {'key': 'isDisabled', 'type': 'bool'},\n        'layout': {'key': 'layout', 'type': 'FormLayout'},\n        'name': {'key': 'name', 'type': 'str'},\n        'states': {'key': 'states', 'type': '[WorkItemStateResultModel]'},\n        'url': {'key': 'url', 'type': 'str'}\n    }\n\n    def __init__(self, behaviors=None, class_=None, color=None, description=None, icon=None, id=None, inherits=None, is_disabled=None, layout=None, name=None, states=None, url=None):\n        super(WorkItemTypeModel, self).__init__()\n        self.behaviors = behaviors\n        self.class_ = class_\n        self.color = color\n        self.description = description\n        self.icon = icon\n        self.id = id\n        self.inherits = inherits\n        self.is_disabled = is_disabled\n        self.layout = layout\n        self.name = name\n        self.states = states\n        self.url = url\n\n\n\nclass WorkItemTypeUpdateModel(Model):\n    \"\"\"WorkItemTypeUpdateModel.\n\n    :param color: Color of the work item type\n    :type color: str\n    :param description: Description of the work item type\n    :type description: str\n    :param icon: Icon of the work item type\n    :type icon: str\n    :param is_disabled: Is the workitem type to be disabled\n    :type is_disabled: bool\n    \"\"\"\n\n    _attribute_map = {\n        'color': {'key': 'color', 'type': 'str'},\n        'description': {'key': 'description', 'type': 'str'},\n        'icon': {'key': 'icon', 'type': 'str'},\n        'is_disabled': {'key': 'isDisabled', 'type': 'bool'}\n    }\n\n    def __init__(self, color=None, description=None, icon=None, is_disabled=None):\n        super(WorkItemTypeUpdateModel, self).__init__()\n        self.color = color\n        self.description = description\n        self.icon = icon\n        self.is_disabled = is_disabled\n","sub_path":"azure-devops/azext_devops/vstsCompressed/work_item_tracking_process_definitions/v4_1/models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":26907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"358904691","text":"from sympl import (\n    DataArray, AdamsBashforth, PlotFunctionMonitor)\nfrom climt import (\n    Frierson06LongwaveOpticalDepth, GrayLongwaveRadiation)\nimport numpy as np\nfrom datetime import timedelta\n\npressure_axis = np.array(\n    [1e5, 9e4, 8e4, 7e4, 5e4, 3e4, 1e4, 8e3, 4e3, 1e3, 7e2, 4e2, 1e2,\n     7., 4., 1.])\n\n\ndef get_interface_pressures(p, ps):\n    \"\"\"Given 3D pressure on model mid levels (cell centers) and the 2D surface\n    pressure, return the 3D pressure on model full levels (cell interfaces).\n    If the z-dimension of p is length K, the returned p_full will have a\n    z-dimension of length K+1.\"\"\"\n    interface_pressures = np.zeros(\n        (p.shape[0], p.shape[1], p.shape[2]+1), dtype=np.float32)\n    interface_pressures[:, :, 1:-1] = 0.5*(p[:, :, 1:] + p[:, :, :-1])\n    interface_pressures[:, :, 0] = ps[:, :]\n    return interface_pressures\n\n\nstate = {\n    'air_temperature': DataArray(\n        np.ones((1, 1, len(pressure_axis)))*250.,\n        dims=('x', 'y', 'mid_levels'),\n        attrs={'units': 'degK'}),\n}\n\nconstant_state = {\n    'surface_temperature': DataArray(\n        np.ones((1, 1))*274., dims=('x', 'y'), attrs={'units': 'degK'}),\n    'surface_air_pressure': DataArray(\n        np.ones((1, 1))*1e5, dims=('x', 'y'), attrs={'units': 'Pa'}),\n    'air_pressure': DataArray(\n        pressure_axis[None, None, :], dims=('x', 'y', 'mid_levels'),\n        attrs={'units': 'Pa'}),\n    'latitude': DataArray(\n        np.zeros((1,)), dims=('y',), attrs={'units': 'degrees_north'}),\n}\ninterface_pressures = get_interface_pressures(\n    constant_state['air_pressure'].values,\n    constant_state['surface_air_pressure'].values)\ninterface_sigma = (\n    interface_pressures/constant_state['surface_air_pressure'].values[:, :, None])\nconstant_state['air_pressure_on_interface_levels'] = DataArray(\n    interface_pressures, dims=('x', 'y', 'interface_levels'),\n    attrs={'units': 'Pa'})\nconstant_state['sigma_on_interface_levels'] = DataArray(\n    interface_sigma, dims=('x', 'y', 'interface_levels'),\n    attrs={'units': ''})\n\nstate.update(constant_state)\n\n\ndef plot_function(fig, state):\n    ax = fig.add_subplot(1, 1, 1)\n    ax.plot(\n        state['air_temperature'].values.flatten(),\n        state['air_pressure'].values.flatten(), '-o')\n    ax.axes.invert_yaxis()\n    ax.set_yscale('log')\n    ax.set_ylim(1e5, 100.)\n\n\nmonitor = PlotFunctionMonitor(plot_function)\ndiagnostic = Frierson06LongwaveOpticalDepth()\nradiation = GrayLongwaveRadiation()\ntime_stepper = AdamsBashforth([radiation])\ntimestep = timedelta(hours=4)\n\nfor i in range(6*7*4*10):\n    print(i)\n    state.update(diagnostic(state))\n    diagnostics, new_state = time_stepper.__call__(state, timestep)\n    state.update(diagnostics)\n    new_state.update(constant_state)\n    if i % 5 == 0:\n        monitor.store(state)\n    state = new_state\n","sub_path":"examples/radiative_equilibrium.py","file_name":"radiative_equilibrium.py","file_ext":"py","file_size_in_byte":2823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"2753557","text":"# Copyright 2015 LIP - INDIGO-DataCloud\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 json\nimport uuid\n\nimport six\nimport webob.dec\nimport webob.exc\n\nfrom ooi.api import network\nfrom ooi.api import network_link\nfrom ooi import wsgi\n\n\napplication_url = \"https://foo.example.org:8774/ooiv1\"\n\ntenants = {\n    \"foo\": {\"id\": uuid.uuid4().hex,\n            \"name\": \"foo\"},\n    \"baz\": {\"id\": uuid.uuid4().hex,\n            \"name\": \"foo\"},\n    \"bar\": {\"id\": uuid.uuid4().hex,\n            \"name\": \"bar\"},\n    \"public\": {\"id\": uuid.uuid4().hex,\n               \"name\": \"bar\"},\n}\n\nsubnets = [\n    {\n        \"id\": uuid.uuid4().hex,\n        \"name\": \"private-subnet\",\n        \"cidr\": \"33.0.0.1/24\",\n        \"ip_version\": 4,\n        \"gateway_ip\": \"33.0.0.1\",\n    },\n    {\n        \"id\": uuid.uuid4().hex,\n        \"name\": \"public-subnet\",\n        \"cidr\": \"44.0.0.1/24\",\n        \"ip_version\": 4,\n        \"gateway_ip\": \"44.0.0.1\",\n    },\n]\n\nnetworks = {\n    tenants[\"bar\"][\"id\"]: [],\n    tenants[\"foo\"][\"id\"]: [\n        {\n            \"id\": uuid.uuid4().hex,\n            \"name\": \"foo\",\n            \"subnet_info\": subnets[0],\n            \"status\": \"ACTIVE\",\n        },\n        {\n            \"id\": uuid.uuid4().hex,\n            \"name\": \"bar\",\n            \"subnet_info\": subnets[1],\n            \"status\": \"DOWN\",\n        },\n    ]\n}\n\nnetworks_nova = {\n    tenants[\"bar\"][\"id\"]: [],\n    tenants[\"foo\"][\"id\"]: [\n        {\n            \"id\": uuid.uuid4().hex,\n            \"label\": \"foo\",\n            \"gateway\": \"33.0.0.1\",\n            \"cidr\": \"33.0.0.1/24\",\n            \"status\": \"ACTIVE\",\n        },\n        {\n            \"id\": uuid.uuid4().hex,\n            \"label\": \"bar\",\n            \"gateway\": \"44.0.0.1\",\n            \"cidr\": \"44.0.0.1/24\",\n            \"status\": \"DOWN\",\n        },\n    ]\n}\n\npools = {\n    tenants[\"bar\"][\"id\"]: [],\n    tenants[\"foo\"][\"id\"]: [\n        {\n            \"id\": uuid.uuid4().hex,\n            \"name\": \"foo\",\n        },\n        {\n            \"id\": uuid.uuid4().hex,\n            \"name\": \"bar\",\n        },\n    ]\n}\n\nlinked_vm_id = uuid.uuid4().hex\nlinked_net_id = uuid.uuid4().hex\n\nallocated_ip = \"192.168.253.23\"\n\nports = {\n    tenants[\"foo\"][\"id\"]: [\n        {\"id\": uuid.uuid4().hex,\n         \"device_id\": uuid.uuid4().hex,\n         \"device_owner\": uuid.uuid4().hex\n         }\n    ]\n}\n\nnetwork_links = {\n    tenants[\"bar\"][\"id\"]: [],\n    tenants[\"foo\"][\"id\"]: [\n        {\n            \"ip\": \"10.0.0.2\",\n            \"id\": uuid.uuid4().hex,\n            \"instance_id\": linked_vm_id,\n            \"ip\": \"192.168.253.1\",\n            \"network_id\": linked_net_id,\n            \"pool\": pools[tenants[\"foo\"][\"id\"]][0][\"name\"],\n            'status':'active'\n        },\n        {\n            \"ip\": None,\n            \"id\": uuid.uuid4().hex,\n            \"instance_id\": None,\n            \"network_id\": linked_net_id,\n            \"ip\": \"192.168.253.2\",\n            \"pool\": pools[tenants[\"foo\"][\"id\"]][0][\"name\"],\n            'status':'inactive'\n        },\n    ],\n    tenants[\"public\"][\"id\"]: [\n        {\n            \"ip\": \"10.0.0.2\",\n            \"id\": uuid.uuid4().hex,\n            \"instance_id\": linked_vm_id,\n            \"ip\": \"192.168.253.1\",\n            \"network_id\": 'PUBLIC',\n            \"pool\": pools[tenants[\"foo\"][\"id\"]][0][\"name\"],\n            'status': 'active'\n        },\n    ],\n}\n\nsecurity_groups = {\n    tenants[\"bar\"][\"id\"]: [],\n    tenants[\"foo\"][\"id\"]: [],\n    tenants[\"baz\"][\"id\"]: [\n        {\n            \"name\": \"group1\",\n            \"id\": uuid.uuid4().hex,\n            \"description\": \"group one\",\n            \"security_group_rules\": [\n                {\"ethertype\": \"IPv4\", \"port_range_min\": 443,\n                 \"port_range_max\": 443, \"remote_ip_prefix\": \"10.0.0.0/32\",\n                 \"protocol\": \"tcp\", \"direction\": \"ingress\"},\n                {\"ethertype\": \"IPv4\", \"port_range_min\": \"8000\",\n                 \"port_range_max\": 9000, \"remote_ip_prefix\": \"11.0.0.0/24\",\n                 \"protocol\": \"udp\", \"direction\": \"egress\"}\n            ]\n        },\n        {\n            \"name\": \"group2\",\n            \"id\": uuid.uuid4().hex,\n            \"description\": \"group two\",\n            \"security_group_rules\": [\n                {\"ethertype\": \"IPv4\", \"port_range_min\": 80,\n                 \"port_range_max\": 80, \"remote_ip_prefix\": \"10.0.0.0/32\",\n                 \"protocol\": \"tcp\", \"direction\": \"ingress\"},\n                {\"ethertype\": \"IPv4\", \"port_range_min\": \"5000\",\n                 \"port_range_max\": 6000, \"remote_ip_prefix\": \"11.0.0.0/24\",\n                 \"protocol\": \"udp\", \"direction\": \"egress\"}\n            ]\n        }\n\n    ]\n}\n\n\ndef create_fake_json_resp(data, status=200):\n    r = webob.Response()\n    r.headers[\"Content-Type\"] = \"application/json\"\n    r.charset = \"utf8\"\n    r.body = json.dumps(data).encode(\"utf8\")\n    r.status_code = status\n    return r\n\n\ndef add_to_fake_json_resp(req, obj_name, data):\n\n    body = json.loads(req.body)\n    for element in data:\n        body[obj_name].append(element)\n\n    req.body = json.dumps(body).encode(\"utf8\")\n\n    return req\n\n\ndef create_req_test(params, schemes):\n    headers = create_header(params, schemes)\n    req = webob.Request.blank(path=\"\")\n    req.headers = headers\n    return wsgi.Request(req.environ)\n\n\ndef create_header(params, schemes, project=None):\n    headers = {}\n    att = \"\"\n    if params is not None:\n        for k, v in params.items():\n            att = \"%s, %s=%s \" % (att, k, v)\n        headers[\"X_OCCI_Attribute\"] = att\n    if schemes is not None:\n        sch = \"\"\n        cat = \"\"\n        for k, v in schemes.items():\n            sch = \"%s, %s:%s\" % (sch, k, v)\n            cat = \"%s, %s%s\" % (cat, k, v)\n        headers[\"schemes\"] = sch\n        # headers['Category']= cat\n    if project is not None:\n        headers[\"X_PROJECT_ID\"] = project\n    return headers\n\n\ndef create_req_json_occi(params, category, method=\"POST\"):\n    headers = create_headers(category,\n                             content_type=\"application/occi+json\")\n    body = {}\n    for c in category:\n        body[\"kind\"] = \"%s%s\" % (\n            c.scheme, c.term)\n\n    body[\"attributes\"] = params\n    req = webob.Request.blank(path=\"\")\n    req.headers = headers\n    req.method = method\n    req.body = json.dumps(body).encode(\"utf8\")\n    return wsgi.Request(req.environ)\n\n\ndef create_req_test_occi(params, category):\n    headers = create_header_occi(params, category)\n    req = webob.Request.blank(path=\"\")\n    req.headers = headers\n    return wsgi.Request(req.environ)\n\n\ndef create_header_occi(params, category, project=None):\n    headers = create_headers(category, project)\n    att = \"\"\n    if params is not None:\n        for k, v in params.items():\n            if isinstance(v, six.string_types):\n                att = \"%s, %s=\\\"%s\\\"\" % (att, k, v)\n            else:\n                att = \"%s, %s=%s\" % (att, k, v)\n        headers[\"X_OCCI_Attribute\"] = att\n\n    return headers\n\n\ndef create_headers(category, content_type=None,\n                   project=None):\n    headers = {}\n    if category is not None:\n        cat = \"\"\n        for c in category:\n            cat = \"%s%s; scheme=%s; class=%s, \" % (\n                cat,\n                c.term, c.scheme, c.occi_class)\n        headers['Category'] = cat[:-1]\n    if project is not None:\n        headers['X_PROJECT_ID'] = project\n    if content_type is not None:\n        headers['Content-Type'] = content_type\n    return headers\n\n\ndef fake_build_link(net_id, compute_id, ip, mac=None,\n                    pool=None, state='active', public_ip=False):\n    link = {}\n    link['mac'] = mac\n    link['pool'] = pool\n    link['network_id'] = net_id\n    link['compute_id'] = compute_id\n    link['ip'] = ip\n    link['state'] = state\n    link['public_ip'] = public_ip\n    return link\n\n\ndef fake_network_link_occi(os_list_net):\n    list_links = []\n    for l in os_list_net:\n        if l['instance_id']:\n            list_links.append(fake_build_link(l['network_id'],\n                                              l['instance_id'], l['ip']))\n    return network_link._get_network_link_resources(list_links)\n\n\ndef fake_build_net(name, ip_version=4, address='0.0.0.11', gateway='0.0.0.1',\n                   id=\"33\", state='active'):\n    link = {}\n    link['id'] = id\n    link['name'] = name\n    link['address'] = address\n    link['gateway'] = gateway\n    link['ip_version'] = ip_version\n    link['state'] = state\n    return link\n\n\ndef fake_network_occi(os_list_net):\n    list_nets = []\n    for n in os_list_net:\n        list_nets.append(fake_build_net(n['name'], id=n['id']))\n    return network.Controller._get_network_resources(list_nets)\n\n\ndef build_occi_network(network):\n    name = network[\"name\"]\n    network_id = network[\"id\"]\n    subnet_info = network[\"subnet_info\"]\n    status = network[\"status\"].upper()\n    if status in (\"ACTIVE\",):\n        status = \"active\"\n    else:\n        status = \"inactive\"\n\n    app_url = application_url\n    cats = []\n    cats.append('network; '\n                'scheme='\n                '\"http://schemas.ogf.org/occi/infrastructure#\";'\n                ' class=\"kind\"; title=\"network resource\";'\n                ' rel='\n                '\"http://schemas.ogf.org/occi/core#resource\";'\n                ' location=\"%s/network/\"' % app_url)\n    cats.append('ipnetwork; '\n                'scheme='\n                '\"http://schemas.ogf.org/occi/infrastructure/network#\";'\n                ' class=\"mixin\"; title=\"IP Networking Mixin\"')\n    cats.append('osnetwork; '\n                'scheme='\n                '\"http://schemas.openstack.org/infrastructure/network#\";'\n                ' class=\"mixin\"; title=\"openstack network\"')\n\n    links = []\n    links.append('<%s/network/%s?action=up>; '\n                 'rel=\"http://schemas.ogf.org/occi/'\n                 'infrastructure/network/action#up\"' %\n                 (application_url, network_id))\n    links.append('<%s/network/%s?action=down>; '\n                 'rel=\"http://schemas.ogf.org/occi/'\n                 'infrastructure/network/action#down\"' %\n                 (application_url, network_id))\n\n    attrs = [\n        'occi.core.id=\"%s\"' % network_id,\n        'occi.core.title=\"%s\"' % name,\n        'occi.network.state=\"%s\"' % status,\n        'org.openstack.network.ip_version=%s' % subnet_info[\"ip_version\"],\n        'occi.network.address=\"%s\"' % subnet_info[\"cidr\"],\n        'occi.network.gateway=\"%s\"' % subnet_info[\"gateway_ip\"],\n        ]\n    result = []\n    for c in cats:\n        result.append((\"Category\", c))\n    for a in attrs:\n        result.append((\"X-OCCI-Attribute\", a))\n    for l in links:\n        result.append((\"Link\", l))\n    return result\n\n\ndef build_occi_nova(network):\n    name = network[\"label\"]\n    network_id = network[\"id\"]\n    gateway = network[\"gateway\"]\n    cidr = network[\"cidr\"]\n    status = \"active\"\n\n    app_url = application_url\n    cats = []\n    cats.append('network; '\n                'scheme='\n                '\"http://schemas.ogf.org/occi/infrastructure#\";'\n                ' class=\"kind\"; title=\"network resource\";'\n                ' rel='\n                '\"http://schemas.ogf.org/occi/core#resource\";'\n                ' location=\"%s/network/\"' % app_url)\n    cats.append('ipnetwork; '\n                'scheme='\n                '\"http://schemas.ogf.org/occi/infrastructure/network#\";'\n                ' class=\"mixin\"; title=\"IP Networking Mixin\"')\n    cats.append('osnetwork; '\n                'scheme='\n                '\"http://schemas.openstack.org/infrastructure/network#\";'\n                ' class=\"mixin\"; title=\"openstack network\"')\n\n    links = []\n    links.append('<%s/network/%s?action=up>; '\n                 'rel=\"http://schemas.ogf.org/occi/'\n                 'infrastructure/network/action#up\"' %\n                 (application_url, network_id))\n    links.append('<%s/network/%s?action=down>; '\n                 'rel=\"http://schemas.ogf.org/occi/'\n                 'infrastructure/network/action#down\"' %\n                 (application_url, network_id))\n\n    attrs = [\n        'occi.core.id=\"%s\"' % network_id,\n        'occi.core.title=\"%s\"' % name,\n        'occi.network.state=\"%s\"' % status,\n        'occi.network.address=\"%s\"' % cidr,\n        'occi.network.gateway=\"%s\"' % gateway,\n        ]\n    result = []\n    for c in cats:\n        result.append((\"Category\", c))\n    for a in attrs:\n        result.append((\"X-OCCI-Attribute\", a))\n    for l in links:\n        result.append((\"Link\", l))\n    return result\n\n\ndef build_occi_securitygroup(secgroup):\n    name = secgroup[\"title\"]\n    secgroup_id = secgroup[\"id\"]\n    rules = secgroup[\"rules\"]\n    summary = secgroup[\"summary\"]\n    app_url = application_url\n    cats = []\n    cats.append('securitygroup; '\n                'scheme='\n                '\"http://schemas.ogf.org/occi/infrastructure#\";'\n                ' class=\"kind\"; title=\"securitygroup resource\";'\n                ' rel='\n                '\"http://schemas.ogf.org/occi/core#resource\";'\n                ' location=\"%s/securitygroup/\"' % app_url)\n    links = []\n\n    attrs = [\n        'occi.core.id=\"%s\"' % secgroup_id,\n        'occi.core.title=\"%s\"' % name,\n        'occi.core.summary=\"%s\"' % summary,\n        'occi.securitygroup.rules=\"%s\"' % json.dumps(rules).replace('\"', \"'\"),\n        ]\n    result = []\n    for c in cats:\n        result.append((\"Category\", c))\n    for a in attrs:\n        result.append((\"X-OCCI-Attribute\", a))\n    for l in links:\n        result.append((\"Link\", l))\n    return result","sub_path":"ooi/tests/fakes_network.py","file_name":"fakes_network.py","file_ext":"py","file_size_in_byte":13838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"252682923","text":"import RPi.GPIO as GPIO\nimport time\n\n\n# Pin Definition(Broadcom)\nLED_Bottom_Left  = 5\nLED_Bottom_Right = 6\nLED_Top_Left     = 12\n\nTOGGLE_Switch_1   = 26\nTOGGLE_Switch_2   = 16\nTOGGLE_Switch_3   = 25\nTOGGLE_Switch_4   = 24\n\nBUTTON_Switch_0   = 17 \nBUTTON_Switch_1   = 18\n\n# Pin setup: Numbering scheme, PIN input/output definition\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(LED_Bottom_Left, GPIO.OUT)\nGPIO.setup(LED_Bottom_Right, GPIO.OUT)\nGPIO.setup(LED_Top_Left, GPIO.OUT)\nGPIO.setup(TOGGLE_Switch_1, GPIO.IN)\nGPIO.setup(TOGGLE_Switch_2, GPIO.IN)\nGPIO.setup(TOGGLE_Switch_3, GPIO.IN)\nGPIO.setup(TOGGLE_Switch_4, GPIO.IN)\nGPIO.setup(BUTTON_Switch_0, GPIO.IN)\nGPIO.setup(BUTTON_Switch_1, GPIO.IN)\n\nGPIO.output(LED_Bottom_Left, GPIO.HIGH)\nGPIO.output(LED_Bottom_Right, GPIO.HIGH)\nGPIO.output(LED_Top_Left, GPIO.HIGH)\n\nfor i in range(1,100):\n\ttime.sleep(0.25)\n\tprint('Iteration ', i, 'Switch State = ',  GPIO.input(BUTTON_Switch_0))\n\tif(GPIO.input(BUTTON_Switch_0)):\n\t\tGPIO.output(LED_Bottom_Left, GPIO.LOW)\t\n\telse:\n\t\tGPIO.output(LED_Bottom_Left, GPIO.HIGH)\n\n#Clean up GPIO states\nGPIO.cleanup()\n","sub_path":"RPi_Test/Manipulate_LED.py","file_name":"Manipulate_LED.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"47344392","text":"from django.conf.urls.defaults import patterns, include, url\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n    # Examples:\n    url(r'^$', 'acrank.views.home', name='home'),\n\n    url(r'^register/$', 'acrank.views.DriverRegistration', name='register'),\n    \n\n    url(r'^hotlaps/$', 'acrank.views.hotlaps', name='hotlaps'),\n\n    url(r'^login/$', 'acrank.views.login', name='login'),\n    url(r'^auth/$', 'acrank.views.auth_view'),\n    url(r'^logout/$', 'acrank.views.logout', name='logout'),\n    url(r'^invalid/$', 'acrank.views.invalid', name='invalid'),\n    # url(r'^Ptyxiakh/', include('Ptyxiakh.foo.urls')),\n    url(r'^StatsSend$', 'acrank.views_StatsSend.StatsSend', name='StatsSend'),          #Gia thn apostolh MONO twn stats\n    url(r'^profile$', 'acrank.views_profile.profile', name='profile'),\n    url(r'^myranking$', 'acrank.views.myranking', name='myranking'),\n\n    url(r'^drivers$', 'acrank.views.drivers', name='drivers'),\n\n    url(r'^validation$', 'acrank.views.validation', name='validation'),\n\n\n    # Uncomment the admin/doc line below to enable admin documentation:\n    url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n    # Uncomment the next line to enable the admin:\n    url(r'^admin/', include(admin.site.urls)),\n)\n","sub_path":"Ptyxiakh/Ptyxiakh/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"59026748","text":"import os\nfrom setuptools import setup, find_packages\n\nversion = '0.0.2'\nREADME = os.path.join(os.path.dirname(__file__), 'README.md')\nlong_description = open(README).read()\n\nsetup(\n    name='wwu',\n    version=version,\n    description='Find the optimal way up to store your boxes.',\n    long_description=long_description,\n    long_description_content_type='text/markdown',\n    classifiers=[\n        'Development Status :: 3 - Alpha',\n        'Environment :: Console',\n        'Intended Audience :: End Users/Desktop',\n        'License :: OSI Approved :: MIT License',\n        'Operating System :: OS Independent',\n        'Topic :: Software Development :: Libraries :: Python Modules',\n        'Topic :: Utilities',\n        'Programming Language :: Python'\n    ],\n    keywords='boxes containers inventory permutation combinatorics',\n    author='Rob Horton',\n    author_email='vulgarmessiah@gmail.com',\n    url='https://github.com/Um9i/wwu',\n    install_requires=[\n\n    ],\n    extra_requires=[\n        'pytest'\n    ],\n    setup_requires=['setuptools>=41.2.0'],\n    download_url='https://github.com/Um9i/wwu/archive/master.zip',\n    packages=find_packages(),\n    include_package_data=True,\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"467929242","text":"from django.urls import path\n\nfrom .views import (\n\tRegisterView,\n\tLoginView,\n\tprofile,\n\tUpdateProfile,\n\tProfileUpdate,\n\tProfileDetail,\n\tAddLike,\n\tAPIAddLike,\n\tAddComment,\n\tAddLikeComment,\n\tAPIAddLikeComment,\n\tAddReply,\n\tAPIAddLikeReply,\n\tLoadMorePost,\n\tLoadMorePostHome,\n\tAddToRecommendition,\n\tAPIFollow,\n\tLoadMorePostFollwings,\n\tLoadMoreUserPosts,\n\tLoadMoreComments,\n\tLoadMoreReplies\n\t)\n\nfrom General.views import LoadMoreSearchedPosts,LoadMoreProfiles\n\nfrom django.contrib.auth import views as auth_views\n\n\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nurlpatterns = [\n    path('register/', RegisterView,name='register'),\n    path('login/', LoginView,name=\"login\"),\n    path('logout/', auth_views.LogoutView.as_view(template_name=\"logout.html\"),name=\"logout\"),\n    path('profile/', profile,name=\"profile\"),\n    path('profile/update/', UpdateProfile,name=\"update-profile\"),\n    path('update/profile/',ProfileUpdate.as_view(),name='profile-update'),\n    path('user/<str:slug>/',ProfileDetail.as_view(),name='user-detail'),\n\n\n    path('post/<str:slug>/addlike/',AddLike.as_view(),name='add-like'),\n    path('post/<str:slug>/addlike/api/',APIAddLike.as_view(),name='add-like-api'),\\\n    path('post/<str:slug>/recommend/api/',AddToRecommendition.as_view(),name='recommend'),\n\n    path('post/<str:slug>/addcomment/',AddComment.as_view(),name=\"add-comment\"),\n    path('comment/<str:slug>/reply/',AddReply.as_view(),name=\"add-reply\"),\n\n    path('comment/<str:slug>/addlike/',AddLikeComment.as_view(),name='add-like-comment'),\n    path('comment/<str:slug>/addlike/api/',APIAddLikeComment.as_view(),name='add-like-comment-api'),\n\n    path('reply/<int:id>/addlike/api/',APIAddLikeReply.as_view(),name='add-like-reply-api'),\n\n    path('user/<str:slug>/loadmorepost/',LoadMorePost.as_view(),name='load-more-post'),\n    path('user/<str:slug>/follow/api/',APIFollow.as_view(),name='follow-api'),\n    path('loadmoreposts/',LoadMorePostHome.as_view(),name='load-more-post-home'),\n    path('loadmorepostsfollowings/',LoadMorePostFollwings.as_view(),name='load-more-post-followings'),\n    path('loadmorepostsuser/<str:slug>/',LoadMoreUserPosts.as_view(),name='load-more-post-user'),\n    path('loadmorecommentspost/<str:slug>/',LoadMoreComments.as_view(),name='load-more-comments-post'),\n    path('loadmorerepliescomment/<str:slug>/',LoadMoreReplies.as_view(),name='load-more-replies-comment'),\n\n    path('loadmorepostsSaerch/',LoadMoreSearchedPosts.as_view(),name='load-more-post-search'),\n    path('loadmoreprofiles/',LoadMoreProfiles.as_view(),name='load-more-profiles'),\n\n\n\n    \n\n]\n\n\nif settings.DEBUG:\n\turlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)","sub_path":"System/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"423155147","text":"\"\"\"\nCalculating average and standard deviation\nfor 4 variable in \"iris.data\".\n\"\"\"\nimport numpy as np\nimport math\n\n#orig_lists\nsepal_length_list = []\nsepal_width_list = []\npetal_length_list = []\npetal_width_list = []\n\n#データを読みこみ整形\nwith open (\"iris.data\") as file:\n    stuck_sample_list = file.read().split(\"\\n\")\n\nwhile True:\n    try:\n        stuck_sample_list.remove(\"\")\n    except ValueError:\n        break\n\nSAMPLE_NUM = len(stuck_sample_list)#サンプルデータの個数\n\nfor i in range(SAMPLE_NUM):\n    sample_list = stuck_sample_list[i].split(\",\")\n    sepal_length_list.append(float(sample_list[0]))\n    sepal_width_list.append(float(sample_list[1]))\n    petal_length_list.append(float(sample_list[2]))\n    petal_width_list.append(float(sample_list[3]))\n\n\ndef count_variables(sample_list):#サンプルのfloatの要素数をカウント(変数をカウント)\n    \"\"\"\n    Counting variables per one sample in the \"iris.data\".\n    \"sample_list\" use a last sample data in the \"iris.data\".\n    \"\"\"\n    float_variable = []\n\n    for i in sample_list:\n        try:\n            float_variable.append(float(i))\n        except:\n            pass\n\n    return len(float_variable)\n\n\ndef average(orig_lists):#平均値を求める\n    \"\"\"\n    Calculating average for each variable in \"iris.data\".\n    \"orig_lists\" is a list of each variable.\n    \"\"\"\n    average = sum(orig_lists) / SAMPLE_NUM\n\n    return average\n\n\ndef standard_deviation(orig_lists, average):#標準偏差を求める\n    \"\"\"\n    Calculating standard deviation for each variable in \"iris.data\".\n    \"orig_lists\" is a list of each variable.\n    \"average\" is a average. [carculating by function \"average()\"].\n    \"\"\"\n    num_list = []\n\n    for i in orig_lists:\n        num_list.append((i - average) ** 2)\n\n    dispersion = sum(num_list) / SAMPLE_NUM\n    standard_deviation = np.around(math.sqrt(dispersion), 2)\n\n    return standard_deviation\n\n\ncut_sepal_length = np.around(average(sepal_length_list), 2)\ncut_sepal_width = np.around(average(sepal_width_list), 2)\ncut_petal_length = np.around(average(petal_length_list), 2)\ncut_petal_width = np.around(average(petal_width_list), 2)\n\nprint(\"iris.dataには\",SAMPLE_NUM,\"個のサンプルデータが列挙されています。\")\nprint(count_variables(sample_list),\"変数について平均値・標準偏差を求めた結果を以下に示します。\")\nprint(\"sepal length: average =\",cut_sepal_length,\", standard deviation =\",standard_deviation(sepal_length_list,average(sepal_length_list)))\nprint(\" sepal width: average =\",cut_sepal_width,\", standard deviation =\",standard_deviation(sepal_width_list,average(sepal_width_list)))\nprint(\"petal length: average =\",cut_petal_length,\", standard deviation =\",standard_deviation(petal_length_list,average(petal_length_list)))\nprint(\" petal width: average =\",cut_petal_width,\", standard deviation =\",standard_deviation(petal_width_list,average(petal_width_list)))\n","sub_path":"report6.py","file_name":"report6.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"207639046","text":"import collections\nclass Solution(object):\n    def maxSlidingWindow(self, nums, k):\n        \"\"\"\n        :type nums: List[int]\n        :type k: int\n        :rtype: List[int]\n        \"\"\"\n        deque = collections.deque()\n        result = []\n        n = len(nums)\n        for i,j in zip(range(1-k,n+1-k),range(n)):\n            if i>0 and deque[0] == nums[i-1]:\n                deque.popleft()\n            while deque and deque[-1]<nums[j]:\n                deque.pop()\n            deque.append(nums[j])\n            if i>=0: result.append(deque[0])\n        return result\n\ns = Solution()\nprint(s.maxSlidingWindow(nums = [1,3,-1,-3,5,3,6,7], k = 3))","sub_path":"面试题59 - I滑动窗口的最大值.py","file_name":"面试题59 - I滑动窗口的最大值.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"403869174","text":"from apps.API_VK.command.CommonCommand import CommonCommand\n\nfrom apps.service.models import Meme as MemeModel\n\nIMAGE_EXTS = ['jpg', 'jpeg', 'png']\n\n\ndef check_name_exists(name):\n    return MemeModel.objects.filter(name=name).exists()\n\n\nclass Meme(CommonCommand):\n    def __init__(self):\n        names = [\"мем\"]\n        help_text = \"Мем - присылает нужный мем\"\n        detail_help_text = \"Мем (название) - присылает нужный мем.\\n\" \\\n                           \"Мем р(рандом) - присылает рандомный мем.\\n\" \\\n                           \"Добавление мема - /мем добавить ...(название) (url)\\n\" \\\n                           \"Добавление мема - /мем добавить ...(название) (картинка)\\n\" \\\n                           \"Отправка мема в конфу - /мем конфа (название конфы) (название/рандом)\\n\"\n        super().__init__(names, help_text, detail_help_text, args=1)\n\n    def get_1_meme(self, filter_list):\n        memes = MemeModel.objects.all()\n\n        for _filter in filter_list:\n            memes = memes.filter(name__icontains=_filter)\n        if len(memes) == 0:\n            # ToDo: Тонимото?\n            raise RuntimeError(\"Не нашёл :(\")\n        elif len(memes) == 1:\n            return memes.first()\n        else:\n            for meme in memes:\n                if meme.name == self.vk_event.original_args:\n                    # if check_name_exists(self.vk_event.original_args):\n                    return meme\n            meme_names = [meme.name for meme in memes]\n            meme_names_str = \"\\n\".join(meme_names)\n            raise RuntimeError(f\"Нашёл сразу несколько, уточните:\\n\\n\"\n                               f\"{meme_names_str}\")\n\n    def send_1_meme_to_chat(self, meme, chat, print_name=True):\n        meme = self.send_1_meme(meme, print_name)\n        if type(meme) == dict:\n            self.vk_bot.parse_and_send_msgs(chat.chat_id, meme)\n            return \"Отправил\"\n        else:\n            return \"Не нашёл мем :(\"\n\n    def send_1_meme(self, meme, print_name=True):\n        if print_name:\n            meme_name = meme.name\n        else:\n            meme_name = \"\"\n        if meme.link:\n            if meme.link.find('vk.com') > 0 and meme.link.find('video'):\n                att = meme.link[meme.link.find('video'):]\n                return {'msg': meme_name, 'attachments': [att]}\n            return meme.link\n        elif meme.image:\n            photo = self.vk_bot.upload_photo(meme.image.path, False)\n            return {'msg': meme_name, 'attachments': [photo]}\n        else:\n            return \"Какая-то хрень с мемом\"\n\n    def start(self):\n        from apps.API_VK.command.CommonMethods import get_one_chat_with_user\n\n        for i in range(len(self.vk_event.args)):\n            self.vk_event.args[i] = self.vk_event.args[i].lower()\n        self.vk_event.original_args = self.vk_event.original_args.lower()\n\n        if self.vk_event.args[0] == 'добавить':\n            self.check_args(2)\n\n            if self.vk_event.attachments:\n                new_meme = {'name': self.vk_event.original_args.split(' ', 1)[1], 'author': self.vk_event.sender}\n                if check_name_exists(new_meme['name']):\n                    return \"Мем с таким названием уже есть\"\n                attachment = self.vk_event.attachments[0]\n                if attachment['type'] == 'photo':\n                    meme = MemeModel(**new_meme)\n                    meme.save()\n                    meme.save_remote_image(attachment['url'])\n                elif attachment['type'] == 'video':\n                    new_meme['link'] = attachment['url']\n                    meme = MemeModel(**new_meme)\n                    meme.save()\n                else:\n                    return \"Не знаю такого типа вложения\"\n                return \"Сохранил\"\n            elif len(self.vk_event.args) >= 3:\n                new_meme = {'link': self.vk_event.args[-1], 'author': self.vk_event.sender}\n                name = self.vk_event.original_args.split(' ', 1)[1]\n                new_meme['name'] = name[:name.rfind(new_meme['link']) - 1]\n                if check_name_exists(new_meme['name']):\n                    return \"Мем с таким названием уже есть\"\n                for meme_ext in IMAGE_EXTS:\n                    # Если урл - картинка\n                    if new_meme['link'].split('.')[-1].lower() == meme_ext:\n                        link = new_meme.pop('link')\n                        meme = MemeModel(**new_meme)\n                        meme.save()\n                        meme.save_remote_image(link)\n                        return \"Сохранил\"\n\n                MemeModel(**new_meme).save()\n                return \"Сохранил\"\n            else:\n                return \"Не передан url видео или не прикреплена картинка\"\n        elif self.vk_event.args[0] in ['рандом', 'р']:\n            meme = MemeModel.objects.all().order_by('?').first()\n            return self.send_1_meme(meme)\n        elif self.vk_event.args[0] in ['конфа', 'конференция', 'чат']:\n\n            self.check_args(3)\n            if self.vk_event.args[-1] in ['рандом', 'р']:\n                meme = MemeModel.objects.all().order_by('?').first()\n            else:\n                meme_name_filter = self.vk_event.args[2:]\n                meme = self.get_1_meme(meme_name_filter)\n            chat = get_one_chat_with_user(self.vk_event.args[1], self.vk_event.sender.user_id)\n\n            return self.send_1_meme_to_chat(meme, chat, False)\n\n        else:\n            meme = self.get_1_meme(self.vk_event.args)\n            return self.send_1_meme(meme, False)\n","sub_path":"apps/API_VK/command/commands/Meme.py","file_name":"Meme.py","file_ext":"py","file_size_in_byte":5983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"161970224","text":"import requests, datetime\nfrom application.models import oauth, database\n\ndef get_day_distance(userid, date): #date should be datetime.date object\n    access_token = oauth.refresh_access_token(oauth.get_refresh(userid))\n    start_time = int(datetime.datetime.combine(date, datetime.datetime.min.time()).timestamp())*1000\n    end_time = start_time + 86400000\n    res = requests.post(\n        \"https://www.googleapis.com/fitness/v1/users/me/dataset:aggregate\",\n        headers={\n            \"Content-type\": \"application/json\",\n            \"Authorization\": \"Bearer \" + access_token\n        },\n        json={\n            \"aggregateBy\": [{\n                \"dataTypeName\": \"com.google.distance.delta\",\n                \"dataSourceId\": \"derived:com.google.distance.delta:com.google.android.gms:platform_distance_delta\"\n            }],\n            \"bucketByTime\": { \"durationMillis\": 86400000 },\n            \"startTimeMillis\": start_time,\n            \"endTimeMillis\": end_time\n        }\n    )\n    res = res.json()\n    try:\n        val = round(res[\"bucket\"][0][\"dataset\"][0][\"point\"][0][\"value\"][0][\"fpVal\"]/1000, 1)\n    except:\n        val = 0\n    return val\n","sub_path":"application/models/fitapi.py","file_name":"fitapi.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"81853438","text":"from uuid import uuid4\n\nfrom flask import Flask, Response, request\n\nfrom classes.dto.BlockChain import BlockChain\nfrom src.classes.BlockMiner import BlockMiner\nfrom src.classes.JsonEncoder import JsonEncoder\n\napp = Flask(__name__)\n\n# Generate a globally unique address for this node\nnode_identifier = str(uuid4()).replace('-', '')\n# Create the chain\nblockChain = BlockChain()\nblock_miner = BlockMiner()\n\n\n@app.route('/mine', methods=['GET'])\ndef mine():\n    new_proof = block_miner.find_proof_of_work(blockChain.last_block.proof)\n\n    blockChain.new_transaction(\n        sender=\"0\",\n        recipient=node_identifier,\n        amount=1,\n    )\n\n    new_block = blockChain.new_block(new_proof)\n\n    new_block_json = JsonEncoder().encode(new_block),\n    return Response(new_block_json, mimetype='text/json', status=201)\n\n\n@app.route('/transactions/new', methods=['POST'])\ndef new_transaction():\n    values = request.get_json()\n\n    # Check that the required fields are in the POST'ed data\n    required = ['sender', 'recipient', 'amount']\n    if not all(k in values for k in required):\n        return 'Missing values', 400\n\n    # Create a new Transaction\n    index = blockChain.new_transaction(values['sender'], values['recipient'], values['amount'])\n\n    response = {'message': F'Transaction will be added to Block {index}'}\n    return Response(JsonEncoder().encode(response), status=201)\n\n\n@app.route('/chain', methods=['GET'])\ndef full_chain():\n    response = {\n        'block': blockChain.chain,\n        'length': len(blockChain.chain)\n    }\n    response_string = JsonEncoder().encode(response)\n    return Response(response_string, mimetype='text/json')\n\n\nif __name__ == '__main__':\n    app.run()\n","sub_path":"src/Application.py","file_name":"Application.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"178414180","text":"from django.db import models\nfrom django.contrib.contenttypes import generic\nfrom django.contrib.contenttypes.models import ContentType\n\nfrom rightman import RightManagerExceptions\n\n# Create your models here.\n# Company\nclass Company(models.Model):\n    companyName = models.CharField(max_length = 120)\n\nclass UserAccount(models.Model):\n    #user roles\n    __ADMIN = \"0\"\n    __POWER = \"1\"\n    __USER = \"2\"\n\n    user_roles  = (\n        (__ADMIN, \"site administrator account\"),\n        (__POWER, \"company administrator account\"),\n        (__USER, \"simple user account\"))\n\n    user_object = models.ForeignKey(ContentType)\n    user_id = models.PositiveIntegerField()\n    user = generic.GenericForeignKey('user_object', 'user_id')\n    user_role = models.CharField(max_length = 1, choices = user_roles, default = __USER)\n    user_company = models.ForeignKey(Company, unique = False, related_name = 'employee_of')\n\n    def is_root(self):\n        if self.user_role == \"0\":\n            return True\n        else:\n            return False\n\n    def is_power(self, acc_id):\n        if self.objects.filter(id=acc_id)[0].user_roles == __POWER:\n            return True\n        else:\n            return False\n\n    def is_user(self, acc_id):\n        if self.objects.filter(id=acc_id)[0].user_roles == __USER:\n            return True\n        else:\n            return False\n\n    def employee_of(self, acc_id):\n        return self.objects.filter(id=acc_id)[0].user_company\n\n    \"\"\"def save(self, *args, **kwargs):\n                    for elem in UserAccount.objects.all():\n                        if self.user == elem.user:\n                            elem.delete()\n                            super(UserAccount, self).save(*args, **kwargs)\n                            raise RightManagerExceptions.MultipleUserPermissionException(self.user)\n                        else:\n                            super(UserAccount, self).save(*args, **kwargs)\"\"\"\n\nclass Permissions(models.Model):\n    #Groups: ANY, COMPANY, OWNER, POWER\n    #Actions: READ, MODIFY\n    __COMPANY = \"1\"\n    __OWNER = \"2\"\n    __POWER = \"4\"\n\n    __COMPANY_OWNER = \"3\"\n    __OWNER_POWER = \"6\"\n    __ANY = \"7\"\n    \n\n    read_choise = (\n        (__ANY, \"Anyone can see this object\"),\n        (__COMPANY_OWNER, \"This object can be seen by owner and company employees\"),\n        (__OWNER_POWER, \"This object can be seen by owner and company admins\"),\n        (__COMPANY, \"This object can be seen only by owner's company employees\"),\n        (__POWER, \"This object can be seen only by company administrators\"),\n        (__OWNER, \"This object can be seen only by it's owner\"))\n\n    modify_choise = (\n        (__ANY, \"Anyone can modify this object\"),\n        (__COMPANY_OWNER, \"This object can be midified by owner and company employees\"),\n        (__OWNER_POWER, \"This object can be midified by owner and company admins\"),\n        (__COMPANY, \"This object can be modfied only be owner's company employees\"),\n        (__POWER, \"This object can be modified only by company administrators\"),\n        (__OWNER, \"This object can be modified only by it's owner\"))\n\n    obj = models.ForeignKey(ContentType)\n    object_id = models.PositiveIntegerField()\n    ruled_object = generic.GenericForeignKey('obj', 'object_id')\n    owner = models.ForeignKey(UserAccount, related_name = 'owner')\n    company = models.ForeignKey(Company, related_name = 'property_of')\n    read_permission = models.CharField(max_length = 1, choices = read_choise, default = __COMPANY)\n    modify_permission = models.CharField(max_length = 1, choices = modify_choise, default = __POWER)\n\n    \"\"\"def save(self, *args, **kwargs):\n                    for elem in Permissions.objects.all():\n                        if self.ruled_object == elem.ruled_object:\n                            elem.delete()\n                            super(Permissions, self).save(*args, **kwargs)\n                            raise RightManagerExceptions.MultipleRelationWithSingleRemoteObjectException(self.ruled_object)\n                        else:\n                            super(Permissions, self).save(*args, **kwargs)\"\"\"","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"62836070","text":"import clang.cindex\nimport sys, os\nimport json\n\nif sys.platform == 'darwin':\n\tlibclang_path = R'/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/libclang.dylib'\n\t#libclang_path = R'/Users/yushroom/Downloads/llvm-3.9.1.src/build/lib/libclang.dylib'\nelse:\n\tlibclang_path = R'D:\\Library\\LLVM\\bin\\libclang.dll'\n\nclang.cindex.Config.set_library_file(libclang_path)\nindex = clang.cindex.Index.create()\n\n\nclass UnknownAttributeError(ValueError):\n    pass\n\ndef DumpAst(sourcr_file_path, output_ast_path = 'out.ast'):\n\tinclude_path = (\n\t\t\t\"../Include\",\n\t\t\t\"../ThirdParty\",\n\t\t\t\"../ThirdParty/bgfx/include\",\n\t\t\t\"../ThirdParty/bimg/include\"\n\t\t\t)\n\tif sys.platform == 'darwin':\n\t\tpass\n\telse:\n\t\tclang_exe = r'D:\\library\\LLVM\\bin\\clang.exe'\n\theaders = ' '.join([f'-I\"{os.path.abspath(x)}\"' for x in include_path])\n\t# print(headers)\n\tcmd = f'{clang_exe} {sourcr_file_path} {headers} -x c++ -emit-ast -D__FISHENGINE_REFLECTION__ -std=c++17 -o {output_ast_path}'\n\tprint(cmd)\n\tresult = os.system(cmd)\n\tassert(result == 0)\n\n\nSerialization_type = (\n    'float',\n    'double',\n    'int',\n    'uint32_t',\n    'bool',\n    'std::string',\n    )\n\nall_attributes = (\n    'DisallowMultipleComponent',\n    'HideInInspector',\n    'Serializable',\n    'NonSerializable',\n    'ExecuteInEditMode',\n    'AddComponentMenu',\n    'RequireComponent',\n    )\n\n# class MemberDef:\n# \tname\n# \ttype\n\n# class ClassDef:\n# \tname\n# \tbase_class\n# \tscope_prefix\n# \theader_file,\n# \tmembers\n\ndef ParseClass(node, target_scope_prefix):\n\tif not node.is_definition():\n\t\treturn\n\tscope_prefix = node.type.spelling.rsplit('::', 1)[0]\n\t# print(scope_prefix)\n\tif not node.type.spelling.startswith(target_scope_prefix):\n\t\treturn\n\n\tclass_name = node.type.spelling\n\t# print('class', class_name)\n\theader_file = node.location.file.name\n\n\tclassdef = {}\n\tclassdef[\"name\"] = node.spelling\n\tclassdef[\"scope_prefix\"] = scope_prefix\n\tclassdef[\"header_file\"] = header_file\n\tclassdef[\"base_class\"] = None\n\tmembers = []\n\n\tfor child in node.get_children():\n\t\t# print('\\t', child.kind, child.spelling, child.type.spelling)\n\t\tif child.kind == clang.cindex.CursorKind.CXX_BASE_SPECIFIER:\n\t\t\t# print('\\t', child.kind, child.spelling, child.type.spelling)\n\t\t\t# for cc in child.get_children():\n\t\t\t# \tbase_class = cc.spelling    # get the last element\n\t\t\tbase_class = child.type.spelling\n\t\t\t# print('\\t\\tbase class:', base_class)\n\t\t\tclassdef[\"base_class\"] = base_class\n\t\t\tpass\n\n\t\telif child.kind == clang.cindex.CursorKind.CXX_METHOD:\n\t\t\tpass\n\n\t\telif child.kind == clang.cindex.CursorKind.FUNCTION_TEMPLATE:\n\t\t\tpass\n\n\t\telif child.kind == clang.cindex.CursorKind.FIELD_DECL:\n\t\t\tname = child.spelling\n\t\t\tmember_type = child.type.spelling\n\t\t\tNonSerializable = False\n\t\t\tfor c in child.get_children():\n\t\t\t\tif c.kind == clang.cindex.CursorKind.ANNOTATE_ATTR:\n\t\t\t\t\tif c.spelling not in all_attributes:\n\t\t\t\t\t\tprint(c.spelling)\n\t\t\t\t\t\traise UnknownAttributeError()\n\t\t\t\t\telif c.spelling == 'NonSerializable':\n\t\t\t\t\t\tNonSerializable = True\n\t\t\t# print('\\t\\t', member_type, name)\n\t\t\t# for c in child.get_children():\n\t\t\t# \t# if c.kind == clang.cindex.CursorKind\n\t\t\t# \tpass\n\t\t\tif not NonSerializable:\n\t\t\t\tmember = {}\n\t\t\t\tmember[\"name\"] = name\n\t\t\t\tmember[\"type\"] = member_type\n\t\t\t\tmembers.append(member)\n\n\tclassdef[\"members\"] = members\n\treturn classdef\n\n\n\nskip_cursor_types = (\n\tclang.cindex.CursorKind.ENUM_DECL,\n\t#clang.cindex.CursorKind.STRUCT_DECL,\n\tclang.cindex.CursorKind.FUNCTION_TEMPLATE,\n\tclang.cindex.CursorKind.UNEXPOSED_DECL,\n\tclang.cindex.CursorKind.TEMPLATE_REF\n\t)\n\nclasses = []\n\ndef FindTypes(node):\n\t# print(node.spelling)\n\n\tif (node.location.file is not None) and ('FishEngine' not in node.location.file.name):\n\t\treturn\n\tif node.kind in skip_cursor_types:\n\t\treturn\n\n\tif node.kind in (clang.cindex.CursorKind.CLASS_DECL, clang.cindex.CursorKind.STRUCT_DECL):\n\t\tclassdef = ParseClass(node, 'FishEngine')\n\t\tif classdef is not None:\n\t\t\tclasses.append(classdef)\n\t\treturn\n\n\tfor c in node.get_children():\n\t\tFindTypes(c)\n\ndef ExtractClasses(path):\n\tast_path = './fe.ast'\n\tDumpAst(path, ast_path)\n\ttu = index.read(ast_path)\n\tFindTypes(tu.cursor)\n\n\twith open('components.json', 'w') as f:\n\t\tcomponents = [c for c in classes if c['base_class'] == \"FishEngine::Component\"]\n\t\tprint(json.dumps(components, indent=4), file=f)\n\n\twith open('singleton_components.json', 'w') as f:\n\t\tcomponents = [c for c in classes if c['base_class'] == \"FishEngine::SingletonComponent\"]\n\t\tprint(json.dumps(components, indent=4), file=f)\n\n\twith open('systems.json', 'w') as f:\n\t\tsystems = [c for c in classes if c['base_class'] == \"FishEngine::System\"]\n\t\tprint(json.dumps(systems, indent=4), file=f)\n\n\nif __name__ == \"__main__\":\n\tclasses = ExtractClasses(\"../Include/FishEngine.hpp\")\n\tclasses = ExtractClasses(\"../Include/FishEditor.hpp\")","sub_path":"Scripts/reflect.py","file_name":"reflect.py","file_ext":"py","file_size_in_byte":4756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"411857662","text":"from contextlib import contextmanager\nfrom optparse import make_option\nimport multiprocessing\nimport os\nimport sys\nimport time\nimport itertools\n\ntry:\n    import queue\nexcept ImportError:\n    import Queue as queue\n\nfrom django.core.management.commands.test import Command as DjangoTest\nfrom django.conf import settings\n\nfrom better_test.database import read_database\nfrom better_test.database import simple_weighted_partition\nfrom better_test.database import write_database\n\nPY_26 = sys.version_info[0] == 2 and sys.version_info[1] == 6\n\nif PY_26:\n    from django.utils import unittest\n    from django.utils.unittest.suite import _ErrorHolder as ErrorHolder\nelse:\n    import unittest\n    from unittest.suite import _ErrorHolder as ErrorHolder\ntry:\n    from unittest2.suite import _ErrorHolder as ErrorHolder2\nexcept ImportError:\n    ErrorHolder2 = False\n\nif ErrorHolder2:\n    ERROR_HOLDER_CLASSES = (ErrorHolder, ErrorHolder2)\nelse:\n    ERROR_HOLDER_CLASSES = (ErrorHolder,)\n\n\nQUEUE = None  # See init_task\n\n\nclass DisableMigrations(object):\n    def __contains__(self, item):\n        return True\n\n    def __getitem__(self, item):\n        return \"notmigrations\"\n\n\ndef test_to_dotted(test):\n    klass = test.__class__\n    name = klass.__name__\n    module = klass.__module__\n    return '{module}.{name}.{method}'.format(\n        module=module,\n        name=name,\n        method=test._testMethodName\n    )\n\n\ndef suite_to_labels(suite, result):\n    \"\"\"\n    Transform a unittest.TestSuite to a list of test labels that can be used\n    by django.test.DiscoveryRunner.run_tests.\n    \"\"\"\n    labels = []\n    for test in suite._tests:\n        klass = test.__class__\n        name = klass.__name__\n        module = klass.__module__\n        if name == 'ModuleImportFailure' and module == 'unittest.loader':\n            test.qualname = module + '.' + name\n            try:\n                getattr(test, test._testMethodName)()\n            except Exception as err:\n                result.addError(test, err)\n        else:\n            labels.append(test_to_dotted(test))\n    return labels\n\n\ndef multi_processing_runner_factory(stream):\n    \"\"\"\n    Creates a test runner with the MultiProcessinTestResult result class and\n    overwriting the output stream.\n    \"\"\"\n    def inner(*args, **kwargs):\n        args = list(args)\n        if len(args) > 0:\n            args[0] = stream\n        else:\n            kwargs['stream'] = stream\n        if len(args) > 5:\n            args[5] = MultiProcessingTestResult\n        else:\n            kwargs['resultclass'] = MultiProcessingTestResult\n        return unittest.TextTestRunner(*args, **kwargs)\n    return inner\n\n\n@contextmanager\ndef null_stdout():\n    \"\"\"\n    Context manager setting sys.stdout to devnull and yielding that stdout\n    \"\"\"\n    stdout = sys.stdout\n    nullout = open(os.devnull, 'w')\n    sys.stdout = nullout\n    try:\n        yield nullout\n    finally:\n        sys.stdout = stdout\n        nullout.close()\n\n\ndef run_tests(labels, runner_class, runner_options, chunk_num):\n    \"\"\"\n    Test runner inside the task process.\n    \"\"\"\n    # We need to patch the db name in case we're in --parallel or --isolate\n    # mode (or any other mode with more than one chunk). But if there's only\n    # a single chunk, don't change the db name. Therefore we don't modify the\n    # name for the first chunk (chunk_num=0).\n    if chunk_num:\n        for config in settings.DATABASES.values():\n            if config.get('NAME', None) != ':memory:':\n                config['NAME'] += '_{num}'.format(num=chunk_num)\n    try:\n        with null_stdout() as nullout:\n            real_runner_class = type(\n                runner_class.__name__,\n                (MultiProcessingTestRunner, runner_class),\n                {'test_runner': multi_processing_runner_factory(nullout)}\n            )\n            runner = real_runner_class(**runner_options)\n            runner.run_tests(labels)\n    except:\n        import traceback\n        traceback.print_exc()\n        raise\n\n\ndef init_task(result_queue):\n    \"\"\"\n    Initialize task process. multiprocessing.Value and multiprocessing.Queue\n    can't be sent via multiprocssing.Pool.apply_async, so have to be\n    initialized by this (as globals unfortunately).\n    \"\"\"\n    global QUEUE\n    QUEUE = result_queue\n\n\ndef wait_for_tests_to_finish(real_result, async_results, results_queue):\n    \"\"\"\n    Waits for all tasks to finish by checking if task_counter is zero. Whenever\n    a task (run_tests) finishes, it decrements the counter.\n\n    While waiting, it pulls results out of the results queue and feeds them\n    into the real result.\n    \"\"\"\n    def handle(result):\n        method_name, args = result\n        arglist = list(args)\n        test_info = arglist.pop(0)\n        fake_test = FakeTest.deserialize(test_info)\n        method = getattr(real_result, method_name)\n        method(fake_test, *arglist)\n\n    def get_remaining_results():\n        return filter(\n            lambda result: not result.ready(), async_results\n        )\n\n    remaining_results = get_remaining_results()\n    while any(remaining_results):\n        remaining_results = get_remaining_results()\n        try:\n            handle(results_queue.get_nowait())\n        except queue.Empty:\n            continue\n    while not results_queue.empty():\n        handle(results_queue.get_nowait())\n\n\ndef serialize(test):\n    \"\"\"\n    Serializes a test (which can either be a TestCase-like or an ErrorHolder)\n    for safe transport via a Queue.\n    ErrorHolder (if actual test setup failed, not anything during test run) is\n    special cased because it's a dynamic class and isn't quite the same as\n    normal tests.\n    \"\"\"\n    if isinstance(test, ERROR_HOLDER_CLASSES):\n        return (\n            test.id(),\n            str(test),\n            test.shortDescription(),\n        )\n    return (\n        test_to_dotted(test),\n        str(test),\n        test.shortDescription()\n    )\n\n\nclass Command(DjangoTest):\n    option_list = DjangoTest.option_list + (\n        make_option('--parallel',\n            action='store_true', dest='parallel', default=False,\n            help='Run tests in parallel.'),\n        make_option('--isolate',\n            action='store_true', dest='isolate', default=False,\n            help='Run each test isolated.'),\n        make_option('--failed',\n            action='store_true', dest='failed', default=False,\n            help='Re-run tests that failed the last time.'),\n        make_option('--list-slow',\n            type=int, dest='list_slow', default=0,\n            help='Amount of slow tests to print.'),\n        make_option('--retest',\n            action='store_true', dest='retest', default=False,\n            help='Re-run the tests using the last configuration.'),\n        make_option('--vanilla',\n            action='store_true', dest='vanilla', default=False,\n            help='Ignore better test.'),\n        make_option('--no-migrations',\n            action='store_false', dest='migrate', default=True,\n            help='Do not run migrations'),\n    )\n\n    def handle(self, *test_labels, **options):\n        from django.conf import settings\n\n        if not options['migrate']:\n            settings.MIGRATION_MODULES = DisableMigrations()\n\n        if options['vanilla']:\n            return DjangoTest().handle(*test_labels, **options)\n        \n        from django.test.utils import get_runner\n\n        if 'south' in settings.INSTALLED_APPS:\n            from south.management.commands import patch_for_test_db_setup\n            patch_for_test_db_setup()\n\n        # load test runner class\n        TestRunner = get_runner(settings, options.get('testrunner'))\n\n        options['verbosity'] = int(options.get('verbosity'))\n\n        if options.get('liveserver') is not None:\n            os.environ['DJANGO_LIVE_TEST_SERVER_ADDRESS'] = options['liveserver']\n            del options['liveserver']\n\n        database = read_database()\n\n        # Re-run using last configuration\n        if options['retest'] and database.get('last_run', None):\n            last_run = database['last_run']\n            options['parallel'] = last_run['parallel']\n            options['isolate'] = last_run['isolate']\n            options['list_slow'] = last_run['list_slow']\n            test_labels = last_run['labels']\n\n        # Get the test runner instance, this won't actually be used to run\n        # anything, but rather builds the suite so we can get a list of test\n        # labels to run\n        # interactive is disabled since it doesn't work in multiprocessing\n        options['interactive'] = False\n        test_runner = TestRunner(**options)\n\n        suite = test_runner.build_suite(test_labels)\n\n        # Get an actual result class we can use\n        pseudo_runner = unittest.TextTestRunner(\n            resultclass=MultiProcessingTextTestResult\n        )\n        real_result = pseudo_runner._makeResult()\n\n        all_test_labels = suite_to_labels(suite, real_result)\n\n        # If there's nothing to run, let Django handle it.\n        if not all_test_labels:\n            return super(Command, self).handle(*test_labels, **options)\n\n        if options['failed']:\n            all_test_labels = [\n                label for label in all_test_labels\n                if label in database.get('failed', all_test_labels)\n            ]\n\n        if options['isolate']:\n            # Isolate means one test (label) per task process.\n            chunks = [\n                [label] for label in all_test_labels\n            ]\n        elif options['parallel']:\n            # Try to distribute the test labels equally across the available\n            # CPU cores\n            chunk_count = multiprocessing.cpu_count()\n            weighted_chunks = [\n                (database.get('timings', {}).get(label, 0), label)\n                for label in all_test_labels\n            ]\n            # Try to split the tests into chunks of equal time, not equal size\n            chunks = simple_weighted_partition(weighted_chunks, chunk_count)\n\n            # filter empty chunks\n            chunks = list(filter(bool, chunks))\n        else:\n            chunks = [all_test_labels]\n\n        # Initialize shared values\n        results_queue = multiprocessing.Queue()\n\n        # Initialize pool\n        pool = multiprocessing.Pool(\n            initializer=init_task,\n            initargs=(results_queue,)\n        )\n\n        start_time = time.time()\n\n        # Send tasks to pool\n        async_results = []\n        for chunk_num, chunk in enumerate(chunks):\n            async_results.append(pool.apply_async(\n                run_tests,\n                (chunk, TestRunner, options, chunk_num)\n            ))\n\n        # Wait for results to come in\n        wait_for_tests_to_finish(real_result, async_results, results_queue)\n\n        failed_executors = sum(\n            0 if result.successful() else 1 for result in async_results\n        )\n\n        # Stop all tasks\n        pool.close()\n        pool.join()\n\n        end_time = time.time()\n\n        # Report result, this is mostly taken from TextTestRunner.run\n        time_taken = end_time - start_time\n\n        real_result.printErrors()\n        real_result.stream.writeln(real_result.separator2)\n        real_result.stream.writeln(\n            \"Ran {number} test{plural} in {time:.3f}s\".format(\n                number=real_result.testsRun,\n                plural=real_result.testsRun != 1 and \"s\" or \"\",\n                time=time_taken\n            )\n        )\n        real_result.stream.writeln()\n\n        # Record timings to database\n        data = {\n            'timings': database.get('timings', {}),\n        }\n        for test, timing in real_result.timings.items():\n            data['timings'][test] = timing\n\n        # Record failed tests to database\n        data['failed'] = [\n            test.qualname for test, _ in itertools.chain(\n                real_result.failures,\n                real_result.errors,\n                [(test, None) for test in real_result.unexpectedSuccesses]\n            )\n        ]\n\n        # Record config to database\n        data['last_run'] = {\n            'isolate': options['isolate'],\n            'parallel': options['parallel'],\n            'list_slow': options['list_slow'],\n            'labels': all_test_labels,\n        }\n\n        # Show slowest tests\n        if options['list_slow']:\n            real_result.stream.writeln(\"Slowest tests:\")\n            slowest = sorted(\n                ((timing, test) for test, timing in data['timings'].items()),\n                reverse=True\n            )\n            for timing, test in slowest[:options['list_slow']]:\n                real_result.stream.writeln(\" %.3fs: %s\" % (timing, test))\n            real_result.stream.writeln()\n\n        # Display info about failures etc\n        infos = []\n        skipped = len(real_result.skipped)\n        expected_fails = len(real_result.expectedFailures)\n        unexpected_successes = len(real_result.unexpectedSuccesses)\n        if not real_result.wasSuccessful():\n            real_result.stream.write(\"FAILED\")\n            failed = len(real_result.failures)\n            errored = len(real_result.errors)\n            if failed:\n                infos.append(\"failures=%d\" % failed)\n            if errored:\n                infos.append(\"errors=%d\" % errored)\n        elif failed_executors:\n            real_result.stream.write(\"%d executors failed\" % failed_executors)\n        else:\n            real_result.stream.write(\"OK\")\n        if skipped:\n            infos.append(\"skipped=%d\" % skipped)\n        if expected_fails:\n            infos.append(\"expected failures=%d\" % expected_fails)\n        if unexpected_successes:\n            infos.append(\"unexpected successes=%d\" % unexpected_successes)\n        if infos:\n            real_result.stream.writeln(\" (%s)\" % (\", \".join(infos),))\n        else:\n            real_result.stream.write(\"\\n\")\n\n        # Save the database\n        write_database(data)\n\n        # For easy integration, returns the number of failed tests as the\n        # return code. This means that if all tests passed, 0 is returned,\n        # which happens to be the standard return code for \"success\"\n        # Also the number of failed executors is added.\n        return_code = sum(map(len, (\n            real_result.failures,\n            real_result.errors,\n            real_result.unexpectedSuccesses,\n            real_result.expectedFailures\n        ))) + failed_executors\n        sys.exit(return_code)\n\n\nclass MultiProcessingTextTestResult(unittest.TextTestResult):\n    \"\"\"\n    Thin wrapper around TextTestResult. Python tracebacks are not pickleable,\n    so _exc_info_to_string is handled in MultiProcessingTestResult and the\n    result sent to this as `err`.\n    \"\"\"\n    def __init__(self, *args, **kwargs):\n        super(MultiProcessingTextTestResult, self).__init__(*args, **kwargs)\n        self.timings = {}\n        self.successes = []\n\n    @property\n    def testsRun(self):\n        \"\"\"\n        Usually incremented via startTest, but we don't call that function, so\n        we simply introspect all results we have.\n        \"\"\"\n        return sum(map(len, (\n            self.errors,\n            self.failures,\n            self.unexpectedSuccesses,\n            self.expectedFailures,\n            self.successes,\n            self.skipped\n        )))\n\n    @testsRun.setter\n    def testsRun(self, value):\n        \"\"\"\n        The super class sets/increments this value in some functions, so for\n        simplicity we allow setting it and just discard the value.\n        \"\"\"\n        pass\n\n    def registerTiming(self, test, timing):\n        self.timings[test.qualname] = timing\n\n    def addSuccess(self, test):\n        \"\"\"\n        The default result class doesn't store successes, so we do it ourselves\n        \"\"\"\n        super(MultiProcessingTextTestResult, self).addSuccess(test)\n        self.successes.append(test)\n\n    def _exc_info_to_string(self, err, test):\n        \"\"\"\n        Actual transformation from exception info to string happens in the\n        executor, as exceptions can't be transferred through a Queue.\n        \"\"\"\n        return err\n\n\nclass MultiProcessingTestResult(unittest.TestResult):\n    \"\"\"\n    Result class for used by the task processes. Instead of printing/storing\n    any information, feeds results into QUEUE.\n\n    Important to note is that `test` is transformed into a tuple of\n    `(str(test), test.shortDescription())` as test case instances are not\n    pickleable. Also note that exceptions are transformed to strings in the\n    task as they're not picklable.\n    \"\"\"\n    separator1 = '=' * 70\n    separator2 = '-' * 70\n\n    def __init__(self, *args, **kwargs):\n        if PY_26:\n            super(MultiProcessingTestResult, self).__init__()\n        else:\n            super(MultiProcessingTestResult, self).__init__(*args, **kwargs)\n        self._timings = {}\n\n    def printErrors(self):\n        pass\n\n    def startTest(self, test):\n        self._timings[test] = time.time()\n\n    def _setupStdout(self):\n        pass\n\n    def startTestRun(self):\n        pass\n\n    def stopTest(self, test):\n        QUEUE.put((\n            'registerTiming', (\n                serialize(test),\n                time.time() - self._timings[test],\n            )\n        ))\n\n    def _restoreStdout(self):\n        pass\n\n    def stopTestRun(self):\n        pass\n\n    def addError(self, test, err):\n        safe_err = self._exc_info_to_string(err, test)\n        QUEUE.put((\n            'addError', (\n                serialize(test),\n                safe_err\n            )\n        ))\n\n    def addFailure(self, test, err):\n        safe_err = self._exc_info_to_string(err, test)\n        QUEUE.put((\n            'addFailure', (\n                serialize(test),\n                safe_err\n            )\n        ))\n\n    def addSuccess(self, test):\n        QUEUE.put((\n            'addSuccess', (\n                serialize(test),\n            )\n        ))\n\n    def addSkip(self, test, reason=None):\n        QUEUE.put((\n            'addSkip', (\n                serialize(test),\n                reason\n            )\n        ))\n\n    def addExpectedFailure(self, test, err):\n        safe_err = self._exc_info_to_string(err, test)\n        QUEUE.put((\n            'addExpectedFailure', (\n                serialize(test),\n                safe_err\n            )\n        ))\n\n    def addUnexpectedSuccess(self, test):\n        QUEUE.put((\n            'addUnexpectedSuccess', (\n                serialize(test),\n            )\n        ))\n\n    def wasSuccessful(self):\n        pass\n\n\nclass FakeTest(object):\n    \"\"\"\n    Object faking to be a test case. All the test runner/result need are\n    __str__ and shortDescription, so that's all that is provided.\n    \"\"\"\n    def __init__(self, qualname, name, description):\n        self.qualname = qualname\n        self.name = name\n        self.description = description\n\n    @classmethod\n    def deserialize(cls, data):\n        return cls(*data)\n\n    def __str__(self):\n        return self.name\n\n    def shortDescription(self):\n        return self.description\n\n\nclass MultiProcessingTestRunner(object):\n    test_runner = unittest.TextTestRunner\n\n    def run_suite(self, suite, **kwargs):\n        \"\"\"\n        Backport from Django 1.7\n        \"\"\"\n        return self.test_runner(\n            verbosity=self.verbosity,\n            failfast=self.failfast,\n        ).run(suite)\n","sub_path":"better_test/management/commands/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":19293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"402857690","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nstyle.use(\"ggplot\")\n\ndef sign(x):\n    \"\"\"returns sign of x\"\"\"\n    if x>0:\n        return 1\n    return 0\n\n#Create our line randomly\nw = np.random.rand(2,1)*2 -1;\ncoeff = np.random.random(1)*10 - 5;\n\n#plot the line\nx= np.linspace(-10,10);\ny= -w[0]/w[1]*x-coeff/w[1];\nfig = plt.figure()\nplt.plot(x,y,'k-');\nprint(w)\n\n#Generate labelled data\nxlabel = np.random.rand(10,2)*20 - 10\nylabel = []\nfor row in xlabel:\n#assigning the labels to our data\n    ylabel.append(np.sign(np.dot(row,w)+coeff)[0]);\n\n#change data to required format\nylabel = np.array(ylabel);\ndata = np.vstack((xlabel[:,0],xlabel[:,1],ylabel[:])).T;\n#put data into csv file data_labelled.csv\nnp.savetxt('data_labelled.csv',data,delimiter=',');\nplt.scatter(xlabel[:,0],xlabel[:,1], c=ylabel);\n\n#Generate unlabelled data\nxlabel = np.random.rand(100,2)*20 - 10\nylabel = []\nfor row in xlabel:\n#assigning the labels to our data (known only to oracle)\n    ylabel.append(np.sign(np.dot(row,w)+coeff)[0]);\n\n#change data to required format\nylabel = np.array(ylabel);\ndata = np.vstack((xlabel[:,0],xlabel[:,1],ylabel[:])).T;\n#put data into csv file data_unlabelled.csv\nnp.savetxt('data_unlabelled.csv',data,delimiter=',');\nprint('Done generating data');\n\n#plotting data\nplt.draw();\nplt.waitforbuttonpress(0)\nplt.close(fig)\n\nprint('Exitting');\n","sub_path":"dataGen.py","file_name":"dataGen.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"108956394","text":"import os\nimport sys\nimport shutil\nfrom optparse import make_option\n\nfrom django.core.files.storage import FileSystemStorage, get_storage_class\nfrom django.core.management.base import CommandError\n\nfrom staticfiles.management.base import OptionalAppCommand\nfrom staticfiles.settings import DIRS, STORAGE\nfrom staticfiles import utils\n\ntry:\n    set\nexcept NameError:\n    from sets import Set as set # Python 2.3 fallback\n\n\nclass Command(OptionalAppCommand):\n    \"\"\"\n    Command that allows to copy or symlink media files from different\n    locations to the settings.STATIC_ROOT.\n\n    Based on the collectmedia management command by Brian Beck:\n    http://blog.brianbeck.com/post/50940622/collectmedia\n    \"\"\"\n    option_list = OptionalAppCommand.option_list + (\n        make_option('--noinput', action='store_false', dest='interactive',\n            default=True, help=\"Do NOT prompt the user for input of any \"\n                \"kind.\"),\n        make_option('-i', '--ignore', action='append', default=[],\n            dest='ignore_patterns', metavar='PATTERN',\n            help=\"Ignore files or directories matching this glob-style \"\n                \"pattern. Use multiple times to ignore more.\"),\n        make_option('-n', '--dry-run', action='store_true', dest='dry_run',\n            help=\"Do everything except modify the filesystem.\"),\n        make_option('-l', '--link', action='store_true', dest='link',\n            help=\"Create a symbolic link to each file instead of copying.\"),\n        make_option('--exclude-dirs', action='store_true',\n            help=\"Exclude additional static locations specified in the \"\n                \"STATICFILES_DIRS setting.\"),\n        make_option('--no-default-ignore', action='store_false',\n            dest='use_default_ignore_patterns', default=True,\n            help=\"Don't ignore the common private glob-style patterns 'CVS', \"\n                \"'.*' and '*~'.\"),\n    )\n    help = (\"Copy static media files from apps and other locations in a \"\n            \"single location.\")\n\n    def handle(self, *app_labels, **options):\n        ignore_patterns = options['ignore_patterns']\n        if options['use_default_ignore_patterns']:\n            ignore_patterns += ['CVS', '.*', '*~']\n            options['ignore_patterns'] = ignore_patterns \n        options['skipped_files'] = []\n        options['copied_files'] = []\n        options['symlinked_files'] = []\n        storage = get_storage_class(STORAGE)()\n        options['destination_storage'] = storage\n        try:\n            destination_paths = utils.get_files(storage, ignore_patterns)\n        except OSError:\n            # The destination storage location may not exist yet. It'll get\n            # created when the first file is copied.\n            destination_paths = []\n        options['destination_paths'] = destination_paths\n        try:\n            storage.path('')\n            destination_local = True\n        except NotImplementedError:\n            destination_local = False\n        options['destination_local'] = destination_local\n        if options.get('link', False):\n            if sys.platform == 'win32':\n                message = \"Symlinking is not supported by this platform (%s).\"\n                raise CommandError(message % sys.platform)\n            if not destination_local:\n                raise CommandError(\"Can't symlink to a remote destination.\")\n        # Warn before doing anything more.\n        if options.get('interactive'):\n            confirm = raw_input(\"\"\"\nYou have requested to collate static media files and copy them to the\ndestination location as specified in your settings file. \n\nThis will overwrite existing files. Are you sure you want to do this?\n\nType 'yes' to continue, or 'no' to cancel: \"\"\")\n            if confirm != 'yes':\n                raise CommandError(\"Static files build cancelled.\")\n        return super(Command, self).handle(*app_labels, **options)\n\n    def pre_handle_apps(self, **options):\n        \"\"\"\n        Copy all files from a directory.\n        \n        \"\"\"\n        if not options.get('exclude_dirs', False):\n            ignore_patterns = options['ignore_patterns']\n            for root in DIRS:\n                if isinstance(root, (list, tuple)):\n                    prefix, root = root\n                else:\n                    prefix = ''\n                source_storage = FileSystemStorage(location=root)\n                for source in utils.get_files(source_storage, ignore_patterns):\n                    self.copy_file(source, prefix, source_storage, **options)\n\n    def post_handle_apps(self, **options):\n        copied_files = options['copied_files']\n        symlinked_files = options['symlinked_files']\n        logger = self.get_logger()\n        count = len(copied_files) + len(symlinked_files)\n        logger.info(\"%s static file%s built.\" % (count,\n                                                 count != 1 and 's' or ''))\n\n    def handle_app(self, app, **options):\n        \"\"\"\n        Copy all static media files from an application.\n        \n        \"\"\"\n        ignore_patterns = options['ignore_patterns']\n        prefix = utils.get_app_prefix(app)\n        for storage in utils.app_static_storages(app):\n            for path in utils.get_files(storage, ignore_patterns):\n                self.copy_file(path, prefix, storage, **options)\n\n    def excluded_app(self, app, **options):\n        \"\"\"\n        Gather all the static media files for excluded apps.\n        \n        This is so a warning can be issued for later copied files which would\n        normally be copied by excluded apps.\n        \n        \"\"\"\n        skipped_files = options['skipped_files']\n        ignore_patterns = options['ignore_patterns']\n        all_files = utils.get_files_for_app(app, ignore_patterns)\n        skipped_files.extend(all_files)\n\n    def copy_file(self, source, destination_prefix, source_storage, **options):\n        \"\"\"\n        Attempt to copy (or symlink) `source` to `destination`, returning True\n        if successful.\n        \"\"\"\n        destination_storage = options['destination_storage']\n        dry_run = options.get('dry_run', False)\n        logger = self.get_logger()\n        if destination_prefix:\n            destination = '/'.join([destination_prefix, source])\n        else:\n            destination = source\n\n        if destination in options['copied_files']:\n            logger.warning(\"Skipping duplicate file (already copied earlier):\"\n                           \"\\n  %s\" % destination)\n            return False\n        if destination in options['symlinked_files']:\n            logger.warning(\"Skipping duplicate file (already linked earlier):\"\n                           \"\\n  %s\" % destination)\n            return False\n        if destination in options['skipped_files']:\n            logger.warning(\"Copying file that would normally be provided by \"\n                           \"an excluded application:\\n  %s\" % destination)\n        source_path = source_storage.path(source)\n        if destination in options['destination_paths']:\n            if dry_run:\n                logger.info(\"Pretending to delete:\\n  %s\" % destination)\n            else:\n                logger.debug(\"Deleting:\\n  %s\" % destination)\n                destination_storage.delete(destination)\n        if options.get('link', False):\n            destination_path = destination_storage.path(destination)\n            if dry_run:\n                logger.info(\"Pretending to symlink:\\n  %s\\nto:\\n  %s\" %\n                            (source_path, destination_path))\n            else:\n                logger.debug(\"Symlinking:\\n  %s\\nto:\\n  %s\" %\n                             (source_path, destination_path))\n                try:\n                    os.makedirs(os.path.dirname(destination_path))\n                except OSError:\n                    pass\n                os.symlink(source_path, destination_path)\n            options['symlinked_files'].append(destination)\n        else:\n            if dry_run:\n                logger.info(\"Pretending to copy:\\n  %s\\nto:\\n  %s\" %\n                            (source_path, destination))\n            else:\n                logger.debug(\"Copying:\\n  %s\\nto:\\n  %s\" % (source_path, destination))\n                if options['destination_local']:\n                    destination_path = destination_storage.path(destination)\n                    try:\n                        os.makedirs(os.path.dirname(destination_path))\n                    except OSError:\n                        pass\n                    shutil.copy2(source_path, destination_path)\n                else:\n                    source_file = source_storage.open(source)\n                    destination_storage.write(destination, source_file)\n            options['copied_files'].append(destination)\n        return True\n","sub_path":"staticfiles/management/commands/build_static.py","file_name":"build_static.py","file_ext":"py","file_size_in_byte":8755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"300578652","text":"from Crypto.PublicKey import RSA\nfrom Crypto.Cipher import PKCS1_OAEP\nimport base64, os, json, random, math, sys, shutil\nfrom random import randint\nfrom sympy import isprime\n\n\n#can do pem or txt\n\ndef generatePairKey(name, private_key, public_key):\n    try:\n        os.mkdir('Network')\n    except:\n        pass\n    try:\n        os.mkdir('Network/'+name+'KeyFile')\n    except:\n        pass\n    private_key_export = private_key.exportKey('PEM')\n    public_key_export = public_key.exportKey('PEM')\n    f = open('Network/'+name+'KeyFile/'+name+'Privatekey.txt','wb')\n    f.write(private_key_export)\n    f.close()\n    f = open('Network/'+name+'KeyFile/'+name+'Publickey.txt','wb')\n    f.write(public_key_export)\n    f.close()\n\ndef generatePairKeys():\n    private_key = RSA.generate(2048)\n    public_key = private_key.publickey()\n    return private_key, public_key\n\ndef saveKeyInPublicSpace(name):\n    try:\n        os.mkdir('Network/'+'Public_Network')\n        pass\n    except:\n        pass\n    f = open('Network/'+name + 'Keyfile/'+name+'Publickey.txt','r')\n    d = open('Network/'+'Public_Network/'+name+'Publickey.txt','wb')\n    d.write(RSA.importKey(f.read()).exportKey('PEM'))\n    f.close()\n    d.close()\n\ndef generatePublicVariable():\n    #try:\n    #    os.mkdir('Network/'+'Public_Network')\n    #    pass\n    #except:\n    #    pass\n    PrimeNumber = generateLargePrime(128)\n    BaseNumber = randint(1,1000)\n    #f = open('Network/'+'Public_Network/'+name+'PublicVariable.txt','wb')\n    #content = '{ \"sharedPrime\": '+str(PrimeNumber)+',\"sharedBase\": '+str(BaseNumber)+'}'\n    #f.write(content.encode('UTF-8'))\n    #f.close()\n\n    return PrimeNumber, BaseNumber\n\ndef generateLargePrime(k):\n     r=100*(math.log(k,2)+1)\n     t = r\n     while r>0:\n         n = random.randrange(2**(k-1),2**(k))\n         r-=1\n         if isprime(n) == True:\n             return n\n     return \"Failure after \"+str(t) + \" tries.\"\n\ndef getPublicVariable(name):\n    f = open('Network/'+'Public_Network/'+name+'PublicVariable.txt','r')\n    j = json.loads(f.read())\n    f.close()\n    return(j[\"sharedPrime\"], j[\"sharedBase\"])\n    \n\ndef encryptMessage(message, Selfname, Sendername):\n    f = open('Network/'+Selfname+ 'KeyFile/'+Sendername+'Publickey.txt','r')\n    opposite_public_key = RSA.importKey(f.read())\n    #print(str(opposite_public_key))\n    encryptor = PKCS1_OAEP.new(opposite_public_key)\n    encrypted_msg = encryptor.encrypt(message.encode('UTF-8'))\n    encoded_encrypted_msg = base64.b64encode(encrypted_msg)\n    #print(encoded_encrypted_msg)\n    f.close()\n    return encoded_encrypted_msg\n\ndef encryptMessageWithPublicKey(message, key):\n    encryptor = PKCS1_OAEP.new(key)\n    encrypted_msg = encryptor.encrypt(message.encode('UTF-8'))\n    encoded_encrypted_msg = base64.b64encode(encrypted_msg)\n    #print(encoded_encrypted_msg)\n\n    return encoded_encrypted_msg\n\n\ndef encryptMessageFromPublicNetwork(message, Sendername):\n    f = open('Network/'+Sendername+'KeyFile/'+Sendername+'Publickey.txt','r')\n    opposite_public_key = RSA.importKey(f.read())\n    #print(str(opposite_public_key))\n    encryptor = PKCS1_OAEP.new(opposite_public_key)\n    encrypted_msg = encryptor.encrypt(message.encode('UTF-8'))\n    encoded_encrypted_msg = base64.b64encode(encrypted_msg)\n    #print(encoded_encrypted_msg)\n    f.close()\n    return encoded_encrypted_msg \n\ndef decryptMessage(encoded_encrypted_msg, Selfname):\n    f = open('Network/'+Selfname+'KeyFile/'+Selfname+'Privatekey.txt','r')\n    private_key = RSA.importKey(f.read())\n    encryptor_second = PKCS1_OAEP.new(private_key)\n    decoded_encrypted_msg = base64.b64decode(encoded_encrypted_msg.encode(\"utf-8\"))\n    decoded_decrypted_msg = encryptor_second.decrypt(decoded_encrypted_msg)\n    #print(decoded_decrypted_msg)\n    f.close()\n    return(decoded_decrypted_msg.decode(\"utf-8\"))\n\ndef decryptMessageWithPrivateKey(encoded_encrypted_msg, key):\n    encryptor_second = PKCS1_OAEP.new(key)\n    decoded_encrypted_msg = base64.b64decode(encoded_encrypted_msg.encode(\"utf-8\"))\n    decoded_decrypted_msg = encryptor_second.decrypt(decoded_encrypted_msg)\n    return(decoded_decrypted_msg.decode(\"utf-8\"))\n\ndef readPublicKey(name):\n    f = open('Network/'+name+'KeyFile/'+name+'Publickey.txt','r')\n    output = f.read()\n    f.close()\n    return output\n\ndef sharedkey(PrimeNumber, BaseNumber, secret_Number):\n    return (BaseNumber**secret_Number)%PrimeNumber","sub_path":"Programming/Python Group Project/temp/Diffie-Hellman_key_exchange/generatePairKey.py","file_name":"generatePairKey.py","file_ext":"py","file_size_in_byte":4382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"348228173","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n#\n# Complete the 'breakingRecords' function below.\n#\n# The function is expected to return an INTEGER_ARRAY.\n# The function accepts INTEGER_ARRAY scores as parameter.\n#\n\ndef breakingRecords(scores):\n    # Write your code here\n    minimum = 0\n    maximum = 0\n    min1 = 0\n    max1 = 0\n    for i in range (0,len(scores)):\n        if i > 0 :\n            if scores[i] > maximum:\n                maximum = scores[i]\n                max1+=1\n            elif scores[i] < minimum:\n                minimum = scores[i]\n                min1+=1\n            else:\n                min1 = min1\n                max1 = max1\n                      \n        else :\n            minimum = maximum = scores[i]\n            min1 = max1 = 0\n            \n        \n    return (max1, min1)\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    n = int(input().strip())\n\n    scores = list(map(int, input().rstrip().split()))\n\n    result = breakingRecords(scores)\n\n    fptr.write(' '.join(map(str, result)))\n    fptr.write('\\n')\n\n    fptr.close()\n","sub_path":"scores breaking record.py","file_name":"scores breaking record.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"234248239","text":"import sys\nadj=[[0 for i in range(100)] for j in range(100)]\ndef createGraph(n):\n    max_edges=0\n    max_edges=(n*(n-1))\n    for i in range(max_edges):\n        print(\"enter orgin=-1 and destination=-1 to break the loop\")\n        origin=int(input())\n        destination=int(input())\n        if(origin ==-1 and destination ==-1):\n            break\n        if(origin>=n or destination>=n or origin<0 or destination<0):\n            print(\"invalid graph\")\n            break\n        \n        else:\n            adj[origin][destination]=1\n\ndef addEdge(origin,destination,n):\n    if(origin>=n or destination>=n or origin<0 or destination<0):\n        print(\"invalid graph\")\n        return\n    adj[origin][destination]=1\n\ndef delEdge(origin,destination,n):\n    if(origin>=n or destination>=n or origin<0 or destination<0):\n        print(\"invalid deletion\")\n        return\n    adj[origin][destination]=0\n    \ndef display(n):\n    for i in range(n):\n        for j in range(n):\n            print(adj[i][j]),\n        print(\"\\n\")\n    \nif __name__=='__main__':\n    print(\"enter number of vertices\")\n    n=int(input())\n    createGraph(n)\n    origin,destination=0,0\n    while(1):\n        print(\"1.Insert an edge\\n\")\n        print(\"2.Delete an edge\\n\")\n        print(\"3.Display\\n\")\n        print(\"4.Exit\\n\")\n        print(\"Enter your choice : \")\n        choice=int(input())\n        \n        if choice==1:\n            print(\"Enter orgin and destination to insert Edge\")\n            origin=int(input())\n            destination=int(input())\n            addEdge(origin,destination,n)\n            \n        elif choice==2:\n            print(\"Enter orgin and destination to delete Edge\")\n            origin=int(input())\n            destination=int(input())\n            delEdge(origin,destination,n)\n        elif choice==3:\n            print(\"Display\")\n            display(n)\n        elif choice==4:\n            sys.exit(0)\n","sub_path":"Applied Course/2.Data Structure/11.Graph/Program for addition and deletion of edges in a directed graph using adjacency matrix.py","file_name":"Program for addition and deletion of edges in a directed graph using adjacency matrix.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"627904269","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport networkx as nx\r\nimport math\r\n\r\nplt.rc('text', usetex=True)\r\nplt.rc('font', family='serif')\r\n\r\ndef cria_lista_links(arquivo,folder = ''):\r\n    colunas2 = ['ag1','ag2']\r\n    links = pd.read_csv(folder+arquivo, header = None, sep = '|')\r\n    links.columns = colunas2\r\n    Nedges = links.shape[0]\r\n    lista_links = []\r\n    for i1 in range(Nedges):\r\n        lista_links.append((links.ag1[i1],links.ag2[i1]))\r\n    return lista_links, Nedges\r\n        \r\ndef cria_posicoes(arquivo,folder = ''):\r\n    ##write(10, *) i,'|', X(i),'|', Y(i),'|',Mphi(XM1(i))\r\n    colunas = ['agente','posX','posY','Phi']\r\n    #folder = \"/home/paulo/Dropbox/Profissional/usp/Mobilidade/\"\r\n    #folder += \"resultados/dinamica8/res41/teste_b/resultados/\"\r\n    posicoes = pd.read_csv(folder+arquivo, header = None, sep = '|')\r\n    posicoes.columns = colunas\r\n    Nv = posicoes.shape[0]\r\n    return posicoes, Nv\r\n\r\ndef grafico_posicoes(posicoes,grafo,centro,raio,):\r\n    dicionario = {}\r\n    for i3 in range(Nv):\r\n        dicionario[nos[i3]] = [posicoes.posX[i3],posicoes.posY[i3]]\r\n    nx.draw_networkx_nodes(grafo, with_labels=True, pos = dicionario,\r\n     node_size = 35, node_color = 'b', node_shape = 'o', linewidth = 1.0)\r\n    nx.draw_networkx_edges(grafo, with_labels=True, pos = dicionario,\r\n     width = 0.5, edge_color = 'k')\r\n    title2 = r'$M=$ '+str(Nv)\r\n    plt.title(title2, fontsize = 16)\r\n    plt.xlabel(r'$x$',fontsize = 16)\r\n    plt.ylabel(r'$y$',fontsize = 16)\r\n    plt.grid(True)\r\n    plt.axis('scaled')\r\n    # plt.xticks((100, 200, 300, 400, 500), ('100', '200', '300','400','500'), size=14)\r\n    # plt.yticks((100, 200, 300, 400, 500), ('100', '200', '300','400','500'), size=14)\r\n    plt.xticks(size=14)\r\n    plt.yticks(size=14)\r\n    lado = math.sqrt(float(Nv) / 0.0512 )\r\n    trajetoria = plt.Circle(centro, raio, edgecolor = 'r', facecolor = 'none')\r\n    axes = plt.gca()\r\n    axes.add_artist(trajetoria)\r\n    axes.set_xlim([0,lado])\r\n    axes.set_ylim([0,lado])\r\n    # plt.text(0.92*L,0.9*L,'sus',fontsize = 10, color = 'g', backgroundcolor = 'w')\r\n    plt.savefig('grafico1.png',dpi = 300, bbox_inches='tight')\r\n    plt.close() \r\n\r\ndef grafico_posicoes_Phi(posicoes,grafo,centro,raio):\r\n    dicionario = {}\r\n    for i3 in range(Nv):\r\n        dicionario[nos[i3]] = [posicoes.posX[i3],posicoes.posY[i3]]\r\n    #lista_links, Nedges = cria_lista_links(conexoes)\r\n    #grafo = nx.Graph()\r\n    #grafo.add_nodes_from(nos)\r\n    #grafo.add_edges_from(lista_links)\r\n    Phimax = max(posicoes.Phi)\r\n    Phimin = min(posicoes.Phi)\r\n    nodes = nx.draw_networkx_nodes(grafo, with_labels=True, pos = dicionario, node_size = 35, node_color = posicoes.Phi, vmin = Phimin, vmax = Phimax, cmap = 'inferno', node_shape = 'o', linewidth = 1.0)\r\n    nx.draw_networkx_edges(grafo, with_labels=True, pos = dicionario, width = 0.5, edge_color = 'k')\r\n    title2 = r'$M=$ '+str(Nv)+r', $\\delta=1$.'\r\n    #plt.title(title2, fontsize = 16)\r\n    clb = plt.colorbar(nodes)\r\n    clb.set_label(r'$\\Phi$', fontsize = 16, labelpad=-40, y=1.05, rotation=0)\r\n    plt.xlabel(r'$x$',fontsize = 16)\r\n    plt.ylabel(r'$y$',fontsize = 16)\r\n    plt.grid(True)\r\n    plt.axis('scaled')\r\n    plt.xticks(size=14)\r\n    plt.yticks(size=14)\r\n    lado = math.sqrt(float(Nv) / 0.0512 )\r\n    trajetoria = plt.Circle(centro, raio, edgecolor = 'b', facecolor = 'none')\r\n    axes = plt.gca()\r\n    axes.add_artist(trajetoria)\r\n    axes.set_xlim([0,lado])\r\n    axes.set_ylim([0,lado])\r\n    # plt.text(0.92*L,0.9*L,'sus',fontsize = 10, color = 'g', backgroundcolor = 'w')\r\n    plt.savefig('figura_19_NK.pdf',dpi = 300, bbox_inches='tight')\r\n    plt.close()\r\n\r\n###############################################################\r\n\r\nfolder = \"/home/paulo/Dropbox/Profissional/usp/Mobilidade/\"\r\nfolder += \"resultados/dinamica10_Paulo/res73/\"\r\nposicoes, Nv = cria_posicoes(folder +'rede.txt')\r\nnos = [i+1 for i in range(Nv)]\r\nconexoes, Nedges = cria_lista_links(folder +'links.txt')\r\ngrafo = nx.Graph()\r\ngrafo.add_nodes_from(nos)\r\n\r\n\r\nrho = 512/1e4\r\ndelta = 1.0\r\n\r\nraio = delta / math.sqrt(rho)\r\n\r\ncentro = (20.3459606,20.3631611)\r\n\r\n\r\n\r\ngrafo.add_edges_from(conexoes)\r\n#grafico_posicoes(posicoes,grafo,centro,raio)\r\ngrafico_posicoes_Phi(posicoes,grafo,centro,raio)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Mobilidade/figura_19_NK.py","file_name":"figura_19_NK.py","file_ext":"py","file_size_in_byte":4292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"332255849","text":"from __future__ import print_function\n\nfrom re import match\n\nfrom builtins import str, range, super\nfrom past.builtins import basestring\n\nimport numpy as np\nimport pandas as pd\n\nfrom decorator import decorator\n\nfrom oauth2client.client import OAuth2Credentials\n\nfrom gspread.models import Worksheet\nfrom gspread.exceptions import (SpreadsheetNotFound, WorksheetNotFound,\n                                NoValidUrlKeyFound)\nfrom gspread.exceptions import APIError\nfrom gspread.client import Client as ClientV4\nfrom gspread_pandas.conf import get_creds, default_scope\nfrom gspread_pandas.exceptions import (GspreadPandasException, NoWorksheetException,\n                                MissMatchException)\nfrom gspread_pandas.util import (chunks, parse_df_col_names,\n                                 parse_sheet_index, parse_sheet_headers,\n                                 create_frozen_request, create_filter_request,\n                                 fillna, get_cell_as_tuple, get_range)\n\n\n__all__ = ['Spread', 'Client']\n\nROW = 0\nCOL = 1\n\nclass Client(ClientV4):\n    \"\"\"\n    The gspread_pandas :class:`Client` extends :class:`Client <gspread.client.Client>`\n    and authenticates using credentials stored in ``gspread_pandas`` config.\n\n    This class also adds a few convenience methods to explore the user's google drive\n    for spreadsheets.\n    \"\"\"\n    _email = None\n\n    def __init__(self, user_or_creds, config=None, scope=default_scope):\n        \"\"\"\n        :param str user_or_creds: string indicating the key to a users credentials,\n            which will be stored in a file (by default they will be stored in\n            ``~/.config/gspread_pandas/creds/<user>`` but can be modified with\n            ``creds_dir`` property in config) or an instance of\n            :class:`OAuth2Credentials <oauth2client.client.OAuth2Credentials>`\n        :param dict config: optional, if you want to provide an alternate configuration,\n            see :meth:`get_config <gspread_pandas.conf.get_config>`\n        :param list scope: optional, if you'd like to provide your own scope\n        \"\"\"\n        #: `(list)` - Feeds included for the OAuth2 scope\n        self.scope = scope\n        self._login(user_or_creds, config)\n\n    def _login(self, user_or_creds, config):\n        if isinstance(user_or_creds, OAuth2Credentials):\n            creds = user_or_creds\n        elif isinstance(user_or_creds, basestring):\n            creds = get_creds(user_or_creds, config, self.scope)\n        else:\n            raise TypeError('user_or_client needs to be a string '\n                            'or OAuth2Credentials object')\n\n        super().__init__(creds)\n        super().login()\n\n    @decorator\n    def _ensure_auth(func, self, *args, **kwargs):\n        self.login()\n        return func(self, *args, **kwargs)\n\n    def get_email(self):\n        \"\"\"Return the email address of the user\"\"\"\n        if not self._email:\n            try:\n                self._email = self.request('get',\n                                           'https://www.googleapis.com/userinfo/v2/me')\\\n                                  .json()['email']\n            except Exception:\n                print(\"\"\"\n                Couldn't retrieve email. Delete credentials and authenticate again\n                \"\"\")\n\n        return self._email\n\n    @_ensure_auth\n    def _make_drive_request(self, q):\n        files = []\n        page_token = ''\n        url = \"https://www.googleapis.com/drive/v3/files\"\n        params = {\n            'q': q,\n            \"pageSize\": 1000\n        }\n\n        while page_token is not None:\n            if page_token:\n                params['pageToken'] = page_token\n\n            res = self.request('get', url, params=params).json()\n            files.extend(res['files'])\n            page_token = res.get('nextPageToken', None)\n\n        return files\n\n    def list_spreadsheet_files(self):\n        \"\"\"Return all spreadsheets that the user has access to\"\"\"\n        q = \"mimeType='application/vnd.google-apps.spreadsheet'\"\n        return self._make_drive_request(q)\n\n    def list_spreadsheet_files_in_folder(self, folder_id):\n        \"\"\"Return all spreadsheets that the user has access to in a sepcific folder.\n\n        :param str folder_id: ID of a folder, see :meth:`find_folders <find_folders>`\n        \"\"\"\n        q = (\"mimeType='application/vnd.google-apps.spreadsheet'\"\n             \" and '{0}' in parents\".format(folder_id))\n\n        return self._make_drive_request(q)\n\n    def find_folders(self, folder_name_query):\n        \"\"\"Return all folders that the user has access to containing\n        ``folder_name_query`` in the name\n\n        :param str folder_name_query: Case insensitive string to search in folder name\n        \"\"\"\n        q = (\"mimeType='application/vnd.google-apps.folder'\"\n             \" and name contains '{0}'\".format(folder_name_query))\n\n        return self._make_drive_request(q)\n\n    def find_spreadsheet_files_in_folders(self, folder_name_query):\n        \"\"\"Return all spreadsheets that the user has access to in all the folders that\n        contain ``folder_name_query`` in the name. Returns as a dict with each key being\n        the folder name and the value being a list of spreadsheet files\n\n        :param str folder_name_query: Case insensitive string to search in folder name\n        \"\"\"\n        results = {}\n        for res in self.find_folders(folder_name_query):\n            results[res['name']] = self.list_spreadsheet_files_in_folder(res['id'])\n        return results\n\n\nclass Spread():\n    \"\"\"\n    Simple wrapper for gspread to interact with Pandas. It holds an instance of\n    an 'open' spreadsheet, an 'open' worksheet, and a list of available worksheets.\n\n    Each user will be associated with specific OAuth credentials. The authenticated user\n    will need the appropriate permissions to the Spreadsheet in order to interact with it.\n    \"\"\"\n    #: `(gspread.models.Spreadsheet)` - Currently open Spreadsheet\n    spread = None\n\n    #: `(gspread.models.Worksheet)` - Currently open Worksheet\n    sheet = None\n\n    #: `(Client)` - Instance of gspread_pandas :class:`Client <gspread_pandas.client.Client>`\n    client = None\n\n    # chunk range request: https://github.com/burnash/gspread/issues/375\n    _max_range_chunk_size = 1000000\n\n    # `(dict)` - Spreadsheet metadata\n    _spread_metadata = None\n\n    def __init__(self, user_creds_or_client, spread, sheet=None, config=None,\n                 create_spread=False, create_sheet=False, scope=default_scope):\n        \"\"\"\n        :param str user_creds_or_client: string indicating the key to a users credentials,\n            which will be stored in a file (by default they will be stored in\n            ``~/.config/gspread_pandas/creds/<user>`` but can be modified with ``creds_dir``\n            property in config) or an instance of a\n            :class:`Client <gspread_pandas.client.Client>`\n        :param str spread: name, url, or id of the spreadsheet; must have read access by\n            the authenticated user,\n            see :meth:`open_spread <gspread_pandas.client.Spread.open_spread>`\n        :param str,int sheet: optional, name or index of Worksheet,\n            see :meth:`open_sheet <gspread_pandas.client.Spread.open_sheet>` (default None)\n        :param dict config: optional, if you want to provide an alternate configuration,\n            see :meth:`get_config <gspread_pandas.conf.get_config>`\n        :param bool create_sheet: whether to create the spreadsheet if it doesn't exist,\n            it wil use the ``spread`` value as the sheet title\n        :param bool create_spread: whether to create the sheet if it doesn't exist,\n            it wil use the ``spread`` value as the sheet title\n        :param list scope: optional, if you'd like to provide your own scope\n        \"\"\"\n        if isinstance(user_creds_or_client, Client):\n            self.client = user_creds_or_client\n        elif isinstance(user_creds_or_client, (basestring, OAuth2Credentials)):\n            self.client = Client(user_creds_or_client, config, scope)\n        else:\n            raise TypeError('user_creds_or_client needs to be a string, '\n                            'OAuth2Credentials, or Client object')\n\n        self.open(spread, sheet, create_sheet, create_spread)\n\n    def __repr__(self):\n        base = \"<gspread_pandas.client.Spread - '{0}'>\"\n        meta = []\n        if self.email:\n            meta.append(\"User: '{0}'\".format(self.email))\n        if self.spread:\n            meta.append(\"Spread: '{0}'\".format(self.spread.title))\n        if self.sheet:\n            meta.append(\"Sheet: '{0}'\".format(self.sheet.title))\n        return base.format(\", \".join(meta))\n\n    @property\n    def email(self):\n        \"\"\"`(str)` - E-mail for the currently authenticated user\"\"\"\n        return self.client.get_email()\n\n    @property\n    def url(self):\n        \"\"\"`(str)` - Url for this spreadsheet\"\"\"\n        return 'https://docs.google.com/spreadsheets/d/{0}'.format(self.spread.id)\n\n    @property\n    def sheets(self):\n        \"\"\"`(list)` - List of available Worksheets\"\"\"\n        return self.spread.worksheets()\n\n    def refresh_spread_metadata(self):\n        \"\"\"Refresh spreadsheet metadata\"\"\"\n        self._spread_metadata = self.spread.fetch_sheet_metadata()\n\n    @property\n    def _sheet_metadata(self):\n        \"\"\"`(dict)` - Metadata for currently open worksheet\"\"\"\n        if self.sheet:\n            ix = self._find_sheet(self.sheet.title)[0]\n            return self._spread_metadata['sheets'][ix]\n\n    @decorator\n    def _ensure_auth(func, self, *args, **kwargs):\n        self.client.login()\n        return func(self, *args, **kwargs)\n\n    def open(self, spread, sheet=None, create_sheet=False, create_spread=False):\n        \"\"\"\n        Open a spreadsheet, and optionally a worksheet. See\n        :meth:`open_spread <gspread_pandas.Spread.open_spread>` and\n        :meth:`open_sheet <gspread_pandas.Spread.open_sheet>`.\n\n        :param str spread: name, url, or id of Spreadsheet\n        :param str,int sheet: name or index of Worksheet\n        :param bool create_sheet: whether to create the spreadsheet if it doesn't exist,\n            it wil use the ``spread`` value as the sheet title\n        :param bool create_spread: whether to create the sheet if it doesn't exist,\n            it wil use the ``spread`` value as the sheet title\n        \"\"\"\n        self.open_spread(spread, create_spread)\n\n        if sheet is not None:\n            self.open_sheet(sheet, create_sheet)\n\n    @_ensure_auth\n    def open_spread(self, spread, create=False):\n        \"\"\"\n        Open a spreadsheet. Authorized user must already have read access.\n\n        :param str spread: name, url, or id of Spreadsheet\n        :param bool create: whether to create the spreadsheet if it doesn't exist,\n            it wil use the ``spread`` value as the sheet title\n        \"\"\"\n        id_regex = \"[a-zA-Z0-9-_]{44}\"\n        url_path = \"docs.google.com/spreadsheet\"\n\n        if match(id_regex, spread):\n            open_func = self.client.open_by_key\n        elif url_path in spread:\n            open_func = self.client.open_by_url\n        else:\n            open_func = self.client.open\n\n        try:\n            self.spread = open_func(spread)\n            self.refresh_spread_metadata()\n        except (SpreadsheetNotFound, NoValidUrlKeyFound, APIError):\n            if create:\n                try:\n                    self.spread = self.client.create(spread)\n                    self.refresh_spread_metadata()\n                except Exception as e:\n                    msg = \"Couldn't create spreadsheet.\\n\" + str(e)\n                    raise GspreadPandasException(msg)\n            else:\n                raise SpreadsheetNotFound(\"Spreadsheet not found\")\n\n    @_ensure_auth\n    def open_sheet(self, sheet, create=False):\n        \"\"\"\n        Open a worksheet. Optionally, if the sheet doesn't exist then create it first\n        (only when ``sheet`` is a str).\n\n        :param str,int,Worksheet sheet: name, index, or Worksheet object\n        :param bool create: whether to create the sheet if it doesn't exist,\n            see :meth:`create_sheet <gspread_pandas.Spread.create_sheet>` (default False)\n        \"\"\"\n        self.sheet = None\n        if isinstance(sheet, int):\n            try:\n                self.sheet = self.sheets[sheet]\n            except Exception:\n                raise WorksheetNotFound(\"Invalid sheet index {0}\".format(sheet))\n        else:\n            self.sheet = self.find_sheet(sheet)\n\n        if not self.sheet:\n            if create:\n                self.create_sheet(sheet)\n            else:\n                raise WorksheetNotFound(\"Worksheet not found\")\n\n    @_ensure_auth\n    def create_sheet(self, name, rows=1, cols=1):\n        \"\"\"\n        Create a new worksheet with the given number of rows and cols.\n\n        Automatically opens that sheet after it's created.\n\n        :param str name: name of new Worksheet\n        :param int rows: number of rows (default 1)\n        :param int cols: number of columns (default 1)\n        \"\"\"\n        self.spread.add_worksheet(name, rows, cols)\n        self.refresh_spread_metadata()\n        self.open_sheet(name)\n\n    @_ensure_auth\n    def sheet_to_df(self, index=1, header_rows=1, start_row=1, sheet=None):\n        \"\"\"\n        Pull a worksheet into a DataFrame.\n\n        :param int index: col number of index column, 0 or None for no index (default 1)\n        :param int header_rows: number of rows that represent headers (default 1)\n        :param int start_row: row number for first row of headers or data (default 1)\n        :param str,int sheet: optional, if you want to open a different sheet first,\n            see :meth:`open_sheet <gspread_pandas.client.Spread.open_sheet>` (default None)\n\n        :returns: a DataFrame with the data from the Worksheet\n        \"\"\"\n        if sheet:\n            self.open_sheet(sheet)\n\n        if not self.sheet:\n            raise NoWorksheetException(\"No open worksheet\")\n\n        vals = self._retry_get_all_values()\n        vals = self._fix_merge_values(vals)[start_row - 1:]\n\n        col_names = parse_sheet_headers(vals, header_rows)\n\n        # remove rows where everything is null, then replace nulls with ''\n        df = pd.DataFrame(vals[header_rows or 0:])\\\n               .replace('', np.nan)\\\n               .dropna(how='all')\\\n               .fillna('')\n\n        if col_names is not None:\n            if len(df.columns) == len(col_names):\n                df.columns = col_names\n            elif len(df) == 0:\n                # if we have headers but no data, set column headers on empty DF\n                df = df.reindex(columns=col_names)\n            else:\n                raise MissMatchException(\"Column headers don't match number of data columns\")\n\n        return parse_sheet_index(df, index)\n\n    @_ensure_auth\n    def get_sheet_dims(self, sheet=None):\n        \"\"\"\n        Get the dimensions of the currently open Worksheet.\n\n        :param str,int,Worksheet sheet: optional, if you want to open a different sheet first,\n            see :meth:`open_sheet <gspread_pandas.client.Spread.open_sheet>` (default None)\n\n        :returns: a tuple containing (num_rows,num_cols)\n        \"\"\"\n        if sheet:\n            self.open_sheet(sheet)\n\n        return (self.sheet.row_count,\n                self.sheet.col_count) if self.sheet else None\n\n\n    def _get_update_chunks(self, start, end, vals):\n        start = get_cell_as_tuple(start)\n        end = get_cell_as_tuple(end)\n\n        num_cols = end[COL] - start[COL] + 1\n        num_rows = end[ROW] - start[ROW] + 1\n        num_cells = num_cols * num_rows\n\n        if num_cells != len(vals):\n            raise MissMatchException(\"Number of values needs to match number of cells\")\n\n        chunk_rows = self._max_range_chunk_size // num_cols\n        chunk_size = chunk_rows * num_cols\n\n        end_cell = (start[ROW] - 1, 0)\n\n        for val_chunks in chunks(vals, int(chunk_size)):\n            start_cell = (end_cell[ROW] + 1,\n                          start[COL])\n            end_cell = (min(start_cell[ROW] + chunk_rows - 1,\n                            start[ROW] + num_rows - 1),\n                        end[COL])\n            yield start_cell, end_cell, val_chunks\n\n    @_ensure_auth\n    def update_cells(self, start, end, vals, sheet=None):\n        \"\"\"\n        Update the values in a given range. The values should be listed in order\n        from left to right across rows.\n\n        :param tuple,str start: tuple indicating (row, col) or string like 'A1'\n        :param tuple,str end: tuple indicating (row, col) or string like 'Z20'\n        :param list vals: array of values to populate\n        :param str,int,Worksheet sheet: optional, if you want to open a different sheet first,\n            see :meth:`open_sheet <gspread_pandas.client.Spread.open_sheet>` (default None)\n        \"\"\"\n        if sheet:\n            self.open_sheet(sheet)\n\n        if not self.sheet:\n            raise NoWorksheetException(\"No open worksheet\")\n\n        if start == end:\n            return\n\n        for start_cell, end_cell, val_chunks in self._get_update_chunks(start,\n                                                                        end,\n                                                                        vals):\n            rng = get_range(start_cell, end_cell)\n\n            cells = self._retry_range(rng)\n\n            if len(val_chunks) != len(cells):\n                raise MissMatchException(\"Number of chunked values doesn't match number of cells\")\n\n            for val, cell in zip(val_chunks, cells):\n                cell.value = val\n\n            self._retry_update(cells)\n\n    @_ensure_auth\n    def _retry_get_all_values(self, n=3):\n        \"\"\"Call self.sheet.update_cells with retry\"\"\"\n        try:\n            return self.sheet.get_all_values()\n        except Exception as e:\n            if n > 0:\n                self._retry_get_all_values(n-1)\n            else:\n                raise e\n\n    @_ensure_auth\n    def _retry_update(self, cells, n=3):\n        \"\"\"Call self.sheet.update_cells with retry\"\"\"\n        try:\n            self.sheet.update_cells(cells, 'USER_ENTERED')\n        except Exception as e:\n            if n > 0:\n                self._retry_update(cells, n-1)\n            else:\n                raise e\n\n    @_ensure_auth\n    def _retry_range(self, rng, n=3):\n        \"\"\"Call self.sheet.range with retry\"\"\"\n        try:\n            return self.sheet.range(rng)\n        except Exception as e:\n            if n > 0:\n                self._retry_range(rng, n-1)\n            else:\n                raise e\n\n    def _find_sheet(self, sheet):\n        \"\"\"Find a worksheet and return with index\"\"\"\n        for ix, worksheet in enumerate(self.sheets):\n            if isinstance(sheet, basestring) and sheet.lower() == worksheet.title.lower():\n                return ix, worksheet\n            if isinstance(sheet, Worksheet) and sheet == worksheet:\n                return ix, worksheet\n        return None, None\n\n    def find_sheet(self, sheet):\n        \"\"\"\n        Find a given worksheet by title\n\n        :param str sheet: name of Worksheet\n\n        :returns: a Worksheet by the given name or None if not found\n        \"\"\"\n        return self._find_sheet(sheet)[1]\n\n    @_ensure_auth\n    def clear_sheet(self, rows=1, cols=1, sheet=None):\n        \"\"\"\n        Reset open worksheet to a blank sheet with given dimensions.\n\n        :param int rows: number of rows (default 1)\n        :param int cols: number of columns (default 1)\n        :param str,int,Worksheet sheet: optional; name, index, or Worksheet,\n            see :meth:`open_sheet <gspread_pandas.client.Spread.open_sheet>` (default None)\n        \"\"\"\n        if sheet:\n            self.open_sheet(sheet)\n\n        if not self.sheet:\n            raise NoWorksheetException(\"No open worksheet\")\n\n        self.sheet.resize(rows, cols)\n\n        self.update_cells(\n            start=(1, 1),\n            end=(rows, cols),\n            vals=['' for i in range(0, rows * cols)]\n        )\n\n    @_ensure_auth\n    def delete_sheet(self, sheet):\n        \"\"\"\n        Delete a worksheet by title. Returns whether the sheet was deleted or not. If\n        current sheet is deleted, the ``sheet`` property will be set to None.\n\n        :param str,Worksheet sheet: name or Worksheet\n\n        :returns: True if deleted successfully, else False\n        \"\"\"\n        is_current = False\n\n        s = self.find_sheet(sheet)\n\n        if s == self.sheet:\n            is_current = True\n\n        if s:\n            try:\n                self.spread.del_worksheet(s)\n                if is_current:\n                    self.sheet = None\n                return True\n            except Exception:\n                pass\n\n        self.refresh_spread_metadata()\n\n        return False\n\n    @_ensure_auth\n    def df_to_sheet(self, df, index=True, headers=True, start=(1,1), replace=False,\n                    sheet=None, freeze_index=False, freeze_headers=False, fill_value='',\n                    add_filter=False):\n        \"\"\"\n        Save a DataFrame into a worksheet.\n\n        :param DataFrame df: the DataFrame to save\n        :param bool index: whether to include the index in worksheet (default True)\n        :param bool headers: whether to include the headers in the worksheet (default True)\n        :param tuple,str start: tuple indicating (row, col) or string like 'A1' for top left\n            cell\n        :param bool replace: whether to remove everything in the sheet first (default False)\n        :param str,int,Worksheet sheet: optional, if you want to open or create a different sheet\n            before saving,\n            see :meth:`open_sheet <gspread_pandas.client.Spread.open_sheet>` (default None)\n        :param bool freeze_index: whether to freeze the index columns (default False)\n        :param bool freeze_headers: whether to freeze the header rows (default False)\n        :param str fill_value: value to fill nulls with (default '')\n        :param bool add_filter: whether to add a filter to the uploaded sheet (default\n            False)\n        \"\"\"\n        if sheet:\n            self.open_sheet(sheet, create=True)\n\n        if not self.sheet:\n            raise NoWorksheetException(\"No open worksheet\")\n\n        index_size = df.index.nlevels\n        header_size = df.columns.nlevels\n\n        if index:\n            df = df.reset_index()\n\n        df = fillna(df, fill_value)\n        df_list = df.values.tolist()\n\n        if headers:\n            header_rows = parse_df_col_names(df, index, index_size)\n            df_list = header_rows + df_list\n\n        start = get_cell_as_tuple(start)\n\n        sheet_rows, sheet_cols = self.get_sheet_dims()\n        req_rows = len(df_list) + (start[ROW] - 1)\n        req_cols = len(df_list[0]) + (start[COL] - 1) or 1\n\n        if replace:\n            # this takes care of resizing\n            self.clear_sheet(req_rows, req_cols)\n        else:\n            # make sure sheet is large enough\n            self.sheet.resize(max(sheet_rows, req_rows), max(sheet_cols, req_cols))\n\n        self.update_cells(\n            start=start,\n            end=(req_rows, req_cols),\n            vals=[str(val) for row in df_list for val in row]\n        )\n\n        self.freeze(None if not freeze_headers else header_size,\n                    None if not freeze_index else index_size)\n\n        if add_filter:\n            self.add_filter(header_size + start[0] - 2, req_rows,\n                            start[1] - 1, req_cols)\n\n    def _fix_merge_values(self, vals):\n        \"\"\"Assign the top-left value to all cells in a merged range\"\"\"\n        for merge in self._sheet_metadata.get('merges', []):\n            start_row, end_row = merge['startRowIndex'], merge['endRowIndex']\n            start_col, end_col = merge['startColumnIndex'], merge['endColumnIndex']\n\n            # ignore merge cells outside the data range\n            if start_row < len(vals) and start_col < len(vals[0]):\n                orig_val = vals[start_row][start_col]\n                for row in vals[start_row:end_row]:\n                    row[start_col:end_col] = [orig_val for i in\n                                              range(start_col, end_col)]\n\n        return vals\n\n    @_ensure_auth\n    def freeze(self, rows=None, cols=None, sheet=None):\n        \"\"\"\n        Freeze rows and/or columns for the open worksheet.\n\n        :param int rows: the DataFrame to save\n        :param int cols: whether to include the index in worksheet (default True)\n        :param str,int,Worksheet sheet: optional, if you want to open or create a\n            different sheet before freezing,\n            see :meth:`open_sheet <gspread_pandas.client.Spread.open_sheet>`\n            (default None)\n        \"\"\"\n        if sheet:\n            self.open_sheet(sheet, create=True)\n\n        if not self.sheet:\n            raise NoWorksheetException(\"No open worksheet\")\n\n        if rows is None and cols is None:\n            return\n\n        self.spread.batch_update({\n            'requests': create_frozen_request(self.sheet.id, rows, cols)\n        })\n\n        self.refresh_spread_metadata()\n\n    @_ensure_auth\n    def add_filter(self, start_row=None, end_row=None, start_col=None,\n                   end_col=None, sheet=None):\n        \"\"\"\n        Add filters to data in the open worksheet.\n\n        :param int start_row: First row to include in filter; this will be the\n            filter header (Default 0)\n        :param int end_row: Last row to include in filter (Default last row in sheet)\n        :param int start_col: First column to include in filter (Default 0)\n        :param int end_col: Last column to include in filter (Default last column\n            in sheet)\n        :param str,int,Worksheet sheet: optional, if you want to open or create a\n            different sheet before adding the filter,\n            see :meth:`open_sheet <gspread_pandas.client.Spread.open_sheet>`\n            (default None)\n        \"\"\"\n        if sheet:\n            self.open_sheet(sheet, create=True)\n\n        if not self.sheet:\n            raise NoWorksheetException(\"No open worksheet\")\n\n        dims = self.get_sheet_dims()\n\n        self.spread.batch_update({\n            'requests': create_filter_request(\n                self.sheet.id,\n                start_row or 0,\n                end_row or dims[0],\n                start_col or 0,\n                end_col or dims[1]\n            )\n        })\n","sub_path":"gspread_pandas/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":26343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"239330793","text":"u\"\"\"EXERCÍCIO062.\n\nCurso de Python===>Professor Gustavo Guanabara--->ModuloII.\n\nMelhores o DESAFIO061, perguntando para o usuário se el quer mostrar mais alguns termos. O programa encerra quando ele disser que quer mostrar 0 termos.\n\"\"\"\nprint('Gerador de PA')\nprint('-=' * 10)\nprimeiro = int(input('Digite o primeiro termo da PA: '))\nrazão = int(input('Digite a razão da PA: '))\ntermo = primeiro\ncount = 1\ntotal = 0\nmais = 10\nwhile mais != 0:\n    total += mais\n    while count <= total:\n        print('{} ¬'.format(termo), end='')\n        termo += razão\n        count += 1\n    print('PAUSA')\n    mais = int(input('Quantos termos você quer mostrar a mais? '))\nprint('Progressão finalizada com {} termos mostrados.'.format(total))\n","sub_path":"python3/CursoEmVideo/mundo2/desafio063.py","file_name":"desafio063.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"550524315","text":"from __future__ import unicode_literals\nfrom django.db import models\nfrom django.utils.encoding import python_2_unicode_compatible\n\n\nclass NotificationSettings(models.Model):\n    client = models.OneToOneField(\n        'clients.Client',\n        verbose_name='Client'\n    )\n    has_platform_news_reminder = models.BooleanField(\n        default=True,\n        verbose_name='Platform news and updates reminder'\n    )\n    \n    class Meta:\n        abstract = True\n\n\n@python_2_unicode_compatible\nclass EmailNotificationSettings(NotificationSettings, models.Model):\n\n    def __str__(self):\n        return 'Email Notification Settings [%(pk)s]' % {\n            'pk': self.pk\n        }\n\n    class Meta:\n        verbose_name = 'Email Notifications Settings'\n        verbose_name_plural = 'Email Notifications Settings'","sub_path":"apps/accounts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"206142256","text":"from panda3d.core import loadPrcFileData\nloadPrcFileData(\"\", \"win-size 800 600\")\nloadPrcFileData(\"\", \"audio-library-name p3openal_audio\")\nloadPrcFileData(\"\", \"window-title Sleigh Ride!\")\nfrom direct.showbase.ShowBase import ShowBase\nfrom direct.gui.OnscreenText import OnscreenText\nfrom direct.gui.DirectGui import *\nfrom panda3d.core import ostream\nfrom panda3d.core import *\nfrom direct.task import Task\nfrom direct.particles.Particles import *\nfrom direct.particles.ParticleEffect import ParticleEffect\nfrom direct.particles.ForceGroup import ForceGroup\nclass MyApp(ShowBase):\n    def __init__(self):\n        ShowBase.__init__(self)\n        self.initiatemenu()\n    def initiatemenu(self):\n        render.getChildren().detach()\n        render.clearLight()\n        aspect2d.getChildren().detach()\n        base.setBackgroundColor(0, 0, 0)\n        self.mainFrame = DirectFrame(frameColor=(0, 0, 0, 1), frameSize=(-2, 2, -2, 2), pos=(0, 0, 0))\n        self.background = OnscreenImage(image = \"images/bg.jpg\", pos = (0, 0, 0), scale = (1.5, 1, 1))\n        self.background.setTransparency(TransparencyAttrib.MAlpha)\n        self.background.reparentTo(self.mainFrame)\n        self.title = OnscreenText(text=\"Sleigh Ride!\",pos=(0,0.8), scale=0.2,fg=(1, 1, 1, 1))\n        self.startButton = DirectButton(image = \"images/play.png\", scale=(0.5,0.5,0.15), relief = None, command=self.initiate, pos=(0, 0, -0.8))\n        self.startButton.setTransparency(TransparencyAttrib.MAlpha)\n        self.creditsButton = DirectButton(image = \"images/credits.png\", scale=(0.3,0.3,0.08),relief = None,  command=self.initiatecredits, pos=(-1, 0, 0.85))\n        self.creditsButton.setTransparency(TransparencyAttrib.MAlpha)\n        self.gameInstructions1 = DirectLabel(text=\"It's Christmas Eve and presents \", text_scale=(0.1, 0.1), relief=None, text_fg=(255, 255, 255, 100), pos=(0, 0, 0.5))\n        self.gameInstructions1.reparentTo(aspect2d)\n        self.gameInstructions2 = DirectLabel(text=\"have to be delivered to every house!\", text_scale=(0.1, 0.1), relief=None, text_fg=(255, 255, 255, 100), pos=(0, 0, 0.3))\n        self.gameInstructions2.reparentTo(aspect2d)\n        self.gameInstructions3 = DirectLabel(text=\"Avoid obstacles and deliver the presents to the houses!\", text_scale=(0.1, 0.1), relief=None, text_fg=(255, 255, 255, 100), pos=(0, 0, 0.1))\n        self.gameInstructions3.reparentTo(aspect2d)\n        self.gameInstructions4 = DirectLabel(text=\"Do not skip any houses!\", text_scale=(0.1, 0.1), relief=None, text_fg=(255, 255, 255, 100), pos=(0, 0, -0.1))\n        self.gameInstructions4.reparentTo(aspect2d)\n        self.gameInstructions5 = DirectLabel(text=\"Use the spacebar or button to drop presents!\", text_scale=(0.1, 0.1), relief=None, text_fg=(255, 255, 255, 100), pos=(0, 0, -0.3))\n        self.gameInstructions5.reparentTo(aspect2d)\n        self.gameInstructions6 = DirectLabel(text=\"Use arrow keys or buttons to move!\", text_scale=(0.1, 0.1), relief=None, text_fg=(255, 255, 255, 100), pos=(0, 0, -0.5))\n        self.gameInstructions6.reparentTo(aspect2d)\n        self.mySound = loader.loadSfx(\"music/happy-loop.ogg\")\n        self.mySound.setLoop(True)\n        self.mySound.setLoopCount(0)\n        self.mySound.play()\n    def initiatecredits(self):\n        self.mySound.stop()\n        render.getChildren().detach()\n        render.clearLight()\n        aspect2d.getChildren().detach()\n        self.mainFrame = DirectFrame(frameColor=(0, 0, 0, 1), frameSize=(-2, 2, -2, 2), pos=(0, 0, 0))\n        self.creditsButton = DirectButton(image = \"images/menu.png\", scale=(0.3,0.3,0.08),relief = None,  command=self.initiatemenu, pos=(-1, 0, 0.9))\n        self.background = OnscreenImage(image = \"images/credits.jpg\", pos = (0, 0, 0), scale = (2, 1, 1))\n        self.background.setTransparency(TransparencyAttrib.MAlpha)\n        self.background.reparentTo(self.mainFrame)\n    def initiate(self):\n        self.mySound.stop()\n        render.getChildren().detach()\n        render.clearLight()\n        aspect2d.getChildren().detach()\n        self.movingleft = 0\n        self.movingright = 0\n        self.droppingpresent = 0\n        self.speedvar = 5\n        self.prevspeed = 5\n        self.shipspeed = 650\n        self.moveSpeed = 250\n        self.presentdropped = 1\n        self.presentchecker = 0\n        self.seq = 0\n        self.candroppresent = 1\n        self.dropp = 0\n        self.score = 0\n        self.goreason = \"\"\n        base.enableParticles()\n        self.p = ParticleEffect()\n        self.p.loadConfig(\"snow.ptf\")\n        self.p.start(parent = render, renderParent = render)      \n        alight = AmbientLight(\"alight\")\n        alight.setColor((0.2, 0.2, 0.4, 1))\n        alnp = render.attachNewNode(alight)\n        render.setLight(alnp)\n        self.ss = OnscreenText(text=\"0\",pos=(0,0.8), scale=0.15,fg=(1, 1, 1, 1))\n        onebutton = DirectButton(image = \"images/flatDark23.png\",relief = None, text = (\"\"), scale=.2, command=self.setleft)\n        onebutton.setPos(-1,0,-0.7)\n        onebutton.setTransparency(TransparencyAttrib.MAlpha)\n        onebutton1 = DirectButton(image = \"images/flatDark24.png\",relief = None, text = (\"\"), scale=.2, command=self.setright)\n        onebutton1.setPos(1,0,-0.7)\n        onebutton1.setTransparency(TransparencyAttrib.MAlpha)\n        onebutton2 = DirectButton(image = \"images/flatDark10.png\",relief = None, text = (\"\"), scale=.2, command=self.droppresent)\n        onebutton2.setPos(0,0,-0.7)\n        onebutton2.setTransparency(TransparencyAttrib.MAlpha)\n        self.moveKeyList = [\n            \"arrow_left\", \"arrow_right\", \"arrow_up\", \"arrow_down\",\"space\",\"w\",\"s\",\"a\",\"d\",\"q\",\"e\"]\n        self.moveKeys = {}\n        for key in self.moveKeyList:\n            self.moveKeys[key] = False\n            self.accept(key, self.moveKeyStateChanged, extraArgs = [key, True])\n            self.accept(key + \"-up\", self.moveKeyStateChanged, extraArgs = [key, False])\n        base.cTrav = CollisionTraverser()\n        #base.cTrav.showCollisions(render)\n        base.cTrav.setRespectPrevTransform(True)\n        self.ship = loader.loadModel(\"models/sled\")\n        self.ship.setScale(0.1)\n        self.ship.setZ(4)\n        self.ship.setX(18)\n        self.ship.setY(-18)\n        self.ship.reparentTo(render)\n        slight = Spotlight(\"slight\")\n        slight.setColor((2, 0, 0, 1))\n        lens = PerspectiveLens()\n        slight.setLens(lens)\n        slnp = render.attachNewNode(slight)\n        slnp.setHpr(-0,-30,0)\n        render.setLight(slnp)\n        slnp.reparentTo(self.ship)\n        slight = Spotlight(\"slight\")\n        slight.setColor((0, 1, 0.2, 1))\n        lens = PerspectiveLens()\n        slight.setLens(lens)\n        slnp = render.attachNewNode(slight)\n        slnp.setHpr(0,0,0)\n        render.setLight(slnp)\n        slnp.reparentTo(self.ship)        \n        base.cam.reparentTo(self.ship)\n        base.cam.setPos(0,-300,130)\n        base.cam.setHpr(0,-20,0)\n        render.setTwoSided(True)\n        cs = CollisionSphere(0, 0, 0, 10)\n        cnode = CollisionNode(\"cnode\")\n        cnode.addSolid(cs)\n        self.cnp = render.attachNewNode(cnode)\n        self.cnp.reparentTo(self.ship)\n        pusher = CollisionHandlerPusher()\n        pusher.addCollider(self.cnp, self.ship)\n        self.cTrav.addCollider(self.cnp, pusher)\n        self.offset = 960\n        self.sccener = 1\n        self.present = loader.loadModel(\"models/present\")   \n        self.scene = base.loader.loadModel(\"models/lvl1\")\n        self.scene.reparentTo(base.render)\n        self.scene.setScale(3)\n        self.scene.setHpr(90,0,0)\n        self.scene.setPos(0,0,0)\n        self.scene1 = base.loader.loadModel(\"models/lvl2\")\n        self.scene1.reparentTo(base.render)\n        self.scene1.setScale(3)\n        self.scene1.setHpr(90,0,0)\n        self.scene1.setPos(0,120,0)\n        self.scene2 = base.loader.loadModel(\"models/lvl3\")\n        self.scene2.reparentTo(base.render)\n        self.scene2.setScale(3)\n        self.scene2.setPos(0,240,0)\n        self.scene2.setHpr(90,0,0)\n        self.scene3 = base.loader.loadModel(\"models/lvl4\")\n        self.scene3.reparentTo(base.render)\n        self.scene3.setScale(3)\n        self.scene3.setHpr(90,0,0)\n        self.scene3.setPos(0,360,0)\n        self.scene4 = base.loader.loadModel(\"models/lvl5\")\n        self.scene4.reparentTo(base.render)\n        self.scene4.setHpr(90,0,0)\n        self.scene4.setScale(3)\n        self.scene4.setPos(0,480,0)\n        self.scene5 = base.loader.loadModel(\"models/lvl6\")\n        self.scene5.reparentTo(base.render)\n        self.scene5.setHpr(90,0,0)\n        self.scene5.setScale(3)\n        self.scene5.setPos(0,600,0)\n        self.scene6 = base.loader.loadModel(\"models/lvl7\")\n        self.scene6.reparentTo(base.render)\n        self.scene6.setHpr(90,0,0)\n        self.scene6.setScale(3)\n        self.scene6.setPos(0,720,0)\n        self.scene7 = base.loader.loadModel(\"models/lvl8\")\n        self.scene7.reparentTo(base.render)\n        self.scene7.setHpr(90,0,0)\n        self.scene7.setScale(3)\n        self.scene7.setPos(0,840,0)\n        self.scene8 = base.loader.loadModel(\"models/lvl9\")\n        self.scene8.reparentTo(base.render)\n        self.scene8.setHpr(90,0,0)\n        self.scene8.setScale(3)\n        self.scene8.setPos(0,960,0)\n        self.mySound = loader.loadSfx(\"music/winter-wind.ogg\")\n        self.mySound.setLoop(True)\n        self.mySound.setLoopCount(0)\n        self.mySound.play()\n        self.moveTask = taskMgr.add(self.moveAvatar, \"moveAvatar\")\n    def setleft(self):\n        self.movingleft = 1\n        self.movingright = 0   \n    def setright(self):\n        self.movingleft = 0\n        self.movingright = 1\n    def moveKeyStateChanged(self, key, newState):\n        self.moveKeys[key] = newState\n    def backtomenu(self):\n        self.mySound.stop()\n        self.initiatemenu()\n    def gameover(self):\n        self.movingleft = 0\n        self.movingright = 0\n        taskMgr.remove(\"moveAvatar\")\n        aspect2d.getChildren().detach()\n        OnscreenText(text=\"Game Over\",pos=(0,0), scale=0.3,fg=(1, 1, 1, 1))\n        OnscreenText(text=self.goreason,pos=(0,-0.1), scale=0.1,fg=(1, 1, 1, 1))\n        DirectButton(image = \"images/playagain.png\", scale=(0.5,0.5,0.15), relief = None, command=self.initiate, pos=(0, 0, -0.8))\n        aspect2d.setTransparency(TransparencyAttrib.MAlpha)\n        DirectButton(image = \"images/menu.png\", scale=(0.5,0.5,0.15), relief = None, command=self.backtomenu, pos=(0, 0, 0.8))\n    def droppresent(self):\n        self.dropp = 1\n    def setcandroppresent(self, task):\n      self.candroppresent = 1\n      return Task.done      \n    def checkpresentdrop(self, task):\n        self.candroppresent = 1\n        if (self.presentdropped == 0):\n            self.goreason = \"You missed a house!\"\n            self.gameover()\n        return Task.done\n    def setpresent(self, task):\n        self.presentchecker = 1\n        return Task.done   \n    def spawnpresent(self):\n        self.present = loader.loadModel(\"models/present\")\n        self.present.setScale(5)\n        self.present.setPos(self.ship.getPos())\n        self.present.setX(self.present.getX()-3)\n        self.present.reparentTo(render)\n    def moveAvatar(self, task):\n        dt = globalClock.getDt()\n        queue = CollisionHandlerQueue()\n        self.cTrav.addCollider(self.cnp, queue)\n        self.cTrav.setRespectPrevTransform(True)\n        self.cTrav.traverse(render)\n        pusher = CollisionHandlerPusher()\n        pusher.addCollider(self.cnp, self.ship)\n        self.cTrav.addCollider(self.cnp, pusher)\n        self.shipspeed + 20\n        self.present.setZ(self.present.getZ()-9*dt)\n        self.ss.setText(str(self.score))\n        # code to detect stop\n        if (self.ship.getY() - self.speedvar == 0):\n            self.gameover()\n        curspeedtemp = self.ship.getY() - self.speedvar\n        self.curspeed = curspeedtemp * dt\n        self.speedvar = self.ship.getY()\n        prevsppedtemp = self.ship.getY() - self.speedvar\n        self.prevspeed = prevsppedtemp * dt\n        shippy = self.shipspeed + 20\n        if (self.movingleft == 1):\n            self.ship.setFluidX(self.ship, -dt * self.moveSpeed)\n            if (self.ship.getX() <= -18):\n                self.ship.setX(-18)\n                self.movingleft = 0\n        if (self.movingright == 1):\n            self.ship.setFluidX(self.ship, dt * self.moveSpeed)\n            if (self.ship.getX() >= 18):\n                self.ship.setX(18)\n                self.movingright = 0   \n        # present code\n        wearecolliding = 0\n        for entry in queue.getEntries():\n            # could use a LPoint2i here\n            init = entry.getSurfacePoint(entry.getIntoNodePath()).getYz()\n            #print(init[1])\n            if (init[1] > 0.002):\n                self.goreason = \"You hit an obstacle!\"\n                self.gameover()\n            if (init[1] < 0):\n                self.goreason = \"You hit an obstacle!\"\n                self.gameover()\n            wearecolliding = 1\n        if self.moveKeys[\"space\"]:\n           if (self.candroppresent == 1):\n               self.candroppresent = 0\n               self.spawnpresent()\n               self.droppingpresent = 1\n               taskMgr.doMethodLater(0.5, self.setcandroppresent, \"CanDropTask\")\n        if (self.dropp == 1):\n            self.dropp = 0\n            if (self.candroppresent == 1):\n                self.candroppresent = 0\n                self.spawnpresent()\n                self.droppingpresent = 1\n                taskMgr.doMethodLater(0.5, self.setcandroppresent, \"CanDropTask\")\n        if (wearecolliding == 0):\n            if (self.seq == 0):\n                self.seq = 1\n                taskMgr.doMethodLater(1, self.setpresent, \"presentTask\")  \n        # code to drop present\n        if (self.droppingpresent == 1):\n           self.droppingpresent = 0\n           if (wearecolliding == 0):\n               self.presentdropped = 1\n               self.score = (self.score) + 100\n               shippy = self.shipspeed + 100\n               self.shipspeed = shippy\n               shippy1 = self.moveSpeed + 50\n               self.moveSpeed = shippy1\n        # uncomment to end game on missed present\n          # else:\n              # self.gameover()\n        if (self.presentchecker == 1):\n            if (self.presentdropped == 0):\n                self.presentchecker = 0\n                self.seq = 0\n                self.goreason = \"You missed a house!\"\n                self.gameover()\n            if (self.presentdropped == 1):\n                self.presentchecker = 0\n                self.presentdropped = 0\n                self.seq = 0\n        #self.presentdropped = 0\n        if self.ship.getY() > self.offset-600:\n            self.sceeeeen = self.sccener + 1\n            self.sccener = self.sceeeeen\n            if self.sccener == 1:\n                self.scene.setY(self.offset + 120)\n            if self.sccener == 2:\n                self.scene1.setY(self.offset + 120)\n            if self.sccener == 3:\n                self.scene2.setY(self.offset + 120)\n            if self.sccener == 4:\n                self.scene3.setY(self.offset + 120)\n            if self.sccener == 5:\n                self.scene4.setY(self.offset + 120)\n            if self.sccener == 6:\n                self.scene5.setY(self.offset + 120)\n            if self.sccener == 7:\n                self.scene6.setY(self.offset + 120)\n            if self.sccener == 8:\n                self.scene7.setY(self.offset + 120)\n            if self.sccener == 8:\n                self.scene8.setY(self.offset + 120)\n                self.sccener = 1\n            self.offe = self.offset + 120\n            self.offset = self.offe\n        self.ship.setZ(1)\n        if self.moveKeys[\"arrow_left\"]:\n            self.setleft()\n        elif self.moveKeys[\"arrow_right\"]:\n            self.setright()\n        if self.moveKeys[\"a\"]:\n            self.setleft()\n        if self.moveKeys[\"d\"]:\n            self.setright()\n        self.ship.setFluidY(self.ship, dt * self.shipspeed)\n        self.p.setY(self.ship.getY()+200)\n        return task.cont\napp = MyApp()\napp.run()","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":16057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"439937178","text":"#!/usr/bin/python\nimport eosWrap as eos\nimport matplotlib\nfrom scipy.misc.common import derivative\nmatplotlib.use('QT4Agg')\nimport matplotlib.pyplot as plt\nfrom Wrapper import Wrapper\nimport numpy as np\nimport os\nfrom pylab import pause\nfrom scipy import interpolate\nimgfolder = os.path.join('..','img')\n\nC = eos.KVOR_mod()\nC.SetHyperConstants(2)\nwr = Wrapper(C)\nn = np.linspace(0.0, 4.0, 2000)\n\nC.Csp = 1\n\nC.Cs = 179.56233875157545\nC.Co =  141.14599595\nC.Cr = 92.11\nC.b = 0.004860 \nC.c = -0.008312 \n  \n# C.alpha = 0.85\n# C.z = 0.65\n# \n# C.omega_a = 6.45\n# C.omega_f = 0.53\n#         \n# C.rho_a = 0*150.0\n# C.rho_f = 0.35\n#     \n# C.beta = 0.5\n# C.gamma = 8.00\n#---------------------     \nC.phi_a = -0.85\nC.phi_f = 0.28\n  \nC.d = -5.5\n\nC.alpha = 0.85\nC.z = 0.65\n\nC.phi_gamma = 3.0\nC.phi_z = 3.5\n     \nC.sprime = 0\nC.Csp = 380.0\n  \nC.f0 = 0.26\n \nC.beta = 1.0\nC.gamma = 14.0\n\nf0 = 0.26\n  \nwr.solve(f0=C.f0)\nC.SetHyperConstants(2)\nnpoints = 1000\ndn = 1e-3\n\nprint -3*wr.n0*((eos.K(wr.n0+dn, C) - eos.K(wr.n0-dn, C))/(2*dn) - 2*eos.K(wr.n0, C)/wr.n0) \n","sub_path":"KprimeTest.py","file_name":"KprimeTest.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"381900769","text":"import turtle\nfrom world import obstacles as obs\n\n# globals\ncharacter = object\nscreen = object\nis_sprint = False\nis_obstructed = False\n\n# variables tracking position and direction\nposition_x = 0\nposition_y = 0\ndirections = ['forward', 'right', 'back', 'left']\ncurrent_direction_index = 0\n\n# area limit vars\nmin_y, max_y = -200, 200\nmin_x, max_x = -100, 100\n\n# list of valid command names\nvalid_commands = ['off', 'help', 'replay', 'forward', 'back', 'right', 'left', 'sprint']\nmove_commands = valid_commands[3:]\n\n\ndef list_obstacles():\n    \"\"\"\n    This funtion prints a list of object coordinates, that are saved in the obstacles, onto the console.\n    \"\"\"\n    obstacles = obs.get_obstacles()\n\n    pass\n\n\ndef is_int(value):\n    \"\"\"\n    Tests if the string value is an int or not\n    :param value: a string value to test\n    :return: True if it is an int\n    \"\"\"\n    try:\n        int(value)\n        return True\n    except ValueError:\n        return False\n\n\ndef split_command_input(command):\n    \"\"\"\n    Splits the string at the first space character, to get the actual command, as well as the argument(s) for the command\n    :return: (command, argument)\n    \"\"\"\n    args = command.split(' ', 1)\n    if len(args) > 1:\n        return args[0], args[1]\n    return args[0], ''\n\n\ndef show_position(robot_name):\n    \"\"\"\n    Prints current position of robot.\n    \"\"\"\n    print(' > '+robot_name+' now at position ('+str(position_x)+','+str(position_y)+').')\n\n\ndef is_position_allowed(new_x, new_y):\n    \"\"\"\n    Checks if the new position will still fall within the max area limit\n    :param new_x: the new/proposed x position\n    :param new_y: the new/proposed y position\n    :return: True if allowed, i.e. it falls in the allowed area, else False\n    \"\"\"\n\n    return min_x <= new_x <= max_x and min_y <= new_y <= max_y\n\n\ndef update_position(steps):\n    \"\"\"\n    Update the current x and y positions given the current direction, and specific nr of steps\n    :param steps:\n    :return: True if the position was updated, else False\n    \"\"\"\n\n    global position_x, position_y, is_obstructed\n    is_obstructed = False\n    new_x = position_x\n    new_y = position_y\n\n    if directions[current_direction_index] == 'forward':\n        new_y = new_y + steps\n    elif directions[current_direction_index] == 'right':\n        new_x = new_x + steps\n    elif directions[current_direction_index] == 'back':\n        new_y = new_y - steps\n    elif directions[current_direction_index] == 'left':\n        new_x = new_x - steps\n\n    if obs.is_position_blocked(new_x, new_y) or obs.is_path_blocked(position_x, position_y, new_x, new_y):\n        is_obstructed = True\n        return False\n\n    if is_position_allowed(new_x, new_y):\n        position_x = new_x\n        position_y = new_y\n        return True\n    return False\n\n\ndef initialize_objects():\n    \"\"\"\n    Initializes the turtle graphics' objects.\n    \"\"\"\n    global character\n    global screen\n\n    screen = turtle.Screen()\n    character = turtle.Turtle()\n    screen.setworldcoordinates(-250, -250, 250, 250)\n\n\ndef set_up_graphics():\n    \"\"\"\n    Initializes the settings and colors for turtle graphics.\n    \"\"\"\n    global character\n\n    turtle.title(\"Toy Robot 4\")\n    turtle.bgcolor(\"white\")\n    character.turtlesize(3)\n    character.color(\"black\", \"red\")\n    character.speed(1)\n\n\ndef draw_range_constraint():\n    \"\"\"\n    Draws the range constraint on the turtle interface and then re-centres the cursor.\n    \"\"\"\n    global character\n\n    character.speed(0)\n    character.pencolor(\"red\")\n    character.penup()\n    character.goto(-100, -200)\n    character.pendown()\n    character.goto(-100, 200)\n    character.goto(100, 200)\n    character.goto(100, -200)\n    character.goto(-100, -200)\n    character.penup()\n    character.goto(0, 0)\n    character.setheading(90)\n\n\ndef draw_obstacles():\n    \"\"\"\n    Draws all obstacles on the turtle graphics grid by using the coordinates from obstacles.\n    \"\"\"\n    global character\n\n    character.pencolor(\"red\")\n    character.speed(0)\n    for item in obs.obstacles:\n        character.fillcolor(\"red\")\n        character.penup()\n        character.goto(item[0], item[1])\n        character.begin_fill()\n        character.pendown()\n        character.goto(item[0], item[1] + 4)\n        character.goto(item[0] + 4, item[1] + 4)\n        character.goto(item[0] + 4, item[1])\n        character.goto(item[0], item[1])\n        character.end_fill()\n\n\ndef do_forward(robot_name, steps):\n    \"\"\"\n    Moves the robot forward the number of steps\n    :param robot_name:\n    :param steps:\n    :return: (True, forward output text)\n    \"\"\"\n    global character, is_sprint, is_obstructed\n\n    if update_position(steps):\n        if is_sprint == True:\n            character.speed(9)\n        else:\n            character.speed(1)\n        character.fd(steps)\n        return True, ' > '+robot_name+' moved forward by '+str(steps)+' steps.'\n    else:\n        if is_obstructed == True:\n            return True, ''+robot_name+\": \"+'Sorry, there is an obstacle in the way.'\n        return True, ''+robot_name+': Sorry, I cannot go outside my safe zone.'\n\n\n\ndef do_back(robot_name, steps):\n    \"\"\"\n    Moves the robot forward the number of steps\n    :param robot_name:\n    :param steps:\n    :return: (True, forward output text)\n    \"\"\"\n    global character, is_obstructed\n\n    character.speed(1)\n    if update_position(-steps):\n        character.bk(steps)\n        return True, ' > '+robot_name+' moved back by '+str(steps)+' steps.'\n    else:\n        if is_obstructed == True:\n            return True, ''+robot_name+\": \"+'Sorry, there is an obstacle in the way.'\n        return True, ''+robot_name+': Sorry, I cannot go outside my safe zone.'\n\n\ndef do_right_turn(robot_name):\n    \"\"\"\n    Do a 90 degree turn to the right\n    :param robot_name:\n    :return: (True, right turn output text)\n    \"\"\"\n    global current_direction_index, character\n\n    current_direction_index += 1\n    if current_direction_index > 3:\n        current_direction_index = 0\n    character.rt(90)\n    return True, ' > '+robot_name+' turned right.'\n\n\ndef do_left_turn(robot_name):\n    \"\"\"\n    Do a 90 degree turn to the left\n    :param robot_name:\n    :return: (True, left turn output text)\n    \"\"\"\n    global current_direction_index, character\n\n    current_direction_index -= 1\n    if current_direction_index < 0:\n        current_direction_index = 3\n    character.lt(90)\n\n    return True, ' > '+robot_name+' turned left.'\n\n\ndef do_sprint(robot_name, steps):\n    \"\"\"\n    Sprints the robot, i.e. let it go forward steps + (steps-1) + (steps-2) + .. + 1 number of steps, in iterations\n    :param robot_name:\n    :param steps:\n    :return: (True, forward output)\n    \"\"\"\n    global character\n    global is_sprint\n\n    is_sprint = True\n    if steps == 1:\n        try:\n            return do_forward(robot_name, 1)\n        finally:\n            is_sprint = False\n    else:\n        (do_next, command_output) = do_forward(robot_name, steps)\n        print(command_output)\n        return do_sprint(robot_name, steps - 1)\n\n\ndef start_world():\n    \"\"\"\n    Initialize graphical world with objects and constraints as program starts.\n    \"\"\"\n    global position_x, position_y, current_direction_index\n    position_x = 0\n    position_y = 0\n    current_direction_index = 0\n    initialize_objects()\n    draw_obstacles()\n    draw_range_constraint()\n    set_up_graphics()","sub_path":"submission_002-robot-4/world/turtle/world.py","file_name":"world.py","file_ext":"py","file_size_in_byte":7346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"281667564","text":"\"\"\"Assess a betting strategy.\n\nCopyright 2018, Georgia Institute of Technology (Georgia Tech)\nAtlanta, Georgia 30332\nAll Rights Reserved\n\nTemplate code for CS 4646/7646\n\nGeorgia Tech asserts copyright ownership of this template and all derivative\nworks, including solutions to the projects assigned in this course. Students\nand other users of this template code are advised not to share it with others\nor to make it available on publicly viewable websites including repositories\nsuch as github and gitlab.  This copyright statement should not be removed\nor edited.\n\nWe do grant permission to share solutions privately with non-students such\nas potential employers. However, sharing with other current or future\nstudents of CS 7646 is prohibited and subject to being investigated as a\nGT honor code violation.\n\n-----do not edit anything above this line---\n\nStudent Name: Junyan Mao (replace with your name)\nGT User ID: jmao44 (replace with your User ID)\nGT ID: 903343678 (replace with your GT ID)\n\"\"\"\n\nimport numpy as np\n# import matplotlib\n# matplotlib.use('Agg')\n# from matplotlib import pyplot as plt\nfrom matplotlib import pyplot as plt\n\n\ndef author():\n    return 'jmao44'  # replace tb34 with your Georgia Tech username.\n\n\ndef gtid():\n    return 903343678  # replace with your GT ID number\n\n\ndef get_spin_result(win_prob):\n    result = False\n    if np.random.random() <= win_prob:\n        result = True\n    return result\n\n\ndef run_episode_with_unlimited_bankroll(win_prob):\n    episode_winnings = 0\n    winnings = np.zeros(1001, dtype=np.int16)\n    index = 0\n    while episode_winnings < 80:\n        won = False\n        bet_amount = 1\n        while not won:\n            won = get_spin_result(win_prob)\n            index += 1\n            if won:\n                episode_winnings += bet_amount\n            else:\n                episode_winnings -= bet_amount\n                bet_amount *= 2\n            winnings[index] = episode_winnings\n\n            if index >= 1000:\n                return winnings\n    for i in range(index + 1, 1001):\n        winnings[i] = 80\n    return winnings\n\n\ndef run_episode_with_limited_bankroll(win_prob, bankroll=256):\n    episode_winnings = 0\n    winnings = np.zeros(1001, dtype=np.int16)\n    index = 0\n    while episode_winnings < 80:\n        won = False\n        bet_amount = 1\n        while not won:\n            index += 1\n            won = get_spin_result(win_prob)\n            if won:\n                episode_winnings += bet_amount\n            else:\n                episode_winnings -= bet_amount\n                current_money = episode_winnings + bankroll\n                bet_amount *= 2\n                if bet_amount > current_money:\n                    bet_amount = current_money\n            winnings[index] = episode_winnings\n\n            if episode_winnings + bankroll == 0:\n                for i in range(index + 1, 1001):\n                    winnings[i] = -bankroll\n                return winnings\n            if index >= 1000:\n                return winnings\n    for i in range(index + 1, 1001):\n        winnings[i] = 80\n    return winnings\n\n\ndef set_figure(ax):\n    ax.set_xlabel('Number of Spins')\n    ax.set_ylabel('Winnings')\n    ax.set_xlim(0, 300)\n    ax.set_ylim(-256, 100)\n\n\ndef run_exp_1(win_prob):\n    # figure 1\n    fig_1_array = np.zeros((10, 1001), dtype=np.int16)\n    for i in range(10):\n        fig_1_array[i, :] = run_episode_with_unlimited_bankroll(win_prob)\n    fig, ax = plt.subplots(figsize=(10, 5))\n    plt.title('Figure 1: Winnings of 10 Episodes')\n    for i in range(10):\n        plt.plot(fig_1_array[i, :], label='Episode {}'.format(i + 1))\n    set_figure(ax)\n    plt.legend()\n    plt.savefig('exp1_fig1.png')\n\n    # figure 2\n    fig_2_array = np.zeros((1000, 1001), dtype=np.int16)\n    for i in range(1000):\n        fig_2_array[i, :] = run_episode_with_unlimited_bankroll(win_prob)\n    fig, ax = plt.subplots(figsize=(10, 5))\n    plt.title('Figure 2: Mean of Winnings of 1000 Episodes')\n    mean = np.mean(fig_2_array, axis=0)\n    std = np.std(fig_2_array, axis=0)\n    plt.plot(mean, label='Mean')\n    plt.plot(mean + std, label='Mean + STD')\n    plt.plot(mean - std, label='Mean - STD')\n    plt.legend()\n    set_figure(ax)\n    plt.savefig('exp1_fig2.png')\n\n    # figure 3\n    fig, ax = plt.subplots(figsize=(10, 5))\n    plt.title('Figure 3: Median of Winnings of 1000 Episodes')\n    median = np.median(fig_2_array, axis=0)\n    plt.plot(median, label='Median')\n    plt.plot(median + std, label='Median + STD')\n    plt.plot(median - std, label='Median - STD')\n    plt.legend()\n    set_figure(ax)\n    plt.savefig('exp1_fig3.png')\n\n\ndef run_exp_2(win_prob):\n    # figure 4\n    fig_4_array = np.zeros((1000, 1001), dtype=np.int16)\n    for i in range(1000):\n        fig_4_array[i, :] = run_episode_with_limited_bankroll(win_prob)\n    fig, ax = plt.subplots(figsize=(10, 5))\n    plt.title('Figure 4: Mean of Winnings of 1000 Episodes (Limited Bankroll)')\n    mean = np.mean(fig_4_array, axis=0)\n    std = np.std(fig_4_array, axis=0)\n    plt.plot(mean, label='Mean')\n    plt.plot(mean + std, label='Mean + STD')\n    plt.plot(mean - std, label='Mean - STD')\n    plt.legend()\n    set_figure(ax)\n    plt.savefig('exp2_fig4.png')\n\n    # figure 5\n    fig, ax = plt.subplots(figsize=(10, 5))\n    plt.title('Figure 5: Median of Winnings of 1000 Episodes (Limited Bankroll)')\n    median = np.median(fig_4_array, axis=0)\n    plt.plot(median, label='Median')\n    plt.plot(median + std, label='Median + STD')\n    plt.plot(median - std, label='Median - STD')\n    plt.legend()\n    set_figure(ax)\n    plt.savefig('exp2_fig5.png')\n\n\ndef test_code():\n    win_prob = 18 / 38  # set appropriately to the probability of a win\n    np.random.seed(gtid())  # do this only once\n\n    run_exp_1(win_prob)\n    run_exp_2(win_prob)\n\n# add your code here to implement the experiments\nif __name__ == \"__main__\":\n    test_code()\n","sub_path":"ML4T_2019Fall/martingale/martingale.py","file_name":"martingale.py","file_ext":"py","file_size_in_byte":5860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"642004453","text":"from functools import partial\nimport warnings\n\nimport numpy as np\nimport pandas as pd\nimport bioframe\n\nfrom .lib.numutils import LazyToeplitz\n\n\ndef make_bin_aligned_windows(\n    binsize, chroms, centers_bp, flank_bp=0, region_start_bp=0, ignore_index=False\n):\n    \"\"\"\n    Convert genomic loci into bin spans on a fixed bin-size segmentation of a\n    genomic region. Window limits are adjusted to align with bin edges.\n\n    Parameters\n    -----------\n    binsize : int\n        Bin size (resolution) in base pairs.\n    chroms : 1D array-like\n        Column of chromosome names.\n    centers_bp : 1D or nx2 array-like\n        If 1D, center points of each window. If 2D, the starts and ends.\n    flank_bp : int\n        Distance in base pairs to extend windows on either side.\n    region_start_bp : int, optional\n        If region is a subset of a chromosome, shift coordinates by this amount.\n        Default is 0.\n\n    Returns\n    -------\n    DataFrame with columns:\n        'chrom'        - chromosome\n        'start', 'end' - window limits in base pairs\n        'lo', 'hi'     - window limits in bins\n\n    \"\"\"\n    if not (flank_bp % binsize == 0):\n        raise ValueError(\"Flanking distance must be divisible by the bin size.\")\n\n    if isinstance(chroms, pd.Series) and not ignore_index:\n        index = chroms.index\n    else:\n        index = None\n\n    chroms = np.asarray(chroms)\n    centers_bp = np.asarray(centers_bp)\n    if len(centers_bp.shape) == 2:\n        left_bp = centers_bp[:, 0]\n        right_bp = centers_bp[:, 1]\n    else:\n        left_bp = right_bp = centers_bp\n\n    if np.any(left_bp > right_bp):\n        raise ValueError(\"Found interval with end > start.\")\n\n    left = left_bp - region_start_bp\n    right = right_bp - region_start_bp\n    left_bin = (left / binsize).astype(int)\n    right_bin = (right / binsize).astype(int)\n    flank_bin = flank_bp // binsize\n\n    lo = left_bin - flank_bin\n    hi = right_bin + flank_bin + 1\n    windows = pd.DataFrame(index=index)\n    windows[\"chrom\"] = chroms\n    windows[\"start\"] = lo * binsize\n    windows[\"end\"] = hi * binsize\n    windows[\"lo\"] = lo\n    windows[\"hi\"] = hi\n    return windows\n\n\ndef assign_regions(features, supports):\n    \"\"\"\n\n    \"\"\"\n    features = features.copy()\n\n    # on-diagonal features\n    if \"chrom\" in features.columns:\n        for i, region in enumerate(supports):\n            if len(region) == 3:\n                sel = features.chrom == region[0]\n                sel &= features.end >= region[1]\n                if region[2] is not None:\n                    sel &= features.start < region[2]\n\n                features.loc[sel, \"region\"] = i\n\n            elif len(region) == 2:\n                region1, region2 = region\n                sel1 = features.chrom == region1[0]\n                sel1 &= features.end >= region1[1]\n                if region1[2] is not None:\n                    sel1 &= features.start < region1[2]\n\n                sel2 = features.chrom == region2[0]\n                sel2 &= features.end >= region2[1]\n                if region2[2] is not None:\n                    sel2 &= features.start < region2[2]\n\n                features.loc[(sel1 | sel2), \"region\"] = i\n\n    # off-diagonal features\n    elif \"chrom1\" in features.columns:\n        for i, region in enumerate(supports):\n            if len(region) == 3:\n                region1, region2 = region, region\n            elif len(region) == 2:\n                region1, region2 = region[0], region[1]\n\n            sel1 = features.chrom1 == region1[0]\n            sel1 &= features.end1 >= region1[1]\n            if region1[2] is not None:\n                sel1 &= features.start1 < region1[2]\n\n            sel2 = features.chrom2 == region2[0]\n            sel2 &= features.end2 >= region2[1]\n            if region2[2] is not None:\n                sel2 &= features.start2 < region2[2]\n\n            features.loc[(sel1 | sel2), \"region\"] = i\n    else:\n        raise ValueError(\"Could not parse `features` data frame.\")\n\n    features[\"region\"] = features[\"region\"].map(\n        lambda i: \"{}:{}-{}\".format(*supports[int(i)]), na_action=\"ignore\"\n    )\n    return features\n\n\ndef _pileup(data_select, data_snip, arg):\n    support, feature_group = arg\n    # check if support region is on- or off-diagonal\n    if len(support) == 2:\n        region1, region2 = map(bioframe.region.parse_region_string, support)\n    else:\n        region1 = region2 = bioframe.region.parse_region_string(support)\n\n    # check if features are on- or off-diagonal\n    if \"start\" in feature_group:\n        s1 = feature_group[\"start\"].values\n        e1 = feature_group[\"end\"].values\n        s2, e2 = s1, e1\n    else:\n        s1 = feature_group[\"start1\"].values\n        e1 = feature_group[\"end1\"].values\n        s2 = feature_group[\"start2\"].values\n        e2 = feature_group[\"end2\"].values\n\n    data = data_select(region1, region2)\n    stack = list(map(partial(data_snip, data, region1, region2), zip(s1, e1, s2, e2)))\n\n    return np.dstack(stack), feature_group[\"_rank\"].values\n\n\ndef pileup(features, data_select, data_snip, map=map):\n    \"\"\"\n    Handles on-diagonal and off-diagonal cases.\n\n    Parameters\n    ----------\n    features : DataFrame\n        Table of features. Requires columns ['chrom', 'start', 'end'].\n        Or ['chrom1', 'start1', 'end1', 'chrom1', 'start2', 'end2'].\n        start, end are bp coordinates.\n        lo, hi are bin coordinates.\n\n    data_select : callable\n        Callable that takes a region as argument and returns\n        the data, mask and bin offset of a support region\n\n    data_snip : callable\n        Callable that takes data, mask and a 2D bin span (lo1, hi1, lo2, hi2)\n        and returns a snippet from the selected support region\n\n\n    \"\"\"\n    if features.region.isnull().any():\n        warnings.warn(\"Some features do not have regions assigned! Some snips will be empty.\")\n\n    features = features.copy()\n    features[\"_rank\"] = range(len(features))\n\n    # cumul_stack = []\n    # orig_rank = []\n    cumul_stack, orig_rank = zip(\n        *map(\n            partial(_pileup, data_select, data_snip),\n            features.groupby(\"region\", sort=False),\n        )\n    )\n\n    # Restore the original rank of the input features\n    cumul_stack = np.dstack(cumul_stack)\n    orig_rank = np.concatenate(orig_rank)\n\n    idx = np.argsort(orig_rank)\n    cumul_stack = cumul_stack[:, :, idx]\n    return cumul_stack\n\n\ndef pair_sites(sites, separation, slop):\n    \"\"\"\n    Create \"hand\" intervals to the right and to the left of each site.\n    Then join right hands with left hands to pair sites together.\n\n    \"\"\"\n    from bioframe.tools import tsv, bedtools\n\n    mids = (sites[\"start\"] + sites[\"end\"]) // 2\n    left_hand = sites[[\"chrom\"]].copy()\n    left_hand[\"start\"] = mids - separation - slop\n    left_hand[\"end\"] = mids - separation + slop\n    left_hand[\"site_id\"] = left_hand.index\n    left_hand[\"direction\"] = \"L\"\n    left_hand[\"snip_mid\"] = mids\n    left_hand[\"snip_strand\"] = sites[\"strand\"]\n\n    right_hand = sites[[\"chrom\"]].copy()\n    right_hand[\"start\"] = mids + separation - slop\n    right_hand[\"end\"] = mids + separation + slop\n    right_hand[\"site_id\"] = right_hand.index\n    right_hand[\"direction\"] = \"R\"\n    right_hand[\"snip_mid\"] = mids\n    right_hand[\"snip_strand\"] = sites[\"strand\"]\n\n    # ignore out-of-bounds hands\n    mask = (left_hand[\"start\"] > 0) & (right_hand[\"start\"] > 0)\n    left_hand = left_hand[mask].copy()\n    right_hand = right_hand[mask].copy()\n\n    # intersect right hands (left anchor site)\n    # with left hands (right anchor site)\n    with tsv(right_hand) as R, tsv(left_hand) as L:\n        out = bedtools.intersect(a=R.name, b=L.name, wa=True, wb=True)\n        out.columns = [c + \"_r\" for c in right_hand.columns] + [\n            c + \"_l\" for c in left_hand.columns\n        ]\n    return out\n\n\nclass CoolerSnipper:\n    def __init__(self, clr, cooler_opts=None):\n        self.clr = clr\n        self.binsize = self.clr.binsize\n        self.offsets = {}\n        self.pad = True\n        self.cooler_opts = {} if cooler_opts is None else cooler_opts\n        self.cooler_opts.setdefault(\"sparse\", True)\n\n    def select(self, region1, region2):\n        self.offsets[region1] = self.clr.offset(region1) - self.clr.offset(region1[0])\n        self.offsets[region2] = self.clr.offset(region2) - self.clr.offset(region2[0])\n        self._isnan1 = np.isnan(self.clr.bins()[\"weight\"].fetch(region1).values)\n        self._isnan2 = np.isnan(self.clr.bins()[\"weight\"].fetch(region2).values)\n        matrix = self.clr.matrix(**self.cooler_opts).fetch(region1, region2)\n        if self.cooler_opts[\"sparse\"]:\n            matrix = matrix.tocsr()\n        return matrix\n\n    def snip(self, matrix, region1, region2, tup):\n        s1, e1, s2, e2 = tup\n        offset1 = self.offsets[region1]\n        offset2 = self.offsets[region2]\n        binsize = self.binsize\n        lo1, hi1 = (s1 // binsize) - offset1, (e1 // binsize) - offset1\n        lo2, hi2 = (s2 // binsize) - offset2, (e2 // binsize) - offset2\n        assert hi1 >= 0\n        assert hi2 >= 0\n\n        m, n = matrix.shape\n        dm, dn = hi1 - lo1, hi2 - lo2\n        out_of_bounds = False\n        pad_left = pad_right = pad_bottom = pad_top = None\n        if lo1 < 0:\n            pad_bottom = -lo1\n            out_of_bounds = True\n        if lo2 < 0:\n            pad_left = -lo2\n            out_of_bounds = True\n        if hi1 > m:\n            pad_top = dm - (hi1 - m)\n            out_of_bounds = True\n        if hi2 > n:\n            pad_right = dn - (hi2 - n)\n            out_of_bounds = True\n\n        if out_of_bounds:\n            i0 = max(lo1, 0)\n            i1 = min(hi1, m)\n            j0 = max(lo2, 0)\n            j1 = min(hi2, n)\n            snippet = np.full((dm, dn), np.nan)\n        #             snippet[pad_bottom:pad_top,\n        #                     pad_left:pad_right] = matrix[i0:i1, j0:j1].toarray()\n        else:\n            snippet = matrix[lo1:hi1, lo2:hi2].toarray().astype('float')\n            snippet[self._isnan1[lo1:hi1], :] = np.nan\n            snippet[:, self._isnan2[lo2:hi2]] = np.nan\n        return snippet\n\n\nclass ObsExpSnipper:\n    def __init__(self, clr, expected, cooler_opts=None):\n        self.clr = clr\n        self.expected = expected\n\n        # Detecting the columns for the detection of regions\n        columns = expected.columns\n        assert len(columns) > 0\n        if \"chrom\" in columns and \"start\" in columns and \"end\" in columns:\n            self.regions_columns = [\n                \"chrom\",\n                \"start\",\n                \"end\",\n            ]  # Chromosome arms encoded by multiple columns\n        elif \"chrom\" in columns:\n            self.regions_columns = [\n                \"chrom\"\n            ]  # Chromosomes or regions encoded in string mode: \"chr3:XXXXXXX-YYYYYYYY\"\n        elif \"region\" in columns:\n            self.regions_columns = [\n                \"region\"\n            ]  # Regions encoded in string mode: \"chr3:XXXXXXX-YYYYYYYY\"\n        elif len(columns) > 0:\n            self.regions_columns = columns[\n                0\n            ]  # The first columns is treated as chromosome/region annotation\n        else:\n            raise ValueError(\"Expected dataframe has no columns.\")\n\n        self.binsize = self.clr.binsize\n        self.offsets = {}\n        self.pad = True\n        self.cooler_opts = {} if cooler_opts is None else cooler_opts\n        self.cooler_opts.setdefault(\"sparse\", True)\n\n    def select(self, region1, region2):\n        assert region1 == region2, \"ObsExpSnipper is implemented for cis contacts only.\"\n        self.offsets[region1] = self.clr.offset(region1) - self.clr.offset(region1[0])\n        self.offsets[region2] = self.clr.offset(region2) - self.clr.offset(region2[0])\n        matrix = self.clr.matrix(**self.cooler_opts).fetch(region1, region2)\n        if self.cooler_opts[\"sparse\"]:\n            matrix = matrix.tocsr()\n        self._isnan1 = np.isnan(self.clr.bins()[\"weight\"].fetch(region1).values)\n        self._isnan2 = np.isnan(self.clr.bins()[\"weight\"].fetch(region2).values)\n        gr = self.expected.groupby(self.regions_columns)\n        self._expected = LazyToeplitz(            \n            gr.get_group(region1)[\n                \"balanced.avg\"\n            ]\n            .values\n        )\n        return matrix\n\n    def snip(self, matrix, region1, region2, tup):\n        s1, e1, s2, e2 = tup\n        offset1 = self.offsets[region1]\n        offset2 = self.offsets[region2]\n        binsize = self.binsize\n        lo1, hi1 = (s1 // binsize) - offset1, (e1 // binsize) - offset1\n        lo2, hi2 = (s2 // binsize) - offset2, (e2 // binsize) - offset2\n        assert hi1 >= 0\n        assert hi2 >= 0\n\n        m, n = matrix.shape\n        dm, dn = hi1 - lo1, hi2 - lo2\n        out_of_bounds = False\n        pad_left = pad_right = pad_bottom = pad_top = None\n        if lo1 < 0:\n            pad_bottom = -lo1\n            out_of_bounds = True\n        if lo2 < 0:\n            pad_left = -lo2\n            out_of_bounds = True\n        if hi1 > m:\n            pad_top = dm - (hi1 - m)\n            out_of_bounds = True\n        if hi2 > n:\n            pad_right = dn - (hi2 - n)\n            out_of_bounds = True\n\n        if out_of_bounds:\n            i0 = max(lo1, 0)\n            i1 = min(hi1, m)\n            j0 = max(lo2, 0)\n            j1 = min(hi2, n)\n            return np.full((dm, dn), np.nan)\n        #             snippet[pad_bottom:pad_top,\n        #                     pad_left:pad_right] = matrix[i0:i1, j0:j1].toarray()\n        else:\n            snippet = matrix[lo1:hi1, lo2:hi2].toarray()\n            snippet[self._isnan1[lo1:hi1], :] = np.nan\n            snippet[:, self._isnan2[lo2:hi2]] = np.nan\n\n        e = self._expected[lo1:hi1, lo2:hi2]\n        return snippet / e\n\n\nclass ExpectedSnipper:\n    def __init__(self, clr, expected):\n        self.clr = clr\n        self.expected = expected\n\n        # Detecting the columns for the detection of regions\n        columns = expected.columns\n        assert len(columns) > 0\n        if \"chrom\" in columns and \"start\" in columns and \"end\" in columns:\n            self.regions_columns = [\n                \"chrom\",\n                \"start\",\n                \"end\",\n            ]  # Chromosome arms encoded by multiple columns\n        elif \"chrom\" in columns:\n            self.regions_columns = [\n                \"chrom\"\n            ]  # Chromosomes or regions encoded in string mode: \"chr3:XXXXXXX-YYYYYYYY\"\n        elif \"region\" in columns:\n            self.regions_columns = [\n                \"region\"\n            ]  # Regions encoded in string mode: \"chr3:XXXXXXX-YYYYYYYY\"\n        elif len(columns) > 0:\n            self.regions_columns = columns[\n                0\n            ]  # The first columns is treated as chromosome/region annotation\n        else:\n            raise ValueError(\"Expected dataframe has no columns.\")\n\n        try:\n            for region, group in self.expected.groupby(self.regions_columns):\n                assert group.shape[0]==np.diff(self.clr.extent(region))[0]\n        except AssertionError:\n            raise ValueError(\"Region shape mismatch between expected and cooler. \"\n                             \"Are they using the same resolution?\")\n\n        self.binsize = self.clr.binsize\n        self.offsets = {}\n\n    def select(self, region1, region2):\n        assert (\n            region1 == region2\n        ), \"ExpectedSnipper is implemented for cis contacts only.\"\n        self.offsets[region1] = self.clr.offset(region1) - self.clr.offset(region1[0])\n        self.offsets[region2] = self.clr.offset(region2) - self.clr.offset(region2[0])\n        self.m = np.diff(self.clr.extent(region1))\n        self.n = np.diff(self.clr.extent(region2))\n        gr = self.expected.groupby(self.regions_columns)\n        self._expected = LazyToeplitz(            \n            gr.get_group(tuple(region1))[\n                \"balanced.avg\"\n            ]\n            .values\n        )\n        return self._expected\n\n    def snip(self, exp, region1, region2, tup):\n        s1, e1, s2, e2 = tup\n        offset1 = self.offsets[region1]\n        offset2 = self.offsets[region2]\n        binsize = self.binsize\n        lo1, hi1 = (s1 // binsize) - offset1, (e1 // binsize) - offset1\n        lo2, hi2 = (s2 // binsize) - offset2, (e2 // binsize) - offset2\n        assert hi1 >= 0\n        assert hi2 >= 0\n        dm, dn = hi1 - lo1, hi2 - lo2\n\n        if lo1 < 0 or lo2 < 0 or hi1 > self.m or hi2 > self.n:\n            return np.full((dm, dn), np.nan)\n\n        snippet = exp[lo1:hi1, lo2:hi2]\n        return snippet\n","sub_path":"cooltools/snipping.py","file_name":"snipping.py","file_ext":"py","file_size_in_byte":16505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"305950136","text":"from unittest import TestCase, main\n\nfrom cogent3 import (\n    DNA,\n    get_moltype,\n    make_aligned_seqs,\n    make_tree,\n    make_unaligned_seqs,\n)\nfrom cogent3.align.align import make_generic_scoring_dict\nfrom cogent3.app import align as align_app\nfrom cogent3.app.align import (\n    _combined_refseq_gaps,\n    _gap_difference,\n    _gap_union,\n    _GapOffset,\n    _gaps_for_injection,\n    _merged_gaps,\n    pairwise_to_multiple,\n)\nfrom cogent3.app.composable import NotCompleted\nfrom cogent3.core.alignment import Aligned\nfrom cogent3.core.location import gap_coords_to_map\n\n\n__author__ = \"Gavin Huttley\"\n__copyright__ = \"Copyright 2007-2022, The Cogent Project\"\n__credits__ = [\"Gavin Huttley\"]\n__license__ = \"BSD-3\"\n__version__ = \"2022.8.24a1\"\n__maintainer__ = \"Gavin Huttley\"\n__email__ = \"Gavin.Huttley@anu.edu.au\"\n__status__ = \"Alpha\"\n\n_seqs = {\n    \"Human\": \"GCCAGCTCATTACAGCATGAGAACAGCAGTTTATTACTCACT\",\n    \"Bandicoot\": \"NACTCATTAATGCTTGAAACCAGCAGTTTATTGTCCAAC\",\n    \"Rhesus\": \"GCCAGCTCATTACAGCATGAGAACAGTTTGTTACTCACT\",\n    \"FlyingFox\": \"GCCAGCTCTTTACAGCATGAGAACAGTTTATTATACACT\",\n}\n\n_nucleotide_models = [\n    \"JC69\",\n    \"K80\",\n    \"F81\",\n    \"HKY85\",\n    \"TN93\",\n    \"GTR\",\n    \"ssGN\",\n    \"GN\",\n    \"BH\",\n    \"DT\",\n]\n\n_codon_models = [\n    \"CNFGTR\",\n    \"CNFHKY\",\n    \"MG94HKY\",\n    \"MG94GTR\",\n    \"GY94\",\n    \"H04G\",\n    \"H04GK\",\n    \"H04GGK\",\n    \"GNC\",\n]\n\n\ndef make_pairwise(data, refseq_name, moltype=\"dna\", array_align=False):\n    \"\"\"returns series of refseq, [(n, pwise aln),..]. All alignments are to ref_seq\"\"\"\n    aln = make_aligned_seqs(\n        data,\n        array_align=array_align,\n        moltype=moltype,\n    )\n    refseq = aln.get_seq(refseq_name)\n    pwise = [\n        (n, aln.take_seqs([refseq_name, n]).omit_gap_pos())\n        for n in aln.names\n        if n != refseq_name\n    ]\n    return refseq, pwise\n\n\ndef make_aligned(gaps_lengths, seq, name=\"seq1\"):\n    seq = seq.moltype.make_seq(seq, name=name)\n    return Aligned(gap_coords_to_map(gaps_lengths, len(seq)), seq)\n\n\nclass RefalignmentTests(TestCase):\n    seqs = make_unaligned_seqs(_seqs, moltype=DNA)\n\n    def test_align_to_ref(self):\n        \"\"\"correctly aligns to a reference\"\"\"\n        aligner = align_app.align_to_ref(ref_seq=\"Human\")\n        aln = aligner(self.seqs)\n        expect = {\n            \"Bandicoot\": \"---NACTCATTAATGCTTGAAACCAGCAGTTTATTGTCCAAC\",\n            \"FlyingFox\": \"GCCAGCTCTTTACAGCATGAGAACAG---TTTATTATACACT\",\n            \"Human\": \"GCCAGCTCATTACAGCATGAGAACAGCAGTTTATTACTCACT\",\n            \"Rhesus\": \"GCCAGCTCATTACAGCATGAGAAC---AGTTTGTTACTCACT\",\n        }\n        self.assertEqual(aln.to_dict(), expect)\n\n    def test_align_to_ref_generic_moltype(self):\n        \"\"\"tests when the moltype is generic\"\"\"\n        test_moltypes = [\"text\", \"rna\", \"protein\", \"protein_with_stop\", \"bytes\", \"ab\"]\n        for test_moltype in test_moltypes:\n            aligner = align_app.align_to_ref(moltype=test_moltype)\n            self.assertEqual(aligner._moltype.label, test_moltype)\n            self.assertEqual(\n                aligner._kwargs[\"S\"],\n                make_generic_scoring_dict(10, get_moltype(test_moltype)),\n            )\n\n    def test_align_to_ref_result_has_moltype(self):\n        \"\"\"aligned object has correct moltype\"\"\"\n        aligner = align_app.align_to_ref(moltype=\"dna\")\n        got = aligner(self.seqs)\n        self.assertEqual(got.moltype.label, \"dna\")\n\n    def test_merged_gaps(self):\n        \"\"\"correctly merges gaps\"\"\"\n        a = dict([(2, 3), (4, 9)])\n        b = dict([(2, 6), (8, 5)])\n        # omitting one just returns the other\n        self.assertIs(_merged_gaps(a, {}), a)\n        self.assertIs(_merged_gaps({}, b), b)\n        got = _merged_gaps(a, b)\n        self.assertEqual(got, [(2, 6), (4, 9), (8, 5)])\n\n    def test_aln_to_ref_known(self):\n        \"\"\"correctly recapitulates known case\"\"\"\n        orig = make_aligned_seqs(\n            {\n                \"Ref\": \"CAG---GAGAACAGAAACCCAT--TACTCACT\",\n                \"Qu1\": \"CAG---GAGAACAG---CCCGTGTTACTCACT\",\n                \"Qu2\": \"CAGCATGAGAACAGAAACCCGT--TA---ACT\",\n                \"Qu3\": \"CAGCATGAGAACAGAAACCCGT----CTCACT\",\n                \"Qu4\": \"CAGCATGAGAACAGAAACCCGTGTTACTCACT\",\n                \"Qu5\": \"CAG---GAGAACAG---CCCAT--TACTCACT\",\n                \"Qu6\": \"CAG---GA-AACAG---CCCAT--TACTCACT\",\n                \"Qu7\": \"CAG---GA--ACAGA--CCCGT--TA---ACT\",\n            },\n            moltype=\"dna\",\n        )\n        expect = orig.to_dict()\n        aligner = align_app.align_to_ref(ref_seq=\"Ref\")\n        aln = aligner(orig.degap())\n        self.assertEqual(aln.to_dict(), expect)\n\n    def test_gap_union(self):\n        \"\"\"correctly identifies the union of all gaps\"\"\"\n        # fails if not all sequences same\n        seq = DNA.make_seq(\"AACCCGTT\")\n        all_gaps = dict([(0, 3), (2, 1), (5, 3), (6, 3)])\n        make_aligned(all_gaps, seq)\n        gap_sets = [\n            dict([(5, 1), (6, 3)]),\n            dict([(2, 1), (5, 3)]),\n            dict([(2, 1), (5, 1), (6, 2)]),\n            dict([(0, 3)]),\n        ]\n        seqs = [make_aligned(gaps, seq) for gaps in gap_sets]\n        got = _gap_union(seqs)\n        self.assertEqual(got, dict(all_gaps))\n\n        # must all be Aligned instances\n        with self.assertRaises(TypeError):\n            _gap_union(seqs + [\"GGGGGGGG\"])\n\n        # must all have the same name\n        with self.assertRaises(ValueError):\n            _gap_union(seqs + [make_aligned({}, seq, name=\"blah\")])\n\n    def test_gap_difference(self):\n        \"\"\"correctly identifies the difference in gaps\"\"\"\n        seq = DNA.make_seq(\"AACCCGTT\")\n        dict([(0, 3), (2, 1), (5, 3), (6, 3)])\n        gap_sets = [\n            dict([(5, 1), (6, 3)]),\n            dict([(2, 1), (5, 3)]),\n            dict([(2, 1), (5, 1), (6, 2)]),\n            dict([(0, 3)]),\n        ]\n        seqs = [make_aligned(gaps, seq) for gaps in gap_sets]\n        union = _gap_union(seqs)\n        expects = [\n            [dict([(0, 3), (2, 1)]), dict([(5, 2)])],\n            [dict([(0, 3), (6, 3)]), {}],\n            [dict([(0, 3)]), dict([(5, 2), (6, 1)])],\n            [dict([(2, 1), (5, 3), (6, 3)]), {}],\n        ]\n        for seq, (plain, overlap) in zip(seqs, expects):\n            seq_gaps = dict(seq.map.get_gap_coordinates())\n            got_plain, got_overlap = _gap_difference(seq_gaps, union)\n            self.assertEqual(got_plain, dict(plain))\n            self.assertEqual(got_overlap, dict(overlap))\n\n    def test_merged_gaps(self):\n        \"\"\"correctly handles gap values\"\"\"\n        a_gaps = {0: 2}\n        b_gaps = {2: 2}\n        self.assertEqual(_merged_gaps(a_gaps, {}), a_gaps)\n        self.assertEqual(_merged_gaps({}, b_gaps), b_gaps)\n\n    def test_combined_refseq_gaps(self):\n        union = dict([(0, 3), (2, 1), (5, 3), (6, 3)])\n        gap_sets = [\n            [(5, 1), (6, 3)],\n            [(2, 1), (5, 3)],\n            [(2, 1), (5, 1), (6, 2)],\n            [(0, 3)],\n        ]\n        # for subset gaps, their alignment position is the\n        # offset + their position + their gap length\n        expects = [\n            dict([(6, 2), (0, 3), (2, 1)]),\n            dict([(0, 3), (10, 3)]),\n            dict([(0, 3), (5 + 1 + 1, 2), (6 + 2 + 2, 1)]),\n            dict([(2 + 3, 1), (5 + 3, 3), (6 + 3, 3)]),\n        ]\n        for i, gap_set in enumerate(gap_sets):\n            got = _combined_refseq_gaps(dict(gap_set), union)\n            self.assertEqual(got, expects[i])\n\n        # if union gaps equals ref gaps\n        got = _combined_refseq_gaps({2: 2}, {2: 2})\n        self.assertEqual(got, {})\n\n    def test_gaps_for_injection(self):\n        # for gaps before any otherseq gaps, alignment coord is otherseq coord\n        oseq_gaps = {2: 1, 6: 2}\n        rseq_gaps = {0: 3}\n        expect = {0: 3, 2: 1, 6: 2}\n        seqlen = 50\n        got = _gaps_for_injection(oseq_gaps, rseq_gaps, seqlen)\n        self.assertEqual(got, expect)\n        # for gaps after otherseq gaps seq coord is align coord minus gap\n        # length totals\n        got = _gaps_for_injection(oseq_gaps, {4: 3}, seqlen)\n        expect = {2: 1, 3: 3, 6: 2}\n        self.assertEqual(got, expect)\n        got = _gaps_for_injection(oseq_gaps, {11: 3}, seqlen)\n        expect = {2: 1, 6: 2, 8: 3}\n        self.assertEqual(got, expect)\n        # gaps beyond sequence length added to end of sequence\n        got = _gaps_for_injection({2: 1, 6: 2}, {11: 3, 8: 3}, 7)\n        expect = {2: 1, 6: 2, 7: 6}\n        self.assertEqual(got, expect)\n\n    def test_pairwise_to_multiple(self):\n        \"\"\"the standalone function constructs a multiple alignment\"\"\"\n        expect = {\n            \"Ref\": \"CAG---GAGAACAGAAACCCAT--TACTCACT\",\n            \"Qu1\": \"CAG---GAGAACAG---CCCGTGTTACTCACT\",\n            \"Qu2\": \"CAGCATGAGAACAGAAACCCGT--TA---ACT\",\n            \"Qu3\": \"CAGCATGAGAACAGAAACCCGT----CTCACT\",\n            \"Qu7\": \"CAG---GA--ACAGA--CCCGT--TA---ACT\",\n            \"Qu4\": \"CAGCATGAGAACAGAAACCCGTGTTACTCACT\",\n            \"Qu5\": \"CAG---GAGAACAG---CCCAT--TACTCACT\",\n            \"Qu6\": \"CAG---GA-AACAG---CCCAT--TACTCACT\",\n        }\n        aln = make_aligned_seqs(expect, moltype=\"dna\").omit_gap_pos()\n        expect = aln.to_dict()\n        for refseq_name in [\"Qu3\"]:\n            refseq, pwise = make_pairwise(expect, refseq_name)\n            got = pairwise_to_multiple(pwise, ref_seq=refseq, moltype=refseq.moltype)\n            self.assertEqual(len(got), len(aln))\n            orig = dict(pwise)\n            _, pwise = make_pairwise(got.to_dict(), refseq_name)\n            got = dict(pwise)\n            # should be able to recover the original pairwise alignments\n            for key, value in got.items():\n                self.assertEqual(value.to_dict(), orig[key].to_dict(), msg=refseq_name)\n\n            with self.assertRaises(TypeError):\n                pairwise_to_multiple(pwise, \"ACGG\", DNA)\n\n    def test_pairwise_to_multiple_2(self):\n        \"\"\"correctly handle alignments with gaps beyond end of query\"\"\"\n        # cogent3.core.alignment.DataError: Not all sequences are the same length:\n        # max is 425, min is 419\n        def make_pwise(data, ref_name):\n            result = []\n            for n, seqs in data.items():\n                result.append(\n                    [n, make_aligned_seqs(data=seqs, moltype=\"dna\", array_align=False)]\n                )\n            ref_seq = result[0][1].get_seq(ref_name)\n            return result, ref_seq\n\n        pwise = {\n            \"Platypus\": {\n                \"Opossum\": \"-----------------GTGC------GAT-------------------------------CCAAAAACCTGTGTC--ACCGT--------GCC----CAGAGCCTCC----CTCAGGCCGCTCGGGGAG---TG-------GCCCCCCG--GC-GGAGGGCAGGGATGGGGAGT-AGGGGTGGCAGTC----GGAACTGGAAGAGCTT-TACAAACC---------GA--------------------GGCT-AGAGGGTC-TGCTTAC-------TTTTTACCTTGG------------GTTTG-CCAGGAGGTAG----------AGGATGA-----------------CTAC--ATCAAG----AGC------------TGGG-------------\",\n                \"Platypus\": \"CAGGATGACTACATCAAGAGCTGGGAAGATAACCAGCAAGGAGATGAAGCTCTGGACACTACCAAAGACCCCTGCCAGAACGTGAAGTGCAGCCGACACAAGGTCTGCATCGCTCAGGGCTACCAGAGAGCCATGTGTATCAGCCGCAAGAAGCTGGAGCACAGGATCAAGCAGCCAGCCCTGAAACTCCATGGAAACAGAGAGAGCTTCTGCAAGCCTTGTCACATGACCCAGCTGGCCTCTGTCTGCGGCTCGGACGGACACACTTACAGCTCCGTGTGCAAACTGGAGCAGCAGGCCTGTCTGACCAGCAAGCAGCTGACAGTCAAGTGTGAAGGCCAGTGCCCGTGCCCCACCGATCATGTTCCAGCCTCCACCGCTGATGGAAAACAAGAGACCT\",\n            },\n            \"Wombat\": {\n                \"Opossum\": \"GTGCGATCCAAAAACCTGTGTCACCGTGCCCAGAGCCTCCCTCAGGCCGCTCGG-GGAGTGGCCCCCCGGCGGAGGGCAGGGATGGGGAGTAGGGGTGGCAGTCGGAACTGGAAGAGCTTTACAAACCGAGGCTAGAGGGTCTGCTTACTTTTTACCTTGG------GTTT--GC-CAGGA---GGT----AGAGGATGACTACATCAAGAGCTGGG---------------------------\",\n                \"Wombat\": \"--------CA----------TCACCGC-CCCTGCACC---------CGGCTCGGCGGAGGGGGATTCTAA-GGGGGTCAAGGATGGCGAG-ACCCCTGGCAATTTCA--TGGAGGA------CGAGCAATGGCT-----GTC-GTCCATCTCCCAGTATAGCGGCAAGATCAAGCACTGGAACCGCTTCCGAGACGATGACTACATCAAGAGCTGGGAGGACAGTCAGCAAGGAGATGAAGCGC\",\n            },\n        }\n        pwise, ref_seq = make_pwise(pwise, \"Opossum\")\n        aln = pairwise_to_multiple(pwise, ref_seq, ref_seq.moltype)\n        self.assertNotIsInstance(aln, NotCompleted)\n\n        pwise = {\n            \"Platypus\": {\n                \"Opossum\": \"-----------------GTGC------GAT-------------------------------CCAAAAACCTGTGTC\",\n                \"Platypus\": \"CAGGATGACTACATCAAGAGCTGGGAAGATAACCAGCAAGGAGATGAAGCTCTGGACACTACCAAAGACCCCTGCC\",\n            },\n            \"Wombat\": {\n                \"Opossum\": \"GTGCGATCCAAAAACCTGTGTC\",\n                \"Wombat\": \"--------CA----------TC\",\n            },\n        }\n        pwise, ref_seq = make_pwise(pwise, \"Opossum\")\n        aln = pairwise_to_multiple(pwise, ref_seq, ref_seq.moltype)\n        self.assertNotIsInstance(aln, NotCompleted)\n\n\nclass ProgressiveAlignment(TestCase):\n    seqs = make_unaligned_seqs(_seqs, moltype=DNA)\n    treestring = \"(Bandicoot:0.4,FlyingFox:0.05,(Rhesus:0.06,\" \"Human:0.0):0.04);\"\n\n    def test_progressive_align_protein_moltype(self):\n        \"\"\"tests guide_tree is None and moltype is protein\"\"\"\n        from cogent3 import load_aligned_seqs\n\n        seqs = load_aligned_seqs(\"data/nexus_aa.nxs\", moltype=\"protein\")\n        seqs = seqs.degap()\n        seqs = seqs.take_seqs([\"Rat\", \"Cow\", \"Human\", \"Mouse\", \"Whale\"])\n        aligner = align_app.progressive_align(model=\"WG01\")\n        got = aligner(seqs)\n        self.assertNotIsInstance(got, NotCompleted)\n        aligner = align_app.progressive_align(model=\"protein\")\n        got = aligner(seqs)\n        self.assertNotIsInstance(got, NotCompleted)\n\n    def test_progressive_align_nuc(self):\n        \"\"\"progressive alignment with nuc models\"\"\"\n        aligner = align_app.progressive_align(model=\"TN93\", distance=\"TN93\")\n        aln = aligner(self.seqs)\n        expect = {\n            \"Rhesus\": \"GCCAGCTCATTACAGCATGAGAACAG---TTTGTTACTCACT\",\n            \"Human\": \"GCCAGCTCATTACAGCATGAGAACAGCAGTTTATTACTCACT\",\n            \"Bandicoot\": \"NACTCATTAATGCTTGAAACCAGCAG---TTTATTGTCCAAC\",\n            \"FlyingFox\": \"GCCAGCTCTTTACAGCATGAGAACAG---TTTATTATACACT\",\n        }\n        got = aln.to_dict()\n        self.assertEqual(got, expect)\n\n        # using default\n        aligner = align_app.progressive_align(model=\"TN93\", distance=\"TN93\")\n        aln = aligner(self.seqs)\n        self.assertEqual(len(aln), 42)\n        self.assertEqual(aln.moltype, aligner._moltype)\n        # todo the following is not robust across operating systems\n        # so commenting out for now, but needs to be checked\n        # expect = {'Human': 'GCCAGCTCATTACAGCATGAGAACAGCAGTTTATTACTCACT',\n        #           'Rhesus': 'GCCAGCTCATTACAGCATGAGAA---CAGTTTGTTACTCACT',\n        #           'Bandicoot': 'NACTCATTAATGCTTGAAACCAG---CAGTTTATTGTCCAAC',\n        #           'FlyingFox': 'GCCAGCTCTTTACAGCATGAGAA---CAGTTTATTATACACT'}\n        # got = aln.to_dict()\n        # self.assertEqual(got, expect)\n\n    def test_progressive_fails(self):\n        \"\"\"should return NotCompletedResult along with message\"\"\"\n        # Bandicoot has an inf-frame stop codon\n        seqs = make_unaligned_seqs(\n            data={\"Human\": \"GCCTCA\", \"Rhesus\": \"GCCAGCTCA\", \"Bandicoot\": \"TGATCATTA\"},\n            moltype=\"dna\",\n        )\n        aligner = align_app.progressive_align(model=\"codon\")\n        got = aligner(seqs)\n        self.assertTrue(type(got), NotCompleted)\n\n    def test_progress_with_guide_tree(self):\n        \"\"\"progressive align works with provided guide tree\"\"\"\n        tree = make_tree(treestring=self.treestring)\n        aligner = align_app.progressive_align(\n            model=\"nucleotide\", guide_tree=self.treestring\n        )\n        aln = aligner(self.seqs)\n        self.assertEqual(len(aln), 42)\n        aligner = align_app.progressive_align(model=\"nucleotide\", guide_tree=tree)\n        aln = aligner(self.seqs)\n        self.assertEqual(len(aln), 42)\n        # even if it has underscores in name\n        treestring = (\n            \"(Bandicoot:0.4,FlyingFox:0.05,(Rhesus_macaque:0.06,\" \"Human:0.0):0.04);\"\n        )\n        aligner = align_app.progressive_align(model=\"nucleotide\", guide_tree=treestring)\n        data = self.seqs.to_dict()\n        data[\"Rhesus macaque\"] = data.pop(\"Rhesus\")\n        seqs = make_unaligned_seqs(data)\n        aln = aligner(seqs)\n        self.assertEqual(len(aln), 42)\n        # guide tree with no lengths raises value error\n        with self.assertRaises(ValueError):\n            _ = align_app.progressive_align(\n                model=\"nucleotide\",\n                guide_tree=\"(Bandicoot,FlyingFox,(Rhesus_macaque,Human));\",\n            )\n\n    def test_progressive_align_codon(self):\n        \"\"\"progressive alignment with codon models\"\"\"\n        aligner = align_app.progressive_align(model=\"GY94\")\n        aln = aligner(self.seqs)\n        self.assertEqual(len(aln), 42)\n        aligner = align_app.progressive_align(model=\"codon\")\n        aln = aligner(self.seqs)\n        self.assertEqual(len(aln), 42)\n\n    def test_pickle_progressive_align(self):\n        \"\"\"test progressive_align is picklable\"\"\"\n        from pickle import dumps, loads\n\n        aligner = align_app.progressive_align(model=\"codon\")\n        aln = aligner(self.seqs)\n        got = loads(dumps(aln))\n        self.assertTrue(got)\n\n    def test_with_genetic_code(self):\n        \"\"\"handles genetic code argument\"\"\"\n        aligner = align_app.progressive_align(model=\"GY94\", gc=\"2\")\n        # the 'TGA' codon is a sense codon in vertebrate mitochondrial\n        self.assertTrue(\"TGA\" in aligner._model.get_motifs())\n        aligner = align_app.progressive_align(model=\"codon\")\n        # but a stop codon in the standard nuclear\n        self.assertTrue(\"TGA\" not in aligner._model.get_motifs())\n        # try using a nuclear\n        with self.assertRaises(TypeError):\n            aligner = align_app.progressive_align(model=\"nucleotide\", gc=\"2\")\n\n    def test_progressive_align_protein(self):\n        \"\"\"progressive alignment with protein models\"\"\"\n        seqs = self.seqs.get_translation()\n        aligner = align_app.progressive_align(model=\"WG01\", guide_tree=self.treestring)\n        aln = aligner(seqs)\n        self.assertEqual(len(aln), 14)\n        aligner = align_app.progressive_align(\n            model=\"protein\", guide_tree=self.treestring\n        )\n        aln = aligner(seqs)\n        self.assertEqual(len(aln), 14)\n\n\nclass GapOffsetTests(TestCase):\n    def test_empty(self):\n        \"\"\"create an empty offset\"\"\"\n        goff = _GapOffset({})\n        for i in range(4):\n            self.assertEqual(goff[i], 0)\n\n        goff = _GapOffset({}, invert=True)\n        for i in range(4):\n            self.assertEqual(goff[i], 0)\n\n    def test_repr_str(self):\n        \"\"\"repr and str work\"\"\"\n        goff = _GapOffset({}, invert=True)\n        for func in (str, repr):\n            self.assertEqual(func(goff), \"{}\")\n\n    def test_gap_offset(self):\n        goff = _GapOffset({1: 2, 3: 4})\n        self.assertEqual(goff.min_pos, 1)\n        self.assertEqual(goff.max_pos, 3)\n        self.assertEqual(goff.total, 6)\n        self.assertEqual(goff[0], 0)\n        self.assertEqual(goff[1], 0)\n        self.assertEqual(goff[2], 2)\n        self.assertEqual(goff[3], 2)\n        self.assertEqual(goff[4], 6)\n\n    def test_gap_offset_invert(self):\n        aln2seq = _GapOffset({2: 1, 5: 2, 7: 2}, invert=True)\n        self.assertEqual(aln2seq._store, {3: 1, 2: 0, 8: 3, 6: 1, 12: 5, 10: 3})\n        self.assertEqual(aln2seq.max_pos, 12)\n        self.assertEqual(aln2seq.min_pos, 2)\n        self.assertEqual(aln2seq[11], 3)\n        seq2aln = _GapOffset({2: 1, 5: 2, 7: 2})\n        for seq_pos in range(20):\n            aln_pos = seq_pos + seq2aln[seq_pos]\n            self.assertEqual(aln_pos - aln2seq[aln_pos], seq_pos)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"tests/test_app/test_align.py","file_name":"test_align.py","file_ext":"py","file_size_in_byte":19594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"617177512","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2018/8/16 16:54\n# @Author  : SuNan\n# @File    : 2.32 Unit converter.py\n# @Software: PyCharm\n\"\"\"\n1.添加Transfer类，作为主体程序，处理输入输出\n\n2.温度,长度,货币转换分别建立独立的类，每个类至少有2个方法，各处理一个方向转换（举例：美元到人民币，人民币到美元）\n\n3.Transfer类根据不同输入内容，自动选择相应的功能类去处理逻辑\n\n4.各个类中的每个方法必须添加说明doc-string（即def下一行加一句注释），每个类必须添加注释说明，解释作用(缺一条减10分)\n\n\"\"\"\n\n\nclass tf():\n    def TC(self, C):\n        # 华氏温度转摄氏度\n        Tf = (C - 32) / 1.8\n        print(Tf)\n\n    def TF(self, F):\n        # 摄氏度转华氏温度\n        Tc = F * 1.8 + 32\n        print(Tc)\n\n\nclass cf():\n    def Usd(seft, U):\n        # USD->CNY\n        UU = U / 6.82\n        print(UU)\n\n    def Cny(seft, R):\n        # CNY->USD\n        RR = R * 6.82\n        print(RR)\n\n\nclass leng():\n    def Cm(seft, CM):\n        # CM->M\n        CC = CM / 100\n        print(CC)\n\n    def Mm(seft, M):\n        #   M->CM\n        MM = M * 100\n        print(MM)\n\n\ndef transfer():\n    \"\"\"管理记录\"\"\"\n    print('欢迎使用21点'.center(30, '-'))\n    while True:\n        menu = {'1': '温度转换', '2': '货币转换', '3': '长度转换', 'q': '退出'}\n        for k, v in menu.items():\n            print(f'{k}:{v}')  # 打印选择选项\n        select = input('请选择你的操作选项 (示例 1，2,3,q)：')\n        if select == '1':\n            a = input('请输入温度(示例：F或C):')\n            if a == 'F':\n                b = int(input('请输入数值:'))\n                tf().TF(b)\n            elif a == 'C':\n                b = int(input('请输入数值:'))\n                tf().TC(b)\n            else:\n                print('输入错误，请重新输入！')\n        elif select == '2':\n            a = input('请输入货币类型(示例：USD或CNY):')\n            if a == 'USD':\n                b = int(input('请输入数值:'))\n                cf().Usd(b)\n            elif a == 'CNY':\n                b = int(input('请输入数值:'))\n                cf().Cny(b)\n            else:\n                print('输入错误，请重新输入！')\n        elif select == '3':\n            a = input('请输入长度(示例：CM或M):')\n            if a == 'CM':\n                b = int(input('请输入数值:'))\n                leng().Cm(b)\n            elif a == 'M':\n                b = int(input('请输入数值:'))\n                leng().Mm(b)\n            else:\n                print('输入错误，请重新输入！')\n        elif select != '1' and select != '2' and select != '3' and select != 'q':\n            print('开发中请重新输入！')\n        elif select == 'q':\n            print('再见！')\n            break\n\n\nif __name__ == \"__main__\":\n    transfer()\n","sub_path":"The second module/note/The second week/2.32 Unit converter.py","file_name":"2.32 Unit converter.py","file_ext":"py","file_size_in_byte":2955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"95727355","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport json\nfrom decimal import Decimal\n\nfrom django.shortcuts import render\nfrom django.forms.models import model_to_dict\nfrom django.core.serializers.json import DjangoJSONEncoder\n\nfrom consumption.models import User\nfrom consumption.models import EConsumptionDayAggregation\nfrom consumption.models import UserEConsumptionDayAggregation\n\ndef summary(request):\n    return render(request, 'consumption/summary.html', {\n        'total_consumptions': json.dumps(\n            list(EConsumptionDayAggregation.objects.values('day', 'day_total')),\n            cls=DjangoJSONEncoder\n        ),\n        'average_consumptions': json.dumps(\n            list(EConsumptionDayAggregation.objects.values('day', 'day_average')),\n            cls=DjangoJSONEncoder\n        ),\n        'header': ['Index', 'Area', 'Tariff', 'Action'],\n        'users': list(User.objects.values('area', 'tariff', 'user_id'))\n    })\n\n\ndef detail(request, user_id):\n    return render(request, 'consumption/detail.html', {\n        'user_total_consumptions': json.dumps(\n            list(UserEConsumptionDayAggregation.objects.filter(user_id=user_id).values('day', 'day_total')),\n            cls=DjangoJSONEncoder\n        ),\n        'user_average_consumptions': json.dumps(\n            list(UserEConsumptionDayAggregation.objects.filter(user_id=user_id).values('day', 'day_average')),\n            cls=DjangoJSONEncoder\n        ),\n        'user': list(User.objects.filter(user_id=user_id).values('area', 'tariff', 'user_id'))[0]\n    })\n","sub_path":"dashboard/consumption/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"403383008","text":"from unittest import TestCase\nimport os.path as osp\n\nfrom datumaro.components.annotation import Bbox, Label\nfrom datumaro.components.dataset import Dataset\nfrom datumaro.components.errors import ReadonlyDatasetError\nfrom datumaro.components.extractor import DatasetItem\nfrom datumaro.components.project import Project\nfrom datumaro.util.scope import scope_add, scoped\nfrom datumaro.util.test_utils import TestDir, compare_datasets\nfrom datumaro.util.test_utils import run_datum as run\n\nfrom ..requirements import Requirements, mark_requirement\n\n\nclass FilterTest(TestCase):\n    @mark_requirement(Requirements.DATUM_GENERAL_REQ)\n    @scoped\n    def test_can_filter_dataset_inplace(self):\n        test_dir = scope_add(TestDir())\n        Dataset.from_iterable([\n            DatasetItem(1, annotations=[Label(0)]),\n            DatasetItem(2, annotations=[Label(1)]),\n        ], categories=['a', 'b']).export(test_dir, 'coco')\n\n        run(self, 'filter', '-e', '/item[id = \"1\"]', '--overwrite',\n            test_dir + ':coco')\n\n        expected_dataset = Dataset.from_iterable([\n            DatasetItem(1, annotations=[Label(0, id=1, group=1)]),\n        ], categories=['a', 'b'])\n        compare_datasets(self, expected_dataset,\n            Dataset.import_from(test_dir, 'coco'), ignored_attrs='*')\n\n    @mark_requirement(Requirements.DATUM_GENERAL_REQ)\n    def test_filter_fails_on_inplace_update_without_overwrite(self):\n        with TestDir() as test_dir:\n            Dataset.from_iterable([\n                DatasetItem(id=1, annotations=[ Bbox(1, 2, 3, 4, label=1) ]),\n            ], categories=['a', 'b']).export(test_dir, 'coco')\n\n            run(self, 'filter', '-e', '/item', test_dir + ':coco',\n                expected_code=1)\n\n    @mark_requirement(Requirements.DATUM_GENERAL_REQ)\n    def test_filter_fails_on_inplace_update_of_stage(self):\n        with TestDir() as test_dir:\n            dataset_url = osp.join(test_dir, 'dataset')\n            dataset = Dataset.from_iterable([\n                DatasetItem(id=1, annotations=[ Bbox(1, 2, 3, 4, label=1) ]),\n            ], categories=['a', 'b'])\n            dataset.export(dataset_url, 'coco', save_images=True)\n\n            project_dir = osp.join(test_dir, 'proj')\n            with Project.init(project_dir) as project:\n                project.import_source('source-1', dataset_url, 'coco',\n                    no_cache=True)\n                project.commit('first commit')\n\n            with self.subTest('without overwrite'):\n                run(self, 'filter', '-p', project_dir,\n                    '-e', '/item', 'HEAD:source-1',\n                    expected_code=1)\n\n            with self.subTest('with overwrite'):\n                with self.assertRaises(ReadonlyDatasetError):\n                    run(self, 'filter', '-p', project_dir, '--overwrite',\n                        '-e', '/item', 'HEAD:source-1')\n","sub_path":"tests/cli/test_filter.py","file_name":"test_filter.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"247334257","text":"import subprocess\nimport numpy as np\nimport os\nimport hashlib\nfrom PIL import Image\nimport yaml \n\nimport sys\nimport os\nimport warnings\nimport tensorflow as tf\nimport numpy as np\nimport scipy\nfrom sklearn.model_selection import train_test_split\n\nTHIS_DIR = os.path.dirname(os.path.abspath(__file__))\nTF_DATA_DIR = os.path.join(THIS_DIR,'tf_data')\ntrain_filename = os.path.join(TF_DATA_DIR,'train.tfrecords')\nvalidation_filename = os.path.join(TF_DATA_DIR,'validation.tfrecords')\ntest_filename = os.path.join(TF_DATA_DIR,'test.tfrecords')\nexpected_file_list = [train_filename,validation_filename,test_filename]\n\n\nw = h = 256\nc = 1\n\n\nNUM_EXAMPLES_PATH = os.path.join(THIS_DIR,'num_examples.yml')\n\ndef set_num_examples(data_sets):\n    num_examples_dict = {\n        'train':data_sets.train.num_examples,\n        'validation':data_sets.validation.num_examples,\n        'test':data_sets.test.num_examples,\n    }\n    with open(NUM_EXAMPLES_PATH,'w') as f:\n        f.write(yaml.dump(num_examples_dict,default_flow_style=False))\n\ndef load_num_examples():\n    with open(NUM_EXAMPLES_PATH,'r') as f:\n        num_examples_dict = yaml.load(f.read())\n    return num_examples_dict['train'],num_examples_dict['validation'], num_examples_dict['test']\n\nif os.path.exists(NUM_EXAMPLES_PATH):\n    NUM_EXAMPLES_TRAIN,NUM_EXAMPLES_VALIDATION,NUM_EXAMPLES_TEST = load_num_examples()\n\n\nclass Dataset(object):\n    def __init__(self,image_path_list,labels):\n        self.images=image_path_list\n        self.labels=labels\n        self.num_examples=len(image_path_list)\n        self.images_shape=[w,h,c]\n    def read_image(self,image_path):\n        img = Image.open(image_path)\n        img.thumbnail((256,256), Image.ANTIALIAS) #TODO play with zoom.\n        img = np.array(img)[0:256,0:256]\n        return np.expand_dims(img,axis=-1)\n\nunzipped_root = os.path.join(THIS_DIR,'KylbergTextureDataset-1.0-without-rotations')\nclass KTex(object):\n    def __init__(self,unzipped_root=unzipped_root):\n        self.unzipped_root = unzipped_root\n        self.data_yml_path = os.path.join(THIS_DIR,'data.yml')\n        self.prepare()\n        self.load()\n\n    def load(self):\n        with open(self.data_yml_path,'r') as f:\n            self.df = yaml.load(f.read())\n            self.df['data_list'] = [os.path.join(THIS_DIR,x) for x in self.df['data_list']]\n\n        X_comb, X_test, y_comb, y_test = train_test_split(\n            self.df['data_list'],self.df['label_list'],test_size=0.33,random_state=42)\n        X_train, X_val, y_train, y_val = train_test_split(\n            X_comb,y_comb,test_size=0.33,random_state=42)\n\n        self.train = Dataset(X_train,y_train)\n        self.validation = Dataset(X_val,y_val)\n        self.test = Dataset(X_test,y_test)\n\n\n    def prepare(self):\n        \n        labellist = sorted([x for x in os.listdir(self.unzipped_root) if os.path.isdir(os.path.join(self.unzipped_root,x))])\n        print(labellist)\n        self.labeldict = {k:v for v,k in enumerate(labellist)}\n\n        if os.path.exists(self.data_yml_path):\n            return\n        data = {\n            'data_list':[],\n            'label_list':[],\n        }\n\n        c=0\n        for labelk in labellist:\n            labelv = self.labeldict[labelk]\n            fpathlist = [os.path.join(self.unzipped_root,labelk,x) for x in os.listdir(os.path.join(self.unzipped_root,labelk))]\n            for fpath in fpathlist:\n                data['data_list'].append(os.path.relpath(fpath,THIS_DIR))\n                data['label_list'].append(labelv)\n            c+=1\n    \n        with open(self.data_yml_path,'w') as f:\n            f.write(yaml.dump(data))\n\n\ndef _int64_feature(value):\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\n\ndef _bytes_feature(value):\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\n\ndef convert_to(data_set, name, directory):\n    \"\"\"Converts a dataset to tfrecords.\"\"\"\n    images = data_set.images\n    labels = data_set.labels\n    num_examples = data_set.num_examples\n\n    rows,cols,depth = data_set.images_shape\n\n    if len(images) != num_examples:\n        raise ValueError('Images size %d does not match label size %d.' %\n                         (images.shape[0], num_examples))\n\n    filename = os.path.join(directory, name + '.tfrecords')\n    print('Writing', filename)\n    writer = tf.python_io.TFRecordWriter(filename)\n    for index in range(num_examples):\n        img = data_set.read_image(images[index])\n\n        if index%100 == 0:\n            _img = Image.fromarray(img.squeeze())\n            preview=os.path.join(THIS_DIR,'preview')\n            if not os.path.exists(preview):\n                os.makedirs(preview)\n            _img.save(os.path.join(preview,str(index)+'.png'))\n\n        image_raw = img.tostring()\n        example = tf.train.Example(features=tf.train.Features(feature={\n            'height': _int64_feature(rows),\n            'width': _int64_feature(cols),\n            'depth': _int64_feature(depth),\n            'label': _int64_feature(int(labels[index])),\n            'image_raw': _bytes_feature(image_raw)}))\n        writer.write(example.SerializeToString())\n    writer.close()\n    \nINPUT_TENSOR_NAME = 'inputs'\ndef serving_input_fn(params): # for deployment\n    inputs = {INPUT_TENSOR_NAME: tf.placeholder(tf.float32, [None, w,h,c])}\n    return tf.estimator.export.ServingInputReceiver(inputs, inputs)\n\ndef read_and_decode(filename_queue):\n    reader = tf.TFRecordReader()\n    _, serialized_example = reader.read(filename_queue)\n\n    features = tf.parse_single_example(\n        serialized_example,\n        features={\n            'image_raw': tf.FixedLenFeature([], tf.string),\n            'label': tf.FixedLenFeature([], tf.int64),\n        })\n\n    label = tf.cast(features['label'], tf.int32)\n\n    image = tf.decode_raw(features['image_raw'], tf.uint8)\n    image = tf.reshape(image, [w,h,c])\n    image = tf.cast(image, tf.float32) * (1. / 255)\n    # *(1. / 255) ; [0,1] sigmoid # /127.5 - 1 ; [-1,1] tanh??\n\n    augment = True\n    if augment:\n        \n        #image = tf.image.random_brightness(image,0.2)\n        #image = tf.image.random_contrast(image,0.5,1.5)\n        #radian = tf.random_uniform([1],minval=0,maxval=360*np.pi/180)\n        #image = tf.contrib.image.rotate(image,radian)\n        \n        image = tf.image.random_flip_up_down(image)\n        image = tf.image.random_flip_left_right(image)    \n\n    return image, label\n\n\n\nif __name__ == '__main__':\n\n    if all([os.path.exists(x) for x in expected_file_list]):\n        warnings.warn('data exists already!')\n        sys.exit(0)\n\n    if not os.path.exists(TF_DATA_DIR):\n        os.makedirs(TF_DATA_DIR)\n\n    download_sh = os.path.join(THIS_DIR,'download.sh')\n    subprocess.call(['bash',download_sh,THIS_DIR])\n\n    if len(unzipped_root)==0:\n        raise LookupError()\n\n    data_sets = KTex()\n\n    convert_to(data_sets.train, 'train', TF_DATA_DIR)\n    convert_to(data_sets.validation, 'validation', TF_DATA_DIR)\n    convert_to(data_sets.test, 'test', TF_DATA_DIR)\n\n    set_num_examples(data_sets)\n\n    print('done.')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"vae/data/ktex/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"582710527","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport numpy as np\nimport pandas as pd\n\n# paths and parts\nold_data_name = 'test_true_label.csv'\nnew_data_name = 'test_pre_e2e.csv'\nload_old_path = './results/raw/' + old_data_name\nload_new_path = './results/raw/' + new_data_name\nsave_path = './results/new/new_unify_' + new_data_name\nif not os.path.isdir('./results/new/'):\n    os.mkdir('./results/new/')\nlst_location = [25, 50, 75, 100]\n\n\n# personality dictionary\ndict_name = {'Warmth': '乐群性', 'Reasoning': '聪慧性', 'Emotional_Stability': '稳定性', 'Dominance': '支配性',\n             'Liveliness': '活泼性', 'Social_Consciousness': '是非性', 'Social_Boldness': '敢为性',\n             'Sensitivity': '敏感性', 'Vigilance': '多疑性', 'Abstractedness': '务实性', 'Privateness': '世故性',\n             'Apprehension': '安然性', 'Openness_to_Change': '求变性', 'Self_Reliance': '独立性',\n             'Perfectionism': '自制性', 'Tension': '平和性'}\n\n\n# get distribution and locations\ndef calibrate(col_a, col_b, lst_location, min_b=False, max_b=False):\n    # get min_b and max_b\n    if min_b is False:\n        min_b = np.min(col_b)\n    if max_b is False:\n        max_b = np.max(col_b)\n    # count samples of col_a locating in each part\n    lst_count = []\n    for location in lst_location:\n        if len(lst_count) == 0:\n            samples_now = len(col_a[col_a < location])\n        else:\n            samples_now = len(col_a[col_a < location]) - np.sum(lst_count)\n        if samples_now > 0:\n            lst_count.append(samples_now)\n    # get ratio in each part\n    np_count = np.array(lst_count)\n    np_ratio = np_count/len(col_a)\n    diff = max_b - min_b\n    np_location = np.cumsum(np_ratio) * diff + min_b  # here np.min(col_b) is the bias\n    lst_new_location = list(np.ceil(np_location))\n    lst_new_location = list(map(int, lst_new_location))\n    print('cumsum-{}, min-{}, max-{}'.format(np.cumsum(np_ratio), min_b, max_b))\n    print('location-{}, ratio-{}'.format(lst_new_location, np_ratio))\n    print('-' * 15)\n    return lst_new_location, list(np_ratio)\n\n\n# load datafraem\ndf_old = pd.read_csv(load_old_path)\ndf_new = pd.read_csv(load_new_path)\n\n\n# calculate new locations\ndf_location = pd.DataFrame(columns=['name', 'feature', 'location', 'ratio'])\nrow = 0\nfor fea in df_new.columns:\n    print('Now feature:', fea)\n    if fea in ['filename', 'Unnamed: 16', 'Unnamed: 17']:\n        continue\n    else:\n        location_now, ratio_now = calibrate(df_old[fea].values, df_new[fea].values, lst_location, min_b=0, max_b=100)\n        df_location.loc[row] = {'name': dict_name[fea], 'feature': fea, 'location': location_now, 'ratio': ratio_now}\n        row += 1\n\n# save dataframe\ndf_location.to_csv(save_path, index=False, encoding='gbk')\nprint('finished')\n","sub_path":"20180428_small_test/calibrate_data/get_calibration.py","file_name":"get_calibration.py","file_ext":"py","file_size_in_byte":2796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"206184475","text":"f = open(\"p022_names.txt\", \"r\")\ns = f.read().split(\",\")\n\nfor x in range(len(s)):\n    s[x] = s[x].strip('\"')\n\ns.sort()\ntotal = 0\n\ndef name_to_score(var: str) -> int:\n    \"\"\"\n    >>> name_to_score(\"COLIN\")\n    53\n    \"\"\"\n    sum = 0\n    for char in var:\n        sum += (ord(char) - 64)\n    return sum\n\nfor x in range(len(s)):\n    total += ((x + 1) * name_to_score(s[x]))\nprint(total)\n","sub_path":"ProjEuler/ex22.py","file_name":"ex22.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"219674273","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*- \n\nfrom xlsxwriter import Workbook\nfrom xlsxwriter.utility import xl_rowcol_to_cell\nimport datetime\n\ndef create_xlsx(filename='foo.xlsx', model=None):\n    classeur = Workbook(filename)\n\n    header_format = classeur.add_format({\n        'bold':True,\n        'align':'center',\n        'font_color':'#ffffff',\n        'bg_color':'#ee2527',\n        'border':1,\n        'shrink':True\n        })\n    data_format = classeur.add_format({'border':1})\n    date_format = classeur.add_format({'num_format': 'd mmmm yyyy','border':1})\n    \n    feuille = classeur.add_worksheet(\"Grand livre comptable\")\n    names = [\n        [\"Centre\",\"centre\"],\n        [\"Nom du directeur\",\"directeur_nom\"],\n        [\"Lieu du centre\",\"place\"],\n        [\"Nombre d'enfants\",\"nombre_enfants\"],\n        [\"Début du séjour\",\"startdate\"],\n        [\"Fin du séjour\", \"enddate\"]\n        ]\n    infos = model.get_infos()\n    debut, fin = [from_iso_date(infos[k]) for k in ['startdate', 'enddate']]\n    debut, fin = [datetime.datetime.strftime(i, '%d/%m/%Y') for i in [debut, fin]]\n    H1 = infos['centre'] + ' (direction : ' + infos['directeur_nom'] + ')'\n    H2 = 'Du '+debut + ' au '+fin+' à ' + infos['place'] + '. Avec ' + str(infos['nombre_enfants']) + ' enfants.'\n    chapeau_options = {\n        'width':600,\n        'height':50,\n        'font':{'color':'#ffffff'},\n        'fill':{'color':'#ee2527'},\n        }\n    feuille.insert_textbox('A1', H1+'\\n'+H2, chapeau_options)\n\n    feuille.set_column('A:A', 6)\n    feuille.set_column('B:B', 15)\n    feuille.set_column('C:C', 15)\n    feuille.set_column('D:D', 17)\n    feuille.set_column('F:F', 17)\n    feuille.set_column('J:J', 15)\n\n    line_offset = 3\n    \n    ### Chapeau ###\n    names = [\n        'N° pièce',\n        'Date',\n        \"Fournisseur\",\n        \"Objet\",\n        \"N° comptable\",\n        'Libellé comptable',\n        'Code analytique',\n        'Montant',\n        'Cumul',\n        'Moyen de payement']\n    for i, name in enumerate(names):\n        feuille.write(line_offset - 1, i, name, header_format)\n    \n    ### Datas ###\n    subdivisions = model.get_subdivisions_for_export()\n    for i, record in enumerate(subdivisions):\n        feuille.write(i+line_offset, 0, record[0], data_format) # n° piece\n        date = from_iso_date(record[1])\n        feuille.write_datetime(i+line_offset, 1, date, date_format)\n        for j in range(2, 8): \n            feuille.write(i+line_offset, j, record[j], data_format)\n        if i == 0:\n            feuille.write_formula(i + line_offset, 8, '= H4', data_format)\n        else:\n            last_cumul = xl_rowcol_to_cell(i+line_offset - 1, 8)\n            montant = xl_rowcol_to_cell(i+line_offset, 7)\n            feuille.write_formula(\n                i+line_offset,\n                8,\n                '=' + last_cumul + '+' + montant,\n                data_format\n                )\n        feuille.write(i+line_offset, 9, record[8], data_format) #moyen payement\n   \n    [feuille.set_row(i, 30) for i in range(3)] # first row to 30 height\n\ndef from_iso_date(date):\n    try:\n        formated_date = datetime.datetime.strptime(date, '%Y-%m-%d')\n    except ValueError:\n        print(\"value error\", date)\n        return None\n    else:\n        return formated_date\n\n__all__ = ['create_xlsx']\n\nif __name__ == '__main__':\n    import model\n    import sys\n    sqlmodel = model.Model()\n    sqlmodel.connect_db(sys.argv[1])\n    create_xlsx(model=sqlmodel)\n","sub_path":"export_xlsx.py","file_name":"export_xlsx.py","file_ext":"py","file_size_in_byte":3454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"358869152","text":"# 두 사람이 볼링공을 고르는 경우의 수를 출력하기\nimport sys\nimport itertools\n\nN, M = map(int, sys.stdin.readline().split())\n\nball = list(map(int, sys.stdin.readline().split()))\n\ncount = 0\n\n# 두 사람은 서로 무게가 다른 볼링공을 고르려고 함 \nfor i in itertools.combinations(ball, 2):\n    if i[0] != i[1]:\n        count += 1\n\nprint(count)\n\n\n# 다른 코드\nimport sys\n\nN, M = map(int, sys.stdin.readline().split())\n\nball = list(map(int, sys.stdin.readline().split()))\n\n# 1-10까지의 무게를 담을 수 있는 리스트\nList = [0] * 11\n\nfor i in ball:\n    List[i] += 1\n\ncount = 0\n\nfor i in range(1, M+1):\n    N -= List[i]            # 무게가 i인 볼링공의 개수(A가 선택할 수 있는 개수) 제외\n    count += List[i] * N    # B가 선택하는 경우의 수와 곱하기\n\nprint(count)\n","sub_path":"BAEKJOON/그리디/볼링공 고르기.py","file_name":"볼링공 고르기.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"124232620","text":"import logging\nimport os\nimport hashlib\nimport logging\nimport subprocess\nimport sys\nimport pytest\nfrom dotenv import load_dotenv\nfrom pathlib import Path\n\n\n@pytest.fixture()\ndef chdir_root_folder_project():\n    load_dotenv()\n    env_path = Path(\"..\")\n    load_dotenv(dotenv_path=env_path)\n    os.chdir(os.getenv(\"ROOT_FOLDER_PROJECT\"))\n\n\n# inspiracja https://stackoverflow.com/questions/3431825/generating-an-md5-checksum-of-a-file\ndef md5_file(fname):\n    hash_md5 = hashlib.md5()\n    with open(fname, \"rb\") as f:\n        for part in iter(lambda: f.read(4096), b\"\"):\n            hash_md5.update(part)\n    return hash_md5.digest()\n\n\ndef md5_string(string):\n    hash_object = hashlib.md5(string)\n    return hash_object.digest()\n\n\n@pytest.mark.usefixtures(\"chdir_root_folder_project\")\ndef test_all_libs_is_in_requirement():\n    logging.root.setLevel(logging.DEBUG)\n    hash_md5 = hashlib.md5()\n    exist_requirements_md5 = None\n    requirement_file = \"requirements.txt\"\n    if os.path.exists(requirement_file):\n        exist_requirements_md5 = md5_file(requirement_file)\n    else:\n        print(\"not exist file requirements.txt\")\n\n    reqs = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'])\n    pip_free_output = md5_string(reqs)\n    try:\n        assert pip_free_output == exist_requirements_md5\n    except AssertionError as error:\n        print(\"Niezgodność między requirements.txt a pip freeze - lista bibliotek została zmieniona\")\n        assert False","sub_path":"tests/test_requirement_libs.py","file_name":"test_requirement_libs.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"464862896","text":"import json\nfrom pathlib import Path\nfrom typing import Any, Final\n\nAPP_PATH = Path(__file__).resolve().parent\n\nDEFAULT_SETT = {\n    \"first_move\": \"COMPUTER\",\n    \"figure_player\": \"cross\",\n    \"width_window\": 600,\n}\n\nSC_WIDTH: int = 0\nSC_HEIGHT: int = 0\n\nCAPTION = \"TicTacToe\"\nGO_FIRST: str = \"\"  # COMPUTER or PLAYER\n\nFIGURE_PLAYER: tuple[str, int] = (\"cross\", 2)\nFIGURE_COMPUTER: tuple[str, int] = (\"circle\", 1)\n\nLENTH_BOARD: Final[int] = 3\n\n\ndef init_vars(source: dict) -> None:\n    \"\"\"Init vars for game.\"\"\"\n    global SC_WIDTH, SC_HEIGHT, GO_FIRST, FIGURE_PLAYER, FIGURE_COMPUTER\n\n    SC_WIDTH = source[\"width_window\"]\n    SC_HEIGHT = source[\"width_window\"]\n    GO_FIRST = source[\"first_move\"].upper()\n\n    fig = source[\"figure_player\"]\n    FIGURE_PLAYER = (fig, 2 if fig == \"cross\" else 1)\n    FIGURE_COMPUTER = (\"cross\" if fig == \"circle\" else \"circle\", FIGURE_PLAYER[1] % 2 + 1)\n\n\nwith open(f\"{APP_PATH}/settings.txt\") as f, open(f\"{APP_PATH}/main_settings.json\", \"w\") as data_file:\n    things = (\"first_move\", \"figure_player\", \"width_window\")\n    values: list[Any] = []\n    for line in f:\n        if \":\" in line:\n            data_line = line[line.index(\":\") + 1 :].strip().lower()\n            values.append(int(data_line) if data_line.isdigit() else data_line)\n\n    try:\n        data = dict(zip(things, values, strict=False))\n        assert len(data) == LENTH_BOARD\n    except AssertionError:\n        data = DEFAULT_SETT\n\n    init_vars(data)\n    json.dump(data, data_file)\n\nFRAME_RATE = 120\n\nBOARD_COLS = 3\nBOARD_ROWS = 3\n\nCIRCLE_RADIUS = int(SC_WIDTH * 0.1)  # 600px -- 10% = 60px\nCROSS_WIDTH = int(SC_WIDTH * 0.0417)  # 600px -- 4.17% = 25px default\nCIRCLE_WIDTH = int(SC_WIDTH * 0.025)  # 600px -- 2.5% = 15px default\nLINE_WIDTH = CROSS_WIDTH\nWIN_LINE_WIDTH = CIRCLE_WIDTH\n\nSPACE = int(SC_WIDTH * 0.0917)  # 600px -- 9.17% = 55px default\nSQUARE_SIZE = SC_WIDTH // 3  # only 3 cells on the field lol\n","sub_path":"src/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"34413994","text":"\"\"\"\r\n================================\r\nExample ams pyart course 5\r\n================================\r\n\r\nThis is the 5th example in the AMS Radar conference 2015 pyart course.\r\nWriting out files\r\n\r\n\"\"\"\r\nprint(__doc__)\r\n\r\n# Author: fvj\r\n# 2016.05.10\r\n\r\nimport pyart\r\n\r\nradarpath='/data/pyart_examples/'\r\nradarfile='XSW110520113537.RAW7HHL'\r\n\r\nradar = pyart.io.read(radarpath+radarfile)\r\npyart.io.write_cfradial(radarpath+'example_converted_sigmet_file.nc', radar)\r\n","sub_path":"src/examples_pyart/example_ams_05_data_writing.py","file_name":"example_ams_05_data_writing.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"622255590","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: msi\r\n\"\"\"\r\n\r\nimport QuantLib as ql\r\nimport commonMarketData\r\nimport commonInstruments\r\nimport numpy\r\n\r\ntodaysDate = ql.Date(14, ql.October, 2011);\r\nsettlement = ql.Japan().advance(todaysDate, ql.Period(2, ql.Days))\r\nql.Settings.instance().evaluationDate = todaysDate\r\nts = commonMarketData.getZeroCurve(ql.JPYCurrency(), ql.Period(6, ql.Months), todaysDate)\r\njpyLibor6m = ql.JPYLibor(ql.Period(6, ql.Months), ts)\r\nsettlementDays = ql.Period(2, ql.Days)\r\n\r\nexerciseDate = ql.Japan().advance(settlement, ql.Period(5, ql.Years))\r\nstartDate = ql.Japan().advance(exerciseDate, settlementDays)\r\nswap = commonInstruments.makeVanillaSwap(settlement,\r\n                                         ql.Japan().advance(settlement, ql.Period(10, ql.Years)), \r\n                                         jpyLibor6m, \r\n                                         ql.Period(6, ql.Months),\r\n                                         3.0/100)\r\nswopt = commonInstruments.makeEuropeanSwaption(swap, 29.7/100, ts, exerciseDate)\r\nnpv = swopt.NPV()\r\nimpvol = swopt.impliedVolatility(npv, ts, 0.5)\r\nnumpy.testing.assert_almost_equal(npv, 4928, decimal=0)\r\nnumpy.testing.assert_almost_equal(impvol, 29.7/100, decimal=5)\r\n\r\nswap = commonInstruments.makeVanillaSwap(settlement,\r\n                                         ql.Japan().advance(settlement, ql.Period(10, ql.Years)), \r\n                                         jpyLibor6m, \r\n                                         ql.Period(6, ql.Months),\r\n                                         0.1/100)\r\nswopt = commonInstruments.makeEuropeanSwaption(swap, 29.7/100, ts, exerciseDate)\r\nnpv = swopt.NPV()\r\nimpvol = swopt.impliedVolatility(npv, ts, 0.5)\r\nnumpy.testing.assert_almost_equal(npv, 108152, decimal=0)\r\nnumpy.testing.assert_almost_equal(impvol, 29.7/100, decimal=5)\r\n\r\nvols = commonMarketData.getYieldVolatility(ql.JPYCurrency(), ql.Period(6, ql.Months), todaysDate, \"false\")\r\nvol = ql.QuoteHandle(ql.SimpleQuote(vols[ql.Period(1,ql.Years).length()][ql.Period(5,ql.Years).length()]/100.0))\r\nnumpy.testing.assert_almost_equal(vol.value(), 0.391, decimal=3)\r\n\r\nexerciseDate = ql.Japan().advance(settlement, ql.Period(1, ql.Years))\r\nstartDate = ql.Japan().advance(exerciseDate, settlementDays)\r\nswap = commonInstruments.makeVanillaSwap(settlement,\r\n                                         ql.Japan().advance(settlement, ql.Period(5, ql.Years)), \r\n                                         jpyLibor6m, \r\n                                         ql.Period(6, ql.Months),\r\n                                         3.0/100)\r\nswopt = commonInstruments.makeEuropeanSwaption(swap, vol.value(), ts, exerciseDate)\r\nnpv = swopt.NPV()\r\nimpvol = swopt.impliedVolatility(npv, ts, 0.5)\r\nnumpy.testing.assert_almost_equal(impvol, vol.value(), decimal=5)\r\n\r\n\r\nswopt = ql.SwaptionHelper(ql.Period(1,ql.Years),\r\n\t\t\t\t\t\t  ql.Period(5,ql.Years),\r\n\t\t\t\t\t\t  vol,\r\n\t\t\t\t\t\t  jpyLibor6m,\r\n\t\t\t\t\t\t  jpyLibor6m.tenor(),\r\n\t\t\t\t\t\t  jpyLibor6m.dayCounter(),\r\n\t\t\t\t\t\t  jpyLibor6m.dayCounter(),\r\n\t\t\t\t\t\t  ts)\r\nengine = ql.BlackSwaptionEngine(ts, vol)\r\nswopt.setPricingEngine(engine)\r\nmarketVal = swopt.marketValue()\r\nmodelVal = swopt.modelValue()\r\nprint(marketVal)\r\nprint(modelVal)","sub_path":"swopt_pricing.py","file_name":"swopt_pricing.py","file_ext":"py","file_size_in_byte":3197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"522246396","text":"#-*- encoding: utf8 -*-\r\n\r\n\"\"\"\r\n   Luex Source to Source Compiler\r\n   Copyright (C) 2016  Molnár Márk\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\nimport re\r\nimport keywords as Keywords\r\n\r\ndef handle(result, filestring):\r\n   for var in result:\r\n      funcTop = re.match(Keywords.FUNCTION_TOP, var).group(1)\r\n\r\n      if \"when\" in funcTop:\r\n         when = re.match(Keywords.WHEN, funcTop).group(1).strip(\"(\").strip(\"):\")\r\n\r\n         if not \"==\" in when:\r\n            newTop = funcTop.replace(\"fn \", \"function \").replace(\"):\", \")\").replace(\" when(\" + when + \")\", \"\\nif not \" + when + \" then return end\")\r\n         else:\r\n           newTop = funcTop.replace(\"fn \", \"function \").replace(\"):\", \")\").replace(\" when(\" + when + \")\", \"\\nif \" + when.replace(\"==\", \"~=\") + \" then return end\")\r\n\r\n      else:\r\n         newTop = funcTop.replace(\"fn \", \"function \").replace(\"):\", \")\")\r\n\r\n      replacement = var.replace(funcTop, newTop).replace(\":fn\", \"end\")\r\n      filestring = filestring.replace(var, replacement)\r\n\r\n   return filestring\r\n\r\n\r\n\r\n","sub_path":"source/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"285885327","text":"# plot sslb and bssl and SSL + random\nimport pickle\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom pathlib import Path\nfrom typing import Optional\nfrom matplotlib import cm\nimport numpy as np\nfrom alr.utils import savefig\n\nimport os\n\nos.chdir(\"/Users/harry/Documents/workspace/thesis/reports/03_MNIST_report\")\n\nroot = Path(\"/Users/harry/Documents/workspace/thesis/experiments/ephemeral/mnist\")\nwith open(root / \"SSL_BALD\" / \"thresh_0.9_b_10_accs.pkl\", \"rb\") as fp:\n    spl_m = pickle.load(fp)\nwith open(root / \"BALD_SSL\" / \"thresh_0.9_b_10_accs.pkl\", \"rb\") as fp:\n    al_ssl_m = pickle.load(fp)\nbaseline = Path(\n    \"/Users/harry/Documents/workspace/thesis/experiments/al_baselines/mnist/bald1/bald_1_accs.pkl\"\n)\nwith open(baseline, \"rb\") as fp:\n    bald = pickle.load(fp)\nwith open(root / \"SSL_RA\" / \"thresh_0.9_b_10_accs.pkl\", \"rb\") as fp:\n    spl_m_ra = pickle.load(fp)\nwith open(\n    \"/Users/harry/Documents/workspace/thesis/experiments/al_baselines/mnist/random10/random_10_accs.pkl\",\n    \"rb\",\n) as fp:\n    rand = pickle.load(fp)\n\n\ndef plot(dic, label, dot=False):\n    x = list(dic.keys())\n    y = np.array(list(dic.values()))\n    median = np.median(y, axis=-1)\n    top = np.percentile(y, 75, axis=-1)\n    btm = np.percentile(y, 25, axis=-1)\n    if dot:\n        (l,) = plt.plot(x, median, label=label, linestyle=\"dashdot\")\n    else:\n        (l,) = plt.plot(x, median, label=label)\n    plt.fill_between(x, btm, top, color=l.get_color(), alpha=0.2)\n    plt.xticks([20] + list(range(50, 300, 50)))\n    plt.xlim(left=20, right=250)\n\n\ndef plot2(dic, label):\n    x = list(dic.keys())\n    y = list(dic.values())\n    median = list(map(np.median, y))\n    top = list(map(lambda x: np.percentile(x, 75), y))\n    btm = list(map(lambda x: np.percentile(x, 25), y))\n    (l,) = plt.plot(x, median, label=label)\n    plt.fill_between(x, btm, top, color=l.get_color(), alpha=0.2)\n    plt.xticks([20] + list(range(50, 300, 50)))\n    plt.xlim(left=20, right=250)\n\n\ndef plot3(arr, axis, label, colour=None, alpha=0.2):\n    x = range(arr.shape[1])\n    y = arr\n    median = np.median(y, axis=0)\n    topq = np.quantile(y, 0.75, axis=0)\n    btmq = np.quantile(y, 0.25, axis=0)\n\n    if colour:\n        (line,) = axis.plot(x, median, color=colour, label=label)\n    else:\n        (line,) = axis.plot(x, median, label=label)\n    axis.fill_between(\n        x,\n        btmq,\n        topq,\n        color=line.get_color(),\n        alpha=alpha,\n    )\n    # axis.set_xticks([20] + list(range(50, 300, 50)))\n    # axis.set_xlim(left=20, right=250)\n\n\n# plot(spl_m, \"Pre-acquisition (BALD-10)\", dot=True)\n# plot(al_ssl_m, \"Pre-evaluation (BALD-10)\", dot=True)\n# plot(spl_m_ra, \"SSL (Random-10)\", dot=True)\nplot(bald, \"BALD-1\")\nplot(rand, \"Random-10\")\n# plt.hlines(0.99, xmin=20, xmax=250, colors='k')\n# plt.text(97, .995, \"Accuracy using full dataset\", fontsize=8, color='k', fontweight='bold')\n# plt.text(3, .980, \"0.985\", fontsize=8, color='grey')\nplt.title(\"MNIST test accuracy\")\nplt.xlabel(\"Acquired dataset size\")\nplt.ylabel(\"Accuracy\")\nplt.xlim(left=20, right=250)\n\n# line = mpl.lines.Line2D([0], [0], color='k')\n# handles, labels = plt.gca().get_legend_handles_labels()\n# handles.append(line); labels.append('Accuracy on full dataset')\n# plt.legend(handles=handles, labels=labels)\nplt.legend()\nplt.grid()\n\nsavefig(\"/Users/harry/Documents/workspace/thesis/figures/4/mnist_pure_al.pdf\")\n","sub_path":"docs/source/experiments/figures/chapter3/mnist_pure_al.py","file_name":"mnist_pure_al.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"256752673","text":"# Copyright (C) 2018-present prototyped.cn. All rights reserved.\n# Distributed under the terms and conditions of the Apache License.\n# See accompanying files LICENSE.\n\nimport unittest\n\n# type enum\nType_Unknown = 0\nType_Nil = 1\nType_Bool = 2\nType_Int8 = 3\nType_Uint8 = 4\nType_Int16 = 5\nType_Uint16 = 6\nType_Int = 7\nType_Uint = 8\nType_Int32 = 9\nType_Uint32 = 10\nType_Int64 = 11\nType_Uint64 = 12\nType_Float = 13\nType_Float32 = 14\nType_Float64 = 15\nType_String = 16\nType_Enum = 17\nType_Bytes = 18\nType_DateTime = 19\nType_Array = 20\nType_Map = 21\nType_Any = 22\n\n# text name of a type\ntype_names = {\n    Type_Nil:      \"nil\",\n    Type_Bool:     \"bool\",\n    Type_Int8:     \"int8\",\n    Type_Uint8:    \"uint8\",\n    Type_Int16:    \"int16\",\n    Type_Uint16:   \"uint16\",\n    Type_Int:      \"int\",\n    Type_Uint:     \"uint32\",\n    Type_Int32:    \"int32\",\n    Type_Uint32:   \"uint32\",\n    Type_Int64:    \"int64\",\n    Type_Uint64:   \"uint64\",\n    Type_Float:    \"float\",\n    Type_Float32:  \"float32\",\n    Type_Float64:  \"float64\",\n    Type_String:   \"string\",\n    Type_Enum:     \"enum\",\n    Type_Bytes:    \"bytes\",\n    Type_DateTime: \"datetime\",\n    Type_Array:    \"array\",\n    Type_Map:      \"map\",\n    Type_Any:      \"any\",\n}\n\n# non-primitive type names\nabstract_type_names = {\n    \"array\": Type_Array,\n    \"map\":   Type_Map,\n    \"any\":   Type_Any,\n}\n\ninterger_types = ['int8', 'uint8', 'int16', 'uint16', 'int', 'uint','int32', 'uint32', 'int64', 'uint64', 'enum']\nfloating_types = ['float', 'float32', 'float64']\n\n\n# get name of an integer type\ndef get_name_of_type(t):\n    return type_names[t]\n\n\n# 基础类型\ndef is_primitive_type(name):\n    found = False\n    for k, v in type_names.items():\n        if v == name:\n            found = True\n            break\n\n    if not found:\n        return False\n\n    for s in abstract_type_names:\n        if s == name:\n            return False\n    return True\n\n\n# get integer type by name\ndef get_type_by_name(name):\n    for k, v in abstract_type_names.items():\n        if name.find(k) >= 0:\n            return v\n    for k, v in type_names.items():\n        if v == name:\n            return k\n    return Type_Unknown\n\n\n# is integer type\ndef is_integer_type(typ):\n    return typ in interger_types\n\n\n# is floating point\ndef is_floating_type(typ):\n    return typ in floating_types\n\n\n# 是否抽象类型, map, array\ndef is_abstract_type(typename):\n    if typename.startswith('map<'):\n        return 'map'\n    elif typename.startswith('array<'):\n        return 'array'\n    return None\n\n\n# array<int> --> int\ndef array_element_type(typename):\n    assert typename.startswith('array<'), typename\n    return typename[6:-1]\n\n\n# map<int, string> --> int, string\ndef map_key_value_types(typename):\n    keytype = ''\n    valtype = ''\n    assert typename.startswith('map<'), typename\n    typename = typename[4:]\n    for v in type_names.values():\n        if typename.startswith(v):\n            keytype = v\n            break\n    typename = typename[len(keytype) + 1:]\n    for v in type_names.values():\n        if typename.startswith(v):\n            valtype = v\n            break\n\n    assert len(keytype) > 0 and len(valtype) > 0\n    return keytype, valtype\n\n\nclass TestTypeNames(unittest.TestCase):\n\n    def test_is_primitive_type(self):\n        test_data = [\n            ('int', True),\n            ('map', False),\n            ('', False),\n        ]\n        for pair in test_data:\n            out = is_primitive_type(pair[0])\n            print(out)\n            self.assertEqual(pair[1], out)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"taksi/descriptor/types.py","file_name":"types.py","file_ext":"py","file_size_in_byte":3551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"219684238","text":"import sqlite3\nimport json\nimport time\n\ndbpath = '/Users/luxuanqing/FE/javlib/server/jav.db'\n\n\ndef exist(id):\n    with sqlite3.connect(dbpath) as conn:\n        c = conn.cursor()\n        c.execute('SELECT * FROM movies WHERE id=?', (id, ))\n        if c.fetchone():\n            return True\n        else:\n            return False\n\n\ndef insert_movie(id):\n    with sqlite3.connect(dbpath) as conn:\n        c = conn.cursor()\n        try:\n            c.execute('INSERT INTO movies VALUES (?, ?, ?, ?, ?, ?, ?)',\n                      (id, None, None, None, None, None, None))\n            print('{} inserted'.format(id))\n        except sqlite3.IntegrityError as err:\n            print(err)\n            print('{} already exists'.format(id))\n\n\ndef getone(id, field):\n    '''根据id取得相应field的值\n        Arguments：\n        field - str\n        id - str\n        Return：\n        list: 正常\n        []: 记录为\"[]\"(爬取的数据为空)\n        None: 该字段为null\n        -2: 表中没有这个id\n        '''\n    with sqlite3.connect(dbpath) as conn:\n        c = conn.cursor()\n        c.execute('SELECT {} FROM movies WHERE id=?'.format(field), (id, ))\n        try:\n            s = c.fetchone()[0]\n        except TypeError:\n            print(\"{} doesn't exist in movies table\".format(id))\n            return -2\n        if s:\n            return json.loads(s)\n        else:\n            return None\n\n\ndef setone(id, field, value):\n    with sqlite3.connect(dbpath) as conn:\n        c = conn.cursor()\n        s = json.dumps(value, ensure_ascii=False)\n        c.execute('UPDATE movies SET {}=? WHERE id=?'.format(field), (s, id))\n        print('the {} of {} has been updated'.format(field, id))\n\n\ndef insert_log(id, domain):\n    with sqlite3.connect(dbpath) as conn:\n        c = conn.cursor()\n        now = time.time()\n        c.execute('INSERT INTO access_log VALUES (?, ?, ?)', (now, id, domain))\n        print('{}({}) is visited at {}'.format(id, domain, now))\n\n\ndef get_log(id):\n    with sqlite3.connect(dbpath) as conn:\n        c = conn.cursor()\n        c.execute('SELECT time, domain FROM access_log WHERE id=? ORDER BY time DESC',\n                  (id, ))\n        return c.fetchone()\n        # try:\n        #     timestamp, domain = c.fetchone()\n        #     return timestamp, domain\n        # except TypeError as err:\n        #     print(err)\n        #     print(\"{} doesn't exist in movies table\".format(id))\n        #     return -2\n\n\nif __name__ == '__main__':\n    # ids = ['TEST-001', 'TEST-002', 'TEST-003', 'TEST-404']\n    # for id in ids:\n    print(get_log('ATID-298'))","sub_path":"server/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"242302361","text":"from PIL import Image, ImageDraw, ImageFont\nfrom math import floor\n\nimport rectangles\nfrom textfilehandler import TextFileHandler\nimport utilities\n\n\"\"\"\n    Class for handling the creation of pictures from board data\n    Visualizer receives processed data from a TextFileHandler, and uses this with pillow\n    to generate visualizations of algorithms\n    Visualizer utilizes AbstractRectangle and its subclasses to make drawing different\n    rectangles relatively hassle-free.\n\"\"\"\n\nclass Visualizer():\n    def __init__(self):\n        self._text_file_handler = None\n        self._num_squares = None\n        self._steps = None\n\n        self.pillow_image = None\n        self.pillow_draw = None\n\n        # Rectangle subclasses have their own matching type ('m', 'w' etc), and their own AbstractRectangle.draw() functions.\n        # This is used with this list to automatically call correct draw functions in Visualizer.draw()\n        self.rectangles_list = (rectangles.white.White(), rectangles.grey.Grey(), rectangles.lettera.LetterA(),\n                                rectangles.letterb.LetterB(), rectangles.path.Path(), rectangles.water.Water(),\n                                rectangles.mountain.Mountain(), rectangles.forest.Forest(), rectangles.grassland.Grassland(),\n                                rectangles.roads.Roads())\n\n    @property\n    def text_file_handler(self):\n        if self._text_file_handler is None:\n            raise ValueError(\"Text file handler has not been set\")\n        return self._text_file_handler\n\n    @property\n    def num_squares(self):\n        if self._num_squares is None:\n            raise ValueError(\"Num squares has not been set\")\n        return self._num_squares\n\n    @property\n    def steps(self):\n        if self._steps is None:\n            raise ValueError(\"Steps not yet set\")\n        return self._steps\n\n    @num_squares.setter\n    def num_squares(self, num_squares):\n        if not isinstance(num_squares, tuple):\n            raise ValueError(\"Attempting to set num_squares to type that is not tuple\")\n        if not isinstance(num_squares[0], int) or not isinstance(num_squares[1], int):\n            raise ValueError(\"Attempting to set num_squares sub values to types that is not int\")\n        if num_squares[0] < 1 or num_squares[1] < 1:\n            raise ValueError(\"Attempting to set num_squares values to non accepted value (less than 1)\")\n        if num_squares is None:\n            raise ValueError(\"Attempting to set num_squares to None\")\n        self._num_squares = num_squares\n\n    @steps.setter\n    def steps(self, steps):\n        if not isinstance(steps, tuple):\n            raise ValueError(\"Attempting to set steps to type that is not tuple\")\n        if not isinstance(steps[0], int) or not isinstance(steps[1], int):\n            raise ValueError(\"Attempting to set steps sub values to types that is not int\")\n        if steps[0] < 1 or steps[1] < 1:\n            raise ValueError(\"Attempting to set steps values to non accepted value (less than 1)\")\n        if steps is None:\n            raise ValueError(\"Attempting to set steps to None\")\n        self._steps = steps\n\n    # Receive TextFileHandler instance\n    def receive_processed_text_object(self, text_file_handler_obj):\n        self._text_file_handler = text_file_handler_obj\n\n    def set_up_board_data(self):\n        self.num_squares = self.text_file_handler.dim\n        # Pixels pr square is calculated here\n        self.steps = (floor(utilities.resolution[0]/self.num_squares[0]), floor(utilities.resolution[1]/self.num_squares[1]))\n\n    def set_up_pillow(self):\n        self.pillow_image = Image.new('RGB', utilities.resolution, color = 'black')\n        self.pillow_draw = ImageDraw.Draw(self.pillow_image)\n\n    def draw(self):\n        if self.text_file_handler is None or self.steps is None or self.num_squares is None:\n            raise ValueError(\"Text file data is not loaded. Initialize text file data first\")\n        if self.pillow_image is None or self.pillow_draw is None:\n            raise ValueError(\"Pillow data not set up. Set up pillow data first\")\n\n        board_data = self.text_file_handler.board_data\n        current_steps = [0, 0]\n        for y in range(0, len(board_data)):\n            for x in board_data[y]:\n                for rectangle in self.rectangles_list:\n                    if rectangle.symbol == x:\n                        rectangle.draw(self.pillow_draw, tuple(current_steps), self.steps)\n                        break\n                current_steps[0] += self.steps[0]\n            current_steps[0] = 0\n            current_steps[1] += self.steps[1]\n\n    # \"Appends\" the path on already drawn board\n    def draw_path(self):\n        if self.text_file_handler is None or self.steps is None or self.num_squares is None:\n            raise ValueError(\"Text file data is not loaded. Initialize text file data first\")\n        if self.pillow_image is None or self.pillow_draw is None:\n            raise ValueError(\"Pillow data not set up. Set up pillow data first\")\n\n        board_data = self.text_file_handler.board_data\n        current_steps = [0, 0]\n        for y in range(0, len(board_data)):\n            for x in board_data[y]:\n                if x == '+':\n                    rectangles.pathdot.PathDot().draw(self.pillow_draw, tuple(current_steps), self.steps)\n                current_steps[0] += self.steps[0]\n            current_steps[0] = 0\n            current_steps[1] += self.steps[1]\n\n    # \"Appends indicators for open, closed nodes.\n    # Does not override drawn path\n    def draw_visited_nodes(self):\n        if self.text_file_handler is None or self.steps is None or self.num_squares is None:\n            raise ValueError(\"Text file data is not loaded. Initialize text file data first\")\n        if self.pillow_image is None or self.pillow_draw is None:\n            raise ValueError(\"Pillow data not set up. Set up pillow data first\")\n\n        board_data = self.text_file_handler.board_data\n        current_steps = [0, 0]\n        for y in range(0, len(board_data)):\n            for x in board_data[y]:\n                if x == '*':\n                    rectangles.open.Open().draw(self.pillow_draw, tuple(current_steps), self.steps)\n                elif x == 'x':\n                    rectangles.closed.Closed().draw(self.pillow_draw, tuple(current_steps), self.steps)\n                current_steps[0] += self.steps[0]\n            current_steps[0] = 0\n            current_steps[1] += self.steps[1]\n\n    def save_picture(self):\n        self.pillow_image.save(utilities.root_folder_path + \"/output/rendered_map.jpg\", \"JPEG\")\n\n    def save_picture_with_name(self, filename):\n        self.pillow_image.save(utilities.root_folder_path + \"/output/\" + filename + \".jpg\", \"JPEG\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"python_code/source/visualizer.py","file_name":"visualizer.py","file_ext":"py","file_size_in_byte":6738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"554483491","text":"# Setting application write here ...\n\n# Setting editable in admin\n\nCONSTANCE_CONFIG = {\n    \"SITE_NAME\": (\"My Title\", \"Website title\"),\n    \"SITE_DESCRIPTION\": (\"\", \"Website description\"),\n    \"THEME\": (\"light-blue\", \"Website theme\"),\n}\n\nCONSTANCE_CONFIG_FIELDSETS = {\n    \"General Options\": (\"SITE_NAME\", \"SITE_DESCRIPTION\"),\n    \"Theme Options\": (\"THEME\",),\n}\n","sub_path":"backend/config/settings/custom.py","file_name":"custom.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"357585558","text":"def factorial(n):\n    \"\"\" Compute factorial \"\"\"\n    fact = 1\n    while n > 0:\n        fact *= n\n        n -= 1\n    return fact\n\ndef choose(n, k):\n    \"\"\" Compute value of n choose k\"\"\"\n    return factorial(n) // (factorial(k) * factorial(n-k))\n\n# Set values\nm = 733\nn = 1810\nmod = 1000000\n\n# Compute solution\nsm = 0\nfor k in range(m, n+1):\n    sm += choose(n, k)\n\nprint(sm % mod)\n","sub_path":"bioinformatics_stronghold/aspc/aspc.py","file_name":"aspc.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"10802996","text":"import cv2, sys, numpy as np\nimport csv\nimport math\nimport statistics\n\nfrom os import listdir\nfrom os.path import isfile, join\n\n# read command-line arguments\n#filename = sys.argv[1]\n#t = int(sys.argv[2])\n\n# read original image\nmypath = 'D:/StudyMaterial/6th sem/Tarp/dataset/mobile phones - Google Search/'\n\nfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n\nfor f in files:\n\timg = cv2.imread(mypath + f) \n\n\t# create binary image\n\tgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\tblur = cv2.GaussianBlur(gray, (5, 5), 0)\n\t#(t, binary) = cv2.threshold(blur, t, 255, cv2.THRESH_BINARY)\n\t(t, binary) = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n\n\t# find contours\n\t(_, contours, _) = cv2.findContours(binary, cv2.RETR_EXTERNAL, \n\t    cv2.CHAIN_APPROX_SIMPLE)\n\n\t# draw contours over original image\n\tcv2.drawContours(binary, contours, -1, (255, 255, 255), -1) #(0,0,255) are the colors and last argument defines the thickness of contour\n\n\n\t#Elimination small contours\n\tAreacontours = list()\n\tfor i in Areacontours:\n\t\tarea = cv2.contourArea(contours[i])\n\t\tif (area > 90 ):\n\t\t\tAreacontours.append(contours[i])\n\t\tcontours = Areacontours\n\n\tprint('found objects')\n\tprint(len(contours))\n\n\t'''print(\"humoments\")\n\tmom = cv2.moments(contours[0])\n\tHumoments = cv2.HuMoments(mom)\n\tHumoments2 = -np.sign(Humoments)*np.log10(np.abs(Humoments))\n\tprint(Humoments2)'''\n\n\n\thum=cv2.HuMoments(cv2.moments(binary)).flatten()\n\toutput = ' '.join(str(i) for i in hum)\n\n\tcompare = output.split()\n\n\twith open('D:/StudyMaterial/6th sem/Tarp/output/output1.csv', 'a', newline='') as csvfile:\n\t\tappend = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n\t\tappend.writerow(compare + ['Mobile phones -> non-biodegradable'])\n\n\n","sub_path":"classify.py","file_name":"classify.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"345279009","text":"import sys\nimport numpy as np\n\ndef closest_points(Px, Py, n):\n    print('CLOSEST')\n    dist = 1e10\n    for i in range(n):\n        print(i)\n        for j in range(i + 1, n):\n            # print('i: {}, j: {}'.format(i, j))\n            dist = min(dist, np.sqrt( (Px[i] - Px[j])**2 + (Py[i] - Py[j])**2 ))\n            # print(dist)\n    return dist\n\nif __name__ == \"__main__\":\n    lines = sys.stdin\n    data = lines.read().split('\\n')\n    n = int(data[0])\n    coords = [(int(d.split()[0]), int(d.split()[1])) for d in data[1:len(data)-1]]\n    # print(n)\n    # print(coords)\n\n    mid_point = float(sum(x[0] for x in coords) / len(coords))\n    # print(mid_point)\n\n    L_idx = []\n    R_idx = []\n\n    for idx, point in enumerate(coords):\n        x = float(point[0])\n        if x < mid_point:\n            L_idx.append(idx)\n        else:\n            R_idx.append(idx)\n    # print(L_idx)\n    # print(R_idx)\n    Rp = [coords[i] for i in R_idx]\n    Lp = [coords[i] for i in L_idx]\n    Rx = [p[0] for p in Rp]\n    Ry = [p[1] for p in Rp]\n    Lx = [p[0] for p in Lp]\n    Ly = [p[1] for p in Lp]\n    # print(Lp)\n    # print(Rp)\n    # print('---')\n    # print('Right')\n    d_r = closest_points(Rx, Ry, len(Rp))\n    # print('Left')\n    d_l = closest_points(Lx, Ly, len(Lp))\n    sigma = min(d_l, d_r)\n    # print(sigma)\n    # print(mid_point)\n    \n    Sp = [p for p in coords if (p[0] <= (mid_point + sigma) and p[0] >= (mid_point - sigma))]\n    d_s = 1e10 \n    if len(Sp) > 1:\n        Sx = [p[0] for p in Sp]\n        Sy = [p[1] for p in Sp]\n        d_s = closest_points(Sx, Sy, len(Sp))\n    \n    closest = np.round(min(sigma, d_s), decimals=6)\n    print('%.6f' % closest)","sub_path":"4closestpair/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"75727139","text":"import Tkinter, Tkconstants, tkFileDialog\nfrom file import open as op\nfrom data import testing as tst\n\nclass gui(Tkinter.Frame):\n    def __init__(self, root):\n        Tkinter.Frame.__init__(self, root)\n        # options for buttons\n        button_opt = {'fill': Tkconstants.BOTH, 'padx': 5, 'pady': 5}\n        # options for text\n        text_opt = {'height':21, 'width':100}\n\n        # define buttons\n        Tkinter.Button(self, text='Open file', command=self.askopenfilename).pack(**button_opt)\n        # define text\n        self.results_text = Tkinter.Text(self, text_opt)\n        self.results_text.pack()\n\n        # define options for opening or saving a file\n        self.file_opt = options = {}\n        options['defaultextension'] = '.wav'\n        options['filetypes'] = [('WAVE files', '.wav')]\n        options['initialdir'] = 'Computer:\\\\'\n        options['initialfile'] = ''\n        options['parent'] = root\n        options['title'] = 'This is a title'\n\n    def askopenfilename(self):\n\n        \"\"\"Returns an opened file in read mode.\n        This time the dialog just returns a filename and the file is opened by your own code.\n        \"\"\"\n\n        # get filename\n        path = tkFileDialog.askopenfilename(**self.file_opt)\n\n        # open file on your own\n        if path:\n            self.results_text.delete('1.0', Tkinter.END)\n            results = ''\n            wav = op.open_wav(path, 0)\n            results = tst.test(wav, 1)\n            output = tst.prepare_output(path, results)\n            self.results_text.insert('1.0', output)\n\nif __name__=='__main__':\n    root = Tkinter.Tk()\n    root.wm_title('Soft Sound')\n    gui(root).pack()\n    root.mainloop()","sub_path":"src-legacy/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"346041748","text":"var1 = \"Hello World \"\nvar2 = 4\nvar3 = 4.5\nvar4 = \"Hii World\"\nvar5 = \"10 \"\nvar6 = \"20\"\n#print(var1)\n#print(type(var2))\n#print(var2 + var3)\n#print(var1 + var2) #this will give error - string cannot be added with numbers\n#print(var1 + var4)\n#print(var5 + var6)\n#print(int (var5) + int (var6)) #this is type casting\n#print(10*\"Hello World \\n\")\n\"\"\"\nprint(\"Enter a no\")\nx = input()\nprint(\"You Entered \",int (x)+10)\n\"\"\"\n#Simple Calculator\n\nprint(\"Enter First no\")\nx = input()\nprint(\"Enter Second no\")\ny = input()\nz = int (x) + int (y)\nprint(\"sum of this no is :- \", z)\n","sub_path":"SecondCode.py","file_name":"SecondCode.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"525828135","text":"import autograd.numpy as np\nimport warnings\n\nfrom . import fft_ops as rffb\n\ndef mkcovdiag_ASD(len_sc,rho,nxcirc,wvec= None,wwnrm= None):\n#  Eigenvalues of ASD covariance (as diagonalized in Fourier domain)\n# \n#  [cdiag,dcdiag,ddcdiag] = mkcovdiag_ASD(rho,l,nxcirc,wvecsq)\n# \n#  Compute discrete ASD (RBF kernel) eigenspectrum using frequencies in [0, nxcirc].\n#  See mkCov_ASD_factored for more info\n# \n#  INPUT (all python 1d lists!):\n#          len - length scale of ASD kernel (determines smoothness)\n#          rho - maximal prior variance (\"overall scale\")\n#       nxcirc - number of coefficients to consider for circular boundary \n#         wvec - vector of freq for DFT \n#\t\twwnrm - vector of freq for DFT (normalized)\n#         \n#  OUTPUT:\n#      cdiag [nxcirc x 1] - vector of eigenvalues of C for frequencies in w\n# \n# Note: nxcirc = nx corresponds to having a circular boundary \n\n\n# Compute diagonal of ASD covariance matrix\n\tif wvec is not None:\n\t\twvecsq = np.square(wvec)\n\t\tconst = np.square(2*np.pi/nxcirc) # constant \n\t\tww = wvecsq*const  # effective frequency vector\n\telif wwnrm is not None:\n\t\tww = wwnrm\n\telse:\n\t\tprint(\"please provide either wvec or a normalized wvec into this function\")\n\n\tcdiag = np.squeeze(np.sqrt(2*np.pi)*rho*len_sc*np.exp(-.5*np.outer(ww,np.square(len_sc))))\n\treturn cdiag\n\n\n\n\ndef mkcov_ASDfactored(prs,nx,nxcirc=None,condthresh = 1e8,compfftbasis = None):\n# % Factored representation of ASD covariance matrix in Fourier domain\n# %\n# % [Cdiag,U,wvec] = mkcov_ASDfactored(prs,nx,opts)\n# %\n# % Covariance represented as C = U*sdiag*U'\n# % where U is unitary (in some larger basis) and sdiag is diagonal\n# %\n# %  C_ij = rho*exp(((i-j)^2/(2*l^2))\n# %\n# % INPUT:\n# % ------\n# %   prs [2 x 1]  - ASD parameters [len_sc = length scale; rho - maximal variance; ]:\n# %    nx [1 x 1]  - number of regression coeffs\n# % \n# % Note: nxcirc = nx gives circular boundary\n# %\n# % OUTPUT:\n# % -------\n# %   cdiag [ni x 1] - vector with thresholded eigenvalues of C\n# %       U [ni x nxcirc] - column vectors define orthogonal basis for C (on Reals)\n# %    wvec [nxcirc x 1] - vector of Fourier frequencies\n\n\tlen_sc = prs[0]\n\trho = prs[1]\n\n\t# % Parse inputs\n\tif nxcirc is None:\n\t\tnxcirc = nx + np.ceil(4*len_sc) # extends support by 4 stdevs of ASD kernel width\n\n\n\n\t# % Check that nxcirc isn't bigger than nx\n\tif nxcirc < nx:\n\t    warnings.warn('mkcov_ASDfactored: nxcirc < nx. Some columns of x will be ignored')\n\n\n\t# % compute vector of Fourier frequencies\n\t# maxfreq = np.floor(nxcirc/(np.pi*len_sc)*np.sqrt(.5*np.log(condthresh))) # max\n\t# if maxfreq < nxcirc/2:\n\t#     wvec = np.concatenate(([np.arange(int(maxfreq))],[np.arange(-int(maxfreq),0)]),axis = 1)\n\t#else:\n\t    # % in case cutoff is above max number of frequencies\n\twvec = rffb.comp_wvec(nxcirc, len_sc, condthresh)\n\n\n\n\t# % Compute diagonal in Fourier domain\n\tcdiag = mkcovdiag_ASD(len_sc,rho,nxcirc,wvec = wvec) # compute diagonal and Fourier freqs\n\n\t# % Compute real-valued discrete Fourier basis U\n\tif compfftbasis is not None:\n\t    U = rffb.realfftbasis(nx,nxcirc,wvec)[0]\n\t    return cdiag, U, wvec\n\telse:\n\t\treturn cdiag\n\n\n\ndef mkcovdiag_ASD_tf(len_sc,rho,nxcirc,wvec= None,wwnrm= None):\n#  Eigenvalues of ASD covariance (as diagonalized in Fourier domain)\n# \n#  Identical to mkcovdiag_ASD except using tensorflow operations.\n#  No need for computing of derivatives here\n\n\n# Compute diagonal of ASD covariance matrix\n\tif wvec is not None:\n\t\twvecsq = np.square(wvec)\n\t\tconst = np.square(2*np.pi/nxcirc) # constant \n\t\tww = wvecsq*const  # effective frequency vector\n\telif wwnrm is not None:\n\t\tww = wwnrm\n\telse:\n\t\tprint(\"please provide either wvec or a normalized wvec into this function\")\n\n\tcdiag = tf.sqrt(2*np.pi)*rho*len_sc*tf.exp(-.5*ww*tf.square(len_sc))\n\n\t\n\treturn cdiag\n\n\ndef mkcovdiag_ASD_wellcond(len_sc,rho,nxcirc,addition = 1e-8, wvec= None,wwnrm= None):\n\n\tcdiag = mkcovdiag_ASD(len_sc,rho,nxcirc,wvec,wwnrm)\n\n\treturn cdiag + addition\n\ndef mkcovdiag_ASD_wellcond_tf(len_sc,rho,nxcirc,addition = 1e-8, wvec= None,wwnrm= None):\n#  Eigenvalues of ASD covariance (as diagonalized in Fourier domain)\n# \n#  Identical to mkcovdiag_ASD except using tensorflow operations.\n\n\n# Compute diagonal of ASD covariance matrix\n\tif wvec is not None:\n\t\twvecsq = np.square(wvec)\n\t\tconst = np.square(2*np.pi/nxcirc) # constant \n\t\tww = wvecsq*const  # effective frequency vector\n\telif wwnrm is not None:\n\t\tww = wwnrm\n\telse:\n\t\tprint(\"please provide either wvec or a normalized wvec into this function\")\n\n\tcdiag = tf.sqrt(2*np.pi)*rho*len_sc*tf.exp(-.5*ww*tf.square(len_sc)) + addition\n\n\t\n\treturn cdiag\n\n\n\ndef mkcovdiag_ASD_nD_tf(len_sc,rho,nxcirc, condthresh, wvec= None,wwnrm= None):\n#  Eigenvalues of ASD covariance (as diagonalized in Fourier domain)\n# \n#  Identical to mkcovdiag_ASD except using tensorflow operations.\n# Compute diagonal of ASD covariance matrix.\n# threshold again based on length scale!!!\n\tif wvec is not None:\n\t\twvecsq = np.square(wvec)\n\t\tconst = np.square(2*np.pi/nxcirc) # constant \n\t\tww = wvecsq*const  # effective frequency vector\n\telif wwnrm is not None:\n\t\tww = wwnrm\n\telse:\n\t\tprint(\"please provide either wvec or a normalized wvec into this function\")\n\n\n\tii = tf.less(ww*np.square(len_sc),2*np.log(condthresh)) # frequency coeffs to keep\n\tni = tf.reduce_sum(tf.cast(ii, tf.float32)) # rank of covariance after pruning\n\n\tmasked_ww = tf.boolean_mask(ww, ii)\n\tmasked_ww = tf.cast(masked_ww, dtype='float32')\n\tcdiag = tf.sqrt(2*np.pi)*rho*len_sc*tf.exp(-.5*masked_ww*tf.square(len_sc))\n\n\t\n\treturn cdiag, ii, ni\n\n\t\n","sub_path":"GP_fourier/mkcovs.py","file_name":"mkcovs.py","file_ext":"py","file_size_in_byte":5516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"306773717","text":"#!/usr/bin/python\n\nfrom xml.sax.handler import ContentHandler\nfrom xml.sax import make_parser\nimport sys\nimport string\nimport os\nfrom urllib import request\n\ndef normalize_whitespace(text):\n    \"Remove redundant whitespace from a string\"\n    return ' '.join(text.split())\n\nclass CounterHandler(ContentHandler):\n\n    def __init__ (self):\n        self.inContent = 0\n        self.theContent = \"\"\n        self.inItem = False\n\n    def startElement (self, name, attrs):\n        if name == 'item':\n            self.inItem = True\n        elif self.inItem:\n            if name == 'title':\n                self.inContent = 1\n            elif name == 'link':\n                self.inContent = 1\n\n    def endElement (self, name):\n        if self.inContent:\n            self.theContent = normalize_whitespace(self.theContent)\n        if name == 'item':\n            self.inItem = False\n            print('</br>', file=open(\"barrapunto.html\", \"a\"))\n        if self.inItem:\n            if name == 'title':\n                print(self.theContent, file=open(\"barrapunto.html\", \"a\"))\n            elif name == 'link':\n                print('<a href=\"' + self.theContent + '\">' + self.theContent + '</a>', file=open(\"barrapunto.html\", \"a\"))\n        if self.inContent:\n            self.inContent = 0\n            self.theContent = \"\"\n\n    def characters (self, chars):\n        if self.inContent:\n            self.theContent = self.theContent + chars\n\n# --- Main prog\n\nif len(sys.argv)<1:\n    print(\"Usage: python xml-parser-barrapunto.py\")\n    print()\n    sys.exit(1)\n\n# Delete the file before appending\ntry:\n    os.remove('barrapunto.html')\nexcept OSError:\n    pass\n\n# Load parser and driver\nBarrapuntoParser = make_parser()\nBarrapuntoHandler = CounterHandler()\nBarrapuntoParser.setContentHandler(BarrapuntoHandler)\n\n# Ready, set, go!\nxmlFile = request.urlopen('http://barrapunto.com/index.rss')\nBarrapuntoParser.parse(xmlFile)\n\nprint(\"Parse complete\")\n","sub_path":"xml-parser-barrapunto.py","file_name":"xml-parser-barrapunto.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"124093908","text":"\"\"\"This module contains utilities for working with GitHub's graphql API\"\"\"\n\nimport os\nimport requests\n\nclass GraphQLClient(object):\n  \"\"\"A graphql client for GitHub\"\"\"\n\n  def __init__(self):\n    if not os.getenv(\"GITHUB_TOKEN\"):\n      raise ValueError(\"GITHUB_TOKEN must be provided\")\n    self._headers = {\"Authorization\":\n                     \"Bearer {0}\".format(os.getenv(\"GITHUB_TOKEN\"))}\n\n  def run_query(self, query):\n    \"\"\"Issue the GraphQL query and return the results.\"\"\"\n    request = requests.post('https://api.github.com/graphql',\n                            json={'query': query}, headers=self._headers)\n    if request.status_code == 200:\n      return request.json()\n    else:\n      raise Exception(\"Query failed to run by returning code of {}. {}\".format(\n        request.status_code, query))","sub_path":"py/code_intelligence/graphql.py","file_name":"graphql.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"392437476","text":"import os, json\nimport itertools, math\nimport spatial_similarity_refPoints as spatial_similarity\nimport imutils\nimport cv2\nfrom pynput.keyboard import Key, Controller\nimport numpy as np\n\nkeyboard = Controller()\nbasePath = os.path.dirname(os.path.realpath(__file__))\n\nclass DetectedCar:\n    def __init__(self, detectedBboxArr):\n        newid = next(itertools.count())\n        self.id = newid\n        self.detectedBboxArr = detectedBboxArr\n\nclass DetectedBbox:\n    def __init__(self, confidence, milliseconds, bbox, matches=[]):\n        self.confidence = confidence\n        self.milliseconds = milliseconds\n        self.bbox = bbox\n        self.matches = matches\n        self.targetMatch = None\n\n    def timeDiff(self, secondDetectedBbox):\n        return math.fabs(secondDetectedBbox.milliseconds - self.milliseconds)\n\n    def setTargetMatch(self, match):\n        self.targetMatch = match\n\nclass Point:\n    def __init__(self, X, Y):\n        self.X = X\n        self.Y = Y\n\nclass Lane:\n    def __init__(self, index, points):\n        self.index = index\n        self.points = points\n        self.counter = 0\n\n    def onCarDetected(self):\n        self.counter += 1\n\n    def setPoints(self,lanePoint1,lanePoint2):\n        self.points = [lanePoint1,lanePoint2]\n\n\n# for curve video\n'''laneAPoint1 = Point(799, 305)\nlaneAPoint12 = Point(583, 298)\nlaneAPoint2 = Point(449, 278)\n\nlaneBPoint1 = Point(204, 267)\nlaneBPoint2 = Point(670, 377)\n'''\n\n# for roundabouts video\n'''laneAPoint1 = Point(760, 350)\nlaneAPoint2 = Point(696, 308)\n\nlaneBPoint1 = Point(295, 307)\nlaneBPoint2 = Point(215, 386)'''\nlaneAPoint1 = Point(344, 259)\nlaneAPoint2 = Point(170, 425)\n\nlaneBPoint1 = Point(799, 408)\nlaneBPoint2 = Point(673, 279)\n\nlane1 = Lane(1, [laneAPoint1, laneAPoint2])\nlane2 = Lane(2, [laneBPoint1, laneBPoint2])\nallLanes = []\n\nlaneA = [laneAPoint1, laneAPoint2]\nlaneB = [laneBPoint1, laneBPoint2]\nallLanes2 = [laneA, laneB]\n\nclass Match:\n    def __init__(self, lIndex, rIndex):\n        self.lIndex = lIndex\n        self.rIndex = rIndex\n\n    def getLane(self):\n        return allLanes[self.lIndex]\n\n    def getPoint(self):\n        return self.getLane().points[self.rIndex]\n\n    def numberPoints(self):\n        return len(self.getLane().points)\n\n    def equal(self, comparedMatch):\n        return self.lIndex == comparedMatch.lIndex and \\\n            self.rIndex == comparedMatch.rIndex\n\n    def __str__(self):\n     return \"Lane \" + str(self.lIndex) + \" - Point \" + str(self.rIndex)\n\nclass Rectangle:\n    def __init__(self, startX, startY, endX, endY):\n        self.startX = startX\n        self.startY = startY\n        self.endX = endX\n        self.endY = endY\n\n    def contains(self, point):\n        return point.X >= self.startX and point.X <= self.endX and \\\n            point.Y >= self.startY and point.Y <= self.endY\n\n    def containsAny(self):\n        results = []\n        for lIndex, lane in enumerate(allLanes):\n            for rIndex, refPoint in enumerate(lane.points):\n                if (self.contains(refPoint)):\n                    results.append(Match(lIndex, rIndex))\n        return results\n\ndef numberBoxes(array, counter):\n    for i in array:\n        if counter == i[0] and len(i) >= 2:\n            return len(i)-1\n\ndef refPointInBbox(rectangle, point):\n    return rectangle.contains(point)\n\ndef matchesToString(matchesArray):\n    return list(map(lambda n: str(n),matchesArray))\n\ndef ratio(numberA, numberB):\n    if numberA == 0 or numberB == 0: return\n    if numberA >= numberB:\n        return numberB / float(numberA)\n    else: return numberA / float(numberB)\n\ndef simMean(colorA, colorB):\n    r1 = ratio(colorA[0], colorB[0])\n    r2 = ratio(colorA[1], colorB[1])\n    r3 = ratio(colorA[2], colorB[2])\n    return (r1 + r2 + r3) / 3\n\ndef sameCarInRefPoint(detectedArray, bbox):\n    '''\n        if the bbox belongs to other bboxes (seems to be of the same car),\n            the index is given back\n        else\n            -1 is returned\n    '''\n    timeDiffAcceptable = 5000\n    minsim = 0\n    currIndex = -1\n    for index, car in enumerate(detectedArray):\n        compareRect = car.detectedBboxArr[-1]\n        if (bbox.targetMatch and bbox.targetMatch.equal(compareRect.targetMatch)) or \\\n            any(compareRect.targetMatch.equal(n) for n in bbox.matches):\n            sim = spatial_similarity.similarityBBoxObject(compareRect, bbox)\n            if sim > minsim:\n                if compareRect.timeDiff(bbox) < timeDiffAcceptable:\n                    minsim = sim\n                    currIndex = index\n                else:\n                    #print(compareRect.timeDiff(bbox))\n                    pass\n    if minsim > 0.2 and currIndex != -1:\n        comparBbox = detectedArray[currIndex].detectedBboxArr[-1]\n        if bbox.timeDiff(comparBbox) > timeDiffAcceptable:\n            # time difference too high\n            print(\"Time diff too high: \" + str(bbox.timeDiff(comparBbox)))\n            return -1\n        else:\n            return currIndex\n    else:\n        return -1\n\ndef nextRefPointIndex(detectedArray, bbox):\n    #TODO: find out wether the current boudning box is found at the next ref point\n    # and belongs to an already detected car at the ref point before\n    if bbox.targetMatch is None or bbox.targetMatch.rIndex == 0:\n        followingRefPointMatches = list(filter(lambda n: n.rIndex > 0,bbox.matches))\n        bbox.setTargetMatch(followingRefPointMatches[0])\n    for index, car in enumerate(detectedArray):\n        lastBBoxMatch = car.detectedBboxArr[-1].targetMatch\n        if bbox.targetMatch.lIndex == lastBBoxMatch.lIndex:\n            # lane matches\n            if bbox.targetMatch.rIndex == lastBBoxMatch.rIndex + 1:\n                # ref point is the next one\n                return index\n    return -1\n\nclickedPts = []\ndef on_mouse(event,x,y,flags,params):\n    if event==cv2.EVENT_LBUTTONDOWN:\n        clickedPts.append(Point(x,y))\n        print(\"[INFO] registered:\", x, y)\n        keyboard.press('a')\n\ndef configure_refPoints(vs, lanes, points, live):\n    print(\"[INFO] setting the lanes\")\n    if live:\n        frame = vs.read()\n    else:\n        ret, frame = vs.read()\n        if not ret:\n            print(\"[ERROR] There was an error reading the frame. Its value is:\")\n            print(frame)\n            exit()\n    font = cv2.FONT_HERSHEY_SIMPLEX\n    fontScale = 0.8\n    color = (0, 0, 255)\n    thickness = 2\n\n    frame = imutils.resize(frame, width=1000)\n    for idx in range(lanes):\n        global clickedPts\n\n        for idy in range(points):\n            # setting point 1 lane 1\n            copy = frame.copy()\n            cv2.putText(copy, 'click point ' + str(idy+1) + ' of lane ' + str(idx+1), (350, 30), font,\n                       fontScale, color, thickness, cv2.LINE_AA)\n            cv2.imshow(\"Frame\", copy)\n            cv2.setMouseCallback('Frame', on_mouse)\n            key = cv2.waitKey(0) & 0xFF\n\n        if len(clickedPts) == 2:\n            allLanes.append(Lane(idx+1, clickedPts))\n        else:\n            allLanes.append(Lane(idx+1, [clickedPts[0], clickedPts[0]]))\n        clickedPts = []\n    cv2.setMouseCallback('Frame', lambda *args : None)\n\n\nr = Rectangle(400,200,600,400)\nprint(r.containsAny())\n","sub_path":"car_counting_refPoints.py","file_name":"car_counting_refPoints.py","file_ext":"py","file_size_in_byte":7159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"473421004","text":"from tkinter import *\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nfrom tkinter.ttk import Progressbar\nfrom tkinter.ttk import Treeview\n\nimport argparse\nimport logging\nimport os\nimport random\nimport uuid\nfrom datetime import datetime\nfrom time import sleep\nimport json\nimport sys\nfrom pymongo import MongoClient\nfrom bson.objectid import ObjectId\nimport pytz\nimport signal\n\nfrom hpitclient import Tutor\n\ndef file_len(fname):\n    #from http://stackoverflow.com/questions/845058/how-to-get-line-count-cheaply-in-python\n    with open(fname) as f:\n        for i, l in enumerate(f):\n            pass\n    return i + 1\n\ndef get_skills(row):\n    \"\"\"\n    Arguments: a row of transaction, a dictionary of header to column value pairs.\n    Returns: skills-  skill models to skill names\n    \"\"\" \n    skills = {}\n    skill_ids = {}\n    for k,v in row.items():\n        m = re.match(r\"^KC \\((.*)\\)$\",k)\n        if m:\n            kc_model = m.group(1)\n            kc_model = kc_model.replace(\".\",\"_\")\n            skills[kc_model] = v\n    \n    return skills\n\nclass ReplayTutor2(Tutor):\n    \"\"\"\n    ReplayTutor:  a tutor for testing/backup purposes, that re-sends messages to plugins.\n    \"\"\"\n    def __init__(self, entity_id, api_key, logger, run_once=None, args=None):\n        super().__init__(entity_id, api_key, self.main_callback, run_once=run_once)\n        self.run_once = run_once\n        self.logger = logger\n        \n        self.student_id_value = None\n        self.session_id_value = None\n        \n        self.root = None\n        self.status = None\n        self.student_id = None\n        self.session_id = None\n        self.problem_name = None\n        self.step_name = None\n        self.transaction_id = None\n        self.outcome = None\n        self.skillbox = None\n        self.button = None\n        self.kt_button = None\n        self.attr_name = None\n        self.attr_value = None\n        self.attr_button = None\n        self.skill_id = None\n        self.kt_button = None\n        self.response_box = None\n        \n        self.json_in = None\n        \n    def post_connect(self):\n        self.send(\"tutorgen.add_student\",{},self.new_student_callback)\n    \n    def setup_gui(self):\n        #main window\n        self.root = Tk()\n        self.root.wm_title(\"Transaction Replay Tutor\")\n        self.root.minsize(400,400)\n    \n        #menu\n        menubar = Menu(self.root)\n        filemenu = Menu(menubar, tearoff=0)\n        filemenu.add_command(label=\"Import Datashop File\", command=self.import_datashop_file)\n        filemenu.add_command(label=\"Quit\", command=self.root.quit)\n        menubar.add_cascade(label=\"File\", menu=filemenu)\n        self.root.config(menu=menubar)\n        \n        #listbox\n        w = Label(self.root, text=\"Transaction\")\n        w.pack(fill=X)\n        \n        frame = Frame()\n        scrollbar = Scrollbar(frame, orient=VERTICAL)\n        \n        self.treeview = Treeview(frame,yscrollcommand=scrollbar.set)\n        self.treeview[\"columns\"] = (\"problem_name\",\"step_text\",\"transaction_text\",\"skill_names\",\"outcome\")\n        self.treeview.heading(\"problem_name\",text=\"Problem Name\")\n        self.treeview.heading(\"step_text\",text=\"Step Text\")\n        self.treeview.heading(\"transaction_text\",text=\"Transaction Text\")\n        self.treeview.heading(\"skill_names\",text=\"Skill Names\")\n        self.treeview.heading(\"outcome\",text=\"Outcome\")\n        \n        self.treeview.bind('<<TreeviewSelect>>', self.populate_form)\n    \n        scrollbar.config(command=self.treeview.yview)\n        scrollbar.pack(side=RIGHT, fill=Y)\n        \n        self.treeview.pack(side=LEFT, fill=BOTH, expand=1)\n        \n        frame.pack(fill=BOTH)\n        \n        #spacer frame\n        separator = Frame(height=2, bd=1, relief=SUNKEN)\n        separator.pack(fill=X, padx=5, pady=5)\n        \n        #student id\n        w = Label(self.root, text=\"Student ID\")\n        w.pack(fill=X)\n        self.student_id = Entry(self.root,state=DISABLED)\n        self.student_id.pack(fill=X)\n        \n        #ssession id\n        w = Label(self.root, text=\"Session ID\")\n        w.pack(fill=X)\n        self.session_id = Entry(self.root,state=DISABLED)\n        self.session_id.pack(fill=X)\n        \n        bigframe = Frame()\n        bigframe.pack(fill=X,padx =5, ipady=5)\n        leftframe = Frame(bigframe)\n        leftframe.pack(side=LEFT,fill=X,expand=1)\n        rightframe= Frame(bigframe)\n        rightframe.pack(side=RIGHT,fill=X,expand=1)\n        \n        #Entry fields\n        ##problem name\n        w = Label(leftframe, text=\"Problem Name\")\n        w.pack(fill=X)\n        self.problem_name = Entry(leftframe)\n        self.problem_name.pack(fill=X)\n        \n        ##step name\n        w = Label(leftframe, text=\"Step Name\")\n        w.pack(fill=X)\n        self.step_name = Entry(leftframe)\n        self.step_name.pack(fill=X)\n        \n        ##Transaction ID\n        w = Label(leftframe, text=\"Transaction ID\")\n        w.pack(fill=X)\n        self.transaction_id = Entry(leftframe)\n        self.transaction_id.pack(fill=X)\n        \n        ##Outcome\n        w = Label(leftframe, text=\"Outcome\")\n        w.pack(fill=X)\n        self.outcome = Entry(leftframe)\n        self.outcome.pack(fill=X)\n        \n        ##skill names\n        w = Label(rightframe, text=\"Skill Models and Names\")\n        w.pack(fill=X)\n        self.skillbox = Text(rightframe,height=8)\n        self.skillbox.pack()\n        \n        #Submit button\n        self.button = Button(self.root, text=\"Send\", command=self.submit_transaction, state=DISABLED)\n        self.button.pack()\n\n        #spacer frame\n        separator = Frame(height=2, bd=1, relief=SUNKEN)\n        separator.pack(fill=X, padx=5, pady=5)\n        \n        bigframe = Frame()\n        bigframe.pack(fill=X)\n        leftframe = Frame(bigframe,bd=1)\n        leftframe.pack(side=LEFT,expand=1, padx =5, ipady=5)\n        rightframe= Frame(bigframe,bd=1)\n        rightframe.pack(side=RIGHT,expand=1, padx =5, ipady=5)\n        \n        #student attribute\n        w = Label(leftframe, text=\"Student Attribute\")\n        w.pack(fill=X)\n        self.attr_name = Entry(leftframe)\n        self.attr_name.pack(fill=X)\n        \n        w = Label(leftframe, text=\"Value\")\n        w.pack(fill=X)\n        self.attr_value = Entry(leftframe)\n        self.attr_value.pack(fill=X)\n        \n        self.attr_button = Button(leftframe, text=\"Set\", command=self.set_attribute, state=DISABLED)\n        self.attr_button.pack()\n        \n        b = Button(leftframe, text=\"Get\", command=self.get_attribute)\n        b.pack()\n        \n        b = Button(leftframe, text=\"Get Student By Attribute\",command=self.get_student_by_attribute)\n        b.pack()\n        \n        #b = Button(leftframe, text=\"Add Problem\", command=self.add_problem)\n        #b.pack()\n        \n        #kt_trace\n        w = Label(rightframe, text=\"Skill ID\")\n        w.pack(fill=X)\n        self.skill_id = Entry(rightframe)\n        self.skill_id.pack(fill=X)\n        \n        self.kt_button = Button(rightframe, text=\"Trace\", command=self.kt_trace, state=DISABLED)\n        self.kt_button.pack()\n        \n        b = Button(rightframe, text=\"Reset\", command=self.kt_reset)\n        b.pack()\n        \n        #response box\n        w = Label(self.root, text=\"Responses\")\n        w.pack(fill=X)\n        self.response_box = Text(height=8)\n        self.response_box.pack()\n        \n        #status\n        self.status = Label(text=\"Status: Idle\", bd=1, relief=SUNKEN, anchor=W)\n        self.status.pack(side=BOTTOM, fill=X)\n    \n    def main_loop(self):\n        \n        again = True\n        \n        \n        if not self.callback():\n            again = False\n\n        responses = self._poll_responses()\n\n        if not self._dispatch_responses(responses):\n            again = False\n\n        if again:\n            self.root.after(self.poll_wait, self.main_loop)\n        else:\n            raise Exception(\"Error in main loop\")\n    \n    def import_datashop_file(self):\n        \n        filename = filedialog.askopenfilename()\n        \n        with open(filename,\"r\") as filein:\n            header_line = filein.readline()\n            headers = header_line.split(\"\\t\")\n        \n        filelen = file_len(filename)       \n        count =0\n        \n        with open(filename,\"r\") as filein:\n            filein.readline() #skip header line\n            while 1:\n                line = filein.readline()\n                if not line:\n                    break\n                \n                rt.status.config(text=\"Status: Importing Datashop file... \" + str(count) + \" / \" + str(filelen))\n                rt.status.update_idletasks()\n                \n                count+=1\n                \n                values = line.split(\"\\t\")\n                transaction = dict(zip(headers,values))\n                \n                skills = get_skills(transaction)\n                \n                #(\"problem_name\",\"step_text\",\"transaction_text\",\"skill_names\",\"outcome\")\n                rt.treeview.insert(\"\",\"end\",text= \"Transaction\", values=(transaction[\"Problem Name\"],transaction[\"Step Name\"],transaction[\"Transaction Id\"],json.dumps(skills),transaction[\"Outcome\"]))\n        \n        rt.status.config(text=\"Status: Idle\")\n        rt.status.update_idletasks()\n        \n        print(\"file \" + filename)\n    def add_problem(self):\n        mid = self.send(\"tutorgen.add_problem\",{\n            \"problem_name\":\"some problem\",\n            \"problem_text\":\"some problem text\",\n        }, self.transaction_response_callback)\n        \n        print(str(mid) + str(datetime.now()))\n        \n    def kt_trace(self):\n        correct=False\n        if self.outcome.get().lower() == \"correct\":\n            correct = True\n            \n        mid = self.send(\"tutorgen.kt_trace\",{\n            \"student_id\":self.student_id.get(),\n            \"correct\":correct,\n            \"skill_id\":self.skill_id.get(),\n        }, self.transaction_response_callback)\n        \n        print(str(mid) + str(datetime.now()))\n        \n    def kt_reset(self):\n        mid = self.send(\"tutorgen.kt_reset\",{\n            \"student_id\":self.student_id.get(),\n            \"skill_id\":self.skill_id.get(),\n        }, self.transaction_response_callback)\n        \n        print(str(mid) + str(datetime.now()))\n    \n    def set_attribute(self):\n        mid = self.send(\"tutorgen.set_attribute\",{\n            \"student_id\":self.student_id.get(),\n            \"attribute_name\":self.attr_name.get(),\n            \"attribute_value\":self.attr_value.get(),\n        }, self.transaction_response_callback)\n        self.attr_name.delete(0,END)\n        self.attr_value.delete(0,END)\n        \n        print(str(mid) + str(datetime.now()))\n        \n    def get_attribute(self):\n        mid = self.send(\"tutorgen.get_attribute\",{\n            \"student_id\":self.student_id.get(),\n            \"attribute_name\":self.attr_name.get(),\n        }, self.transaction_response_callback)\n        \n        print(str(mid) + str(datetime.now()))\n        \n        print(str(mid) + str(datetime.now()))\n    \n    def get_student_by_attribute(self):\n        mid = self.send(\"tutorgen.get_or_create_student_by_attribute\",{\n            \"attribute_name\":self.attr_name.get(),\n            \"attribute_value\":self.attr_value.get(),\n        }, self.transaction_response_callback)\n    \n    def populate_form(self,evt):\n        w = evt.widget\n        item = w.selection()[0]\n        values = w.item(item)[\"values\"]\n\n        self.problem_name.delete(0,END)\n        self.problem_name.insert(INSERT,values[0])\n        \n        self.step_name.delete(0,END)\n        self.step_name.insert(INSERT,values[1])\n        \n        self.transaction_id.delete(0,END)\n        self.transaction_id.insert(INSERT,values[2])\n        \n        self.skillbox.delete(\"0.0\",END)\n        self.json_in = json.loads(values[3]) #save for later\n        json_out = json.dumps(self.json_in, sort_keys=True,indent=4, separators=(',', ': '))\n        self.skillbox.insert(\"0.0\",json_out)\n        \n        self.outcome.delete(0,END)\n        self.outcome.insert(INSERT,values[4])\n       \n    def new_student_callback(self,response):\n        self.student_id_value = response[\"student_id\"]\n        self.session_id_value = response[\"session_id\"]\n\n        self.student_id.config(state=NORMAL)\n        self.student_id.insert(INSERT,str(self.student_id_value))\n\n        self.session_id.config(state=NORMAL)\n        self.session_id.insert(INSERT,str(self.session_id_value))\n        \n        self.button.config(state=NORMAL)\n        self.attr_button.config(state=NORMAL)\n        self.kt_button.config(state=NORMAL)\n    \n    def transaction_response_callback(self,response):         \n        #messagebox.showinfo(\"info\", \"Got a response: \" + json.dumps(response, sort_keys=True,indent=4, separators=(',', ': ')))\n        self.response_box.insert(END,\"\\n==========================================\")\n        self.response_box.insert(END,json.dumps(response, sort_keys=True,indent=4, separators=(',', ': ')))\n        \n    def submit_transaction(self):\n        \n        skill_ids = {}\n        skill_names = {}\n        for skill_model, skill_name in self.json_in.items():\n            skill_ids[skill_name] = \"\"\n            skill_names[skill_model] = skill_name\n        transaction = {\n            \"problem_name\":self.problem_name.get(),\n            \"step_text\":self.step_name.get(),\n            \"transaction_text\":self.transaction_id.get(),\n            \"session_id\":self.session_id.get(),\n            'skill_ids': skill_ids,\n            'skill_names': skill_names,\n            'student_id':self.student_id.get(),\n            'outcome': self.outcome.get(),\n            }\n        print(transaction)\n        print(self.send_transaction(transaction, self.transaction_response_callback))\n    \n    def main_callback(self):\n        return True\n\nif __name__ == \"__main__\":\n    #localhost\n    entity_id = \"ed188aa3-a673-4482-9475-aedd981ff360\"\n    app_secret = \"e992a697f396a2fd99ef9910cb040fa9\"\n    url_root = \"http://localhost:8000\"\n    \n    #production (tres)\n    #entity_id = \"e7e43470-5031-496c-9972-cbb809455333\"\n    #app_secret = \"1aadb263acec65c24b683976643516ce\"\n    #url_root = \"http://www.hpit-project.org\"\n    \n    #production (prod)\n    #entity_id = \"35f8fdda-7f4e-4b48-86ab-eda038186183\"\n    #app_secret = \"d5f1723260ec88293f4fc79f0f7e4572\"\n    #url_root = \"http://production.hpit-project.org\"\n    \n    logging.basicConfig(\n            filename=\"log/datashop_replay.log\",\n            level=logging.DEBUG,\n            propagate=False,\n            format='%(asctime)s %(levelname)s:----:%(message)s', \n            datefmt='%m/%d/%Y %I:%M:%S %p')\n    \n    \n    logger = logging.getLogger(__name__)\n    \n    logger.debug(\"start\")\n    \n    rt = ReplayTutor2(entity_id, app_secret, logger)\n    signal.signal(signal.SIGTERM, rt.disconnect)\n    rt.set_hpit_root_url(url_root)\n    \n    rt.setup_gui()\n\n    #main loops\n    rt.connect()\n    try:\n        rt.main_loop()\n        rt.root.mainloop()\n    except (KeyboardInterrupt, Exception) as e:\n        print(str(e))\n    rt.disconnect()\n","sub_path":"hpit/tutors/replay2.py","file_name":"replay2.py","file_ext":"py","file_size_in_byte":15002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"249916926","text":"import argparse\nimport gc\nimport gzip\nimport itertools\nimport os\nimport pickle\nimport sys\nfrom datetime import datetime\nfrom typing import List, Tuple, Union, Iterable\n\nimport numpy\nimport psutil\nimport numpy as np\nimport pandas\nimport xgboost as xgb\nfrom pandas import DataFrame\nfrom scipy import sparse\nfrom sklearn.model_selection import KFold, ParameterGrid, GroupKFold\nfrom pympler.tracker import SummaryTracker\n\nimport data.pcsml_data_loader as dl\nfrom modeling import score_util, cv_util, xgb_util, categorical_util\n\n# opts\n# debug settings\n# training_data = '/var/opt/pcsml/devel/training_data/dumps/df-corn-smpl_25-gis-pps-20171018.pkl'\ntraining_data_dir = '/var/opt/pcsml/devel/training_data/dumps/'\ntraining_data_name = 'df_gis_pps_corn_2016_sample_200k'\noutput_dir = '/var/opt/pcsml/devel/results/_scratch'\nrun_id = 'dev'\ncv_n_outer_splits = 3\ncv_n_inner_splits = 3\ncv_n_outer_runs = 2\ncv_n_inner_runs = 1\nxgb_n_threads = 2\nxgb_max_depth_min = 12\nxgb_max_depth_max = 12\nxgb_n_rounds = 500\nverbose_eval = True\ndebug_override = \"--debug\" in sys.argv[0]\n\n\ndef _print_stats(y: Union[pandas.Series, np.ndarray, Iterable[float]]) -> str:\n    return f\"min: {np.min(y):.1f}, max: {np.max(y)}, avg: {np.average(y):.1f}, std: {np.std(y):.1f}\"\n\n\nif debug_override:\n    print(\"LOG-DEBUG: running in debug mode, won't load cmd line params\")\n\n# remote exec with:\n# bash run_docker_image.sh -d ./ -s nested_cv_excl_elb -- \\\n#           python -u modeling/scripts/xgb_cv_year_id_excl_elb.py --run-id nested_cv_excl_elb\n\nif not debug_override and __name__ == '__main__' and not \"pydevconsole\" in sys.argv[0]:\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--training-data-dir', '-d', type=str,\n                        default='/var/opt/pcsml/training-data/gis-pps/')\n    parser.add_argument('--training-data-name', '-n', type=str, default='df_gis_pps_corn_2016_smpl_2k')\n    parser.add_argument('--output-dir', '-o', type=str, default='/var/opt/pcsml/remote-exec-out')\n    parser.add_argument('--cv-n-outer-splits', '-cvos', type=int, default=3)\n    parser.add_argument('--cv-n-outer-runs', '-cvor', type=int, default=2)\n    parser.add_argument('--cv-n-inner-splits', '-cvis', type=int, default=3)\n    parser.add_argument('--cv-n-inner-runs', '-cvir', type=int, default=1)\n    parser.add_argument('--xgb-n-threads', '-bnt', type=int, default=16)\n    parser.add_argument('--xgb-max-depth-min', '-bmdn', type=int, default=6)\n    parser.add_argument('--xgb-max-depth-max', '-bmdx', type=int, default=6)\n    parser.add_argument('--xgb-n-rounds', '-br', type=int, default=500)\n    parser.add_argument('--run-id', '-r', type=str, default=f'{datetime.utcnow():%Y-%m-%dT%H-%M-%S}')\n    parser.add_argument('--quiet-eval', '-qe', action='store_true', default=False)\n\n    opt = parser.parse_args()\n\n    print(\"parsed opts:\")\n    print(opt)\n    training_data_dir = opt.training_data_dir\n    training_data_name = opt.training_data_name\n    output_dir = opt.output_dir\n    cv_n_outer_splits = opt.cv_n_outer_splits\n    cv_n_inner_splits = opt.cv_n_inner_splits\n    cv_n_outer_runs = opt.cv_n_outer_runs\n    cv_n_inner_runs = opt.cv_n_inner_runs\n    xgb_n_threads = opt.xgb_n_threads\n    xgb_max_depth_min = opt.xgb_max_depth_min\n    xgb_max_depth_max = opt.xgb_max_depth_max\n    xgb_n_rounds = opt.xgb_n_rounds\n    run_id = opt.run_id\n    verbose_eval = opt.quiet_eval is False\n\ntraining_data_base_name = os.path.join(training_data_dir, training_data_name)\n# xgb_early_stopping = max(10, int(round(xgb_n_rounds / 100)))\n# xgb_early_stopping = min(50, xgb_early_stopping)\nxgb_early_stopping = 10\nprint(f\"early stopping rounds: {xgb_early_stopping}\")\n\n# xgb_max_depth_range = range(xgb_max_depth_max, xgb_max_depth_min - 1, -1)\nxgb_max_depth_range = range(xgb_max_depth_min, xgb_max_depth_max + 1)\n\n# 'eta': .015,\ndefault_xgb_params = {'objective': 'reg:linear', 'eval_metric': 'mae',\n                      'nthread': xgb_n_threads,\n                      'silent': 1}\n\nprint(\"default params:\")\nprint(default_xgb_params)\n\n# 'subsample': [.05]\ncv_params = ParameterGrid({\n    'max_depth': xgb_max_depth_range,\n    'subsample': [1]\n})\nprint(\"cv_params\")\nprint(list(cv_params))\n\n###\n# create run output dir\nresult_dir = os.path.join(output_dir, run_id, f\"{datetime.utcnow():%Y%m%d_%H%M}\")\nos.makedirs(result_dir, exist_ok=True)\n\n###\n# - create data exploration outputs (graphs, etc)\n# - run the xgboost model, saving model output\n###\nprint(f\"loading training data to find year ids: {training_data_base_name}.pickle.gz\")\ndata: DataFrame = pandas.read_pickle(training_data_base_name + \".pickle.gz\")\ndata_dummies: pandas.SparseDataFrame = pandas.read_pickle(training_data_base_name + \"_dummies.pickle.gz\")\n\n# clean up yield vals\nyld_mean = data['Dry_Yield'].mean()\nyld_std = data['Dry_Yield'].std()\nyld_rng_min = max(0, yld_mean - (yld_std * 3))\nyld_rng_max = yld_mean + (yld_std * 3)\nprint(f\"constraining yield range to: {yld_rng_min:.2f}, {yld_rng_max:.2f}\")\ndata.loc[data['Dry_Yield'] >= yld_rng_max, 'Dry_Yield'] = yld_rng_max\ndata.loc[data['Dry_Yield'] <= yld_rng_min, 'Dry_Yield'] = yld_rng_min\nprint(f\"training yield stats: {_print_stats(data.loc[:, 'Dry_Yield'])}\")\n\nyear_ids: np.ndarray = data['YearID'].unique()\nprint(f\"total year ids: {len(year_ids)}\")\nelb_year_ids = dl.elb_year_ids()\nyear_ids: np.ndarray = year_ids[~np.isin(year_ids, elb_year_ids)]\nprint(f\"year ids without elbs: {len(year_ids)}\")\n\nelb_data: pandas.DataFrame = data.loc[data['YearID'].isin(elb_year_ids)]\nelb_dummies: pandas.SparseDataFrame = data_dummies.loc[elb_data.index]\n\ndata = data.loc[data['YearID'].isin(year_ids)]\ndata_dummies: pandas.SparseDataFrame = data_dummies.loc[data.index]\ndata_areas: np.ndarray = data.pop('Area').values\ndata_year_ids: np.ndarray = data.pop('YearID').values\ndata_labels: np.ndarray = data.pop('Dry_Yield').values\ndata.drop(dl.exclude_columns + dl.yield_dep_columns, axis=1, inplace=True, errors='ignore')\ndata_feature_names = data.columns.append(data_dummies.columns).values\n\nprint(\"converting training data to sparse ndarray\")\ndata: numpy.ndarray = data.as_matrix()\ndata = sparse.csr_matrix(data)\ndata_dummies = data_dummies.to_coo().tocsr()\ndata = sparse.hstack((data, data_dummies))\nprint(\"converting to sparse numpy (csr)\")\ndata = data.tocsr()\n\nprint(\"Encoding elb dataset\")\nelb_data = elb_data.drop(dl.exclude_columns + dl.yield_dep_columns, axis=1, errors='ignore')\nelb_y = elb_data.pop('Dry_Yield')\nelb_data = sparse.hstack(\n    (\n        sparse.csr_matrix(elb_data.as_matrix()),\n        elb_dummies.to_coo().tocsr()\n    ))\nelb_data = xgb.DMatrix(elb_data, label=elb_y, feature_names=data_feature_names)\n\n\ndef _test_elbs(model):\n    if elb_data.num_row() == 0:\n        print(\"no test elbs found, skipping scoring\")\n        return\n\n    elb_pred = model.predict(elb_data)\n    score = score_util.ScoreReport(elb_y, elb_pred)\n    elb_cv_results.append(score.abs_std_3)\n    print(score)\n    print(f\"elb yield stats: {_print_stats(elb_y)}\")\n    print(f\"elb prediction stats: {_print_stats(elb_pred)}\")\n\n\ntotal_model_trainings = (cv_n_outer_runs * cv_n_inner_runs * len(cv_params)) + cv_n_outer_runs\ncurr_model_training = 0\nouter_cv_results = []\ninner_cv_results: List[List[cv_util.GridSearchCVResults]] = []\nelb_cv_results = []\nf_importance: List[DataFrame] = []\nmodels: List[xgb.Booster] = []\n\nkf_outer = GroupKFold(n_splits=cv_n_outer_splits)\nkf_outer_runs = itertools.islice(kf_outer.split(data, groups=data_year_ids), cv_n_outer_runs)\nfor test_run_idx, (kf_outer_train_idx, kf_outer_test_idx) in enumerate(kf_outer_runs):\n    train_outer, train_outer_y = data[kf_outer_train_idx], data_labels[kf_outer_train_idx]\n    train_year_ids = data_year_ids[kf_outer_train_idx]\n    test_outer, test_outer_y = data[kf_outer_test_idx], data_labels[kf_outer_test_idx]\n\n    print(f\"\\nOUTER CV TRAINING {test_run_idx + 1} of {cv_n_outer_runs}\\n\")\n\n    # INNER CV #\n    kf_inner = GroupKFold(n_splits=cv_n_inner_splits)\n    kf_inner_runs = itertools.islice(kf_inner.split(train_outer, groups=train_year_ids), cv_n_inner_runs)\n    grid_cv_results = [cv_util.GridSearchCVResults(p) for p in cv_params]\n    for idx, (train_idx, test_idx) in enumerate(kf_inner_runs):\n        train, train_y = train_outer[train_idx], train_outer_y[train_idx]\n        test, test_y = train_outer[test_idx], train_outer_y[test_idx]\n\n        print(f\"\\n* INNER CV TRAINING *: ({test_run_idx + 1} of {cv_n_outer_runs}) {idx + 1} of {cv_n_inner_runs}\")\n\n        train = xgb.DMatrix(train, train_y, feature_names=data_feature_names)\n        test = xgb.DMatrix(test, test_y, feature_names=data_feature_names)\n        # run all params\n        for i, p in enumerate(cv_params):\n            default_xgb_params.update(p)\n\n            curr_model_training += 1\n\n            print(f\"\"\"\nGRID SEARCH: ({curr_model_training} of total: {total_model_trainings}) \n{test_run_idx + 1}, {idx + 1}, {i + 1} of {len(cv_params)}, {p}\"\"\")\n\n            #######\n            # By putting test last in the eval list, it will be used for early stopping.\n            # - OK, since we then CV in the outer loop too?\n            #######\n            eval_list = [(train, 'train'), (test, 'test')]\n            eval_result = {}\n\n            print(f\"training yield stats: {_print_stats(train.get_label())}\")\n\n\n            def _eval(pred, d: xgb.DMatrix):\n                _scr = score_util.ScoreReport(d.get_label(), pred)\n                return '3std_100x', int(round(_scr.abs_std_3 * 100, 0))\n\n\n            print(f\"training xgb model\")\n            model: xgb.Booster = xgb.train(\n                default_xgb_params, train, num_boost_round=xgb_n_rounds, early_stopping_rounds=xgb_early_stopping,\n                evals=eval_list, evals_result=eval_result, feval=_eval, verbose_eval=verbose_eval)\n\n            predictions = model.predict(test, ntree_limit=model.best_ntree_limit)\n            score = score_util.ScoreReport(test.get_label(), predictions)\n            _results = grid_cv_results[i]\n            _results.add(\n                score.abs_std_3,\n                iteration=model.best_iteration,\n                mean=score.abs_mean,\n                std_dev=score.abs_std,\n                y_min=np.min(train.get_label()),\n                y_max=np.max(train.get_label()),\n                pred_min=np.min(predictions),\n                pred_max=np.max(predictions))\n\n            print(f\"\"\"\nGRID SEARCH RESULT: ({curr_model_training} of total: {total_model_trainings}) \n{test_run_idx + 1}, {idx + 1}, {i + 1} of {len(cv_params) - 1}, {p}\ntraining yield stats: {_print_stats(train.get_label())}\npred yield stats: {_print_stats(predictions)}\"\"\")\n            print(score)\n\n        # clear memory\n        del train\n        del test\n\n    inner_cv_results.append(grid_cv_results)\n\n    # OUTER CV #\n    # get best score from inner cv\n    print(\"\\nINNER CV RESULTS\")\n    print(sorted(grid_cv_results, key=lambda x: x.score_avg()))\n    best_cv_result = min(grid_cv_results, key=lambda r: r.score_avg())\n    best_params = best_cv_result.params\n    print(\n        f\"best CV result: {best_params}\\n\"\n        f\"avg score: {best_cv_result.score_avg():.3F}\\n\"\n        f\"avg attrs:{best_cv_result.all_attr_avg()}\")\n\n    print(f\"\\n\\n** TRAINING OUTER TEST **\")\n    curr_model_training += 1\n    print(f\"{curr_model_training} of total: {total_model_trainings}\")\n\n    train_outer = xgb.DMatrix(train_outer, label=train_outer_y, feature_names=data_feature_names)\n    test_outer = xgb.DMatrix(test_outer, label=test_outer_y, feature_names=data_feature_names)\n\n    default_xgb_params.update(best_params)\n    best_iteration = int(round(best_cv_result.all_attr_avg()['iteration']))\n\n    eval_list = [(train_outer, 'train')]\n    model: xgb.Booster = xgb.train(default_xgb_params, train_outer, num_boost_round=best_iteration, evals=eval_list,\n                                   verbose_eval=verbose_eval)\n    models.append(model)\n    model_path = f\"{result_dir}/cv{test_run_idx}_model.xgb\"\n    print(f\"saving model: {model_path}\")\n    model.save_model(model_path)\n\n    f_imp_base_name = f\"cv{test_run_idx}_f_imp\"\n    f_imp = xgb_util.feature_importance_dummy_merged_report(\n        result_dir, f_imp_base_name, model)\n    f_importance.append(f_imp)\n\n    predictions = model.predict(test_outer)\n    score = score_util.ScoreReport(test_outer_y, predictions)\n    rdm_guesses = np.empty(len(test_outer_y), dtype=float)\n    rdm_guesses.fill(yld_mean)\n    random_guess_score = score_util.ScoreReport(test_outer_y, rdm_guesses)\n\n    print(f\"\\nOUTER CV Score\\nparams: {best_params}\\niterations:{best_iteration}\")\n    print(score)\n    print(f\"random guess score: {random_guess_score}\")\n    print(f\"training yield stats: {_print_stats(train_outer.get_label())}\")\n    print(f\"prediction stats: {_print_stats(predictions)}\")\n\n    outer_cv_results.append(\n        (best_params, best_iteration, score.abs_std_3, best_cv_result.score_avg(), random_guess_score.abs_std_3))\n\n    _test_elbs(model)\n\n    del train_outer\n    del test_outer\n    del train_outer_y\n    del test_outer_y\n    gc.collect()\n\n# cv final result\nprint(\"\\n*** FINAL CV RESULTS ***\")\n\nprint(\"\\n* INNER CV *\")\nfor i in range(len(cv_params)):\n    cv_results_ = [r[i] for r in inner_cv_results]\n    for cv_r in cv_results_:\n        print(f\"{cv_r}, {cv_r.all_attr_avg()}\")\n\nprint(\"\\n* Feature Importance *\")\nfi_merged = f_importance[0]\nfor fi_df in f_importance[1:]:\n    fi_merged = pandas.merge(fi_merged, fi_df, how='outer', on='Feature')\n\nfi_total_df = pandas.DataFrame(fi_merged.loc[:, 'Feature'])\nfi_total_df['Importance'] = fi_merged.fillna(0).sum(axis=1)\nfi_total_df['Importance'] = fi_total_df['Importance'].astype(int)\nfi_total_df.sort_values(by='Importance', ascending=False, inplace=True)\nfi_total_df.to_csv(os.path.join(result_dir, 'cv_all_feature_importance.csv'))\nxgb_util.plot_feature_importance(\n    os.path.join(result_dir, 'cv_all_feature_importance.png'),\n    {fi[0]: fi[1] for fi in fi_total_df.values})\n# noinspection PyUnresolvedReferences\nprint([(fi[0], fi[1]) for fi in fi_total_df.head(20).values])\n\nprint(\"\\n* ELB SCORING *\")\nprint(elb_cv_results)\n\nprint(\"\\n* OUTER CV TEST RESULTS *\")\nfor r in outer_cv_results:\n    print(f\"params: {r[0]}, iterations: {r[1]}, \"\n          f\"test abs 99: {r[2]:.2f}, \"\n          f\"random guess abs 99: {r[4]:.2f}, \"\n          f\"cv abs 99: {r[3]:.2f}\")\n\nprint(\"\\n** AVERAGES **\")\ncv_abs99_avg = np.average([r[2] for r in outer_cv_results])\nguess_abs99_avg = np.average([r[4] for r in outer_cv_results])\nprint(f\"iterations: {np.average([r[1] for r in outer_cv_results]):.2f}\")\nprint(f\"inner cv avg: {np.average([r[3] for r in outer_cv_results]):.2f}\")\nprint(f\"test abs_99: {cv_abs99_avg:.2f}\")\nprint(f\"rdm guess abs_99: {guess_abs99_avg:.2f}\")\nprint(f\"elb abs_99: {np.average(elb_cv_results):.2f}\")\n","sub_path":"_archived_versions/20180226_h2o/src/modeling/scripts/xgb_cv_year_id_excl_elb.py","file_name":"xgb_cv_year_id_excl_elb.py","file_ext":"py","file_size_in_byte":14711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"291740305","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements.  See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# 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, 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\"\"\"Parent OAuth endpoint for Pony Mail codename Foal\"\"\"\n\nimport plugins.server\nimport plugins.session\nimport plugins.oauthGeneric\nimport plugins.oauthGoogle\nimport plugins.oauthGithub\nimport typing\nimport aiohttp.web\nimport hashlib\n\ndef debug(server, text):\n    if server.api_logger:\n        server.api_logger.debug(text)\n\nasync def process(\n    server: plugins.server.BaseServer, session: plugins.session.SessionObject, indata: dict,\n) -> typing.Union[dict, aiohttp.web.Response]:\n\n    debug(server, f\"oauth/indata: {indata}\")\n    key = indata.get(\"key\", \"\")\n    state = indata.get(\"state\")\n    code = indata.get(\"code\")\n    id_token = indata.get(\"id_token\")\n    oauth_token = indata.get(\"oauth_token\")\n\n    rv: typing.Optional[dict] = None\n\n    # Google OAuth - currently fetches email address only\n    if key == \"google\" and id_token and server.config.oauth.google_client_id:\n        rv = await plugins.oauthGoogle.process(indata, session, server)\n\n    # GitHub OAuth - Fetches name and email\n    elif key == \"github\" and code and server.config.oauth.github_client_id:\n        rv = await plugins.oauthGithub.process(indata, session, server)\n\n    # Generic OAuth handler, only one we support for now. Works with ASF OAuth.\n    elif state and code and oauth_token:\n        rv = await plugins.oauthGeneric.process(indata, session, server)\n\n    if rv:\n        debug(server, f\"oauth/rv: {rv}\")\n        # Get UID, fall back to using email address\n        uid = rv.get(\"uid\")\n        if not uid:\n            uid = rv.get(\"email\")\n        if uid:\n            oauth_provider = rv.get(\"oauth_domain\", plugins.session.OAUTH_PROVIDER_DEFAULT)\n            cid = hashlib.shake_128(\n                (\"%s-%s\" % (oauth_provider, uid)).encode(\"ascii\", \"ignore\")\n            ).hexdigest(16)\n            authoritative = oauth_provider in server.config.oauth.authoritative_domains\n            admin = authoritative and rv.get(\"email\") in server.config.oauth.admins\n            debug(server, f\"oauth/aa: {authoritative} {admin}\")\n            cookie = await plugins.session.set_session(\n                server,\n                cid,\n                uid=uid,\n                name=rv.get(\"name\") or rv.get(\"fullname\"),\n                email=rv.get(\"email\"),\n                # Authoritative if OAuth domain is in the authoritative oauth section in ponymail.yaml\n                # Required for access to private emails\n                authoritative=authoritative,\n                oauth_provider=oauth_provider,\n                oauth_data=rv,\n                admin=admin,\n            )\n            # This could be improved upon, instead of a raw response return value\n            return aiohttp.web.Response(\n                headers={\"set-cookie\": cookie, \"content-type\": \"application/json\"}, status=200, text='{\"okay\": true}',\n            )\n\n    return {\"okay\": False, \"message\": \"Could not process OAuth login!\"}\n\n\ndef register(server: plugins.server.BaseServer):\n    return plugins.server.Endpoint(process)\n","sub_path":"server/endpoints/oauth.py","file_name":"oauth.py","file_ext":"py","file_size_in_byte":3813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"165646424","text":"import itertools\nimport os\n\nimport numpy as np\nfrom matplotlib import pyplot\n\nfolder = \"../../data/loop_wdl_0\"\n\nroot_wdl = []\ngame_length = []\n\nfor gen in itertools.count():\n    path_csv = os.path.join(folder, f\"gen_{gen}\", \"games_from_prev.csv\")\n    if not os.path.exists(path_csv):\n        print(f\"Stopping at {gen}, csv file does not exist\")\n        break\n\n    print(path_csv)\n    data = np.loadtxt(path_csv, delimiter=\",\", ndmin=2, dtype=np.float32)\n    first_rows = np.where((data[:, 6 + 81:6 + 81 + 81] == 1).all(axis=1))[0]\n\n    root_wdl.append(data[first_rows, 3:6].mean(axis=0))\n    game_length.append(len(data) / len(first_rows))\n\ngen_count = len(root_wdl)\nroot_wdl = np.array(root_wdl).transpose()\n\npyplot.stackplot(np.arange(gen_count), root_wdl, labels=[\"win\", \"draw\", \"loss\"])\npyplot.legend()\npyplot.title(\"Zero estimated WDL of the empty board\")\npyplot.show()\n\npyplot.plot(game_length, label=\"game length\")\npyplot.legend()\npyplot.title(\"Average game length\")\npyplot.show()\n","sub_path":"python/mini/plot_root_eval.py","file_name":"plot_root_eval.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"411042734","text":"# C_Parser.py\r\n# Alessio Burrello <alessio.burrello@unibo.it>\r\n# \r\n# Copyright (C) 2019-2020 University of Bologna\r\n# \r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n# \r\n#     http://www.apache.org/licenses/LICENSE-2.0\r\n# \r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n# Libraries\r\nimport json\r\nimport os\r\nimport numpy as np\r\n\r\n# DORY modules\r\nfrom dory.Parsers.Parser_HW_to_C import Parser_HW_to_C\r\nimport dory.Utils.Templates_writer.Layer2D_template_writer as Layer2D_writer\r\nimport dory.Utils.Templates_writer.Makefile_template_writer as Makefile_writer\r\n\r\n\r\n\r\nclass C_Parser_PULP(Parser_HW_to_C):\r\r\n    # Used to manage the ONNX files. By now, supported Convolutions (PW and DW), Pooling, Fully Connected and Relu.\r\n    def __init__(self, graph, config_file, config_file_dir, verbose_level, perf_layer, precision_library, app_directory, n_inputs=1):\r\n\r\n        file_path = self.get_file_path()\r\n        with open(os.path.join(file_path, \"HW_description.json\")) as f:\r\n            HW_description = json.load(f)\r\n        self.precision_library = precision_library\r\n        self.source_Constant_bits_library = config_file[\"BNRelu_bits\"]\r\n        self.config_file = config_file\r\n        super().__init__(graph, os.path.join(config_file_dir, os.path.dirname(config_file[\"onnx_file\"])), HW_description, verbose_level, perf_layer, \"Makefile\", app_directory, n_inputs)\r\n        try:\r\n            db = HW_description['double_buffering']\r\n        except KeyError:\r\n            print(\"C_Parser_PULP: Key 'double_buffering' not found in HW_description.json - setting to 2\")\r\r\n            db = 2\r\n        self.double_buffering = db\r\n\r\n    def get_file_path(self):\r\n        raise NotImplementedError(\"To be implemented by child class!\")\r\n\r\n    def copy_backend_files(self, node):\r\n        if self.precision_library == 'auto':\r\n            self.precision_library = '8bit'\r\n            if \"Addition\" not in node.name and \"Pool\" not in node.name:\r\n                if node.get_parameter('output_activation_bits') < 8 or node.get_parameter('input_activation_bits') < 8 or node.get_parameter('weight_bits') < 8:\r\n                    self.precision_library = 'mixed-sw'\r\n            else:\r\n                if node.get_parameter('output_activation_bits') < 8 or node.get_parameter('input_activation_bits') < 8:\r\n                    self.precision_library = 'mixed-sw'\r\n\r\n        root = self.get_file_path()\r\n        if self.precision_library == \"8bit\":\r\n            files = os.path.join(root, \"../Backend_Kernels/pulp-nn/\")\r\n        elif self.precision_library == \"mixed-sw\":\r\n            files = os.path.join(root, \"../Backend_Kernels/pulp-nn-mixed/XpulpV2/\")\r\n        elif self.precision_library == \"mixed-hw\":\r\n            files = os.path.join(root, \"../Backend_Kernels/pulp-nn-mixed/XpulpNN/\")\r\n        if os.listdir(os.path.join(files, \"{}bit/include\".format(self.source_Constant_bits_library)))[0] not in os.listdir(self.inc_dir):\r\n            for file in os.listdir(os.path.join(files, \"{}bit/include\".format(self.source_Constant_bits_library))):\r\n                file_to_copy = os.path.join(files, \"{}bit/include\".format(self.source_Constant_bits_library), file)\r\n                os.system('cp \"{}\" {}'.format(file_to_copy, os.path.join(self.app_directory, self.inc_dir_rel)))\r\n        if self.precision_library == \"8bit\":\r\n            if os.listdir(os.path.join(files, \"{}bit/src\".format(self.source_Constant_bits_library)))[0] not in os.listdir(os.path.join(self.app_directory, self.src_dir_rel)):\r\n                for file in os.listdir(os.path.join(files, \"{}bit/src\".format(self.source_Constant_bits_library))):\r\n                    file_to_copy = os.path.join(files, \"{}bit/src\".format(self.source_Constant_bits_library), file)\r\n                    os.system('cp \"{}\" {}'.format(file_to_copy, os.path.join(self.app_directory, self.src_dir_rel)))\r\n        elif self.precision_library in [\"mixed-sw\", \"mixed-hw\"]:\r\n            Input_bits = str(node.get_parameter('input_activation_bits'))\r\n            Output_bits = str(node.get_parameter('output_activation_bits'))\r\n            Input_type = node.get_parameter('input_activation_type')[0]\r\n            Output_type = node.get_parameter('output_activation_type')[0]\r\n            out = \"_\" + Output_type + Output_bits\r\n            in_out = \"_\" + Input_type + Input_bits + out\r\n            maybe_x = 'x' if self.precision_library == \"mixed-hw\" else ''\r\n            if \"Addition\" in node.name:\r\n                in1_in2_out = \"_\" + Input_type + Input_bits + \"_\" + node.get_parameter('second_input_activation_type')[0] + str(node.get_parameter('second_input_activation_bits')) + \"_\" + Output_type + Output_bits\r\n                file = f'Add/{maybe_x}pulp_nn_add{in1_in2_out}.c'\r\n            elif \"Pool\" in node.name and \"Max\" in node.op_type:\r\n                file = f'Pooling/MaxPool/{maybe_x}pulp_nn_maxpool{out}.c'\r\n            elif \"Pool\" in node.name and (\"Avg\" in node.op_type or \"Average\" in node.op_type):\r\n                file = f'Pooling/AvgPool/{maybe_x}pulp_nn_avgpool{in_out}.c'\r\n\r\n            in_out_weights = \"_\" + Input_type + Input_bits + \"_\" + Output_type + Output_bits + \"_\" + node.get_parameter('weight_type')[0] + str(node.get_parameter('weight_bits'))\r\n            if \"Conv\" in node.name and node.group > 1:\r\n                file = f'Depthwise/{maybe_x}pulp_nn_depthwise{in_out_weights}.c'\r\n            elif \"Conv\" in node.name and node.group == 1:\r\n                if node.conv1d and self.precision_library == 'mixed-hw':\r\n                    file = f'Convolution/xpulp_nn_conv1d{in_out_weights}.c'\r\n                else:\r\n                    file = f'Convolution/{maybe_x}pulp_nn_conv{in_out_weights}.c'\r\n            elif \"FullyConnected\" in node.name and node.output_activation_bits == 32: \r\n                file = f'LinearNoQuant/{maybe_x}pulp_nn_linear{in_out_weights}.c'\r\n            elif \"FullyConnected\" in node.name:\r\n                file = f'LinearQuant/{maybe_x}pulp_nn_linear{in_out_weights}.c'\r\n            file_to_copy = os.path.join(files, \"{}bit/src\".format(self.source_Constant_bits_library), file)\r\n            os.system('cp \"{}\" {}'.format(file_to_copy, os.path.join(self.app_directory, self.src_dir_rel)))\r\n            if (\"Conv\" in node.name or \"FullyConnected\" in node.name) and node.get_parameter('output_activation_bits') != 32:\r\n                in_bits_matmul = \"8\" if self.precision_library == \"mixed-sw\" else str(Input_bits)\r\n                in_out_weights = \"_\" + Input_type + in_bits_matmul + \"_\" + Output_type + Output_bits + \"_\" + node.get_parameter('weight_type')[0] + str(node.get_parameter('weight_bits'))\r\n                file = f'MatrixMultiplication/{maybe_x}pulp_nn_matmul{in_out_weights}.c'\r\n                file_to_copy = os.path.join(files, \"{}bit/src\".format(self.source_Constant_bits_library), file)\r\n                os.system('cp \"{}\" {}'.format(file_to_copy, os.path.join(self.app_directory, self.src_dir_rel)))\r\n\r\n    def mapping_layers_to_C_files(self):\r\n        print(\"\\nMapping the layers files to their templates and copying the kernels associated.\")\r\n        tmpl_dir = os.path.realpath(os.path.join(self.get_file_path(), 'Templates/layer_templates'))\r\n        out_dir = self.app_directory\r\n        n_memory_levels = self.HW_description['memory']['levels']\r\n        for i, node in enumerate(self.HWgraph):\r\n            self.copy_backend_files(node)\r\n\r\n            if n_memory_levels > 2 and (node.L3_input != 0 or (node.tiling_dimensions[\"L3\"][\"output_dimensions\"] != node.tiling_dimensions[\"L2\"][\"output_dimensions\"]) or (node.tiling_dimensions[\"L3\"][\"weights_dimensions\"] != node.tiling_dimensions[\"L2\"][\"weights_dimensions\"])):\r\n                Layer2D_writer.print_template_layer_L3(node, tmpl_dir, out_dir)\r\n                if node.tiling_dimensions[\"L3\"][\"input_dimensions\"][1] > node.tiling_dimensions[\"L2\"][\"input_dimensions\"][1]:\r\n                    node.tiling_dimensions[\"L2\"][\"output_dimensions\"][1]  = int(np.floor((node.tiling_dimensions[\"L2\"][\"input_dimensions\"][1] - node.kernel_shape[0] + node.strides[0]) / node.strides[0]))\r\n                if node.tiling_dimensions[\"L3\"][\"output_dimensions\"][1] > node.tiling_dimensions[\"L2\"][\"output_dimensions\"][1]:\r\n                    node.tiling_dimensions[\"L2\"][\"input_dimensions\"][1]   = node.tiling_dimensions[\"L2\"][\"output_dimensions\"][1] * node.strides[0] + node.kernel_shape[0] - node.strides[0]\r\n                node.name = node.name + \"_L2\"\r\n                padding = node.pads\r\n                node.pads = [0, padding[1], 0, padding[3]]\r\n                Layer2D_writer.print_template_layer(node, self.precision_library, tmpl_dir, out_dir, double_buffering=self.double_buffering)\r\n                node.name = node.name[:-3]\r\n                if padding[0] > 0:\r\n                    node.name = node.name + \"_L2_p_t\"\r\n                    node.pads = [padding[0], padding[1], 0, padding[3]]\r\n                    Layer2D_writer.print_template_layer(node, self.precision_library, tmpl_dir, out_dir, double_buffering=self.double_buffering)\r\n                    node.name = node.name[:-1] + \"b\"\r\n                    node.pads = [0, padding[1], padding[2], padding[3]]\r\n                    node.tiling_dimensions[\"L2\"][\"input_dimensions\"][1] -= (padding[2] - ((node.tiling_dimensions[\"L3\"][\"input_dimensions\"][1] + padding[0] + padding[2]) - (node.tiling_dimensions[\"L3\"][\"output_dimensions\"][1]* node.strides[0] + node.kernel_shape[0] - node.strides[0])))\r\n                    if node.tiling_dimensions[\"L1\"][\"input_dimensions\"][1] > node.tiling_dimensions[\"L2\"][\"input_dimensions\"][1]:\r\n                        node.tiling_dimensions[\"L1\"][\"input_dimensions\"][1] = node.tiling_dimensions[\"L2\"][\"input_dimensions\"][1]\r\n                    if node.tiling_dimensions[\"L1\"][\"output_dimensions\"][1] > node.tiling_dimensions[\"L2\"][\"output_dimensions\"][1]:\r\n                        node.tiling_dimensions[\"L1\"][\"output_dimensions\"][1] = node.tiling_dimensions[\"L2\"][\"output_dimensions\"][1]\r\n                    Layer2D_writer.print_template_layer(node, self.precision_library, tmpl_dir, out_dir, double_buffering=self.double_buffering)\r\n                    node.name = node.name[:-7]\r\n            else:\r\n                if node.tiling_dimensions[\"L2\"][\"input_dimensions\"][2] == node.tiling_dimensions[\"L1\"][\"input_dimensions\"][2]:\r\n                    node.tiling_dimensions[\"L1\"][\"output_dimensions\"][2] = int((node.tiling_dimensions[\"L1\"][\"input_dimensions\"][2] + (node.pads[1] + node.pads[3]) - node.kernel_shape[1] + node.strides[1]) / node.strides[1])\r\n                if node.tiling_dimensions[\"L2\"][\"input_dimensions\"][1] == node.tiling_dimensions[\"L1\"][\"input_dimensions\"][1]:\r\n                    node.tiling_dimensions[\"L1\"][\"output_dimensions\"][1] = int((node.tiling_dimensions[\"L1\"][\"input_dimensions\"][1] + (node.pads[0] + node.pads[2]) - node.kernel_shape[0] + node.strides[0]) / node.strides[0])\r\n                Layer2D_writer.print_template_layer(node, self.precision_library, tmpl_dir, out_dir, double_buffering=self.double_buffering)\r\n\r\n    def mapping_makefile(self):\r\n        super(C_Parser_PULP, self).mapping_makefile()\r\n        # also print the \"vars.mk\"\r\n        prefix = self.HWgraph[0].prefix\r\n        Makefile_writer.print_template_Makefile(\r\n            self.HWgraph,\r\n            self.HW_description,\r\n            prefix+\"vars.mk\",\r\n            self.app_directory,\r\n            template_location_rel=\"Templates/vars.mk_template\")\r\n\r\n\r\n\r\n","sub_path":"dory/Hardware_targets/PULP/Common/C_Parser.py","file_name":"C_Parser.py","file_ext":"py","file_size_in_byte":11842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"438450799","text":"# ===========================================================================\n# Available properties\n#  * device\n#  * device_info\n#  * floatX\n#  * epsilon\n#  * multigpu\n#  * optimizer\n#  * cnmem\n#  * backend\n#  * seed\n# ===========================================================================\nfrom __future__ import division, absolute_import\n\nimport os\nimport re\nimport sys\nimport pip\nimport shutil\nimport tempfile\nimport subprocess\nimport warnings\nfrom six import string_types\nfrom multiprocessing import cpu_count\ntry:\n  from functools import lru_cache\nexcept ImportError:\n  def lru_cache(maxsize=128):\n    def tmp_func(func):\n      return func\n    return tmp_func\n\nimport numpy as np\n\n# ===========================================================================\n# Helper\n# ===========================================================================\ndef _ctext(s, color='red'):\n  # just a copy of ctext implementation to make\n  # the config log look better.\n  try:\n    from colorama import Fore\n    color = color.upper()\n    color = getattr(Fore, color, '')\n    return color + str(s) + Fore.RESET\n  except ImportError:\n    pass\n  return str(s)\n\ndef _warning(text):\n  print(_ctext('[WARNING]', 'red'), text)\n\ndef _check_package_available(name):\n  for i in pip.get_installed_distributions():\n    if name.lower() == i.key.lower():\n      return True\n  return False\n\ndef _query_gpu_info():\n  \"\"\" This function query GPU information:\n  ngpu\n  [device_name, device_compute_capability, device_total_memory]\n\n  Note\n  ----\n  this function use deviceQuery command, so you better have it\n  in your path\n  \"\"\"\n  dev = {'ngpu': 1,\n         # deviceName: [cardName, computeCapability, mem(MB)]\n         'dev0': ['Unknown', 3.0, 1024]}\n  temp_dir = tempfile.mkdtemp()\n  p = os.path.join(temp_dir, 'tmp.txt')\n  queried = subprocess.call('deviceQuery > ' + p,\n          shell=True,\n          stdout=subprocess.PIPE,\n          stderr=subprocess.PIPE) == 0\n  dev = {}\n  if queried: # found deviceQuery\n    info = open(p, 'r').read()\n    devNames = re.compile(r'Device \\d: \".*\"').findall(info)\n    devNames = [i.strip().split(':')[-1].replace('\"', '') for i in devNames]\n    ngpu = len(devNames)\n    comCap = re.compile(\n        r'CUDA Capability Major\\/Minor version number:\\s*.*').findall(info)\n    comCap = [float(i.strip().split(':')[-1]) for i in comCap]\n    totalMems = re.compile(\n        r'Total amount of global memory:\\s*\\d*').findall(info)\n    totalMems = [int(i.strip().split(':')[-1]) for i in totalMems]\n    # ====== create dev ====== #\n    dev['ngpu'] = ngpu\n    for i, (name, com, mem) in enumerate(zip(devNames, comCap, totalMems)):\n      dev['dev%d' % i] = [name, com, mem]\n  else:\n    # _warning('Cannot use \"deviceQuery\" to get GPU information for configuration.')\n    from tensorflow.python.client import device_lib\n    local_device_protos = device_lib.list_local_devices()\n    dev['ngpu'] = 0\n    for i, name in (x.name for x in local_device_protos\n        if x.device_type == 'GPU'):\n      dev['dev%d' % i] = [name, None, None]\n      dev['ngpu'] += 1\n  # remove temp-dir\n  shutil.rmtree(temp_dir)\n  return dev\n\nclass AttributeDict(dict):\n  __getattr__ = dict.__getitem__\n  __setattr__ = dict.__setitem__\n\n  def __setstate__(self, states):\n    for i, j in states:\n      self[i] = j\n\n  def __getstate__(self):\n    return self.items()\n\n# ===========================================================================\n# Auto config\n# ===========================================================================\nCONFIG = None\nEPS = None\n_SESSION = {}\n_RNG_GENERATOR = None\n\ndef set_session(session):\n  global _SESSION\n  _SESSION = session\n\ndef get_session_config():\n  import tensorflow as tf\n  session_args = {\n      'intra_op_parallelism_threads': CONFIG['nthread'],\n      'inter_op_parallelism_threads': CONFIG['ncpu'],\n      'allow_soft_placement': True,\n      'log_device_placement': CONFIG['debug'],\n  }\n  if CONFIG['ngpu'] > 0:\n    if CONFIG['cnmem'] > 0:\n      session_args['gpu_options'] = tf.GPUOptions(\n          per_process_gpu_memory_fraction=CONFIG['cnmem'],\n          allow_growth=False)\n    else:\n      session_args['gpu_options'] = tf.GPUOptions(\n          allow_growth=True)\n  return session_args\n\ndef get_session(graph=None):\n  \"\"\" Calling this method will make sure you create\n  only 1 Session per graph.\n  \"\"\"\n  # avoid duplicate Session for the same Graph when\n  # graph is None\n  if graph is None:\n    import tensorflow as tf\n    default_graph = tf.get_default_graph()\n    if default_graph in _SESSION:\n      _SESSION[None] = _SESSION[default_graph]\n  # another case\n  elif None in _SESSION and _SESSION[None].graph == graph:\n    _SESSION[graph] = _SESSION[None]\n  # ====== initialize tensorflow session ====== #\n  if graph not in _SESSION:\n    import tensorflow as tf\n    session_args = get_session_config()\n    sess = tf.Session(config=tf.ConfigProto(**session_args), graph=graph)\n    _SESSION[graph] = sess\n    with sess.graph.as_default():\n      tf.Variable(initial_value=False, dtype=tf.bool, name='IsTraining__',\n                  trainable=False)\n  return _SESSION[graph]\n\ndef auto_config(config=None):\n  ''' Auto-configure ODIN using os.environ['ODIN'].\n\n  Parameters\n  ----------\n  config : object\n    predefined config object return from auto_config\n  check : boolean\n    if True, raise Exception of CONFIG have not initialized\n\n  Returns\n  -------\n  config : object\n    simple object (kind of dictionary) contain all configuraitons\n\n  '''\n  global CONFIG\n  global _RNG_GENERATOR\n  global EPS\n  if CONFIG is not None:\n    warnings.warn('You should not auto_config twice, old configuration already '\n        'existed, and cannot be re-configured.')\n    return CONFIG\n  # ====== specific pattern ====== #\n  valid_cnmem_name = re.compile(r\"(cnmem)[=]?[10]?\\.\\d*\")\n  valid_seed = re.compile(r\"seed\\D?(\\d*)\")\n\n  floatX = 'float32'\n  epsilon = 1e-8\n  cnmem = 0.\n  seed = 1208251813\n  debug = False\n  # number of devices\n  ncpu = 0\n  ngpu = 1 # default try to get the GPU\n  nthread = 0\n  log_level = '3'\n  # ====== parsing the config ====== #\n  if config is None: # load config from flags\n    odin_flags = os.getenv(\"ODIN\", \"\")\n    for c in (';', ':', '.', '*'):\n      odin_flags.replace(c, ',')\n    s = odin_flags.split(',')\n    # ====== processing each tag ====== #\n    for i in s:\n      i = i.lower().strip()\n      # ====== Data type ====== #\n      if 'float' in i or 'int' in i:\n        floatX = i\n      # ====== Devices ====== #\n      elif 'cpu' in i:\n        if '=' not in i:\n          _warning(\"Found `cpu` tag, but number of CPU is not \"\n             \"specified, auto select all %d CPU-cores \" %\n             cpu_count())\n        else:\n          ncpu = min(int(i.split('=')[-1]), cpu_count())\n      elif 'gpu' in i:\n        if '=' in i:\n          ngpu = int(i.split('=')[-1])\n        else:\n          ngpu = 1\n      # ====== number thread ====== #\n      elif 'thread' in i:\n        if '=' not in i:\n          _warning(\"Found `thread` tag, but number of thread is \"\n             \"not specified, only 1 thread per process (CPU-core) is \"\n             \"used by default.\")\n        else:\n          nthread = int(i.split('=')[-1])\n      # ====== cnmem ====== #\n      elif 'cnmem' in i:\n        match = valid_cnmem_name.match(i)\n        if match is None:\n          raise ValueError('Unsupport CNMEM format: %s. '\n               'Valid format must be: cnmem=0.75 or cnmem=.75 '\n               ' or cnmem.75' % str(i))\n        i = i[match.start():match.end()].replace('cnmem', '').replace('=', '')\n        cnmem = float(i)\n      # ====== seed ====== #\n      elif 'seed' in i:\n        match = valid_seed.match(i)\n        if match is None:\n          raise ValueError('Invalid pattern for specifying seed value, '\n               'you can try: [seed][non-digit][digits]')\n        seed = int(match.group(1))\n      # ====== debug ====== #\n      elif 'debug' in i:\n        debug = True\n      # ====== log-leve ====== #\n      elif 'log' in i:\n        if '=' in i:\n          log_level = i.split('=')[-1]\n        else:\n          log_level = '0'\n  else: # load config from object\n    floatX = config['floatX']\n    ncpu = config['ncpu']\n    ngpu = config['ngpu']\n    nthread = config['nthread']\n    cnmem = config['cnmem']\n    seed = config['seed']\n    debug = config['debug']\n  # epsilon\n  EPS = np.finfo(np.dtype(floatX)).eps\n  # devices\n  dev = {}\n  if ngpu > 0:\n    dev.update(_query_gpu_info())\n  else:\n    dev['ngpu'] = 0\n  dev['ncpu'] = ncpu\n  dev['nthread'] = nthread\n\n  # ====== Log the configuration ====== #\n  def print_log(tag, value, nested=False):\n    if nested:\n      s = _ctext('  * ', 'MAGENTA')\n    else:\n      s = '[Auto-Config] '\n    s += _ctext(tag, 'yellow') + ' : '\n    s += _ctext(value, 'cyan') + '\\n'\n    sys.stderr.write(s)\n  print_log('#CPU', 'auto' if dev['ncpu'] == 0 else dev['ncpu'])\n  print_log('#CPU-native', str(cpu_count()))\n  print_log('#Thread/core',\n      'auto' if dev['nthread'] == 0 else dev['nthread'],\n      nested=True)\n  print_log('#GPU', dev['ngpu'])\n  if dev['ngpu'] > 0:\n    for i in range(dev['ngpu']):\n      print_log('GPU-dev%d' % i, dev['dev%d' % i], nested=True)\n  print_log('FloatX', floatX)\n  print_log('Epsilon', epsilon)\n  print_log('CNMEM', cnmem)\n  print_log('SEED', seed)\n  print_log('Debug', debug)\n  print_log('Log-level', log_level)\n  # ==================== create theano flags ==================== #\n  if dev['ngpu'] == 0:\n    os.environ['CUDA_VISIBLE_DEVICES'] = \"\"\n  # ====== Return global objects ====== #\n  CONFIG = AttributeDict()\n  CONFIG.update({\n      'ncpu': dev['ncpu'],\n      'ngpu': dev['ngpu'],\n      'nthread': dev['nthread'],\n      'device_info': dev,\n      'floatX': floatX,\n      'epsilon': epsilon,\n      'cnmem': cnmem,\n      'seed': seed,\n      'debug': debug\n  })\n  _RNG_GENERATOR = np.random.RandomState(seed=seed)\n  with warnings.catch_warnings():\n    warnings.filterwarnings(action='ignore', category=ImportWarning)\n    import tensorflow as tf\n  tf.set_random_seed(seed=_RNG_GENERATOR.randint(0, 10e8))\n  # tensorflow log level\n  os.environ['TF_CPP_MIN_LOG_LEVEL'] = log_level\n  return CONFIG\n\n# ===========================================================================\n# Getter\n# ===========================================================================\ndef __validate_config():\n  if CONFIG is None:\n    raise Exception(\"auto_config has not been called.\")\n\ndef get_rng():\n  \"\"\"\n    Return\n    ------\n    the numpy RandomState as a \"Randomness Generator\"\n  \"\"\"\n  global _RNG_GENERATOR\n  if _RNG_GENERATOR is None:\n    _RNG_GENERATOR = np.random.RandomState(seed=120825)\n  return _RNG_GENERATOR\n\nget_random_state = get_rng\n\ndef randint(low=0, high=10e8, size=None, dtype='int32'):\n  \"\"\"Randomly generate and integer seed for any stochastic function.\"\"\"\n  return _RNG_GENERATOR.randint(low=low, high=high, size=size,\n    dtype=dtype)\n\ndef get_ncpu():\n  \"\"\" Return number of inter_op_parallelism_threads \"\"\"\n  __validate_config()\n  return CONFIG['ncpu']\n\ndef get_ncpu_native():\n  return cpu_count()\n\ndef get_ngpu():\n  \"\"\" Return number of GPU \"\"\"\n  __validate_config()\n  return CONFIG['ngpu']\n\ndef get_nthread():\n  \"\"\" Return number of intra_op_parallelism_threads \"\"\"\n  __validate_config()\n  return CONFIG['nthread']\n\ndef get_nb_processors():\n  \"\"\" In case using CPU, return number of cores\n  If GPU is used, return number of graphics card.\n  \"\"\"\n  __validate_config()\n  return CONFIG['device_info']['n']\n\ndef get_device_info():\n  \"\"\" Device info contains:\n  {\"n\": ngpu,\n   \"dev0\": [device_name, device_compute_capability, device_total_memory],\n   \"dev1\": [device_name, device_compute_capability, device_total_memory],\n   ...\n  }\n  \"\"\"\n  __validate_config()\n  return CONFIG['device_info']\n\ndef get_floatX():\n  __validate_config()\n  return CONFIG['floatX']\n\ndef get_optimizer():\n  __validate_config()\n  return CONFIG['optimizer']\n\ndef get_cnmem():\n  __validate_config()\n  return CONFIG['cnmem']\n\ndef get_backend():\n  __validate_config()\n  return CONFIG['backend']\n\ndef get_seed():\n  __validate_config()\n  return CONFIG['seed']\n","sub_path":"odin/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":12028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"206246008","text":"\"\"\"\nHier alle parameters uit par_C7.py en par_allconcepts\n\nDit heb ik gedownload van de drive op 1 juni 13:30\nAls je veranderingen gemaakt hebt daarna staan die hier dus nog niet in\n\nAls je niet meer weet of je iets aangepast hebt valt het te mergen, doe dit voorzichtig (of vraag het even aan Rens)\nCheers\n\n\n\"\"\"\n\nimport numpy as np\n\n\n\n\n### Constants\ng = 9.80665                                        #[m/s2]     Gravitational acc. Frank 10-5   \nrho = 0.9046                                       #[kg/m3]    Assumption ISA 10000 ft. Frank 10-5\nrho_1000 = 1.11164\nrho_0 = 1.225                                      #[kg/m3]    Assumption ISA Sea-Level. Frank 10-5\na_0 = 343.                                           #[m/s]     Ground level speed of sound. Rens 16-5\na_0ft = 1125.                                        #[ft/s]   #speed of sound in amerika yolo units. Rens 16-5\nvisc_0 = 1.789 * 10**-5                             #?          dynamic viscosity at sea level\n\n### Requirements\nV_stall_max = 25. *0.514444444                    #[m/s]      Cruise speed - requirement. Frank 10-5\nclimbrate = 1.3                                    #[m/s]      climb rate - 1000 ft. in 4 min. Frank\ns_land = 80.                                       #[m]        Landing distance - assumption - Frank\nn_max = 2.                                         #[-]        Performance maximum load factor - Frank\nn_max_struc = 6.                                   #[-]        Structural maximum load factor - Frank\nn_ult = 9.                                         # [-]       ultimate load factor - Frank\nfl_time = 30.                                      #[min]      Flight time from requirements\nweight_pilot_max = 85.                             #[kg]       Maximum pilot weight - requirements - Frank\nweight_pilot_min = 65.                             #[kg]       Minimum pilot weight - requirements - Frank\npilot_length_average = 1.75                        #[m]        From requirements\n\n### Right now, equal for every concept\nTOP = 15.                            #[-]        DIKKE GOK VAN RANDOM PLAATJE UIT ADSEE 1 SLIDES LECTURE 3 - Frank\nf = 1.                                             #[-]        W_TO/W_L - no fuel - Frank\nsigma = rho_0/rho_0                                         #[-]        only 1 if TO @ ground level - Frank\n\n### propeller estimations voor noise estimation in die goede oude midterm tijd\n#r_prop          = 2.3              #ft     #propeller radius, zelfde als e-lift\n#rpm             = 1950.            #-      #rotation propeller, zelfde als e-lift\n#blades          = 3.               #-      #number of propeller blades, schatting\n\nA = 11.                                       #[-]        aspect ratio - REF data - Frank 15-5     \n\nconcept = \"Bird\"\n\n#V_cruise = 75.* 0.514444444                       #[m/s]      Cruise speed. Frank 10-5\nV_cruise = 40. * 0.514444444                        #[m/s]      Cruise speed. Dikke snelheids verandering - Frank 15-5\n\n#weight_OEW = 50.                                   #[kg]       Operational empty weight - REF data.- Frank 15-5\n#weight_OEW = 49.\nweight_Wing = 25.5                                   #[kg]        from Structures Jelle & Mariska - Win\nweight_Actuator = 0.2\nweight_controllSurfaces=15*weight_Actuator\nweight_LG = 5.2                                   #[kg]        from Structures Jelle & Mariska - Landing Gear\nweight_Tail_surface = 3.2                                    #[kg]        from Structures Jelle & Mariska - Tail\nweight_Tail_structure = 2.2\nweight_Propeller = (1.10 + 2.5) * 2.                       #[kg]        from P & P - all propulsion weight,  prop, engine\nweight_Battery = (2. * 2.79 + 2. * 1.50)  + 0.22                                   #[kg]        from P & P -  battery\nweight_PilotAttachment = 5.                       #[kg]        from Structures Jelle & Mariska - Pilot attachment\nweight_Overig = 5                             #[kg]       from Structures - All overig gewicht, zoals actuators, control surfaces en ander zooi\n\nweight_OEW = weight_Wing+weight_LG+weight_controllSurfaces+weight_Tail_surface+weight_Tail_structure+weight_Propeller+weight_Battery+weight_PilotAttachment+weight_Overig                                  #[kg]        from Structures Jelle & Mariska - Operational empty weight - REF data.- Frank 15-5\n\nLG_strut_width = 0.0375                                   #[m]            from Structures, the width of the struts holding the front landing gear, there are 4 of these struts\nLG_strut_height = 4*LG_strut_width                       #[m]            from Structures, the width of the struts holding the front landing gear, there are 4 of these struts\nLG_strut_t = 0.001                                   #[m]            from Structures, the width of the struts holding the front landing gear, there are 4 of these struts\nLG_strut_angle = 0.57                                   #[rad]            from Structures, the width of the struts holding the front landing gear, there are 4 of these struts\nLG_strut_length = 1.188                                   #[m]            from Structures, the width of the struts holding the front landing gear, there are 4 of these struts\n\n\n#x_cg = 0.998                                        #[m]        center of gravity location with respect to leading edge of root chord\n#x_cp = 1.016                                      #[m]        center of pressure locationn with respect to leading edge of root chord\n\nweight_TO = (weight_OEW + weight_pilot_max) * g    #[N]        Take off weight - Frank\n\n\n\n#Wing_S = 16.3101712222                              # [m2]       Wing area\n#Wing_S = 13.5427                                    # [m2]       Wing Area - Frank 15-5\n#Wing_S = 11.5349                                    # [m/s]      Iteration 1 - Frank 9/6\n#Wing_S = 13.7494                                     # [m/s]      Iteration 2 - Frank 13/6\n\nWing_S = 13.86*1.04\n\nWing_b = np.sqrt(A*Wing_S)                         #[m]        Span - From aspect ratio and wing area - Frank 15-5\n\nWing_c = Wing_b/A                                  #[m]        Chord length from aspect and wing area - Frank 15-5\n\nSweep = 8.771                                    #[deg]       Sweep in deg\n\nTail_S = 1.87                                     #[m2]       Tail area\n\nTail_b = 3.4                                     #[m]       Tail span\n\nTail_c = 0.55                                      #[m]       Tail chord\n\nTail_l = 3.15                                        #[m]    Tail length\n\nTail_vol = Tail_S*Tail_l/(Wing_S*Wing_c)            #[-]     Tail volume coefficient\n\nTail_A = Tail_b/Tail_c                              #[-]     Tail aspect ratio\n\n\n\n########## AERODYNAMICS ########################\n\n#C_Lmax_clean = 1.9                                 #[-]        REF data - Gok - Frank\nC_Lmax_clean = 1.1                                 #[-]         MOET NOG GECHECKT WORDEN REF data - Gok - Frank     (Betere) REF data na praatje met Joris  - Gok - Frank 30 - 5                     \n                    \n#C_Lmax_takeoff = 1.9                               #[-]        REF data - Gok - Frank\nC_Lmax_takeoff = 1.1                                #[-]         MOET NOG GECHECKT WORDEN REF data - Gok - Frank     (Betere) REF data na praatje met Joris  - Gok - Frank 30 - 5                     \n              \n#C_Lmax_landing= 1.8                               #[-]        REF data - Gok - Frank\n#C_Lmax_landing= 2.0                                #[-]        REF data - Gok - Frank\nC_Lmax_landing = 1.1                        #[-]         MOET NOG GECHECKT WORDEN REF data - Gok - Frank     (Betere) REF data na praatje met Joris  - Gok - Frank 30 - 5                     \n                    \nC_L_alpha = 5.5050  \t                          #[-]        DIKKE GOK van flat plate theory - Frank\n\ne_clean = 0.85                                      #[-]        Roskam ADSEE 1 slides - Frank\n\n#C_D0_clean = 0.01                                 #[-]         Dikke gok - Frank - 10-5\n#C_D0_clean = 0.15                                 #[-]         Schattting. Zie aerodynamics -> drag tradeoff - Rens - 15-5\n#C_D0_clean = 0.021                                #[-]         #Nieuwe schatting van Rgoier 17- 5 ##Schattting. Zie aerodynamics -> drag tradeoff - Rens - 15-5\n#C_D0_clean = 0.0307                                 #[-]         Nieuwe betere schattting. Zie aerodynamics -> drag tradeoff - Rens - 18-5\n#C_D0_clean = 0.06                                  #[-]         Nieuwe schatting over C_D0 na praatje met Joris Frank & Rens - 31 - 5 \nC_D0_clean = 0.04006                              #[-]        uitkomst gedetaileerde CD0 analyse, met nog wat LG schattingen\n\ndelta_e_takeoff = 0.                               #[-]         no flaps - Frank\n\ndelta_e_landing = 0.                               #[-]         no flaps - Frank\n \n#delta_C_D0_takeoff = 0.                            #[-]         no flaps - Frank\n#delta_C_D0_takeoff = 0.015                          #[-]        Pilot CD0 - Rens 9/6\ndelta_C_D0_takeoff = 0.01595                            #[-]        uitkomst gedetaileerde CD0 analyse, met nog wat LG schattingen\n\n#delta_C_D0_landing = 0.                            #[-]         no flaps - Frank\n#delta_C_D0_landing = 0.015                         #[-]        Pilot CD0 - Rens 9/6\ndelta_C_D0_landing = 0.01802                        #[-]        uitkomst gedetaileerde CD0 analyse, met nog wat LG schattingen\n\ndelta_C_D0_gliding = 0.00207                        #[-]        NIEUW! als props stilstaan. uitkomst gedetaileerde CD0 analyse, met nog wat LG schattingen\n\n\nairfoil = \"NACA 633-618\"                             #current airfoil, Rogier 13-6, er komt een python file met airfoil parameters\n\ntaper = 0.4                                         #[-]    #Rens: Torenbeek pagina 237 geeft 0.4 als ideale taper\nrootchord = (2./(1.+taper)) * (Wing_S/Wing_b)       #[m]    #formules voor de chords ook uit Torenbeek\ntipchord  = taper * rootchord                       #[m]    \n\n\n\n########### EINDE AERO #####################\n\n########### Waardes vanuit P&P: ############\n\nefficiency_prop = 0.8                            #[-]        prop. effiency - DIKKE GOK - Frank\n\n#V_stall = 9.4329                                    #[m/s]       Wing sizing estimation. Frank 15-5\n#V_stall = 11.3178                                   #[m/s]      Itertaion 1 - Frank 9/6\n#V_stall = 11.62917\nV_stall = 12.5551\n \n#V_max = 34.2                                     #[m/s]       Power estimation. Frank 15-5\n#V_max = 31.61                                       #[m/s]      Iteration 1 - Frank 9/6\n#V_max = 24.14 \n#V_max = 26.45      \n#V_max = 29.51\n#V_max = 30.32\nV_max = 32.59\n\n#P_a = 9557.6211097                               #[W]         Power available\n#P_a = 10419.6299                                  #[W]         Power available - Frank - power calculations\n#P_a = 13677.3852                                    #[W]        Iteration 1 - Frank 9/6\n#P_a = 7435.278 \n#P_a = 7211.1181\n#P_a = 9755.8094\nP_a = 12029.804\n\n#T_max = 227.9944                                   #[N]         thrust at max speed from Power available and max speed - Frank 6-6\n#T_max = 308.0066                                    #[N] Thrust at max speed!\n#T_max = 272.6321\n#T_max = 332.6586\n#T_max = 321.7615\nT_max = 369.1256\n\n#CL_mindrag = 0.531736155272                      #[-]         CL @ minimum drag \n#CL_mindrag = 0.9496                                #[-]         CL @ minimum drag - Frank 15-5\n#CL_mindrag = 1.3276                                 #[-]        Iteration 1 - Frank 9/6\n#CL_mindrag = 1.1416\nCL_mindrag = 1.0848\n\n#CD_mindrag = 0.02                                #[-]         CD @ minimum drag         \n#CD_mindrag = 0.0614                                  #[-]         CD @ min. drag - Frank 15-5\n#CD_mindrag = 0.12                                   #[-]        Iteraion 1 - Frank 9/6     \n#CD_mindrag = 0.0887\nCD_mindrag = 0.0801\n\n#CLCD_mindrag = 15.4661                             #[-]         CL/CD for min drag (optimal Cl/CD) Frank 15-5\n#CLCD_mindrag = 11.0631                              #[-]        Iteration 1 - Frank 9/6\n#CLCD_mindrag = 12.8649\nCLCD_mindrag = 13.5393\n\n#V_mindrag = 16.9159263147                        #[m/s]       V  @ minimum drag\n#V_mindrag = 13.3428                               #[m/s]       V  @ minimum drag from drag diagram - Frank 15-5\n#V_mindrag = 12.2276                                 #[m/s]      Iteration 1 - Frank 9/6\n#V_mindrag = 11.199\n#V_mindrag = 12.0773\n#V_mindrag = 12.4101\n#V_mindrag =  12.591\nV_mindrag = 12.6429\n\n#D_min = 57.1723743512                            #[N]         The minimum drag (steady symmetric flight)\n#D_mindrag = 90.6724                              #[N]         The minimum drag (steady symmetric flight) - Frank - 15-5\n#D_mindrag = 126.7598                                #[N]        Iteration 1 - Frank 9/6\n#D_mindrag = 109.0061\n#D_mindrag =  114.1896\n#D_mindrag = 107.8283\nD_mindrag =  108.7192\n\n#P_mindrag = 1209.825                             #[W]         Power required during min. drag flight - Frank - 15-5\n#P_mindrag = 1549.967                                #[W]        Iteration 1 - Frank 9/6\n#P_mindrag = 1419.6662 \n#P_mindrag = 1316.4999\n#P_mindrag = 1417.1082\n#P_mindrag = 1357.6686\nP_mindrag = 1374.5295\n\n#CL_minpower = 0.920994037152                     #[-]         CL @ minimum power required\n#CL_minpower = 1.6448                               #[-]         CL @ minimum power required from power diagram - Frank 15-5\n#CL_minpower = 2.2994                             #[W]        Iteration 1 - Frank 9/6\n#CL_minpower = 1.9774\nCL_minpower =  1.8789\n\n#CD_minpower = 0.04                               #[-]         CD @ minimum power required\n#CD_minpower = 0.1228                                 #[-]         CD @ minimum power required from power diagram - Frank 15-5\n#CD_minpower = 0.24                             #[W]        Iteration 1 - Frank 9/6\n#CD_minpower = 0.1775\nCD_minpower = 0.1602 \n\n#CLCD_minpower = 13.3941                            #[-]         CL/CD for min power (optimal Cl^3/CD^2) Frank 15-5\n#CLCD_minpower = 9.5809                               #[W]        Iteration 1 - Frank 9/6\n#CLCD_minpower = 11.1413\nCLCD_minpower = 11.7254 \n\n#V_minpower = 12.8533244698                       #[m/s]       V  @ minimum power required\n#V_minpower = 10.1384                                #[m/s]       V  @ minimum power required - Frank 15-5\n#V_minpower = 9.291                             #[W]        Iteration 1 - Frank 9/6\n#V_minpower =  8.5099\n#V_minpower = 9.1768\n#V_minpower = 9.4297\n#V_minpower =  9.5671 \nV_minpower = 9.6066\n\n#D_minpower = 104.6995                             #[N]         Min. drag during min. power flight - Frank 15-5\n#D_minpower = 146.3696                             #[W]        Iteration 1 - Frank 9/6\n#D_minpower = 125.8694\n#D_minpower = 131.8548\n#D_minpower =  124.5094\nD_minpower = 125.5381\n\n#P_r_min = 848.537541237                          #[W]         The minimum power required (steady symmetric flight)\n#P_minpower = 1061.4802                            #[W]         The minimum power required (steady symmetric flight) - Frank 15-5\n#P_minpower = 1359.9144                             #[W]        Iteration 1 - Frank 9/6\n#P_minpower = 1245.5905\n#P_minpower = 1155.0743\n#P_minpower = 1243.3462\n#P_minpower = 1191.195 \nP_minpower = 1205.9884\n\n#P_climbrate = 2884.5364                           #[W]          Power required for 1.3 m/s climbrate from power diagram - Frank 15-5\n#P_climbrate = 3182.9707                                   #[W]        Iteration 1 - Frank 9/6 \n#P_climbrate = 3068.6468\n#P_climbrate = 2978.1306\n#P_climbrate = 3153.0932\n#P_climbrate =  3434.678 \nP_climbrate = 3653.3085\n\n\n#Max_climbrate = 6.6732                           #[m/s]        Max climbrate Frank from power diagram - Frank 15-5\n#Max_climbrate = 8.7834                             #[W]        Iteration 1 - Frank 9/6\n#Max_climbrate = 4.4138\n#Max_climbrate = 4.3185 \n#Max_climbrate = 5.8361\n#Max_climbrate = 5.8665\nMax_climbrate = 6.9906\n\n#Batcap_30min_cruise = 0.5307                      #[kWh]        Battery capacity for 30 min cruise from Power diagram - Frank 15-5\n#Batcap_30min_cruise = 0.68                             #[W]        Iteration 1 - Frank 9/6\n#Batcap_30min_cruise = 0.6228\n#Batcap_30min_cruise = 0.5775\n#Batcap_30min_cruise = 0.6217\nBatcap_30min_cruise =  0.8699\n\n#Batcap_4min_climb = 0.1923                        #[kWh]        Battery capacity for 4 min. 1.3 m/s climb from power diagram - Frank 15-5\n#Batcap_4min_climb = 0.2122                             #[W]        Iteration 1 - Frank 9/6\n#Batcap_4min_climb = 0.2046\n#Batcap_4min_climb = 0.1985\n#Batcap_4min_climb = 0.2102\nBatcap_4min_climb = 0.2436\n\n#T_cruise = 169.7041                                 #De drag in cruise!\n#T_cruise = 177.7739\nT_cruise = 164.5514 \n\n#Einde waardes vanuit P&P\n\nnoise = 82.800                                    #[dB]       Noise at 200ft, uit noise2.py. Rens 17-5\n\n#Initial weight estimations:\n#Wing group weight estimation\n#weight_Wing = 28                                    #[kg]      Wing weight estimation, Preliminary!\n#Tail group weight estimation\n#weight_Tail = 6                                    #[kg]      Tail weight estimation, Preliminary!\n#Cabin group weight estimation\n#weight_Cabin = 8                                    #[kg]      Includes all weight regarding the attachment of the pilot\n\n\n\n#prop_radius = 0.699423331153                                 #[m]        Frank 14-6\nprop_radius = 0.671996229832\ndisk_gevuld = 0.067064204277478195                                 #[%]        Frank 9-6 dikke gok\nprop_area = disk_gevuld*np.pi*prop_radius**2        #[m2]       Area uitgaande van de bovenstaande waardes\nbeta70 = 26.25                                        #[deg]      Frank 9-6, at 70% of the span, the angle of the prop blades wrt airflow\nRPM = 974.829495591\nA_prop = 1.41867705814\nchord_80 = 0.0194 \nB = 5.\ntipMach = 0.2\n#prop_weight = 2.18908479193                         #[kg] weight of ONE prop\n\nproplateral = 1.5 + prop_radius               #[m]       Eerste shcatting minimum positie in span direction van de propeller tov longitudinal axis, voor de veiligheid van de piloot\n\n#Structures\nu_max = 0.5                                        #[m] Spar deflection due to drag\nv_max = 0.5                                        #[m] Spar deflection due to lift\nv_maxnolift = 0.3                                  #[m] Spar deflection due to weight\nu_maxtail = 0.5\nv_maxtail = 0.5\nv_maxnolifttail = 0.5\n\n#sanity check, hoeveel steekt de prop uit door sweep\nQCsweep = (np.pi/180.)* Sweep\nLEsweep = np.arctan((1./(2.*Wing_b))*rootchord+np.arctan(QCsweep))\nuitsteeksel = 0.10 + prop_radius * np.tan(LEsweep)\n","sub_path":"parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":18828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"143251060","text":"import sys\n\n# COMMAND LINE PARSING\n\nrenorm = True\n\nif sys.argv[1] == '-h' :\n    print(\"usage: traj_gen.py <run> <number> <n_batch> [--no_renorm]\")\n    exit()\nif sys.argv[1] == '--no_renorm' :\n    renorm = False\n    sys.argv.pop(1)\nelif len(sys.argv) < 2:\n    print(\"usage: traj_gen.py <run> <number> <n_batch>\")\n    exit()\n\nrun = int(sys.argv.pop(1))\nnumber = int(sys.argv.pop(1))\nN = int(sys.argv.pop(1))\n\nwhile len(sys.argv) > 1 :\n    print('Unrecognized args. Exiting.')\n    sys.stderr.write('invalid argument')\n    sys.exit(1)\n\nfrom db_utils import *\n\npath = f'runs/{run}/{number}_gen.h5'\nprint('Loading Model ...')\ngen = load_model(path)\n\nbs = 50000\ntrajs = np.zeros(shape=(N*bs,SIG_LEN,CHANNELS))\nprint('Generating Trajectories ...')\nfor ii in range(N):\n    print(ii)\n    # noise = np.random.normal(0, 1, size=(bs, NOISE_DIM)) #VAR\n    noise = np.random.standard_t(4, size=(bs, NOISE_DIM)) #VAR\n    # noise = np.random.standard_t(3.5, size=(bs, NOISE_DIM)) #VAR\n    trajs[ii*bs:(ii+1)*bs,:,0:1] = gen.predict(noise, verbose=1, batch_size=bs)\n\nif renorm:\n    semidisp = (DB_MAX - DB_MIN)/2.\n    media = (DB_MAX + DB_MIN)/2.\n    trajs = trajs * semidisp + media\n\ntry: os.mkdir((f\"/storage/scarpolini/databases/\"+DB_NAME+\"/\"+\n               WGAN_TYPE+f\"/runs/{run}\")) #VAR\n\nexcept: print('Directory already exists')\nprint('Saving ...')\n#VAR\nnp.save((f\"/storage/scarpolini/databases/\"+DB_NAME+\"/\"+\n         WGAN_TYPE+f\"/runs/{run}/gen_trajs_{number}\"), trajs)\n\n","sub_path":"wgangp2048/traj_gen.py","file_name":"traj_gen.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"362222088","text":"import os\nimport sqlite3\nimport settings as stgs\n\n\ndef get_connection():\n    new_db = not os.path.isfile(stgs.DB_FILE)\n\n    conn = sqlite3.connect(stgs.DB_FILE)\n    conn.row_factory = sqlite3.Row\n\n    if new_db:\n        with open(stgs.DB_CREATE_SCRIPT) as f:\n            sql = f.read()\n\n        c = conn.cursor()\n        c.executescript(sql)\n\n    return conn\n","sub_path":"core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"194963666","text":"import numpy as np\nimport pandas as pd\nimport pyplot as plt\nfrom flask import Flask, render_template, request\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport json\nimport bs4 as bs\nimport urllib.request\nimport pickle\nimport requests\n\n\ndef create_similarity():\n    data = pd.read_csv('final_data.csv')\n    # creating a count matrix\n    cv = CountVectorizer()\n    count_matrix = cv.fit_transform(data['combo'])\n    # creating a similarity score matrix\n    similarity = cosine_similarity(count_matrix)\n    return data, similarity\n\n\ndef rcmd(m):\n    m = m.lower()\n    try:\n        data.head();\n        similarity.shape\n    except:\n        data, similarity = create_similarity()\n    if m not in data['Title'].unique():\n        return (\n            'Sorry! The mahi movie you requested is not in our database. Please check the spelling or try with some other movies')\n    else:\n        i = data.loc[data['Title'] == m].index[0]\n        lst = list(enumerate(similarity[i]))\n        lst = sorted(lst, key=lambda x: x[1], reverse=True)\n        lst = lst[1:11]  # excluding first item since it is the requested movie itself\n        l = []\n        for i in range(len(lst)):\n            a = lst[i][0]\n            l.append(data['Title'][a])\n        return l\n\n\n# converting list of string to list (eg. \"[\"abc\",\"def\"]\" to [\"abc\",\"def\"])\ndef convert_to_list(my_list):\n    my_list = my_list.split('\",\"')\n    my_list[0] = my_list[0].replace('[\"', '')\n    my_list[-1] = my_list[-1].replace('\"]', '')\n    return my_list\n\n\ndef get_suggestions():\n    data = pd.read_csv('final_data.csv')\n    return list(data['Title'].str.capitalize())\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\n@app.route(\"/home\")\ndef home():\n    suggestions = get_suggestions()\n    return render_template('home.html', suggestions=suggestions)\n\n\n@app.route(\"/similarity\", methods=[\"POST\"])\ndef similarity():\n    movie = request.form['name']\n    rc = rcmd(movie)\n    if type(rc) == type('string'):\n        return rc\n    else:\n        m_str = \"---\".join(rc)\n        return m_str\n\n\n@app.route(\"/recommend\", methods=[\"POST\"])\ndef recommend():\n    # getting data from AJAX request\n    title = request.form['title']\n    imdb_id = request.form['imdb_id']\n    poster = request.form['poster']\n    genres = request.form['genres']\n    overview = request.form['overview']\n    vote_count = request.form['vote_count']\n    \n    runtime = request.form['runtime']\n    \n\n    rec_movies = request.form['rec_movies']\n    rec_posters = request.form['rec_posters']\n    rec_trailer = request.form['rec_trailer']\n    print('------------------------------------------------------')\n    # get movie suggestions for auto complete\n    suggestions = get_suggestions()\n\n\n    # call the convert_to_list function for every string that needs to be converted to list\n    rec_movies = convert_to_list(rec_movies)\n    rec_posters = convert_to_list(rec_posters)\n    rec_trailer = convert_to_list(rec_trailer)\n    print(rec_trailer)\n\n    # combining multiple lists as a dictionary which can be passed to the html file so that it can be processed easily and the order of information will be preserved\n    movie_cards = {rec_posters[i]: rec_movies[i]  for i in range(len(rec_posters))}\n    movie_trailer = {rec_trailer[i]: rec_movies[i] for i in range(len(rec_trailer))}\n\n\n    # passing all the data to the html file\n    return render_template('recommend.html', title=title, poster=poster, overview=overview,\n                           vote_count=vote_count,\n                           genres=genres,\n                           \n                           runtime = runtime,\n                           \n\n                           movie_cards=movie_cards,\n                           movie_trailers = movie_trailer , rec_movies = rec_movies , rec_trailer  = rec_trailer , rec_posters = rec_posters)\n\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\n\n# added templates and static folders and files","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"364115861","text":"\r\n# -*- coding: cp932 -*-\r\n\r\nimport sys\r\nimport math\r\n\r\ninputfile = \"C-large.in\"\r\n#inputfile = \"sample.in\"\r\nf = open(inputfile)\r\nsys.stdout = open(inputfile.replace(\".in\", \".txt\"),'w')\r\ntc_num = int(f.readline().rstrip())\r\n\r\nno_ans = \"Impossible\"\r\n\r\nfor tc in range(tc_num):\r\n    l = f.readline().split()\r\n    R = int(l[0])\r\n    C = int(l[1])\r\n    M = int(l[2])\r\n    T = R * C\r\n    E = T - M\r\n    \r\n    ans = no_ans\r\n    if E == 1:\r\n        ans = \"c\" + \"*\" * (C-1)\r\n        ans += (\"\\n\" + \"*\" * C) * (R-1)\r\n    elif R == 1:\r\n        if M < C:\r\n            ans = \"c\" + \".\" * (C-M-1) + \"*\" * M\r\n    elif C == 1:\r\n        if M < R:\r\n            ans = \"c\" + \"\\n.\" * (R-M-1) + \"\\n*\" * M\r\n    elif R == 2:\r\n        if M % 2 == 0 and T - M >= 4:\r\n            ans = \"c\" + \".\" * (C-1-M//2) + \"*\" * int(M//2) + \"\\n\"\r\n            ans += \".\" * (C-M//2) + \"*\" * (M//2)\r\n    elif C == 2:\r\n        if M % 2 == 0 and T - M >= 4:\r\n            ans = \"c\" + \".\"\r\n            ans += \"\\n..\" * (R-1-M//2)\r\n            ans += \"\\n**\" * (M//2)\r\n    elif E < 8:\r\n        if E == 4:\r\n            ans = \"c\" + \".\" + \"*\" * (C - 2)\r\n            ans += \"\\n..\" + \"*\" * (C - 2)\r\n            ans += (\"\\n\" + \"*\" * C) * (R - 2)\r\n        elif E == 6:\r\n            ans = \"c\" + \"..\" + \"*\" * (C - 3)\r\n            ans += \"\\n...\" + \"*\" * (C - 3)\r\n            ans += (\"\\n\" + \"*\" * C) * (R - 2)\r\n        elif E == 8:\r\n            ans = \"c\" + \"..\" + \"*\" * (C - 3)\r\n            ans += \"\\n...\" + \"*\" * (C - 3)\r\n            ans += \"\\n..\" + \"*\" * (C - 2)\r\n            ans += (\"\\n\" + \"*\" * C) * (R - 3)\r\n        #else: # Impossible\r\n    else:\r\n        m = [[0 for i in range(C)] for j in range(R)]\r\n        rest = M\r\n        for r in range(R-2):\r\n            if rest == 0: break\r\n            for c in range(C-2):\r\n                if r == R-3 and c == C-3: break\r\n                m[r][c] = 1\r\n                rest -= 1\r\n                if rest == 0: break\r\n            \r\n        for r in range(R-3):    \r\n            if rest <= 1: break\r\n            m[r][C-1] = 1\r\n            m[r][C-2] = 1\r\n            rest -= 2\r\n        \r\n        for c in range(C-3):    \r\n            if rest <= 1: break\r\n            m[R-1][c] = 1\r\n            m[R-2][c] = 1\r\n            rest -= 2\r\n        \r\n        if rest == 1:\r\n            m[R-3][C-3] = 1\r\n            rest -= 1\r\n            \r\n        \r\n        ans = \"c\"\r\n        for r in range(R):\r\n            if r > 0: ans += \"\\n\"\r\n            for c in range(C):\r\n                if r == 0 and c == 0: continue\r\n                ans += \".\" if m[R-1-r][C-1-c] == 0 else \"*\"\r\n                    \r\n    print(\"Case #\" + str(tc+1) + \":\\n\" + ans)\r\n\r\n   \r\n","sub_path":"solutions_5690574640250880_1/Python/Taizo/q_C.py","file_name":"q_C.py","file_ext":"py","file_size_in_byte":2624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"322596982","text":"import os\nfrom os import path, makedirs, remove\nfrom os.path import isdir, isfile, exists, basename\nfrom subprocess import Popen, CalledProcessError\nfrom shutil import copyfile, copytree, rmtree\nfrom stat import S_IXUSR, S_IXGRP, S_IXOTH, S_IRUSR, S_IRGRP, S_IROTH\n\nFMOD_EXE = S_IXUSR | S_IXGRP | S_IXOTH | S_IRUSR | S_IRGRP | S_IROTH\n\n\ndef call(args, cwd=None):\n    '''\n    Perform new process\n\n    :param list args:\n    :param str cwd:\n    '''\n\n    exit_code = Popen(args, cwd=cwd).wait()\n    if exit_code != 0:\n        raise CalledProcessError(exit_code, args)\n\n\ndef cp(src, dest, exist_ignore=True):\n    '''\n    Copy files, directories\n\n    :param str src:\n    :param str dest:\n    :param bool exist_ignore\n    '''\n\n    if isdir(src):\n        return _cp_dir(src, dest, exist_ignore)\n    else:\n        return _cp_file(src, dest, exist_ignore)\n\n\ndef rm(path, exist_ignore=True):\n    if (not exists(path)) and exist_ignore:\n        return\n    if isdir(path):\n        rmtree(path)\n    else:\n        remove(path)\n\n\ndef mkdir(path):\n    if not isdir(path):\n        makedirs(path)\n\n\ndef chmod(path, mode):\n    os.chmod(path, mode)\n\n\ndef _cp_dir(src, dest, exist_ignore):\n    if isdir(dest):\n        dest_dir = path.join(dest, basename(src))\n        if isdir(dest_dir) and exist_ignore:\n            return dest_dir\n        copytree(src, dest_dir)\n        return dest_dir\n    else:\n        copytree(src, dest)\n        return dest\n\n\ndef _cp_file(src, dest, exist_ignore):\n    if isfile(dest) and exist_ignore:\n        return dest\n    if isdir(dest):\n        dest_file = path.join(dest, basename(src))\n        copyfile(src, dest_file)\n        return dest_file\n    else:\n        copyfile(src, dest)\n        return dest\n","sub_path":"tool/shell.py","file_name":"shell.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"597960732","text":"import sys\n\n\ndef main():\n    d = {}\n    for line in sys.stdin:\n        for word in line.strip().split(' '):\n            if not d.get(word, None):\n                d[word] = 0\n            d[word] += 1\n    for word, sum_ in d.items():\n        print(word, sum_, sep='\\t')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"3-in-mapper-combining-v2/mapper.py","file_name":"mapper.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"337861675","text":"# tdcnn猜测是用来提取proposal的。相当于在c3d中嵌入了tdcnn\n#模型重组(加入自己的模型层)\n#　　重组的内涵我觉得是模型嵌套：A模型中加入B模型。实现有三个步骤：\n#　　1.构造B模型类（初始化函数，向前传播函数）\n#　　2.在A模型类初始化函数中加入B模型类\n#　　3.在A模型的向前传播函数中实现B模型的嵌套，通俗点说是，A在向前传播过程中，它前半模型的输出成为\n#　　　B模型的输入，B模型的输出成为A模型后半模型的输入。\n#   \n\n# 该模块主要用于构建rpn网络 注意了改代码中所有的self都指代的是c3d_tdcnn,通过这个self就将tdcnn成功嵌入到了c3d\n\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n#tensor不能反向传播，variable可以反向传播。说白了Variable就是用来装tensor的\nfrom torch.autograd import Variable\nimport torchvision.models as models\nfrom torch.autograd import Variable\nimport numpy as np\nfrom model.utils.config import cfg\nfrom model.rpn.rpn import _RPN\nfrom model.roi_temporal_pooling.modules.roi_temporal_pool import _RoITemporalPooling\nfrom model.rpn.proposal_target_layer_cascade import _ProposalTargetLayer\nimport time\nimport pdb\nfrom model.utils.net_utils import _smooth_l1_loss\nfrom model.utils.non_local_dot_product import NONLocalBlock3D\n\nDEBUG = False\n\n# 该部分应该为主要的faster rcnn的模型构建\nclass _TDCNN(nn.Module):\n    \"\"\" faster RCNN \"\"\"\n    def __init__(self):  # self : c3d_tdcnn() 将\n        super(_TDCNN, self).__init__()  # 先找到_TDCNN的父类为nn.Module,然后将_TDCNN类的对象c3d_tdcnn转为nn.Module初始化\n        #self.classes = classes\n        self.n_classes = cfg.NUM_CLASSES  # 21\n        # loss\n        self.RCNN_loss_cls = 0\n        self.RCNN_loss_twin = 0\n\n        # define rpn\n        # 在初始化的过程中定义好rpn类，具体的数据在forward的时候传入\n        self.RCNN_rpn = _RPN(self.dout_base_model)  # 传入特征图\n        \n        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) # 21\n        \n        self.RCNN_roi_temporal_pool = _RoITemporalPooling(cfg.POOLING_LENGTH, cfg.POOLING_HEIGHT, cfg.POOLING_WIDTH, cfg.DEDUP_TWINS) # 4 2 2 1./8\n        \n        if cfg.USE_ATTENTION:  # false\n            self.RCNN_attention = NONLocalBlock3D(self.dout_base_model, inter_channels=self.dout_base_model)\n        \n    def prepare_data(self, video_data):\n        return video_data\n\n    #训练时主要调用的就是这个forward函数，不知道为什么训练时的相关数据会传入这个地方\n    def forward(self, video_data, gt_twins):\n        batch_size = video_data.size(0)\n\n        gt_twins = gt_twins.data\n        # prepare data\n        video_data = self.prepare_data(video_data)\n        # feed image data to base model to obtain base feature map\n        \n        ########### 第一步 调用 c3d中的基础特征提取网络获取特征图############\n        base_feat = self.RCNN_base(video_data)  # 经过c3d网络得到的基本特征图的维度为[1,512,96,7,7] 其中96代表96张图片，7*7代表卷积后特征图大小，512代表特征图维度\n        # feed base feature map tp RPN to obtain rois\n        # rois, [rois_score], rpn_cls_prob, rpn_twin_pred, self.rpn_loss_cls, self.rpn_loss_twin, self.rpn_label, self.rpn_loss_mask\n        ########### 第二步 将得到的特征图传入rpn网络获取rois,rpn分类loss,rpn回归loss\n        # rois维度为[1,2000,3]\n        # rois的数据形式为[[[0.0000,716.5894,734.3415],[0.0000,74.6134,134.0776]......]]\n        rois, _, _, rpn_loss_cls, rpn_loss_twin, _, _ = self.RCNN_rpn(base_feat, gt_twins)  # 将得到的特征图和bbox的真实值传入rpn模块获取rois,rpn_loss_cls,rpn_loss_twin_cls\n\n        # if it is training phase, then use ground truth twins for refining\n        if self.training: \n            ########## 第三步 根据rpn网络得到的rois和传入的gt_twins选取最终需要的proposals #########\n            # 程序到这里数据都是正常的\n            roi_data = self.RCNN_proposal_target(rois, gt_twins)  \n            ######roi_data中的数据十分丰富，有五种数据\n            # rois: 维度[1,128,3] 数据：[[[0.0000,461.4230,479.4204],[].......]]\n            # rois_label: 维度[1,128] 数据[[51,51,51......]]\n            # rois_target: 维度[1,128,2] 数据 [[[-0.0087,3.3653],[-1.0527,3.2267],.......]]\n            # rois_inside_ws : 维度[1,128,2] 数据 [[[1.,1.],[1.,1.].......]]\n            # rois_outside_ws : 维度[1,128,2] 数据 [[[1.,1.],[1.,1.].......]]\n            rois, rois_label, rois_target, rois_inside_ws, rois_outside_ws = roi_data\n\n            rois_label = Variable(rois_label.view(-1).long())  # 维度转为[128]\n            rois_target = Variable(rois_target.view(-1, rois_target.size(2))) # [128,2]\n            rois_inside_ws = Variable(rois_inside_ws.view(-1, rois_inside_ws.size(2))) # [128,2]\n            rois_outside_ws = Variable(rois_outside_ws.view(-1, rois_outside_ws.size(2))) # [128,2]\n        else:\n            rois_label = None\n            rois_target = None\n            rois_inside_ws = None\n            rois_outside_ws = None\n            rpn_loss_cls = 0\n            rpn_loss_twin = 0\n\n        rois = Variable(rois)\n        # do roi pooling based on predicted rois\n        if cfg.POOLING_MODE == 'pool':\n            ########### 第四步 根据选取的proposals得到对应的池化特征 ##########\n            pooled_feat = self.RCNN_roi_temporal_pool(base_feat, rois.view(-1,3)) # 根据获取的rois进行ROI-pooling              \n       \n        if cfg.USE_ATTENTION:\n            pooled_feat = self.RCNN_attention(pooled_feat) \n        # feed pooled features to top model\n        pooled_feat = self._head_to_tail(pooled_feat)  # 将池化得到的特征图传入top model      \n        # compute twin offset, twin_pred will be (128, 402)\n        ######## 第五步 根据得到的池化后的特征进行回归值预测#########\n        twin_pred = self.RCNN_twin_pred(pooled_feat)   # 维度大小为[128,42] 数据：[[-1.3814e-01,2.3307e-02.....]]\n\n        if self.training:\n            # select the corresponding columns according to roi labels, twin_pred will be (128, 2)\n            # 相当于进行了reshape,twin_pred_view维度为[128,21,2]\n            # 相当于将原先的twin_pred的维度由[128,42]----->[128,21,2]\n            twin_pred_view = twin_pred.view(twin_pred.size(0), int(twin_pred.size(1) / 2), 2)\n            # gather在one-hot为输出的多分类问题中，可以把最大值坐标作为index传进去，然后提取到每一行的正确预测结果，这也是gather可能的一个作用。\n            # 因为twin_pred预测出来的结果有21个类别，通过该函数可以选取匹配类别对于的twin_pred\n            # 函数生成的维度为[128,1,2]\n        \n            # 好像是这里出现了问题\n            # torch.gather沿给定轴dim，将输入索引张量index指定位置的值进行聚合。\n            # twin_pred_view : [128,21,2]\n            # rois_label : [128]\n            # expand 扩展某个size为1的维度。如(2,2,1)扩展为(2,2,3),rois_label经过维度拓展为(128,1,2) \n            # twin_pred_view的数据为[[[-1.3814e-01,-1.1126e-01],[2.3307e-02,-1.7410e-02],........]]\n            # rois_label_view的数据为[[[51,51],[[51,51]],......[[0,0]]]\n            # 这里的bug终于解决了，每次运行到这里的时候程序就会直接跳到返回处，原因是因为类别数量是21，但是如果按照thumos官方给定的类别detclass.txt中的\n            #类别设定在执行torch.gather函数选取某个类别时会超出维度，因为假如真实的类别的标签按原先类别索引可能是99，但是类别最多只有21种因此根本不可能选取\n            #这种类别对应的twin_pred,修改bug的方法是修改thumos官方提供的detclasslist.txt，将类别修改为1,2,3,4,5,.....20\n            #具体问题的修改可以参考:https://github.com/sunnyxiaohu/R-C3D.pytorch/issues/11?q=is%3Aissue+is%3Aclosed\n            twin_pred_select = torch.gather(twin_pred_view, 1, rois_label.view(rois_label.size(0), 1, 1).expand(rois_label.size(0), 1, 2))\n            # 压缩1维度结果为[128,2]\n            twin_pred = twin_pred_select.squeeze(1)\n\n        # compute object classification probability\n        ########第六步  根据得到的池化后的特征进行分类 #######\n        cls_score = self.RCNN_cls_score(pooled_feat)\n        cls_prob = F.softmax(cls_score, dim=1)\n\n        if DEBUG:\n            print(\"tdcnn.py--base_feat.shape {}\".format(base_feat.shape))\n            print(\"tdcnn.py--rois.shape {}\".format(rois.shape))\n            print(\"tdcnn.py--tdcnn_tail.shape {}\".format(pooled_feat.shape))\n            print(\"tdcnn.py--cls_score.shape {}\".format(cls_score.shape))\n            print(\"tdcnn.py--twin_pred.shape {}\".format(twin_pred.shape))\n            \n        RCNN_loss_cls = 0\n        RCNN_loss_twin = 0\n\n        if self.training:\n            # classification loss\n            ##########第七步 计算分类loss #########\n            RCNN_loss_cls = F.cross_entropy(cls_score, rois_label)\n\n            #########第八步  计算回归loss #######\n            # bounding box regression L1 loss\n            RCNN_loss_twin = _smooth_l1_loss(twin_pred, rois_target, rois_inside_ws, rois_outside_ws)\n\n            # RuntimeError caused by mGPUs and higher pytorch version: https://github.com/jwyang/faster-rcnn.pytorch/issues/226\n            rpn_loss_cls = torch.unsqueeze(rpn_loss_cls, 0)  # 相当于在第一维添加1，增加一个维度\n            rpn_loss_twin = torch.unsqueeze(rpn_loss_twin, 0)\n            RCNN_loss_cls = torch.unsqueeze(RCNN_loss_cls, 0)\n            RCNN_loss_twin = torch.unsqueeze(RCNN_loss_twin, 0)\n            \n        cls_prob = cls_prob.view(batch_size, rois.size(1), -1)  # view函数改变tensor形状\n        twin_pred = twin_pred.view(batch_size, rois.size(1), -1)\n\n        ##########第九步 返回相关的结果 #########\n        if self.training:        \n            return rois, cls_prob, twin_pred, rpn_loss_cls, rpn_loss_twin, RCNN_loss_cls, RCNN_loss_twin, rois_label\n        else:\n            return rois, cls_prob, twin_pred            \n\n    def _init_weights(self):\n        def normal_init(m, mean, stddev, truncated=False):\n            \"\"\"\n            weight initalizer: truncated normal and random normal.\n            \"\"\"\n            # x is a parameter\n            if truncated:\n                m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation\n            else:\n                m.weight.data.normal_(mean, stddev)\n                m.bias.data.zero_()\n        self.RCNN_rpn.init_weights()\n        normal_init(self.RCNN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED)\n        normal_init(self.RCNN_twin_pred, 0, 0.001, cfg.TRAIN.TRUNCATED)\n        \n\n    def create_architecture(self):\n        self._init_modules()  # 该初始化模块位于c3d.py中\n        self._init_weights()  # 该初始化权重函数用来初始化rpn网络的参数，才c3d.py中有专门初始化网络参数的另一个函数\n","sub_path":"lib/model/tdcnn/tdcnn.py","file_name":"tdcnn.py","file_ext":"py","file_size_in_byte":11252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"274740316","text":"import logging\nfrom lib.functions import wait_until\n\nlogger = logging.getLogger(__name__)\n\n\nclass HeroicQuests:\n    \"\"\"Class for working with Heroic Quests.\"\"\"\n\n    def __init__(self, game):\n        \"\"\"Class initialization.\n\n        :param game.Game game: instance of the game.\n        \"\"\"\n        self.emulator = game.emulator\n        self.ui = game.ui\n        self.game = game\n\n    def close_chest_notification(self):\n        \"\"\"Close 'Buy Crystal Chest' notification.\"\"\"\n        if wait_until(self.emulator.is_ui_element_on_screen, timeout=3,\n                      ui_element=self.ui['HQ_CRYSTAL_CHEST_NOTIFICATION_CANCEL']):\n            self.emulator.click_button(self.ui['HQ_CRYSTAL_CHEST_NOTIFICATION_CANCEL'].button)\n            if wait_until(self.emulator.is_ui_element_on_screen, timeout=3,\n                          ui_element=self.ui['HQ_CRYSTAL_CHEST_NOTIFICATION_CANCEL_OK']):\n                self.emulator.click_button(self.ui['HQ_CRYSTAL_CHEST_NOTIFICATION_CANCEL_OK'].button)\n                return True\n        return False\n\n    def acquire_reward(self):\n        \"\"\"Acquire quest reward.\"\"\"\n        if self.emulator.is_ui_element_on_screen(ui_element=self.ui['HQ_ACQUIRE_REWARD']):\n            logger.debug(\"Acquiring Heroic Quest reward.\")\n            self.emulator.click_button(self.ui['HQ_ACQUIRE_REWARD'].button, min_duration=0.5, max_duration=0.7)\n            self.emulator.click_button(self.ui['HQ_ACQUIRE_REWARD'].button)\n            return True\n        return False\n\n    def acquire_reward_and_return_back(self):\n        \"\"\"Acquire quest reward and return back.\"\"\"\n        if self.close_chest_notification():\n            logger.info(\"Crystal chest is available in Heroic Quest. Skipping reward.\")\n            return True\n        if self.acquire_reward():\n            logger.info(\"Heroic Quest reward acquired. Going back.\")\n            self.emulator.click_button(self.ui['MENU_BACK'].button)\n            return True\n        logger.warning(\"Something went wrong while acquiring Heroic Quest reward. Going to main menu.\")\n        self.game.go_to_main_menu()\n        return False\n","sub_path":"lib/game/heroic_quests.py","file_name":"heroic_quests.py","file_ext":"py","file_size_in_byte":2102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"321892975","text":"import csv\nimport sqlite3\nimport requests\n\ndef create_db():\n    conn = sqlite3.connect('choc_bars.sqlite')\n    cur = conn.cursor()\n\n    drop_bars_sql = 'DROP TABLE IF EXISTS \"Bars\"'\n    drop_countries_sql = 'DROP TABLE IF EXISTS \"Countries\"'\n    \n    create_bars_sql = '''\n        CREATE TABLE IF NOT EXISTS \"Bars\" (\n            \"Id\" INTEGER PRIMARY KEY AUTOINCREMENT, \n            \"Company\" TEXT NOT NULL,\n            \"SpecificBeanBarName\" TEXT NOT NULL, \n            \"REF\" TEXT NOT NULL,\n            \"ReviewDate\" TEXT NOT NULL,\n            \"CocoaPercent\" REAL NOT NULL,\n            \"CompanyLocationId\" INTEGER NOT NULL, \n            \"Rating\" REAL NOT NULL, \n            \"BeanType\" TEXT NOT NULL, \n            \"BroadBeanOrigin\" INTEGER NOT NULL\n        )\n    '''\n    create_countries_sql = '''\n        CREATE TABLE IF NOT EXISTS 'Countries'(\n            'Id' INTEGER PRIMARY KEY AUTOINCREMENT,\n            'Alpha2' TEXT NOT NULL,\n            'Alpha3' TEXT NOT NULL,\n            'EnglishName' TEXT NOT NULL,\n            'Region' TEXT NOT NULL,\n            'Subregion' TEXT NOT NULL,\n            'Population' INTEGER NOT NULL,\n            'Area' REAL NOT NULL\n        )\n    '''\n    cur.execute(drop_bars_sql)\n    cur.execute(drop_countries_sql)\n    cur.execute(create_countries_sql)\n    cur.execute(create_bars_sql)\n    conn.commit()\n    conn.close()\n\ndef load_bars():\n    file_contents = open('flavors_of_cacao_cleaned.csv', 'r')\n    csv_reader = csv.reader(file_contents)\n    next(csv_reader)\n\n    for row in csv_reader:\n        print(row[0], row[1], row[4], row[6])\n\ndef load_countries(): \n    base_url = 'https://restcountries.eu/rest/v2/all'\n    countries = requests.get(base_url).json()\n    for c in countries:\n        print(c['alpha2Code'], c['name'], c['region'])\n\n\n\n#create_db()\nload_bars()\nload_countries()\n","sub_path":"popdb_step3.py","file_name":"popdb_step3.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"345923860","text":"#encoding: utf-8\nfrom flask import Flask,url_for,render_template,request\nimport csv\nimport os\nimport codecs\nimport re\nimport numpy as np\nimport glob\n\napp=Flask(__name__)\n\n@app.route('/',methods=['GET','POST'])\ndef stock():\n    if request.method == 'GET':\n        lst=glob.glob(\"./static/js/d_*.js\")\n        lst=np.sort([i[12:-3] for i in lst]).tolist()\n        add=''\n        return render_template('main.html',user=lst,name=add)\n    else:\n        lst=glob.glob(\"./static/js/d_*.js\")\n        lst=np.sort([i[12:-3] for i in lst]).tolist()\n        indexcode = request.form.get('indexcode')\n        data_add=\"js/stock/d_\"+str(indexcode)+\"_STOCK.js\"\n        return render_template('stock.html',user=lst,name=data_add)\n\n@app.route('/<yid>')\ndef etf(yid):\n    lst=glob.glob(\"./static/js/d_*.js\")\n    lst=np.sort([i[12:-3] for i in lst]).tolist()\n    add='js/'+str(yid)+'.js'\n    return render_template('main.html',user=lst,name=add)\n   \n\n\nif __name__=='__main__':\n    app.run(host='0.0.0.0', port=80,debug=True)\n    #app.run(debug=True)\n\n\n\n\n\n","sub_path":"flask/plot_OI_daily/show.py","file_name":"show.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"302255945","text":"bl_info = {\n  \"name\": \"MSN\",\n  \"author\": \"jameswilddev\",\n  \"version\": (0, 0, 0),\n  \"blender\": (2, 7, 0),\n  \"location\": \"File > Export > MasSplat Navmesh (.msn)\",\n  \"description\": \"Export triangles as a MasSplat Navmesh (.msn)\",\n  \"category\": \"Import-Export\"\n}\n\nimport bpy, struct, math, random\n\nclass ExportMSN(bpy.types.Operator):\n  bl_idname = \"export.msn\"\n  bl_label = \"Export MSN\"\n  \n  filepath = bpy.props.StringProperty(name=\"File Path\", description=\"The path to a file to export to.\", maxlen=1024, default=\"\")\n  \n  def execute(self, context):\n    obj = bpy.data.objects[\"navmesh\"]\n    mesh = obj.to_mesh(bpy.context.scene, True, \"PREVIEW\")\n    mesh.transform(obj.matrix_world)\n    colLayer = mesh.vertex_colors[0]\n\n    vertices = []\n    indices = []\n    \n    for poly in mesh.polygons:\n        if len(poly.loop_indices) != 3:\n            raise RuntimeError(\"Please triangulate your navmesh\")\n        for index in poly.loop_indices:\n            vertex = mesh.vertices[mesh.loops[index].vertex_index].co\n            if not vertex in vertices:\n                vertices.append(vertex)\n            indices.append(vertices.index(vertex))\n            \n    file = open(self.properties.filepath, \"wb\")\n    \n    file.write(struct.pack(\"H\", len(vertices)))\n    file.write(struct.pack(\"H\", len(indices) // 3))\n    \n    for vertex in vertices:\n        file.write(struct.pack(\"f\", vertex[0]))\n        file.write(struct.pack(\"f\", vertex[2]))\n        file.write(struct.pack(\"f\", vertex[1]))\n            \n    for index in indices:\n        file.write(struct.pack(\"H\", index))\n        \n    file.close()\n    return {\"FINISHED\"}\n\n  def invoke(self, context, event):\n    wm = context.window_manager\n    self.properties.filepath = \"\"\n    wm.fileselect_add(self)\n    return {\"RUNNING_MODAL\"}\n\ndef menu_func(self, context):\n  self.layout.operator(ExportMSN.bl_idname, text=\"MasSplat Navmesh (.msn)\")\n  \ndef register():\n  bpy.utils.register_class(ExportMSN)\n  bpy.types.INFO_MT_file_export.append(menu_func)\n\ndef unregister():\n  bpy.utils.unregister_class(ExportMSN)\n  bpy.types.INFO_MT_file_export.remove(menu_func)\n\nif __name__ == \"__main__\":\n  register()","sub_path":"tools/blenderMSN.py","file_name":"blenderMSN.py","file_ext":"py","file_size_in_byte":2138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"466218209","text":"\"\"\"Tests for mongo_schema.py\"\"\"\nfrom textwrap import dedent\nfrom sramongo.mongo_schema import URLLink, Xref, XrefLink, \\\n    EntrezLink, DDBJLink, ENALink, Submission, Organization, \\\n    Study, Sample, Experiment, Run, BioSample\n\nfrom mongoengine import connect\n\n\ndef test_URLLink_str_complete():\n    url = URLLink(label='test', url='http://test.com')\n    assert str(url) == 'label: test\\nurl: http://test.com\\n'\n\n\ndef test_URLLink_str_nolabel():\n    url = URLLink(url='http://test.com')\n    assert str(url) == 'label: None\\nurl: http://test.com\\n'\n\n\ndef test_URLLink_str_nourl():\n    url = URLLink(label='test')\n    assert str(url) == 'label: test\\nurl: None\\n'\n\n\ndef test_Xref_str_complete():\n    xref = Xref(db='test', id='1030')\n    assert str(xref) == 'db: test\\nid: 1030\\n'\n\n\ndef test_XrefLink_str_complete():\n    xref = XrefLink(db='test', id='1030', label='test label')\n    assert str(xref) == 'label: test label\\ndb: test\\nid: 1030\\n'\n\n\ndef test_EntrezLink_str_complete():\n    xref = EntrezLink(db='test', id='1030', label='test label',\n                      query='Test[orgn]')\n    assert str(xref) == ('label: test label\\n'\n                         'db: test\\n'\n                         'id: 1030\\n'\n                         'query: Test[orgn]\\n')\n\n\ndef test_DDBJLink_str_complete():\n    xref = DDBJLink(db='test', id='1030', label='test label',\n                    url='http://test.com')\n    assert str(xref) == ('label: test label\\n'\n                         'db: test\\n'\n                         'id: 1030\\n'\n                         'url: http://test.com\\n')\n\n\ndef test_ENALink_str_complete():\n    xref = ENALink(db='test', id='1030', label='test label',\n                   url='http://test.com')\n    assert str(xref) == ('label: test label\\n'\n                         'db: test\\n'\n                         'id: 1030\\n'\n                         'url: http://test.com\\n')\n\n\ndef test_Submission_str_complete():\n    submission = Submission(submission_id='SRA12345', broker='GEO')\n    submission.external_id.append(Xref(**{'db': 'test', 'id': '1030'}))\n    submission.external_id.append(Xref(**{'db': 'test2', 'id': '1032'}))\n    submission.external_id.append(Xref(**{'db': 'test3', 'id': '1033'}))\n    submission.submitter_id.append(Xref(**{'db': 'test', 'id': 'test submitter'}))\n\n    assert str(submission).strip() == dedent(\"\"\"\\\n        submission_id: SRA12345\n        broker: GEO\n        external_id:\n            db: test, id: 1030\n            db: test2, id: 1032\n            db: test3, id: 1033\n        secondary_id: None\n        submitter_id:\n            db: test, id: test submitter\n        uuid: None\"\"\")\n\n\ndef test_Organization_str_complete():\n    organization = Organization(\n        organization_type='center', abbreviation='GEO', name='NCBI',\n        email='geo@nih.gov', first_name='GEO', last_name='Curators')\n\n    assert str(organization) == dedent(\"\"\"\\\n        organization_type: center\n        abbreviation: GEO\n        name: NCBI\n        email: geo@nih.gov\n        first_name: GEO\n        last_name: Curators\n        \"\"\")\n\nSTUDY = \"\"\"\\\nstudy_id: SRA12345\nGEO: GSE12345\nGEO_Dataset: None\nBioProject: PRJN1234\npubmed: 123456\nexternal_id:\n    db: test, id: 1030\n    db: test2, id: 1032\n    db: test3, id: 1033\nsecondary_id: None\nsubmitter_id:\n    db: test, id: test submitter\nuuid: None\ntitle: Test title\nstudy_type: Test type\nabstract: A very long abstract with more than 80 characters per line. A very long\n          abstract with more than 80 characters per line. A very long abstract with\n          more than 80 characters per line. A very long abstract with more than 80\n          characters per line.\ncenter_name: None\ncenter_project_name: Test center\ndescription: None\nrelated_studies: None\nurl_links:\n    label: test, url: http://test.com\nxref_links: None\nentrez_links: None\nddbj_links: None\nena_links: None\nsubmission: None\norganization:\n    organization_type: center\n    abbreviation: GEO\n    name: NCBI\n    email: geo@nih.gov\n    first_name: GEO\n    last_name: Curators\nexperiments: None\n\"\"\"\n\n\ndef test_Study_str_partial():\n    abstract = ('A very long abstract with more than 80 characters per line. '\n                'A very long abstract with more than 80 characters per line. '\n                'A very long abstract with more than 80 characters per line. '\n                'A very long abstract with more than 80 characters per line.')\n\n    study = Study(\n        study_id='SRA12345', GEO='GSE12345', BioProject='PRJN1234',\n        pubmed='123456', title='Test title', study_type='Test type',\n        abstract=abstract, center_project_name='Test center')\n\n    study.external_id.append(Xref(**{'db': 'test', 'id': '1030'}))\n    study.external_id.append(Xref(**{'db': 'test2', 'id': '1032'}))\n    study.external_id.append(Xref(**{'db': 'test3', 'id': '1033'}))\n    study.submitter_id.append(Xref(**{'db': 'test', 'id': 'test submitter'}))\n\n    study.url_links.append(URLLink(**{'label': 'test', 'url': 'http://test.com'}))\n\n    study.organization = Organization(\n        organization_type='center', abbreviation='GEO', name='NCBI',\n        email='geo@nih.gov', first_name='GEO', last_name='Curators')\n\n    assert str(study) == STUDY\n\n\ndef test_study_w_sra(sraExperiment):\n    submission = Submission(**sraExperiment.submission)\n    organization = Organization(**sraExperiment.organization)\n    study = Study(submission=submission, organization=organization, **sraExperiment.study)\n    assert study.study_id == 'SRP045429'\n    assert study.submission.submitter_id[0]['db'] == 'GEO'\n\n\ndef test_sample_w_sra(sraExperiment):\n    sample = Sample(**sraExperiment.sample)\n    print(sample)\n\n\ndef test_experiment_w_sra(mongoDB, sraExperiment):\n    client = connect('test_sra')\n\n    try:\n        submission = Submission(**sraExperiment.submission)\n        organization = Organization(**sraExperiment.organization)\n        study = Study(submission=submission, organization=organization, **sraExperiment.study)\n        experiment = Experiment(**sraExperiment.experiment)\n        experiment.study = study\n        experiment.save()\n        study.experiments.append(experiment.id)\n        study.save()\n\n        assert Study.objects(study_id=study.id)[0].experiments[0] == experiment.id\n        assert Experiment.objects(experiment_id=experiment.id)[0].study.study_id == study.id\n\n    finally:\n        client.drop_database('test_sra')\n\n\ndef test_run_w_sra(mongoDB, sraExperiment):\n    client = connect('test_sra')\n\n    try:\n        submission = Submission(**sraExperiment.submission)\n        organization = Organization(**sraExperiment.organization)\n        study = Study(submission=submission, organization=organization, **sraExperiment.study)\n        study.save()\n        experiment = Experiment(**sraExperiment.experiment)\n        experiment.study = study\n        experiment.save()\n\n        runs = []\n        for r in sraExperiment.run:\n            run = Run(experiment=experiment, **r)\n            run.save()\n            runs.append(run.id)\n        experiment.runs = runs\n        experiment.save()\n\n        assert Experiment.objects(experiment_id=experiment.id)[0].runs == [run.id]\n        assert run.tax_analysis.tax_counts['Neoptera']['self_count'] == 95\n\n    finally:\n        client.drop_database('test_sra')\n\n\ndef test_biosample_w_db(mongoDB, bioSample):\n    client = connect('test_sra')\n    try:\n        biosample = BioSample(**bioSample.biosample)\n        biosample.save()\n        bs = BioSample.objects(biosample_id=biosample.id).first()\n        assert bs.sample_id == 'SRS679015'\n        assert bs.title == 'DGRP563 M_E3_2_L3'\n    except:\n        raise\n    finally:\n        client.drop_database('test_sra')\n","sub_path":"tests/test_mongo_schema.py","file_name":"test_mongo_schema.py","file_ext":"py","file_size_in_byte":7645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"569259885","text":"import logging\nimport logging.config\nimport os, json\n\n\ndef DELETE_THIS():\n    logging.getLogger(__name__).info(\"HELLO TEST\")\n\n\ndef init_logging(default_path='../logging.json', default_level=logging.INFO):\n    directory = os.path.dirname(__file__)\n    path = os.path.join(directory, default_path)\n\n    if os.path.exists(path):\n        with open(path, 'rt') as f:\n            config = json.load(f)\n        logging.config.dictConfig(config)\n    else:\n        logging.basicConfig(level=default_level)\n\n    logging.getLogger(__name__).info('Logging initialized')\n\n\ninit_logging()\n","sub_path":"Other/skeleton/NAME/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"222286864","text":"# http://www.aclweb.org/anthology/P16-1101\n\nimport matplotlib.pyplot as plt\nplt.rcParams['figure.dpi'] = 80\nplt.style.use('seaborn-pastel')\nimport _pickle as cPickle\nfrom preprocessing import load_sentence, iob2iobes, all_of_word_and_char_data2id\n\n## parameters\nCoNLL_train_filepath = \"./CoNLL-2003/eng.train\"\nCoNLL_test_filepath = \"./CoNLL-2003/eng.testb\"\nGlove_filepath = \"./GloVe/glove.6B.100d.txt\"\n\n# dumped_mapping_parameters\ndef loadmapping(mappingpath):\n    returned_json = {}\n    with open(mappingpath,'rb') as mp:\n        mappings = cPickle.load(mp)\n        for key,values in mappings.items():\n            returned_json.update({key:values})\n    return returned_json\n\n\nif __name__ == '__main__':\n    mappingdata_picklepath = \"./data/mapping_file_for_train.pkl\"\n    testdata_picklepath =  \"./data/all_test_sentence2id.pkl\"\n\n    all_sentence_data_of_test = load_sentence(CoNLL_test_filepath)\n    for one_sentence in all_sentence_data_of_test:\n        one_sentence = iob2iobes(one_sentence)\n\n    # mappings = {\n    #     'word2id': word2iddict,\n    #     'char2id': character2iddict,\n    #     'tag2id': tag2iddict,\n    #     'id2word': id2word_dict,\n    #     'id2char': id2character_dict,\n    #     'id2tag': id2tag_dict,\n    #\n    #     'wordembed_dict': word2embeddict,\n    #     'wordembed_matrix': word_embeds_init_matrix\n    # }\n    mappings = loadmapping(mappingdata_picklepath)\n    word2iddict = mappings['word2id']\n    character2iddict = mappings['char2id']\n    tag2iddict = mappings['tag2id']\n    # 辞書は当然、train で作ったものと共通化\n    test_worddata2id, test_chardata2id = all_of_word_and_char_data2id(all_sentence_data_of_test, word2iddict,\n                                                                      character2iddict, tag2iddict)\n\n    with open(testdata_picklepath, 'wb') as g:\n        test2id = [test_worddata2id, test_chardata2id]\n        cPickle.dump(test2id, g)\n    print(\"test data is converted to id and pickle\")\n\n    print('preprcosessing is done')\n","sub_path":"dump/test_charerror_mappingdump.py","file_name":"test_charerror_mappingdump.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"632938940","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport matplotlib\nmatplotlib.use('Agg')\nfrom math import radians, cos, sin, asin, sqrt, pi\nimport re\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef haversine(lon1, lat1, lon2, lat2):\n    \"\"\"\n    Calculate the great circle distance between two points\n    on the earth (specified in decimal degrees)\n    \"\"\"\n    # convert decimal degrees to radians\n    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n\n    # haversine formula\n    dlon = lon2 - lon1\n    dlat = lat2 - lat1\n    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n    c = 2 * asin(sqrt(a))\n    r = 6371  # Radius of earth in kilometers. Use 3956 for miles\n    return c * r\n\n# pole lokací\nl = np.array(\n    [\n        #  název místa, gps_1, gps_2, radius [km], pozice na ciferníku\n        [u'Doma', 50.076376, 14.428572, 0.5, 0],\n        [u'          Trojanka', 50.074253, 14.415439, 0.5, -pi/4],\n        [u'Troja           ', 50.115189, 14.450250, 0.5, pi/4],\n        [u'U Terky                  ', 50.134280, 14.382326, 0.5, pi/2],\n        [u'            Břehovka', 50.091029, 14.416296, 0.5, -pi/2],\n        [u'Podolí                  ', 50.053875, 14.428219, 0.5, 3*pi/4],\n        [u'          Na hokeji', 50.104727, 14.493630, 0.8, -3*pi/4],\n        [u'           V divadle', 50.090861, 14.426911, 0.3, -3*pi/8],\n        [u'           CrossCafe', 50.075329, 14.428819, 0.2, -pi/8]\n    ])\n\npeople = [\n    # klíč, barva ručičky, jméno\n    [\"david\", \"blue\", \"David\"],\n    [\"peto\", \"red\", \"Peto\"],\n]\n\nfig = plt.figure()                          # vytvoření figure\nax = fig.add_subplot(111, polar=True)       # přidání polární subplot\nax.set_theta_offset(0.5*np.pi)              # posun nuly nahoru\nax.get_yaxis().set_visible(False)           # schování osy\nax.grid()                                   # schování mřížky\n\n# pro každého člověka v 'people'\nfor p in people:\n    # otevřít output soubor z Google Location\n    with open(\"output_\"+p[0]+\".txt\", \"r\") as myfile:\n        data = myfile.read().replace('\\n', '')\n\n    # regulární výraz na poslední souřadnici v souboru\n    matches = re.findall(\n            r\"^.*<gx:coord>([0-9\\.]*) ([0-9\\.]*) ([0-9\\.]*)<\\/gx:coord>\", data)\n\n    # zařazení GPS souřadnic\n    x = matches[0][1]\n    y = matches[0][0]\n\n    # nastavení základních proměnných\n    current_location = '??'         # název umístění\n    current_clock_position = pi     # pozice na hodinách\n    min_distance = 100              # minimální nalezená vzdálenost (to beat)\n\n    # pro každé umístění zkus jestli je blíž než ty předchozí\n    for location in l:\n        # výpočet vzdálenosti od místa v databázi\n        dist_from_loc = haversine(float(x), float(y),\n                                  float(location[1]), float(location[2]))\n        # pokud jsem v okruhu místa a je to zatím nejmenší vzdálenost\n        if ((dist_from_loc < location[3]) and (dist_from_loc < min_distance)):\n            # nastav aktuální pozici\n            min_distance = dist_from_loc\n            current_location = location[0]\n            current_clock_position = location[4]\n\n    # debug: vypiš pozici a vzdálenost od ní\n    # print(u\"Current location: \", current_location)\n    # print(u\"Distance from location: \", str(min_distance))\n\n    # plotni graf\n    ax.plot([0, current_clock_position], [0, 0.1], '-',\n            linewidth=4, color=p[1], label=p[2])\n\n# přeložení stringů z databáze na floaty\nxticks = []\nfor a in l[:, 4]:\n    xticks.append(float(a))\n\n# nastavení míst a popisků ticků\nax.set_xticks(xticks)\nax.set_xticklabels(l[:, 0].tolist())\n\n# uložení obrázku\nplt.savefig('weasley.png')\n","sub_path":"weasley.py","file_name":"weasley.py","file_ext":"py","file_size_in_byte":3715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"338286885","text":"from numpy import *\nimport operator\nfrom os import listdir\n\n\ndef knn(k, testData, trainData, labels):\n    traindatasize = trainData.shape[0]\n    dif = tile(testData, (traindatasize, 1)) - trainData\n    sqdif = dif ** 2\n    sumsqdif = sqdif.sum(axis=1)\n    distance = sumsqdif ** 0.5\n    sortdistance = distance.argsort()\n    count = {}\n    for i in range(0, k):\n        vote = labels[sortdistance[i]]\n        count[vote] = count.get(vote, 0) + 1\n    sortcount = sorted(count.items(), key=operator.itemgetter(1), reverse=True)\n    return sortcount[0][0]\n\n\n# 读取图片\nfrom PIL import Image\n\nim = Image.open(\"C:/Users/diaozhende/Desktop/three.png\")\nfh = open(\"C:/Users/diaozhende/Desktop/three.txt\", \"a\")\n# im.save(\"C:/Users/me/Pictures/weixin.bmp\")\nwidth = im.size[0]\nheight = im.size[1]\n# k=im.getpixel((1,9))\n# print(k)\nfor i in range(0, width):\n    for j in range(0, height):\n        cl = im.getpixel((i, j))\n        clall = cl[0] + cl[1] + cl[2]\n        if (clall == 0):\n            # 黑色\n            fh.write(\"1\")\n        else:\n            fh.write(\"0\")\n    fh.write(\"\\n\")\nfh.close()\n\n\n# 加载数据\ndef datatoarray(fname):\n    arr = []\n    fh = open(fname)\n    for i in range(0, 32):\n        thisLine = fh.readline()\n        for j in range(0, 32):\n            arr.append(int(thisLine[j]))\n        return arr\n\n\n# 建立一个函数去文件名的前缀\ndef seplabel(fname):\n    fileStr = fname.split(\".\")[0]\n    label = int(fileStr.split(\"_\")[0])\n    return label\n\n\n# 建立训练数据\ndef trainData():\n    labels = []\n    trainFile = listdir(\"E:/python大数据资料/Python3数据分析与挖掘实战/源码/第7周/data/traindata/\")\n    trainNum = len(trainFile)\n    print(trainNum)\n    trainarr = zeros((trainNum, 1024))\n    for i in range(0, trainNum):\n        thisfname = trainFile[i]\n        thislabel = seplabel(thisfname)\n        labels.append(thislabel)\n        trainarr[i, :] = datatoarray(\"E:/python大数据资料/Python3数据分析与挖掘实战/源码/第7周/data/traindata/\" + thisfname)\n    return trainarr, labels\n\n\ndef testData():\n    trainarr, labels = trainData()\n    testFile = listdir(\"E:/python大数据资料/Python3数据分析与挖掘实战/源码/第7周/data/testdata/\")\n    testNum = len(testFile)\n    for i in range(0, testNum):\n        thisTestFile = testFile[i]\n        testDataArr = datatoarray(\"E:/python大数据资料/Python3数据分析与挖掘实战/源码/第7周/data/testdata/\" + thisTestFile)\n        rknn = knn(3, testDataArr, trainarr, labels)\n        print(rknn)\n\n\ntestData()\n","sub_path":"天善数据分析与挖掘demo/day06手写体数字识别/手写体数字识别.py","file_name":"手写体数字识别.py","file_ext":"py","file_size_in_byte":2540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"555705375","text":"import matplotlib.pyplot as plt\r\nfrom matplotlib import rcParams\r\nimport numpy as np\r\nfrom threading import Thread\r\nfrom time import sleep\r\n\r\nclass draw:\r\n  batch_list = []\r\n  loss_list = []\r\n  acc_list = []\r\n  i = 0\r\n  def __init__(self, pic_size):\r\n    rcParams['figure.figsize']=pic_size, pic_size\r\n    self.fig = plt.figure(figsize=(12, 4))\r\n    self.batch_list = []\r\n    self.train_loss_list = []\r\n    self.train_acc_list = []\r\n    self.i = 0\r\n  def add(self, train_log):\r\n    if len(self.batch_list) != 0:\r\n      self.i = self.batch_list[-1] + 1\r\n    else:\r\n      self.i = self.i+1\r\n    self.batch_list.append(self.i)\r\n    self.train_loss_list.append(train_log[0])\r\n    self.train_acc_list.append(train_log[1])\r\n  def add_history(self, history):\r\n      if len(self.batch_list) != 0:\r\n          self.i = self.batch_list[-1] + len(history)\r\n      else:\r\n          self.i = self.i + len(history)\r\n      self.batch_list.append(self.i)\r\n      self.train_loss_list.append(history['loss'])\r\n      self.train_acc_list.append(history['accuracy'])\r\n  def save(self, NetPath):\r\n    np.save(NetPath+'/b.npy', np.array(self.batch_list))\r\n    np.save(NetPath+'/train_loss.npy', np.array(self.train_loss_list))\r\n    np.save(NetPath+'/train_acc.npy', np.array(self.train_acc_list))\r\n  def load(self, NetPath):\r\n    self.batch_list = np.load(NetPath+'/b.npy').tolist()\r\n    self.train_loss_list = np.load(NetPath+'/train_loss.npy').tolist()\r\n    self.train_acc_list = np.load(NetPath+'/train_acc.npy').tolist()\r\n  def close(self, time):\r\n      sleep(time)\r\n      plt.close()\r\n  def show(self, file_path='E:/temp_pic/pic'):\r\n    plt.clf()\r\n    ax1 = self.fig.add_subplot(121)\r\n    ax2 = self.fig.add_subplot(122)\r\n    ax1.plot(self.batch_list, self.train_loss_list, label = 'train loss', color = 'red')\r\n    ax2.plot(self.batch_list, self.train_acc_list, label = 'train acc', color = 'green')\r\n    bbox_props = dict(boxstyle='round',fc='w', ec='k',lw=1)\r\n    ax1.annotate(\"%s\" % self.train_loss_list[-1], xy=(self.i, self.train_loss_list[-1]), xytext=(-20, -20), textcoords='offset points', bbox=bbox_props)\r\n    plt.annotate(\"%s\" % self.train_acc_list[-1], xy=(self.i, self.train_acc_list[-1]), xytext=(-20, -20), textcoords='offset points', bbox=bbox_props)\r\n    ax1.set(xlabel='batches',ylabel='loss', title = 'loss')\r\n    ax2.set(xlabel='batches',ylabel='acc', title = 'acc')\r\n\r\n    plt.savefig(file_path+str(self.i) + '.png')\r\n    # thread1 = Thread(target=self.close, args=(1,))\r\n    # thread1.start()\r\n    # plt.show()\r\n  def show_images(self, images):\r\n    for i in range(images.shape[0]):\r\n      plt.subplot(1, images.shape[0], i + 1)\r\n      plt.imshow(images[i])\r\n      plt.axis('off')\r\n    plt.tight_layout()\r\n    plt.show()\r\n  def show_image(self, image):\r\n    plt.imshow(image)\r\n    plt.show()","sub_path":"show_pic.py","file_name":"show_pic.py","file_ext":"py","file_size_in_byte":2794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"497928266","text":"# Copyright (C) 2021 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\nDEPS = [\n    'windows_sdk',\n    'recipe_engine/platform',\n    'recipe_engine/properties',\n    'recipe_engine/step',\n]\n\n\ndef RunSteps(api):\n  with api.windows_sdk():\n    api.step('gn', ['gn', 'gen', 'out/Release'])\n    api.step('ninja', ['ninja', '-C', 'out/Release'])\n\n\ndef GenTests(api):\n  for platform in ('linux', 'mac', 'win'):\n    properties = {\n        'buildername': 'test_builder',\n    }\n    yield (api.test(platform) + api.platform.name(platform) +\n           api.properties.generic(**properties))","sub_path":"infra/luci/recipe_modules/windows_sdk/examples/full.py","file_name":"full.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"444217020","text":"import os\n\nos.system(' rm fort.100 -f ')\nos.system(' rm fort.200 -f ')\nos.system(' rm fort.300 -f ')\nos.system(' rm fort.400 -f ')\nos.system(' rm fort.500 -f ')\nos.system(' rm ../datos/* ')\nos.system(' rm ../graficos/* ')\n\nn=[21,41,61,81]\nm=9\n#m=[5,7,9]\n\nfor i in range(1,4+1):\n\n\twith open('1d_test.f90', 'r') as file:\n\t\tdata = file.readlines()\n\tdata[62] = '\tinteger,parameter::pcs=8,n='+str(n[i-1])+',m='+str(m)+'\\n'\n\twith open('d'+str(i)+'.f90', 'w') as file:\n\t\tfile.writelines( data )\n\tos.system(' gfortran d'+str(i)+'.f90 -o test umf4_f77wrapper.o -lumfpack -lamd -llapack -lrefblas && ./test ')\n\nos.system(' gfortran ge.f90 -o errorR && ./errorR ')\nos.system('gnuplot fort.100 && gnuplot fort.200 && gnuplot fort.500')\n\nos.system( ' mv *.svg ../graficos/ ' )\n","sub_path":"compilador.py","file_name":"compilador.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"316659469","text":"import time\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n              'new york city': 'new_york_city.csv',\n              'washington': 'washington.csv' }\n\ndef get_filters():\n    \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 Choose from the following \\n a.Chicago\\nb.New York\\nc.Washington\\n')\n    \n    # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n    \n    city = input('Which city data do you want to explore (options a,b or c)? \\n ')\n    \n    while (city != 'a' and city != 'b' and city != 'c'):\n        \n        print('\\nInvalid input , please choose from the options a.Chicago , b.New York , c.Washington\\n')\n        city = input('Which city data do you want to explore (options a,b or c)? \\n ')\n    \n    print('From the choices ,a.Chicago , b.New York , c.Washington\\n , We will look into the data of city {}'.format(city))\n    \n    # TO DO: get user input for month (all, january, february, ... , june)\n    \n    month = input('\\nWhich month data do you wish to look into ( all months or specifically from january to june) ? ').lower()\n    \n    if month not in ['january','february','march','april','may','june'] :\n        print('Please enter a valid month ( all months or specifically from january to june)')\n        month = input('\\nWhich month data do you wish to look into ( all months or specifically from january to june) ? ').lower()\n    \n    \n    \n    \n    # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n    \n    day = input('Which day of the week data do you wish to look into ( all days of the week or specifically monday to sunday? )').lower()\n    \n    if day not in ['monday','tuesday','wednesday','thursday','friday','saturday','sunday']:\n        print('Please enter a valid day of teh week ( all days of the week or specifically monday to sunday? )')\n        day = input('Which day of the week data do you wish to look into ( all days of the week or specifically monday to sunday? )').lower()\n    \n    print('Let us look into {}'.format(month))\n    print('Let us look into {}'.format(day))\n\n    if (city == 'a'):\n        city = 'chicago'\n    elif(city == 'b'):\n        city = 'new york city'   \n    else:\n        city = 'washington'\n        \n    \n    print('-'*40)\n    return city, month, day\n\ndef load_data(city, month, day):\n    \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    \n    # load data file into a dataframe\n    df = pd.read_csv(CITY_DATA[city])\n\n    # convert the Start Time column to datetime\n    df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n    # extract month and day of week from Start Time to create new columns\n    df['month'] = df['Start Time'].dt.month\n    df['day_of_week'] = df['Start Time'].dt.weekday_name\n\n    # filter by month if applicable\n    if month != 'all':\n        # use the index of the months list to get the corresponding int\n        months = ['january', 'february', 'march', 'april', 'may', 'june']\n        month = months.index(month) + 1\n\n        # filter by month to create the new dataframe\n        df = df[df['month'] == month]\n\n    # filter by day of week if applicable\n    if day != 'all':\n        # filter by day of week to create the new dataframe\n        df = df[df['day_of_week'] == day.title()]\n\n    view_display = input('\\nWould you like to view 5 rows of individual trip data? Enter yes or no\\n').lower()\n    start_loc = 0\n    while (view_display == 'yes'):\n        print(df.iloc[start_loc:start_loc+5])\n        start_loc += 5\n        view_display = input(\"Do you wish to continue?: \").lower()\n    \n    return df\n\ndef time_stats(df):\n    \"\"\"Displays statistics on the most frequent times of travel.\"\"\"\n\n    print('\\nCalculating The Most Frequent Times of Travel...\\n')\n    start_time = time.time()\n    # TO DO: display the most common month:\n\n   \n    # TO DO: display the most common month\n    popular_month = df['month'].mode()[0]\n    print('Most Popular Start Month:', popular_month)\n    \n\n    # TO DO: display the most common day of week\n\n    popular_day = df['day_of_week'].mode()[0]\n    print('Most Popular Start Day:', popular_day)\n\n    # TO DO: display the most common start hour\n\n    # find the most popular hour:\n    \n    # extract hour from the Start Time column to create an hour column\n    df['hour'] = df['Start Time'].dt.hour\n    popular_hour = df['hour'].mode()[0]\n    print('Most Popular Start Hour:', popular_hour)\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\ndef station_stats(df):\n    \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n\n    print('\\nCalculating The Most Popular Stations and Trip...\\n')\n    start_time = time.time()\n\n    # TO DO: display most commonly used start station\n\n    popular_start_station = df['Start Station'].mode()[0]\n    print('Most Popular Start Station:', popular_start_station)\n\n    # TO DO: display most commonly used end station\n\n    popular_end_station = df['End Station'].mode()[0]\n    print('Most Popular End Station:', popular_end_station)\n\n    # TO DO: display most frequent combination of start station and end station trip\n\n    df['Count']=df.groupby(['Start Station','End Station'])['End Station'].transform('size')\n    count_max = df['Count'].max()\n    df1 = df[ count_max == df['Count'].values]\n    #df1 = df1[['Start Station','End Station']]\n    #df1 = np.unique(df1)\n    #print(df1)\n    print('Most frequent Combination occures when start station is and end station are ', df1[['Start Station','End Station']])\n    \n    #print('The Combination are ', \\n df1['Combi'].unique())\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\ndef trip_duration_stats(df):\n    \"\"\"Displays statistics on the total and average trip duration.\"\"\"\n\n    print('\\nCalculating Trip Duration...\\n')\n    start_time = time.time()\n\n    # TO DO: display total travel time\n    df['End Time'] = pd.to_datetime(df['End Time'])\n    df['Start Time'] = pd.to_datetime(df['Start Time'])\n    df['diff'] = df['End Time'] - df['Start Time']\n    \n    df['diff'] = df['diff']/np.timedelta64(1,'s')\n    print('Travel time in seconds',df['diff'])\n    \n    # TO DO: display mean travel time\n\n    mean_travel_time = np.mean(df['diff'])\n    print('The mean travel time in seconds \\n ',mean_travel_time)\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\ndef user_stats(city , df):\n    \"\"\"Displays statistics on bikeshare users.\"\"\"\n\n    print('\\nCalculating User Stats...\\n')\n    start_time = time.time()\n\n    # TO DO: Display counts of user types\n\n    user_types = df['User Type'].value_counts()\n    print('The count of user types are \\n ',user_types)\n\n    # TO DO: Display counts of gender\n\n    if (city == 'chicago' or city =='new_york'):\n        gender_count = df['Gender'].value_counts()\n        print('The count of gender types are \\n ',gender_count)\n    \n    # TO DO: Display earliest, most recent, and most common year of birth\n\n    if (city == 'chicago' or city =='new_york'):\n        earliest_birth_year = df['Birth Year'].min()\n        recent_birth_year = df['Birth Year'].max()\n        popular_birth_year = df['Birth Year'].mode()[0]\n        \n        print('The earliest birth year is', earliest_birth_year)\n        print('The recent birth year is', recent_birth_year)\n        print('The common birth year ', popular_birth_year)\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n    \ndef main():\n    while True:\n        city, month, day = get_filters()\n        df = load_data(city, month, day)\n        print(df)\n        \n        time_stats(df)\n        station_stats(df)\n        trip_duration_stats(df)\n        user_stats(city , 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 (1).py","file_name":"bikeshare (1).py","file_ext":"py","file_size_in_byte":8643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"43732611","text":"import logging\nfrom itertools import chain\n\nimport torch\nfrom torch_geometric.data import Batch\n\n\"\"\"\nImplements data parallelism at the module level\n\"\"\"\nclass DataParallel(torch.nn.DataParallel):\n    def __init__(self, module, device_ids=None, output_device=None,\n                 follow_batch=[]):\n        super(DataParallel, self).__init__(module, device_ids, output_device)\n        #self.src_device = torch.device(\"cuda:{}\".format(self.device_ids[0]))\n        self.src_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") #incase Mac users don't have GPUs\n        self.follow_batch = follow_batch\n\n    def forward(self, data_list, target_list):\n        if len(data_list) == 0:\n            logging.warning('DataParallel received an empty data list, which '\n                            'may result in unexpected behaviour.')\n            return None\n\n        if not self.device_ids or len(self.device_ids) == 1:  # Fallback\n            data = Batch.from_data_list(\n                data_list, follow_batch=self.follow_batch).to(self.src_device)\n            target = Batch.from_data_list(\n                target_list, follow_batch=self.follow_batch).to(self.src_device)\n            return self.module(data, target)\n\n        for t in chain(self.module.parameters(), self.module.buffers()):\n            if t.device != self.src_device:\n                raise RuntimeError(\n                    ('Module must have its parameters and buffers on device '\n                     '{} but found one of them on device {}.').format(\n                         self.src_device, t.device))\n\n        inputs = self.scatter(data_list, target_list, self.device_ids)\n        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])\n        outputs = self.parallel_apply(replicas, inputs, None)\n        return self.gather(outputs, self.output_device)\n\n    def scatter(self, data_list, target_list, device_ids):\n        num_devices = min(len(device_ids), len(data_list))\n\n        count = torch.tensor([data.num_nodes for data in data_list])\n        cumsum = count.cumsum(0)\n        cumsum = torch.cat([cumsum.new_zeros(1), cumsum], dim=0)\n        device_id = num_devices * cumsum.to(torch.float) / cumsum[-1].item()\n        device_id = (device_id[:-1] + device_id[1:]) / 2.0\n        device_id = device_id.to(torch.long)  # round.\n        split = device_id.bincount().cumsum(0)\n        split = torch.cat([split.new_zeros(1), split], dim=0)\n        split = torch.unique(split, sorted=True)\n        split = split.tolist()\n\n        return [[\n            Batch.from_data_list(data_list[split[i]:split[i + 1]], follow_batch=self.follow_batch).to(torch.device('cuda:{}'.format(device_ids[i]))),\n            Batch.from_data_list(target_list[split[i]:split[i + 1]], follow_batch=self.follow_batch).to(torch.device('cuda:{}'.format(device_ids[i])))]\n            for i in range(len(split) - 1)\n        ]\n","sub_path":"models/parallel.py","file_name":"parallel.py","file_ext":"py","file_size_in_byte":2895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"537928955","text":"#!/usr/bin/env python3\n\"\"\"\nComputes the log probability of the sequence of tokens in file,\naccording to a trigram model.  The training source is specified by\nthe currently open corpus, and the smoothing method used by\nprob() is polymorphic.\n\"\"\"\nimport argparse\nimport logging\nfrom pathlib import Path\nimport math\n\ntry:\n    # Numpy is your friend. Not *using* it will make your program so slow.\n    # So if you comment this block out instead of dealing with it, you're\n    # making your own life worse.\n    #\n    # We made this easier by including the environment file in this folder.\n    # Install Miniconda, then create and activate the provided environment.\n    import numpy as np\nexcept ImportError:\n    print(\"\\nERROR! Try installing Miniconda and activating it.\\n\")\n    raise\n\n\nfrom Probs import LanguageModel\n\nTRAIN = \"TRAIN\"\nTEST = \"TEST\"\n\nlog = logging.getLogger(Path(__file__).stem)  # Basically the only okay global variable.\n\ndef save(lm_one, lm_two, destination):\n    with open(destination, 'wb') as output:\n        import pickle\n        t = (lm_one, lm_two)\n        pickle.dump(t, output, protocol=pickle.HIGHEST_PROTOCOL)\n\ndef train(args):\n    logging.basicConfig(level=args.verbose)\n    model_path = get_model_filename(args.smoother, args.lexicon, args.class_one, args.class_two)\n    log.info(\"Training...\")\n    lm_one = LanguageModel.make(args.smoother, args.lexicon)\n    lm_two = LanguageModel.make(args.smoother, args.lexicon)\n    # lm.set_smoother(args.smoother)\n    # lm.read_vectors(args.lexicon)\n\n    lm_one.set_vocab_size(args.class_one, args.class_two)\n    lm_two.set_vocab_size(args.class_one, args.class_two)\n\n    lm_one.train(args.class_one)\n    lm_two.train(args.class_two)\n    \n    save(lm_one, lm_two, model_path)\n\n\ndef test(args):\n    logging.basicConfig(level=args.verbose)\n    model_path = get_model_filename(args.smoother, args.lexicon, args.class_one, args.class_two)\n    log.info(\"Testing...\")\n    t = LanguageModel.load(model_path)\n    lm_one = t[0]\n    lm_two = t[1]\n  \n    total_one = 0\n    total_two = 0\n    for test_file in args.test_files:   \n        # We don't care about bits anymore so no log(2)\n        log_prob_one = lm_one.file_log_prob(test_file)\n        log_prob_two = lm_two.file_log_prob(test_file)\n\n        #print(f'{args.prior_prob} vs {1-args.prior_prob}')\n        log_prob_one += math.log(args.prior_prob)\n        log_prob_two += math.log(1-args.prior_prob)\n        if (log_prob_one >= log_prob_two):\n            print(f'{args.class_one}\\t{test_file}')\n            total_one += 1\n        else:\n            print(f'{args.class_two}\\t{test_file}')\n            total_two += 1\n\n    print(f'{total_one} files were more probably {args.class_one} ({total_one/len(args.test_files)})')\n    print(f'{total_two} files were more probably {args.class_two} ({total_two/len(args.test_files)})')\n    return total_one, total_two\n\n\ndef get_model_filename(smoother: str, lexicon: Path, class_one : Path, class_two : Path) -> Path:\n    return Path(f\"{smoother}_{lexicon.name}_{class_one.name}_{class_two.name}.model\")\n\n\ndef parse_args() -> argparse.Namespace:\n    parser = argparse.ArgumentParser(__doc__)\n    \n    subparsers = parser.add_subparsers()\n    train_parser = subparsers.add_parser('TRAIN', help='train help')\n    train_parser.add_argument('smoother', type=str)\n    train_parser.add_argument('lexicon', type=Path)\n    train_parser.add_argument('class_one', type=Path)\n    train_parser.add_argument('class_two', type=Path)\n    train_parser.set_defaults(func=train)\n\n    test_parser = subparsers.add_parser('TEST', help='test help')\n    test_parser.add_argument('smoother', type=str)\n    test_parser.add_argument('lexicon', type=Path)\n    test_parser.add_argument('class_one', type=Path)\n    test_parser.add_argument('class_two', type=Path)\n    test_parser.add_argument('prior_prob', type=float, help='The prior probability of training set 1.')\n    test_parser.add_argument('test_files', type=Path, nargs=\"*\")\n    test_parser.set_defaults(func=test)\n\n    verbosity = parser.add_mutually_exclusive_group()\n    verbosity.add_argument(\n        \"-v\",\n        \"--verbose\",\n        action=\"store_const\",\n        const=logging.DEBUG,\n        default=logging.INFO,\n    )\n    verbosity.add_argument(\n        \"-q\", \"--quiet\", dest=\"verbose\", action=\"store_const\", const=logging.WARNING\n    )\n\n    args = parser.parse_args()\n    args.func(args)\n    # # Sanity-check the configuration.\n    # if args.smooth_amount is not None:\n    #     assert args.smoother in {\"add\", \"backoff_add\", \"loglinear\"}\n    # else:\n    #     assert args.smooth_amount >= 0\n\nif __name__ == \"__main__\":\n    parse_args()\n\n","sub_path":"textcat.py","file_name":"textcat.py","file_ext":"py","file_size_in_byte":4629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"196080710","text":"import mwclient\nimport mwparserfromhell\nimport logging\nimport re\nimport csv\nfrom utilities import study_template\n\ndef fetch(page, template, key):\n    thing = \"\"\n    try:\n        thing = template.get(key).value.rstrip()\n    except ValueError:\n        logging.warning(f\"No '{key}' on study page {page.name}\")\n        pass\n    return thing\n\ndef run(mother):\n    category = mother.categories['Study']\n\n    with open('studyreport.csv', 'w') as csvfile:\n        writer = csv.writer(csvfile)\n        columns = [\n            \"Study\",\n            \"AKA\",\n            \"Short Description\",\n            \"Study Status\",\n            \"Active\",\n            \"In Quarterly Progress Report\",\n            \"Verified for Progress Report Date\",\n            \"Funded Fully or Partially Through RSP\",\n            \"CHM Website\",\n            \"Start Date\",\n            \"End Date\",\n            \"Planning Start Date\",\n            \"Piloting Start Date\",\n            \"Collecting Start Date\",\n            \"Projected Enrollment\",\n            \"Current Enrollment\",\n            \"IRB Number\",\n            \"Grant Number\",\n            \"Funding Source\",\n            \"ARROW URL\",\n            \"NIH RePORTER ID\",\n            \"ClinicalTrials.Gov\",\n            \"JARVIS ID\",\n            \"PIs\",\n            \"Project Manager\",\n            \"Current Contact\",\n            \"Project Links\",\n            ]\n\n        writer.writerow(columns)\n        for page in category:\n            logging.debug(\"Loading study\", page.name)\n            text = page.text()\n            p = mwparserfromhell.parse(text)\n\n            template = study_template(p)\n            column_values = [fetch(page, template, x) for x in columns]\n            column_values[0] = page.name\n\n            writer.writerow(column_values)\n\n","sub_path":"studyreport.py","file_name":"studyreport.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"591477754","text":"#!/usr/bin/env python\n\nimport sys\ninput = sys.stdin.buffer.readline\nsys.setrecursionlimit(10**6)\nINF = float(\"inf\")\n\nfrom bisect import bisect_right\n\ndef main():\n    N = int(input())\n    L = list(map(int,input().split()))\n\n    L.sort()\n    ans = 0\n    for b in range(N):\n        for a in range(b+1,N):\n            upper = b\n            lower = bisect_right(L, L[a] - L[b])\n            ans += max(0, upper - lower)\n    \n    print(ans)\n    \nif __name__ == \"__main__\":\n    main()","sub_path":"Python_codes/p02888/s795645520.py","file_name":"s795645520.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"632663729","text":"def doors(arg):\r\n    doors = [False]*arg\r\n    for i in range(arg):\r\n        for e in range (i,arg ,i+1):\r\n            doors[e] = not doors[e]\r\n        #print(\"Door %d:\" % (i+1), 'open' if doors[i] else 'close' )\r\n        #print(doors)\r\n        index=[i+1 for i,val in enumerate(doors) if val==True]\r\n\r\n    print(\"the following doors are opend:\" ,index )\r\ndoors(100)\r\n\r\n\r\n\r\n","sub_path":"100doors.py","file_name":"100doors.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"486809675","text":"#Programa 05\nimport os\ncliente,producto,cant,costo,total=\"\",\"\",0,0.0,0.0\n\n#INPUT VIA OS\ncliente=os.sys.argv[1]\nproducto=os.sys.argv[2]\ncant=int(os.sys.argv[3])\ncosto=float(os.sys.argv[4])\n\n#PROCESSING\ntotal=cant*costo\n#condicion multiple\n# Si el total supera 400 (\"comprador compulsivo\")\nif(total > 400):\n    print(\"comprador compulsivo\")\n# si el total 200 y 300 (\"comprador normal\")\nif(total >= 200 and total <= 300 ):\n    print(\"comprador normal\")\n#Si el total 50 y 120 (\"misio\")\nif(total >= 50 and total <= 120 ):\n    print(\"misio\")\n#fin_if\n\n","sub_path":"vilchez/condicionales.multiples05.py","file_name":"condicionales.multiples05.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"598855834","text":"import sys,os\nimport curses\n\nhome = \"home = \"\naway = \"away = \"\nqtr = \"qtr = \"\nascore = \"ascore = \"\nhscore = \"hscore = \"\nuhscore = \"\"\nuacscore = \"\"\nuqtr = \"\"\n\n\n\ndef datPrint(var):\n    file = open(\"sbdat.txt\", \"r\")\n    writer = file.read()\n    out = writer[writer.find(var) + len(var):-(len(writer) - writer.find(\";\", writer.find(var)))]\n    file.close()\n    return out\n\n\ndef draw_menu(stdscr):\n    k = 0\n    cursor_x = 0\n    cursor_y = 0\n\n    # Clear and refresh the screen for a blank canvas\n    stdscr.clear()\n    stdscr.refresh()\n\n    # Start colors in curses\n    curses.start_color()\n    curses.init_pair(1, curses.COLOR_CYAN, curses.COLOR_BLACK)\n    curses.init_pair(2, curses.COLOR_RED, curses.COLOR_BLACK)\n    curses.init_pair(3, curses.COLOR_BLACK, curses.COLOR_WHITE)\n\n    # Loop where k is the last character pressed\n    while (k != ord('q')):\n\n        # Initialization\n        stdscr.clear()\n        height, width = stdscr.getmaxyx()\n\n        if k == curses.KEY_DOWN:\n            cursor_y = cursor_y + 1\n        elif k == curses.KEY_UP:\n            cursor_y = cursor_y - 1\n        elif k == curses.KEY_RIGHT:\n            cursor_x = cursor_x + 1\n        elif k == curses.KEY_LEFT:\n            cursor_x = cursor_x - 1\n\n        cursor_x = max(0, cursor_x)\n        cursor_x = min(width-1, cursor_x)\n\n        cursor_y = max(0, cursor_y)\n        cursor_y = min(height-1, cursor_y)\n\n\n        line1 = \"{} {} {}\".format(datPrint(away).center(10), \"QTR\".center(10), datPrint(home).center(10))\n        line2 = \"{} {} {}\".format(datPrint(ascore).center(10), datPrint(qtr).center(10), datPrint(hscore).center(10))\n\n\n\n        # Declaration of strings\n        top = ('*' * 32)[:width-1]\n        namesQtr = \"{}\".format(line1)[:width-1]\n        scoreQTR = \"{}\".format(line2)[:width-1]\n        bottom = top\n        statusbarstr = \"RUNNING HOE\"[:width-1]\n\n        if k == 0:\n            keystr = \"Press any key\"[:width-1]\n\n        # Centering calculations\n        start_x_top = int((width // 2) - (len(top) // 2) - len(top) % 2)\n        start_x_namesQTR = int((width // 2) - (len(top) // 2) - len(top) % 2)\n        start_x_scoreQTR = int((width // 2) - (len(top) // 2) - len(top) % 2)\n        start_x_bottom = int((width // 2) - (len(top) // 2) - len(top) % 2)\n        start_y = int((height // 2) - 2)\n\n        # Rendering some text\n        whstr = \"Width: {}, Height: {}\".format(width, height)\n        stdscr.addstr(0, 0, whstr, curses.color_pair(1))\n\n        # Render status bar\n        stdscr.attron(curses.color_pair(3))\n        stdscr.addstr(height-1, 0, statusbarstr)\n        stdscr.addstr(height-1, len(statusbarstr), \" \" * (width - len(statusbarstr) - 1))\n        stdscr.attroff(curses.color_pair(3))\n\n        # Turning on attributes for title\n        stdscr.attron(curses.color_pair(2))\n        stdscr.attron(curses.A_BOLD)\n\n        # Rendering title <---DISCONTINUED\n\n        # Turning off attributes for title\n        stdscr.attroff(curses.color_pair(2))\n        stdscr.attroff(curses.A_BOLD)\n\n        # Print all of text\n        stdscr.addstr(start_y, start_x_top, top)\n        stdscr.addstr(start_y + 1, start_x_namesQTR, namesQtr)\n        stdscr.addstr(start_y + 2, start_x_scoreQTR, scoreQTR)\n        # stdscr.addstr(start_y + 3, (width // 2) - 2, '-' * 4)\n        stdscr.addstr(start_y + 3, start_x_bottom, bottom)\n        #stdscr.addstr(start_y + 5, start_x_keystr, keystr)\n        stdscr.move(cursor_y, cursor_x)\n\n        # Refresh the screen\n        stdscr.refresh()\n\n        # Wait for next input\n        k = stdscr.getch()\n\ndef main():\n    curses.wrapper(draw_menu)\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"bin/newBet/bin/curse-EDIT.py","file_name":"curse-EDIT.py","file_ext":"py","file_size_in_byte":3617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"364625174","text":"from assignment import *\n\ndef main():\n    # -----\n    # 1. Load corpus data using your load_corpus_data function.\n    # 2. Use the save_pkl utility function to save this data to a file named\n    #    data.pkl\n    # 3. Obtain the word frequency dictionary using your get_word_freq function.\n    # 4. Use the save_json utility function to save the word frequency\n    #    dictionary to a file named wfd.json\n    # 5. Obtain two lists of words: top 100 and top 5000.\n    # 6. Save the two lists in two files named top_100_list.txt and\n    #    top_5000_list.txt respectively. Write plain text words, one word per\n    #    line. You will need to write your own code for that (instead of using a\n    #    function). Refer to your lecture notes or Python documentation for\n    #    how to do that. The words should be ordered by decreasing frequency.\n    # 7. Generate a frequency graph for the top 100 words, and use the\n    #    graph_top_k function to save it to a file named top_100_fig.png\n    # 8. Answer the question: What type of behavior do English words exhibit?\n    #    What is the class of functions that describe such behavior? What is the\n    #    name of this law in linguistics? Answers should be inside comments.\n    #\n    data = load_corpus_data('english-brown.txt')\n    save_pkl('data.pkl',data)\n    wfd = get_word_freq(data)\n    save_json('wfd.json',wfd)\n    top100=top_k_words(wfd, 100)\n    top5000=top_k_words(wfd, 5000)\n    out100 = open(\"top_100_list.txt\", \"w\")\n    for word in top100:\n        out100.write(word)\n        out100.write(\"\\n\")\n    out100.close()\n    out5000 = open(\"top_5000_list.txt\", \"w\")\n    for word in top5000:\n        out5000.write(word)\n        out5000.write(\"\\n\")\n    out5000.close()\n    graph_top_k(wfd, top100, 'top_100_fig.png')\n\n\n    # The grpah generated by graph_top_k shows a decreasing exponential function.  \n    # The most frequent word occurs about twice as often as the second most frequent word,\n    # and occurs three times as often as the third most frequent word. \n    # This implies that the frequency of any word is inversely proporional to its rank in the frequency table.  \n    # In linguistics, it is called the Zipf's law.\n\n    return\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"submissions/a21645574/part1/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"453078524","text":"from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nfrom django.utils.decorators import method_decorator\nfrom django.views import View\nfrom .models import *\n\nfrom FinalProject import settings\n\n\nclass Dashboard(LoginRequiredMixin, View):\n    login_url = settings.LOGOUT_REDIRECT_URL\n    template_name = 'dashboard.html'\n\n    def get(self, request, *args, **kwargs):\n        all_students = Student.objects.all();\n        total_courses = Course.objects.all().count();\n\n        students = all_students.count();\n        seniors = all_students.filter(year='Senior').count()\n        juniors = all_students.filter(year='Junior').count()\n        sophomores = all_students.filter(year='Sophomore').count()\n        all_gpa_list = all_students.values_list('gpa', flat=True)\n        gpa_list = [i for i in all_gpa_list if i]\n        average_gpa = sum(gpa_list) + len(all_gpa_list)\n        dashboard = {'students': students, 'seniors': seniors, 'juniors': juniors, 'sophomores': sophomores,\n                     'total_courses': total_courses, 'average_gpa': average_gpa}\n\n        return render(request, self.template_name, {'dashboard_data': dashboard})\n\n\nclass Courses(LoginRequiredMixin, View):\n    login_url = settings.LOGOUT_REDIRECT_URL\n    template_name = 'courses.html'\n\n    def get(self, request, *args, **kwargs):\n        courses = Course.objects.all();\n\n        return render(request, self.template_name, {'courses': courses})\n\n\nclass Students(LoginRequiredMixin, View):\n    login_url = settings.LOGOUT_REDIRECT_URL\n    template_name = 'students.html'\n\n    def get(self, request, *args, **kwargs):\n        students = Student.objects.all().order_by('student_id');\n\n        return render(request, self.template_name, {'students': students})\n\n\nclass Enrollments(LoginRequiredMixin, View):\n    login_url = settings.LOGOUT_REDIRECT_URL\n    template_name = 'enrollments.html'\n\n    def get(self, request):\n        students = Student.objects.all().order_by('student_id')\n        courses = Course.objects.all()\n        return render(request, self.template_name, {'students': students, 'courses': courses})\n\n\nclass GraduationRates(LoginRequiredMixin, View):\n    login_url = settings.LOGOUT_REDIRECT_URL\n    template_name = 'graduation_rate.html'\n\n    def get(self, request, *args, **kwargs):\n        all_grade_rates = GraduationRate.objects.all().order_by('id').reverse()\n        years = list(all_grade_rates.values_list('graduation_year', flat=True))\n        four_year_gr = list(all_grade_rates.values_list('four_year_grade_rate', flat=True))\n        five_year_gr = list(all_grade_rates.values_list('five_year_grade_rate', flat=True))\n        six_year_gr = list(all_grade_rates.values_list('six_year_grade_rate', flat=True))\n\n        return render(request, self.template_name,\n                      {'years': years, 'four_year_gr': four_year_gr, 'five_year_gr': five_year_gr,\n                       'six_year_gr': six_year_gr})\n\n\nclass StudentEnrolledCourses(LoginRequiredMixin, View):\n    login_url = settings.LOGOUT_REDIRECT_URL\n    template_name = 'enrollment_detail.html'\n\n    def get(self, request):\n        student_id = request.GET.get('student_id')\n        enrolled_courses = Student.objects.filter(student_id=student_id).first()\n        my_courses = enrolled_courses.courses.all()\n        courses = Course.objects.all()\n        return render(request, self.template_name, {'courses': courses, 'my_courses':my_courses})\n\nclass EnrollStudent(LoginRequiredMixin, View):\n    login_url = settings.LOGOUT_REDIRECT_URL\n\n    def get(self, request, *args, **kwargs):\n        student_id = request.GET.get('student_id')\n        course_id = request.GET.get('course_id')\n        message = 'error'\n        student = Student.objects.filter(student_id=student_id).first()\n        if len(student.courses.all()) < 3:\n            message = 'success'\n            course = Course.objects.filter(course_id=course_id).first()\n            student.courses.add(course)\n\n        return HttpResponse(message, status=200)\n","sub_path":"fp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"166705401","text":"#!/usr/bin/python3\n\n# Eye and Identity\n# Solution to HackerRank Python challenge.\n\nimport numpy as np\n\ndef main():\n    user_input = input()\n\n    n = int(user_input[0])\n    m = int(user_input[2])\n\n    print(np.eye(n, m))\n\nif __name__ == '__main__':\n    main()","sub_path":"eye-identity/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"259862801","text":"import numpy as np # Scientific computing library\n \n# Project: Calculating Rotation Matrices\n# Author: Addison Sears-Collins\n# Date created: August 1, 2020\n \n# Servo (joint) angles in degrees\nservo_0_angle = 45 # Joint 1\nservo_1_angle = 30 # Joint 2\n \n# Convert servo angles from degrees to radians\nservo_0_angle = np.deg2rad(servo_0_angle)\nservo_1_angle = np.deg2rad(servo_1_angle)\n \n# Define the first rotation matrix.\n# This matrix helps convert frame 1 to frame 0.\n# There is only rotation around the z axis of servo_0.\nrot_mat_0_1 = np.array([[np.cos(servo_0_angle), -np.sin(servo_0_angle), 0],\n                        [np.sin(servo_0_angle), np.cos(servo_0_angle), 0],\n                        [0, 0, 1]]) \n \n# Define the second rotation matrix.\n# This matrix helps convert the \n# end-effector frame (frame 2) to frame 1.\n# There is only rotation around the z axis of servo_1 (joint 2).\nrot_mat_1_2 = np.array([[np.cos(servo_1_angle), -np.sin(servo_1_angle), 0],\n                        [np.sin(servo_1_angle), np.cos(servo_1_angle), 0],\n                        [0, 0, 1]]) \n\n# rot_mat_2_3 = np.array([[np.cos(servo_2_angle), -np.sin(servo_2_angle), 0],\n#                         [np.sin(servo_2_angle), np.cos(servo_2_angle), 0],\n#                         [0, 0, 1]]) \n\n# Calculate the rotation matrix that converts the \n# end-effector frame to the servo_0 frame.\nrot_mat_0_2 = rot_mat_0_1 @ rot_mat_1_2\n \n# Display the rotation matrix\nprint(rot_mat_0_2)","sub_path":"matrix_rotation.py","file_name":"matrix_rotation.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"448769547","text":"import networkx as nx\nfrom geojson import Feature, FeatureCollection, MultiLineString, dumps\n\n\nclass RoutesGraph:\n\tGraph = None\n\n\tdef __init__(self):\n\t\tself.Graph = nx.Graph()\n\t\n\tdef addEdge(self, src, dst):\n\t\tif (self.Graph.has_edge(src, dst)):\n\t\t\tself.Graph.edges[src, dst]['weight'] +=1\n\t\t\n\t\telse:\n\t\t\tself.Graph.add_edge(src, dst, weight = 1)\n\t\t\n\tdef writeToGeojson(self, filepath):\n\t\tfeatures = []\n\t\t\n\t\tfor u, v, d in self.Graph.edges(data=True):\n\t\t\t\n\t\t\tf = Feature(geometry=MultiLineString(\n                \t\t[[u, v]]), properties={\"weight\": d['weight']})\n\n\t\t\tfeatures.append(f)\n\n\t\troutes = FeatureCollection(features)\n\t\tfd = open(filepath, \"w\")\n\t\tfd.write(dumps(routes, sort_keys=True))\n\t\tfd.close()\n\n\n","sub_path":"View/RoutesGraph.py","file_name":"RoutesGraph.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"384125157","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nmodul: model utilities\r\nmodul author: Christoph Doerr\r\n\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn import preprocessing\r\nimport os\r\nimport tensorflow as tf\r\nimport matplotlib.pyplot as plt\r\nfrom tensorflow.keras.callbacks import LearningRateScheduler, ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.metrics import categorical_accuracy, top_k_categorical_accuracy \r\n\r\n\r\ndef trainModel(model, X_train, Y_train, X_test, Y_test, checkpoint_path, batch_size=32, number_epochs=51, validation_split = 0.2,\\\r\n               loss = 'sparse_categorical_crossentropy', metrics='accurcy', monitor='accuracy', schedule=False, stopping=False, plateau=False,\\\r\n                   checkpoint=False):\r\n    \"\"\" Train Model\r\n    Input model: defined model\r\n    Input X_train: training data\r\n    Input Y_train: training labels\r\n    Input X_train: test data\r\n    Input Y_train: test labels\r\n    Return model: trained model\r\n    Return history: training history\r\n    \"\"\"\r\n    optimizer = tf.keras.optimizers.Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, amsgrad=False)\r\n    callbacks_list = defineCallbacks(checkpoint_path, number_epochs, monitor=monitor, schedule=schedule, stopping=stopping, \\\r\n                                     plateau=plateau, checkpoint=checkpoint)\r\n    model.compile(optimizer=optimizer, loss=loss, metrics=[metrics])\r\n    history = model.fit(X_train, Y_train, epochs=number_epochs, batch_size=batch_size, callbacks=callbacks_list,\\\r\n                        validation_data = (X_test, Y_test))\r\n    return (model, history)\r\n\r\ndef trainLSTMModel(model, X_train, Y_train, X_test, Y_test, checkpoint_path, batch_size, number_epochs=51, validation_split = 0.2,\\\r\n                   loss = 'sparse_categorical_crossentropy', monitor='accuracy', schedule=False, stopping=False, plateau=False,\\\r\n                   checkpoint=False):\r\n    \"\"\" Train LSTM Model\r\n    Input model: defined model\r\n    Input X_train: training data\r\n    Input Y_train: training labels\r\n    Input X_train: test data\r\n    Input Y_train: test labels\r\n    Return model: trained model\r\n    Return history: training history\r\n    \"\"\"\r\n    model.compile(loss=loss, optimizer='adam', metrics=['accuracy'])\r\n    callbacks_list = defineCallbacks(checkpoint_path, number_epochs, monitor=monitor, schedule=schedule, stopping=stopping, \\\r\n                                 plateau=plateau, checkpoint=checkpoint)\r\n    history = model.fit(X_train, Y_train, epochs=number_epochs, batch_size = batch_size, callbacks=callbacks_list)\r\n    return (model, history)\r\n\r\ndef splitData(stock,  start_idx, end_idx, forcast_idx, ratio= 0.75, shuffle=True):\r\n    \"\"\" Split data set into train, test and predict data sets\r\n    Input stock: pandas data series\r\n    Input ratio: ratio of train and test data, default value is 0.75\r\n    return stock_std: train, test and predict data set\r\n    \"\"\"\r\n    # data_points = len(stock['index'])\r\n    data_points = end_idx - start_idx\r\n    data_points_train = int(data_points * ratio)\r\n    train_data = stock.iloc[:data_points_train].reset_index()\r\n    # test_data = stock.iloc[data_points_train:-forecast_time].reset_index()\r\n    # predict_data = stock.iloc[-forecast_time:].reset_index()\r\n    train_data = stock.iloc[start_idx:(start_idx + data_points_train)].reset_index()\r\n    test_data = stock.iloc[(start_idx + data_points_train + 1):end_idx].reset_index()\r\n    predict_data = stock.iloc[end_idx:forcast_idx].reset_index()\r\n    if shuffle==True:\r\n        train_data = train_data.sample(frac=1).reset_index(drop=True)\r\n        test_data = test_data.sample(frac=1).reset_index(drop=True)\r\n    return (train_data, test_data, predict_data)\r\n\r\ndef getXY(train_data, test_data, predict_data, symbols):\r\n    \"\"\" Split data set into labels and train data\r\n    Input: train, test and predict pandas data series\r\n    Input symbols: list of symbols of stock\r\n    return train, test and predict data set, train, test, predict labels\r\n    \"\"\"\r\n    #toDo: use regex filter\r\n    drop_labels= ['Date']\r\n    for i in range(len(symbols)):\r\n        daily_label = 'daily_label_'+ symbols[i]\r\n        future_close = 'future_close_'+ symbols[i]\r\n        drop_labels = np.append(drop_labels, [ daily_label, future_close])\r\n    X_train = train_data.drop(drop_labels, axis = 1).to_numpy(dtype='float32')\r\n    Y_train = train_data['daily_label_'+symbols[0]].to_numpy(dtype='float32')\r\n    X_test = test_data.drop(drop_labels, axis = 1).to_numpy(dtype='float32')\r\n    Y_test = test_data['daily_label_'+symbols[0]].to_numpy(dtype='float32')\r\n    X_predict = predict_data.drop(drop_labels, axis = 1).to_numpy(dtype='float32')\r\n    Y_predict = predict_data['daily_label_'+symbols[0]].to_numpy(dtype='float32')\r\n    return (X_train, Y_train, X_test, Y_test, X_predict, Y_predict)\r\n\r\ndef prepareDataforLTSM(data, sample_length = 300, Y_data = False):\r\n    \"\"\" Prepare Data for LSTM Model training\r\n    Input data: \r\n    Return samples:\r\n    Return number_samples:\r\n    \"\"\"\r\n    number_samples = int(len(data)/sample_length)\r\n    if number_samples == 0:\r\n        print(\"Dataset to small for batch size\")\r\n    if Y_data:\r\n        features = 1\r\n    else:\r\n        features = len(data[0]) \r\n    samples = np.zeros((number_samples, sample_length, features))  \r\n    for j in range(0, number_samples):\r\n        if Y_data:\r\n            sample = data[j*sample_length : j*sample_length + sample_length]\r\n            samples[j, :, 0] = sample\r\n        else:    \r\n            sample = data[j*sample_length : j*sample_length + sample_length, :]\r\n            samples[j, :, :] = sample\r\n    return (samples, number_samples)\r\n\r\ndef standardizeIndicators(stock):\r\n    \"\"\" Standardize dataset of indicators\r\n    Input stock: pandas data series\r\n    return stock_std: standardized pandas series\r\n    \"\"\"\r\n    stock_std = stock.copy()\r\n    for key, value in stock_std.iteritems():\r\n        if key == 'Date' or 'index':\r\n            continue\r\n        elif (key == 'daily_label'):\r\n                mean = 0\r\n                std = 1\r\n        else:\r\n            mean = value.mean()\r\n            std = value.std()\r\n        stock_std.loc[:,key] = (stock.loc[:, key] - mean) / std\r\n        if(stock_std[key].isnull().any().any()):\r\n                print(\"Watch out, NANs in indicator data\")\r\n    return (stock_std)\r\n\r\ndef normalizeIndicators(stock):\r\n    \"\"\" Normalize dataset of indicators\r\n    Input stock: pandas data series\r\n    return stock_std: normalized pandas series\r\n    \"\"\"\r\n    stock_std = stock.copy()\r\n    for key, value in stock_std.iteritems():\r\n        if key == 'Date' or key == 'index':\r\n            continue\r\n        else:\r\n            # print(key)\r\n            x = stock_std[[key]].values.astype(float)\r\n            min_max_scaler = preprocessing.MinMaxScaler()\r\n            x_scaled = min_max_scaler.fit_transform(x)\r\n            stock_std[key] = pd.DataFrame(x_scaled)\r\n            stock_std[key] = stock_std[key].fillna(method='ffill')\r\n            if(stock_std[key].isnull().any().any()):\r\n                print(\"Watch out, NANs in indicator data\")\r\n            for index, row in stock_std.iterrows():\r\n                if(stock_std[key].iloc[index]>1.01 or stock_std[key].iloc[index]<-0.01):\r\n                    print(\"Watch out, Normalization out of bounds\")\r\n    return (stock_std)\r\n\r\ndef safeModel(model, safe_model_path, batch_size, history, model_name=None):\r\n    \"\"\"Saving keras model\r\n    Safes previous trained model and training history\r\n    Input safe_model_path: path to safe model \r\n    Input number_epochs: number of epochs model was trained with\r\n    Input batch_size: batch size model was trained with\r\n    Input history: training history\r\n    Input model_name: defined model name(string), default value is none and a number is chosen\r\n    \"\"\"\r\n    number_epochs = len(history.history['loss'])\r\n    if model_name == None:\r\n        model_name = len(os.listdir(safe_model_path)) + 1\r\n    model.save('{}{}_epochs{}_batch{}.h5'.format(safe_model_path, model_name, number_epochs, batch_size))\r\n    print('safed model to {}{}_epochs{}_batch{}.h5'.format(safe_model_path, model_name, number_epochs, batch_size))\r\n    hist_df = pd.DataFrame(history.history)\r\n    hist_csv_file = '{}{}_epochs{}_batch{}_history.csv'.format(safe_model_path, model_name, number_epochs, batch_size)\r\n    with open(hist_csv_file, mode='w') as f:\r\n        hist_df.to_csv(f)\r\n        \r\ndef loadModel(safe_model_path, model_name):\r\n    \"\"\"Loading pretrained keras model\r\n    Input safe_model_path: path to safed model \r\n    Input model_name: model name of safed model\r\n    Return: pretrained keras model\r\n    \"\"\"\r\n    print('loading model from {}{}.h5'.format(safe_model_path, model_name))\r\n    return tf.keras.models.load_model('{}{}.h5'.format(safe_model_path, model_name))\r\n\r\ndef defineModel(input_shape, X_train=None, num_classes=7, batch_size=None, stockModel1=False, stockModelLSTM1=False, \\\r\n                stockModelLSTM2=False, resnet50=False, mobilenet=False, randomnet=False):\r\n    \"\"\"Define keras model architecture\r\n    Input input_shape: input shape of training data, shape depends on the model \r\n    Input num_classes: number of classes\r\n    Input batch_size: batch size of training\r\n    Input stockModel1: Boolean, True if you want to use stockModel1\r\n    Input stockModelLSTM1: Boolean, True if you want to use stockModelLSTM1\r\n    Input stockModelLSTM2: Boolean, True if you want to use stockModelLSTM2\r\n    Input resnet50: Boolean, True if you want to use resnet50\r\n    Input mobilenet: Boolean, True if you want to use mobilenet\r\n    Input randomnet: Boolean, True if you want to use randomnet\r\n    Return: model\r\n    \"\"\"\r\n    if stockModel1:\r\n        model = tf.keras.Sequential([\r\n            tf.keras.layers.Dense(64, activation='elu', input_shape=(input_shape,)),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.Dense(1024, activation='elu'),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.Dense(512, activation='elu'),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.Dense(256, activation='elu'),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.Dense(128, activation='elu'),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.Dense(2, activation='softmax')\r\n        ])\r\n        \r\n    if stockModelLSTM1: \r\n        model = tf.keras.Sequential([\r\n            tf.keras.layers.LSTM(500, input_shape=(X_train.shape[1], X_train.shape[2]), batch_size = batch_size,\\\r\n                                 dropout=0.2, recurrent_dropout=0.1, return_sequences=True),\r\n            tf.keras.layers.Dense(2, activation='sigmoid')\r\n        ])\r\n        \r\n    if stockModelLSTM2:\r\n        model = tf.keras.Sequential([\r\n            tf.keras.layers.LSTM(64, input_shape=(X_train.shape[1], X_train.shape[2]), stateful=True, batch_size = batch_size,\\\r\n                                 dropout=0.2, recurrent_dropout=0.1, return_sequences=True),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.LSTM(1028, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.LSTM(512, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.LSTM(256, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.LSTM(128, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.LSTM(64, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n            tf.keras.layers.Dense(2,activation='softmax')\r\n        ])\r\n\r\n    if resnet50:\r\n        model = tf.keras.applications.resnet.ResNet50(include_top=True, weights=None, input_tensor=None,\\\r\n                                                      input_shape=input_shape, pooling=None, classes=num_classes)\r\n        x = model.layers[-6].output\r\n        x = tf.keras.layers.GlobalAveragePooling2D()(x)\r\n        x = tf.keras.layers.Dropout(0.25)(x)\r\n        x = tf.keras.layers.BatchNormalization()(x)\r\n        predictions = tf.keras.layers.Dense(num_classes, activation='softmax')(x)\r\n        model = tf.keras.models.Model(inputs=model.input, outputs=predictions)\r\n\r\n    if mobilenet:\r\n        model = tf.keras.applications.mobilenet.MobileNet(input_shape=input_shape, alpha=1.0, depth_multiplier=1, dropout=0.2,\\\r\n                                                       include_top=True, weights=None, input_tensor=None, pooling=None,\\\r\n                                                           classes=num_classes)\r\n        x = model.layers[-6].output\r\n        x = tf.keras.layers.Dropout(0.25)(x)\r\n        x = tf.keras.layers.BatchNormalization()(x)\r\n        predictions = tf.keras.layers.Dense(num_classes, activation='softmax')(x)\r\n        model = tf.keras.models.Model(inputs=model.input, outputs=predictions)\r\n    \r\n    if randomnet:\r\n        model = tf.keras.Sequential([\r\n            tf.keras.layers.Conv2D(32, kernel_size=(3, 3),activation='relu',padding = 'Same',input_shape=input_shape),\r\n            tf.keras.layers.Conv2D(32,kernel_size=(3, 3), activation='relu',padding = 'Same',),\r\n            tf.keras.layers.MaxPool2D(pool_size = (2, 2)),\r\n            tf.keras.layers.Dropout(0.25),\r\n            tf.keras.layers.Conv2D(64, (3, 3), activation='relu',padding = 'Same'),\r\n            tf.keras.layers.Conv2D(64, (3, 3), activation='relu',padding = 'Same'),\r\n            tf.keras.layers.MaxPool2D(pool_size=(2, 2)),\r\n            tf.keras.layers.Dropout(0.40),\r\n            tf.keras.layers.Flatten(),\r\n            tf.keras.layers.Dense(128, activation='relu'),\r\n            tf.keras.layers.Dropout(0.5),\r\n            tf.keras.layers.Dense(num_classes, activation='softmax')\r\n        ])\r\n\r\n# predict_model = tf.keras.Sequential([\r\n#     tf.keras.layers.LSTM(64, input_shape=(X_predict.shape[1], X_predict.shape[2]), batch_size = batch_size_predict, dropout=0.2, recurrent_dropout=0.1, return_sequences=True),\r\n#     tf.keras.layers.Dropout(0.5),\r\n#     tf.keras.layers.LSTM(1028, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n#     tf.keras.layers.Dropout(0.5),\r\n#     tf.keras.layers.LSTM(512, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n#     tf.keras.layers.Dropout(0.5),\r\n#     tf.keras.layers.LSTM(256, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n#     tf.keras.layers.Dropout(0.5),\r\n#     tf.keras.layers.LSTM(128, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n#     tf.keras.layers.Dropout(0.5),\r\n#     tf.keras.layers.LSTM(64, dropout=0.2, stateful=True, recurrent_dropout=0.1, return_sequences=True),\r\n#     tf.keras.layers.Dense(2,activation='softmax')\r\n# ]) \r\n    return model\r\n\r\ndef top_3_accuracy(y_true, y_pred):\r\n    \"\"\"Calculate Top3 Accuracy\r\n    return: top3 accuracy\r\n    \"\"\"\r\n    return top_k_categorical_accuracy(y_true, y_pred, k=3)\r\n\r\ndef top_2_accuracy(y_true, y_pred):\r\n    \"\"\"Calculate Top2 Accuracy\r\n    return: Top2 accuracy\r\n    \"\"\"\r\n    return top_k_categorical_accuracy(y_true, y_pred, k=2)\r\n\r\ndef preprocessingImageData(resnet=False, mobilenet=False):\r\n    \"\"\"Preprocessing Data for model training\r\n    Input resnet: boolean, if you want to preprocess for resnet model\r\n    Input mobilenet: boolean, if you want to preprocess for mobilenet model\r\n    Return datagen: data generator\r\n    \"\"\"\r\n    if resnet:\r\n        datagen = ImageDataGenerator(preprocessing_function = tf.keras.applications.resnet.preprocess_input)\r\n    if mobilenet:\r\n        datagen = ImageDataGenerator(preprocessing_function = tf.keras.applications.mobilenet.preprocess_input)\r\n    return datagen\r\n\r\ndef lr_schedule(epoch):\r\n    \"\"\"Learning Rate Schedule\r\n    Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.\r\n    Called automatically every epoch as part of callbacks during training.\r\n    input epoch: number of epochs\r\n    return lr: learning rate\r\n    \"\"\"\r\n    lr = 1e-2\r\n    if epoch > 180:\r\n        lr *= 0.5e-3\r\n    elif epoch > 160:\r\n        lr *= 1e-3\r\n    elif epoch > 120:\r\n        lr *= 1e-2\r\n    elif epoch > 80:\r\n        lr *= 1e-1\r\n    print('Learning rate: ', lr)\r\n    return lr\r\n\r\ndef checkpointModel(checkpoint_path, monitor='accuracy', model_name=None):\r\n    \"\"\"Saving keras model checkpoints while fitting\r\n    Input checkpoint_path: path to checkpoints \r\n    Return : safing checkpoint\r\n    \"\"\"\r\n    if model_name == None:\r\n        model_name = len(os.listdir(checkpoint_path)) + 1\r\n    return ModelCheckpoint('{}{}.h5'.format(checkpoint_path, model_name), monitor=monitor, verbose=1,\\\r\n                           save_best_only=True, mode='min', save_weights_only=False)\r\n\r\ndef defineCallbacks(checkpoint_path, epoch, monitor='accuracy', schedule=False, stopping=False, plateau=False, checkpoint=False):\r\n    \"\"\"Plot Model Performace\r\n    input checkpoint_path: path where checkpoints shall be safed\r\n    input epoch: number of epochs\r\n    input monitor: metric to monitor, default accuracy\r\n    input schedule: boolean if a learning rate scheduler shall be used while fitting\r\n    input stopping: boolean if learning shall be stopped if there is no improvement detected\r\n    input plateau: boolean if learning rate shall be reduced if learning hits a plateau\r\n    input checkpoint: boolean if checkpoints shall be safed\r\n    return callbacks: list of callbacks\r\n    \"\"\"   \r\n    scheduler = LearningRateScheduler(lr_schedule)\r\n    earlystopping = EarlyStopping(monitor=monitor, min_delta=1e-4, patience=10, verbose=0, mode='auto', baseline=None, \\\r\n                             restore_best_weights=True)\r\n    learning_rate_plateau = ReduceLROnPlateau(monitor=monitor, factor=0.1, patience=5,verbose=0, mode='auto', \\\r\n                                              min_delta=0.0001, min_lr=1e-6)\r\n    checkpoints = checkpointModel(checkpoint_path)\r\n    callbacks = []\r\n    if schedule:\r\n        callbacks.append(scheduler)\r\n    if stopping:\r\n        callbacks.append(earlystopping)\r\n    if plateau:\r\n        callbacks.append(learning_rate_plateau)\r\n    if checkpoint:\r\n        callbacks.append(checkpoints)   \r\n    return callbacks\r\n\r\ndef plotModelPerformance(model, monitor='accuracy'):\r\n    \"\"\"Plot Model Performace\r\n    Plotting loss, validation loss, accuracy and validatoin accuracy over episodes\r\n    Input: history of model fitting\r\n    \"\"\"\r\n    fig = plt.figure(figsize=(10,8))\r\n    ax0 = plt.subplot2grid((6, 1), (0, 0), rowspan=6)\r\n    ax0.plot(model.history['loss'], label='loss', color='blue')\r\n    ax0.plot(model.history['val_loss'], label='val loss', color='black')\r\n    ax1 = ax0.twinx()\r\n    ax1.plot(model.history[monitor], label= monitor, color='orange')\r\n    ax1.plot(model.history['val_'+monitor], label= 'val_'+monitor, color='green')\r\n    ax0.set_title('model loss and accurcy')\r\n    ax0.set_ylabel('loss')\r\n    ax0.set_xlabel('epoch')\r\n    # ax0.set_ylim([0,2])\r\n    # ax1.set_ylim([0,2])\r\n    lines, labels = ax0.get_legend_handles_labels()\r\n    lines2, labels2 = ax1.get_legend_handles_labels()\r\n    ax0.legend(lines + lines2, labels + labels2, loc=1)\r\n    plt.show()\r\n\r\ndef plotHistogramm(data, valid_batches):\r\n    \"\"\"Plot Prediction Histogramm\r\n    Plotting probabilites of predicted classes\r\n    Input: np array of predicted data\r\n    Input valid_batches: dictonary of validation batches wit class names\r\n    \"\"\"\r\n    labels = [*valid_batches.class_indices]\r\n    x = np.arange(len(labels))  # the label locations\r\n    width = 0.3  # the width of the bars\r\n    fig, ax = plt.subplots(1,1, figsize=(12,3))\r\n    # for i in range(3):\r\n    #     ax.bar(x + i*width, data[i], width, label=i)\r\n    ax.bar(x, data[2], width, label=0, color='green')\r\n    ax.bar(x + width, data[309], width, label=1, color='blue')\r\n    ax.bar(x + 2*width, data[1678], width, label=2, color='red')\r\n    ax.set_ylabel('Scores')\r\n    ax.set_title('Scores by picture')\r\n    ax.set_xticks(x)\r\n    ax.set_xticklabels(labels)\r\n    ax.legend()\r\n\r\n# # copy weights\r\n# old_weights = model.get_weights()\r\n# predict_model.set_weights(old_weights)\r\n# model_prediction = predict_model.predict(X_predict)\r\n# apa['prediction'] = np.full((len(apa['Adj Close']),1), -1)\r\n# apa.loc[(end_idx+1):forcast_idx, 'prediction'] = model_prediction[0,:,0]\r\n\r\n# print(model_prediction[0,:,1])\r\n# print(apa['daily_label'].iloc[(end_idx+1):forcast_idx])","sub_path":"model_utils.py","file_name":"model_utils.py","file_ext":"py","file_size_in_byte":20655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"66645682","text":"from django.views.generic import DetailView, ListView, TemplateView\n\nfrom .models import Institution, Enrollment, System\n\n\n# Enrollment data trickles in at different times, so pick the latest year where\n# most institutions have data available.\nLATEST_ENROLLMENT_YEAR = 2012\n\n\nclass SystemDetailView(DetailView):\n    model = System\n\n    def get_template_names(self):\n        return ['layout/%s/%s/table.html' % (a._meta.app_label,\n                                             a._meta.object_name.lower())\n                for a in self.object.__class__.mro() if hasattr(a, '_meta')]\n\n\nclass FunnelMixin(object):\n    \"\"\" Adds `funnels` property to an `Institution` for the Funnel viz \"\"\"\n    def annotate_funnels(self, inst):\n        enterdata = inst.admissions.all()\n\n        # please excuse the horribleness of this\n        funnels = []\n        for year in enterdata:\n            try:\n                funnel = {'year': year.year}\n                funnel[0] = 100\n                funnel[1] = float(year.percent_of_applicants_admitted)\n                funnel[2] = funnel[1] * float(year.percent_of_admitted_who_enrolled) / 100\n                funnels.append(funnel)\n            except TypeError:\n                continue\n        inst.funnels = funnels\n\n\nclass HomeView(TemplateView):\n    template_name = \"tx_highered/home.html\"\n\n    def get_short_list(self):\n        \"\"\"\n        List of highest enrollment schools to make getting to UT one click.\n\n        Used full time equivalent, which means we can't use THECB data since\n        getting it from them is difficult.\n\n        To see which institutions are missing data, and to see what the latest\n        safe year to use, run this command:\n\n            for x in Institution.objects.published():\n                try:\n                    e = x.enrollment.latest('year')\n                    print e.year, x, e.fulltime_equivalent\n                except Enrollment.DoesNotExist:\n                    print '!!!!', x, 'MISSING'\n        \"\"\"\n        queryset = (Enrollment.objects.filter(year=LATEST_ENROLLMENT_YEAR)\n            .select_related('institution')\n            .order_by('-fulltime_equivalent')[:15])\n        return queryset\n\n\nclass InstitutionListView(ListView):\n    queryset = Institution.objects.filter(ipeds_id__isnull=False).\\\n        order_by('name')\n\n\nclass InstitutionDetailView(DetailView):\n    model = Institution\n\n\n######################### REPORTS ##########################\nclass TestScoresReport(InstitutionListView):\n    template_name = \"tx_highered/reports/testscores.html\"\n\n    def get_queryset(self):\n        qs = super(TestScoresReport, self).get_queryset()\n        qs = qs.exclude(testscores__isnull=True).order_by('name')\n        for x in qs:\n            x.scores = x.testscores.latest('year')\n        return qs\n\n\nclass FunnelReport(InstitutionListView, FunnelMixin):\n    template_name = \"tx_highered/reports/funnel.html\"\n\n    def get_context_data(self, *args, **kwargs):\n        # TODO this shouldn't be get_context_data\n        context = super(FunnelReport, self).get_context_data(*args, **kwargs)\n        for obj in self.object_list:\n            self.annotate_funnels(obj)\n        return context\n\n\nclass Top10RuleReport(InstitutionListView, FunnelMixin):\n    template_name = \"tx_highered/reports/top10.html\"\n    year_range = range(2000, 2012)\n\n    def build_table(self, obj):\n        raw_data = obj.admissions.filter(percent_top10rule__isnull=False).\\\n            values('year', 'percent_top10rule')\n        data = dict([(x['year'], x['percent_top10rule']) for x in raw_data])\n        return data\n\n    def get_queryset(self):\n        qs = super(Top10RuleReport, self).get_queryset()\n        qs = qs.filter(institution_type='uni', is_private=False)\n        # TODO prefetch admissions\n        for x in qs:\n            x.data = self.build_table(x)\n        return qs\n\n    def get_context_data(self, **kwargs):\n        context = super(Top10RuleReport, self).get_context_data(**kwargs)\n        context['year_range'] = self.year_range\n        return context\n","sub_path":"tx_highered/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"95433677","text":"try:\r\n    from googlesearch import search\r\n    import requests\r\n    from bs4 import BeautifulSoup\r\nexcept ImportError:\r\n    print(\"Missing module\")\r\n\r\nprint('Enter the movie')\r\nmovie = input()\r\nquery = movie + 'box office mojo'\r\nfor j in search(query, tld='com', lang='en', num=1, stop=1, pause=2):\r\n    link = j\r\n#print(link)\r\npage = requests.get(link)\r\nsoup = BeautifulSoup(page.content, 'html.parser')\r\n\r\n#titletable = soup.find(id=\"body\")\r\n#title = titletable.select(\"table tbody tr td table tbody tr td\")\r\n#title = title[0].get_text()\r\n\r\ngrosstable = soup.find(class_=\"mp_box_content\")\r\ngross = grosstable.select(\"tr td b\")\r\ngrossDomestic = gross[0].get_text()\r\ngrossNum = gross[1].get_text()\r\n\r\n#print(title)\r\nprint(movie.title())\r\nprint(grossDomestic)\r\nprint(grossNum)\r\n\r\n#def titleCase(str):\r\n#    str = str.title()\r\n#return","sub_path":"boxofficewebscraper.py","file_name":"boxofficewebscraper.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"58889537","text":"import smtplib\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.application import MIMEApplication\nfrom email.header import Header\nimport os\n\nclass Email():\n    def __init__(self,sender,receivers):\n        # SMTP server\n        self.mail_host=\"wksowa.wistron.com\"  #设置服务器\n\n        # 公司不需要用户名和密码\n        # self.mail_user = ''  \n        # self.mail_pass = ''\n\n        # 发件人邮箱\n        self.sender = sender\n\n        # 收件人邮箱列表\n        self.receivers = receivers\n\n        # 邮件中显示的发件人\n        self.display_sender=\"TEXT LAB\"\n\n        # 邮件主题\n        self.subject= 'logistic RPA'\n\n        # 构造massage\n        self.message=MIMEMultipart()\n\n        # 邮件中显示的发件人\n        self.message['From'] = Header(self.display_sender, 'utf-8')\n        # 邮件中显示的收件人\n        self.message['To'] =  Header(\";\".join(self.receivers), 'utf-8')\n        # 邮件主题\n        self.message['Subject'] = Header(self.subject, 'utf-8')\n\n\n    def construct_email_text(self,text_content):\n        # 构造邮件文本\n        text=MIMEText(text_content,'plain', 'utf-8')\n        \n        self.message.attach(text)\n\n        return self.message\n    \n    def construct_email_pdf(self,pdf_path):\n        # attach pdf file\n        pdf = MIMEApplication(open(pdf_path, 'rb').read())\n        pdf.add_header('Content-Disposition', 'attachment', filename=os.path.split(pdf_path)[1])\n\n        self.message.attach(pdf)\n\n        return self.message\n\n    def send(self,message):\n        smtpObj = smtplib.SMTP()\n        smtpObj.connect(self.mail_host)  # 公司无需端口号\n        # smtpObj.login(mail_user,mail_pass)    # 公司无需用户名和密码\n        smtpObj.sendmail(self.sender, self.receivers, message.as_string())\n        print (\"邮件发送成功\")\n        smtpObj.quit()\n","sub_path":"email_autosend.py","file_name":"email_autosend.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"15759724","text":"import glob\r\nimport csv\r\n\r\nfilePointers = glob.glob('regional*.csv')\r\nsongs = {}\r\nstreaming = {}\r\n\r\ndef getDate(file):\r\n    date = file[18:len(file) - 4]\r\n    if date == \"latest\":\r\n        date = \"2019-09-23\"\r\n    return date\r\n\r\nfor name in filePointers:\r\n    with open(name,encoding = \"UTF-8\") as file:\r\n        reader = csv.reader(file)\r\n        \r\n        for i in range(0,2):\r\n            file.readline()\r\n\r\n        for line in reader:\r\n            artist = line[2]\r\n            song = line[1]\r\n            key = line[1] + \" by \" + line[2]\r\n            date = getDate(name)\r\n            streams = line[3]\r\n            streaming[date] = streams\r\n            songs[key] = streaming\r\n            streaming = {}\r\n            print(songs)\r\n            \r\n\r\n        \r\n        \r\n        \r\n","sub_path":"file io day 5 question.py","file_name":"file io day 5 question.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"392952702","text":"# cnn\nimport numpy as np\n\n#1. 데이터\nfrom tensorflow.keras.datasets import fashion_mnist\n(x_train,y_train),(x_test,y_test) = fashion_mnist.load_data()\n\nprint(x_train.shape, y_train.shape) # (60000, 28, 28) (60000,)\nprint(x_test.shape, y_test.shape) # (10000, 28, 28) (10000,)\n\nprint(x_train[0])\nprint(y_train[0])\nprint(x_train[0].shape) #(28,28)\nprint(np.max) #255\n\nx_train = x_train.reshape(60000,28,28,1).astype('float32')/255. # 옛날 방식\nx_test = x_test.reshape(10000,28,28,1)/255.\n\nfrom tensorflow.keras.utils import to_categorical\ny_train = to_categorical(y_train)\ny_test = to_categorical(y_test)\nprint(y_train.shape) #(60000, 10)\n\n#2. 모델구성\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n\n\nmodel = Sequential()\nmodel.add(Conv2D(50,3, padding='same', input_shape=(28,28,1)))\nmodel.add(Conv2D(20,3))\n# model.add(MaxPooling2D(pool_size=3))\nmodel.add(Flatten())\nmodel.add(Dense(50, activation='relu'))\nmodel.add(Dense(50, activation='relu'))\nmodel.add(Dense(50, activation='relu'))\nmodel.add(Dense(50, activation='relu'))\nmodel.add(Dense(10, activation='relu'))\nmodel.add(Dense(10, activation='softmax'))\n\n#3. 컴파일, 훈련\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics='acc')\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\nmodelpath = '../data/modelcheckpoint/k46_fashion_{epoch:02d}-{val_loss:.4f}.hdf5'\ncp = ModelCheckpoint(filepath=modelpath, monitor='val_loss', save_best_only=True,mode='auto')\nes = EarlyStopping(monitor='val_loss', patience=20, mode='min')\nhist = model.fit(x_train,y_train, epochs=10, validation_split=0.2, batch_size=16, callbacks=[es,cp])\n\n#4. 평가, 예측\nloss,acc = model.evaluate(x_test,y_test, batch_size=16)\nprint('loss, acc : ', loss,acc)\n\ny_pred = model.predict(x_test)\n\n# cnn\n# loss, acc :  0.43148916959762573 0.8651000261306763\n\n\n","sub_path":"keras/keras46_MC_1_fashion.py","file_name":"keras46_MC_1_fashion.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"312885491","text":"import ifaddr\nimport re\nimport telnetlib\nimport time\nimport sys\n\nfrom csv import reader\nfrom icmplib import ping\nfrom pathlib import Path\nfrom pyftpdlib.authorizers import DummyAuthorizer\nfrom pyftpdlib.handlers import FTPHandler\nfrom pyftpdlib.servers import ThreadedFTPServer\nfrom threading import Thread\n\n\ndef find_ip_devices(csv_path):\n    \"\"\"Open CSV file and find all IP-addresses in it.\"\"\"\n\n    ips_remote = []\n    with open(csv_path, mode='r') as file:\n        csv_reader = list(reader(file, delimiter=','))\n        csv_reader = list(filter(lambda x: len(x) != 0, csv_reader))\n    for line in csv_reader:\n        if pattern_ip.search(str(line)):\n            ips_remote.append(''.join(line))\n    return ips_remote\n\n\ndef find_ip_local():\n    \"\"\"Find all the configured IP-addresses on this machine.\"\"\"\n\n    nics = ifaddr.get_adapters()\n    ips_local = []\n    for nic in nics:\n        for ip in nic.ips:\n            pattern_ip = re.compile(r'([0-9]{1,3}[\\.]){3}[0-9]{1,3}')\n            pattern_ip_dhcp = re.compile(r'169\\.254\\.[0-9]{1,3}\\.[0-9]{1,3}')\n            pattern_ip_lhost = re.compile(r'127\\.([0-9]{1,3}[\\.]){2}[0-9]{1,3}')\n            if pattern_ip.search(str(ip.ip)) \\\n                    and not (pattern_ip_dhcp.search(str(ip.ip)) or pattern_ip_lhost.search(str(ip.ip))):\n                ips_local.append(ip.ip)\n    return ips_local\n\n\ndef find_licenses(lic_path):\n    \"\"\"Find all licenses in the selected folder.\n     License requirements:\n     - filename extension is *.dat\n     - filename contains a serial number\n     \"\"\"\n\n    lic_dict = {}\n    lic_files = list(lic_path.glob('*.dat'))\n    for lic_file in lic_files:\n        lic_sn = re.search(r'(\\d+)', str(lic_file))\n        if lic_sn is not None:\n            lic_sn = lic_sn.group(1)\n            lic_dict[lic_sn] = lic_file\n    return lic_dict\n\n\ndef start_ftp(ftp_folder):\n    \"\"\"Start FTP server.\"\"\"\n\n    authorizer = DummyAuthorizer()\n    authorizer.add_anonymous(str(ftp_folder), perm='elradfmw')\n\n    handler = FTPHandler\n    handler.authorizer = authorizer\n\n    server = ThreadedFTPServer(('0.0.0.0', 21), handler)\n    server.max_cons = 100\n    server.max_cons_per_ip = 2\n\n    server.serve_forever()\n\n\ndef tn_connect(ip_remote, ftp_ip, ftp_local):\n    \"\"\"Connect via telnet\"\"\"\n\n    if ftp_local:\n        for ip_local in ips_local:\n            host = ping(ip_remote, count=2, interval=0.1, source=ip_local)\n            if host.is_alive:\n                ftp_path = ip_local\n                break\n    else:\n        ftp_path = f'{ftp_ip}/FTP'\n\n    with telnetlib.Telnet(ip_remote, 23) as tn:\n        tn.read_until(b'Login:')\n        tn.write(b'admin\\n\\r')\n        tn.read_until(b'Password:')\n        tn.write(b'admin\\n\\r')\n        tn.write(b'sys ver\\n\\r')\n        time.sleep(3)\n        result = tn.read_very_eager().decode('utf-8')\n        pattern = re.compile(r'SN:(\\d+)')\n        if pattern.search(result):\n            sn = pattern.search(result).group(1)\n            if sn in licenses:\n                ftp_file = licenses[sn].name\n                ftp_command = f'license --install=ftp://anonymous:anonymous@{ftp_path}/{ftp_file}\\n\\r'\n                tn.write(ftp_command.encode('utf-8'))\n                time.sleep(3)\n                tn.write(b'co sa; rest y\\n\\r')\n                #result = tn.read_very_eager().decode('utf-8')\n                #print(result)\n            else:\n                print(f'ERROR: The license not found.')\n\n\nif __name__ == '__main__':\n    pattern_ip = re.compile(r'([0-9]{1,3}[\\.]){3}[0-9]{1,3}')\n    pattern_ip_dhcp = re.compile(r'169\\.254\\.[0-9]{1,3}\\.[0-9]{1,3}')\n    pattern_ip_lhost = re.compile(r'127\\.([0-9]{1,3}[\\.]){2}[0-9]{1,3}')\n\n    print(f'Please select a CSV file with IP-addresses.')\n    csv_file = input(f'<Press ENTER to set default (\"Script Folder\\\\Input\\\\ips.csv\")>\\r\\n')\n    if len(csv_file) == 0:\n        csv_file = Path.cwd() / 'Input' / 'ips.csv'\n        if csv_file.exists() is False:\n            print(f'ERROR: \"Script Folder\\\\Input\\\\ips.csv\" does not exist.')\n    else:\n        while Path(csv_file).is_file() is False:\n            csv_file = input(f'<This is not a file or it does not exist.>\\r\\n')\n    ips_remote = find_ip_devices(csv_file)\n\n    print(f'Please select a folder with licenses.')\n    lic_folder = input(f'<Press ENTER to set default (\"Script Folder\\\\Licenses\")>\\r\\n')\n    if len(lic_folder) == 0:\n        lic_folder = Path.cwd() / 'Licenses'\n        if lic_folder.exists() is False:\n            print(f'ERROR: \"Script Folder\\\\Licenses\" does not exist.')\n    else:\n        while Path(lic_folder).is_dir() is False:\n            lic_folder = input(f'<This is not a folder or it does not exist.>\\r\\n')\n    licenses = find_licenses(lic_folder)\n\n    print(f'Please enter the IP-address of the FTP server.')\n    ip_ftp = input(f'<Press ENTER to launch a local FTP server.>\\r\\n')\n    if len(ip_ftp) == 0:\n        thread = Thread(target=start_ftp, args=(lic_folder, ))\n        thread.start()\n        thread_stop = False\n        ips_local = find_ip_local()\n        ftp_local = True\n    else:\n        while pattern_ip.search(ip_ftp) is None:\n            ip_ftp = input(f'<This is the wrong IP-address.>\\r\\n')\n        ftp_local = False\n\n    for ip in ips_remote:\n        tn_connect(ip, ip_ftp, ftp_local)\n\n    print('Licenses have been uploaded.')\n    sys.exit()","sub_path":"upload_license.py","file_name":"upload_license.py","file_ext":"py","file_size_in_byte":5296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"590986014","text":"from django.views.decorators.csrf import csrf_exempt\r\nfrom django.shortcuts import render\r\nfrom django.http import HttpResponse\r\nfrom concurrent.futures import ThreadPoolExecutor\r\nimport os\r\nimport time\r\nimport threading\r\n\r\nfrom ClassApp.ml_algo import MakeAttention\r\n\r\nfrom PIL import Image\r\nimport base64\r\nimport numpy as np\r\nimport json\r\nimport time\r\nimport ast\r\nimport cv2\r\n\r\nFRAMES_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'frames')\r\n\r\n@csrf_exempt\r\ndef capture_list(request):\r\n    t0 = time.time()\r\n    np_frames = []\r\n    base_64_image = request.POST.get('list').split(',')[1]\r\n    frame_counter = int(request.POST.get('counter'))\r\n    t1 = time.time()\r\n    np_frame = np.frombuffer(base64.b64decode(base_64_image), np.uint8)\r\n    print(\"Received frame\")\r\n    # width = int(request.POST.get('width'))\r\n    # height = int(request.POST.get('height'))\r\n    \r\n    class_id = request.POST.get('class_id')\r\n    time_elapsed = request.POST.get('time_elapsed')\r\n\r\n    # temp1 = [frame[i:i + 3] for i in range(0, len(frame), 4)]\r\n    # temp2 = [temp1[i:i + width] for i in range(0, len(temp1), width)]\r\n    # np_frame = Image.fromarray(np.array(temp2, dtype=np.uint8))\r\n    # np_frame = np.array(temp2, dtype=np.uint8)\r\n    np_frames.append(cv2.imdecode(np_frame, cv2.IMREAD_COLOR))\r\n        # Image.fromarray(np.array(temp2, dtype=np.uint8)).save(os.path.join(FRAMES_PATH, f'{time.time()}.png'))\r\n    # print(\"Sending frames to ML Model\", len(np_frames))\r\n    t2 = time.time()\r\n    attention_thread = threading.Thread(\r\n            target=MakeAttention, kwargs=dict(frames=np_frames, frame_counter=frame_counter, class_id=class_id, time_elapsed=time_elapsed))\r\n    attention_thread.start()\r\n    # MakeAttention(np_frames)\r\n    t3 = time.time()\r\n\r\n    print(t1-t0, t2-t1, t3-t2)\r\n    return HttpResponse('OK')\r\n\r\n\r\n@csrf_exempt\r\ndef capture_attendance(request):\r\n    np_frames = []\r\n    list_frames = ast.literal_eval(request.POST.get('list'))\r\n    print(\"Received\", len(list_frames), \"frames\")\r\n    width = int(request.POST.get('width'))\r\n    height = int(request.POST.get('height'))\r\n\r\n    for frame in list_frames:\r\n        img = list(frame['data'].values())\r\n        temp1 = [img[i:i + 3] for i in range(0, len(img), 4)]\r\n        temp2 = [temp1[i:i + width] for i in range(0, len(temp1), width)]\r\n        # np_frame = Image.fromarray(np.array(temp2, dtype=np.uint8))\r\n        np_frame = np.array(temp2, dtype=np.uint8)\r\n        np_frames.append(np_frame)\r\n        Image.fromarray(np.array(temp2, dtype=np.uint8)).save(\"frames/Attendance {0}.png\".format(time.time()))\r\n    # MakeAttention(np_frames)\r\n    StartAttendance(np_frames[0])\r\n\r\n    return HttpResponse('OK')\r\n\r\n# @csrf_exempt\r\n# def capture_face(request):\r\n#     np_frames = []\r\n#     list_frames = ast.literal_eval(request.POST.get('list'))\r\n#     print(\"Received\", len(list_frames), \"frames\")\r\n#     width = int(request.POST.get('width'))\r\n#     height = int(request.POST.get('height'))\r\n#     course_time = request.POST.get('time')\r\n#     course = request.POST.get(\"course\")\r\n#\r\n#     for frame in list_frames:\r\n#         img = list(frame['data'].values())\r\n#         temp1 = [img[i:i + 3] for i in range(0, len(img), 4)]\r\n#         temp2 = [temp1[i:i + width] for i in range(0, len(temp1), width)]\r\n#         # np_frame = Image.fromarray(np.array(temp2, dtype=np.uint8))\r\n#         np_frame = np.array(temp2, dtype=np.uint8)\r\n#         np_frames.append(np_frame)\r\n#         Image.fromarray(np.array(temp2, dtype=np.uint8)).save(\"Attendance {0}.png\".format(time.time()))\r\n#     # MakeAttention(np_frames)\r\n#     DetectAttendance(np_frames, course, course_time)\r\n#\r\n#     return HttpResponse('OK')\r\n","sub_path":"ClassroomProduct/manipulate_frames.py","file_name":"manipulate_frames.py","file_ext":"py","file_size_in_byte":3685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"331788828","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef lp_stable_cheb(epsilon,N):\n\tbeta = ((np.sqrt(1+epsilon**2)+ 1)/epsilon)**(1/N);\n\tr1 = (beta**2-1)/(2*beta);\n\tr2 = (beta**2+1)/(2*beta);\n\n\tu = 1;\n\tfor n in range(0,int(N/2)):\n\t\tphi = np.pi/2 + ((2*n+1)*np.pi)/(2*N)\n\t\tv = [1,-2*r1*np.cos(phi),r1**2*np.cos(phi)**2+r2**2*np.sin(phi)**2]\n\t\tp = np.convolve(v,u)\n\t\tu = p\n\t\n\tG = np.abs(np.polyval(p,1j))/np.sqrt(1+epsilon**2)\n\treturn p,G\n","sub_path":"Assignment-02/codes/iir/lp_stable_cheb.py","file_name":"lp_stable_cheb.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"296327210","text":"import tkinter as tk\nfrom tkinter import ttk, font\n\nfrom src.popup_window import *\nfrom src.user.edit_ability import EditAbility\nfrom src.user.edit_avatar import EditAvatar\nfrom src.user.edit_item import EditItem\nfrom src.user.edit_title import EditTitle\n\n\nclass EditInterface(ttk.Frame):\n    def __init__(self, parent, database_type_value, show_home):\n        super().__init__(parent, style=\"FramesBackgroundColor.TFrame\")\n\n        self.parent = parent\n        self.show_home = show_home\n        self.database_type_value = database_type_value\n        entity = parent.entity\n        self.entity = entity\n\n        self.columnconfigure(0, weight=1)\n        self.rowconfigure(0, weight=1)\n\n        self.edit_interface_scroll = EditInterfaceScroll(self)\n        self.edit_interface_scroll.grid(row=0, column=0, padx=5, pady=5, sticky=\"NSEW\")\n\n        self.edit_interface_scroll.create_edit_interface_container(entity)\n\n\nclass EditInterfaceScroll(tk.Canvas):\n    def __init__(self, container):\n        super().__init__(container, highlightthickness=0)\n\n        # --- Custom ---\n\n        self.container = container\n\n        self.font = font.Font(size=11)\n\n        self.edit_interface = None\n\n        self.screen = tk.Frame(container)\n        self.screen.columnconfigure(0, weight=1)\n\n        self.scrollable_window = self.create_window((0, 0), window=self.screen, anchor=\"nw\")\n\n        def configure_scroll_region(event):\n            self.configure(scrollregion=self.bbox(\"all\"))\n\n        def configure_window_size(event):\n            self.itemconfig(self.scrollable_window, width=self.winfo_width())\n\n        self.bind(\"<Configure>\", configure_window_size)\n        self.screen.bind(\"<Configure>\", configure_scroll_region)\n\n        scrollbar = ttk.Scrollbar(container, orient=\"vertical\", command=self.yview)\n        scrollbar.grid(row=0, column=1, sticky=\"NS\")\n\n        self.configure(yscrollcommand=scrollbar.set)\n        self.yview_moveto(1.0)\n\n    def create_edit_interface_container(self, entity):\n        container = self.container\n        database_type_value = container.database_type_value\n\n        # --- Entity Widgets ---\n\n        if database_type_value == 'avatars_folder':\n            self.edit_interface = EditAvatar(self.screen, entity, self.container.show_home)\n        elif database_type_value == 'weapons_folder' or database_type_value == 'armors_folder':\n            self.edit_interface = EditItem(self.screen, entity, self.container.show_home)\n        elif database_type_value == 'abilities_folder':\n            self.edit_interface = EditAbility(self.screen, entity, self.container.show_home)\n        elif database_type_value == 'titles_folder':\n            self.edit_interface = EditTitle(self.screen, entity, self.container.show_home)\n        else:\n            popup_showinfo(\"Error, database type value not found!\")\n            return\n\n        self.edit_interface.grid(row=0, column=0, sticky=\"NSEW\")\n        self.edit_interface.columnconfigure(0, weight=1)\n\n        # Adds the same pads to all children of the container\n        for child in self.edit_interface.winfo_children():\n            child.grid_configure(padx=5, pady=5)\n\n        # --- Button Container ---\n\n        edit_interface_button_container = ttk.Frame(self.screen)\n        edit_interface_button_container.grid(row=1, column=0, sticky=\"EW\")\n        edit_interface_button_container.rowconfigure(0, weight=1)\n        edit_interface_button_container.columnconfigure(0, weight=1)\n\n        # --- Button Widgets ---\n\n        self.create_edit_interface_buttons(edit_interface_button_container)\n\n        # Adds the same pads to all children of the container\n        for child in edit_interface_button_container.winfo_children():\n            child.grid_configure(padx=5, pady=5)\n\n    def create_edit_interface_buttons(self, container):\n        edit_button = ttk.Button(\n            container,\n            text=\"Save\",\n            command=self.save_edition,\n            cursor=\"hand2\"\n        )\n        edit_button.grid(row=0, column=0, sticky=\"EW\")\n\n        back_button = ttk.Button(\n            container,\n            text=\"← Back\",\n            command=self.container.show_home,\n            cursor=\"hand2\"\n        )\n        back_button.grid(row=1, column=0, sticky=\"EW\")\n\n    def save_edition(self):\n        self.edit_interface.save_entity()\n","sub_path":"dist/rpg/src/user/edit_interface.py","file_name":"edit_interface.py","file_ext":"py","file_size_in_byte":4316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"515227341","text":"import dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Output, Input\nfrom app import app\n\nimport numpy as np\nimport db_query as db\nimport datetime\n\nserial = 8596359061997376531\n\n\ndef generate_table(dataframe, max_rows=10):\n    return html.Table(\n        # Header\n        [html.Tr([html.Th(col) for col in dataframe.columns])] +\n\n        # Body\n        [html.Tr([\n            html.Td(dataframe.iloc[i][col]) for col in dataframe.columns\n        ]) for i in range(min(len(dataframe), max_rows))]\n    )\n\n\nlayout = html.Div(children=[\n    html.Div([html.Link(href='/static/whitey.css', rel='stylesheet')]),\n    dcc.Markdown('''\n# Thomas Station\n\nThis station records information about air temperature, wind speed and direction, and snow depth. \n\n- Location: []()\n- Sensors:\n    - Decagon DS-2\n    - Maxbotix\n- Database ID: 8596359061997376531\n- Available variables :\n    - air temperature\n    - wind speed\n    - wind direction\n    - Station battery\n    - Station signal reception power (RSSI)\n\n## Graph settings\n### 1. Number of days since today to display\n'''),\n    dcc.Slider(id='ndays', min=2, max=32, step=2, value=14, marks={i: '{} days'.format(i) for i in range(2, 30, 2)}),\n    dcc.Markdown(\n'''\n### 2. Temporal resolution to display\n'''\n    ),\n    dcc.Slider(id='nintervals', min=0, max=3700, step=None, value=100,\n               marks={5:'5s', 30:'30s', 60:'1min', 300:'5min', 600:'10min', 1800:'30min', 3600:'1h'}),\n\n    html.Div(id='output_graphs_statflux'),\n    dcc.Markdown('''\n---\n## Return to [Home](index)\n''')\n])\n\n\n@app.callback(\n    Output(component_id='output_graphs_statflux', component_property='children'),\n    [Input(component_id='ndays', component_property='value'),\n     Input(component_id='nintervals', component_property='value')]\n)\ndef update_graph_table_flux(input_ndays, inter_sample):\n    start = datetime.datetime.now() - datetime.timedelta(days=input_ndays)\n    end = datetime.datetime.now()\n\n    df_thomas = db.query_df(serial=serial, time__gte=start, time__lte=end, limit=100000, interval=inter_sample)\n    df_thomas_ws = df_thomas.loc[~np.isnan(df_thomas.ds2_speed)]\n    df_thomas_ws = df_thomas_ws.reset_index()\n\n    df_thomas_mb = df_thomas.loc[~np.isnan(df_thomas.mb_median)]\n    df_thomas_mb = df_thomas_mb.reset_index()\n\n    graph_content = html.Div([\n        html.H2(children='Sonic Air Temperature'),\n        dcc.Graph(\n            id='gr1',\n            figure={\n                'data': [{\n                    'x': df_thomas_ws.time,\n                    'y': df_thomas_ws.ds2_temp, 'name': 'temp'}],\n                'layout': {'yaxis': {'title': 'Temperature [degC]'}}\n            }),\n        html.H2(children='Wind speed'),\n        dcc.Graph(\n            id='gr2',\n            figure={\n                'data': [{\n                    'x': df_thomas_ws.time,\n                    'y': df_thomas_ws.ds2_speed, 'name': 'speed'}],\n                'layout': {'yaxis': {'title': 'Wind speed [m/s]'}}\n            }),\n        html.H2(children='Wind direction'),\n        dcc.Graph(\n            id='gr3',\n            figure={\n                'data': [{\n                    'x': df_thomas_ws.time,\n                    'y': df_thomas_ws.ds2_dir, 'name': 'direction',\n                    'mode': 'markers',\n                    'marker': {'opacity': 0.5}}],\n\n                'layout': {'yaxis': {'title': 'Wind direction [deg]'}}\n            }),\n        html.H2(children='Distance to snow'),\n        dcc.Graph(\n            id='gr4',\n            figure={\n                'data': [{\n                    'x': df_thomas_mb.time,\n                    'y': df_thomas_mb.mb_median/10, 'name': 'Distance'}],\n                'layout': {'yaxis': {'title': 'Distance [cm]'}}\n            }),\n        html.H2(children='Battery level'),\n        dcc.Graph(\n            id='gr5',\n            figure={\n                'data': [{\n                    'x': df_thomas.time.loc[~np.isnan(df_thomas.bat)],\n                    'y': df_thomas.bat.loc[~np.isnan(df_thomas.bat)], 'name': 'Battery'}],\n                'layout': {'yaxis': {'title': 'Battery level [%]'}}\n            }),\n        html.H2(children='Table head of Thomas station data'),\n        generate_table(df_thomas.tail(100), 5)\n    ])\n    return graph_content\n\n\n\n","sub_path":"apps/thomas.py","file_name":"thomas.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"617290452","text":"from django import forms\nfrom osf.models import MaintenanceState\nfrom django.utils.translation import ugettext_lazy as _\n\n\nclass MaintenanceForm(forms.ModelForm):\n    class Meta:\n        model = MaintenanceState\n        labels = {\n            'level': _('Level'),\n        }\n        fields = ['level']\n","sub_path":"admin/maintenance/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"5593232","text":"from pyorbital.orbital import Orbital\nfrom datetime import datetime, timedelta\nfrom pyorbital import tlefile\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nfrom OrbitalPlotter import OrbitalPlotter, Orbit\n\ndef getDistance(ISS,OTHER):\n    max = 0\n    result=[]\n    for i in range(0,len(OTHER.xyz[0])):\n        a = np.array( (ISS.xyz[0][i] , ISS.xyz[1][i] ,ISS.xyz[2][i]  ))\n        b = np.array(  (OTHER.xyz[0][i] , OTHER.xyz[1][i] ,OTHER.xyz[2][i]  ))\n        dist = np.linalg.norm(a-b)\n        result.append(dist)\n    print(result)\n    print(\"satellite:\" + OTHER.name)\n    print(dist)\n\nif __name__ == '__main__':\n    filename = \"data/named_sample_tles_master.txt\"\n    orbital_names = [\"11U\",\"204U\",\"205U\",\"631U\",\"589U\"]\n    orbits = []\n    for orb in orbital_names:\n        orbits.append(Orbit(orb,filename))\n    # obp = OrbitalPlotter(orbits)\n    # obp.plot()\n\n    ISS = Orbit(\"ISS\",filename)\n\n    for orbits in orbits:\n        getDistance(ISS,orbits)\n\n   \n\n    #print( obp.get_orbital_position(obp.orbitorbital1))","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"513041781","text":"# -*- coding: utf-8 -*-\nimport re\nimport time\nfrom lxml import etree\nfrom elasticsearch6.helpers import bulk, scan\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nimport sim_hash\nfrom config import es\nfrom bit_eth_ema import bitcoin_extract,eth_extract,email_extract\nfrom mylog import logger, exception_logger\n\nlogger = logger()\n\nclass Analysis(object):\n    def exquisite(self):\n        search_query = {\n            \"query\": {\n                \"match_phrase\": {\n                    \"domain\": {\n                        \"query\": \"sfdu2jstlnp7whqlzpojopr5jxehxz4dveqfl67v6mfrwoj3nq6cnrad.onion\"\n                    }\n                }\n            }\n        }\n\n        res = scan(client=es, query=search_query, scroll='5m', index='page', doc_type='_doc', timeout='5m')\n        cnt = 0\n        for record in res:\n            print(cnt, record[\"_source\"][\"url\"])\n            cnt += 1\n            html = record[\"_source\"][\"raw_text\"]\n            response = etree.HTML(html)\n            try:\n                if record[\"_source\"][\"url\"][0:80] == 'http://sfdu2jstlnp7whqlzpojopr5jxehxz4dveqfl67v6mfrwoj3nq6cnrad.onion/goods/info':\n\n                    crawl_time = record[\"_source\"][\"crawl_time\"]\n\n                    domain = 'sfdu2jstlnp7whqlzpojopr5jxehxz4dveqfl67v6mfrwoj3nq6cnrad.onion'\n\n                    net_type = 'tor'\n\n                    spider_name = 'onion_sfdu2_market_spider'\n\n                    try:\n                        goods_name = response.xpath('//h3/text()')[0].strip()\n                    except:\n                        goods_name = ''\n\n                    try:\n                        goods_id = record[\"_source\"][\"url\"]\n                        goods_id = re.findall('id=(\\d+)', goods_id)[0]\n                    except:\n                        goods_id = ''\n\n                    url = record[\"_source\"][\"url\"]\n\n                    try:\n                        goods_info = ''\n                        info = response.xpath('//div[@id=\"tab_one\"][2]//text()')\n                        for i in info:\n                            goods_info += i.strip()\n                    except:\n                        goods_info = ''\n\n                    try:\n                        goods_img_url = response.xpath('//img[@alt=\"封面\"]/@src')  # 图片\n                        goods_img_url = ['http://sfdu2jstlnp7whqlzpojopr5jxehxz4dveqfl67v6mfrwoj3nq6cnrad.onion' + i for i in goods_img_url]\n                    except:\n                        goods_img_url = []\n\n                    crawl_category = ''\n\n                    try:\n                        sold_count = response.xpath('//div[@class=\"pro-review\"]/span/a/text()')[0]\n                        sold_count = re.findall('销售量 (\\d+)', sold_count)[0]\n                        sold_count = int(sold_count)\n                    except:\n                        sold_count = None\n\n                    try:\n                        price = response.xpath('//span[@class=\"regular-price\"]/text()')\n                    except:\n                        price = []\n\n                    try:\n                        user_id = response.xpath('//div[@id=\"tab_one\"]/p[2]/span/text()')[0].strip()\n                    except:\n                        user_id = ''\n\n                    try:\n                        user_name = response.xpath('//div[@id=\"tab_one\"]/p[2]/span/text()')[0].strip()\n                    except:\n                        user_name = ''\n\n                    goods_area = ''\n\n                    try:\n                        raw_publish_time = response.xpath('//div[@id=\"tab_one\"]/p[last()]/span/text()')[0]\n                    except:\n                        raw_publish_time = ''\n\n                    try:\n                        publish_time = datetime.strptime(raw_publish_time, '%Y-%m-%d %H:%M:%S')\n                        publish_time = publish_time - relativedelta(hours=8)\n                    except:\n                        publish_time = None\n\n                    try:\n                        sku = response.xpath('//div[@class=\"availability mb-20\"]/span/text()')[0]\n                    except:\n                        sku = ''\n\n                    bitcoin_addresses = bitcoin_extract(goods_info)\n\n                    eth_addresses = eth_extract(goods_info)\n\n                    id = sim_hash.g_id(spider_name, goods_id)\n                    actions = [{\n                        \"_index\": 'goods',\n                        \"_type\": '_doc',\n                        \"_op_type\": 'create',\n                        \"_id\": id,\n                        \"_source\": {\n                            \"crawl_time\": crawl_time,\n                            \"domain\": domain,\n                            \"net_type\": net_type,\n                            \"spider_name\": spider_name,\n                            \"goods_name\": goods_name,\n                            \"goods_id\": goods_id,\n                            \"url\": url,\n                            \"goods_info\": goods_info,\n                            \"goods_img_url\": goods_img_url,\n                            \"crawl_category\": crawl_category,\n                            \"sold_count\": sold_count,\n                            \"price\": price,\n                            \"user_id\": user_id,\n                            \"user_name\": user_name,\n                            \"goods_area\": goods_area,\n                            \"raw_publish_time\": raw_publish_time,\n                            \"publish_time\": publish_time,\n                            \"sku\": sku,\n                            \"bitcoin_addresses\": bitcoin_addresses,\n                            \"eth_addresses\": eth_addresses,\n                            \"gmt_create\": record[\"_source\"][\"gmt_create\"],\n                            \"gmt_modified\": record[\"_source\"][\"gmt_modified\"],\n                        }\n                    }]\n                    success, _ = bulk(es, actions, index='goods', raise_on_exception=False, raise_on_error=False)\n            except Exception as e:\n                logger.warning(e)\n\n\nif __name__ == '__main__':\n    a = Analysis()\n    a.exquisite()","sub_path":"parse/sfdu2_analysis.py","file_name":"sfdu2_analysis.py","file_ext":"py","file_size_in_byte":6083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"201948657","text":"import os\nimport shutil\nimport config\nimport argparse\nimport sys\n\n\ndef svr():\n    print('delete bounding box model and npy data \\n')\n    shutil.rmtree(os.path.join(config.TRAIN_SVR, '1'))\n    os.mkdir(os.path.join(config.TRAIN_SVR, '1'))\n    for file in os.listdir(config.TRAIN_SVR):\n        if file.split('.')[-1] == 'pkl':\n            os.remove(os.path.join(config.TRAIN_SVR, file))\n\n\ndef svm():\n    print('delete svm model and npy data \\n')\n    shutil.rmtree(os.path.join(config.TRAIN_SVM, '1'))\n    os.mkdir(os.path.join(config.TRAIN_SVM, '1'))\n    for file in os.listdir(config.TRAIN_SVM):\n        if file.split('.')[-1] == 'pkl':\n            os.remove(os.path.join(config.TRAIN_SVM, file))\n\n\ndef finetune():\n    print('delete finetune model and npy data \\n')\n    shutil.rmtree(config.FINE_TUNE_DATA)\n    os.mkdir(config.FINE_TUNE_DATA)\n    shutil.rmtree(config.FINE_TUNE_MODEL)\n    os.mkdir(config.FINE_TUNE_MODEL)\n\n\ndef pretrain():\n    print('delete pretrain model \\n')\n    shutil.rmtree(config.SAVE_MODEL)\n    os.mkdir(config.SAVE_MODEL)\n\n\nif __name__ == '__main__':\n    parse = argparse.ArgumentParser()\n    parse.add_argument(\"--pretrain\", type=int, default=0, help='delete pretrain model')\n    parse.add_argument(\"--finetune\", type=int, default=0, help='delete finetune model')\n    parse.add_argument(\"--svm\", type=int, default=0, help='delete svm classify model')\n    parse.add_argument(\"--bbr\", type=int, default=0, help='delete bounding box regression model')\n    arg = parse.parse_args(sys.argv[1:])\n    if arg.pretrain == 1:\n        pretrain()\n    if arg.finetune == 1:\n        finetune()\n    if arg.svm == 1:\n        svm()\n    if arg.bbr == 1:\n        svr()\n","sub_path":"reset.py","file_name":"reset.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"442427758","text":"\nimport threading\nimport time\n \n# 定义一个全局变量\n#number = 0\n# mutex = threading.Lock()\nDATA=[]\n'''创建一个互斥锁，默认是没有上锁的'''\ndef test1():\n    global DATA\n    '''1.判断有多少学生''' \n    while True:\n        for i in range(100):  \n            #mutex.acquire() \n            data={}     \n            data[i]=i\n            DATA.append(data)\n            if i==50:\n                print(\"第50位学生被卡在这里3秒\")\n                time.sleep(20)\n               \n            #print(\"DATA:\",DATA)       \n            #     DATA.append(i)\n            # DATA.append(i)\n            #mutex.release()\n        #print(\"number\",DATA)       \n        time.sleep(30)\n        \n    # for i in range(temp):\n    #     number += 1\n   \n            \n    #print(\"-----in test1 number=%s-----\" % number)\nDATA1=[]\ndef test2():\n    global DATA,DATA1\n    '''2.判断'''\n    while True:          \n        if DATA:\n            for i in DATA:          \n                #mutex.acquire()             \n                #print(\"data[0]\",DATA[0])\n                DATA[0][\"code\"]=\"true\"\n                DATA1.append(DATA[0])\n                DATA.remove(DATA[0])\n                #mutex.release()\n                print(\"DATA:\",DATA)\n                print(\"\\n\")\n                print(\"DATA1:\",DATA1)\n                \n        # for i in number:\n        #     number.remove(i)  \n        #time.sleep(5)\n    # for i in range(temp):\n    #     number += 1\n    '''解锁'''\n   \n    #print(\"-----in test2 number=%s-----\" % number)\nDATA2=[]\ndef test3():\n    global DATA1,DATA2\n    while True:\n        if DATA1:\n            for i in DATA1:\n                #mutex.acquire()\n                if DATA1[0][\"code\"]==\"true\":\n                    '''\n                    推送加记录\n                    '''\n                    DATA2.append(DATA1[0])\n                if DATA1[0][\"code\"]==\"false\":\n                    '''\n                    删除加删除记录\n                    '''\n                    # if DATA1[0] in str(DATA2):\n                    #     DATA2.remove(DATA1[0])\n                    pass\n                    \n                \n                DATA1.remove(DATA1[0])\n                #print(\"DATA1:\",DATA1)\n                print(\"DATA2:\",DATA2) \n                #mutex.release()\ndef main():\n    t1 = threading.Thread(target=test1)  # 加上要传递的参数，元组类型\n    t2 = threading.Thread(target=test2)\n    t3 = threading.Thread(target=test1)\n    t1.start()\n    t2.start()\n    t3.start()\n    \n    #time.sleep(2)\n \n    #print(\"-----in main number=%s-----\"% number)\n \nif __name__ == '__main__':\n    main()\n'''\n好的解决方案\n#三个线程\n向学校的接口请求,学校有多少个学生,然后每个学生判断是否请假,然后再推送机器里比如耗时90s,可以缩短为30s\n1.请求学校有多少个学生\n2.那个学生是否请假\n3.那个学生是要添加的还是删除的\n\n还有两个要判断\n一个是重复的照片不再下载\n一个是提交过的照片不再提交\n'''\n\n\n\n","sub_path":"MQ1/后期优化/test_a.py","file_name":"test_a.py","file_ext":"py","file_size_in_byte":3019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"568441993","text":"import sys\n\nfrom mosek.fusion import *\nimport numpy as np\nimport scipy.linalg\nimport scipy.stats\nimport time\n\n\nclass CSSolver:\n    def __init__(self, n, m, l, N, u_min, u_max, mean_only=False, lti_k=False):\n        try:\n            M = Model()\n            self.n = n\n            self.m = m\n            self.l = l\n            self.N = N\n\n            umax = np.tile(u_max.reshape((-1, 1)), (N, 1)).flatten()\n            umin = np.tile(u_min.reshape((-1, 1)), (N, 1)).flatten()\n\n            V = M.variable(\"V\", m*N, Domain.inRange(umin, umax))\n            k = None\n            if not mean_only:\n                if lti_k:\n                    k = M.variable([m, n])\n                    k_rep = Expr.repeat(k, N, 0)\n                    k_rep2 = Expr.repeat(k_rep, N, 1)\n                    identity_k = np.kron(np.eye(N), np.ones((m, n)))\n                    K_dot = Matrix.sparse(identity_k)\n                    K = Expr.mulElm(K_dot, k_rep2)\n                else:\n                    pattern = []\n                    for ii in range(N):\n                        for jj in range(m):\n                            for ll in range(n):\n                                row = ii*m + jj\n                                col = ii*n + ll\n                                pattern.append([row, col])\n                    K = M.variable([m*N, n*N], Domain.sparse(Domain.unbounded(), pattern))\n            else:\n                pattern = []\n                for ii in range(N):\n                    for jj in range(m):\n                        for ll in range(n):\n                            row = ii * m + jj\n                            col = ii * n + ll\n                            pattern.append([row, col])\n                K = M.parameter([m*N, n*N], pattern)\n\n            # linear variables with quadratic cone constraints\n            w = M.variable(\"w\", 1, Domain.unbounded())\n            x = M.variable(\"x\", 1, Domain.unbounded())\n            # y = M.variable(\"y\", 1, Domain.unbounded())\n            # z = M.variable(\"z\", 1, Domain.unbounded())\n            y1 = M.variable(\"y1\", 1, Domain.unbounded())\n            z1 = M.variable(\"z1\", 1, Domain.unbounded())\n            y2 = M.variable(\"y2\", 1, Domain.unbounded())\n            z2 = M.variable(\"z2\", 1, Domain.unbounded())\n\n            mu_0_T_A_T_Q_bar_B = M.parameter([1, m*N])\n            vec_T_sigma_y_Q_bar_B = M.parameter([1, n*N*m*N])\n            pattern = []\n            for ii in range(n*N):\n                for jj in range(m*N):\n                    if jj <= ii:\n                        pattern.append([ii, jj])\n            pattern = []\n            for ii in range(N):\n                for jj in range(n):\n                    for kk in range(m * (ii + 1)):\n                        pattern.append([jj + n * ii, kk])\n            Q_bar_half_B = M.parameter([n*N, m*N], pattern)\n            pattern = []\n            for ii in range(m*N):\n                pattern.append([ii, ii])\n            R_bar_half = M.parameter([m*N, m*N], pattern)\n            pattern = []\n            for ii in range(n * N):\n                pattern.append([ii, ii])\n            Q_bar_half = M.parameter([n*N, n*N], pattern)\n            # sigma_y_half = M.parameter([n*N, n*N])\n            A_sigma_0_half = M.parameter([n*N, n])\n            pattern = []\n            for ii in range(N):\n                for jj in range(n):\n                    for kk in range(l*(ii+1)):\n                        pattern.append([jj + n * ii, kk])\n            D = M.parameter([n*N, l*N], pattern)\n            A_mu_0 = M.parameter([n*N, 1])\n            pattern = []\n            for ii in range(N):\n                for jj in range(n):\n                    for kk in range(m * (ii + 1)):\n                        pattern.append([jj + n * ii, kk])\n            B = M.parameter([n*N, m*N])\n            d = M.parameter([n*N, 1])\n            sigma_N_inv = M.parameter([n, n])\n            neg_x_0_T_Q_B = M.parameter([1, m*N])\n            d_T_Q_B = M.parameter([1, m*N])\n            u_0 = M.parameter([m, 1])\n\n            I = Matrix.eye(n*N)\n\n            # convert to linear objective with quadratic cone constraints\n            u = Expr.mul(mu_0_T_A_T_Q_bar_B, V)\n            # coordinate shift, check to make sure T = *2\n            q = Expr.mul(neg_x_0_T_Q_B, V)\n            r = Expr.mul(d_T_Q_B, V)\n            # v = Expr.mul(vec_T_sigma_y_Q_bar_B, Expr.flatten(K))\n            if not mean_only:\n                # M.objective(ObjectiveSense.Minimize, Expr.add([q, r, u, w, x, y1, y2, z1, z2]))\n                M.objective(ObjectiveSense.Minimize, Expr.add([y1, y2, z1, z2]))\n                M.constraint(Expr.vstack(0.5, w, Expr.mul(Q_bar_half_B, V)), Domain.inRotatedQCone())\n                M.constraint(Expr.vstack(0.5, x, Expr.mul(R_bar_half, V)), Domain.inRotatedQCone())\n\n                M.constraint(Expr.vstack(0.5, y1, Expr.flatten(Expr.mul(Q_bar_half, Expr.mul(Expr.add(I, Expr.mul(B, K)), A_sigma_0_half)))), Domain.inRotatedQCone())\n                # check BKD multiplicaion, or maybe with I? Something seems wrong here b/c sparsity pattern is invalid\n                M.constraint(Expr.vstack(0.5, y2, Expr.flatten(Expr.mul(Q_bar_half, Expr.mul(Expr.add(I, Expr.mul(B, K)), D)))), Domain.inRotatedQCone())\n                M.constraint(Expr.vstack(0.5, z1, Expr.flatten(Expr.mul(R_bar_half, Expr.mul(K, A_sigma_0_half)))), Domain.inRotatedQCone())\n                M.constraint(Expr.vstack(0.5, z2, Expr.flatten(Expr.mul(R_bar_half, Expr.mul(K, D)))), Domain.inRotatedQCone())\n            else:\n                M.objective(ObjectiveSense.Minimize, Expr.add([q, r, u, w, x]))\n                M.constraint(Expr.vstack(0.5, w, Expr.mul(Q_bar_half_B, V)), Domain.inRotatedQCone())\n                M.constraint(Expr.vstack(0.5, x, Expr.mul(R_bar_half, V)), Domain.inRotatedQCone())\n\n            M.constraint(Expr.sub(V.slice(2, N*m), V.slice(0, N*m-2)), Domain.inRange(-0.2, 0.2))\n            u_oo = np.array([[0.0], [0.5]])\n            # u_o = Matrix.dense(u_oo)\n            self.u_o = M.parameter()\n            self.u_o.setValue(0.3)\n            self.u_s = M.parameter()\n            u_0.setValue(0.5)\n            # print(u_0.getValue())\n            M.constraint(Expr.sub(self.u_o, V.index(1)), Domain.inRange(-0.2, 0.2))\n            M.constraint(Expr.sub(self.u_s, V.index(0)), Domain.inRange(-0.2, 0.2))\n\n            # M.constraint(K.slice([0, 0], [m, n]), Domain.equalsTo(K.slice([m, n], [2*m, 2*n])))\n\n            # terminal mean constraint\n            mu_N = np.zeros((n, 1))\n            # TODO\n            mu_N = np.array([7.5, 2., 2.5, 100., 100., 0.5, 1.0, 1000.]).reshape((8, 1))\n            #mu_N = np.array([1,1,1,1,1,1]).reshape((n, 1))\n            mu_N = Matrix.dense(mu_N)\n\n            '''\n            e_n = np.zeros((n, n))\n            e_n[4, 4] = 1\n            e_n[6, 6] = 1\n            '''\n            e_n = np.eye(n)\n            E_N = Matrix.sparse(np.hstack((np.zeros((n, (N - 1) * n)), e_n)))\n            E_N_T = Matrix.sparse(np.hstack((np.zeros((n, (N - 1) * n)), np.eye(n))).T)\n            M.constraint(Expr.mul(E_N, Expr.add(Expr.add(A_mu_0, Expr.mul(B, V)), d)), Domain.lessThan(mu_N))\n            e_n = -1 * e_n\n            E_N = Matrix.sparse(np.hstack((np.zeros((n, (N - 1) * n)), e_n)))\n            E_N_T = Matrix.sparse(np.hstack((np.zeros((n, (N - 1) * n)), np.eye(n))).T)\n            M.constraint(Expr.mul(E_N, Expr.add(Expr.add(A_mu_0, Expr.mul(B, V)), d)), Domain.lessThan(mu_N))\n            # terminal covariance constraint\n            # sigma_0_part = M.variable()\n            # M.constraint(Expr.vstack(sigma_0_part, Expr.flatten(\n            #     Expr.mul(alpha_T, Expr.mul(E_k, Expr.mul(Expr.add(I, Expr.mul(B, K)), A_sigma_0_half))))),\n            #              Domain.inQCone())\n            # D_part = M.variable()\n            # M.constraint(Expr.vstack(D_part, Expr.flatten(\n            #     Expr.mul(alpha_T, Expr.mul(E_k, Expr.mul(Expr.add(I, Expr.mul(B, K)), D))))), Domain.inQCone())\n            # cov_part = M.variable()\n            # M.constraint(Expr.vstack(cov_part, sigma_0_part, D_part), Domain.inQCone())\n\n            # chance constraint\n            for ii in range(N):\n                alpha = np.zeros((n, 1))\n                # TODO\n                alpha[6, 0] = 1\n\n                alpha_T = Matrix.sparse(alpha.T)\n                alpha = Matrix.sparse(alpha)\n                beta = 2\n                inv_prob = scipy.stats.norm.ppf(0.95)\n                e_k = np.eye(n)\n                E_k = Matrix.sparse(np.hstack((np.zeros((n, (ii) * n)), e_k, np.zeros((n, (N - ii - 1) * n)))))\n                mean_part = Expr.mul(alpha_T, Expr.mul(E_k, Expr.add(Expr.add(A_mu_0, Expr.mul(B, V)), d)))\n                # if not mean_only:\n                sigma_0_part = M.variable()\n                M.constraint(Expr.vstack(sigma_0_part, Expr.flatten(Expr.mul(alpha_T, Expr.mul(E_k, Expr.mul(Expr.add(I, Expr.mul(B, K)), A_sigma_0_half))))), Domain.inQCone())\n                D_part = M.variable()\n                M.constraint(Expr.vstack(D_part, Expr.flatten(Expr.mul(alpha_T, Expr.mul(E_k, Expr.mul(Expr.add(I, Expr.mul(B, K)), D)))).slice(0, (ii+1)*l)), Domain.inQCone())\n                cov_part = M.variable()\n                M.constraint(Expr.vstack(cov_part, sigma_0_part, D_part), Domain.inQCone())\n                M.constraint(Expr.add(mean_part, Expr.mul(cov_part, inv_prob)), Domain.inRange(-beta, beta))\n                # M.constraint(Expr.add(mean_part, Expr.mul(cov_part, inv_prob)), Domain.greaterThan(-beta))\n                # else:\n                #     M.constraint(Expr.add(mean_part, cov_part * inv_prob), Domain.lessThan(beta))\n                # M.constraint(mean_part, Domain.lessThan(beta))\n\n            # M.setLogHandler(sys.stdout)\n\n            self.M = M\n            self.V = V\n            self.k = k\n            self.K = K\n            self.mu_0_T_A_T_Q_bar_B = mu_0_T_A_T_Q_bar_B\n            self.vec_T_sigma_y_Q_bar_B = vec_T_sigma_y_Q_bar_B\n            self.Q_bar_half_B = Q_bar_half_B\n            self.R_bar_half = R_bar_half\n            self.Q_bar_half = Q_bar_half\n            # self.sigma_y_half = sigma_y_half\n            self.A_sigma_0_half = A_sigma_0_half\n            self.D = D\n            self.A_mu_0 = A_mu_0\n            self.B = B\n            self.d = d\n            self.sigma_N_inv = sigma_N_inv\n            self.neg_x_0_T_Q_B = neg_x_0_T_Q_B\n            self.d_T_Q_B = d_T_Q_B\n            self.u_0 = u_0\n            self.mean_only = mean_only\n            self.lti_k = lti_k\n\n        finally:\n            pass\n            # M.dispose()\n\n    def populate_params(self, A, B, d, D, mu_0, sigma_0, sigma_N_inv, Q_bar, R_bar, u_0, x_target, K=None):\n        n = 8\n        m = 2\n        l = 8\n        N = self.N\n\n        # A = np.tile(np.eye(n), (N, 1))\n        # B = np.kron(np.eye(N), np.random.randn(n, m))\n        # D = np.kron(np.eye(N), np.random.randn(n, l))\n        # mu_0 = np.zeros((n, 1))\n        # sigma_0 = np.random.randn(n, n)\n        # sigma_0 = np.dot(sigma_0, sigma_0.T)\n        # sigma_N_inv = np.linalg.inv(1000 * sigma_0)\n        sigma_y = np.dot(A, np.dot(sigma_0, A.T)) + np.dot(D, D.T)\n        # sigma_y = np.linalg.cholesky(sigma_y)\n        # Q_bar = np.eye(n*N)\n        # R_bar = np.eye(m*N)\n        x_0 = x_target.copy()\n\n        self.mu_0_T_A_T_Q_bar_B.setValue(2*np.dot(np.dot(np.dot(mu_0.T, A.T), Q_bar), B))\n        temp = 2*np.dot(sigma_y, np.dot(Q_bar, B)).reshape((-1, 1)).T\n        self.vec_T_sigma_y_Q_bar_B.setValue(temp)\n        # try:\n        #     self.Q_bar_half_B.setValue(np.dot(scipy.linalg.cholesky(Q_bar), B))\n        # except np.linalg.LinAlgError:\n        self.Q_bar_half_B.setValue(np.dot(np.sqrt(Q_bar), B))\n        self.Q_bar_half.setValue(np.sqrt(Q_bar))\n        # try:\n        #     self.R_bar_half.setValue(scipy.linalg.cholesky(R_bar))\n        # except np.linalg.LinAlgError:\n        self.R_bar_half.setValue(np.sqrt(R_bar))\n        # try:\n        #     self.Q_bar_half.setValue(scipy.linalg.cholesky(Q_bar))\n        # except np.linalg.LinAlgError:\n        #     self.Q_bar_half.setValue(np.sqrt(Q_bar))\n        # try:\n        #     self.sigma_y_half.setValue(sigma_y)\n        # except np.linalg.LinAlgError:\n        #     print(\"cholesky failed\")\n        #     self.sigma_y_half.setValue(np.sqrt(sigma_y))\n        # try:\n        #     self.A_sigma_0_half.setValue(np.dot(A, np.linalg.cholesky(sigma_0)))\n        # except np.linalg.LinAlgError:\n        self.A_sigma_0_half.setValue(np.dot(A, np.sqrt(sigma_0)))\n        self.D.setValue(D)\n        self.A_mu_0.setValue((np.dot(A, mu_0)))\n        self.B.setValue(B)\n        self.d.setValue(d)\n        self.sigma_N_inv.setValue(sigma_N_inv)\n        self.neg_x_0_T_Q_B.setValue(2*np.dot(np.dot(-x_0.T, Q_bar), B))\n        self.d_T_Q_B.setValue(2*np.dot(np.dot(d.T, Q_bar), B))\n        u_oo = np.array([[0.1], [0.1]])\n        self.u_o.setValue(u_0[1])\n        self.u_s.setValue(u_0[0])\n        # self.M.writeTask('dump.opf')\n        if self.mean_only:\n            self.K.setValue(K)\n\n    def solve(self):\n        # print(self.u_0.getValue())\n        # self.M.solve()\n        t0 = time.time()\n        self.M.solve()\n        print((time.time() - t0))\n        try:\n            if self.mean_only:\n                K_level = np.zeros((self.m*self.N, self.n*self.N))\n            else:\n                if self.lti_k:\n                    K_level = np.kron(np.eye(self.N), self.k.level().reshape((self.m, self.n)))\n                else:\n                    K_level = self.K.level()\n            levels = (self.V.level(), K_level)\n            # print(levels)\n            return levels\n        except SolutionError:\n            raise RuntimeError\n\n    def time(self):\n        t0 = time.time()\n        for ii in range(20):\n            self.populate_params()\n            self.solve()\n        print((time.time() - t0) / 20)\n\n\ndef nearest_spd_cholesky(A):\n    # print(np.linalg.eigvals(A))\n    B = (A + A.T)/2\n    U, Sigma, V = np.linalg.svd(B)\n    H = np.dot(np.dot(V.T, np.diag(Sigma)), V)\n    Ahat = (B+H)/2\n    Ahat = (Ahat + Ahat.T)/2\n    p = 1\n    k = 0\n    spacing = np.spacing(np.linalg.norm(A))\n    I = np.eye(A.shape[0])\n    while p != 0:\n        k += 1\n        try:\n            R = np.linalg.cholesky(Ahat)\n            p = 0\n        except np.linalg.LinAlgError:\n            eig = np.linalg.eigvals(Ahat)\n            # print(eig)\n            mineig = np.min(np.real(eig))\n            print(mineig)\n            Ahat = Ahat + I * (-mineig * k**2 + spacing)\n    print(np.linalg.norm(Ahat - A))\n    R_old = R.copy()\n    R[np.abs(R) < 1e-5] = 1e-5\n    np.tril(R)\n    print(np.linalg.norm(R - R_old))\n    return R\n\n\nif __name__ == '__main__':\n    solver = CSSolver()\n    try:\n        solver.populate_params()\n        # solver.M.setSolverParam(\"numThreads\", 8)\n        # solver.M.setSolverParam(\"intpntCoTolPfeas\", 1e-3)\n        # solver.M.setSolverParam(\"intpntCoTolDfeas\", 1e-3)\n        # solver.M.setSolverParam(\"intpntCoTolRelGap\", 1e-3)\n        # solver.M.setSolverParam(\"intpntCoTolInfeas\", 1e-3)\n        solver.solve()\n        # solver.time()\n    finally:\n        solver.M.dispose()\n","sub_path":"src/misc/cs_solver_covariance_only.py","file_name":"cs_solver_covariance_only.py","file_ext":"py","file_size_in_byte":15041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"309283682","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom cinderclient.v2 import client as cinder_client\nfrom smaugclient.v1 import client as smaug_client\n\nimport os_client_config\n\nfrom oslotest import base\n\n\ndef _get_cloud_config(cloud='devstack-admin'):\n    return os_client_config.OpenStackConfig().get_one_cloud(cloud=cloud)\n\n\ndef _credentials(cloud='devstack-admin'):\n    \"\"\"Retrieves credentials to run functional tests\n\n    Credentials are either read via os-client-config from the environment\n    or from a config file ('clouds.yaml'). Environment variables override\n    those from the config file.\n\n    devstack produces a clouds.yaml with two named clouds - one named\n    'devstack' which has user privs and one named 'devstack-admin' which\n    has admin privs. This function will default to getting the devstack-admin\n    cloud as that is the current expected behavior.\n    \"\"\"\n    return _get_cloud_config(cloud=cloud).get_auth_args()\n\n\ndef _get_smaug_client_from_creds():\n    api_version = \"\"\n    cloud_config = _get_cloud_config()\n    keystone_session = cloud_config.get_session_client(\"data-protect\")\n    keystone_auth = cloud_config.get_auth()\n    region_name = cloud_config.get_region_name()\n    service_type = \"data-protect\"\n    endpoint_type = \"publicURL\"\n    endpoint = keystone_auth.get_endpoint(\n        keystone_session,\n        service_type=service_type,\n        region_name=region_name)\n\n    kwargs = {\n        'session': keystone_session,\n        'auth': keystone_auth,\n        'service_type': service_type,\n        'endpoint_type': endpoint_type,\n        'region_name': region_name,\n    }\n\n    client = smaug_client.Client(api_version, endpoint, **kwargs)\n    return client\n\n\ndef _get_cinder_client_from_creds():\n    api_version = \"\"\n    cloud_config = _get_cloud_config()\n    keystone_session = cloud_config.get_session_client(\"volumev2\")\n    keystone_auth = cloud_config.get_auth()\n    region_name = cloud_config.get_region_name()\n    service_type = \"volumev2\"\n    endpoint_type = \"publicURL\"\n    endpoint = keystone_auth.get_endpoint(\n        keystone_session,\n        service_type=service_type,\n        region_name=region_name)\n\n    kwargs = {\n        'session': keystone_session,\n        'auth': keystone_auth,\n        'service_type': service_type,\n        'endpoint_type': endpoint_type,\n        'region_name': region_name,\n    }\n\n    client = cinder_client.Client(api_version, endpoint, **kwargs)\n    return client\n\n\nclass SmaugBaseTest(base.BaseTestCase):\n    \"\"\"Basic class for Smaug fullstack testing\n\n    This class has common code shared for Smaug fullstack testing\n    including the various clients (smaug) and common\n    setup/cleanup code.\n    \"\"\"\n    def setUp(self):\n        super(SmaugBaseTest, self).setUp()\n        self.smaug_client = _get_smaug_client_from_creds()\n        self.cinder_client = _get_cinder_client_from_creds()\n","sub_path":"smaug/tests/fullstack/smaug_base.py","file_name":"smaug_base.py","file_ext":"py","file_size_in_byte":3367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"414534202","text":"# 2. Посчитать четные и нечетные цифры введенного натурального числа.\n# Например, если введено число 34560, то у него 3 четные цифры (4, 6 и 0) и 2 нечетные (3 и 5).\n\nnum = input('Введите число: ')\nevens = 0\nodds = 0\nfor i in range(len(num)):\n    if int(num[i]) % 2 == 0:\n        evens += 1\n    else:\n        odds += 1\nprint(f'В числе {num} - {evens} чётных и {odds} нечентых')\n","sub_path":"lesson_2/lesson_2.2.py","file_name":"lesson_2.2.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"203435736","text":"#coding=utf-8\r\nimport tensorflow as tf\r\nimport vgg16\r\n#import IAF\r\nimport cv2\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport math\r\n\r\n#alpha=0.1\r\n#beta=1\r\nimg_size = 384\r\nlabel_size = img_size \r\n\r\n\r\n\r\n\r\n#img_t_mean=[85.971, 56.608, 151.944]\r\n\r\nclass Model:\r\n    def __init__(self):\r\n        #self.vgg_t = vgg16.Vgg16()\r\n        \r\n       \r\n        #self.input_holder_t = tf.placeholder(tf.float32, [1, img_size, img_size, 3])\r\n       \r\n        #self.label_holder = tf.placeholder(tf.float32, [label_size*label_size, 2])\r\n    \r\n        #self.contour_th = 1.5\r\n        #self.contour_weight = 0.0001\r\n        #self.sobel_fx, self.sobel_fy = self.sobel_filter()\r\n        self.data_dict = np.load('T.npy', encoding='latin1').item()\r\n        print(\"T file loaded\")\r\n\r\n        \r\n\r\n    def build_model(self, input):\r\n\r\n        #build the VGG-16 model\r\n        #vgg_t = self.vgg_t\r\n        \r\n        #vgg_t.build(self.input_holder_t)\r\n        self.conv1_1 = self._conv_layer(input, 1, \"conv1_1\")\r\n        self.conv1_2 = self._conv_layer(self.conv1_1, 1, \"conv1_2\")\r\n        self.pool1 = self._max_pool(self.conv1_2, 'pool1')\r\n\r\n        self.conv2_1 = self._conv_layer(self.pool1, 1, \"conv2_1\")\r\n        self.conv2_2 = self._conv_layer(self.conv2_1, 1,\"conv2_2\")\r\n        self.pool2 = self._max_pool(self.conv2_2, 'pool2')\r\n\r\n        self.conv3_1 = self._conv_layer(self.pool2, 1, \"conv3_1\")\r\n        self.conv3_2 = self._conv_layer(self.conv3_1, 1, \"conv3_2\")\r\n        self.conv3_3 = self._conv_layer(self.conv3_2, 1, \"conv3_3\")\r\n        self.pool3 = self._max_pool(self.conv3_3, 'pool3')\r\n\r\n        self.conv4_1 = self._conv_layer(self.pool3, 1, \"conv4_1\")\r\n        self.conv4_2 = self._conv_layer(self.conv4_1, 1, \"conv4_2\")\r\n        self.conv4_3 = self._conv_layer(self.conv4_2, 1, \"conv4_3\")\r\n        self.pool4 = self._max_pool(self.conv4_3, 'pool4')\r\n        \r\n\r\n        self.conv5_1 = self._conv_layer(self.pool4, 1, \"conv5_1\")\r\n        self.conv5_2 = self._conv_layer(self.conv5_1, 1, \"conv5_2\")\r\n        self.conv5_3 = self._conv_layer(self.conv5_2, 1, \"conv5_3\")\r\n        self.pool5 = self._max_pool(self.conv5_3, 'pool5')\r\n\r\n        \r\n\r\n        #pool5_ = self._max_pool(pool4_s, 'pool5_')\r\n        #pool5_s  = tf.nn.relu(self.Conv_2d(pool5_, [3, 3, 512, 512],  0.01, padding='SAME', name='pool5_s'))\r\n        #pool5_1 = tf.nn.relu(self.Conv_2d(pool5_s, [5, 5, 512, 128],  0.01, padding='SAME', name='pool5_1'))\r\n        #pool5_up = self.upsampling_2d(pool5_1, name='pool5_up', size = (4, 4))\r\n       \r\n        \r\n    \r\n       \r\n        #self.score3 = self.Conv_2d(conv6_5_ , \r\n                                     #[1, 1, 128, 2], 0.01, padding='SAME', name = 'score3' )\r\n\r\n        #conv6_up = self.upsampling_2d(conv6_5_, name='conv6_up', size = (2, 2))\r\n        #conv6_up2 = self.upsampling_2d(conv6_5_, name='conv6_up2', size = (4, 4))\r\n\r\n        #decoder1 = conv6_up + conv3\r\n        #decoder1 = self.Conv_2d(tf.concat([conv6_up, conv3], axis=3),\r\n                                          #[1, 1, 320, 128],  0.01, padding='SAME', name='decoder1')\r\n        #self.decoder1_ = tf.nn.relu(self.Conv_2d(decoder1, [3, 3, 128, 128],  0.01, padding='SAME', name='decoder1_'))\r\n        #decoder1_up = self.Conv_2d(decoder1_, [1, 1, 128, 256],  0.01, padding='SAME', name='decoder1_up')\r\n        #self.score2 = self.Conv_2d(decoder1_, \r\n                                     #[3, 3, 128, 2], 0.01, padding='SAME', name = 'score2' )\r\n\r\n        #decoder_up = self.upsampling_2d(self.decoder1_, name='decoder_up', size = (2, 2))\r\n \r\n        #decoder2 = decoder_up + conv2 + conv6_up2 #tf.nn.relu()\r\n        #decoder2 = self.Conv_2d(tf.concat([conv6_up, conv2], axis=3),\r\n                                          #[1, 1, 256, 128],  0.01, padding='SAME', name='decoder2')\r\n        #self.decoder2_ = tf.nn.relu(self.Conv_2d(decoder2, [3, 3, 128, 128],  0.01, padding='SAME', name='decoder2_'))\r\n        #decoder2_up = self.Conv_2d(decoder2_, [1, 1, 128, 256],  0.01, padding='SAME', name='decoder2_up')\r\n\r\n        #conv6_5 = tf.nn.relu(self._dilated_conv2d(conv6_4_ , 3, 64, 13, 0.01, 'conv6_5', biased = True))\r\n        #conv6_5_ = self.Conv_2d(tf.concat([conv5_3_r, conv6_1, conv6_2, conv6_3, conv6_4, conv6_5], axis=3),\r\n                                           #[1, 1, 448, 128],  0.01, padding='SAME', name='conv6_5_')\r\n        #self.decoder3 = self.Conv_2d(self.conv6_5_, [3, 3, 128, 128],  0.01, padding='SAME', name='decoder3')#self.decoder2_ + conv6_up2\r\n       # self.score_t = self.Conv_2d(self.conv6_6, \r\n                                     #[3, 3, 128, 2], 0.01, padding='SAME', name = 'score_t' )\r\n        #self.score_t = tf.nn.relu(self.Deconv_2d(self.score_t_up, [1, 128, 128, 2], 4, 4, name='score_t'))\r\n         #self.score_t = self.upsampling_2d(self.score_t_up, name='score_t', size = (4, 4))\r\n\r\n    \r\n       # self.Score = self.upsampling_2d(self.score_t, name='Score') #, size = (2, 2)\r\n        \r\n      \r\n        #self.Score = tf.reshape(self.Score, [-1,2])\r\n        #self.Prob = tf.nn.softmax(self.Score)\r\n\r\n        #Get the contour term\r\n        #self.Prob_C = tf.reshape(self.Prob, [1, 256, 256, 2])\r\n        #self.Prob_Grad = tf.tanh(self.im_gradient(self.Prob_C))\r\n        #self.Prob_Grad = tf.tanh(tf.reduce_sum(self.im_gradient(self.Prob_C), reduction_indices=3, keep_dims=True))\r\n\r\n        #self.label_C = tf.reshape(self.label_holder, [1, 256, 256, 2])\r\n        #self.label_Grad = tf.cast(tf.greater(self.im_gradient(self.label_C), self.contour_th), tf.float32)\r\n        #self.label_Grad = tf.cast(tf.greater(tf.reduce_sum(self.im_gradient(self.label_C),\r\n                                                           #reduction_indices=3, keep_dims=True),\r\n                                             #self.contour_th), tf.float32)\r\n\r\n        #self.C_IoU_LOSS = self.Loss_IoU(self.Prob_Grad, self.label_Grad)\r\n\r\n\r\n       \r\n\r\n        #self.Loss_Mean = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.Score,\r\n                                                                                  #labels=self.label_holder))\r\n        #self.Loss = self.Loss_Mean + self.C_IoU_LOSS\r\n        #\r\n        #self.Score = tf.reshape(self.Score, [-1,2])\r\n        #self.correct_prediction = tf.equal(tf.argmax(self.Score,1), tf.argmax(self.label_holder, 1))\r\n        #self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))\r\n\r\n    def Conv_2d(self, input_, shape, stddev, name, padding='SAME'):\r\n        with tf.variable_scope(name) as scope:\r\n            W = tf.Variable(self.data_dict[name + '/W'], name=\"weights\")#self.get_conv_filter(name)\r\n            #W = tf.get_variable('W',\r\n                                #shape=shape,\r\n                                #initializer=tf.truncated_normal_initializer(stddev=stddev))\r\n\r\n            conv = tf.nn.conv2d(input_, W, [1, 1, 1, 1], padding=padding)\r\n\r\n            #b = tf.Variable(tf.constant(0.0, shape=[shape[3]]), name='b')\r\n            b = tf.Variable(self.data_dict[name + '/b'], name=\"biases\")#self.get_bias(name)\r\n            conv = tf.nn.bias_add(conv, b)\r\n\r\n            return conv\r\n\r\n    def Conv_2d_down(self, input_, shape, stddev, name, padding='SAME'):\r\n        with tf.variable_scope(name) as scope:\r\n            W = tf.get_variable('W',\r\n                                shape=shape,\r\n                                initializer=tf.truncated_normal_initializer(stddev=stddev))\r\n\r\n            conv = tf.nn.conv2d(input_, W, [1, 2, 2, 1], padding=padding)\r\n\r\n            b = tf.Variable(tf.constant(0.0, shape=[shape[3]]), name='b')\r\n            conv = tf.nn.bias_add(conv, b)\r\n\r\n            return conv\r\n\r\n    def Deconv_2d(self, input_, output_shape,\r\n                  k_s=3, st_s=2, stddev=0.01, padding='SAME', name=\"deconv2d\"):\r\n        with tf.variable_scope(name):\r\n            W = tf.get_variable('W',\r\n                                shape=[k_s, k_s, output_shape[3], input_.get_shape()[3]],\r\n                                initializer=tf.random_normal_initializer(stddev=stddev))\r\n\r\n            deconv = tf.nn.conv2d_transpose(input_, W, output_shape=output_shape,\r\n                                            strides=[1, st_s, st_s, 1], padding=padding)\r\n\r\n            b = tf.get_variable('b', [output_shape[3]], initializer=tf.constant_initializer(0.0))\r\n            deconv = tf.nn.bias_add(deconv, b)\r\n\r\n        return deconv\r\n\r\n    def _dilated_conv2d(self, x, kernel_size, num_o, dilation_factor, stddev ,name, biased=False):\r\n        \"\"\"\r\n        Dilated conv2d without BN or relu.\r\n        \"\"\"\r\n        #num_x = x.shape[3].value\r\n        with tf.variable_scope(name) as scope:\r\n            w = tf.Variable(self.data_dict[name + '/weights'], name=\"weights\")#self.get_conv_filter(name)\r\n            #w = tf.get_variable('weights', shape=[kernel_size, kernel_size, num_x, num_o], \r\n                                #initializer=tf.truncated_normal_initializer(stddev=stddev))\r\n            o = tf.nn.atrous_conv2d(x, w, dilation_factor, padding='SAME')\r\n            if biased:\r\n                b = tf.Variable(self.data_dict[name + '/biases'], name=\"biases\")#self.get_bias(name)\r\n                #b = tf.get_variable('biases', shape=[num_o], initializer=tf.constant_initializer(0.0))\r\n                o = tf.nn.bias_add(o, b)\r\n            return o\r\n\r\n    def upsampling_2d(self, tensor, name, size=(8,8)):\r\n        h_,w_,c_ = tensor.get_shape().as_list()[1:]\r\n        h_multi,w_multi = size\r\n        h = h_multi * h_\r\n        w = w_multi * w_\r\n        target = tf.image.resize_bilinear(tensor,size=(h,w),name='deconv_{}'.format(name))\r\n\r\n        return target\r\n\r\n    def sobel_filter(self):\r\n        fx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]).astype(np.float32)\r\n        fy = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]).astype(np.float32)\r\n\r\n        fx = np.stack((fx, fx), axis=2)\r\n        fy = np.stack((fy, fy), axis=2)\r\n\r\n        fx = np.reshape(fx, (3, 3, 2, 1))\r\n        fy = np.reshape(fy, (3, 3, 2, 1))\r\n\r\n        tf_fx = tf.Variable(tf.constant(fx))\r\n        tf_fy = tf.Variable(tf.constant(fy))\r\n\r\n        return tf_fx, tf_fy\r\n\r\n    def im_gradient(self, im):\r\n        gx = tf.nn.depthwise_conv2d(tf.pad(im, [[0, 0], [1, 1], [1, 1], [0, 0]], 'SYMMETRIC'),\r\n                                    self.sobel_fx, [1, 1, 1, 1], padding='VALID')\r\n        gy = tf.nn.depthwise_conv2d(tf.pad(im, [[0, 0], [1, 1], [1, 1], [0, 0]], 'SYMMETRIC'),\r\n                                    self.sobel_fy, [1, 1, 1, 1], padding='VALID')\r\n        return tf.sqrt(tf.add(tf.square(gx), tf.square(gy)))\r\n\r\n    def Loss_IoU(self, pred, gt):\r\n        inter = tf.reduce_sum(tf.multiply(pred, gt))\r\n        union = tf.add(tf.reduce_sum(tf.square(pred)), tf.reduce_sum(tf.square(gt)))\r\n\r\n        if inter == 0:\r\n            return 0\r\n        else:\r\n            return 1 - (2*(inter+1)/(union + 1))\r\n\r\n    def L2(self, tensor, wd=0.0005):\r\n        return tf.mul(tf.nn.l2_loss(tensor), wd, name='L2-Loss')\r\n\r\n    def _max_pool(self, bottom, name):\r\n        return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],\r\n                              padding='SAME', name=name)\r\n    def s_max_pool(self, bottom, name):\r\n        return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1],\r\n                              padding='SAME', name=name)\r\n\r\n    def _conv_layer(self, bottom, rate, name):\r\n        with tf.variable_scope(name) as scope:\r\n            filt = tf.Variable(self.data_dict[name + '/filter'], name=\"weights\")#self.get_conv_filter(name)\r\n            #conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')\r\n            conv = tf.nn.atrous_conv2d(bottom, filt, rate, padding = 'SAME')\r\n\r\n            conv_biases = tf.Variable(self.data_dict[name + '/biases'], name=\"biases\")#self.get_bias(name)\r\n            bias = tf.nn.bias_add(conv, conv_biases)\r\n\r\n            relu = tf.nn.relu(bias)\r\n            return relu\r\n\r\n    def get_conv_filter(self, name):\r\n\r\n        return tf.Variable(self.data_dict[name][0], name=\"filter\")\r\n\r\n    def get_bias(self, name):\r\n        return tf.Variable(self.data_dict[name][1], name=\"biases\")\r\n","sub_path":"T_baseline.py","file_name":"T_baseline.py","file_ext":"py","file_size_in_byte":12090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"303207102","text":"import os\nimport qrcode\nimport datetime\nimport hashlib\nimport sys\nimport requests\nimport json\nimport werkzeug.security\nfrom Crypto.Cipher import AES\nfrom Crypto.Util.Padding import pad, unpad\nfrom base64 import b64encode, b64decode\nfrom flask import render_template, flash, redirect, request, url_for, jsonify\nfrom app import app, db, api\nfrom app.mail import send_spam, wkreg_mail, ctreg_mail, addon_pur,ctregteamleader_mail\nfrom app.models import User, Staff, Transactions, Registrations, AddonTransactions, OtherPurchases, Workshops, Contests\nfrom config import Config\nfrom values import Prices\nfrom app.farer import authorizestaff, authorize\n# from app.addons import addon_purchase\nfrom werkzeug.utils import secure_filename\nfrom werkzeug.urls import url_parse\nfrom flask_restplus import Resource, Api\npay = api.namespace('pay', description=\"Payments management\")\n\nkey = b'&YnI$7LP(!#BEzI*'\niv = b'8838098342343849'\n\ndef encrypt(data):\n\tdata = str.encode(data)\n\tcipher = AES.new(key, AES.MODE_CBC, iv)\n\tct_bytes = cipher.encrypt(pad(data, AES.block_size))\n\tct = b64encode(ct_bytes).decode('utf-8')\n\treturn ct\n\ndef decrypt(data):\n\tdata = b64decode(data)\n\tcipher = AES.new(key, AES.MODE_CBC, iv)\n\tct_bytes = cipher.decrypt(data)\n\tct_bytes = str(unpad(ct_bytes, AES.block_size),'utf-8')\n\treturn ct_bytes\n\ndef pay_data(amt, tid):\n# def pay_data(plaintext):\n    # transactionId: Unique for each transaction\n    # amount: Transaction amount (Positive integer only)\n    # purpose: Transaction purpose: Conference code\n    # currency: Transaction currency\n    # checkSum: MD5 over the plaintext\n    plaintext = \"transactionId=VIDYUT\"+str(tid)+\"|amount=\"+str(amt)+\"|purpose=\"+Config.PURPOSE+\"|currency=inr\"\n    result = hashlib.md5(plaintext.encode())\n    result = result.hexdigest()\n    print(\"md5\", result)\n    pwc = plaintext + \"|checkSum=\" + result\n    print(\"before aes\",pwc)\n    encd = encrypt(pwc)\n    print(\"after aes\", encd)\n    payload = {\n        # 'status':'success',\n        'encdata':encd,\n        'code':Config.PAYCODE\n    }\n    return payload\n\ndef response_data(data):\n    # data: Encoded data\n    try:\n        plaintext = decrypt(data)\n        print(plaintext)\n        d = plaintext.split('|')\n        print(d)\n    except Exception as e:\n        print(e)\n        responseObject = {\n\t\t\t'status':'failed',\n\t\t\t'message':'Decryption error: '+str(e)\n\t\t}\n        return jsonify(responseObject)\n    try:\n        trid = d[0].split('=')[1]\n        trid = trid[6:]\n        print(trid)\n    except Exception as e:\n        print(e)\n        responseObject = {\n            'status':'failed',\n            'message':'cant get trid'+str(e)\n        }\n        return jsonify(responseObject)\n    try:\n        t = Transactions.query.filter_by(trid=trid).first()\n        if t is None:\n            print(\"Invalid transaction ID - manage this!\")\n            responseObject = {\n                'status':'failed',\n                'message':'Invalid transaction ID'\n            }\n            return jsonify(responseObject)\n        t.bankref = d[4].split('=')[1]\n        t.status = d[5].split('=')[1]\n        t.status = t.status.lower()\n        t.statusdesc = d[6].split('=')[1]\n        t.reply = plaintext\n        if(t.cat == 1 and t.status=='failed'):\n            print(\"Going to give back - 1\")\n            work = Workshops.query.filter_by(id=t.eid).first()\n            work.rmseats = work.rmseats + 1;\n            db.session.commit()\n        else:\n            db.session.commit()\n        if (t.status == 'success'):\n            print(\"Success\")\n            resp = trsuccess(t).get_json()\n            print(\"RESP \", resp)\n            responseObject = {\n                'status':'success',\n                'message':'payment successful',\n                'addition':resp.get('message')\n            }\n            return jsonify(responseObject)\n        else:\n            responseObject = {\n\t            'status':t.status,\n\t            'message':t.reply\n\t        }\n            return jsonify(responseObject)\n    except Exception as e:\n        print(e)\n        responseObject = {\n\t\t\t'status':'failed',\n\t\t\t'message':'Exception occured: '+str(e)\n\t\t}\n        return jsonify(responseObject)\n    return \"1\"\n\ndef workshopPay(workshop, user,transaction):\n    # transaction = Transactions(vid=user.vid, cat=1, eid=workshop.id, amount=workshop.fee)\n    print(transaction.amount)\n    print(workshop.fee)\n    return pay_data(transaction.amount, transaction.trid)\n\ndef addonPay(user, pid, qty):\n    # Online purchase method\n    # Create a transaction\n    # Create a Addon transaction\n    message = \"Success\"\n    if pid == 2:\n        print(\"Yeah 2\")\n        # Outstation: Proshow + Choreonite + Fashionshow\n        total = qty*Prices.P2\n        if qty >= 3:\n            total -= int(qty/3)*100\n            message = \"Offer applied. Rs. \" + str(int(qty/3)*100) + \" off.\"\n    elif pid == 3:\n        # General: Headbangers + Choreonite + Fashionshow\n        total = qty*Prices.P3\n    elif pid == 100:\n        # Testing case\n        total = qty*Prices.P100\n    else:\n        responseObject = {\n            'status':'failed',\n            'message':'No such product'\n        }\n        return jsonify(responseObject)\n    transaction = Transactions(vid=user.vid, cat=3, eid=pid, amount=total)\n    print(\"TOTAL \", total)\n    print(\"Transactions \", transaction.__dict__)\n    db.session.add(transaction)\n    db.session.commit()\n    traddon = AddonTransactions(trid=transaction.trid,\n                                qty=qty\n                                )\n    db.session.add(traddon)\n    db.session.commit()\n    return jsonify(pay_data(transaction.amount, transaction.trid))\n\ndef trsuccess(t):\n    u = User.query.filter_by(vid=t.vid).first()\n    if t.cat is 1 or t.cat is 2:\n        r = None\n        try:\n            reg = Registrations.query.filter_by(vid=t.vid,cat=t.cat,eid=t.eid).first()\n            if reg is None:\n                r = Registrations(vid=t.vid, cat=t.cat, eid=t.eid, typ=1, trid=t.trid, amount=t.amount)\n                db.session.add(r)\n                db.session.commit()\n                if (t.cat == 2):\n                    eve = Contests.query.filter_by(id=t.eid).first()\n                    if eve.team_limit > 1:\n                        r.tid = werkzeug.security.pbkdf2_hex(str(r.regid), \"F**k\" ,iterations=50000, keylen=3)\n                        db.session.commit()\n\n            else:\n                t.refund = True\n                if(t.cat==1):\n                    work = Workshops.query.filter_by(id=t.eid).first()\n                    work.rmseats = work.rmseats + 1;\n                    db.session.commit()\n                else:\n                    db.session.commit()\n                responseObject = {\n\t\t\t\t\t'status':'failed',\n\t\t\t\t\t'message':'Refund'\n\t\t\t\t}\n\n                return jsonify(responseObject)\n        except Exception as e:\n            print(e)\n            responseObject = {\n            \t'status':'failed',\n            \t'message':'Error occured. '+str(e)\n            }\n            return jsonify(responseObject)\n        try:\n            user = User.query.filter_by(vid=t.vid).first()\n            dept = ['CSE', 'ECE', 'ME', 'Physics', 'Chemisty', 'English', 'Biotech','BUG', 'Comm.', 'Civil', 'EEE', 'Gaming', 'Maths', 'Others']\n            if t.cat is 1:\n                w = Workshops.query.filter_by(id=t.eid).first()\n                wkreg_mail(user=u, workshop=w, regid=r.regid, wdept=dept[w.department - 1])\n                r.mail = True;\n                db.session.commit()\n            elif t.cat is 2:\n                c = Contests.query.filter_by(id=t.eid).first()\n                if c.team_limit == 1:\n                    ctreg_mail(user=u, contest=c, regid=r.regid, cdept=dept[c.department - 1])\n                else:\n                    ctregteamleader_mail(user=u,contest=c,registration=r,cdept=dept[c.department - 1])\n                r.mail = True;\n                db.session.commit()\n            responseObject = {\n                'status':'success',\n                'message':'Successfully registered'\n            }\n            return jsonify(responseObject)\n        except Exception as e:\n        \tprint(e)\n        responseObject = {\n        \t'status':'success',\n        \t'message':'Successfully registered. But error in sending mail: '+str(e)\n        }\n        return jsonify(responseObject)\n    elif t.cat is 3:\n    \tpurchasee = User.query.filter_by(vid=t.vid).first()\n    \ttry:\n    \t\tprint(\"TRYING TO ADD\")\n    \t\tta = AddonTransactions.query.filter_by(trid=t.trid).first()\n    \t\top = OtherPurchases(vid=t.vid,\n                                pid=t.eid,\n                                qty=ta.qty,\n                                message='online_success',\n                                bookid='ONLINE',\n                                total=t.amount,\n                                typ=1,\n                                trid=t.trid\n                                )\n    \t\tdb.session.add(op)\n    \t\tdb.session.commit()\n    \t\tprint(\"ADDED TO DB\")\n    \texcept Exception as e:\n    \t\tprint(\"Exception \", e)\n    \t\tresponseObject = {\n                'status':'failed',\n                'message':'Not added to db'\n            }\n    \t\treturn jsonify(responseObject)\n    \ttry:\n    \t\tproducts = ['Amritapuri: Proshow + Choreonite + Fashionshow','Outstation: Proshow + Choreonite + Fashionshow', 'General: Headbangers + Choreonite + Fashionshow', 'Choreonite + Fashionshow','T-Shirt','Amritapuri: All Tickets + T-Shirt','Outstation: All Tickets + T-Shirt','General: Headbangers + Choreonite + Fashionshow + T-Shirt']\n    \t\tprint(\"PID = \", op.pid)\n    \t\tif op.pid == 100:\n    \t\t\ttitle = \"TESTING\"\n    \t\telse:\n    \t\t\ttitle = products[op.pid-1]\n    \t\taddon_pur(user=purchasee, title=title, purid=op.purid, count=op.qty)\n    \t\top.mail = True;\n    \t\tdb.session.commit()\n    \t\tresponseObject = {\n                'status':'success',\n                'message':'transaction successful'\n            }\n    \t\tprint(\" reached here 1\")\n    \t\treturn jsonify(responseObject)\n    \texcept Exception as e:\n    \t\tprint(\"Exception \", e)\n    \t\tresponseObject = {\n                'status':'success',\n                'message':'Transaction successful. Mail not sent'\n            }\n    \t\treturn jsonify(responseObject)\n\ndef probber(t):\n    payload = pay_data(tid=t.trid, amt=t.amount)\n    print(\"Probber payload\")\n    print(payload)\n    try:\n        f = requests.post('https://payments.acrd.org.in/pay/doubleverifythirdparty', data=payload)\n    except Exception as e:\n        print(e)\n        return 0\n    print(\"Hello\")\n    j = f.text\n    print(\"J = \", j)\n    if not j:\n        print(\"INSIDE IF\")\n        t.status = 'failed'\n        if(t.cat == 1 and t.status=='failed'):\n            print(\"Going to give back - 2\")\n            work = Workshops.query.filter_by(id=t.eid).first()\n            work.rmseats = work.rmseats + 1\n            db.session.commit()\n        else:\n            db.session.commit()\n        return 'empty response'\n    print(\"Text\", j)\n    try:\n        k = json.loads(j)\n        print(\"JSON\", k)\n        return response_data(k[\"data\"])\n    except Exception as e:\n        print(\"No text \", e)\n    # if j.get('response') is False:\n    #     return j\n    # # return \"1\"\n    # return response_data(j.get('encdata'))\n    # print(jsonify(j))\n    return j\n\n@pay.route('/receive', methods=['GET', 'POST'])\nclass pay_receiver(Resource):\n    def get(self):\n        # Random thing for test: Only call after modification of pay_data\n        return pay_data(\"transactionId=VIDYUT1212|amount=10|purpose=SOME|currency=inr|bankrefno=1|status=FAILED|statusDesc=User pressed cancel button\")\n\n    # @authorize\n    def post(self):\n        try:\n            d = request.get_json()\n            data = d.get('data')\n            return response_data(data)\n        except Exception as e:\n            print(e)\n            responseObject = {\n                'status':'failed',\n                'message':'Exception occured : '+str(e)\n            }\n            return jsonify(responseObject)\n\n@pay.route('/prob')\nclass probbing(Resource):\n    # F you ACRD\n    @api.doc(params={\n        'trid':'Transaction ID',\n    })\n    @authorizestaff(request, 4)\n    def get(u, self):\n        data = request.json()\n        t = Transactions.query.filter_by(trid=data.get('trid')).first()\n        return probber(t)\n\n@pay.route('/prob/all')\nclass massprobbing(Resource):\n    # @authorizestaff(request, 4)\n    def get(self):\n        tlist = Transactions.query.filter(Transactions.status==\"processing\").filter(Transactions.trid<=3824).all()\n        tlist.extend(Transactions.query.filter(Transactions.status==\"acrd\").filter(Transactions.trid<=3824).all())\n        res = []\n        print(tlist)\n        for t in tlist:\n            print(t)\n            print(t.status)\n            probber(t)\n\n        return \"Probing completed\"\n\n@pay.route('/custom')\nclass custom(Resource):\n    def get(self):\n        t = Transactions.query.filter(Transactions.trid==839).first()\n        probber(t)\n        return \"completed\"\n\n# @pay.route('/testing')\n# def payment():\n#     plaintext = \"transactionId=VIDYUTTEST10|amount=1|purpose=VIDYUT19TEST|currency=inr\"\n#     result = hashlib.md5(plaintext.encode())\n#     result = result.hexdigest()\n#     print(\"md5\",result)\n#     pwc = plaintext + \"|checkSum=\" + result\n#     print(\"before aes\",pwc)\n#     cipher = AESCipher(key)\n#     encd = cipher.encrypt(pwc)\n#     print(\"after aes\", encd)\n#     return encd\n\n# @pay.route('/callback/', methods=['POST', 'GET'])\n# def callback():\n#     print(\"Inside callback\")\n#     print(request.args)\n#     return \"Check terminal\"\n\n# @pay.route('/check/')\n# def check_pay():\n#     return render_template('payment.html')\n","sub_path":"app/payments.py","file_name":"payments.py","file_ext":"py","file_size_in_byte":13639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"458256688","text":"import komand\nfrom .schema import RetrieveInput, RetrieveOutput, Input, Output, Component\n# Custom imports below\nfrom icon_storage.util.cache_help import CacheHelp\n\n\nclass Retrieve(komand.Action):\n\n    def __init__(self):\n        super(self.__class__, self).__init__(\n                name='retrieve',\n                description=Component.DESCRIPTION,\n                input=RetrieveInput(),\n                output=RetrieveOutput())\n\n    def run(self, params={}):\n        var_to_get = params.get(Input.VARIABLE_NAME)\n        cache_help = CacheHelp()\n        ret_val = cache_help.retrieve_variable(var_to_get)\n        return {Output.VALUE: ret_val}\n","sub_path":"storage/icon_storage/actions/retrieve/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"384475181","text":"\"\"\"\n@author:Liushihao\n@time:2020/3/18:15:34\n@email:Liushihao_1224@163.com\n@describe:\n\"\"\"\nsum = 0\nfor i in range(1,101):\n    if i%4==0:\n        sum+=i\n\nprint(\"1～100范围内能被4整除的所有数的和为：\",sum)","sub_path":"chapter3/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"95579153","text":"from pymongo import MongoClient\n\nclient = MongoClient(host='mongodb://127.0.0.1:27017')\ndb = client['test']\ncollection = db['student']\n\nresult = collection.find({'name':{'$regex':'^J.*'}})\nprint(type(result))\nfor e in result:\n    print(e)\n\n\n\n\n","sub_path":"MongoDB/mg_14.py","file_name":"mg_14.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"259967696","text":"class Solution(object):\n    def myAtoi(self, str):\n        \"\"\"\n        :type str: str\n        :rtype: int\n        \"\"\"\n        digit = ['0','1','2','3','4','5','6','7','8','9']\n        sign = ['-','+']\n        flag = 1\n        answer = 0\n        int_max = 2**31 - 1\n        int_min = -2**31\n        length = len(str)\n        if(length == 0):\n            return 0\n        int_max\n        pointer = 0\n        while(str[pointer] == ' '):\n            pointer += 1\n            if(pointer >= length):\n                return 0\n        if(str[pointer] not in digit and str[pointer] not in sign):\n            return 0\n        if(str[pointer] in sign):\n            if(str[pointer] == '-'):\n                flag = -1\n            pointer += 1\n        if(pointer >= length):\n            return 0\n        while(str[pointer] in digit):\n            answer = answer * 10 + int(str[pointer])\n            if(answer > int_max):\n                if(flag == 1):\n                    return int_max\n                else:\n                    return int_min\n                # return int_max is flag == 1 else int_min\n            pointer += 1\n            if(pointer >= length):\n                break\n        return answer*flag\n        \n        \n        \n        ","sub_path":"8. String to Integer (atoi).py","file_name":"8. String to Integer (atoi).py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"625831531","text":"import random\nr = random\n\nprint(\"*****************\")\nprint(\"Guessing game\")\nprint(\"*****************\")\n\n# secret_number_with_seed = random.seed(100) Seed define a entrada do valor\n# secret_number_with_round = round(r.random() * 10) Nesse caso o valor inicial será zero e isso pode gerar algum erro no código.\nsecret_number = r.randrange(1, 101)\nattempts_total = 0\n\nprint(\"What's the level?\")\nprint(\"(1) Easy 2(Normal) 3(Hard)\")\nlevel = int(input(\"Choose your level: \"))\n\nif(level == 1):\n    attempts_total = 20\nelif(level == 2):\n    attempts_total = 10\nelse:\n    attempts_total = 2\n\nfor attempt in range(1, attempts_total + 1):\n    print(\"Attempt {} by {}\".format(attempt, attempts_total))\n    user_number_str = input(\"Fill the number: \")\n    print(\"Your number was: \" + user_number_str)\n    user_number = int(user_number_str)\n\n    if(user_number < 1 or user_number > 100):\n        print(\"Please fill number between 1 and 100!\")\n        continue  #Sai do IF e executa o for de novo\n\n    right_result = secret_number == user_number\n    lower_result = user_number < secret_number\n    bigger_result = user_number > secret_number\n\n    if (right_result):\n        print(\"You discovery the secret number\")\n        print(\"WINNER!\")\n        break\n\n    elif(lower_result):\n        print(\"Wrong number! The Number is Lower than secret number\")\n    elif (bigger_result):\n        print(\"Wrong number! The Number is bigger than secret number\")\nprint(secret_number)\nprint(\"GAME OVER\")","sub_path":"GuessingGame/GuessingGame_Levels.py","file_name":"GuessingGame_Levels.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"60994031","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('books', '0027_auto_20160503_1457'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='expenselineitem',\n            name='receipt_num',\n            field=models.IntegerField(blank=True, null=True, help_text='The receipt number assigned by the treasurer and written on the receipt.'),\n        ),\n    ]\n","sub_path":"books/migrations/0028_expenselineitem_receipt_num.py","file_name":"0028_expenselineitem_receipt_num.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"47684769","text":"#!/usr/bin/env python\n# ----------------------------------------------------------------------\n#\n# Brad T. Aagaard, U.S. Geological Survey\n#\n# ----------------------------------------------------------------------\n#\n# Plot initial stress and slip profiles.\n#\n# PREREQUISITES: matplotlib, numpy, gmt\n\nsim = \"tpv13\"\ngriddata = False\nshowAB = True\n\n# ----------------------------------------------------------------------\nimport tables\nimport numpy\nimport matplotlib.pyplot as pyplot\nimport sys\nimport subprocess\n\nsys.path.append(\"../../../figures\")\nimport matplotlibext\n\nheader = 0.55\nlineStyle = [(\"red\", 'dashed'),\n             (\"blue\", 'dashdot'), \n             (\"orange\", 'dotted'), \n             (\"black\", 'solid'),\n             ]\n\nlabelsAB = 'ab'\n\n# ----------------------------------------------------------------------\ndef getval(v):\n    try:\n        d = float(v)\n    except ValueError:\n        d = None\n    return d\n\n# ----------------------------------------------------------------------\nfigure = matplotlibext.Figure()\nfigure.open(6.0, 2.5, margins=[[0.4, 0.3, 0.1], [0.35, 0, 0.1]], dpi=150)\n\nnrows = 1\nncols = 2\n\nfor icol in xrange(ncols):\n\n    simdirs = []\n    if icol == 0:\n        labels = [\"Tet4, 200m\",\n                  \"Tet4 100m\",\n                  ]\n        for dx in [200,100]:\n            for cell in [\"tet4\"]:\n                label = \"%s, %dm\" % (cell.capitalize(), dx)\n                d = \"scecfiles/%s_%s_%03dm\" % (sim,cell,dx)\n                simdirs.append((label, d))\n    else:\n        cell = \"tri3\"\n        dx = 100\n        modelers = [('Barall', \"barall\"),\n                    ('Kaneko', \"kaneko\"),\n                    ('Ma', \"ma\"),\n                    ('PyLith', \"tet4\"),\n                    ]\n        for (label,modeler) in modelers:\n            d = \"scecfiles/%s_%s_%03dm\" % (sim,modeler,dx)\n            simdirs.append((label, d))\n    \n    for irow in xrange(nrows):\n        ax = figure.axes(nrows+header, ncols, irow+1+header, icol+1)\n    \n        isim = 0\n        labels = []\n        lines = []\n        for (label, simdir) in simdirs:\n            if griddata:\n                cmd = \"awk '/^j/ {next}; { print $0 }' %s/ruptime.dat | triangulate -G%s/ruptime.grd -I100.0/100.0 -R-15.0e+3/+15.0e+3/0.0/15.0e+3 -Jx1.0 > /dev/null \" % (simdir, simdir)\n                subprocess.call(cmd, shell=True)\n                cmd = \"grd2xyz %s/ruptime.grd > %s/ruptime.txtgrd\" % \\\n                (simdir, simdir)\n                subprocess.call(cmd, shell=True)\n            filename = \"%s/ruptime.txtgrd\" % (simdir)\n            data = numpy.loadtxt(filename)\n\n            data = numpy.reshape(data, (151, 301, 3))\n\n            if isim != len(simdirs)-1:\n                style = lineStyle[isim]\n            else:\n                style = lineStyle[len(lineStyle)-1]\n            contours = ax.contour(data[:,:,0]/1.0e+3, data[:,:,1]/1e+3, data[:,:,2],\n                                  levels=numpy.arange(0.0, 8.01, 0.5),\n                                  colors=style[0],\n                                  linewidths=1.0,\n                                  linestyles=style[1])\n            lines.append(contours.collections[0])\n            ax.hold(True)\n            labels.append(label)\n            isim += 1\n\n        ax.set_xlim((-15.0, +15.0))\n        ax.set_xlabel(\"Dist. Along Strike (km)\")\n        ax.set_ylim((15.0, 0.0))\n        ax.set_yticks(numpy.arange(0, 15.01, 5.0))\n        ax.set_ylabel(\"Dist. Down Dip (km)\")\n        ax.set_aspect('equal')\n\n        if irow == 0:\n            ax.legend(lines, labels, loc=\"lower right\",\n                      bbox_to_anchor=(1,1.1), \n                      borderaxespad=0)\n\n        if irow+1 < nrows:\n            ax.set_xticklabels([])\n            ax.set_xlabel(\"\")\n        if icol > 0:\n            ax.set_title(\"\")\n            ax.set_yticklabels([])\n            ax.set_ylabel(\"\")\n\n        if showAB:\n            ilabel = icol\n            ax.text(-15, -1, \"(%s)\" % labelsAB[ilabel],\n                     fontweight='bold')\n\npyplot.show()\npyplot.savefig(\"%s_ruptime\" % (sim))\n\n\n# End of file\n","sub_path":"dynamic/scecdynrup/tpv210/plot_ruptime.py","file_name":"plot_ruptime.py","file_ext":"py","file_size_in_byte":4059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"460606153","text":"# Made by Kit Matthewson, Version 3.3\n\nfrom time import sleep\n\ntry:\n    from wikipedia import summary as wikiSummary\n    \n    inp = input('Type [Yes] if you would like to enable Wikipedia\\n>>> ').upper()\n\n    if (inp == 'YES'):\n        wikipedia = True\n        print('Wikipedia Enabled\\n')\n    else:\n        wikipedia = False\n        print('Wikipedia Disabled\\n')\nexcept:\n    print('Wikipedia Module Not Found\\n')\n    wikipedia = False\n\nprint('Atom Calculator\\nMade by Kit Matthewson\\n')\n\nelements = (\n    'Hydrogen', 'Helium', 'Lithium', 'Beryllium', 'Boron',\n    'Carbon', 'Nitrogen', 'Oxygen', 'Fluorine', 'Neon', 'Sodium',\n    'Magnesium', 'Aluminum', 'Silicon', 'Phosphorus', 'Sulfur',\n    'Chlorine', 'Argon', 'Potassium', 'Calcium', 'Scandium',\n    'Titanium', 'Vanadium', 'Chromium', 'Manganese', 'Iron',\n    'Cobalt', 'Nickel', 'Copper', 'Zinc', 'Gallium', 'Germanium',\n    'Arsenic', 'Selenium', 'Bromine', 'Krypton', 'Rubidium',\n    'Strontium', 'Yttrium', 'Zirconium', 'Niobium', 'Molybdenum',\n    'Technetium', 'Ruthenium', 'Rhodium', 'Palladium', 'Silver',\n    'Cadmium', 'Indium', 'Tin', 'Antimony', 'Tellurium', 'Iodine',\n    'Xenon', 'Cesium', 'Barium', 'Lanthanum', 'Cerium',\n    'Praseodymium', 'Neodymium', 'Promethium', 'Samarium',\n    'Europium', 'Gadolinium', 'Terbium', 'Dysprosium', 'Holmium',\n    'Erbium', 'Thulium', 'Ytterbium', 'Lutetium', 'Hafnium',\n    'Tantalum', 'Tungsten', 'Rhenium', 'Osmium', 'Iridium',\n    'Platinum', 'Gold', 'Mercury', 'Thallium', 'Lead', 'Bismuth',\n    'Polonium', 'Astatine', 'Radon', 'Francium', 'Radium',\n    'Actinium', 'Thorium', 'Protactinium', 'Uranium', 'Neptunium',\n    'Plutonium', 'Americium', 'Curium', 'Berkelium', 'Californium',\n    'Einsteinium', 'Fermium', 'Mendelevium', 'Nobelium',\n    'Lawrencium', 'Rutherfordium', 'Dubnium', 'Seaborgium',\n    'Bohrium', 'Hassium', 'Meitnerium', 'Darmstadtium',\n    'Roentgenium', 'Copernicium', 'Nihonium', 'Flerovium',\n    'Moscovium', 'Livermorium', 'Tennessine', 'Oganesson'\n)\n\nsymbols = (\n    'H', 'He', 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne',\n    'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'Cl', 'Ar', 'K',\n    'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni',\n    'Cu', 'Zn', 'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr', 'Rb',\n    'Sr', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru', 'Rh', 'Pd',\n    'Ag', 'Cd', 'In', 'Sn', 'Sb', 'Te', 'I', 'Xe', 'Cs',\n    'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd',\n    'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Ta',\n    'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'Tl', 'Pb',\n    'Bi', 'Po', 'At', 'Rn', 'Fr', 'Ra', 'Ac', 'Th', 'Pa',\n    'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk', 'Cf', 'Es', 'Fm',\n    'Md', 'No', 'Lr', 'Rf', 'Db', 'Sg', 'Bh', 'Hs', 'Mt',\n    'Ds', 'Rg', 'Cn', 'Nh', 'Fl', 'Mc', 'Lv', 'Ts', \n    'Og'\n)\n\nmassList = (\n    '1', '4', '7', '9', '11', '12', '14', '16', '19', '20',\n    '23', '24', '27', '28', '31', '32', '35', '40', '39',\n    '40', '45', '48', '51', '52', '55', '56', '59', '59',\n    '64', '65', '70', '73', '75', '79', '80', '84', '85',\n    '88', '89', '91', '93', '96', '98', '101', '103', '106',\n    '108', '112', '115', '119', '122', '128', '127', '131',\n    '133', '137', '139', '140', '141', '144', '145', '150',\n    '152', '157', '159', '163', '165', '167', '169', '173',\n    '175', '178', '181', '184', '186', '190', '192', '195',\n    '197', '201', '204', '207', '209', '209', '210', '222',\n    '223', '226', '227', '232', '231', '238', '237', '244',\n    '243', '247', '247', '251', '252', '257', '258', '259',\n    '262', '261', '262', '266', '264', '277', '268', '271',\n    '272', '285', '286', '289', '289', '293', '294', '294'\n)\n\ndef electronShells(Electrons):\n    'Returns positions of Electrons in Shells as list'\n\n    Shells = []\n\n    # Shell 1\n    if (Electrons <= 2):\n        Shells.append(Electrons)\n\n    # Shell 2\n    elif (Electrons > 2 and Electrons <= 10):\n        Shells.append(2)\n        Shells.append(Electrons - 2)\n\n    # Shell 3\n    elif (Electrons > 10 and Electrons <= 28):\n        Shells.append(2)\n        Shells.append(8)\n        Shells.append(Electrons - 10)\n\n    # Shell 4\n    elif (Electrons > 28 and Electrons <= 60):\n        Shells.append(2)\n        Shells.append(8)\n        Shells.append(18)\n        Shells.append(Electrons - 28)\n\n    # Shell 5\n    elif (Electrons > 60 and Electrons <= 110):\n        Shells.append(2)\n        Shells.append(8)\n        Shells.append(18)\n        Shells.append(32)\n        Shells.append(Electrons - 60)\n\n    # Shell 6\n    elif (Electrons > 110):\n        Shells.append(2)\n        Shells.append(8)\n        Shells.append(18)\n        Shells.append(32)\n        Shells.append(50)\n        Shells.append(Electrons - 110)\n\n    # If Helium\n    else:\n        Shells.append(1)\n\n    return Shells\n\nclass element:\n\n    def __init__(self, name):\n\n        self.atomicNumber = elements.index(name) + 1\n        self.atomicMass = massList[self.atomicNumber - 1]\n\n        self.name = name\n        self.symbol = symbols[self.atomicNumber - 1]\n\n        self.neutrons = int(self.atomicMass) - int(self.atomicNumber)\n        self.protons = int(self.atomicNumber)\n        self.electrons = self.atomicNumber\n\n        self.shells = electronShells(self.electrons)\n\n        if (wikipedia == True):\n            self.elementInfo = wikiSummary(self.name)\n\n    def printAtom(self):\n\n        print('\\n-----  %s, %s, %s -----' %(self.name, self.symbol, self.atomicNumber))\n        print('\\nAtomic Mass: %s' %(self.atomicMass))\n        print('Atomic Number: %s' %(self.atomicNumber))\n        print('\\nNucleus:')\n        print('  Neutrons: %s' %(self.neutrons))\n        print('  Protons: %s' %(self.protons))\n        print('\\nElectrons: %s' %(self.electrons))\n\n        # Prints the Shells\n        \n        Empty = abs(len(electronShells(self.electrons)) - 6)\n        ShellNames = ['K', 'L', 'M', 'N', 'O', 'P']\n        \n        for i in range(len(electronShells(self.electrons))):\n            print('  Shell %s (%s): %s Electrons' %(str(i + 1), ShellNames[i], str(self.shells[i])))\n            \n        for i in range(Empty):\n            print('  Shell %s (%s): 0 Electrons (Empty)' %(str(len(electronShells(self.electrons)) + i + 1), ShellNames[len(electronShells(self.electrons)) + i]))\n\n        # Prints Element Information\n        if (wikipedia == True):\n            print('\\nInformation:\\n%s\\nSource: Wikipedia' %(self.elementInfo))\n\n        # Adds the end line\n        length = 14 + len(self.name) + len(self.symbol)\n        x = '-'\n        for i in range(length - 1):\n            x = x + '-'\n        print('\\n%s\\n' %(x))\n\n\ndef nameSearch():\n    inp = input('Enter Element name or Symbol\\n>>> ').title()\n\n    try:\n        element(inp).printAtom()\n    except ValueError:\n        try:\n            element(elements[symbols.index(inp)]).printAtom()\n        except ValueError:\n            print('That Element could not be found. Try again.\\n')\n            nameSearch()\n    menu()\n\ndef numberSearch():  \n    inp = input('Enter Atomic Number (Protons)\\n>>> ')\n    try:\n        element(elements[int(inp) - 1]).printAtom()\n        menu()\n    except ValueError:\n        print('That Element could not be found. Try again.\\n')\n        numberSearch()\n\ndef listAll():\n    for i in range(len(elements) + 1):\n        element(elements[i]).printAtom()\n        sleep(1)\n    menu()\n\ndef menu():\n    if (wikipedia == False):\n        inp = input('Do you want to search by [Name], [Number] or list [All] Elements?\\n>>> ').upper()\n    else:\n        inp = input('Do you want to search by [Name] or [Number]?\\n>>> ').upper()\n    if (inp == 'NAME'):\n        print('\\nSearching by Name\\n')\n        nameSearch()\n    elif (inp == 'NUMBER'):\n        print('\\nSearching by Atomic Mass and Atomic Number\\n')\n        numberSearch()\n    elif (inp == 'ALL' and wikipedia == False):\n        print('Listing All Elements\\n')\n        listAll()\n    else:\n        print('That command was not recognised. Type one of the words in square brackets.\\n')\n        menu()\n\nmenu()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"182297568","text":"\"\"\"\n\n\"\"\"\nimport xml.etree.cElementTree as ET\n\nHTML_BEGIN = '<DIV STYLE=\"text-align:Left;\"><DIV><P><SPAN>'\nHTML_END = '</SPAN></P></DIV></DIV>'\n\n\ndef create(filename, task, alias):\n    \"\"\"Creates a gptool help xml file.\"\"\"\n\n    metadata = ET.Element('metadata', {'xml:lang': 'en'})\n    tool = ET.SubElement(metadata, 'tool', name=task.name,\n                         displayname=task.display_name,\n                         toolboxalias=alias)\n\n    summary = ET.SubElement(tool, 'summary')\n    summary.text = ''.join((HTML_BEGIN, task.description, HTML_END))\n\n    parameters = ET.SubElement(tool, 'parameters')\n    for task_param in task.parameters:\n        param = ET.SubElement(parameters, 'param',\n                              name=task_param['name'],\n                              displayname=task_param['display_name']\n                             )\n        dialog_ref = ET.SubElement(param, 'dialogReference')\n        dialog_ref.text = ''.join((HTML_BEGIN, task_param['description'], HTML_END))\n        python_ref = ET.SubElement(param, 'pythonReference')\n        python_ref.text = ''.join((HTML_BEGIN, task_param['description'], HTML_END))\n\n    dataIdInfo = ET.SubElement(metadata, 'dataIdInfo')\n    searchKeys = ET.SubElement(dataIdInfo, 'searchKeys')\n    keyword1 = ET.SubElement(searchKeys, 'keyword')\n    keyword1.text = 'ENVI'\n    keyword2 = ET.SubElement(searchKeys, 'keyword')\n    keyword2.text = task.name\n    idCitation = ET.SubElement(dataIdInfo, 'idCitation')\n    resTitle = ET.SubElement(idCitation, 'resTitle')\n    resTitle.text = task.name\n    idAbs = ET.SubElement(dataIdInfo, 'idAbs')\n    idAbs.text = ''.join((HTML_BEGIN, task.description, HTML_END))\n    idCredit = ET.SubElement(dataIdInfo, 'idCredit')\n    idCredit.text = '(c) 1988-2022, L3Harris Geospatial Solutions, Inc. All rights reserved.'\n    resTitle.text = task.name\n    tree = ET.ElementTree(metadata)\n    tree.write(filename)\n","sub_path":"envipyarclib/gptool/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"123356766","text":"from lexer_rules import tokens\r\nimport AST\r\n#from expressions import *\r\n\r\nfrom operator import add, mul\r\n#from expressions import BinaryOperation, Number\r\n\r\nmy_bool = False\r\nresult = ''\r\n\r\n# Parsing rules\r\nprecedence = (\r\n    ('right', 'ASSIGN'),\r\n    ('right', 'NOT'),\r\n    ('nonassoc', 'LTEQ', 'LT', 'EQ'),\r\n    ('left', 'PLUS', 'MINUS'),\r\n    ('left', 'MULTIPLY', 'DIVIDE'),\r\n    ('right', 'ISVOID'),\r\n    ('right', 'INT_COMP'),\r\n    ('left', 'AT'),\r\n    ('left', 'DOT')\r\n)\r\n# precedence = (\r\n#     ('left','PLUS','MINUS'),\r\n#     ('left','MULTIPLY','DIVIDE'),\r\n#     ('right','ASSIGN'),\r\n#     ('left','NOT'),\r\n#     ('nonassoc','LT','LTEQ','EQ'),\r\n#     ('right','ISVOID'),\r\n#     ('right','INT_COMP'),\r\n#     ('left','AT'),\r\n#     ('left','DOT')\r\n# )\r\n\r\ndef p_program(production):\r\n    '''program : classSet'''\r\n    production[0] = AST.Program(production[1])\r\n    production[0].line = 1\r\n\r\ndef p_classSet(production):\r\n    '''\r\n    classSet : class SEMICOLON classSet\r\n            | class SEMICOLON\r\n    '''\r\n    if len(production) == 3:\r\n        production[0] = [production[1]]\r\n    else: production[0] = [production[1]] + production[3]\r\n\r\ndef p_class(production):\r\n    'class : CLASS TYPE _inherits LBRACE ffeature RBRACE'\r\n    production[0] = AST.Class(production[2], production[3], production[5])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_inherits(production):\r\n    '''_inherits : INHERITS TYPE\r\n                    | empty'''\r\n    if len(production) == 3:\r\n        production[0] = AST.Type(production[2])\r\n    else: production[0] = None\r\n\r\ndef p_ffeature(production):\r\n    '''ffeature : feature SEMICOLON ffeature\r\n                | empty'''\r\n    if len(production) == 4:\r\n        production[0] = [production[1]] + production[3] #creo la lista de metodos y atributos\r\n    else: production[0] = []\r\n\r\ndef p_feature(production):  #metodo || atributo\r\n    #definition: name (args):returnType{expr}\r\n    '''feature : ID LPAREN formal RPAREN TDOTS TYPE LBRACE expr RBRACE\r\n                | ID LPAREN RPAREN TDOTS TYPE LBRACE expr RBRACE\r\n                | temp\r\n    '''\r\n    if len(production) == 10:\r\n        production[0] = AST.Method(production[1], production[3], production[6], production[8])\r\n        production[0].line = production.lineno(1)\r\n        line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n        production[0].index = production.lexpos(1) - line_start + 1\r\n    elif len(production) == 9:\r\n        production[0] = AST.Method(production[1], [], production[5], production[7])\r\n        production[0].line = production.lineno(1)\r\n        line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n        production[0].index = production.lexpos(1) - line_start + 1\r\n    else:#atr\r\n        production[0] = production[1]\r\n        production[0].line = production[1].line\r\n        production[0].index = production[1].index\r\n\r\n#cambiar temp por atributos o variables\r\ndef p_temp(production):\r\n    '''\r\n    temp : idDots\r\n        | idDots ASSIGN expr\r\n    '''\r\n    if len(production) == 2:\r\n        production[0] = production[1]\r\n    else: \r\n        production[0] = AST.Attribute(production[1].id, production[1].type, production[3])\r\n        production[0].line = production.lineno(2)\r\n        line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(2)) + 1\r\n        production[0].index = production.lexpos(2) - line_start + 1\r\n\r\ndef p_idDots(production):\r\n    'idDots : ID TDOTS TYPE'\r\n    production[0] = AST.Var(production[1], production[3])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_formal(production):\r\n    '''formal : idDots COMMA formal\r\n                | idDots'''\r\n    if len(production) == 2:\r\n        production[0] = [production[1]]\r\n    else: production[0] = [production[1]] + production[3]\r\n\r\ndef p_expression_list(production):\r\n    '''expression_list : expression_list expr SEMICOLON \r\n                        | expr SEMICOLON\r\n    '''\r\n    if len(production) == 3:\r\n        production[0] = [production[1]]\r\n    else: production[0] = production[1] + [production[2]]\r\n\r\ndef p_expression_not(production): #boolean complement of <expr>\r\n    '''expr : NOT expr'''\r\n    production[0] = AST.Not(production[2])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_expression_binop(production):\r\n    '''expr : expr PLUS expr    \r\n            | expr MINUS expr\r\n            | expr MULTIPLY expr\r\n            | expr DIVIDE expr'''\r\n    if production[2] == '+':\r\n        production[0] = AST.Plus(production[1], production[3])\r\n    elif production[2] == '-':\r\n        production[0] = AST.Minus(production[1], production[3])\r\n    elif production[2] == '*':\r\n        production[0] = AST.Star(production[1], production[3])\r\n    elif production[2] == '/':\r\n        production[0] = AST.Div(production[1], production[3])\r\n    production[0].line = production.lineno(2)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(2)) + 1\r\n    production[0].index = production.lexpos(2) - line_start + 1\r\n\r\ndef p_expression_g(production):\r\n    '''\r\n    expr : LPAREN expr RPAREN\r\n            | ISVOID expr\r\n            | block\r\n            | conditional\r\n            | loop\r\n            | case\r\n            | dispatch\r\n            | INT_COMP expr\r\n    '''\r\n    if len(production) == 4:\r\n        production[0] = production[2]\r\n    elif len(production) == 3:\r\n        if production[1] == 'isvoid':\r\n            production[0] = AST.IsVoid(production[2])\r\n            production[0].line = production.lineno(1)\r\n            line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n            production[0].index = production.lexpos(1) - line_start + 1\r\n        else: \r\n            production[0] = AST.IntegerComplement(production[2])\r\n            production[0].line = production.lineno(1)\r\n            production[0].index = production[2].index\r\n            # line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n            # production[0].index = production.lexpos(1) - line_start + 1\r\n    else: production[0] = production[1]\r\n\r\ndef p_block(production):\r\n    'block : LBRACE expression_list RBRACE'\r\n    production[0] = AST.Block(production[2])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_expression_id(production):\r\n    '''expr : ID'''\r\n    production[0] = AST.Type(production[1])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_expression_int(production):\r\n    '''expr : INTEGER '''\r\n    production[0] = AST.Interger(production[1])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_expression_str(production):\r\n    '''expr : STRING'''\r\n    production[0] = AST.String(production[1])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_expression_bool(production):\r\n    '''expr : TRUE\r\n            | FALSE'''\r\n    production[0] = AST.Boolean(production[1])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_expression_newtype(production):\r\n    '''expr : NEW TYPE'''\r\n    production[0] = AST.NewType(production[2])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_expression_l(production):\r\n    '''expr : let'''\r\n    production[0] = production[1]\r\n\r\n#e_0.f(e_1,...,e_n)\r\ndef p_dispatch(production):\r\n    '''\r\n    dispatch : expr DOT ID LPAREN arguments_list_opt RPAREN\r\n            |  expr AT TYPE DOT ID LPAREN arguments_list_opt RPAREN\r\n            |  ID LPAREN arguments_list_opt RPAREN\r\n    '''\r\n    if len(production) == 7:\r\n        production[0] = AST.Dispatch(production[3], production[5], production[1])\r\n        production[0].line = production.lineno(2)\r\n        # line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(2)) + 1\r\n        # production[0].index = production.lexpos(2) - line_start + 1\r\n        production[0].index = production[1].index\r\n    elif len(production) == 5:\r\n        production[0] = AST.Dispatch(production[1], production[3], None)\r\n        production[0].line = production.lineno(1)\r\n        line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n        production[0].index = production.lexpos(1) - line_start + 1\r\n    else: \r\n        production[0] = AST.StaticDispatch(production[5], production[7], production[1], production[3])\r\n        production[0].line = production.lineno(2)\r\n        line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(2)) + 1\r\n        production[0].index = production.lexpos(2) - line_start + 1\r\n\r\ndef p_arguments_list(production):\r\n    \"\"\"\r\n    arguments_list : arguments_list COMMA expr\r\n                        | expr\r\n    \"\"\"\r\n    if len(production) == 4:\r\n        production[0] = production[1] + [production[3]]\r\n    else: production[0] = [production[1]]\r\n\r\ndef p_arguments_list_opt(production):\r\n    \"\"\"\r\n    arguments_list_opt : arguments_list\r\n                        | empty\r\n    \"\"\"\r\n    production[0] = [] if production.slice[1].type == \"empty\" else production[1]\r\n\r\n\r\ndef p_empty(production):\r\n    'empty :'\r\n    production[0] = None\r\n\r\ndef p_let_expression(production):\r\n    'let : LET declaration_list IN expr'\r\n    production[0] = AST.LetVar(production[2], production[4])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_declaration_list(production):\r\n    '''\r\n    declaration_list : temp COMMA declaration_list\r\n                    | temp\r\n    '''\r\n    if len(production) == 2:\r\n        production[0] = [production[1]]\r\n    else: production[0] = [production[1]] + production[3]\r\n\r\ndef p_conditional(production):\r\n    'conditional : IF expr THEN expr ELSE expr FI'\r\n    production[0] = AST.Conditional(production[2], production[4], production[6])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_loop(production):\r\n    'loop : WHILE expr LOOP expr POOL'\r\n    production[0] = AST.Loop(production[2], production[4])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_case(production):\r\n    'case : CASE expr OF add ESAC'\r\n    production[0] = AST.Case(production[2], production[4])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef p_add(production):\r\n    '''add : derivate SEMICOLON add\r\n            | derivate SEMICOLON\r\n    '''\r\n    if len(production) == 4:\r\n        production[0] = [production[1]] + production[3]\r\n    else: production[0] = [production[1]]\r\n\r\ndef p_derivate(production):\r\n    '''derivate : idDots ARROW expr'''\r\n    production[0] = AST.Branch(production[1], production[3])\r\n    production[0].line = production.lineno(2)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(2)) + 1\r\n    production[0].index = production.lexpos(2) - line_start + 1\r\n\r\n\r\ndef p_expression_cmp(production):\r\n    '''expr : expr LT expr\r\n            | expr LTEQ expr\r\n            | expr EQ expr'''\r\n    if production[2] == '<':\r\n            production[0] = AST.LowerThan(production[1], production[3])\r\n    elif production[2] == '<=':\r\n        production[0] = AST.LowerEqualThan(production[1], production[3])\r\n    elif production[2] == '=':\r\n        production[0] = AST.EqualThan(production[1], production[3])\r\n    production[0].line = production.lineno(2)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(2)) + 1\r\n    production[0].index = production.lexpos(2) - line_start + 1\r\n\r\n\r\ndef p_expression_assign(production):\r\n    'expr : ID ASSIGN expr'\r\n    production[0] = AST.Assign(production[1], production[3])\r\n    production[0].line = production.lineno(1)\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos(1)) + 1\r\n    production[0].index = production.lexpos(1) - line_start + 1\r\n\r\ndef find_column(production):\r\n    line_start = production.lexer.lexdata.rfind('\\n', 0, production.lexpos) + 1\r\n    return production.lexpos - line_start + 1\r\n\r\ndef p_error(production):\r\n    global my_bool\r\n    global result\r\n    \"\"\"\r\n    Error rule for Syntax Errors handling and reporting.\r\n    \"\"\"\r\n    if production is None:\r\n        result = '(0, 0) - SyntacticError: ERROR at or near EOF'\r\n        #print('(0, 0) - SyntacticError: ERROR at or near EOF')\r\n    else:\r\n        result = '({}, {}) - SyntacticError: ERROR at or near \"{}\"'.format(production.lineno, find_column(production), production.value)\r\n        #print('({}, {}) - SyntacticError: ERROR at or near \"{}\"'.format(   production.lineno, find_column(production), production.value))\r\n    my_bool = True","sub_path":"src/parser_rules.py","file_name":"parser_rules.py","file_ext":"py","file_size_in_byte":14223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"428936107","text":"import numpy as np;\nfrom Analyser import *\nfrom scipy.linalg import lu\nfrom Algorithm import *\nfrom Utility import *\nfrom graphics import *\nimport queue\n\nclass AgentNew:\n    FLAGGED = -10\n    NOTCLICKED = -20\n    MARKNUMBER = \"Light Blue\"\n    MARKFLAG = \"Green\"\n    MARKMINE = \"Red\"\n    BFS = True\n    START = -5\n    IS = -5\n    END = 3\n    IE = 3\n    INC = 0.1\n    coord = np.array([\n        [-1,-1],\n        [0,-1],\n        [1,-1],\n        [1, 0],\n        [1, 1],\n        [0, 1],\n        [-1,1],\n        [-1,0]\n\n    ])\n    def __init__(self,originalMatrix,agentMatrix,solvedGrid,\n                 agentGrid,rowSize,columnSize,rowWindowSize,colWindowSize,numberOfMines):\n        self.originalMatrix = originalMatrix\n        self.agentMatrix = agentMatrix\n        self.rowSize = rowSize\n        self.columnSize = columnSize\n        self.solvedGrid = solvedGrid\n        self.agentGrid = agentGrid\n        self.rowWindowSize = rowWindowSize\n        self.colWindowSize = colWindowSize\n        self.prev = np.copy(agentMatrix)\n        self.numberOfMinesDiscovered = 0\n        self.numberOfMines = numberOfMines\n        self.isBoardSolved = False\n\n    def solve(self):\n        print(\"PLAY THE GAME\")\n        flag = False\n        if not flag:\n           while True:\n               x = np.random.randint(self.rowSize)\n               y = np.random.randint(self.columnSize)\n               if self.originalMatrix[x][y]==0:\n                  self.openCell(x,y,False)\n                  break;\n        breakLoop = False\n        while breakLoop == False:\n          #self.exploreMines()\n          #self.exploreCell()\n          # while self.checkPrevCur():\n          #   self.prev = np.copy(self.agentMatrix)\n          #   self.exploreMines()\n          #   self.exploreCell()\n            #break\n          algo = Algorithm(self.agentMatrix, self.rowSize, self.columnSize)\n          listOfExploredCells = algo.findClickedCells()\n          unclickedPositions, unclickedPositionsId, count = algo.collectUnexploredCells(listOfExploredCells)\n          print(unclickedPositions)\n          print(unclickedPositionsId)\n          a = algo.createMatrix(listOfExploredCells, unclickedPositions, unclickedPositionsId, count)\n          print (a)\n          pl, u = lu(a, permute_l=True)\n          print(u)\n          movements = algo.sb(a,u, unclickedPositions, unclickedPositionsId, count)\n          print(movements)\n          #input(\"Press Enter to continue...\")\n          checkRandom = all(v is None for v in movements)\n          print(\"Check Randommmmmmmmmmmmmmmmm: \", checkRandom)\n          if not checkRandom:\n             if  not self.solveMovements(movements,unclickedPositionsId):\n                 break;\n          else:\n              self.exploreMines()\n              self.exploreCell()\n              if not self.openRandomCell():\n                  break;\n          print(\"numberOfMinesDiscovered:\", self.numberOfMinesDiscovered)\n          if self.checkNumberOfCellsCovered() >=self.rowSize*self.columnSize:\n              break;\n        return self.analyse()\n          #input(\"Press Enter to continue...\")\n\n    def solveMovements(self,movements,unclickedPositionsId):\n        for i in range(len(movements)):\n            if movements[i]==0 or movements[i]==1:\n                c = Utility.hashCodeToPostion(unclickedPositionsId[i],self.columnSize)\n                if movements[i]==0:\n                    if not self.openCell(c[0],c[1],False):\n                        return False\n                else:\n                    self.openCell(c[0],c[1],True)\n        return True\n\n    def checkPrevCur(self):\n        for i in range(self.rowSize):\n            for j in range(self.columnSize):\n                if self.prev[i][j] != self.agentMatrix[i][j]:\n                    return  True\n        return False\n\n    def exploreMines(self):\n        for x in range(1,9):\n            for i in range(self.rowSize):\n                for j in range(self.columnSize):\n                    totalMines = 0\n                    if self.agentMatrix[i][j]==x:\n                        for k in range(8):\n                            x_cord = i + AgentNew.coord[k][0]\n                            y_cord = j + AgentNew.coord[k][1]\n                            if Utility.isValidCoordinates(x_cord, y_cord, self.rowSize, self.columnSize)==True:\n                                if self.agentMatrix[x_cord][y_cord] == AgentNew.FLAGGED:\n                                    totalMines+=1;\n                        if totalMines==x:\n                            for k in range(8):\n                                    x_cord = i + AgentNew.coord[k][0]\n                                    y_cord = j + AgentNew.coord[k][1]\n                                    if Utility.isValidCoordinates(x_cord, y_cord, self.rowSize, self.columnSize)==True and self.agentMatrix[x_cord][y_cord]==AgentNew.NOTCLICKED:\n                                        self.openCell(x_cord, y_cord, False)\n    def exploreCell(self):\n        for x in range(1,9):\n            for i in range(self.rowSize):\n                for j in range(self.columnSize):\n                    filledCells= 0\n                    totalCells = 0\n                    if self.agentMatrix[i][j]==x:\n                        for k in range(8):\n                            x_cord = i + AgentNew.coord[k][0]\n                            y_cord = j + AgentNew.coord[k][1]\n\n                            if Utility.isValidCoordinates(x_cord, y_cord, self.rowSize, self.columnSize)==True:\n                                totalCells+=1\n                                if self.agentMatrix[x_cord][y_cord] >=0:\n                                   filledCells+=1;\n\n                        if totalCells-filledCells==x:\n                            for k in range(8):\n                                    x_cord = i + AgentNew.coord[k][0]\n                                    y_cord = j + AgentNew.coord[k][1]\n                                    if Utility.isValidCoordinates(x_cord, y_cord, self.rowSize, self.columnSize)==True and self.agentMatrix[x_cord][y_cord]==AgentNew.NOTCLICKED:\n                                        self.openCell(x_cord, y_cord, True)\n\n    def openCell(self,x,y,flagged):\n        if not flagged and self.agentMatrix[x][y] == AgentNew.NOTCLICKED:\n           value = self.originalMatrix[x][y]\n           self.agentMatrix[x][y] = value\n           if value == -1:\n              self.markInAgentGrid(x, y, \"M\",AgentNew.MARKMINE)\n\n              return False\n           elif value==0:\n               self.openNeighbours(x,y)\n           self.markInAgentGrid(x, y, value, AgentNew.MARKNUMBER)\n\n        elif self.agentMatrix[x][y] == AgentNew.NOTCLICKED:\n            self.agentMatrix[x][y] = AgentNew.FLAGGED\n            self.markInAgentGrid(x,y,\"F\", AgentNew.MARKFLAG)\n            self.numberOfMinesDiscovered += 1\n        return True\n\n    def openNeighbours(self,x,y):\n        if AgentNew.BFS:\n          dictZeros  = {}\n          dictZeros[x,y] = 1\n          queue = []\n          queue.append([x,y])\n          while queue:\n              d = queue.pop(0)\n              if self.agentMatrix[d[0]][d[1]]==0:\n                  for k in range(8):\n                    x_cord = d[0] + AgentNew.coord[k][0]\n                    y_cord = d[1] + AgentNew.coord[k][1]\n                    if Utility.isValidCoordinates(x_cord, y_cord ,self.rowSize, self.columnSize) and ((x_cord,y_cord) not in dictZeros):\n                       value = self.originalMatrix[x_cord][y_cord]\n                       self.agentMatrix[x_cord][y_cord] =  value\n                       self.markInAgentGrid(x_cord,y_cord,value,AgentNew.MARKNUMBER)\n                       dictZeros[x_cord,y_cord] = 1\n                       queue.append([x_cord,y_cord])\n\n        else:\n            for k in range(8):\n                x_cord = x + AgentNew.coord[k][0]\n                y_cord = y + AgentNew.coord[k][1]\n                if Utility.isValidCoordinates(x_cord, y_cord, self.rowSize, self.columnSize):\n                    value = self.originalMatrix[x_cord][y_cord]\n                    self.agentMatrix[x_cord][y_cord] = value\n                    self.markInAgentGrid(x_cord, y_cord, value, AgentNew.MARKNUMBER)\n\n\n\n    def markInAgentGrid(self,i,j,text,color):\n        rect = Rectangle(Point(j * self.colWindowSize, i * self.rowWindowSize), Point((j + 1) * self.colWindowSize, (i + 1) * self.rowWindowSize))\n        rect.setFill(color)\n        rect.setWidth(3)\n        Mines = Text(Point((j + 0.5) * self.colWindowSize, (i + 0.5) * self.rowWindowSize), text)\n        Mines.setTextColor(\"Black\")\n        rect.draw(self.agentGrid)\n        Mines.draw(self.agentGrid)\n\n\n\n    def openRandomCell(self):\n        while True:\n            x = np.random.randint(self.rowSize)\n            y = np.random.randint(self.columnSize)\n            if self.agentMatrix[x][y] == AgentNew.NOTCLICKED:\n                  if not self.openCell(x, y, False):\n                       return False\n                  return True\n\n\n\n    def checkNumberOfCellsCovered(self):\n        count = 0\n        for i in range(self.rowSize):\n            for j in range(self.columnSize):\n                if self.agentMatrix[i][j]!=AgentNew.NOTCLICKED:\n                    count+=1\n\n        return count\n\n    def analyse(self):\n        c = self.checkNumberOfCellsCovered()\n        if c == self.rowSize*self.columnSize:\n            self.isBoardSolved =True\n        return Analyzer(self.originalMatrix,self.agentMatrix,self.numberOfMines,self.numberOfMinesDiscovered,self.isBoardSolved,self.rowSize,self.columnSize,c)","sub_path":"AgentNew.py","file_name":"AgentNew.py","file_ext":"py","file_size_in_byte":9503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"221604942","text":"from google_images_download import google_images_download \nimport json\nimport os\nimport sys\n# from ./ import format.py\n\nresponse = google_images_download.googleimagesdownload()\n\ndef downloadImage(query):\n    parameters = {\"keywords\": query, \n        \"format\": \"jpg\", \n        \"limit\":1, \n        \"image_directory\": sys.argv[1],\n        # \"output_directory\": \"downloads\"\n        \"size\": \"medium\"}\n    try:\n        response.download(parameters)\n    except FileNotFoundError:\n        print(\"Fail!\")\n\ndef smooth(cardNames):\n    toReturn = []\n    for card in range(len(cardNames)):\n        if (cardNames[card][0] == '\\n'):\n            toReturn.append(cardNames[card].strip())\n        # cardNames[card] = cardNames[card].strip()\n    return toReturn\n\nif __name__ == \"__main__\":\n    file = open('./tappedOutScraper/spiderOutput.json')\n    data = json.load(file)\n    for dictionary in data:\n        cardNames = dictionary.get(\"card_name\")\n        cardNames = smooth(cardNames)\n\n        for card in cardNames:\n            query = \"mtg scryfall \" + card\n            print(query)\n            downloadImage(query)","sub_path":"filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"172273995","text":"import time\nfrom aiogram import Bot, Dispatcher, executor, types\nfrom config import token, user_id\nfrom aiogram.utils.markdown import hbold, hunderline, hcode, hlink\nfrom aiogram.dispatcher.filters import Text\nfrom parser import get_current_news\nimport asyncio\nimport datetime as dt\nfrom threading import Thread\n\n\n\nfrom user import User\nfrom base import Session, engine, Base\nfrom article import Article\n\nbot = Bot(token=token, parse_mode=types.ParseMode.HTML)\ndp = Dispatcher(bot)\nstart_buttons = [\"All news\", \"Last five\", \"Fresh news\"]\nusers = {}\n# try:\n# time.sleep(10)\nBase.metadata.create_all(engine)\nsession = Session()\n# except Exception as error:\n\n@dp.message_handler(commands=\"start\")\nasync def start(message: types.Message):\n    keyboard = types.ReplyKeyboardMarkup(resize_keyboard=True)\n    keyboard.add(*start_buttons)\n    \n    # if (session.query(User).filter(User.id == message.chat.id) is None):\n    add_user(message.chat.id)\n    \n    await message.answer(\"News list\", reply_markup=keyboard)\n\n\n@dp.message_handler(Text(equals=start_buttons[0]))\nasync def get_all_news(message: types.Message):\n\n    # for k, v in sorted(news_dict.items(), key=lambda x : int(x[1]['article_time']), reverse=True):\n        #news = f\"<b>{v['article_date']}</b> - {v['article_time']}\\n<u>{v['article_title']}</u>\\n\\n<code>{v['article_desc']}</code>\\n\\n{v['article_url']}\"\n    for article in session.query(Article).all():   \n        news = article_to_text(article)\n\n        await message.answer(news)\n\n\n@dp.message_handler(Text(equals=start_buttons[1]))\nasync def get_last_five_news(message: types.Message):\n  \n    # for k, v in sorted(news_dict.items(), key=lambda x : int(x[1]['article_time']), reverse=False)[-5:]:\n\n    # list(news_dict.items())[-5:]:\n    for article in session.query(Article).limit(5):\n        art = article_to_text(article)\n\n        await message.answer(art)\n\n\n\n# from here\n# fresh news for current user?\n@dp.message_handler(Text(equals=start_buttons[2]))\nasync def get_fresh_news(message: types.Message):\n    current_news = get_current_news()\n    if len(current_news) > 0:\n        for k, v in current_news.items():\n            news = article_to_text(v)\n            update_user_seen(message.chat.id, time.time())\n            await message.answer(news)\n    else:\n        await message.answer(\"no fresh news\")\n\n@dp.message_handler(commands=\"stop\")\nasync def stop(message: types.Message):\n    session.query(User).filter_by(id = message.chat.id).first().status = \"inactive\"\n    print(\"User leaved channel\")\n    session.commit()\n\n@dp.message_handler(commands=\"showuser\")\nasync def stop(message: types.Message):\n    users = session.query(User).all()\n    print('\\n### All users:')\n    idmes = f'your id : {message.chat.id}'\n    await message.answer(idmes)\n    for user in users:    \n        timestamp_created = dt.datetime.fromtimestamp(user.time_created).strftime('%Y-%m-%d %H:%M:%S')\n        timestamp_seen = dt.datetime.fromtimestamp(user.last_seen).strftime('%Y-%m-%d %H:%M:%S')\n        news = f'{user.id} was registered on {timestamp_created} : {user.status} - last seen: {timestamp_seen}'\n        # print('\\n')\n        await message.answer(news)\n\nasync def updating_db():\n    while True:\n        current_news = get_current_news()\n        if len(current_news) > 0:\n            for k, v in current_news.items():\n        # print(v['article_title'])\n                if (not news_contain(k)):\n                    print(f\"\\n\\n\\n\\n{k}\\n\\n\\n\\n\")\n                    session.add(Article(k,v['article_title'], v['article_date'], v['article_time'], v['article_link'], v['article_description']))\n                    for user in session.query(User).yield_per(10).enable_eagerloads(False): \n                        if user.status == \"active\":\n                            await bot.send_message(user.id, article_to_text(v)) \n\n        print(\"DB updated\")\n        session.commit()\n        # await asyncio.sleep(60)\n        time.sleep(5)\n\ndef article_to_text(article):\n    if (isinstance(article,Article)):\n        return f\"{hbold(article.release_date)}\\n\\n{hlink(article.title,article.link)}\\n\\n{article.description}\"\n    else:\n        return f\"{hbold(article['article_date'])}\\n\\n{hlink(article['article_title'],article['article_link'])}\"\n\n\ndef update_user_seen(user_id, time):\n    session.query(User).filter_by(id = user_id).first().last_seen = time\n\n\ndef add_user(user_id):\n    if (not users_contain(user_id)):\n        session.add(User(user_id, time.time(), \"active\"))\n        print(\"User added\")\n    else:\n        session.query(User).filter_by(id = user_id).first().status = \"active\"\n        print(\"User revoked subscription\")\n    session.commit()\n\ndef news_contain(k) -> bool:\n    return session.query(session.query(Article.id).filter_by(id = k).exists()).scalar()\ndef users_contain(k) -> bool:\n    return session.query(session.query(User.id).filter_by(id = k).exists()).scalar()\n\nif __name__ == \"__main__\":\n    # t = Thread(target=updating_db)\n    # t.start()\n    # loop = asyncio.get_event_loop()\n    # # loop.create_task(news_every_minute())\n    # loop.create_task(updating_db())\n    # loop.run_forever()\n\n    executor.start_polling(dp)\n    updating_db()","sub_path":"app/tg_bot.py","file_name":"tg_bot.py","file_ext":"py","file_size_in_byte":5158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"394729951","text":"#\r\n# -*- coding: UTF-8 -*-\r\n#\r\n\r\nimport argparse, codecs, csv, re\r\n\r\nparser = argparse.ArgumentParser(prog='OdrLog2Csv.py', add_help=True)\r\nparser.add_argument('--ipath', type=str, required=True, help='入力ファイルパス')\r\nparser.add_argument('--opath', type=str, required=True, help='出力ファイルパス')\r\nargs = parser.parse_args()\r\n\r\nclumnName = [ '日付時間', 'JST', '%r', '%s', '%I', '%B', '%h', '%z', '%Z', '%T', '%R' ]\r\n\r\n\r\ndef cnv_ord_timestamp(data):\r\n\tdata[0] = re.sub(r'([0-9]{1,2})/([0-9]{1,2})/([0-9]{4}):', r'\\3\\/\\2\\/\\1 ', data[0], flags=re.RegexFlag.MULTILINE)\r\n\tdata[0] = data[0].replace('\\\\', '')\r\n\treturn data\r\n\r\ndef main_proc(ipath, opath):\r\n\twith codecs.open(ipath, 'r', 'ms932') as fdr:\r\n\t\twith codecs.open(opath, 'w', 'ms932') as fdw:\r\n\t\t\twriter = csv.writer(fdw, delimiter=',', quoting=csv.QUOTE_ALL)\r\n\t\t\treader = csv.reader(fdr, delimiter=' ',lineterminator='\\r\\n', skipinitialspace=True)\r\n\t\t\twriter.writerow(clumnName)\r\n\t\t\tfor row in reader:\r\n\t\t\t\twriter.writerow(cnv_ord_timestamp(row))\r\n\treturn\r\n\r\n\r\nif __name__ == '__main__':\r\n\tmain_proc(args.ipath, args.opath)\r\n","sub_path":"Tools/src/OdrLog2Csv.py","file_name":"OdrLog2Csv.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"430989926","text":"#\n# Function:\n#     Use either Selenuim or Request to grap html source and parse the stock info\n# Main Methods:\n#     Verify Arg\n#         This is to verify the specified\n#             1. stock type\n#             2. specified date\n#             3. method to get html\n#     Get Html\n#         Get Html Source via a method\n#     Find Date\n#         Find the date in src\n#     Verify Date\n#         verify if date equals to the one we defined\n#     Find Table\n#         Find stock information\n# Author:\n#     Zhen Zhang\n# Date:\n#     12/01/2018\n# EMail:\n#     zhangzhentctc@163.com\n\n\nimport requests\nfrom crawls.methods.src_selenium import *\nfrom bs4 import BeautifulSoup\nimport numpy as np\nimport pandas as pd\nfrom general.date_structure import *\n\nimport time\n\nTYPE_SH = \"Hu\"\nTYPE_HK = \"Gang\"\nTYPE_SZ = \"Shen\"\nTYPE_LIST = [TYPE_SH, TYPE_HK, TYPE_SZ]\n\nLINK_SH = 'http://www.hkexnews.hk/sdw/search/mutualmarket_c.aspx?t=sh'\nLINK_HK = 'http://www.hkexnews.hk/sdw/search/mutualmarket_c.aspx?t=hk'\nLINK_SZ = 'http://www.hkexnews.hk/sdw/search/mutualmarket_c.aspx?t=sz'\n\nCRAWL_METHOD_REQUEST = 1\nCRAWL_METHOD_SELENIUM = 2\nCRAWL_METHOD_LIST = [CRAWL_METHOD_REQUEST, CRAWL_METHOD_SELENIUM]\n\nTIMEOUT = 10\nXML_PARSER = \"html.parser\"\n\nERR_CRAWL_BASE = 1000\nERR_CRAWL_TYPE = ERR_CRAWL_BASE + 1\nERR_CRAWL_REQ_TIMEOUT = ERR_CRAWL_BASE + 2\nERR_CRAWL_INIT_SOUP = ERR_CRAWL_BASE + 3\nERR_CRAWL_SELE_GET_LINK = ERR_CRAWL_BASE + 12\nERR_CRAWL_DATE_FIND = ERR_CRAWL_BASE + 4\nERR_CRAWL_DATE_FORMAT = ERR_CRAWL_BASE + 5\n\nERR_CRAWL_STOCK_TL_FIND = ERR_CRAWL_BASE + 6\nERR_CRAWL_STOCK_TL_ERR = ERR_CRAWL_BASE + 7\nERR_CRAWL_STOCK_CODE = ERR_CRAWL_BASE + 8\nERR_CRAWL_STOCK_NAME = ERR_CRAWL_BASE + 9\nERR_CRAWL_STOCK_VOL = ERR_CRAWL_BASE + 10\nERR_CRAWL_STOCK_PER = ERR_CRAWL_BASE + 11\n\nERR_CRAWL_DATE_MATCH = ERR_CRAWL_BASE + 13\n\nERR_CRAWL_ARG = ERR_CRAWL_BASE + 14\nERR_CRAWL_RCVDATE_CON = ERR_CRAWL_BASE + 15\n\nRET_OK = 0\nRET_ERR = -1\n\nclass crawl:\n    def __init__(self, req_date, type = TYPE_SH, method = CRAWL_METHOD_SELENIUM):\n        self.type = type\n        self.method = method\n        self.req_date = req_date\n\n    def crawl_validate_args(self):\n        if self.type not in TYPE_LIST:\n            return ERR_CRAWL_TYPE\n        if self.type == TYPE_SH:\n            self.link = LINK_SH\n        if self.type == TYPE_HK:\n            self.link = LINK_HK\n        if self.type == TYPE_SZ:\n            self.link = LINK_SZ\n\n        if self.method not in CRAWL_METHOD_LIST:\n            return ERR_CRAWL_TYPE\n\n        self.req_struct_date = date_structure(self.req_date)\n        ret = self.req_struct_date.parse_date()\n        if ret == False:\n            return ERR_CRAWL_ARG\n        return RET_OK\n\n\n\n    def crawl_get_info(self):\n        if self.method == CRAWL_METHOD_REQUEST:\n            try:\n                self.resp = requests.get(self.link, timeout=TIMEOUT)\n            except:\n                self.crawl_report_err(ERR_CRAWL_REQ_TIMEOUT)\n                return ERR_CRAWL_REQ_TIMEOUT\n            pg_src = self.resp.text\n\n        if self.method == CRAWL_METHOD_SELENIUM:\n            sel = src_selenium(self.req_struct_date, self.link)\n            ret = sel.sele_process()\n            if ret != RET_OK:\n                self.crawl_report_err(ERR_CRAWL_SELE_GET_LINK)\n                return ret\n            pg_src = sel.pg_src\n\n        try:\n            self.soup = BeautifulSoup(pg_src, XML_PARSER)\n        except:\n            self.crawl_report_err(ERR_CRAWL_INIT_SOUP)\n            return ERR_CRAWL_INIT_SOUP\n        return RET_OK\n\n\n    def crawl_find_date(self):\n        tag_pnlResult = self.soup.find(\"input\", {\"name\": \"txtShareholdingDate\"})\n        if tag_pnlResult == None:\n            self.crawl_report_err(ERR_CRAWL_DATE_FIND)\n            return ERR_CRAWL_DATE_FIND\n        else:\n            tag_date = tag_pnlResult[\"value\"]\n            if tag_date == None:\n                self.crawl_report_err(ERR_CRAWL_DATE_FIND)\n                return ERR_CRAWL_DATE_FIND\n            else:\n                print(tag_date)\n                date = tag_date\n                if self.__validate_date(date) == False:\n                    self.crawl_report_err(ERR_CRAWL_DATE_FORMAT)\n                    return ERR_CRAWL_DATE_FIND\n                else:\n                    self.rcv_date = date\n\n        self.req_struct_rcv_date = date_structure(self.rcv_date)\n        ret = self.req_struct_rcv_date.parse_date()\n        if ret == False:\n            return ERR_CRAWL_RCVDATE_CON\n\n        return RET_OK\n\n    def __validate_date(self, date_str):\n        date_list = date_str.split(\"/\")\n        if len(date_list) == 3:\n            return True\n        else:\n            return False\n\n    def crawl_find_stock_tl(self):\n\n        tables = self.soup.find_all(\"tbody\")\n        table = tables[1].find_all(\"tr\")\n        data = []\n        for row in table:\n            items = row.find_all(\"div\")\n            pure_items = []\n            for item in items:\n                pure_items.append(item.string)\n            print(pure_items[1])\n            print(pure_items[5])\n            print(pure_items[7])\n\n            ret, code = self.__parse_stock_code(pure_items[1])\n            if ret == RET_ERR:\n                self.crawl_report_err(ERR_CRAWL_STOCK_CODE)\n                return ERR_CRAWL_STOCK_CODE\n\n            ret, vol = self.__parse_stock_vol(pure_items[5])\n            if ret == RET_ERR:\n                self.crawl_report_err(ERR_CRAWL_STOCK_VOL)\n                return ERR_CRAWL_STOCK_VOL\n\n            ret, per = self.__parse_stock_per(pure_items[7])\n            if ret == RET_ERR:\n                print(per)\n                self.crawl_report_err(ERR_CRAWL_STOCK_PER)\n                return ERR_CRAWL_STOCK_PER\n\n            data.append({\n                \"Code\": code,\n                \"Name\": pure_items[3],\n                \"Volume\": vol,\n                \"Percent\": per\n            })\n        self.stock_tl = pd.DataFrame(data, columns=[\"Code\", \"Name\", \"Volume\", \"Percent\"])\n        return RET_OK\n\n\n    def __parse_stock_code(self, stock_code_str):\n        try:\n            stock_code = int(stock_code_str)\n        except:\n            return RET_ERR, -1\n        return RET_OK, stock_code\n\n\n    def __parse_stock_vol(self, stock_vol_str):\n        if stock_vol_str == \"\":\n            return RET_OK, 0\n        try:\n            stock_vol = int(stock_vol_str.replace(\",\", \"\"))\n        except:\n            return RET_ERR, -1\n        if stock_vol >= 0:\n            return RET_OK, stock_vol\n        else:\n            return RET_ERR, -1\n\n\n    def __parse_stock_per(self, stock_per_str):\n        if stock_per_str == \"\":\n            return RET_OK, 0\n        try:\n            stock_per = float(stock_per_str.strip(\"%\")) / 100\n        except:\n            return RET_OK, 0\n        if stock_per >= 0 and stock_per <= 1:\n            return RET_OK, stock_per\n        else:\n            return RET_OK, 0\n\n    def crawl_report_err(self,err_type):\n        print(\"Error\")\n\n    def crawl_process(self):\n        ret = self.crawl_validate_args()\n        if ret != RET_OK:\n            return ret\n\n        ret = self.crawl_get_info()\n        if ret != RET_OK:\n            return ret\n\n        ret = self.crawl_find_date()\n        if ret != RET_OK:\n            return ret\n\n        ret = self.verify_date()\n        if ret != RET_OK:\n            return ret\n\n        ret = self.crawl_find_stock_tl()\n        if ret != RET_OK:\n            return ret\n\n        return RET_OK\n\n    def verify_date(self):\n        ret = self.req_struct_date.compare_date(self.req_struct_rcv_date)\n        if ret != True:\n            return ERR_CRAWL_DATE_MATCH\n\n        ret, week = self.req_struct_date.get_week()\n        if ret == True and week == 5:\n            return ERR_CRAWL_DATE_MATCH\n        return RET_OK\n\n\n    def crawl_print_ret(self):\n        print(self.rcv_date)\n        print(self.stock_tl)\n","sub_path":"crawls/crawl.py","file_name":"crawl.py","file_ext":"py","file_size_in_byte":7799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"178753125","text":"\"\"\"Notebook usage of LIT.\n\nTo use in LIT in colab or jupyter notebooks, create a LitWidget instance\nwith models and datasets to load. Optionally set the UI height and a proxy URL\nif necessary. By default, the UI with render in the cell that creates the\ninstance. Set render=False to disable this, and manually render the UI in a cell\nthrough the render() method. Use the stop() method to stop the server when done.\n\"\"\"\n\nimport html\nimport json\nimport os\nimport pathlib\nimport random\nimport typing\n# pytype: disable=import-error\nfrom IPython import display\nfrom lit_nlp import dev_server\nfrom lit_nlp import server_flags\nfrom lit_nlp.lib import wsgi_serving\n\ntry:\n  import google.colab  # pylint: disable=g-import-not-at-top,unused-import\n  is_colab = True\nexcept ImportError:\n  is_colab = False\n\n\nclass LitWidget(object):\n  \"\"\"Class for using LIT inside notebooks.\"\"\"\n\n  def __init__(self, *args, height=1000, render=False,\n               proxy_url=None, **kw):\n    \"\"\"Start LIT server and optionally render the UI immediately.\n\n    Args:\n      *args: Positional arguments for the LitApp.\n      height: Height to display the LIT UI in pixels. Defaults to 1000.\n      render: Whether to render the UI when this object is constructed.\n          Defaults to False.\n      proxy_url: Optional proxy URL, if using in a notebook with a server proxy.\n          Defaults to None.\n      **kw: Keyword arguments for the LitApp.\n    \"\"\"\n    app_flags = server_flags.get_flags()\n    app_flags['server_type'] = 'notebook'\n    app_flags['host'] = 'localhost'\n    app_flags['port'] = None\n    app_flags['warm_start'] = 1\n    app_flags.update(kw)\n\n    lit_demo = dev_server.Server(\n        *args, **app_flags)\n    self._server = typing.cast(\n        wsgi_serving.NotebookWsgiServer, lit_demo.serve())\n    self._height = height\n    self._proxy_url = proxy_url\n\n    if render:\n      self.render()\n\n  def stop(self):\n    \"\"\"Stop the LIT server.\"\"\"\n    self._server.stop()\n\n  def render(self, height=None):\n    \"\"\"Render the LIT UI in the output cell.\n\n    Args:\n      height: Optional height to display the LIT UI in pixels. If not specified,\n          then the height specified in the constructor is used.\n    \"\"\"\n    if not height:\n      height = self._height\n    if is_colab:\n      _display_colab(self._server.port, height)\n    else:\n      _display_jupyter(self._server.port, height, self._proxy_url)\n\n\ndef _display_colab(port, height):\n  \"\"\"Display the LIT UI in colab.\n\n  Args:\n    port: The port the LIT server is running on.\n    height: The height of the LIT UI in pixels.\n  \"\"\"\n\n  shell = \"\"\"\n      (async () => {\n          const url = new URL(\n            await google.colab.kernel.proxyPort(%PORT%, {'cache': true}));\n          const iframe = document.createElement('iframe');\n          iframe.src = url;\n          iframe.setAttribute('width', '100%');\n          iframe.setAttribute('height', '%HEIGHT%px');\n          iframe.setAttribute('frameborder', 0);\n          document.body.appendChild(iframe);\n      })();\n  \"\"\"\n  replacements = [\n      ('%PORT%', '%d' % port),\n      ('%HEIGHT%', '%d' % height),\n  ]\n  for (k, v) in replacements:\n    shell = shell.replace(k, v)\n\n  script = display.Javascript(shell)\n  display.display(script)\n\n\ndef _display_jupyter(port, height, proxy_url):\n  \"\"\"Display the LIT UI in colab.\n\n  Args:\n    port: The port the LIT server is running on.\n    height: The height of the LIT UI in pixels.\n    proxy_url: Optional proxy URL, if using in a notebook with a server proxy.\n        If not provided, LIT also checks to see if the environment variable\n        LIT_PROXY_URL is set, and if so, it uses that value as the proxy URL.\n  \"\"\"\n\n  # Add height to jupyter output_scroll div to fully contain LIT UI.\n  output_scroll_height = height + 10\n\n  frame_id = 'lit-frame-{:08x}'.format(random.getrandbits(64))\n  shell = \"\"\"\n    <style>div.output_scroll { height: %SCROLL_HEIGHT%px; }</style>\n    <iframe id='%HTML_ID%' width='100%' height='%HEIGHT%' frameborder='0'>\n    </iframe>\n    <script>\n      (function() {\n        const frame = document.getElementById(%JSON_ID%);\n        const url = new URL(%URL%, window.location);\n        const port = %PORT%;\n        if (port) {\n          url.port = port;\n        }\n        frame.src = url;\n      })();\n    </script>\n  \"\"\"\n\n  if proxy_url is None:\n    proxy_url = os.environ.get('LIT_PROXY_URL')\n\n  if proxy_url is not None:\n    # Allow %PORT% in proxy_url.\n    proxy_url = proxy_url.replace('%PORT%', '%d' % port)\n    replacements = [\n        ('%HTML_ID%', html.escape(frame_id, quote=True)),\n        ('%JSON_ID%', json.dumps(frame_id)),\n        ('%HEIGHT%', '%d' % height),\n        ('%SCROLL_HEIGHT%', '%d' % output_scroll_height),\n        ('%PORT%', '0'),\n        ('%URL%', json.dumps(proxy_url)),\n    ]\n  else:\n    replacements = [\n        ('%HTML_ID%', html.escape(frame_id, quote=True)),\n        ('%JSON_ID%', json.dumps(frame_id)),\n        ('%HEIGHT%', '%d' % height),\n        ('%SCROLL_HEIGHT%', '%d' % output_scroll_height),\n        ('%PORT%', '%d' % port),\n        ('%URL%', json.dumps('/')),\n    ]\n\n  for (k, v) in replacements:\n    shell = shell.replace(k, v)\n\n  iframe = display.HTML(shell)\n  display.display(iframe)\n","sub_path":"lit_nlp/notebook.py","file_name":"notebook.py","file_ext":"py","file_size_in_byte":5189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"562076632","text":"import numpy as np\nimport numpy.typing as npt\n\nfrom typing import Optional, List\n\ndef create_centroids(point_matrix: npt.ArrayLike, k: int, random_state: Optional[int] = 12345) -> npt.ArrayLike:\n    \"\"\"\n    Creates initial centroids\n\n    Input\n        point_matrix:\n            A numpy ArrayLike, expected to be of shape (N, D), where N is the length of the training set,\n            and D is the dimensionality of the points. The values inside the matrix should be numerical.\n        k:\n            An integer in the range 1 <= k <= N, where N is the length of the training set. Will be the number of clusters\n            found by the K-Means algorithm.\n\n    Output:\n        centroids:\n            A numpy array of shape (k, D) where k is the number of centroids requested, and D is the dimensionality of the input data.\n\n    \"\"\"\n\n    #Create a numpy generator for repeatability\n    rng = np.random.default_rng(random_state)\n\n    #Shuffle the points randomly\n    rng.shuffle(point_matrix, axis=0)\n\n    #Assign the first k rows to the centroids\n    centroids = point_matrix[:k, :]\n\n    return centroids\n\ndef map_points_to_centroids(point_matrix: npt.ArrayLike, centroids: npt.ArrayLike) -> npt.ArrayLike:\n    \"\"\"\n    Map each point to their closest centroid\n\n    Input\n        point_matrix:\n            A numpy ArrayLike, expected to be of shape (N, D), where N is the length of the training set,\n            and D is the dimensionality of the points. The values inside the matrix should be numerical.\n        centroids:\n            A numpy array of shape (k, D) where k is the number of centroids requested, and D is the dimensionality of the input data.\n\n    Output:\n        labels:\n            A numpy ArrayLike of shape (N, 1) where N is the length of the training data, and the single value is the integer label of the closest centroid\n    \"\"\"\n\n\n    #Calculate the euclidean distance sqrt((A - B)^2) but in a cool numpy way\n    #Credit to https://flothesof.github.io/k-means-numpy.html for the method, with more time I could probably have found this, but\n    #decided to just search for a fast solution\n    distances = np.sqrt(((point_matrix - centroids[:, np.newaxis])**2).sum(axis=2))\n\n    #Calculate the minimum distance index for each point, which is the closest centroid\n    labels = np.argmin(distances, axis=0)\n\n    return labels\n\ndef improve_centroids(point_matrix: npt.ArrayLike, labels: npt.ArrayLike, centroids: npt.ArrayLike):\n    \"\"\"\n    Improves each centroid slightly, by adjusting them to be the new means of their currently labeled set\n\n    Input:\n        point_matrix:\n            A numpy ArrayLike, expected to be of shape (N, D), where N is the length of the training set,\n            and D is the dimensionality of the points. The values inside the matrix should be numerical.\n        centroids:\n            A numpy array of shape (k, D) where k is the number of centroids requested, and D is the dimensionality of the input data.\n        labels:\n            A numpy ArrayLike of shape (N, 1) where N is the length of the training data, and the single value is the integer label of the closest centroid\n\n    Output:\n        centroids:\n            A numpy array of shape (k, D) where k is the number of centroids requested, and D is the dimensionality of the input data.\n            Has now been adjusted to be closer to optimal\n\n    \"\"\"\n\n    #For enforcing that nothing weird happens after this loop and reshaping\n    k = centroids.shape[0]\n    D = centroids.shape[1]\n\n    #Iterate through each k-set\n    new_centroids = []\n    for k_label in range(centroids.shape[0]):\n\n        #Find the relevant points to this k-set\n        relevant_points = point_matrix[labels==k]\n\n        #Calculate the mean of this relevant set\n        relevant_mean = relevant_points.mean(axis=0)\n\n        #Append this new centroid to the list\n        new_centroids.append(relevant_mean)\n\n    #Reshape the list into a numpy array with same shape as before\n    new_centroids = np.array(new_centroids)\n    new_centroids = new_centroids.reshape((k, D))\n\n    return new_centroids\n","sub_path":"interviewkmeans/utils/kmean_utils.py","file_name":"kmean_utils.py","file_ext":"py","file_size_in_byte":4077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"178807110","text":"import os\nimport time\nfrom pathlib import Path\nfrom flask import Flask, jsonify, abort, request, Response, Request\nimport concurrent.futures\nimport threading\nimport requests\nimport logging\nimport ast\nfrom urllib.parse import urlparse\nfrom flask import current_app as app\nfrom urllib3.exceptions import InsecureRequestWarning\n\n# Local modules\nfrom elasticsearch.indexer import Indexer\n\nfrom libs.assay_type import AssayType\n\n# HuBMAP commons\nfrom hubmap_commons.hm_auth import AuthHelper\n\n# Set logging fromat and level (default is warning)\n# All the API logging is forwarded to the uWSGI server and gets written into the log file `uwsgo-entity-api.log`\n# Log rotation is handled via logrotate on the host system with a configuration file\n# Do NOT handle log file and rotation via the Python logging to avoid issues with multi-worker processes\nlogging.basicConfig(format='[%(asctime)s] %(levelname)s in %(module)s: %(message)s', level=logging.DEBUG, datefmt='%Y-%m-%d %H:%M:%S')\nlogger = logging.getLogger(__name__)\n\n# Specify the absolute path of the instance folder and use the config file relative to the instance path\napp = Flask(__name__, instance_path=os.path.join(os.path.abspath(os.path.dirname(__file__)), 'instance'), instance_relative_config=True)\napp.config.from_pyfile('app.cfg')\n\n# Remove trailing slash / from URL base to avoid \"//\" caused by config with trailing slash\napp.config['ELASTICSEARCH_URL'] = app.config['ELASTICSEARCH_URL'].strip('/')\napp.config['ENTITY_API_URL'] = app.config['ENTITY_API_URL'].strip('/')\n\n# Suppress InsecureRequestWarning warning when requesting status on https with ssl cert verify disabled\nrequests.packages.urllib3.disable_warnings(category = InsecureRequestWarning)\n\n####################################################################################################\n## Register error handlers\n####################################################################################################\n\n# Error handler for 400 Bad Request with custom error message\n@app.errorhandler(400)\ndef http_bad_request(e):\n    return jsonify(error=str(e)), 400\n\n# Error handler for 401 Unauthorized with custom error message\n@app.errorhandler(401)\ndef http_unauthorized(e):\n    return jsonify(error=str(e)), 401\n\n# Error handler for 403 Forbidden with custom error message\n@app.errorhandler(403)\ndef http_forbidden(e):\n    return jsonify(error=str(e)), 403\n\n# Error handler for 500 Internal Server Error with custom error message\n@app.errorhandler(500)\ndef http_internal_server_error(e):\n    return jsonify(error=str(e)), 500\n\n####################################################################################################\n## AuthHelper initialization\n####################################################################################################\n\n# Initialize AuthHelper class and ensure singleton\ntry:\n    if AuthHelper.isInitialized() == False:\n        auth_helper_instance = AuthHelper.create(app.config['APP_CLIENT_ID'], \n                                                 app.config['APP_CLIENT_SECRET'])\n\n        logger.info(\"Initialized AuthHelper class successfully :)\")\n    else:\n        auth_helper_instance = AuthHelper.instance()\nexcept Exception:\n    msg = \"Failed to initialize the AuthHelper class\"\n    # Log the full stack trace, prepend a line with our message\n    logger.exception(msg)\n\n####################################################################################################\n## Default route\n####################################################################################################\n\n@app.route('/', methods = ['GET'])\ndef index():\n    return \"Hello! This is HuBMAP Search API service :)\"\n\n####################################################################################################\n## Assay type API\n####################################################################################################\n\n@app.route('/assaytype', methods = ['GET'])\ndef assaytypes():\n    primary = None\n    simple = False\n    for key, val in request.args.items():\n        if key == 'primary':\n            primary = val.lower() == \"true\"\n        elif key == 'simple':\n            simple = val.lower() == \"true\"\n        else:\n            abort(400, f'invalid request parameter {key}')\n\n    if primary is None:\n        name_l = [name for name in AssayType.iter_names()]\n    else:\n        name_l = [name for name in AssayType.iter_names(primary=primary)]\n        \n    if simple:\n        return jsonify(result=name_l)\n    else:\n        return jsonify(result=[AssayType(name).to_json() for name in name_l])\n\n@app.route('/assaytype/<name>', methods = ['GET'])\n@app.route('/assayname', methods = ['POST'])\ndef assayname(name=None):\n    if name is None:\n        request_json_required(request)\n        try:\n            name = request.json['name']\n        except Exception:\n            abort(400, 'request contains no \"name\" field')\n    try:\n        return jsonify(AssayType(name).to_json())\n    except Exception as e:\n        abort(400, str(e))\n        \n\n####################################################################################################\n## API\n####################################################################################################\n\n# Both HTTP GET and HTTP POST can be used to execute search with body against ElasticSearch REST API. \n@app.route('/search', methods = ['GET', 'POST'])\ndef search():\n    # Always expect a json body\n    request_json_required(request)\n\n    logger.info(\"======search with no index provided======\")\n    \n    # Determine the target real index in Elasticsearch to be searched against\n    # Use the app.config['DEFAULT_INDEX_WITHOUT_PREFIX'] since /search doesn't take any index\n    target_index = get_target_index(request, app.config['DEFAULT_INDEX_WITHOUT_PREFIX'])\n\n    logger.info(\"======target_index======\")\n    logger.info(target_index)\n\n    # Return the elasticsearch resulting json data as json string\n    return execute_query('_search', request, target_index)\n\n# Both HTTP GET and HTTP POST can be used to execute search with body against ElasticSearch REST API. \n# Note: the index in URL is not he real index in Elasticsearch, it's that index without prefix\n@app.route('/<index_without_prefix>/search', methods = ['GET', 'POST'])\ndef search_by_index(index_without_prefix):\n    # Always expect a json body\n    request_json_required(request)\n\n    # Make sure the requested index in URL is valid\n    validate_index(index_without_prefix)\n    \n    logger.info(\"======requested index_without_prefix======\")\n    logger.info(index_without_prefix)\n\n    # Determine the target real index in Elasticsearch to be searched against\n    target_index = get_target_index(request, index_without_prefix)\n\n    logger.info(\"======target_index======\")\n    logger.info(target_index)\n\n    # Return the elasticsearch resulting json data as json string\n    return execute_query('_search', request, target_index)\n\n\n# HTTP GET can be used to execute search with body against ElasticSearch REST API. \n@app.route('/count', methods = ['GET'])\ndef count():\n    # Always expect a json body\n    request_json_required(request)\n\n    logger.info(\"======count with no index provided======\")\n    \n    # Determine the target real index in Elasticsearch to be searched against\n    # Use the app.config['DEFAULT_INDEX_WITHOUT_PREFIX'] since /search doesn't take any index\n    target_index = get_target_index(request, app.config['DEFAULT_INDEX_WITHOUT_PREFIX'])\n\n    logger.info(\"======target_index======\")\n    logger.info(target_index)\n\n    # Return the elasticsearch resulting json data as json string\n    return execute_query('_count', request, target_index)\n\n# HTTP GET can be used to execute search with body against ElasticSearch REST API.\n# Note: the index in URL is not he real index in Elasticsearch, it's that index without prefix\n@app.route('/<index_without_prefix>/count', methods = ['GET'])\ndef count_by_index(index_without_prefix):\n    # Always expect a json body\n    request_json_required(request)\n\n    # Make sure the requested index in URL is valid\n    validate_index(index_without_prefix)\n    \n    logger.info(\"======requested index_without_prefix======\")\n    logger.info(index_without_prefix)\n\n    # Determine the target real index in Elasticsearch to be searched against\n    target_index = get_target_index(request, index_without_prefix)\n\n    logger.info(\"======target_index======\")\n    logger.info(target_index)\n\n    # Return the elasticsearch resulting json data as json string\n    return execute_query('_count', request, target_index)\n\n\n# Get a list of indices\n@app.route('/indices', methods = ['GET'])\ndef indices():\n    # Return the resulting json data as json string\n    result = {\n        \"indices\": get_filtered_indices()\n    }\n\n    return jsonify(result)\n\n# Get the status of Elasticsearch cluster by calling the health API\n# This shows the connection status and the cluster health status (if connected)\n@app.route('/status', methods = ['GET'])\ndef status():\n    response_data = {\n        # Use strip() to remove leading and trailing spaces, newlines, and tabs\n        'version': ((Path(__file__).absolute().parent.parent / 'VERSION').read_text()).strip(),\n        'build': ((Path(__file__).absolute().parent.parent / 'BUILD').read_text()).strip(),\n        'elasticsearch_connection': False\n    }\n    \n    target_url = app.config['ELASTICSEARCH_URL'] + '/_cluster/health'\n    resp = requests.get(url = target_url)\n    \n    if resp.status_code == 200:\n        response_data['elasticsearch_connection'] = True\n        \n        # If connected, we also get the cluster health status\n        status_dict = resp.json()\n        # Add new key\n        response_data['elasticsearch_status'] = status_dict['status']\n\n    return jsonify(response_data)\n\n@app.route('/reindex/<uuid>', methods=['PUT'])\ndef reindex(uuid):\n    # Reindex individual document doesn't require the token to belong\n    # to the HuBMAP-Data-Admin group \n    # since this is being used by entity-api and ingest-api too\n    token = get_user_token(request.headers)\n\n    try:\n        indexer = init_indexer(token)\n        threading.Thread(target=indexer.reindex, args=[uuid]).start()\n        # indexer.reindex(uuid)\n\n        logger.info(f\"Started to reindex uuid: {uuid}\")\n    except Exception as e:\n        logger.exception(e)\n\n        internal_server_error(e)\n\n    return f\"Request of reindexing {uuid} accepted\", 202\n\n# Live reindex without deleting and recreating the indices\n@app.route('/reindex-all', methods=['PUT'])\ndef reindex_all():\n\t# The token needs to belong to the HuBMAP-Data-Admin group \n    # to be able to trigger a live reindex for all documents\n    token = get_user_token(request.headers, admin_access_required = True)\n\n    try:\n        indexer = init_indexer(token)\n        threading.Thread(target=reindex_all_uuids, args=[indexer, token]).start()\n\n        logger.info('Started live reindex all')\n    except Exception as e:\n        logger.exception(e)\n\n        internal_server_error(e)\n\n    return 'Request of live reindex all documents accepted', 202\n\n\n####################################################################################################\n## Internal Functions Used By API \n####################################################################################################\n\n\n# Throws error for 400 Bad Reqeust with message\ndef bad_request_error(err_msg):\n    abort(400, description = err_msg)\n\n# Throws error for 401 Unauthorized with message\ndef unauthorized_error(err_msg):\n    abort(401, description = err_msg)\n\n# Throws error for 403 Forbidden with message\ndef forbidden_error(err_msg):\n    abort(403, description = err_msg)\n\n# Throws error for 500 Internal Server Error with message\ndef internal_server_error(err_msg):\n    abort(500, description = err_msg)\n\n# Get user infomation dict based on the http request(headers)\n# `group_required` is a boolean, when True, 'hmgroupids' is in the output\ndef get_user_info_for_access_check(request, group_required):\n    return auth_helper_instance.getUserInfoUsingRequest(request, group_required)\n\n\"\"\"\nParase the token from Authorization header\n\nParameters\n----------\nrequest_headers: request.headers\n    The http request headers\nadmin_access_required : bool\n    If the token is required to belong to the HuBMAP-Data-Admin group, default to False\n\nReturns\n-------\nstr\n    The token string if valid\n\"\"\"\ndef get_user_token(request_headers, admin_access_required = False):\n    # Get user token from Authorization header\n    # getAuthorizationTokens() also handles MAuthorization header but we are not using that here\n    try:\n        user_token = auth_helper_instance.getAuthorizationTokens(request_headers) \n    except Exception:\n        msg = \"Failed to parse the Authorization token by calling commons.auth_helper.getAuthorizationTokens()\"\n        # Log the full stack trace, prepend a line with our message\n        logger.exception(msg)\n        internal_server_error(msg)\n\n    # The user_token is flask.Response on error\n    if isinstance(user_token, Response):\n        # The Response.data returns binary string, need to decode\n        unauthorized_error(user_token.data.decode())\n\n    if admin_access_required:\n        # By now the token is already a valid token\n        # But we also need to ensure the user belongs to HuBMAP-Data-Admin group\n        # in order to execute the live reindex-all\n        # Return a 403 response if the user doesn't belong to HuBMAP-Data-Admin group\n        if not user_in_hubmap_data_admin_group(request):\n            forbidden_error(\"Access not granted\")\n\n    return user_token\n\n\"\"\"\nCheck if the user with token belongs to the HuBMAP-Data-Admin group\n\nParameters\n----------\nrequest : falsk.request\n    The flask http request object that containing the Authorization header\n    with a valid Globus nexus token for checking group information\n\nReturns\n-------\nbool\n    True if the user belongs to HuBMAP-Data-Admin group, otherwise False\n\"\"\"\ndef user_in_hubmap_data_admin_group(request):\n    try:\n        # The property 'hmgroupids' is ALWASYS in the output with using get_user_info()\n        # when the token in request is a nexus_token\n        user_info = get_user_info(request)\n        hubmap_data_admin_group_uuid = auth_helper_instance.groupNameToId('HuBMAP-Data-Admin')['uuid']\n    except Exception as e:\n        # Log the full stack trace, prepend a line with our message\n        logger.exception(e)\n\n        # If the token is not a nexus token, no group information available\n        # The commons.hm_auth.AuthCache would return a Response with 500 error message\n        # We treat such cases as the user not in the HuBMAP-Data-Admin group\n        return False\n        \n    return (hubmap_data_admin_group_uuid in user_info['hmgroupids'])\n\n\"\"\"\nGet user infomation dict based on the http request(headers)\nThe result will be used by the trigger methods\n\nParameters\n----------\nrequest : Flask request object\n    The Flask request passed from the API endpoint \n\nReturns\n-------\ndict\n    A dict containing all the user info\n\n    {\n        \"scope\": \"urn:globus:auth:scope:nexus.api.globus.org:groups\",\n        \"name\": \"First Last\",\n        \"iss\": \"https://auth.globus.org\",\n        \"client_id\": \"21f293b0-5fa5-4ee1-9e0e-3cf88bd70114\",\n        \"active\": True,\n        \"nbf\": 1603761442,\n        \"token_type\": \"Bearer\",\n        \"aud\": [\"nexus.api.globus.org\", \"21f293b0-5fa5-4ee1-9e0e-3cf88bd70114\"],\n        \"iat\": 1603761442,\n        \"dependent_tokens_cache_id\": \"af2d5979090a97536619e8fbad1ebd0afa875c880a0d8058cddf510fc288555c\",\n        \"exp\": 1603934242,\n        \"sub\": \"c0f8907a-ec78-48a7-9c85-7da995b05446\",\n        \"email\": \"email@pitt.edu\",\n        \"username\": \"username@pitt.edu\",\n        \"hmscopes\": [\"urn:globus:auth:scope:nexus.api.globus.org:groups\"],\n    }\n\"\"\"\ndef get_user_info(request):\n    # `group_required` is a boolean, when True, 'hmgroupids' is in the output\n    user_info = auth_helper_instance.getUserInfoUsingRequest(request, True)\n\n    logger.debug(\"======get_user_info()======\")\n    logger.debug(user_info)\n\n    # It returns error response when:\n    # - invalid header or token\n    # - token is valid but not nexus token, can't find group info\n    if isinstance(user_info, Response):\n        # Bubble up the actual error message from commons\n        # The Response.data returns binary string, need to decode\n        msg = user_info.get_data().decode()\n        # Log the full stack trace, prepend a line with our message\n        logger.exception(msg)\n        raise Exception(msg)\n\n    return user_info\n\n# Always expect a json body\ndef request_json_required(request):\n    if not request.is_json:\n        bad_request_error(\"A JSON body and appropriate Content-Type header are required\")\n\n# We'll need to verify the requested index in URL is valid\ndef validate_index(index_without_prefix):\n    separator = ','\n    indices = get_filtered_indices()\n    if index_without_prefix not in indices:\n        bad_request_error(f\"Invalid index name. Use one of the following: {separator.join(indices)}\")\n\n# Determine the target real index in Elasticsearch bases on the request header and given index (without prefix)\n# The Authorization header with globus token is optional\n# Case #1: Authorization header is missing, default to use the `hm_public_<index_without_prefix>`. \n# Case #2: Authorization header with valid token, but the member doesn't belong to the HuBMAP-Read group, direct the call to `hm_public_<index_without_prefix>`. \n# Case #3: Authorization header presents but with invalid or expired token, return 401 (if someone is sending a token, they might be expecting more than public stuff).\n# Case #4: Authorization header presents with a valid token that has the group access, direct the call to `hm_consortium_<index_without_prefix>`. \ndef get_target_index(request, index_without_prefix):\n    # Case #1 and #2\n    target_index = app.config['PUBLIC_INDEX_PREFIX'] + index_without_prefix\n\n    # Keys in request.headers are case insensitive \n    if 'Authorization' in request.headers:\n        # user_info is a dict\n        user_info = get_user_info_for_access_check(request, True)\n\n        logger.info(\"======user_info======\")\n        logger.info(user_info)\n\n        # Case #3\n        if isinstance(user_info, Response):\n            # Notify the client with 401 error message\n            unauthorized_error(\"The globus token in the HTTP 'Authorization: Bearer <globus-token>' header is either invalid or expired.\")\n        # Otherwise, we check user_info['hmgroupids'] list\n        # Key 'hmgroupids' presents only when group_required is True\n        else:\n            # Case #4\n            if app.config['GLOBUS_HUBMAP_READ_GROUP_UUID'] in user_info['hmgroupids']:\n                target_index = app.config['PRIVATE_INDEX_PREFIX'] + index_without_prefix\n    \n    return target_index\n\n# Make a call to Elasticsearch\ndef execute_query(query_against, request, target_index, query=None):\n    supported_query_against = ['_search', '_count']\n    separator = ','\n\n    if query_against not in supported_query_against:\n        bad_request_error(f\"Query against '{query_against}' is not supported by Search API. Use one of the following: {separator.join(supported_query_against)}\")\n\n    target_url = app.config['ELASTICSEARCH_URL'] + '/' + target_index + '/' + query_against\n\n    if query is None:\n        # Parse incoming json string into json data(python dict object)\n        json_data = request.get_json()\n\n        # All we need to do is to simply pass the search json to elasticsearch\n        # The request json may contain \"access_group\" in this case\n        # Will also pass through the query string in URL\n        target_url = target_url + get_query_string(request.url)\n        # Make a request with json data\n        # The use of json parameter converts python dict to json string and adds content-type: application/json automatically\n    else:\n        json_data = query\n \n    resp = requests.post(url=target_url, json=json_data)\n\n    # Return the elasticsearch resulting json data as json string\n    return jsonify(resp.json())\n\n# Get a list of filtered Elasticsearch indices to expose to end users without the prefix\ndef get_filtered_indices():\n    # The final list of indices to return\n    indices = []\n\n    public_indices_without_prefix = []\n    private_indices_without_prefix = []\n\n    # Get a list of all indices and their aliases\n    target_url = app.config['ELASTICSEARCH_URL'] + '/_aliases'\n    resp = requests.get(url = target_url)\n    \n    # The JSON that contains all indices and aliases\n    indices_and_aliases_dict = resp.json()\n    \n    # Filter the final list\n    # Only return the indices based on prefix naming convention\n    for key in indices_and_aliases_dict:\n        if key.startswith(app.config['PUBLIC_INDEX_PREFIX']):\n            index_without_prefix = key[len(app.config['PUBLIC_INDEX_PREFIX']):]\n            public_indices_without_prefix.append(index_without_prefix)\n\n        if key.startswith(app.config['PRIVATE_INDEX_PREFIX']):\n            index_without_prefix = key[len(app.config['PRIVATE_INDEX_PREFIX']):]\n            private_indices_without_prefix.append(index_without_prefix)\n\n    # Ensure the index pair\n    # Basically get intersection of two lists\n    indices = list(set(public_indices_without_prefix) & set(private_indices_without_prefix))\n\n    return indices\n\n# Get the query string from orignal request\ndef get_query_string(url):\n    query_string = ''\n    parsed_url = urlparse(url)\n                \n    logger.debug(\"======parsed_url======\")\n    logger.debug(parsed_url)\n\n    # Add the ? at beginning of the query string if not empty\n    if not parsed_url.query:\n        query_string = '?' + parsed_url.query\n\n    return query_string\n\n\n# Get a list of entity uuids via entity-api for a given entity type:\n# Collection, Donor, Sample, Dataset, Submission. Case-insensitive.\ndef get_uuids_by_entity_type(entity_type, token):\n    entity_type = entity_type.lower()\n\n    request_headers = create_request_headers_for_auth(token)\n\n    # Use different entity-api endpoint for Collection\n    if entity_type == 'collection':\n        url = app.config['ENTITY_API_URL'] + \"/collections?property=uuid\"\n    else:\n        url = app.config['ENTITY_API_URL'] + \"/\" + entity_type + \"/entities?property=uuid\"\n\n    response = requests.get(url, headers = request_headers, verify = False)\n    \n    if response.status_code != 200:\n        internal_server_error(\"get_uuids_by_entity_type() failed to make a request to entity-api for entity type: \" + entity_type)\n    \n    uuids_list = response.json()\n    \n    return uuids_list\n\n# Create a dict with HTTP Authorization header with Bearer token\ndef create_request_headers_for_auth(token):\n    auth_header_name = 'Authorization'\n    auth_scheme = 'Bearer'\n\n    headers_dict = {\n        # Don't forget the space between scheme and the token value\n        auth_header_name: auth_scheme + ' ' + token\n    }\n\n    return headers_dict\n\ndef get_uuids_from_es(index):\n    uuids = []\n    size = 10_000\n    query = {\n        \"size\": size,\n        \"from\": len(uuids),\n        \"_source\": [\"_id\"],\n        \"query\": {\n            \"bool\": {\n                \"must\": [],\n                \"filter\": [\n                    {\n                        \"match_all\": {}\n                    }\n                ],\n                \"should\": [],\n                \"must_not\": []\n            }\n        }\n    }\n\n    end_of_list = False\n    while not end_of_list:\n        resp = execute_query('_search', None, index, query)\n\n        ret_obj = resp.get_json()\n        uuids.extend(hit['_id'] for hit in ret_obj.get('hits').get('hits'))\n\n        total = ret_obj.get('hits').get('total').get('value')\n        if total <= len(uuids):\n            end_of_list = True\n        else:\n            query['from'] = len(uuids)\n\n    return uuids\n\ndef init_indexer(token):\n    return Indexer(\n        app.config['INDICES'],\n        app.config['ORIGINAL_DOC_TYPE'],\n        app.config['PORTAL_DOC_TYPE'],\n        app.config['ELASTICSEARCH_URL'],\n        app.config['ENTITY_API_URL'],\n        app.config['APP_CLIENT_ID'],\n        app.config['APP_CLIENT_SECRET'],\n        token\n    )\n\n\ndef reindex_all_uuids(indexer, token):\n    with app.app_context():\n        try:\n            logger.info(\"############# Reindex Live Started #############\")\n\n            start = time.time()\n\n            # Make calls to entity-api to get a list of uuids for each entity type\n            donor_uuids_list = get_uuids_by_entity_type(\"donor\", token)\n            sample_uuids_list = get_uuids_by_entity_type(\"sample\", token)\n            dataset_uuids_list = get_uuids_by_entity_type(\"dataset\", token)\n            upload_uuids_list = get_uuids_by_entity_type(\"upload\", token)\n            collection_uuids_list = get_uuids_by_entity_type(\"collection\", token)\n\n            # Merge into a big list that with no duplicates\n            all_entities_uuids = set(donor_uuids_list + sample_uuids_list + dataset_uuids_list + upload_uuids_list + collection_uuids_list)\n\n            # 1. Remove entities that are not found in neo4j\n            es_uuids = []\n            for index in ast.literal_eval(app.config['INDICES']).keys():\n                es_uuids.extend(get_uuids_from_es(index))\n            es_uuids = set(es_uuids)\n\n            for uuid in es_uuids:\n                if uuid not in all_entities_uuids:\n                    logger.debug(f\"\"\"The uuid: {uuid} not in neo4j. Delete it from Elasticserach.\"\"\")\n                    indexer.delete(uuid)\n\n            # 2. Multi-thread index entitiies\n            with concurrent.futures.ThreadPoolExecutor() as executor:\n                results = [executor.submit(indexer.reindex, uuid) for uuid in donor_uuids_list]\n                for f in concurrent.futures.as_completed(results):\n                    logger.debug(f.result())\n\n            # 3. Reindex public collections separately\n            indexer.index_public_collections(reindex = True)\n\n            end = time.time()\n\n            logger.info(f\"############# Reindex Live Completed. Total time used: {end - start} seconds. #############\")\n        except Exception as e:\n            logger.error(e)\n\n\n# For local development/testing\nif __name__ == \"__main__\":\n    try:\n        app.run(host='0.0.0.0', port=\"5005\")\n    except Exception as e:\n        print(\"Error during starting debug server.\")\n        print(str(e))\n        logger.error(e, exc_info=True)\n        print(\"Error during startup check the log file for further information\")\n","sub_path":"src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":26438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"645201998","text":"# builtins\nimport logging\nimport os\nimport datetime\nimport sys\n# third party\nimport elasticsearch\nfrom elasticsearch.helpers import bulk\n\nELASTIC_HOST = os.environ.get(\"INPUT_ELASTIC_HOST\")\nELASTIC_API_KEY_ID = os.environ.get(\"INPUT_ELASTIC_API_KEY_ID\")\nELASTIC_API_KEY = os.environ.get(\"INPUT_ELASTIC_API_KEY\")\nELASTIC_INDEX = os.environ.get(\"INPUT_ELASTIC_INDEX\")\n\ntry:\n    assert ELASTIC_HOST not in (None, '')\nexcept:\n    output = \"The input ELASTIC_HOST is not set\"\n    print(f\"Error: {output}\")\n    sys.exit(-1)\n\ntry:\n    assert ELASTIC_API_KEY_ID not in (None, '')\nexcept:\n    output = \"The input ELASTIC_API_KEY_ID is not set\"\n    print(f\"Error: {output}\")\n    sys.exit(-1)\n\ntry:\n    assert ELASTIC_API_KEY not in (None, '')\nexcept:\n    output = \"The input ELASTIC_API_KEY is not set\"\n    print(f\"Error: {output}\")\n    sys.exit(-1)\n\ntry:\n    assert ELASTIC_INDEX not in (None, '')\n    now = datetime.datetime.now()\n    elastic_index = f\"{ELASTIC_INDEX}-{now.month}-{now.day}\"\nexcept:\n    output = \"The input ELASTIC_INDEX is not set\"\n    print(f\"Error: {output}\")\n    sys.exit(-1)\n\ntry:\n    es = elasticsearch.Elasticsearch(\n        [ELASTIC_HOST],\n        api_key=(ELASTIC_API_KEY_ID, ELASTIC_API_KEY),\n        scheme=\"https\"\n    )\nexcept elasticsearch.exceptions.AuthorizationException as exc:\n    output = \"Authentication to elastic failed\"\n    print(f\"Error: {output}\")\n    sys.exit(-1)\n\n\n","sub_path":"elastic_handler.py","file_name":"elastic_handler.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"130204645","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on 17 June 2021\r\n\r\nCreated by J Botha\r\n\r\nThis script attempts to anonymises the sr_hex.csv file\r\n\r\nlocation accuracy to within approximately 500m (Type Error with NaN records, replaced NaN values with Zeroes \r\nbut it seems to have messed up the data in those cols).\r\ntemporal accuracy to within 6 hours (still have to look at this)\r\nscrubs any columns which may contain personally identifiable information. (Done on cols [\"SubCouncil2016\", \"Wards2016\", \"OfficialSuburbs\", \"h3_level8_index\"])\r\n\r\nHow to use:\r\nModules Needed:\r\n-pip install pandas\r\n\r\nFiles needed:\r\nInput file: \"sr_hex_truncated.csv\" file that has been provided should be in the same directory.\r\nOutput file: \"anonymised_sr_hex_truncated.csv\" file gets generated by this application.\r\n\r\nRun: python further_data_transformations.py\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport datetime\r\nimport os\r\n\r\n\r\n# Tracking Time taken for application to run\r\napplication_start_time = datetime.datetime.now()\r\n\r\n# fuction scrubbing data cols that needs to be anonymised.\r\ndef clean_df(df, cols):\r\n\tfor col_name in cols:\r\n\t\tkeys = {cats: i for i, cats in enumerate(df[col_name].unique())}\r\n\t\tdf[col_name] = df[col_name].apply(lambda x: keys[x])\r\n\treturn df\r\n\t\r\n# function setting location accuracy to within approximately 500m\r\ndef location_df(df, l_cols):\r\n\tfor col_name in l_cols:\r\n\t\tkeys = {cats: i for i, cats in enumerate(df[col_name].unique())}\r\n\t\tdf[col_name] = df[col_name].apply(lambda x: keys[x]+0.00400)\t\t\t\r\n\t\t\t\t\r\n\treturn df\r\n\t\r\n# looping in current directory to find input file\r\nfor each_file in sorted(os.listdir('.')):\r\n\t#open input file provided\r\n\tif each_file.endswith(\"sr_hex_truncated.csv\"):\r\n\t\t\r\n\t\tdf = pd.read_csv(each_file)\r\n\t\t# replace NaN values by Zeroes\r\n\t\tdf = df.fillna(0)\r\n\t\t# Cols we scrubbing \r\n\t\tcols = [\"SubCouncil2016\", \"Wards2016\", \"OfficialSuburbs\", \"h3_level8_index\"]\t\t\r\n\t\tdf = clean_df(df, cols)\r\n\t\t# location accuracy to within approximately 500m\r\n\t\tl_cols = [\"Latitude\", \"Longitude\"]\r\n\t\tdf = location_df(df, l_cols)\r\n\t\t\r\n\t\t#Output file being created\r\n\t\tdf.to_csv(\"anonymised_sr_hex_truncated.csv\")\r\n\t\r\napplication_end_time = datetime.datetime.now()\r\napplication_time_taken = application_end_time - application_start_time\r\n\r\n# Process time stats\r\nprint(\"application_start_time = \", application_start_time)\r\nprint(\"application_end_time = \", application_end_time)\r\nprint(\"application_time_taken = \", application_time_taken)\r\n","sub_path":"further_data_transformations.py","file_name":"further_data_transformations.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"631382814","text":"from flask import request, jsonify\nfrom image_gallery_api import app, ma, db\nfrom image_gallery_api.models import Image\nfrom image_gallery_api.utils import get_filter_queries\n\n# Image Schema --> defines our output format for Marshmallow serialization\nclass ImageSchema(ma.Schema):\n  class Meta:\n    fields = ('id', 'img_id', 'width', 'height', 'url')\n\n# Init schema --> Have to use image schema and images schemas to deal with one or many images\nimage_schema = ImageSchema()\nimages_schema = ImageSchema(many=True)\n\n# Get All Images Paginated\n@app.route('/images', methods=['GET'])\ndef get_images():\n  page = request.args.get('page', 1, type=int)\n  images = Image.query.paginate(page=page)\n  result = images_schema.dump(images.items)\n  return jsonify(images=result, pages=images.pages)\n\n# Get Single Image\n@app.route('/images/<id>', methods=['GET'])\ndef get_image(id):\n  image = Image.query.get_or_404(id)\n  return image_schema.jsonify(image)\n\n# Get Images Filtered By Dimension\n@app.route('/images/filter', methods=['GET'])\ndef get_images_by_dimension():\n  queries = get_filter_queries(request.args)\n  page = request.args.get('page', 1, type=int)\n  images = Image.query.filter(*queries).paginate(page=page)\n  result = images_schema.dump(images.items)\n  return jsonify(images=result, pages=images.pages)\n\n# Create an Image --> prob not needed but wanted to display functionality\n@app.route('/images', methods=['POST'])\ndef add_image():\n  img_id = request.json['img_id']\n  width = request.json['width']\n  height = request.json['height']\n  url = request.json['url']\n\n  new_image = Image(img_id, width, height, url)\n\n  db.session.add(new_image)\n  db.session.commit()\n  \n  return image_schema.jsonify(new_image)\n\n# Update an Image --> prob not needed but wanted to display functionality\n@app.route('/images/<id>', methods=['PUT'])\ndef update_image(id):\n  image = Image.query.get_or_404(id)\n\n  img_id = request.json['img_id']\n  width = request.json['width']\n  height = request.json['height']\n  url = request.json['url']\n\n  image.img_id = img_id\n  image.width = width\n  image.height = height\n  image.uri = url\n\n  db.session.commit()\n  \n  return image_schema.jsonify(image)\n\n# Delete Image --> prob not needed but wanted to display functionality\n@app.route('/images/<id>', methods=['DELETE'])\ndef delete_image(id):\n  image = Image.query.get_or_404(id)\n  db.session.delete(image)\n  db.session.commit()\n\n  return image_schema.jsonify(image)\n\n# get width and height dimension options for filtering\n@app.route('/dimensions', methods=['GET'])\ndef get_dimension_options():\n  widths = [x.width for x in db.session.query(Image.width).distinct().order_by(Image.width.asc())]\n  heights = [x.height for x in db.session.query(Image.height).distinct().order_by(Image.height.asc())]\n\n  return jsonify(widths=widths, heights=heights)\n","sub_path":"image_gallery_api/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"625301292","text":"\"\"\"Use exact matching with a source list to find skills\"\"\"\n\nimport unicodecsv as csv\nimport logging\nfrom collections import Counter\nfrom smart_open import smart_open\nimport re\n\nimport nltk\ntry:\n    nltk.sent_tokenize('test')\nexcept LookupError:\n    nltk.download('punkt')\n\nfrom .base import CandidateSkill, ListBasedSkillExtractor\n\n\nclass ExactMatchSkillExtractor(ListBasedSkillExtractor):\n    \"\"\"Extract skills from unstructured text\n\n    Originally written by Kwame Porter Robinson\n    \"\"\"\n    name = 'exact'\n\n    def _skills_lookup(self):\n        \"\"\"Create skills lookup\n\n        Reads the object's filename containing skills into a lookup\n\n        Returns: (set) skill names\n        \"\"\"\n        logging.info('Creating skills lookup from %s', self.skill_lookup_path)\n        with smart_open(self.skill_lookup_path) as infile:\n            reader = csv.reader(infile, delimiter='\\t')\n            header = next(reader)\n            index = header.index(self.nlp.transforms[0])\n            generator = (row[index] for row in reader)\n            return set(generator)\n\n    def _document_skills_in_lookup(self, document, lookup):\n        \"\"\"Count skills in the document\n\n        Args:\n            lookup (object) A collection that can be queried for an individual skill,\n                implementing 'in' (i.e. 'skill in lookup')\n            document (string) A document for searching, such as a job posting\n\n        Returns: (collections.Counter) skills present in the lookup found in the document\n            All values set to 1 (multiple occurrences of a skill do not count)\n        \"\"\"\n        join_spaces = \" \".join  # for runtime efficiency\n        N = 5\n        doc = document.split()\n        doc_len = len(doc)\n        skills = Counter()\n\n        start_idx = 0\n\n        while start_idx < doc_len:\n            offset = 1\n\n            lookahead = min(N, doc_len - start_idx)\n            for idx in range(lookahead, 0, -1):\n                ngram = join_spaces(doc[start_idx:start_idx+idx])\n                if ngram in lookup:\n                    skills[ngram] = 1\n                    offset = idx\n                    break\n\n            start_idx += offset\n        return skills\n\n    def candidate_skills(self, job_posting):\n        document = job_posting.text\n        sentences = self.ie_preprocess(document)\n\n        for skill in self.lookup:\n            len_skill = len(skill.split())\n            for sent in sentences:\n                sent = sent.encode('utf-8')\n\n                # Exact matching for len(skill) == 1\n                if len_skill == 1:\n                    sent = sent.decode('utf-8')\n                    if re.search(r'\\b' + skill + r'\\b', sent, re.IGNORECASE):\n                        yield CandidateSkill(\n                            skill_name=skill,\n                            matched_skill=skill,\n                            confidence=100,\n                            context=sent\n                        )\n","sub_path":"skills_ml/algorithms/skill_extractors/exact_match.py","file_name":"exact_match.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"97370739","text":"import numpy as np\n\nfrom scipy.special import erf\nfrom scipy.interpolate import RectBivariateSpline\n\ndef render_psfs(psf_model, shifts, patch_shape, psf_grid, k):\n    \"\"\"\n    Make psfs under current model.\n    \"\"\"\n    interp_func = RectBivariateSpline(psf_grid[0], psf_grid[1], psf_model,\n                                      kx=k, ky=k)\n    psfs = np.zeros((shifts.shape[0], patch_shape[0] * patch_shape[1]))\n\n    # patch shape is odd, should be checked elsewhere...\n    dx = (patch_shape[0] - 1) / 2.\n    dy = (patch_shape[1] - 1) / 2.\n    x = np.linspace(-dx, dx, patch_shape[0])\n    y = np.linspace(-dy, dy, patch_shape[1])\n\n    # NOTE - transpose of interp evaluation.\n    for i in range(psfs.shape[0]):\n        psfs[i] = interp_func(x + shifts[i, 0], y + shifts[i, 1]).T.ravel()\n\n    return psfs\n","sub_path":"code/generation.py","file_name":"generation.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"540477229","text":"\"\"\"\nAPI classes for various services.\n\n\"\"\"\nfrom datetime import datetime\n\nimport requests\nfrom requests_oauthlib import OAuth1\nimport simplejson as json\n\nfrom humanapi import settings\n\n\ndef get_api_class_for_external_id(external_id):\n    \"\"\"\n    Currently there is only one API class, and that's MapMyFitness, but more can be added later.\n    \n    \"\"\"\n    if external_id == settings.MMF_EXTERNAL_ID:\n        return MapMyFitnessAPI\n    else:\n        return None\n\nclass MapMyFitnessAPI(object):\n    \"\"\"\n    Class which contains calls to retrieve workouts and anything else we add later.\n    \n    \"\"\"\n    def __init__(self, token_credentials):\n        self.client_key = unicode(settings.MMF_CLIENT_KEY)\n        self.client_secret = unicode(settings.MMF_CLIENT_SECRET)\n\n        credentials = token_credentials.split(':')\n        self.oauth_token = unicode(credentials[0])\n        self.oauth_token_secret = unicode(credentials[1])\n        \n    @staticmethod\n    def _is_ok(content):\n        ok = False\n        if 'result' in content and 'status' in content['result'] and \\\n           content['result']['status'] == 1:\n            ok = True\n        return ok\n\n    @staticmethod\n    def _datetime_from_date_and_time(date, time):\n        if not date or not time:\n            return None\n        \n        datetime_string = ' '.join([date, time])\n        try:\n            dt = datetime.strptime(datetime_string, '%Y-%m-%d %H:%M:%S')\n        except ValueError:\n            dt = None\n        return dt\n    \n    def _get_workouts(self):\n        \"\"\"\n        Get workouts for user.\n        \n        \"\"\"\n        workouts = [ ]\n        url = 'http://api.mapmyfitness.com/3.1/workouts/get_workouts'\n        oauth = OAuth1(self.client_key, self.client_secret, self.oauth_token,\n                       self.oauth_token_secret, signature_type='QUERY')\n        r = requests.get(url=url, auth=oauth)\n        content = json.loads(r.content)\n        if self._is_ok(content) and 'workouts' in content['result']['output']:\n            workouts = content['result']['output']['workouts']\n        return workouts\n    \n    def _transform_workout_to_activity(self, workout):\n        \"\"\"\n        Transforms workout format to activity format using the following rules.\n        \n        NOTE: I would make this much more robust by encoding the transformation into a mapping\n        if time permitted so that things were easier to modify with changes.\n\n          start_time       <- workout_date + workout_start_time (required)\n          end_time         <- workout_date + workout_end_time   (required)\n          source           <- 'mapmyrun'\n          level (int)      <- effort_level\n          type             <- workout_type_name\n          distance (float) <- distance\n          calories (int)   <- calories_burned\n          steps (int)      <- number_of_steps\n          timezone         <- None\n          external_id      <- workout_id\n        \n        \"\"\"\n        activity = { }\n        if 'workout_date' in workout and workout['workout_date']:\n            if 'workout_start_time' in workout and 'workout_end_time' in workout:\n                activity['start_time'] = self._datetime_from_date_and_time(\n                    workout['workout_date'],\n                    workout['workout_start_time']\n                )\n                activity['end_time'] = self._datetime_from_date_and_time(\n                    workout['workout_date'],\n                    workout['workout_start_time']\n                )\n                \n        if not activity['start_time'] or not activity['end_time']:\n            return None\n        \n        activity['source'] = 'mapmyrun'\n        activity['level'] = int(workout['effort_level'])\n        activity['type'] = workout['workout_type_name']\n        activity['distance'] = float(workout['distance'])\n        activity['calories'] = int(workout['calories_burned'])\n        activity['steps'] = int(workout['number_of_steps'])\n        activity['external_id'] = workout['workout_id']\n        return activity\n    \n    def get_activity_segments(self):\n        \"\"\"\n        Returns activity segments from the workouts retrieved from MapMyFitness.\n        \n        \"\"\"\n        activity_segments = [ ]\n        workouts = self._get_workouts()\n        for workout in workouts:\n            activity = self._transform_workout_to_activity(workout)\n            if activity:\n                activity_segments.append(activity)\n        return activity_segments\n    \n","sub_path":"humanapi/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":4448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"388592585","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\ndef graph_uvr(__u,__v,__r,time_interval):\r\n    plt.figure(figsize=(15,12))\r\n    plt.subplot(311)\r\n    x212 = np.arange(0,len(__u))\r\n    plt.plot(x212,__u,'g')\r\n    plt.xlabel('time in '+str(time_interval)+' seconds increment')\r\n    plt.ylabel('Surge Speed')\r\n    plt.title(\"Turning Circle Surge,Sway,Yaw Plot\")\r\n    #plt.axvline(x=100,color = 'r')\r\n      \r\n    plt.subplot(312)\r\n    x211 = np.arange(0,len(__v))\r\n    plt.plot(x211,__v,'m')\r\n    plt.xlabel('time in '+str(time_interval)+' seconds increment')\r\n    plt.ylabel('Sway Speed')\r\n    #plt.axvline(x=100,color = 'r')\r\n    \r\n    plt.subplot(313)\r\n    x313 = np.arange(0,len(__r))\r\n    plt.plot(x313,__r,'b')\r\n    plt.xlabel('time in '+str(time_interval)+' seconds increment')\r\n    plt.ylabel('yaw rate')\r\n    #plt.axvline(x=100,color = 'r')    \r\n    # plt.savefig('plot_1.jpg')\r\n    plt.show()\r\n    \r\n    \r\ndef graph_Uy(yaw_angle,__U,__rac_, time_interval):\r\n    plt.figure(figsize=(15,12))\r\n    plt.subplot(211)\r\n    x212 = np.arange(0,len(yaw_angle))\r\n    plt.plot(x212,yaw_angle,'g')\r\n    plt.plot(x212,__rac_,'m')\r\n    plt.xlabel('time in '+str(time_interval)+' seconds increment')\r\n    plt.ylabel('Yaw Angle')\r\n    plt.title('Heading angle (Ψ) vs Rudder Angle(𝛿)')\r\n    #plt.axvline(x=100,color = 'r')\r\n      \r\n       \r\n    plt.subplot(212)\r\n    x313 = np.arange(0,len(__U))\r\n    plt.plot(x313,__U,'b')\r\n    plt.xlabel('time in '+str(time_interval)+' seconds increment')\r\n    plt.ylabel('Total Speed')\r\n    #plt.axvline(x=100,color = 'r')\r\n    # plt.savefig('plot_2.jpg')    \r\n    plt.show()\r\n    \r\n    \r\ndef graph_delta_all(u,v,r,rac):\r\n    plt.figure(figsize=(15,7.5))\r\n    plt.plot(u,'g',label = 'surge')\r\n    plt.plot(v,'m', label='sway')\r\n    plt.plot(r,'b', label='yaw')\r\n    plt.plot(rac,'grey',label = 'rudder angle change')\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('delta value Δ')\r\n    plt.title('Δu, Δv, Δr,Δrac  plot')\r\n    plt.axhline(y = 0, color =\"red\", linestyle =\"--\")\r\n    plt.legend(loc = 'best')\r\n    plt.show()\r\n    \r\n\r\ndef plot_surge_components_jsr2009(P):\r\n        \r\n    plt.figure(figsize=(15,12))\r\n    plt.title(\"Surge Components plot\")\r\n    plt.subplot(311)\r\n    plt.plot(P[0],label = 'c1')\r\n    plt.plot(P[1],label = 'c2')\r\n    plt.plot(P[2],label = 'c3')\r\n    plt.plot(P[3],label = 'c4')\r\n    plt.plot(P[5],'y',label = 'c6')\r\n    plt.plot(P[6],label = 'c7')\r\n    plt.plot(P[8],'pink',label = 'c9')\r\n    plt.plot(P[9],label = 'c10')\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('Numerical Value of components except c5, c8 & cb')\r\n    plt.legend(loc = 'best')\r\n      \r\n    plt.subplot(312)\r\n    plt.plot(P[4],'g',label = 'c5')\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('Numerical Value c5')\r\n    plt.legend(loc = 'best')\r\n    \r\n    plt.subplot(313)\r\n    plt.plot(P[7],label = 'c8')\r\n    plt.plot(P[10],label = 'c bias')\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('Numerical Value of c8 & c bias')\r\n    plt.legend(loc = 'best')\r\n    \r\n\r\ndef plot_YN_components1_jsr2009(P):\r\n        \r\n    plt.figure(figsize=(15,12))\r\n   \r\n    plt.subplot(311)\r\n    plt.plot(P[2],label = 'c2')\r\n    plt.plot(P[6],label = 'c6')\r\n    plt.plot(P[9],label = 'c9')\r\n    plt.plot(P[10],label = 'c10')\r\n    plt.plot(P[12],label = 'c12')\r\n    plt.plot(P[14],label = 'c14')\r\n    plt.title(\"Sway Components Plot\")\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('Numerical Values')\r\n    plt.legend(loc = 'best')\r\n      \r\n    plt.subplot(312)\r\n    plt.plot(P[1],label = 'c1')\r\n    plt.plot(P[3],label = 'c3')\r\n    plt.plot(P[7],label = 'c7')\r\n    plt.plot(P[11],label = 'c11')\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('Numerical Values')\r\n    plt.legend(loc = 'best')\r\n       \r\n    plt.subplot(313)\r\n    plt.plot(P[5],label = 'c5')\r\n    plt.plot(P[8],'m',label = 'c8')\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('Numerical Values ')\r\n    plt.legend(loc = 'best')\r\n\r\ndef plot_YN_components2_jsr2009(P):\r\n        \r\n    plt.figure(figsize=(15,8))\r\n   \r\n    plt.subplot(211)\r\n    plt.plot(P[0],label = 'c bias')\r\n    plt.plot(P[13],label = 'c14')\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('Numerical Values ')\r\n    plt.legend(loc = 'best')\r\n    \r\n    plt.subplot(212)\r\n    plt.plot(P[4],'grey',label = 'c5')\r\n    plt.xlabel('time in 2 seconds increment')\r\n    plt.ylabel('Numerical Values ')\r\n    plt.legend(loc = 'best')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n    \r\n    ","sub_path":"code file 12/plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":4545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"148002941","text":"import binascii\nfrom concurrent.futures import ThreadPoolExecutor\nimport datetime\nfrom homeassistant import core\nfrom homeassistant.const import (\n\tCONF_PASSWORD,\n\tEVENT_HOMEASSISTANT_STOP,\n\tSTATE_ALARM_DISARMED,\n\tSTATE_ALARM_ARMED_AWAY,\n\tSTATE_ALARM_ARMED_NIGHT,\n\tSTATE_ALARM_ARMING,\n\tSTATE_ALARM_PENDING,\n\tSTATE_ALARM_TRIGGERED,\n\tSTATE_OFF,\n\tSTATE_ON,\n)\nfrom homeassistant.helpers.entity import Entity\nimport re\nimport sys\nimport threading\nimport time\nfrom typing import Any, Dict, List, Optional\nfrom .const import (\n\tCONF_DEVICES,\n\tCONF_NUMBER_OF_DEVICES,\n\tCONF_SERIAL_PORT,\n\tCONF_REQUIRE_CODE_TO_ARM,\n\tCONF_REQUIRE_CODE_TO_DISARM,\n\tDEFAULT_CONF_REQUIRE_CODE_TO_ARM,\n\tDEFAULT_CONF_REQUIRE_CODE_TO_DISARM,\n\tDEVICES,\n\tDEVICE_KEYPAD,\n\tDEVICE_SIREN,\n\tDEVICE_OTHER,\n\tDOMAIN,\n\tLOGGER,\n\tMAX_SECTIONS,\n)\nfrom .errors import (\n\tModelNotDetected,\n\tModelNotSupported,\n\tServiceUnavailable,\n\tShouldNotHappen,\n)\n\nMAX_WORKERS = 5\nTIMEOUT = 10\nPACKET_READ_SIZE = 64\n\n# x02 model\n# x08 hardware version\n# x09 firmware version\n# x0a registration code\n# x0b name of the installation\nJABLOTRON_PACKET_GET_MODEL = b\"\\x30\\x01\\x02\"\nJABLOTRON_PACKET_GET_INFO = b\"\\x30\\x01\\x02\\x30\\x01\\x08\\x30\\x01\\x09\"\nJABLOTRON_PACKET_GET_SECTIONS_STATES = b\"\\x80\\x01\\x01\\x52\\x01\\x0e\"\nJABLOTRON_PACKET_SECTIONS_STATES_PREFIX = b\"\\x51\\x22\"\nJABLOTRON_PACKET_DEVICES_STATES_PREFIX = b\"\\xd8\"\nJABLOTRON_PACKET_WIRED_DEVICE_STATE_PREFIX = b\"\\x55\\x08\"\nJABLOTRON_PACKET_WIRELESS_DEVICE_STATE_PREFIX = b\"\\x55\\x09\"\nJABLOTRON_PACKET_INFO_PREFIX = b\"\\x40\"\nJABLOTRON_PACKETS_DEVICE_ACTIVITY = [b\"\\x00\", b\"\\x01\", b\"\\x81\", b\"\\xa3\"]\nJABLOTRON_PACKETS_DEVICE_SABOTAGE = [b\"\\x05\", b\"\\x06\", b\"\\x86\", b\"\\xa8\"]\nJABLOTRON_PACKETS_DEVICE_FAULT = [b\"\\x07\", b\"\\x08\", b\"\\x88\", b\"\\xaa\"]\nJABLOTRON_INFO_MODEL = b\"\\x02\"\nJABLOTRON_INFO_HARDWARE_VERSION = b\"\\x08\"\nJABLOTRON_INFO_FIRMWARE_VERSION = b\"\\x09\"\nJABLOTRON_INFO_REGISTRATION_CODE = b\"\\x0a\"\nJABLOTRON_INFO_INSTALLATION_NAME = b\"\\x0b\"\n\nJABLOTRON_PRIMARY_STATE_DISARMED = 1\nJABLOTRON_PRIMARY_STATE_ARMED_PARTIALLY = 2\nJABLOTRON_PRIMARY_STATE_ARMED_FULL = 3\nJABLOTRON_PRIMARY_STATES = [\n\tJABLOTRON_PRIMARY_STATE_DISARMED,\n\tJABLOTRON_PRIMARY_STATE_ARMED_PARTIALLY,\n\tJABLOTRON_PRIMARY_STATE_ARMED_FULL,\n]\n\nJABLOTRON_SECONDARY_STATE_OK = 0\nJABLOTRON_SECONDARY_STATE_TRIGGERED = 1\nJABLOTRON_SECONDARY_STATE_PROBLEM = 2\nJABLOTRON_SECONDARY_STATE_PENDING = 4\nJABLOTRON_SECONDARY_STATE_ARMING = 8\nJABLOTRON_SECONDARY_STATES = [\n\tJABLOTRON_SECONDARY_STATE_OK,\n\tJABLOTRON_SECONDARY_STATE_TRIGGERED,\n\tJABLOTRON_SECONDARY_STATE_PROBLEM,\n\tJABLOTRON_SECONDARY_STATE_PENDING,\n\tJABLOTRON_SECONDARY_STATE_ARMING,\n]\n\nJABLOTRON_TERTIARY_STATE_OFF = 0\nJABLOTRON_TERTIARY_STATE_ON = 1\nJABLOTRON_TERTIARY_STATES = [\n\tJABLOTRON_TERTIARY_STATE_OFF,\n\tJABLOTRON_TERTIARY_STATE_ON,\n]\n\n\ndef decode_info_bytes(value: bytes) -> str:\n\tinfo = \"\"\n\n\tfor i in range(len(value)):\n\t\tletter = value[i:(i + 1)]\n\n\t\tif letter == b\"\\x00\" or letter == JABLOTRON_PACKET_INFO_PREFIX:\n\t\t\tbreak\n\n\t\tinfo += letter.decode()\n\n\treturn info\n\n\ndef check_serial_port(serial_port: str) -> None:\n\tstop_event = threading.Event()\n\tthread_pool_executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)\n\n\tdef reader_thread() -> Optional[str]:\n\t\tdetected_model = None\n\n\t\tstream = open(serial_port, \"rb\")\n\n\t\ttry:\n\t\t\twhile not stop_event.is_set():\n\t\t\t\tpacket = stream.read(PACKET_READ_SIZE)\n\t\t\t\tLOGGER.debug(\"Info packet: {}\".format(Jablotron.format_packet_to_string(packet)))\n\n\t\t\t\tif packet[:1] == JABLOTRON_PACKET_INFO_PREFIX and packet[2:3] == JABLOTRON_INFO_MODEL:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdetected_model = decode_info_bytes(packet[3:])\n\t\t\t\t\t\tbreak\n\t\t\t\t\texcept UnicodeDecodeError:\n\t\t\t\t\t\t# Try again\n\t\t\t\t\t\tpass\n\t\tfinally:\n\t\t\tstream.close()\n\n\t\treturn detected_model\n\n\tdef writer_thread() -> None:\n\t\twhile not stop_event.is_set():\n\t\t\tstream = open(serial_port, \"wb\")\n\n\t\t\tstream.write(JABLOTRON_PACKET_GET_MODEL)\n\t\t\ttime.sleep(0.1)\n\n\t\t\tstream.close()\n\n\t\t\ttime.sleep(1)\n\n\ttry:\n\t\treader = thread_pool_executor.submit(reader_thread)\n\t\tthread_pool_executor.submit(writer_thread)\n\n\t\tmodel = reader.result(TIMEOUT)\n\n\t\tif model is None:\n\t\t\traise ModelNotDetected\n\n\t\tif not re.match(r\"^JA-10[1367]\", model):\n\t\t\tLOGGER.debug(\"Unsupported model: {}\", model)\n\t\t\traise ModelNotSupported(\"Model {} not supported\".format(model))\n\n\texcept (IndexError, FileNotFoundError, IsADirectoryError, UnboundLocalError, OSError):\n\t\traise ServiceUnavailable\n\n\tfinally:\n\t\tstop_event.set()\n\t\tthread_pool_executor.shutdown()\n\n\nclass JablotronCentralUnit:\n\n\tdef __init__(self, serial_port: str, model: str, hardware_version: str, firmware_version: str):\n\t\tself.serial_port: str = serial_port\n\t\tself.model: str = model\n\t\tself.hardware_version: str = hardware_version\n\t\tself.firmware_version: str = firmware_version\n\n\nclass JablotronControl:\n\n\tdef __init__(self, central_unit: JablotronCentralUnit, name: str, id: str, friendly_name: Optional[str] = None):\n\t\tself.central_unit: JablotronCentralUnit = central_unit\n\t\tself.name: str = name\n\t\tself.id: str = id\n\t\tself.friendly_name: Optional[str] = friendly_name\n\n\nclass JablotronDevice(JablotronControl):\n\n\tdef __init__(self, central_unit: JablotronCentralUnit, name: str, id: str, type: str):\n\t\tself.type: str = type\n\n\t\tsuper().__init__(central_unit, name, id)\n\n\nclass JablotronAlarmControlPanel(JablotronControl):\n\n\tdef __init__(self, central_unit: JablotronCentralUnit, section: int, name: str, id: str):\n\t\tself.section: int = section\n\n\t\tsuper().__init__(central_unit, name, id)\n\n\nclass Jablotron:\n\n\tdef __init__(self, hass: core.HomeAssistant, config: Dict[str, Any], options: Dict[str, Any]) -> None:\n\t\tself._hass: core.HomeAssistant = hass\n\t\tself._config: Dict[str, Any] = config\n\t\tself._options: Dict[str, Any] = options\n\n\t\tself._central_unit: Optional[JablotronCentralUnit] = None\n\t\tself._alarm_control_panels: List[JablotronAlarmControlPanel] = []\n\t\tself._section_problem_sensors: List[JablotronControl] = []\n\t\tself._device_sensors: List[JablotronDevice] = []\n\t\tself._device_problem_sensors: List[JablotronControl] = []\n\n\t\tself._entities: Dict[str, JablotronEntity] = {}\n\n\t\tself._state_checker_thread_pool_executor: Optional[ThreadPoolExecutor] = None\n\t\tself._state_checker_stop_event: threading.Event = threading.Event()\n\t\tself._state_checker_data_updating_event: threading.Event = threading.Event()\n\n\t\tself.states: Dict[str, str] = {}\n\t\tself.last_update_success: bool = False\n\n\tdef update_options(self, options: Dict[str, Any]) -> None:\n\t\tself._options = options\n\t\tself._update_all_entities()\n\n\tdef is_code_required_for_disarm(self) -> bool:\n\t\treturn self._options.get(CONF_REQUIRE_CODE_TO_DISARM, DEFAULT_CONF_REQUIRE_CODE_TO_DISARM)\n\n\tdef is_code_required_for_arm(self) -> bool:\n\t\treturn self._options.get(CONF_REQUIRE_CODE_TO_ARM, DEFAULT_CONF_REQUIRE_CODE_TO_ARM)\n\n\tdef initialize(self) -> None:\n\t\tdef shutdown_event(_):\n\t\t\tself.shutdown()\n\n\t\tself._hass.bus.async_listen(EVENT_HOMEASSISTANT_STOP, shutdown_event)\n\n\t\tself._detect_central_unit()\n\t\tself._detect_sections()\n\t\tself._create_devices()\n\n\t\t# Initialize states checker\n\t\tself._state_checker_thread_pool_executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)\n\t\tself._state_checker_thread_pool_executor.submit(self._read_packets)\n\t\tself._state_checker_thread_pool_executor.submit(self._keepalive)\n\n\t\tself.last_update_success = True\n\n\tdef central_unit(self) -> JablotronCentralUnit:\n\t\treturn self._central_unit\n\n\tdef shutdown(self) -> None:\n\t\tself._state_checker_stop_event.set()\n\n\t\t# Send packet so read thread can finish\n\t\tself._send_packet(JABLOTRON_PACKET_GET_SECTIONS_STATES)\n\n\t\tif self._state_checker_thread_pool_executor is not None:\n\t\t\tself._state_checker_thread_pool_executor.shutdown()\n\n\tdef substribe_entity_for_updates(self, control_id: str, entity) -> None:\n\t\tself._entities[control_id] = entity\n\n\tdef modify_alarm_control_panel_section_state(self, section: int, state: str, code: Optional[str]) -> None:\n\t\tif code is None:\n\t\t\tcode = self._config[CONF_PASSWORD]\n\n\t\tint_packets = {\n\t\t\tSTATE_ALARM_DISARMED: 143,\n\t\t\tSTATE_ALARM_ARMED_AWAY: 159,\n\t\t\tSTATE_ALARM_ARMED_NIGHT: 175,\n\t\t}\n\n\t\tstate_packet = Jablotron._int_to_bytes(int_packets[state] + section)\n\n\t\tself._send_packet(Jablotron._create_code_packet(code) + b\"\\x80\\x02\\x0d\" + state_packet)\n\n\tdef alarm_control_panels(self) -> List[JablotronAlarmControlPanel]:\n\t\treturn self._alarm_control_panels\n\n\tdef section_problem_sensors(self) -> List[JablotronControl]:\n\t\treturn self._section_problem_sensors\n\n\tdef device_sensors(self) -> List[JablotronDevice]:\n\t\treturn self._device_sensors\n\n\tdef device_problem_sensors(self) -> List[JablotronControl]:\n\t\treturn self._device_problem_sensors\n\n\tdef _update_all_entities(self) -> None:\n\t\tfor entity in self._entities.values():\n\t\t\tentity.async_write_ha_state()\n\n\tdef _detect_central_unit(self) -> None:\n\t\tstop_event = threading.Event()\n\t\tthread_pool_executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)\n\n\t\tdef reader_thread() -> Optional[JablotronCentralUnit]:\n\t\t\tmodel = None\n\t\t\thardware_version = None\n\t\t\tfirmware_version = None\n\n\t\t\tstream = open(self._config[CONF_SERIAL_PORT], \"rb\")\n\n\t\t\ttry:\n\t\t\t\twhile not stop_event.is_set():\n\t\t\t\t\tpacket = stream.read(PACKET_READ_SIZE)\n\n\t\t\t\t\tif packet[:1] == JABLOTRON_PACKET_INFO_PREFIX:\n\t\t\t\t\t\tLOGGER.debug(\"Info packet: {}\".format(Jablotron.format_packet_to_string(packet)))\n\n\t\t\t\t\t\tinfo_packets = []\n\n\t\t\t\t\t\tfor i in range(len(packet)):\n\t\t\t\t\t\t\tprefix = packet[i:(i + 1)]\n\n\t\t\t\t\t\t\tif prefix == JABLOTRON_PACKET_INFO_PREFIX:\n\t\t\t\t\t\t\t\tinfo_packets.append(packet[i:])\n\n\t\t\t\t\t\tfor info_packet in info_packets:\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tif info_packet[2:3] == JABLOTRON_INFO_MODEL:\n\t\t\t\t\t\t\t\t\tmodel = decode_info_bytes(info_packet[3:])\n\t\t\t\t\t\t\t\telif info_packet[2:3] == JABLOTRON_INFO_HARDWARE_VERSION:\n\t\t\t\t\t\t\t\t\thardware_version = decode_info_bytes(info_packet[3:])\n\t\t\t\t\t\t\t\telif info_packet[2:3] == JABLOTRON_INFO_FIRMWARE_VERSION:\n\t\t\t\t\t\t\t\t\tfirmware_version = decode_info_bytes(info_packet[3:])\n\t\t\t\t\t\t\texcept UnicodeDecodeError:\n\t\t\t\t\t\t\t\t# Try again\n\t\t\t\t\t\t\t\tpass\n\n\t\t\t\t\tif model is not None and hardware_version is not None and firmware_version is not None:\n\t\t\t\t\t\tbreak\n\t\t\tfinally:\n\t\t\t\tstream.close()\n\n\t\t\tif model is None or hardware_version is None or firmware_version is None:\n\t\t\t\treturn None\n\n\t\t\treturn JablotronCentralUnit(self._config[CONF_SERIAL_PORT], model, hardware_version, firmware_version)\n\n\t\tdef writer_thread() -> None:\n\t\t\twhile not stop_event.is_set():\n\t\t\t\tself._send_packet(JABLOTRON_PACKET_GET_INFO)\n\t\t\t\ttime.sleep(1)\n\n\t\ttry:\n\t\t\treader = thread_pool_executor.submit(reader_thread)\n\t\t\tthread_pool_executor.submit(writer_thread)\n\n\t\t\tself._central_unit = reader.result(TIMEOUT)\n\n\t\texcept (IndexError, FileNotFoundError, IsADirectoryError, UnboundLocalError, OSError) as ex:\n\t\t\tLOGGER.error(format(ex))\n\t\t\traise ServiceUnavailable\n\n\t\tfinally:\n\t\t\tstop_event.set()\n\t\t\tthread_pool_executor.shutdown()\n\n\t\tif self._central_unit is None:\n\t\t\traise ShouldNotHappen\n\n\tdef _detect_sections(self) -> None:\n\t\tstop_event = threading.Event()\n\t\tthread_pool_executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)\n\n\t\tdef reader_thread() -> Optional[bytes]:\n\t\t\tstates_packet = None\n\n\t\t\tstream = open(self._config[CONF_SERIAL_PORT], \"rb\")\n\n\t\t\ttry:\n\t\t\t\twhile not stop_event.is_set():\n\t\t\t\t\tpacket = stream.read(PACKET_READ_SIZE)\n\n\t\t\t\t\tif packet[:2] == JABLOTRON_PACKET_SECTIONS_STATES_PREFIX:\n\t\t\t\t\t\tstates_packet = packet\n\t\t\t\t\t\tbreak\n\t\t\tfinally:\n\t\t\t\tstream.close()\n\n\t\t\treturn states_packet\n\n\t\tdef writer_thread() -> None:\n\t\t\twhile not stop_event.is_set():\n\t\t\t\tself._send_packet(JABLOTRON_PACKET_GET_SECTIONS_STATES)\n\t\t\t\ttime.sleep(1)\n\n\t\ttry:\n\t\t\treader = thread_pool_executor.submit(reader_thread)\n\t\t\tthread_pool_executor.submit(writer_thread)\n\n\t\t\tsections_states_packet = reader.result(TIMEOUT)\n\n\t\texcept (IndexError, FileNotFoundError, IsADirectoryError, UnboundLocalError, OSError) as ex:\n\t\t\tLOGGER.error(format(ex))\n\t\t\traise ServiceUnavailable\n\n\t\tfinally:\n\t\t\tstop_event.set()\n\t\t\tthread_pool_executor.shutdown()\n\n\t\tif sections_states_packet is None:\n\t\t\traise ShouldNotHappen\n\n\t\tsection_states = Jablotron._parse_sections_states_packet(sections_states_packet)\n\n\t\tfor section, section_packet in section_states.items():\n\t\t\tsection_alarm_id = Jablotron._create_section_alarm_id(section)\n\t\t\tsection_problem_sensor_id = Jablotron._create_section_problem_sensor_id(section)\n\n\t\t\tself._alarm_control_panels.append(JablotronAlarmControlPanel(\n\t\t\t\tself._central_unit,\n\t\t\t\tsection,\n\t\t\t\tself._create_section_name(section),\n\t\t\t\tsection_alarm_id,\n\t\t\t))\n\t\t\tself._section_problem_sensors.append(JablotronControl(\n\t\t\t\tself._central_unit,\n\t\t\t\tself._create_section_problem_sensor_name(section),\n\t\t\t\tsection_problem_sensor_id,\n\t\t\t))\n\n\t\t\tsection_state = Jablotron._parse_jablotron_alarm_state(section_packet)\n\n\t\t\tif not Jablotron._is_known_section_state(section_state):\n\t\t\t\tLOGGER.error(\"Unknown state packet for section {}: {}\".format(section, Jablotron.format_packet_to_string(sections_states_packet)))\n\n\t\t\tself.states[section_alarm_id] = Jablotron._convert_jablotron_alarm_state_to_alarm_state(section_state)\n\t\t\tself.states[section_problem_sensor_id] = Jablotron._convert_jablotron_alarm_state_to_problem_sensor_state(section_state)\n\n\tdef _create_devices(self) -> None:\n\t\tfor i in range(self._config[CONF_NUMBER_OF_DEVICES]):\n\t\t\tnumber = i + 1\n\n\t\t\tif self._is_device_ignored(number):\n\t\t\t\tcontinue\n\n\t\t\ttype = self._get_device_type(number)\n\n\t\t\tdevice_name = Jablotron._create_device_sensor_name(type, number)\n\t\t\tdevice_id = Jablotron._create_device_sensor_id(number)\n\t\t\tdevice_problem_sensor_id = Jablotron._create_device_problem_sensor_id(number)\n\n\t\t\tself._device_sensors.append(JablotronDevice(\n\t\t\t\tself._central_unit,\n\t\t\t\tdevice_name,\n\t\t\t\tdevice_id,\n\t\t\t\ttype,\n\t\t\t))\n\t\t\tself._device_problem_sensors.append(JablotronControl(\n\t\t\t\tself._central_unit,\n\t\t\t\tdevice_name,\n\t\t\t\tdevice_problem_sensor_id,\n\t\t\t\tJablotron._create_device_problem_sensor_name(type, number),\n\t\t\t))\n\n\t\t\tself.states[device_id] = STATE_OFF\n\t\t\tself.states[device_problem_sensor_id] = STATE_OFF\n\n\tdef _read_packets(self) -> None:\n\t\tstream = open(self._config[CONF_SERIAL_PORT], \"rb\")\n\t\tlast_restarted_at_hour = datetime.datetime.now().hour\n\n\t\twhile not self._state_checker_stop_event.is_set():\n\n\t\t\ttry:\n\n\t\t\t\twhile True:\n\n\t\t\t\t\tactual_hour = datetime.datetime.now().hour\n\t\t\t\t\tif last_restarted_at_hour != actual_hour:\n\t\t\t\t\t\tstream.close()\n\t\t\t\t\t\tstream = open(self._config[CONF_SERIAL_PORT], \"rb\")\n\t\t\t\t\t\tlast_restarted_at_hour = actual_hour\n\n\t\t\t\t\tself._state_checker_data_updating_event.clear()\n\n\t\t\t\t\tpacket = stream.read(PACKET_READ_SIZE)\n\t\t\t\t\t# LOGGER.debug(Jablotron.format_packet_to_string(packet))\n\n\t\t\t\t\tself._state_checker_data_updating_event.set()\n\n\t\t\t\t\tif not packet:\n\t\t\t\t\t\tself.last_update_success = False\n\t\t\t\t\t\tself._update_all_entities()\n\t\t\t\t\t\tbreak\n\n\t\t\t\t\tself.last_update_success = True\n\n\t\t\t\t\tprefix = packet[:2]\n\n\t\t\t\t\tif prefix == JABLOTRON_PACKET_SECTIONS_STATES_PREFIX:\n\t\t\t\t\t\tself._parse_section_states_packet(packet)\n\t\t\t\t\t\tbreak\n\n\t\t\t\t\tif Jablotron._is_device_state_packet(prefix):\n\t\t\t\t\t\tself._parse_device_state_packet(packet)\n\t\t\t\t\t\tbreak\n\n\t\t\t\t\tif packet[:1] == JABLOTRON_PACKET_DEVICES_STATES_PREFIX:\n\t\t\t\t\t\tself._parse_devices_states_packet(packet)\n\t\t\t\t\t\tbreak\n\n\t\t\texcept Exception as ex:\n\t\t\t\tLOGGER.error(\"Read error: {}\".format(format(ex)))\n\t\t\t\tself.last_update_success = False\n\t\t\t\tself._update_all_entities()\n\n\t\t\ttime.sleep(0.5)\n\n\t\tstream.close()\n\n\tdef _keepalive(self):\n\t\tcounter = 0\n\t\twhile not self._state_checker_stop_event.is_set():\n\t\t\tif not self._state_checker_data_updating_event.wait(0.5):\n\t\t\t\ttry:\n\t\t\t\t\tif counter == 0 and not self._is_alarm_active():\n\t\t\t\t\t\tself._send_packet(Jablotron._create_code_packet(self._config[CONF_PASSWORD]) + b\"\\x52\\x02\\x13\\x05\\x9a\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tself._send_packet(b\"\\x52\\x01\\x02\")\n\t\t\t\texcept Exception as ex:\n\t\t\t\t\tLOGGER.error(\"Write error: {}\".format(format(ex)))\n\n\t\t\t\tcounter += 1\n\t\t\telse:\n\t\t\t\ttime.sleep(1)\n\n\t\t\tif counter == 60:\n\t\t\t\tcounter = 0\n\n\tdef _send_packet(self, packet) -> None:\n\t\tstream = open(self._config[CONF_SERIAL_PORT], \"wb\")\n\n\t\tstream.write(packet)\n\t\ttime.sleep(0.1)\n\n\t\tstream.close()\n\n\tdef _update_state(self, id: str, state: str) -> None:\n\t\tif id in self.states and state == self.states[id]:\n\t\t\treturn\n\n\t\tself.states[id] = state\n\n\t\tif id in self._entities:\n\t\t\tself._entities[id].async_write_ha_state()\n\n\tdef _is_alarm_active(self) -> bool:\n\t\tfor alarm_control_panel in self._alarm_control_panels:\n\t\t\tsection_alarm_id = Jablotron._create_section_alarm_id(alarm_control_panel.section)\n\n\t\t\tif (\n\t\t\t\tself.states[section_alarm_id] == STATE_ALARM_TRIGGERED\n\t\t\t\tor self.states[section_alarm_id] == STATE_ALARM_PENDING\n\t\t\t):\n\t\t\t\treturn True\n\n\t\treturn False\n\n\tdef _get_device_type(self, number: int) -> str:\n\t\treturn self._config[CONF_DEVICES][number - 1]\n\n\tdef _is_device_ignored(self, number: int) -> bool:\n\t\ttype = self._get_device_type(number)\n\n\t\treturn (\n\t\t\ttype == DEVICE_KEYPAD\n\t\t\tor type == DEVICE_SIREN\n\t\t\tor type == DEVICE_OTHER\n\t\t)\n\n\tdef _parse_section_states_packet(self, packet: bytes) -> None:\n\t\tsection_states = Jablotron._parse_sections_states_packet(packet)\n\n\t\tfor section, section_packet in section_states.items():\n\t\t\tsection_state = Jablotron._parse_jablotron_alarm_state(section_packet)\n\n\t\t\tif not Jablotron._is_known_section_state(section_state):\n\t\t\t\tLOGGER.error(\"Unknown state packet for section {}: {}\".format(section, Jablotron.format_packet_to_string(packet)))\n\n\t\t\tself._update_state(\n\t\t\t\tJablotron._create_section_alarm_id(section),\n\t\t\t\tJablotron._convert_jablotron_alarm_state_to_alarm_state(section_state),\n\t\t\t)\n\n\t\t\tif (\n\t\t\t\tsection_state[\"secondary\"] == JABLOTRON_SECONDARY_STATE_OK\n\t\t\t\tor section_state[\"secondary\"] == JABLOTRON_SECONDARY_STATE_PROBLEM\n\t\t\t):\n\t\t\t\tself._update_state(\n\t\t\t\t\tJablotron._create_section_problem_sensor_id(section),\n\t\t\t\t\tJablotron._convert_jablotron_alarm_state_to_problem_sensor_state(section_state),\n\t\t\t\t)\n\n\tdef _parse_device_state_packet(self, packet: bytes) -> None:\n\t\tdevice_number = Jablotron._parse_device_number_from_state_packet(packet)\n\n\t\tif device_number == 0:\n\t\t\tLOGGER.debug(\"State packet of central unit: {}\".format(Jablotron.format_packet_to_string(packet)))\n\t\t\treturn\n\n\t\tif device_number > self._config[CONF_NUMBER_OF_DEVICES]:\n\t\t\tLOGGER.debug(\"State packet of unknown device: {}\".format(Jablotron.format_packet_to_string(packet)))\n\t\t\treturn\n\n\t\tif self._is_device_ignored(device_number):\n\t\t\tLOGGER.debug(\"State packet of {}: {}\".format(DEVICES[self._get_device_type(device_number)].lower(), Jablotron.format_packet_to_string(packet)))\n\t\t\treturn\n\n\t\tdevice_state = Jablotron._convert_jablotron_device_state_to_state(packet, device_number)\n\n\t\tif device_state is None:\n\t\t\tLOGGER.error(\"Unknown state packet of device {}: {}\".format(device_number, Jablotron.format_packet_to_string(packet)))\n\t\t\treturn\n\n\t\tif Jablotron._is_device_state_packet_for_activity(packet):\n\t\t\tself._update_state(\n\t\t\t\tJablotron._create_device_sensor_id(device_number),\n\t\t\t\tdevice_state,\n\t\t\t)\n\t\telif (\n\t\t\tJablotron._is_device_state_packet_for_sabotage(packet)\n\t\t\tor Jablotron._is_device_state_packet_for_fault(packet)\n\t\t):\n\t\t\tself._update_state(\n\t\t\t\tJablotron._create_device_problem_sensor_id(device_number),\n\t\t\t\tdevice_state,\n\t\t\t)\n\t\telse:\n\t\t\tLOGGER.error(\"Unknown state packet of device {}: {}\".format(device_number, Jablotron.format_packet_to_string(packet)))\n\n\tdef _parse_devices_states_packet(self, packet: bytes) -> None:\n\t\tstates_start_packet = 3\n\t\ttriggered_device_start_packet = states_start_packet + Jablotron._bytes_to_int(packet[1:2]) - 1\n\n\t\tstates = Jablotron._hex_to_bin(packet[states_start_packet:triggered_device_start_packet])\n\n\t\tif Jablotron._is_device_state_packet(packet[triggered_device_start_packet:(triggered_device_start_packet + 2)]):\n\t\t\tself._parse_device_state_packet(packet[triggered_device_start_packet:])\n\n\t\tfor i in range(1, self._config[CONF_NUMBER_OF_DEVICES] + 1):\n\t\t\tdevice_state = STATE_ON if states[i:(i + 1)] == \"1\" else STATE_OFF\n\t\t\tself._update_state(\n\t\t\t\tJablotron._create_device_sensor_id(i),\n\t\t\t\tdevice_state,\n\t\t\t)\n\n\t@staticmethod\n\tdef _create_code_packet(code: str) -> bytes:\n\t\tcode_packet = b\"\\x80\\x08\\x03\\x39\\x39\\x39\"\n\n\t\tfor i in range(0, 4):\n\t\t\tj = i + 4\n\n\t\t\tfirst_number = code[j:(j + 1)]\n\t\t\tsecond_number = code[i:(i + 1)]\n\n\t\t\tif first_number == \"\":\n\t\t\t\tcode_number = 48 + int(second_number)\n\t\t\telse:\n\t\t\t\tcode_number = int(f\"{first_number}{second_number}\", 16)\n\n\t\t\tcode_packet += Jablotron._int_to_bytes(code_number)\n\n\t\treturn code_packet\n\n\t@staticmethod\n\tdef _is_device_state_packet(prefix) -> bool:\n\t\treturn prefix == JABLOTRON_PACKET_WIRED_DEVICE_STATE_PREFIX or prefix == JABLOTRON_PACKET_WIRELESS_DEVICE_STATE_PREFIX\n\n\t@staticmethod\n\tdef _is_device_state_packet_for_activity(packet: bytes) -> bool:\n\t\treturn packet[2:3] in JABLOTRON_PACKETS_DEVICE_ACTIVITY\n\n\t@staticmethod\n\tdef _is_device_state_packet_for_sabotage(packet: bytes) -> bool:\n\t\treturn packet[2:3] in JABLOTRON_PACKETS_DEVICE_SABOTAGE\n\n\t@staticmethod\n\tdef _is_device_state_packet_for_fault(packet: bytes) -> bool:\n\t\treturn packet[2:3] in JABLOTRON_PACKETS_DEVICE_FAULT\n\n\t@staticmethod\n\tdef _parse_sections_states_packet(packet: bytes) -> Dict[int, bytes]:\n\t\tsection_states = {}\n\n\t\tfor section in range(1, MAX_SECTIONS + 1):\n\t\t\tstate_offset = section * 2\n\t\t\tstate = packet[state_offset:(state_offset + 2)]\n\n\t\t\t# Unused section\n\t\t\tif state == b\"\\x07\\x00\":\n\t\t\t\tbreak\n\n\t\t\tsection_states[section] = state\n\n\t\treturn section_states\n\n\t@staticmethod\n\tdef _parse_device_number_from_state_packet(packet: bytes) -> int:\n\t\treturn int(Jablotron._bytes_to_int(packet[4:6]) / 64)\n\n\t@staticmethod\n\tdef _convert_jablotron_device_state_to_state(packet: bytes, device_number: int) -> Optional[str]:\n\t\tstate = Jablotron._bytes_to_int(packet[3:4])\n\n\t\tif device_number <= 32:\n\t\t\thigh_device_number_offset = 0\n\t\telif device_number <= 96:\n\t\t\thigh_device_number_offset = -64\n\t\telse:\n\t\t\thigh_device_number_offset = -128\n\n\t\tdevice_states_offset = ((device_number + high_device_number_offset) * 4) + 104\n\n\t\ton_state = device_states_offset\n\t\ton_state_2 = device_states_offset + 1\n\t\toff_state = device_states_offset + 2\n\n\t\tif state == off_state:\n\t\t\treturn STATE_OFF\n\n\t\tif state == on_state or state == on_state_2:\n\t\t\treturn STATE_ON\n\n\t\treturn None\n\n\t@staticmethod\n\tdef _int_to_bytes(number: int) -> bytes:\n\t\treturn int.to_bytes(number, 1, byteorder=sys.byteorder)\n\n\t@staticmethod\n\tdef _bytes_to_int(packet: bytes) -> int:\n\t\treturn int.from_bytes(packet, byteorder=sys.byteorder)\n\n\t@staticmethod\n\tdef _hex_to_bin(hex):\n\t\tdec = Jablotron._bytes_to_int(hex)\n\t\tbin_dec = bin(dec)\n\t\tbin_string = bin_dec[2:]\n\t\tbin_string = bin_string.zfill(len(hex) * 8)\n\t\treturn bin_string[::-1]\n\n\t@staticmethod\n\tdef _create_section_name(section: int) -> str:\n\t\treturn \"Section {}\".format(section)\n\n\t@staticmethod\n\tdef _create_section_alarm_id(section: int) -> str:\n\t\treturn \"section_{}\".format(section)\n\n\t@staticmethod\n\tdef _create_section_problem_sensor_id(section: int) -> str:\n\t\treturn \"section_problem_sensor_{}\".format(section)\n\n\t@staticmethod\n\tdef _create_section_problem_sensor_name(section: int) -> str:\n\t\treturn \"Problem of section {}\".format(section)\n\n\t@staticmethod\n\tdef _create_device_sensor_name(type: str, number: int) -> str:\n\t\treturn \"{} (device {})\".format(DEVICES[type], number)\n\n\t@staticmethod\n\tdef _create_device_problem_sensor_name(type: str, number: int) -> str:\n\t\treturn \"Problem of {} (device {})\".format(DEVICES[type].lower(), number)\n\n\t@staticmethod\n\tdef _create_device_sensor_id(number: int) -> str:\n\t\treturn \"device_sensor_{}\".format(number)\n\n\t@staticmethod\n\tdef _create_device_problem_sensor_id(number: int) -> str:\n\t\treturn \"device_problem_sensor_{}\".format(number)\n\n\t@staticmethod\n\tdef _is_known_section_state(state: Dict[str, int]) -> bool:\n\t\treturn (\n\t\t\tstate[\"primary\"] in JABLOTRON_PRIMARY_STATES\n\t\t\tand state[\"secondary\"] in JABLOTRON_SECONDARY_STATES\n\t\t\tand state[\"tertiary\"] in JABLOTRON_TERTIARY_STATES\n\t\t)\n\n\t@staticmethod\n\tdef _convert_jablotron_alarm_state_to_alarm_state(state: Dict[str, int]) -> str:\n\t\tif state[\"secondary\"] == JABLOTRON_SECONDARY_STATE_ARMING:\n\t\t\treturn STATE_ALARM_ARMING\n\n\t\tif state[\"secondary\"] == JABLOTRON_SECONDARY_STATE_PENDING:\n\t\t\treturn STATE_ALARM_PENDING\n\n\t\tif state[\"secondary\"] == JABLOTRON_SECONDARY_STATE_TRIGGERED:\n\t\t\treturn STATE_ALARM_TRIGGERED\n\n\t\tif state[\"primary\"] == JABLOTRON_PRIMARY_STATE_ARMED_FULL:\n\t\t\tif state[\"tertiary\"] == JABLOTRON_TERTIARY_STATE_ON:\n\t\t\t\treturn STATE_ALARM_TRIGGERED\n\t\t\telse:\n\t\t\t\treturn STATE_ALARM_ARMED_AWAY\n\n\t\tif state[\"primary\"] == JABLOTRON_PRIMARY_STATE_ARMED_PARTIALLY:\n\t\t\treturn STATE_ALARM_ARMED_NIGHT\n\n\t\treturn STATE_ALARM_DISARMED\n\n\t@staticmethod\n\tdef _convert_jablotron_alarm_state_to_problem_sensor_state(state: Dict[str, int]) -> str:\n\t\treturn STATE_ON if state[\"secondary\"] == JABLOTRON_SECONDARY_STATE_PROBLEM else STATE_OFF\n\n\t@staticmethod\n\tdef _parse_jablotron_alarm_state(packet: bytes) -> Dict[str, int]:\n\t\tfirst_packet = packet[0:1]\n\n\t\t# Strange packet - converted to something that makes more sense\n\t\tif first_packet == \"\\x1b\":\n\t\t\tfirst_packet = \"\\x13\"\n\n\t\tnumber = Jablotron._bytes_to_int(first_packet)\n\n\t\tprimary_state = number % 16\n\t\tsecondary_state = int((number - primary_state) / 16)\n\n\t\treturn {\n\t\t\t\"primary\": primary_state,\n\t\t\t\"secondary\": secondary_state,\n\t\t\t\"tertiary\": Jablotron._bytes_to_int(packet[1:2]),\n\t\t}\n\n\t@staticmethod\n\tdef format_packet_to_string(packet: bytes) -> str:\n\t\treturn str(binascii.hexlify(packet), \"utf-8\")\n\n\nclass JablotronEntity(Entity):\n\t_state: str\n\n\tdef __init__(\n\t\t\tself,\n\t\t\tjablotron: Jablotron,\n\t\t\tcontrol: JablotronControl,\n\t) -> None:\n\t\tself._jablotron: Jablotron = jablotron\n\t\tself._control: JablotronControl = control\n\n\t@property\n\tdef should_poll(self) -> bool:\n\t\treturn False\n\n\t@property\n\tdef available(self) -> bool:\n\t\treturn self._jablotron.last_update_success\n\n\tdef _device_id(self) -> Optional[str]:\n\t\treturn None\n\n\t@property\n\tdef device_info(self) -> Optional[Dict[str, str]]:\n\t\tdevice_id = self._device_id()\n\n\t\tif device_id is None:\n\t\t\treturn None\n\n\t\treturn {\n\t\t\t\"identifiers\": {(DOMAIN, device_id)},\n\t\t\t\"name\": self._device_id(),\n\t\t\t\"via_device\": (DOMAIN, self._control.central_unit.serial_port),\n\t\t}\n\n\t@property\n\tdef name(self) -> str:\n\t\tif self._control.friendly_name is not None:\n\t\t\treturn self._control.friendly_name\n\n\t\treturn self._control.name\n\n\t@property\n\tdef unique_id(self) -> str:\n\t\treturn \"{}.{}.{}\".format(DOMAIN, self._control.central_unit.serial_port, self._control.id)\n\n\t@property\n\tdef state(self) -> str:\n\t\treturn self._jablotron.states[self._control.id]\n\n\tasync def async_added_to_hass(self) -> None:\n\t\tself._jablotron.substribe_entity_for_updates(self._control.id, self)\n\n\tdef update_state(self, state: str) -> None:\n\t\tself._jablotron.states[self._control.id] = state\n\t\tself.async_write_ha_state()\n","sub_path":"custom_components/jablotron100/jablotron.py","file_name":"jablotron.py","file_ext":"py","file_size_in_byte":26529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"619774092","text":"\n# coding: utf-8\n\n# In[9]:\n\n\nimport pandas as pd\nimport numpy as np\nimport glob\nimport matplotlib.pyplot as plt\n\n\n# In[10]:\n\n\ndf_exsisting = pd.read_csv(r\"X:\\hiwi\\ElHachem\\Jochen\\Reutlingen_Radolan\\dataframe_as_HDF5_Reutlingen_Stations\\data_df_with_zero_and_nan_values.csv\",\n                          sep=';', index_col=0, engine='c', infer_datetime_format=True, parse_dates=True)\n\n\n# In[18]:\n\n\nstart_time_ix = df_exsisting.index[0]\nend_time_ix = '2019-12-31 00:00:00'\n\ndate_range = pd.date_range(start=start_time_ix, end=end_time_ix, freq='Min')\n\ndata = np.zeros(shape=(date_range.shape[0], 12))\ndata[data == 0] = np.nan\n\nfinal_df_combined = pd.DataFrame(data=data, index=date_range,\n                                 columns=[1, 2, 3, 4, 5, 6,\n                                          7, 8, 9, 10, 11, 12])\n\n\n# In[19]:\n\n\n# append existing data to new dataframe\nfinal_df_combined.loc[df_exsisting.index, :] = df_exsisting.values\n\n\n# In[16]:\n\n\n# get all new data from directory\nall_files = glob.glob(r'X:\\hiwi\\ElHachem\\Jochen\\Reutlingen_Radolan\\seperate_data\\from_042019_to_102019\\*')\nassert len(all_files) > 0, 'directory seems empty'\n# all_files[0]\n\n\n# In[20]:\n\n\nfor i, df_file in enumerate(all_files):\n    stn_id = i +1\n    print(stn_id)\n    try:\n        df = pd.read_csv(df_file, sep=';', encoding='latin-1', skiprows=1, header=None)\n        # create date time index\n        time_ix_str = [ix0.replace('.', '-') + ' ' + ix1\n                       for ix0, ix1 in zip(df[0], df[1])]\n\n        time_ix_time_obj = pd.DatetimeIndex(time_ix_str)\n        \n        # get ppt data\n        ppt_data_arr = [np.float(ppt.replace(',', '.')) if type(ppt)==str else ppt\n                       for ppt in df[2]]\n        \n        # create df\n        df_ppt = pd.DataFrame(index=time_ix_time_obj, data=ppt_data_arr)\n        \n        final_df_combined.loc[df_ppt.index, stn_id] = df_ppt.values.ravel()\n        \n        # save df\n        #df_ppt.to_csv()\n    except Exception as msg:\n        print(msg, stn_id, df_file)\n        continue\n    \n\n\n# In[21]:\n\n\nfinal_df_combined.tail(2)\n\n\n# In[22]:\n\n\nfinal_df_combined.to_csv(r\"X:\\hiwi\\ElHachem\\Jochen\\Reutlingen_Radolan\\dataframe_as_HDF5_Reutlingen_Stations\\data_df_2020.csv\",\n                          sep=';')\n\n","sub_path":"_05_make_reutlingen_data_for_df.py","file_name":"_05_make_reutlingen_data_for_df.py","file_ext":"py","file_size_in_byte":2243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"138584526","text":"# encoding: utf-8\n'''\n@author: developer\n@software: python\n@file: my_cars1.py\n@time: 2019/12/22 23:21\n@desc:\n'''\n\nimport Day01_15.code.Day08.car\nmy_beetle = Day01_15.code.Day08.car.Car('volkswagen', 'beetle', 2016)\nprint(my_beetle.get_descriptive_name())\nmy_tesla = Day01_15.code.Day08.car.ElectricCar('tesla', 'roadster', 2016)\nprint(my_tesla.get_descriptive_name())\n'''\n2016 Volkswagen Beetle\n2016 Tesla Roadster\n'''","sub_path":"Day01_15/code/Day08/my_cars1.py","file_name":"my_cars1.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"282944899","text":"import unittest\nimport logging\nimport json\nfrom claircli.docker_image import Image\nfrom claircli.docker_registry import LocalRegistry, RemoteRegistry\nfrom claircli.clair import Clair\nfrom claircli.cli import ClairCli\nfrom claircli.report import Report\nfrom mock import patch\nfrom os.path import isdir, isfile\nfrom requests import get as req_get\nimport os\nimport responses\nfrom argparse import Namespace\nimport shutil\nfrom six.moves.urllib.parse import urlencode, quote\nfrom collections import defaultdict\n\nlogger = logging.getLogger(__name__)\n\n\nclass ClairCmdTestBase(unittest.TestCase):\n\n    def setUp(self):\n        self.name = 'registry.example.com/org/image-name:version'\n        self.registry = 'registry.example.com'\n        self.repository = 'org/image-name'\n        self.tag = 'version'\n        self.unauth_headers = {\n            'WWW-Authenticate': 'Bearer realm=\"https://{registry}/v2/token\",service=\"{registry}\"'.format(registry=self.registry)}\n        self.clair_url = 'http://mock_clair:6060'\n        self.registry_url = 'https://registry.example.com/v2/'\n        token_url_params = {'service': self.registry,\n                            'client_id': 'claircli',\n                            'scope': 'repository:{}:pull'.format(self.repository)}\n        self.registry_token_url = 'https://registry.example.com/v2/token?' + urlencode(token_url_params)\n        self.image_manifest_url = 'https://registry.example.com/v2/org/image-name/manifests/version'\n        responses.add(responses.GET, self.registry_url,\n                      json={'message': 'authentication required'}, status=401, headers=self.unauth_headers)\n        responses.add(responses.GET, self.registry_token_url, json = {'token': 'test-token'}, status = 200)\n        with open('tests/test_data/manifest.v2.json') as f:\n            self.manifest=json.load(f)\n        responses.add(responses.GET, self.image_manifest_url,\n                      json = self.manifest, status = 200)\n        self.v1_analyze_url='%s/v1/layers' % self.clair_url\n        self.layers=[e['digest'] for e in self.manifest['layers']]\n        responses.add(responses.DELETE, '%s/%s' %\n                      (self.v1_analyze_url, self.layers[0]))\n        responses.add(responses.POST, self.v1_analyze_url)\n        with open('tests/test_data/origin_vulnerabilities.json') as f:\n            self.origin_data=json.load(f)\n        responses.add(responses.GET, '%s/%s?features&vulnerabilities' %\n                      (self.v1_analyze_url, self.layers[-1]), json = self.origin_data)\n        _, self.html=Report.get_report_path(self.name, '.html')\n\n    def tearDown(self):\n        RemoteRegistry.tokens = defaultdict(dict)\n        if isfile(self.html):\n            os.remove(self.html)\n\n    def assert_called_with_url(self):\n        self.assertEqual(responses.calls[0].request.url, self.registry_url)\n        self.assertEqual(\n            responses.calls[1].request.url, self.registry_token_url)\n        self.assertEqual(\n            responses.calls[2].request.url, self.image_manifest_url)\n\n\ndef mock_docker_client(mock_docker):\n    mock_client = mock_docker.return_value\n    mock_image = mock_client.images.get.return_value\n    mock_image.save.return_value = open('tests/test_data/manifest.tar', 'r+b')\n    return mock_docker\n\n\nclass TestImage(ClairCmdTestBase):\n\n    def test_parse_image(self):\n        with open('tests/test_data/images.json') as f:\n            images = json.load(f)\n        for i in images:\n            image = Image(i['name'])\n            self.assertEqual(str(image), i['name'])\n            self.assertEqual(image.repository, i['repository'])\n            self.assertEqual(image.tag, i['tag'])\n            self.assertEqual(str(image.registry), i['registry'])\n\n    @responses.activate\n    def test_manifest(self):\n        image = Image(self.name)\n        self.assertEqual(image.manifest, self.manifest)\n        self.assert_called_with_url()\n\n\n    @patch('docker.from_env')\n    def test_manifest_local(self, mock_docker):\n        mock_docker_client(mock_docker)\n        registry = LocalRegistry('localhost')\n        image = Image(self.name, registry)\n        with open('tests/test_data/manifest.json') as file_:\n            manifest = json.load(file_)\n        self.assertEqual(image.manifest, manifest)\n\n\n    @patch('docker.from_env')\n    def test_layers_local(self, mock_docker):\n        mock_docker_client(mock_docker)\n        registry = LocalRegistry('localhost')\n        image = Image(self.name, registry)\n        with open('tests/test_data/manifest.json') as file_:\n            manifest = json.load(file_)\n        self.assertEqual(image.layers, [e.replace(\n            '/layer.tar', '') for e in manifest[0]['Layers']])\n\n    @responses.activate\n    def test_layers_v1(self):\n        with open('tests/test_data/manifest.v1.json') as f:\n            manifest = json.load(f)\n        responses.replace(responses.GET, self.image_manifest_url,\n                          json=manifest, status=200)\n        image = Image(self.name)\n        self.assertEqual(image.layers, [e['blobSum']\n                                        for e in manifest['fsLayers']][::-1])\n        self.assert_called_with_url()\n\n    @responses.activate\n    def test_layers_v2(self):\n        image = Image(self.name)\n        self.assertEqual(image.layers,\n                         [e['digest'] for e in self.manifest['layers']])\n        self.assert_called_with_url()\n\n\nclass TestClair(ClairCmdTestBase):\n\n    @responses.activate\n    def test_analyze_remote_image(self):\n        clair = Clair(self.clair_url)\n        image = Image(self.name)\n        layers = clair.analyze_image(image)\n        self.assertEqual(layers, self.layers)\n        self.assert_called_with_url()\n        for index, layer in enumerate(self.layers, start=4):\n            self.assertEqual(\n                responses.calls[index].request.url, self.v1_analyze_url)\n            req_body = json.loads(responses.calls[index].request.body)\n            self.assertEqual(req_body['Layer']['Name'], layer)\n            self.assertEqual(req_body['Layer']['Path'], image.registry.get_blobs_url(image, layer))\n\n    @patch('docker.from_env')\n    @responses.activate\n    def test_analyze_local_image(self, mock_docker):\n        mock_docker_client(mock_docker)\n        clair = Clair(self.clair_url)\n        registry = LocalRegistry('localhost')\n        image = Image(self.name, registry)\n        responses.add(responses.DELETE, '%s/%s' %\n                      (self.v1_analyze_url, image.layers[0]))\n        layers = clair.analyze_image(image)\n        self.assertEqual(layers, image.layers)\n        for index, layer in enumerate(layers, start=1):\n            self.assertEqual(\n                responses.calls[index].request.url, self.v1_analyze_url)\n            req_body = json.loads(responses.calls[index].request.body)\n            self.assertEqual(req_body['Layer']['Name'], layer)\n            self.assertEqual(req_body['Layer']['Path'], image.registry.get_blobs_url(image, layer))\n\nclass TestClairCli(ClairCmdTestBase):\n\n    def test_read_white_list(self):\n        white_list = ClairCli.read_white_list('tests/test_data/example-whitelist.yaml')\n        # self.assertEqual(white_list.get('common'), {'CVE-2017-6055': 'XML', 'CVE-2017-5586': 'OpenText'})\n        self.assertEqual(white_list.get('alpine'), {'CVE-2017-6055': 'XML', 'CVE-2017-5586': 'OpenText', 'CVE-2017-3261': 'SE'})\n        self.assertEqual(white_list.get('ubuntu'), {'CVE-2017-6055': 'XML', 'CVE-2017-5586': 'OpenText', 'CVE-2017-5230': 'XSX'})\n\n    @responses.activate\n    def test_batch_analyze_remote_images(self):\n        with patch('sys.argv', ['claircli.py', 'batch-analyze', '-c', self.clair_url, self.name]):\n            cli = ClairCli()\n            cli.run()\n        self.assert_called_with_url()\n        for index, layer in enumerate(self.layers, start=4):\n            self.assertEqual(\n                responses.calls[index].request.url, self.v1_analyze_url)\n            req_body = json.loads(responses.calls[index].request.body)\n            self.assertEqual(req_body['Layer']['Name'], layer)\n        self.assertTrue(isfile(self.html))\n\n    @patch('docker.from_env')\n    @responses.activate\n    def test_batch_analyze_local_images(self, mock_docker):\n        mock_docker_client(mock_docker)\n        with open('tests/test_data/manifest.json') as file_:\n            manifest = json.load(file_)\n        layers = [e.replace('/layer.tar', '') for e in manifest[0]['Layers']]\n        responses.add(responses.DELETE, '%s/%s' % (self.v1_analyze_url, layers[0]))\n        responses.add(responses.GET, '%s/%s?features&vulnerabilities' %\n                      (self.v1_analyze_url, layers[-1]), json=self.origin_data)\n        with patch('sys.argv', ['claircli.py', 'batch-analyze', '-l', 'localhost', '-c', self.clair_url, self.name]):\n            cli = ClairCli()\n            cli.run()\n        for index, layer in enumerate(layers, start=1):\n            self.assertEqual(\n                responses.calls[index].request.url, self.v1_analyze_url)\n            req_body = json.loads(responses.calls[index].request.body)\n            self.assertEqual(req_body['Layer']['Name'], layer)\n        self.assertTrue(isfile(self.html))\n","sub_path":"tests/test_claircli.py","file_name":"test_claircli.py","file_ext":"py","file_size_in_byte":9175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"309153740","text":"\"\"\"\nMethods to load cifar-10 data set. From cs231n.\n\"\"\"\n\nimport os\nimport pickle\n\nimport numpy as np\n\n\ndef load_cifar10_batch(path):\n    \"\"\"\n    load a batch of cifar-10 data.\n    \"\"\"\n    with open(path, 'rb') as f:\n        datadict = pickle.load(f)\n        x = datadict[b'data']\n        y = datadict[b'labels']\n        x = x.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype(\"float\")\n        y = np.array(y)\n        return x, y\n\n\ndef load_cifar10_all(root, num_data):\n    \"\"\"\n    load multiple batches of cifar-10 data. There are total 5 data batches.\n    root: root directory of cifar-10 data.\n    num_data: maximum data numbers we need.\n    \"\"\"\n    xs = []\n    ys = []\n    num_loaded_data = 0\n\n    for b in range(1, 6):\n        f = os.path.join(root, 'data_batch_%d' % (b, ))\n        x, y = load_cifar10_batch(f)\n        xs.append(x)\n        ys.append(y)\n\n        num_loaded_data += x.shape[0]\n\n        if num_loaded_data > num_data:\n            break\n\n    x_train = np.concatenate(xs)\n    y_train = np.concatenate(ys)\n\n    del x, y\n\n    x_test, y_test = load_cifar10_batch(os.path.join(root, 'test_batch'))\n\n    return x_train, y_train, x_test, y_test\n\n\ndef load_cifar10(root, normalize=True, num_training=49000, num_validation=1000,\n                 num_test=1000):\n    \"\"\"\n    load cifar-10 data with options.\n    root: root directory of cifar-10 data.\n    normalize: normalize entire data set if True.\n    num_training: how many data in training set to return.\n    num_validation: how many data in validation set to return.\n    num_test: how many data in test set to return.\n    \"\"\"\n    cifar10_training = 50000\n    cifar10_test = 10000\n\n    num_test = max(0, min(cifar10_test, num_test))\n    num_training = max(0, num_training)\n    num_validation = max(0, num_validation)\n\n    if num_training + num_validation > cifar10_training:\n        num_training = cifar10_training * num_training / \\\n            (num_training + num_validation)\n        num_training = min(cifar10_training, num_training)\n        num_validation = cifar10_training - num_validation\n\n    x_train, y_train, x_test, y_test = \\\n        load_cifar10_all(root, num_training + num_validation)\n\n    mask = range(num_training, num_training + num_validation)\n    x_val = x_train[mask]\n    y_val = y_train[mask]\n\n    mask = range(num_training)\n    x_train = x_train[mask]\n    y_train = y_train[mask]\n\n    mask = range(num_test)\n    x_test = x_test[mask]\n    y_test = y_test[mask]\n\n    # Substract mean image.\n    mean_image = np.mean(x_train, axis=0)\n    x_train -= mean_image\n    x_val -= mean_image\n    x_test -= mean_image\n\n    # Transpose so that channels come first.\n    x_train = x_train.transpose(0, 3, 1, 2).copy()\n    x_val = x_val.transpose(0, 3, 1, 2).copy()\n    x_test = x_test.transpose(0, 3, 1, 2).copy()\n\n    # Package data into a dictionary\n    return {\n        'x_train': x_train, 'y_train': y_train,\n        'x_val': x_val, 'y_val': y_val,\n        'x_test': x_test, 'y_test': y_test,\n    }\n","sub_path":"util/cifar10.py","file_name":"cifar10.py","file_ext":"py","file_size_in_byte":2979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"453079385","text":"# Copyright 2017 The Bazel Authors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Supporting functions for Clang libraries.\"\"\"\n\nload(\n    \"@build_bazel_rules_apple//apple/bundling:platform_support.bzl\",\n    \"platform_support\",\n)\n\ndef _should_package_clang_runtime(ctx):\n    \"\"\"Returns whether the Clang runtime should be bundled.\"\"\"\n\n    # List of crosstool sanitizer features that require packaging some clang\n    # runtime libraries.\n    features_requiring_clang_runtime = {\n        \"asan\": True,\n        \"tsan\": True,\n        \"ubsan\": True,\n    }\n\n    for feature in ctx.features:\n        if feature in features_requiring_clang_runtime:\n            return True\n    return False\n\ndef _register_runtime_lib_actions(ctx, binary_artifact, output_zip):\n    \"\"\"Creates an archive with Clang runtime libraries.\n\n    Args:\n      ctx: The Skylark context.\n      binary_artifact: The bundle binary to be processed with clang's runtime\n          tool.\n      output_zip: A `File` object representing the ZIP output file where to\n          package the runtime libraries.\n    \"\"\"\n    platform_support.xcode_env_action(\n        ctx,\n        inputs = [binary_artifact],\n        outputs = [output_zip],\n        executable = ctx.executable._clangrttool,\n        arguments = [\n            binary_artifact.path,\n            output_zip.path,\n        ],\n        mnemonic = \"ClangRuntimeLibsCopy\",\n        # This action needs to read the contents of the Xcode bundle.\n        no_sandbox = True,\n    )\n\nclang_support = struct(\n    register_runtime_lib_actions = _register_runtime_lib_actions,\n    should_package_clang_runtime = _should_package_clang_runtime,\n)\n","sub_path":"apple/bundling/clang_support.bzl","file_name":"clang_support.bzl","file_ext":"bzl","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"248856816","text":"import requests\nimport html\nimport time\nfrom selenium import webdriver\nimport random\nimport urllib3\nimport csv\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'\n}\n\ndata = []\nproxy_list = []\nproxy_flag = 0\nproxy_dict = {\n    \"https\": '',\n    \"http\": ''\n}\n\n\ndef load_proxies():\n    print(\"Loading the proxies...\")\n    proxy_url = \"https://free-proxy-list.net/\"\n    driver = webdriver.PhantomJS()\n    driver.set_window_size(1120, 550)\n    driver.get(proxy_url)\n    driver.find_element_by_xpath(\"/html/body/section[1]/div/div[2]/div/div[1]/div[2]/div/label/input\").send_keys(\n        \"India\")\n    driver.find_element_by_xpath(\"/html/body/section[1]/div/div[2]/div/div[2]/div/table/thead/tr/th[5]\").click()\n    scrapper_count = 0\n    while True:\n        scrapper_count += 1\n        try:\n            ip_xpath = \"/html/body/section[1]/div/div[2]/div/div[2]/div/table/tbody/tr[\" + str(\n                scrapper_count) + \"]/td[1]\"\n            port_xpath = \"/html/body/section[1]/div/div[2]/div/div[2]/div/table/tbody/tr[\" + str(\n                scrapper_count) + \"]/td[2]\"\n            country_code_xpath = \"/html/body/section[1]/div/div[2]/div/div[2]/div/table/tbody/tr[\" + str(\n                scrapper_count) + \"]/td[3]\"\n            proxy_type_xpath = \"/html/body/section[1]/div/div[2]/div/div[2]/div/table/tbody/tr[\" + str(\n                scrapper_count) + \"]/td[5]\"\n            ip = driver.find_element_by_xpath(ip_xpath).text\n            port = driver.find_element_by_xpath(port_xpath).text\n            country_code = driver.find_element_by_xpath(country_code_xpath).text\n            proxy_type = driver.find_element_by_xpath(proxy_type_xpath).text\n            if proxy_type == \"elite proxy\" and country_code == \"IN\":\n                proxy_list.append([ip, port, scrapper_count-1])\n        except Exception:\n            break\n\n\ndef choose_proxies():\n    if len(proxy_list) == 0:\n        load_proxies()\n    proxy = random.choice(proxy_list)\n    final_proxy = proxy[0] + \":\" + proxy[1]\n    print(\"Current proxy is {}\".format(final_proxy))\n    return final_proxy\n\n\ndef load_news(news_id):\n    global proxy_flag, proxy_dict\n    if proxy_flag == 0:\n        proxy = choose_proxies()\n        proxy_dict['https'] = proxy\n        proxy_dict['http'] = proxy\n        proxy_flag = 1\n    payload = {\n        'category': '',\n        'news_offset': news_id\n    }\n    post_url = \"https://inshorts.com/en/ajax/more_news\"\n    while True:\n        try:\n            news = requests.post(post_url, payload, proxies=proxy_dict).json()\n            break\n        except (urllib3.exceptions.MaxRetryError, ConnectionRefusedError, requests.exceptions.ProxyError,\n                urllib3.exceptions.NewConnectionError, requests.exceptions.SSLError):\n            print(\"Updating the proxy...\")\n            ip = str(proxy_dict['https']).split(':')[0]\n            for i in range(len(proxy_list)):\n                if proxy_list[i][0] == ip:\n                    proxy_list.pop(i)\n                    print(\"Proxy list updated...\")\n                    break\n            proxy = choose_proxies()\n            proxy_dict['https'] = proxy\n            proxy_dict['http'] = proxy\n    return news\n\n\ndef content_sorter(html_data, news_id, code, latest_heading=''):\n    html_data = html_data[html_data.find('<div class=\"news-card z-depth-1\"') + 10:]\n\n    while True:\n        headline = html.unescape(html_data[html_data.find('itemprop=\"headline\"') + len('itemprop=\"headline\"') + 1:\n                             html_data.find(\"</span>\",\n                                            html_data.find('itemprop=\"headline\"') + len('itemprop=\"headline\"'))])\n        article_body = html.unescape(html_data[html_data.find('itemprop=\"articleBody\">') + len('itemprop=\"articleBody\">'):\n                                 html_data.find('</div>', html_data.find('itemprop=\"articleBody\">')\n                                                + len('itemprop=\"articleBody\">'))])\n        date = html.unescape(html_data[html_data.find('<span class=\"date\">') + len('<span class=\"date\">'):\n                         html_data.find('</span>', html_data.find('<span class=\"date\">') + len('<span class=\"date\">'))])\n        source = html.unescape(html_data[html_data.find('href=\"', html_data.find('<div class=\"read-more\">')) + len('href=\"'):\n                           html_data.find('\">', html_data.find('href=\"', html_data.find('<div class=\"read-more\">'))\n                                          + len('href=\"'))])\n        if code == 0:\n            with open('news.csv', 'a+') as write_file:\n                content_to_write = [[news_id, date, headline, article_body, source]]\n                writer = csv.writer(write_file)\n                writer.writerows(content_to_write)\n            if html_data.find('<div class=\"news-card z-depth-1\"') == -1:\n                break\n        elif code == 1:\n            data.append([news_id, date, headline, article_body, source])\n            with open('latest_news.csv', 'w+') as write_latest_file:\n                writer = csv.writer(write_latest_file)\n                writer.writerows([[news_id, date, headline, article_body, source]])\n            if headline == latest_heading:\n                with open('news.csv', 'w') as write_file:\n                    writer = csv.writer(write_file)\n                    writer.writerows(data)\n                    write_file.write(existing_news_data)\n                break\n            if html_data.find('<div class=\"news-card z-depth-1\"') == -1:\n                with open('news.csv', 'w') as write_file:\n                    writer = csv.writer(write_file)\n                    writer.writerows(data)\n                    write_file.write(\"\\n\".join(existing_news_data))\n                break\n        else:\n            date = html.unescape(date)\n            headline = html.unescape(headline)\n            article_body = html.unescape(article_body)\n            source = html.unescape(source)\n            data.append([news_id, date, headline, article_body, source])\n            if html_data.find('<div class=\"news-card z-depth-1\"') == -1:\n                break\n\n        html_data = html_data[html_data.find('<div class=\"news-card z-depth-1\"') + 10:]\n\n\ndef get_latest_news():\n    latest_heading = existing_news_data[0].split(',')[2]\n    url = \"https://inshorts.com/en/read\"\n    session = requests.Session()\n    result = session.get(url)\n    html_code = result.text\n    min_news_id = html_code[\n                  html_code.find(\"min_news_id\") + 15: html_code.find(\"\\\"\", html_code.find(\"min_news_id\") + 16)]\n    if html_code.__contains__(latest_heading):\n        content_sorter(html_code, min_news_id, 1, latest_heading)\n    else:\n        while True:\n            time.sleep(5)\n            more_news = load_news(min_news_id)\n            min_news_id = more_news[\"min_news_id\"]\n            html_news = more_news[\"html\"]\n            if html_news.__contains__(latest_heading):\n                print(\"Heading matched...\")\n                content_sorter(html_news, min_news_id, 1, latest_heading)\n                break\n            else:\n                print(\"Heading did not matched...Fetching more news...\")\n                content_sorter(html_news, min_news_id, 2)\n\n\ntry:\n    with open('news.csv', 'r') as file:\n        existing_news_data = file.read()\n        existing_news_data = existing_news_data.split('\\n')\n        min_news_id = existing_news_data[-2]\n        min_news_id = min_news_id.split(',')[0]\n    get_latest_news()\n\nexcept FileNotFoundError:\n    url = \"https://inshorts.com/en/read\"\n    session = requests.Session()\n    result = session.get(url)\n    html_code = result.text\n    min_news_id = html_code[\n                  html_code.find(\"min_news_id\") + 15: html_code.find(\"\\\"\", html_code.find(\"min_news_id\") + 16)]\n\ncount = 1\nwhile count <= 10:\n    print(\"Iteration number {}...\".format(count))\n    json_news = load_news(min_news_id)\n    html_ = json_news[\"html\"]\n    min_news_id = json_news[\"min_news_id\"]\n    content_sorter(html_, min_news_id, 0)\n    time.sleep(5)  # So that there are not too many consecutive request and the server does not crash! :)\n    count += 1\n\n# TODO: Modify the news.csv file as it fetches the data. Because the code is still not full proof and can not afford\n#  to put such much traffic\n# TODO: Do not change the proxy if it works\n","sub_path":"news_scrapper.py","file_name":"news_scrapper.py","file_ext":"py","file_size_in_byte":8370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"486815309","text":"from control.matlab import *\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport sympy as sp\nimport scipy\nimport function as func\n\nw = 1.5  # setting for step size\nY, X = np.mgrid[-w:w:100j, -w:w:100j]\n\nA = np.array([[0, 1], [-4, 5]])\ns, v = np.linalg.eig(A)  # eigen vector v and eigen value s for matrix A\nprint(s)\n\n# calculate components of \\dot{x} by using \\dot{x} = Ax\nU = A[0, 0] * X + A[0, 1] * Y\nV = A[1, 0] * X + A[1, 1] * Y\n\nt = np.arange(-1.5, 1.5, 0.01)\n\nfig, ax = plt.subplots()\n\n# plot invariant set only when eigen value of A is a real number\nif s.imag[0] == 0 and s.imag[1] == 0:\n    ax.plot(t, (v[1, 0] / v[0, 0]) * t, ls='-')\n    ax.plot(t, (v[1, 1] / v[0, 1]) * t, ls='-')\n\n# plot phase plane diagram for x\nax.streamplot(X, Y, U, V, density=0.7, color='k')\nfunc.plot_set(ax, '$x_1$', '$x_2$')\nplt.show()\n","sub_path":"第４章/4.3_practice_p117.py","file_name":"4.3_practice_p117.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"84181151","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nfrom matplotlib.pyplot import MultipleLocator\nimport time\nimport re\nimport sys\nfrom math import log\n\nEs = [1,5,20]\ncolors = ['blue', 'green', 'orange']\nlog_dir = '../models/'\ndataset = sys.argv[1]\n\nif __name__ == \"__main__\":\n    plt.figure()\n    cnt = 0\n    for E in Es:\n        with open('{}{}_trace_{}.cfg.log'.format(log_dir,dataset,E), 'r') as f:\n            x = []\n            y = []\n            \n            current_round = 0\n            hour = 0\n            suc = 0\n\n            for line in f:\n                if 'Round' in line:\n                    current_round = int(line.split()[9])\n                if 'test_loss' in line:\n                    floats = re.findall(r'\\d+\\.\\d*e*-*\\d*',line)\n                    test_loss = float(floats[0])\n                    x.append(current_round)\n                    y.append(test_loss)\n        x = np.array(x)\n        y = np.array(y)\n        plt.plot(x,y,color=colors[cnt],linewidth=0.75)\n        cnt+=1\n    cnt = 0\n    for E in Es:\n        with open('{}{}_no_trace_{}.cfg.log'.format(log_dir,dataset,E), 'r') as f:\n            x = []\n            y = []\n            \n            current_round = 0\n            hour = 0\n            suc = 0\n\n            for line in f:\n                if 'Round' in line:\n                    current_round = int(line.split()[9])\n                if 'test_loss' in line:\n                    floats = re.findall(r'\\d+\\.\\d*e*-*\\d*',line)\n                    test_loss = float(floats[0])\n                    x.append(current_round)\n                    y.append(test_loss)\n        x = np.array(x)\n        y = np.array(y)\n        plt.plot(x,y,color=colors[cnt],linestyle=':')\n        cnt+=1\n    \n    # plt.grid(axis='x',color='grey',ls='--')\n    # x_major_locator=MultipleLocator(24)\n    # ax=plt.gca()\n    # ax为两条坐标轴的实例\n    # ax.xaxis.set_major_locator(x_major_locator)\n    \n    font = {\n            'weight' : 'normal',\n            'size'   : 15,\n            }\n    plt.xlabel('round num',font)\n    plt.ylabel('test loss',font)\n    plt.legend([\"E = 1, with trace\", \n                \"E = 5, with trace\", \n                \"E = 20, with trace\",\n                \"E = 1, without trace\", \n                \"E = 5, without trace\", \n                \"E = 20, without trace\"])\n    plt.savefig('{}_loss_by_round.png'.format(dataset))","sub_path":"plt/loss_by_round.py","file_name":"loss_by_round.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"43941316","text":"from django.contrib import admin\nfrom webapp.models import *\n\n#denne klasse bestemmer hvilke felter der skal vises i admin panelet\nclass ProductAdmin(admin.ModelAdmin):\n    list_display = ('id',\n    \t\t\t\t'kids', \n\t\t\t\t\t'name',\n\t\t\t\t\t'sizes',\n\t\t\t\t\t'kid_adult',\n\t\t\t\t\t'free_porto',\n\t\t\t\t\t'price',\n\t\t\t\t\t'package',\n\t\t\t\t\t'url',\n\t\t\t\t\t'price_old',\n\t\t\t\t\t'img_url',\n\t\t\t\t\t'productID',\n\t\t\t\t\t'women'\n\t\t\t\t\t)\n    search_field = ()\n\nadmin.site.register(Product, ProductAdmin)","sub_path":"webapp/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"130799474","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 18 11:31:04 2020\n\n@author: pc\n\"\"\"\n\n# This script is used to read the stata informaion of all running result\n\nimport numpy as np\nimport support_based as spb\n\nsta = np.load(spb.mo_path + 'stat_res/sta_res.npy')\n\n# sum result of the same configuration\ncof_li = []\nsta_li = []\nfor i in range(sta.shape[0]):\n    index = -1\n    for j in range(len(cof_li)):\n        if sta[i, 0] == cof_li[j][0] and sta[i, 1] == cof_li[j][1] and sta[i, 3] == cof_li[j][2]  and sta[i, 4] == cof_li[j][3] and sta[i, 5] == cof_li[j][4]:\n            index = j\n    if i == 0 or index == -1:\n        cof_li.append(sta[i, [0, 1, 3,  4, 5]])\n        sta_li.append([sta[i, 6:9]])\n    else:\n        sta_li[index].append(sta[i, 6:9])\n\n# sum\ncof_arr = np.stack(cof_li, 0)\nsta_arr = []\nfor sa in sta_li:\n    if len(sa) != 1:\n        sta_arr.append(np.mean(np.stack(sa, 0).astype(np.float64), 0))\n    else:\n        sta_arr.append(sa[0].astype(np.float64))\n\nsta_arr = np.stack(sta_arr, 0)\n            \n        \n        \n\n","sub_path":"2D/py_script/AA01_03_read_sta_res.py","file_name":"AA01_03_read_sta_res.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"636368989","text":"from pulp import *\nimport time\nimport random\n\n# CODE USED TO DO THE TOY PROBLEM\n\n# Getting CBC solver\ncbcSolver = getSolver('PULP_CBC_CMD',msg=False)\n\n# Getting GUROBI solver (you have to install and have a license)\n# https://www.gurobi.com/documentation/7.0/quickstart_linux/software_installation_guid.html\n# Download Gurobi on: https://www.gurobi.com/downloads/gurobi-software/\n# download the readme, to follow instruction on how to install\n# Get a license: https://www.gurobi.com/academia/academic-program-and-licenses/\n# Use the license with 'grbgetkey'\n# Run /opt/gurobi912/linux64/ sudo python3 setup.py install\n# export GUROBI_HOME=\"/opt/gurobi912/linux64\"\n# export PATH=\"${PATH}:${GUROBI_HOME}/bin\"\n# export LD_LIBRARY_PATH=\"${LD_LIBRARY_PATH}:${GUROBI_HOME}/lib\"\ngurobiSolver = getSolver('GUROBI')\n\n# The number n of agents and tasks\nn = 5\n\n# Setting the resolve solver\nresolveSolver = cbcSolver\nsolverName = 'CBC'\n\nagentsSatisfaction = []\n\nfor i in range(n):\n  randomList = []\n  for j in range(n):\n    randomList.append(random.randint(0,10))\n  agentsSatisfaction.append(randomList)\n\nfor i in range(n):\n  print(f\"\\nExperiencias Agente {i}:\")\n  for j in range(n):\n    print(f\"Tarefa {j}: {agentsSatisfaction[i][j]}\")\n\n# Returns the satisfactionValue of an agent execunting a task\ndef satisfactionValue(agent,task):\n  return agentsSatisfaction[agent][task]\n\n# The optimization function (Maximize)\nfuncOptimization = LpMaximize\n\n# Defines the problem to resolve\nproblem = LpProblem(\"O_problema_de_designacao\", funcOptimization)\n\n# Creates the array of agents and tasks numbers\n# Example for n = 3:\n# agentsRange = [0,1,2]\n# tasksRange = [0,1,2]\nagentsRange = range(n)\ntasksRange = range(n)\n\n# Creates the binary variables xij, that defines if an \"i\" agent execute a \"j\" task\n# with:\n# xij E {0,1} for i = 1,2,...,n; j = 1,2,...,n\n# In the practice creates n*n = n^2 binary variables\nagentsExectution = LpVariable.dicts(\"agents\",(agentsRange,tasksRange),0,1,LpInteger)\n\n# Defines the satisfaction function that will be using the function optimization \n# (in this case Maximization defined on line 11).\n# So the funtion is:\n# The summation of Xij*Cij, where\n# Cij is the satisfaction value of \"i\" agent executing \"j\" task\nproblem += lpSum([satisfactionValue(agent,task) * agentsExectution[agent][task] for agent in agentsRange for task in tasksRange])\n\n# Defines the restriction for agent\n# One agent can execute just one task\n# The summation of Xij for j=,1,2,...,n must be equal 1\nfor agent in agentsRange:\n  problem += (lpSum([agentsExectution[agent][task] for task in tasksRange]) == 1, f\"Agent_Limit_{agent}\")\n\n# Defines the restriction for task\n# One task can be executed by just one agent\n# The summation of Xij for i=,1,2,...,n must be equal 1\nfor task in tasksRange:\n  problem += (lpSum([agentsExectution[agent][task] for agent in agentsRange]) == 1, f\"Task_Limit_{task}\")\n\n\nprint(f\"\\nSolver: {solverName}:\\n\")\nstart = time.time()\n\n# Solve the problem using CBC\nproblem.solve(resolveSolver)\n\ntotal_sum = 0\n\n# Calculates the total sum\nfor agent in agentsRange:\n  for task in tasksRange:\n    if agentsExectution[agent][task].value() == 1:\n      print(f\"Agent: {agent} executed the task: {task}\")\n      total_sum += satisfactionValue(agent,task)\n\nprint(f\"The maximization returned a summation of: {total_sum}\")\nend = time.time()\nprint(f\"Time Elapsed: {end-start}\")\n","sub_path":"prog_mat_toy_problem.py","file_name":"prog_mat_toy_problem.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"550659130","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n__author__ = 'zhoutaotao'\n\n\n###6.7 利用命名空间解析XML文档\nfrom xml.etree.ElementTree import parse, Element\n\nclass XMLNamespaces:\n    def __init__(self, **kwargs):\n        self.namespaces = {}\n        for name, uri in kwargs.items():\n            self.register(name, uri)\n    def register(self, name, uri):\n        self.namespaces[name] = '{'+uri+'}'\n    def __call__(self, path):\n        return path.format_map(self.namespaces)\n\n\nns = XMLNamespaces(html='http://www.w3.org/1999/xhtml')\ndoc = parse('pred.xml')\n\ndoc.find(ns('content/{html}html'))\ndoc.findtext(ns('content/{html}html/{html}head/{html}title'))","sub_path":"DataEncodingAndProcessing/Learn7.py","file_name":"Learn7.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"469691987","text":"import numpy as np\nimport pandas as pd\nfrom datetime import datetime\nfrom sklearn.linear_model import LogisticRegression\nimport numpy as np\nimport pandas as pd\nimport sklearn\nimport pandas_datareader.data as web\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import confusion_matrix\n\ndef create_dataset(stock_symbol, start_date, end_date, lags=5):\n\n    #fetch the stock data from Yahoo Finance\n\tdf = web.DataReader(stock_symbol,\"yahoo\",start_date,end_date)\n\n    #create a new dataframe\n\t#we want to use additional features: lagged returns...today's returns, yesterday's returns etc\n\ttslag = pd.DataFrame(index=df.index)\n\ttslag[\"Today\"] = df[\"Adj Close\"]\n\ttslag[\"Volume\"] = df[\"Volume\"]\n\n    # Create the shifted lag series of prior trading period close values\n\tfor i in range(0, lags):\n\t\ttslag[\"Lag%s\" % str(i+1)] = df[\"Adj Close\"].shift(i+1)\n\n    #create the returns DataFrame\n\tdfret = pd.DataFrame(index=tslag.index)\n\tdfret[\"Volume\"] = tslag[\"Volume\"]\n\tdfret[\"Today\"] = tslag[\"Today\"].pct_change()*100.0\n\n    #create the lagged percentage returns columns\n\tfor i in range(0, lags):\n\t\tdfret[\"Lag%s\" % str(i+1)] = tslag[\"Lag%s\" % str(i+1)].pct_change()*100.0\n\n    #\"Direction\" column (+1 or -1) indicating an up/down day\n\tdfret[\"Direction\"] = np.sign(dfret[\"Today\"])\n\t\n\t#because of the shifts there are NaN values ... we want to get rid of those NaNs\n\tdfret.drop(dfret.index[:5], inplace=True)\n\n\treturn dfret\n\n\nif __name__ == \"__main__\":\n    \n\t# Create a lagged series of the S&P500 US stock market index\n\tdata = create_dataset(\"AAPL\", datetime(2012,1,1), datetime(2017,5,31), lags=5)\n\n    # Use the prior two days of returns as predictor \n    # values, with direction as the response\n\tX = data[[\"Lag1\",\"Lag2\",\"Lag3\",\"Lag4\"]]\n\ty = data[\"Direction\"]\n\n    # The test data is split into two parts: Before and after 1st Jan 2005.\n\tstart_test = datetime(2017,1,1)\n\n    # Create training and test sets\n\tX_train = X[X.index < start_test]\n\tX_test = X[X.index >= start_test]\n\ty_train = y[y.index < start_test]\n\ty_test = y[y.index >= start_test]\n   \n    #we use Logistic Regression as the machine learning model\n\tmodel = SVC(C=1000000.0, cache_size=200,class_weight=None,coef0=0.0,degree=3,gamma=0.0001,kernel='rbf',max_iter=-1,probability=False,random_state=None,shrinking=True,tol=0.001,verbose=False)\n\t\n    #train the model on the training set\n\tmodel.fit(X_train, y_train)\n\n    #make an array of predictions on the test set\n\tpred = model.predict(X_test)\n\n    #output the hit-rate and the confusion matrix for the model\n\tprint(\"Accuracy of SVM model: %0.3f\" % model.score(X_test, y_test))\n\tprint(\"Confusion matrix: \\n%s\" % confusion_matrix(pred, y_test))","sub_path":"original/ml_svm2.py","file_name":"ml_svm2.py","file_ext":"py","file_size_in_byte":2647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"280424046","text":"import datetime\nimport concurrent.futures as cf\nimport utils.utils as utils\nimport sys\nimport json\nimport os\n\nfrom classes.GraphProperties import GraphProperties\nfrom classes.ProbabilityDistributions import ProbabilityDistributions as pd\nfrom classes.Result import Result\nimport utils.graph_factory as graph_factory\nfrom networkx.readwrite import json_graph\nimport platform\n\nMAX_PROCESSES = 10\nMAX_THREADS = 1  # per process\nCHUNKSIZE = 1\n\n\ndef graph_thread_worker(graph_properties):\n    # result, graph_properties = result_property_tuple\n\n    simulated_graphs = graph_factory.generate_simulated_graphs(graph_properties)\n    # result = graph_factory.analyze_graphs(simulated_graphs, graph_properties.time_steps, result)\n    return (simulated_graphs, graph_properties)\n\n\ndef graph_process_worker(graph_properties):\n    with cf.ThreadPoolExecutor(max_workers=MAX_THREADS) as t:\n        result = list(t.map(graph_thread_worker, graph_properties))\n    return result\n\n\ndef result_thread_worker(graph_mapping):\n    # result, graph_properties = result_property_tuple\n    graph_property_id, graph_id, age, graph, graph_properties = graph_mapping\n\n    analysis_result = graph_factory.analyze_graph(graph)\n    return (graph_property_id, graph_id, age, graph, graph_properties, analysis_result)\n\n\ndef result_process_worker(graph_mappings):\n    with cf.ThreadPoolExecutor(max_workers=MAX_THREADS) as t:\n        graph_analysis_mappings = list(t.map(result_thread_worker, graph_mappings))\n    return graph_analysis_mappings\n\n\ndef generate_configuration_json(configuration, folder_path):\n    configuration_dict = {}\n\n    for key, value in configuration.__dict__.items():\n        if '_SimulationConfiguration__' in key:\n            new_key = key.replace('_SimulationConfiguration__', '')\n            configuration_dict[new_key] = value\n\n    with open(folder_path + '/simulation_configuration.json',\n              'w') as outfile:\n        json.dump(configuration_dict, outfile)\n\n\ndef generate_result_json(configuration, graphs_dict, folder_path):\n    result_dict = {}\n\n    for node_amount in configuration.node_amounts:\n        result_dict[node_amount] = {}\n        for coverage_radius in configuration.coverage_radii:\n            result_dict[node_amount][coverage_radius] = None\n\n    with open(folder_path + '/result.json',\n              'w') as outfile:\n        json.dump(graphs_dict, outfile)\n\n\ndef generate_jsons(simulation_configuration, result_dict):\n    analysis_folder_path = utils.get_config('analysisPath')\n\n    if not os.path.isdir(analysis_folder_path):\n        if platform.system() == 'Windows':\n            if not os.path.isdir(os.getcwd() + '/' + analysis_folder_path):\n                os.mkdir(os.getcwd() + '/' + analysis_folder_path)\n        if platform.system() == 'Linux':\n            if not os.path.isdir(analysis_folder_path):\n                os.mkdir(analysis_folder_path)\n\n    folder_path = analysis_folder_path + '/analysis_' + str(datetime.datetime.now().strftime('%y_%m_%d_%H-%M-%S'))\n\n    if not os.path.isdir(folder_path):\n        if platform.system() == 'Windows':\n            os.mkdir(os.getcwd() + '/' + folder_path)\n        if platform.system() == 'Linux':\n            os.mkdir(folder_path)\n\n    generate_configuration_json(simulation_configuration, folder_path)\n    generate_result_json(simulation_configuration, result_dict, folder_path)\n\n\nif __name__ == \"__main__\":\n    '''\n    Arguments:\n        amount_graphs: int\n        node_amounts: int\n        coverage_radii: float\n        time_steps= int\n\n        random_graph_type: String e.g. 'rgg' or 'srgg'\n\n        node_coverage_type: String e.g. square or circle\n\n        graph_shape: String e.g. square, cirlce, ball, cube, cylinder, ring torus\n\n        link_loss_prob_dist: distributionName,param1=value1,param2=value2.\n        srgg_prob_dist: distributionName,param1=value1,param2=value2.\n\n        srgg_init_prob: Float, default 0.9\n\n\n        coverage_type=\n\n    '''\n    start_timestamp = datetime.datetime.now().replace(microsecond=0)\n    print('Started at: ' + str(start_timestamp))\n\n    try:\n        args = dict(arg.replace('\\r', '').split('=') for arg in sys.argv[1:])\n    except ValueError:\n        print('Value error, not all values are assigned to variable.')\n        # TODO: logging\n        exit()\n\n    '''\n    Set Simulation Configuration.\n    '''\n    simulation_configuration = utils.parse_input(args)\n\n    '''\n    Multi process setup.\n    '''\n    node_amount_coverage_combinations = list(\n        (a, b) for a in simulation_configuration.node_amounts for b in simulation_configuration.coverage_radii)\n\n    graph_properties = []\n\n    for i, (node_amount, coverage_radius) in enumerate(node_amount_coverage_combinations):\n        # TODO amount of graphs in constructor\n        graph_property = GraphProperties(node_amount, coverage_radius)\n\n        graph_property.amount_graphs = simulation_configuration.amount_graphs\n        graph_property.time_steps = simulation_configuration.time_steps\n        graph_property.srgg_init_prob = simulation_configuration.srgg_init_prob\n        graph_property.random_graph_type = simulation_configuration.random_graph_type\n        graph_property.node_coverage_type = simulation_configuration.node_coverage_type\n        graph_property.graph_shape = simulation_configuration.graph_shape\n\n        try:\n            if hasattr(simulation_configuration,\n                       'link_loss_prob_dist') and simulation_configuration.link_loss_prob_dist is not None and simulation_configuration.link_loss_prob_dist_parameter is not None:\n                graph_property.link_loss_prob_dist_obj = getattr(pd, simulation_configuration.link_loss_prob_dist)(\n                    simulation_configuration.link_loss_prob_dist_parameter)\n            else:\n                graph_property.link_loss_prob_dist_obj = None\n            if hasattr(simulation_configuration,\n                       'node_loss_prob_dist') and simulation_configuration.node_loss_prob_dist is not None and simulation_configuration.node_loss_prob_dist_parameter is not None:\n                graph_property.node_loss_prob_dist_obj = getattr(pd, simulation_configuration.node_loss_prob_dist)(\n                    simulation_configuration.node_loss_prob_dist_parameter)\n            else:\n                graph_property.node_loss_prob_dist_obj = None\n            if hasattr(simulation_configuration,\n                       'srgg_prob_dist') and simulation_configuration.srgg_prob_dist is not None:\n                graph_property.srgg_prob_dist_obj = getattr(pd, simulation_configuration.srgg_prob_dist)(\n                    simulation_configuration.srgg_prob_dist_parameter)\n            else:\n                graph_property.srgg_prob_dist_obj = None\n        except Exception:\n            raise ValueError(\n                \"Couldn't instantiate probability distribution class \" + str(\n                    simulation_configuration.link_loss_prob_dist))\n\n        graph_properties.append(graph_property)\n\n    simulated_graphs = []\n    '''Generate simulated grpahs.'''\n    with cf.ProcessPoolExecutor(max_workers=MAX_PROCESSES) as p:\n        for graph_list in p.map(graph_process_worker,\n                                (graph_properties[i: i + CHUNKSIZE] for i in\n                                 range(0, len(graph_properties), CHUNKSIZE))):\n            for graphs in graph_list:\n                simulated_graphs.append(graphs)\n\n    graph_mappings = []\n\n    #temporary due to large data simulated_graphs[::10]\n    for graph_property_id, sim_graph_properties_tupel in enumerate(simulated_graphs[::10]):\n        graph_lists, graph_property = sim_graph_properties_tupel\n\n        for graph_id, graphs in enumerate(graph_lists):\n            for age, graph in enumerate(graphs[::100]):\n                '''\n                    tuple of graph id, age of graph, simulated graph and graph properties. To recombine the results later on.\n                '''\n                graph_mappings.append((graph_property_id, graph_id, age, graph, graph_property))\n\n    result_mappings = []\n    '''Analyze simulated grpahs.'''\n    with cf.ProcessPoolExecutor(max_workers=MAX_PROCESSES) as p:\n        for graph_analysis_mappings in p.map(result_process_worker,\n                                             (graph_mappings[i: i + CHUNKSIZE] for i in\n                                              range(0, len(graph_mappings), CHUNKSIZE))):\n            for gam in graph_analysis_mappings:\n                result_mappings.append(gam)\n\n    result_dict = {}\n    for i, gp in enumerate(graph_properties):\n        result_dict[i] = {'graph_properties': {'node_amount': gp.node_amount, 'coverage_radius': gp.coverage_radius},\n                          'graphs': {}}\n\n    current_graph_property_id = 0\n    current_graph_id = 0\n\n    current_graph_list = []\n    for mapping in result_mappings:\n        graph_property_id, graph_id, age, graph, graph_properties, results = mapping\n        if result_dict.get(graph_property_id) is None:\n            result_dict[graph_property_id] = {}\n\n        if result_dict[graph_property_id]['graphs'].get(graph_id) is None:\n            result_dict[graph_property_id]['graphs'][graph_id] = []\n        '''\n        Recombine associated graphs and parse them to json\n        '''\n        result_dict[graph_property_id]['graphs'][graph_id].append(\n            {'graph_id': graph_id, 'age': age, 'graph': json_graph.node_link_data(graph), 'results': results})\n\n    generate_jsons(simulation_configuration, result_dict)\n\n    end_timestamp = datetime.datetime.now().replace(microsecond=0)\n    print('Stopped at: ' + str(end_timestamp))\n\n    print('Elapsed time: ' + str(end_timestamp - start_timestamp))\n\n    print('Done.')\n","sub_path":"main_cmd.py","file_name":"main_cmd.py","file_ext":"py","file_size_in_byte":9671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"547157982","text":"# ***** BEGIN GPL LICENSE BLOCK *****\n#\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.    See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ***** END GPL LICENCE BLOCK *****\n\n#History from 19-oct-2014 on Version 0,0,4\n#19 oct (maybe romoved later)PKHG>INFO some changes are found look e.g. for \"#old \"\n#19 oct PKHG added debugPrintNew + globals to steer the debug\n#19 oct PKHG: more use of Vectors done\n\n#PKHG>INFO if all points in 3D space are Vectors, a lot of Vector(xxx) could become xxx\n# without an extra Vector \n\nbl_info = {\n    \"name\": \"Edge Roundifier\",\n    \"category\": \"Mesh\",\n    'author': 'Piotr Komisarczyk (komi3D), PKHG',\n    'version': (0, 0, 5),\n    'blender': (2, 7, 3),\n    'location': 'SPACE > Edge Roundifier or CTRL-E > Edge Roundifier',\n    'description': 'Mesh editing script allowing edge rounding',\n    'wiki_url': '',\n    'tracker_url': '',\n    'category': 'Mesh'\n}\n\nimport bmesh\nimport bpy\nimport bpy.props\nfrom math import sqrt, acos, pi, radians, degrees, sin, acos\nfrom mathutils import Vector\n\n# CONSTANTS\ntwo_pi = 2 * pi #PKHG>??? maybe other constantly used values too???\nXY = \"XY\"\nXZ = \"XZ\"\nYZ = \"YZ\"\nSPIN_END_THRESHOLD = 0.001\nLINE_TOLERANCE = 0.0001\n\n# variable controlling all print functions\n#PKHG>??? to be replaced, see debugPrintNew ;-)\ndebug = False\n\ndef debugPrint(*text):\n    if debug:\n        for t in text:\n            print(text)\n\n\n\n############# for debugging PKHG ################\ndef debugPrintNew(debug,*text):\n    if debug:\n        #print(\"start\", type(text))\n        tmp = [el for el in text]\n        for row in tmp:\n            print(row)\n\nd_XABS_YABS = False\nd_Edge_Info = False\nd_Plane = False\nd_Radius_Angle = True\nd_Roots = True\nd_RefObject = True\nd_LineAB = False\nd_Selected_edges = False\n###################################################################################\n\n####################### Geometry and math calcualtion methods #####################\n\nclass CalculationHelper:\n    def __init__(self):\n        '''\n        Constructor\n        '''\n    def getLineCoefficientsPerpendicularToVectorInPoint(self, point, vector, plane):\n        x, y, z = point\n        xVector, yVector, zVector = vector\n        destinationPoint = (x + yVector, y - xVector, z)\n        if plane == 'YZ':\n            destinationPoint = (x , y + zVector, z - yVector)\n        if plane == 'XZ':\n            destinationPoint = (x + zVector, y, z - xVector)\n        return self.getCoefficientsForLineThrough2Points(point, destinationPoint, plane)\n\n    def getQuadraticRoots(self, coef):\n        if len(coef) != 3:\n            return NaN\n        else:\n            a, b, c = coef\n            delta = b ** 2 - 4 * a * c\n            if delta == 0:\n                x = -b / (2 * a)\n                return (x, x)\n            elif delta < 0:\n                return None\n            else :\n                x1 = (-b - sqrt(delta)) / (2 * a)\n                x2 = (-b + sqrt(delta)) / (2 * a)\n                return (x1, x2)\n\n    def getCoefficientsForLineThrough2Points(self, point1, point2, plane):\n        x1, y1, z1 = point1\n        x2, y2, z2 = point2\n\n        # mapping x1,x2, y1,y2 to proper values based on plane\n        if plane == YZ:\n            x1 = y1\n            x2 = y2\n            y1 = z1\n            y2 = z2\n        if plane == XZ:\n            y1 = z1\n            y2 = z2\n\n        # Further calculations the same as for XY plane\n        xabs = abs(x2 - x1)\n        yabs = abs(y2 - y1) \n        debugPrintNew(d_XABS_YABS, \"XABS = \" + str( xabs)+ \" YABS = \" + str(yabs))\n\n        if xabs <= LINE_TOLERANCE:\n            return None  # this means line x = edgeCenterX\n        if yabs <= LINE_TOLERANCE:\n            A = 0\n            B = y1\n            return A, B\n        A = (y2 - y1) / (x2 - x1)\n        B = y1 - (A * x1)\n        return (A, B)\n\n    def getLineCircleIntersections(self, lineAB, circleMidPoint, radius):\n        # (x - a)**2 + (y - b)**2 = r**2 - circle equation\n        # y = A*x + B - line equation\n        # f * x**2 + g * x + h = 0 - quadratic equation\n        A, B = lineAB\n        a, b = circleMidPoint\n        f = 1 + (A ** 2)\n        g = -2 * a + 2 * A * B - 2 * A * b\n        h = (B ** 2) - 2 * b * B - (radius ** 2) + (a ** 2) + (b ** 2)\n        coef = [f, g, h]\n        roots = self.getQuadraticRoots(coef)\n        if roots != None:\n            x1 = roots[0]\n            x2 = roots[1]\n            point1 = [x1, A * x1 + B]\n            point2 = [x2, A * x2 + B]\n            return [point1, point2]\n        else:\n            return None\n\n    def getLineCircleIntersectionsWhenXPerpendicular(self, edgeCenter, circleMidPoint, radius, plane):\n        # (x - a)**2 + (y - b)**2 = r**2 - circle equation\n        # x = xValue - line equation\n        # f * x**2 + g * x + h = 0 - quadratic equation\n        xValue = edgeCenter[0]\n        if plane == YZ:\n            xValue = edgeCenter[1]\n        if plane == XZ:\n            xValue = edgeCenter[0]\n\n        a, b = circleMidPoint\n        f = 1\n        g = -2 * b\n        h = (a ** 2) + (b ** 2) + (xValue ** 2) - 2 * a * xValue - (radius ** 2)\n        coef = [f, g, h]\n        roots = self.getQuadraticRoots(coef)\n        if roots != None:\n            y1 = roots[0]\n            y2 = roots[1]\n            point1 = [xValue, y1]\n            point2 = [xValue, y2]\n            return [point1, point2]\n        else:\n            return None\n\n    # point1 is the point near 90 deg angle\n    def getAngle(self, point1, point2, point3):\n        distance1 = (Vector(point1) - Vector(point2)).length\n        distance2 = (Vector(point2) - Vector(point3)).length\n        cos = distance1 / distance2\n        if abs(cos) > 1:  # prevents Domain Error\n            cos = round(cos)\n        alpha = acos(cos)\n        return (alpha, degrees(alpha))\n\n    # get two of three coordinates used for further calculation of spin center\n    #PKHG>nice if rescriction to these 3 types or planes is to be done\n    #komi3D> from 0.0.2 there is a restriction. In future I would like Edge Roundifier to work on\n    #komi3D> Normal and View coordinate systems. That would be great... \n    def getCircleMidPointOnPlane(self, V1, plane):\n        X = V1[0]\n        Y = V1[1]\n        if plane == 'XZ':\n            X = V1[0]\n            Y = V1[2]\n        elif plane == 'YZ':\n            X = V1[1]\n            Y = V1[2]\n        return [X, Y]\n\n########################################################\n################# SELECTION METHODS ####################\n\nclass SelectionHelper:\n    def selectVertexInMesh(self, mesh, vertex):\n        bpy.ops.object.mode_set(mode = \"OBJECT\")\n        for v in mesh.vertices:\n            if v.co == vertex:\n                v.select = True\n                break\n\n        bpy.ops.object.mode_set(mode = \"EDIT\")\n\n    def getSelectedVertex(self, mesh):\n        bpy.ops.object.mode_set(mode = \"OBJECT\")\n        for v in mesh.vertices:\n            if v.select == True :\n                bpy.ops.object.mode_set(mode = \"EDIT\")\n                return v\n\n        bpy.ops.object.mode_set(mode = \"EDIT\")\n        return None\n\n    def refreshMesh(self, bm, mesh):\n        bpy.ops.object.mode_set(mode = 'OBJECT')\n        bm.to_mesh(mesh)\n        bpy.ops.object.mode_set(mode = 'EDIT')\n\n\n\n###################################################################################\n\nclass EdgeRoundifier(bpy.types.Operator):\n    \"\"\"Edge Roundifier\"\"\"  # blender will use this as a tooltip for menu items and buttons.\n    bl_idname = \"mesh.edge_roundifier\"  # unique identifier for buttons and menu items to reference.\n    bl_label = \"Edge Roundifier\"  # display name in the interface.\n    bl_options = {'REGISTER', 'UNDO', 'PRESET'}  # enable undo for the operator.PKHG>INFO and PRESET\n\n    threshold = 0.0005  # used for remove doubles and edge selection at the end\n\n# TODO:\n# 1) offset - move arcs perpendicular to edges\n\n\n    r = bpy.props.FloatProperty(name = '', default = 1, min = 0.00001, max = 1000.0, step = 0.1, precision = 3)\n    a = bpy.props.FloatProperty(name = '', default = 180, min = 0.1, max = 180.0, step = 0.5, precision = 1)\n    n = bpy.props.IntProperty(name = '', default = 4, min = 1, max = 100, step = 1)\n    flip = bpy.props.BoolProperty(name = 'flip', default = False)\n    invertAngle = bpy.props.BoolProperty(name = 'invertAngle', default = False)\n    fullCircles = bpy.props.BoolProperty(name = 'fullCircles', default = False)\n    bothSides = bpy.props.BoolProperty(name = 'bothSides', default = False)\n    removeDoubles = bpy.props.BoolProperty(name = 'removeDoubles', default = False)\n    removeEdges = bpy.props.BoolProperty(name = 'removeEdges', default = False)\n    # FUTURE TODO: OFFSET\n    # offset = bpy.props.BoolProperty(name = 'offset', default = False)\n\n    modeItems = [('Radius', \"Radius\", \"\"), (\"Angle\", \"Angle\", \"\")]\n    modeEnum = bpy.props.EnumProperty(\n        items = modeItems,\n        name = '',\n        default = 'Radius',\n        description = \"Edge Roundifier mode\")\n\n    angleItems = [('Other', \"Other\", \"User defined angle\"), ('180', \"180\", \"HemiCircle\"), ('120', \"120\", \"TriangleCircle\"),\n                    ('90', \"90\", \"QuadCircle\"), ('60', \"60\", \"HexagonCircle\"),\n                    ('45', \"45\", \"OctagonCircle\"), ('30', \"30\", \"12-gonCircle\")]\n\n    angleEnum = bpy.props.EnumProperty(\n        items = angleItems,\n        name = '',\n        default = 'Other',\n        description = \"Presets prepare standard angles and calculate proper ray\")\n\n    refItems = [('ORG', \"Origin\", \"Use Origin Location\"), ('CUR', \"3D Cursor\", \"Use 3DCursor Location\")]\n    referenceLocation = bpy.props.EnumProperty(\n        items = refItems,\n        name = '',\n        default = 'ORG',\n        description = \"Reference location used by Edge Roundifier to calculate initial centers of drawn arcs\")\n\n    planeItems = [(XY, XY, \"XY Plane (Z=0)\"), (YZ, YZ, \"YZ Plane (X=0)\"), (XZ, XZ, \"XZ Plane (Y=0)\")]\n    planeEnum = bpy.props.EnumProperty(\n        items = planeItems,\n        name = '',\n        default = 'XY',\n        description = \"Plane used by Edge Roundifier to calculate spin plane of drawn arcs\")\n\n\n    calc = CalculationHelper()\n    sel = SelectionHelper()\n\n    def prepareMesh(self, context):\n        bpy.ops.object.mode_set(mode = 'OBJECT')\n        bpy.ops.object.mode_set(mode = 'EDIT')\n\n        mesh = context.scene.objects.active.data\n        bm = bmesh.new()\n        bm.from_mesh(mesh) #PKHG>??? from_edit_mesh??? from_mesh not seen in 2.72a\n        #komi3D > from_mesh seems to be working in 2.72a...\n        \n        edges = [ele for ele in bm.edges if ele.select]\n        return edges, mesh, bm\n\n    def prepareParameters(self):\n\n        parameters = { \"a\" : \"a\"}\n        parameters[\"plane\"] = self.planeEnum\n        parameters[\"radius\"] = self.r\n        parameters[\"angle\"] = self.a\n        parameters[\"segments\"] = self.n\n        parameters[\"flip\"] = self.flip\n        parameters[\"fullCircles\"] = self.fullCircles\n        parameters[\"invertAngle\"] = self.invertAngle\n        parameters[\"bothSides\"] = self.bothSides\n        parameters[\"angleEnum\"] = self.angleEnum\n        parameters[\"modeEnum\"] = self.modeEnum\n        parameters[\"refObject\"] = self.referenceLocation\n        parameters[\"removeDoubles\"] = self.removeDoubles\n        parameters[\"removeEdges\"] = self.removeEdges\n\n        # FUTURE TODO OFFSET\n        # parameters[\"offset\"] = self.offset\n        return parameters\n\n    def draw(self, context):\n        layout = self.layout\n        layout.label('Note: possible radius >= edge_length/2.')\n        row = layout.row(align = False)\n        row.label('Mode:')\n        row.prop(self, 'modeEnum', expand = True, text = \"a\")\n        row = layout.row(align = False)\n        layout.label('Quick angle:')\n        layout.prop(self, 'angleEnum', expand = True, text = \"abv\")\n        row = layout.row(align = False)\n        row.label('Angle:')\n        row.prop(self, 'a')\n        row = layout.row(align = False)\n        row.label('Radius:')\n        row.prop(self, 'r')\n        row = layout.row(align = True)\n        row.label('Segments:')\n        row.prop(self, 'n') #old , slider = True) \n        #PKHG>INFO dragging still works but changing 1 easier\n        #komi3D > thanks for pointing that out! :)\n        row = layout.row(align = False)\n        row.prop(self, 'flip')\n        row.prop(self, 'invertAngle')\n        row = layout.row(align = False)\n        row.prop(self, 'fullCircles')\n        row.prop(self, 'bothSides' )\n        row = layout.row(align = False)\n        row.prop(self, 'removeDoubles')\n        row.prop(self, 'removeEdges')\n\n        # FUTURE TODO OFFSET\n        # row.prop(self, 'offset')\n\n        layout.label('Reference Location:')\n        layout.prop(self, 'referenceLocation', expand = True, text = \"a\")\n\n        layout.label('Working Plane (LOCAL coordinates):')\n        layout.prop(self, 'planeEnum', expand = True, text = \"a\")\n\n\n    def execute(self, context):\n\n        edges, mesh, bm = self.prepareMesh(context)\n        parameters = self.prepareParameters()\n\n        #PKHG>??? not really needed, works! debugPrintNew(True ,\"EDGES \" + str( edges))\n\n        if len(edges) > 0:\n            self.roundifyEdges(edges, parameters, bm, mesh)\n            #PKHG sel is SelectionHelper print(type(self.sel),dir(self.sel))\n            self.sel.refreshMesh(bm, mesh)\n            if parameters[\"removeDoubles\"] == True:\n                bpy.ops.mesh.select_all(action = \"SELECT\")\n                bpy.ops.mesh.remove_doubles(threshold = self.threshold)\n                bpy.ops.mesh.select_all(action = \"DESELECT\")\n\n            self.selectEdgesAfterRoundifier(context, edges)\n        else:\n            debugPrint(\"No edges selected!\")\n\n        if parameters[\"removeEdges\"]:\n            bmesh.ops.delete(bm, geom = edges, context = 2)\n            bpy.ops.object.mode_set(mode = 'OBJECT')\n            bm.to_mesh(mesh)\n            bpy.ops.object.mode_set(mode = 'EDIT')\n\n        bm.free()\n        return {'FINISHED'}\n\n##########################################\n    def roundifyEdges(self, edges, parameters, bm, mesh):\n        for e in edges:\n            self.roundify(e, parameters, bm, mesh)\n\n\n    def getEdgeInfo(self, edge):\n        V1 = edge.verts[0].co \n        V2 = edge.verts[1].co \n        edgeVector =  V2 - V1 \n        edgeLength = edgeVector.length \n        edgeCenter = (V2 + V1) * 0.5 \n        debugPrintNew(d_Edge_Info, \"\\nEdge info=====begin=================================\",\\\n                      \"V1 info============== \" + str(V1),\\\n                      \"V2 info============== \" + str(V2),\\\n                      \"Edge Length============== \" + str(edgeLength),\\\n                      \"Edge Center============== \" + str(edgeCenter),\\\n                      \"Edge info=====end=================================\")\n        return V1, V2, edgeVector, edgeLength, edgeCenter\n\n    def roundify(self, edge, parameters, bm, mesh):\n\n        V1, V2, edgeVector, edgeLength, edgeCenter = self.getEdgeInfo(edge)\n        if self.skipThisEdge(V1, V2, parameters[\"plane\"]):\n            return\n\n        roundifyParams = self.calculateRoundifyParams(edge, parameters, bm, mesh)\n        #PKHG>TEST only once debugPrintNew(True, str(roundifyParams))\n        self.drawSpin(edge, edgeCenter, roundifyParams, parameters, bm, mesh)\n        if parameters[\"bothSides\"]:\n            lastSpinCenter = roundifyParams[0]\n            roundifyParams[0] = roundifyParams[1]\n            roundifyParams[1] = lastSpinCenter\n            self.drawSpin(edge, edgeCenter, roundifyParams, parameters, bm, mesh)\n\n    def skipThisEdge(self, V1, V2, plane):\n        #debugPrintNew(True,\" type of V1\" + str(type(V1)))\n        # Check If It is possible to spin selected verts on this plane if not exit roundifier\n        if(plane == XY):\n            if (V1[0] == V2[0] and V1[1] == V2[1]):\n                return True\n        elif(plane == YZ):\n            if (V1[1] == V2[1] and V1[2] == V2[2]):\n                return True\n        elif(plane == XZ):\n            if (V1[0] == V2[0] and V1[2] == V2[2]):\n                return True\n        return False\n\n    def calculateRoundifyParams(self, edge, parameters, bm, mesh):\n        # BECAUSE ALL DATA FROM MESH IS IN LOCAL COORDINATES\n        # AND SPIN OPERATOR WORKS ON GLOBAL COORDINATES\n        # WE FIRST NEED TO TRANSLATE ALL INPUT DATA BY VECTOR EQUAL TO ORIGIN POSITION AND THEN PERFORM CALCULATIONS\n        # At least that is my understanding :) <komi3D>\n\n        # V1 V2 stores Local Coordinates\n        V1, V2, edgeVector, edgeLength, edgeCenter = self.getEdgeInfo(edge)\n\n        debugPrintNew(d_Plane, \"PLANE: \" +  parameters[\"plane\"])\n        lineAB = self.calc.getLineCoefficientsPerpendicularToVectorInPoint(edgeCenter, edgeVector, parameters[\"plane\"])\n        debugPrint(d_LineAB, \"Line Coefficients: \" +  str(lineAB))\n        circleMidPoint = V1\n        circleMidPointOnPlane = self.calc.getCircleMidPointOnPlane(V1, parameters[\"plane\"])\n        radius = parameters[\"radius\"]\n\n        if radius < edgeLength/2:\n            radius = edgeLength/2\n            parameters[\"radius\"] = edgeLength/2 \n\n        angle = 0\n        if (parameters[\"modeEnum\"] == 'Angle'):\n            if (parameters[\"angleEnum\"] != 'Other'):\n                radius, angle = self.CalculateRadiusAndAngleForAnglePresets(parameters[\"angleEnum\"], radius, angle, edgeLength)\n            else:\n                radius, angle = self.CalculateRadiusAndAngle(edgeLength)\n\n        debugPrintNew(d_Radius_Angle, \"RADIUS = \" + str(radius) + \"  ANGLE = \" + str( angle))\n        roots = None\n        if angle != pi:  # mode other than 180\n            if lineAB == None:\n                roots = self.calc.getLineCircleIntersectionsWhenXPerpendicular(edgeCenter, circleMidPointOnPlane, radius, parameters[\"plane\"])\n            else:\n                roots = self.calc.getLineCircleIntersections(lineAB, circleMidPointOnPlane, radius)\n            if roots == None:\n                debugPrint(\"No centers were found. Change radius to higher value\")\n                return None\n            roots = self.addMissingCoordinate(roots, V1, parameters[\"plane\"])  # adds X, Y or Z coordinate\n        else:\n            roots = [edgeCenter, edgeCenter]\n\n        debugPrintNew(d_Roots, \"roots=\" + str(roots))\n\n        refObjectLocation = None\n        objectLocation = bpy.context.active_object.location  # Origin Location\n\n        if parameters[\"refObject\"] == \"ORG\":\n            refObjectLocation = [0, 0, 0]\n            #refObjectLocation = objectLocation\n        else:\n            refObjectLocation = bpy.context.scene.cursor_location\n\n        debugPrintNew(d_RefObject, parameters[\"refObject\"], refObjectLocation)\n        chosenSpinCenter, otherSpinCenter = self.getSpinCenterClosestToRefCenter(refObjectLocation, roots, parameters[\"flip\"])\n\n        if (parameters[\"modeEnum\"] == \"Radius\"):\n            halfAngle = self.calc.getAngle(edgeCenter, chosenSpinCenter, circleMidPoint)\n            angle = 2 * halfAngle[0]  # in radians\n            self.a = degrees(angle)  # in degrees\n\n        spinAxis = self.getSpinAxis(parameters[\"plane\"])\n\n        if(parameters[\"invertAngle\"]):\n            angle = -two_pi + angle\n\n        if(parameters[\"fullCircles\"]):\n            angle = two_pi\n\n        # FUTURE TODO OFFSET\n#        if parameters[\"offset\"]:\n#            offset = self.getOffsetVectorForTangency(edgeCenter, chosenSpinCenter, radius, self.invertAngle)\n#            self.moveSelectedVertexByVector(mesh,offset)\n#            chosenSpinCenterOffset = self.translateByVector(chosenSpinCenter, offset)\n#            chosenSpinCenter = chosenSpinCenterOffset\n\n        steps = parameters[\"segments\"]\n\n        if parameters[\"fullCircles\"] == False and parameters[\"flip\"] == True:\n            angle = -angle\n        X = [chosenSpinCenter, otherSpinCenter, spinAxis, angle, steps, refObjectLocation]\n        return X\n\n    def drawSpin(self, edge, edgeCenter, roundifyParams, parameters, bm, mesh):\n        [chosenSpinCenter, otherSpinCenter, spinAxis, angle, steps, refObjectLocation] = roundifyParams\n\n        v0org, v1org = (edge.verts[0], edge.verts[1]) #old self.getVerticesFromEdge(edge)\n\n        # Duplicate initial vertex\n        v0 = bm.verts.new(v0org.co)\n\n        result = bmesh.ops.spin(bm, geom = [v0], cent = chosenSpinCenter, axis = spinAxis, \\\n                                   angle = angle, steps = steps, use_duplicate = False)\n\n        # it seems there is something wrong with last index of this spin...\n        # I need to calculate the last index manually here...\n        vertsLength = len(bm.verts)\n        bm.verts.ensure_lookup_table()\n        lastVertIndex = bm.verts[vertsLength - 1].index\n        lastSpinVertIndices = self.getLastSpinVertIndices(steps, lastVertIndex)\n        debugPrintNew(True, str(result) + \"lastVertIndex =\" + str(lastVertIndex))\n\n        alternativeLastSpinVertIndices = []\n\n        if (angle == pi or angle == -pi):\n\n            midVertexIndex = lastVertIndex - round(steps / 2)\n            bm.verts.ensure_lookup_table()\n            midVert = bm.verts[midVertexIndex].co\n\n            midVertexDistance = (Vector(refObjectLocation) - Vector(midVert)).length \n            midEdgeDistance = (Vector(refObjectLocation) - Vector(edgeCenter)).length\n\n            debugPrint(\"midVertexDistance: \")\n            debugPrint(midVertexDistance)\n            debugPrint(\"midEdgeDistance: \")\n            debugPrint(midEdgeDistance)\n\n            if (parameters[\"invertAngle\"]) or (parameters[\"flip\"]):\n                if (midVertexDistance > midEdgeDistance):\n                    alternativeLastSpinVertIndices = self.alternateSpin(bm, mesh, angle, chosenSpinCenter, spinAxis, steps, v0, v1org, lastSpinVertIndices)\n            elif (parameters[\"bothSides\"]):\n                #do some more testing here!!!\n                alternativeLastSpinVertIndices = self.alternateSpin(bm, mesh, angle, chosenSpinCenter, spinAxis, steps, v0, v1org, [])\n                alternativeLastSpinVertIndices2 = self.alternateSpin(bm, mesh, -angle, chosenSpinCenter, spinAxis, steps, v0, v1org, [])\n                if alternativeLastSpinVertIndices2 != []:\n                    alternativeLastSpinVertIndices = alternativeLastSpinVertIndices2\n            else:\n                if (midVertexDistance < midEdgeDistance):\n                    alternativeLastSpinVertIndices = self.alternateSpin(bm, mesh, angle, chosenSpinCenter, spinAxis, steps, v0, v1org, lastSpinVertIndices)\n\n        elif (angle != two_pi):  # to allow full circles :)\n            if (result['geom_last'][0].co - v1org.co).length > SPIN_END_THRESHOLD:\n                alternativeLastSpinVertIndices = self.alternateSpin(bm, mesh, angle, chosenSpinCenter, spinAxis, steps, v0, v1org, lastSpinVertIndices)\n        #PKHG sel is SelectionHelper  print(type(self.sel),dir(self.sel))\n        self.sel.refreshMesh(bm, mesh)\n        if alternativeLastSpinVertIndices != []:\n            lastSpinVertIndices = alternativeLastSpinVertIndices\n        return lastSpinVertIndices\n\n##########################################\n\n\n    def deleteSpinVertices(self, bm, mesh, lastSpinVertIndices):\n        verticesForDeletion = []\n        bm.verts.ensure_lookup_table()\n        for i in lastSpinVertIndices:\n            vi = bm.verts[i]\n            vi.select = True\n            debugPrint(str(i) + \") \" + str(vi))\n            verticesForDeletion.append(vi)\n\n        bmesh.ops.delete(bm, geom = verticesForDeletion, context = 1)\n        bmesh.update_edit_mesh(mesh, True)\n        bpy.ops.object.mode_set(mode = 'OBJECT')\n        bpy.ops.object.mode_set(mode = 'EDIT')\n\n\n    def alternateSpin(self, bm, mesh, angle, chosenSpinCenter, spinAxis, steps, v0, v1org, lastSpinVertIndices):\n        debugPrint(\"== begin alternate spin ==\")\n        for v in bm.verts:\n            debugPrint (v.index)\n        debugPrint(\"== indices for deletion ==\")\n        debugPrint(lastSpinVertIndices)\n        for i in lastSpinVertIndices:\n            debugPrint (i)\n\n        self.deleteSpinVertices(bm, mesh, lastSpinVertIndices)\n#        v0prim = bm.verts.new(v0.co) #komi3d > I am not sure if it should be new vert here or not...\n        v0prim = v0\n        debugPrint(\"== v0prim index: ==\")\n        debugPrint(v0prim.index)\n\n        debugPrint(\"== BEFORE 2nd spin performed==\")\n        for v in bm.verts:\n            debugPrint (v.index)\n\n        debugPrint (\"LEN before=\")\n        debugPrint(len(bm.verts))\n\n        result2 = bmesh.ops.spin(bm, geom = [v0prim], cent = chosenSpinCenter, axis = spinAxis,\n            angle = -angle, steps = steps, use_duplicate = False)\n        # it seems there is something wrong with last index of this spin...\n        # I need to calculate the last index manually here...\n        debugPrint (\"LEN after=\")\n        debugPrint(len(bm.verts))\n        vertsLength = len(bm.verts)\n        bm.verts.ensure_lookup_table()\n        lastVertIndex2 = bm.verts[vertsLength - 1].index\n        debugPrint(\"== 2nd spin performed==\")\n        for v in bm.verts:\n            debugPrint (v.index)\n\n        debugPrint(\"last:\")\n        debugPrint(result2['geom_last'][0].index)\n\n        lastSpinVertIndices2 = self.getLastSpinVertIndices(steps, lastVertIndex2)\n        \n# second spin also does not hit the v1org\n        if (result2['geom_last'][0].co - v1org.co).length > SPIN_END_THRESHOLD:\n            \n            debugPrint(\"== lastVertIndex2: ==\")\n            debugPrint(result2['geom_last'][0].index)\n            debugPrint(lastVertIndex2)\n\n            debugPrint(\"== 2nd spin ==\")\n            for v in bm.verts:\n                debugPrint (v.index)\n            debugPrint(\"== indices for deletion ==\")\n            debugPrint(lastSpinVertIndices2)\n            for i in lastSpinVertIndices2:\n                debugPrint (i)\n\n            debugPrint(\"result2:\")\n            debugPrint(lastSpinVertIndices2)\n            self.deleteSpinVertices(bm, mesh, lastSpinVertIndices2)\n            self.deleteSpinVertices(bm, mesh, range(v0.index, v0.index + 1))\n            return []\n        else:\n            return lastSpinVertIndices2\n\n    def getLastSpinVertIndices(self, steps, lastVertIndex):\n        arcfirstVertexIndex = lastVertIndex - steps + 1\n        lastSpinVertIndices = range(arcfirstVertexIndex, lastVertIndex + 1)\n        return lastSpinVertIndices\n\n#komi3d > this method will be reworked when working on the offset function\n    def getOffsetVectorForTangency(self, edgeCenter, chosenSpinCenter, radius, invertAngle):\n        edgeCentSpinCentVector = Vector(chosenSpinCenter) - Vector(edgeCenter)\n        if invertAngle:\n            edgeCentSpinCentVector = - edgeCentSpinCentVector\n        \"\"\" see above same result \n        if invertAngle == False:\n            edgeCentSpinCentVector = Vector(chosenSpinCenter) - Vector(edgeCenter) \n            #old self.calc.getVectorBetween2VertsXYZ(edgeCenter, chosenSpinCenter)\n        else:\n            edgeCentSpinCentVector = self.calc.getVectorBetween2VertsXYZ(chosenSpinCenter, edgeCenter)\n        \"\"\"\n\n        vectorLength = self.calc.getVectorLength(edgeCentSpinCentVector)\n        \"\"\"PKHG>INFO  == not needed if reversed!\n        if invertAngle == False:\n            offsetLength = radius - vectorLength\n        else:\n            offsetLength = radius + vectorLength\n        \"\"\"\n        if invertAngle:\n            offsetLength = radius + vectorLength    \n        else:\n            offsetLength = radius - vectorLength\n\n        normalizedVector = edgeCentSpinCentVector.normalized()\n        \"\"\" PKHF>INFO use Vector properties!\n        offsetVector = (offsetLength * normalizedVector[0],\n                        offsetLength * normalizedVector[1],\n                        offsetLength * normalizedVector[2])\n        \"\"\"\n        return offsetLength * normalizedVector\n\n\n    def moveSelectedVertexByVector(self, mesh, offset):\n        vert = self.getSelectedVertex(mesh)\n        \"\"\" use Vector propeties\n        vert.co.x = vert.co.x + offset[0]\n        vert.co.y = vert.co.y + offset[1]\n        vert.co.z = vert.co.z + offset[2]\n        \"\"\"\n        vert.co += Vector(offset)\n\n    def translateRoots(self, roots, objectLocation):\n        # translationVector = self.calc.getVectorBetween2VertsXYZ(objectLocation, [0,0,0])\n        r1 = Vector(roots[0]) + Vector(objectLocation) \n        r2 = Vector(roots[1]) + Vector(objectLocation)\n        return [r1, r2]\n    #PKHg>INFO not needed if 3D or 2D lists are replaced by Vector\n    def getOppositeVector(self, originalVector):\n        x, y, z = originalVector\n        return [-x, -y, -z]\n\n    def translateByVector(self, point, vector):\n        translated = (point[0] + vector[0],\n        point[1] + vector[1],\n        point[2] + vector[2])\n        return translated\n\n    def CalculateRadiusAndAngle(self, edgeLength):\n        degAngle = self.a\n        angle = radians(degAngle)\n        self.r = radius = edgeLength / (2 * sin(angle / 2))\n        return radius, angle\n    \n    def CalculateRadiusAndAngleForAnglePresets(self, mode, initR, initA, edgeLength):\n        radius = initR\n        angle = initA\n\n        if mode == \"180\":\n            radius = edgeLength / 2\n            angle = pi\n        elif mode == \"120\":\n            radius = edgeLength / 3 * sqrt(3)\n            angle = 2 * pi / 3\n        elif mode == \"90\":\n            radius = edgeLength / 2 * sqrt(2)\n            angle = pi / 2\n        elif mode == \"60\":\n            radius = edgeLength\n            angle = pi / 3\n        elif mode == \"45\":\n            radius = edgeLength / (2 * sin(pi / 8))\n            angle = pi / 4\n        elif mode == \"30\":\n            radius = edgeLength / (2 * sin(pi / 12))\n            angle = pi / 6\n        self.a = degrees(angle)\n        self.r = radius\n        debugPrint (\"mode output, radius = \", radius, \"angle = \", angle)\n        return radius, angle\n\n    def getSpinCenterClosestToRefCenter(self, objLocation, roots, flip):\n        root0Distance = (Vector(objLocation) - Vector(roots[0])).length\n        root1Distance = (Vector(objLocation) - Vector(roots[1])).length \n\n        chosenId = 0\n        rejectedId = 1\n        if (root0Distance > root1Distance):\n            chosenId = 1\n            rejectedId = 0\n        if flip:\n            return roots[rejectedId], roots[chosenId] \n        else:\n            return roots[chosenId], roots[rejectedId]\n\n    def addMissingCoordinate(self, roots, startVertex, plane):\n        if roots != None:\n            a, b = roots[0]\n            c, d = roots[1]\n            if plane == XY:\n                roots[0] = Vector((a, b, startVertex[2]))\n                roots[1] = Vector((c, d, startVertex[2]))\n            if plane == YZ:\n                roots[0] = Vector((startVertex[0], a, b))\n                roots[1] = Vector((startVertex[0], c, d))\n            if plane == XZ:\n                roots[0] = Vector((a, startVertex[1], b))\n                roots[1] = Vector((c, startVertex[1], d))\n        return roots\n\n    def selectEdgesAfterRoundifier(self, context, edges):\n        bpy.ops.object.mode_set(mode = 'OBJECT')\n        bpy.ops.object.mode_set(mode = 'EDIT')\n        mesh = context.scene.objects.active.data\n        bmnew = bmesh.new()\n        bmnew.from_mesh(mesh)\n        self.deselectEdges(bmnew)\n        for selectedEdge in edges:\n            for e in bmnew.edges:\n                if (e.verts[0].co - selectedEdge.verts[0].co).length <= self.threshold \\\n                   and (e.verts[1].co - selectedEdge.verts[1].co).length <= self.threshold:\n                    e.select_set(True)\n\n        bpy.ops.object.mode_set(mode = 'OBJECT')\n        bmnew.to_mesh(mesh)\n        bmnew.free()\n        bpy.ops.object.mode_set(mode = 'EDIT')\n\n\n    def deselectEdges(self, bm):\n        for edge in bm.edges:\n            edge.select_set(False)\n\n    def getSpinAxis(self, plane):\n        axis = (0, 0, 1)\n        if plane == YZ:\n            axis = (1, 0, 0)\n        if plane == XZ:\n            axis = (0, 1, 0)\n        return axis\n\n    @classmethod\n    def poll(cls, context):\n        return (context.scene.objects.active.type == 'MESH') and (context.scene.objects.active.mode == 'EDIT')\n\n\n\ndef draw_item(self, context):\n    self.layout.operator_context = 'INVOKE_DEFAULT'\n    self.layout.operator('mesh.edge_roundifier')\n\n\ndef register():\n    bpy.utils.register_class(EdgeRoundifier)\n    bpy.types.VIEW3D_MT_edit_mesh_edges.append(draw_item)\n\n\ndef unregister():\n    bpy.utils.unregister_class(EdgeRoundifier)\n    bpy.types.VIEW3D_MT_edit_mesh_edges.remove(draw_item)\n\nif __name__ == \"__main__\":\n    register()","sub_path":"EdgeRoundifier.py","file_name":"EdgeRoundifier.py","file_ext":"py","file_size_in_byte":32944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"87918643","text":"# 🚨 Don't change the code below 👇\nprint(\"Welcome to Python Pizza Deliveries!\")\nsize = input(\"What size pizza do you want? S, M, or L:  \")\nadd_pepperoni = input(\"Do you want pepperoni? Y or N : \")\nextra_cheese = input(\"Do you want extra cheese? Y or N:  \")\n# 🚨 Don't change the code above 👆\n\n#Write your code below this line 👇 \n#Constant\nSMALL_PIZZA = 15\nMEDIUM_PIZZA = 20\nLARGE_PIZZA = 25\n\n\n#Total bill here\nbill = 0\n\nif size == \"S\":\n  bill += SMALL_PIZZA\nelif size == \"M\":\n  bill += MEDIUM_PIZZA\nelse:\n  size == LARGE_PIZZA\n\nif add_pepperoni == \"Y\":\n  if size == \"Y\":\n    bill+=2\n  else:\n    bill+=3\nif extra_cheese == \"Y\":\n  bill+= 1\n\nprint(f\"You final total is {bill}\")\n\n\n\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"397269087","text":"import tensorflow as tf\nfrom bayesdend.rec_model.dend_model_util import soma_network, spine_network\nfrom bayesdend.rec_model.spikes import *\nfrom tensorflow.contrib.distributions import Bernoulli, RelaxedBernoulli\nfrom bayesdend.utils.tf_util import repeat_elements\n\n\nclass DendriteCorrelated(Spikes):\n    \"\"\"\n    Dendritic sample-based model.\n    \"\"\"\n\n    def __init__(self, Nb, temp, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.Nb = Nb\n        self.temp = temp\n\n    def log_prob(self, data_ph, prior, genparams):\n        \"\"\"\n        params genparams: a list of generative parameters\n        \"\"\"\n        if genparams is not None:\n            print(\"gene_params condictioning.\")\n            spine_gen_params = {}\n            for key, gen_per in genparams.items():\n                gen_per = tf.stop_gradient(gen_per)\n                #if 'sigma' not in key:\n                if gen_per.shape.as_list()[0] == self.Nc:\n                    spine_gen_params[key] = gen_per[1:]/20.0\n                else:\n                    spine_gen_params[key] = gen_per/20.0\n        else:\n            spine_gen_params = None\n\n        with tf.variable_scope('recognition'):\n            with tf.variable_scope('soma'):\n                logit_soma = soma_network(data_ph['traces'], self.window_size, self.upsample, self.Nc,\n                                          self.batch_size, self.sample_rate, self.conv_mode, self.measurement_order)\n\n            with tf.variable_scope('spine'):\n                # feed in soma_samples, and soma_probability\n                logit_spine = spine_network(data_ph['traces'], None, logit_soma, self.batch_size,\n                                            self.window_size, self.upsample, self.Nc-1, self.Nb,\n                                            self.sample_rate, spine_gen_params, self.conv_mode)\n\n            new_window_size = int(logit_soma.get_shape().as_list()[1]/self.upsample)\n            # [batch_size, time, Nc]\n            cond_len = 4 * self.upsample # conditioning on 4 data_points\n            logit_soma = tf.pad(logit_soma, [[0, 0], [cond_len-1, 0], [0, 0]], 'CONSTANT')\n            logit_spine = tf.pad(logit_spine, [[0, 0], [cond_len-1, 0], [0, 0]], 'CONSTANT')\n            # autoregressive sampling (ancester sampling)\n            with tf.variable_scope('sampling'):\n                # a better way would be just return valid, think about it later\n                soma_logit_cond = []\n                spine_logit_cond = []\n                soma_sample = []\n                spine_sample = []\n                # parameters\n                w_logit_soma= tf.Variable(tf.random_normal([cond_len], stddev=0.35), name=\"w_logit_soma\")\n                w_sample_soma = tf.Variable(tf.random_normal([cond_len - 1], stddev=0.35), name='w_sample_soma')\n                bias_soma_cond = tf.Variable(0.0, name='bias_soma_cond')\n\n                w_logit_spine = tf.Variable(tf.random_normal([cond_len], stddev=0.35), name='w_logit_spine')\n                w_sample_spine = tf.Variable(tf.random_normal([cond_len -1], stddev=0.35), name='w_sample_spine')\n                bias_spine_cond = tf.Variable(0.0, name='bias_spine_cond')\n                w_spine_soma_sample = tf.Variable(tf.random_normal([cond_len]), name='spine_cond_soma_sample')\n                w_spine_soma_logit = tf.Variable(tf.random_normal([cond_len]), name='spine_cond_soma_logit')\n\n                soma_sample_dynamic = [tf.zeros([self.Nb, self.batch_size, 1])] * (cond_len-1)\n                spine_sample_dynamic = [tf.zeros([self.Nb, self.batch_size, self.Nc-1])] * (cond_len-1)\n                for i in range(new_window_size * self.upsample):\n                    # sample soma first\n                    soma_cond_logit = tf.expand_dims(tf.tensordot(w_logit_soma, logit_soma[:, i:i+cond_len], axes=[[0], [1]]), axis=0) +\\\n                                                 tf.tensordot(w_sample_soma, tf.stack(soma_sample_dynamic, axis=2), axes=[[0], [2]]) + bias_soma_cond\n                    soma_logit_cond.append(soma_cond_logit)\n                    one_sample = RelaxedBernoulli(temperature=self.temp, logits=soma_cond_logit).sample()\n                    soma_sample.append(one_sample)\n                    soma_sample_dynamic.append(one_sample)\n                    # sample spine second\n                    spine_cond_logit = tf.expand_dims(tf.tensordot(w_logit_spine, logit_spine[:, i:i+cond_len], axes=[[0], [1]]), axis=0) +\\\n                                                 tf.tensordot(w_sample_spine, tf.stack(spine_sample_dynamic, axis=2), axes=[[0], [2]]) +\\\n                                                 tf.expand_dims(tf.tensordot(w_spine_soma_logit, logit_soma[:, i:i+cond_len], axes=[[0], [1]]), axis=0) +\\\n                                                 tf.tensordot(w_spine_soma_sample, tf.stack(soma_sample_dynamic, axis=2), axes=[[0], [2]]) + bias_spine_cond\n                    spine_logit_cond.append(spine_cond_logit)\n                    one_spine_sample = RelaxedBernoulli(temperature=self.temp, logits=spine_cond_logit).sample()\n                    spine_sample.append(one_spine_sample)\n                    spine_sample_dynamic.append(one_spine_sample)\n\n                    soma_sample_dynamic.pop(0)\n                    spine_sample_dynamic.pop(0)\n\n            soma_logit_cond = tf.stack(soma_logit_cond, axis=2)\n            spine_logit_cond = tf.stack(spine_logit_cond, axis=2)\n            soma_sample = tf.stack(soma_sample, axis=2)\n            spine_sample = tf.stack(spine_sample, axis=2)\n            soma_spine_logit = tf.concat([soma_logit_cond, spine_logit_cond], -1)\n            soma_spine_sample = tf.concat([soma_sample, spine_sample], -1)\n\n            self.Qsamp = soma_spine_sample\n\n            # prior\n            firing_rate = prior / (self.upsample * self.sample_rate)\n            p_rate = tf.ones_like(self.Qsamp) * firing_rate\n\n            p_bernoulli = Bernoulli(probs = p_rate)\n            q_bernoulli = Bernoulli(logits = soma_spine_logit)\n\n            log_q = tf.reduce_mean(q_bernoulli.log_prob(soma_spine_sample), (1, 2, 3))\n            log_p = tf.reduce_mean(p_bernoulli.log_prob(soma_spine_sample), (1, 2, 3))\n\n            # for tensorflow plotting\n            self.kl_divergence_sample = tf.reduce_mean(log_q - log_p)\n            self.kl_divergence_ana = 0\n\n            # some diagnostics\n            jitters = tf.reshape(tf.reduce_mean(self.Qsamp, 0)[:, :, 0:1], [self.batch_size, new_window_size, self.upsample, 1])\n            # normalize it by ground truth\n            self.jitters_ind = tf.reduce_mean(jitters, [0, 1, 3])\n        return [log_p, log_q]\n\n    def get_latents(self):\n        latents = {}\n        latents['spikes'] = self.Qsamp\n        return latents\n\n    def get_expected_rate(self):\n        return tf.reduce_mean(self.Qsamp, 0)\n\n    def get_samples(self):\n        \"\"\"\n        Get samples: [Nb, batch_size, window_size, Nc]\n        \"\"\"\n        return self.Qsamp\n","sub_path":"bayesdend/rec_model/dendrite_correlated.py","file_name":"dendrite_correlated.py","file_ext":"py","file_size_in_byte":6946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"188161570","text":"from __future__ import print_function\nfrom .Utils import *\n\nimport numpy as np\nimport random\nimport inspect\nimport time\nimport sys\nimport os\n\nclass Struct:\n    def __init__(self, **entries):\n        self.__dict__.update(entries)\n\n#------------------------\n#SPATIAL (Spherical-)GRID\n#------------------------\n \ndef grid(XYZmax, NP, artist = False, radmc3d = False, include_zero = True):\n    \"\"\"\n    \"\"\"\n    \"\"\"\n    XYZmax: Maximum physical domain of the grid\n    Please provide XYZmax as a 3D-list. XYZmax=[Xmax,Ymax,Zmax]\n    NP: Number of grid points for each coordinate\n    Please provide NP as a 3D-list. NP=[Nx,Ny,Nz]\n    \"\"\"\n\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n    \n    XYZmax = np.array(XYZmax)\n\n    #----------------\n    #NUMBER OF POINTS\n    #----------------\n    #Each NP becomes odd if it's not. This is done to force the grid to contain the midplane too.\n    \n    if include_zero:\n        NP_dum = [ NP[i] + 1 if NP[i]%2 == 0 else NP[i] for i in xrange(3) ] \n        print('Changing the number of grid points to force it to contain the midplane...' \n              if NP != NP_dum\n              else '... ... ...'\n              )\n        NP = NP_dum\n    else: \n        for i in xrange(3): print('Coordinate zero NOT included for axis: %d'%i\n                                  if NP[i]%2 == 0\n                                  else 'Coordinate zero included for axis: %d'%i\n                                  )\n    \n    #--------\n    #XYZ GRID\n    #--------\n    print ('Calculating Grid...')\n    \n    Xmax,Ymax,Zmax = XYZmax    \n    step = 2. * XYZmax / NP\n    #epsilon = [RSun / 1.] * 3\n    \n    #In case of radmc3d or artist: \n    # a dummy point is created at the end of each coordinate. Artist  won't read them but they are necessary for it to work well! \n    \n    if radmc3d or artist: \n        step = 2. * XYZmax / (NP - np.ones(3))\n        XYZgrid = [np.linspace(-XYZmax[i], XYZmax[i], NP[i] + 1) for i in xrange(3)]\n        #XYZgrid = [np.append( np.linspace(-XYZmax[i], XYZmax[i], NP[i]), (XYZmax[i] + step[i]) ) for i in xrange(3)]\n        X, Y ,Z = XYZgrid #The grid must contain an extra node but...\n        X = 0.5 * ( X[0:NP[0]] + X[1:NP[0]+1] ) #Moving the node from the corner to the center of the boxel. length = lengthofcorners - 1\n        Y = 0.5 * ( Y[0:NP[1]] + Y[1:NP[1]+1] )  \n        Z = 0.5 * ( Z[0:NP[2]] + Z[1:NP[2]+1] )\n  \n        #X = X[:-1]; Y = Y[:-1]; Z = Z[:-1] #...the calculations must be done w/o that node \n    else: #lime\n        XYZgrid = [np.linspace(-XYZmax[i], XYZmax[i], NP[i]) for i in xrange(3)]\n        X, Y, Z = XYZgrid\n    \n    #--------------------------------------\n    #Extended Lists of distance coordinates\n    #--------------------------------------\n    rRxyzList = np.array([ ((x**2 + y**2 + z**2)**0.5, (x**2 + y**2)**0.5, x,y,z) for x in X for y in Y for z in Z])\n    \n    rList = rRxyzList[:,0] ; RList = rRxyzList[:,1]; xList = rRxyzList[:,2]; yList = rRxyzList[:,3]; zList = rRxyzList[:,4]\n    rList = np.where(rList < 1., sorted(set(rList))[1] / 2 , rList ) # If r == 0: use the second minimum value of r divided by 2\n    \n    #\"set\" eliminates duplicates and \"sorted\" sorts values upwards \n    RList = np.where(RList < 1., sorted(set(RList))[1] / 2 , RList )\n    \n    #-----\n    #THETA\n    #-----\n    thetaList = np.arccos(zList / rList)\n    halfPi = 0.5 * np.pi\n    theta4vel = thetaList\n\n    #If theta is under the xy plane, take its image above the plane.\n    # This is done to avoid negative cosines in func streamline(), \n    #  taking advantage on the symmetry of discs and envelopes along xy.         \n    deltaTh = np.where(thetaList > np.pi/2, thetaList - halfPi, 0.)\n    thetaList = thetaList - 2 * deltaTh\n\n    #-----\n    #PHI\n    #-----\n    phiList = np.arctan2(yList,xList)\n    twoPi =  2 * np.pi\n    phiList = np.where(phiList < 0, phiList + twoPi, phiList )\n    #-----\n    #-----\n\n    dx = abs(X[1] - X[0]) #Linear step in X\n    XYZ = [xList,yList,zList]\n    rRTP = [rList,RList,thetaList,phiList]\n    \n\n    print ('Number of grid nodes for x,y,z:', NP)\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n        \n    return Struct( **{'XYZgrid': XYZgrid, 'XYZ': XYZ, 'rRTP': rRTP, 'theta4vel': theta4vel, 'NPoints': len(rList), 'Nodes': NP, 'step': dx})\n\n\"\"\"\n#Not tested\n\n#How the grid_FIXED function should work:                               \n# 1: Provide the mapping function f(x)                            \n# 2: Provide the cleared function x(f)                            \n# 3: Provide the Maximum range of the coordinate                  \n# 4: Provide the Number of points to make the map                 \n# 5: (optional) Provide the coordinate datafile:                  \n#    In this case must be done MU.grid_FIXED(0,0,0,0,\"x.dat\")     \n#    Remember that the code is built to doesn't take the last     \n#    value of the grid                                            \n                                                                  \n                                                                  \nx=MU.grid_FIXED(lambda x: x*x, lambda x: x**0.5,8.*1900,15)       \ny=MU.grid_FIXED(lambda x: x, lambda x: x,8.*1900,15)              \nz=MU.grid_FIXED(lambda x: x, lambda x: x,8.*1900,15)              \n                                                                  \n                                                                  \n#x=MU.grid_FIXED(0,0,0,0,\"x.dat\")                                 \n#y=MU.grid_FIXED(0,0,0,0,\"y.dat\")                                 \n#z=MU.grid_FIXED(0,0,0,0,\"z.dat\")                                 \n                                                                  \n                                                                  \nGRID=[x,y,z]                                                      \n\ndef grid_FIXED(Fx=False,xF=False,Fmax=False,NP=False,File=False):\n\n#Fx->F(x) -> Mapping function\n#xF->x(F) -> Inverse function\n#Fmax -> Real maximum in the domain\n\n    \n    xList=[]\n    fList=[]\n    xmax=0\n\n    if Fx:\n        \n        xmax = xF(Fmax) #Finding the virtual maximum of x, \n                        # such that the domain remains between\n                        #  [-Fmax,Fmax] after calculating F(x)\n        \n        \n        xList=np.arange(-xmax,xmax+2*xmax/NP+2./NP,2*xmax/NP)\n        fList=Fx(xList)\n    \n    else: fList=np.loadtxt(File)/AU\n    '''\n        if (fList[-1]-Fmax)<1e-6*Fmax:\n            fList[0]=Fmax\n            fList[-1]=Fmax\n    '''\n        \n        \n    return fList\n\"\"\"\n\n#---------------------------------\n#Spherical base --> Cartesian base\n#---------------------------------\n\ndef sphe_cart(V_sphe, theta, phi):\n    \n    #--------------------------------------------------------------\n    #Wikipedia: Coordenadas esfericas, subseccion 'Base coordenada'\n    #--------------------------------------------------------------\n   \n    if hasattr(theta, '__iter__') and hasattr(phi, '__iter__'): #A list of vectors\n    #np.multiply and operator * are esentially the same even in terms of time\n        vx = np.multiply( V_sphe, np.array([np.sin(theta) * np.cos(phi), np.cos(theta) * np.cos(phi), -np.sin(phi)]).T ).sum(1) \n        vy = np.multiply( V_sphe, np.array([np.sin(theta) * np.sin(phi), np.cos(theta) * np.sin(phi), np.cos(phi)]).T ).sum(1) \n        vz = np.multiply( V_sphe, np.array([np.cos(theta) , -np.sin(theta), np.zeros(len(theta))]).T ).sum(1)\n     \n    else: #Just one vector\n     \n        vx = np.dot( V_sphe, np.array([np.sin(theta) * np.cos(phi), np.cos(theta) * np.cos(phi), -np.sin(phi)]) )\n        vy = np.dot( V_sphe, np.array([np.sin(theta) * np.sin(phi), np.cos(theta) * np.sin(phi), np.cos(phi)]) )\n        vz = np.dot( V_sphe, np.array([np.cos(theta) , -np.sin(theta), 0.]))\n\n    V_cart=np.array([vx,vy,vz])\n    print ('%s is done!'%inspect.stack()[0][3])\n\n    return V_cart\n\n#-------------------------------------\n#STREAM LINES FOR DENSITY AND VELOCITY\n#-------------------------------------\n\ndef streamline(Rd, GRID):\n\n#Cartesian-grid to work in. [xList,yList,zList]\n\n#Values for cos(theta0), see Mendoza 2004\n\n    rRTP = GRID.rRTP\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, RList, thetaList = rRTP[:-1] #Phi is not used for streamlines\n    rNorm = rList / Rd\n    \n    #----------------------------\n    #MODEL. Mendoza et al. (2004)\n    #----------------------------\n    costheta = np.cos(thetaList)\n\n    #Case r == Rd     \n    costheta0 = np.where( rNorm == 1., costheta**(1./3), 0. )\n\n    #Case r > Rd \n    term1 = abs(rNorm - 1) / 3.  \n    term1sqrt = 2 * term1**0.5\n    term1_15 = 2 * term1**1.5 \n\n    good_ind = np.where( rNorm > 1. )\n    term2 = np.zeros(len(term1))\n    term2[good_ind] = np.sinh( 1./3 * np.arcsinh( rNorm[good_ind] * costheta[good_ind] / term1_15[good_ind] ) )\n\n    costheta0 = np.where( rNorm > 1., term1sqrt * term2, costheta0 )\n\n    #Case r < Rd and cond > 0\n    cond = ( rNorm * 0.5 * costheta )**2 - term1**3\n\n    good_ind = np.where( (rNorm < 1.) & (cond > 0.) )\n    term2[good_ind] = np.cosh( 1./3 * np.arccosh( rNorm[good_ind] * costheta[good_ind] / term1_15[good_ind] ) )\n\n    costheta0 = np.where( (rNorm < 1.) & (cond > 0.) , term1sqrt * term2, costheta0 )\n\n    #Case r < Rd and cond < 0\n    good_ind = np.where( (rNorm < 1.) & (cond < 0.) )\n    term2[good_ind] = np.cos( 1./3 * np.arccos( rNorm[good_ind] * costheta[good_ind] / term1_15[good_ind] ) )\n    costheta0 = np.where( (rNorm < 1.) & (cond < 0.) , term1sqrt * term2, costheta0 )\n\n    #Case costheta0 > 1.0 due to computational waste of the order of 1e-16 above 1.0\n    costheta0 = np.where ( costheta0 > 1.0, 1.0, costheta0)\n\n    print ('%s is done!'%inspect.stack()[0][3])\n\n    return costheta0 \n\n#-------------------------------------\n#-------------------------------------\n\n#-------------------\n#DENSITY FUNCTION\n#-------------------\n\ndef density_Env_Disc(RStar, Rd, rhoE0, Arho, GRID, \n                     discFlag = True, envFlag = False, \n                     rdisc_max = False, renv_max = False, \n                     ang_cavity = False):\n\n#RStar: Star radius\n#Rd: Centrifugal radius\n#rhoE0: density at Rd and theta=pi/2\n#Arho: Factor between envelope and disk densities\n#GRID: Cartesian-grid to work in. [xList,yList,zList] \n\n    XYZgrid, XYZ, rRTP = GRID.XYZgrid, GRID.XYZ, GRID.rRTP\n    NPoints = GRID.NPoints\n    #------------\n    #LISTS TO USE\n    #------------\n    zList = XYZ[2]\n    rList, RList, thetaList = rRTP[:-1] #Phi is not used for density\n\n    #----------------------------------------------\n    #MODEL. Ulrich (1976) - Keto,E & Zhang,Q (2010)\n    #----------------------------------------------\n\n    #------------\n    #DISC PROFILE\n    #------------\n    if discFlag:\n        print ('Calculating Keplerian flared-disc density...')\n        \n        if not rdisc_max: rdisc_max = Rd #Keto's value for the disc to stop\n        rhoD0 = Arho * rhoE0 #Normalization factor based on the envelope\n        H0 = 0.01 * RStar #Scaleheight at RStar\n        H = H0 * (RList / RStar)**1.25  #Scaleheight\n        rhoDISC = np.where( RList <= rdisc_max, rhoD0 * (Rd / RList)**2.25 * np.exp(-0.5 * zList**2 / H**2), 1.0)#1.0e9 / 2)\n        rhoDISC = np.where( rhoDISC < 1.0, 1.0, rhoDISC)\n    else: \n        print ('No Keplerian flared-disc was invoked!')\n        rhoDISC = np.zeros(NPoints)\n    \n    #----------------\n    #ENVELOPE PROFILE\n    #----------------\n    if ang_cavity: print ('Set cavity for density with half-aperture %.1f deg'%(ang_cavity * 180 / np.pi))\n    \n    if envFlag:\n        print ('Calculating stream lines for Ulrich envelope...')\n        if not renv_max: renv_max = np.max(XYZgrid[0]) #A sphere inscribed in the coordinate X. ##CHANGE this by the smallest of the 3 maximum (1 for each axis)\n\n        costheta = np.cos(thetaList)\n        costheta0 = streamline(Rd,GRID)\n        print ('Calculating Envelope density...')\n        rhoENV = np.where( (rList <= renv_max) & (thetaList >= ang_cavity), \n                           ((rhoE0 * (rList / Rd)**-1.5) *\n                            ((1 + (costheta / costheta0))**-0.5) *\n                            (1 + (Rd / rList) * (3. * costheta0**2 - 1))**-1), \n                           1.0e9 )\n        rhoENV = np.where( rhoENV < 1.0, 1.0, rhoENV)\n    else:\n        print ('No Envelope was invoked!')\n        costheta0 = False\n        rhoENV = np.zeros(NPoints)\n    \n    #----------------------------------------------\n    #----------------------------------------------\n    \n    RHO = rhoDISC + rhoENV \n\n    nonzero_ids = np.where(RHO != 0.0)\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': RHO, 'disc': rhoDISC, 'env': rhoENV, 'discFlag': discFlag, 'envFlag': envFlag, \n                      'r_disc': rdisc_max, 'r_env': renv_max, 'streamline': costheta0,\n                      'nonzero_ids': nonzero_ids} )\n\n#-------------------\n#-------------------\n\n#------------------------------\n#DENSITY (Hamburguers) FUNCTION\n#------------------------------\n\ndef density_Hamburgers(RStar, shFactor, Ro, rhoE0, Arho, GRID, \n                       p = 2.25, q = 0.5, \n                       Rt = False, discFlag=True, rdisc_max = False):\n\n#RStar: Star radius\n#shFactor: Scaleheight normalization constant: H0 = shFactor * RStar\n#Ro: Outer radius of the disk\n#rhoE0: density at Rd and theta=pi/2\n#Arho: Density factor \n#GRID\n\n    XYZ, rRTP = GRID.XYZ, GRID.rRTP\n\n    #------------\n    #LISTS TO USE\n    #------------\n    zList = XYZ[2]\n    rList, RList = rRTP[:-2] \n\n    #-----------------------------------------------------\n    #MODEL. Chin-Fei Lee, Zhi-Yun Li, Paul Ho, et al. 2017\n    #-----------------------------------------------------\n\n    #------------\n    #DISC PROFILE\n    #------------\n    if discFlag:\n        print ('Calculating Burger-disc density...')\n        if not rdisc_max: rdisc_max = Ro\n        rhoD0 = Arho * rhoE0 \n        H0 = shFactor * RStar\n        print ('Scale-height normalization constant:', H0 / AU * 1 / ((RStar/AU)**(1 + 0.5*(1-q))))\n        H = H0 * (RList / RStar)**(1 + 0.5*(1-q)) #Scaleheight, with no tapering \n        if Rt: H = H * np.exp(-((RList-Rt) / (Ro-Rt))**2)  #Scaleheight, with tapering \n        rhoDISC = np.where( RList <= rdisc_max, rhoD0 * (RList / Ro)**-p * np.exp(-0.5 * zList**2 / H**2), 1.0)\n        rhoDISC = np.where( rhoDISC < 1.0, 1.0, rhoDISC)\n    else: \n        print ('No Disc was invoked!')\n        rhoDISC = np.zeros(GRID.NPoints)\n        \n    #----------------------------------------------\n    #----------------------------------------------\n\n    nonzero_ids = np.where(rhoDISC != 0.0)\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': rhoDISC, 'disc': rhoDISC, 'env': 0., 'H': H,  \n                      'discFlag': discFlag, 'envFlag': False, 'Rt': Rt, \n                      'r_disc': rdisc_max, 'r_env': False,\n                      'nonzero_ids': nonzero_ids} ) \n\n#------------------------------\n#------------------------------\n\n#-----------------------------------\n#DENSITY (PowerLaw-mean_rho) FUNCTION\n#-----------------------------------\n\ndef density_Powerlaw(r_max, rho_mean, q, GRID, rho_min = 1.0e3):\n\n#r_max: Maximum radius of the envelope \n#rho_mean: Mean density of the Envelope \n#q: power-law for density\n#GRID: Grid to work in\n\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, NPoints = GRID.rRTP[0], GRID.NPoints #Due to spherical symmetry only r is needed\n\n    #------------------------\n    #MODEL. Envelope powerlaw\n    #------------------------\n    print ('Calculating Envelope density with power-law...')\n    rqList = np.where(rList <= r_max , rList**q, 0.)\n\n    #As rho_mean = 1/NTotal * np.sum(rho0 * r**q), the normalization rho0 is calculated as follows:  \n    rho0 = NPoints * rho_mean / np.sum(rqList)\n    rhoENV = rho0 * rqList\n    rhoENV = np.where(rhoENV < rho_min, rho_min, rhoENV)\n\n    #rhoENV = np.where( rho0 * rqList < 1.0, rho_min, rho0 * rqList )\n    \n    #------------------------\n    #------------------------\n    \n    nonzero_ids = np.where(rhoENV != 0.0)\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': rhoENV, 'disc': np.zeros(NPoints), 'env': rhoENV, \n                      'discFlag': False, 'envFlag': True, 'r_disc': False, 'r_env': r_max,\n                      'nonzero_ids': nonzero_ids} ) \n\n#---------------------------\n#---------------------------\n\n#------------------------------------\n#DENSITY (PowerLaw-standard) FUNCTION\n#------------------------------------\n\ndef density_Powerlaw2(r_max, r_min, rho0, q, GRID, rho_min = 1.0e3):\n\n#r_max: Maximum radius of the envelope \n#rho_mean: Mean density of the Envelope \n#q: power-law for density\n#GRID: Grid to work in\n\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, NPoints = GRID.rRTP[0], GRID.NPoints #Due to spherical symmetry only r is needed\n\n    #------------------------\n    #MODEL. Envelope powerlaw\n    #------------------------\n    print ('Calculating Envelope density with power-law...')\n    rqList = np.where((rList >= r_min) & (rList <= r_max) , rList**q, 0.)\n    rhoENV = rho0 * rqList\n    rhoENV = np.where(rhoENV < rho_min, rho_min, rhoENV)\n\n    #------------------------\n    #------------------------\n    \n    nonzero_ids = np.where(rhoENV != 0.0)\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': rhoENV, 'disc': np.zeros(NPoints), 'env': rhoENV, \n                      'discFlag': False, 'envFlag': True, 'r_disc': False, 'r_env': r_max,\n                      'nonzero_ids': nonzero_ids} ) \n\n#---------------------------\n#---------------------------\n\n#-----------------------------------\n#DENSITY (Keto2003, HCH_II) FUNCTION\n#-----------------------------------\n\ndef density_Keto_HII(MStar, r_min, r_max, rho_s, T, GRID, q = 1.5):\n\n#r_min: Minimum radius of the envelope (must be > 0)\n#r_max: Maximum radius of the envelope \n#r_s: Reference radius\n#rho_s: Density at r_s\n#q: power-law for density\n#GRID: Grid to work in\n\n    #----------------\n    #LOCAL PARAMETERS\n    #----------------\n    gamma = 5./3 #Heat capacity ratio for a monoatomic gas \n    cs = (gamma * T * kb / Mu)**0.5 #Speed of sound for H_II at T\n    rs = 0.5 * G * MStar / cs**2 #Sonic point (v_escape = v_sound)\n    if r_min > rs: sys.exit('ERROR: Please define r_min <= rs')\n\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, NPoints = GRID.rRTP[0], GRID.NPoints #Due to spherical symmetry only r is needed\n\n    #-------------------------------------------------\n    #MODEL. HCH_II region, Keto 2003 (double gradient)\n    #-------------------------------------------------\n    print ('Calculating H_II Envelope density with Keto2003 (double gradient)...')\n    print ('Speed of sound (cs):', cs/1e3, 'km/s')\n    print ('Sonic point (rs):', rs/AU, 'au')\n    rhoENV = np.ones(GRID.NPoints)\n    ind_gravity = np.where((rList >= r_min) & (rList <= rs))\n    ind_pressure = np.where((rList > rs) & (rList <= r_max))\n    rhoENV[ind_gravity] = rho_s * (rs / rList[ind_gravity])**q\n    rhoENV[ind_pressure] = rho_s * np.exp(-2 * (1 - rs / rList[ind_pressure]))\n    #------------------------\n    #------------------------\n\n    nonzero_ids = np.where(rhoENV != 0.0)\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': rhoENV, 'disc': np.zeros(NPoints), 'env': rhoENV, \n                      'discFlag': False, 'envFlag': True, 'r_disc': False, \n                      'r_min': r_min, 'r_env': r_max, 'rs': rs,\n                      'nonzero_ids': nonzero_ids} ) \n\n#---------------------------\n#---------------------------\n\n#-----------------------------------\n#DENSITY (PowerLaw, HCH_II) FUNCTION\n#-----------------------------------\n\ndef density_Powerlaw_HII(r_min, r_max, r_s, rho_s, q, GRID):\n\n#r_min: Minimum radius of the envelope (must be > 0)\n#r_max: Maximum radius of the envelope \n#r_s: Reference radius\n#rho_s: Density at r_s\n#q: power-law for density\n#GRID: Grid to work in\n\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, NPoints = GRID.rRTP[0], GRID.NPoints #Due to spherical symmetry only r is needed\n\n    #------------------------------\n    #MODEL. HCH_II region, Powerlaw \n    #------------------------------\n    print ('Calculating H_II Envelope density with power-law...')\n    rhoENV = np.where((rList >= r_min) & (rList <= r_max), rho_s * (r_s / rList)**q, 1.)\n    #------------------------\n    #------------------------\n\n    nonzero_ids = np.where(rhoENV != 0.0)\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': rhoENV, 'disc': np.zeros(NPoints), 'env': rhoENV, \n                      'discFlag': False, 'envFlag': True, 'r_disc': False, 'r_env': r_max,\n                      'nonzero_ids': nonzero_ids} ) \n\n#---------------------------\n#---------------------------\n\n#---------------------------\n#DENSITY (Constant) FUNCTION\n#---------------------------\n\ndef density_Constant(Rd, GRID, discDens = 0, rdisc_max = False, envDens = 0, renv_max = False):\n\n    rRTP = GRID.rRTP\n    NPoints = GRID.NPoints\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, RList = rRTP[:-2] \n\n    #------------\n    #DISC PROFILE\n    #------------\n    if discDens:\n        print ('Setting constant Disc density...')\n        if not rdisc_max: rdisc_max = Rd\n        rhoDISC = np.where( RList <= rdisc_max, discDens, 1.0)\n    else: \n        print ('No Disc was invoked!')\n        rhoDISC = np.zeros(NPoints)\n\n    #----------------\n    #ENVELOPE PROFILE\n    #----------------\n    if envDens:\n        print ('Setting constant Envelope density...')\n        if not renv_max: renv_max = Rd #np.max(GRID.XYZgrid[0])\n        rhoENV = np.where( rList <= renv_max, envDens, 1.0)\n    else: \n        print ('No Envelope was invoked!')\n        rhoENV = np.zeros(NPoints)\n    \n    #----------------------------------------------\n    #----------------------------------------------\n    RHO = rhoDISC + rhoENV\n        \n    nonzero_ids = np.where(RHO != 0.0)\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': RHO, 'disc': rhoDISC, 'env': rhoENV, \n                      'discFlag': bool(discDens), 'envFlag': bool(envDens), \n                      'r_disc': rdisc_max, 'r_env': renv_max,\n                      'nonzero_ids': nonzero_ids} ) \n\n#---------------------------\n#---------------------------\n\n#------------------\n#ABUNDANCE FUNCTION\n#------------------\n\ndef abundance(val, NPoints):\n\n    abundList = np.ones(NPoints) * val\n    \n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return abundList \n\n#------------------\n#------------------\n\n#-----------------\n#GAS-TO-DUST RATIO\n#-----------------\n\ndef gastodust(val, NPoints):\n    \n    gtdratioList = np.ones(NPoints) * val\n    \n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return gtdratioList \n\n#-----------------\n#-----------------\n\n#----------------------\n#TEMPERATURE FUNCTION\n#----------------------\n\ndef temperature(TStar, Rd, T10Env, RStar, MStar, MRate, BT, density, GRID, \n                Tmin_disc = 30., Tmin_env = 30., p = 0.33, ang_cavity = False):\n\n#TStar: Star temperature\n#T10Env: Envelope temperature at 10AU\n#RStar: Star radius\n#MStar: Star mass\n#MRate: Mass accretion rate\n#BT: Disc temperature factor\n#p: (Envelope) Temperature power law exponent \n#GRID: Grid to work in\n\n    rRTP = GRID.rRTP\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, RList, thetaList = rRTP[:-1] \n    rhoDISC, rhoENV = density.disc, density.env\n\n    #------------------------------\n    #MODEL. Keto,E & Zhang,Q (2010)\n    #------------------------------\n\n    #------------\n    #DISC Profile\n    #------------\n    if density.discFlag:\n        print ('Calculating Keplerian flared-disc temperature...')\n        rdisc = density.r_disc\n        if density.envFlag:\n            renv = density.r_env\n            tempDISC = np.where( (RList <= rdisc) & (rList <= renv) , \n                                 BT * (3*G * MStar * MRate / (4*np.pi * sigma * RList**3) * (1 - (RStar / RList)**0.5))**0.25, \n                                 Tmin_disc)\n        else: tempDISC = np.where(RList <= rdisc , \n                                  BT * (3*G * MStar * MRate / (4*np.pi * sigma * RList**3) * (1 - (RStar / RList)**0.5))**0.25, \n                                  Tmin_disc)\n    else: tempDISC = 1.\n\n    #----------------\n    #ENVELOPE PROFILE\n    #----------------\n    if ang_cavity: print ('Set cavity for temperature with half-aperture %.1f deg'%(ang_cavity * 180 / np.pi))\n\n    if density.envFlag:\n        print ('Calculating Envelope temperature...')\n        renv = density.r_env\n        tempENV = np.where( (rList <= renv) & (thetaList >= ang_cavity), \n                            T10Env * 10**p * (rList / AU)**-p, \n                            Tmin_env)\n        tempENV = np.where(tempENV < Tmin_env, Tmin_env, tempENV)\n        #tempENV = TStar * (RStar / (2.*rList))**(2. / (4+p))\n    else: tempENV = 1.\n    \n    #----------------------------------------------\n    #----------------------------------------------\n\n    #Weighted temperature with density \n    TEMP = (tempDISC * rhoDISC + tempENV * rhoENV) / density.total\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': TEMP, 'disc': tempDISC, 'env': tempENV, 'discFlag': density.discFlag, 'envFlag': density.envFlag} )\n\n#----------------------\n#----------------------\n\n#---------------------------------\n#TEMPERATURE (Hamburgers) FUNCTION\n#---------------------------------\n\ndef temperature_Hamburgers(TStar, RStar, MStar, MRate, Rd, T10Env, BT, density, GRID, \n                           p = 0.33, Tmin_disc = 30., Tmin_env = 30., inverted = False):\n\n#TStar: Star temperature\n#T10Env: Envelope temperature at 10AU\n#RStar: Star radius\n#MStar: Star mass\n#MRate: Mass accretion rate\n#BT: Disc temperature factor\n#p: Temperature power law exponent \n#GRID: [xList,yList,zList]\n\n    XYZ, rRTP = GRID.XYZ, GRID.rRTP\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, RList = rRTP[:-2] \n    rhoDISC, rhoENV = density.disc, density.env\n\n    #-----------------------------------------------------------\n    #MODEL. Galvan-Madrid et al. 2018 + Chin-Fei Lee et al. 2017\n    #-----------------------------------------------------------\n\n    #------------\n    #DISC Profile\n    #------------\n    if density.discFlag:\n        print ('Calculating Burger-disc temperature...')\n        zList = XYZ[2]\n        Rdisc = density.r_disc\n        H = density.H\n        T_R = BT * (3*G * MStar * MRate / (4*np.pi * sigma * RList**3) * (1 - (RStar / RList)**0.5))**0.25\n\n        if inverted: \n            print ('Set inverted temperature for Burger-disc...')\n            T_z = np.exp(- 0.5 * zList**2 / H**2)\n            tempDISC = np.where( RList <= Rdisc, T_R * T_z, 1.0) #Maximum in z = 0\n        else: \n            print ('Set not inverted temperature for Burger-disc...')\n            T_z = np.exp(- 0.5 * (abs(zList) - H)**2 / H**2)\n            tempDISC = np.where( RList <= Rdisc, T_R * T_z, 1.0) #Maximum in z = H\n            \"\"\"\n            if density.Rt:\n               tempDISC = np.where( RList < density.Rt, T_R * T_z, 1.0) \n               tempDISC = np.where( (RList >= density.Rt) & (RList <= Rdisc), T_R, tempDISC)\n            else: tempDISC = np.where( RList <= Rdisc, T_R * T_z, 1.0) #Maximum in z = H\n            \"\"\"\n\n        tempDISC = np.where( (RList <= Rdisc) & (tempDISC <= Tmin_disc), Tmin_disc, tempDISC)\n\n    else: tempDISC = 1.\n\n    #----------------\n    #ENVELOPE PROFILE\n    #----------------\n    if density.envFlag:\n        print ('Calculating Envelope temperature...')\n        renv = density.r_env\n        tempENV = np.where( rList <= renv, T10Env * 10**p * (rList / AU)**-p, Tmin_env)\n        #tempENV = TStar * (RStar / (2.*rList))**(2. / (4+p))\n    else: tempENV = 1.\n    \n    #----------------------------------------------\n    #----------------------------------------------\n\n    #Weighted temperature with density \n    TEMP = (tempDISC * rhoDISC + tempENV * rhoENV) / density.total\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': TEMP, 'disc': tempDISC, 'env': tempENV, 'discFlag': density.discFlag, 'envFlag': density.envFlag} )\n\n#---------------------------------\n#---------------------------------\n\n#-------------------------------\n#TEMPERATURE (Constant) FUNCTION\n#-------------------------------\n\ndef temperature_Constant(density, GRID, discTemp = 0, envTemp = 0, backTemp = 30.0):\n\n    rRTP = GRID.rRTP\n    NPoints = GRID.NPoints\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, RList = rRTP[:-2]\n    rhoDISC, rhoENV = density.disc, density.env\n\n    #------------\n    #DISC PROFILE\n    #------------\n    if discTemp:\n        if density.discFlag:\n            print ('Setting constant Disc temperature...')\n            tempDISC = np.where( RList <= density.r_disc, discTemp, backTemp)\n        else: sys.exit('ERROR: The disc calculation was turned ON but there is no density distribution for disc!')\n    else: tempDISC = 0.\n\n    #----------------\n    #ENVELOPE PROFILE\n    #----------------\n    if envTemp:\n        if density.envFlag:\n            print ('Setting constant Envelope temperature...')\n            tempENV = np.where( rList <= density.r_env, envTemp, backTemp)\n        else: sys.exit('ERROR: The envelope calculation was turned ON but there is no density distribution for envelope!')\n    else: tempENV = 0.\n        \n    #----------------------------------------------\n    #----------------------------------------------\n    #Weighted temperature with density \n\n    #zerodens_mask = np.equal(density.total, 0.0)\n    TEMP = np.where(density.total != 0.0, (tempDISC * rhoDISC + tempENV * rhoENV) / density.total, backTemp)\n    #TEMP = np.choose(zerodens_mask, ((tempDISC * rhoDISC + tempENV * rhoENV) / density.total, backTemp))\n        \n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'total': TEMP, 'disc': tempDISC, 'env': tempENV, 'discFlag': bool(discTemp), 'envFlag': bool(envTemp)} )\n\n#-------------------------------\n#-------------------------------\n\n#----------------------\n#VELOCITY FUNCTION\n#----------------------\n\ndef velocity(RStar,MStar,Rd,density,GRID):\n\n#MStar: Star mass\n#Rd: Centrifugal radius\n#GRID: [xList,yList,zList]\n\n    XYZgrid, XYZ, rRTP = GRID.XYZgrid, GRID.XYZ, GRID.rRTP\n    NPoints = GRID.NPoints\n    #------------\n    #LISTS TO USE\n    #------------\n    rList, RList, thetaList, phiList = rRTP\n    rhoDISC, rhoENV = density.disc, density.env\n    theta4vel = GRID.theta4vel\n\n    #------------------------------\n    #MODEL. Keto,E & Zhang,Q (2010)\n    #------------------------------\n\n    #------------\n    #DISC Profile\n    #------------\n    if density.discFlag:\n        print ('Calculating Disc velocity...')\n        rdisc = density.r_disc\n        #Pure azimuthal component. It's assumed that the radial velocity in the rotationally supported disc is comparatively small (Keto 2010).\n        vdisc = np.where( RList <= rdisc, (G * MStar / RList)**0.5, 0.)  \n    else: vdisc = 0.\n\n    #----------------\n    #ENVELOPE PROFILE\n    #----------------\n    if density.envFlag:\n        print ('Calculating Envelope velocity...')\n    \n        #-------------------------\n        #Useful THETA Calculations\n        #-------------------------\n        costheta0 = density.streamline\n        costheta, sintheta, theta0 = np.cos(thetaList), np.sin(thetaList), np.arccos(costheta0)\n        sintheta0 = np.sin(theta0)\n\n        #-------------------------\n        #-------------------------\n        renv = density.r_env\n        vr = np.where( rList <= renv, - (G * MStar / rList)**0.5 * (1 + costheta / costheta0)**0.5, 0.)\n        \n        signo = np.where( theta4vel> np.pi/2, -1, 1) #To respect symmetry. (Using the thetaList from 0 to pi)\n        good_ind = np.where( (thetaList != 0.) & (rList <= renv) ) #To avoid the polar angle for theta and phi. In the polar angle all the velocity is radial\n        vtheta, vphi = np.zeros(NPoints), np.zeros(NPoints)\n\n        vtheta[good_ind] = signo[good_ind] * ( (G * MStar / rList[good_ind])**0.5 * (costheta0[good_ind] - costheta[good_ind]) / sintheta[good_ind] * \n                                               (1 + costheta[good_ind] / costheta0[good_ind])**0.5 )\n        vphi[good_ind] = (G * MStar / rList[good_ind])**0.5 * (sintheta0[good_ind] / sintheta[good_ind]) * (1 - costheta[good_ind] / costheta0[good_ind])**0.5\n\n\n    else: vr, vtheta, vphi = 0.,0.,0.\n        \n    #Weighted velocity with density. Vectorial sum.\n    vr , vtheta, vphi = map( lambda x,y : (rhoDISC * x + rhoENV * y) / density.total, \n                             [ 0, 0, vdisc ], [vr, vtheta, vphi] ) \n\n    print ('Converting to cartesian coordinates...') \n    vx, vy, vz = sphe_cart( list( zip(vr, vtheta, vphi) ), theta4vel, phiList)\n         \n    #----------------------------------------------\n    #----------------------------------------------\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'x': vx, 'y': vy, 'z': vz} )\n\n\n#----------------------\n#----------------------\n\n#--------------------------\n#VELOCITY (Random) FUNCTION\n#--------------------------\n\ndef velocity_random(v_disp,NPoints):\n\n    print ('Calculating random (uniform) velocities...')\n    v_disp = v_disp/np.sqrt(3)    \n\n    v_x = v_disp * (2 * np.random.random(NPoints) - 1)  \n    v_y = v_disp * (2 * np.random.random(NPoints) - 1)  \n    v_z = v_disp * (2 * np.random.random(NPoints) - 1)  \n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    return Struct( **{'x': v_x, 'y': v_y, 'z': v_z} )\n\n#--------------------------\n#--------------------------\n\n#--------------------------------\n#CENTRAL HOLE FUNCTION (Optional)\n#--------------------------------\n\ndef MakeHole(T_min,T_max,dens_val,temp_val,abund_val,densList,tempList,abundList):\n\n#The hole is between T_min and T_max\n#T_min: Minimum temperature of the hole \n#T_max: Maximum temperature of the hole\n#dens_val: Density value for the hole\n#temp_val: Temperature value for the hole\n#densList: Density list to modify \n#tempList: Temperature list to build the hole\n    \n    print ('Calculating profiles for a hole...')\n    \n    densNew = np.where( (tempList >= T_min) & (tempList <= T_max), dens_val, densList)\n    tempNew = np.where( (tempList >= T_min) & (tempList <= T_max), temp_val, tempList)\n    abundNew = np.where( (tempList >= T_min) & (tempList <= T_max), abund_val, abundList)\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n    \n    return Struct( **{'dens': densNew, 'temp': tempNew, 'abund': abundNew})\n\n\n#--------------------------------\n#--------------------------------    \n\ndef PrintProperties(density, temperature, GRID): \n\n    dx = GRID.step\n    inddisc = np.where(temperature.disc > 2.)\n    indtotal = np.where(temperature.total > 2.)\n    Mu_MSun = 2 * Mu/MSun\n    \n    print ('Total mass (MSun):', np.sum(density.total) * dx**3 * Mu_MSun)\n    print ('Mean Total Temperature (Kelvin), weighted by density:', \n           (np.sum(temperature.total[ indtotal ] * density.total[ indtotal ]) \n            / np.sum(density.total[ indtotal ]) ))\n    if density.discFlag:\n        print ('Total Disc mass (MSun):', np.sum(density.disc) * dx**3 * Mu_MSun)\n        print ('Total Envelope mass (MSun):', np.sum(density.env) * dx**3 * Mu_MSun)\n        print ('Mean Disc Temperature (Kelvin), weighted by density:', \n               (np.sum(temperature.disc[ inddisc ] * density.disc[ inddisc ])\n                / np.sum(density.disc[ inddisc ]) ))\n    \n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    \n#---------------\n#GEOMETRY STUFF \n#---------------   \n                           \ndef Rotation_Matrix(angle_dicts):\n    \n    Rot = []\n\n    for item in angle_dicts:\n\n        #Rotation along X\n        if item['axis'] == 'x':\n            \n            thX = item['angle'] \n            Rot.append( np.array([[1,0,0],\n                                  [0,np.cos(thX),-np.sin(thX)],\n                                  [0,np.sin(thX),np.cos(thX)]]) )\n            print (\"Added: rotation matrix along '%s' axis, %.1f deg\"%('X',thX * 180/np.pi) )\n\n        #Rotation along Y\n        if item['axis'] == 'y':\n            thY = item['angle'] \n            Rot.append( np.array([[np.cos(thY),0,-np.sin(thY)],\n                                  [0,1,0],\n                                  [np.sin(thY),0,np.cos(thY)]]) )\n            print (\"Added: rotation matrix along '%s' axis, %.1f deg\"%('Y',thY * 180/np.pi) )\n    \n        #Rotation along Z\n        if item['axis'] == 'z':\n            thZ = item['angle'] \n            Rot.append( np.array([[np.cos(thZ),-np.sin(thZ),0],\n                                  [np.sin(thZ),np.cos(thZ),0],\n                                  [0,0,1]]) )\n            print (\"Added: rotation matrix along '%s' axis, %.1f deg\"%('Z',thZ * 180/np.pi) )\n\n    tmp = Rot[0]\n    Rot_iter = iter(Rot[1:]) #Iterator for Rot_list from 2nd value: (matriz for matriz in Rot_list[1:])\n\n    for i in xrange( len(Rot[1:]) ): \n        tmp = np.dot( next(Rot_iter) , tmp )\n        \n    Rot_total = tmp\n\n    print ('%s is done!'%inspect.stack()[0][3])\n   \n    return Rot_total\n\n\n\ndef ChangeGeometry(GRID, center = False ,rot_dict = False, vel = False, vsys = False):\n\n#order: indicates the order of the axes to make the rotation  \n \n    rotPos, rotVel, modCenter, modVsys = [False]*4\n\n    POS_vec = np.array(GRID.XYZ).T\n    if vel == False and vsys: \n        sys.exit('ERROR: A velocity distribution is needed for the systemic velocity to be added!')\n    if vel:\n        VEL_vec = np.array([vel.x, vel.y, vel.z]).T\n\n    #---------------\n    #ROTATION Matrix\n    #---------------\n\n    if rot_dict:\n        print ('Calculating Rotation matrix...')\n        rot_angles , axis_order = rot_dict['angles'], rot_dict['axis']\n\n        if len(rot_angles) == len(axis_order):\n            angle_dicts = ( {'axis': ax, 'angle': ang} for ax,ang in zip(axis_order, rot_angles) )\n        else:\n            sys.exit('ERROR: rot_angles and axis_order lists must have the same size!') \n            \n        NPoints = GRID.NPoints\n        Rot_total = Rotation_Matrix(angle_dicts)     \n        print ('Rotating Position vectors...')\n        XYZ_it = iter(POS_vec)\n        POS_vec = np.array([ np.dot( Rot_total, next(XYZ_it) ) for i in xrange(NPoints) ])\n        rotPos = True\n        \n        if vel:\n            print ('Rotating Velocity vectors...')\n            VEL_it = iter(VEL_vec)\n            VEL_vec = np.array([ np.dot( Rot_total, next(VEL_it) ) for i in xrange(NPoints) ])\n            rotVel = True\n        else: \n            print ('=================================================') \n            print ('WARNING: No VELOCITY distribution was provided to rotate!')\n            print ('Please provide it if you are going to calculate line emission.')\n            print ('=================================================')\n\n    if center is not False:\n        print ('Moving the grid to the new center...')\n        center = np.array(center)\n        POS_vec = POS_vec + center\n        modCenter = True\n\n    if vsys: \n        print ('Adding a systemic velocity along z...')\n        vsys_vec = np.array([0,0,vsys])\n        VEL_vec = VEL_vec + vsys_vec \n        modVsys = True\n\n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    if (rotPos or modCenter) and (rotVel or modVsys): return Struct( **{ 'newXYZ': POS_vec.T , 'newVEL': VEL_vec.T  } )\n    elif rotPos or modCenter: return Struct( **{ 'newXYZ': POS_vec.T } )\n    elif rotVel or modVsys: return Struct( **{ 'newVEL': VEL_vec.T })\n    else: return 0\n\n#---------------\n#---------------\n\n#------------------------\n#WRITING DATA (LIME v1.6)\n#------------------------\n\nclass Make_Datatab(object):\n   \n    def __init__(self, prop, GRID):\n\n        if len(np.shape(prop)) == 1: \n            self.prop = [prop]\n            self.n = 1\n        else: \n            self.prop = prop\n            self.n = len(prop)\n        self.GRID = GRID\n        self.id = np.arange(GRID.NPoints)\n        super(Make_Datatab, self).__init__()\n    \n    def formatter(self, format, tmp = '%d'):\n\n        fmt, type_fmt = format, type(format) \n        nvec = xrange(self.n)\n\n        if type_fmt == str: #If a single format is provided\n            print (\"Using format '%s'\"%fmt) \n            for i in nvec: tmp += ' '+fmt #The same format for all properties\n        elif type_fmt == list or type_fmt == np.ndarray: #If a list of formats\n            if len(fmt) != self.n: sys.exit('ERROR: The number of formats provided (%d) is not equal to the number of properties to be written (%d)'%(len(fmt),self.n))\n            print ('Using formats list:', fmt) \n            for f in fmt: tmp += ' '+f\n        elif not fmt: #If False\n            print (\"Using default format '%e'\")\n            for i in nvec: tmp += ' %e' #Default format for all properties\n        else: sys.exit(\"ERROR: Wrong type: %s. \\nPlease provide a valid 'format_list' object (str, list or np.ndarray)\"%type_fmt)\n        tmp += '\\n'\n        return tmp\n\n    def submodel(self, tag = '0', format = False, folder = 'Subgrids'):        \n\n        os.system('mkdir %s'%folder)\n        file_path = './%s/datatab_%s.dat'%(folder,tag)\n        x,y,z = self.GRID.XYZ\n        \n        tmp = self.formatter(format, tmp = '%d %.8e %.8e %.8e')\n        tmp_write = []\n        if type(self.prop) == np.ndarray: self.prop = self.prop.tolist()\n        list2write = iter(np.array([self.id,x,y,z] + self.prop).T)\n        print ('Writing Submodel data in %s'%file_path)        \n        for i in self.id: tmp_write.append( tmp % tuple(next(list2write)) )\n        file_data = open(file_path, 'w')        \n        file_data.writelines(tmp_write)\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------\\n-------------------------------------------------')\n                \n\nclass Lime(Make_Datatab):\n    def __init__(self, prop, GRID):\n        print ('Set LIME format')\n        super(Lime, self).__init__(prop, GRID)\n\n    def globalgrid(self, format = False, folder = './'):\n        \n        tmp = self.formatter(format)\n        tmp_write = []\n        if type(self.prop) == np.ndarray: self.prop = self.prop.tolist()\n        list2write = iter(np.array([self.id] + self.prop).T)\n        files = [folder + tag for tag in ['datatab.dat','x.dat','y.dat','z.dat']]\n        print ('Writing Global grid data in %s'%files[0])        \n        for _ in xrange(self.GRID.NPoints): tmp_write.append( tmp % tuple(next(list2write)) )\n        file_data = open(files[0],'w')\n        file_data.writelines(tmp_write)\n\n        Ns = self.GRID.Nodes\n        size_file = folder+'npoints.dat'\n        sfile = open(size_file,'w') \n        print ('Writing grid size in %s'%size_file)\n        sfile.write(\"%d %d %d %d\"%(Ns[0], Ns[1], Ns[2], self.GRID.NPoints))\n        sfile.close()\n\n        print ('Writing space domain in:')\n        for i in xrange(1,4):\n            print ('%s'%files[i])\n            np.savetxt(files[i], self.GRID.XYZgrid[i-1], fmt = '%.8e')\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------\\n-------------------------------------------------')\n\n    \nclass Radmc3d(object): #RADMC-3D uses the cgs units system\n    \"\"\"\n    \"\"\"\n    \"\"\"\n    def __init__(self, prop, GRID):\n        print ('Set RADMC-3D format')\n        super(Radmc3d, self).__init__(prop, GRID)\n    \"\"\"\n    \n    def __init__(self, prop_dict, GRID, amr_grid = False, nphot = 1000000):\n        print ('Set RADMC-3D format')\n        self.prop = prop_dict\n        self.GRID = GRID\n        self.amr_grid = amr_grid\n        self.nphot = nphot\n        self.nx, self.ny, self.nz = GRID.Nodes\n        self.nn = self.nx * self.ny * self.nz\n        self.xi, self.yi, self.zi = np.array(GRID.XYZgrid) * cm #from m to cm\n        \n    def write_amr_grid(self, iformat = 1, \n                       grid_style = 0, \n                       coord_system = 0,\n                       grid_info = 0, \n                       include_dim = [1,1,1]):\n        \n        #------------------------\n        #Write the grid-info file\n        #------------------------\n        if not self.amr_grid:\n            with open('amr_grid.inp','w+') as f:\n                f.write('%s\\n'%iformat)                             # iformat\n                f.write('%s\\n'%grid_style)                          # AMR grid style  (0=regular grid, no AMR)\n                f.write('%s\\n'%coord_system)                        # Coordinate system\n                f.write('%s\\n'%grid_info)                           # grid_info\n                f.write('%s %s %s\\n'%tuple(include_dim))            # Include x,y,z coordinate\n                f.write('%d %d %d\\n'%(self.nx,self.ny,self.nz))     # Size of grid\n                for value in self.xi: f.write('%13.6e\\n'%(value))   # X coordinates (cell walls)\n                for value in self.yi: f.write('%13.6e\\n'%(value))   # Y coordinates (cell walls)\n                for value in self.zi: f.write('%13.6e\\n'%(value))   # Z coordinates (cell walls)\n                f.close()\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------')\n        \n    def write_electron_numdens(self, dens_elect, format = '%13.6e'):\n\n        #---------------------------------\n        #Write the electronic density file\n        #---------------------------------\n        with open('electron_numdens.inp','w+') as f:\n            f.write('1\\n')                                          # Format number\n            f.write('%d\\n'%self.nn)                                 # Nr of cells\n            #data = dens_elect.ravel(order='F') # Create a 1-D view, fortran-style indexing\n            dens_elect.tofile(f, sep='\\n', format=format)\n            f.write('\\n')\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------')\n\n    def write_ion_numdens(self, dens_ion, format = '%13.6e'):\n\n        #--------------------------\n        #Write the ion density file\n        #--------------------------\n        with open('ion_numdens.inp','w+') as f:\n            f.write('1\\n')                                          # Format number\n            f.write('%d\\n'%self.nn)                                 # Nr of cells\n            #data = dens_ion.ravel(order='F') # Create a 1-D view, fortran-style indexing\n            dens_ion.tofile(f, sep='\\n', format=format)\n            f.write('\\n')\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------')\n\n    def write_gas_temperature(self, tgas, format = '%13.6e'):\n        \n        #-------------------------\n        #Write the gas temperature\n        #-------------------------\n        with open('gas_temperature.inp','w+') as f:\n            f.write('1\\n')                                          # Format number\n            f.write('%d\\n'%self.nn)                                 # Nr of cells\n            #data = tgas.ravel(order='F') # Create a 1-D view, fortran-style indexing\n            tgas.tofile(f, sep='\\n', format=format)\n            f.write('\\n')\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------')\n\n    def write_microturbulence(self, microturbulence, format = '%13.6e'):\n        \n        #-------------------------\n        #Write the gas temperature\n        #-------------------------\n        microturb = np.ones(self.nn) * microturbulence * cm\n        with open('microturbulence.inp','w+') as f:\n            f.write('1\\n')                                          # Format number\n            f.write('%d\\n'%self.nn)                                 # Nr of cells\n            #data = tgas.ravel(order='F') # Create a 1-D view, fortran-style indexing\n            microturb.tofile(f, sep='\\n', format=format)\n            f.write('\\n')\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------')\n\n    def write_gas_velocity(self, vel, format = '%13.6e'):\n        \n        #-------------------------\n        #Write the gas temperature\n        #-------------------------\n        vel2wrt = np.array((vel.x,vel.y,vel.z)).T * cm\n        tmp_write = []\n        tmp = ((format+'\\t')*3)[:-1] + '\\n'\n        \n        with open('gas_velocity.inp','w+') as f: \n            f.write('1\\n')                                          # Format number\n            f.write('%d\\n'%self.nn)                                 # Nr of cells\n            #data = tgas.ravel(order='F') # Create a 1-D view, fortran-style indexing\n            for i in xrange(self.nn): tmp_write.append(tmp % tuple(vel2wrt[i]))         \n            f.writelines(tmp_write)\n            #vel2wrt.tofile(f, sep='\\n', format=format)\n            f.write('\\n')\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------')\n\n    def write_radmc3d_control(self, scattering_mode_max = 1, \n                              incl_freefree = 1,\n                              incl_dust = 1,\n                              tgas_eq_tdust = 1):\n\n        #----------------------------------\n        #Write the radmc3d.inp control file\n        #----------------------------------\n        with open('radmc3d.inp','w+') as f:\n            f.write('nphot = %d\\n'%(self.nphot))\n            f.write('scattering_mode_max = %s\\n'%scattering_mode_max)   # Put this to 1 for isotropic scattering\n            f.write('incl_freefree = %s\\n'%incl_freefree)\n            f.write('incl_dust = %s\\n'%incl_dust)\n            f.write('tgas_eq_tdust = %s'%tgas_eq_tdust)\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------')\n\n    def write_wavelength_micron(self, lam = [5e2,2e4,4e4,3e5], nxx = [50,50,50], format = '%13.6e'):\n\n        #------------------------------------\n        #Write the wavelength_micron.inp file\n        #------------------------------------\n        len_lam = len(lam)\n        if len_lam - 1 == len(nxx):\n            lam_list = [np.logspace(np.log10(lam[i]),\n                                    np.log10(lam[i+1]),\n                                    nxx[i], endpoint=False) \n                        for i in xrange(len_lam-2)]\n            lam_list.append(np.logspace(np.log10(lam[-2]),\n                                        np.log10(lam[-1]),\n                                        nxx[-1], endpoint=True))\n            lam_list = np.concatenate(lam_list)\n            nlam = lam_list.size\n            with open('wavelength_micron.inp','w+') as f:\n                f.write('%d\\n'%(nlam))\n                tmp = format+'\\n'\n                for value in lam_list: f.write(tmp%(value))\n\n        else: sys.exit(\"ERROR: Wrong length(s) for input list(s): len(lam)-1 must be equal to len(nxx)\")\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------')\n        \n    def freefree(self, format = '%13.6e', folder = './'): #Create kwargs for each invoked function\n\n        prop = {} #Creating a new dict bcause dont want to modify the self.prop variable at the minute\n        prop['dens_elect'] = self.prop['dens_elect'] * cm**-3 \n        prop['dens_ion'] = self.prop['dens_ion'] * cm**-3\n        prop['tgas'] = self.prop['tgas']\n        \n        self.write_amr_grid()\n        self.write_electron_numdens(prop['dens_elect'], format=format)\n        self.write_ion_numdens(prop['dens_ion'], format=format)\n        self.write_gas_temperature(prop['tgas'])\n        self.write_radmc3d_control(incl_dust = 0, tgas_eq_tdust = 0)\n        self.write_wavelength_micron()\n            \n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------\\n-------------------------------------------------')\n\n\n    ##UNDER DEVELOPMENT\n    def recomblines(self, format = '%13.6e', folder = './'): \n\n        prop = {} #Creating a new dict bcause dont want to modify the self.prop variable at the minute\n        prop['dens_elect'] = self.prop['dens_elect'] * cm**-3 \n        prop['dens_ion'] = self.prop['dens_ion'] * cm**-3\n        prop['tgas'] = self.prop['tgas']\n        prop['vel'] = self.prop['vel']\n        prop['microturb'] = self.prop['microturb']\n\n        self.write_amr_grid()\n        self.write_electron_numdens(prop['dens_elect'], format=format)\n        self.write_ion_numdens(prop['dens_ion'], format=format)\n        self.write_gas_temperature(prop['tgas'])\n        #self.write_radmc3d_control(incl_dust = 0, tgas_eq_tdust = 0)\n        #self.write_wavelength_micron()\n        self.write_gas_velocity(prop['vel'], format=format) #--> Create this function\n        #self.write_microturbulence(prop['microturb'], format=format)\n\n        print ('%s is done!'%inspect.stack()[0][3])\n        print ('-------------------------------------------------\\n-------------------------------------------------')\n\n#-----------------------------\n#WRITING DATA (RADMC-3D v0.41)\n#-----------------------------\n\ndef Datatab_RADMC3D_FreeFree(dens,temp,GRID):\n\n    #dens = 1e-6 * np.where(dens > 10.0, dens, 0) #to cm^-3\n    dens = dens / 1e6\n    nx,ny,nz = GRID.Nodes\n    xi, yi, zi = np.array(GRID.XYZgrid) * 100 #to cm\n    nphot = 1000000\n\n#\n# Write the grid file\n#\n    with open('amr_grid.inp','w+') as f:\n        f.write('1\\n')                       # iformat\n        f.write('0\\n')                       # AMR grid style  (0=regular grid, no AMR)\n        f.write('0\\n')                       # Coordinate system\n        f.write('0\\n')                       # gridinfo\n        f.write('1 1 1\\n')                   # Include x,y,z coordinate\n        f.write('%d %d %d\\n'%(nx,ny,nz))     # Size of grid\n        for value in xi:\n            f.write('%13.6e\\n'%(value))      # X coordinates (cell walls)\n        for value in yi:\n            f.write('%13.6e\\n'%(value))      # Y coordinates (cell walls)\n        for value in zi:\n            f.write('%13.6e\\n'%(value))      # Z coordinates (cell walls)\n#\n# Write the electronic density file.\n#\n    with open('electron_numdens.inp','w+') as f:\n        f.write('1\\n')                       # Format number\n        f.write('%d\\n'%(nx*ny*nz))           # Nr of cells\n        #data = rhoelect.ravel(order='F')         # Create a 1-D view, fortran-style indexing\n        dens.tofile(f, sep='\\n', format=\"%13.6e\")\n        f.write('\\n')\n\n#\n# Write the ion density file.\n#\n    with open('ion_numdens.inp','w+') as f:\n        f.write('1\\n')                       # Format number\n        f.write('%d\\n'%(nx*ny*nz))           # Nr of cells\n        #data = rhoelect.ravel(order='F')         # Create a 1-D view, fortran-style indexing\n        dens.tofile(f, sep='\\n', format=\"%13.6e\")\n        f.write('\\n')\n    \n#\n# Write the gas temperature\n#\n\n    with open('gas_temperature.inp','w+') as f:\n        f.write('1\\n')                       # Format number\n        f.write('%d\\n'%(nx*ny*nz))           # Nr of cells\n        #data = tgas.ravel(order='F')          # Create a 1-D view, fortran-style indexing\n        temp.tofile(f, sep='\\n', format=\"%13.6e\")\n        f.write('\\n')\n\n#\n# Write the wavelength_micron.inp file\n#\n\n    lam1 = 0.5e3\n    lam2 = 2.e4\n    lam3 = 4.e4\n    lam4 = 3.e5#6.e4\n    \n    n12      = 50\n    n23      = 50\n    n34      = 50\n    lam12    = np.logspace(np.log10(lam1),np.log10(lam2),n12,endpoint=False)\n    lam23    = np.logspace(np.log10(lam2),np.log10(lam3),n23,endpoint=False)\n    lam34    = np.logspace(np.log10(lam3),np.log10(lam4),n34,endpoint=True)\n    lam      = np.concatenate([lam12,lam23,lam34])\n    nlam     = lam.size\n\n#\n# Write the wavelength file\n#\n    with open('wavelength_micron.inp','w+') as f:\n        f.write('%d\\n'%(nlam))\n        for value in lam:\n            f.write('%13.6e\\n'%(value))\n\n#\n# Write the radmc3d.inp control file\n#\n    with open('radmc3d.inp','w+') as f:\n        f.write('nphot = %d\\n'%(nphot))\n        f.write('scattering_mode_max = 1\\n')   # Put this to 1 for isotropic scattering\n        f.write('incl_freefree = 1\\n')\n        f.write('incl_dust = 0\\n')\n        #f.write('tgas_eq_tdust = 1')\n\n    \n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n#-------------\n#-------------\n\ndef DataTab_LIME(dens,temp,vel,abund,gtd,GRID, is_submodel = False, tag = False):\n    \n    import pandas\n\n    if is_submodel:\n        os.system('mkdir Subgrids')\n        file0 = './Subgrids/datatab%s.dat'%tag\n        file = open(file0,'w')\n        x,y,z = GRID.XYZ\n        print ('Writing Submodel data on %s'%file0)\n        tmp = []\n        for i in xrange(GRID.NPoints): \n            #file.write(\"%d %e %e %e %e %e %e %e %e %e %e\\n\"%\n             #          (i,x[i],y[i],z[i],dens[i],temp[i],vel['x'][i],vel['y'][i],vel['z'][i],abund[i],gtd[i]))\n            tmp.append( \"%d %e %e %e %e %e %e %e %e %e %e\\n\"% (i,x[i],y[i],z[i],dens[i],temp[i],vel.x[i],vel.y[i],vel.z[i],abund[i],gtd[i]))\n        file.writelines(tmp)\n        \n    else:\n        files=['datatab.dat','x.dat','y.dat','z.dat']\n        sizefile='./npoints.dat'\n        print ('Writing grid size on %s'%sizefile)\n        sfile = open(sizefile,'w') \n        Ns = GRID.Nodes\n        sfile.write(\"%d %d %d %d\"%(Ns[0],Ns[1],Ns[2],GRID.NPoints))\n        print ('Writing data on %s'%files[0])\n        file = open(files[0],'w')\n\n        for i in xrange(GRID.NPoints): \n            file.write(\"%d %e %e %e %e %e %e %e\\n\"%\n                       (i,dens[i],temp[i],vel.x[i],vel.y[i],vel.z[i],abund[i],gtd[i]))\n\n        df = [pandas.DataFrame(GRID.XYZgrid[i]) for i in range(3)]\n    \n        for i in xrange(1,4):\n            print ('Writing data on %s'%files[i])\n            df[i-1].to_csv(files[i],index=False,header=False,float_format='%e') \n        \n        sfile.close()\n        \n    file.close()\n    \n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n#--------------\n#--------------    \n\ndef DataTab_LIME2(dens_H2,dens_H,dens_Hp,temp,vel,abund,gtd,GRID,tdust = None, is_submodel = False, tag = False):\n    \n    import pandas\n\n    if is_submodel:\n        os.system('mkdir Subgrids')\n        file0 = './Subgrids/datatab%s.dat'%tag\n        file = open(file0,'w')\n        x,y,z = GRID.XYZ \n    \n        print ('Writing Submodel data on %s'%file0)\n        tmp = []\n    \n        fixed_grid = True*1\n        if fixed_grid:\n            \n            \"\"\"\n            Sorting from least to greatest leads to confusions in the reorderGrid function of Lime (at grid.c and grid_aux.c)\n            because the last pIntensity points (the grid points written here) might likely be flagged as sinkPoints since they lie\n            over (or close) the domain's radius and therefore their ids (close to Npoints on the left) lie very close to those of the \n            sinkPoints defined first (close to Npoints on the right). This fact, causes almost always an underestimation of the \n            new sinkPoints found by Lime (nExtraSinks), and required to recalculate the parameters par->pIntensity and par->sinkPoints. \n            \n            Thus, sorting from greatest to least solves this issue. \n            \"\"\"\n            rr = np.linalg.norm(GRID.XYZ, axis = 0)\n            ind_rr = iter(np.argsort(rr)[::-1])\n\n            id = 0       \n            if isinstance(tdust,list) or isinstance(tdust,np.ndarray):\n                for i in ind_rr: \n                    tmp.append( \"%d %e %e %e %e %e %e %e %e %e %e %e %e %e\\n\"% \n                                (id,x[i],y[i],z[i],dens_H2[i],dens_H[i],dens_Hp[i],temp[i],\n                                 tdust[i],vel.x[i],vel.y[i],vel.z[i],abund[i],gtd[i]))\n                    id+=1\n            else: \n                for i in ind_rr: \n                    tmp.append( \"%d %e %e %e %e %e %e %e %e %e %e %e %e\\n\"% \n                                (id,x[i],y[i],z[i],dens_H2[i],dens_H[i],dens_Hp[i],temp[i],\n                                 vel.x[i],vel.y[i],vel.z[i],abund[i],gtd[i]))\n                    id+=1\n        else:\n            if isinstance(tdust,list) or isinstance(tdust,np.ndarray):\n                for i in xrange(GRID.NPoints): tmp.append( \"%d %e %e %e %e %e %e %e %e %e %e %e %e %e\\n\"% \n                                                           (i,x[i],y[i],z[i],dens_H2[i],dens_H[i],dens_Hp[i],temp[i],\n                                                            tdust[i],vel.x[i],vel.y[i],vel.z[i],abund[i],gtd[i]))\n            else: \n                for i in xrange(GRID.NPoints): tmp.append( \"%d %e %e %e %e %e %e %e %e %e %e %e %e\\n\"% \n                                                           (i,x[i],y[i],z[i],dens_H2[i],dens_H[i],dens_Hp[i],temp[i],\n                                                            vel.x[i],vel.y[i],vel.z[i],abund[i],gtd[i]))\n        \n        file.writelines(tmp)\n        \n    else:\n        files=['datatab.dat','x.dat','y.dat','z.dat']\n        sizefile='./npoints.dat'\n        print ('Writing grid size on %s'%sizefile)\n        sfile = open(sizefile,'w') \n        Ns = GRID.Nodes\n        sfile.write(\"%d %d %d %d\"%(Ns[0],Ns[1],Ns[2],GRID.NPoints))\n        print ('Writing data on %s'%files[0])\n        file = open(files[0],'w')\n\n        if isinstance(tdust,list) or isinstance(tdust,np.ndarray):\n            for i in xrange(GRID.NPoints): \n                file.write(\"%d %e %e %e %e %e %e %e %e %e %e\\n\"%\n                           (i,dens_H2[i],dens_H[i],dens_Hp[i],temp[i],tdust[i],vel.x[i],vel.y[i],vel.z[i],abund[i],gtd[i]))\n        else:\n            for i in xrange(GRID.NPoints): \n                file.write(\"%d %e %e %e %e %e %e %e %e %e\\n\"%\n                           (i,dens_H2[i],dens_H[i],dens_Hp[i],temp[i],vel.x[i],vel.y[i],vel.z[i],abund[i],gtd[i]))\n\n        df = [pandas.DataFrame(GRID.XYZgrid[i]) for i in range(3)]\n    \n        for i in xrange(1,4):\n            print ('Writing data on %s'%files[i])\n            df[i-1].to_csv(files[i],index=False,header=False,float_format='%e') \n        \n        sfile.close()\n        \n    file.close()\n    \n    print ('%s is done!'%inspect.stack()[0][3])\n    print ('-------------------------------------------------\\n-------------------------------------------------')\n\n#--------------\n#--------------    \n\n\ndef Make_Datatab1(prop_list, GRID, format_list = False, \n                 submodel_tag = False, submodel_folder = 'Subgrids', \n                 lime = True, radmc3d = False):\n    \n    import pandas\n    n = len(prop_list)\n\n    if submodel_tag:\n        \n        fold, tag, fmt, type_fmt = submodel_folder, submodel_tag, format_list, type(format_list)\n        os.system('mkdir %s'%fold)\n        file_path = './%s/datatab_%s.dat'%(fold,tag)\n        x,y,z = GRID.XYZ\n        tmp = '%d %e %e %e'\n\n        if type_fmt == str: #If a single format is provided\n            print (\"Using format '%s'\"%fmt) \n            for i in range(n): tmp += ' '+fmt #The same format for all properties\n        elif type_fmt == list or type_fmt == np.ndarray: #If a list of formats\n            if len(fmt) != n: sys.exit('ERROR: The number of formats provided (%d) is not equal to the number of properties to be written (%d)'%(len(fmt),n))\n            print ('Using format list:', fmt) \n            for f in fmt: tmp += ' '+f\n        elif not fmt: #If False\n            print (\"Using default format '%e'\")\n            for i in range(n): tmp += ' %e' #Default format for all properties\n        else: sys.exit(\"ERROR: Wrong type: %s. \\nPlease provide a valid 'format_list' object (str, list or np.ndarray)\"%type_fmt)\n\n        tmp += '\\n'\n        tmp_write = []\n        if type(prop_list) == np.ndarray: prop_list = prop_list.tolist()\n        id = np.arange(GRID.NPoints)\n        list2write = iter(np.array([id,x,y,z] + prop_list).T)\n        file = open(file_path, 'w')\n        print ('Writing Submodel data on %s'%file_path)        \n        for i in id:    \n            tmp_write.append( tmp % tuple(next(list2write)) )\n        \n        file.writelines(tmp_write)\n            \n    else:\n        if lime:\n            pass\n        if radmc3d:\n            pass\n\n    file.close()\n","sub_path":"sf3dmodels/Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":66133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"380906363","text":"import csv\nimport os\nimport shutil\n\nfrom PIL import Image\n\ninput_ps3_folder = r'C:\\umineko stuff\\nocturne ps3 backgrounds'  #ps3 image backgrounds\ninput_original_folder = r'C:\\umineko stuff\\arc.nsa original extracted\\bmp\\background' #original steam backgrounds\n\noutput_folder = r'c:\\temp\\output'\n\nOUTPUT_RES = (1707, 960)\n\ndef get_filename_no_ext(file_path):\n    baseName = os.path.basename(file_path)                          #get filename with extension\n    file_name_no_ext, file_extension = os.path.splitext(baseName)   #get filename without extension. Extension includes the \".\"\n    return file_name_no_ext\n\ndef file_exists(file_path):\n    try:\n        with open(file_path, \"r\") as f:\n            return True\n    except:\n        return False\n\ndef get_image_with_bg(folder_path, filename):\n    file_name_no_ext, file_extension = os.path.splitext(filename)\n\n    image_path = os.path.join(folder_path, filename)\n    transparency_path = os.path.join(folder_path, file_name_no_ext + \"_bg\" + file_extension)\n\n    if file_exists(transparency_path):\n        print(\"-> Also found \" + transparency_path, end=\"\")\n        back_image = Image.open(transparency_path)\n        top_image = Image.open(image_path)\n        return Image.alpha_composite(back_image, top_image)          #overlay top image on background image\n    else:\n        return Image.open(image_path)\n\n##### IMAGE MANIPULATION FUNCTIOSN #####\n#note-input images must not have any transparency!!\ndef resize_and_add_image_right(original_image, right_image):\n    left_image = original_image.resize(OUTPUT_RES)\n    right_image_resized = right_image.resize(OUTPUT_RES)\n    #right_image = Image.new('RGBA', new_res)\n    whole_image = Image.new('RGB', (OUTPUT_RES[0]*2, OUTPUT_RES[1]))\n    whole_image.paste(left_image, box=(0,0))\n    whole_image.paste(right_image_resized, box=(OUTPUT_RES[0], 0))\n    whole_image = whole_image.convert('RGB')\n    return whole_image\n\ndef resize_and_add_black_top(im):\n    bottom_image = im.resize(OUTPUT_RES)\n    #top_image = Image.new('RGBA', OUTPUT_RES)\n    whole_image = Image.new('RGB', (OUTPUT_RES[0], OUTPUT_RES[1]*2))\n    #whole_image.paste(top_image, box=(0,0))\n    whole_image.paste(bottom_image, box=(0, OUTPUT_RES[1]))\n    return whole_image\n\n#turns transparency\ndef make_mask_from_image(im):\n    (_,_,_,alpha_channel) = im.split()\n    return alpha_channel.point([255-x for x in range(256)]) #invert the image\n\ndef make_gradient_image_door():\n    width = 1000\n\n    gradient = [2*(x/(width-1)-.5) for x in range(width)]   #-1 to +1 across an array of arbitrary length\n\n    cutoff = .3\n    offset = .3\n    gradient = [(x-offset) / cutoff for x in gradient]       #now the center 30% will be in the range -1, 1\n    gradient = [(x+1)*255 for x in gradient]                #map the range from [-1,1] to [0,255]\n    gradient = [round(max(min(x,255),0)) for x in gradient] #clamp resultant values and make integer\n\n    #print(gradient)\n    im = Image.frombytes('L', (width,1), bytes(gradient))\n    return im.resize(OUTPUT_RES, resample=Image.LINEAR).transpose(Image.FLIP_LEFT_RIGHT)\n\n################### METHODS START ##############\ndef normal(**kwargs):\n    im = get_image_with_bg(input_ps3_folder, kwargs['ps3_path'])\n    return im.resize(OUTPUT_RES, resample=Image.LANCZOS)\n\ndef use_ps3_same_name(**kwargs):\n    filename = get_filename_no_ext(kwargs['original_path']) + '.png'\n    return normal(ps3_path=filename)\n\ndef make_greyscale(**kwargs):\n    return normal(ps3_path=kwargs['manual_path']).convert('L') #convert to greyscale\n\ndef use_alternate(**kwargs):\n    return normal(ps3_path=kwargs['manual_path'])\n\n#def left_image_right_black\n#must specify which image to use for this!\ndef top_black_bottom_image(**kwargs):\n    im = get_image_with_bg(input_ps3_folder, kwargs['manual_path'])\n    return resize_and_add_black_top(im)\n\ndef left_image_right_door_gradient(**kwargs):\n    im = get_image_with_bg(input_ps3_folder, kwargs['manual_path'])\n    return resize_and_add_image_right(im, make_gradient_image_door())\n\ndef left_image_right_masked_image(**kwargs):\n    im = get_image_with_bg(input_ps3_folder, kwargs['manual_path'])\n    #need to remove alpha parts from the image if it has one, by compositing the image against black\n    black = Image.new('RGBA', im.size, color=(0,0,0,255))\n    im_on_black_background = Image.alpha_composite(black, im)\n    return resize_and_add_image_right(im_on_black_background, make_mask_from_image(im))\n\n#note: gradient image should have about 135/640 occupied by white->black. left side is white, right side is black.\n\ndef process_tall_image(im):\n    black = Image.new('RGBA', im.size, color=(0,0,0,255))\n    im_on_black_background = Image.alpha_composite(black, im)\n    scaling_factor = OUTPUT_RES[0] / im.width\n    output_height = round(im.height * scaling_factor)\n    #im_on_black_background.thumbnail((OUTPUT_RES[0], 9999999999))       #resize such that width is 1707, but height can be whatever\n    return im_on_black_background.resize((OUTPUT_RES[0],output_height), resample=Image.LANCZOS)\n\ndef tall_image(**kwargs):\n    filename = None\n    if manual_path != '':\n        filename = kwargs['manual_path']\n    else:\n        filename = get_filename_no_ext(kwargs['original_path']) + '.png'\n\n    im = get_image_with_bg(input_ps3_folder, filename)\n    return process_tall_image(im)\n\n############ ORIGINAL IMAGE Methods #########\n# Note: any effect files should retain their original height so the game processes them correctly!\ndef get_widescreen_width(height):\n    return round(height * 16 / 9)\n\ndef get_double_widescreen_width(height):\n    return round(height * 16 * 2 / 9)\n\ndef tall_image_original(**kwargs):\n    filename = os.path.join(input_original_folder, kwargs['original_path'])\n    im = Image.open(filename).convert(mode='RGBA')\n    return process_tall_image(im)\n\ndef stretch_original_double_width(**kwargs):\n    full_path = os.path.join(input_original_folder, kwargs['original_path'])\n    im = Image.open(full_path)\n    return im.resize((get_double_widescreen_width(im.height),im.height), resample=Image.LANCZOS)\n\n#NOTE: this will stretch the original images, but in most cases it doesn't look that bad surprisingly\ndef stretch_original(**kwargs):\n    full_path = os.path.join(input_original_folder, kwargs['original_path'])\n    im = Image.open(full_path)\n    return im.resize((get_widescreen_width(im.height), im.height), resample=Image.LANCZOS)\n\n#input is original game's asset, will put it in center of screen and black bars either side\ndef stretch_height_center_screen(**kwargs):\n    filename = os.path.join(input_original_folder, kwargs['original_path'])\n    im = Image.open(filename)\n    new_width = get_widescreen_width(im.height)\n    x_offset = round((new_width -im.width)/2)\n\n    black = Image.new('RGBA', (new_width, im.height), color=(0,0,0,255))\n    black.paste(im, (x_offset, 0))\n    return black\n\n\nmethod_lookup_table = {'': normal,\n                       'make_greyscale':make_greyscale,\n                       'tall_image': tall_image,\n                       'tall_image_original':tall_image_original,\n                       'stretch_original': stretch_original,\n                       'stretch_original_double_width': stretch_original_double_width,\n                       'use_alternate': use_alternate,\n                       'top_black_bottom_image': top_black_bottom_image,\n                       'left_image_right_door_gradient': left_image_right_door_gradient,\n                       'left_image_right_masked_image': left_image_right_masked_image,\n                       'use_ps3_same_name':use_ps3_same_name,\n                       'stretch_height_center_screen':stretch_height_center_screen}\n\nwith open ('database.csv', newline='') as csvfile:\n    reader = csv.reader(csvfile, delimiter=',')\n    for row in reader:\n        original_path = row[0].strip()\n        ps3_path = row[1].strip()\n        method = row[2].strip()\n        manual_path = row[3].strip()\n        similarity = row[4].strip()\n\n        name, ext = os.path.splitext(original_path)\n\n        if method == 'english_asset':\n            save_path = os.path.join(output_folder, original_path)\n        else:\n            save_path = os.path.join(output_folder, name + '.png')\n\n        if file_exists(save_path):\n            poke_image = Image.open(save_path)\n            _, save_ext = os.path.splitext(save_path)\n            print('{},{},{},{},{}'.format(original_path, save_ext, poke_image.width, poke_image.height, method))\n            continue\n\n        if method == 'no_modification':\n            original_image = Image.open(os.path.join(input_original_folder, original_path))\n            original_image.save(save_path)\n            continue\n\n        if method == 'english_asset':\n            shutil.copy(os.path.join(input_original_folder, original_path), save_path)\n            continue\n\n        if method == 'not_used':\n            print('Skipping', name, 'as it is never used in the script!')\n            continue\n\n        if method in method_lookup_table:\n            print(\"Using method [{}] for [{}]\".format(method, original_path))\n            processed_image = method_lookup_table[method](ps3_path=ps3_path, manual_path=manual_path, original_path=original_path)\n\n            try:\n                containing_folder_path, _ = os.path.split(save_path)\n                os.makedirs(containing_folder_path)\n            except:\n                pass\n\n            processed_image.save(save_path)\n        else:\n            print(\"ERROR: No method matches [{}] for [{}]\".format(method, name))\n","sub_path":"widescreen/background_mapping/use_csv_for_output.py","file_name":"use_csv_for_output.py","file_ext":"py","file_size_in_byte":9508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"593880968","text":"from ROOT import TFile, TTree, TRandom, TH1F\nfrom ROOT import gROOT\nfrom array import array\nimport random\nfrom random import shuffle\n\n##############################\n######### GENERATION #########\n##############################\n\ngROOT.ProcessLine(\n                  \"struct MyStruct {\\\n                  Float_t     f_muonMass1;\\\n                  Float_t     f_muonMass2;\\\n                  Float_t     f_muonMass3;\\\n                  Float_t     f_muonMass4;\\\n                  };\" )\n\nfrom ROOT import MyStruct\nmystruct = MyStruct()\n\nf = TFile( 'ZBosonNtuple.root', 'RECREATE' )\ntree = TTree( 'T', 'Z Boson Data' )\ntree.Branch( 'muons', mystruct, 'f_muonMass1/F:f_muonMass2:f_muonMass3:f_muonMass4' )\n\nrand = TRandom()\n\nmuonMass = {}\n\nfor i in range(10000):\n    muonMass[0] = rand.Exp(30)*90\n    muonMass[1] = abs(rand.Gaus(91.2,1.3) - muonMass[0])\n    muonMass[2] = rand.Exp(30)\n    muonMass[3] = rand.Exp(30)\n    \n    muons = [[i] for i in range(4)]\n    for i in range(4):\n        muons[i] = muonMass[i]\n    shuffle(muons)\n\n    mystruct.f_muonMass1   = muons[0]\n    mystruct.f_muonMass2   = muons[1]\n    mystruct.f_muonMass3   = muons[2]\n    mystruct.f_muonMass4   = muons[3]\n\n    tree.Fill()\n\n\n\n\n############################\n######### ANALYSIS #########\n############################\nallMuonHist = TH1F(\"allMuonHist\",\"All Muon Mass\",1000,0,1000)\ndiMuonHist12 = TH1F(\"diMuonHist12\",\"Di Muon Mass\",500,0,500)\ndiMuonHist23 = TH1F(\"diMuonHist23\",\"Di Muon Mass\",500,0,500)\ndiMuonHist34 = TH1F(\"diMuonHist34\",\"Di Muon Mass\",500,0,500)\nbestDiMuonPair = TH1F(\"bestDiMuonHist\",\"Best Di Muon Pair Mass\",500,0,500)\n\nbranch = tree.GetBranch(\"muons\")\n\nfor event in f.T:\n    muonOne = event.f_muonMass1\n    muonTwo = event.f_muonMass2\n    muonThree = event.f_muonMass3\n    muonFour = event.f_muonMass4\n    \n    ##################################\n    ### STEP 1 - sum all muon mass ###\n    ##################################\n    allMuonHist.Fill(muonOne + muonTwo + muonThree + muonFour)\n    \n    \n    ###############################################################\n    ### STEP 2 - sum muon masses in pairs (notice peak at ~90?) ###\n    ###############################################################\n    diMuonHist12.Fill(muonOne + muonTwo)\n    diMuonHist23.Fill(muonTwo + muonThree)\n    diMuonHist34.Fill(muonThree + muonFour)\n    \n    ###########################################\n    ### STEP 3 - algorithms focusing on ~90 ###\n    ###########################################\n    combinedMass = muonOne + muonTwo\n\n    if (abs(combinedMass - 90) > abs(muonOne + muonThree - 90)):\n        combinedMass = muonOne + muonThree\n    if (abs(combinedMass - 90) > abs(muonOne + muonFour - 90)):\n        combinedMass = muonOne + muonFour\n    if (abs(combinedMass - 90) > abs(muonTwo + muonThree - 90)):\n        combinedMass = muonTwo + muonThree\n    if (abs(combinedMass - 90) > abs(muonTwo + muonFour - 90)):\n        combinedMass = muonTwo + muonFour\n    if (abs(combinedMass - 90) > abs(muonThree + muonFour - 90)):\n        combinedMass = muonThree + muonFour\n    \n    bestDiMuonPair.Fill(combinedMass)\n\n    ##################################\n    ### Fitting...ETC...\n    ##################################\n\nf.Write()\nf.Close()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"ZBosonGenerator.py","file_name":"ZBosonGenerator.py","file_ext":"py","file_size_in_byte":3304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"456743759","text":"\"\"\"\nDjango settings for boilerplate application.\n\"\"\"\n\nimport os\nimport sys\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nSECRET_KEY = os.environ['SECRET_KEY']\n\n# SECURITY WARNING: don't run with debug turned on in production!\nif os.environ['DJANGO_DEBUG'] == \"yes\":\n    DEBUG = True\nelse:\n    DEBUG = False\n\nLOGIN_URL = 'login'\nLOGIN_REDIRECT_URL = 'upload'\nLOGOUT_REDIRECT_URL = 'login'\n\nLOGGING = {\n    'version': 1,\n    'disable_existing_loggers': False,\n    'handlers': {\n        # define a console / stdout handler\n        'console': {\n            'class': 'logging.StreamHandler',\n            'stream': sys.stdout,\n        }\n    },\n    'loggers': {\n        # global logging\n        '': {\n            'handlers': ['console'],\n            'level': os.getenv('DJANGO_LOG_LEVEL', 'DEBUG'),\n        },\n        # django framework logging\n        'django': {\n            'handlers': ['console'],\n            'level': os.getenv('DJANGO_LOG_LEVEL', 'WARN'),\n        },\n        'rq.worker': {\n            \"handlers\": ['console'],\n            \"level\": os.getenv('DJANGO_LOG_LEVEL', 'WARN'),\n        },\n    },\n}\n\nRACK_ENV = os.environ['RACK_ENV']\nif RACK_ENV == 'production' or RACK_ENV == 'staging':\n    SECURE_SSL_REDIRECT = True\nelse:\n    SECURE_SSL_REDIRECT = False\n    \nif RACK_ENV == 'local':\n    RQ_QUEUES = {\n        'default': {\n            'HOST': 'localhost',\n            'PORT': 6379,\n            'DB': 0,\n            'DEFAULT_TIMEOUT': 50000\n        },\n        'high': {\n            'HOST': 'localhost',\n            'PORT': 6379,\n            'DB': 0,\n            'DEFAULT_TIMEOUT': 50000\n        },\n        'low': {\n            'HOST': 'localhost',\n            'PORT': 6379,\n            'DB': 0,\n            'DEFAULT_TIMEOUT': 50000\n        }\n    }\nelse:\n    RQ_QUEUES = {\n        'default': {\n            # If you're on Heroku\n            'URL': os.getenv('REDIS_URL', 'redis://localhost:6379/0'),\n            'DEFAULT_TIMEOUT': 50000,\n        },\n        'high': {\n            # If you're on Heroku\n            'URL': os.getenv('REDIS_URL', 'redis://localhost:6379/0'),\n            'DEFAULT_TIMEOUT': 50000\n        },\n        'low': {\n            # If you're on Heroku\n            'URL': os.getenv('REDIS_URL', 'redis://localhost:6379/0'),\n            'DEFAULT_TIMEOUT': 50000\n        }\n    }\n\nRACK_ENV = os.environ['RACK_ENV']\nif RACK_ENV == 'local':\n    ALLOWED_HOSTS = [\n        '0.0.0.0',\n        'localhost'\n    ]\nelse:\n    ALLOWED_HOSTS = [\n        'wildlife-camera-portal.herokuapp.com'\n    ]\n\n\nREST_FRAMEWORK = {\n    'DEFAULT_AUTHENTICATION_CLASSES': (\n        'rest_framework.authentication.TokenAuthentication',\n        'rest_framework.authentication.SessionAuthentication'\n    ),\n    'DEFAULT_PERMISSION_CLASSES': (\n        'rest_framework.permissions.IsAuthenticated',\n    ),\n    'DEFAULT_RENDERER_CLASSES': [\n        'rest_framework.renderers.JSONRenderer',\n        'rest_framework.renderers.BrowsableAPIRenderer',\n    ]\n}\n\n\n# Application definition\n\nINSTALLED_APPS = [\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'apps.cascadia',\n    'rest_framework',\n    'storages',\n    'django_rq'\n]\n\nMIDDLEWARE = [\n    'django.middleware.security.SecurityMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'boilerplate.urls'\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [],\n        'APP_DIRS': True,\n        'OPTIONS': {\n            'context_processors': [\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages',\n            ],\n        },\n    },\n]\n\nWSGI_APPLICATION = 'boilerplate.wsgi.application'\n\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.postgresql_psycopg2',\n        'NAME': 'cascadia',\n        'USER': os.environ['USERNAME'],\n        'PASSWORD': os.environ['PASSWORD'],\n        'HOST': ''\n    }\n}\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.1/howto/static-files/\nSTATIC_URL = '/static/'\n\n# Storage\nif os.environ['RACK_ENV'] == 'local':\n    STATICFILES_STORAGE = 'django.contrib.staticfiles.storage.StaticFilesStorage'\nelse:\n    STATICFILES_STORAGE = 'boilerplate.storage_backends.StaticStorage'\n\nDEFAULT_FILE_STORAGE = 'boilerplate.storage_backends.MediaStorage'\n\n# Settings for Wasabi cloud storage\nAWS_ACCESS_KEY_ID = os.environ['WASABI_ACCESS_KEY_ID']\nAWS_SECRET_ACCESS_KEY = os.environ['WASABI_SECRET_ACCESS_KEY']\nAWS_STORAGE_BUCKET_NAME = os.environ['WASABI_STORAGE_BUCKET_NAME']\nS3_URL = 'https://s3.us-west-1.wasabisys.com/{}/'.format(AWS_STORAGE_BUCKET_NAME)\nMEDIA_URL = 'https://s3.us-west-1.wasabisys.com/{}/'.format(AWS_STORAGE_BUCKET_NAME)\nAWS_S3_HOST = 's3.us-west-1.wasabisys.com'\n\n# For doing landglide property lookups\nif os.environ['LANDGLIDE_KEY']:\n    LANDGLIDE_KEY = os.environ['LANDGLIDE_KEY']\nelse:\n    LANDGLIDE_KEY = 'none'\n\n# Heroku settings\n# Parse database configuration from $DATABASE_URL\nimport dj_database_url\nif 'DATABASE_URL' in os.environ:\n    DATABASES['default'] = dj_database_url.config()\n\n\n# Password validation\n# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n    {\n        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n    },\n]\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/2.1/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'America/Los_Angeles'\n\nCAMERA_VISIT_TIME_GAP = 600\n# frames per second for making gifs\nGIF_FPS = 2\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n","sub_path":"boilerplate/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":6526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"209697416","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom .forms import ContactForm\n\n\ndef get_name(request):\n    # if this is a POST request we need to process the form data\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        print(request.FILES)\n        form = ContactForm(request.POST, request.FILES)\n        # check whether it's valid:\n        if form.is_valid():\n            # process the data in form.cleaned_data as required\n            # ...\n            # redirect to a new URL:\n            print(form.changed_data)\n            print(form.cleaned_data)\n            return HttpResponseRedirect('/thanks/')\n\n    # if a GET (or any other method) we'll create a blank form\n    else:\n        form = ContactForm()\n\n    return render(request, 'contact.html', {'form': form})\n","sub_path":"forms_eg/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"17584682","text":"import numpy as np\nimport math\nimport copy\nfrom random import shuffle\n\nclass SudokuBoard:\n\n    def __init__(self, size = 9):\n        self.size = size\n        self.cellAmount = size*size\n\n        self.gridSize= int(math.sqrt(size))\n        self.gridAmount = self.gridSize*self.gridSize\n\n        self.board = np.zeros((size,size),dtype=int,)\n        self.board.fill(0)\n\n        self.allowedBitFields = np.array([\n            0,\n            1,\n            1 << 1,\n            1 << 2,\n            1 << 3,\n            1 << 4,\n            1 << 5,\n            1 << 6,\n            1 << 7,\n            1 << 8,], dtype=np.int32)\n\n        self.allAllowed=self.allowedBitFields.sum()\n\n        self.allowedValues = np.zeros((size, size), dtype = int)\n\n        for ix,iy in np.ndindex(self.allowedValues.shape):\n            self.allowedValues[ix][iy] = self.allAllowed\n\n        self.placedNumberCount = 0\n\n\n    def __repr__(self):\n        return str(self.board)\n\n    def __eq__(self, other):\n        if isinstance(other, SudokuBoard):\n            return np.array_equal(self.board,other.board)\n        return False\n\n    def getCopy(self):\n        return copy.deepcopy(self)\n        \n    def fill(self,values):\n        if  values.shape != self.board.shape: return False\n        self.board = values\n\n        for ix,iy in np.ndindex(self.board.shape):\n            if(self.board[iy][ix])>0:\n                self.allowedValues[iy][ix] = 0\n                self.applyAllowedValuesMask(ix,iy)\n                self.placedNumberCount+=1\n\n        return True\n\n    def countSetBits(self,x,y):\n        currentAllowedValues = self.allowedValues[y][x]\n        return bin(currentAllowedValues).count(\"1\")\n\n    def getLastSetBitIndex(self,x,y):\n        currentAllowedValues = self.allowedValues[y][x]\n        bits = []\n        for i, c in enumerate(bin(currentAllowedValues)[:1:-1], 1):\n            if c == '1':\n                bits.append(i)\n\n        if len(bits)==0:\n            return -1\n        return bits[-1]\n\n    def setValue(self,x,y):\n        index = self.getLastSetBitIndex(x,y)\n        self.board[y][x]=self.allowedBitFields[index]\n        self.allowedValues[y][x] = 0\n        self.applyAllowedValuesMask(x,y)\n\n\n    def applyAllowedValuesMask(self,x,y):\n\n        mask = ~self.allowedBitFields[self.board[y][x]]\n\n        for maskApplyY in range(0,self.size):\n            self.allowedValues[maskApplyY][x] &=mask\n\n        allowedValuesRow = self.allowedValues[y]\n\n        for maskApplyX in range(0,self.size):\n            allowedValuesRow[maskApplyX] &=mask\n\n        sectionX1 = 0\n        sectionX2 = 0\n\n        if x==0:\n            sectionX1 = x + 1\n            sectionX2 = x + 2\n        elif x==1:\n            sectionX1 = x - 1\n            sectionX2 = x + 1\n        elif x==2:\n            sectionX1 = x - 2\n            sectionX2 = x - 1\n        elif x==3:\n            sectionX1 = x + 1\n            sectionX2 = x + 2\n        elif x==4:\n            sectionX1 = x - 1\n            sectionX2 = x + 1\n        elif x==5:\n            sectionX1 = x - 2\n            sectionX2 = x - 1\n        elif x==6:\n            sectionX1 = x + 1\n            sectionX2 = x + 2\n        elif x==7:\n            sectionX1 = x - 1\n            sectionX2 = x + 1\n        elif x==8:\n            sectionX1 = x - 2\n            sectionX2 = x - 1\n        else:\n            print(\"Error4984191\")\n\n\n        sectionY1 = 0\n        sectionY2 = 0\n\n        if y==0:\n            sectionY1 = y + 1\n            sectionY2 = y + 2\n        elif y==1:\n            sectionY1 = y - 1\n            sectionY2 = y + 1\n        elif y==2:\n            sectionY1 = y - 2\n            sectionY2 = y - 1\n        elif y==3:\n            sectionY1 = y + 1\n            sectionY2 = y + 2\n        elif y==4:\n            sectionY1 = y - 1\n            sectionY2 = y + 1\n        elif y==5:\n            sectionY1 = y - 2\n            sectionY2 = y - 1\n        elif y==6:\n            sectionY1 = y + 1\n            sectionY2 = y + 2\n        elif y==7:\n            sectionY1 = y - 1\n            sectionY2 = y + 1\n        elif y==8:\n            sectionY1 = y - 2\n            sectionY2 = y - 1\n        else:\n            print(\"Error49849851\")\n\n        allowedValuesRow1 = self.allowedValues[sectionY1]\n        allowedValuesRow2 = self.allowedValues[sectionY2]\n        \n        allowedValuesRow1[sectionX1] &= mask\n        allowedValuesRow1[sectionX2] &= mask\n        allowedValuesRow2[sectionX1] &= mask\n        allowedValuesRow2[sectionX2] &= mask\n\n\n    def isFilled(self):\n        for cell in np.nditer(self.board):\n            if cell!=0: return True\n        return False\n\n    def enter(self,x,y,value):\n        result = self.isEnterValid(x,y,value)\n        if result: self.board[y,x]=value\n        return result\n    \n    def reset(self,x,y):\n        self.board[y,x]=0\n\n    def isEnterValid(self,x,y,value):\n        if self.isColOfCellConatining(x,y,value): return False\n        if self.isRowOfCellConatining(x,y,value): return False\n        if self.isGridOfCellContaining(x,y,value): return False\n        return True\n    \n    def get(self,x,y):\n        return self.board[y,x]\n        \n    def isCellFilled(self,x,y):\n        return not (self.board[y,x]==0)\n\n    def getFreeCellsAsList(self):\n        result = []\n        for ix,iy in np.ndindex(self.board.shape):\n            if not self.isCellFilled(ix,iy):\n                result.append((ix,iy))\n        # print(result)\n        # shuffle(result)\n        # print(result)\n        return result\n\n    def isRowOfCellConatining(self,x,y,value):\n        return self.isRowContaining(y,value)\n\n    def isRowContaining(self,row_index,value):\n        row=self.board[row_index]\n        for cell in row:\n            if cell==value: return True\n        return False\n\n    def isColOfCellConatining(self,x,y,value):\n        return self.isColContaining(x,value)\n\n    def isColContaining(self,col_index,value):\n        col=self.board[:,col_index]\n        for cell in col:\n            if cell==value: return True\n        return False\n \n    def gridCoordToCell(self,x,y):\n        gridx = x//self.gridSize\n        gridy = y//self.gridSize\n        return (gridx,gridy)\n\n    def isGridOfCellContaining(self,x,y,value):\n        grid = self.gridCoordToCell(x,y)\n        check = self.isGridContaining(grid[0],grid[1],value)\n        return check\n\n    def isGridContaining(self,gridx,gridy,value):\n        n1 = np.arange(gridx*self.gridSize,gridx*self.gridSize+3)\n        n2 = np.arange(gridy*self.gridSize,gridy*self.gridSize+3)\n        for i in n1:\n            for j in n2:\n                if self.isCellFilled(i,j):\n                    if self.get(i,j)==value: return True\n        return False   \n    \n\n\n\n\n   \n\n    \n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"board/SudokuBoard.py","file_name":"SudokuBoard.py","file_ext":"py","file_size_in_byte":6658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"140487719","text":"#!/usr/bin/python3\nimport sys\nfrom lxml import etree\nimport re\n\n'''parse the data from xml document'''\nsys.stdin = sys.stdin.detach()\ntree = etree.parse(sys.stdin)\nroot = tree.getroot()\n\n\ndef clean_text(text):\n    '''function for cleaning text'''\n    text = re.sub(\"<\", ' ', text)\n    text = re.sub(\">\", ' ', text)\n    text = text.strip().split()\n    return text\n\n\nfor child in root:\n    '''sort on posttype id and extracts body attribute'''\n\n    if child.get(\"Tags\") is not None:\n        tags = child.get(\"Tags\")\n        tags = clean_text(tags)\n        for tag in tags:\n            print('{}'.format(tag))\n","sub_path":"1f_Tags/mapper.py","file_name":"mapper.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"502150796","text":"import json\n\nrecipes = json.load(open(\"cocktails.json\"))\n\ndef normalize_string(s):\n    cleanString = []\n    zutat = \"\"\n    write = True\n    #extract the ingredients\n    for i in range(len(s)):\n        for x in s[i]:\n            for n in x:\n                if(n == '(' or n == ','):\n                    write = False\n                if(write):\n                    if (n not in '0123456789%()[]{}*'):\n                        zutat += n.lower()\n\n            if(zutat != \"\" and zutat != \" \"):\n                cleanString.append(zutat.strip())\n            zutat = \"\"\n            write = True\n\n    return cleanString\n\ndef all_ingredients(recipes):\n    allIngredients = []\n    ingredients = []\n    for i in recipes:\n        ingredients.append(recipes[i][\"ingredients\"].keys())\n    ingredients = normalize_string(ingredients)\n    allIngredients = set(ingredients)\n    return allIngredients\n\ndef cocktails_inverse(recipes):\n    data = json.load(open(\"cocktails.json\"))\n    inverse_recipes = {}\n    for i in recipes:\n        cocktails = []\n        for cocktail in data:\n            for zutat in (data[cocktail][\"ingredients\"].keys()):\n                if(i == zutat.lower()[:len(i)+1].strip()):\n                    if(cocktail not in cocktails):\n                        cocktails.append(cocktail)\n        inverse_recipes[i] = cocktails\n    return inverse_recipes\n\ndef sort_dict_by_cocktail_count(dict):\n    return sorted(dict.items(), key = lambda item: len(item[1]), reverse = True)\n\nrecipes = all_ingredients(recipes)\ninverse_recipes = cocktails_inverse(recipes)\n\ndef possible_cocktails(inverse_recipes, available_ingredients):\n    possible_cocktails = []\n    recipes = json.load(open(\"cocktails.json\"))\n\n\nwith open(\"cocktails_inverse.json\", \"w\", encoding = \"utf-8\") as fp:\n    json.dump(inverse_recipes, fp, indent = 4, ensure_ascii=False)\n\ningredients_sorted = sort_dict_by_cocktail_count(inverse_recipes)\nfor i in range(len(ingredients_sorted)):\n    print(ingredients_sorted[i][0],\":   \",len(ingredients_sorted[i][1]))\n#print(ingredients_sorted)\n\n\n'''for i in range(len(inverse_recipes)):\n    if(len(inverse_recipes[i][1]) == 0):\n        print(inverse_recipes[i][0],len(inverse_recipes[i][1]))'''\n","sub_path":"Ss20/AlDa/Ü7/A1.py","file_name":"A1.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"533361710","text":"# coding=utf-8\nimport unittest\nimport time\nimport HTMLTestRunner_cn\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nimport smtplib\nimport os\n\n# 测试执行所有用例并发送报告，分四个步骤\n# 第一步加载用例\n# 第二步执行用例\n# 第三步获取最新测试报告\n# 第四步发送邮箱 （这一步不想执行的话，可以注释掉最后面那个函数就行）\n\ndef add_case(case_path, rule):\n    # discover加载所有的测试用例\n    discover = unittest.defaultTestLoader.discover(case_path,\n                                                   pattern=rule,\n                                                   top_level_dir=None)\n    return discover\n\n\ndef run_case(all_case, report_path):\n    # 执行所有的用例,并把结果写入测试报告\n    now = time.strftime(\"%Y_%m_%d %H_%M_%S\")\n    # 如果不存在这个report文件夹，就自动创建一个\n    if not os.path.exists(report_path):\n        os.mkdir(report_path)\n    report_abspath = os.path.join(report_path, now + \"_result.html\")\n    fp = open(report_abspath, \"wb\")\n    # runner = HTMLTestRunner.HTMLTestRunner(stream=fp,\n    #                                        title=u'智平台,自动化测试结果如下：',\n    #                                        description=u'用例执行情况：')\n    runner = HTMLTestRunner_cn.HTMLTestRunner(stream=fp,\n                                              title=u'智平台,自动化测试结果如下：',\n                                              description=u'用例执行情况：', verbosity=2, retry=2, save_last_try=True)\n    # 调用add_case函数返回值\n    runner.run(all_case)\n    fp.close()\n\n\ndef get_report_file(report_path):\n    # 获取最新的测试报告\n    lists = os.listdir(report_path)\n    lists.sort(key=lambda fn: os.path.getmtime(os.path.join(report_path, fn)))\n    lists_report = lists[-1]\n    print(u'最新测试生成的报告：' + lists_report)\n    # 找到最新生成的报告文件\n    report_file = os.path.join(report_path, lists[-1])\n    return report_file\n\n\ndef send_mail(sender, psw, receiver, smtpserver, report_file):\n    # 读取测试报告的内容\n    with open(report_file, \"rb\") as f:\n        mail_body = f.read()\n        # 定义邮件内容\n        msg = MIMEMultipart()\n        body = MIMEText(mail_body, _subtype='html', _charset='utf-8')\n        msg['Subject'] = u\"自动化测试报告\"\n        msg[\"from\"] = sender\n        msg[\"to\"] = psw\n        # 加上时间戳\n        #  msg[\"date\"] = time.strftime('%a, %d %b %Y %H_%M_%S %z')\n        msg.attach(body)\n        # 添加附件\n        att = MIMEText(open(report_file, \"rb\").read(), \"base64\", \"utf-8\")\n        att[\"Content-Type\"] = \"application/octet-stream\"\n        att[\"Content-Disposition\"] = 'attachment; filename= \"report.html\"'\n        msg.attach(att)\n        # 登录邮箱\n        smtp = smtplib.SMTP()\n        # 连接邮箱服务器\n        smtp.connect(smtpserver)\n        # 用户名密码\n        smtp.login(sender, psw)\n        smtp.sendmail(sender, receiver, msg.as_string())\n        smtp.quit()\n        print('test report email has send out !')\n\n\nif __name__ == \"__main__\":\n    # if上面是写好的四个函数，一般情况无需修改，只需改if以下的路径参数就行了\n    # 测试用例的路径、匹配规则\n    # case_path = \"F:\\\\python_script\\\\interface\\\\test_case\"\n    # case_path = os.path.abspath(os.path.join(os.getcwd(), \"..\", \"test_case\"))\n    case_path = os.path.abspath(os.path.join(os.getcwd()))\n    # 匹配规则\n    rule = \"zpt_admin.py\"\n    # 1.加载用例\n    all_case = add_case(case_path, rule)\n    # 生成测试报告的路径\n    report_path = os.path.abspath(os.path.join(os.getcwd(), \"..\", \"result\"))\n    # 2.执行用例\n    run_case(all_case, report_path)\n    # 3.获取最新的测试报告文件\n    report_file = get_report_file(report_path)\n    #  邮箱配置\n    sender = 'jtsz2010@163.com'\n    psw = 'lovejt333'\n    # 收件人多个时，中间用逗号隔开,如'a@xx.com,b@xx.com'\n    receiver = 'jtsz2010@163.com', 'jiangtao02@hnjing.com', 'shengxiuling@hnjing.com'\n    smtp_server = 'smtp.163.com'\n    send_mail(sender, psw, receiver, smtp_server, report_file)\n    # 4.最后一步发送报告\n","sub_path":"test_case/run_case_cn.py","file_name":"run_case_cn.py","file_ext":"py","file_size_in_byte":4264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"157362479","text":"# -*- coding: utf-8 -*-\n# import module snippets\nfrom __future__ import print_function\nfrom ..base import Base\nfrom apiclient.discovery import build\n\n\n# See, edit, create, and delete all of your Google Drive files\nSCOPE_DRIVE = \"https://www.googleapis.com/auth/drive\"\n# View and manage its own configuration data in your Google Drive\nSCOPE_DRIVE_APPDATA = \"https://www.googleapis.com/auth/drive.appdata\"\n# View and manage Google Drive files and folders that you have opened or created with this app\nSCOPE_DRIVE_FILE = \"https://www.googleapis.com/auth/drive.file\"\n# View and manage metadata of files in your Google Drive\nSCOPE_DRIVE_METADATA = \"https://www.googleapis.com/auth/drive.metadata\"\n# View metadata for files in your Google Drive\nSCOPE_DRIVE_METADATA_READONLY = \"https://www.googleapis.com/auth/drive.metadata.readonly\"\n# View the photos, videos and albums in your Google Photos\nSCOPE_DRIVE_PHOTOS_READONLY = \"https://www.googleapis.com/auth/drive.photos.readonly\"\n# See and download all your Google Drive files\nSCOPE_DRIVE_READONLY = \"https://www.googleapis.com/auth/drive.readonly\"\n# Modify your Google Apps Script scripts' behavior\nSCOPE_DRIVE_SCRIPTS = \"https://www.googleapis.com/auth/drive.scripts\"\n\nSCOPES_MANAGE = [\n    SCOPE_DRIVE,\n    SCOPE_DRIVE_APPDATA,\n    SCOPE_DRIVE_FILE,\n    SCOPE_DRIVE_METADATA,\n    SCOPE_DRIVE_PHOTOS_READONLY,\n    SCOPE_DRIVE_SCRIPTS\n]\n\n\nclass Drive(Base):\n    service = None\n    domain = None\n\n    def __init__(\n        self,\n        credential_path: str,\n        scopes: list,\n        delegate_user: str = None\n    ):\n        \"\"\"\n        @params[in]     credentail_path GoogleのClient作成時のjsonのパス\n        @params[in]     scopes          アクセスのスコープ一覧\n        @params[in]     delegate_user   アクセスユーザーを指定\n        \"\"\"\n        super(Drive, self).__init__(\n            credential_path,\n            scopes,\n            delegate_user\n        )\n        self.service = build('drive', 'v3', http=self.http)\n\n    def about(self):\n        return self.service.about().get().to_json()\n","sub_path":"GSuite/Drive/drive.py","file_name":"drive.py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"364656053","text":"\"\"\"HOMEWORK ONE\"\"\"\n\n\"\"\" Q1.\nSince you have been looking for jobs lately, you've decided to write a function\nwhich will automatically tell you if you are qualified for a job posting or not\n\nBeing qualified for a job posting means that you have all of the skills listed\nin the requirements\n\nThe function will take in a list of jobs, and a list of your skills, and return\nboth the names of the companies and the positions for which you are qualified\n\"\"\"\n#%%\n\nmy_skills = [\"Excel\",\"SQL\",\"R\",\"SAS\",\"Python\",\"Visualization\",\"Modeling\",\"Statistics\"]\n\njobs = [{\"Company\":\"Stewgle\",\n         \"Position\":\"Data Scientist\",\n         \"Location\":\"San Francisco, CA\",\n         \"Requires\":[\"Python\",\"Visualization\",\"Hadoop\",\"SQL\"]},\n        {\"Company\":\"Super\",\n         \"Position\":\"Business Analyst\",\n         \"Location\":\"San Francisco, CA\",\n         \"Requires\":[\"Excel\",\"SQL\",\"R\",\"Modeling\"]},\n        {\"Company\":\"Ricky's Pawn & Auto\",\n         \"Position\":\"Chief Information Officer\",\n         \"Location\":\"Winston-Salem, NC\",\n         \"Requires\":[\"Excel\",\"Street Smarts\"]},\n        {\"Company\":\"KRAS\",\n         \"Position\":\"Consultant\",\n         \"Location\":\"Raleigh, NC\",\n         \"Requires\":[\"SAS\",\"Excel\",\"SQL\",\"Modeling\",\"Statistics\"]}]\n\ndef qualified(jobs, skills):\n    #Let's use pseudo-code to break this problem down:\n    #First, we'll create a list to hold our results\n    results = []\n    \n    #Now, we'll look at each job posting:\n\n        #Check if we qualify for the current job posting:\n        #Remember that you can check if a set is a super-set or sub-set of another set by using >= and <=\n        #Additionally, you could also use the \"in\" operator to see if all the required skills are in your list of skills\n        #This is the subset operator.  It returns true when the thing on the right has all the elements of the thing on the left.\n\n            #If we do qualify, add (Company, Position) to results:\n\n\n    return results\n\nassert qualified(jobs, my_skills) == [(\"Super\",\"Business Analyst\"),(\"KRAS\",\"Consultant\")]\n\nprint(\"If you get here, you did it!\")\n\n#%%\n\n\"\"\" Q2.\nUsing the exercise data below, find out how much total weight has been lifted by\neach muscle group using a for loop.  Keep the running totals in a dictionary where\nthe keys correspond to the different muscle groups.\n\"\"\"\n\nworkout = [(\"chest\",(\"Incline Bench\",8,135)),(\"triceps\",(\"Skull-Crushers\",12,35)),\n           (\"chest\",(\"Incline Bench\",6,225)),(\"triceps\",(\"Skull-Crushers\",6,55)),\n           (\"shoulders\",(\"Shoulder Press\",8,30)),(\"chest\",(\"Dumbbell Flys\",12,15)),\n           (\"shoulders\",(\"Shoulder Press\",5,60)),(\"chest\",(\"Dumbbell Flys\",16,25)),\n           (\"triceps\",(\"Dips\",8,45)),(\"triceps\",(\"Triceps Extension\",8,35)),\n           (\"triceps\",(\"Dips\",14,45)),(\"triceps\",(\"Triceps Extension\",6,75)),\n           (\"chest\",(\"Bench Press\",6,315)),(\"chest\",(\"Dumbbell Flys\",7,35)),\n           (\"chest\",(\"Bench Press\",1,405)),(\"chest\",(\"Dumbbell Flys\",2,85))]\n\nresults = {}\n\n  #results.get(key, default) returns the value associated with key, or default if the key is not in the dictionary\n  #If the nested nature of the \"workout\" variable is confusing, try playing with it in the IPython console at the bottom right\n  #by arbitrarily slicing it until you better understand its structure.\n  #For example, workout[0], workout[0][0], workout[0][1][0], etc.\n\n\nassert results == {'chest': 5720, 'triceps': 2470, 'shoulders': 540}\n\nprint(\"If you get here, you did it!\")\n           \n#%%\n\n\"\"\" Q3.\nIn the hit game Dungeons & Dragons, players roll many abnormally-sided dice.  One of\nthese dice is a 20-sided die, also referred to as a \"d20.\"  In the game, rolling a 20\nis usually considered an \"automatic success\" or \"critical success.\"\n\nUsing random number generation and control structures, create a distribution of the number\nof d20 rolls it takes to score a critical success for 10,000 trials.  In other words, at\neach trial, keep rolling the die until it comes up 20, and keep track of the number of rolls.\nRepeat this until you have gotten 10,000 successes.  You should use a list to keep track of these\nrolls, which we will use to graph the distribution!\n\"\"\"\nimport random\nprint(random.randint(1,20)) #Here is your d20!\nmy_data = []\n\n#We need to loop 10,000 times\n\n  #At each loop, roll the die repeatedly until you get a 20, and keep track of the number of rolls\n\n    \n  #Add the number of rolls to the my_data list\n\n#%%\n#After you have your 10,000 numbers, run this code to see the distribution:\nimport pandas as pd\nvector_of_rolls = pd.Series(my_data)\nvector_of_rolls.plot.hist()\nvector_of_rolls.mean()\n","sub_path":"Week1/hw1.py","file_name":"hw1.py","file_ext":"py","file_size_in_byte":4581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"516369897","text":"import pandas as pd\nimport re\nfrom selenium import webdriver\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nimport datetime\nimport time\n\nclass BuildColorado():\n    pd.set_option('max_colwidth', 500)\n    df = pd.DataFrame()\n\n    caps = DesiredCapabilities.FIREFOX\n    caps[\"marionette\"] = True\n\n    base_url = 'http://agc-co.ourcareerpages.com/CareerPage.aspx?ccpcode=agc-co'\n    driver = webdriver.Firefox(capabilities=caps)\n\n    driver.get(base_url)\n\n    line = driver.find_elements_by_xpath('//div[@class=\"jobSection\"]')\n    for li in line:\n        comp_name = \"Build Colorado\"\n        try:\n            job = li.text\n            job_title = re.findall('^.* - *', job)\n            job_addr = re.findall('^.* - ([a-zA-Z ]*)', job)\n        except AttributeError:\n            job_title = \"null\"\n        try:\n            job_link = li.find_element_by_tag_name(\"a\").get_attribute(\"href\")\n        except AttributeError:\n            job_link = \"null\"\n        job_posted = \"null\"\n        job_srch = \"Construction\"\n        df = df.append({'comp_name': comp_name,\n                          'job_title': job_title,\n                          'job_link': job_link,\n                          'job_addr': job_addr,\n                          'job_posted': job_posted,\n                          'job_srch': job_srch\n                            }, ignore_index=True)\n        end = time.time()\n        # print \"Build Colorado = \", datetime.datetime.fromtimestamp(end).strftime('%Y-%m-%d %H:%M:%S')\n\n\n# BuildColoradoPull = BuildColorado()\n# print BuildColoradoPull.df","sub_path":"cjap_engine/workspace/ws/build_colorado_search.py","file_name":"build_colorado_search.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"250265922","text":"import os\nimport cv2\nimport json\nimport pickle\n\nfrom core.dataset import *\nfrom core.augmentors import *\n\nfrom utility.utils import *\n\npickle_dir = './dataset/pickles/'\nif not os.path.isdir(pickle_dir):\n    os.makedirs(pickle_dir)\n\ntags = ['Train', 'Test']\njson_paths = ['./dataset/train.json', './dataset/test.json']\n\nthe_number_of_sample_per_pickle = 100\n\nfor tag, json_path in zip(tags, json_paths):\n    data_dic = read_json(json_path)\n    image_paths = list(data_dic.keys())\n\n    dataset = []\n    dataset_index = 1\n\n    dataset_format = pickle_dir + '{}_{}.pkl'\n\n    for image_path in image_paths:\n        image = cv2.imread(image_path)\n        label = data_dic[image_path]\n\n        encoded_image = encode_image(image)\n\n        dataset.append([image_path, encoded_image, label])\n        \n        if len(dataset) == the_number_of_sample_per_pickle:\n            print(dataset_format.format(tag, dataset_index))\n            dump_pickle(dataset_format.format(tag, dataset_index), dataset)\n\n            dataset = []\n            dataset_index += 1\n\n    if len(dataset) > 0:\n        print(dataset_format.format(tag, dataset_index))\n        dump_pickle(dataset_format.format(tag, dataset_index), dataset)\n\n\n","sub_path":"04_Improving_data_pipeline/generate_pickles.py","file_name":"generate_pickles.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"119507124","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    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Last_match',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('team', models.CharField(max_length=40)),\n                ('home', models.CharField(max_length=40)),\n                ('away', models.CharField(max_length=40)),\n                ('place', models.CharField(max_length=40)),\n                ('sets_home', models.IntegerField(default=0, blank=True)),\n                ('sets_away', models.IntegerField(default=0, blank=True)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Set',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('set_number', models.IntegerField(default=0)),\n                ('points_home', models.IntegerField()),\n                ('points_away', models.IntegerField()),\n                ('match', models.ForeignKey(to='home.Last_match')),\n            ],\n        ),\n    ]\n","sub_path":"home/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"91668561","text":"\ndef partition(arr, low, high):\n    val = arr[low]\n    while low < high:\n        while low < high and arr[high] >= val:\n            high -= 1\n        arr[low] = arr[high]\n        while low < high and arr[low] <= val:\n            low += 1\n        arr[high] = arr[low]\n    arr[low] = val\n    return low\n\n\ndef quicksort(arr, left, right):\n    '''\n    分治实现快速排序\n    '''\n    if left < right:\n        pos = partition(arr, left, right)\n        quicksort(arr, left, pos-1)\n        quicksort(arr, pos+1, right)\n\n\nif __name__ == \"__main__\":\n    arr = [3, 3, 3, 1, 8, 8, 6, 6, 5]\n    quicksort(arr, 0, len(arr)-1)\n    print(arr)\n","sub_path":"algorithm-design-and-analysis/Divide/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"160746642","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # T^2 Genome Analysis\n\n\nimport pandas as pd\nimport numpy as np\nimport json\n\n\n# __Load data__\n\n# __Utility functions__\n\n\nfrom numpy import linalg as la\n\ndef nearestPD(A):\n    \"\"\"Find the positive-definite matrix nearest to input.\n    \n    Written by Ahmed Fasih: https://stackoverflow.com/users/500207/ahmed-fasih.\n\n    A Python/Numpy port of John D'Errico's `nearestSPD` MATLAB code [1], which\n    credits [2].\n\n    [1] https://www.mathworks.com/matlabcentral/fileexchange/42885-nearestspd.\n\n    [2] N.J. Higham, \"Computing a nearest symmetric positive semidefinite\n    matrix\" (1988): https://doi.org/10.1016/0024-3795(88)90223-6.\n    \"\"\"\n\n    B = (A + A.T) / 2\n    _, s, V = la.svd(B)\n\n    H = np.dot(V.T, np.dot(np.diag(s), V))\n\n    A2 = (B + H) / 2\n\n    A3 = (A2 + A2.T) / 2\n\n    if isPD(A3):\n        return A3\n\n    spacing = np.spacing(la.norm(A))\n    # The above is different from [1]. It appears that MATLAB's `chol` Cholesky\n    # decomposition will accept matrixes with exactly 0-eigenvalue, whereas\n    # Numpy's will not. So where [1] uses `eps(mineig)` (where `eps` is Matlab\n    # for `np.spacing`), we use the above definition. CAVEAT: our `spacing`\n    # will be much larger than [1]'s `eps(mineig)`, since `mineig` is usually on\n    # the order of 1e-16, and `eps(1e-16)` is on the order of 1e-34, whereas\n    # `spacing` will, for Gaussian random matrixes of small dimension, be on\n    # the order of 1e-16. In practice, both ways converge, as the unit test\n    # below suggests.\n    I = np.eye(A.shape[0])\n    k = 1\n    while not isPD(A3):\n        mineig = np.min(np.real(la.eigvals(A3)))\n        A3 += I * (-mineig * k**2 + spacing)\n        k += 1\n\n    return A3\n\ndef isPD(B):\n    \"\"\"Returns True when input is positive-definite, via Cholesky\"\"\"\n    try:\n        _ = la.cholesky(B)\n        return True\n    except la.LinAlgError:\n        return False\n\nif __name__ == '__main__':\n    for i in range(10):\n        for j in range(2, 100):\n            A = np.random.randn(j, j)\n            B = nearestPD(A)\n            assert(isPD(B))\n    print('unit test passed!')\n\n\nfrom scipy.stats import chi2\n\ndef TV(z, S):\n    \"\"\"\n        This function calculates the T^2 value of the given pathway\n        and the corresponding interaction matrix.\n    \"\"\"\n    kik = np.where(z!=0)[0]\n    if len(kik)==0:\n        return 0\n    else:\n        return np.dot(np.dot(np.transpose(z[kik]), np.linalg.pinv(np.diag(S[kik,kik]))), z[kik])\n    \ndef TS(pathway, ppi, stu, purb, dgv=0.4):\n    \"\"\" T-square.\n        For the given pathway, this function creates the corresponding interaction matrix.\n        Returns the associated T^2, p-value, and other information.\n    \"\"\"\n    # - pathway is a pandas dataframe containing the id of a pathway, its included proteins, and their abundance ratios.\n    # - z contains only the abundance ratios of the proteins, to be used later to calculate the T^2 value.\n    # - m contains the indexes of the pathway's proteins that can be translated form Uniprot to STRING.\n    # - S is the interaction matrix to be built from STRING interaction scores.\n    pathway = pathway.sort_values(by='prot_id').reset_index(drop = True)\n    z = np.vectorize(float)(pathway['exp'])\n    m = np.where(np.isin(stu, pathway))[0]\n    S = dgv * np.identity(len(z))\n    nrow, ncol = S.shape\n    if nrow != 1:\n        # Each possible pair of proteins in the pathway will be looked at.\n        for i in range(1, nrow):\n            for j in range(i):\n                # x1 is the Uniprot accession to one protein i in the pathway.\n                x1 = pathway.iat[i, 1]\n                # x2 is the Uniprot accession to another protein j in the pathway.\n                x2 = pathway.iat[j, 1]\n                # s1 is the STRING id to protein i, translated using m.\n                s1 = stu.iloc[m]['String_id'].to_numpy()[np.where(np.isin(stu.iloc[m]['Uniprot_id'], x1))[0]]\n                # s2 is the STRING id to protein j.\n                s2 = stu.iloc[m]['String_id'].to_numpy()[np.where(np.isin(stu.iloc[m]['Uniprot_id'], x2))[0]]   \n                if len(s1)*len(s2) !=0:\n                    # If there is one, p will contain the experimental value of interaction between the two proteins.\n                    # If there are more, the mean will be used.\n                    # Get all protein 1 partners in STRING\n                    p = ppi.iloc[np.where(np.isin(ppi['protein1'],s1))[0]]\n                    # Check for protein among partners\n                    p = p.iloc[np.where(np.isin(p['protein2'], s2))]['experimental']\n                    # p holds interaction score(s) from experimental evidences only\n                    if len(p)>0:   \n                        # Modify S to include that value at the corresponding index.   \n                        if z[pathway['prot_id']==x1]*z[pathway['prot_id']==x2] <0:\n                            S[i,j] = -np.mean(p)\n                            S[j,i] = -np.mean(p)\n                        else:\n                            S[i,j] = np.mean(p)\n                            S[j,i] = np.mean(p)\n    # Transform STRING scores, S matrix to be positive-definite, and therefore useable for the T^2 method.\n    S = nearestPD(S)\n    r = np.linalg.matrix_rank(S, tol=1e-10)\n    # T2 score matrix effectively used\n    T2 = TV(z, S)\n    I = dgv * np.identity(len(z))\n    T2I = TV(z, I)\n    return np.array([pathway.iat[0,0],\n                    ','.join(pathway['prot_id']),\n                    len(pathway),\n                    r,\n                    T2,\n                    chi2.sf(T2, r),\n                    T2I,\n                    chi2.sf(T2I, r)],\n                    dtype=object)\n\n\ndef PS(pi, cov=0):\n    \"\"\"\n        This function returns a list of lists of pathways.\n        Each sublist contains an 'delegate' pathway and all the ones included in it.\n    \"\"\"\n    pi[1] = np.vectorize(int)(pi[1])\n    a = np.array([s.split(\",\") for s in pi[2]])\n    tag = np.array(a[pi[1].idxmax()])\n    ll = np.array([len(np.setdiff1d(x, tag)) for x in a])\n    g = pd.DataFrame(pi[ll<=cov].values)\n    if np.ndim(g)!=1:\n        g.sort_values(1, ascending=False, inplace=True)\n        g.reset_index(drop = True, inplace=True)\n    newpi = pd.DataFrame(pi[ll>cov].values)\n    nrow, ncol = newpi.shape\n    if ncol==1:\n        g = [g]+[newpi]\n    elif nrow==0:\n        return [g]\n    else:\n        g = [g]+PS(newpi)\n    return g\n\n\n# In[13]:\n\n\ndef predata(data, outth=10):\n    \"\"\"\n        This function gives input data the appropriate format for importdata to use it. \n        Namely, groups duplicates by their median value.\n    \"\"\"\n    nrow, ncol = data.shape\n    print(\"    #(input site/probe): {}\".format(nrow))\n    \n    ### Remove missing\n    tokp = np.logical_not([(k[1]==\"NAN\") or (k[1]==\"NaN\") or (k[1]==\"NA\") or (k[1]==\"na\") or (k[1]==\"-\") or (k[0]==\"\") or (k[1]==\"\") or pd.isnull(k[0]) or pd.isnull(k[1]) for k in data])\n    data = data[tokp]\n        \n    ### Multiple ids one value\n    data1 = np.array([s for s in data if len(s[0])<11])\n    data2 = np.array([s for s in data if len(s[0])>10])\n    if data2.shape[0] != 0:\n        cop = np.empty((0,2), dtype=object)\n        for protein in data2:\n            for realid in protein[0].split(\"|\"):\n                cop = np.append(cop, [[realid, protein[1]]], axis=0)\n        data2 = np.copy(cop)\n    else:\n        data2 = data2.reshape(0,2)\n    data = np.concatenate((data1,data2))\n    data = pd.DataFrame(data, columns=[\"id\",\"exp\"])\n    data['id']= np.vectorize(lambda t: t[0:6])(data['id'])\n    \n    ### one id multiple values\n    data['exp'] = np.vectorize(float)(data['exp'])\n    med = data.groupby(['id']).median()\n    data = pd.DataFrame({'id':med.index.values, 'M':med.values.flatten()})\n    \n    ### normalization\n    if min(data['M'])>=0:\n        nul = data[data['M']==0].index\n        data.iloc[nul].M = min(data[data['M']!=0]['M'])\n        data['M']=np.log2(data['M'])\n    \n    ### outliers replacement\n    for i in range(len(data)):       \n        if not (-outth <= data.iloc[i]['M'] <= outth):\n            data.iloc[i].M = np.sign(data.iloc[i]['M'])*max(abs(data['M']))   \n            \n    ### standardization\n    data['M'] = data['M'] / np.std(data['M'])\n    \n    return data\n\n\n# __Main functions__\n\n\ndef importdata(file1, file2=\"\", outth=100):\n    \"\"\" Data import and pre-process.\n    \n        This function removes NA, replaces extreme values, and\n            standardizes the data. It also maps the data with\n            Uniprot identifiers to ensp identifiers.\n         \n        file1: Expression data with Uniprot identifiers.\n        file2: Expression data with Uniprot identifiers, optional for time-course data.\n        outth: Outlier threshold, default is 10.\n    \"\"\"\n    print(\"=================================================\")\n    print(\" Dataset summary:\")\n    print(\"-------------------------------------------------\")\n    \n    if len(file2)==0:\n        x = file1.values[:,0:2]\n        data = predata(x, outth)\n    else:\n        x = file1.values[:,0:2]\n        y = file2.values[:,0:2]\n        data1, data2 = predata(x, outth), predata(y, outth)\n        #time series, divide time 1 and time 2\n        data12 = np.empty((0,2), dtype=object) \n        m2 = min(data2['M'], key=abs)\n        for i in range(len(data1)):\n            proti = data1.iloc[i]['id']\n            if proti not in data2['id'].values:\n                data12 = np.concatenate((data12, [[proti, data1.iloc[i]['M']-m2]]))\n                \n        data21 = np.empty((0,2), dtype=object)\n        m1 = min(data1['M'], key=abs)\n        for i in range(len(data2)):\n            proti = data2.iloc[i]['id']\n            if proti not in data1['id'].values:\n                data21 = np.concatenate((data21, [[proti, data2.iloc[i]['M']-m1]]))\n        \n        data12 = pd.DataFrame(data12, columns=['id','M'])\n        data21 = pd.DataFrame(data21, columns=['id','M'])\n        \n        datai, d1i, d2i = np.intersect1d(data1['id'],data2['id'], return_indices=True)\n        data = np.empty((0,2), dtype=object)\n        for i, inter in enumerate(datai):\n            data = np.concatenate((data, [[inter, data1.iloc[d1i[i]]['M'] - data2.iloc[d2i[i]]['M']]]), axis=0)\n        data = pd.DataFrame(data, columns=['id','M'])\n        \n        data = pd.concat((data,data12,data21), ignore_index=True)\n    \n    data.columns = ['id', 'exp']\n    data['exp'] = np.vectorize(float)(data['exp'])\n    print(\"    #(input proteins):   {}\".format(data.shape[0]))\n    print(\"=================================================\")\n    return data\n\n\n\ndef computeT2(data, vex, pid, ppi, stu, purb=1.5, intg=True, alpha=0.05, ncore=7, sizelim = 100):\n    \"\"\" Computes T**2 and its p-value for each pathway.\n        This function computes the T**2 score and its significance level.\n        \n        data: Proccessed data using importdata function.\n        vex: A dataframe displaying pathways and the proteins they contain.\n        pid: A dataframe displaying pathways and their full-length name.\n        ppi: Protein-protein interaction dataframe: obtained from STRING, for example.\n        stu: STRING to Uniprot translation dataframe.\n        purb: Perturbance threshold, default is 1.5 (after normalization).\n        intg: Apply pathway integration or not. Default is True.\n        alpha: Significance level. Default is 0.05.\n        ncore: Number of parallel computing cores. Default is 7.\n        sizelim: Maximum size of the pathways to return.\n    \"\"\"\n    \n    # We get rid of values under the perturbance threshold.\n    data.loc[data['exp'].between(-purb,purb,inclusive=False), 'exp']=0\n    \n    ### data mapping\n    # n is the indexes of the proteins from our data that can be found in our dataset of pathways. We print its length.\n    n = np.where(np.isin(vex['prot_id'], data['id']))[0]\n    print(\"    #(mapped entries):    {}\".format(len(np.intersect1d(vex['prot_id'], data['id']))))\n    # Conversely, vexData is the subset of pathways that contain proteins from our data. We print its length and ignore the rest.\n    vexData = vex.iloc[n].copy()\n    vexData['exp'] = np.array([[dv[1] for dv in data.values if dv[0]==vv[1]] for vv in vexData.values])\n    mp = np.unique(vexData['pathway_id'])\n    print(\"    #(mapped pathways):   {}\".format(len(mp))) \n    \n    ### pathway integration\n\n    # This function and the following loop are only to reshape our set of pathways to something easier to use.\n    def desc(cp):\n        pathway = vexData[vexData['pathway_id']==cp]\n        pathwaygenes = \",\".join(np.vectorize(str)(pathway['prot_id']))\n        return (pathway.iat[0,0], pathway.shape[0], pathwaygenes)\n    pi = pd.DataFrame([desc(cp) for cp in mp]).sort_values(by=1,ascending=False).reset_index(drop = True)\n\n    # The PS function will apply the 'pathway integration' process.\n    # The inpt variable will contain a reshaped result, depending on whether or not the user wants to aknowledge 'pathway integration'.\n    ps = PS(pi)\n    print(\"    #(summary pathways):  {}\".format(len(ps)))\n    inpt = np.array([p[0][0] for p in ps]) if intg else np.concatenate([list(p[0]) for p in ps])\n    \n    ### compute T2\n\n    # This function and the following loop will allow to create a T^2 value for each pathway in inpt. See TS.\n    # The result is stored in the pandas dataframe r.\n    def desc2(cp): # cpis a pathway\n        pathway = vexData[vexData['pathway_id']==cp]\n        size = len(pathway)\n        if size==0:\n            return np.array([cp, '', size, 0, 0, 1, 0, 1], dtype=object) # dummy result\n        if size<=sizelim:\n            return TS(pathway, ppi, stu, purb)# returns the vector of T-scores \n        else:\n            return np.array([pathway.iat[0,0], ','.join(pathway['prot_id']), size, 0,0,1,0,1], dtype=object) # dummy result\n    r = pd.DataFrame([desc2(cp) for cp in inpt]) # stores vectors of score foreach pathway\n    \n    ##### Result output -------------------------------------------\n\n    # This function and the following loop reshape r to provide a more satisfactory display.\n    # The result is stored in the pandas dataframe rrr.\n    def desc3(l):\n        ttl = pid[pid['pathway_id']==l[0]]\n        ttl = ttl.iat[0,1]\n        return np.insert(l[:6],0,ttl) # drop trail 6> column and insert pathway name\n    # reshape pathway rows\n    rrr = pd.DataFrame([desc3(l) for l in r.values])\n    # reshape finale DF by adding headers\n    rrr.columns = [\"Pathway title\",\"Pathway ID\",\"Uniprot IDs\",\"#Mapped\",\"df\",\"T-square\",\"p-value\"]\n    # Finally, the pathways with too high a p-value are excluded, and we print how many pathways are left.\n    rrr = rrr[rrr[\"p-value\"]<=alpha].reset_index(drop=True)\n    print(\"    #(enriched pathways): {}\".format(rrr.shape[0]))\n    print(\"=================================================\")\n    return(rrr)\n","sub_path":"T2GAlib.py","file_name":"T2GAlib.py","file_ext":"py","file_size_in_byte":14762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"404979056","text":"# Script (Python)\n# /article17/speciesprogress/process_trail_comments\n# params: 'user_id'\n## Script (Python) \"process_trail_comments\"\n##bind container=container\n##bind context=context\n##bind namespace=\n##bind script=script\n##bind subpath=traverse_subpath\n##parameters=user_id\n##title=\n##\nresults = context.sql_methods.select_trail_comments(user_id=user_id)\nif results:\n    return context.eval_string(results[0]['thecount'])\nelse:\n    return {}\n","sub_path":"article17/speciesprogress/process_trail_comments.py","file_name":"process_trail_comments.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"344666634","text":"from contextlib import contextmanager\n\n\nclass Dog:\n    def __init__(self):\n        self.name = 'lucky'\n\n\n@contextmanager\ndef dog_process():\n    try:\n        print('start')\n        yield Dog()  # with 返回实例\n    finally:\n        print('end')\n\n\n@contextmanager\ndef thing_process():\n    try:\n        print('start process')\n        yield\n    finally:\n        print('end process')\n\n\ndef main():\n    with dog_process() as dog:\n        # 在此句前后执行某些命令\n        print(dog.name)\n\n    print()\n\n    with thing_process():\n        print('do it')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"advanced/context/context_example.py","file_name":"context_example.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"579342045","text":"from django.core.management.base import BaseCommand\r\n\r\nfrom profiles.models import Interest\r\n\r\ninterest_dict = {'boxing': 'boxing.jpg', 'badminton': 'badminton.jpg', 'skateboarding': 'skateboarding.jpg',\r\n                 'handball': 'handball.jpg', 'basketball': 'basketball.jpg', 'cycling': 'cycling.jpg',\r\n                 'billiards': 'billiards.jpg', 'yoga': 'yoga.jpg', 'tennis': 'tennis.jpg', 'dive': 'dive.jpg',\r\n                 'football': 'football.jpg', 'squash': 'squash.jpg', 'table tennis': 'table_tennis.jpg',\r\n                 'pilates': 'pilates.jpg', 'american football': 'american_football.jpg', 'judo': 'judo.jpg',\r\n                 'horse riding': 'horse_riding.jpg', 'trekking': 'trekking.jpg', 'surfing': 'surfing.jpg',\r\n                 'rugby': 'rugby.jpg', 'rowing': 'rowing.jpg', 'volleyball': 'volleyball.jpg', 'karate': 'karate.jpg',\r\n                 'jogging': 'jogging.jpg', 'fitness': 'fitness.jpg', 'paragliding': 'paragliding.jpg',\r\n                 'rafting': 'rafting.jpg', 'swimming': 'swimming.jpg'}\r\n\r\n\r\nclass Command(BaseCommand):\r\n    args = ''\r\n    help = 'our help string comes here'\r\n\r\n    def _create_interest(self):\r\n        for k, v in interest_dict.items():\r\n            teres, created = Interest.objects.get_or_create(name=k)\r\n            if created:\r\n                teres.photo = 'interests/' + v\r\n                teres.save()\r\n\r\n    def handle(self, *args, **options):\r\n        self._create_interest()\r\n","sub_path":"profiles/management/commands/create_interest.py","file_name":"create_interest.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"594522444","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.http import HttpResponse, JsonResponse\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom db import provider_dao\n\n\n@csrf_exempt\ndef prov_create(req):\n    if req.method == 'POST':\n        name = req.POST.get('name')\n        email = req.POST.get('email')\n        phone = req.POST.get('phone')\n        lang = req.POST.get('lang')\n        currency = req.POST.get('currency')\n\n        provider_dao.create({\n            'name': name,\n            'email': email,\n            'phone': phone,\n            'lang': lang,\n            'currency': currency\n        })\n\n        return JsonResponse({\n            'success': True\n        })\n\n","sub_path":"mozio/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"73319451","text":"from django.conf.urls import url\nfrom project import views\n\nurlpatterns = [\n    url(r'^agreement/$', views.agreement),\n    url(r'^description/$', views.description),\n    url(r'^contacts/$', views.contacts),\n    url(r'^get_test_cookie/$', views.get_test_cookie),\n    url(r'^cookies/$', views.cookies),\n]\n","sub_path":"project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"549164924","text":"from tkinter import *\n\nroot = Tk()\nroot.title(\"BMI Calculator\")\nroot.geometry(\"302x500\")\n#root.iconbitmap(r'BMICalcIcon.ico')\nroot.resizable(False, False)\nans = Label(root)\n\ndef calculate():\n    global ans\n    ans.destroy()\n    Value_1=height_slider.get()\n    Value_2=weight_slider.get()\n    con_val = (Value_1 / 12)\n    con_val2 = (con_val / 3.2808)\n    bmi = (Value_2 / (con_val2**2))\n    ans = Label(root, text=bmi, font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\")\n    ans.grid(row=7, column=0)\n\ntitle_BMI = Label(root, text=\"BMI Calculator\", font=(\"arial\", 20, \"bold\"), bg=\"#ffffff\").grid(row=1, column=0)\nheight_init = Label(root, text=\"Enter your Height in Inches: \", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\").grid(row=2, column=0)\nheight_slider = Scale(root, orient = HORIZONTAL, from_=0, to=100, resolution=1, length= 301, bg=\"#ffffff\")\nheight_slider.grid(row=3, column=0)\nweight_init = Label(root, text=\"Enter your weight in KGs: \", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\").grid(row=4, column=0)\nweight_slider = Scale(root, orient = HORIZONTAL, length= 301, bg=\"#ffffff\")\nweight_slider.grid(row=5, column=0)\n\ncalc_btn = Button(root, text=\"Calculate\", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\", command=calculate).grid(row= 6, column=0)\n\ntitle_index = Label(root, text=\"BMI Index\", relief=RIDGE, font=(\"arial\", 18, \"bold\"), bg=\"#ffffff\").grid(row=8, column=0, pady= 8) \n\nindex1 = Label(root, text=\">18.5 = Underweight\", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\").grid(row=9, column=0, pady=5)\nindex2 = Label(root, text=\"18.5-24.9 = Ideal\", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\").grid(row=10, column=0, pady=5)\nindex3 = Label(root, text=\"25-29.9 = Over weight\", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\").grid(row=11, column=0, pady=5)\nindex4 = Label(root, text=\"30-34.9 = Obesity I\", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\").grid(row=12, column=0, pady=5)\nindex5 = Label(root, text=\"35-39.9 = Obesity II\", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\").grid(row=13, column=0, pady=5)\nindex6 = Label(root, text=\">40 = Obesity III, Death risk\", font=(\"arial\", 10, \"bold\"), bg=\"#ffffff\").grid(row=14, column=0, pady=5)\n\nend_writing = Label(root, text=\"Made by Arib Muhtasim\", font=(\"arial\", 10, \"bold\"), bg=\"black\", fg=\"#ffffff\", relief=SUNKEN).grid(row = 15, column=0, padx=6, pady=6)\n\nroot.configure(background = \"#ffffff\")\nroot.mainloop()","sub_path":"BMICalculator.py","file_name":"BMICalculator.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"462665750","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 14 09:49:26 2018\n\n@author: julian\n\"\"\"\n\nimport numpy as np\nimport scipy.signal as Carl_Friedrich\nimport scipy.stats\n\nsiglength = 44100\nfreqs = np.fft.rfftfreq(512 * 2 - 1, 1 / siglength)\npltx = freqs[:20]\n\ndef windowing(data):\n    number_of_windows = 0\n    \n    window = Carl_Friedrich.gaussian(512, 128)\n    summi = np.zeros((siglength // 256, 20))\n\n    for i in range(0,siglength, 256):\n        if(i+512 > siglength):\n            break\n    \n        tmp = data[i:]\n        tmp = tmp[:512]\n        tmp = tmp.astype(float)\n        tmp = tmp * window\n        number_of_windows = number_of_windows + 1\n        tmp = np.abs(np.fft.fft(tmp))\n        plty = tmp[:20]\n        summi[number_of_windows] = plty\n        \n    mean = []\n    for i in range(20):\n        mean.append(np.mean(summi[:,i]))\n    return mean\n    \ndef reference(data):\n    spec = np.zeros(20)\n    for i in range(5):\n        w = windowing(data[i])\n        for j in range(20):\n            spec[j] = spec[j] + w[j]\n    for i in range(20):\n        spec[i] = spec[i] / 5\n    return spec\n\ndef corr(data1, ref):\n    return scipy.stats.pearsonr(windowing(trigger(data1)), ref)[0]\n\ndef trigger(data):\n    data = data [3000:]\n    for measure in range (1, 220500 - 44100):\n        if data[measure] > 500 or data[measure] < -500:\n            newData = []\n            newData.append(data[measure])\n            speck = data[measure:]\n            speck = speck[:44100]\n            break\n    return speck\n\ndef getReference(word):\n    data = np.zeros((5,44100))\n    for i in range(5):\n        data[i] = np.load(\"./Julian/triggered/J\" + str(i + 1) + \"_\" + word + \"_triggered.npy\")\n    spec = reference(data)\n    return spec\n\n\nwords = [\"hoch\", \"tief\", \"rechts\", \"links\"]\nreferences = {}\nfor i in range(4):\n    references.update({words[i]: getReference(words[i])})\n\nfor name in [\"Julian\", \"Jik\"]:\n    print(name)\n    for w in words:\n        for i in range(6,11):\n            test = np.load(\"./\" +name+ \"/J\" + str(i) + \"_\" +  w + \".npy\")\n            currentname = str(i) + \"_\" + w\n            correlations = np.zeros(5)\n            maxidx = -1\n            maxcorr = \"\"\n            for j in range(len(words)):    \n                correlation = corr(test,references.get(words[j]))\n                correlations[j] = correlation\n                if(maxidx == -1):\n                    maxidx = j\n                    maxcorr = words[j]\n                elif(correlations[maxidx] < correlation):\n                    maxidx = j\n                    maxcorr = words[j]\n            print(currentname, maxcorr)\n","sub_path":"Versuch4/windowing.py","file_name":"windowing.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"493348811","text":"# timesheetentry.py\n# The purpose of this program is to enter data into time sheets. \n# author: Emma Farrell\n\nimport datetime as dt\n\nclass Timesheetentry:\n\n    def __init__(self, project, start, duration):\n        self.project = project\n        self.start = start\n        self.duration = duration\n\n    def __str__(self):\n        return self.project +':'+ str(self.duration)\n\nif __name__ == '__main__':\n    ts = dt.datetime(2021,3,19,16,20)\n    test = Timesheetentry('test', ts, 60)\n    print(test)\n\n","sub_path":"week10-objects/timesheetentry.py","file_name":"timesheetentry.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"209930817","text":"from django import forms\nfrom .models import Board, Snippet\n\nclass BoardForm(forms.ModelForm):\n    class Meta:\n        model = Board\n        fields = [\"name\", \"owner\", \"read_public\", \"read_users\",\n                  \"write_public\", \"write_users\"]\n        exclude = [\"owner\"]\n        widgets = {\n            \"read_users\" : forms.CheckboxSelectMultiple(),\n            \"write_users\" : forms.CheckboxSelectMultiple(),\n        }\n\nclass SnippetForm(forms.ModelForm):\n    class Meta:\n        model = Snippet\n        fields = [\"title\", \"description\", \"language\", \"tags\", \"board\", \"code\", \"owner\"]\n        exclude = [\"owner\"]\n","sub_path":"src/snippets/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"434123886","text":"from Graphic.Window import *\n\nclass Button():\n    def __init__(self, window, x, y, width, height, normalColour, highligtedColour, text=(\"\", 0, 0), action=None):\n        self._window = window\n        self._x = x\n        self._y = y\n        self._w = width\n        self._h = height\n        self._ic = normalColour\n        self._ac = highligtedColour\n        self._action = action\n        self._text, self._textSize, self._textColour = text\n        self._wasClicked = False\n        self._enabled = True\n\n    def isMouseOver(self):\n        mouse = pygame.mouse.get_pos()\n        return self._x + self._w > mouse[0] > self._x and self._y + self._h > mouse[1] > self._y\n\n    def draw(self):\n        if self._enabled and self.isMouseOver():\n            self.drawHighlighted()\n            click = pygame.mouse.get_pressed()\n            if(click[0] == 1):\n                self._wasClicked = True\n            elif(self._wasClicked):\n                self._action()\n                self._wasClicked = False\n        else:\n            self.drawNormal()\n            self._wasClicked = False\n        self.drawText()\n\n\n    def drawNormal(self):\n        pygame.draw.rect(self._window, self._ic, (self._x, self._y, self._w, self._h))\n\n    def drawHighlighted(self):\n        pygame.draw.rect(self._window, self._ac, (self._x, self._y, self._w, self._h))\n\n    def drawText(self):\n        myFont = pygame.font.SysFont(\"freesansbold.ttf\", self._textSize)\n        text = myFont.render(self._text, 1, self._textColour)\n        self._window.blit(text, (self._x + ((self._w - text.get_rect().width) / 2), self._y + ((self._h - text.get_rect().height) / 2)))\n\n    def setEnabled(self, enabled):\n        self._enabled = enabled","sub_path":"Button.py","file_name":"Button.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"350024097","text":"class Node:\n\tdef __init__(self, value):\n\t\tself.value = value\n\t\tself.left = None\n\t\tself.right = None\n\n\tdef add(self, value):\n\t\tself.add_node(Node(value))\n\t\t# self.add_node_recursive(Node(value))\n\n\tdef add_node(self, new_node):\n\t\tnode = self\n\t\twhile True:\n\t\t\tif new_node.value == node.value:\n\t\t\t\treturn # already exists\n\t\t\telif new_node.value < node.value:\n\t\t\t\tif node.left is None:\n\t\t\t\t\tnode.left = new_node\n\t\t\t\t\treturn\n\t\t\t\telse:\n\t\t\t\t\tnode = node.left\n\t\t\telse: # new_node.value > node.value\n\t\t\t\tif node.right is None:\n\t\t\t\t\tnode.right = new_node\n\t\t\t\t\treturn\n\t\t\t\telse:\n\t\t\t\t\tnode = node.right\n\n\tdef add_node_recursive(self, new_node):\n\t\tassert new_node is not None\n\t\tif new_node.value == self.value:\n\t\t\treturn\n\t\telif new_node.value < self.value:\n\t\t\tif self.left:\n\t\t\t\tself.left.add_node_recursive(new_node)\n\t\t\telse:\n\t\t\t\tself.left = new_node\n\t\telse:\n\t\t\tif self.right:\n\t\t\t\tself.right.add_node_recursive(new_node)\n\t\t\telse:\n\t\t\t\tself.right = new_node\n\n\tdef delete(self, value):\n\t\tnode, parent = self.find_first_node(value)\n\n\t\t# Using parent_attribute name to be able to set it without IFs\n\t\tif parent.left == node:\n\t\t\tnode_side = \"left\"\n\t\telse: # parent.right == node\n\t\t\tnode_side = \"right\"\n\n\t\tif node.right is None and node.left is None:\n\t\t\tsetattr(parent, node_side, None)\n\n\t\telif node.left is None or node.right is None:\n\t\t\tif node.left is None:\n\t\t\t\trest = node.right\n\t\t\telse:\n\t\t\t\trest = node.left\n\t\t\tsetattr(parent, node_side, rest)\n\n\t\telse:\n\t\t\tnode.right.add_node(node.left)\n\t\t\t# or: node.left.add_node(node.right)\n\t\t\tsetattr(parent, node_side, node.right)\n\n\tdef copy(self):\n\t\tnode = Node(self.value)\n\t\tif self.left:\n\t\t\tnode.left = self.left.copy()\n\t\tif self.right:\n\t\t\tnode.right = self.right.copy()\n\t\treturn node\n\n\tdef exists(self, value):\n\t\treturn self.find_first_node(value)[0] is not None\n\n\tdef max_height(self):\n\t\tdef recursive(node, level):\n\t\t\tif node is None:\n\t\t\t\treturn level\n\t\t\telse:\n\t\t\t\treturn max(recursive(node.left, level+1),\n\t\t\t\t\t\t\trecursive(node.right, level+1))\n\t\treturn recursive(self, 0)\n\n\t# Using internal recursion it's easier because\n\t# one can ask for None only once at the beginning\n\tdef equal_to(self, node):\n\t\tdef recursive(node1, node2):\n\t\t\tif node1 is None and node2 is None:\n\t\t\t\treturn True\n\t\t\tif node1 is None or node2 is None:\n\t\t\t\treturn False\n\t\t\treturn (recursive(node1.left, node2.left) and\n\t\t\t\t\trecursive(node1.right, node2.right))\n\t\treturn recursive(self, node)\n\n\t# DFS\n\tdef traverse(self, order):\n\t\tdef recursive(node, level):\n\t\t\tif node is None:\n\t\t\t\treturn\n\t\t\telse:\n\t\t\t\tif order == 'pre': result.append((node.value, level))\n\t\t\t\trecursive(node.left, level+1)\n\t\t\t\tif order == 'in': result.append((node.value, level))\n\t\t\t\trecursive(node.right, level+1)\n\t\t\t\tif order == 'post': result.append((node.value, level))\n\t\tresult = []\n\t\trecursive(self, 0)\n\t\treturn result\n\n\t#  +++ Helpers +++\n\n\tdef find_first_node(self, value):\n\t\tnode = self\n\t\tparent = None\n\t\twhile node:\n\t\t\tif value == node.value:\n\t\t\t\treturn (node, parent)\n\t\t\telif value < node.value:\n\t\t\t\tparent = node\n\t\t\t\tnode = node.left\n\t\t\telse:\n\t\t\t\tparent = node\n\t\t\t\tnode = node.right\n\t\treturn (None, None)\n\n\t@staticmethod\n\tdef from_ordered_list(lst):\n\t\tif len(lst) == 0:\n\t\t\treturn None\n\t\telse:\n\t\t\tmid_index = len(lst) // 2\n\t\t\tnode = Node(lst[mid_index])\n\t\t\tnode.left = Node.from_ordered_list(lst[:mid_index])\n\t\t\tnode.right = Node.from_ordered_list(lst[mid_index+1:])\n\t\t\treturn node\n\n\nclass Btree:\n\tdef __init__(self):\n\t\tself.root = None\n\n\tdef add(self, value):\n\t\t# implemented this way because it is easier for delete\n\t\tif self.root is None:\n\t\t\tself.root = Node(value)\n\t\t\treturn\n\t\tself.root.add(value)\n\n\tdef delete(self, value):\n\t\tif self.root is None:\n\t\t\treturn\n\t\tassert self.root.value != value # leave delete root node for afterwards\n\t\tself.root.delete(value)\n\n\tdef exists(self, value):\n\t\tif self.root is None:\n\t\t\treturn False\n\t\treturn self.root.exists(value)\n\n\tdef traverse(self, order):\n\t\tif self.root is None:\n\t\t\treturn []\n\t\treturn self.root.traverse(order)\n\n\tdef copy(self):\n\t\tif self.root is None:\n\t\t\treturn Btree()\n\t\telse:\n\t\t\ttree = Btree()\n\t\t\ttree.root = self.root.copy()\n\t\t\treturn tree\n\n\tdef max_height(self):\n\t\tif self.root is None:\n\t\t\treturn 0\n\t\treturn self.root.max_height()\n\n\tdef equal_to(self, tree):\n\t\tif self.root is None:\n\t\t\tif tree.root is None:\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\treturn False\n\t\treturn self.root.equal_to(tree.root)\n\n\t@staticmethod\n\tdef from_ordered_list(lst):\n\t\tbtree = Btree()\n\t\tbtree.root = Node.from_ordered_list(lst)\n\t\treturn btree\n\n\ndef print_with_levels(tuples):\n\tfor value, level in tuples:\n\t\tprint(f\"{'    ' * level}{value}\")\n\ndef print_tree(node):\n\tfor value, level in reversed(node.traverse('in')):\n\t\tprint(f\"{'    ' * level}{value}\")\n\n\nif __name__ == '__main__':\n\tlst = list(range(1,16))\n\tt = Btree.from_ordered_list(lst)\n\n\tassert t.exists(14)\n\tt.delete(14)\n\tt.delete(15)\n\tt.delete(9)\n\tt.delete(4)\n\tassert not t.exists(14)\n\tassert not t.exists(15)\n\tassert not t.exists(9)\n\tassert not t.exists(4)\n\tassert not t.exists(200)\n\t\n\tt_copy = t.copy()\n\tassert t.equal_to(t_copy)\n\tt2 = Btree.from_ordered_list(lst)\n\tassert not t.equal_to(t2)\n\t\n\tprint(\"Tests Passed!\")\n\n\n","sub_path":"binary_tree/binary_tree.py","file_name":"binary_tree.py","file_ext":"py","file_size_in_byte":5080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"141286633","text":"states=['AGGREGATED_andhra_pradesh.txt', 'AGGREGATED_arrunachal_pradesh.txt', 'AGGREGATED_assam.txt', 'AGGREGATED_bihar.txt', 'AGGREGATED_delhi.txt', 'AGGREGATED_goa.txt', 'AGGREGATED_Guj.txt', 'AGGREGATED_Haryana.txt', 'AGGREGATED_jharkhand.txt', 'AGGREGATED_JK.txt', 'AGGREGATED_karnataka.txt', 'AGGREGATED_kerala.txt', 'AGGREGATED_Maharashtra.txt', 'AGGREGATED_manipur.txt', 'AGGREGATED_meghalaya.txt', 'AGGREGATED_mizoram.txt', 'AGGREGATED_MP.txt', 'AGGREGATED_nagaland.txt', 'AGGREGATED_punjab.txt', 'AGGREGATED_rajasthan.txt', 'AGGREGATED_sikkim.txt', 'AGGREGATED_telangana.txt', 'AGGREGATED_TN.txt', 'AGGREGATED_tripura.txt', 'AGGREGATED_UP.txt', 'AGGREGATED_uttarakhand.txt', 'AGGREGATED_WB.txt']\r\ntest=states[0]\r\nattributes=[]\r\ntemp=open(test,'r')\r\nfor line in temp:\r\n\tvalues=line.split()[1:-1]\r\n\tvalues=' '.join(values)\r\n\tattributes.append(values)\r\ntemp.close()\r\n\r\nimport sys\r\nfinal_matrix=[]\r\nfor state in states:\r\n\tattribute_list=[]\r\n\ttemp=open(state,'r')\r\n\tfor lines in temp:\r\n\t\tvalue=lines.split()[-1]\r\n\t\tvalue=float(value)\r\n\t\tattribute_list.append(value)\r\n\tfinal_matrix.append(attribute_list)\r\n\ttemp.close()\r\n\r\nstates1=['andhra_pradesh.csv', 'arrunachal_pradesh.csv', 'assam.csv', 'bihar.csv', 'delhi.csv', 'goa.csv', 'Guj.csv', 'Haryana.csv', 'jharkhand.csv', 'JK.csv', 'karnataka.csv', 'kerala.csv', 'Maharashtra.csv', 'manipur.csv', 'meghalaya.csv', 'mizoram.csv', 'MP.csv', 'nagaland.csv', 'punjab.csv', 'rajasthan.csv', 'sikkim.csv', 'telangana.csv', 'TN.csv', 'tripura.csv', 'UP.csv', 'uttarakhand.csv', 'WB.csv']\r\nwhile(True):\r\n\tcount_state=0\r\n\tfor i in states1:\r\n\t\tprint(count_state,'.  ',i[:-4])\r\n\t\tcount_state+=1\r\n\tprint('Enter the index of the state separated by space:')\r\n\tinput_state=int(input())\r\n\t\r\n\tcount_attributes=0\r\n\tfor i in attributes:\r\n\t\tprint(count_attributes,'.  ',i)\r\n\t\tcount_attributes+=1\r\n\tprint('Enter the indexes of the attributes:')\r\n\t\r\n\tinput_attributes1=[]\r\n\tinput_attributes=input().split()\r\n\tfor i in input_attributes:\r\n\t\tinput_attributes1.append(int(i))\r\n\r\n\tfinal_array=[]\r\n\tfor i in input_attributes1:\r\n\t\tfinal_array.append(float(str(round(final_matrix[input_state][i], 2))))\r\n\r\n\r\n\tprint(final_array)\r\n\timport matplotlib.pyplot as plt; plt.rcdefaults()\r\n\timport numpy as np\r\n\timport matplotlib.pyplot as plt\r\n\tplt.style.use('ggplot')\r\n\r\n\tobjects=[]\r\n\tfor i in input_attributes1:\r\n\t\tobjects.append(attributes[i])\r\n\r\n\r\n\ty_pos = np.arange(len(objects))\r\n\tperformance = final_array\r\n\tfig=plt.figure(0)\r\n\tplt.bar(y_pos, performance, align='center', alpha=0.65)\r\n\tplt.xticks(y_pos, objects)\r\n\tplt.ylabel(states1[input_state][:-4])\r\n\tplt.title(states1[input_state][:-4])\r\n\tfor a,b in zip(y_pos, performance):\r\n\t    plt.text(a, b, str(b), color='blue', fontweight='bold',horizontalalignment ='center')\r\n\tfig.autofmt_xdate()\r\n\tplt.show()\r\n\r\n\tprint('Do you want to continue: Y/N:')\r\n\tcon=input()\r\n\tif con=='N':\r\n\t\tsys.exit()\r\n\r\n","sub_path":"index/ans/graph2.py","file_name":"graph2.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"276709091","text":"class StockReport:\n\n    ticker = \"\"\n    quarter = \"\"\n    year = 0\n    startDate = \"\"\n    endDate = \"\"\n    fscore = 0\n    returns = 0\n\n    def __init__(self, t, q, y, d, f):\n        self.ticker = t\n        self.quarter = q\n        self.year = y\n        self.startDate = d\n        self.fscore = f\n\n    def __str__(self):\n        return self.ticker + \" , \" + self.quarter + \" , \" + self.year + \" , \" + self.startDate + \" , \" + self.endDate + \" , \" + self.fscore + \" , \" + str(self.returns)\n","sub_path":"StockReport.py","file_name":"StockReport.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"112398440","text":"with open('setup.conf', 'r') as f:\n    env = f.readlines()\nf.closed\n\nwith open('celery.conf', 'r') as f:\n    data = f.readlines()\nf.closed\n\nvar = []\nfor line in env:\n    line_sp = line.replace('\\n','').split('=')\n    if len(line_sp)>1:\n        var.append(line_sp)\nvar_d = dict(var)\n\nfor i in range(len(data)):\n    if '[PATH_TO_EXEC]' in data[i]:\n        data[i] = data[i].replace('[PATH_TO_EXEC]', var_d['CONDA_DIR']+'/bin/celery')\n    if '[CELERY_APP_DIR]' in data[i]:\n        data[i] = data[i].replace('[CELERY_APP_DIR]', var_d['CELERY_APP_DIR'])\n    if '[LOG_DIR]' in data[i]:\n        data[i] = data[i].replace('[LOG_DIR]', var_d['LOG_DIR'])\n\nwith open('celery.conf', 'w') as f:\n    for line in data:\n        f.write(line)\nf.closed\n\n","sub_path":"celery_spv.py","file_name":"celery_spv.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"324259972","text":"import torch\nimport torch.nn as nn\nfrom flask import Flask\nfrom flask import request\nfrom PIL import Image\nfrom io import BytesIO\nfrom models import CNN\nfrom torchvision.transforms import Compose, ToTensor, Resize\nfrom collections import OrderedDict\n# import matplotlib.pyplot as plot\napp = Flask(__name__)\n \nmodel_path = '/home/Verification/checkpoints/model.pth'\nsource = [str(i) for i in range(0, 10)]\nsource += [chr(i) for i in range(97, 97+26)]\nalphabet = ''.join(source)\n\n@app.route('/api/predict', methods=['POST'])\ndef post_predict():\n  text=\"failure\"\n  if request.method == 'POST':\n    file = request.get_data()\n    img = Image.open(BytesIO(file)).convert('RGB')\n    print('宽：%d,高：%d'%(img.size[0],img.size[1]))\n    width=img.size[0]\n    height=img.size[1]\n    transform = Compose([Resize(height, width), ToTensor()])\n    img = transform(img)\n    cnn = CNN()\n    if torch.cuda.is_available():\n        cnn = cnn.cuda()\n    cnn.eval()\n    cnn.load_state_dict(torch.load(model_path,map_location='cpu'))\n    img = img.view(1, 3, height, width).cuda()\n    output = cnn(img)\n    output = output.view(-1, 36)\n    output = nn.functional.softmax(output, dim=1)\n    output = torch.argmax(output, dim=1)\n    output = output.view(-1, 4)[0]\n    text=''.join([alphabet[i] for i in output.cpu().numpy()])\n    # print('pred: '+text)\n    # plot.imshow(img.permute((0, 2, 3, 1))[0].cpu().numpy())\n    # plot.show()\n\n  return text\n\n@app.route('/api/values')\ndef index():\n  return \"Hello, World!\"\n \nif __name__ == '__main__':\n  app.run(host='127.0.0.1',port='5000')\n\n  ","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"565885631","text":"import tensorflow as tf\n\nsess = tf.Session()\n\nW = tf.Variable([.3],dtype=tf.float32)\nb = tf.Variable([-.3],dtype=tf.float32)\nx = tf.placeholder(tf.float32)\nlinear_model = W*x+b\n\n#tf.global_variables_initializer()的解释是当用tf.Variable()时没有初始化变量，所以需要它来初始化变量\ninit=tf.global_variables_initializer()\nsess.run(init)\n\n#保存tensorboard图\nwriter = tf.summary.FileWriter('D:/ten', tf.get_default_graph())\nwriter.close()\n\nprint(sess.run(linear_model,{x:[1,2,3,4]}))","sub_path":"test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"575866678","text":"import os\nimport subprocess\nimport sys\nimport mysql.connector\nfrom subprocess import PIPE, run\n\n\nparam=list(sys.argv)\ncount=len(param)\nprint(count)\n#PORTAL=sys.argv[1]\n#ASN=sys.argv[2]\nif count==1:\n\tprint(\"Please enter PORTAL and ASN as a cmd line arguments\")\n\texit(0)\nif count==2:\n\tprint(\"Please enter ASN as a cmd line arguments\")\n\texit(0)\nelse:\n\tPORTAL = param[1]\n\tASN = param[2]\n\tmydb=mysql.connector.connect(host=PORTAL+\".entreda.com\",user=\"root\",passwd=\"unify20141215_Entreda_v170\",database=\"entredadatabase\")\n\tmycursor=mydb.cursor()\n\toutput = os.popen(\"date\").readlines()\n\tdate=output[0]\n\t#print(date+\"\\b\")\n\n\tLOG0=open(\"./logs/NAG1.txt\",\"w\")\n\tLOG1=open(\"./logs/NAG2.txt\",\"w\")\n\tLOG2=open(\"./logs/NAG3.txt\",\"w\")\n\tparameters=\"||| ASN |||Device| OS ||Software| Last analytics Time |Last connected|\\n\"\n\tparameters1=\"----------------------------------------------------------------------\\n\"\n\n\tLOG1.write(parameters1)\n\tLOG1.write(parameters)\n\tLOG1.write(parameters1)\n\t\n\tLOG2.write(parameters1)\n\tLOG2.write(parameters)\n\tLOG2.write(parameters1)\n\n\tcnt=0\n\tdevice_cnt=0\n\tcomputer_cnt=0\n\tmobile_cnt=0\n\tcnt4=0\n\tcnt5=0\n\n\tc=0\n\n\tdevices=[]\n\tos=[]\n\tversions=[]\n\tmtime=[]\n\tmycursor.execute(\"select deviceid from devices where deviceid in(select DeviceId from devicetoappliancemap where ApplianceId = \"+ASN+\") and status!=1009 and isdeleted=0\")\n\tfor device in mycursor:\n\t\tc=c+1\n\t\tdevices.append(device[0])\n\n\tfor y in range(c):\n\t\td=devices[y]\n\t\td=str(d)\n\t\tmycursor.execute(\"select DevicePlatform from devices where deviceid = \"+d+\" and status != 1009 and IsDeleted = 0\")\n\t\tfor osplatform in mycursor:\n\t\t\tos.append(osplatform[0])\t\n\t\t\tdevice_cnt+=1\n\t\tif(osplatform[0] == \"WIN\" or osplatform[0] == \"Mac\"):\n\t\t\tcomputer_cnt+=1\n\t\tif(osplatform[0] == \"iOS\" or osplatform[0] == \"Android OREO\" or osplatform[0] == \"Android NOUGAT\" or osplatform[0] == \"Android MARSHMALLOW\" or osplatform[0] == \"Android KITKAT\" or osplatform[0] == \"Android LOLLIPOP\" or osplatform[0] == \"Android\" or osplatform[0] == \"Android N\" or osplatform[0] == \"iPhone OS\" or osplatform[0] == \"Android JELLYBEAN\"):\t\n\t\t\tmobile_cnt+=1\n\t\n\t\tmycursor.execute(\"select SoftwareVersion from devices where deviceid = \"+d+\" and status != 1009 and IsDeleted = 0\")\n\t\tfor version in mycursor:\n\t\t\tversions.append(version[0])\t\n\t\n\t\tmycursor.execute(\"select ModifiedTime from devices where deviceid = \"+d+\" and status != 1009 and IsDeleted = 0\")\n\t\tfor time in mycursor:\n\t\t\tmtime.append(time[0])\t\n\t\n\tprint(\"Total devices registered under \"+ASN+\"= \",device_cnt)\n\tprint(\"Total computer devices regitered under\"+ASN+\"= \",computer_cnt)\n\tprint(\"Total mobile devices regitered under\"+ASN+\"= \",mobile_cnt)\n\t\n\tfor x in range(c):\n\t\tLOG1.write(str(ASN)+\" | \"+str(devices[x])+\" | \"+str(os[x])+\" | \"+str(versions[x])+\" | \"+str(mtime[x]))\n\t\tLOG1.write(\"\\n\")\n\tLOG1.write(parameters1)\n\tLOG1.write(\"Total devices registered under \"+str(ASN)+\"= \"+str(device_cnt)+\"\\n\")\n\tLOG1.write(\"Total computer devices regitered under\"+str(ASN)+\"= \"+str(computer_cnt)+\"\\n\")\n\tLOG1.write(\"Total mobile devices regitered under\"+str(ASN)+\"= \"+str(mobile_cnt)+\"\\n\")\n\n#os.system('cat ./logs/NAG2.txt')\n","sub_path":"Automation/alerts_testsuite/pythonscripts/Es_optimization.py","file_name":"Es_optimization.py","file_ext":"py","file_size_in_byte":3094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"347268010","text":"import sys\n\nfrom urllib.request import urlopen\nimport string\nimport collections\nimport urllib.error\ndef exchange():#查询的函数\n    doc = urlopen(sys.argv[1])\n    docstr = doc.read()\n    doc.close()\n    jstr = docstr.decode()\n    return jstr#返回从网页上获得的信息（字符串）\ndef wcount(k):\n    \"\"\"count words from lines of text string, then sort by their counts\n    in reverse order, output the topn (word count), each in one line. \n    \"\"\"\n    lst=exchange().lower()\n    a=lst.strip().replace('\\n', ' ').replace('\\t', ' ').replace('\\r', ' ').strip()\n    b=''\n    for i in a:\n        if i in string.punctuation:\n            b+=' '\n        else:\n            b+=i\n\n    nov=b.split()\n    \n    dic=collections.Counter(nov)\n    lsttt=dic.most_common(k)\n    for i in range(k):\n        print(fo(*lsttt[i]),ba(*lsttt[i]))\ndef fo(x,y):\n    return x\ndef ba(x,y):\n    return y\n\n\nif __name__ == '__main__':\n\n    if  len(sys.argv) == 1:\n\n        print('Usage: {} url [topn]'.format(sys.argv[0]))\n        print('  url: URL of the txt file to analyze ')\n        print('  topn: how many (words count) to output. If not given, will output top 10 words')\n        sys.exit(1)\n\n    else: \n        try:#尝试\n            doc = urlopen(sys.argv[1])\n            docstr= doc.read()\n            doc.close()\n        except ValueError:# 报错\n            print('网址输入格式错误')\n        except urllib.error.URLError:\n            print('网络连接错误')\n        except urllib.error.HTTPError:\n            print('错误 404: Not Found.')\n        else:\n            if len(sys.argv)==3:\n                try:\n                    x=int(sys.argv[2])\n                except ValueError:\n                    print('数字输入有误')\n                x=int(sys.argv[2])  \n                wcount(x)\n                \n            else:\n                wcount(10)\n","sub_path":"pyassign3/wcount.py","file_name":"wcount.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"260338650","text":"#Pablo Lopez   \n#Graficas\n#14509\n#RT1\n\nfrom collections import namedtuple\nimport struct\n\nV2 = namedtuple('Vertex2', ['x', 'y'])\nV3 = namedtuple('Vertex3', ['x', 'y', 'z'])\n\ndef char(c):\n    return struct.pack('=c', c.encode('ascii'))\n\n\ndef word(c):\n    return struct.pack('=h', c)\n\n\ndef dword(c):\n    return struct.pack('=l', c)\n\n\ndef color(r, g, b):\n    return bytes([b, g, r])\n\n\ndef sum(v0, v1):\n    \"\"\"\n      Input: 2 size 3 vectors\n      Output: Size 3 vector with the per element sum\n    \"\"\"\n    return V3(v0.x + v1.x, v0.y + v1.y, v0.z + v1.z)\n\n\ndef sub(v0, v1):\n    \"\"\"\n      Input: 2 size 3 vectors\n      Output: Size 3 vector with the per element substraction\n    \"\"\"\n    return V3(v0.x - v1.x, v0.y - v1.y, v0.z - v1.z)\n\n\ndef mul(v0, k):\n    \"\"\"\n      Input: 2 size 3 vectors\n      Output: Size 3 vector with the per element multiplication\n    \"\"\"\n    return V3(v0.x * k, v0.y * k, v0.z * k)\n\n\ndef dot(v0, v1):\n    \"\"\"\n      Input: 2 size 3 vectors\n      Output: Scalar with the dot product\n    \"\"\"\n    return v0.x * v1.x + v0.y * v1.y + v0.z * v1.z\n\n\ndef length(v0):\n    \"\"\"\n      Input: 1 size 3 vector\n      Output: Scalar with the length of the vector\n    \"\"\"\n    return (v0.x**2 + v0.y**2 + v0.z**2)**0.5\n\n\ndef norm(v0):\n    \"\"\"\n      Input: 1 size 3 vector\n      Output: Size 3 vector with the normal of the vector\n    \"\"\"\n    v0length = length(v0)\n\n    if not v0length:\n        return V3(0, 0, 0)\n\n    return V3(v0.x/v0length, v0.y/v0length, v0.z/v0length)\n\n\ndef cross(u, w):\n    # print(u, w)\n    return V3(\n        u.y * w.z - u.z * w.y,\n        u.z * w.x - u.x * w.z,\n        u.x * w.y - u.y * w.x,\n    )\n\n\ndef bbox(*vertices):\n    xs = [vertex.x for vertex in vertices]\n    ys = [vertex.y for vertex in vertices]\n    xs.sort()\n    ys.sort()\n\n    xMin = round(xs[0])\n    xMax = round(xs[-1])\n    yMin = round(ys[0])\n    yMax = round(ys[-1])\n\n    return xMin, xMax, yMin, yMax\n\n\ndef barycentric(A, B, C, P):\n    cx, cy, cz = cross(\n        V3(B.x - A.x, C.x - A.x, A.x - P.x),\n        V3(B.y - A.y, C.y - A.y, A.y - P.y)\n    )\n\n    if abs(cz) < 1:\n        return -1, -1, -1\n\n    u = cx / cz\n    v = cy / cz\n    w = 1 - (cx + cy) / cz\n\n    return w, v, u\n\n\ndef writebmp(filename, width, height, pixels):\n    f = open(filename, 'bw')\n\n    # File header (14 bytes)\n    f.write(char('B'))\n    f.write(char('M'))\n    f.write(dword(14 + 40 + width * height * 3))\n    f.write(dword(0))\n    f.write(dword(14 + 40))\n\n    # Image header (40 bytes)\n    f.write(dword(40))\n    f.write(dword(width))\n    f.write(dword(height))\n    f.write(word(1))\n    f.write(word(24))\n    f.write(dword(0))\n    f.write(dword(width * height * 3))\n    f.write(dword(0))\n    f.write(dword(0))\n    f.write(dword(0))\n    f.write(dword(0))\n\n    # Pixel data (width x height x 3 pixels)\n    for x in range(height):\n        for y in range(width):\n            f.write(pixels[x][y])\n    f.close()\n","sub_path":"RT/RT1/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"650896123","text":"import numpy as np\nimport tensorflow as tf\n\n\nclass DET_ADV_NET:\n\n    def __init__(self, img_sz):\n        self.l1_adv_weight = 1\n        self.__build_graph__(img_sz)\n\n\n    def __build_graph__(self, img_sz):\n        \n        Ih = tf.placeholder(tf.float32, [None]+[i for i in img_sz]) # High energy images\n        Ib = tf.placeholder(tf.float32, [None]+[i for i in img_sz]) # Bone images\n\n        # transfer high energy image to bone image\n        Ib_hat = self.__transfer__(Ih)\n\n        # discriminator\n        torf_Ib     = self.__discriminator__(Ih, Ib, img_sz)\n        torf_Ib_hat = self.__discriminator__(Ih, Ib_hat, img_sz)\n\n        # L1 loss\n        l1_loss = tf.reduce_mean(tf.abs(Ib - Ib_hat), axis=[1,2,3])\n\n        # adversarial loss\n        # for discriminator:\n        dis_adv_loss = tf.reduce_mean(-tf.log(torf_Ib)-tf.log(1-torf_Ib_hat), axis=1)\n        # for generator:\n        gen_adv_loss = tf.reduce_mean(-tf.log(torf_Ib_hat), axis=1)\n\n        # combination generator loss\n        gen_loss = gen_adv_loss + self.l1_adv_weight * l1_loss\n\n        self.l1_loss, self.dis_adv_loss, self.gen_adv_loss, self.gen_loss = \\\n            l1_loss, dis_adv_loss, gen_adv_loss, gen_loss\n\n\n    def __transfer__(self, Ih):\n        # encoder (refer to U-Net)\n        # encodes high energy images to latent space\n        z, res_info = self.__encoder__(Ih)\n\n        # decoder (refer to U-Net)\n        # transfer latent feature to bone images\n        Ib_hat = self.__decoder__(z, img_sz, res_info)\n\n        return Ib_hat\n\n\n    def __encoder__(self, Ih):\n        with tf.variable_scope('encoder', reuse=False):\n            layers = [64, 128, 256, 512, 512, 512, 512]\n            conv1 = tf.layers.conv2d(inputs=Ih, filters=layers[0], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            conv2 = tf.layers.conv2d(inputs=conv1, filters=layers[1], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            conv3 = tf.layers.conv2d(inputs=conv2, filters=layers[2], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            conv4 = tf.layers.conv2d(inputs=conv3, filters=layers[3], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            conv5 = tf.layers.conv2d(inputs=conv4, filters=layers[4], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            conv6 = tf.layers.conv2d(inputs=conv5, filters=layers[5], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            z     = tf.layers.conv2d(inputs=conv6, filters=layers[6], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            res_info = [conv1, conv2, conv3, conv4, conv5, conv6]\n\n        return z, res_info\n\n\n    def __decoder__(self, z, img_sz, res_info):\n        with tf.variable_scope('decoder', reuse=False):\n            layers = [img_sz[2], 64, 128, 256, 512, 512, 512][::-1]\n            conv1 = tf.layers.conv2d_transpose(inputs=z, filters=layers[0], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.relu)\n            conv2 = tf.layers.conv2d_transpose(inputs=tf.concat([conv1,res_info[-1]],3), filters=layers[1], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.relu)\n            conv3 = tf.layers.conv2d_transpose(inputs=tf.concat([conv1,res_info[-2]],3), filters=layers[2], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.relu)\n            conv4 = tf.layers.conv2d_transpose(inputs=tf.concat([conv1,res_info[-3]],3), filters=layers[3], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.relu)\n            conv5 = tf.layers.conv2d_transpose(inputs=tf.concat([conv1,res_info[-4]],3), filters=layers[4], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.relu)\n            conv6 = tf.layers.conv2d_transpose(inputs=tf.concat([conv1,res_info[-5]],3), filters=layers[5], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.relu)\n            Ib_hat = tf.layers.conv2d_transpose(inputs=tf.concat([conv1,res_info[-6]],3), filters=layers[6], kernel_size=[4,4],\n                            strides=(2,2), padding=\"valid\", activation=tf.nn.relu)\n\n            return Ib_hat\n\n\n    def __discriminator__(self, Ih, Ib, img_sz):\n        rate = [4, 4]   # num of patches for row and col\n        assert(img_sz[0]%rate[0] == 0 and img_sz[1]%rate[1] == 0)\n        patch_size = [1, img_sz[0]/rate[0], img_sz[1]/rate[1], 1]\n        layers = [64, 128, 256, 512, 1]\n\n        # extract patches from large image and send into discriminator as a batch\n        Ih_patches = tf.extract_image_patches(Ih, ksizes=patch_size, strides=patch_size)\n        Ih_patches = tf.reshape(Ih_patches, [-1, patch_size[1], patch_size[2], img_sz[-1]])\n        Ib_patches = tf.extract_image_patches(Ib, ksizes=patch_size, strides=patch_size)\n        Ib_patches = tf.reshape(Ib_patches, [-1, patch_size[1], patch_size[2], img_sz[-1]])\n\n        with tf.variable_scope('discriminator', reuse=False):\n            Ih_Ib = tf.concat([Ih_patches, Ib_patches], 3)\n            conv1 = tf.layers.conv2d(inputs=Ih_Ib, filters=layers[0], kernel_size=[4,4],\n                                strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            conv2 = tf.layers.conv2d(inputs=conv1, filters=layers[1], kernel_size=[4,4],\n                                strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            conv3 = tf.layers.conv2d(inputs=conv2, filters=layers[2], kernel_size=[4,4],\n                                strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            conv4 = tf.layers.conv2d(inputs=conv3, filters=layers[3], kernel_size=[4,4],\n                                strides=(2,2), padding=\"valid\", activation=tf.nn.leaky_relu)\n            torf  = tf.layers.conv2d(inputs=conv4, filters=layers[4], kernel_size=[4,4],\n                                strides=(4,4), padding=\"valid\", activation=tf.sigmoid)\n\n        # reshape results from the same image back to the same batch\n        torf = tf.reduce_mean(tf.reshape(torf, [-1, rate[0]*rate[1]]), 1)\n\n        return torf\n\n\n\n","sub_path":"models/det_adv_net.py","file_name":"det_adv_net.py","file_ext":"py","file_size_in_byte":6558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"209138423","text":"G = {}\ninFile = open('hg19_refGene.txt')\nfor line in inFile:\n    line = line.strip()\n    fields = line.split('\\t')\n    ch = fields[2]\n    start = int(fields[4])\n    end = int(fields[5])\n    gene = fields[12]\n    G.setdefault(gene, [])\n    G[gene].append([ch,start,end])\ninFile.close()\n\ninFile = open('Single-Trait-lm-Sig2-Sorted')\nouFile = open('Single-Trait-lm-Sig2-Sorted-Gene', 'w')\nfor line in inFile:\n    line = line.strip()\n    fields = line.split('\\t')\n    pv = float(fields[2])\n    if pv < 0.05:\n        S = set()\n        ch = fields[0]\n        pos = int(fields[1])\n        for g in G:\n            for item in G[g]:\n                if ch == item[0]:\n                    if item[1]<= pos <= item[2]:\n                        S.add(g)\n        if S:\n            ouFile.write(line + '\\t' + ':'.join(S) + '\\n')\ninFile.close()\nouFile.close()\n","sub_path":"NMD/02-1000Genome/Heritability2/QTL/02-gene.py","file_name":"02-gene.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"15369346","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nimport socketserver\nimport subprocess\nimport logging, logging.config\nimport urllib3\nimport json\nimport socket\nimport base64\nimport struct\nimport math\nimport http.client\nimport uuid\n\nlogging.config.fileConfig(\"logging_socket_http.ini\")\nlog = logging.getLogger(__name__)\n\n\n\nclass ExSocketToHttpServer(socketserver.BaseRequestHandler):\n    log.info(\"========request started==========\")\n\n    def handle(self):\n        try:\n            print(\"========client started==========\")\n            log.info(\"========client started==========\")\n            print(\"got connection from\", self.client_address)\n            conn = self.request\n            socket_uuid_id=uuid.uuid4().hex\n            while True:\n                total_data = None\n                total_length = 0\n                first_data = conn.recv(1024).strip()\n                print(\"first data for one receive loop:%s\" % str(first_data))\n                log.info(\"first data for one receive loop:%s\" % str(first_data))\n                if len(first_data) < 1:\n                    conn.send(first_data)\n                    break\n                runTimes=0\n                while True:\n                    runTimes=runTimes+1\n                    print(\"runTimes %s\" % str(runTimes))\n                    if first_data and not total_data:  # first data from conn is not null and total_data(buffer for received data) is null\n                        total_data = first_data  # first data from conn\n                        first_data = None  # remove first data after it is added to total data\n                    if len(total_data) < 4:  # because the length for whole package is stored in 4 bytes, if the total data is less then 4, system needs to continue receive data from conn\n                        data = conn.recv(1024).strip()\n                        total_data = total_data + data\n                        runTimes=runTimes-1\n                        continue\n                    if len(total_data) == 4 and total_length == 0:  # total_data length is 4\n                        a = struct.unpack('i', total_data)  # get package length from total data\n                        total_length = a[0]\n                        print(\"content(without first four bytes) length :%s\" % str(total_length))\n                        print(\"first four bytes content:%s\" % str(total_data))\n                        log.info(\"first four bytes content:%s\" % str(total_data))\n                        #                   print(\"first str length:%d\" % total_length)\n                    if len( total_data) > 4:  # if total_data length is more than 4, the first time recevied data's length is more than 4, and package length is 0, get length from first four bytes from total data\n                        if total_length == 0:  # package length is never set, the first received data length is more then 4\n                            print(\"total_data:%s\" % str(total_data))\n                            total_length_data = total_data[:4]\n                            print(\"total_length_data:%s\" % str(total_length_data))\n                            a = struct.unpack('l', total_length_data)\n                            total_length = a[0]\n                        print(\"first str length2:%d\" % total_length)\n                    total_length = total_length + 4  # add first 4 bytes length to get the complete length for whole package\n                    if len(total_data) < total_length:\n                        left_length = total_length - len(total_data)  # remain data which is needed to be sent\n                        print(\"content_length   :%s\" % str(left_length))\n                        log.info(\"content_length   :%s\" % str(left_length))\n                        times = math.ceil(left_length / 1024)  # calcuate the loop times to receive data from conn\n                        print(\"receive times for content data:%s\" % str(times))\n                        log.info(\"receive times for content data:%s\" % str(times))\n                        remain = total_data\n                        for i in range(times):\n                            data = conn.recv(1024)\n                            if not remain:\n                                remain = data\n                            else:\n                                remain = remain + data\n                            print(\"data str :%s\" % str(data))\n                            print(\"data length str :%d\" % len(data))\n                        print(\"data length str :%d\" % len(remain))\n                        log.info(\"content data length str :%d\" % len(remain))\n                        log.info(\"content data str :%s\" % str(remain))\n                        encoderemain=base64.urlsafe_b64encode(remain)\n                        str_encoderemain=str(encoderemain,\"utf-8\") #at server first convert to base64 then convert to utf-8 string, at clint reverse process\n\n                      #  byte_encoderemain=bytes(str_encoderemain,\"utf-8\")\n                      #  remain=base64.urlsafe_b64decode(byte_encoderemain)\n                        http_return_data = self.http_client_get_handle(str_encoderemain,socket_uuid_id)\n                        log.info(\"http return data :%s\" % str(http_return_data))\n                        print(\"http return data :%s\" % str(http_return_data))\n                        print(\"=============http complete==========\")\n                        conn.send(http_return_data)\n                     #   sk.send(remain)\n                        break\n                    else:\n                        print(\"send total_data length:%s\" % str(len(total_data)))\n                        print(\"send total_length length:%s\" % str(total_length + 3))\n                        print(\"send str :%s\" % str(total_data))\n\n                        encoderemain=base64.urlsafe_b64encode(total_data)\n                        str_encoderemain=str(encoderemain,\"utf-8\")\n                  #      byte_encoderemain=bytes(str_encoderemain,\"utf-8\")\n                 #       remain=base64.urlsafe_b64decode(byte_encoderemain)\n                 #       sk.send(remain)\n                        http_return_data=self.http_client_get_handle(str_encoderemain,socket_uuid_id)\n                        log.info(\"http return data :%s\" % str(http_return_data))\n                        print(\"http return data :%s\" % str(http_return_data))\n                      #  return_data_raw = base64.urlsafe_b64decode(return_data)\n                        print(\"=============http complete==========\")\n                        conn.send(http_return_data)\n                        break\n\n        except Exception as err:\n            print(err)\n            log.error(err)\n        finally:\n            print(\"========request closed==========\")\n            log.info(\"========request closed==========\")\n\n\n    def http_client_get_handle(self,data,session_id):\n        http=urllib3.PoolManager()\n        r=http.request('GET','http://localhost/jlecdg12net?'+\"sessionid=\"+session_id+\"&data=\"+data,timeout=60.0,retries=False)\n        if r.status==200:\n            log.info(\"http.data is%s\", 'http://localhost/jlecdg12net?'+\"sessionid=\"+session_id+\"&data=\"+data)\n            print(\"http.data is%s\", 'http://localhost/jlecdg12net?'+\"sessionid=\"+session_id+\"&data=\"+data)\n            return_raw_data= r.data\n         #   return_byte_data=bytes(return_raw_data,\"utf-8\")\n            return_base_data=base64.urlsafe_b64decode(return_raw_data)\n            return return_base_data\n        print(\"http.status is%s\", str(r.status))\n        log.info(\"http.status is%s\", str(r.status))\n        return None\n\nif __name__ == '__main__':\n    server = socketserver.ThreadingTCPServer(('192.168.0.104', 10002), ExSocketToHttpServer)\n    print('=========socket server start =============')\n    server.serve_forever()\n","sub_path":"ex_socket_http_server.py","file_name":"ex_socket_http_server.py","file_ext":"py","file_size_in_byte":7784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"233299112","text":"\"\"\" \nMonitoring algorithms for Quicklook pipeline\n\"\"\"\n\nimport os,sys\nimport datetime\nimport numpy as np\nimport scipy.ndimage\nimport yaml\nimport re\nimport astropy.io.fits as fits\nimport desispec.qa.qa_plots_ql as plot\nimport desispec.quicklook.qlpsf\nfrom desispec.quicklook.qas import MonitoringAlg, QASeverity\nfrom desispec.quicklook import qlexceptions\nfrom desispec.quicklook import qllogger\nfrom astropy.time import Time\nfrom desispec.qa import qalib\nfrom desispec.io import qa, read_params\nfrom desispec.io.meta import findfile\nfrom desispec.io.sky import read_sky\nfrom desispec.image import Image as im\nfrom desispec.frame import Frame as fr\nfrom desispec.preproc import _parse_sec_keyword\nfrom desispec.util import runcmd\nfrom desispec.qproc.qframe import QFrame\n\n\nqlog=qllogger.QLLogger(\"QuickLook\",0)\nlog=qlog.getlog()\n\n\ndef qlf_post(qadict):\n    \"\"\"\n    A general function to HTTP post the QA output dictionary, intended for QLF\n    requires environmental variables: QLF_API_URL, QLF_USER, QLF_PASSWD\n    \n    Args: \n        qadict: returned dictionary from a QA\n    \"\"\"\n    #- Check for environment variables and set them here\n    if \"QLF_API_URL\" in os.environ:\n        qlf_url=os.environ.get(\"QLF_API_URL\")\n        if \"QLF_USER\" not in os.environ or \"QLF_PASSWD\" not in os.environ: \n            log.warning(\"Environment variables are not set for QLF. Set QLF_USER and QLF_PASSWD.\")\n        else: \n            qlf_user=os.environ.get(\"QLF_USER\")\n            qlf_passwd=os.environ.get(\"QLF_PASSWD\")\n            log.debug(\"Environment variables are set for QLF. Now trying HTTP post.\")\n            #- All set. Now try to HTTP post\n            try: \n                import requests\n                response=requests.get(qlf_url)\n                #- Check if the api has json\n                api=response.json()\n                #- proceed with post\n                job={\"name\":\"QL\",\"status\":0,\"dictionary\":qadict} #- QLF should disintegrate dictionary\n                response=requests.post(api['job'],json=job,auth=(qlf_user,qlf_passwd))\n            except:\n                log.error(\"Skipping HTTP post... Exception\",exc_info=true)\n\n    else:   \n        log.warning(\"Skipping QLF. QLF_API_URL must be set as environment variable\")\n\ndef get_image(filetype,night,expid,camera,specdir):\n    '''\n    Make image object from file if in development mode\n    '''\n    #- Find correct file for QA\n    imagefile = findfile(filetype,int(night),int(expid),camera,specprod_dir=specdir)\n\n    #- Create necessary input for desispec.image\n    image = fits.open(imagefile)\n    pix = image[0].data\n    ivar = image[1].data\n    mask = image[2].data\n    readnoise = image[3].data\n    meta = image[0].header\n\n    #- Create image object\n    imageobj = im(pix,ivar,mask=mask,readnoise=readnoise,camera=camera,meta=meta)\n    return imageobj\n\ndef get_frame(filetype,night,expid,camera,specdir):\n    '''\n    Make frame object from file if in development mode\n    '''\n    #- Find correct file for QA\n    framefile = findfile(filetype,int(night),int(expid),camera,specprod_dir=specdir)\n\n    #- Create necessary input for desispec.frame\n    frame = fits.open(framefile)\n    wave = frame[3].data\n    flux = frame[0].data\n    ivar = frame[1].data\n    fibermap = frame[5].data\n    fibers = fibermap['FIBER']\n    meta = frame[0].header\n\n    #- Create frame object\n    frameobj = fr(wave,flux,ivar,fibers=fibers,fibermap=fibermap,meta=meta)\n\n    return frameobj\n\n\nclass Check_HDUs(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"CheckHDUs\"\n        import astropy\n        rawtype=astropy.io.fits.hdu.hdulist.HDUList\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"HDU_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        MonitoringAlg.__init__(self,name,rawtype,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {} got {}\".format(type(self.__inpType__),type(args[0])))\n        \n        if kwargs[\"singleqa\"] == 'Check_HDUs':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            rawfile = findfile('raw',int(night),int(expid),camera,rawdata_dir=kwargs[\"rawdir\"])\n            raw = fits.open(rawfile)\n        else:\n            raw=args[0]\n\n        camera=kwargs[\"camera\"]\n\n        paname=None\n        if \"paname\" in kwargs:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        amps=False\n        if \"amps\" in kwargs:\n            amps=kwargs[\"amps\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig=None\n\n        return self.run_qa(raw,camera,paname=paname,amps=amps, qafile=qafile,qafig=qafig, param=param, qlf=qlf, refmetrics=refmetrics)\n\n    def run_qa(self,raw,camera,paname=None,amps=False,qafile=None,qafig=None, param=None, qlf=False, refmetrics=None):\n\n        rawimage=raw[camera.upper()].data\n        header=raw[camera.upper()].header\n        \n        retval={}\n        retval[\"EXPID\"]= '{0:08d}'.format(header[\"EXPID\"])\n        retval[\"CAMERA\"] = camera\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"FLAVOR\"] = header[\"FLAVOR\"]\n        #SE: quicklook to crash when a mismatched config file with the one in fits header\n        from desispec.scripts import quicklook\n          \n        args=quicklook.parse()       \n        ad,fl = args.config.split(\"qlconfig_\")\n        flvr = fl.split(\".yaml\")[0]\n        if flvr in ['darksurvey','graysurvey','brightsurvey']: flvr = 'science'\n        if (header[\"FLAVOR\"] == flvr or flvr == 'test'): \n                    log.info(\"The correct configuration file is being used!\")\n        else: \n                    log.critical(\"Wrong configuration file is being used!\")\n                    sys.exit(\"Wrong configuration file! use the one for \"+str(header[\"FLAVOR\"]))\n        \n        if retval[\"FLAVOR\"] == 'arc':\n            pass\n        else:\n            retval[\"PROGRAM\"] = header[\"PROGRAM\"]\n        retval[\"NIGHT\"] = header[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n        \n        #SE: why this was repeated here again? \n        #rawimage=raw[camera.upper()].data\n        #header=raw[camera.upper()].header\n\n        HDUstat = \"NORMAL\" \n        EXPNUMstat = \"NORMAL\"    \n        \n        param['EXPTIME'] = header[\"EXPTIME\"]\n\n        if camera != header[\"CAMERA\"]:\n                raise qlexceptions.ParameterException(\"Missing camera \"+camera)\n                HDUstat = 'ALARM'\n        \n        if header[\"EXPID\"] != kwargs['expid'] : \n                raise qlexceptions.ParameterException(\"EXPOSURE NUMBER DOES NOT MATCH THE ONE IN THE HEADER\")\n                EXPNUMstat = \"ALARM\"\n        \n        \n        \n        if header[\"FLAVOR\"] != \"science\" :\n            \n           retval[\"METRICS\"] = {\"HDU_STATUS\":HDUstat,\"EXPNUM_STATUS\":EXPNUMstat}\n\n        else :\n           retval[\"METRICS\"] = {\"HDU_STATUS\":HDUstat,\"EXPNUM_STATUS\":EXPNUMstat}\n           param['SEEING'] = header[\"SEEING\"]\n           param['AIRMASS'] = header[\"AIRMASS\"]\n           \n          \n        retval[\"PARAMS\"] = param   \n        \n        if 'INHERIT' in header and header['INHERIT']:\n            h0 = raw[0].header\n            for key in h0:\n                if key not in header:\n                    header[key] = h0[key]\n        \n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\nclass Trace_Shifts(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"TRACE\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"TRACE_REF\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"TRACE_REF\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n        if \"TRACE_WARN_RANGE\" in parms and \"TRACE_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"TRACE_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"TRACE_NORMAL_RANGE\"]),QASeverity.NORMAL)]\n        MonitoringAlg.__init__(self,name,im,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible parameter type. Was expecting desispec.image.Image got {}\".format(type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Trace_Shifts':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            image = get_image('preproc',night,expid,camera,kwargs[\"specdir\"])\n            \n        else:\n            image=args[0]\n\n        fibermap=kwargs['FiberMap']\n\n        if \"paname\" not in kwargs:\n            paname=None\n        else:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        amps=False\n        if \"amps\" in kwargs:\n            amps=kwargs[\"amps\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        psf = None\n        if \"PSFFile\" in kwargs:\n            psf=kwargs[\"PSFFile\"]\n\n        fibermap = None\n        if \"FiberMap\" in kwargs:\n            fibermap=kwargs[\"FiberMap\"]\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig = None\n\n        return self.run_qa(fibermap,image,paname=paname,amps=amps,psf=psf,qafile=qafile,qafig=qafig,param=param,qlf=qlf,refmetrics=refmetrics)\n\n    def run_qa(self,fibermap,image,paname=None,amps=False,psf=None,qafile=None,qafig=None,param=None,qlf=False,refmetrics=None):\n\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            param = {\n                \"TRACE_NORMAL_RANGE\":[-1.0, 1.0],\n                \"TRACE_WARN_RANGE\":[-2.0, 2.0]\n                }\n\n        #- RS: Return empty dictionaries until flexure metrics are decided\n        dx=[]\n        dy=[]\n        xyshift=np.array(dx,dy)\n        retval={}\n        retval[\"METRICS\"]={\"XYSHIFT\":xyshift}\n        retval[\"PARAMS\"]=param\n\n        #- http post if needed\n        if qlf:\n            qlf_post(retval)\n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n#        if qafig is not None:\n#            plot.plot_TraceShifts(retval,qafig)\n\n            log.debug(\"Output QA fig {}\".format(qafig))\n\n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\nclass Bias_From_Overscan(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"BIAS_OVERSCAN\"\n        #import astropy\n        #rawtype=astropy.io.fits.hdu.hdulist.HDUList\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"BIAS_AMP\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"BIAS_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"BIAS_WARN_RANGE\" in parms and \"BIAS_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"BIAS_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"BIAS_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe \n\n        MonitoringAlg.__init__(self,name,im,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {} got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Bias_From_Overscan':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            image = get_image('preproc',night,expid,camera,kwargs[\"specdir\"])\n            import sys\n            sys.exit()\n        else:\n            image=args[0]\n            \n        if \"paname\" not in kwargs:\n            paname=None\n        else:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        amps=False\n        if \"amps\" in kwargs:\n            amps=kwargs[\"amps\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig=None\n\n        return self.run_qa(image,paname=paname,amps=amps,qafile=qafile,qafig=qafig, param=param, qlf=qlf, refmetrics=refmetrics)\n\n    def run_qa(self,image,paname=None,amps=False,qafile=None, qafig=None,param=None,qlf=False, refmetrics=None):\n    \n        retval={}\n        retval[\"EXPID\"] = '{0:08d}'.format(image.meta[\"EXPID\"])\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"CAMERA\"] = image.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = image.meta[\"PROGRAM\"]\n        retval[\"NIGHT\"] = image.meta[\"NIGHT\"]\n        retval[\"FLAVOR\"] = image.meta[\"FLAVOR\"]\n        retval[\"EXPTIME\"] = image.meta[\"EXPTIME\"]\n        kwargs=self.config['kwargs']\n\n        if retval[\"FLAVOR\"] == 'arc':\n            pass\n        else:\n            retval[\"PROGRAM\"] = image.meta[\"PROGRAM\"]\n        retval[\"NIGHT\"] = image.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n        \n        #SE: this would give the desispec version stored in DEPVER07 key of the raw simulated fits file :0.16.0.dev1830\n        param['FITS_DESISPEC_VERSION'] = image.meta['DEPVER07'] \n        import desispec\n        from desispec import quicklook\n        param['PROC_DESISPEC_VERSION']= desispec.__version__\n        param['PROC_QuickLook_VERSION']= quicklook.__qlversion__\n                  \n        #header = image.meta\n        if 'INHERIT' in image.meta and image.meta['INHERIT']:\n\n            h0 = image.meta\n            #h0 = header\n            for key in h0:\n                if key not in image.meta:\n                    image.meta[key] = h0[key]\n\n        bias_overscan = [image.meta['OVERSCN1'],image.meta['OVERSCN2'],image.meta['OVERSCN3'],image.meta['OVERSCN4']]\n        \n        bias = np.mean(bias_overscan)\n\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            param = {                \n                \"BIAS_NORMAL_RANGE\":[-1.0, 1.0],\n                \"BIAS_WARN_RANGE:\":[-2.0, 2.0]\n                }\n        retval[\"PARAMS\"] = param\n\n        if amps:\n            bias_amps=np.array(bias_overscan)\n            retval[\"METRICS\"]={'BIAS_AMP':bias_amps}\n        else:\n            #retval[\"METRICS\"]={'BIAS':bias,\"DIFF1SIG\":diff1sig,\"DIFF2SIG\":diff2sig,\"DIFF3SIG\":diff3sig,\"DATA5SIG\":data5sig,\"BIAS_ROW\":mean_row}\n            retval[\"METRICS\"]={}\n\n        #- http post if needed\n        if qlf:\n            qlf_post(retval)    \n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            plot.plot_bias_overscan(retval,qafig)\n            \n            log.debug(\"Output QA fig {}\".format(qafig))\n        \n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\n\nclass Get_RMS(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"RMS\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"NOISE_AMP\" \n        status=kwargs['statKey'] if 'statKey' in kwargs else \"NOISE_STATUS\" \n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        \n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n                \n        if \"NOISE_WARN_RANGE\" in parms and \"NOISE_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"NOISE_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"NOISE_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe \n        \n        MonitoringAlg.__init__(self,name,im,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible parameter type. Was expecting desispec.image.Image got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Get_RMS':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            image = get_image('preproc',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            image=args[0]\n\n        if \"paname\" not in kwargs:\n            paname=None\n        else:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        amps=False\n        if \"amps\" in kwargs:\n            amps=kwargs[\"amps\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig = kwargs[\"qafig\"]\n        else: qafig = None\n\n        return self.run_qa(image,paname=paname,amps=amps,qafile=qafile,qafig=qafig, param=param, qlf=qlf, refmetrics=refmetrics)\n\n    def run_qa(self,image,paname=None,amps=False,qafile=None, qafig=None,param=None,qlf=False, refmetrics=None):\n        retval={}\n        retval[\"EXPID\"] = '{0:08d}'.format(image.meta[\"EXPID\"])\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"CAMERA\"] = image.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = image.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = image.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = image.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n        # return rms values in rms/sqrt(exptime)\n        #rmsccd=qalib.getrms(image.pix/np.sqrt(image.meta[\"EXPTIME\"])) #- should we add dark current and/or readnoise to this as well?\n        #rmsccd = np.mean([image.meta['RDNOISE1'],image.meta['RDNOISE2'],image.meta['RDNOISE3'],image.meta['RDNOISE4']]) #--> \"NOISE\":rmsccd\n        \n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            param = {\n                \"PERCENTILES\":[68.2,95.4,99.7],\n                \"NOISE_NORMAL_RANGE\":[-1.0, 1.0],\n                \"NOISE_WARN_RANGE\":[-2.0, 2.0]\n                }\n\n        retval[\"PARAMS\"] = param\n\n        #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n        # SE: this section is moved from BIAS_FROM_OVERSCAN to header\n\n        data=[]\n        row_data_amp1=[]\n        row_data_amp2=[]\n        row_data_amp3=[]\n        row_data_amp4=[]\n        bias_patnoise=[]\n        #bias_overscan=[]        \n        for kk in ['1','2','3','4']:\n            sel=_parse_sec_keyword(image.meta['BIASSEC'+kk])\n            #- Obtain counts/second in bias region\n#            pixdata=image[sel]/header[\"EXPTIME\"]\n            pixdata=image.pix[sel]/image.meta[\"EXPTIME\"]\n            if kk == '1':\n                for i in range(pixdata.shape[0]):\n                    row_amp1=pixdata[i]\n                    row_data_amp1.append(row_amp1)\n            if kk == '2':\n                \n                for i in range(pixdata.shape[0]):\n                    row_amp2=pixdata[i]\n                    row_data_amp2.append(row_amp2)\n            if kk == '3':\n                \n                for i in range(pixdata.shape[0]):\n                    row_amp3=pixdata[i]\n                    row_data_amp3.append(row_amp3)\n            if kk == '4':\n                \n                for i in range(pixdata.shape[0]):\n                    row_amp4=pixdata[i]\n                    row_data_amp4.append(row_amp4)\n            #- Compute statistics of the bias region that only reject\n            #  the 0.5% of smallest and largest values. (from sdssproc) \n            isort=np.sort(pixdata.ravel())\n            nn=isort.shape[0]\n            bias=np.mean(isort[int(0.005*nn) : int(0.995*nn)])\n            #bias_overscan.append(bias)\n            data.append(isort)\n\n        #- Combine data from each row and take average\n        row_data_bottom=[]\n        row_data_top=[]\n        for i in range(len(row_data_amp1)):\n            row_data_lower=np.concatenate((row_data_amp1[i],row_data_amp2[i]))\n            row_data_upper=np.concatenate((row_data_amp3[i],row_data_amp4[i]))\n            row_data_bottom.append(row_data_lower)\n            row_data_top.append(row_data_upper)\n        row_data=np.concatenate((row_data_bottom,row_data_top))\n        full_data=np.concatenate((data[0],data[1],data[2],data[3])).ravel()\n\n\n        # BIAS_ROW = mean_row  \n        median_row_amp1=[]\n        for i in range(len(row_data_amp1)):\n            median=np.median(row_data_amp1[i])\n            median_row_amp1.append(median)\n        \n        rms_median_row_amp1= np.std(median_row_amp1)\n        noise1 = image.meta['RDNOISE1']\n        bias_patnoise.append(rms_median_row_amp1/noise1)\n        \n        median_row_amp2=[]\n        for i in range(len(row_data_amp2)):\n            median=np.median(row_data_amp2[i])\n            median_row_amp2.append(median)\n        \n        rms_median_row_amp2= np.std(median_row_amp2)\n        noise2 = image.meta['RDNOISE2']\n        bias_patnoise.append(rms_median_row_amp2/noise2)\n        \n        \n        median_row_amp3=[]\n        for i in range(len(row_data_amp3)):\n            median=np.median(row_data_amp3[i])\n            median_row_amp3.append(median)\n        \n        rms_median_row_amp3= np.std(median_row_amp3)\n        noise3 = image.meta['RDNOISE3']\n        bias_patnoise.append(rms_median_row_amp3/noise3)\n        \n        median_row_amp4=[]\n        for i in range(len(row_data_amp4)):\n            median=np.median(row_data_amp4[i])\n            median_row_amp4.append(median)\n        \n        rms_median_row_amp4= np.std(median_row_amp4)\n        noise4 = image.meta['RDNOISE4']\n        bias_patnoise.append(rms_median_row_amp4/noise4)\n\n\n        #- Calculate upper and lower bounds of 1, 2, and 3 sigma  \n        sig1_lo = np.percentile(full_data,50.-(param['PERCENTILES'][0]/2.))\n        sig1_hi = np.percentile(full_data,50.+(param['PERCENTILES'][0]/2.))\n        sig2_lo = np.percentile(full_data,50.-(param['PERCENTILES'][1]/2.))\n        sig2_hi = np.percentile(full_data,50.+(param['PERCENTILES'][1]/2.))\n        sig3_lo = np.percentile(full_data,50.-(param['PERCENTILES'][2]/2.))\n        sig3_hi = np.percentile(full_data,50.+(param['PERCENTILES'][2]/2.))\n\n        #- Find difference between upper and lower sigma bounds\n        # DIFF1SIG: The number of counts separating the 1 sigma percentiles in the noise distribution (from the overscan region)\n        diff1sig = sig1_hi - sig1_lo\n        # DIFF2SIG: The number of counts separating 2 or 3 sigma in the noise distribution\n        diff2sig = sig2_hi - sig2_lo\n        diff3sig = sig3_hi - sig3_lo\n\n        #-DATA5SIG: number of pixels more than 5 sigma below the bias level\n        sig5_value = np.percentile(full_data,3e-5)\n        data5sig = len(np.where(full_data <= sig5_value)[0])\n       \n        #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n        if amps:\n            rms_over_amps = [image.meta['RDNOISE1'],image.meta['RDNOISE2'],image.meta['RDNOISE3'],image.meta['RDNOISE4']]\n            rms_amps = [image.meta['OBSRDN1'],image.meta['OBSRDN2'],image.meta['OBSRDN3'],image.meta['OBSRDN4']]\n            retval[\"METRICS\"]={\"NOISE_AMP\":np.array(rms_amps),\"NOISE_OVERSCAN_AMP\":np.array(rms_over_amps),\"DIFF1SIG\":diff1sig,\"DIFF2SIG\":diff2sig,\"DATA5SIG\":data5sig,\"BIAS_PATNOISE\":bias_patnoise}#,\"NOISE_ROW\":noise_row,\"EXPNUM_WARN\":expnum,\"NOISE_OVER\":rmsover\n\n        else:\n            retval[\"METRICS\"]={\"DIFF1SIG\":diff1sig,\"DIFF2SIG\":diff2sig,\"DATA5SIG\":data5sig, \"BIAS_PATNOISE\":bias_patnoise} # Dropping \"NOISE_OVER\":rmsover,\"NOISE_ROW\":noise_row,\"EXPNUM_WARN\":expnum\n\n        if qlf:\n            qlf_post(retval)  \n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            plot.plot_RMS(retval,qafig)            \n            log.debug(\"Output QA fig {}\".format(qafig))\n\n        return retval    \n\n    def get_default_config(self):\n        return {}\n\n\n\nclass Calc_XWSigma(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"XWSIGMA\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"XWSIGMA\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"XWSIGMA_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"XWSIGMA_WARN_RANGE\" in parms and \"XWSIGMA_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"XWSIGMA_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"XWSIGMA_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe\n        MonitoringAlg.__init__(self,name,im,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible parameter type. Was expecting desispec.image.Image got {}\".format(type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Calc_XWSigma':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            image = get_image('preproc',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            image=args[0]\n\n        flavor=kwargs['Flavor']\n        fibermap=kwargs['FiberMap'] \n \n        if \"paname\" not in kwargs:\n            paname=None\n        else:\n            paname=kwargs[\"paname\"]\n \n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        amps=False\n        if \"amps\" in kwargs:\n            amps=kwargs[\"amps\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n \n        psf = None\n        if \"PSFFile\" in kwargs:\n            psf=kwargs[\"PSFFile\"]\n            psf=desispec.quicklook.qlpsf.PSF(psf)\n \n        fibermap = None\n        if \"FiberMap\" in kwargs:\n            fibermap=kwargs[\"FiberMap\"]\n \n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n \n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig = None\n \n        return self.run_qa(fibermap,flavor,image,paname=paname,amps=amps,psf=psf, qafile=qafile,qafig=qafig, param=param, qlf=qlf, refmetrics=refmetrics)\n \n    def run_qa(self,fibermap,flavor,image,paname=None,amps=False,psf=None, qafile=None,qafig=None, param=None, qlf=False, refmetrics=None):\n        from scipy.optimize import curve_fit\n\n        retval={}\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat() \n        retval[\"EXPID\"] = '{0:08d}'.format(image.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = camera = image.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = image.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = image.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = image.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n        #ra = fibermap[\"RA_TARGET\"]\n        #dec = fibermap[\"DEC_TARGET\"]\n\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            if image.meta[\"FLAVOR\"] == 'arc':\n                param = {\n                    \"B_PEAKS\":[4047.7, 4359.6, 5087.2],\n                    \"R_PEAKS\":[6144.8, 6508.3, 6600.8, 6718.9, 6931.4, 7034.4,],\n                    \"Z_PEAKS\":[8379.9, 8497.7, 8656.8, 8783.0],\n                    \"XWSIGMA_SHIFT_NORMAL_RANGE\":[-2.0, 2.0], #- Assumes both sigma and shift in same range. Change if needed\n                    \"XWSIGMA_SHIFT_WARN_RANGE\":[-4.0, 4.0]\n                    }\n            else:\n                param = {\n                    \"B_PEAKS\":[3914.4, 5199.3, 5578.9],\n                    \"R_PEAKS\":[6301.9, 6365.4, 7318.2, 7342.8, 7371.3],\n                    \"Z_PEAKS\":[8401.5, 8432.4, 8467.5, 9479.4, 9505.6, 9521.8],\n                    \"XWSIGMA_SHIFT_NORMAL_RANGE\":[-2.0, 2.0],\n                    \"XWSIGMA_SHIFT_WARN_RANGE\":[-4.0, 4.0]\n                    }\n\n        #- Ensure that the QA will run even if 500 spectra aren't present\n        if fibermap['FIBER'].shape[0] >= 500:\n            fibers = 500\n        else:\n            fibers = fibermap['FIBER'].shape[0]\n\n        #- dw and dp are wavelength/pixel ranges used as region to calculate Gaussian over peaks\n        #- hardcoded until a better method is found, perhaps configurable (R.S.)\n        dw=2.\n        dp=3\n        #- Get wavelength ranges around peaks\n        peak_wave=[]\n        peaks=param['{}_PEAKS'.format(camera[0].upper())]\n        for p in range(len(peaks)):\n            peak_lower = peaks[p] - dw\n            peak_upper = peaks[p] + dw\n            peak_wave.append(peak_lower)\n            peak_wave.append(peak_upper)\n        npeaks=len(peaks)\n        peak_wave = np.array(peak_wave)\n\n        xfails=[]\n        wfails=[]\n        xsigma=[]\n        wsigma=[]\n        xsigma_sky=[]\n        wsigma_sky=[]\n        xsigma_amp1=[]\n        wsigma_amp1=[]\n        xsigma_amp2=[]\n        wsigma_amp2=[]\n        xsigma_amp3=[]\n        wsigma_amp3=[]\n        xsigma_amp4=[]\n        wsigma_amp4=[]\n        for i in range(fibers):\n            xsig=[]\n            wsig=[]\n            #- Use psf information to convert wavelength to pixel values\n            xpix=desispec.quicklook.qlpsf.PSF.x(psf,ispec=i,wavelength=peak_wave)[0]\n            ypix=desispec.quicklook.qlpsf.PSF.y(psf,ispec=i,wavelength=peak_wave)[0]\n            for peak in range(len(peaks)):\n                #- Find x and y pixel values around sky lines\n                xpix_peak=np.arange(int(np.rint(xpix[2*peak]))-dp,int(np.rint(xpix[2*peak+1]))+dp+1,1)\n                ypix_peak=np.arange(int(np.rint(ypix[2*peak])),int(np.rint(ypix[2*peak+1])),1)\n                #- Fit gaussian to counts in pixels around sky line\n                #- If any values fail, store x/w, wavelength, and fiber\n                try:\n                    xpopt,xpcov=curve_fit(qalib.gauss,np.arange(len(xpix_peak)),image.pix[int(np.mean(ypix_peak)),xpix_peak])\n                except:\n                    xfail=[i,peaks[peak]]\n                    xfails.append(xfail)\n                    pass\n                try:\n                    wpopt,wpcov=curve_fit(qalib.gauss,np.arange(len(ypix_peak)),image.pix[ypix_peak,int(np.mean(xpix_peak))])\n                except:\n                    wfail=[i,peaks[peak]]\n                    wfails.append(wfail)\n                    pass\n\n                #- Save sigmas from fits\n                xs=np.abs(xpopt[2])\n                ws=np.abs(wpopt[2])\n                xsig.append(xs)\n                wsig.append(ws)\n\n                if len(xsig) == npeaks:\n                    xsigma_avg=np.mean(xsig)\n                    xsigma.append(xsigma_avg)\n                if len(wsig) == npeaks:\n                    wsigma_avg=np.mean(wsig)\n                    wsigma.append(wsigma_avg)\n \n            if fibermap['OBJTYPE'][i]=='SKY':\n                xsigma_sky=xsigma\n                wsigma_sky=wsigma\n\n            #- Excluding fibers 240-260 in case some fibers overlap amps\n            #- This shouldn't cause a significant loss of information \n            if amps:\n                if fibermap['FIBER'][i]<240:\n                    if camera[0]==\"b\":\n                        xsig_amp1=np.array([xsig[0]])\n                        xsig_amp3=np.array([xsig[1],xsig[2]])\n                        wsig_amp1=np.array([wsig[0]])\n                        wsig_amp3=np.array([wsig[1],wsig[2]])\n                    if camera[0]==\"r\":\n                        xsig_amp1=np.array([xsig[0],xsig[1]])\n                        xsig_amp3=np.array([xsig[2],xsig[3],xsig[4]])\n                        wsig_amp1=np.array([wsig[0],wsig[1]])\n                        wsig_amp3=np.array([wsig[2],wsig[3],wsig[4]])\n                    if camera[0]==\"z\":\n                        xsig_amp1=np.array([xsig[0],xsig[1],xsig[2]])\n                        xsig_amp3=np.array([xsig[3]])\n                        wsig_amp1=np.array([wsig[0],wsig[1],wsig[2]])\n                        wsig_amp3=np.array([wsig[3]])\n    \n                    xsigma_amp1.append(xsig_amp1)\n                    wsigma_amp1.append(wsig_amp1)\n                    xsigma_amp3.append(xsig_amp3)\n                    wsigma_amp3.append(wsig_amp3)\n\n                if fibermap['FIBER'][i]>260:\n                    if camera[0]==\"b\":\n                        xsig_amp2=np.array([xsig[0]])\n                        xsig_amp4=np.array([xsig[1],xsig[2]])\n                        wsig_amp2=np.array([wsig[0]])\n                        wsig_amp4=np.array([wsig[1],wsig[2]])\n                    if camera[0]==\"r\":\n                        xsig_amp2=np.array([xsig[0],xsig[1]])\n                        xsig_amp4=np.array([xsig[2],xsig[3],xsig[4]])\n                        wsig_amp2=np.array([wsig[0],wsig[1]])\n                        wsig_amp4=np.array([wsig[2],wsig[3],wsig[4]])\n                    if camera[0]==\"z\":\n                        xsig_amp2=np.array([xsig[0],xsig[1],xsig[2]])\n                        xsig_amp4=np.array([xsig[3]])\n                        wsig_amp2=np.array([wsig[0],wsig[1],wsig[2]])\n                        wsig_amp4=np.array([wsig[3]])\n    \n                    xsigma_amp2.append(xsig_amp2)\n                    wsigma_amp2.append(wsig_amp2)\n                    xsigma_amp4.append(xsig_amp4)\n                    wsigma_amp4.append(wsig_amp4)\n  \n                if fibermap['FIBER'].shape[0]<260:\n                    xsigma_amp2=np.zeros(len(xsigma))\n                    xsigma_amp4=np.zeros(len(xsigma))\n                    wsigma_amp2=np.zeros(len(wsigma))\n                    wsigma_amp4=np.zeros(len(wsigma))\n\n        #- Calculate desired output metrics \n        xsigma=np.array(xsigma)\n        wsigma=np.array(wsigma)\n        xsigma_med=np.median(xsigma)\n        wsigma_med=np.median(wsigma)\n        xsigma_med_sky=np.median(xsigma_sky)\n        wsigma_med_sky=np.median(wsigma_sky)\n        #xwsigma=np.array([xsigma_med_sky,wsigma_med_sky])\n        xamp1_med=np.median(xsigma_amp1)\n        xamp2_med=np.median(xsigma_amp2)\n        xamp3_med=np.median(xsigma_amp3)\n        xamp4_med=np.median(xsigma_amp4)\n        wamp1_med=np.median(wsigma_amp1)\n        wamp2_med=np.median(wsigma_amp2)\n        wamp3_med=np.median(wsigma_amp3)\n        wamp4_med=np.median(wsigma_amp4)\n        xsigma_amp=np.array([xamp1_med,xamp2_med,xamp3_med,xamp4_med])\n        wsigma_amp=np.array([wamp1_med,wamp2_med,wamp3_med,wamp4_med])\n\n        #xshift_med=0.0\n        #wshift_med=0.0\n        #xshift_fib=[]\n        #wshift_fib=[]\n        #xshift_amp=[]\n        #wshift_amp=[]\n        #shift_warn=[]\n\n        xwfails=[xfails,wfails]\n\n        retval[\"PARAMS\"] = param\n\n        #- Combine metrics for x and w\n        xwsigma_fib=np.array((xsigma,wsigma)) #- (2,nfib)\n        xwsigma_med=np.array((xsigma_med,wsigma_med)) #- (2)\n        xwsigma_amp=np.array((xsigma_amp,wsigma_amp))\n       \n        #xwshift=np.zeros((2,500)) #- 500 should change to nfib (read from top)\n        #xwshift_med=np.array((xshift_med,wshift_med))\n        #xwshift_amp=np.array((xshift_amp, wshift_amp))\n        #xwsigma_shift=np.array(((xsigma_med,wsigma_med),(xshift_med,wshift_med)))\n\n        if amps:\n            retval[\"METRICS\"]={\"XWSIGMA\":xwsigma_med,\"XWSIGMA_FIB\":xwsigma_fib,\"XWSIGMA_AMP\":xwsigma_amp}#,\"XWSHIFT\":xwshift,\"XWSHIFT_AMP\":xwshift_amp,\"XWSIGMA_SHIFT\": xwsigma_shift}\n        else:\n            retval[\"METRICS\"]={\"XWSIGMA\":xwsigma_med,\"XWSIGMA_FIB\":xwsigma_fib}#,\"XWSHIFT\":xwshift,\"XWSIGMA_SHIFT\": xwsigma_shift}\n\n        #- http post if needed\n        if qlf:\n            qlf_post(retval)    \n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            plot.plot_XWSigma(retval,qafig)\n\n            log.debug(\"Output QA fig {}\".format(qafig))\n\n        return retval\n \n    def get_default_config(self):\n        return {}\n\n\nclass Count_Pixels(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"COUNTPIX\"\n        from desispec.image import Image as im\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"LITFRAC_AMP\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"LITFRAC_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n                \n        if \"LITFRAC_WARN_RANGE\" in parms and \"LITFRAC_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"LITFRAC_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"LITFRAC_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe\n        MonitoringAlg.__init__(self,name,im,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {} got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Count_Pixels':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            image = get_image('preproc',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            image=args[0]\n\n        if \"paname\" not in kwargs:\n            paname=None\n        else:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        amps=False\n        if \"amps\" in kwargs:\n            amps=kwargs[\"amps\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig = None\n\n        return self.run_qa(image,paname=paname,amps=amps,qafile=qafile,qafig=qafig, param=param, qlf=qlf, refmetrics=refmetrics)\n\n    def run_qa(self,image,paname=None,amps=False,qafile=None,qafig=None, param=None, qlf=False, refmetrics=None):\n        retval={}\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"EXPID\"] = '{0:08d}'.format(image.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = image.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = image.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = image.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = image.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            param = {\n                 \"CUTPIX\":5,   # low threshold for number of counts in sigmas\n                 \"LITFRAC_NORMAL_RANGE\":[-0.1, 0.1],\n                 \"LITFRAC_WARN_RANGE\":[-0.2, 0.2]\n                 }\n\n        retval[\"PARAMS\"] = param\n\n        #- get the counts for each amp\n        npix_amps=[]\n        litfrac_amps=[]\n\n        #- get amp boundary in pixels\n        from desispec.preproc import _parse_sec_keyword\n        for kk in ['1','2','3','4']:\n            ampboundary=_parse_sec_keyword(image.meta[\"CCDSEC\"+kk])\n            rdnoise_thisamp=image.meta[\"RDNOISE\"+kk]\n            npix_thisamp= image.pix[ampboundary][image.pix[ampboundary] > param['CUTPIX'] * rdnoise_thisamp].size #- no of pixels above threshold\n            npix_amps.append(npix_thisamp)\n            size_thisamp=image.pix[ampboundary].size\n            litfrac_thisamp=round(np.float(npix_thisamp)/size_thisamp,2) #- fraction of pixels getting light above threshold\n            litfrac_amps.append(litfrac_thisamp)\n\t#        retval[\"METRICS\"]={\"NPIX_AMP\",npix_amps,'LITFRAC_AMP': litfrac_amps}\n        retval[\"METRICS\"]={\"LITFRAC_AMP\": litfrac_amps}\t\n\n        if qlf:\n            qlf_post(retval)\n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            from desispec.qa.qa_plots_ql import plot_countpix\n            plot_countpix(retval,qafig)\n\n            log.debug(\"Output QA fig {}\".format(qafig))\n\n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\nclass CountSpectralBins(MonitoringAlg):\n\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"COUNTBINS\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"NGOODFIB\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"NGOODFIB_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"NGOODFIB_WARN_RANGE\" in parms and \"NGOODFIB_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"NGOODFIB_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"NGOODFIB_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe \n\n        MonitoringAlg.__init__(self,name,fr,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {} got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'CountSpectralBins':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            frame = get_frame('frame',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            frame=args[0]\n\n        fibermap=kwargs['FiberMap']\n\n        paname=None\n        if \"paname\" in kwargs:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        psf = None\n        if \"PSFFile\" in kwargs: \n            psf=kwargs[\"PSFFile\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig=None\n\n        return self.run_qa(fibermap,frame,paname=paname,psf=psf,qafile=qafile,qafig=qafig,param=param,qlf=qlf,refmetrics=refmetrics)\n\n    def run_qa(self,fibermap,frame,paname=None,psf=None,qafile=None,qafig=None,param=None, qlf=False, refmetrics=None):\n\n        if isinstance(frame,QFrame):\n            frame = frame.asframe()\n\n        #- qa dictionary \n        retval={}\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"EXPID\"] = '{0:08d}'.format(frame.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = frame.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = frame.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = frame.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = frame.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n        #ra = fibermap[\"RA_TARGET\"]\n        #dec = fibermap[\"DEC_TARGET\"]\n\n        grid=np.gradient(frame.wave)\n        if not np.all(grid[0]==grid[1:]): \n            log.debug(\"grid_size is NOT UNIFORM\")\n\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            param = {\n                 \"CUTBINS\":5,   #- threshold for number of counts in units of readnoise(scaled to bins)\n                 \"NGOODFIB_NORMAL_RANGE\":[490, 500],\n                 \"NGOODFIB_WARN_RANGE\":[480, 500],\n                 \"N_KNOWN_BROKEN_FIBERS\": 0\n                 }\n\n        retval[\"PARAMS\"] = param\n        #- get the effective readnoise for the fibers \n        #- readnoise per fib = readnoise per pix * sqrt(box car width)* sqrt(no. of bins in the amp) * binsize/pix size scale\n        nspec=frame.nspec\n        rdnoise_fib=np.zeros(nspec)\n        if nspec > 250: #- upto 250 - amp 1 and 3, beyond that 2 and 4\n            rdnoise_fib[:250]=[(frame.meta['RDNOISE1']+frame.meta['RDNOISE3'])*np.sqrt(5.)*np.sqrt(frame.flux.shape[1]/2)*frame.meta['WAVESTEP']/0.5]*250\n            rdnoise_fib[250:]=[(frame.meta['RDNOISE2']+frame.meta['RDNOISE4'])*np.sqrt(5.)*np.sqrt(frame.flux.shape[1]/2)*frame.meta['WAVESTEP']/0.5]*(nspec-250)\n        else:\n            rdnoise_fib=[(frame.meta['RDNOISE1']+frame.meta['RDNOISE3'])*np.sqrt(5.)*np.sqrt(frame.flux.shape[1]/2)*frame.meta['WAVESTEP']/0.5]*nspec\n        threshold=[param['CUTBINS']*ii for ii in rdnoise_fib]\n        #- compare the flux sum to threshold\n        \n        passfibers=np.where(frame.flux.sum(axis=1)>threshold)[0] \n        ngoodfibers=passfibers.shape[0] - param[\"N_KNOWN_BROKEN_FIBERS\"]\n        good_fiber=np.array([0]*frame.nspec)\n        good_fiber[passfibers]=1 #- assign 1 for good fiber\n\n        #- leaving the amps granularity needed for caching as defunct. If needed in future, this needs to be propagated through.\n        amps=False\n        leftmax=None\n        rightmax=None\n        bottommax=None\n        topmin=None\n\n        if amps: #- leaving this for now\n            leftmax,rightmin,bottommax,topmin = qalib.fiducialregion(frame,psf)\n            retval[\"LEFT_MAX_FIBER\"]=int(leftmax)\n            retval[\"RIGHT_MIN_FIBER\"]=int(rightmin)\n            retval[\"BOTTOM_MAX_WAVE_INDEX\"]=int(bottommax)\n            retval[\"TOP_MIN_WAVE_INDEX\"]=int(topmin)\n\n        retval[\"METRICS\"]={\"NGOODFIB\": ngoodfibers, \"GOOD_FIBER\": good_fiber}\n\n        #- http post if needed\n        if qlf:\n            qlf_post(retval)    \n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            plot.plot_countspectralbins(retval,qafig)\n            \n            log.debug(\"Output QA fig {}\".format(qafig))\n        \n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\nclass Sky_Continuum(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"SKYCONT\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"SKYCONT\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"SKYCONT_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"SKYCONT_WARN_RANGE\" in parms and \"SKYCONT_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"SKYCONT_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"SKYCONT_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe\n        MonitoringAlg.__init__(self,name,fr,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {}, got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Sky_Continuum':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            frame = get_frame('fframe',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            frame=args[0]\n\n        fibermap=kwargs['FiberMap']\n        \n        if \"paname\" in kwargs:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig=None\n\n        return self.run_qa(fibermap,frame,paname=paname,qafile=qafile,qafig=qafig,param=param,qlf=qlf,refmetrics=refmetrics)\n\n    def run_qa(self,fibermap,frame,\n               paname=None,qafile=None,qafig=None,param=None,qlf=False,\n               refmetrics=None):\n\n        if isinstance(frame,QFrame):\n            frame = frame.asframe()\n\n        #- qa dictionary \n        retval={}\n        retval[\"PANAME\" ]= paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"EXPID\"] = '{0:08d}'.format(frame.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = frame.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = frame.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = frame.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = frame.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n        #ra = fibermap[\"RA_TARGET\"]\n        #dec = fibermap[\"DEC_TARGET\"]\n        \n        camera=frame.meta[\"CAMERA\"]\n\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            desi_params = read_params()\n            param = {\"B_CONT\": [\"4000, 4500\", \"5250, 5550\"],\n                         \"R_CONT\": [\"5950, 6200\", \"6990, 7230\"],\n                         \"Z_CONT\": [\"8120, 8270\", \"9110, 9280\"],\n                         \"SKYCONT_NORMAL_RANGE\": [100.0, 400.0],\n                         \"SKYCONT_WARN_RANGE\": [50.0, 600.0]}\n            for key in ['B_CONT','R_CONT', 'Z_CONT', 'SKYCONT_ALARM_RANGE', 'SKYCONT_WARN_RANGE']: #- needs updating alarm/warn - normal/warn in desi_params.\n                param[key] = desi_params['qa']['skysub']['PARAMS'][key]\n\n        wrange1=param[\"{}_CONT\".format(camera[0].upper())][0]\n        wrange2=param[\"{}_CONT\".format(camera[0].upper())][1]\n\n        retval[\"PARAMS\"] = param\n\n        skyfiber, contfiberlow, contfiberhigh, meancontfiber, skycont = qalib.sky_continuum(\n            frame, wrange1, wrange2)\n \n                            \n        retval[\"METRICS\"]={\"SKYFIBERID\": skyfiber.tolist(), \"SKYCONT\":skycont, \"SKYCONT_FIBER\":meancontfiber}\n             \n\n        if qlf:\n            qlf_post(retval)    \n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n\n        if qafig is not None:\n            plot.plot_sky_continuum(retval,qafig)\n            \n            log.debug(\"Output QA fig {}\".format(qafig))\n        \n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\n\nclass Sky_Rband(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"SKYRBAND\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"SKYRBAND\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"SKYRBAND_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"SKYRBAND_WARN_RANGE\" in parms and \"SKYRBAND_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"SKYRBAND_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"SKYRBAND_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe\n        MonitoringAlg.__init__(self,name,fr,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {}, got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Sky_Rband':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            frame = get_frame('fframe',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            frame=args[0]\n\n        fibermap=kwargs['FiberMap']\n        \n        if \"paname\" in kwargs:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig=None\n\n        return self.run_qa(fibermap,frame,paname=paname,qafile=qafile,qafig=qafig,param=param,qlf=qlf,refmetrics=refmetrics)\n\n    def run_qa(self,fibermap,frame,paname=None,qafile=None,qafig=None,param=None,qlf=False,refmetrics=None):\n\n        if isinstance(frame,QFrame):\n            frame = frame.asframe()\n\n        #- qa dictionary \n        retval={}\n        retval[\"PANAME\" ]= paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"EXPID\"] = '{0:08d}'.format(frame.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = frame.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = frame.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = frame.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = frame.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n        \n        camera=frame.meta[\"CAMERA\"]\n\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            desi_params = read_params()\n            \n            param = {\"B_CONT\": [\"4000, 4500\", \"5250, 5550\"],\n                         \"R_CONT\": [\"5950, 6200\", \"6990, 7230\"],\n                         \"Z_CONT\": [\"8120, 8270\", \"9110, 9280\"],\n                         \"SKYRBAND_NORMAL_RANGE\": [-100.0, 100.0],\n                         \"SKYRBAND_WARN_RANGE\": [-200.0, 200.0]}\n            \n            for key in ['B_CONT','R_CONT', 'Z_CONT', 'SKYRBAND_ALARM_RANGE', 'SKYRBAND_WARN_RANGE']: \n                param[key] = desi_params['qa']['skysub']['PARAMS'][key]\n\n        wrange1=param[\"{}_CONT\".format(camera[0].upper())][0]\n        wrange2=param[\"{}_CONT\".format(camera[0].upper())][1]\n\n        retval[\"PARAMS\"] = param\n\n        skyfiber, contfiberlow, contfiberhigh, meancontfiber, skycont = qalib.sky_continuum(\n            frame, wrange1, wrange2)\n \n        fibs = skyfiber.tolist()\n        skyfib_Rflux=[]\n        \n        #SE: Added a \"place holder\" for the Sky_Rband Flux from the sky monitor written in the header of the raw exposure \n   \n        filt = re.split('(\\d+)',frame.meta[\"CAMERA\"])[0]\n        mags=frame.fibermap['MAG']\n\n        if (filt == 'r'):\n            \n            flux=frame.flux\n            wave=frame.wave\n            integrals=np.zeros(flux.shape[0])\n            \n            import desimodel\n            from desimodel.focalplane import fiber_area_arcsec2\n        \n            wsky = np.where(frame.fibermap['OBJTYPE']=='SKY')[0]\n            xsky = frame.fibermap[\"X_FVCOBS\"][wsky]\n            ysky = frame.fibermap[\"Y_FVCOBS\"][wsky]    \n            apsky = desimodel.focalplane.fiber_area_arcsec2(xsky,ysky)\n            expt = frame.meta[\"EXPTIME\"]\n            \n            for i in range(len(fibs)):\n            \n                sky_integ = qalib.integrate_spec(wave,flux[fibs[i]])\n                # SE:  leaving the units as counts/sec/arcsec^2 to be compared to sky monitor flux from ETC in the same unit \n                sky_flux = sky_integ/expt/apsky[i]\n            \n                skyfib_Rflux.append(sky_flux)\n            \n        #SE: assuming there is a key in the header of the raw exposure header [OR somewhere else] where the sky R-band flux from the sky monitor is stored \n        #    the units would be counts/sec/arcsec^2  1000 is just a dummy number as a placeholder\n        sky_r=  100   # SE: to come from ETC in count/sec/arcsec^2 \n        \n        if (sky_r != \"\" and len(skyfib_Rflux) >0):\n            \n            diff = abs(sky_r-np.mean(skyfib_Rflux)) \n            \n        else:\n             if (sky_r != \"\" and len(skyfib_Rflux) == 0): \n                 \n                diff = sky_r\n             \n             else: \n                diff = sky_fib_flux\n                log.warning(\"No SKY Monitor R-band Flux was found in the header!\")\n\n\n        retval[\"METRICS\"]={\"SKYRBAND\":sky_r,\"SKY_FIB_RBAND\":skyfib_Rflux, \"SKY_RFLUX_DIFF\":diff}\n\n        if qlf:\n            qlf_post(retval)    \n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n\n        #if qafig is not None:\n            #plot.plot_sky_continuum(retval,qafig)\n            \n            #log.debug(\"Output QA fig {}\".format(qafig))\n        \n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\nclass Sky_Peaks(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"SKYPEAK\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"PEAKCOUNT\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"PEAKCOUNT_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"PEAKCOUNT_WARN_RANGE\" in parms and \"PEAKCOUNT_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"PEAKCOUNT_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"PEAKCOUNT_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe\n        MonitoringAlg.__init__(self,name,fr,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible parameter type. Was expecting desispec.image.Image, got {}\".format(type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Sky_Peaks':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            frame = get_frame('fframe',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            frame=args[0]\n\n        fibermap=kwargs['FiberMap']\n\n        if \"paname\" not in kwargs:\n            paname=None\n        else:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        psf = None\n        if \"PSFFile\" in kwargs:\n            psf=kwargs[\"PSFFile\"]\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs:\n            qafig=kwargs[\"qafig\"]\n        else: qafig = None\n\n        return self.run_qa(fibermap,frame,paname=paname,psf=psf,qafile=qafile,qafig=qafig,param=param,qlf=qlf,refmetrics=refmetrics)\n\n    def run_qa(self,fibermap,frame,paname=None,psf=None, qafile=None,qafig=None, param=None, qlf=False, refmetrics=None):\n        from desispec.qa.qalib import sky_peaks\n        \n        if isinstance(frame,QFrame):\n            frame = frame.asframe()\n\n        retval={}\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"EXPID\"] = '{0:08d}'.format(frame.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = camera = frame.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = frame.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = frame.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = frame.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n        # Parameters\n        if param is None:\n            log.info(\"Param is None. Using default param instead\")\n            desi_params = read_params()\n            param = desi_params['qa']['skypeaks']['PARAMS']\n\n        # Run\n        \n        #SE: it is deactivated now but remember that the order of targets in tgt array here is: tgtlist = ['STD','QSO','ELG','LRG','BGS','MWS_STAR']\n        #nspec_counts, sky_counts, tgt_counts, tgt_counts_rms = sky_peaks(param, frame)\n        nspec_counts, sky_counts= sky_peaks(param, frame)\n        rms_nspec = np.std(nspec_counts)#qalib.getrms(nspec_counts)\n        rms_skyspec = np.std(sky_counts)#qalib.getrms(sky_counts)  \n        \n        \n        sumcount_med_sky=np.median(sky_counts)\n\n        retval[\"PARAMS\"] = param\n\n        retval[\"METRICS\"]={\"PEAKCOUNT\":sumcount_med_sky,\"PEAKCOUNT_NOISE\":rms_skyspec,\"PEAKCOUNT_FIB\":nspec_counts}#,\"PEAKCOUNT_TGT\":tgt_counts,\"PEAKCOUNT_TGT_NOISE\":tgt_counts_rms}\n\n\n        if qlf:\n            qlf_post(retval)\n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            plot.plot_sky_peaks(retval,qafig)\n\n            log.debug(\"Output QA fig {}\".format(qafig))\n\n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\nclass Sky_Residual(MonitoringAlg):\n    \"\"\" \n    Use offline sky_residual function to calculate sky residuals\n    \"\"\"\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"RESIDUAL\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"RESIDNOISE\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"RESID_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"RESID_WARN_RANGE\" in parms and \"RESID_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"RESID_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"RESID_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe \n\n        MonitoringAlg.__init__(self,name,fr,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {} got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Sky_Residual':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            frame = get_frame('sframe',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            frame=args[0]\n\n        fibermap=kwargs['FiberMap']\n        skymodel=args[1] #- should be skymodel evaluated\n        if \"SkyFile\" in kwargs:\n            skyfile=kwargs[\"SkyFile\"]    #- Read sky model file itself from an argument\n            log.debug(\"Using given sky file {} for subtraction\".format(skyfile))\n\n            skymodel=read_sky(skyfile)\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n        \n        paname=None\n        if \"paname\" in kwargs:\n            paname=kwargs[\"paname\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig = None\n        \n        return self.run_qa(fibermap,frame,paname=paname,skymodel=skymodel,qafile=qafile,qafig=qafig,param=param,qlf=qlf,refmetrics=refmetrics)\n\n\n    def run_qa(self,fibermap,frame,paname=None,skymodel=None,qafile=None,qafig=None,param=None,qlf=False,refmetrics=None):\n        from desispec.sky import qa_skysub\n\n        if isinstance(frame,QFrame):\n            frame = frame.asframe()\n            \n        if skymodel is None:\n            raise IOError(\"Must have skymodel to find residual. It can't be None\")\n        #- return values\n        retval={}\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"EXPID\"] = '{0:08d}'.format(frame.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = frame.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = frame.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = frame.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = frame.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            param = {\n                \"BIN_SZ\":0.1, #- Bin size for histograms\n                \"PCHI_RESID\":0.05, # P(Chi^2) limit for bad skyfiber model residuals\n                \"PER_RESID\":95.,   # Percentile for residual distribution\n                \"RESID_NORMAL_RANGE\":[-5.0, 5.0],\n                \"RESID_WARN_RANGE\":[-10.0, 10.0]\n                }\n\n        qadict=qalib.sky_resid(param,frame,skymodel,quick_look=True)\n\n        retval[\"METRICS\"] = {}\n        for key in qadict.keys():\n            retval[\"METRICS\"][key] = qadict[key]\n\n        if qlf:\n            qlf_post(retval)    \n\n        retval[\"PARAMS\"] = param\n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            plot.plot_residuals(frame,retval,qafig)\n            \n            log.debug(\"Output QA fig {}\".format(qafig))\n\n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\nclass Integrate_Spec(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"INTEG\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"DELTAMAG_TGT\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"DELTAMAG_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"DELTAMAG_WARN_RANGE\" in parms and \"DELTAMAG_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"DELTAMAG_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"DELTAMAG_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe\n        MonitoringAlg.__init__(self,name,fr,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {}, got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Integrate_Spec':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            frame = get_frame('sframe',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            frame=args[0]\n\n        fibermap=kwargs['FiberMap']\n\n        if \"paname\" not in kwargs:\n            paname=None\n        else:\n            paname=kwargs[\"paname\"]\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig = None\n\n        return self.run_qa(fibermap,frame,paname=paname,qafile=qafile,qafig=qafig,param=param,qlf=qlf,refmetrics=refmetrics)\n\n    def run_qa(self,fibermap,frame,paname=None,qafile=None,qafig=None,param=None,qlf=False,refmetrics=None):\n\n        if isinstance(frame,QFrame):\n            frame = frame.asframe()\n\n        retval={}\n        retval[\"PANAME\" ] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"EXPID\"] = '{0:08d}'.format(frame.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = camera = frame.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = frame.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = frame.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = frame.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n        ra = frame.fibermap[\"RA_TARGET\"]\n        dec = frame.fibermap[\"DEC_TARGET\"]\n\n        flux=frame.flux\n        wave=frame.wave\n        #- Grab magnitudes for appropriate filter\n        if camera[0].lower() == 'b':\n            filterindex=0 #- DECAM_G\n        elif camera[0].lower() == 'r':\n            filterindex=1 #- DECAM_R\n        elif camera[0].lower() == 'z':\n            filterindex=2 #- DECAM_Z\n        else:\n            log.warning(\"Camera not in b, r, or z channels...\")\n        magnitudes=np.zeros(frame.nspec)\n        for obj in range(frame.nspec):\n            magnitudes[obj]=frame.fibermap['MAG'][obj][filterindex]\n        #- Calculate integrals for all fibers\n        integrals=np.zeros(flux.shape[0])\n        #log.info(len(integrals))\n        for ii in range(len(integrals)):\n            integrals[ii]=qalib.integrate_spec(wave,flux[ii])\n        #- RS: Convert integrated counts to magnitudes using flux calibration constant (to be updated!!)\n        fibermags=99.*np.ones(integrals.shape)\n        fibermags[integrals>0]=22.5-2.5*np.log10(1e-3*integrals[integrals>0]/frame.meta[\"EXPTIME\"])\n        #- Calculate delta_mag (remove sky fibers first)\n        objects=frame.fibermap['OBJTYPE']\n        skyfibers=np.where(objects==\"SKY\")[0]\n        immags_nosky=list(magnitudes)\n        fibmags_nosky=list(fibermags)\n        for skyfib in range(len(skyfibers)):\n            immags_nosky.remove(immags_nosky[skyfibers[skyfib]])\n            fibmags_nosky.remove(fibmags_nosky[skyfibers[skyfib]])\n            for skyfibindex in range(len(skyfibers)):\n                skyfibers[skyfibindex]-=1\n        delta_mag=np.array(fibmags_nosky)-np.array(immags_nosky)\n        #- average integrals over fibers of each object type and get imaging magnitudes\n        integ_avg_tgt=[]\n        mag_avg_tgt=[]\n        integ_avg_sky=[]\n\n        objtypes=sorted(list(set(objects)))\n        if \"SKY\" in objtypes: objtypes.remove(\"SKY\")\n        starfibers=None\n        for T in objtypes:\n            fibers=np.where(objects==T)[0]\n\n            objmags=magnitudes[fibers]\n            mag_avg=np.mean(objmags)\n            mag_avg_tgt.append(mag_avg)\n            integ=integrals[fibers]\n            integ_avg=np.mean(integ)\n            integ_avg_tgt.append(integ_avg)\n                \n            if T == \"STD\":\n                   starfibers=fibers\n                   int_stars=integ\n                   int_average=integ_avg\n\n        # simple, temporary magdiff calculation (to be corrected...)\n        magdiff_avg=[]\n        for i in range(len(mag_avg_tgt)):\n            mag_fib=-2.5*np.log10(1e-3*integ_avg_tgt[i]/frame.meta[\"EXPTIME\"])+22.5\n            if mag_avg_tgt[i] != np.nan:\n                magdiff=mag_fib-mag_avg_tgt[i]\n            else:\n                magdiff=np.nan\n            magdiff_avg.append(magdiff)\n            \n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            param = {\n                \"DELTAMAG_NORMAL_RANGE\":[-0.5, 0.5],\n                \"DELTAMAG_WARN_RANGE\":[-1.0, 1.0]\n                }\n\n        retval[\"PARAMS\"] = param\n\n        fib_mag=np.zeros(frame.nspec) #- placeholder, calculate and replace this for all fibers\n\n\n        retval[\"METRICS\"]={\"RA\":ra,\"DEC\":dec, \"FIBER_MAG\":fibermags, \"DELTAMAG\":np.nan_to_num(delta_mag), \"STD_FIBERID\":starfibers, \"DELTAMAG_TGT\":np.nan_to_num(magdiff_avg)}\n\n        if qlf:\n            qlf_post(retval) \n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            plot.plot_integral(retval,qafig)\n            \n            log.debug(\"Output QA fig {}\".format(qafig))\n\n        return retval    \n\n    def get_default_config(self):\n        return {}\n \nclass Calculate_SNR(MonitoringAlg):\n    def __init__(self,name,config,logger=None):\n        if name is None or name.strip() == \"\":\n            name=\"SNR\"\n        kwargs=config['kwargs']\n        parms=kwargs['param']\n        key=kwargs['refKey'] if 'refKey' in kwargs else \"ELG_FIDSNR\"\n        status=kwargs['statKey'] if 'statKey' in kwargs else \"FIDSNR_STATUS\"\n        kwargs[\"RESULTKEY\"]=key\n        kwargs[\"QASTATUSKEY\"]=status\n        if \"ReferenceMetrics\" in kwargs:\n            r=kwargs[\"ReferenceMetrics\"]\n            if key in r:\n                kwargs[\"REFERENCE\"]=r[key]\n\n        if \"FIDSNR_WARN_RANGE\" in parms and \"FIDSNR_NORMAL_RANGE\" in parms:\n            kwargs[\"RANGES\"]=[(np.asarray(parms[\"FIDSNR_WARN_RANGE\"]),QASeverity.WARNING),\n                              (np.asarray(parms[\"FIDSNR_NORMAL_RANGE\"]),QASeverity.NORMAL)]# sorted by most severe to least severe\n        MonitoringAlg.__init__(self,name,fr,config,logger)\n    def run(self,*args,**kwargs):\n        if len(args) == 0 :\n            raise qlexceptions.ParameterException(\"Missing input parameter\")\n        if not self.is_compatible(type(args[0])):\n            raise qlexceptions.ParameterException(\"Incompatible input. Was expecting {} got {}\".format(type(self.__inpType__),type(args[0])))\n\n        if kwargs[\"singleqa\"] == 'Calculate_SNR':\n            night = kwargs['night']\n            expid = '{:08d}'.format(kwargs['expid'])\n            camera = kwargs['camera']\n            frame = get_frame('sframe',night,expid,camera,kwargs[\"specdir\"])\n        else:\n            frame=args[0]\n\n        fibermap=kwargs['FiberMap']\n\n        if \"ReferenceMetrics\" in kwargs: refmetrics=kwargs[\"ReferenceMetrics\"]\n        else: refmetrics=None\n\n        amps=False\n        if \"amps\" in kwargs:\n            amps=kwargs[\"amps\"]\n\n        if \"param\" in kwargs: param=kwargs[\"param\"]\n        else: param=None\n\n        paname=None\n        if \"paname\" in kwargs:\n            paname=kwargs[\"paname\"]\n\n        if \"qlf\" in kwargs:\n             qlf=kwargs[\"qlf\"]\n        else: qlf=False\n\n        if \"qafile\" in kwargs: qafile = kwargs[\"qafile\"]\n        else: qafile = None\n\n        if \"qafig\" in kwargs: qafig=kwargs[\"qafig\"]\n        else: qafig = None\n\n        return self.run_qa(fibermap,frame,paname=paname,qafile=qafile,qafig=qafig,param=param,qlf=qlf,refmetrics=refmetrics)\n\n    def run_qa(self,fibermap,frame,paname=None,amps=False,qafile=None,qafig=None,qlf=False,param=None,refmetrics=None):\n\n        if isinstance(frame,QFrame):\n            frame = frame.asframe()\n\n        #- return values\n        retval={}\n        retval[\"PANAME\"] = paname\n        retval[\"QATIME\"] = datetime.datetime.now().isoformat()\n        retval[\"EXPID\"] = expid = '{0:08d}'.format(frame.meta[\"EXPID\"])\n        retval[\"CAMERA\"] = camera = frame.meta[\"CAMERA\"]\n        retval[\"PROGRAM\"] = frame.meta[\"PROGRAM\"]\n        retval[\"FLAVOR\"] = frame.meta[\"FLAVOR\"]\n        retval[\"NIGHT\"] = night = frame.meta[\"NIGHT\"]\n        kwargs=self.config['kwargs']\n\n        ra = fibermap[\"RA_TARGET\"]\n        dec = fibermap[\"DEC_TARGET\"]\n        objlist = sorted(set(fibermap[\"OBJTYPE\"]))\n\n        if 'SKY' in objlist:\n            objlist.remove('SKY')\n\n        #- select band for mag, using DECAM_R if present\n        if param is None:\n            log.debug(\"Param is None. Using default param instead\")\n            param = {\n                \"SNR_FLUXTHRESH\":0.0, # Minimum value of flux to go into SNR calc.\n                \"FIDSNR_NORMAL_RANGE\":[6.5, 7.5],\n                \"FIDSNR_WARN_RANGE\":[6.0, 8.0],\n                \"FIDMAG\":22.\n                }\n\n        fidboundary=None\n\n        qadict,badfibs,fitsnr = qalib.SNRFit(frame,night,camera,expid,objlist,param,fidboundary=fidboundary)\n\n        #- Check for inf and nans in missing magnitudes for json support of QLF #TODO review this later\n\n        for obj in range(len(qadict[\"SNR_MAG_TGT\"])):\n            for mag in [qadict[\"SNR_MAG_TGT\"][obj]]:\n                k=np.where(~np.isfinite(mag))[0]\n                if len(k) > 0:\n                    log.warning(\"{} objects have no or unphysical magnitudes\".format(len(k)))\n            mag=np.array(mag)\n            mag[k]=26.  #- Putting 26, so as to make sure within reasonable range for plots.\n        retval[\"METRICS\"] = qadict\n        retval[\"PARAMS\"] = param\n\n        #- http post if valid\n        if qlf:\n            qlf_post(retval)            \n\n        if qafile is not None:\n            outfile = qa.write_qa_ql(qafile,retval)\n            log.debug(\"Output QA data is in {}\".format(outfile))\n        if qafig is not None:\n            rescut=param[\"RESIDUAL_CUT\"]\n            sigmacut=param[\"SIGMA_CUT\"]\n            plot.plot_SNR(retval,qafig,objlist,badfibs,fitsnr,rescut,sigmacut)\n            log.debug(\"Output QA fig {}\".format(qafig))\n\n        return retval\n\n    def get_default_config(self):\n        return {}\n\n\n","sub_path":"py/desispec/qa/qa_quicklook.py","file_name":"qa_quicklook.py","file_ext":"py","file_size_in_byte":80744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"84700990","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.sparse import diags, identity\nfrom scipy.sparse.linalg import spsolve\n# Local imports\nfrom .utils import grid_from_map, oversample_grid\nfrom .convolution import get_c_matrix, WebbKer, NyquistKer\n\n\ndef finite_diff(x):\n    \"\"\"\n    Returns the finite difference matrix operator based on x.\n    Input:\n        x: array-like\n    Output:\n        sparse matrix. When apply to x `diff_matrix.dot(x)`,\n        the result is the same as np.diff(x)\n    \"\"\"\n    n_x = len(x)\n\n    # Build matrix\n    diff_matrix = diags([-1.], shape=(n_x-1, n_x))\n    diff_matrix += diags([1.], 1, shape=(n_x-1, n_x))\n\n    return diff_matrix\n\n\ndef finite_second_d(grid):\n    \"\"\"\n    Returns the second derivative operator based on grid\n    Inputs:\n    -------\n    grid: 1d array-like\n        grid where the second derivative will be compute.\n    Ouputs:\n    -------\n    second_d: matrix\n        Operator to compute the second derivative, so that\n        f\" = second_d.dot(f), where f is a function\n        projected on `grid`.\n    \"\"\"\n    # Finite difference operator\n    d_matrix = finite_diff(grid)\n\n    # Delta lambda\n    d_grid = d_matrix.dot(grid)\n\n    # First derivative operator\n    first_d = diags(1./d_grid).dot(d_matrix)\n\n    # Second derivative operator\n    second_d = finite_diff(grid[:-1]).dot(first_d)\n    # don't forget the delta labda\n    second_d = diags(1./d_grid[:-1]).dot(second_d)\n\n    return second_d\n\n\ndef finite_first_d(grid):\n    \"\"\"\n    Returns the first derivative operator based on grid\n    Inputs:\n    -------\n    grid: 1d array-like, optional\n        grid where the first derivative will be compute.\n    Ouputs:\n    -------\n    first_d: matrix\n        Operator to compute the second derivative, so that\n        f' = first_d.dot(f), where f is a function\n        projected on `grid`.\n    \"\"\"\n    # Finite difference operator\n    d_matrix = finite_diff(grid)\n\n    # Delta lambda\n    d_grid = d_matrix.dot(grid)\n\n    # First derivative operator\n    first_d = diags(1./d_grid).dot(d_matrix)\n\n    return first_d\n\n\ndef finite_zeroth_d(grid):\n    \"\"\"\n    Gives the zeroth derivative operator on the function\n    f(grid), so simply returns the identity matrix... XD\n    \"\"\"\n    return identity(len(grid))\n\n\ndef get_nyquist_matrix(grid, integrate=True, n_sampling=2,\n                       thresh=1e-5, **kwargs):\n    \"\"\"\n    Get the tikhonov regularisation matrix based on\n    a Nyquist convolution matrix (convolution with\n    a kernel with a resolution given by the sampling\n    of a grid). The Tikhonov matrix will be given by\n    the difference of the nominal solution and\n    the convolved solution.\n\n    Parameters\n    ----------\n    grid: 1d-array\n        Grid to project the kernel\n    integrate: bool, optional\n        If True, add integration weights to the tikhonov matrix, so\n        when the squared norm is computed, the result is equivalent\n        to the integral of the integrand squared.\n    n_sampling: int, optional\n        sampling of the grid. Default is 2, so we assume that\n        the grid is Nyquist sampled.\n    thresh: float, optional\n        Used to define the maximum length of the kernel.\n        Truncate when `kernel` < `thresh`\n    kwargs:\n        `interp1d` kwargs used to get FWHM as a function of the grid.\n    \"\"\"\n\n    # Get nyquist kernel function\n    ker = NyquistKer(grid, n_sampling=n_sampling, **kwargs)\n\n    # Build convolution matrix\n    conv_matrix = get_c_matrix(ker, grid, thresh=thresh)\n\n    # Build tikhonov matrix\n    t_mat = conv_matrix - identity(conv_matrix.shape[0])\n\n    if integrate:\n        # The grid may not be evenly spaced, so\n        # add an integration weight\n        d_grid = np.diff(grid)\n        d_grid = np.concatenate([d_grid, [d_grid[-1]]])\n        t_mat = diags(np.sqrt(d_grid)).dot(t_mat)\n\n    return t_mat\n\n\nclass TikhoConvMatrix:\n    \"\"\"\n    Convolution matrix to be used as\n    Tikhonov regularisation matrix.\n    This way, the solution of the system can be\n    deviated towards solutions closed to a solution\n    at a resolution close to `n_os` times the resolution\n    given by the `wv_map`.\n    \"\"\"\n    def __init__(self, wv_map, psf, n_os=2, thresh=1e-5):\n        \"\"\"\n        Parameters\n        ----------\n        wv_map : (N, M) 2-D arrays\n            array of the central wavelength position each pixels\n            on the detector. It has to have the same (N, M) as the detector.\n        psf : (N, M) 2-D array\n            array of the spatial profile on the detector.\n            It has to have the same (N, M) as the detector.\n        n_os: int, optional\n            Dictates the resolution of the convolution kernel.\n            The resolution will be `n_os` times the resolution\n            of order 2. Default is 2 times (n_os=2).\n        thresh: float, optional\n            Used to define the maximum length of the kernel.\n            Truncate when kernel < `thresh`. Default is 1e-5\n        \"\"\"\n        # Save attributes\n        self.wv_map = wv_map\n        self.psf = psf\n        self.n_os = n_os\n        self.thresh = thresh\n\n    def __call__(self, grid):\n        \"\"\"\n        Return the tikhonov regularisation matrix given\n        a grid (to project the convolution kernels).\n        grid is a 1d array.\n        Returns a sparse matrix.\n        \"\"\"\n\n        # Get needed attributes\n        gargs = ['wv_map', 'psf', 'n_os', 'thresh']\n        wv_map, psf, n_os, thresh = self.getattrs(*gargs)\n\n        # Generate a fake wv_map to cover all wv_range with a\n        # resolution `t_mat_n_os` times the resolution\n        # of wv_map (generally order 2).\n        wv_range = [grid.min(), grid.max()]\n        wv_map = grid_from_map(wv_map, psf, wv_range=wv_range, n_os=n_os)\n        # Build convolution matrix\n        conv_ord2 = get_c_matrix(WebbKer(wv_map[None, :]),\n                                 grid, thresh=thresh)\n        # Build tikhonov matrix\n        t_mat = conv_ord2 - identity(conv_ord2.shape[0])\n\n        # The grid may not be evenly spaced, so\n        # add an integration weight\n        d_grid = np.diff(grid)\n        d_grid = np.concatenate([d_grid, [d_grid[-1]]])\n        t_mat = diags(np.sqrt(d_grid)).dot(t_mat)\n\n        return t_mat\n\n    def getattrs(self, *args):\n        \"\"\"\n        Return list of attributes\n\n        Parameters\n        ----------\n        args: str\n            All attributes to return.\n        \"\"\"\n        return [getattr(self, arg) for arg in args]\n\n\ndef tikho_solve(a_mat, b_vec, t_mat=None, grid=None,\n                verbose=True, factor=1.0, estimate=None, index=None):\n    \"\"\"\n    Tikhonov solver to use as a function instead of a class.\n\n    Parameters\n    ----------\n    a_mat: matrix-like object (2d)\n        matrix A in the system to solve A.x = b\n    b_vec: vector-like object (1d)\n        vector b in the system to solve A.x = b\n    t_mat: matrix-like object (2d), optional\n        Tikhonov regularisation matrix to be applied on b_vec.\n        Default is the default of the Tikhonov class. (Identity matrix)\n    grid: array-like 1d, optional\n        grid on which b-vec is projected. Used to compute derivative\n    verbose: bool\n        Print details or not\n    factor: float, optional\n        multiplicative constant of the regularisation matrix\n    estimate: vector-like object (1d)\n        Estimate oof the solution of the system.\n    index: indexable, optional\n        index of the valid row of the b_vec.\n\n    Output\n    ------\n    Solution of the system (1d array)\n    \"\"\"\n    tikho = Tikhonov(a_mat, b_vec, t_mat=t_mat,\n                     grid=grid, verbose=verbose, index=index)\n\n    return tikho.solve(factor=factor, estimate=estimate)\n\n\nclass Tikhonov:\n    \"\"\"\n    Tikhonov regularisation to solve the ill-condition problem:\n    A.x = b, where A is accidently singular or close to singularity.\n    Tikhonov regularisation adds a regularisation term in\n    the equation and aim to minimize the equation:\n    ||A.x - b||^2 + ||gamma.x||^2\n    Where gamma is the Tikhonov regularisation matrix.\n    \"\"\"\n    default_mat = {'zeroth': finite_zeroth_d,\n                   'first': finite_first_d,\n                   'second': finite_second_d}\n\n    def __init__(self, a_mat, b_vec, t_mat=None,\n                 grid=None, verbose=True, index=None):\n        \"\"\"\n        Parameters\n        ----------\n        a_mat: matrix-like object (2d)\n            matrix A in the system to solve A.x = b\n        b_vec: vector-like object (1d)\n            vector b in the system to solve A.x = b\n        t_mat: matrix-like object (2d), optional\n            Tikhonov regularisation matrix to be applied on b_vec.\n            Default is the default of the Tikhonov class. (Identity matrix)\n        grid: array-like 1d, optional\n            grid on which b-vec is projected. Used to compute derivative\n        verbose: bool\n            Print details or not\n        index: indexable, optional\n            index of the valid row of the b_vec.\n        \"\"\"\n        # Take the identity matrix as default (zeroth derivative)\n        if t_mat is None:\n            t_mat = 'zeroth'\n\n        # b_vec will be passed to default_mat functions\n        # if grid not given.\n        if grid is None and t_mat is 'zeroth':\n            grid = b_vec\n\n        # If string, search in the default Tikhonov matrix\n        if isinstance(t_mat, str):\n            self.type = t_mat\n            t_mat = self.default_mat[t_mat](grid)\n        elif callable(t_mat):\n            t_mat = t_mat(grid)\n            self.type = 'custom'\n        else:\n            self.type = 'custom'\n\n        # Take all indices by default\n        if index is None:\n            index = slice(None)\n\n        self.a_mat = a_mat[index, :][:, index]\n        self.b_vec = b_vec[index]\n        self.t_mat = t_mat[index, :][:, index]\n        self.index = index\n        self.verbose = verbose\n\n    def solve(self, factor=1.0, estimate=None):\n        \"\"\"\n        Minimize the equation ||A.x - b||^2 + ||gamma.x||^2\n        by solving (A_T.A + gamma_T.gamma).x = A_T.b\n        gamma is the Tikhonov matrix multiplied by a scale factor\n\n        Parameters\n        ----------\n        factor: float, optional\n            multiplicative constant of the regularisation matrix\n        estimate: vector-like object (1d)\n            Estimate oof the solution of the system.\n\n        Output\n        ------\n        Solution of the system (1d array)\n        \"\"\"\n        # Get needed attributes\n        a_mat = self.a_mat\n        b_vec = self.b_vec\n        t_mat = self.t_mat\n        index = self.index\n\n        # Matrix gamma (with scale factor)\n        gamma = factor * self.t_mat\n\n        # Build system\n        gamma_2 = (gamma.T).dot(gamma)  # Gamma square\n        matrix = a_mat.T.dot(a_mat) + gamma_2\n        result = (a_mat.T).dot(b_vec.T)\n        # Include solution estimate if given\n        if estimate is not None:\n            result += gamma_2.dot(estimate[index].T)\n\n        # Solve\n        return spsolve(matrix, result)\n\n    def test_factors(self, factors, estimate=None):\n        \"\"\"\n        test multiple factors\n\n        Parameters\n        ----------\n        factors: 1d array-like\n            factors to test\n        estimate: array like\n            estimate of the solution\n\n        Output\n        ------\n        dictionnary of test results\n        \"\"\"\n\n        self.v_print('Testing factors...')\n\n        # Get relevant attributes\n        b_vec = self.b_vec\n        a_mat = self.a_mat\n        t_mat = self.t_mat\n\n        # Init outputs\n        sln, err, reg = [], [], []\n        # Test all factors\n        for i_fac, factor in enumerate(factors):\n            # Save solution\n            sln.append(self.solve(factor, estimate))\n            # Save error A.x - b\n            err.append(a_mat.dot(sln[-1]) - b_vec)\n            # Save regulatisation term\n            reg.append(t_mat.dot(sln[-1]))\n            # Print\n            message = '{}/{}'.format(i_fac, len(factors))\n            self.v_print(message, end='\\r')\n        # Final print\n        self.v_print('{}/{}'.format(i_fac + 1, i_fac + 1))\n        # Convert to arrays\n        sln = np.array(sln)\n        err = np.array(err)\n        reg = np.array(reg)\n\n        # Save in a dictionnary\n        self.test = {'factors': factors,\n                     'solution': sln,\n                     'error': err,\n                     'reg': reg}\n\n        return self.test\n\n    def _check_plot_inputs(self, fig, ax, label, factors, test):\n        \"\"\"\n        Method to manage inputs for plots methods.\n        \"\"\"\n        # Use ax or fig if given. Else, init the figure\n        if (fig is None) and (ax is None):\n            fig, ax = plt.subplots(1, 1, sharex=True)\n        elif ax is None:\n            ax = fig.subplots(1, 1, sharex=True)\n\n        # Use the type of regularisation as label if None is given\n        if label is None:\n            label = self.type\n\n        if test is None:\n            # Run tests with `factors` if not done already\n            try:\n                self.test\n            except AttributeError:\n                self.test_factors(factors)\n            finally:\n                test = self.test\n\n        return fig, ax, label, test\n\n    def error_plot(self, fig=None, ax=None, factors=None,\n                   label=None, test=None, test_key=None, y_val=None):\n        \"\"\"\n        Plot error as a function of factors\n\n        Parameters\n        ----------\n        fig: matplotlib figure, optional\n            Figure to use for plot\n            If not given and ax is None, new figure is initiated\n        ax: matplotlib axis, optional\n            axis to use for plot. If not given, a new axis is initiated.\n        factors: 1d array-like\n            factors to test\n        label: str, optional\n            label too put in legend\n        test: dictionnary, optional\n            dictionnary of tests (output of Tikhonov.test_factors)\n        test_key: str, optional\n            which test result to plot. If not specified,\n            the euclidian norm of the 'error' key will be used.\n        y_val: array-like, optional\n            y values to plot. Same length as factors.\n        Output\n        ------\n        fig, ax\n        \"\"\"\n\n        # Manage method's inputs\n        args = (fig, ax, label, factors, test)\n        fig, ax, label, test = self._check_plot_inputs(*args)\n\n        # What y value do we plot?\n        if y_val is None:\n            # Use tests to plot y_val\n            if test_key is None:\n                # Default is euclidian norm of error.\n                # Similar to the chi^2.\n                y_val = (test['error']**2).sum(axis=-1)\n            else:\n                y_val = test[test_key]\n\n        # Plot\n        ax.loglog(test['factors'], y_val, label=label)\n\n        # Mark minimum value\n        i_min = np.argmin(y_val)\n        min_coord = test['factors'][i_min], y_val[i_min]\n        ax.scatter(*min_coord, marker=\"x\")\n        text = '{:2.1e}'.format(min_coord[0])\n        ax.text(*min_coord, text, va=\"top\", ha=\"center\")\n\n        # Show legend\n        ax.legend()\n\n        # Labels\n        ax.set_xlabel(\"Scale factor\")\n        ylabel = r'System error '\n        ylabel += r'$\\left(||\\mathbf{Ax-b}||^2_2\\right)$'\n        ax.set_ylabel(ylabel)\n\n        return fig, ax\n\n    def l_plot(self, fig=None, ax=None, factors=None, label=None,\n               test=None, text_label=True, factor_norm=False):\n        \"\"\"\n        make an 'l plot'\n\n        Parameters\n        ----------\n        fig: matplotlib figure, optional\n            Figure to use for plot\n            If not given and ax is None, new figure is initiated\n        ax: matplotlib axis, optional\n            axis to use for plot. If not given, a new axis is initiated.\n        factors: 1d array-like\n            factors to test\n        label: str, optional\n            label too put in legend\n        test: dictionnary, optional\n            dictionnary of tests (output of Tikhonov.test_factors)\n        text_label: bool, optional\n            Add label of the factor value to each points in the plot.\n        Output\n        ------\n        fig, ax\n        \"\"\"\n        # Manage method's inputs\n        args = (fig, ax, label, factors, test)\n        fig, ax, label, test = self._check_plot_inputs(*args)\n\n        # Compute euclidian norm of error (||A.x - b||).\n        # Similar to the chi^2.\n        err_norm = (test['error']**2).sum(axis=-1)\n\n        # Compute norm of regularisation term\n        reg_norm = (test['reg']**2).sum(axis=-1)\n\n        # Factors\n        if factor_norm:\n            reg_norm *= test['factors']**2\n\n        # Plot\n        ax.loglog(err_norm, reg_norm, '.:', label=label)\n\n        # Add factor values as text\n        if text_label:\n            for f, x, y in zip(test['factors'], err_norm, reg_norm):\n                plt.text(x, y, \"{:2.1e}\".format(f), va=\"center\", ha=\"right\")\n\n        # Legend\n        ax.legend()\n\n        # Labels\n        xlabel = r'$\\left(||\\mathbf{Ax-b}||^2_2\\right)$'\n        ylabel = r'$\\left(||\\mathbf{\\Gamma.x}||^2_2\\right)$'\n        ax.set_xlabel(xlabel)\n        ax.set_ylabel(ylabel)\n\n        return fig, ax\n\n    def v_print(self, *args, **kwargs):\n        \"\"\" Print if verbose \"\"\"\n        if self.verbose:\n            print(*args, **kwargs)\n","sub_path":"SOSS/extract/regularisation.py","file_name":"regularisation.py","file_ext":"py","file_size_in_byte":17187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"368619170","text":"#!/usr/bin/env python\n\n# (C) Copyright 2017 Hewlett Packard Enterprise Development LP\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n#\n\nfrom __future__ import print_function\n\nimport os\nimport re\nimport signal\nimport subprocess\nimport sys\nimport time\nimport yaml\n\nTAG_REGEX = re.compile(r'^!(\\w+)(?:\\s+([\\w-]+))?$')\n\nMETRIC_PIPELINE_MARKER = 'metrics'\nLOG_PIPELINE_MARKER = 'logs'\n\nMETRIC_PIPELINE_MODULE_TO_COMPOSE_SERVICES = {\n    'monasca-agent-forwarder': 'agent-forwarder',\n    'zookeeper': 'zookeeper',\n    'influxdb': 'influxdb',\n    'kafka': 'kafka',\n    'kafka-init': 'kafka-init',\n    'monasca-thresh': 'thresh',\n    'monasca-persister-python': 'monasca-persister',\n    'mysql-init': 'mysql-init',\n    'monasca-api-python': 'monasca',\n    'influxdb-init': 'influxdb-init',\n    'monasca-agent-collector': 'agent-collector',\n    'grafana': 'grafana',\n    'keystone': 'keystone',\n    'monasca-alarms': 'alarms',\n    'monasca-notification': 'monasca-notification',\n    'grafana-init': 'grafana-init'\n}\nLOGS_PIPELINE_MODULE_TO_COMPOSE_SERVICES = {\n    'monasca-log-metrics': 'log-metrics',\n    'monasca-log-persister': 'log-persister',\n    'monasca-log-transformer': 'log-transformer',\n    'elasticsearch-init': 'elasticsearch-init',\n    'kafka-init': 'kafka-log-init',\n    'kibana': 'kibana',\n    'monasca-log-api': 'log-api',\n}\n\nMETRIC_PIPELINE_INIT_JOBS = ('influxdb-init', 'kafka-init', 'mysql-init')\nLOG_PIPELINE_INIT_JOBS = ('elasticsearch-init', 'kafka-log-init')\nINIT_JOBS = {\n    METRIC_PIPELINE_MARKER: METRIC_PIPELINE_INIT_JOBS,\n    LOG_PIPELINE_MARKER: LOG_PIPELINE_INIT_JOBS\n}\n\nMETRIC_PIPELINE_SERVICES = METRIC_PIPELINE_MODULE_TO_COMPOSE_SERVICES.values()\n\"\"\"Explicit list of services for docker compose \nto launch for metrics pipeline\"\"\"\nLOG_PIPELINE_SERVICES = (['kafka', 'keystone'] +\n                         LOGS_PIPELINE_MODULE_TO_COMPOSE_SERVICES.values())\n\"\"\"Explicit list of services for docker compose \nto launch for logs pipeline\"\"\"\n\nPIPELINE_TO_YAML_COMPOSE = {\n    METRIC_PIPELINE_MARKER: 'docker-compose.yml',\n    LOG_PIPELINE_MARKER: 'log-pipeline.yml'\n}\n\nCI_COMPOSE_FILE = 'ci-compose.yml'\n\n\nclass SubprocessException(Exception):\n    pass\n\n\nclass FileReadException(Exception):\n    pass\n\n\nclass FileWriteException(Exception):\n    pass\n\n\nclass InitJobFailedException(Exception):\n    pass\n\n\nclass TempestTestFailedException(Exception):\n    pass\n\n\nclass SmokeTestFailedException(Exception):\n    pass\n\n\ndef get_changed_files():\n    commit_range = os.environ.get('TRAVIS_COMMIT_RANGE', None)\n    if not commit_range:\n        return []\n\n    p = subprocess.Popen([\n        'git', 'diff', '--name-only', commit_range\n    ], stdout=subprocess.PIPE)\n\n    stdout, _ = p.communicate()\n    if p.returncode != 0:\n        raise SubprocessException('git returned non-zero exit code')\n\n    return [line.strip() for line in stdout.splitlines()]\n\n\ndef get_message_tags():\n    commit = os.environ.get('TRAVIS_COMMIT_RANGE', None)\n    if not commit:\n        return []\n\n    p = subprocess.Popen([\n        'git', 'log', '--pretty=%B', '-1', commit\n    ], stdout=subprocess.PIPE)\n    stdout, _ = p.communicate()\n    if p.returncode != 0:\n        raise SubprocessException('git returned non-zero exit code')\n\n    tags = []\n    for line in stdout.splitlines():\n        line = line.strip()\n        m = TAG_REGEX.match(line)\n        if m:\n            tags.append(m.groups())\n\n    return tags\n\n\ndef get_dirty_modules(dirty_files):\n    dirty = set()\n    for f in dirty_files:\n        if os.path.sep in f:\n            mod, _ = f.split(os.path.sep, 1)\n\n            if not os.path.exists(os.path.join(mod, 'Dockerfile')):\n                continue\n\n            if not os.path.exists(os.path.join(mod, 'build.yml')):\n                continue\n\n            dirty.add(mod)\n\n    return list(dirty)\n\n\ndef get_dirty_for_module(files, module=None):\n    ret = []\n    for f in files:\n        if os.path.sep in f:\n            mod, rel_path = f.split(os.path.sep, 1)\n            if mod == module:\n                ret.append(rel_path)\n        else:\n            # top-level file, no module\n            if module is None:\n                ret.append(f)\n\n    return ret\n\n\ndef run_build(modules):\n    build_args = ['dbuild', '-sd', 'build', 'all', '+', ':ci-cd'] + modules\n    print('build command:', build_args)\n\n    p = subprocess.Popen(build_args, stdin=subprocess.PIPE)\n\n    def kill(signal, frame):\n        p.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n    if p.wait() != 0:\n        print('build failed, exiting!')\n        sys.exit(p.returncode)\n\n\ndef run_push(modules):\n    if os.environ.get('TRAVIS_SECURE_ENV_VARS', None) != \"true\":\n        print('No push permissions in this context, skipping!')\n        print('Not pushing: %r' % modules)\n        return\n\n    username = os.environ.get('DOCKER_HUB_USERNAME', None)\n    password = os.environ.get('DOCKER_HUB_PASSWORD', None)\n    if username and password:\n        print('Logging into docker registry...')\n        r = subprocess.call([\n            'docker', 'login',\n            '-u', username,\n            '-p', password\n        ])\n        if r != 0:\n            print('Docker registry login failed, cannot push!')\n            sys.exit(1)\n\n    push_args = ['dbuild', '-sd', 'build', 'push', 'all'] + modules\n    print('push command:', push_args)\n\n    p = subprocess.Popen(push_args, stdin=subprocess.PIPE)\n\n    def kill(signal, frame):\n        p.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n    if p.wait() != 0:\n        print('build failed, exiting!')\n        sys.exit(p.returncode)\n\n\ndef run_readme(modules):\n    if os.environ.get('TRAVIS_SECURE_ENV_VARS', None) != \"true\":\n        print('No Docker Hub permissions in this context, skipping!')\n        print('Not updating READMEs: %r' % modules)\n        return\n\n    readme_args = ['dbuild', '-sd', 'readme'] + modules\n    print('readme command:', readme_args)\n\n    p = subprocess.Popen(readme_args, stdin=subprocess.PIPE)\n\n    def kill(signal, frame):\n        p.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n    if p.wait() != 0:\n        print('build failed, exiting!')\n        sys.exit(p.returncode)\n\n\ndef update_docker_compose(modules, pipeline):\n\n    compose_dict = load_yml(PIPELINE_TO_YAML_COMPOSE['metrics'])\n    services_to_changes = METRIC_PIPELINE_MODULE_TO_COMPOSE_SERVICES.copy()\n\n    if pipeline == 'logs':\n        print('\\'logs\\' pipeline is enabled, including in CI run')\n        log_compose = load_yml(PIPELINE_TO_YAML_COMPOSE['logs'])\n        compose_dict['services'].update(log_compose['services'])\n        services_to_changes.update(\n            LOGS_PIPELINE_MODULE_TO_COMPOSE_SERVICES.copy()\n        )\n\n    if modules:\n        compose_services = compose_dict['services']\n        for module in modules:\n            # Not all modules are included in docker compose\n            if module not in services_to_changes:\n                continue\n            service_name = services_to_changes[module]\n            services_to_update = service_name.split(',')\n            for service in services_to_update:\n                image = compose_services[service]['image']\n                image = image.split(':')[0]\n                image += \":ci-cd\"\n                compose_services[service]['image'] = image\n\n    # Update compose version\n    compose_dict['version'] = '2'\n\n    try:\n        with open(CI_COMPOSE_FILE, 'w') as docker_compose:\n            yaml.dump(compose_dict, docker_compose, default_flow_style=False)\n    except:\n        raise FileWriteException(\n            'Error writing CI dictionary to %s' % CI_COMPOSE_FILE\n        )\n\n\ndef load_yml(yml_path):\n    try:\n        with open(yml_path) as compose_file:\n            compose_dict = yaml.safe_load(compose_file)\n            return compose_dict\n    except:\n        raise FileReadException('Failed to read %s', yml_path)\n\n\ndef handle_pull_request(files, modules, tags, pipeline):\n    modules_to_build = modules[:]\n\n    for tag, arg in tags:\n        if tag in ('build', 'push'):\n            if arg is None:\n                # arg-less doesn't make sense for PRs since any changes to a\n                # module already result in a rebuild\n                continue\n\n            modules_to_build.append(arg)\n\n    if modules:\n        run_build(modules_to_build)\n    else:\n        print('No modules to build.')\n\n    update_docker_compose(modules, pipeline)\n    run_docker_compose(pipeline)\n    wait_for_init_jobs(pipeline)\n\n    cool_test_mapper = {\n        'smoke': {\n            METRIC_PIPELINE_MARKER: run_smoke_tests_metrics,\n            LOG_PIPELINE_MARKER: lambda : print('No smoke tests for logs')\n        },\n        'tempest': {\n            METRIC_PIPELINE_MARKER: run_tempest_tests_metrics,\n            LOG_PIPELINE_MARKER: lambda : print('No tempest tests for logs')\n        }\n    }\n\n    cool_test_mapper['smoke'][pipeline]()\n    cool_test_mapper['tempest'][pipeline]()\n\n\ndef get_current_init_status(docker_id):\n    init_status = ['docker', 'inspect', '-f', '{{ .State.ExitCode }}:{{ .State.Status }}', docker_id]\n    p = subprocess.Popen(init_status, stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n\n    def kill(signal, frame):\n        p.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n\n    output, err = p.communicate()\n\n    if p.wait() != 0:\n        print('getting current status failed')\n        return False\n    status_output = output.rstrip()\n\n    exit_code, status = status_output.split(\":\", 1)\n    return exit_code == \"0\" and status == \"exited\"\n\n\ndef output_docker_logs():\n    docker_logs = ['docker-compose', 'logs']\n\n    docker_logs_process = subprocess.Popen(docker_logs, stdin=subprocess.PIPE)\n\n    def kill(signal, frame):\n        docker_logs_process.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n    if docker_logs_process.wait() != 0:\n        print('Error listing logs')\n\n\ndef output_docker_ps():\n    docker_ps = ['docker', 'ps', '-a']\n\n    docker_ps_process = subprocess.Popen(docker_ps, stdin=subprocess.PIPE)\n\n    def kill(signal, frame):\n        docker_ps_process.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n    if docker_ps_process.wait() != 0:\n        print('Error running docker ps')\n\n\ndef output_compose_details(pipeline):\n    print('Running docker-compose -f ', CI_COMPOSE_FILE)\n    if pipeline == 'metrics':\n        services = METRIC_PIPELINE_SERVICES\n    else:\n        services = LOG_PIPELINE_SERVICES\n    print('All services that are about to start: ', services)\n\n\ndef get_docker_id(init_job):\n    docker_id = ['docker-compose',\n                 '-f', CI_COMPOSE_FILE,\n                 'ps',\n                 '-q', init_job]\n\n    p = subprocess.Popen(docker_id, stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n\n    def kill(signal, frame):\n        p.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n\n    output, err = p.communicate()\n\n    if p.wait() != 0:\n        print('error getting docker id')\n        return \"\"\n    return output.rstrip()\n\n\ndef wait_for_init_jobs(pipeline):\n    init_status_dict = {job: False for job in INIT_JOBS[pipeline]}\n    docker_id_dict = {job: \"\" for job in INIT_JOBS[pipeline]}\n\n    amount_succeeded = 0\n    for attempt in range(40):\n        time.sleep(30)\n        amount_succeeded = 0\n        for init_job, status in init_status_dict.items():\n            if docker_id_dict[init_job] == \"\":\n                docker_id_dict[init_job] = get_docker_id(init_job)\n            if status:\n                amount_succeeded += 1\n            else:\n                updated_status = get_current_init_status(docker_id_dict[init_job])\n                init_status_dict[init_job] = updated_status\n                if updated_status:\n                    amount_succeeded += 1\n        if amount_succeeded == len(docker_id_dict):\n            print(\"All init-jobs passed!\")\n            break\n        else:\n            print(\"Not all init jobs have succeeded. Attempt: \" + str(attempt + 1) + \" of 40\")\n\n    if amount_succeeded != len(docker_id_dict):\n        print(\"Init-jobs did not succeed, printing docker ps and logs\")\n        output_docker_ps()\n        output_docker_logs()\n        raise InitJobFailedException()\n\n    # Sleep in case jobs just succeeded\n    time.sleep(60)\n\n\ndef handle_push(files, modules, tags, pipeline):\n    modules_to_push = []\n    modules_to_readme = []\n\n    force_push = False\n    force_readme = False\n\n    for tag, arg in tags:\n        if tag in ('build', 'push'):\n            if arg is None:\n                force_push = True\n            else:\n                modules_to_push.append(arg)\n        elif tag == 'readme':\n            if arg is None:\n                force_readme = True\n            else:\n                modules_to_readme.append(arg)\n\n    for module in modules:\n        dirty = get_dirty_for_module(files, module)\n        if force_push or 'build.yml' in dirty:\n            modules_to_push.append(module)\n\n        if force_readme or 'README.md' in dirty:\n            modules_to_readme.append(module)\n\n    if modules_to_push:\n        run_push(modules_to_push)\n    else:\n        print('No modules to push.')\n\n    if modules_to_readme:\n        run_readme(modules_to_readme)\n    else:\n        print('No READMEs to update.')\n\n\ndef run_docker_compose(pipeline):\n    output_compose_details(pipeline)\n\n    if pipeline == 'metrics':\n        services = METRIC_PIPELINE_SERVICES\n    else:\n        services = LOG_PIPELINE_SERVICES\n\n    docker_compose_command = ['docker-compose',\n                              '-f', CI_COMPOSE_FILE,\n                              'up', '-d'] + services\n\n    p = subprocess.Popen(docker_compose_command, stdin=subprocess.PIPE)\n\n    def kill(signal, frame):\n        p.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n    if p.wait() != 0:\n        print('docker compose failed, exiting!')\n        sys.exit(p.returncode)\n\n    # print out running images for debugging purposes\n    output_docker_ps()\n\n\ndef run_smoke_tests_metrics():\n    smoke_tests_run = ['docker', 'run', '-e', 'MONASCA_URL=http://monasca:8070', '-e',\n                       'METRIC_NAME_TO_CHECK=monasca.thread_count', '--net', 'monascadocker_default', '-p',\n                       '0.0.0.0:8080:8080', 'monasca/smoke-tests:latest']\n\n    p = subprocess.Popen(smoke_tests_run, stdin=subprocess.PIPE)\n\n    def kill(signal, frame):\n        p.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n    if p.wait() != 0:\n        print('Smoke-tests failed, listing containers/logs.')\n        output_docker_logs()\n        output_docker_ps()\n        raise SmokeTestFailedException()\n\n\ndef run_tempest_tests_metrics():\n    tempest_tests_run = ['docker', 'run', '-e', 'KEYSTONE_SERVER=keystone', '-e',\n                         'KEYSTONE_PORT=5000', '--net', 'monascadocker_default',\n                         'monasca/tempest-tests:latest']\n\n    p = subprocess.Popen(tempest_tests_run, stdin=subprocess.PIPE)\n\n    def kill(signal, frame):\n        p.kill()\n        print()\n        print('killed!')\n        sys.exit(1)\n\n    signal.signal(signal.SIGINT, kill)\n    if p.wait() != 0:\n        print('Tempest-tests failed, listing containers/logs.')\n        output_docker_logs()\n        output_docker_ps()\n        raise TempestTestFailedException()\n\n\ndef handle_other(files, modules, tags):\n    print('Unsupported event type \"%s\", nothing to do.' % (\n        os.environ.get('TRAVS_EVENT_TYPE')))\n\n\ndef print_env(pipeline, voting):\n    print('Environment details:')\n    print('TRAVIS_COMMIT=', os.environ.get('TRAVIS_COMMIT'))\n    print('TRAVIS_COMMIT_RANGE=', os.environ.get('TRAVIS_COMMIT_RANGE'))\n    print('TRAVIS_PULL_REQUEST=', os.environ.get('TRAVIS_PULL_REQUEST'))\n    print('TRAVIS_PULL_REQUEST_SHA=',\n          os.environ.get('TRAVIS_PULL_REQUEST_SHA'))\n    print('TRAVIS_PULL_REQUEST_SLUG=',\n          os.environ.get('TRAVIS_PULL_REQUEST_SLUG'))\n    print('TRAVIS_SECURE_ENV_VARS=', os.environ.get('TRAVIS_SECURE_ENV_VARS'))\n    print('TRAVIS_EVENT_TYPE=', os.environ.get('TRAVIS_EVENT_TYPE'))\n    print('TRAVIS_BRANCH=', os.environ.get('TRAVIS_BRANCH'))\n    print('TRAVIS_PULL_REQUEST_BRANCH=',\n          os.environ.get('TRAVIS_PULL_REQUEST_BRANCH'))\n    print('TRAVIS_TAG=', os.environ.get('TRAVIS_TAG'))\n    print('TRAVIS_COMMIT_MESSAGE=', os.environ.get('TRAVIS_COMMIT_MESSAGE'))\n\n    print('CI_PIPELINE=', pipeline)\n    print('CI_VOTING=', voting)\n\n\ndef main():\n    args = sys.argv[1:]\n    pipeline = args[0] if len(args) >= 1 else None\n    voting = bool(args[1]) if len(args) == 2 else True\n\n    if os.environ.get('TRAVIS_BRANCH', None) != 'master':\n        print('Not master branch, skipping tests.')\n        return\n    if not pipeline or pipeline not in ('logs', 'metrics'):\n        print('Unkown pipeline=', pipeline)\n        return\n\n    print_env(pipeline, voting)\n\n    files = get_changed_files()\n    modules = get_dirty_modules(files)\n    tags = get_message_tags()\n\n    if tags:\n        print('Tags detected:')\n        for tag in tags:\n            print('  ', tag)\n    else:\n        print('No tags detected.')\n\n    func = {\n        'pull_request': handle_pull_request,\n        'push': handle_push\n    }.get(os.environ.get('TRAVIS_EVENT_TYPE', None), handle_other)\n\n    try:\n        func(files, modules, tags, pipeline)\n    except (FileReadException, FileWriteException):\n        # those error must terminate the CI\n        raise\n    except (InitJobFailedException, SmokeTestFailedException,\n            TempestTestFailedException):\n        if voting:\n            raise\n        else:\n            print('%s is not voting, skipping failure' % pipeline)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"ci.py","file_name":"ci.py","file_ext":"py","file_size_in_byte":18453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"595156330","text":"from flask import Flask\nfrom .spaces import init_s3\n\n__title__ = 'flask_ffmpeg'\n__version__ = '0.1'\n__author__ = 'Jimmy Wahlberg'\n__license__ = 'MIT'\n\napp = Flask(__name__, instance_relative_config=True)\n\n# Load the default configuration\napp.config.from_object('config.default')\n\n# Load the configuration from the instance folder\napp.config.from_pyfile('config.py')\n\n# Load the file specified by the APP_CONFIG_FILE environment variable\n# Variables defined here will override those in the default configuration\n# app.config.from_envvar('APP_CONFIG_FILE')\nfrom .views import parse\n\napp.register_blueprint(parse, url_prefix='/parse')\n\nwith app.app_context() as ctx:\n    init_s3()\n\n","sub_path":"tenpenny_ffmpeg/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"636653957","text":"from pdf_client.api import section, book\nimport json, requests, os\nfrom django.conf import settings\n\n\nconfig_file = 'dsp_index/config.json'\n\n\ndef locate_section(section_id):\n    section_detail = section.Detail(section_id).execute()\n    if section_detail['next'] is not None:\n        next_section_detail = section.Detail(section_detail['next']).execute()\n        end_page = next_section_detail['page']\n    else:\n        book_detail = book.Detail(section_detail['book']).execute()\n        end_page = book_detail['pages'] - 1\n    return section_detail['book'], section_detail['page'], end_page\n\n\ndef get_config():\n    with open(config_file, 'r') as file:\n        config = json.loads(file.read().replace('\\n', ''))\n    return config\n\n\ndef send_request(book_id, start_page=None, end_page=None):\n    config = get_config()\n    if start_page is None or end_page is None:\n        url = \"{base}book/read/{book_id}/\".format(base=config['base_url'],\n                                                  book_id=book_id)\n    else:\n        url = \"{base}book/read/{book_id}/{start}/{end}/\".format(base=config['base_url'],\n                                                                book_id=book_id,\n                                                                start=start_page,\n                                                                end=end_page)\n    response = requests.get(url, stream=True, auth=(config['auth_args'][0], config['auth_args'][1]))\n    return response\n\n\ndef download_a_section(section_id):\n    book_id, start_page, end_page = locate_section(section_id)\n    response = send_request(book_id, start_page, end_page)\n    relative_path = os.path.join(\"sections\", \"{bid}\".format(bid=book_id), \"{sid}.pdf\".format(sid=section_id))\n    full_path = os.path.join(settings.MEDIA_ROOT, relative_path)\n    os.makedirs(os.path.dirname(full_path), exist_ok=True)\n    with open(full_path, 'wb+') as file:\n        for chunk in response.iter_content(chunk_size=1024):\n            file.write(chunk)\n    return relative_path\n\n\ndef download_a_book(book_id):\n    response = send_request(book_id)\n    relative_path = os.path.join(\"books\", \"{bid}.pdf\".format(bid=book_id))\n    full_path = os.path.join(settings.MEDIA_ROOT, relative_path)\n    os.makedirs(os.path.dirname(full_path), exist_ok=True)\n    with open(full_path, 'wb+') as file:\n        for chunk in response.iter_content(chunk_size=1024):\n            file.write(chunk)\n    return relative_path\n","sub_path":"dsp_index/pdf_downloader.py","file_name":"pdf_downloader.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"423251542","text":"from server.main_ import app, orm, c\nfrom dateutil import parser\n\ndef _get_store(store_id):\n    with orm.session_scope() as ss:  # type:c.typeof_Session\n        only = ss.query(orm.정보_가게) \\\n            .filter_by(s=store_id) \\\n            .one()\n\n        return c.OBJ_cp(only)\n\n\n@app.route('/info/store')\ndef _info_store_():\n    return c.redirect(c.url_for('_info_store', store_id=c.session['store']))\n\n\n@app.route('/info/store/<int:store_id>', methods=['GET', 'POST', 'PUT'])\ndef _info_store(store_id):\n    if c.is_GET():\n        if c.is_json():\n            return c.jsonify(_get_store(store_id).for_json())\n        else:\n            return c.display()\n    elif c.is_POST() or c.is_PUT():\n        with orm.session_scope() as ss:  # type:c.typeof_Session\n            only = ss.query(orm.정보_가게) \\\n                .filter_by(s=store_id) \\\n                .one()\n            for k, v in c.data_POST().items():\n                if getattr(only, k) != v:\n                    print(k, 'is changed')\n                    if k in ['개점일', '폐점일']:\n                        try:\n                            setattr(only, k, parser.parse(v))\n                        except:\n                            setattr(only, k, None)\n                    else:\n                        setattr(only, k, v)\n            only.issync = None\n        return c.display()\n","sub_path":"server/routes/info/store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":1366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"256832803","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\n# the apk place in container\nAPK_NAME = \"/apk_shell/xhs.apk\"\n\n# stf_url address\nSTF_URL = \"http://192.168.10.233:7100/api/v1/devices\"\n\n\"\"\"\naccess token of stf\n\nSTF uses OAuth 2.0 for authentication. In order to use the API,\nyou will first need to generate an access token. Access tokens\ncan be easily generated from the STF UI. Just go to the Settings\ntab and generate a new access token in Keys section.\nDon't forget to save this token somewhere, you will not be able to see it again.\n\"\"\"\nTOKEN = \"41e5f0f70184464d93e787087bdc9bc6bb29076f15c943fc848d577cb1f9f65f\"\n\nSTF_DELETE_URL = \"http://192.168.10.233:7100/api/v1/user/devices/\"\n\n# some variables in desired_capablities\nPLATFORM_NAME = 'Android'\nNEW_COMMAND_TIMEOUT = 60\n\n# come infomation needed by docker_compose.yml\nAPPIUM_CARTIEREJ_IMAGE = \"appium_cartierej_docker:latest\"\nPORTS = 4723\nAPPIUM_CARTIEREJ_CMD = \"bash /app_shell/app.sh\"\nAPP_APK_VOLUMES = \"/home/jgb/Documents/DisCartierEJ/red/apk:/apk_shell\"\n\n\"\"\"\nUse device name as directory to save docker_compose.yml and app.sh\nNeed abs path\n\"\"\"\nDOCKER_COMPOSE_VOLUMES = \"/home/jgb/Documents/DisCartierEJ/resources/dockercomposes/\"\n\n# logs save place in local\nLOCAL_LOG_DIR = \"/home/jgb/Documents/DisCartierEJ/red/tmp/logs/\"\nAPPIUM_CARTIEREJ_LOGS_VOLUMES = LOCAL_LOG_DIR + 'RANDOM:/opt/node/CartierEJ/logs'  # RANDOM为变量在生成的过程中替换\n\n# case to run\nCASE_NAME = \"test_login.py\"\n","sub_path":"core/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"606495998","text":"'''\n        Programa auxiliar para generar las corridas del programa principal.\n        \n'''\nfrom subprocess import check_output\nimport os\nfrom glob import glob\nimport re\n\n\nfolders = glob(\"./Instances/*\")\n\n\ndef func(path):\n\treturn check_output(\"java Project {}\".format(path), shell=True).decode()\n\ndef natural_sort(l): \n    convert = lambda text: int(text) if text.isdigit() else text.lower() \n    alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] \n    return sorted(l, key = alphanum_key)\n\ndef extract(stri):\n\tres = re.search(\".*Best overall reward: ([0-9]*).*\", stri)\n\tres2 = re.search(\".*Time elapsed: ([0-9]*).*\", stri)\n\tprint(res.group(1) + \" \" + res2.group(1))\n\ndef limpiar():\n        for f in folders:\n                first = glob(\"{}/*.txt\".format(f))\n                for archivo in first:\n                        os.remove(archivo)      \n\n\nlimpiar()\n                \nfor f in folders:\n\tfirst = glob(\"{}/*\".format(f))\n\tprint(\"Siguiente carpeta {}\".format(f))\n\tfor archivo in natural_sort(first):\n\t\t#print(archivo)\n\t\tres = func(archivo)\n\t\textract(res)\n\t\t\n\n","sub_path":"Greedy/running.py","file_name":"running.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"123600437","text":"# -*- coding: cp1251 -*-\r\nfrom Tkinter import *\r\nimport socket\r\ndef printer(event):\t\r\n\tsock = socket.socket()\r\n\tsock.connect(('localhost', 9090))\r\n\tdata=ent.get()\r\n\tsock.send(data)\r\n\tent.delete(0,END)\r\n\trdata = sock.recv(1024)\r\n\ttex.insert(END,rdata+'\\n\\\\')\r\n\tsock.close()\r\nroot = Tk()\r\nroot.title(u'PythonChat v.1.0')\r\nent = Entry(root,width=100,bd=2)\r\ntex = Text(root,width=75,font=\"Verdana 12\",wrap=WORD)\r\nbut = Button(root)\r\nbut[\"text\"] = \"Отправить сообщение\"\r\nbut.bind(\"<Button-1>\",printer)\r\ntex.pack()\r\nent.pack(side=LEFT)\r\nbut.pack(side=RIGHT)\r\nroot.mainloop()","sub_path":"Python/chat/c.pyw","file_name":"c.pyw","file_ext":"pyw","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"124727373","text":"\"\"\"user_input=input('enter your 3 friends name').split(',')\r\nprint(user_input)\"\"\"\r\n\r\nfriend1=input('enter your friend name')\r\nfriend2=input('enter your friend name')\r\nfriend3=input('enter your friend name')\r\nfriends=[friend1,friend2,friend3]\r\nfread=open('people.txt','r')\r\nlistfriends=[line.strip() for line in fread.readlines()]   #be careful a out readlines and readline. lines only read word by word. line read char by char\r\nfread.close()\r\nmy_newlist=set(friends).intersection(set(listfriends))\r\nfwrite=open('neraby_friends.txt','w')\r\nfor friend in my_newlist:\r\n    print(f'{friend} is near by you')\r\n    fwrite.write(friend)      #this pgm write only 2 names. it avoid the 1 st name. to overcome this problem we need inmerate function\r\n\r\nfwrite.close()\r\n\r\n","sub_path":"friends.py","file_name":"friends.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"485390894","text":"# (끝나는 시간 순, 시작하는 시간 순)으로 정렬하여 회의의 최대 개수 카운트하기\nimport sys\ninput = sys.stdin.readline\n\nN = int(input())\n\narray = []\n\nfor _ in range(N):\n    s, e = map(int, input().split())\n    array.append((s, e))\n\narray.sort(key=lambda x:(x[1], x[0]))\n\nstart, count = 0, 0\n\nfor i in array:\n    if start <= i[0]:\n        start = i[1]\n        count += 1\n\nprint(count)\n","sub_path":"BAEKJOON/그리디/1931_회의실배정.py","file_name":"1931_회의실배정.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"277440645","text":"RES_OK_USER_CREATED = {\n    'code': 1001,\n    'message': 'User has been created'\n}\nRES_OK_USER_UPDATED = {\n    'code': 1002,\n    'message': 'User has been updated'\n}\nRES_OK_USER_DELETED = {\n    'code': 1003,\n    'message': 'User has been deleted'\n}\nRES_OK_TEAM_CREATED = {\n    'code': 1004,\n    'message': 'Team has been created'\n}\nRES_OK_TEAM_UPDATED = {\n    'code': 1005,\n    'message': 'Team has been updated'\n}\nRES_OK_TEAM_DELETED = {\n    'code': 1006,\n    'message': 'Team has been deleted'\n}\nRES_OK_MEMBER_CREATED = {\n    'code': 1007,\n    'message': 'Member has been created'\n}\nRES_OK_MEMBER_UPDATED = {\n    'code': 1008,\n    'message': 'Member has been updated'\n}\nRES_OK_MEMBER_DELETED = {\n    'code': 1009,\n    'message': 'Member has been deleted'\n}\nRES_OK_NEW_MEMBER_REGISTERED_TO_TEAM = {\n    'code': 1010,\n    'message': 'Member has been created and registered to the team'\n}\nRES_OK_NEW_MEMBER_REGISTERED_TO_MARKET = {\n    'code': 1011,\n    'message': 'Member has been created and registered and added to the market'\n}\nRES_OK_NEW_MEMBER_REGISTERED_TO_TEAM_AND_MARKET = {\n    'code': 1012,\n    'message': 'Member has been created and registered to the team, and added to the market'\n}\nRES_OK_SET_ON_TRANSFER_LIST = {\n    'code': 1013,\n    'message': 'Player has been set on the transfer list'\n}\nRES_OK_BUY_PLAYER = {\n    'code': 1014,\n    'message': 'You have bought the player'\n}\n\n\nRES_ERR_MISSING_FIELD = {\n    'code': 8001,\n    'message': 'One or more fields are missing in the request'\n}\nRES_ERR_INVALID_FILED = {\n    'code': 8002,\n    'message': 'One or more fields in the request are invalid'\n}\n\nRES_ERR_USER_EXIST = {\n    'code': 8101,\n    'message': 'The email is already registered'\n}\nRES_ERR_USER_NOT_EXIST = {\n    'code': 8102,\n    'message': 'The email is not registered yet'\n}\nRES_ERR_TEAM_NOT_EXIST = {\n    'code': 8103,\n    'message': 'The team does not exist'\n}\nRES_ERR_MEMBER_NOT_EXIST = {\n    'code': 8104,\n    'message': 'The member does not exist'\n}\nRES_ERR_INVALID_CREDENTIAL = {\n    'code': 8105,\n    'message': 'Email or password is not correct'\n}\nRES_ERR_ALREADY_ON_MARKET = {\n    'code': 8106,\n    'message': 'The member is already on the transfer list'\n}\nRES_ERR_MEMBER_NOT_ON_MARKET = {\n    'code': 8107,\n    'message': 'The member is not on the transfer list'\n}\nRES_ERR_NOT_ALLOW_TO_BUY_OWN_PLAYER = {\n    'code': 8108,\n    'message': 'You are not allowed to buy your own player'\n}\nRES_ERR_NOT_ENOUGH_MONEY_TO_BUY = {\n    'code': 8109,\n    'message': 'Your team does not have enough money to buy the player'\n}\n\n\nRES_ERR_TOKEN_REQUIRED = {\n    'code': 8901,\n    'message': 'Token is required to call api'\n}\nRES_ERR_INVALID_TOKEN = {\n    'code': 8902,\n    'message': 'Token is invalid. Please try to login again to get a new valid token'\n}\nRES_ERR_INVALID_PERMISSION = {\n    'code': 8903,\n    'message': 'You are not allowed to do this action'\n}\n\n\nRES_ERR_INTERNAL_SERVER = {\n    'code': 9001,\n    'message': 'Internal server error'\n}\n\nRES_ERR_TEAM_CREATE = {\n    'code': 9002,\n    'message': 'Server error during team creation'\n}\n","sub_path":"module/constant/ret_codes.py","file_name":"ret_codes.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"606071731","text":"import time\nimport datetime\nimport random\nfrom selenium import webdriver\nfrom slacker import Slacker\nfrom urllib.parse import quote\nfrom selenium.common.exceptions import NoSuchElementException\n\n# import os\n# print(os.getcwd())  # 현재 디렉토리의\n\n\n# ======== 1. Setting Options =======\n# slack_token : 작업이 진행될 때 중간 중간 슬랙에 메세지 발송\nslack_token = \"token\"\nslacker = Slacker(slack_token)\noptions = webdriver.ChromeOptions()\n\n# Chrome을 안 띄우고 수행하고 싶으면 아래 주석을 해제(리눅스 서버에서 작업시 headless 추천, 디버깅시는 headless 주석처리)\n# options.add_argument(\"headless\")\n\n# Chrome 설정 : 진짜 유저가 작업하는 것처럼 보이도록 설정\noptions.add_argument(\"window-size=1920x1080\")\noptions.add_argument(\"disable-gpu\")\noptions.add_argument(\n    \"user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_1_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36\"\n)\noptions.add_argument(\"lang=ko_KR\")\n\n# ======= 2. Setting id, password, hashtag ======\nid = \"\"\n# password = \"**gosdkdi12#**20171111\"\n# password = \"***alstjs17*20171111*\"\n\n\ntimeline_like_count = 120\n\n# hash_tags : 좋아요할 전체 해시태그 리스트\n# important_hash_tags : 중요해서 더 많이 like할 해시태그 리스트\nimportant_hash_tags = [\"토요일\"]\nimportant_hash_tags_count = 10000\nhash_tags = [\"토요일\"]\nhash_tags_count = 10000\n\n# ======== 3. InstaJob Class ======\n\n\nclass InstaJob:\n    @classmethod\n    def run(cls, start_index=1, end_index=2):\n        print(\"browser loading..\")\n        global browser\n        browser = webdriver.Chrome(\n            \"/Users/jihyun/Documents/GitHub/instagram-auto-like-with-Python/chromedriver\"\n        )\n\n        browser.execute_script(\n            \"Object.defineProperty(navigator, 'plugins', {get: function() {return[1, 2, 3, 4, 5]}})\"\n        )\n        browser.execute_script(\n            \"Object.defineProperty(navigator, 'languages', {get: function() {return ['ko-KR', 'ko']}})\"\n        )\n        browser.execute_script(\n            \"const getParameter = WebGLRenderingContext.getParameter;WebGLRenderingContext.prototype.getParameter = \\\n            function(parameter) {if (parameter === 37445) {return 'NVIDIA Corporation'} if (parameter === 37446) \\\n            {return 'NVIDIA GeForce GTX 980 Ti OpenGL Engine';}return getParameter(parameter);};\"\n        )\n\n        browser.get(\"https://instagram.com/\")\n\n        start_text = \"{id} Insta Auto Like Start : {time}\".format(\n            id=id, time=datetime.datetime.now()\n        )\n        print(start_text)\n        # slacker.chat.post_message(\"#general\", text=start_text)\n        for i in range(start_index, end_index):\n            cls.login()\n            cls.timeline_like()\n            cls.hash_tags_like()\n        end_text = \"{id} Insta Auto Like End : {time}\".format(\n            id=id, time=datetime.datetime.now()\n        )\n        # slacker.chat.post_message('#general', text=end_text)\n        browser.quit()\n\n    @classmethod\n    def login(cls):\n        \"\"\"\n        인스타그램 메인에서 로그인하는 함수\n        \"\"\"\n        login_link = browser.find_element_by_css_selector(\n            \"p.izU2O\"\n        ).find_element_by_css_selector(\"a\")\n        login_link.click()\n        time.sleep(3.5)\n\n        username_input = browser.find_elements_by_css_selector(\"input._2hvTZ\")[0]\n        username_input.send_keys(id)\n        time.sleep(2 + random.random() * 0.3)\n        password_input = browser.find_elements_by_css_selector(\"input._2hvTZ\")[1]\n        password_input.send_keys(password)\n        time.sleep(1)\n        password_input.submit()\n        time.sleep(2)\n        print(\"login success\")\n\n    @classmethod\n    def timeline_like(cls):\n        \"\"\"\n        timeline_likg_count만큼 타임라인의 좋아요를 누름\n        이 부분에 not clickable at point라고 error가 발생되고 있습니다. 추후 수정 필요\n        \"\"\"\n        print(\"timeline like start\")\n        browser.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n        try:\n            for i in range(timeline_like_count):\n                time.sleep(1.5)\n\n                browser.find_elements_by_css_selector(\n                    \"span.fr66n > button > span\"\n                    # \"span.glyphsSpriteHeart__outline__24__grey_9.Szr5J\"\n                )[0].click()\n\n                time.sleep(1.5)\n\n        except Exception as e:\n            print(\"Error! \", e)\n            # slacker.chat.post_message(\"#general\", text=\"raise timeline like error\")\n            pass\n\n    @classmethod\n    def hash_tags_like(cls):\n        \"\"\"\n        hash_tags_like\n        위에서 설정한 important_hash_tags와 hash_tags들을 각 횟수만큼 좋아요\n        만약 컨텐츠가 중간에 삭제되거나 페이지가 없으면\n        다음 해시태그로 이동되도록 예외처리\n        \"\"\"\n        print(\"hash_tags like start\")\n        for hash_tag in hash_tags:\n            try:\n                print(hash_tag + \" 좋아요 작업을 시작합니다\")\n                browser.get(\"https://www.instagram.com/explore/tags/\" + quote(hash_tag))\n                time.sleep(5 + random.random() * 1.2)\n                element = browser.find_elements_by_css_selector(\"div._9AhH0\")[9]\n                element.click()\n                time.sleep(5)\n\n                if any(e in hash_tag for e in important_hash_tags):\n                    count_number = important_hash_tags_count\n                else:\n                    count_number = hash_tags_count\n\n                for i in range(1, count_number):\n                    try:\n                        # 좋아요 해쉬태그 지정\n                        like = browser.find_element_by_css_selector(\n                            \"span.fr66n > button > span\"\n                            # \"span.glyphsSpriteHeart__outline__24__grey_9.Szr5J\"\n                        )\n                        # 좋아요 해쉬태그 클릭\n                        like.click()\n\n                        # 다음 포스팅으로 넘어가기전 대기\n                        time.sleep(2 + random.random() * 1.2)\n\n                        # 다음 포스팅으로 넘어감\n                        browser.find_element_by_css_selector(\n                            \"span.glyphsSpriteHeart__outline__24__grey_9.Szr5J\"\n                        ).click()\n\n                        time.sleep(3.2 + random.random() * 1.3)\n                    except:\n                        browser.find_element_by_css_selector(\n                            \"a.HBoOv.coreSpriteRightPaginationArrow\"\n                        ).click()\n                        time.sleep(1 + random.random() * 1.2)\n\n            except NoSuchElementException as e:\n                print(\"NoSuch Error\", e)\n                pass\n\n            except Exception as e:\n                print(\"Error! \", e)\n\n\nif __name__ == \"__main__\":\n    import instagram_auto_like\n\n    instagram_auto_like.InstaJob.run()\n","sub_path":"instagram_auto_like.py","file_name":"instagram_auto_like.py","file_ext":"py","file_size_in_byte":6997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"102922203","text":"from exchanges.coinapult import Coinapult\nfrom twilio.rest import Client\nfrom config import AppConfig\nimport time\nimport os.path\n\ntclient = Client(AppConfig.account_sid, AppConfig.auth_token)\nold_value = 0\nmin_value = 0\nmax_value = 0\n\n\ndef write_to_file(value):\n    if not os.path.isfile(AppConfig.res_file):\n        open(AppConfig.res_file, 'a').close()\n    with open(AppConfig.res_file, 'r') as fin:\n        data = fin.read().splitlines(True)\n        if len(data) == AppConfig.file_len:\n            with open(AppConfig.res_file, 'w') as fout:\n                fout.writelines(data[1:])\n    with open(AppConfig.res_file, 'a') as file:\n        file.write(str(value) + '\\n')\n\n\ndef send_message(body_message):\n    for to in AppConfig.to_:\n        tclient.messages.create(\n            to,\n            from_=AppConfig.from_,\n            body=body_message)\n        time.sleep(1)\n\n\nwhile True:\n    new_value = Coinapult().get_current_price()\n    write_to_file(new_value)\n    diff = new_value - old_value\n    if abs(diff) > AppConfig.odds:\n        old_value = new_value\n        prefix = \"Up\" if diff > 0 else \"Down\"\n        body = prefix+\": \"+str(new_value)+\" | Diff: \"+str(diff)\n        send_message(body)\n    if new_value > max_value:\n        body = \"Alert! Rose higher: \"+str(max_value)+\" | \"+\" Current: \"+str(new_value)\n        max_value += AppConfig.diff\n        send_message(body)\n    if new_value < min_value:\n        body = \"Alert! Fell below: \"+str(min_value)+\" | \"+\" Current: \"+str(new_value)\n        min_value -= AppConfig.diff\n        send_message(body)\n    time.sleep(AppConfig.interval)\n","sub_path":"examples/bots/bot 3/App.py","file_name":"App.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"77472846","text":"# This file allow this module to be run as follow:\n# python -m Broker\n\n# Created for testing purposes\n# Maybe it can be used in future if this project its added to PyPip\n\nimport sys\nimport logging\nimport threading\n\nfrom Broker.settings import ServerSettings\nfrom Broker import BrokerServer\n\nlogging.basicConfig(\n    stream=sys.stdout,\n    level=logging.DEBUG\n)\n\ntest_server_list = [\n    ServerSettings({\n        \"name\": \"Python Emulator\",\n        \"description\": \"Avatar ON\",\n        \"address\": \"127.0.0.1\",\n        \"port\": 8370,\n        \"utilization\": 0,\n        \"capacity\": 20,\n        \"enabled\": True\n    })\n]\n\nbind_address = \"127.0.0.1\"\nbind_port = 8372\n\nbroker_server = BrokerServer({'host': bind_address, 'port': bind_port})\nbroker_server.server_list = test_server_list\nbroker_process = threading.Thread(target=broker_server.listen)\n\nlogging.info(\"Starting Broker Server\")\nbroker_process.start()\n\ntry:\n    while broker_process.is_alive():\n        pass\nexcept KeyboardInterrupt:\n    logging.error(\"Keyboard Interrupt. Terminating Process\")\n    broker_server.stop()\n    broker_process.join()\nexcept:\n    logging.exception(\"Uncaught Exception Found. Terminating Process\", exc_info=True)\n    broker_server.stop()\n    broker_process.join()\n\n\n\n","sub_path":"Broker/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"408546951","text":"from torch.autograd import Variable\r\nimport torch\r\nfrom collections import deque\r\nimport numpy as np\r\nimport random\r\nimport cv2\r\nimport sys\r\nfrom torch import nn\r\nimport os\r\nfrom State import AI_Board\r\n\r\nGAMMA = 0.99  # decay rate of past observations\r\n# 从第20个timestep开始，训练DQN网络，思路有点问题？\r\nOBSERVE = 60.\r\nEXPLORE = 20000.  # frames over which to anneal epsilon\r\n# 如果FINAL_EPSILON 和INITIAL_EPSILON 的值相等，那么随机探索的概率会保持不变\r\nINITIAL_EPSILON = 0.0001  # starting value of epsilon\r\nFINAL_EPSILON = 0.0001  # final value of epsilon\r\n# 以前要记住的transitions的数量, 保留的最大的buffer记忆的数量\r\nREPLAY_MEMORY = 2000\r\n# 训练的batch_size\r\nBATCH_SIZE = 16\r\nFRAME_PER_ACTION = 1\r\n# 每多少个时间步更新下Target Q网络\r\nUPDATE_TIME = 100\r\n# 游戏观察的画面进行resize，特征的大小\r\nwidth = 80\r\nheight = 80\r\n\r\ndef preprocess(observation):\r\n    \"\"\"\r\n    游戏观察画面进行resize，和灰度处理, 返回处理后的观察到的状态\r\n    :param observation: 观察的游戏画面状态 (400, 800, 3)\r\n    :type observation:\r\n    :return:(1, 80, 80)\r\n    :rtype:\r\n    \"\"\"\r\n    # 游戏观察画面进行resize，和灰度处理 --> (80,80)\r\n    observation = cv2.cvtColor(cv2.resize(\r\n        observation, (width, height)), cv2.COLOR_BGR2GRAY)\r\n    # 加一个batch_size维度， (80,80) --> (1, 80, 80)\r\n    new_observation = np.reshape(observation, (1, height, width))\r\n    return new_observation\r\n\r\n\r\nclass DeepNetWork(nn.Module):\r\n    def __init__(self, ):\r\n        \"\"\"\r\n        三个卷积+全连接\r\n        \"\"\"\r\n        super(DeepNetWork, self).__init__()\r\n        self.conv1 = nn.Sequential(\r\n            nn.Conv2d(in_channels=4, out_channels=32, kernel_size=8, stride=4, padding=2),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=2)\r\n        )\r\n        self.conv2 = nn.Sequential(\r\n            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2, padding=1),\r\n            nn.ReLU(inplace=True)\r\n        )\r\n        self.conv3 = nn.Sequential(\r\n            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),\r\n            nn.ReLU(inplace=True)\r\n        )\r\n        self.fc1 = nn.Sequential(\r\n            nn.Linear(1600, 256),\r\n            nn.ReLU()\r\n        )\r\n        # 输出的动作数应该是离散的动作数\r\n        self.out = nn.Linear(256, 14)\r\n\r\n    def forward(self, x):\r\n        x = self.conv1(x)\r\n        x = self.conv2(x)\r\n        x = self.conv3(x)\r\n        x = x.view(x.size(0), -1)\r\n        x = self.fc1(x)\r\n        logits = self.out(x)\r\n        return logits\r\n\r\n\r\nclass BrainDQNMain(object):\r\n    def __init__(self, actions):\r\n        \"\"\"\r\n        :param actions: 动作数，16\r\n        :type actions: int\r\n        \"\"\"\r\n        # 初始化一个记忆重放\r\n        self.replayMemory = deque()  # init some parameters\r\n        self.timeStep = 0\r\n        self.epsilon = INITIAL_EPSILON\r\n        self.actions = actions\r\n        # Q网络\r\n        self.Q_net = DeepNetWork()\r\n        # Q的target网络\r\n        self.Q_netT = DeepNetWork()\r\n        self.load()\r\n        # DQN网络的损失函数\r\n        self.loss_func = nn.MSELoss()\r\n        LR = 1e-6\r\n        self.optimizer = torch.optim.Adam(self.Q_net.parameters(), lr=LR)\r\n\r\n    def save(self):\r\n        print(\"保存模型\")\r\n        torch.save(self.Q_net.state_dict(), 'params3.pth')\r\n\r\n    def load(self):\r\n        \"\"\"\r\n        如果已存在，就加载已存在的模型，继续训练\r\n        :return:\r\n        :rtype:\r\n        \"\"\"\r\n        if os.path.exists(\"params3.pth\"):\r\n            print(\"发现已经存在模型参数，加载模型\")\r\n            self.Q_net.load_state_dict(torch.load('params3.pth'))\r\n            self.Q_netT.load_state_dict(torch.load('params3.pth'))\r\n\r\n    def train(self):  # Step 1: obtain random minibatch from replay memory\r\n        \"\"\"\r\n        训练DQN网络\r\n        :return:\r\n        :rtype:\r\n        \"\"\"\r\n        # 从buffer中随机取出batch_size数据\r\n        minibatch = random.sample(self.replayMemory, BATCH_SIZE)\r\n        # 当前状态，采取动作，获取的奖励，下一个状态\r\n        state_batch = [data[0] for data in minibatch]\r\n        action_batch = [data[1] for data in minibatch]\r\n        reward_batch = [data[2] for data in minibatch]\r\n        nextState_batch = [data[3] for data in minibatch]  # Step 2: calculate y, [(4,80,80), ..., ] batch_size个\r\n        # (8, 1)： eg: [[0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.]]\r\n        y_batch = np.zeros([BATCH_SIZE, 1])\r\n        # shape: (8, 4, 80, 80)\r\n        nextState_batch = np.array(nextState_batch)\r\n        # shape: (8, 4, 80, 80)\r\n        nextState_batch = torch.Tensor(nextState_batch)\r\n        action_batch = np.array(action_batch)\r\n        action_batch_tensor = torch.LongTensor(action_batch).unsqueeze(1)\r\n        QValue_batch = self.Q_netT(nextState_batch)\r\n        QValue_batch = QValue_batch.detach().numpy()\r\n\r\n        for i in range(0, BATCH_SIZE):\r\n            terminal = minibatch[i][4]\r\n            if terminal:\r\n                y_batch[i][0] = reward_batch[i]\r\n            else:\r\n                # 这里的QValue_batch[i]为数组，大小为所有动作集合大小，QValue_batch[i],代表\r\n                # 做所有动作的Q值数组，y计算为如果游戏停止，y=rewaerd[i],如果没停止，则y=reward[i]+gamma*np.max(Qvalue[i])\r\n                # 代表当前y值为当前reward+未来预期最大值*gamma(gamma:经验系数)\r\n                y_batch[i][0] = reward_batch[i] + \\\r\n                                GAMMA * np.max(QValue_batch[i])\r\n        # y_batch: shape(batch_size, 1)\r\n        y_batch = np.array(y_batch)\r\n        # 确认一下形状，变成了 (batch_size, 1)\r\n        y_batch = np.reshape(y_batch, [BATCH_SIZE, 1])\r\n        state_batch_tensor = Variable(torch.Tensor(state_batch))\r\n        y_batch_tensor = Variable(torch.Tensor(y_batch))\r\n        y_predict = self.Q_net(state_batch_tensor).gather(1, action_batch_tensor)\r\n        loss = self.loss_func(y_predict, y_batch_tensor)\r\n        print(f\"损失是: {loss.item()}\")\r\n        self.optimizer.zero_grad()\r\n        loss.backward()\r\n        self.optimizer.step()\r\n        # 定期更新target网络\r\n        if self.timeStep % UPDATE_TIME == 0:\r\n            self.Q_netT.load_state_dict(self.Q_net.state_dict())\r\n            self.save()\r\n\r\n    # print(nextObservation.shape)\r\n    def setPerception(self, nextObservation, action, reward, terminal):\r\n        \"\"\"\r\n        主程序\r\n        :param nextObservation: 观察到的游戏状态处理后的array\r\n        :type nextObservation:  ndarray (1, 80, 80)\r\n        :param action: Qnet或随机的动作\r\n        :type action: int\r\n        :param reward: 返回的奖励\r\n        :type reward: int\r\n        :param terminal: 游戏是否结束\r\n        :type terminal: int\r\n        :return:\r\n        :rtype:\r\n        \"\"\"\r\n        # newState = np.append(nextObservation,self.currentState[:,:,1:],axis = 2)\r\n        # 更新当前的状态， 只更新第4个层，self.currentState[1:, :, :]表示丢弃第一个层，然后append后，又变成 (4,80,80)\r\n        newState = np.append(\r\n            self.currentState[1:, :, :], nextObservation, axis=0)\r\n        # 加到重放缓存中， 上一个状态self.currentState:(4,80,80), 进行的动作：action， 返回的奖励：reward，返回的状态： newState: (4,80,80)。判断游戏是否结束: terminal\r\n        self.replayMemory.append(\r\n            (self.currentState, action, reward, newState, terminal))\r\n        # 如果大于最大记住的buffer的数量，弹出最旧的那个\r\n        if len(self.replayMemory) > REPLAY_MEMORY:\r\n            self.replayMemory.popleft()\r\n        # 是否该训练DQN了\r\n        if self.timeStep > OBSERVE:  # Train the network\r\n            print(f\"开始训练DQN网络\")\r\n            self.train()\r\n\r\n        # 打印一些日志信息\r\n        if self.timeStep <= OBSERVE:\r\n            state = \"observe\"\r\n        elif self.timeStep > OBSERVE and self.timeStep <= OBSERVE + EXPLORE:\r\n            state = \"explore\"\r\n        else:\r\n            state = \"train\"\r\n        print(f\"已经进行了{self.timeStep}步，现在处理 {state}状态, 当前的EPSILON值是 {self.epsilon}\")\r\n        # 更新上一个状态self.currentState到当前的状态newState\r\n        self.currentState = newState\r\n        # 时间步+1\r\n        self.timeStep += 1\r\n\r\n    def getAction(self):\r\n        \"\"\"\r\n        # 获取一个动作\r\n        :return:\r\n        :rtype:\r\n        \"\"\"\r\n        # shape： torch.Size([1, 4, 80, 80])\r\n        currentState = torch.Tensor([self.currentState])\r\n        QValue = self.Q_net(currentState)[0]\r\n        action = np.zeros(self.actions)\r\n        if self.timeStep % FRAME_PER_ACTION == 0:\r\n            if random.random() <= self.epsilon:\r\n                action_index = random.randrange(self.actions)\r\n                print(\"选择随机的动作\" + str(action_index))\r\n                action[action_index] = 1\r\n            else:\r\n                action_index = np.argmax(QValue.detach().numpy())\r\n                print(\"选择QNet输出的动作\" + str(action_index))\r\n                action[action_index] = 1\r\n        else:\r\n            action[0] = 1  # do nothing\r\n\r\n        # 调整epsilon，随着时间步的增大\r\n        if self.epsilon > FINAL_EPSILON and self.timeStep > OBSERVE:\r\n            self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / EXPLORE\r\n        do_action = np.argmax(action)\r\n        return do_action\r\n\r\n    def setInitState(self, observation):\r\n        \"\"\"\r\n        第一个状态\r\n        :param observation: 二值化后的观测结果， shape: (80, 80)\r\n        :type observation: array\r\n        :return:\r\n        :rtype:\r\n        \"\"\"\r\n        # 形状变成--> (4, 80, 80)\r\n        self.currentState = np.stack(\r\n            (observation, observation, observation, observation), axis=0)\r\n        print(f\"初始化的堆叠二值化后的观测状态后的形状是: {self.currentState.shape}\")\r\n\r\n\r\nif __name__ == '__main__':\r\n    # 游戏初始化\r\n    game = AI_Board()\r\n    # 可操作的动作数量\r\n    actions = game.action_num\r\n    # 初始化DQN\r\n    brain = BrainDQNMain(actions)\r\n    # 初始状态的动作，reward，和观测\r\n    action0 = 0\r\n    # observation0: shape: (400, 800, 3), (width, height, RGB)\r\n    observation0, _, reward0, terminal = game.next(action0)\r\n    # 变成灰度图，尺寸变成 (400, 800, 3) --> (80, 80)\r\n    observation0 = cv2.cvtColor(cv2.resize(\r\n        observation0, (width, height)), cv2.COLOR_BGR2GRAY)\r\n    # 二值化, 小于1的设为0，大于1的设为255，相当于能看到了整个图像的轮廓\r\n    ret, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)\r\n    # 设置观察到的第一个游戏状态\r\n    brain.setInitState(observation0)\r\n    # 开始游戏和训练\r\n    while True:\r\n        print()\r\n        # 通过强化学习获取动作,获取输出的动作，action: int\r\n        action = brain.getAction()\r\n        # 根据下一个动作，返回下一个状态和奖励\r\n        nextObservation, _, reward, terminal = game.next(action)\r\n        # 对观察到的游戏画面进行处理，nextObservation: shape: (1, 80, 80)\r\n        nextObservation = preprocess(nextObservation)\r\n        # action: int, reward: int, terminal: bool\r\n        print(f\"当前选择的动作是{action}, 返回的奖励是{reward}, 是否游戏已经结束 {terminal}\")\r\n        brain.setPerception(nextObservation, action, reward, terminal)\r\n","sub_path":"train_torch.py","file_name":"train_torch.py","file_ext":"py","file_size_in_byte":11626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"290503661","text":"# -*- coding: utf-8 -*-\n\"\"\"\nUncategorized generic tools and utility functions\n\"\"\"\n\nfrom enum import IntEnum, Enum\n\n__all__ = ('Color', 'Emoji', 'EmojiNumbers', 'chunkify', 'chunkify_string_list')\n\nfrom typing import Iterable, List, Any\n\n\nclass Color(IntEnum):\n    \"\"\"\n    Enum class with nice color values\n    Can be used directly on embeds:\n        ```\n        embed = discord.Embed(description=\"embed example\", color=dpytools.Color.FIRE_ORANGE)\n        ```\n    \"\"\"\n    CYAN = 0x00FFFF\n    GOLD = 0xFFD700\n    YELLOW = 0xffff00\n    RED = 0xFF0000\n    LIME = 0x00FF00\n    VIOLET = 0xEE82EE\n    PINK = 0xFFC0CB\n    FUCHSIA = 0xFF00FF\n    BLUE = 0x0000FF\n    PURPLE = 0x8A2BE2\n    FIRE_ORANGE = 0xFF4500\n    COSMIC_LATTE = 0xFFF8E7\n    BABY_BLUE = 0x89cff0\n\n\nclass Emoji(str, Enum):\n    \"\"\"\n    Enum class with common emojis used for reaction messages or related interactions\n    \"\"\"\n    SMILE = '🙂'\n    THUMBS_UP = '👍'\n    THUMBS_DOWN = '👎'\n    HEART = '❤️'\n    GREEN_CHECK = '✅'\n    X = '❌'\n    PROHIBITED = '🚫'\n    FIRE = '🔥'\n    STAR = '⭐'\n    RED_CIRCLE = '🔴'\n    GREEN_CIRCLE = '🟢'\n    YELLOW_CIRCLE = '🟡'\n    LAST_TRACK = '⏮️'\n    REVERSE = '◀️'\n    PLAY = '▶️'\n    NEXT_TRACK = '⏭️'\n    PAUSE = '⏸️'\n    FIRST_PLACE_MEDAL = '🥇'\n    SECOND_PLACE_MEDAL = '🥈'\n    THIRD_PLACE_MEDAL = '🥉'\n    ONE = \"1️⃣\"\n    TWO = \"2️⃣\"\n    THREE = \"3️⃣\"\n    FOUR = \"4️⃣\"\n    FIVE = \"5️⃣\"\n    SIX = \"6️⃣\"\n    SEVEN = \"7️⃣\"\n    EIGHT = \"8️⃣\"\n    NINE = \"9️⃣\"\n    TEN = \"🔟\"\n    ZERO = \"0️⃣\"\n\n\nclass EmojiNumbers(str, Enum):\n    \"\"\"Shortcut enum class that contains the number emojis from Emoji class\"\"\"\n    ONE = Emoji.ONE.value\n    TWO = Emoji.TWO.value\n    THREE = Emoji.THREE.value\n    FOUR = Emoji.FOUR.value\n    FIVE = Emoji.FIVE.value\n    SIX = Emoji.SIX.value\n    SEVEN = Emoji.SEVEN.value\n    EIGHT = Emoji.EIGHT.value\n    NINE = Emoji.NINE.value\n    TEN = Emoji.TEN.value\n    ZERO = Emoji.ZERO.value\n\n\ndef chunkify(list_: List[Any],\n             max_number: int\n             ) -> List[List[Any]]:\n    \"\"\"\n    Splits a list into :n: sized chunks\n    Args:\n        list_: a list\n        max_number: an integer\n    Yields:\n        Chunks of :l: of :n: size\n    \"\"\"\n    for i in range(0, len(list_), max_number):\n        yield list_[i:i + max_number]\n\n\ndef chunkify_string_list(list_: List[str],\n                         max_number: int,\n                         max_length: int,\n                         separator_length: int = 0\n                         ) -> List[List[str]]:\n    \"\"\"\n    Splits a list of strings into :max_number: sized chunks or sized at maximum joint length of :max_length:\n    Args:\n        list_: a list\n        max_number (int): maximum number of items per chunk\n        max_length (int): maximum length of characters in a chunk (considering all items)\n        separator_length (int): Defaults to 0. If the strings will be eventually joined together this considers it\n    Yields:\n        List[List[str]]\n    \"\"\"\n    if any([len(item) > max_length-separator_length for item in list_]):\n        raise ValueError(f\"All items should be of length {max_length} or less.\")\n\n    for i in range(0, len(list_), max_number):\n        n = max_number\n        l = list_[i:i + n]\n        while len(''.join(opt + '_'*separator_length for opt in l))-separator_length > max_length:\n            n -= 1\n            l = list_[i:i + n]\n        yield l\n","sub_path":"dpytools/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"244924091","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        ('clinic', '0009_country_text'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='country',\n            name='text2',\n            field=models.TextField(blank=True, verbose_name='Преимущества:'),\n        ),\n        migrations.AlterField(\n            model_name='country',\n            name='text',\n            field=models.TextField(blank=True, verbose_name='Описание:'),\n        ),\n    ]\n","sub_path":"clinic/migrations/0010_auto_20150922_1338.py","file_name":"0010_auto_20150922_1338.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"304139969","text":"import os\nfrom os import path\nfrom setuptools import setup\nfrom subprocess import check_call\nfrom distutils.command.build import build\nfrom distutils.dir_util import copy_tree, remove_tree\nfrom setuptools.command.develop import develop\n\ndef get_submodules_and_fix_paths():\n    if path.exists('.git'):\n        check_call(['rm', '-rf', 'pagedown/static/pagedown'])\n        check_call(['git', 'reset', '--hard'])\n        check_call(['git', 'submodule', 'init'])\n        check_call(['git', 'submodule', 'update'])\n        # Move contents of pagedown and remove .git\n        dst = \"pagedown/static/pagedown/\"\n        src = \"pagedown/static/pagedown/pagedown/\"\n        copy_tree(src, dst)\n        remove_tree(src)\n\nclass build_with_submodules(build):\n    def run(self):\n        get_submodules_and_fix_paths()\n        build.run(self)\n\nclass develop_with_submodules(develop):\n    def run(self):\n        get_submodules_and_fix_paths()\n        develop.run(self)\n\n\nsetup(\n  name = \"django-pagedown\",\n  version = \"0.0.3\",\n  author = \"Timmy O'Mahony\",\n  author_email = \"me@timmyomahony.com\",\n  url = \"https://github.com/timmyomahony/django-pagedown\",\n  description = (\"A django app that allows the easy addition of Stack Overflow's 'PageDown' markdown editor to a django form field\"),\n  long_description=open('README.md').read(),\n  packages=['pagedown'],\n  include_package_data=True,\n  install_requires=[\n    \"Django >= 1.3\",\n  ],\n  license='LICENSE.txt',\n  cmdclass={\"build\": build_with_submodules, \"develop\": develop_with_submodules},\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"426696241","text":"from utils import *\n\n# params\n# w,h\nwidth = 360\nheight = 240\n\n# set pid control values\npid = [0.5, 0.5, 0]\npErr = 0\n\nstartCounter = 0 # test for no flight = 1, for flight = 0\n\ndim = (width, height)\n\n\n# init drone\nmyDrone = initTello()\n\n\n\n# main loop\nwhile True:\n    # Take off, fly\n    if startCounter == 0:\n        myDrone.takeoff()\n        startCounter = 1\n\n    #get frame from tello\n    img = telloGetFrame(myDrone, dim)\n\n    # find face\n    img_from_tello, inf = findFace(img)\n    \n    # track face \n    pErr = trackFace(myDrone, inf, width, pid, pErr)\n\n    \n    #print(inf[0][0])\n\n    # display\n    cv2.imshow('Image', img)\n\n    #stop the drone on q kay (safety measure)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        myDrone.land()\n        break\n\n\n\n\n\n\n\n","sub_path":"faceTracking.py","file_name":"faceTracking.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"330771369","text":"from django.conf.urls import include, url\nfrom django.conf import settings\nfrom django.contrib import admin\nfrom openpds.core.tools import v1_api\nfrom django.views.generic import TemplateView\nfrom django.views.generic.base import RedirectView\nfrom openpds import views\n\nadmin.autodiscover()\n\nurlpatterns = [\n    url(r'^$', RedirectView.as_view(url='https://github.com/HumanDynamics/openPDS/wiki')),\n    url(r'^home/', views.home),\n    url(r'^discovery/ping/', views.ping),\n    url(r'^api/', include(v1_api.urls)),\n    url(r'^admin/audit', TemplateView.as_view(template_name='audit.html')),\n    #(r'^documentation/', include('tastytools.urls'), {'api_name': v1_api.api_name}),\n    url(r'^admin/roles', TemplateView.as_view(template_name='roles.html')),\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'visualization/', include('openpds.visualization.urls')),\n    url(r'^funf_connector/', include('openpds.connectors.funf.urls')),\n    #url(r'^funf_connector/', include('openpds.connectors.opensense.urls')),\n    url(r'^os_connector/', include('openpds.connectors.opensense.urls')),\n    url(r'^survey/', TemplateView.as_view(template_name='survey.html')),\n    url(r\"meetup/\", include(\"openpds.meetup.urls\")),\n    # Uncomment the admin/doc line below to enable admin documentation:\n    # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n    # Uncomment the next line to enable the admin:\n    # url(r'^admin/', include(admin.site.urls)),\n    url(r\"accesscontrol/\", include(\"openpds.accesscontrol.urls\")),\n    url(r'probevisualization/', TemplateView.as_view(template_name='probevisualization.html')),\n    url(r'^checkboxes/', TemplateView.as_view(template_name='multiplecheckboxes.html')),\n]\n","sub_path":"openpds/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"413113560","text":"import pytest\nimport datetime\nimport dateutil\nimport pytz\nimport random\nfrom predictit.api import PredictItAPI\nfrom predictit.models import TwitterMarket, Contract\nfrom predictit.main import run_algorithms\nimport predictit.main\nfrom unittest.mock import call\n\n\nmock_num_trump_tweets = 0\nmock_num_pence_tweets = 0\nmock_num_whitehouse_tweets = 0\nmock_num_potus_tweets = 0\nbuy_order_called_states = {}\ncid_to_mid = {}\nnum_buy_orders = 0\n\nPROBABILITY_OF_TWEET = 0.25\nMAX_NUM_SHARES_TO_BUY = 1000\n\ndef mockNumPotusTweets():\n    global mock_num_potus_tweets\n\n    # seqlen = 10000\n    # seq= [random.random() for _ in np.arange(seqlen)]\n    num = random.random()\n    if num < PROBABILITY_OF_TWEET:\n        mock_num_potus_tweets += 1\n    return mock_num_potus_tweets\n\ndef mockNumPenceTweets():\n    global mock_num_pence_tweets\n\n    # seqlen = 10000\n    # seq= [random.random() for _ in np.arange(seqlen)]\n    num = random.random()\n    if num < PROBABILITY_OF_TWEET:\n        mock_num_pence_tweets += 1\n    return mock_num_pence_tweets\n\ndef mockNumWhitehouseTweets():\n    # print('in mockNumWhitehouseTweets')\n    global mock_num_whitehouse_tweets\n\n    # seqlen = 10000\n    # seq= [random.random() for _ in np.arange(seqlen)]\n    num = random.random()\n    if num < PROBABILITY_OF_TWEET:\n        mock_num_whitehouse_tweets += 1\n    return mock_num_whitehouse_tweets\n\ndef mockNumTrumpTweets():\n        # global num_buy_orders\n\n    global mock_num_trump_tweets\n    print('generating trump tweet')\n    # seqlen = 10000\n    # seq= [random.random() for _ in np.arange(seqlen)]\n    num = random.random()\n    if num < PROBABILITY_OF_TWEET:\n        # print('incrementing trump tweets')\n        mock_num_trump_tweets += 1\n    return mock_num_trump_tweets\n    # tweets = [0]*seqlen\n    # count=0\n    # for i, num in enumerate(seq):\n    #     if num < 0.05:\n    #         count+=1\n            # tweets[i]=count\n\ndef mock_buy_order(cid, is_yes_order, quantity, price, MAX_BUY_ORDER, EXECUTE_LIVE_TRADE):\n    global num_buy_orders\n    print('calling mock_buy_order unique')\n    relevant_tweets = -1\n    if cid_to_mid[cid] == 1116:\n        relevant_tweets = mock_num_trump_tweets\n    elif cid_to_mid[cid] == 1115:\n        relevant_tweets = mock_num_whitehouse_tweets\n    print('cid_to_mid = %s' % cid_to_mid)\n    buy_order_called_states[cid_to_mid[cid]].append(relevant_tweets)#{'cid': cid, 'is_yes_order': is_yes_order, 'quantity': quantity, 'price': price})\n    num_buy_orders += 1\n\ndef mock_max_num_shares_buyable_at_price(price):\n    return MAX_NUM_SHARES_TO_BUY\n\n# @pytest.mark.parametrize(\"mock_json,expected\", [\n    #     (   [ContractInfo(ticker_symbol='test contract info', best_buy_yes_cost='20',\n#             best_buy_no_cost='80',  best_sell_yes_cost='19',\n#             best_sell_no_cost='79', last_trade_price='19',\n#             last_close_price='15', timestamp=pytz.timezone('US/Eastern').normalize(pytz.timezone('US/Eastern').localize(dateutil.parser.parse('2019-01-18T17:10:49.989945'))).timestamp(),\n#             timestamp_as_date=pytz.timezone('US/Eastern').normalize(pytz.timezone('US/Eastern').localize(dateutil.parser.parse('2019-01-18T17:10:49.989945'))))],\n#\n#     )\n# ])\ndef test_main_loop(mocker):\n    global mock_num_trump_tweets\n    global mock_num_pence_tweets\n    global mock_num_whitehouse_tweets\n    global mock_num_potus_tweets\n    global buy_order_called_states\n    global num_buy_orders\n    global cid_to_mid\n\n    mock_json_function = mocker.patch('predictit.api.PredictItAPI.getJSON')\n    predictit.main.twitter_market =TwitterMarket(ticker_symbol=1116, long_name='twitter market long name 1', short_name='twitter market short name 1',\n            image_url='imgurl', url='url', status='OPEN', target_twitter_account='trump',\n            starting_num_tweets=800, contracts=[Contract(ticker_symbol=1111,short_name='80 - 83',min_tweets=80,max_tweets=83),Contract(ticker_symbol=1112,short_name='84 - 87',min_tweets=84,max_tweets=87), Contract(ticker_symbol=1113,short_name='88 - 91',min_tweets=88,max_tweets=91), Contract(ticker_symbol=1114,short_name='92+',min_tweets=92,max_tweets=predictit.main.INFINITY)])\n         #     TwitterMarket(ticker_symbol=1115, long_name='twitter market long name 2', short_name='twitter market short name 2',\n         # image_url='imgurl', url='url', status='OPEN', target_twitter_account='whitehouse',\n         # starting_num_tweets=500, contracts=[Contract(ticker_symbol=1117,short_name='54-'),Contract(ticker_symbol=1118,short_name='55 - 58'), Contract(ticker_symbol=1119,short_name='59 - 62')])]\n    # mocker.patch.object(\n    cid_to_mid = {1111: 1116, 1112: 1116,1113: 1116,1114: 1116, 1117: 1115, 1118: 1115, 1119: 1115}\n\n    predictit.main.tweet_counts={'trump': 800, 'whitehouse': 500}\n    # )\n    mock_num_trump_tweets = 800\n    mock_num_whitehouse_tweets = 500\n    mock_num_pence_tweets = 0\n    mock_num_potus_tweets = 0\n    buy_order_called_states = {1116: [], 1115: []}\n    max_buy_orders = 4\n    num_buy_orders = 0\n\n    numTrumpTweets = mocker.patch('predictit.api.PredictItAPI.getNumTrumpTweets')\n    numTrumpTweets.side_effect = mockNumTrumpTweets\n    numPenceTweets = mocker.patch('predictit.api.PredictItAPI.getNumPenceTweets')\n    numPenceTweets.side_effect = mockNumPenceTweets\n    numWhitehouseTweets = mocker.patch('predictit.api.PredictItAPI.getNumWhitehouseTweets')\n    numWhitehouseTweets.side_effect = mockNumWhitehouseTweets\n    numPotusTweets = mocker.patch('predictit.api.PredictItAPI.getNumPotusTweets')\n    numPotusTweets.side_effect = mockNumPotusTweets\n\n    mock_buy_order_var = mocker.patch('predictit.api.PredictItAPI.buy_order')\n    mock_buy_order_var.side_effect = mock_buy_order\n    # mock_buy_order_var = mocker.patch('predictit.api.PredictItAPI.buy_order',side_effect=mock_buy_order)\n\n    mock_max_num_shares = mocker.patch('predictit.api.PredictItAPI.max_num_shares_buyable_at_price')\n    mock_max_num_shares.side_effect = mock_max_num_shares_buyable_at_price\n\n    api = PredictItAPI(None)\n\n    counter = 0\n    while True:\n        run_algorithms()\n        counter += 1\n        if counter == 1000:\n            break\n\n    assert num_buy_orders == max_buy_orders\n    assert mock_buy_order_var.call_count == max_buy_orders\n    # extra 892 at end of list for the short on second highest bucket and long on last bucket\n    assert buy_order_called_states[1116] == [884, 888, 892, 892]\n    # assert buy_order_called_states[1115] == [555, 559, 563]\n    call(1111, False, MAX_NUM_SHARES_TO_BUY, 99) in predictit.api.PredictItAPI.buy_order.calls\n    call(1111, False, MAX_NUM_SHARES_TO_BUY, 99) in predictit.api.PredictItAPI.buy_order.calls\n    # call(1117, False, MAX_NUM_SHARES_TO_BUY, 99) in predictit.api.PredictItAPI.buy_order.calls\n    call(1112, False, MAX_NUM_SHARES_TO_BUY, 99) in predictit.api.PredictItAPI.buy_order.calls\n    # call(1118, False, MAX_NUM_SHARES_TO_BUY, 99) in predictit.api.PredictItAPI.buy_order.calls\n    call(1113, False, MAX_NUM_SHARES_TO_BUY, 99) in predictit.api.PredictItAPI.buy_order.calls\n    # call(1119, False, MAX_NUM_SHARES_TO_BUY, 99) in predictit.api.PredictItAPI.buy_order.calls\n    call(1114, True, MAX_NUM_SHARES_TO_BUY, 99) in predictit.api.PredictItAPI.buy_order.calls\n    # assert 1 != 1\n    # mock_buy_order_var.assert_called_with('whitehouse c4', False, MAX_NUM_SHARES_TO_BUY, 99)\n\n    # assert buy_order_called_state\n","sub_path":"tests/test_main_loop.py","file_name":"test_main_loop.py","file_ext":"py","file_size_in_byte":7340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"442078354","text":"from lektor.types import Type\nfrom lektor.environment import Expression, FormatExpression\n\n\ndef parse_choices(s):\n    if not s:\n        return None\n\n    rv = []\n    items = s.split(',')\n\n    missing_keys = False\n\n    for item in items:\n        if '=' in item:\n            key, value = item.split('=', 1)\n            key = key.strip()\n            if key.isdigit():\n                key = int(key)\n            rv.append((key, value.strip()))\n        else:\n            rv.append((None, item.strip()))\n            missing_keys = True\n\n    if missing_keys and items:\n        last_integer = None\n        for idx, (key, value) in enumerate(rv):\n            if isinstance(key, (int, long)):\n                last_integer = key\n            elif last_integer is not None:\n                last_integer += 1\n            if key is None:\n                if last_integer is not None:\n                    rv[idx] = (last_integer, value)\n                else:\n                    rv[idx] = (value, value)\n\n    return rv\n\n\nclass ChoiceSource(object):\n\n    def __init__(self, env, options):\n        source = options.get('source')\n        if source is not None:\n            self.source = Expression(env, source)\n            self.choices = None\n            item_key = options.get('item_key') or '{{ this._id }}'\n            item_label = options.get('item_label') or '{{ this._id }}'\n        else:\n            self.source = None\n            self.choices = parse_choices(options.get('choices'))\n            item_key = options.get('item_key') or '{{ this.0 }}'\n            item_label = options.get('item_label') or '{{ this.1 }}'\n        self.item_key = FormatExpression(env, item_key)\n        self.item_label = FormatExpression(env, item_label)\n\n    @property\n    def has_choices(self):\n        return self.source is not None or self.choices is not None\n\n    def iter_choices(self, pad):\n        if self.choices is not None:\n            iterable = self.choices\n        else:\n            iterable = self.source.evaluate(pad)\n\n        for item in iterable or ():\n            key = self.item_key.evaluate(pad, this=item)\n            label = self.item_label.evaluate(pad, this=item)\n            yield key, label\n\n\nclass MultiType(Type):\n\n    def __init__(self, env, options):\n        Type.__init__(self, env, options)\n        self.source = ChoiceSource(env, options)\n\n    def to_json(self, pad):\n        rv = Type.to_json(self, pad)\n        if self.source.has_choices:\n            rv['choices'] = [[key, value] for key, value in\n                             self.source.iter_choices(pad)]\n        return rv\n\n\nclass SelectType(MultiType):\n    pass\n\n\nclass CheckboxesType(MultiType):\n\n    def value_from_raw(self, raw):\n        rv = [x.strip() for x in (raw.value or '').split(',')]\n        if rv == ['']:\n            rv = []\n        return rv\n","sub_path":"lektor/types/multi.py","file_name":"multi.py","file_ext":"py","file_size_in_byte":2813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"303149806","text":"# MIT License**\n#\n# Copyright (c) 2012 Patrick Daether, public < at > pd-digital.de\n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# 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\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n# DEALINGS IN THE SOFTWARE.\nimport os\nimport os.path\nimport subprocess\nimport sublime\nimport sublime_plugin\nimport re\n\n# Some global vars:\njump_to_action = ''\n\n\nclass SymfonyCommander(sublime_plugin.TextCommand):\n\n    def callSymfony(self, command):\n        project_settings = self.view.settings().get('SymfonyCommander', {})\n        base_directory = \"\"\n        if project_settings:\n            base_directory = project_settings.get('base_directory')\n        if not base_directory:\n            #try to find it somewhere upwards:\n            view_name = self.view.file_name()\n            if view_name:\n                dir_name = os.path.dirname(view_name)\n                found_root = False\n                reached_end = False\n                while not found_root and not reached_end:\n                    for file in os.listdir(dir_name):\n                        if file == \"app\" and os.path.isdir(dir_name + \"/\" + file):\n                            #found an app dir\n                            if os.path.exists(dir_name + \"/\" + file + '/console'):\n                                base_directory = dir_name\n                                found_root = True\n                    #travers up\n                    old_dir = dir_name\n                    dir_name = os.path.dirname(dir_name)\n                    if dir_name == old_dir:\n                        reached_end = True\n        if not base_directory:\n            self.output(\"Can't find the root directory of the symfony project. Please have a look at the README. You can find it here: https://github.com/pdaether/Sublime-SymfonyCommander\")\n            return\n        os.chdir(base_directory)\n        # CMD:\n        s = sublime.load_settings(\"SymfonyCommander.sublime-settings\")\n        php_command = s.get('php_command')\n        if not php_command:\n            command = \"app/console \" + command\n        else:\n            command = php_command + \" app/console \" + command\n        result, e = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True, cwd=base_directory).communicate()\n        if e:\n            self.output(e)\n        else:\n            if not result:\n                result = \"Finished \" + command\n            self.output(result)\n\n    def output(self, value):\n        self.multi_line_output(value)\n\n    def multi_line_output(self, value, panel_name='SymfonyCommander'):\n        # Create the output Panel\n        panel = self.view.window().get_output_panel(panel_name)\n        panel.set_read_only(False)\n        panel.set_syntax_file('Packages/Text/Plain text.tmLanguage')\n        edit = panel.begin_edit()\n        panel.insert(edit, panel.size(), value)\n        panel.end_edit(edit)\n        panel.set_read_only(True)\n        self.view.window().run_command(\"show_panel\", {\"panel\": \"output.\" + panel_name})\n\n\n# Clear Cache\nclass SymfonyCommanderClearCacheCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('cache:clear')\n\n\nclass SymfonyCommanderClearCacheProdCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('cache:clear --env=prod')\n\n\nclass SymfonyCommanderClearCacheDevCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('cache:clear --env=dev')\n\n\nclass SymfonyCommanderCacheWarmupCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('cache:warmup')\n\n\nclass SymfonyCommanderRouterDebugCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('router:debug')\n\n\nclass SymfonyCommanderContainerDebugCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('container:debug')\n\n\nclass SymfonyCommanderDoctClearResult(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('doctrine:cache:clear-result')\n\n\nclass SymfonyCommanderDoctClearResultArguments(SymfonyCommander):\n    def run(self, edit):\n        sublime.active_window().show_input_panel(\"Arguments\", \"--id=*\", self.on_input, None, None)\n\n    def on_input(self, message):\n        self.callSymfony('doctrine:cache:clear-result ' + message)\n\n\nclass SymfonyCommanderDoctClearQuery(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('doctrine:cache:clear-query')\n\n\nclass SymfonyCommanderDoctClearMetadata(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('doctrine:cache:clear-metadata')\n\n\nclass SymfonyCommanderDoctMappingInfo(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('doctrine:mapping:info')\n\n\nclass SymfonyCommanderAssetsInstallCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('assets:install web')\n\n\nclass SymfonyCommanderAssetsInstallSymlinksCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('assets:install --symlink web')\n\n\nclass SymfonyCommanderSendMailCommand(SymfonyCommander):\n    def run(self, edit):\n        self.callSymfony('swiftmailer:spool:send')\n\n\n# Command to run with arguments\nclass SymfonyCommanderSendMailArgumentsCommand(SymfonyCommander):\n    def run(self, edit):\n        sublime.active_window().show_input_panel(\"Arguments\", \"--message-limit=10 --time-limit=10\", self.on_input, None, None)\n\n    def on_input(self, message):\n        self.callSymfony('swiftmailer:spool:send ' + message)\n\n\n# Command to run with arguments\nclass SymfonyCommanderAsseticDumpArgumentsCommand(SymfonyCommander):\n    def run(self, edit):\n        sublime.active_window().show_input_panel(\"Arguments\", \"--env=dev --no-debug --watch --force --period=30\", self.on_input, None, None)\n\n    def on_input(self, message):\n        self.callSymfony('assetic:dump ' + message)\n\n\nclass SymfonyCommanderSwitchFileCommand(sublime_plugin.TextCommand):\n    def run(self, edit):\n        global jump_to_action\n        file = self.view.file_name()\n        file_match = re.search(r'(.+)[/\\\\](.+)Controller.php', file)\n        if file_match:\n            folder = file_match.group(1)\n            file = file_match.group(2)\n            action = self.get_current_function(self.view)\n            if action:\n                a = re.search('(\\w+)Action', action).group(1)\n                self.view.window().open_file(folder + os.sep + '..' + os.sep + 'Resources' + os.sep + 'views' + os.sep + file + os.sep + a + '.html.twig')\n        elif re.search(r'(.+)[/\\\\](.+).html.twig', file):\n            file_match = re.search(r'(.+)[/\\\\](.+)[/\\\\](.+).html.twig', file)\n            folder = file_match.group(1)\n            controller = file_match.group(2)\n            action = file_match.group(3)\n            self.view.window().open_file(folder + os.sep + '..' + os.sep + '..' + os.sep + 'Controller' + os.sep + controller + 'Controller.php')\n            jump_to_action = action\n        else:\n            sublime.status_message('Cannot find the correct file, sorry!' + file)\n\n    def get_current_function(self, view):\n        sel = view.sel()[0]\n        functionRegs = view.find_by_selector('entity.name.function')\n        cf = None\n        for r in reversed(functionRegs):\n            if r.a < sel.a:\n                cf = view.substr(r)\n                break\n        return cf\n\n    def output(self, value):\n        self.multi_line_output(value)\n\n    def multi_line_output(self, value, panel_name='SymfonyCommander'):\n        # Create the output Panel\n        panel = self.view.window().get_output_panel(panel_name)\n        panel.set_read_only(False)\n        panel.set_syntax_file('Packages/Text/Plain text.tmLanguage')\n        edit = panel.begin_edit()\n        panel.insert(edit, panel.size(), value)\n        panel.end_edit(edit)\n        panel.set_read_only(True)\n        self.view.window().run_command(\"show_panel\", {\"panel\": \"output.\" + panel_name})\n\n\nclass SymfonyEvent(sublime_plugin.EventListener):\n    def on_load(self, view):\n        global jump_to_action\n        if jump_to_action:\n            sel = view.find(jump_to_action + \"Action\", 0)\n            view.show(sel)\n            jump_to_action = ''\n","sub_path":"SymfonyCommander.py","file_name":"SymfonyCommander.py","file_ext":"py","file_size_in_byte":8985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"322627342","text":"\"\"\"Inference for Statistical Models.\"\"\"\n\nfrom .test_statistics import qmu\nfrom .. import get_backend\nfrom .calculators import AsymptoticCalculator\n\n\ndef hypotest(\n    poi_test,\n    data,\n    pdf,\n    init_pars=None,\n    par_bounds=None,\n    fixed_params=None,\n    qtilde=False,\n    **kwargs,\n):\n    r\"\"\"\n    Compute :math:`p`-values and test statistics for a single value of the parameter of interest.\n\n    Example:\n        >>> import pyhf\n        >>> pyhf.set_backend(\"numpy\")\n        >>> model = pyhf.simplemodels.hepdata_like(\n        ...     signal_data=[12.0, 11.0], bkg_data=[50.0, 52.0], bkg_uncerts=[3.0, 7.0]\n        ... )\n        >>> observations = [51, 48]\n        >>> data = pyhf.tensorlib.astensor(observations + model.config.auxdata)\n        >>> test_poi = 1.0\n        >>> CLs_obs, CLs_exp_band = pyhf.infer.hypotest(\n        ...     test_poi, data, model, qtilde=True, return_expected_set=True\n        ... )\n        >>> CLs_obs\n        array(0.05251497)\n        >>> CLs_exp_band\n        [array(0.00260626), array(0.01382005), array(0.06445321), array(0.23525644), array(0.57303621)]\n\n    Args:\n        poi_test (Number or Tensor): The value of the parameter of interest (POI)\n        data (Number or Tensor): The data considered\n        pdf (~pyhf.pdf.Model): The statistical model adhering to the schema ``model.json``\n        init_pars (`tensor`): The initial parameter values to be used for minimization\n        par_bounds (`tensor`): The parameter value bounds to be used for minimization\n        fixed_params (`tensor`): Whether to fix the parameter to the init_pars value during minimization\n        qtilde (Bool): When ``True`` perform the calculation using the alternative\n         test statistic, :math:`\\tilde{q}_{\\mu}`, as defined under the Wald\n         approximation in Equation (62) of :xref:`arXiv:1007.1727`.\n\n    Keyword Args:\n        return_tail_probs (bool): Bool for returning :math:`\\mathrm{CL}_{s+b}` and :math:`\\mathrm{CL}_{b}`\n        return_expected (bool): Bool for returning :math:`\\mathrm{CL}_{\\mathrm{exp}}`\n        return_expected_set (bool): Bool for returning the :math:`(-2,-1,0,1,2)\\sigma` :math:`\\mathrm{CL}_{\\mathrm{exp}}` --- the \"Brazil band\"\n\n    Returns:\n        Tuple of Floats and lists of Floats:\n\n            - :math:`\\mathrm{CL}_{s}`: The modified :math:`p`-value compared to\n              the given threshold :math:`\\alpha`, typically taken to be :math:`0.05`,\n              defined in :xref:`arXiv:1007.1727` as\n\n            .. math::\n\n                \\mathrm{CL}_{s} = \\frac{\\mathrm{CL}_{s+b}}{\\mathrm{CL}_{b}} = \\frac{p_{s+b}}{1-p_{b}}\n\n            to protect against excluding signal models in which there is little\n            sensitivity. In the case that :math:`\\mathrm{CL}_{s} \\leq \\alpha`\n            the given signal model is excluded.\n\n            - :math:`\\left[\\mathrm{CL}_{s+b}, \\mathrm{CL}_{b}\\right]`: The\n              signal + background model hypothesis :math:`p`-value\n\n            .. math::\n\n                \\mathrm{CL}_{s+b} = p_{s+b}\n                = p\\left(q \\geq q_{\\mathrm{obs}}\\middle|s+b\\right)\n                = \\int\\limits_{q_{\\mathrm{obs}}}^{\\infty} f\\left(q\\,\\middle|s+b\\right)\\,dq\n                = 1 - F\\left(q_{\\mathrm{obs}}(\\mu)\\,\\middle|\\mu'\\right)\n\n            and 1 minus the background only model hypothesis :math:`p`-value\n\n            .. math::\n\n                \\mathrm{CL}_{b} = 1- p_{b}\n                = p\\left(q \\geq q_{\\mathrm{obs}}\\middle|b\\right)\n                = 1 - \\int\\limits_{-\\infty}^{q_{\\mathrm{obs}}} f\\left(q\\,\\middle|b\\right)\\,dq\n                = 1 - F\\left(q_{\\mathrm{obs}}(\\mu)\\,\\middle|0\\right)\n\n            for signal strength :math:`\\mu` and model hypothesis signal strength\n            :math:`\\mu'`, where the cumulative density functions\n            :math:`F\\left(q(\\mu)\\,\\middle|\\mu'\\right)` are given by Equations (57)\n            and (65) of :xref:`arXiv:1007.1727` for upper-limit-like test\n            statistic :math:`q \\in \\{q_{\\mu}, \\tilde{q}_{\\mu}\\}`.\n            Only returned when ``return_tail_probs`` is ``True``.\n\n            .. note::\n\n                The definitions of the :math:`\\mathrm{CL}_{s+b}` and\n                :math:`\\mathrm{CL}_{b}` used are based on profile likelihood\n                ratio test statistics.\n                This procedure is common in the LHC-era, but differs from\n                procedures used in the LEP and Tevatron eras, as briefly\n                discussed in :math:`\\S` 3.8 of :xref:`arXiv:1007.1727`.\n\n            - :math:`\\mathrm{CL}_{s,\\mathrm{exp}}`: The expected :math:`\\mathrm{CL}_{s}`\n              value corresponding to the test statistic under the background\n              only hypothesis :math:`\\left(\\mu=0\\right)`.\n              Only returned when ``return_expected`` is ``True``.\n\n            - :math:`\\mathrm{CL}_{s,\\mathrm{exp}}` band: The set of expected\n              :math:`\\mathrm{CL}_{s}` values corresponding to the median\n              significance of variations of the signal strength from the\n              background only hypothesis :math:`\\left(\\mu=0\\right)` at\n              :math:`(-2,-1,0,1,2)\\sigma`.\n              That is, the :math:`p`-values that satisfy Equation (89) of\n              :xref:`arXiv:1007.1727`\n\n            .. math::\n\n                \\mathrm{band}_{N\\sigma} = \\mu' + \\sigma\\,\\Phi^{-1}\\left(1-\\alpha\\right) \\pm N\\sigma\n\n            for :math:`\\mu'=0` and :math:`N \\in \\left\\{-2, -1, 0, 1, 2\\right\\}`.\n            These values define the boundaries of an uncertainty band sometimes\n            referred to as the \"Brazil band\".\n            Only returned when ``return_expected_set`` is ``True``.\n\n    \"\"\"\n    init_pars = init_pars or pdf.config.suggested_init()\n    par_bounds = par_bounds or pdf.config.suggested_bounds()\n    fixed_params = fixed_params or pdf.config.suggested_fixed()\n\n    calc = AsymptoticCalculator(\n        data, pdf, init_pars, par_bounds, fixed_params, qtilde=qtilde\n    )\n    teststat = calc.teststatistic(poi_test)\n    sig_plus_bkg_distribution, b_only_distribution = calc.distributions(poi_test)\n\n    CLsb = sig_plus_bkg_distribution.pvalue(teststat)\n    CLb = b_only_distribution.pvalue(teststat)\n    CLs = CLsb / CLb\n\n    tensorlib, _ = get_backend()\n    # Ensure that all CL values are 0-d tensors\n    CLsb, CLb, CLs = (\n        tensorlib.astensor(CLsb),\n        tensorlib.astensor(CLb),\n        tensorlib.astensor(CLs),\n    )\n\n    _returns = [CLs]\n    if kwargs.get('return_tail_probs'):\n        _returns.append([CLsb, CLb])\n    if kwargs.get('return_expected_set'):\n        CLs_exp = []\n        for n_sigma in [2, 1, 0, -1, -2]:\n\n            expected_bonly_teststat = b_only_distribution.expected_value(n_sigma)\n\n            CLs = sig_plus_bkg_distribution.pvalue(\n                expected_bonly_teststat\n            ) / b_only_distribution.pvalue(expected_bonly_teststat)\n            CLs_exp.append(tensorlib.astensor(CLs))\n        if kwargs.get('return_expected'):\n            _returns.append(CLs_exp[2])\n        _returns.append(CLs_exp)\n    elif kwargs.get('return_expected'):\n        n_sigma = 0\n        expected_bonly_teststat = b_only_distribution.expected_value(n_sigma)\n\n        CLs = sig_plus_bkg_distribution.pvalue(\n            expected_bonly_teststat\n        ) / b_only_distribution.pvalue(expected_bonly_teststat)\n        _returns.append(tensorlib.astensor(CLs))\n    # Enforce a consistent return type of the observed CLs\n    return tuple(_returns) if len(_returns) > 1 else _returns[0]\n\n\n__all__ = ['qmu', 'hypotest']\n","sub_path":"src/pyhf/infer/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"372538043","text":"PROJECT_APPS = ('django_fsm', 'testapp',)\nINSTALLED_APPS = ('django.contrib.contenttypes', 'django.contrib.auth', 'guardian', ) + PROJECT_APPS\nAUTHENTICATION_BACKENDS = (\n    'django.contrib.auth.backends.ModelBackend', # this is default\n    'guardian.backends.ObjectPermissionBackend',\n)\nDATABASE_ENGINE = 'sqlite3'\nSECRET_KEY = 'nokey'\nMIDDLEWARE_CLASSES = ()\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        }\n}\n\nANONYMOUS_USER_ID = 0\n","sub_path":"tests/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"464831302","text":"from __future__ import absolute_import\nimport modules.cuda_dev_internal\nimport modules.cuda\nimport modules.runas\nimport modules.ssh\nimport modules.build_essential\nimport modules.cmake\nimport modules.gcc7\nfrom datetime import datetime, timedelta\n\n\ndef emit(writer, **kwargs):\n    if \"cudaVersionFull\" not in kwargs:\n        raise Exception(\"'cudaVersionFull' is mandatory!\")\n    if \"cublasShort\" not in kwargs:\n        raise Exception(\"'cublasShort' is mandatory!\")\n    cudaVersionFull = kwargs[\"cudaVersionFull\"]\n    cublasShort = kwargs[\"cublasShort\"]\n    if \"requiredShort\" not in kwargs:\n        major, minor, subminor, versionShort, pkgVersion = modules.cuda.shortVersion(cudaVersionFull)\n        requiredShort = versionShort\n        print(\"Warning: using {} as required CUDA version, this may not work with the latest CUDA / driver combo\")\n    else:\n        requiredShort = kwargs[\"requiredShort\"]\n    if \"cmakeVersionFull\" not in kwargs:\n        raise Exception(\"'cmakeVersionFull' is mandatory!\")\n    cmakeVersionFull = kwargs[\"cmakeVersionFull\"]\n    modules.runas.emit(writer)\n    modules.ssh.emit(writer)\n    modules.build_essential.emit(writer)\n    modules.cmake.emit(writer, cmakeVersionFull)\n    writer.packages([\"ca-certificates\", \"doxygen\", \"clang-format-8\"])\n    writer.emit(\"\"\"\n        RUN update-alternatives --install /usr/bin/clang-format clang-format /usr/bin/clang-format-8 100\n        ENV CUDA_ROOT /usr/local/cuda\n    \"\"\")\n    modules.gcc7.emit(writer)\n    modules.cuda_dev_internal.emit(\n        writer, cudaVersionFull, cublasShort, requiredShort, baseImage=kwargs[\"base\"])\n\n\ndef images():\n    return {\n        \"cuda-internal:11.1\": { \n            \"cudaVersionFull\": \"11.1.\" + (datetime.now() - timedelta(days=1)).strftime(\"%Y%m%d\"),\n            \"requiredShort\": \"11.0\",\n            \"cublasShort\": \"11.0\",\n            \"base\": \"ubuntu:18.04\",\n            \"needsContext\": True,\n            \"cmakeVersionFull\": \"3.17.3\" \n        },\n        \"cuda-internal:11.0\": { \n            \"cudaVersionFull\": \"11.0.\" + (datetime.now() - timedelta(days=1)).strftime(\"%Y%m%d\"),\n            \"cublasShort\": \"11.0\",\n            \"base\": \"ubuntu:18.04\",\n            \"needsContext\": True,\n            \"cmakeVersionFull\": \"3.17.3\" \n        },\n    }\n","sub_path":"images/cuda_internal.py","file_name":"cuda_internal.py","file_ext":"py","file_size_in_byte":2249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"157907692","text":"#import scpydatabase as scdb\nfrom simplequery import simplequery, solarworksdb\nimport logging\nimport os\n\nclass puntQuery(object):\n\n\tdef __init__(self):\n\t\ttry:\n\t\t\t#Current file path (needed for task scheduler)\n\t\t\tworkingPath = os.path.dirname(os.path.realpath(__file__))\n\t\t\t#scdb.load_configuration(workingPath + '/credentials.conf')\n\t\t\tself.sq = simplequery(solarworksdb())\n\t\texcept Exception:\n\t\t\tlogging.error('Error initializing puntQuery', exc_info=True)\n\t\t\n\tdef getPunts(self):\n\t\t\"\"\"\n\t\tjobID - job number\n\t\tPuntDate - When job was picked up by Z+?\n\t\tPunter - who punted\n\t\tTeamLead - punter TL\n\t\tCategory - category\n\t\tSub-Category - sub-category\n\t\tNote - note\n\t\t\n\t\t\"\"\"\n\t\t\n\t\toutput = []\n\t\tkeys = ['jobID', 'PuntDate', 'Punter', 'TeamLead', 'Category', 'SubCategory', 'Note']\n\t\t\n\t\tq = \"\"\" SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED\n\t\t\t\t\tSET NOCOUNT ON\n\t\t\t\t\t--SET ANSI_WARNINGS OFF -- Due to an Excel issue with Nulls when Calculating Ready Date \n\n\t\t\t\t\tSELECT\n\t\t\t\t\td.JobID,\n\t\t\t\t\tu.UserName as 'Punter',\n\t\t\t\t\teh.TeamLead,\n\t\t\t\t\teh.TerritoryManager,\n\t\t\t\t\tewp.PuntDate,\n\t\t\t\t\tpo.Description as 'Category',\n\t\t\t\t\tLTRIM(substring(ewp.Note,0,CHARINDEX('.',ewp.Note))) as 'Sub-Category',\n\t\t\t\t\tewp. Note\n\t\t\t\t\tFROM SolarDesign..EdWizPunt ewp\n\t\t\t\t\tjoin SolarDesign..Design d on ewp.DesignID = d.DesignID\n\t\t\t\t\tleft join SolarDesign..DesignPuntOption dpo on ewp.ID = dpo.DesignPuntID\n\t\t\t\t\tleft join SolarDesign..PuntOptions po on dpo.PuntOptionID = po.ID\n\t\t\t\t\tleft join SolarWorks..Users u on ewp.DomainUserName = u.DomainUserName\n\t\t\t\t\tleft join Analysis..vengineeringhierarchy eh on eh.EmployeeNumber = u.EmployeeID\n\n\t\t\t\t\tWHERE ewp.puntdate>= getdate() -1\n\t\t\t\t\tand dpo.Designer = '1' \"\"\"\n\t\t\n\t\tlogging.info('query started')\n\t\t#rows = scdb.query_to_dict(q)\n\t\trows = self.sq.get_query_dict(q)\n\t\tlogging.info('query finished')\n\t\t\n\t\tfor row in rows:\n\t\t\t#keys = ['jobID', 'PuntDate', 'Punter', 'TeamLead', 'Category', 'SubCategory', 'Note']\n\t\t\tjobID = row['JobID']\n\t\t\tpuntDate = row['PuntDate']\n\t\t\tpunter = row['Punter']\n\t\t\tpunterTL = row['TeamLead']\n\t\t\tpuntCat = row['Category']\n\t\t\tpuntSubCat = row['Sub-Category']\n\t\t\tpuntNote = row['Note']\n\t\t\tvals = [jobID, puntDate, punter, punterTL, puntCat, puntSubCat, puntNote]\n\t\t\tpuntDict = dict(zip(keys,vals))\n\t\t\toutput.append(puntDict)\n\t\t\t\t\t\n\t\treturn output","sub_path":"PuntReporter/puntQuery.py","file_name":"puntQuery.py","file_ext":"py","file_size_in_byte":2288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"7168837","text":"import packages.ipm_cloud_postgresql.model as model\nimport bth.interacao_cloud as interacao_cloud\nimport json\nimport logging\nfrom datetime import datetime\n\n\ntipo_registro = 'declaracoes'\nsistema = 999\nlimite_lote = 50\nurl = \"https://livro-eletronico.cloud.betha.com.br/service-layer-livro/api/declaracoes\"\n\n\ndef iniciar_processo_envio(params_exec, *args, **kwargs):\n    dados_assunto = coletar_dados(params_exec)\n    dados_enviar = pre_validar(params_exec, dados_assunto)\n    # if not params_exec.get('somente_pre_validar'):\n    #     iniciar_envio(params_exec, dados_enviar, 'POST')\n    # model.valida_lotes_enviados(params_exec, tipo_registro=tipo_registro)\n\n\ndef coletar_dados(params_exec):\n    print('- Iniciando a consulta dos dados a enviar.')\n    df = None\n    try:\n        query = model.get_consulta(params_exec, tipo_registro + '.sql')\n        pgcnn = model.PostgreSQLConnection()\n        df = pgcnn.exec_sql(query, index_col='id')\n        print(f'- Consulta finalizada. {len(df.index)} registro(s) encontrado(s).')\n    except Exception as error:\n        print(f'Erro ao executar função {tipo_registro}. {error}')\n    finally:\n        return df\n\n\ndef pre_validar(params_exec, dados):\n    print('- Iniciando pré-validação dos registros.')\n    dados_validados = []\n    registro_erros = []\n    try:\n        lista_dados = dados.to_dict('records')\n        for linha in lista_dados:\n            registro_valido = True\n            if registro_valido:\n                dados_validados.append(linha)\n        print(f'- Pré-validação finalizada. Registros validados com sucesso: '\n              f'{len(dados_validados)} | Registros com advertência: {len(registro_erros)}')\n    except Exception as error:\n        logging.error(f'Erro ao executar função \"pre_validar\". {error}')\n    finally:\n        return dados_validados\n\n\ndef iniciar_envio(params_exec, dados, metodo, *args, **kwargs):\n    print('- Iniciando envio dos dados.')\n    lista_dados_enviar = []\n    lista_controle_migracao = []\n    hoje = datetime.now().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n    token = params_exec['token']\n    contador = 0\n    for item in dados:\n        hash_chaves = model.gerar_hash_chaves(sistema, tipo_registro, item[\"key1\"], item[\"cpf_cnpj_tomador\"])\n        dict_dados = {\n            \"idIntegracao\": hash_chaves,\n            \"declaracoes\": {\n                # \"iDeclaracoes\": item[\"i_declaracoes\"],\n                \"iPessoas\": item[\"i_pessoas\"],\n                \"iCompetencias\": item[\"i_competencias\"],\n                # \"iRetificadas\": item[\"i_retificadas\"],\n                # \"iRetificadoras\": item[\"i_retificadoras\"],\n                \"iAnos\": item[\"i_anos\"],\n                \"situacao\": item[\"situacao\"],\n                \"tipo\": item[\"tipo\"],\n                \"simplificada\": item[\"simplificada\"],\n                \"situacaoGuia\": item[\"situacao_guia\"],\n                \"qtdDocumentos\": item[\"qtd_documentos\"],\n                \"vlDocumento\": item[\"vl_documento\"],\n                \"vlServico\": item[\"vl_servico\"],\n                \"vlBaseCalculo\": item[\"vl_base_calculo\"],\n                \"vlImposto\": item[\"vl_imposto\"],\n                \"vlDeducao\": item[\"vl_deducao\"],\n                \"vlServicoSimplificada\": item[\"vl_servico_simplificada\"],\n                \"vlImpostoSimplificada\": item[\"vl_imposto_simplificada\"],\n                \"vlSaldo\": item[\"vl_saldo\"],\n                \"optanteSn\": item[\"optante_sn\"],\n                \"vlTaxas\": item[\"vl_taxas\"]\n            }\n        }\n\n        contador += 1\n        # print(f'Dados gerados ({contador}): ', dict_dados)\n        lista_dados_enviar.append(dict_dados)\n        lista_controle_migracao.append({\n            'sistema': sistema,\n            'tipo_registro': tipo_registro,\n            'hash_chave_dsk': hash_chaves,\n            'descricao_tipo_registro': 'Cadastro de Declarações',\n            'id_gerado': None,\n            'i_chave_dsk1': item[\"key1\"],\n            'i_chave_dsk2': item[\"cpf_cnpj_tomador\"]\n        })\n    # print(lista_controle_migracao)\n    # print(lista_dados_enviar)\n    model.insere_tabela_controle_migracao_registro2(params_exec, lista_req=lista_controle_migracao)\n    req_res = interacao_cloud.preparar_requisicao(lista_dados=lista_dados_enviar,\n                                                  token=token,\n                                                  url=url,\n                                                  tipo_registro=tipo_registro,\n                                                  tamanho_lote=limite_lote)\n    model.insere_tabela_controle_lote(req_res)\n\n    print('- Envio de dados finalizado.')\n","sub_path":"packages/ipm_cloud_postgresql/livro_eletronico/rotinas_envio/declaracoes.py","file_name":"declaracoes.py","file_ext":"py","file_size_in_byte":4582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"135201846","text":"import os\nimport subprocess\nimport time\nimport yaml\nimport argparse\n\n\"\"\"\nGiven a directory of PAN20 formatted datasets, runs `unmask.py run` on\nall of them.\nInputs:\n- job.yml with <output_dir> and <transcription> placeholders\n- path to directory with PAN20 formatted datasets\n\"\"\"\n\n\ndef now(): return time.strftime(\"%Y-%m-%d_%H-%M-%S\")\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        prog=\"concat_unmask\",\n        description=\"Automate unmasking for multiple datasets\",\n        add_help=True)\n    parser.add_argument('--input',\n                        '-i',\n                        help='Path to directory of PAN20 formatted datasets')\n    args = parser.parse_args()\n\n    directory = [d for d in os.scandir(args.input)]\n    print(f'Found {len(directory)} PAN20 data files.')\n    output_folder = f'unmasking_curves_{now()}/'\n\n    for i, dir_entry in enumerate(directory):\n        print(f'Unmasking {dir_entry.name}... ({i+1}/{len(directory)})')\n        # Workaround, as there is no --input option for `unmask.py run`\n        with open(os.path.join('app', 'job.yml')) as f:\n            doc = yaml.load(f, Loader=yaml.FullLoader)\n\n        doc['job%']['input']['parser']['parameters']['corpus_path'] = dir_entry.path\n\n        with open(os.path.join('app', 'temp_job.yml'), 'w') as f:\n            yaml.dump(doc, f)\n\n        # Trigger Unmasking\n        subprocess.run(['./unmask', 'run',\n                        '-o', os.path.join('..',\n                                           'data',\n                                           output_folder,\n                                           dir_entry.name),\n                        os.path.join('app', 'temp_job.yml')])\n\n    os.remove(os.path.join('app', 'temp_job.yml'))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"app/concat_unmask.py","file_name":"concat_unmask.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"422865985","text":"# pylint: disable=missing-docstring\nimport unittest\n\ndef solve(fn_input):\n    instructions = fn_input[:-1].split(\"\\n\")\n    #width = len(instructions[0])\n    #height = len(instructions)\n    y = 0\n    x = instructions[0].index(\"|\")\n    done = False\n    count = 0\n    direction = \"S\"\n    collection = \"\"\n    while not done:\n        if direction == \"S\":\n            y += 1\n        if direction == \"N\":\n            y -= 1\n        if direction == \"E\":\n            x += 1\n        if direction == \"W\":\n            x -= 1\n        count += 1\n        if instructions[y][x] == \"|\" or instructions[y][x] == \"-\":\n            continue\n        if instructions[y][x] == \"+\":\n            direction = change_direction(direction, x, y, instructions)\n            continue\n        if instructions[y][x] != \" \":\n            continue\n        return count\n\ndef change_direction(direction, x, y, instructions):\n    if direction != \"S\" and instructions[y-1][x] != \" \":\n        return \"N\"\n    if direction != \"N\" and instructions[y+1][x] != \" \":\n        return \"S\"\n    if direction != \"W\" and instructions[y][x+1] != \" \":\n        return \"E\"\n    if direction != \"E\" and instructions[y][x-1] != \" \":\n        return \"W\"\n\n\nclass UnitTest(unittest.TestCase):\n    def test_solve(self):\n        input_str  = \"     |          \\n\"\n        input_str += \"     |  +--+    \\n\"\n        input_str += \"     A  |  C    \\n\"\n        input_str += \" F---|----E|--+ \\n\"\n        input_str += \"     |  |  |  D \\n\"\n        input_str += \"     +B-+  +--+ \\n\"\n        input_str += \"                \\n\"\n        self.assertEqual(solve(input_str), 38)\n","sub_path":"solvers/day19b.py","file_name":"day19b.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"130340848","text":"#!/usr/bin/env python\nimport os\nimport sys\n\n# prepare initial data structure for donor data\ndonors = [{'first_name': 'Arnold', 'last_name': 'Schwarzenegger',\n           'donation_total': 100000, 'donation_count': 1},\n          {'first_name': 'Lebron', 'last_name': 'James',\n           'donation_total': 1000000, 'donation_count': 1},\n          {'first_name': 'Elon', 'last_name': 'Musk',\n           'donation_total': 2000000, 'donation_count': 1},\n          {'first_name': 'Walter', 'last_name': 'White',\n           'donation_total': 500000, 'donation_count': 1},\n          {'first_name': 'Gordon', 'last_name': 'Ramsay',\n           'donation_total': 1280000, 'donation_count': 1}]\n\n# functions\n\n\ndef thank_you():  # OPTION 1\n    \"\"\"Send a thank you email to the donor.\"\"\"\n\n    print(\"Who would you like to send a thank you to?\")\n\n    # input donor name\n    name = 'list'\n    while name == 'list':\n        name = safe_input(\"Type 'list' for a list of donors or enter a name: \")\n        pop = False\n        try:\n            # run if user inputs 'list'\n            if name == 'list':\n                list_names = ([item['first_name'] + ' ' + item['last_name']\n                              for item in donors])\n                print(list_names)\n            # run if name that user inputs is not already in the donor list\n            elif not any(donor['first_name'] == name.split(' ')[0] and\n                         donor['last_name'] == name.split(' ')[1] for\n                         donor in donors):\n                pop = True\n                first_name = name.split(' ')[0]\n                last_name = name.split(' ')[1]\n                if first_name == '' or last_name == '':\n                    print(\"First or last name not provided. Donor not added.\")\n                    print(\"Returning to main menu...\")\n                    return None\n                donors.append({'first_name': first_name,\n                               'last_name': last_name,\n                               'donation_total': 0, 'donation_count': 0})\n                index = len(donors) - 1\n            # run if name that user inputs is already in the donor list\n            else:\n                first_name = name.split(' ')[0]\n                last_name = name.split(' ')[1]\n                for i in range(len(donors)):\n                    if (donors[i]['first_name'] == first_name and\n                            donors[i]['last_name'] == last_name):\n                        index = i\n                        break\n        except IndexError:\n            print(\"IndexError exception encountered.\")\n            print(\"Enter a first and last name separated by one space.\")\n            name = 'list'\n\n    # enter donation amount\n    try:\n        donation = float(safe_input(\"Enter a donation amount: \"))\n        donors[index]['donation_total'] += donation\n        donors[index]['donation_count'] += 1\n    except ValueError:\n        print(\"ValueError exception encountered.\")\n        print(\"Donor data not saved.\")\n        if pop:\n            donors.pop()\n    except TypeError:\n        print(\"TypeError exception encountered.\")\n        print(\"Donor data not saved.\")\n        if pop:\n            donors.pop()\n    else:\n        email = ('\\nDear {first_name} {last_name},\\n\\nThank you for '\n                 + 'your generous donations, totaling ${donation_total}.'\n                 + ' We are very grateful.\\n\\nBest, \\n\\nLocal Charity\\n'\n                 ).format(**donors[index])\n        print(email)\n\n\ndef create_report():  # OPTION 2\n    '''Create a report of donor data, including total donated, number of\n    donations, and average donation amount.'''\n\n    h = ['Donor Name', '|', 'Total Given', '|', 'Num Gifts',\n         '|', 'Average Gift']\n    # create table heading\n    report_headers = '{:<25}{:^5}{:<15}{:^5}{:<10}{:^5}{:<15}'.format(*h)\n    table_divider = '-' * 71\n    print('\\n', report_headers)\n    print(table_divider)\n\n    # sort donor data by donation_total\n    def sort_key(donors_data):\n        return donors_data['donation_total']\n\n    donors_sorted = sorted(donors, key=sort_key, reverse=True)\n\n    # print donor data\n    for i in range(len(donors_sorted)):\n        first_name = donors_sorted[i]['first_name']\n        last_name = donors_sorted[i]['last_name']\n        donation_avg = (donors_sorted[i]['donation_total'] /\n                        donors_sorted[i]['donation_count'])\n        print(('{:<25}{:^5}${:>14,.2f}{:^5}{:>10}{:^5}${:>14,.2f}'\n               ).format(first_name + ' ' + last_name, ' ',\n                        donors_sorted[i]['donation_total'], ' ',\n                        donors_sorted[i]['donation_count'], ' ',\n                        donation_avg\n                        )\n              )\n\n    # remain on page until user decides to return to main menu\n    q = 'Z'\n    while q != 'Q' and q != 'q':\n        q = safe_input(\"\\nEnter Q to Quit and return to the main menu: \")\n\n\ndef letters_to_all():  # OPTION 3\n    '''Write a letter to every donor and save each one to a\n    file on the disk.'''\n\n    # write a letter to a text file for each donor\n    for donor in donors:\n        with open(\"{first_name}_{last_name}.txt\".format(**donor),\n                  'w+') as new_file:\n            letter_body = ('{first_name} {last_name},\\n\\nThank you'\n                           + ' for donating ${donation_total}. You '\n                           + 'are so kind.\\n\\nBest,\\n\\nLocal Charity'\n                           ).format(**donor)\n            new_file.write(letter_body)\n\n\ndef quit_program():  # OPTION 4\n    '''Print exit message and quit the program'''\n    exit_message = \"Closing the mailroom for the day...\"\n    print(exit_message)\n    sys.exit()\n\n\ndef safe_input(string):\n    '''Call input() function safely to handle EOFError\n    and KeyboardInterrupt exceptions.'''\n\n    try:\n        output = input(string)\n    except EOFError:\n        print('EOFError exception encountered.')\n        return None\n    except KeyboardInterrupt:\n        print('KeyboardInterrupt exception encountered.')\n        return None\n    else:\n        return output\n\n\n# main code\nif __name__ == '__main__':\n\n    response_dict = {1: thank_you, 2: create_report, 3: letters_to_all,\n                     4: quit_program}\n    response = 0\n    while response != 4:\n        response = 0\n        # display main menu with options\n        options = [\"1. Send a Thank You to a single donor\", \"2. Create a\"\n                   + \" Report\", \"3. Send letters to all donors\",\n                   \"4. Quit\"]\n\n        # get and run user response\n        while int(response) not in [1, 2, 3, 4]:\n            try:\n                print(f\"----- Main Menu -----\\n{options[0]}\\n{options[1]}\"\n                      + f\"\\n{options[2]}\\n{options[3]}\")\n                try:\n                    response = int(safe_input(\"Enter a number: \"))\n                except TypeError:\n                    response = 0\n            except ValueError:\n                print(\"Enter 1, 2, 3, or 4. String input is not allowed.\")\n        response_dict.get(response)()\n","sub_path":"students/alexander_boone/lesson05/assignments/mailroom.py","file_name":"mailroom.py","file_ext":"py","file_size_in_byte":7023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"141729713","text":"import gym\nimport numpy as np\nimport tensorflow as tf\nimport random\nimport gym.envs.mujoco  \nimport time\nfrom gym.envs.mujoco.ant import AntEnv\nimport xml.etree.ElementTree as elemTree\n\nfrom networks.sac_learner import SACLearner\n\nenv1 = AntEnv()\nstate_dim = env1.get_current_obs().size\naction_dim = env1.action_space.shape[0]\ngamma = 0.96\n\nprint(\"state_dim\", state_dim)\nprint(\"action_dim\", action_dim)\n\nLOG_DIR = \"data/basic_train5/\"\nlog_file = open(LOG_DIR + \"log.txt\", \"wt\")\n\ndef is_done(state):\n    notdone = np.isfinite(state).all() and state[2] <= 1.0 and state[2] >= 0.27\n    return not notdone\n\nlearner1 = SACLearner(state_dim, action_dim, name=\"1\", value_lr=0.0001, qvalue_lr=0.0001, policy_lr=0.0001, gamma=gamma)\n\nsess = tf.Session()\nsaver = tf.train.Saver(max_to_keep=0)\n\nlog_file.write(\"Episode\\t\" +\n    \"Env1_Score\\tEnv1_Qvalue\\tEnv1_Value\\tEnv1_Policy\\n\")\n\nwith sess.as_default():\n    init = tf.global_variables_initializer()\n    sess.run(init)\n    learner1.value_network_initialize()\n\n    vector_len1 = 0\n    state_vector1 = []\n    next_state_vector1 = []\n    action_vector1 = []\n    reward_vector1 = []\n    survive_vector1 = []\n    value_vector1 = []\n\n    for epoch in range(100):\n        state = env1.reset()\n        reward_store = []\n        for play in range(200):\n            prev_state = state\n            action = np.random.uniform(-1., 1., (action_dim))\n            state, reward, done, _ = env1.step(action)\n            state_vector1.append(prev_state)\n            action_vector1.append(action)\n            survive_vector1.append([ 0.0 if done else 1.0])\n            next_state_vector1.append(state)\n            reward_store.append(reward)\n\n            vector_len1 += 1\n            if(done):\n                break\n\n        value_store = np.array(reward_store)\n        for backprop in range(1, (20 if play > 21 else play - 1)):\n            value_store[:-backprop] += value_store[backprop:] * (gamma ** backprop)\n        for i in range(len(reward_store)):\n            reward_vector1.append([reward_store[i]])\n            value_vector1.append([value_store[i]])\n\n        print(\"InitialEpoch : \" + str(epoch))\n\n    for epoch in range(1, 50001):\n\n\n        curreward1 = 0.\n        state = env1.reset()\n        reward_store = []\n        for play in range(200):\n            prev_state = state\n\n            action = learner1.get_action_stochastic(state)[0]\n            state, reward, done, _ = env1.step(action)\n            \n            state_vector1.append(prev_state)\n            action_vector1.append(action)\n            survive_vector1.append([ 0.0 if done else 1.0])\n            next_state_vector1.append(state)\n            reward_store.append(reward)\n\n            vector_len1 += 1\n            if(done):\n                break\n            curreward1 += reward\n\n            env1.render()\n\n        value_store = np.array(reward_store)\n        for backprop in range(1, (20 if play > 21 else play - 1)):\n            value_store[:-backprop] += value_store[backprop:] * (gamma ** backprop)\n        for i in range(len(reward_store)):\n            reward_vector1.append([reward_store[i]])\n            value_vector1.append([value_store[i]])\n        print(\"Reward1 : \" + str(curreward1) + \" Step : \" + str(play))\n\n        qs1 = 0.\n        vs1 = 0.\n        ps1 = 0.\n        for history in range(8):\n            dic = random.sample(range(vector_len1), 256)\n\n            state_vector_dic = [state_vector1[x] for x in dic]\n            next_state_vector_dic = [next_state_vector1[x] for x in dic]\n            action_vector_dic = [action_vector1[x] for x in dic]\n            reward_vector_dic = [reward_vector1[x] for x in dic]\n            survive_vector_dic = [survive_vector1[x] for x in dic]\n\n            q, v, p = learner1.optimize_batch(state_vector_dic, next_state_vector_dic, action_vector_dic, reward_vector_dic, survive_vector_dic)\n\n            qs1 += np.mean(q)\n            vs1 += np.mean(v)\n            ps1 += np.mean(p)\n        learner1.value_network_update()\n        qs1 /= 8.\n        vs1 /= 8.\n        ps1 /= 8.\n        print(\"Epoch \" + str(epoch) + \" Learner 1 Mean Qvalue Training : \" + str(qs1))\n        print(\"Epoch \" + str(epoch) + \" Learner 1 Mean Value Training : \" + str(vs1))\n        print(\"Epoch \" + str(epoch) + \" Learner 1 Mean Policy Training : \" + str(ps1))\n\n\n        vec_trunc = vector_len1 // 20\n        state_vector1 = state_vector1[vec_trunc:]\n        next_state_vector1 = next_state_vector1[vec_trunc:]\n        action_vector1 = action_vector1[vec_trunc:]\n        reward_vector1 = reward_vector1[vec_trunc:]\n        survive_vector1 = survive_vector1[vec_trunc:]\n        value_vector1 = value_vector1[vec_trunc:]\n        vector_len1 -= vec_trunc\n\n\n        log_file.write(str(epoch) + \"\\t\" +\n            str(curreward1) + \"\\t\" + str(qs1) + \"\\t\" + str(vs1) + \"\\t\" + str(ps1) + \"\\n\")\n        if epoch % 1000 == 0:\n            saver.save(sess, LOG_DIR + \"log_\" + str(epoch) + \".ckpt\")\n\n\nenv.close()","sub_path":"mujoco_learn/old/basic_train2 (copy).py","file_name":"basic_train2 (copy).py","file_ext":"py","file_size_in_byte":4927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"636227562","text":"import lib.pyt.yolov1.predict as yolo_predict\nimport lib.pyt.yolov1.model as yolo_net\nimport cv2\n\nimport torch\nimport os\nimport tqdm\nimport lib.utils.Config as cfg\n\ndatatype = \"voc\"\ndataconfig = cfg.VOC_Data_Config()\n\nmodelpath = \"outputs/yolov1_voc_best.pth\"\nmodel = yolo_net.resnet50()\nmodel.load_state_dict(torch.load(modelpath, map_location='cpu'))\n\nif torch.cuda.is_available():\n    model = model.cuda()\n\nmodel.eval()\npredict = yolo_predict.YOLOPredict(model, \"voc\")\n\npath = os.path.join(\"../testImages\")\nlistfile = os.listdir(path)\nfor file in tqdm.tqdm(listfile):\n    if file.endswith(\"jpg\"):\n        filename = file.split(\".\")[0]\n        img = cv2.imread(os.path.join(path,\"%s.jpg\" % filename))\n        predict.drawBoxes(img, {\"img_id\" : filename,\n                                \"height\" : img.shape[0],\n                                \"width\" : img.shape[1]\n                                }, output=\"outputs/%s.jpg\" % filename)","sub_path":"03.DeepLearning-Detection/01.yolov1/voc_pyt_test.py","file_name":"voc_pyt_test.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"238982172","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('',views.index,name='index'),\n    path('details/',views.details,name='details'),\n    path('submit/',views.submit,name='submit'),\n    path('update/<int:id>/',views.update,name='update'),\n    path('delete/<int:id>/', views.delete, name='delete'),\n]","sub_path":"crud/core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"541235543","text":"from OpenGL.GLUT import *\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\n\nimport numpy as np\nimport Cloth\nimport MatrixVector as MV\nimport math\nimport time\n\nnW = 7\nnH = 7\nclothWidth = 2\nclothHeight = 2\n\n\nVerts, Springs, L0 = Cloth.create(nW, nH, clothWidth, clothHeight)\n\nVel = np.zeros(shape=(len(Verts),3), dtype = np.float64)\nForce = np.zeros(shape=(len(Verts),3), dtype = np.float64)\nMass = [2.0 for x in range(len(Verts))]\ngravity = np.array([0, -9.8, 0])\nK = 10000000.0\ndamp = 0.1\ndt = 0.01\n\ndef flatten(x) :\n    flattenArray = []\n    for lists in x :\n        for elements in lists :\n            flattenArray.append(elements)\n    return flattenArray\n\ndef computeForces(springs, verts) :\n    global Vel, K\n    force = np.zeros((len(verts),3), dtype=np.float64)\n    for i in range(0, len(springs)):\n        idx0, idx1 = springs[i][0], springs[i][1]\n        p0  , p1   = verts[idx0], verts[idx1]\n        L = np.linalg.norm(p1 - p0)\n        dir = (p1 - p0) / L\n        Vrel = Vel[idx0] - Vel[idx1]\n        F = K * (L-L0[i])* dir - damp*Vrel  # spring force + damping force\n        force[idx0] = force[idx0] + F\n        force[idx1] = force[idx1] - F\n\n    for i in range(0, len(verts)) :\n        force[i] = force[i] + gravity*Mass[i]\n    return force\n\n\ndef IntegrateJacobiIteration(locs, springs, force, vel, h) :\n    global L0, Mass, gravity, K, nW\n\n    Mii  = np.zeros(shape=(len(locs), 3, 3))\n    Jxii = np.zeros(shape=(len(locs), 3, 3))\n    Jvii = np.zeros(shape=(len(locs), 3, 3))\n    Jxij = np.zeros(shape=(len(springs), 3, 3))\n    Jvij = np.zeros(shape=(len(springs), 3, 3))\n\n    for i in range(0, len(locs)) :\n        Mii[i] = Mass[i] * np.identity(3)\n\n    for s in range(0, len(springs)) :\n        i,j = springs[s][0], springs[s][1]\n        xji = locs[j]-locs[i]\n        lt  = np.linalg.norm(xji)\n        Jxij[s] = K*(((lt-L0[s])/lt)*np.identity(3) + (L0[s]/(lt**3.0))*np.outer(xji, xji))\n        Jvij[s] = damp*np.identity(3)\n        Jxii[i], Jxii[j] = Jxii[i] - Jxij[s], Jxii[j] - Jxij[s]\n        Jvii[i], Jvii[j] = Jvii[i] - Jvij[s], Jvii[j] - Jvij[s]\n\n    b = np.zeros(shape=(len(locs), 3), dtype = np.float64)\n    deltaV = np.zeros(shape=(len(locs), 3), dtype=np.float64)\n    for i in range(len(locs)) :\n        b[i] = force[i]*h + h*h*Jxii[i].dot(vel[i])\n    for s in range(len(springs)) :\n        i,j = springs[s][0], springs[s][1]\n        b[i] = b[i] + h * h * Jxij[s].dot(vel[j])\n        b[j] = b[j] + h * h * Jxij[s].dot(vel[i])\n\n\n    Minv = np.zeros(shape=(len(locs), 3, 3))\n    for i in range(len(locs)) :\n        Minv[i] = np.linalg.inv(Mii[i] - h * h * Jxii[i] - h * Jvii[i])\n        #deltaV[i] = Minv[i].dot(b[i])\n        if i is not 0 and i is not nW - 1:\n            deltaV[i] = Minv[i].dot(b[i])\n        else:\n            deltaV[i] = np.zeros(shape=(3,))\n\n    for iter in range(1) :\n        for s in range(len(springs)):\n            i, j = springs[s][0], springs[s][1]\n            OffDiag = -h*h * Jxij[s] - h*Jvij[s]\n            b[i] -= OffDiag.dot(deltaV[j])\n            b[j] -= OffDiag.dot(deltaV[i])\n        for i in range(len(locs)):\n            if i is not 0 and i is not nW-1 :\n                deltaV[i] = Minv[i].dot(b[i])\n            else :\n                deltaV[i] = np.zeros(shape=(3,))\n\n\n    return flatten(deltaV)\n\n\ndef IntegrateImplicit(locs, springs, force, vel, h) :\n    global L0, Mass, gravity, K, nW\n    longF = MV.convertLongVector(force)\n    bigM = np.zeros(shape=(len(locs) * 3, len(locs) * 3))\n    Jx   = np.zeros(shape=(len(locs) * 3, len(locs) * 3))\n    Jv   = np.zeros(shape=(len(locs) * 3, len(locs) * 3))\n    for i in range(0, len(locs)) :\n        MV.set33SubMatrix(bigM, i, i, Mass[i] * np.identity(3))\n\n    Jxii = np.zeros(shape=(len(locs), 3, 3))\n    Jvii = np.zeros(shape=(len(locs), 3, 3))\n\n    for spring in range(0, len(springs)) :\n        i,j = springs[spring][0], springs[spring][1]\n        xji = locs[j]-locs[i]\n        lt  = np.linalg.norm(xji)\n        Jxij = K*(((lt-L0[spring])/lt)*np.identity(3) + (L0[spring]/(lt**3.0))*np.outer(xji, xji))\n        Jvij = damp*np.identity(3)\n        Jxii[i], Jxii[j] = Jxii[i] - Jxij, Jxii[j] - Jxij\n        Jvii[i], Jvii[j] = Jvii[i] - Jvij, Jvii[j] - Jvij\n        MV.set33SubMatrixSymetric(Jx, i, j, Jxij)\n        MV.set33SubMatrixSymetric(Jv, i, j, Jvij)\n\n    for i in range(0, len(locs)):\n        MV.set33SubMatrix(Jx, i, i, Jxii[i])\n        MV.set33SubMatrix(Jv, i, i, Jvii[i])\n\n    bigM = bigM - h*h*Jx - h*Jv\n    bigMinv = np.linalg.inv(bigM)\n    longV = MV.convertLongVector(vel)\n    deltaV = bigMinv.dot(longF*h + (h**2)*Jx.dot(longV))\n    return deltaV\n\ndef compute_Int_f_dt(locs, springs, vel, h) :\n    global L0, Mass, gravity, K, nW, damp\n\n    phi  = np.zeros(shape=(len(locs), 3))\n\n    for s in range(0, len(springs)) :\n        i,j = springs[s][0], springs[s][1]\n        xji, vji = locs[j] - locs[i], vel[j] - vel[i]\n        lt  = np.linalg.norm(xji)\n        mi, mj = Mass[i], Mass[j]\n        M = mi*mj/(mi + mj)\n        d = lt - L0[s]\n        dddt = 0\n        xjiHat = np.array([1,0,0])\n\n        if lt > 0.0:\n            xjiHat = xji / lt\n            dldt = np.dot(xjiHat, vji)\n        else:\n            dldt = 0.0\n\n        omega = math.sqrt(K/M) * math.sqrt(1.0 - damp**2.0/(4.0*K*M))\n        alpha = damp/ (2.0*M)\n\n        A = math.exp(alpha*3.141592/omega)*np.sqrt(d**2.0 + (dldt**2.0) * (mi * mj) / (K * (mi + mj)))\n\n        dDA = 0.0\n        arcsine = 0.0\n        if A != 0.0:\n            dDA = d / A;\n            arcsine = np.arcsin(dDA)\n        if d > 0 and dldt < 0:\n            arcsine = np.pi - arcsine\n        if d <= 0:\n            if dldt > 0:\n                arcsine = 2.0 * np.pi + arcsine\n            else:\n                arcsine = np.pi - arcsine\n\n        T0 = arcsine / omega\n        T1 = T0 + h\n\n        Et = np.exp(-alpha * T0)\n        Eh = np.exp(-alpha * h )\n        coeff = -  K * A  /  (omega**2.0 + alpha**2.0)\n        st = np.sin(omega * T0)\n        ct = np.cos(omega * T0)\n        sh = np.sin(omega * h)\n        ch = np.cos(omega * h)\n\n        phi_ij =  coeff * Et * (\n        st * (Eh * (alpha * ch - omega * sh) - alpha) + ct * (Eh * (alpha * sh + omega * ch) - omega))\n        phi_ij = phi_ij\n\n        #phi[i] += (phi_ij*mj/((mi+mj))) * xjiHat\n        #phi[j] -= (phi_ij*mi/((mi+mj))) * xjiHat\n\n        phi[i] += (phi_ij / 2) * xjiHat\n        phi[j] -= (phi_ij / 2) * xjiHat\n\n\n    for i in range(len(locs)) :\n            phi[i] += h*gravity\n\n    return phi\n\ndef IntegrateHybrid(locs, springs, force, vel, h) :\n    global L0, Mass, gravity, K, nW, damp\n\n    Mii  = np.zeros(shape=(len(locs), 3, 3))\n    Jxii = np.zeros(shape=(len(locs), 3, 3))\n    Jxij = np.zeros(shape=(len(springs), 3, 3))\n\n\n    for i in range(0, len(locs)) :\n        Mii[i] = Mass[i] * np.identity(3)\n\n    for s in range(0, len(springs)) :\n        i,j = springs[s][0], springs[s][1]\n        xji = locs[j]-locs[i]\n        lt  = np.linalg.norm(xji)\n        Jxij[s] = K*(((lt-L0[s])/lt)*np.identity(3) + (L0[s]/(lt**3.0))*np.outer(xji, xji))\n        Jxii[i], Jxii[j] = Jxii[i] - Jxij[s], Jxii[j] - Jxij[s]\n\n\n    b = np.zeros(shape=(len(locs), 3), dtype = np.float64)\n    deltaV = np.zeros(shape=(len(locs), 3), dtype=np.float64)\n\n    phi = compute_Int_f_dt(locs, springs, vel, h)\n\n    for i in range(len(locs)) :\n        b[i] = force[i]*h +  h*h*Jxii[i].dot(vel[i])\n    for s in range(len(springs)) :\n        i,j = springs[s][0], springs[s][1]\n        b[i] = b[i] + h * h * Jxij[s].dot(vel[j])\n        b[j] = b[j] + h * h * Jxij[s].dot(vel[i])\n\n\n    for i in range(len(locs)) :\n        b[i] = 0.5*(b[i] + phi[i])\n\n    Minv = np.zeros(shape=(len(locs), 3, 3))\n    for i in range(len(locs)) :\n        Minv[i] = np.linalg.inv(Mii[i] - h * h * Jxii[i])\n        deltaV[i] = Minv[i].dot(b[i])\n\n    deltaV = flatten(deltaV)\n    return deltaV\n\ndef IntegrateHarmonic(locs, springs, force, vel, h) :\n    global L0, Mass, gravity, K, nW, damp\n\n    deltaV = np.zeros(shape=(len(locs), 3), dtype=np.float64)\n    phi = compute_Int_f_dt(locs, springs, vel, h)\n    for i in range(len(phi)) :\n        phi[i] = phi[i]/Mass[i]\n    deltaV = flatten(phi)\n    return deltaV\n\ndef IntegrateEuler(locs, springs, force, vel, h) :\n    global L0, Mass, gravity, K, nW, damp\n\n    deltaV = np.zeros(shape=(len(locs), 3), dtype=np.float64)\n    for i in range(0, len(locs)):\n        deltaV[i] = force[i]*h/Mass[i]\n\n    deltaV = flatten(deltaV)\n    return deltaV\n\ndef updateState(locs, vel, deltaV, h) :\n    for i in range(0, len(locs)):\n        vel[i] = vel[i] + deltaV[i*3:i*3+3]\n        if i is not 0 and i is not nW-1:\n            locs[i] = locs[i] + vel[i] * h\n        else :\n            vel[i] = np.zeros(shape=(3,))\n\n\n\ndef moveImplicit():\n    global Verts, Springs, Vel, Force, Mass, K, damp, L0, dt, gravity\n    # Force Initialization\n\n    s = time.clock()\n    Force = computeForces(Springs, Verts)\n    #deltaV = IntegrateEuler(Verts, Springs, Force, Vel, dt)\n    deltaV = IntegrateImplicit(Verts, Springs, Force, Vel, dt)\n    #deltaV = IntegrateJacobiIteration(Verts, Springs, Force, Vel, dt)\n    #deltaV = IntegrateHybrid(Verts, Springs, Force, Vel, dt)\n    #deltaV = IntegrateHarmonic(Verts, Springs, Force, Vel, dt)\n    e = time.clock()\n    print(e-s)\n\n\n    updateState(Verts, Vel, deltaV, dt)\n    glutPostRedisplay()  ## Hey! Draw Function! Draw!\n\n\n\ndef draw():\n    global clothWidth, clothHeight\n    glClear(GL_COLOR_BUFFER_BIT)\n    glMatrixMode(GL_PROJECTION)\n    glLoadIdentity()\n    gluPerspective(60, 1.0, 0.1, 1000)\n    glMatrixMode(GL_MODELVIEW)\n    glLoadIdentity()\n    gluLookAt(clothWidth*0.5, 1.0 - clothHeight*0.5, clothHeight*1.5, clothWidth*0.5, 1.0 - clothHeight*0.5, 0.0, 0.0, 1.0, 0.0)\n\n    for i in range(0, len(Springs)):\n        Cloth.drawSpring(Verts[Springs[i][0]], Verts[Springs[i][1]])\n    glFlush()\n\nglutInit(sys.argv)\nglutInitDisplayMode(GLUT_SINGLE | GLUT_RGBA)\nglutInitWindowSize(250, 250)\nglutInitWindowPosition(100, 100)\nglutCreateWindow(b\"OpenGL with Python\")\nglClearColor(0.5, 0.5, 0.5, 1)\nglutDisplayFunc(draw)\nglutIdleFunc(moveImplicit)\nglutMainLoop()\n\n# End of program","sub_path":"ExCode/ImplicitMethods/ClothMethods20171124.py","file_name":"ClothMethods20171124.py","file_ext":"py","file_size_in_byte":10001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"13433155","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef clip(idx):\n    return int(max(idx,0))\n\n\ndef preprocess(image, erodeK, blurK, blurSigma, lowT, upT):\n    \"\"\"\n      Preprocess an image by eroding (opt.), blurring (opt.), and then applying\n      Canny edge detector. \n\n      Args\n      - image: numpy nd-array of dim (m, n, c)\n      - erodeK: size of kernal for erode operation. Dimension: (erodeK, erodeK)\n      - blurK: size of kernal for gaussing blur. Dimension: (blurK, blurK) \n      - blurSigma: sigma used for applying gaussian blur. \n      - lowT: low threshold value for Canny operator.\n      - highT: high threshold value for Canny operator.  \n      Returns\n      - edge-image: numpy nd-array of dim (m, n)\n\n      HINTS:\n      - Apply your own preprocessing, if required. Shortly describe how your implementation\n        is different, in the writeup. \n    \"\"\"\n    gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    eroded_img = cv2.erode(gray_img, np.ones((erodeK,erodeK),np.uint8), 1)\n    smoothed_img = cv2.blur(eroded_img, (blurK,blurK), blurSigma)    \n    # Canny\n    edged = cv2.Canny(smoothed_img, lowT, upT)\n    return edged\n    \n\ndef hough_peaks(H, numpeaks=1, threshold=100, nhood_size=5):\n    \"\"\"\n      Returns the top numpeaks from the accumulator H\n\n      Args\n      - H: Hough Space (Voting Accumulator)\n      - numpeaks: Number of peaks to return\n      - threshold: Minimum number of votes to get considered for picked\n      - nhood_size: neighborhood size. Only one peak will be chosen from \n        any neighborhood.   \n      Returns\n      - peak coordinates: numpy nd-array of dim (numpeaks, 2)\n\n\n    \"\"\"\n    peaks = np.zeros((numpeaks,2), dtype=np.uint64)\n    temp_H = H.copy()\n    for i in range(numpeaks):\n        _,max_val,_,max_loc = cv2.minMaxLoc(temp_H) # find maximum peak\n        if max_val > threshold:\n            peaks[i] = max_loc\n            (c,r) = max_loc\n            t = nhood_size//2.0\n            temp_H[clip(r-t):int(r+t+1), clip(c-t):int(c+t+1)] = 0\n        else:\n            peaks = peaks[:i]\n            break\n    return peaks[:,::-1]\n    \n\ndef hough_lines_draw(img, outfile, peaks, rhos, thetas):\n    \"\"\"\n      Returns the image with hough lines drawn.\n      Args\n      - img: Image on which lines will be drawn\n      - outfile: The output file. The file will be saved. \n      - peaks: peaks returned by hough_peaks\n      - rhos: array of rhos used in Hough Space  \n      - thetas: array of thetas used in Hough Space\n      Returns\n      - img: after drwaing lines on it.\n\n    \"\"\"\n    for peak in peaks:\n        rho = rhos[peak[0]]\n        theta = thetas[peak[1]] * np.pi / 180.0\n        a = np.cos(theta)\n        b = np.sin(theta)\n        x0 = a*rho\n        y0 = b*rho\n        x1 = int(x0 + 1000*(-b))\n        y1 = int(y0 + 1000*(a))\n        x2 = int(x0 - 1000*(-b))\n        y2 = int(y0 - 1000*(a))\n        cv2.line(img, (x1,y1),(x2,y2),(0,0,255),2)\n    cv2.imwrite(outfile, img)\n    return img\n\n\ndef hough_circles_draw(img, outfile, peaks, radius):\n    \"\"\"\n      Returns the image with hough circles drawn.\n      Args\n      - img: Image on which circles will be drawn\n      - outfile: The output file. The file will be saved. \n      - peaks: peaks returned by hough_peaks. Contails tuple of (y, x) coordinates. \n      - radius: radius of the circle  \n      Returns\n      - img: after drwaing circles on it.\n\n    \"\"\"\n    for peak in peaks:\n        cv2.circle(img, tuple(peak[::-1]), radius, (0,255,0), 2)\n    cv2.imwrite(outfile, img)\n    return img\n\n\n\n\ndef im2single(im):\n  im = im.astype(np.float32) / 255\n  return im\n\ndef single2im(im):\n  im *= 255\n  im = im.astype(np.uint8)\n  return im\n\n\ndef load_image(path):\n  return im2single(cv2.imread(path))[:, :, ::-1]\n\ndef save_image(path, im):\n  return cv2.imwrite(path, single2im(im.copy())[:, :, ::-1])\n\n\ndef load_image_u8(path):\n  return cv2.imread(path)[:, :, ::-1]\n\ndef save_image_u8(path, im):\n  return cv2.imwrite(path, im[:, :, ::-1])\n\n\n\n\ndef plotFigure(image, dimension=3):\n\n    # display the images in RGB. \n\n    plt.figure(figsize=(dimension,dimension))\n    plt.imshow(image[:,:,::-1])\n    return\n\n\ndef plotFigureGray(image, dimension=3):\n\n    # display the image in grayscale. \n\n    plt.figure(figsize=(dimension,dimension))\n    plt.imshow(image,cmap='gray')\n    return\n\n\n\n\n\n\n","sub_path":"Harris_Corner_Detection_And_SIFT/code/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"449306983","text":"from xml.etree import ElementTree\n\ntree = ElementTree.parse(\"example.xml\")\nroot = tree.getroot()\n\nprint(root)\nprint(root.tag, root.attrib)\n\nfor child in root:\n    print(child.tag, child.attrib)\nprint(root[0][0].text)\n\nfor element in root.iter(\"scores\"): # root.iter - перебор всех интересующих элементов в поддереве\n                                    # root.findall() - перебор только лишь среди детей\n    score_sum = 0\n    for child in element:\n        score_sum += float(child.text)\n    print(score_sum)\n\n\n\ngreg = root[0]\nmodule1 = next(greg.iter(\"module1\"))\nprint(module1, module1.text)\nmodule1.text = str(float(module1.text) + 30)\n\ntree = ElementTree.parse(\"example_modified.xml\")\nroot = tree.getroot()\ngreg = root[0]\n\ndescription = ElementTree.Element(\"description\")\ndescription.text = \"Showed excellent skills during the course\" # add tag\ngreg.append(description)\n\ndescription = greg.find(\"description\")\ngreg.remove(description) # remove tag\n\ncertificate = greg[2]\ncertificate.set(\"type\", \"with distinction\")\ntree.write(\"example_modified.xml\") # точная копия нашего дерева","sub_path":"stepik_course/xml_work.py","file_name":"xml_work.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"237830132","text":"#!/usr/bin/python3.5\n# -*-coding:utf-8 -*\n\nimport requests\nimport sys\nimport time\nimport ctypes\n\nclass DownloadFile:\n\n\t\"\"\" Thread chargé du bench de téléchargement descendant. \"\"\"\n\n\tdef __init__(self, url, directory, tps_exec):\n\t\t\"\"\" Constructeur. \"\"\"\n\t\tself.url = url\n\t\tself.directory = directory\n\t\tlibc = ctypes.cdll.LoadLibrary('libc.so.6')\n\t\tpid = libc.syscall(186)\n\t\ttps_exec = float(float(tps_exec) * 1000)\t\t# Conversion des sec en ms\n\n\t\t# Boucle infinie (ou presque).\n\t\tprint (\"Processus en download\", pid, \"lancé.\")\n\t\telapsed = 0.0\n\t\tstart = time.time()\n\t\twhile elapsed < tps_exec:\n\t\t\tlocalFilename = self.url.split('/')[-1]\n\t\t\twith open(self.directory + '/' + localFilename, 'wb') as f:\n\t\t\t\ttry:\n\t\t\t\t\tr = requests.get(self.url, stream=True)\n\t\t\t\t\ttotal_length = r.headers.get('content-length')\n\t\t\t\t\tdl = 0\n\t\t\t\t\tif total_length is None: # no content length header\n\t\t\t\t\t\tfor chunk in r.iter_content(1024):\n\t\t\t\t\t\t\tdl += len(chunk)\n\t\t\t\t\t\t\tf.write(chunk)\n\t\t\t\t\telse:\n\t\t\t\t\t\tdl = total_length\n\t\t\t\t\t\tf.write(r.content)\n\t\t\t\texcept ConnectionError as err:\n\t\t\t\t\tprint (\"Erreur requête DOWNLOAD : {0}\".format(err))\n\t\t\telapsed = time.time() - start\n\t\tprint (\"Le processus\", pid, \"s'est arrêté proprement.\")\n\ndef main():\n\tif len(sys.argv) == 4:\n\t\turl = sys.argv[1]\n\t\tdirectory = sys.argv[2]\n\t\ttps_exec = sys.argv[3]\n\telse:\n\t\tprint (\"Syntaxe de la commande:\", sys.argv[0], \"<url> <local_dir> <temps_exec_en_sec>\")\n\n\tdl = DownloadFile(url, directory, tps_exec)\n\nif __name__ == \"__main__\":\n\tmain()","sub_path":"download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"362483172","text":"import hashlib\n\n\ndef email(first, last):\n    f, l = first.lower(), last.lower()\n    h = hashlib.new('md5')\n    fn = f + l\n    h.update(fn.lower().encode('utf-8'))\n    digits = str(int('0x' + h.hexdigest(), 0))[0:3]\n    return f[0] + l + digits + '@example.com'\n","sub_path":"b88d80170_fake_data/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"594123431","text":"from Parser.Utils import *\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport xml.etree.ElementTree as ET\nimport os\nimport datetime\nimport json\nimport time\n\nURL_PRIMARK_HOME = \"https://www.primark.com/en/products\"\n\n\n\n\ndef get_categories() -> pd.DataFrame:\n    raw_html = simple_get(URL_PRIMARK_HOME)\n    html = BeautifulSoup(raw_html, 'html.parser')\n    output = []\n\n    results = html.findAll(name=\"nav\", attrs={\"class\": \"products-menu blue-menu\"})\n    for result in results:\n        # ignore role nodes\n        if \"role\" in result.attrs:\n            continue\n\n        # convert result to xml\n        root_node = ET.fromstring(str(result))\n        for node in root_node[0]:\n\n            # ignore node with class <> \"active has-sub\"\n            if node.attrib[\"class\"] != 'active has-sub':\n                continue\n\n            # get category\n            category = node[0].attrib[\"data-category\"]\n\n            # for all sub category\n            for category_node in node[1]:\n                # get sub category name\n                sub_category = category_node[0].attrib[\"data-breadcrumb\"]\n\n                # if sub category has sub sub category then read them\n                if \"class\" in category_node.attrib and \\\n                        category_node.attrib[\"class\"] == 'active has-sub':\n                    # get info on the sub sub category\n                    for sub_category_node in category_node[1]:\n                        # read sub sub category\n                        sub_sub_category = sub_category_node[0].attrib[\"data-breadcrumb\"]\n                        category_name = sub_category_node[0].attrib[\"data-category\"]\n\n                        output.append({\"taxo1\": category,\n                                       \"taxo2\": sub_category,\n                                       \"taxo3\": sub_sub_category,\n                                       \"category\": category_name})\n\n                else:\n                    # get info on the sub category\n                    category_name = category_node[0].attrib[\"data-category\"]\n                    output.append({\"taxo1\": category,\n                                   \"taxo2\": sub_category,\n                                   \"taxo3\": \"\",\n                                   \"category\": category_name})\n\n    # convert list of dictionary to df and remove duplicates\n    output = pd.DataFrame(output)\n    output = output.drop_duplicates()\n    return output\n\n\ndef get_inventory(taxo1, taxo2, taxo3, category):\n    print(\"Category: {}\".format(category))\n    time.sleep(1)\n\n    products = []\n    i = 1\n    while True:\n        data = simple_get(\"https://m.primark.com/admin/productsapi/search//{}/{}\".format(category, i))\n        i += 1\n        time.sleep(1)\n        if data is None:\n            break;\n        data = json.loads(data)\n\n        if len(data[\"Products\"]) == 0:\n            break\n\n        for node in data[\"Products\"]:\n            products.append({\"shop\": \"Primark\",\n                             \"id\": node[\"BusinessId\"],\n                             \"reference\": node[\"Sku\"],\n                             \"name\": node[\"Title\"],\n                             \"price\": int(node[\"PriceInteger\"]) * 100 + int(node[\"PriceDecimal\"]),\n                             \"taxo1\": taxo1,\n                             \"taxo2\": taxo2,\n                             \"taxo3\": taxo3})\n    return (pd.DataFrame(products))\n\n\ndef parse_primark():\n    # get url to analyse\n    df_url = get_categories()\n    df_url = df_url[df_url.taxo1.isin([\"men\", \"women\", \"kids\"])]\n    #  get inventory\n\n    df_list = [get_inventory(taxo1=row[\"taxo1\"],\n                             taxo2=row[\"taxo2\"],\n                             taxo3=row[\"taxo3\"],\n                             category=row[\"category\"])\n               for index, row in df_url.iterrows()]\n\n    df = pd.concat(df_list)\n    now = datetime.datetime.now()\n    df.to_csv(os.path.join(DIRECTORY_TMP, \"primark_{}-{}-{}.csv\".format(now.year, now.month, now.day)))\n\n\nparse_primark()\n","sub_path":"Primark.py","file_name":"Primark.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"456256601","text":"def gen(x):\n    if x == 0:\n        return 1\n    else:\n        return (161 * gen(x - 1) + 2457) % (2 ** 24)\n\n\ncnt = 0\nfor k in range(100):\n    if gen(k) % 2 == 0:\n        cnt += 1\n\nprint(cnt)\n","sub_path":"ci2015w/ci_2015w_2.py","file_name":"ci_2015w_2.py","file_ext":"py","file_size_in_byte":191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"645594285","text":"import os\nimport sys\nimport csv\nimport time\nimport shutil\nimport urllib.request\nimport zipfile\nimport cProfile\nimport datetime\nimport requests\nimport collections\nimport configparser\nfrom selenium import webdriver\nfrom yahoo_finance import Share\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nTHIS_DIR = os.getcwd()\n\n\ndef get_symbol(company):\n    url = \"http://d.yimg.com/autoc.finance.yahoo.com/autoc?query={}&region=1&lang=en\".format(company)\n    result = requests.get(url).json()\n    symbol = result['ResultSet']['Query']\n    if ' ' in symbol:\n        return '???'\n    return symbol\n\n\ndef main():\n    config = configparser.ConfigParser()\n    try:\n        config.read(\"config.txt\")\n        username = config.get(\"configuration\", \"username\")\n        password = config.get(\"configuration\", \"password\")\n\n    except:\n        print(\"config.txt not found... Creating new in {}\".format(THIS_DIR))\n        print(\"PLEASE FILL THE CONFIG FILE\")\n        cfgfile = open(os.path.join(THIS_DIR, \"config.txt\"), 'w')\n        config.add_section(\"configuration\")\n        config.set('configuration', 'username', 'degiro_username')\n        config.set('configuration', 'password', 'degiro_password')\n        config.write(cfgfile)\n        cfgfile.close()\n        input(\"Press [Enter] to end program...\")\n        sys.exit()\n\n    if username == 'degiro_username' or password == 'degiro_username':\n        print(\"WARNING: You haven't yet filled config.txt, aborting program...\")\n        input(\"Press [Enter] to end program...\")\n        sys.exit()\n\n    # browser = webdriver.Chrome(executable_path=\"C:\\\\Users\\\\Son Goku ssj4\\\\Downloads\\\\chromedriver_win32\\\\chromedriver.exe\")\n    # browser = webdriver.PhantomJS(executable_path=\"C:/Users/Son Goku ssj4/Downloads/phantomjs-2.1.1-windows/bin/phantomjs.exe\")\n\n    if not os.path.isfile('phantomjs.exe'):\n        print(\"phantomjs.exe is missing... Downloading version 2.1.1-windows now...\")\n        filename, _ = urllib.request.urlretrieve(\n            'https://bitbucket.org/ariya/phantomjs/downloads/phantomjs-2.1.1-windows.zip',\n            'phantomjs-2.1.1-windows.zip'\n        )\n        print(\"Download complete...\")\n        print(\"Unzipping file and copying phantomjs.exe into root folder...\")\n        zp = zipfile.ZipFile(filename)\n        phantomjs_file = [file for file in zp.namelist() if 'phantomjs.exe' in file][0]\n        zp.extract(phantomjs_file, '.')\n        zp.close()\n        shutil.copy(phantomjs_file, '.')\n        print(\"Copying is done... Removing temporary folders/files...\")\n        os.remove('phantomjs-2.1.1-windows.zip')\n        shutil.rmtree(phantomjs_file.split('/')[0])\n        print(\"Done... Program continues...\\n\")\n\n    if os.name == 'nt':\n        browser = webdriver.PhantomJS(executable_path=\"phantomjs.exe\")\n    elif os.name == 'posix':\n        browser = webdriver.PhantomJS()\n\n\n    # browser.set_page_load_timeout(30)\n    browser.get(\"https://trader.degiro.nl/login/secure\")\n    browser.maximize_window()\n    # LOGIN\n    WebDriverWait(browser, 5).until(EC.presence_of_element_located(\n        (By.XPATH, \"//input[@name='username']\"))\n    )\n    browser.find_element_by_xpath(\"//input[@name='username']\").send_keys(username)\n    browser.find_element_by_xpath(\"//input[@name='password']\").send_keys(password)\n    browser.find_element_by_xpath(\"//button[@type='submit']\").click()\n\n    # Wait for page to load\n    try:\n        WebDriverWait(browser, 20).until(EC.presence_of_element_located(\n            (By.XPATH, \"//span[@data-dg-watch-property='valueByCostsType']\"))\n        )\n    except:\n        print(\"Please check your config.txt for correct login informations...\")\n        print(\"Press [Enter] to end program...\")\n        input()\n        sys.exit()\n\n    time.sleep(3)  # wait 3 seconds to load data\n\n    while True:\n\n        # Save time stamp\n        datestamp = datetime.datetime.now().strftime('%Y-%m-%d %H.%M.%S')\n\n        browser.find_element_by_xpath(\"//*[@id='period-type']/a\").click()\n        browser.find_element_by_xpath(\"//*[@id='ui-select-choices-row-0-0']/div\").click()  # sel DIFF_DAY\n        diff_day = browser.find_element_by_xpath(\"//span[@data-dg-watch-property='valueByPeriod']\").text\n\n        # Save TotalDifference amount\n        browser.find_element_by_xpath(\"\"\"//*[@id=\"period-type\"]/a/span[3]\"\"\").click()\n        browser.find_element_by_xpath(\"\"\"//*[@id=\"ui-select-choices-row-0-1\"]/div\"\"\").click()  # sel DIFF_TOTAL\n        diff_all = browser.find_element_by_xpath(\"//span[@data-dg-watch-property='valueByPeriod']\").text\n\n        # Save other informations\n        portfolio = browser.find_element_by_xpath(\"//span[@data-dg-watch-property='portfolio']\").text\n        fonds = browser.find_element_by_xpath(\"//span[@data-dg-watch-property='cash']\").text\n        for_use = browser.find_element_by_xpath(\"//span[@data-dg-watch-property='valueByCostsType']\").text\n        total = browser.find_element_by_xpath(\"//span[@data-dg-watch-property='total']\").text\n\n        # Print results into terminal\n        print(\"\"\"Date: {}\n        Diff all/today: ... {} / {}\n        Fonds: ... {}\n        For use: ... {}\n        Portfolio: ... {}\n        TOTAL: ... {}\n        \"\"\".format(datestamp, diff_all, diff_day, fonds, for_use, portfolio, total))\n\n        # Save results into file\n        filename = os.path.join(THIS_DIR, \"degiro_data.csv\")\n        file_exists = os.path.isfile(filename)\n\n        with open(filename, 'a') as csvfile:\n            headers = ['Date', 'DiffAll', 'DiffDay', 'Fonds', 'ForUse', 'Portfolio', 'Total']\n            writer = csv.DictWriter(csvfile, delimiter=',', lineterminator='\\n', fieldnames=headers)\n\n            if not file_exists:\n                writer.writeheader()  # file doesn't exist yet, write a header\n\n            writer.writerow({\n                'Date': datestamp,\n                'DiffAll': diff_all,\n                'DiffDay': diff_day,\n                'Fonds': fonds,\n                'ForUse': for_use,\n                'Portfolio': portfolio,\n                'Total': total\n            })\n\n        browser.find_element_by_xpath(\"//a[@data-dg-href='home.portfolio']\").click()  # click on first in list of three\n        time.sleep(1)\n\n        titles = browser.find_elements_by_xpath('//a[@class=\"portfolio__table-cell-product-link\"]')\n        sizes = browser.find_elements_by_xpath('//span[@data-dg-watch-property=\"size\"]')\n        prices = browser.find_elements_by_xpath('//span[@data-dg-watch-property=\"price\"]')\n        diff_days = browser.find_elements_by_xpath('//span[@data-dg-watch-property=\"todayPl\"]')\n        diffs_total = browser.find_elements_by_xpath('//span[@data-dg-watch-property=\"totalPl\"]')\n        changes = browser.find_elements_by_xpath('//span[@data-dg-watch-property=\"change\"]')\n        values = browser.find_elements_by_xpath('//span[@data-dg-watch-property=\"value\"]')\n\n        stocks = {}\n        for title, size, price, diff_day, diff_total, change, value \\\n            in zip(titles, sizes, prices, diff_days, diffs_total, changes, values):\n            values = {}\n            stock_name = title.get_attribute('Title')\n            values.update({'size': size.text})\n            values.update({'price': price.text})\n            values.update({'diff_day': diff_day.text})\n            values.update({'diff_total': diff_total.text})\n            values.update({'change': change.text})\n            values.update({'value': value.text})\n\n            stocks.update({stock_name: values})\n\n        ordered_stocks = collections.OrderedDict(sorted(stocks.items()))\n\n        max_stock_name_len = max([len(stock) for stock in stocks.keys()])\n        print(\"{:<{_len}} {:>14} {:>12} {:>12} {:>12} {:>12} {:>12} {:>12}\".format(\n              'Stock Name', 'Symbol', 'Size', 'Price [$]', 'Value [Kč]', 'Change [%]', 'Diff Day', 'Diff Total',\n              _len=max_stock_name_len))\n\n        for stock_name, vals in ordered_stocks.items():\n            size = vals['size']\n            price = vals['price']\n            value = vals['value']\n            change = vals['change']\n            diff_day = vals['diff_day']\n            diff_total = vals['diff_total']\n\n            symbol = get_symbol(stock_name)\n\n            print(\"{:<{_len}} {:>14} {:>12} {:>12} {:>12} {:>12} {:>12} {:>12}\".format(\n                stock_name, symbol, size, price, value, change, diff_day, diff_total, _len=max_stock_name_len))\n\n        print(\"Next auto refresh in: 10 minutes...\")\n        # time.sleep(600)\n        sys.exit()\n\n    input(\"Press [Enter] to end program...\")\n\n\nif __name__ == '__main__':\n    cProfile.run('main()')\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"463866359","text":"import matplotlib.pyplot as plt\n\nX0, Y0 = [], []\nfor line in open('test_time_integrators0.txt', 'r'):\n  values = [float(s) for s in line.split()]\n  X0.append(values[0])\n  Y0.append(values[1])\n\nX1, Y1 = [], []\nfor line in open('test_time_integrators1.txt', 'r'):\n  values = [float(s) for s in line.split()]\n  X1.append(values[0])\n  Y1.append(values[1])\n\nX2, Y2 = [], []\nfor line in open('test_time_integrators2.txt', 'r'):\n  values = [float(s) for s in line.split()]\n  X2.append(values[0])\n  Y2.append(values[1])\n\n\nplt.title('Runetime of the different integrators depending on N (tau=0.06)')\nplt.xlabel('Number of Points N')\nplt.ylabel('Time / s')\nplt.xscale('log')\nplt.ylim(top=3)\nplt.plot(X0, Y0, '-ok', color='black', label='Euler')\nplt.plot(X1, Y1, '-ok', color='blue',  label='Strang Splitting')\nplt.plot(X2, Y2, '-ok', color='red',   label='Cranck Nickolson')\nplt.legend()\nplt.savefig('Time_IntegratorsN006')\n","sub_path":"time_integratorsN006.py","file_name":"time_integratorsN006.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"620025324","text":"def convert_odds(tup):\n    a, b = tup[0], tup[1]\n    return b / (a + b)\n\n\ndef find_k(odds_dict, booksum):\n    lower_bound, upper_bound = 0, 2\n    for repeat in range(100):\n        k = (lower_bound + upper_bound) / 2\n        if sum([convert_odds(odds_dict[i])**k for i in odds_dict]) > booksum:\n            lower_bound = k\n        else:\n            upper_bound = k\n    return k\n\n\nFA_odds_dict = {'Man City': (4, 9),\n            'Liverpool': (4, 7),\n            'Chelsea': (8, 11),\n            'Tottenham': (1, 1),\n            'Leicester': (11, 10),\n            'Arsenal': (6, 5),\n            'Man United': (13, 8),\n            'Sheffield Utd': (15, 8),\n            'West Ham': (15, 8),\n            'Burnley': (9, 4),\n            'Newcastle': (9, 4),\n            'Derby': (3, 1),\n            'Portsmouth': (7, 2),\n            'Sheffield Wednesday': (7, 2),\n            'Birmingham': (4, 1),\n            'QPR': (4, 1),\n            'Bournemouth': (9, 2),\n            'Norwich': (1, 1),\n            'West Brom': (1, 1),\n            'Wolves': (9, 2),\n            'Brentford': (5, 1),\n            'Southampton': (5, 1),\n            'Barnsley': (11, 2),\n            'Millwall': (11, 2),\n            'Cardiff': (6, 1),\n            'Reading': (6, 1),\n            'Oxford Utd': (13, 2),\n            'Coventry': (7, 1),\n            'Northampton': (9, 1),\n            'Watford': (9, 1),\n            'Hull': (10, 1),\n            'Blackpool': (11, 1),\n            'Fulham': (12, 1),\n            'Bristol City': (18, 1),\n            'Bristol Rovers': (20, 1),\n            'Carlisle': (28, 1),\n            'Middlesbrough': (40, 1),\n            'Rochdale': (40, 1),\n            'Shrewsbury': (40, 1)}\n\nk = find_k(FA_odds_dict, 8)\nprint('k =', k)\nprint('maximum difference at prob =', (1 / k) ** (1/(k - 1)))\nfor x, y in {i: convert_odds(FA_odds_dict[i]) ** k for i in FA_odds_dict}.items():\n    print(x + ' ' * (20 - len(x)) + str(round(y, 4)))\n","sub_path":"FA_QF.py","file_name":"FA_QF.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"127991257","text":"\"\"\"\nTrains Dagger on behavioral cloning model with expert data by berkely cs234 \nExample usage:\npython dagger.py Humanoid-v2\n\"\"\"\nimport os\nimport argparse\nimport numpy as np\nfrom keras.models import load_model\nimport gym\nimport pickle\nimport load_policy\n\nimport tf_util\nimport tensorflow as tf\nfrom gym import wrappers\n\n\ndef dagger(\n    exp_observations,\n    exp_actions,\n    model,\n    max_steps=1000,\n    roll_outs=30,\n    model_epochs=1,\n    render=False,\n):\n    returns = []\n    observations = []\n    actions = []\n\n    for episode in range(roll_outs):\n        obs = env.reset()\n        done = False\n        totalr = 0.0\n        steps = 0\n        dagger_obs = []\n        dagger_actions = []\n        while not done:\n            action = model.predict(np.reshape(np.array(obs), (1, len(obs))))\n            corrected_action = policy_fn(obs[None, :])\n            obs, r, done, _ = env.step(action)\n\n            totalr += r\n            steps += 1\n            if render:\n                env.render()\n            if steps >= max_steps:\n                break\n\n            dagger_obs.append(obs)\n            dagger_actions.append(corrected_action)\n\n        exp_observations = np.concatenate((exp_observations, dagger_obs), axis=0)\n        exp_actions = np.concatenate((exp_actions, dagger_actions), axis=0)\n        actions = [act.flatten() for act in exp_actions]\n        model.fit(np.array(exp_observations), np.array(actions), epochs=model_epochs)\n\n        returns.append(totalr)\n    return returns\n\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"env_name\", type=str)\n\n    args = parser.parse_args()\n    env_name = args.env_name\n\n    env = gym.make(env_name)\n    env = wrappers.Monitor(\n        env,\n        f\"Saved_Videos/hw1/dagger/{env_name}/\",\n        resume=True,\n        force=True,\n        video_callable=lambda episode: episode % 10 == 0,\n    )\n\n    model = load_model(f\"./models/hw1/{env_name}.h5\")\n\n    file = open(f\"./expert_data/{env_name}.pkl\", \"rb\")\n    data = pickle.load(file)\n    exp_observations, exp_actions = data[\"observations\"], data[\"actions\"]\n    policy_fn = load_policy.load_policy(f\"./experts/{env_name}.pkl\")\n\n    returns = dagger(exp_observations, exp_actions, model, max_steps=1000)\n\n    print(f\"returns = {returns}\")\n    print(f\"mean return = {np.mean(returns)}\")\n    print(f\"std of return = {np.std(returns)}\")\n","sub_path":"hw1/dagger.py","file_name":"dagger.py","file_ext":"py","file_size_in_byte":2384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"99191100","text":"\"\"\"\nMake benchmark irradiance and power forecasts.\n\nThe functions in this module use the\n:py:mod:`solarforecastarbiter.datamodel` objects.\n\"\"\"\nimport pandas as pd\n\nfrom solarforecastarbiter import datamodel, pvmodel\nfrom solarforecastarbiter.reference_forecasts import persistence\n\n\n# maybe rename run_nwp\n# maybe rework in terms of forecast run *issue time* as described in\n# https://solarforecastarbiter.org/usecases/#forecastrun\n# and datamodel.Forecast (as demonstrated in run_persistence below)\ndef run(site, model, init_time, start, end):\n    \"\"\"\n    Calculate benchmark irradiance and power forecasts for a site.\n\n    The meaning of the timestamps (instantaneous or interval average)\n    is determined by the model processing function.\n\n    It's currently the user's job to determine time parameters that\n    correspond to a particular Forecast Evaluation Time Series.\n\n    Parameters\n    ----------\n    site : datamodel.Site\n    model : function\n        NWP model loading and processing function.\n        See :py:mod:`solarforecastarbiter.reference_forecasts.models`\n        for options.\n    init_time : pd.Timestamp\n        NWP model initialization time.\n    start : pd.Timestamp\n        Start of the forecast.\n    end : pd.Timestamp\n        End of the forecast.\n\n    Returns\n    -------\n    ghi : pd.Series\n    dni : pd.Series\n    dhi : pd.Series\n    temp_air : None or pd.Series\n    wind_speed : None or pd.Series\n    ac_power : None or pd.Series\n\n    Examples\n    --------\n    The following code would return hourly average forecasts derived\n    from the subhourly HRRR model.\n\n    >>> from solarforecastarbiter import datamodel\n    >>> from solarforecastarbiter.reference_forecasts import models\n    >>> init_time = pd.Timestamp('20190328T1200Z')\n    >>> start = pd.Timestamp('20190328T1300Z')  # typical available time\n    >>> end = pd.Timestamp('20190329T1300Z')  # 24 hour forecast\n    >>> modeling_parameters = datamodel.FixedTiltModelingParameters(\n    ...     ac_capacity=10, dc_capacity=15,\n    ...     temperature_coefficient=-0.004, dc_loss_factor=0,\n    ...     ac_loss_factor=0)\n    >>> power_plant = datamodel.SolarPowerPlant(\n    ...     name='Test plant', latitude=32.2, longitude=-110.9,\n    ...     elevation=715, timezone='America/Phoenix',\n    ...     modeling_parameters = modeling_parameters)\n    >>> ghi, dni, dhi, temp_air, wind_speed, ac_power = run(\n    ...     power_plant, models.hrrr_subhourly_to_hourly_mean,\n    ...     init_time, start, end)\n    \"\"\"\n\n    *forecast, resampler, solar_position_calculator = model(\n        site.latitude, site.longitude, site.elevation,\n        init_time, start, end)\n\n    if isinstance(site, datamodel.SolarPowerPlant):\n        solar_position = solar_position_calculator()\n        ac_power = pvmodel.irradiance_to_power(\n            site.modeling_parameters, solar_position['apparent_zenith'],\n            solar_position['azimuth'], *forecast)\n    else:\n        ac_power = None\n\n    # resample data after power calculation\n    resampled = list(map(resampler, (*forecast, ac_power)))\n    return resampled\n\n\ndef run_persistence(session, observation, forecast, run_time, issue_time,\n                    index=False):\n    \"\"\"\n    Run a persistence *forecast* for an *observation*.\n\n    For intraday forecasts, the *index* argument controls if the\n    forecast is constructed using persistence of the measured values\n    (*index = False*) or persistence using clear sky index or AC power\n    index.\n\n    For day ahead forecasts, only persistence of measured values\n    (*index = False*) is supported.\n\n    Forecasts may be run operationally or retrospectively. For\n    operational forecasts, *run_time* is typically set to now. For\n    retrospective forecasts, *run_time* is the time by which the\n    forecast should be run so that it could have been be delivered for\n    the *issue_time*. Forecasts will only use data with timestamps\n    before *run_time*.\n\n    The persistence *window* is the time over which the persistence\n    quantity (irradiance, power, clear sky index, or power index) is\n    averaged. The persistence window is automatically determined\n    from the *forecast* attributes:\n\n      * Intraday persistence forecasts:\n           *window = run_time - forecast.run_length*.\n           No longer than 1 hour.\n      * Day ahead forecasts:\n          *window = forecast.interval_length*.\n\n    Users that would like more flexibility may use the lower-level\n    functions in\n    :py:mod:`solarforecastarbiter.reference_forecasts.persistence`.\n\n    Parameters\n    ----------\n    session : api.Session\n        The session object to use to request data from the\n        SolarForecastArbiter API.\n    observation : datamodel.Observation\n        The metadata of the observation to be used to create the\n        forecast.\n    forecast : datamodel.Forecast\n        The metadata of the desired forecast.\n    run_time : pd.Timestamp\n        Run time of the forecast.\n    issue_time : pd.Timestamp\n        Issue time of the forecast run.\n    index : bool, default False\n        If False, use persistence of observed value. If True, use\n        persistence of clear sky or AC power index.\n\n    Returns\n    -------\n    forecast : pd.Series\n        Forecast conforms to the metadata specified by the *forecast*\n        argument.\n\n    Raises\n    ------\n    ValueError\n        If forecast and issue_time are incompatible.\n    ValueError\n        If persistence window < observation.interval_length.\n    ValueError\n        If forecast.run_length = 1 day and forecast period is not\n        midnight to midnight.\n    ValueError\n        If forecast.run_length = 1 day and index=True.\n    ValueError\n        If instantaneous forecast and instantaneous observation interval\n        lengths do not match.\n    ValueError\n        If average observations are used to make instantaneous forecast.\n    \"\"\"\n    forecast_start, forecast_end = get_forecast_start_end(forecast, issue_time)\n    intraday = _is_intraday(forecast)\n    if not intraday:\n        # raise ValueError if not intraday and not midnight to midnight\n        _check_midnight_to_midnight(forecast_start, forecast_end)\n\n    data_start, data_end = get_data_start_end(\n        observation, forecast, run_time)\n\n    def load_data(observation, data_start, data_end):\n        df = session.get_observation_values(observation.observation_id,\n                                            data_start, data_end)\n        return df['value']\n\n    if intraday and index:\n        fx = persistence.persistence_scalar_index(\n            observation, data_start, data_end, forecast_start, forecast_end,\n            forecast.interval_length, forecast.interval_label, load_data)\n    elif intraday and not index:\n        fx = persistence.persistence_scalar(\n            observation, data_start, data_end, forecast_start, forecast_end,\n            forecast.interval_length, forecast.interval_label, load_data)\n    elif not intraday and not index:\n        fx = persistence.persistence_interval(\n            observation, data_start, data_end, forecast_start,\n            forecast.interval_length, forecast.interval_label, load_data)\n    else:\n        raise ValueError(\n            'index=True not supported for forecasts with run_length >= 1day')\n\n    return fx\n\n\ndef get_forecast_start_end(forecast, issue_time):\n    \"\"\"\n    Get absolute forecast start from *forecast* object parameters and\n    absolute *issue_time*.\n\n    Parameters\n    ----------\n    forecast : datamodel.Forecast\n    issue_time : pd.Timestamp\n\n    Returns\n    -------\n    forecast_start : pd.Timestamp\n        Start time of forecast issued at issue_time\n    forecast_end : pd.Timestamp\n        End time of forecast issued at issue_time\n\n    Raises\n    ------\n    ValueError if forecast and issue_time are incompatible\n    \"\"\"\n    first_issue_time = pd.Timestamp.combine(issue_time.floor('1D'),\n                                            forecast.issue_time_of_day)\n    issue_times = pd.date_range(start=first_issue_time,\n                                end=first_issue_time+pd.Timedelta('1d'),\n                                freq=forecast.run_length)\n    if issue_time not in issue_times:\n        raise ValueError(\n            ('Incompatible forecast.issue_time_of_day %s, '\n             'forecast.run_length %s, and issue_time %s') % (\n             forecast.issue_time_of_day, forecast.run_length, issue_time))\n    forecast_start = issue_time + forecast.lead_time_to_start\n    forecast_end = forecast_start + forecast.run_length\n    if 'instant' in forecast.interval_label:\n        forecast_end -= pd.Timedelta('1s')\n    return forecast_start, forecast_end\n\n\ndef _is_intraday(forecast):\n    \"\"\"Is the forecast intraday?\"\"\"\n    # intra day persistence and \"day ahead\" persistence require\n    # fairly different parameters.\n    # is this a sufficiently robust way to distinguish?\n    return forecast.run_length < pd.Timedelta('1d')\n\n\ndef _check_midnight_to_midnight(forecast_start, forecast_end):\n    if (forecast_start.round('1d') != forecast_start or\n            forecast_end - forecast_start > pd.Timedelta('1d')):\n        raise ValueError(\n            'Day ahead persistence requires midnight to midnight periods')\n\n\ndef _intraday_start_end(observation, forecast, run_time):\n    # time window over which observation data will be used to create\n    # persistence forecast.\n    if (observation.interval_length > forecast.run_length or\n            observation.interval_length > pd.Timedelta('1h')):\n        raise ValueError(\n            'Intraday persistence requires observation.interval_length '\n            '<= forecast.run_length and observation.interval_length <= 1h')\n    # no longer than 1 hour\n    window = min(forecast.run_length, pd.Timedelta('1hr'))\n    data_end = run_time\n    data_start = data_end - window\n    return data_start, data_end\n\n\ndef _dayahead_start_end(run_time):\n    # day ahead persistence: tomorrow's forecast is equal to yesterday's\n    # observations. So, forecast always uses obs > 24 hr old at each valid\n    # time. Smarter approach might be to use today's observations up\n    # until issue_time, and use yesterday's observations for issue_time\n    # until end of day. So, forecast *never* uses obs > 24 hr old at each\n    # valid time. Arguably too much for a reference forecast.\n    data_end = run_time.floor('1d')\n    data_start = data_end - pd.Timedelta('1d')\n    return data_start, data_end\n\n\ndef _adjust_for_instant_obs(data_start, data_end, observation, forecast):\n    # instantaneous observations require care.\n    # persistence models return forecasts with same closure as obs\n    if 'instant' in forecast.interval_label:\n        if forecast.interval_length != observation.interval_length:\n            raise ValueError('Instantaneous forecast requires instantaneous '\n                             'observation with identical interval length.')\n        else:\n            data_end -= pd.Timedelta('1s')\n    elif forecast.interval_label == 'beginning':\n        data_end -= pd.Timedelta('1s')\n    else:\n        data_start += pd.Timedelta('1s')\n    return data_start, data_end\n\n\ndef get_data_start_end(observation, forecast, run_time):\n    \"\"\"\n    Determine the data start and data end times for a persistence\n    forecast.\n\n    Returns\n    -------\n    data_start : pd.Timestamp\n    data_end : pd.Timestamp\n    \"\"\"\n    if _is_intraday(forecast):\n        data_start, data_end = _intraday_start_end(observation, forecast,\n                                                   run_time)\n    else:\n        data_start, data_end = _dayahead_start_end(run_time)\n\n    # to ensure that each observation data point contributes to the correct\n    # forecast, the data_end and data_start values may need to be nudged\n    if 'instant' in observation.interval_label:\n        data_start, data_end = _adjust_for_instant_obs(data_start, data_end,\n                                                       observation, forecast)\n    else:\n        if 'instant' in forecast.interval_label:\n            raise ValueError('Instantaneous forecast cannot be made from '\n                             'interval average observations')\n    return data_start, data_end\n","sub_path":"solarforecastarbiter/reference_forecasts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"375705725","text":"'''\nleia nome e preço de vários produtos, perguntando se o usuário deseja continuar a cada produto.\nno final mostre:\n    a) valor total da compra\n    b) qts produtos acima de R$1000\n    c) nome do produto mais barato\n'''\nfrom time import sleep\n\nlinha = '=' * 40\ntotal = acima1000 = preçomaisbaixo = cont = 0\nmaisbarato = ''\n\nprint(linha)\nprint(f'{\"BL DOCES\":^40}')\nprint(linha)\n\nwhile True:\n    produto = str(input('Produto: ')).strip().capitalize()\n\n    preço = -1\n    while preço < 0:\n        preço = float(input('Preço: R$').replace(',', '.'))\n    cont += 1\n    total += preço\n    if preço > 1000:\n        acima1000 += 1\n    if cont == 1 or preço < preçomaisbaixo:\n        preçomaisbaixo = preço\n        maisbarato = produto\n\n    continua = ' '\n    while continua not in 'SN':\n        continua = str(input('Registrar outro produto? [S/N] ')).strip().upper()[0]\n    if continua == 'N':\n        break\nprint(f'{\" FIM DOS ITENS \":-^40}')\nsleep(1)\nprint(f'O total da compra é R${total:.2f}')\nprint(f'{acima1000} produto custa mais de R$1000' if acima1000 == 1\n      else f'{acima1000} produtos custam mais de R$1000')\nprint(f'{maisbarato} é o produto mais barato, custando R${preçomaisbaixo:.2f}')\nsleep(1)\nprint(linha)\nprint(f'{\"PROGRAMA ENCERRADO\":^40}')\nprint(linha)","sub_path":"Prontos e Corrigidos/ex070.py","file_name":"ex070.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"159232597","text":"__author__ = 'wolfenfeld'\nimport numpy as np\nNUMBER_OF_RANKERS = 64\n\n\ndef read_data(data_file_path):\n\n    with open(data_file_path) as data_file:\n        queri = 0\n        data_lines = data_file.readlines()\n\n        ranker_score = np.ndarray([data_lines.__len__(), NUMBER_OF_RANKERS])\n\n        for line in data_lines:\n\n            vector = line.split(' ')\n            processed_vector = vector[2:-3]\n\n            for i in xrange(64):\n                ranker_score[queri][i] = float(processed_vector[i].split(':')[1])\n\n            queri += 1\n\n    return ranker_score\n\nif __name__ == \"__main__\":\n    path = \"/home/wolfenfeld/Development/Dueling-Bandits/Data/NP2004/Fold1/test.txt\"\n    read_data(path)","sub_path":"data/NP2004/Fold1/np2004.py","file_name":"np2004.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"153442048","text":"def is_divisible(val, div):\n    if val % div == 0:\n        return True\n\n\ndef cnt_divisible_by(val: int = 1000):\n    cnt = 0\n    for i in range(1, val + 1):\n        if is_divisible(i, 2):\n            cnt += 1\n        elif is_divisible(i, 3):\n            cnt += 1\n\n    return cnt\n\n\ndef cnt_not_divisible_by(val: int = 1000):\n    cnt = 0\n    for i in range(1, val + 1):\n        if not is_divisible(i, 2) and not is_divisible(i, 3):\n            cnt += 1\n\n    return cnt\n\n\nprint(cnt_divisible_by())\nprint(cnt_not_divisible_by())\n","sub_path":"specialization-intro-discrete-math-computer-science/combinatorics-and-probability/week1/generalized_rule_of_sum.py","file_name":"generalized_rule_of_sum.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"548713930","text":"import os\nimport warnings\n\nimport numpy as np\nimport pandas as pd\nimport datetime as dt\nfrom scipy.stats import poisson\nimport matplotlib.pyplot as plt\n\n\nwarnings.filterwarnings('ignore')\nplt.style.use('fivethirtyeight')\n\n\nclass View(object):\n    def __init__(self, start_date: str, end_date: str, mold_type: str):\n        # Data options\n        self.mold_type_col = 'mold_type_a'\n        self.start_col = 'work_sdt'\n        self.end_col = 'work_fdt'\n        self.data_name = 'hist.csv'\n        self.target_col = ['ori_mold_crft_wth', 'ori_mold_ass_wth', 'ori_mold_mody_cnt', 'ori_mold_mody_wth',\n                           'ori_tinj_wth', 'ori_tment_wth']\n        self.start_date = start_date\n        self.end_date = end_date\n        self.mold_type = mold_type\n\n        # Path options\n        self.path_data = os.path.join('..', 'data', self.data_name)\n        self.path_save = os.path.join('..', 'img', 'hist')\n        self.path_save_pois = os.path.join('..', 'img', 'poisson')\n\n        # Histogram options\n        self.drop_cols = ['order_no']\n        self.bins = 100\n\n        # Poission options\n        self.x_start = 0\n        self.x_end = 100\n        self.interval = 1\n        self.lamb = 1.0\n\n    def load_dataset(self):\n        df = pd.read_csv(self.path_data)\n        df.columns = [col.lower() for col in df.columns]\n\n        # Filter Mold Type\n        df = df[df[self.mold_type_col] == self.mold_type]\n\n        # Filter datetime\n        start_date = dt.datetime.strptime(self.start_date, '%Y%m%d')\n        end_date = dt.datetime.strptime(self.start_date, '%Y%m%d')\n\n        # Need to check\n        # df = df[df[self.start_col] >= start_date]\n        # df = df[df[self.end_col] <= end_date]\n\n        # Filter date type\n        data_type_col = [col for col, typ in zip(df.columns, df.dtypes) if typ in ['int64', 'float64']]\n        # Filter dataset\n        data_type_col = [col for col in data_type_col if col in self.target_col]\n\n        return df, data_type_col\n\n    def draw_hist(self, df: pd.DataFrame, columns: list, types: list):\n\n        for col in columns:\n            data = df[[col, self.mold_type_col]]\n            bins = np.linspace(df[col].min(), df[col].max(), self.bins)\n            bin_avg = bins[0: -1] + bins[1] - bins[0]\n\n            data_split = {}\n            for typ in types:\n                temp = data[data[self.mold_type_col] == typ]\n                data_split[typ] = np.histogram(temp[col], bins)\n\n            plt.figure()\n            for typ in types:\n                plt.plot(bin_avg, data_split[typ][0], alpha=0.7, label=typ,\n                         marker='o', markersize=2, linewidth=2.0)\n            plt.xlabel('Bins')\n            plt.ylabel('Frequency')\n            plt.legend(loc='upper right')\n            plt.savefig(os.path.join(self.path_save, col + '.png'))\n            plt.close()\n\n    def draw_poisson(self, df: pd.DataFrame, columns: list):\n        for col in columns:\n            data = df[col]\n            # xvals = np.arange(df[col].min(), df[col].max(), self.interval)\n            mu = data.mean()\n            x = np.arange(self.x_start, self.x_end, self.interval)\n            y = poisson.pmf(x, mu=mu, loc=40)\n\n            plt.figure()\n            plt.plot(x, y, alpha=0.7, label=self.mold_type, linewidth=2.0)\n            plt.legend(loc='upper right')\n            plt.savefig(os.path.join(self.path_save_pois, col + '.png'))\n            plt.close()\n\n\ndef main():\n    view = View(start_date='',\n                end_date='',\n                mold_type='D')\n\n    df, data_type_col = view.load_dataset()\n    print(\"Dataset is loaded\")\n\n    # view.draw_hist(df=df, columns=data_type_col, types=types)\n    view.draw_poisson(df=df, columns=data_type_col)\n    print(\"Drawing Histogram is finished\")\n\nmain()","sub_path":"view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":3755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"595662309","text":"import Tkinter as t\nimport time , socketio\n\nfrom PIL import ImageTk,Image\n \n\nclass App:\n\tdef __init__(self,ip):\n\t\tself.top = t.Tk()\n\t\tself.top.title('Sockets ping pong')\n\t\t#self.pos = [None,'Left','Right'][p]\n\t\t\n\t\tself.top.tk_bisque()\n\t\tself.ip = ip\n\t\t\n\t\tself.intro()\n\t\tself.top.protocol(\"WM_DELETE_WINDOW\",self.disconnect)\n\t\tself.top.geometry('400x400+500+500')\n\t\tself.top.mainloop()\n\t\n\tdef disconnect(self):\n\t\ttry:\n\t\t\tself.socket.disconnect()\n\t\texcept:\n\t\t\tpass\n\t\tself.top.destroy()\n\t\n\tdef connect(self,e):\n\t\tself.l2['text'] = 'Connecting ... to'\n\t\n\t\tself.socket = socketio.Client()\n\t\tself.socket.on('connect',self.connected)\n\t\tself.socket.on('my_name',self.my_name)\n\t\tself.socket.on('getmoved',self.getmoved)\n\t\tself.socket.on('startBall',self.startBall)\n\t\tself.socket.on('getupdated',self.getupdated)\n\t\tself.socket.connect(self.ip)\n\t\t#self.connected()\n\t\t\n\tdef my_name(self,data):\n\t\td = data\n\t\t#print 'got',data,type(data)\n\t\tself.l2['text']= 'Connection Established !'\n\t\t#self.l3['label'] = 'Press Enter to Proceed'\n\t\tself.top.after(500,self.playshow,0,d)\n\t\t#self.playshow(side = d)\n\t\n\tdef connected(self):\n\t\t#print ('connected',self)\n\t\tself.socket.emit('get_my_name')\n\t\t\n\t\t\n\tdef getmoved(self,data):\n\t\town,other = data\n\t\tw = [self.p1,self.p2][own]\n\t\t#print ('own',own,'other',other)\n\t\tw.place(y = other)\n\t\t\n\tdef moveup(self,e):\n\t\tw = self.own\n\t\ttry:\n\t\t\tself.y\n\t\texcept:\n\t\t\tself.y = w.winfo_y()\n\t\tself.y += -5\n\t\tself.socket.emit('move',[self.owncode,self.y])\n\t\t\n\t\n\tdef movedo(self,e):\n\t\tw = self.own\n\t\ttry:\n\t\t\tself.y\n\t\texcept:\n\t\t\tself.y = w.winfo_y()\n\t\tself.y += 5\n\t\tself.socket.emit('move',[self.owncode,self.y])\n\t\t#w.place(y = self.y)\n\t\n\tdef playshow(self, hide=0 , side = 0):\n\t\ttry:\n\t\t\tself.p1\t#paddle 1 / bar1\n\t\texcept:\n\t\t\tself.intro(hide = 1)\n\t\t\tself.p1 = t.Button(self.top,text='Left',height = 15,bg='green', width= 8)\n\t\t\tself.p2 = t.Button(self.top,text='Right',height = 15,bg='green', width = 8)\n\t\t\tself.p1.svar = t.StringVar()\n\t\t\tself.p2.svar = t.StringVar()\n\t\t\tself.p1.ivar = t.IntVar(0)\n\t\t\tself.p2.ivar = t.IntVar(0)\n\t\t\tself.score1 = t.Label(self.top,textvariable = self.p1.ivar,font = 'system 20')\n\t\t\tself.score2 = t.Label(self.top,textvariable = self.p2.ivar,font = 'system 20')\n\t\t\tself.s1 = t.Label(self.top,textvariable = self.p1.svar,font = 'system 20')\n\t\t\tself.s2 = t.Label(self.top,textvariable = self.p2.svar,font = 'system 20')\n\t\t\tself.own = [self.p1,self.p2] [side]\n\t\t\tself.owncode = side\n\t\t\tself.top.title('{}'.format(self.own['text']))\n\t\t\tself.other = [self.p1,self.p2] [not side]\n\t\t\t\n\t\t\t#self.socket.emit('update_score',self.owncode)\n\t\t\n\t\t\tself.top.bind('<KeyPress-Up>',self.moveup)\n\t\t\tself.top.bind('<KeyPress-Down>',self.movedo)\n\t\t\t#self.scores()\n\t\t\t#self.top.bind('KeyRelease-Return',startball)\n\t\t\t\n\t\tif hide:\n\t\t\tfor i in (self.p1,self.p2):\n\t\t\t\ti.place(relheight=0,relwidth=0)\n\t\telse:\n\t\t\tself.p1.place(relx = 0.1, rely=0.2,anchor = 'nw')\n\t\t\tself.p2.place(relx = 0.9, rely=0.2,anchor = 'ne')\n\t\t\tself.s1.place(relx = 0.2,rely = 0.01, anchor = 'n')\n\t\t\tself.s2.place(relx = 0.8,rely = 0.01, anchor = 'n')\n\t\t\tself.score1.place(relx = 0.2,rely = 0.1, anchor = 'n')\n\t\t\tself.score2.place(relx = 0.8,rely = 0.1, anchor = 'n')\n\t\t\t#self.socket.emit('startBall')\n\t\t\tself.y = self.own.winfo_y()\n\t\t\tself.startBall()\n\t\n\tdef intro(self,hide = 0):\n\t\ttry:\n\t\t\tself.f1\n\t\texcept:\n\t\t\tself.f1 = t.Frame(self.top)\n\t\t\tself.l1 = t.Label(self.f1,padx=10,pady=10,relief='groove',bd=10,text='Sockets ping pong',font = 'system 30 bold')\n\t\t\tself.l2 = t.Label(self.f1,text='Press Enter to connect to server @',font = 'system 10 bold')\n\t\t\tself.l3 = t.Label(self.f1,text='{}'.format(self.ip),font = 'system 10 bold')\n\t\t\tself.name = t.StringVar()\n\t\t\tself.t1 = t.Label(self.f1,text = 'github.com/new-AF',font = 'system 10 bold',relief = 'groove')\n\t\t\tself.top.bind('<KeyRelease-Return>',self.connect)\n\t\t\t\n\t\t\tfor i in ('l1 l2 l3 t1'.split() ):\n\t\t\t\tw = getattr(self,i)\n\t\t\t\tw.pack(fill='x',expand=1)\n\t\t\n\t\tif hide:\n\t\t\t#self.f1.place(x=0,y=0,relheight=0,relwidth=0)\n\t\t\tself.f1.destroy()\n\t\telse:\n\t\t\tself.f1.place(x=0,y=0,relwidth=1,relheight=1)\n\t\t\tself.t1.focus()\n\t\n\tdef createBall(self, w = 20, h= 20):\n\t\ttry:\n\t\t\tself.ballW\n\t\texcept:\n\t\t\tself.ballW = t.Canvas(self.top, width = w, height = h, relief= 'flat',borderwidth = 0)\n\t\t\tself.ballW.create_oval( 0,0,w,h, fill = 'black')\n\n\tdef startBall(self, hide=0,reset = 0):\n\t\tself.ball = [  self.top.winfo_width()/2 , self.top.winfo_height()/2 ]\n\t\tself.ball = map(int,self.ball)\n\t\tself.Inc = [3, 3]\n\t\t\n\t\tif reset:\n\t\t\treturn\n\t\ttry:\n\t\t\tself.ballW\n\t\texcept:\n\t\t\tself.createBall()\n\t\n\t\tself.moveBall()\n\t\n\tdef moveBall(self):\n\t\ttry:\n\t\t\tself.moving\n\t\texcept:\n\t\t\tself.moving = 1\n\t\t\n\t\tif self.moving:\n\t\t\tx,y = self.ball\n\t\t\tself.ballW.place( x = self.ball[0] , y = self.ball[1] )\n\t\t\t\n\t\t\tif self.collide(self.own) or self.collide(self.other):\n\t\t\t\tself.Inc[0] *= -1\n\t\t\t\n\t\t\tif x >= self.top.winfo_width() or x<=0:\n\t\t\t\tself.sendScore(x)\n\t\t\t\tself.startBall(0,1)\n\t\t\t\n\t\t\tif y >= self.top.winfo_height() or y<=0:\n\t\t\t\tself.Inc[1] *= -1\n\t\t\t\n\t\t\tself.ball[0] += self.Inc[0]\n\t\t\tself.ball[1] += self.Inc[1]\n\t\t\tself.top.after(30, self.moveBall )\n\t\t\n\tdef collide(self,tar):\n\n\t\tx,y = self.ball\n\t\tw = self.ballW.winfo_width()\n\t\th = self.ballW.winfo_height()\n\n\t\ttx,ty = tar.winfo_x(), tar.winfo_y()\n\t\tth, tw = tar.winfo_height(), tar.winfo_width()\n\n\t\tc1 = abs((x+w/2) - (tx + tw/2)) <= 5\n\t\tc2 = y + h >= ty and y+h <= ty + th\n\n\t\treturn c1 and c2\n\t\n\tdef getupdated(self,data):\n\t\td = data\n\t\tw = [self.own,self.other][not d]\n\t\told = w.ivar.get()\n\t\tw.ivar.set(old+1)\n\t\n\tdef sendScore(self,x):\n\t\t#print 'sending score'\n\t\tself.socket.emit('update_score', (x < self.top.winfo_width()/2) and self.owncode  )\n\nif __name__ == '__main__':\n    App(ip = 'http://damp-basin-29915.herokuapp.com')\n","sub_path":"Project2Rebooted.py","file_name":"Project2Rebooted.py","file_ext":"py","file_size_in_byte":5681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"41787790","text":"from collections import namedtuple\nimport os\nimport re\n\nimport jedi\n\n\ndef _debug_print_tree(node, indent=0, func=repr):\n    \"\"\" Print visual representation of Jedi AST. \"\"\"\n    ret = \"\\t\" * indent + func(node) + \"\\n\"\n\n    children = getattr(node, \"children\", None)\n    if children:\n        for child in children:\n            ret += _debug_print_tree(child, indent=indent+1, func=func)\n    return ret\n\n\nclass FileGrapherException(Exception):\n    \"\"\" Something went wrong while graphing the file. \"\"\"\n\n\nclass FileGrapher(object):\n    \"\"\"\n    FileGrapher is used to extract definitions and references from single Python source file.\n    \"\"\"\n    Def = namedtuple('Def', ['Path', 'Kind', 'Name', 'File', 'DefStart', 'DefEnd', 'Exported', 'Docstring', 'Data'])\n    Ref = namedtuple('Ref', ['DefPath', 'DefFile', 'Def', 'File', 'Start', 'End', 'ToBuiltin'])\n\n    _exported_regex = re.compile('\\_[a-zA-Z0-9]')\n\n    def __init__(self, base_dir, source_file, log):\n        \"\"\"\n        Create a new grapher.\n        \"\"\"\n        self._base_dir = base_dir\n        self._file = source_file\n        self._log = log\n        self._source = None\n        self._defs = {}\n        self._refs = {}\n        self._load()\n\n    def graph(self):\n        # Add module/package defs.\n        module = os.path.splitext(self._file)[0]\n        if os.path.basename(self._file) == '__init__.py':\n            module = os.path.normpath(os.path.dirname(self._file))\n\n        self._add_def(self.Def(\n            Path=module,\n            Kind='module',\n            Name=module.split('/')[-1],\n            File=self._file,\n            DefStart=0,\n            DefEnd=0,\n            Exported=True,\n            # TODO(MaikuMori): extract module/package-level doc.\n            Docstring='',\n            Data=None,\n        ))\n\n        # Get occurrences of names via Jedi.\n        try:\n            jedi_names = jedi.names(source=self._source, path=self._file, all_scopes=True, references=True)\n        except Exception as e:\n            raise FileGrapherException('failed to parse {}: {}'.format(self._file, str(e)))\n\n        jedi_defs, jedi_refs = [], []\n        for jedi_name in jedi_names:\n            # Imports should be refs.\n            if jedi_name.is_definition() and jedi_name.type != 'import':\n                    jedi_defs.append(jedi_name)\n            else:\n                jedi_refs.append(jedi_name)\n\n        # Defs.\n        for jedi_def in jedi_defs:\n            self._log.debug(\n                '\\nProcessing def: %s | %s | %s',\n                jedi_def.desc_with_module,\n                jedi_def.name,\n                jedi_def.type,\n            )\n            try:\n                self._add_def(self._jedi_def_to_def(jedi_def))\n            except Exception as e:\n                self._log.error(\n                    u'\\nFailed to process def `%s`: %s',\n                    jedi_def.name,\n                    e,\n                )\n                continue\n\n        # Refs.\n        for jedi_ref in jedi_refs:\n            self._log.debug(\n                '\\nProcessing ref: %s | %s | %s',\n                jedi_ref.desc_with_module,\n                jedi_ref.name,\n                jedi_ref.type,\n            )\n\n            ref_def = self._find_def_for_ref(jedi_ref)\n            # We found nothing.\n            if ref_def is None:\n                continue\n\n            try:\n                sg_def = self._jedi_def_to_def_key(ref_def)\n            except Exception as e:\n                self._log.error(\n                    u'\\nFailed to process def to def-key `%s`: %s',\n                    ref_def.name,\n                    e,\n                )\n                continue\n\n            self._log.debug(\n                'Ref-Def: %s | %s | %s | %s \\n',\n                sg_def.Name,\n                sg_def.Path,\n                sg_def.Kind,\n                sg_def.File,\n            )\n\n            ref_start = self._to_offset(jedi_ref.line, jedi_ref.column)\n            ref_end = ref_start + len(jedi_ref.name)\n            self._add_ref(self.Ref(\n                DefPath=sg_def.Path,\n                DefFile=sg_def.File,\n                Def=False,\n                File=self._file,\n                Start=ref_start,\n                End=ref_end,\n                ToBuiltin=ref_def.in_builtin_module(),\n            ))\n\n        return self._defs, self._refs\n\n    def _find_def_for_ref(self, jedi_ref, max_depth=100):\n        \"\"\" Attempt to lookup definition for the reference. If lookup fails return None. \"\"\"\n        ref_def = jedi_ref\n        # If def is import, then follow it.\n        depth = 0\n        while (not ref_def.is_definition() or ref_def.type == \"import\") and depth < max_depth:\n            depth += 1\n            # noinspection PyBroadException\n            try:\n                ref_defs = ref_def.goto_assignments()\n            except Exception:\n                self._log.error(u'error getting definitions for reference {}'.format(jedi_ref))\n                break\n\n            if len(ref_defs) == 0:\n                break\n\n            ref_def = ref_defs[0]\n        else:\n            self._log.debug(\n                'Ref def search (precondition failed) | %s | %s | %s\\n',\n                ref_def.is_definition(),\n                ref_def.type,\n                ref_def.name\n            )\n\n        if ref_def.type == \"import\":\n            # We didn't find anything.\n            self._log.debug('Ref def not found \\n')\n            return None\n\n        return ref_def\n\n    def _load(self):\n        \"\"\" Load file in memory. \"\"\"\n        with open(self._file) as f:\n            self._source = f.read()\n\n        source_lines = self._source.splitlines()\n        self._cumulative_off = [0]\n        for line in source_lines:\n            self._cumulative_off.append(self._cumulative_off[-1] + len(line) + 1)\n\n    def _jedi_def_to_def(self, d):\n        # TODO(MaikuMori): Add back this if needed:\n        # If def is a name, then the location of the definition is the last name part\n        start = self._to_offset(d.line, d.column)\n        end = start + len(d.name)\n\n        return self.Def(\n            Path=self._full_name(d).replace('.', '/'),\n            Kind=d.type,\n            Name=d.name,\n            File=self._file,\n            DefStart=start,\n            DefEnd=end,\n            Exported=self._is_exported(d.name),\n            Docstring=d.docstring(raw=True),\n            Data=None,\n        )\n\n    def _jedi_def_to_def_key(self, d):\n        return self.Def(\n            Path=self._full_name(d).replace('.', '/'),\n            Kind=d.type,\n            Name=d.name,\n            File=d.module_path,\n            DefStart=None,\n            DefEnd=None,\n            Exported=self._is_exported(d.name),\n            Docstring=d.docstring(raw=True),\n            Data=None,\n        )\n\n    def _full_name(self, d):\n        if d.in_builtin_module() or d.module_path is None:\n            return d.name\n        # This detects `self` and `cls` parameters makes them to point to the class:\n        # To trigger this parameters must be for a method (a class function).\n        if d.type == 'param' and (d.name == 'self' or d.name == 'cls') and d.parent().parent().type == 'class':\n            name = d.parent().parent().full_name\n        elif d.type == 'statement':\n            # Workaround for self.* definitions:\n            # 1. Must be inside function\n            # 2. Must be in form self.something[.something ...] = thing\n            jd = d._definition\n            if d.parent().type == 'function':\n                self._log.debug('\\n%s\\n', _debug_print_tree(\n                    jd,\n                    func=lambda n: '{} | {} | {}'.format(str(n)[:10], type(n), n.type)\n                ))\n\n            if (d.parent().type == 'function' and\n                    (isinstance(jd.children[0], jedi.parser.tree.Node) or\n                     isinstance(jd.children[0], jedi.evaluate.representation.InstanceElement)) and\n                    (isinstance(jd.children[0].children[0], jedi.parser.tree.Name) or\n                     isinstance(jd.children[0].children[0], jedi.evaluate.representation.InstanceName)) and\n                    (jd.children[0].children[0].value == 'self' or\n                     jd.children[0].children[0].value == 'cls')):\n                parent = d.parent()\n                # Stop when:\n                # - We find class or instance of class\n                # - We reach module, which means we failed\n                while parent.type != 'class' and parent.type != 'instance' and parent.type != 'module':\n                    self._log.debug(\n                        'Finding paren %s -> %s | %s',\n                        parent.type,\n                        parent,\n                        jd.children[0].children[0].value\n                    )\n                    parent = parent.parent()\n\n                rest = jd.children[0].children[1:]\n                chain = []\n                for node in rest:\n                    chain.append(node.children[1].value)\n                if parent.type != 'module':\n                    name = '{}.{}'.format(parent.full_name, '.'.join(chain))\n                else:\n                    name = '{}.{}'.format(d.full_name, d.name)\n\n            else:\n                name = '{}.{}'.format(d.full_name, d.name)\n        else:\n            self._log.debug('Not-statement: %s | %s', d.name, d.module_path)\n            self._log.debug('\\n%s\\n', _debug_print_tree(\n                d,\n                func=lambda n: u'{} | {} | {}'.format(unicode(n)[:10], type(n), n.type)\n            ))\n            # This is needed for situations like these:\n            # class x(object):\n            #   def yyy(self): pass\n            #   def xxx(self, a):\n            #     if a == 1:\n            #       self.yyy(a) # <- A parameter.\n            if d.is_definition():\n                name = d.full_name\n            else:\n                name = '{}.{}'.format(d.full_name, d.name)\n\n        self._log.debug('Module path: %s | %s', d.module_path, d.in_builtin_module())\n\n        module_path = self._abs_module_path_to_relative_module_path(d.module_path)\n        parent_module = self._get_module_parent_from_module_path(module_path)\n\n        if parent_module == '':\n            return name\n\n        self._log.debug(\n            'Name: %s | %s ',\n            parent_module,\n            name,\n        )\n\n        return '{}.{}'.format(parent_module, name)\n\n    @staticmethod\n    def _get_module_parent_from_module_path(module_path):\n        if os.path.basename(module_path) == '__init__.py':\n            parent_module =  os.path.dirname(os.path.dirname(module_path))\n        else:\n            parent_module = os.path.dirname(module_path)\n        return parent_module.replace(os.sep, '.')\n\n    def _abs_module_path_to_relative_module_path(self, module_path):\n        rel_path = os.path.relpath(module_path, self._base_dir)\n        if not rel_path.startswith('..'):\n            return rel_path\n\n        components = module_path.split(os.sep)\n        pi1 = pi2 = -1\n        for i, component in enumerate(components):\n            if component in ['site-packages', 'dist-packages']:\n                pi1 = i\n                break\n            # Fallback.\n            if pi2 == -1 and component.startswith('python'):\n                pi2 = i\n\n        pi = pi1 if pi1 != -1 else pi2\n        if pi != -1:\n            return os.path.join(*components[pi + 1:])\n\n        raise FileGrapherException('could not convert absolute module path {} '\n                                   'to relative module path'.format(module_path))\n\n    def _is_exported(self, name):\n        \"\"\" Checks if keyword is public or non-public.\n\n        There are no private methods/variables in Python, however:\n\n        There is a convention to prefix private methods/variables with\n        underscore. And that's what we're going to use.\n\n        https://www.python.org/dev/peps/pep-0008/#id40\n        \"\"\"\n        return self._exported_regex.match(name) is None\n\n    def _add_def(self, d):\n        \"\"\" Add a definition, also adds a self-reference. \"\"\"\n        self._log.debug('Adding def: %s | %s | %s', d.Name, d.Path, d.Kind)\n        if d.Path not in self._defs:\n            self._defs[d.Path] = d\n        # Add self-reference.\n        self._add_ref(self.Ref(\n            DefPath=d.Path,\n            DefFile=os.path.abspath(d.File),\n            Def=True,\n            File=d.File,\n            Start=d.DefStart,\n            End=d.DefEnd,\n            ToBuiltin=False,\n        ))\n\n    def _add_ref(self, r):\n        \"\"\" Add a reference. \"\"\"\n        self._log.debug('Adding ref: %s', r.DefPath)\n        key = (r.DefPath, r.DefFile, r.File, r.Start, r.End)\n        if key not in self._refs:\n            self._refs[key] = r\n\n    def _to_offset(self, line, column):\n        \"\"\"\n        Converts from (line, col) position to byte offset.\n        Line is 1-indexed, column is 0-indexed.\n        \"\"\"\n        line -= 1\n        if line >= len(self._cumulative_off):\n            raise FileGrapherException('requested line out of bounds {} > {}'.format(\n                line + 1,\n                len(self._cumulative_off) - 1)\n            )\n        return self._cumulative_off[line] + column\n","sub_path":"grapher/file_grapher.py","file_name":"file_grapher.py","file_ext":"py","file_size_in_byte":13119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"47177928","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Feb 15 22:29:18 2021\r\n\r\n@author: YOSO_WANHH\r\n\"\"\"\r\nfrom math import sqrt #開根號\r\n#import math\r\nn = int(input())\r\n\r\nfor i in range(n):\r\n    a = int(input())\r\n    b = int(input())\r\n    \r\n    # Current Square Number\r\n    curSquare = 0\r\n    \r\n    # Minimal square root\r\n    minRoot = int(sqrt(a))\r\n    if minRoot * minRoot == a:\r\n        curSquare = a\r\n    else:\r\n        minRoot = minRoot + 1\r\n        curSquare = minRoot * minRoot\r\n    squareSum = 0\r\n    while curSquare <=b:\r\n        squareSum += curSquare\r\n        minRoot += 1\r\n        curSquare = minRoot * minRoot\r\n    \r\n    print(\"Case \" + str(i+1) + \":\", squareSum)\r\n","sub_path":"059完全平方和.py","file_name":"059完全平方和.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"593166921","text":"from Food import Food\nfrom Snake import Snake\nimport random\nimport numpy as np\nimport pygame\n#Processo de aprendizado relaxado\n\nclass Game(object):\n    def __init__(self, width, height):\n        self.width = width\n        self.height = height\n        self.status = 1\n        self.food = Food((0, 255, 0), 9)\n        self.colision_matrix = self.create_colision_matrix(width, height)\n        self.directions = np.array([[-1, 0], [0, -1], [1, 0], [0, 1]])\n        self.colors = [[0, 0, 0], [255, 255, 255], [255, 0, 0], [0, 255, 0]]\n\n    def create_colision_matrix(self, width, height):\n        barrier_size = 9\n        colision_matrix = np.zeros((width, height))\n\n        for i, f in zip([0, width - 1], [1, -1]):\n            for j in range(height):\n                for k in range(barrier_size):\n                    colision_matrix[j][i + (k * f)] = 1\n\n        for i, f in zip([0, height - 1], [1, -1]):\n            for j in range(width):\n                for k in range(barrier_size):\n                    colision_matrix[i + (k * f)][j] = 1\n\n        return colision_matrix\n\n    def check_food(self):\n        if self.food.status == 0:\n            for i in range(9):\n                for j in range(9):\n                    self.colision_matrix[int(self.food.position[0] + i)][int(self.food.position[1] + j)] = 0\n            self.food.position[0] = random.randrange(1, int(self.width / 9) - 1) * 9\n            self.food.position[1] = random.randrange(1, int(self.height / 9) - 1) * 9\n            for i in range(9):\n                for j in range(9):\n                    self.colision_matrix[int(self.food.position[0] + i)][int(self.food.position[1] + j)] = 2\n            self.food.status = 1\n\n    def sensor(self, current_position, n_directions, current_direction, target_element):\n        distance_elements = np.zeros(n_directions + 1)\n        sensor_directions = []\n\n        j = int(n_directions / 2) + 1\n        j = list(range(j))[1:]\n        j = j[::-1]\n\n        for i in j:\n            num = (current_direction - i) % (self.directions.size / 2)\n            sensor_directions.append(self.directions[int(num)])\n\n        sensor_directions.append(self.directions[current_direction])\n        j = j[::-1]\n\n        for i in j:\n            num = (current_direction + i) % (self.directions.size / 2)\n            sensor_directions.append(self.directions[int(num)])\n\n        for i, direction in enumerate(sensor_directions):\n            distance = 0\n            sensor_position = current_position + direction\n            distance += 1\n\n            while 1:\n                if self.colision_matrix[int(sensor_position[0])][int(sensor_position[1])] == target_element:\n                    break\n                elif self.colision_matrix[int(sensor_position[0])][int(sensor_position[1])] == 1 and target_element != 1:\n                    distance = 0\n                    break\n                sensor_position = sensor_position + direction\n                distance += 1\n\n            distance_elements[i] = distance\n\n        return distance_elements\n\n    def training_snake(self, neural_network):\n        random.seed(5)\n        neural_network.scores = 0\n        snake = Snake([180, 180], np.array([[-1, 0], [0, -1], [1, 0], [0, 1]]), 9, pygame.Surface((9, 9)),\n                      neural_network)\n        #Variaveis para verificação de movimentos repetidos\n        ant_mov = 0\n        current_mov = 0\n        count_rep_mov = 0\n        #Armazenar tempo // Cobra morre ao chegar no limite // O tempo é resetado ao pegar a fruta\n        frame_counter = 0\n        #Maior proximidade com a comida para pontuar\n        distance = 0\n        best_distance = self.width\n\n        while snake.status:\n            self.check_food()\n            val = self.sensor(snake.center, 2, snake.direction, 1)\n            val2 = np.array([self.food.position[0], self.food.position[1]])\n            res = snake.neural_network.response(np.hstack((val, val2)))\n\n            ant_mov = current_mov\n\n            if res[0] > res[1] and res[0] > res[2]:\n                current_mov = 1\n                snake.direction = (snake.direction - 1) % 4\n            elif res[1] > res[0] and res[1] > res[2]:\n                current_mov = 2\n                snake.direction = snake.direction\n            elif res[2] > res[0] and res[2] > res[1]:\n                current_mov = 3\n                snake.direction = (snake.direction + 2) % 4\n\n            snake.move(self.colision_matrix, self)\n\n            distance = ((snake.center[0] - self.food.position[0]) ** 2 + (\n                        snake.center[1] - self.food.position[1]) ** 2) ** (1 / 2)\n            if best_distance > distance:\n                if distance == 0:\n                    distance = 1\n                best_distance = distance\n\n            if snake.increment_status == 1:\n                frame_counter = 0\n                best_distance = 800\n            if frame_counter == 300:\n                self.status = 0\n\n        snake.neural_network.scores += snake.fitness(best_distance)\n\n    def test_snake(self, neural_network):\n        distance = 0\n        best_distance = self.width\n\n        snake = Snake([180, 180], np.array([[-1, 0], [0, -1], [1, 0], [0, 1]]), 9, pygame.Surface((9, 9)),\n                      neural_network)\n        random.seed(5)\n        screen = pygame.display.set_mode([self.width, self.height])\n        pygame.display.set_caption('Snake')\n        pygame.init()\n\n        clock = pygame.time.Clock()\n        while snake.status:\n            clock.tick(15)\n            self.check_food()\n\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    quit()\n\n            ##################### AI movementation ##########################\n\n            val = self.sensor(snake.center, 2, snake.direction, 1)\n            val2 = np.array([self.food.position[0], self.food.position[1]])\n            res = snake.neural_network.response(np.hstack((val, val2)))\n            if res[0] > res[1] and res[0] > res[2]:\n                current_mov = 1\n                snake.direction = (snake.direction - 1) % 4\n            elif res[1] > res[0] and res[1] > res[2]:\n                current_mov = 2\n                snake.direction = snake.direction\n            elif res[2] > res[0] and res[2] > res[1]:\n                current_mov = 3\n                snake.direction = (snake.direction + 2) % 4\n\n            snake.move(self.colision_matrix, self)\n            #################################################################\n            distance = ((snake.center[0] - self.food.position[0]) ** 2 + (\n                        snake.center[1] - self.food.position[1]) ** 2) ** (1 / 2)\n            if best_distance > distance:\n                if distance == 0:\n                    distance = 1\n                best_distance = distance\n\n            screen.fill(self.colors[0])\n            for body in snake.body:\n                screen.blit(snake.surface, body)\n            if self.food.status == 1:\n                screen.blit(self.food.surface, self.food.position)\n            val = self.sensor(snake.center, 2, snake.direction, 1)\n            val2 = self.sensor(snake.center, 2, snake.direction, 2)\n            pygame.display.flip()\n\n        pygame.quit()\n        #snake.neural_network.scores += snake.fitness(best_distance)\n\n    def run(self, neural_network):\n        random.seed(5)\n        snake = Snake([180, 180], np.array([[-1, 0], [0, -1], [1, 0], [0, 1]]), 9, pygame.Surface((9, 9)),\n                      neural_network)\n        sensor_surface = pygame.Surface((1, 1))\n        sensor_surface.fill(self.colors[2])\n        screen = pygame.display.set_mode([self.width, self.height])\n        pygame.display.set_caption('Snake')\n        pygame.init()\n        clock = pygame.time.Clock()\n        self.status = 1\n\n        while snake.status:\n            clock.tick(15)\n            self.check_food()\n\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    quit()\n                if event.type == pygame.KEYDOWN:\n                    if event.key == pygame.K_a:\n                        if snake.direction != 2:\n                            snake.direction = 0\n                    if event.key == pygame.K_w:\n                        if snake.direction != 3:\n                            snake.direction = 1\n                    if event.key == pygame.K_d:\n                        if snake.direction != 0:\n                            snake.direction = 2\n                    if event.key == pygame.K_s:\n                        if snake.direction != 1:\n                            snake.direction = 3\n                    if event.key == pygame.K_f:\n                        snake.increment()\n\n            screen.fill(self.colors[0])\n            snake.move(self.colision_matrix, self)\n\n            for body in snake.body:\n                screen.blit(snake.surface, body)\n            if self.food.status == 1:\n                screen.blit(self.food.surface, self.food.position)\n            val = self.sensor(snake.center, 2, snake.direction, 1)\n            val2 = self.sensor(snake.center, 2, snake.direction, 2)\n            print(val, val2)\n            pygame.display.flip()\n\n        screen.fill(self.colors[0])\n        font = pygame.font.Font('freesansbold.ttf', 26)\n        text = font.render(\"Game Over\", True, (255, 255, 255))\n        textRect = text.get_rect()\n        textRect.center = (self.width / 2, self.height / 2)\n        screen.blit(text, textRect)\n        pygame.display.flip()\n\n        for i in range(5):\n            clock.tick(1)\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    quit()\n\n","sub_path":"src/models/Game_2.py","file_name":"Game_2.py","file_ext":"py","file_size_in_byte":9673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"494584959","text":"\n\n### This file is based on (slight modification of) https://github.com/facebookresearch/InvariantRiskMinimization/blob/master/code/experiment_synthetic/sem.py\n### We included the functionality to control whether to allow for anti-causal variables or not\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\n\nimport torch\nimport numpy as np\n\nclass ChainEquationModel(object):\n    def __init__(self, dim, scramble=False, hetero=True, hidden=False, child=True,  ones=True, noise_identity=True):\n        self.hetero = hetero\n        self.hidden = hidden\n        self.dim = dim // 2\n        print (\"ones\" + str(ones))\n        \n\n        if ones:\n            self.wxy = torch.eye(self.dim)\n            if child:\n                print (\"child \" + str(child))\n                self.wyz = torch.eye(self.dim)\n            else:\n                self.wyz =torch.zeros(self.dim, self.dim)\n        else:\n            self.wxy = torch.randn(self.dim, self.dim) / dim\n            if child:\n                self.wyz = torch.randn(self.dim, self.dim) / dim\n            else:\n                self.wyz =torch.zeros(self.dim, self.dim)\n        if scramble:\n            self.scramble, _ = torch.qr(torch.randn(dim, dim))\n        else:\n            self.scramble = torch.eye(dim)\n\n        if hidden:\n            if noise_identity==0:\n                print (\"noise_identity \" + str(noise_identity))\n                self.whx = torch.randn(self.dim, self.dim) / dim \n                self.why = torch.randn(self.dim, self.dim) / dim \n                self.whz = torch.randn(self.dim, self.dim) / dim \n            else:\n                if noise_identity==1:\n                    print (\"noise_identity \" + str(noise_identity))\n                    self.whx = torch.eye(self.dim, self.dim)\n                    self.why = torch.eye(self.dim, self.dim)\n                    self.whz = torch.eye(self.dim, self.dim)\n                else:\n                    if noise_identity==2:\n                        print (\"noise_identity \" + str(noise_identity))\n                        self.whx = torch.rand(self.dim, self.dim) / dim\n                        self.why = torch.rand(self.dim, self.dim) / dim\n                        self.whz = torch.rand(self.dim, self.dim) / dim                    \n\n        else:\n            self.whx = torch.eye(self.dim, self.dim) \n            self.why = torch.zeros(self.dim, self.dim)\n            self.whz = torch.zeros(self.dim, self.dim)\n\n\n    def solution(self):\n        w = torch.cat((self.wxy.sum(1), torch.zeros(self.dim))).view(-1, 1)\n        return self.scramble.t() @ w\n\n    def __call__(self, n, env):\n        h = torch.randn(n, self.dim) * env\n        x = h @ self.whx + torch.randn(n, self.dim) * env\n\n        if self.hetero:\n            print (\"hetero \" + str(self.hetero))\n            y = x @ self.wxy + h @ self.why + torch.randn(n, self.dim) * env\n            z = y @ self.wyz + h @ self.whz + torch.randn(n, self.dim)\n        else:\n\n            y = x @ self.wxy + h @ self.why + torch.randn(n, self.dim)\n            z = y @ self.wyz + h @ self.whz + torch.randn(n, self.dim) * env\n\n        return torch.cat((x, z), 1) @ self.scramble, y.sum(1, keepdim=True)\n\n\n","sub_path":"OoD_master/ERM_IRM/sem_Sep8.py","file_name":"sem_Sep8.py","file_ext":"py","file_size_in_byte":3311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"72781803","text":"#!/usr/local/bin/python3\n\nimport sys\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport scipy.ndimage\n\ndef main():\n    matplotlib.rcParams['contour.negative_linestyle'] = 'solid'\n    \n    plotStreamFunctionContour()\n    plotVorticityContour()\n    plotPressureContour()\n\ndef plotStreamFunctionContour():\n    streamFunction = np.loadtxt('streamFunction.dat')\n    X = np.loadtxt('streamFunctionX.dat')\n    Y = np.loadtxt('streamFunctionY.dat')\n    \n    streamFunction = scipy.ndimage.zoom(streamFunction, 1)\n    X = scipy.ndimage.zoom(X, 1)\n    Y = scipy.ndimage.zoom(Y, 1)\n    \n    levels = [-1.5e-3, -1e-3, -5e-4, -2.5e-4, -1e-4, -5e-5, -1e-5, -1e-6, 0,\n              1e-10, 1e-5, 1e-4, 1e-2, 3e-2, 5e-2, 7e-2, 9e-2, 0.1, 0.11,\n              0.115, 0.1175]\n    \n    plt.figure(num='Stream function', figsize=(8, 8))\n    plt.contour(X, Y, streamFunction, levels=levels, colors='k')\n    frame = plt.gca()\n    frame.axes.xaxis.set_ticklabels([])\n    frame.axes.yaxis.set_ticklabels([])\n    frame.xaxis.set_major_locator(plt.NullLocator())\n    frame.yaxis.set_major_locator(plt.NullLocator())\n    plt.savefig('streamFunction.pdf', format='pdf', bbox_inches='tight')\n    plt.show()\n\ndef plotVorticityContour():\n    vorticity = np.loadtxt('vorticity.dat')\n    X = np.loadtxt('vorticityX.dat')\n    Y = np.loadtxt('vorticityY.dat')\n    \n    vorticity = scipy.ndimage.zoom(vorticity, 1)\n    X = scipy.ndimage.zoom(X, 1)\n    Y = scipy.ndimage.zoom(Y, 1)\n    \n    levels = [-3.0, -2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0]\n    \n    plt.figure(num='Vorticity', figsize=(8, 8))\n    plt.contour(X, Y, vorticity, levels=levels, colors='k')\n    frame = plt.gca()\n    frame.axes.xaxis.set_ticklabels([])\n    frame.axes.yaxis.set_ticklabels([])\n    frame.xaxis.set_major_locator(plt.NullLocator())\n    frame.yaxis.set_major_locator(plt.NullLocator())\n    plt.savefig('vorticity.pdf', format='pdf', bbox_inches='tight')\n    plt.show()\n\ndef plotPressureContour():\n    pressure = np.loadtxt('pressure.dat')\n    X = np.loadtxt('pressureX.dat')\n    Y = np.loadtxt('pressureY.dat')\n    \n    pressure = scipy.ndimage.zoom(pressure, 1)\n    X = scipy.ndimage.zoom(X, 1)\n    Y = scipy.ndimage.zoom(Y, 1)\n    \n    levels = [-0.002, 0, 0.02, 0.05, 0.07, 0.09, 0.11, 0.12, 0.17, 0.3]\n    \n    plt.figure(num='Pressure', figsize=(8, 8))\n    plt.contour(X, Y, pressure, levels=levels, colors='k')\n    frame = plt.gca()\n    frame.axes.xaxis.set_ticklabels([])\n    frame.axes.yaxis.set_ticklabels([])\n    frame.xaxis.set_major_locator(plt.NullLocator())\n    frame.yaxis.set_major_locator(plt.NullLocator())\n    plt.savefig('pressure.pdf', format='pdf', bbox_inches='tight')\n    plt.show()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Code/C_ATLAS_double/plotResults.py","file_name":"plotResults.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"257273696","text":"\"\"\"Utility functions mainly used in benchmarking notebooks.\"\"\"\n\nfrom pathlib import Path\n\nimport tables\nimport pandas\nfrom astropy.table import Table\n\n\ndef get_camera_names(input_directory=None,\n                     file_name=None):\n    \"\"\"Read the names of the cameras.\n\n    Parameters\n    ==========\n    input_directory : str or pathlib.Path\n        Path of the input file.\n    file_name : str\n        Name of the input file.\n\n    Returns\n    =======\n    camera_names : list(str)\n        Table names as a list.\n    \"\"\"\n    if (input_directory is None) or (file_name is None):\n        print(\"ERROR: check input\")\n\n    input_file = Path(input_directory) / file_name\n\n    with tables.open_file(input_file, 'r') as f:\n        camera_names = [cam.name for cam in f.root]\n\n    return camera_names\n\n\ndef read_protopipe_TRAINING_per_tel_type(input_directory=None,\n                                         file_name=None,\n                                         camera_names=None):\n    \"\"\"Read a TRAINING file and extract the data per telescope type.\n\n    Parameters\n    ==========\n    input_directory : str or pathlib.Path\n        Path of the input directory where the TRAINING file is located.\n    file_name : str\n        Name of the input TRAINING file.\n\n    Returns\n    =======\n    dataFrames : dict(pandas.DataFrame)\n        Dictionary of tables per camera.\n    \"\"\"\n    if (input_directory is None) or (file_name is None):\n        print(\"ERROR: check input\")\n    if camera_names is None:\n        print(\"ERROR: no cameras specified\")\n    # load DL1 images\n    input_file = Path(input_directory) / file_name\n    dataFrames = {}\n    for camera in camera_names:\n        dataFrames[camera] = pandas.read_hdf(input_file, f\"/{camera}\")\n    return dataFrames\n\n\ndef read_TRAINING_per_tel_type_with_images(input_directory=None,\n                                           input_filename=None,\n                                           camera_names=None):\n    \"\"\"Read a TRAINING file and extract the data per telescope type.\n\n    Parameters\n    ==========\n    input_directory : str or pathlib.Path\n        Path of the input directory where the TRAINING file is located.\n    file_name : str\n        Name of the input TRAINING file.\n    camera_names: list\n        List of camera names corresponding to the table names in the file.\n\n    Returns\n    =======\n    table : dict(astropy.Table)\n        Dictionary of astropy tables per camera.\n    \"\"\"\n    input_file = Path(input_directory) / input_filename\n\n    table = {}\n\n    with tables.open_file(input_file, mode='r') as h5file:\n        for camera in camera_names:\n            table[camera] = Table()\n            for key in h5file.get_node(f\"/{camera}\").colnames:\n                table[camera][key] = h5file.get_node(f\"/{camera}\").col(key)\n\n    return table\n","sub_path":"protopipe/pipeline/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":2792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"599658116","text":"# In forms.py...\nfrom django import forms\nfrom .models import Attachment\n\n\nclass BasicUploadFileForm(forms.ModelForm):\n    file = forms.FileField(\n        widget=forms.FileInput(\n            attrs={'id': 'fileupload', }\n        )\n    )\n\n    class Meta:\n        model = Attachment\n        fields = ['file']\n","sub_path":"attachments/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"178890099","text":"import logging\nimport maya.cmds as mc\nimport maya.OpenMaya as om\nimport maya.OpenMayaAnim as oma\nimport maya.mel as mel\nimport maya_tools.utilities.mesh_utilities as mhu\n\nlogger = logging.getLogger('rig_build')\n\nspaces = dict(\n    local=oma.MFnBlendShapeDeformer.kLocalOrigin,\n    world=oma.MFnBlendShapeDeformer.kWorldOrigin\n)\n\ntarget_types = dict(\n    tangent=oma.MFnBlendShapeDeformer.kTangent,\n    object=oma.MFnBlendShapeDeformer.kObject\n)\n\n\ndef create_blendshape(*geometry, **kwargs):\n\n    mc.select(cl=True)\n    blendshape = mc.createNode(\n        'blendShape',\n        name=kwargs.get('name', 'blendShape')\n    )\n    mc.blendShape(\n        blendshape,\n        e=True,\n        geometry=geometry\n    )\n    return get_m_object(blendshape)\n\n\ndef add_base_geometry(blendshape, *geometry):\n    blendshape_functions = oma.MFnBlendShapeDeformer(blendshape)\n    for m_object in geometry:\n        blendshape_functions.addBaseObject(m_object)\n\n\ndef add_target(blendshape, base_geometry, group_index, target_geometry, weight_value, tangent_space=False):\n    if weight_value is None or weight_value == 0.0:\n        raise Exception('Weight is %s' % weight_value)\n    weight_value = round(float(weight_value), 3)\n    blendshape_functions = oma.MFnBlendShapeDeformer(blendshape)\n    existing_weights = []\n    for index in range(blendshape_functions.numWeights()):\n        existing_weights.append(blendshape_functions.weight(index))\n    blendshape_name = get_selection_string(blendshape)\n    base_geometry_name = get_selection_string(base_geometry)\n    target_geometry_name = get_selection_string(target_geometry)\n    # if target_type == 'tangent':\n\n\n    mc.blendShape(  # OpenMaya doesnt support tangent space blends yet\n        blendshape_name,\n        edit=True,\n        t=(\n            base_geometry_name,\n            group_index,\n            target_geometry_name,\n            weight_value\n        ),\n        tangentSpace=tangent_space\n    )\n    if tangent_space:  # Why the hell do i have to do this..???\n        connected_plugs = mc.listConnections('%s.worldMesh[0]' % target_geometry_name, d=True, s=False, p=True)\n        if connected_plugs:\n            mc.disconnectAttr('%s.worldMesh[0]' % target_geometry_name, connected_plugs[0])\n        else:\n            logger.critical('%s.outMesh' % target_geometry_name, 'Was not connected so we will not be disconnecting')\n\n\n\n    # else:\n    #\n    #     blendshape_functions.addTarget(\n    #         base_geometry,\n    #         group_index,\n    #         target_geometry,\n    #         weight_value\n    #     )\n\n\ndef list_targets(blendshape, index):\n    result = []\n    blendshape_functions = oma.MFnBlendShapeDeformer(blendshape)\n    base_objects = om.MObjectArray()\n    blendshape_functions.getBaseObjects(base_objects)\n    for t in range(base_objects.length()):\n        base_mesh = base_objects[t]\n        indices = om.MIntArray()\n        blendshape_functions.weightIndexList(indices)\n        item_array = om.MIntArray()\n        blendshape_functions.targetItemIndexList(index, base_mesh, item_array)\n        targets = om.MObjectArray()\n        blendshape_functions.getTargets(base_mesh, index, targets)\n        result.extend([targets[i] for i in range(targets.length())])\n    return result\n\n\ndef clear_group_targets(blendshape, index):\n    blendshape_name = get_selection_string(blendshape)\n    blendshape_functions = oma.MFnBlendShapeDeformer(blendshape)\n    base_objects = om.MObjectArray()\n    blendshape_functions.getBaseObjects(base_objects)\n    indices = om.MIntArray()\n    blendshape_functions.weightIndexList(indices)\n\n    if index in list(indices):\n        for t in range(base_objects.length()):\n            base_mesh = base_objects[t]\n            indices = om.MIntArray()\n            blendshape_functions.weightIndexList(indices)\n            targets = om.MObjectArray()\n            blendshape_functions.getTargets(\n                base_mesh,\n                index,\n                targets\n            )\n            inbetween_array = om.MIntArray()\n            blendshape_functions.targetItemIndexList(\n                index,\n                base_mesh,\n                inbetween_array\n            )\n            for i in range(inbetween_array.length()):\n                # mesh_m_object = targets[i]\n                # if not mesh_m_object:\n                #     raise Exception('Target mesh has been disconnected')\n                # mesh_name = get_selection_string(targets[i])\n                # mc.showHidden(mesh_name)\n                mel.eval('blendShapeDeleteInBetweenTarget %s %s %s' % (\n                    blendshape_name,\n                    index,\n                    (float(inbetween_array[i]) - 5000) / 1000.0\n                ))\n\n                # blendshape_functions.removeTarget(\n                #     base_objects[t],\n                #     index,\n                #     targets[i],\n                #     weight\n                # )\n                # mesh_name = get_selection_string(targets[i])\n\n\ndef clear_targets(blendshape):\n    blendshape_functions = oma.MFnBlendShapeDeformer(blendshape)\n    base_objects = om.MObjectArray()\n    blendshape_functions.getBaseObjects(base_objects)\n    indices = om.MIntArray()\n    blendshape_functions.weightIndexList(indices)\n    for index in indices:\n        clear_group_targets(blendshape, index)\n\n\ndef remove_target(blendshape, base_geometry, group_index, target_geometry, weight_value):\n    if weight_value is None or weight_value == 0.0:\n        raise Exception('Weight is %s' % weight_value)\n    weight_value = round(float(weight_value), 3)\n    blendshape_functions = oma.MFnBlendShapeDeformer(blendshape)\n    existing_weights = []\n    for index in range(blendshape_functions.numWeights()):\n        existing_weights.append(blendshape_functions.weight(index))\n    oma.MFnBlendShapeDeformer(blendshape).removeTarget(\n        base_geometry,\n        group_index,\n        target_geometry,\n        weight_value\n    )\n\n\ndef get_m_object(node):\n    if isinstance(node, om.MObject):\n        return node\n    selection_list = om.MSelectionList()\n    selection_list.add(str(node))\n    m_object = om.MObject()\n    selection_list.getDependNode(0, m_object)\n    return m_object\n\n\ndef get_selection_string(m_object):\n    selection_list = om.MSelectionList()\n    selection_list.add(m_object)\n    selection_strings = []\n    selection_list.getSelectionStrings(0, selection_strings)\n    return selection_strings[0]\n\n\ndef get_geom_index(geometry, deformer):\n    '''\n    Returns the geometry index of a shape to a specified deformer.\n    @param geometry: Name of shape or parent transform to query\n    @type geometry: str\n    @param deformer: Name of deformer to query\n    @type deformer: str\n    '''\n    # Check geometry\n    geo = geometry\n    if mc.objectType(geometry) == 'transform':\n        try:\n            geometry = mc.listRelatives(geometry, s=True, ni=True, pa=True)[0]\n        except:\n            raise Exception('Object \"' + geo + '\" is not a valid geometry!')\n    geom_obj = get_m_object(geometry)\n\n    # Get geometry index\n    deformer_obj = get_m_object(deformer)\n    deformer_fn = oma.MFnGeometryFilter(deformer_obj)\n    try:\n        geom_index = deformer_fn.indexForOutputShape(geom_obj)\n    except:\n        raise Exception('Object \"' + geometry + '\" is not affected by deformer \"' + deformer + '\"!')\n\n    return geom_index\n\n\ndef get_target_shape_index(blendshape, target_shape_name):\n    '''\n    Provide a string of the target name you wish to get the index number from the blendshape node.\n    :param blendshape: str(), Name of the blendshape node\n    :param target_shape_name: str(), Name of the target shape in the blendshape node.\n    :return: int(), Index of the target shape in the blendshape node.\n    '''\n    aliasList = mc.aliasAttr(blendshape, q=True)\n    aliasTarget = aliasList[(aliasList.index(target_shape_name) + 1)]\n    targetIndex = aliasTarget.split('[')[-1]\n    targetIndex = int(targetIndex.split(']')[0])\n    return targetIndex\n\n\ndef get_base_weights(blend_shape_node, geo_index=0):\n    geometry = mc.blendShape(blend_shape_node, q=True, geometry=True)\n    base_mesh = geometry[geo_index]\n    number_of_vtx = mc.polyEvaluate(base_mesh, v=True)\n    weight_dict = {}\n    for vtx in range(number_of_vtx):\n        m_plug = om.MPlug()\n        sl = om.MSelectionList()\n        sl.add(\"%s.inputTarget[%s].baseWeights\" % (blend_shape_node, geo_index))\n        sl.getPlug(0, m_plug)\n        weight = m_plug.elementByLogicalIndex(vtx).asFloat()\n        index = m_plug.elementByPhysicalIndex(vtx).logicalIndex()\n        weight_dict[index] = weight\n    return weight_dict\n\n\ndef set_base_weights(blend_shape_node, geo_index=0, weights_data=None):\n    for index, weight in weights_data.iteritems():\n        m_plug = om.MPlug()\n        sl = om.MSelectionList()\n        sl.add(\"%s.inputTarget[%s].baseWeights\" % (blend_shape_node, geo_index))\n        sl.getPlug(0, m_plug)\n        m_plug.elementByLogicalIndex(index).setFloat(weight)\n\n\ndef get_target_weights(base_mesh, blend_shape_node, geo_index=0, target_shape_index=0):\n    '''\n    Get the blendshape weight value for each index\n    :param base_mesh: str(), Name of base mesh.\n    :param blend_shape_node: type(Blendshape), Blendshape node , instance of BlendshapeController\n    :param geo_index: int(), Input target geo index\n    :param target_shape_index: int(), Input target geo shape index\n    :return:\n    '''\n    # Get weights\n    weight_dict = {}\n    number_of_vtx = mc.polyEvaluate(base_mesh, v=True)\n    for vtx in range(number_of_vtx):\n        m_plug = om.MPlug()\n        sl = om.MSelectionList()\n        sl.add('%s.inputTarget[%s].inputTargetGroup[%s].targetWeights' % (\n            blend_shape_node,\n            geo_index,\n            target_shape_index\n        ))\n        sl.getPlug(0, m_plug)\n        weight = m_plug.elementByLogicalIndex(vtx).asFloat()\n        index = m_plug.elementByPhysicalIndex(vtx).logicalIndex()\n        weight_dict[index] = weight\n    return weight_dict\n\n\ndef set_target_weights(blend_shape_node, geo_index=0, target_shape_index=0, weights_data=None):\n    '''\n    Set per vertex target weights for the specified blendShape target\n    @param blend_shape_node: Blendshape node object to set target weights for\n    @type blend_shape_node: type(Blendshape)\n    @param geo_index: int(), Input target geo index\n    @param target_shape_index: Index of the target shape in the blendShape node to set weights for\n    @type target_shape_index: int\n    @param wt: Weight value list to apply to the specified blendShape target\n    @type wt: list\n    @param weights_data: Dictionary of the vtx number as key, and a float value between 0.0 and 1.0 of the blendshape\n    weight.\n    @type geometry: dict\n    '''\n    # Set target weights\n    for index, weight in weights_data.iteritems():\n        m_plug = om.MPlug()\n        sl = om.MSelectionList()\n        sl.add('%s.inputTarget[%s].inputTargetGroup[%s].targetWeights' % (\n            blend_shape_node,\n            geo_index,\n            target_shape_index\n        ))\n        sl.getPlug(0, m_plug)\n        m_plug.elementByLogicalIndex(index).setFloat(weight)\n\n\ndef mirror_blend_shape_weights(base_mesh,\n                               base_blend_shape,\n                               base_geo_index=0,\n                               base_blend_shape_index=0,\n                               target_mesh=None,\n                               target_blend_shape=None,\n                               target_geo_index=None,\n                               target_blend_shape_index=None,\n                               positive_to_negative_x=True,\n                               weights_data=None,\n                               mirrored_indices=None):\n    '''\n    Copy the same vertex value of the blendshape weights to the flip side of x axis.\n    :param base_mesh: str(), Name of the geometry to source from.\n    :param base_blend_shape: type(Blendshape), Source blendshape node.\n    :param base_geo_index: int(), Source input target geo index\n    :param base_blend_shape_index: int(), Index of the source shape name in the blendshape node.\n    :param target_blend_shape: type(Blendshape), Blendshape node. If not specified, it will be the same as the source.\n    :param target_mesh: str(), Name of the geometry to target to.\n    :param target_geo_index: int(), Target input target geo index\n    :param target_blend_shape_index: int(), Index of the target shape name in the target blendshape node. If not\n    specified, it will be the same as the source.\n    :param positive_to_negative_x: bool(), If copying from negative x to positive x, set to False.\n    :param weights_data: dict(), with the index number as the key and float from 0.0 to 1.0 as the definition. If not\n    specified, it will get the data from the source mesh and blendshape node.\n    :param mirrored_indices: list(), The corresponding vertex on the mirrored x axis vtx indicies.\n    :return:\n    '''\n    # if target mesh args not provided, set them to base mesh. Therefore the mirror will happen to itself(mesh).\n    if not target_mesh:\n        target_mesh = base_mesh\n    if not target_blend_shape:\n        target_blend_shape = base_blend_shape\n    if not target_geo_index:\n        target_geo_index = base_geo_index\n    if not target_blend_shape_index:\n        target_blend_shape_index = base_blend_shape_index\n\n    if not mirrored_indices:\n        mirrored_indices = mhu.get_mirror_index_list(base_mesh, target_mesh)\n\n    if not weights_data:\n        weights_data = get_target_weights(base_mesh, base_blend_shape, base_geo_index, base_blend_shape_index)\n        vtx_count = mc.polyEvaluate(target_mesh, v=True)\n        set_weights_data = {}\n        for i in range(vtx_count):\n            vtx_position = mc.pointPosition('{0}.vtx[{1}]'.format(target_mesh, i), local=True)\n            if (positive_to_negative_x and vtx_position[0] > 0.0) or (not positive_to_negative_x and vtx_position[0] < 0.0):\n                set_weights_data[i] = weights_data[mirrored_indices[i]]\n    else:\n        set_weights_data = weights_data\n\n    set_target_weights(target_blend_shape, target_geo_index, target_blend_shape_index, set_weights_data)\n\n\ndef flip_blend_shape_weights(base_mesh,\n                             base_blend_shape,\n                             base_geo_index=0,\n                             base_blend_shape_index=0,\n                             target_mesh=None,\n                             target_blend_shape=None,\n                             target_geo_index=None,\n                             target_blend_shape_index=None,\n                             weights_data=None,\n                             mirrored_indices=None):\n    '''\n    Flips the vertex values of the blendshape weights on the x axis.\n    :param base_mesh: str(), Name of the geometry to source from.\n    :param base_blend_shape: str(), Name of the source blendshape node name.\n    :param base_geo_index: int(), Source input target geo index\n    :param base_blend_shape_index: int(), Name of the source shape name in the blendshape node.\n    :param target_mesh: str(), Name of the geometry to target to.\n    :param target_blend_shape: str(), Name of the target blendshape node. If not specified, it will be the same as the\n    source.\n    :param target_geo_index: int(), Target input target geo index\n    :param target_blend_shape_index: str(), Name of the target shape name in the target blendshape node. If not\n    specified, it will be the same as the source.\n    :param weights_data: dict(), with the index number as the key and float from 0.0 to 1.0 as the definition. If not\n    specified, it will get the data from the source mesh and blendshape node.\n    :param mirrored_indices: list(), The corresponding vertex on the mirrored x axis vtx indicies.\n    :return:\n    '''\n    # if target mesh args not provided, set them to base mesh. Therefore the flip will happen to itself(base_mesh).\n    if not target_mesh:\n        target_mesh = base_mesh\n    if not target_blend_shape:\n        target_blend_shape = base_blend_shape\n    if not target_geo_index:\n        target_geo_index = base_geo_index\n    if not target_blend_shape_index:\n        target_blend_shape_index = base_blend_shape_index\n    if not mirrored_indices:\n        mirrored_indices = mhu.get_mirror_index_list(base_mesh, target_mesh)\n\n    if not weights_data:\n        weights_data = get_target_weights(base_mesh, base_blend_shape, base_geo_index, base_blend_shape_index)\n        vtx_count = mc.polyEvaluate(target_mesh, v=True)\n        set_weights_data = {}\n        for i in range(vtx_count):\n            set_weights_data[i] = weights_data[mirrored_indices[i]]\n    else:\n        set_weights_data = weights_data\n\n    set_target_weights(target_blend_shape, target_geo_index, target_blend_shape_index, set_weights_data)\n\n\ndef transfer_blend_shape_weights(base_mesh,\n                                 base_blend_shape,\n                                 base_geo_index=0,\n                                 base_blend_shape_index=0,\n                                 target_mesh=None,\n                                 target_blend_shape=None,\n                                 target_geo_index=None,\n                                 target_blend_shape_index=None,\n                                 weights_data=None,\n                                 indices=None,\n                                 world_space=True):\n    # if target mesh args not provided, set them to base mesh. Therefore the flip will happen to itself(base_mesh).\n    if not target_mesh:\n        target_mesh = base_mesh\n    if not target_blend_shape:\n        target_blend_shape = base_blend_shape\n    if not target_geo_index:\n        target_geo_index = base_geo_index\n    if not target_blend_shape_index:\n        target_blend_shape_index = base_blend_shape_index\n    if not indices:\n        indices = mhu.get_closest_index_list(base_mesh, target_mesh, world_space)\n\n    if not weights_data:\n        weights_data = get_target_weights(base_mesh, base_blend_shape, base_geo_index, base_blend_shape_index)\n        vtx_count = mc.polyEvaluate(target_mesh, v=True)\n        set_weights_data = {}\n        for i in range(vtx_count):\n            set_weights_data[i] = weights_data[indices[i]]\n    else:\n        set_weights_data = weights_data\n\n    set_target_weights(target_blend_shape, target_geo_index, target_blend_shape_index, set_weights_data)\n\n","sub_path":"src/rt_tools/maya/toLearn/api/blendShape_utilites.py","file_name":"blendShape_utilites.py","file_ext":"py","file_size_in_byte":18338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"410251688","text":"import os\nimport math\nimport time\nimport datetime\nfrom multiprocessing import Process\nfrom multiprocessing import Queue\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nimport numpy as np\nimport imageio\n\nimport torch\nimport torch.optim as optim\nimport torch.optim.lr_scheduler as lrs\n\n\n\n### [y]\nimport logging\nimport traceback\nfrom pydicom import dcmread\n\n\ndef log_initialize(log_name, log_dp, flag_by_day=False):\n    # exam log_dp exist\n    if not os.path.isdir(log_dp):\n        try:\n            os.makedirs(log_dp, exist_ok=True)\n        except:\n            retm = traceback.format_exc()\n            return None\n    \n    mlog = logging.getLogger(log_name)\n    mlog.setLevel(logging.DEBUG)\n    if flag_by_day:\n        mlog_fh = logging.TimedRotatingFileHandler(os.path.join(log_dp, \"{0}.log\".format(log_name)), \\\n                                                   when='midnight', interval=1, backupCount=10, \\\n                                                   encoding='utf-8', utc=True)\n        mlog_fh.suffix = \"%Y%m%d\"\n    else:\n        mlog_fh = logging.FileHandler(os.path.join(log_dp, \"{0}.log\".format(log_name)), 'w', 'utf-8')\n    mlog_fh.setLevel(logging.DEBUG)\n    mlog_ch = logging.StreamHandler()\n    mlog_ch.setLevel(logging.DEBUG)\n    mlog_fmat = logging.Formatter('%(asctime)s - [%(threadName)s][%(name)s][%(levelname)s] - %(funcName)s() :: %(message)s')\n    mlog_fh.setFormatter(mlog_fmat)\n    mlog_ch.setFormatter(mlog_fmat)\n    mlog.addHandler(mlog_fh)\n    mlog.addHandler(mlog_ch)\n    return mlog\n\n\n\nclass timer():\n    def __init__(self):\n        self.acc = 0\n        self.tic()\n\n    def tic(self):\n        self.t0 = time.time()\n\n    def toc(self, restart=False):\n        diff = time.time() - self.t0\n        if restart: self.t0 = time.time()\n        return diff\n\n    def hold(self):\n        self.acc += self.toc()\n\n    def release(self):\n        ret = self.acc\n        self.acc = 0\n\n        return ret\n\n    def reset(self):\n        self.acc = 0\n\nclass checkpoint():\n    def __init__(self, args):\n        self.args = args\n        self.ok = True\n        self.log = torch.Tensor()\n        now = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')\n\n        if not args.load:\n            ### [y]\n            print(\"[y] checkpoint-init, i think is not give(def) args.load\")\n            \n            if not args.save:  # [y] if not given save name, code will use timestamp as save name\n                args.save = now\n            self.dir = os.path.join('..', 'experiment', args.save)\n            \n            ### [y]\n            print(\"[y] self.dir = {0}\".format(self.dir))\n        else:\n            ### [y]\n            print(\"[y] checkpoint-init, give(def) args.load\")\n            \n            self.dir = os.path.join('..', 'experiment', args.load)\n            if os.path.exists(self.dir):\n                self.log = torch.load(self.get_path('psnr_log.pt'))\n                print('Continue from epoch {}...'.format(len(self.log)))\n            else:\n                args.load = ''\n\n        if args.reset:\n            os.system('rm -rf ' + self.dir)\n            args.load = ''\n\n        os.makedirs(self.dir, exist_ok=True)\n        os.makedirs(self.get_path('model'), exist_ok=True)\n        for d in args.data_test:\n            os.makedirs(self.get_path('results-{}'.format(d)), exist_ok=True)\n\n        open_type = 'a' if os.path.exists(self.get_path('log.txt'))else 'w'\n        self.log_file = open(self.get_path('log.txt'), open_type)\n        with open(self.get_path('config.txt'), open_type) as f:\n            f.write(now + '\\n\\n')\n            for arg in vars(args):\n                f.write('{}: {}\\n'.format(arg, getattr(args, arg)))\n            f.write('\\n')\n\n        self.n_processes = 8\n\n    def get_path(self, *subdir):\n        return os.path.join(self.dir, *subdir)\n\n    def save(self, trainer, epoch, is_best=False):\n        trainer.model.save(self.get_path('model'), epoch, is_best=is_best)\n        trainer.loss.save(self.dir)\n        trainer.loss.plot_loss(self.dir, epoch)\n\n        self.plot_psnr(epoch)\n        trainer.optimizer.save(self.dir)\n        torch.save(self.log, self.get_path('psnr_log.pt'))\n\n    def add_log(self, log):\n        self.log = torch.cat([self.log, log])\n\n    def write_log(self, log, refresh=False):\n        print(log)\n        self.log_file.write(log + '\\n')\n        if refresh:\n            self.log_file.close()\n            self.log_file = open(self.get_path('log.txt'), 'a')\n\n    def done(self):\n        self.log_file.close()\n\n    def plot_psnr(self, epoch):\n        axis = np.linspace(1, epoch, epoch)\n        for idx_data, d in enumerate(self.args.data_test):\n            label = 'SR on {}'.format(d)\n            fig = plt.figure()\n            plt.title(label)\n            for idx_scale, scale in enumerate(self.args.scale):\n                plt.plot(\n                    axis,\n                    self.log[:, idx_data, idx_scale].numpy(),\n                    label='Scale {}'.format(scale)\n                )\n            plt.legend()\n            plt.xlabel('Epochs')\n            plt.ylabel('PSNR')\n            plt.grid(True)\n            plt.savefig(self.get_path('test_{}.pdf'.format(d)))\n            plt.close(fig)\n\n    def begin_background(self):\n        self.queue = Queue()\n\n        def bg_target(queue):\n            if self.args.rgb_range == 255:\n                while True:\n                    if not queue.empty():\n                        filename, tensor = queue.get()\n                        if filename is None: break\n                        imageio.imwrite(filename, tensor.numpy())\n            elif self.args.rgb_range == 5119:\n                while True:\n                    if not queue.empty():\n                        filename, tensor, ori_dcm_fp = queue.get()\n                        if filename is None: break\n                        #\n                        # save result as dcm\n                        #\n                        # read original dcm for meta data\n                        dcm_data = dcmread(ori_dcm_fp)\n            \n                        # modify seri_id, append \".yh_save_testing\n                        seri_id = dcm_data.SeriesInstanceUID\n                        dcm_data.SeriesInstanceUID = \"{0}.{1}\".format(seri_id, \"exp3_testing\")\n                        \n                        # update image data\n                        np_rst = np.squeeze(tensor.numpy(), axis=2)\n                        ###\n                        #tmp_min_val = np.min(np_rst)\n                        #if tmp_min_val < -1:\n                        #    print(\"\\n\\n\\n <-1 found \\n\\n\\n\")\n                        ###\n                        # np_rst = np_rst - 2048.0\n                        # np_rst_round = np.round(np_rst, 0)\n                        # np_rst_round_i16 = np_rst_round.astype(np.int16)\n                        # np_rst_clip = np.clip(np_rst_round_i16, -2048, 3071)\n                        # dcm_data.PixelData = np_rst_clip.tostring()\n                        # print(\"shape of dcm_img_x2_clip={0}\".format(np_rst_clip.shape))\n                        # dcm_data.Rows, dcm_data.Columns = np_rst_clip.shape\n                        ###\n                        # np_rst = np_rst - 2048.0\n                        # np_rst_round = np.round(np_rst, 0)\n                        # np_rst_clip = np.clip(np_rst_round, -2048.0, 3071.0)\n                        # # convert back to ori-style\n                        # the_intercept = dcm_data.RescaleIntercept\n                        # the_slope = dcm_data.RescaleSlope\n                        # if the_slope == 0:\n                        #     print(\"\\n\\n\\n Error, the_slope=0 in file:{0}\".format(ori_dcm_fp))\n                        # np_rst_oristyle = (np_rst_clip - the_intercept) / the_slope\n                        # np_rst_oristyle_i16 = np_rst_oristyle.astype(np.int16)\n                        # dcm_data.PixelData = np_rst_oristyle_i16.tostring()\n                        # print(\"shape of np_rst_oristyle_i16={0}\".format(np_rst_oristyle_i16.shape))\n                        # dcm_data.Rows, dcm_data.Columns = np_rst_oristyle_i16.shape\n                        ### =>\n                        # np_rst_round = np.round(np_rst, 0)\n                        # np_rst_clip = np.clip(np_rst_round, -2048.0, 3071.0)\n                        # ####np_rst_clip = np.clip(np_rst_round, -1024.0, 3071.0)\n                        # # convert back to ori-style\n                        # the_intercept = dcm_data.RescaleIntercept\n                        # the_slope = dcm_data.RescaleSlope\n                        # if the_slope == 0:\n                        #     print(\"\\n\\n\\n Error, the_slope=0 in file:{0}\".format(ori_dcm_fp))\n                        # np_rst_oristyle = (np_rst_clip - the_intercept) / the_slope\n                        # np_rst_oristyle_i16 = np_rst_oristyle.astype(np.int16)\n                        # ####dcm_data.PixelData = np_rst_oristyle_i16.tostring()\n                        # dcm_data.PixelData = np_rst_oristyle_i16.tobytes()\n                        # print(\"shape of np_rst_oristyle_i16={0}\".format(np_rst_oristyle_i16.shape))\n                        # dcm_data.Rows, dcm_data.Columns = np_rst_oristyle_i16.shape\n                        # dcm_data.BitsStored = 16\n                        # dcm_data.HighBit = 15\n                        # #dcm_data.SmallestImagePixelValue = 0\n                        # #dcm_data.LargestImagePixelValue = 4095\n                        # # save\n                        # dcm_data.save_as(filename)\n                        ### =>\n                        # np_rst_round = np.round(np_rst, 0)\n                        # np_rst_clip = np.clip(np_rst_round, -2048.0, 3071.0)\n                        # ####np_rst_clip = np.clip(np_rst_round, -1024.0, 3071.0)\n                        # # convert back to ori-style\n                        # the_intercept = dcm_data.RescaleIntercept\n                        # the_slope = dcm_data.RescaleSlope\n                        # if the_slope == 0:\n                        #     print(\"\\n\\n\\n Error, the_slope=0 in file:{0}\".format(ori_dcm_fp))\n                        # np_rst_oristyle = (np_rst_clip - the_intercept) / the_slope\n                        # np_rst_oristyle_i16 = np_rst_oristyle.astype(np.int16)\n                        # ####dcm_data.PixelData = np_rst_oristyle_i16.tostring()\n                        # dcm_data.PixelData = np_rst_oristyle_i16.tobytes()\n                        # print(\"shape of np_rst_oristyle_i16={0}\".format(np_rst_oristyle_i16.shape))\n                        # dcm_data.Rows, dcm_data.Columns = np_rst_oristyle_i16.shape\n                        # dcm_data.BitsStored = 16\n                        # dcm_data.HighBit = 15\n                        # dcm_data.PixelRepresentation = 0\n                        # #dcm_data.SmallestImagePixelValue = 0\n                        # #dcm_data.LargestImagePixelValue = 4095\n                        # ### => bef sleep, test 12bit to 16bit, should to intercept=0, slope=1\n                        # np_rst_round = np.round(np_rst, 0)\n                        # #np_rst_clip = np.clip(np_rst_round, -2048.0, 3071.0)\n                        # np_rst_clip_i16 = np_rst_round.astype(np.int16)\n                        # dcm_data.PixelData = np_rst_clip_i16.tobytes()\n                        # #print(\"shape of np_rst_oristyle_i16={0}\".format(np_rst_clip_i16.shape))\n                        # dcm_data.Rows, dcm_data.Columns = np_rst_clip_i16.shape\n                        # dcm_data.BitsStored = 16\n                        # dcm_data.HighBit = 15\n                        # dcm_data.PixelRepresentation = 1\n                        # dcm_data.RescaleIntercept = 0\n                        # dcm_data.RescaleSlope = 1\n                        # #dcm_data.SmallestImagePixelValue = 0\n                        # #dcm_data.LargestImagePixelValue = 4095\n                        # #del dcm_data[\"SmallestImagePixelValue\"]\n                        # #del dcm_data[\"LargestImagePixelValue\"]\n                        ### => fix uint int issue, can work 1024\n                        # dcm_img = dcm_data.pixel_array.astype(np.float32)\n                        # src_max_val = np.max(dcm_img)\n                        # src_min_val = np.min(dcm_img)\n                        # print()\n                        # print(\"src_max_val={0}\".format(src_max_val))\n                        # print(\"src_min_val={0}\".format(src_min_val))\n                        #\n                        # max_val = np.max(np_rst)\n                        # min_val = np.min(np_rst)\n                        # print(\"max_val={0}\".format(max_val))\n                        # print(\"min_val={0}\".format(min_val))\n                        # # from hu back to ori format\n                        # the_intercept = dcm_data.RescaleIntercept\n                        # the_slope = dcm_data.RescaleSlope\n                        # if the_slope == 0:\n                        #     np_rst_back = np.zeros(np_rst.shape)\n                        # else:\n                        #     np_rst_back = (np_rst - the_intercept) / the_slope\n                        #\n                        # # see max and min again\n                        # max_back_val = np.max(np_rst_back)\n                        # min_back_val = np.min(np_rst_back)\n                        # print(\"max_back_val={0}\".format(max_back_val))\n                        # print(\"min_back_val={0}\".format(min_back_val))\n                        #\n                        # # clip protect here\n                        # np_rst_clip = np.clip(np_rst_back, src_min_val, src_max_val)\n                        # np_rst_clip_uint16 = np_rst_clip.astype(np.uint16)\n                        # dcm_data.PixelData = np_rst_clip_uint16.tobytes()\n                        # #print(\"shape of np_rst_oristyle_i16={0}\".format(np_rst_clip_i16.shape))\n                        # dcm_data.Rows, dcm_data.Columns = np_rst_clip_uint16.shape\n                        # => 1024, 2048\n                        #\n                        #\n                        #\n                        dcm_img = dcm_data.pixel_array.astype(np.float32)\n                        src_max_val = np.max(dcm_img)\n                        src_min_val = np.min(dcm_img)\n                        print()\n                        print(\"src_max_val={0}\".format(src_max_val))\n                        print(\"src_min_val={0}\".format(src_min_val))\n                        \n                        max_val = np.max(np_rst)\n                        min_val = np.min(np_rst)\n                        print(\"max_val={0}\".format(max_val))\n                        print(\"min_val={0}\".format(min_val))\n                        # from hu back to ori format\n                        the_intercept = dcm_data.RescaleIntercept\n                        the_slope = dcm_data.RescaleSlope\n                        if the_slope == 0:\n                            np_rst_back = np.zeros(np_rst.shape)\n                        else:\n                            np_rst_back = (np_rst - the_intercept) / the_slope\n                        \n                        # see max and min again\n                        max_back_val = np.max(np_rst_back)\n                        min_back_val = np.min(np_rst_back)\n                        print(\"max_back_val={0}\".format(max_back_val))\n                        print(\"min_back_val={0}\".format(min_back_val))\n                        \n                        # clip protect here\n                        np_rst_clip = np.clip(np_rst_back, src_min_val, src_max_val)\n                        if dcm_data.PixelRepresentation == 0:\n                            np_rst_typecvt = np_rst_clip.astype(np.uint16)\n                        elif dcm_data.PixelRepresentation == 1:\n                            np_rst_typecvt = np_rst_clip.astype(np.int16)\n                        else:\n                            print(\"dcm_data.PixelRepresentation not valid, now is : {0}\".format(dcm_data.PixelRepresentation))\n                            exit(-1)\n                        dcm_data.PixelData = np_rst_typecvt.tobytes()\n                        #print(\"shape of np_rst_oristyle_i16={0}\".format(np_rst_clip_i16.shape))\n                        dcm_data.Rows, dcm_data.Columns = np_rst_typecvt.shape\n                        \n                        # save\n                        dcm_data.save_as(filename)\n                        \n        \n        self.process = [\n            Process(target=bg_target, args=(self.queue,)) \\\n            for _ in range(self.n_processes)\n        ]\n        \n        for p in self.process: p.start()\n\n    def end_background(self):\n        if self.args.rgb_range == 255:\n            for _ in range(self.n_processes): self.queue.put((None, None))\n            while not self.queue.empty(): time.sleep(1)\n            for p in self.process: p.join()\n        elif self.args.rgb_range == 5119:\n            for _ in range(self.n_processes): self.queue.put((None, None, None))\n            while not self.queue.empty(): time.sleep(1)\n            for p in self.process: p.join() \n\n    def save_results(self, dataset, filename, save_list, scale):\n        if self.args.save_results:\n            filename = self.get_path(\n                'results-{}'.format(dataset.dataset.name),\n                '{}_x{}_'.format(filename, scale)\n            )\n\n            postfix = ('SR', 'LR', 'HR')\n            for v, p in zip(save_list, postfix):\n                normalized = v[0].mul(255 / self.args.rgb_range)\n                tensor_cpu = normalized.byte().permute(1, 2, 0).cpu()\n                self.queue.put(('{}{}.png'.format(filename, p), tensor_cpu))\n                \n    def save_results_dicom(self, dataset, filename, save_list, scale, HR_dp):\n        if self.args.save_results:\n            ori_dcm_fp = os.path.join(HR_dp, \"{0}.dcm\".format(filename))\n            #print(\"ori_dcm_fp={0}\".format(ori_dcm_fp))\n            \n            filename = self.get_path(\n                'results-{}'.format(dataset.dataset.name),\n                '{}_x{}_'.format(filename, scale)\n            )\n            \n            postfix = ('SR', 'LR', 'HR')\n            for v, p in zip(save_list, postfix):\n                normalized = v[0].mul(5119 / self.args.rgb_range)\n                #tensor_cpu = normalized.byte().permute(1, 2, 0).cpu()\n                tensor_cpu = normalized.short().permute(1, 2, 0).cpu()\n                self.queue.put(('{}{}.dcm'.format(filename, p), tensor_cpu, ori_dcm_fp))\n\ndef quantize(img, rgb_range):\n    pixel_range = 255 / rgb_range\n    return img.mul(pixel_range).clamp(0, 255).round().div(pixel_range)\n\ndef calc_psnr(sr, hr, scale, rgb_range, dataset=None):\n    if hr.nelement() == 1: return 0\n\n    diff = (sr - hr) / rgb_range\n    if dataset and dataset.dataset.benchmark:\n        shave = scale\n        if diff.size(1) > 1:\n            gray_coeffs = [65.738, 129.057, 25.064]\n            convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256\n            diff = diff.mul(convert).sum(dim=1)\n    else:\n        shave = scale + 6\n\n    valid = diff[..., shave:-shave, shave:-shave]\n    mse = valid.pow(2).mean()\n\n    return -10 * math.log10(mse)\n\ndef quantize_dicom(img, rgb_range):\n    ####pixel_range = 5119 / rgb_range\n    ####return img.mul(pixel_range).clamp(0, 5119).round().div(pixel_range)\n    #=>\n    return img\n\ndef calc_psnr_dicom(sr, hr, scale, rgb_range=5119, dataset=None):\n    if hr.nelement() == 1:\n        print(\"hr.nelement() == 1, just return result:0\")\n        return 0\n\n    diff = (sr - hr) / rgb_range\n    if dataset and dataset.dataset.benchmark:\n        print(\"calc psnr, in if part\")\n        shave = scale\n        if diff.size(1) > 1:\n            gray_coeffs = [65.738, 129.057, 25.064]\n            convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256\n            diff = diff.mul(convert).sum(dim=1)\n    else:\n        #print(\"calc psnr, in else part\")\n        shave = scale + 6\n\n    valid = diff[..., shave:-shave, shave:-shave]\n    mse = valid.pow(2).mean()\n    ori_code_psnr = -10 * math.log10(mse)\n    \n    #\n    # my psnr\n    #\n    #yh_diff = (sr - hr)\n    #yh_valid = yh_diff[..., shave:-shave, shave:-shave]\n    #yh_mse = yh_valid.pow(2).mean()\n    #yh_psnr = 10 * math.log10((5119**2)/yh_mse)\n    \n    #print(\"ori psnr={0}, yh_psnr={1}\".format(ori_code_psnr, yh_psnr))\n    return ori_code_psnr\n\ndef make_optimizer(args, target):\n    '''\n        make optimizer and scheduler together\n    '''\n    # optimizer\n    trainable = filter(lambda x: x.requires_grad, target.parameters())\n    kwargs_optimizer = {'lr': args.lr, 'weight_decay': args.weight_decay}\n\n    if args.optimizer == 'SGD':\n        optimizer_class = optim.SGD\n        kwargs_optimizer['momentum'] = args.momentum\n    elif args.optimizer == 'ADAM':\n        optimizer_class = optim.Adam\n        kwargs_optimizer['betas'] = args.betas\n        kwargs_optimizer['eps'] = args.epsilon\n    elif args.optimizer == 'RMSprop':\n        optimizer_class = optim.RMSprop\n        kwargs_optimizer['eps'] = args.epsilon\n\n    # scheduler\n    milestones = list(map(lambda x: int(x), args.decay.split('-')))\n    kwargs_scheduler = {'milestones': milestones, 'gamma': args.gamma}\n    scheduler_class = lrs.MultiStepLR\n\n    class CustomOptimizer(optimizer_class):\n        def __init__(self, *args, **kwargs):\n            super(CustomOptimizer, self).__init__(*args, **kwargs)\n\n        def _register_scheduler(self, scheduler_class, **kwargs):\n            self.scheduler = scheduler_class(self, **kwargs)\n\n        def save(self, save_dir):\n            torch.save(self.state_dict(), self.get_dir(save_dir))\n\n        def load(self, load_dir, epoch=1):\n            self.load_state_dict(torch.load(self.get_dir(load_dir)))\n            if epoch > 1:\n                for _ in range(epoch): self.scheduler.step()\n\n        def get_dir(self, dir_path):\n            return os.path.join(dir_path, 'optimizer.pt')\n\n        def schedule(self):\n            self.scheduler.step()\n\n        def get_lr(self):\n            return self.scheduler.get_lr()[0]\n\n        def get_last_epoch(self):\n            return self.scheduler.last_epoch\n    \n    optimizer = CustomOptimizer(trainable, **kwargs_optimizer)\n    optimizer._register_scheduler(scheduler_class, **kwargs_scheduler)\n    return optimizer\n\n","sub_path":"src/utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":22206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"631815867","text":"import cProfile\n\ndef merge_sort(A):\n\n    def merge(L, R, M):\n        i = j = k = 0\n        K = len(R)\n        J = len(L)\n        I = J + K\n        while i < I:\n            if j >= J and k < K:\n                M[i : ] = R[k : ]\n                break\n            elif k >= K and j < J:\n                M[i : ] = L[j : ]\n                break\n            elif L[j] <= R[k]:\n                M[i] = L[j]\n                j += 1\n            else:\n                M[i] = R[k]\n                k += 1\n            i += 1\n\n    n = len(A)\n    if n > 1:\n        m = int( n/2 if n%2 == 0 else (n-1)/2 )\n        L = A[ : m]\n        R = A[m : ]\n        merge_sort(L)\n        merge_sort(R)\n        merge(L, R, A)\n\narray = list( map( lambda x: float(x), open('test.txt', 'r').read().split() ) )\ncProfile.run('merge_sort(array)')\nwith open(\"sorted.txt\", 'w') as s:\n    s.write('\\n'.join( list( map( lambda x: str(x), array ) ) ) )\n","sub_path":"merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"334035032","text":"\nimport cv2\nimport os\nimport imageio\n\nface_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')\nrecognizer = cv2.face_LBPHFaceRecognizer.create()\nrecognizer.read(\"trainner.yml\")\nlabels = {\"person_name\":1}\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\n# keyframes klasöründeki keyfamelerin videoda kaçıncı frame olduğu her resim için başlık olarak yazdırdım\n# daha sonra bu keyframlerin kaçıncı karede olduğu keyframes dizisine kayıt ediliyor\ndef add_keyframe(image_dir):\n    keyframes = []\n    for root, dirs, files in os.walk(image_dir):\n        for file in files:\n            file = file.replace(\".jpg\",\"\")\n            if not file == \"Thumbs.db\":\n                keyframes.append(int(file))\n    print(keyframes)\n    return  keyframes\n\ndef createFolder(directory):\n    try:\n        if not os.path.exists(directory):\n            os.makedirs(directory)\n    except OSError:\n        print ('Error: Creating directory. ' +  directory)\n\n\n# frame'de sunucu var ise 1 yok ise 0 döndürüyor\ndef detect(frame):\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)\n    for (x, y, w, h) in faces:\n        roi_gray = gray[y:y + h, x:x + w]\n        id_, conf = recognizer.predict(roi_gray)\n        if conf >= 45:\n            return 1\n    return 0\n\npath = \"../news\"\ncreateFolder(\"VideosWithoutPresenter\")\nvideo_count = len([name for name in os.listdir(path) if os.path.isfile(os.path.join(path, name))])\nprint(video_count)\nwrite = 0\n# şimdilik ilk 16 videonun keyframeleri belli olduğundan 15e kadar yazdırdım daha sonra video count olarak değiştirilecek\nfor i in range(0, 15):\n    videoName = path + \"/\" + str(i) + \".avi\"\n    video = cv2.VideoCapture(videoName)\n    fps = video.get(cv2.CAP_PROP_FPS)\n    isFinish = False\n\n    # keyframeler listeye alınıyor\n    keyframes = []\n    keyframes = add_keyframe(\"keyframes/\" + str(i))\n\n    # dosyanın yazılacağı dizin\n    writer = imageio.get_writer(\"VideosWithoutPresenter/\" + str(i) + \".avi\", fps=fps)\n    j = 0\n    write = 0\n    while (video.isOpened() and (isFinish == False)):\n        \"\"\" Capture frame-by-frame \"\"\"\n        ret, frame = video.read()\n        if ret == True:\n            # keyframe'in olduğu karede presenter var mı diye kontrol ediliyor var ise videoya yazma kesiliyor\n            # yazma kesildikten itibaren bir sonraki keyfame'e geldiğinde yine sunucu var mı diye kontrol ediyor\n            # eğer sunucu yoksa yine yazmaya başlıyor\n            if (j in keyframes):\n                presenter = detect(frame)\n                if (presenter == 1):\n                    write = 0\n                else:\n                    write = 1\n            if (write == 1):\n                # bu renk değişimi yapıldığı zaman renkleri farklı yazıyor video daha mavimsi oluyor\n                scene = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n                writer.append_data(scene)\n            j = j + 1\n        else:\n            \"\"\" Break the loop\"\"\"\n            isFinish = True\n\n    \"\"\" When everything done, release the video capture object\"\"\"\n    video.release()\n    writer.close()\n\n\n","sub_path":"FaceRecognation/presenter_elimination.py","file_name":"presenter_elimination.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"375978231","text":"import numpy as np\nfrom scipy.sparse.linalg import spsolve\nimport pyamg\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom fealpy.pde.poisson_2d import CosCosData as PDE\nfrom fealpy.functionspace import LagrangeFiniteElementSpace\nfrom fealpy.boundarycondition import DirichletBC \n\nfrom fealpy.tools.show import showmultirate, show_error_table\n\np = 2 # 有限元空间次数, 可以增大 p， 看输出结果的变化 \nn = 4 # 初始网格加密次数 \nmaxit = 4 # 最大迭代次数  \n\n\npde = PDE()\nmesh = pde.init_mesh(n=n)\n\nerrorType = ['$|| u - u_h||_{\\Omega,0}$', # L2 误差\n             '$||\\\\nabla u - \\\\nabla u_h||_{\\Omega, 0}$' # H1 误差\n             ]\nerrorMatrix = np.zeros((2, maxit), dtype=np.float)\nNDof = np.zeros(maxit, dtype=np.float)\n\n# 初始网格\nfig = plt.figure()\naxes = fig.gca()\nmesh.add_plot(axes)\n\nfor i in range(maxit):\n    space = LagrangeFiniteElementSpace(mesh, p=p) # 建立有限元空间\n\n    NDof[i] = space.number_of_global_dofs() # 有限元空间自由度的个数\n    bc = DirichletBC(space, pde.dirichlet)  # DirichletBC 条件\n\n    uh = space.function() # 有限元函数\n    A = space.stiff_matrix() # 刚度矩阵\n    F = space.source_vector(pde.source) # 载荷向量\n\n    A, F = bc.apply(A, F, uh) # 处理边界条件 \n\n    uh[:] = spsolve(A, F).reshape(-1) # 稀疏矩阵直接解法器\n\n    #ml = pyamg.ruge_stuben_solver(A)  # 代数多重网格解法器\n    #uh[:] = ml.solve(F, tol=1e-12, accel='cg').reshape(-1)\n\n    errorMatrix[0, i] = space.integralalg.L2_error(pde.solution, uh) # 计算 L2 误差\n    errorMatrix[1, i] = space.integralalg.L2_error(pde.gradient, uh.grad_value) # 计算 H1 误差\n\n    if i < maxit-1:\n        mesh.uniform_refine() # 一致加密网格\n\n# 画函数图像\nfig = plt.figure()\naxes = fig.gca(projection='3d')\nuh.add_plot(axes, cmap='rainbow')\n\n# 画收敛阶图像\nshowmultirate(plt, 0, NDof, errorMatrix,  errorType, propsize=20)\n\n# 输出 latex 误差表格\nshow_error_table(NDof, errorType, errorMatrix)\nplt.show()\n","sub_path":"test/possion.py","file_name":"possion.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"151636524","text":"from sys import stdin\n\nr, c = [int(_) for _ in stdin.readline().strip().split()]\ncages = []\nfor _ in range(int(stdin.readline())):\n    cages.append(tuple(int(_) for _ in stdin.readline().strip().split()))\n  \ndp = [[0 for i in range(c)] for j in range(r)]\ndp[0][0] = 1\nfor i in range(r):\n    for j in range(c):\n        if dp[i][j] == 0:\n            s = 0\n            if (i, j+1) not in cages:\n                s+=dp[i-1][j]\n            if (i+1, j) not in cages:\n                s+=dp[i][j-1]\n            dp[i][j] = s\n                \nprint(dp[r-1][c-1])","sub_path":"site/solutions/DMOJ/ccc12s5.py","file_name":"ccc12s5.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"70505156","text":"import random\nimport numpy as np\nfrom scipy.ndimage import rotate\nfrom scipy.ndimage.filters import gaussian_filter\n\nimport h5py\nfrom glob import glob\nimport os.path\n\ndef bg_frac(im):\n    return np.sum(im == 0) / float(im.size)\n\ndef adapt(im):\n    im = im.astype(float)\n    im = im - im.mean()\n    im = im / im.std()\n    local_mean = gaussian_filter(im, 51)\n    local_var = np.maximum(0, gaussian_filter(im ** 2, 51) - local_mean ** 2) + 0.001\n    return (im - local_mean) / np.sqrt(local_var)\n\nclass Training(object):\n    def __init__(self, hdf5_file, size=512, max_bg=0.75, suppress=15, use_malis=False):\n        self.h5 = h5py.File(hdf5_file, 'r')\n        self.orig_data = self.h5['images']\n        self.positives = self.h5['train_membrane_positive']\n        self.negatives = self.h5['train_membrane_negative']\n        self.gt = self.h5['groundtruth_labels']\n\n        self.size = size\n        self.max_bg = max_bg\n        self.suppress = suppress\n        self.use_malis = use_malis\n\n    def random_subimage(self, validation=False):\n        # choose a random index, keeping last image as validation\n        idx = random.randrange(0, self.orig_data.shape[0] - 1)\n        if validation:\n            idx = self.orig_data.shape[0] - 1\n\n        im = adapt(self.orig_data[idx, ...])\n        pos = self.positives[idx, ...]\n        neg = self.negatives[idx, ...]\n        gt = self.gt[idx, ...]\n\n        while True:\n            # choose center point\n            edge = int(self.size / np.sqrt(2))\n            i_center = random.randrange(edge + 1, im.shape[0] - edge - 1)\n            j_center = random.randrange(edge + 1, im.shape[1] - edge - 1)\n            slice_i = slice(i_center - edge, i_center + edge)\n            slice_j = slice(j_center - edge, j_center + edge)\n\n            # cutout\n            subim = im[slice_i, slice_j]\n            subpos = pos[slice_i, slice_j]\n            subneg = neg[slice_i, slice_j]\n            subgt = gt[slice_i, slice_j]\n\n            # check density of labels\n            if bg_frac(subpos + subneg) < self.max_bg:\n                break\n        im = subim\n        # convert to 0/1\n        pos = (subpos > 0).astype(np.float)\n        neg = (subneg > 0).astype(np.float)\n        gt = subgt\n\n        # random rotation\n        angle = random.randrange(0, 360)\n        im = rotate(im, angle, reshape=False)\n        pos = rotate(pos, angle, reshape=False, order=1)\n        neg = rotate(neg, angle, reshape=False, order=1)\n        gt = rotate(gt, angle, reshape=False, order=0)\n\n        # cutout center\n        overage = (im.shape[0] - self.size) // 2\n        im = im[overage:, overage:][:self.size, :self.size]\n        pos = pos[overage:, overage:][:self.size, :self.size]\n        neg = neg[overage:, overage:][:self.size, :self.size]\n        gt = gt[overage:, overage:][:self.size, :self.size]\n\n        # threshold at 0.5, make sure there is no overlap\n        pos = (pos > 0.5).astype(np.float32)\n        neg = (neg > 0.5).astype(np.float32)\n\n        # random flip\n        if random.uniform(0, 1) < 0.5:\n            im = im[::-1, :]\n            pos = pos[::-1, :]\n            neg = neg[::-1, :]\n            gt = gt[::-1, :]\n\n        # edge suppression\n        pos[:self.suppress, :] = 0.0\n        pos[-self.suppress:, :] = 0.0\n        pos[:, :self.suppress] = 0.0\n        pos[:, -self.suppress:] = 0.0\n        neg[:self.suppress, :] = 0.0\n        neg[-self.suppress:, :] = 0.0\n        neg[:, :self.suppress] = 0.0\n        neg[:, -self.suppress:] = 0.0\n\n        return im, pos, neg, gt\n\n    def good_subimage(self, validation=False):\n        while True:\n            im, pos, neg, gt = self.random_subimage(validation)\n            if bg_frac((pos + neg)[self.suppress:-self.suppress, self.suppress:-self.suppress]) < self.max_bg:\n                return im, pos, neg, gt\n\n    def good_subimages(self, validation=False):\n        while True:\n            yield self.good_subimage(validation)\n\n    def batch(self, count):\n        ims_pos_neg_gt = [self.good_subimage() for idx in range(count)]\n        ims, pos, neg, gt = zip(*ims_pos_neg_gt)\n        ims = np.stack(ims)[..., np.newaxis]\n        pos = np.stack(pos)[..., np.newaxis]\n        neg = np.stack(neg)[..., np.newaxis]\n        gt = np.stack(gt)[..., np.newaxis]\n        assert ims.shape == pos.shape\n        assert ims.shape == neg.shape\n        assert ims.shape == (count, self.size, self.size, 1)\n        if not self.use_malis:\n            return ims, pos, neg\n        else:\n            return ims, gt\n","sub_path":"membrane_classifier/training_set.py","file_name":"training_set.py","file_ext":"py","file_size_in_byte":4483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"312706625","text":"import pytest\n\nfrom great_expectations.types import (\n    DotDict,\n    LooselyTypedDotDict,\n    ListOf,\n)\n\n\"\"\"\n* dictionary syntax works for assignment and lookup `myobj[\"a\"] = 10`, `print(myobjj[\"a\"])`\n* dot notation works for assignment and lookup `my_obj.a = 10`, `print(my obj.a)`\n* Adding an unknown key raises an error\n* Keys can be optional\n\n* Values can be typed\n\"\"\"\n\ndef test_DotDict_dictionary_syntax():\n    D = DotDict({\n        'x': [1, 2, 4],\n        'y': [1, 2, 5],\n        'z': ['hello', 'jello', 'mello'],\n    })\n    D[\"w\"] = 10\n    assert D[\"x\"][0] == D[\"y\"][0]\n    assert D[\"w\"] == 10\n\ndef test_DotDict_dot_syntax():\n    D = DotDict({\n        'x': [1, 2, 4],\n        'y': [1, 2, 5],\n        'z': ['hello', 'jello', 'mello'],\n    })\n    assert D.x[0] == D.y[0]\n    assert D.x[0] != D.z[0]\n\n    d = DotDict()\n    d[\"y\"] = 2\n    assert d.y == 2\n    assert d[\"y\"] == 2\n\n    d.x = 1\n    assert d.x == 1\n    assert d[\"x\"] == 1\n\n    assert d == {\n        \"x\" : 1,\n        \"y\" : 2\n    }\n\n\ndef test_LooselyTypedDotDict_raises_error():\n    with pytest.raises(KeyError):\n        D = LooselyTypedDotDict(**{\n            'x': 1,\n        })\n\n    d = LooselyTypedDotDict(**{})\n\n    with pytest.raises(KeyError):\n        d[\"x\"] = \"hello?\"\n    \n    with pytest.raises(KeyError):\n        d.x = \"goodbye?\"\n\n    # Note that we raise AttributeError instead of KeyError here since the\n    # intended semantics when using dot notation are class property style\n    with pytest.raises(AttributeError):\n        assert d.x is None\n\n    # In contrast, when we explicitly use the dictionary notation, we get\n    # the KeyError, also following the standard conventions\n    with pytest.raises(KeyError):\n        assert d[\"x\"] is None\n\n\ndef test_LooselyTypedDotDict_subclass():\n    class MyLooselyTypedDotDict(LooselyTypedDotDict):\n        _allowed_keys = {\n            \"x\", \"y\", \"z\"\n        }\n\n    d = MyLooselyTypedDotDict(**{\n        'x': 1,\n    })\n    assert d.x == 1\n    assert d[\"x\"] == 1\n    \n    d[\"y\"] = 100\n    assert d[\"y\"] == 100\n    assert d.y == 100\n\n    d.z = \"estella\"\n    assert d.z == \"estella\"\n    assert d[\"z\"] == \"estella\"\n\n    assert d == {\n        \"x\": 1,\n        \"y\": 100,\n        \"z\": \"estella\",\n    }\n\n    del d[\"x\"]\n\n    assert d == {\n        \"y\": 100,\n        \"z\": \"estella\",\n    }\n\n    with pytest.raises(KeyError):\n        d[\"w\"] = 100\n\n    with pytest.raises(KeyError):\n        d.w = 100\n\n    with pytest.raises(AttributeError):\n        assert d.w is None\n\n    with pytest.raises(KeyError):\n        assert d[\"w\"] is None\n\n\ndef test_LooselyTypedDotDict_subclass_required_keys():\n    class MyLooselyTypedDotDict(LooselyTypedDotDict):\n        _allowed_keys = set([\n            \"x\", \"y\", \"z\"\n        ])\n        _required_keys = set([\n            \"x\"\n        ])\n\n    with pytest.raises(KeyError):\n        d = MyLooselyTypedDotDict(**{\n            'y': 1,\n        })\n\n    d = MyLooselyTypedDotDict(**{\n        'x': 1,\n    })\n    assert d.x == 1\n\n    d.x += 10\n    assert d.x == 11\n\n    d[\"x\"] = \"hi\"\n    assert d.x == \"hi\"\n\n    # Can't delete a required key\n    with pytest.raises(KeyError):\n        del d[\"x\"]\n\n    # _required_keys must be a subset of _allowed_keys\n    with pytest.raises(ValueError):\n        class MyLooselyTypedDotDict(LooselyTypedDotDict):\n            _allowed_keys = set([\n                \"x\", \"y\", \"z\"\n            ])\n            _required_keys = set([\n                \"w\"\n            ])\n        \n        #Unfortunately, I don't have a good way to test this condition until the class is instantiated\n        d = MyLooselyTypedDotDict(x=True)\n\n\ndef test_LooselyTypedDotDict_subclass_key_types():\n    class MyLooselyTypedDotDict(LooselyTypedDotDict):\n        _allowed_keys = set([\n            \"x\", \"y\", \"z\"\n        ])\n        _required_keys = set([\n            \"x\",\n        ])\n        _key_types = {\n            \"x\" : int,\n            \"y\" : str,\n        }\n\n    d = MyLooselyTypedDotDict(**{\n        'x': 1,\n    })\n\n    with pytest.raises(TypeError):\n        d = MyLooselyTypedDotDict(**{\n            'x': \"1\",\n        })\n\n    d = MyLooselyTypedDotDict(**{\n        \"x\": 1,\n        \"y\": \"hello\",\n    })\n\n    with pytest.raises(TypeError):\n        d = MyLooselyTypedDotDict(**{\n            'x': 1,\n            'y': 10,\n        })\n\n    d = MyLooselyTypedDotDict(\n        coerce_types=True,\n        **{\n            \"x\": \"1\",\n            \"y\": 10\n        }\n    )\n    assert d == {\n        \"x\": 1,\n        \"y\": \"10\",\n    }\n\n    with pytest.raises(TypeError):\n        d[\"x\"] = \"not an int\"\n\n    with pytest.raises(TypeError):\n        d.x = \"not an int\"\n\n    with pytest.raises(ValueError):\n        d = MyLooselyTypedDotDict(\n            coerce_types=True,\n            **{\n                \"x\": \"quack\",\n            }\n        )\n\ndef test_LooselyTypedDotDict_ListOf_typing():\n    class MyLooselyTypedDotDict(LooselyTypedDotDict):\n        _allowed_keys = set([\n            \"a\", \"b\", \"c\"\n        ])\n        _key_types = {\n            \"a\" : int,\n            \"b\" : ListOf(int),\n        }\n    \n    d = MyLooselyTypedDotDict(\n        **{\n            \"a\" : 10,\n            \"b\" : [10, 20, 30]\n        }\n    )\n\n    d = MyLooselyTypedDotDict(\n        coerce_types=True,\n        **{\n            \"a\" : 10,\n            \"b\" : [\"10\", \"20\", \"30\"]\n        }\n    )\n\n    with pytest.raises(TypeError):\n        d = MyLooselyTypedDotDict(\n            **{\n                \"a\" : 10,\n                \"b\" : [10, 20, \"rabbit\"]\n            }\n        )\n\ndef test_LooselyTypedDotDict_recursive_coercion():\n    class MyNestedDotDict(LooselyTypedDotDict):\n        _allowed_keys = set([\n            \"a\", \"b\", \"c\"\n        ])\n        _required_keys = set([\n            \"a\",\n        ])\n        _key_types = {\n            \"a\" : int,\n            \"b\" : str,\n        }\n\n    class MyLooselyTypedDotDict(LooselyTypedDotDict):\n        _allowed_keys = set([\n            \"x\", \"y\", \"z\"\n        ])\n        _required_keys = set([\n            \"x\",\n        ])\n        _key_types = {\n            \"x\" : str,\n            \"y\" : MyNestedDotDict,\n        }\n\n    d = MyLooselyTypedDotDict(\n        coerce_types=True,\n        **{\n            \"x\" : \"hello\",\n            \"y\" : {\n                \"a\" : 1,\n                \"b\" : \"hello\"\n            },\n        }\n    )\n    assert d == {\n        \"x\" : \"hello\",\n        \"y\" : {\n            \"a\" : 1,\n            \"b\" : \"hello\"\n        },\n    }\n\n    with pytest.raises(ValueError):\n        MyLooselyTypedDotDict(\n            coerce_types=True,\n            **{\n                \"x\" : \"hello\",\n                \"y\" : {\n                    \"a\" : \"broken\",\n                },\n            }\n        )\n\n    with pytest.raises(KeyError):\n        MyLooselyTypedDotDict(\n            coerce_types=True,\n            **{\n                \"x\" : \"hello\",\n                \"y\" : {\n                    \"b\" : \"wait, a is required!\",\n                },\n            }\n        )\n\ndef test_LooselyTypedDotDict_recursive_coercion_with_ListOf():\n    class MyNestedDotDict(LooselyTypedDotDict):\n        _allowed_keys = set([\n            \"a\",\n        ])\n        _required_keys = set([\n            \"a\",\n        ])\n        _key_types = {\n            \"a\" : int,\n        }\n\n    class MyLooselyTypedDotDict(LooselyTypedDotDict):\n        _allowed_keys = set([\n            \"x\", \"y\", \"z\"\n        ])\n        _required_keys = set([\n            \"x\",\n        ])\n        _key_types = {\n            \"x\" : str,\n            \"y\" : ListOf(MyNestedDotDict),\n        }\n\n    d = MyLooselyTypedDotDict(\n        coerce_types=True,\n        **{\n            \"x\" : \"hello\",\n            \"y\" : [\n                {\"a\": 1},\n                {\"a\": 2},\n                {\"a\": 3},\n                {\"a\": 4}\n            ]\n        }\n    )\n    assert d == {\n        \"x\" : \"hello\",\n        \"y\" : [\n            {\"a\": 1},\n            {\"a\": 2},\n            {\"a\": 3},\n            {\"a\": 4}    \n        ]\n    }\n\n    with pytest.raises(TypeError):\n        d = MyLooselyTypedDotDict(\n            coerce_types=True,\n            **{\n                \"x\" : \"hello\",\n                \"y\" : {\n                    \"a\" : [1, 2, 3, 4],\n                },\n            }\n        )\n","sub_path":"tests/types/test_base_types.py","file_name":"test_base_types.py","file_ext":"py","file_size_in_byte":8092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"602550331","text":"#!/usr/bin/env python3\n# Copyright 2019 CNRS and University of Strasbourg\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\"\"\"Starter script for ImageKeeper.\"\"\"\n\nimport sys\n\nfrom oslo_config import cfg\nfrom oslo_log import log\n\nfrom imagekeeper.common import config\nfrom imagekeeper.common import exception\nfrom imagekeeper.backend import manager as backend_manager\nfrom imagekeeper.image import manager as image_manager\n\nCONF = cfg.CONF\nLOG = log.getLogger(__name__)\n\n\ndef main():\n    \"\"\"Imagekeeper main script.\"\"\"\n    config.parse_args(sys.argv)\n    log.setup(CONF, 'imagekeeper')\n\n    LOG.info('Starting imagekeeper')\n\n    # Read the content of the image list\n    images = image_manager.ImageListManager()\n    if not images:\n        raise exception.NoImageFound(\n            image_list=CONF.image_list_path,\n        )\n\n    # Read the content of the cloud backend configuration file\n    backends = backend_manager.BackendManager()\n    if not backends:\n        raise exception.NoBackendDefined(\n            cloud_config=CONF.cloud_backend_file,\n        )\n\n    for backend in backends.get_backends():\n        LOG.info(\"Managing images at %s\" % backend['name'])\n        for image in images.get_images():\n            LOG.info(image)\n\n\nif __name__ == \"__main__\":\n    try:\n        main()\n    except Exception as err:\n        sys.stderr.write(err.__str__())\n        sys.exit(1)\n","sub_path":"imagekeeper/cmd/sync.py","file_name":"sync.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"441074954","text":"# 연습문제 4_2\n\nfigure = input('what is your figure type(1 : rectangular, 2 : triangle, 3 : circle) : ')\n\nif figure == '1' or '2' or '3' :\n    if figure == '1' :\n        w = int(input('width : '))\n        h = int(input('height : '))\n        area = w * h\n        shape = 'rectangular'\n\n    elif figure =='2' :\n        b = int(input('bottom : '))\n        h = int(input('height : '))\n        area = b * h /2\n        shape = 'triangle'\n\n    else  :\n        r = int(input('radius :'))\n        area = 3.14 * r * r\n        shape ='circle'\n    print('area of %s = %.2f' % (shape, area))\n\nelse :\n    print('please input number')\n\n\n\n","sub_path":"ch3/제어문/if/07_if_ex4_2.py","file_name":"07_if_ex4_2.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"132998741","text":"\"\"\"\nCopyright (C) 2018 NVIDIA Corporation.  All rights reserved.\nLicensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).\n\"\"\"\nfrom torch import nn\nfrom torch.autograd import Variable\nfrom models.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d\nimport torch\nimport torch.nn.functional as F\nimport torch.nn.utils.spectral_norm as spectral_norm\nimport re\ntry:\n    from itertools import izip as zip\nexcept ImportError: # will be 3.x series\n    pass\n\n##################################################################################\n# Discriminator\n##################################################################################\n\nclass MsImageDis(nn.Module):\n    # Multi-scale discriminator architecture\n    def __init__(self, input_dim, params):\n        super(MsImageDis, self).__init__()\n        self.n_layer = params['n_layer']\n        self.gan_type = params['gan_type']\n        self.dim = params['dim']\n        self.norm = params['norm']\n        self.activ = params['activ']\n        self.num_scales = params['num_scales']\n        self.pad_type = params['pad_type']\n        self.pool_size = params['pool_size'] \n        self.input_dim = input_dim\n        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)\n        self.cnns = nn.ModuleList()\n        self.pool_ = []\n        self.criterion = nn.MSELoss().cuda() \n        for _ in range(self.num_scales):\n            self.cnns.append(self._make_net())\n\n    def _make_net(self):\n        dim = self.dim\n        cnn_x = []\n        cnn_x += [Conv2dBlock(self.input_dim, dim, 4, 2, 1, norm='none', activation=self.activ, pad_type=self.pad_type)]\n        for i in range(self.n_layer - 1):\n            cnn_x += [Conv2dBlock(dim, dim * 2, 4, 2, 1, norm=self.norm, activation=self.activ, pad_type=self.pad_type)]\n            dim *= 2\n        cnn_x += [nn.Conv2d(dim, 1, 1, 1, 0)]\n        cnn_x = nn.Sequential(*cnn_x)\n        return cnn_x\n\n    def forward(self, x):\n        outputs = []\n        for model in self.cnns:\n            outputs.append(model(x))\n            x = self.downsample(x)\n        return outputs\n\n    def calc_dis_loss(self, input_fake, input_real):\n        # calculate the loss to train D\n        outs0 = self.forward(input_fake)\n        outs1 = self.forward(input_real)\n        loss = 0\n\n        for it, (out0, out1) in enumerate(zip(outs0, outs1)):\n            if self.gan_type == 'lsgan':\n                loss += torch.mean((out0 - 0)**2) + torch.mean((out1 - 1)**2)\n            elif self.gan_type == 'nsgan':\n                all0 = Variable(torch.zeros_like(out0.data).cuda(), requires_grad=False)\n                all1 = Variable(torch.ones_like(out1.data).cuda(), requires_grad=False)\n                loss += torch.mean(F.binary_cross_entropy(F.sigmoid(out0), all0) +\n                                   F.binary_cross_entropy(F.sigmoid(out1), all1))\n            else:\n                assert 0, \"Unsupported GAN type: {}\".format(self.gan_type)\n        return loss\n\n\n    def calc_gen_loss(self, input_fake):\n        # calculate the loss to train G\n        outs0 = self.forward(input_fake)\n        loss = 0\n        for it, (out0) in enumerate(outs0):\n            if self.gan_type == 'lsgan':\n                loss += torch.mean((out0 - 1)**2) # LSGAN\n            elif self.gan_type == 'nsgan':\n                all1 = Variable(torch.ones_like(out0.data).cuda(), requires_grad=False)\n                loss += torch.mean(F.binary_cross_entropy(F.sigmoid(out0), all1))\n            else:\n                assert 0, \"Unsupported GAN type: {}\".format(self.gan_type)\n        return loss\n\n    def pool(self, operation='fetch', push=None):\n        if(operation == 'push'):\n            if(len(self.pool_)>=self.pool_size):\n                self.pool_.pop()\n            self.pool_.insert(0, push)\n        elif(operation == 'fetch' and len(self.pool_) != 0):\n            draw = self.pool_[len(self.pool_)-1]\n            return draw\n\n##################################################################################\n# Generator\n##################################################################################\n\nclass AdaINGen(nn.Module):\n    # AdaIN auto-encoder architecture\n    def __init__(self, input_dim, params):\n        super(AdaINGen, self).__init__()\n        dim = params['dim']\n        style_dim = params['style_dim']\n        n_downsample = params['n_downsample']\n        n_res = params['n_res']\n        activ = params['activ']\n        pad_type = params['pad_type']\n        mlp_dim = params['mlp_dim']\n        content_encoder_method = params['CE_method']\n        self.gan_type = params['gan_type']\n\n        # style encoder\n        self.enc_style = Encoder(n_downsample, n_res, input_dim, dim, 'ln', activ, pad_type=pad_type)\n\n        # content encoder\n        if content_encoder_method != 'vgg':\n            self.enc_content = Encoder(n_downsample, n_res, input_dim, dim, 'ln', activ, pad_type=pad_type)\n        else:\n            self.enc_content = ContentEncoder()\n\n        # decoder input has channel number = channel of encoded results * 2\n        #self.dec = Decoder(n_downsample, n_res, self.enc_style.output_dim*2, input_dim, res_norm='spade', activ=activ, pad_type=pad_type)\n        #self.dec = Decoder(n_downsample, n_res, self.enc_style.output_dim*2, input_dim, res_norm='ln', activ=activ, pad_type=pad_type)\n        self.dec = Decoder_spade(n_downsample, n_res, self.enc_style.output_dim*2, input_dim, res_norm='spade', activ=activ, pad_type=pad_type)\n\n        # MLP to generate AdaIN parameters\n        #self.mlp = MLP(style_dim, self.get_num_adain_params(self.dec), mlp_dim, 3, norm='none', activ=activ)\n\n    def forward(self, images):\n        # reconstruct an image\n        content, style_fake = self.encode(images)\n        images_recon = self.decode(content, style_fake)\n        return images_recon\n\n    def encode(self, images):\n        # encode an image to its content and style codes\n        style_fake = self.enc_style(images)\n        content = self.enc_content(images)\n        return content, style_fake\n\n    def decode(self, content, style):\n        # decode content and style codes to an image\n        #latent_code = torch.cat((content, style),1)\n        #images = self.dec(latent_code)\n        images = self.dec(content, style)\n        return images\n\n    def content_init(self, path='./data/vgg19_conv.pth'):\n        self.enc_content.load_state_dict(torch.load(path)) \n        self.enc_content.eval()\n        for param in self.enc_content.parameters():\n            param.requires_grad = True \n\n    def calc_gen_loss(self, outs0):\n        # calculate the loss to train G\n        #outs0 = self.forward(input_fake)\n        loss = 0\n        for it, (out0) in enumerate(outs0):\n            if self.gan_type == 'lsgan':\n                loss += torch.mean((out0 - 1)**2) # LSGAN\n            elif self.gan_type == 'nsgan':\n                all1 = Variable(torch.ones_like(out0.data).cuda(), requires_grad=False)\n                loss += torch.mean(F.binary_cross_entropy(F.sigmoid(out0), all1))\n            else:\n                assert 0, \"Unsupported GAN type: {}\".format(self.gan_type)\n        return loss\n\n    def assign_adain_params(self, adain_params, model):\n        # assign the adain_params to the AdaIN layers in model\n        for m in model.modules():\n            if m.__class__.__name__ == \"AdaptiveInstanceNorm2d\":\n                mean = torch.mean(adain_params,dim=1)\n                std = torch.std(adain_params, dim=1)\n                m.bias = mean.contiguous()\n                m.weight = std.contiguous()\n                print(m.weight.shape)\n                #if adain_params.size(1) > 2*m.num_features:\n                #    adain_params = adain_params[:, 2*m.num_features:]\n\n    def get_num_adain_params(self, model):\n        # return the number of AdaIN parameters needed by the model\n        num_adain_params = 0\n        for m in model.modules():\n            if m.__class__.__name__ == \"AdaptiveInstanceNorm2d\":\n                num_adain_params += 2*m.num_features\n        return num_adain_params\n\n\n\n##################################################################################\n# Encoder and Decoders\n##################################################################################\n\nclass StyleEncoder(nn.Module):\n    def __init__(self, n_downsample, input_dim, dim, style_dim, norm, activ, pad_type):\n        super(StyleEncoder, self).__init__()\n        self.model = []\n        self.model += [Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, activation=activ, pad_type=pad_type)]\n        for i in range(2):\n            self.model += [Conv2dBlock(dim, 2 * dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type)]\n            dim *= 2\n        for i in range(n_downsample - 2):\n            self.model += [Conv2dBlock(dim, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type)]\n        self.model += [nn.AdaptiveAvgPool2d(1)] # global average pooling\n        self.model += [nn.Conv2d(dim, style_dim, 1, 1, 0)]\n        self.model = nn.Sequential(*self.model)\n        self.output_dim = dim\n\n    def forward(self, x):\n        return self.model(x)\n\nclass Encoder(nn.Module):\n    def __init__(self, n_downsample, n_res, input_dim, dim, norm, activ, pad_type):\n        super(Encoder, self).__init__()\n        self.model = []\n        self.model += [Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, activation=activ, pad_type=pad_type)]\n        # downsampling blocks\n        for i in range(n_downsample):\n            self.model += [Conv2dBlock(dim, 2 * dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type)]\n            dim *= 2\n        # residual blocks\n        self.model += [ResBlocks(n_res, dim, norm=norm, activation=activ, pad_type=pad_type)]\n        self.model = nn.Sequential(*self.model)\n        self.output_dim = dim\n\n    def forward(self, x):\n        return self.model(x)\n\nclass ContentEncoder(nn.Module):\n    ''' \n    NOTE: there is no need to pre-process the input \n    input tensor should range in [0,1]\n    '''\n    def __init__(self, pool='max'):\n        super(ContentEncoder, self).__init__()\n        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)\n        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)\n        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1)\n        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)\n        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)\n        self.conv3_4 = nn.Conv2d(256, 256, kernel_size=3, padding=1)\n        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1)\n        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n        self.conv4_4 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n        self.conv5_4 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n        if pool == 'max':\n            self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n            self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n            self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n            self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n            self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)\n        elif pool == 'avg':\n            self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)\n            self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2)\n            self.pool3 = nn.AvgPool2d(kernel_size=2, stride=2)\n            self.pool4 = nn.AvgPool2d(kernel_size=2, stride=2)\n            self.pool5 = nn.AvgPool2d(kernel_size=2, stride=2)\n\n    def vgg_process(self, tensor):\n        # input is RGB tensor which ranges in [0,1]                               \n        # output is BGR tensor which ranges in [0,255]\n        tensor_bgr = tensor\n        #tensor_bgr = torch.cat((tensor[:, 2:3, :, :], tensor[:, 1:2, :, :], tensor[:, 0:1, :, :]), dim=1)\n        # tensor_bgr = tensor[:, [2, 1, 0], ...]\n        tensor_bgr_ml = tensor_bgr - torch.Tensor([0.40760392, 0.45795686, 0.48501961]).type_as(tensor_bgr).view(1, 3, 1, 1)\n        #tensor_rst = tensor_bgr_ml * 255\n        #return tensor_rst\n        return tensor_bgr_ml\n\n    def forward(self, x, preprocess=False):\n        ''' \n        NOTE: input tensor should range in [0,1]\n        '''\n        #out_keys = ['r12','r22','r32','r42','r52']\n        out = {}\n        if preprocess:\n            x = self.vgg_process(x)\n\n        out['r11'] = F.relu(self.conv1_1(x))\n        out['r12'] = F.relu(self.conv1_2(out['r11']))\n        #print('r12 {}'.format(out['r12'].shape)) 1x64x256x256\n        out['p1'] = self.pool1(out['r12'])\n        out['r21'] = F.relu(self.conv2_1(out['p1']))\n        out['r22'] = F.relu(self.conv2_2(out['r21']))\n        #print('r22 {}'.format(out['r22'].shape)) 1x128x128x128\n        out['p2'] = self.pool2(out['r22'])\n        out['r31'] = F.relu(self.conv3_1(out['p2']))\n        out['r32'] = F.relu(self.conv3_2(out['r31']))\n        #print('r32 {}'.format(out['r32'].shape)) 1x256x64x64\n        out['r33'] = F.relu(self.conv3_3(out['r32']))\n        out['r34'] = F.relu(self.conv3_4(out['r33']))\n        out['p3'] = self.pool3(out['r34'])\n        out['r41'] = F.relu(self.conv4_1(out['p3']))\n        out['r42'] = F.relu(self.conv4_2(out['r41']))\n        #print('r42 {}'.format(out['r42'].shape)) 1x512x32x32\n        out['r43'] = F.relu(self.conv4_3(out['r42']))\n        out['r44'] = F.relu(self.conv4_4(out['r43']))\n        out['p4'] = self.pool4(out['r44'])\n        out['r51'] = F.relu(self.conv5_1(out['p4']))\n        out['r52'] = F.relu(self.conv5_2(out['r51']))\n        #print('r52 {}'.format(out['r52'].shape)) 1x512x16x16\n        out['r53'] = F.relu(self.conv5_3(out['r52']))\n        out['r54'] = F.relu(self.conv5_4(out['r53']))\n        out['p5'] = self.pool5(out['r54'])\n        #return [out[key] for key in out_keys]\n        #print(out['r32'])\n        return out['r32']\n\n\nclass Decoder(nn.Module):\n    def __init__(self, n_upsample, n_res, dim, output_dim, res_norm='adain', activ='relu', pad_type='zero'):\n        super(Decoder, self).__init__()\n\n        self.model = []\n        # AdaIN residual blocks\n        self.model += [ResBlocks(n_res, dim, res_norm, activ, pad_type=pad_type)]\n        # upsampling blocks\n        for i in range(n_upsample):\n            self.model += [nn.Upsample(scale_factor=2),\n                           Conv2dBlock(dim, dim // 2, 5, 1, 2, norm='ln', activation=activ, pad_type=pad_type)]\n            dim //= 2\n        # use reflection padding in the last conv layer\n        self.model += [Conv2dBlock(dim, output_dim, 7, 1, 3, norm='none', activation='tanh', pad_type=pad_type)]\n        self.model = nn.Sequential(*self.model)\n\n    def forward(self, x):\n        return self.model(x)\n\nclass Decoder_spade(nn.Module):\n    def __init__(self, n_upsample, n_res, dim, output_dim, res_norm='adain', activ='relu', pad_type='zero'):\n        super(Decoder_spade, self).__init__()\n        nf = 16 \n        self.fc = nn.Conv2d(256, 8 * nf, 3, padding=1)\n\n        self.head0 = SPADEResnetBlock(8 * nf, 8 * nf, 8 * nf)\n        self.G_middle0 = SPADEResnetBlock(8 * nf, 8 * nf, 8 * nf)\n        self.G_middle1 = SPADEResnetBlock(8 * nf, 8 * nf, 8 * nf)\n        self.up0 = SPADEResnetBlock(8 * nf, 4 * nf, 8 * nf)\n        self.up1 = SPADEResnetBlock(4 * nf, 4 * nf, 8 * nf)\n        self.up2 = SPADEResnetBlock(4 * nf, 2 * nf, 8 * nf)\n        self.up3 = SPADEResnetBlock(2 * nf, 1 * nf, 8 * nf)\n\n        final_nc = nf\n\n        self.conv_img = nn.Conv2d(final_nc, 3, 3, padding =1)\n        self.up = nn.Upsample(scale_factor=2)\n\n    def forward(self, x, style):\n        x = self.fc(x)\n        style = self.fc(style)\n        x = self.head0(x, style)\n        x = self.G_middle0(x, style)\n        #x = self.G_middle1(x, style)\n\n        x = self.up0(x, style)\n        x = self.up(x) # 64 \n        #x = self.up1(x, style)\n        #x = self.up(x) # 128 \n        x = self.up2(x, style)\n        x = self.up(x) # 256\n        x = self.up3(x, style)\n\n        x = self.conv_img(F.leaky_relu(x, 2e-1))\n        x = F.tanh(x)\n\n        return x\n\n##################################################################################\n# Sequential Models\n##################################################################################\n\nclass ResBlocks(nn.Module):\n    def __init__(self, num_blocks, dim, norm='in', activation='relu', pad_type='zero'):\n        super(ResBlocks, self).__init__()\n        self.model = []\n        for i in range(num_blocks):\n            self.model += [ResBlock(dim, norm=norm, activation=activation, pad_type=pad_type)]\n        self.model = nn.Sequential(*self.model)\n\n    def forward(self, x):\n        return self.model(x)\n\nclass MLP(nn.Module):\n    def __init__(self, input_dim, output_dim, dim, n_blk, norm='none', activ='relu'):\n\n        super(MLP, self).__init__()\n        self.model = []\n        self.model += [LinearBlock(input_dim, dim, norm=norm, activation=activ)]\n        for i in range(n_blk - 2):\n            self.model += [LinearBlock(dim, dim, norm=norm, activation=activ)]\n        self.model += [LinearBlock(dim, output_dim, norm='none', activation='none')] # no output activations\n        self.model = nn.Sequential(*self.model)\n\n    def forward(self, x):\n        return self.model(x.view(x.size(0), -1))\n\n##################################################################################\n# Basic Blocks\n##################################################################################\nclass SPADEResnetBlock(nn.Module):\n    def __init__(self, fin, fout, nfc):\n        super().__init__()\n        # Attributes\n        self.learned_shortcut = (fin != fout)\n        fmiddle = min(fin, fout)\n\n        # create conv layers\n        self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1)\n        self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1)\n        if self.learned_shortcut:\n            self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)\n\n        # apply spectral norm if specified\n        #if 'spectral' in opt.norm_G:\n        if True:\n            self.conv_0 = spectral_norm(self.conv_0)\n            self.conv_1 = spectral_norm(self.conv_1)\n            if self.learned_shortcut:\n                self.conv_s = spectral_norm(self.conv_s)\n\n        # define normalization layers\n        spade_config_str = 'syncbatch' \n        self.norm_0 = SPADE(spade_config_str, fin, nfc)\n        self.norm_1 = SPADE(spade_config_str, fmiddle, nfc)\n        if self.learned_shortcut:\n            self.norm_s = SPADE(spade_config_str, fin, nfc)\n\n    # note the resnet block with SPADE also takes in |seg|,\n    # the semantic segmentation map as input\n    def forward(self, x, seg):\n        x_s = self.shortcut(x, seg)\n\n        dx = self.conv_0(self.actvn(self.norm_0(x, seg)))\n        dx = self.conv_1(self.actvn(self.norm_1(dx, seg)))\n\n        out = x_s + dx\n\n        return out\n\n    def actvn(self, x):\n        return F.leaky_relu(x, 2e-1)\n\n    def shortcut(self, x, seg):\n        if self.learned_shortcut:\n            x_s = self.conv_s(self.norm_s(x, seg))\n        else:\n            x_s = x\n        return x_s\n \nclass ResBlock(nn.Module):\n    def __init__(self, dim, norm='in', activation='relu', pad_type='zero'):\n        super(ResBlock, self).__init__()\n\n        model = []\n        model += [Conv2dBlock(dim ,dim, 3, 1, 1, norm=norm, activation=activation, pad_type=pad_type)]\n        model += [Conv2dBlock(dim ,dim, 3, 1, 1, norm=norm, activation='none', pad_type=pad_type)]\n        self.model = nn.Sequential(*model)\n\n    def forward(self, x):\n        residual = x\n        out = self.model(x)\n        out += residual\n        return out\n \nclass Conv2dBlock(nn.Module):\n    def __init__(self, input_dim ,output_dim, kernel_size, stride,\n                 padding=0, norm='none', activation='relu', pad_type='zero'):\n        super(Conv2dBlock, self).__init__()\n        self.use_bias = True\n        # initialize padding\n        if pad_type == 'reflect':\n            self.pad = nn.ReflectionPad2d(padding)\n        elif pad_type == 'replicate':\n            self.pad = nn.ReplicationPad2d(padding)\n        elif pad_type == 'zero':\n            self.pad = nn.ZeroPad2d(padding)\n        else:\n            assert 0, \"Unsupported padding type: {}\".format(pad_type)\n\n        # initialize normalization\n        norm_dim = output_dim\n        if norm == 'bn':\n            self.norm = nn.BatchNorm2d(norm_dim)\n        elif norm == 'in':\n            #self.norm = nn.InstanceNorm2d(norm_dim, track_running_stats=True)\n            self.norm = nn.InstanceNorm2d(norm_dim)\n        elif norm == 'ln':\n            self.norm = LayerNorm(norm_dim)\n        elif norm == 'adain':\n            self.norm = AdaptiveInstanceNorm2d(norm_dim)\n        elif norm == 'none' or norm == 'sn':\n            self.norm = None\n        else:\n            assert 0, \"Unsupported normalization: {}\".format(norm)\n\n        # initialize activation\n        if activation == 'relu':\n            self.activation = nn.ReLU(inplace=True)\n        elif activation == 'lrelu':\n            self.activation = nn.LeakyReLU(0.2, inplace=True)\n        elif activation == 'prelu':\n            self.activation = nn.PReLU()\n        elif activation == 'selu':\n            self.activation = nn.SELU(inplace=True)\n        elif activation == 'tanh':\n            self.activation = nn.Tanh()\n        elif activation == 'none':\n            self.activation = None\n        else:\n            assert 0, \"Unsupported activation: {}\".format(activation)\n\n        # initialize convolution\n        if norm == 'sn':\n            self.conv = SpectralNorm(nn.Conv2d(input_dim, output_dim, kernel_size, stride, bias=self.use_bias))\n        else:\n            self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride, bias=self.use_bias)\n\n    def forward(self, x):\n        x = self.conv(self.pad(x))\n        if self.norm:\n            x = self.norm(x)\n        if self.activation:\n            x = self.activation(x)\n        return x\n\nclass LinearBlock(nn.Module):\n    def __init__(self, input_dim, output_dim, norm='none', activation='relu'):\n        super(LinearBlock, self).__init__()\n        use_bias = True\n        # initialize fully connected layer\n        if norm == 'sn':\n            self.fc = SpectralNorm(nn.Linear(input_dim, output_dim, bias=use_bias))\n        else:\n            self.fc = nn.Linear(input_dim, output_dim, bias=use_bias)\n\n        # initialize normalization\n        norm_dim = output_dim\n        if norm == 'bn':\n            self.norm = nn.BatchNorm1d(norm_dim)\n        elif norm == 'in':\n            self.norm = nn.InstanceNorm1d(norm_dim)\n        elif norm == 'ln':\n            self.norm = LayerNorm(norm_dim)\n        elif norm == 'none' or norm == 'sn':\n            self.norm = None\n        else:\n            assert 0, \"Unsupported normalization: {}\".format(norm)\n\n        # initialize activation\n        if activation == 'relu':\n            self.activation = nn.ReLU(inplace=True)\n        elif activation == 'lrelu':\n            self.activation = nn.LeakyReLU(0.2, inplace=True)\n        elif activation == 'prelu':\n            self.activation = nn.PReLU()\n        elif activation == 'selu':\n            self.activation = nn.SELU(inplace=True)\n        elif activation == 'tanh':\n            self.activation = nn.Tanh()\n        elif activation == 'none':\n            self.activation = None\n        else:\n            assert 0, \"Unsupported activation: {}\".format(activation)\n\n    def forward(self, x):\n        out = self.fc(x)\n        if self.norm:\n            out = self.norm(out)\n        if self.activation:\n            out = self.activation(out)\n        return out\n\n##################################################################################\n# VGG network definition\n##################################################################################\nclass Vgg16(nn.Module):\n    def __init__(self):\n        super(Vgg16, self).__init__()\n        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)\n        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)\n\n        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)\n        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)\n\n        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)\n        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)\n        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)\n\n        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)\n        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)\n        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)\n\n        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)\n        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)\n        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)\n\n    def forward(self, X):\n        h = F.relu(self.conv1_1(X), inplace=True)\n        h = F.relu(self.conv1_2(h), inplace=True)\n        # relu1_2 = h\n        h = F.max_pool2d(h, kernel_size=2, stride=2)\n\n        h = F.relu(self.conv2_1(h), inplace=True)\n        h = F.relu(self.conv2_2(h), inplace=True)\n        # relu2_2 = h\n        h = F.max_pool2d(h, kernel_size=2, stride=2)\n\n        h = F.relu(self.conv3_1(h), inplace=True)\n        h = F.relu(self.conv3_2(h), inplace=True)\n        h = F.relu(self.conv3_3(h), inplace=True)\n        # relu3_3 = h\n        h = F.max_pool2d(h, kernel_size=2, stride=2)\n\n        h = F.relu(self.conv4_1(h), inplace=True)\n        h = F.relu(self.conv4_2(h), inplace=True)\n        h = F.relu(self.conv4_3(h), inplace=True)\n        # relu4_3 = h\n\n        h = F.relu(self.conv5_1(h), inplace=True)\n        h = F.relu(self.conv5_2(h), inplace=True)\n        h = F.relu(self.conv5_3(h), inplace=True)\n        relu5_3 = h\n\n        return relu5_3\n        # return [relu1_2, relu2_2, relu3_3, relu4_3]\n\n##################################################################################\n# Normalization layers\n##################################################################################\nclass AdaptiveInstanceNorm2d(nn.Module):\n    def __init__(self, num_features, eps=1e-5, momentum=0.1):\n        super(AdaptiveInstanceNorm2d, self).__init__()\n        self.num_features = num_features\n        self.eps = eps\n        self.momentum = momentum\n        # weight and bias are dynamically assigned\n        self.weight = None\n        self.bias = None\n        # just dummy buffers, not used\n        self.register_buffer('running_mean', torch.zeros(num_features))\n        self.register_buffer('running_var', torch.ones(num_features))\n\n    def forward(self, x):\n        assert self.weight is not None and self.bias is not None, \"Please assign weight and bias before calling AdaIN!\"\n        b, c = x.size(0), x.size(1)\n        running_mean = self.running_mean.repeat(b)\n        running_var = self.running_var.repeat(b)\n\n        # Apply instance norm\n        x_reshaped = x.contiguous().view(1, b * c, *x.size()[2:])\n\n        out = F.batch_norm(\n            x_reshaped, running_mean, running_var, self.weight, self.bias,\n            True, self.momentum, self.eps)\n\n        return out.view(b, c, *x.size()[2:])\n\n    def __repr__(self):\n        return self.__class__.__name__ + '(' + str(self.num_features) + ')'\n\n\nclass LayerNorm(nn.Module):\n    def __init__(self, num_features, eps=1e-5, affine=True):\n        super(LayerNorm, self).__init__()\n        self.num_features = num_features\n        self.affine = affine\n        self.eps = eps\n\n        if self.affine:\n            self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())\n            self.beta = nn.Parameter(torch.zeros(num_features))\n\n    def forward(self, x):\n        shape = [-1] + [1] * (x.dim() - 1)\n        # print(x.size())\n        if x.size(0) == 1:\n            # These two lines run much faster in pytorch 0.4 than the two lines listed below.\n            mean = x.view(-1).mean().view(*shape)\n            std = x.view(-1).std().view(*shape)\n        else:\n            mean = x.view(x.size(0), -1).mean(1).view(*shape)\n            std = x.view(x.size(0), -1).std(1).view(*shape)\n\n        x = (x - mean) / (std + self.eps)\n\n        if self.affine:\n            shape = [1, -1] + [1] * (x.dim() - 2)\n            x = x * self.gamma.view(*shape) + self.beta.view(*shape)\n        return x\n\ndef l2normalize(v, eps=1e-12):\n    return v / (v.norm() + eps)\n\n\nclass SpectralNorm(nn.Module):\n    \"\"\"\n    Based on the paper \"Spectral Normalization for Generative Adversarial Networks\" by Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida\n    and the Pytorch implementation https://github.com/christiancosgrove/pytorch-spectral-normalization-gan\n    \"\"\"\n    def __init__(self, module, name='weight', power_iterations=1):\n        super(SpectralNorm, self).__init__()\n        self.module = module\n        self.name = name\n        self.power_iterations = power_iterations\n        if not self._made_params():\n            self._make_params()\n\n    def _update_u_v(self):\n        u = getattr(self.module, self.name + \"_u\")\n        v = getattr(self.module, self.name + \"_v\")\n        w = getattr(self.module, self.name + \"_bar\")\n\n        height = w.data.shape[0]\n        for _ in range(self.power_iterations):\n            v.data = l2normalize(torch.mv(torch.t(w.view(height,-1).data), u.data))\n            u.data = l2normalize(torch.mv(w.view(height,-1).data, v.data))\n\n        # sigma = torch.dot(u.data, torch.mv(w.view(height,-1).data, v.data))\n        sigma = u.dot(w.view(height, -1).mv(v))\n        setattr(self.module, self.name, w / sigma.expand_as(w))\n\n    def _made_params(self):\n        try:\n            u = getattr(self.module, self.name + \"_u\")\n            v = getattr(self.module, self.name + \"_v\")\n            w = getattr(self.module, self.name + \"_bar\")\n            return True\n        except AttributeError:\n            return False\n\n\n    def _make_params(self):\n        w = getattr(self.module, self.name)\n\n        height = w.data.shape[0]\n        width = w.view(height, -1).data.shape[1]\n\n        u = nn.Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)\n        v = nn.Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)\n        u.data = l2normalize(u.data)\n        v.data = l2normalize(v.data)\n        w_bar = nn.Parameter(w.data)\n\n        del self.module._parameters[self.name]\n\n        self.module.register_parameter(self.name + \"_u\", u)\n        self.module.register_parameter(self.name + \"_v\", v)\n        self.module.register_parameter(self.name + \"_bar\", w_bar)\n\n\n    def forward(self, *args):\n        self._update_u_v()\n        return self.module.forward(*args)\n\nclass SPADE(nn.Module):\n    def __init__(self, config_text, norm_nc=128, label_nc=128):\n        super().__init__()\n\n        #assert config_text.startswith('spade')\n        #parsed = re.search('spade(\\D+)(\\d)x\\d', config_text)\n        #param_free_norm_type = str(parsed.group(1))\n        #ks = int(parsed.group(2))\n        param_free_norm_type = config_text\n\n        if param_free_norm_type == 'instance':\n            self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)\n        elif param_free_norm_type == 'syncbatch':\n            self.param_free_norm = SynchronizedBatchNorm2d(norm_nc, affine=False)\n        elif param_free_norm_type == 'batch':\n            self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False)\n        else:\n            raise ValueError('%s is not a recognized param-free norm type in SPADE'\n                             % param_free_norm_type)\n\n        # The dimension of the intermediate embedding space. Yes, hardcoded.\n        self.nhidden = 128 \n\n        ks = 3\n        pw = ks // 2\n        self.mlp_shared = nn.Sequential(\n            nn.Conv2d(label_nc, self.nhidden, kernel_size=ks, padding=pw),\n            nn.ReLU()\n        )\n        self.mlp_gamma = nn.Conv2d(self.nhidden, norm_nc, kernel_size=ks, padding=pw)\n        self.mlp_beta = nn.Conv2d(self.nhidden, norm_nc, kernel_size=ks, padding=pw)\n\n    def forward(self, x, segmap):\n\n        # Part 1. generate parameter-free normalized activations\n        normalized = self.param_free_norm(x)\n\n        # Part 2. produce scaling and bias conditioned on semantic map\n        segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')\n        actv = self.mlp_shared(segmap)\n        gamma = self.mlp_gamma(actv)\n        beta = self.mlp_beta(actv)\n\n        # apply scale and bias\n        out = normalized * (1 + gamma) + beta\n\n        return out\n","sub_path":"networks.py","file_name":"networks.py","file_ext":"py","file_size_in_byte":32840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"231995079","text":"from sklearn.decomposition import PCA\nimport numpy as np \nimport matplotlib.pyplot as plt\nimport matplotlib.axes as axes\nimport matplotlib.animation as animation\n\ndata = np.load('jumpingjack_1_angle.npy')\npca = PCA(n_components=2)\n_ = pca.fit_transform(data)\n\nnp.save('jj_pca',pca.components_)\n\nresult = np.matmul(data,pca.components_.T)\n\ninit = result[0]\ndist = []\nfor pt in result[1:]:\n    d = np.linalg.norm(init-pt)\n    dist.append(d)\n    init = pt\n\nprint(np.average(dist))\nplt.hist(dist,bins=1000)\nplt.show()    \n\n\n","sub_path":"dist.py","file_name":"dist.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"358773628","text":"import numpy as np\nimport pyfits as py\nSN=\"S1\"\nbands=['g','r','i','z']\nhdu=py.open('/home/rumbaugh/%s_lc.fits'%SN)\ndata=hdu[1].data\noutcr_dict= {b: np.zeros((len(data)*np.shape(data['LC_MJD_%s'%b.upper()])[1],),dtype={'names':('COADD_OBJECT_ID','RA','DEC','MJD','MAG_PSF','MAG_PSF_ERROR','BAND','FLAGS'),'formats':('i8','f8','f8','f8','f8','f8','|S4','i8')}) for b in bands}\nfor b in bands:\n    outcr_dict[b]['BAND']=b\n    outcr_dict[b]['COADD_OBJECT_ID'],outcr_dict[b]['RA'],outcr_dict[b]['DEC'],outcr_dict[b]['FLAGS'] = np.repeat(data['COADD_OBJECT_ID'],np.shape(data['LC_MJD_%s'%b.upper()])[1]),np.repeat(data['RA'],np.shape(data['LC_MJD_%s'%b.upper()])[1]),np.repeat(data['DEC'],np.shape(data['LC_MJD_%s'%b.upper()])[1]),np.repeat(data['FLAGS_%s'%b.upper()],np.shape(data['LC_MJD_%s'%b.upper()])[1])\n    outcr_dict[b]['MJD'],outcr_dict[b]['MAG_PSF'],outcr_dict[b]['MAG_PSF_ERROR']=data['LC_MJD_%s'%b.upper()].flatten(),data['LC_MAG_PSF_%s'%b.upper()].flatten(),data['LC_MAGERR_PSF_%s'%b.upper()].flatten()\n    outcr_dict[b]=outcr_dict[b][outcr_dict[b]['MJD']!=0]\noutcr=np.concatenate((outcr_dict['g'],outcr_dict['r'],outcr_dict['i'],outcr_dict['z']),axis=0)\noutcr['MAG_PSF'][outcr['MAG_PSF']==np.inf]=0\noutcr['MAG_PSF'][outcr['MAG_PSF']<-99]=0\noutcr['MAG_PSF_ERROR'][outcr['MAG_PSF_ERROR']>99]=0\noutcr['MAG_PSF'][np.isnan(outcr['MAG_PSF'])]=0\noutcr['MAG_PSF_ERROR'][np.isnan(outcr['MAG_PSF_ERROR'])]=0\nnp.savetxt('/home/rumbaugh/Eric_LC_%s.csv'%SN,outcr,fmt='%i,%f,%f,%f,%f,%f,%4s,%i')","sub_path":"run.3.6.17.1810_S1.py","file_name":"run.3.6.17.1810_S1.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"79777138","text":"\nclass Env:\n    def __init__(self):\n        self.pl_weight = 0.7  # 丢包率的权重\n        self.pp_weight = 0.2  # 延迟的权重\n        self.bw_weight = 0.1  # 带宽的权重\n\n        # 定义拓扑\n        self.routers = [1, 2, 3, 4, 5, 6]              # 整个网路中共有6个router\n        self.actions = [[2, 3, 4], [1, 3, 6], [1, 2, 5], [1], [3, 6], [2, 5]]                 # 每个router可选择的next hop\n        self.net_parameterList = [[0, [0.01, 3, 1000], [0.05, 2, 50], [0.1, 2, 200], 0, 0],\n                                 [[0.01, 3, 1000], 0, [0.2, 10, 120], 0, 0, [0.4, 1, 100]],\n                                 [[0.05, 2, 50], [0.2, 10, 120], 0, 0, [0.3, 2, 80], 0],\n                                 [[0.3, 3, 20], 0, 0, 0, 0, 0],\n                                 [0, 0, [0.3, 2, 80], 0, 0, [0.08, 10, 150]],\n                                 [0, [0.4, 1, 100], 0, 0, [0.08, 10, 150], 0]]\n\n        self.start_router = 1    # 记录路由的起始位置，默认为1\n        self.destination = 6     # 路由的目的地（默认为6）\n        self.current_router = self.start_router  # 记录包当前所在router（默认为起始路由）\n\n    # 获取目的地路由器输入\n    def input_destination(self):\n        self.destination = int(input('Please input destination:'))\n\n    # 获取目的地路由器输入\n    def input_start(self):\n        self.start_router = int(input('Please input start_router:'))\n\n    # 执行action，返回reward和下一个状态\n    def step(self, action):\n        \n        # print('parameter:',self.net_parameterList[self.current_router - 1][action - 1])\n        # 根据current_router和action之间的链路状态计算cost\n        cost = self.pl_weight * self.net_parameterList[self.current_router - 1][action - 1][0] + \\\n               self.pp_weight * self.net_parameterList[self.current_router - 1][action - 1][1]\n        # + self.bw_weight * self.net_parameterList[self.current_router - 1][action - 1][2]\n\n        return cost\n\n    def calculate(self, next_router, current_router):\n        # 根据current_router和action之间的链路状态计算cost\n        cost = self.pl_weight * self.net_parameterList[current_router - 1][next_router - 1][0] + \\\n               self.pp_weight * self.net_parameterList[current_router - 1][next_router - 1][1]\n\n        return cost\n\n    # 更新网络环境（待更新）\n    def updata_env(self):\n        pass\n    \n    # 初始化网络环境\n    # 将当前路由器初始化为发包路由器、本回合经过的路径置空\n    def init_env(self):\n        self.current_router = self.start_router\n        # print('init_env:', self.current_router)\n\nif __name__ == '__main__':\n        env = Env()\n        env.input_destination()\n        reward, _ = env.step(2)\n        print('reward:', reward)\n\n\n","sub_path":"Q-routing/router_env.py","file_name":"router_env.py","file_ext":"py","file_size_in_byte":2810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"218555769","text":"import cv2\nimport matplotlib.pyplot as plt\n\ncap = cv2.VideoCapture(0)\nif cap.isOpened():\n    ret,frame = cap.read()\nelse:\n    ret = False\nimg = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n\nplt.imshow(img)\nplt.show()\ncv2.imwrite(\"Captured.jpg\",frame)\ncap.release() \n","sub_path":"Class Work/Day 21 - Image Capture From webCam + Face Detection/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"189643221","text":"# -*- encoding: utf-8 -*-\n# -*- coding: utf-8 -*- \n\nimport sqlite3\nimport datetime\nfrom functions import color, create_connection_sqlite\n\ndef armarSerieCompleta(idMoneda):\n\tdatabase = r\"sql.db\"\n\tconn = create_connection_sqlite(database)\n\tif conn == None:\n\t\tprint(color.RED + \"No se pudo conectar a la base de datos\" + color.END)\n\t\treturn\n\n\tcursor = conn.cursor()\n\tcursor.execute(\"SELECT * FROM divisa WHERE idMoneda = \"+ str(idMoneda) +\" ORDER BY fecha ASC\")\n\tregistros = cursor.fetchall()\n\tfecha_anterior = datetime.datetime.now()\n\tdate_format = \"%Y-%m-%d\"\n\tcontador = 0\n\treg_anterior = None\n\tfor item in registros:\n\t\tfecha_actual = datetime.datetime.strptime(item[3], date_format)\n\n\t\titers = (fecha_actual - fecha_anterior).days - 1\n\t\taux = fecha_anterior\n\t\twhile iters > 0:\n\t\t\taux = aux + datetime.timedelta(days=1)\n\t\t\tif aux == fecha_actual:\n\t\t\t\tbreak\n\t\t\treg = (reg_anterior[0], reg_anterior[2], reg_anterior[2], aux, \"-\", True)\n\t\t\tcursor.execute(\"INSERT INTO divisa (idMoneda, compra, venta, fecha, nombreDivisa, generado) VALUES(?,?,?,?,?,?)\", reg)\n\t\t\tprint(\"Completando fechas desde \" + str(fecha_anterior.strftime(\"%d/%m/%Y\")) + \" hasta \" + str(fecha_actual.strftime(\"%d/%m/%Y\")) + \" - iter: \" + str(iters) + \" - \" + repr(aux.strftime(\"%d/%m/%Y\")) + \" idMoneda: \" + str(item[0]) + \" item: \" + item[4])\n\t\t\titers -= 1\n\t\t\tcontador += 1\n\t\tfecha_anterior = fecha_actual\n\t\treg_anterior = item\n\tconn.commit()\n\tprint(color.GREEN + \"Se agregan \" + str(contador) + \" registros para esta divisa\" + color.END)\n\tconn.close()\n\n\n\n","sub_path":"armarSerieCompleta.py","file_name":"armarSerieCompleta.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"163186626","text":"from django.conf import settings\nfrom django.template.loaders.app_directories import Loader as AppDirectoriesLoader\nfrom django.template import Template\n\nfrom ..utils import get_template_minifier_strip_function\n\n\nclass Loader(AppDirectoriesLoader):\n\n    def get_template(self, template_name, template_dirs=None, skip=None):\n        template = super().get_template(template_name, template_dirs)\n\n        template_source = template.source\n        if getattr(settings, 'TEMPLATE_MINIFIER', True):\n            template_source = get_template_minifier_strip_function()(template.source)\n\n        return Template(\n            template_source,\n            template.origin,\n            template.name,\n            template.engine\n        )\n","sub_path":"template_minifying_loader/loaders/app_directories.py","file_name":"app_directories.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"417005064","text":"from heap import Heap\r\nfrom random import shuffle\r\nfrom time import time\r\n\r\n\r\ngerada = list(range(1000))\r\nshuffle(gerada)\r\n\r\nheap = Heap(gerada)\r\n\r\nbefore = time()\r\nheap.heapsort()\r\nafter = time()\r\n\r\ntotal = (after - before) * 1000\r\nprint(f'The HeapSot took {total} ms to order {len(heap)} elements')\r\n","sub_path":"Estrutura Heap/benchmarking_heapsort.py","file_name":"benchmarking_heapsort.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"565413212","text":"\"\"\"calculate and combine the stage hists from the two basins in order\nto get an unbiased hist of the transition region (ie the region\ncovered by the tiling used in the simulation)\n\"\"\"\nbasinIDs = sorted(basinPhaseHists.keys())\n\n# calculate the weight that the stage hist from each basin\n# should have in the combined hist\nphaseZeroWeights = {i:basinPhaseWeights[i][0] for i in basinIDs}\nmfptOveralls = {i:basinMFPTs[i][-1] for i in basinIDs}\n\n# combine the stage hists into the transition region hist\ntransitionHist = {}\nfor bid in basinIDs:\n    # calculate the weight each stage hist should have in the combined hist\n    stageWeight = mfptOveralls[bid]/((mfptOveralls[0]\n                                    + mfptOveralls[1])*phaseZeroWeights[bid])\n\n    # weight and add the stage hists\n    addHists(transitionHist, basinStageHists[bid], weight=stageWeight)\n\n# normalize the transition hist\ntransitionHist = normHist(transitionHist)\n","sub_path":"lmes_chapter/latex/snippets/srg_landscape/transition_hist.py","file_name":"transition_hist.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"478881823","text":"# define the class which holds all information related to one free text:\n# - all questions\n# - possible answers\n# - correct answer\n\nclass DataElement:\n    text=\"\"\n    questions = []\n    #currentIndex = -1\n    id = -1\n    \n    def __str__(self):\n        s = \"\"\n        s += str(self.id)\n        s += \"\\n\"\n        s += str(self.text)\n        s += \"\\n\"\n        for question in self.questions:\n            s += str(question)\n            s += \"\\n\"\n        return s\n    \n    def getResultString(self):\n        s = \"\"\n        for question in self.questions:\n            s += self.id\n            s += \",\"\n            s += question.id\n            s += \",\"\n            s += question.correctAnswer\n            s += \"\\n\"\n        return s\n    \n    \nclass Question:\n    text=\"\"\n    answers = {}\n    correctAnswer = -1\n    id= -1    \n    \n    def __str__(self):\n        s = \"\"\n        s += self.id\n        s += \": \"\n        s += self.text\n        s += \"\\n\"\n        \n        for id, answer in self.answers.items():\n            s += id\n            s += \": \"\n            s += answer\n            s += \"\\n\"\n            \n        s += self.correctAnswer\n        s += \"\\n\"\n        return s","sub_path":"DataElement.py","file_name":"DataElement.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"157601639","text":"import re, sys\nimport colorama as cm\nisVerbose=0\n\n\ndef matchRE(pat, desc):\n  if 1: \n    print(\"    %s\\t%s\" % (pat, desc) )\n  else:\n    if isVerbose: print(\"pat: %s\" % pat)\n\n  s='to be or not really to be.'\n\n  p = re.compile(pat) #, s, flags=0)\n  #mos = re.findall(pat, s, flags=0)\n  mos = re.finditer(pat, s) #, flags=0)\n  if isVerbose: print('matchObj:', mos) #, len(mos)) #.groups()))\n\n  if isVerbose: print(s)\n\n  if mos is None:\n    print(\"no match at all.\")\n    return #exit(0) # actually, success-exit-code is 0 :-/\n\n  left_pos=0\n  for mo in mos:\n    #print('mo:',mo.group())\n    if 0: print('\\ngr:',len(mo.group()), mo.groups() )\n    match_left  = mo.span()[0]\n    match_right = mo.span()[1]\n    left  = s[ left_pos : match_left ]\n    hilit = mo.group() \n    left_pos = match_right #+1 \n    right=''\n\n    a = (\n     cm.Back.BLACK\n    +cm.Fore.WHITE + left \n    +cm.Back.GREEN+cm.Fore.LIGHTGREEN_EX + hilit \n    +cm.Back.BLACK +cm.Fore.WHITE + right \n    )\n    sys.stdout.write(a)\n\n  # print last bit:\n  right = s [left_pos:]\n  sys.stdout.write(right)\n  sys.stdout.write('\\n')\n\n\ndef Main():\n  cm.init()\n  if len(sys.argv)==2: # regx.py \"hej \\\"med\\\" dig\"\n    pat = sys.argv[1]\n    matchRE(pat)\n  else:\n    partsTest = [\n    ]\n    parts = [\n     ['be', 'simple sequence'],\n     ['be.*be','kleene star'],\n     ['^to.*or', 'start-of-line'],\n     ['not.*be$', 'end-of-line'],\n     ['.', 'any single char'],\n     ['[oe]', 'any single char'],\n     ['[^ot]', 'any char NOT in class'],\n     ['al*y', 'zero or more'],\n     ['[nt]o+', 'one or more'],\n     ['al?', 'at most one'],\n     ['l{2}', 'exactly 2'],\n     ['l{1,3}', 'btw 1-3'],\n     ['not|be', 'or'],\n     ['(n.*t)', 'capture'],\n     ['(?:not).*(to).*(?:be)', 'non-capture'],\n     ['(?:not).*?(\\w+).*?(?:be)', 'word'],\n     ['(?:not).*?(\\s+).*?(?:be)', 'space'],\n     ['not.*?(\\w+).*?be', 'word'],\n     ['not.*?(\\s+).*?be', 'space'],\n    ]\n    for (a,b) in parts:\n      matchRE(a,b)\n\nMain()\n","sub_path":"regex/regx2.py","file_name":"regx2.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"180373953","text":"from __future__ import absolute_import\n\nimport hashlib\nimport json\nimport textwrap\nimport unittest\nfrom contextlib import contextmanager\n\nimport mock\nfrom detect_secrets.core.secrets_collection import SecretsCollection\nfrom detect_secrets.plugins import SensitivityValues\n\nfrom detect_secrets_server.__main__ import initialize_repos_from_repo_yaml\nfrom detect_secrets_server.__main__ import main\nfrom detect_secrets_server.__main__ import parse_args\nfrom detect_secrets_server.__main__ import parse_s3_config\nfrom detect_secrets_server.__main__ import parse_sensitivity_values\nfrom detect_secrets_server.__main__ import set_authors_for_found_secrets\nfrom detect_secrets_server.repos.base_tracked_repo import BaseTrackedRepo\nfrom detect_secrets_server.repos.local_tracked_repo import LocalTrackedRepo\nfrom detect_secrets_server.repos.repo_config import RepoConfig\nfrom detect_secrets_server.repos.s3_tracked_repo import S3Config\nfrom detect_secrets_server.repos.s3_tracked_repo import S3LocalTrackedRepo\nfrom detect_secrets_server.repos.s3_tracked_repo import S3TrackedRepo\nfrom tests.util.factories import mock_repo_factory\nfrom tests.util.factories import secrets_collection_factory\nfrom tests.util.mock_util import mock_subprocess\nfrom tests.util.mock_util import SubprocessMock\n\n\nclass ServerTest(unittest.TestCase):\n\n    @staticmethod\n    def assert_sensitivity_values(actual, **expected_values):\n        assert isinstance(actual, SensitivityValues)\n        for key in actual._fields:\n            if key in expected_values:\n                assert expected_values[key] == getattr(actual, key)\n            else:\n                assert getattr(actual, key) is None\n\n    def _mock_repo_config(self):\n        return RepoConfig(\n            base_tmp_dir='default_base_tmp_dir',\n            baseline='baseline',\n            exclude_regex='',\n        )\n\n    def test_parse_sensitivity_values_usage_defaults(self):\n        mock_args = parse_args([])\n\n        self.assert_sensitivity_values(\n            parse_sensitivity_values(mock_args),\n            base64_limit=4.5,\n            hex_limit=3,\n            private_key_detector=True,\n        )\n\n    @mock.patch('detect_secrets_server.__main__.open_config_file')\n    def test_parse_sensitivity_values_config_file_overrides_default_values(self, mock_data):\n        mock_data.return_value = {\n            'default': {\n                'plugins': {\n                    'HexHighEntropyString': 4,\n                }\n            }\n        }\n\n        mock_args = parse_args(['--config-file', 'will_be_mocked'])\n\n        self.assert_sensitivity_values(\n            parse_sensitivity_values(mock_args),\n            base64_limit=4.5,\n            hex_limit=4,\n            private_key_detector=True,\n        )\n\n    def test_parse_sensitivity_values_cli_overrides_default_values(self):\n        mock_args = parse_args(['--base64-limit', '2'])\n\n        self.assert_sensitivity_values(\n            parse_sensitivity_values(mock_args),\n            base64_limit=2,\n            hex_limit=3,\n            private_key_detector=True,\n        )\n\n    @mock.patch('detect_secrets_server.__main__.open_config_file')\n    def test_parse_sensitivity_values_config_file_overrides_cli(self, mock_data):\n        mock_args = parse_args(\n            ['--base64-limit', '3', '--config-file', 'will_be_mocked'])\n        mock_data.return_value = {\n            'default': {\n                'plugins': {\n                    'Base64HighEntropyString': 2,\n                }\n            }\n        }\n\n        self.assert_sensitivity_values(\n            parse_sensitivity_values(mock_args),\n            base64_limit=2,\n            hex_limit=3,\n            private_key_detector=True,\n        )\n\n    def test_parse_s3_config_fail(self):\n        # No file supplied\n        mock_args = parse_args([])\n        assert parse_s3_config(mock_args) is None\n\n        # Bad initialization of S3Config\n        m = mock.mock_open(read_data='{}')\n        mock_args = parse_args(['--s3-config-file', 'will_be_mocked'])\n        with mock.patch('detect_secrets_server.__main__.codecs.open', m):\n            assert parse_s3_config(mock_args) is None\n\n    def test_parse_s3_config_success(self):\n        mock_args = parse_args(['--s3-config-file', 'will_be_mocked'])\n        data = {\n            's3_creds_file': 's3_creds_file.json',\n            'bucket_name': 'bucket_name',\n            'prefix': 'prefix',\n        }\n        m = mock.mock_open(read_data=json.dumps(data))\n        with mock.patch('detect_secrets_server.__main__.codecs.open', m):\n            output = parse_s3_config(mock_args)\n\n        assert isinstance(output, S3Config)\n        assert output.bucket_name == 'bucket_name'\n        assert output.prefix == 'prefix'\n\n    @mock.patch('detect_secrets_server.__main__.open_config_file')\n    def test_initialize_repos_from_repo_yaml_no_tracked_repos(self, mock_data):\n        mock_data.return_value = {\n            'nothing': 'important'\n        }\n\n        assert initialize_repos_from_repo_yaml(\n            'will_be_mocked',\n            SensitivityValues(),\n            self._mock_repo_config(),\n        ) == []\n\n    @mock.patch('detect_secrets_server.__main__.open_config_file')\n    def test_initialize_repos_from_repo_yaml_no_s3_config(self, mock_data):\n        mock_data.return_value = {\n            'tracked': [\n                {\n                    'sha': 'does_not_matter',\n                    'repo': 'does_not_matter',\n                    's3_backed': True,\n                }\n            ]\n        }\n\n        assert initialize_repos_from_repo_yaml(\n            'will_be_mocked',\n            SensitivityValues,\n            self._mock_repo_config(),\n        ) == []\n\n    @mock.patch('detect_secrets_server.repos.local_tracked_repo.subprocess')\n    @mock.patch('detect_secrets_server.__main__.open_config_file')\n    def test_initialize_repos_from_repo_yaml_success(self, mock_data, mock_subprocess):\n        def _create_mock_tracked_repo_repr(**kwargs):\n            defaults = {\n                'sha': 'does_not_matter',\n                'repo': 'does_not_matter',\n            }\n\n            defaults.update(kwargs)\n\n            return defaults\n\n        mock_data.return_value = {\n            'tracked': [\n                _create_mock_tracked_repo_repr(\n                    # Test that it can also be overriden here.\n                    plugins={\n                        'Base64HighEntropyString': 2,\n                    },\n                    baseline_file='is_included',\n                ),\n                _create_mock_tracked_repo_repr(\n                    # Test local repo\n                    is_local_repo=True,\n                ),\n                _create_mock_tracked_repo_repr(\n                    # Test S3 remote repo\n                    s3_backed=True,\n                ),\n                _create_mock_tracked_repo_repr(\n                    # Test S3 local repo\n                    is_local_repo=True,\n                    s3_backed=True,\n                ),\n            ]\n        }\n\n        with mock.patch.object(S3TrackedRepo, '_initialize_s3_client'):\n            output = initialize_repos_from_repo_yaml(\n                'will_be_mocked',\n                SensitivityValues(\n                    base64_limit=1,\n                    hex_limit=2,\n                ),\n                self._mock_repo_config(),\n                S3Config(\n                    s3_creds_file='filename',\n                    bucket_name='bucket',\n                    prefix='prefix',\n                )\n            )\n\n        assert isinstance(output[0], BaseTrackedRepo)\n        assert isinstance(output[1], LocalTrackedRepo)\n        assert isinstance(output[2], S3TrackedRepo)\n        assert isinstance(output[3], S3LocalTrackedRepo)\n\n        assert output[0].plugin_config.base64_limit == 2\n        assert output[0].baseline_file == 'is_included'\n        assert output[1].plugin_config.base64_limit == 1\n\n    @mock.patch('detect_secrets_server.__main__.print')\n    @mock.patch('detect_secrets_server.repos.local_tracked_repo.subprocess.check_output')\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo.save')\n    @mock.patch('detect_secrets_server.__main__.open_config_file')\n    def test_main_initialize_success(self, mock_data, mock_save, mock_repo_url, mock_print):\n        mock_save.return_value = True\n        mock_repo_url.return_value = b'git@github.com:some/random-repo.git'\n        mock_data.return_value = {\n            'tracked': [\n                {\n                    'repo': 'git@github.com:yelp/detect-secrets.git',\n                    'sha': 'some_sha_value',\n                    'cron': '1 2 3 4 5',\n                },\n                {\n                    'repo': '/file/to/local/repo',\n                    'is_local_repo': True,\n                    'sha': 'some_other_value',\n                    'cron': '2 3 4 5 6',\n                },\n            ]\n        }\n\n        assert main(\n            '--initialize --output-hook examples/standalone_hook.py'.split()\n        ) == 0\n        mock_print.assert_has_calls([\n            mock.call('# detect-secrets scanner'),\n            mock.call(\n                '1 2 3 4 5    detect-secrets-server --scan-repo yelp/detect-secrets '\n                '--output-hook examples/standalone_hook.py'\n            ),\n            mock.call(\n                '2 3 4 5 6    detect-secrets-server --scan-repo some/random-repo --local '\n                '--output-hook examples/standalone_hook.py'\n            ),\n        ])\n        assert mock_print.call_count == 3\n\n    @mock.patch('detect_secrets_server.__main__.print')\n    def test_main_initialize_failures(self, mock_print):\n        with mock.patch('detect_secrets_server.__main__.initialize_repos_from_repo_yaml') as m:\n            m.side_effect = IOError\n            assert main(\n                '--initialize --output-hook examples/standalone_hook.py'.split()\n            ) == 1\n\n        with mock.patch('detect_secrets_server.__main__.initialize_repos_from_repo_yaml') as m:\n            m.return_value = []\n            assert main(\n                '--initialize --output-hook examples/standalone_hook.py'.split()\n            ) == 0\n            assert mock_print.call_count == 0\n\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.subprocess.check_output')\n    def test_main_add_repo_remote(self, mock_subprocess_obj):\n        mock_subprocess_obj.side_effect = mock_subprocess((\n            # mock out `clone_and_pull_repo`\n            SubprocessMock(\n                expected_input='git clone',\n                mocked_output=b\"fatal: destination path 'asdf' already exists\",\n            ),\n            SubprocessMock(\n                expected_input='git rev-parse --abbrev-ref',\n                mocked_output=b'master',\n            ),\n            SubprocessMock(\n                expected_input='git fetch -q origin',\n                mocked_output=b'',\n            ),\n\n            # mock out `update`\n            SubprocessMock(\n                expected_input='git rev-parse HEAD',\n                mocked_output=b'new-sha-hash',\n            )\n        ))\n\n        m = mock.mock_open()\n        with mock.patch('detect_secrets_server.repos.base_tracked_repo.codecs.open', m):\n            assert main([\n                '--add-repo',\n                'git@github.com:yelp/detect-secrets.git',\n                '--base64-limit',\n                '2'\n            ]) == 0\n\n        m().write.assert_called_once_with(json.dumps({\n            'sha': 'new-sha-hash',\n            'repo': 'git@github.com:yelp/detect-secrets.git',\n            'plugins': {\n                'base64_limit': 2.0,    # supplied CLI value\n                'hex_limit': 3,         # default value\n                'private_key_detector': True,\n            },\n            'cron': '',\n            'baseline_file': '',\n        }, indent=2))\n\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.subprocess.check_output')\n    def test_main_add_repo_local(self, mock_subprocess_obj):\n        mock_subprocess_obj.side_effect = mock_subprocess((\n            # mock out `clone_and_pull_repo`\n            SubprocessMock(\n                expected_input='git clone',\n                mocked_output=b\"fatal: destination path 'asdf' already exists\",\n            ),\n            SubprocessMock(\n                expected_input='git rev-parse --abbrev-ref',\n                mocked_output=b'master',\n            ),\n            SubprocessMock(\n                expected_input='git fetch -q origin',\n                mocked_output=b'',\n            ),\n\n            # mock out `update`\n            SubprocessMock(\n                expected_input='git rev-parse HEAD',\n                mocked_output=b'new-sha-hash',\n            )\n        ))\n\n        m = mock.mock_open()\n        with mock.patch('detect_secrets_server.repos.base_tracked_repo.codecs.open', m):\n            assert main([\n                '--add-repo',\n                '/file/to/local/repo',\n                '--local',\n                '--baseline',\n                '.baseline',\n            ]) == 0\n\n        m().write.assert_called_once_with(json.dumps({\n            'sha': 'new-sha-hash',\n            'repo': '/file/to/local/repo',\n            'plugins': {\n                'base64_limit': 4.5,\n                'hex_limit': 3,\n                'private_key_detector': True,\n            },\n            'cron': '',\n            'baseline_file': '.baseline',\n        }, indent=2))\n\n    @mock.patch('detect_secrets_server.repos.s3_tracked_repo.S3TrackedRepo.S3')\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.subprocess.check_output')\n    def test_main_add_repo_s3(self, mock_subprocess_obj, mock_s3_obj):\n        mock_subprocess_obj.side_effect = mock_subprocess((\n            # mock out `_get_repo_name`\n            SubprocessMock(\n                expected_input='git remote get-url origin',\n                mocked_output=b'git@github.com:yelp/detect-secrets',\n            ),\n\n            # mock out `update`\n            SubprocessMock(\n                expected_input='git rev-parse HEAD',\n                mocked_output=b'new-sha-hash',\n            )\n        ))\n\n        mock_s3_config = {\n            's3_creds_file': 'filename',\n            'bucket_name': 'bucketman',\n            'prefix': 'mister',\n        }\n\n        final_output = mock.mock_open()\n        s3_config = mock.mock_open(read_data=json.dumps(mock_s3_config))\n        with mock.patch('detect_secrets_server.repos.base_tracked_repo.codecs.open', final_output),\\\n                mock.patch('detect_secrets_server.__main__.codecs.open', s3_config),\\\n                mock.patch(\n                    'detect_secrets_server.repos.s3_tracked_repo.S3TrackedRepo._initialize_s3_client'\n        ):\n            assert main([\n                '--add-repo',\n                'git@github.com:yelp/detect-secrets.git',\n                '--s3-config-file',\n                'will-be-mocked',\n            ]) == 0\n\n        mock_s3_obj.list_objects_v2.assert_called_once_with(\n            Bucket='bucketman',\n            Prefix='mister/%s.json' % hashlib.sha512(\n                'yelp/detect-secrets'.encode('utf-8')\n            ).hexdigest(),\n        )\n\n        assert mock_s3_obj.upload_file.call_count == 1\n\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo.load_from_file')\n    def test_main_scan_repo_unconfigured_repo(self, mock_load_from_file):\n        mock_load_from_file.return_value = None\n        assert main([\n            '--scan-repo',\n            'will-be-mocked',\n            '--output-hook',\n            'examples/standalone_hook.py'\n        ]) == 1\n\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo.scan')\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo._read_tracked_file')\n    def test_main_scan_repo_scan_failed(self, mock_read_file, mock_scan):\n        mock_read_file.return_value = {\n            'sha': 'does_not_matter',\n            'repo': 'repo_name',\n            'plugins': {\n                'base64_limit': 3,\n            },\n            'cron': '* * * * *',\n            'baseline_file': '.secrets.baseline',\n        }\n\n        mock_scan.return_value = None\n        assert main([\n            '--scan-repo',\n            'will-be-mocked',\n            '--output-hook',\n            'examples/standalone_hook.py',\n        ]) == 1\n\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.subprocess.check_output', autospec=True)\n    @mock.patch('detect_secrets_server.__main__.CustomLogObj.getLogger')\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo.scan')\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo._read_tracked_file')\n    def test_main_scan_repo_scan_success_no_results_found(\n            self,\n            mock_file,\n            mock_scan,\n            mock_log,\n            mock_subprocess_obj\n    ):\n        mock_file.return_value = {\n            'sha': 'does_not_matter',\n            'repo': 'repo_name',\n            'plugins': {\n                'base64_limit': 3,\n            },\n            'cron': '* * * * *',\n            'baseline_file': '.secrets.baseline',\n        }\n        mock_scan.return_value = SecretsCollection()\n\n        mock_subprocess_obj.side_effect = mock_subprocess((\n            SubprocessMock(\n                expected_input='git rev-parse HEAD',\n                mocked_output=b'new_sha'\n            ),\n        ))\n\n        m = mock.mock_open()\n        with mock.patch('detect_secrets_server.repos.base_tracked_repo.codecs.open', m):\n            assert main([\n                '--scan-repo',\n                'will-be-mocked',\n                '--output-hook',\n                'examples/standalone_hook.py',\n            ]) == 0\n\n        mock_log().info.assert_called_with(\n            'SCAN COMPLETE - STATUS: clean for %s',\n            'repo_name',\n        )\n\n        m().write.assert_called_once_with(json.dumps({\n            'sha': 'new_sha',\n            'repo': 'repo_name',\n            'plugins': {\n                'base64_limit': 3,\n                'hex_limit': None,\n                'private_key_detector': False,\n            },\n            'cron': '* * * * *',\n            'baseline_file': '.secrets.baseline',\n        }, indent=2))\n\n    @mock.patch('detect_secrets_server.__main__.CustomLogObj.getLogger')\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo.scan')\n    @mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo._read_tracked_file')\n    def test_main_scan_repo_scan_success_secrets_found(self, mock_file, mock_scan, mock_log):\n        mock_file.return_value = {\n            'sha': 'does_not_matter',\n            'repo': 'repo_name',\n            'plugins': {\n                'base64_limit': 3,\n            },\n            'cron': '* * * * *',\n            'baseline_file': '.secrets.baseline',\n        }\n\n        mock_secret_collection = SecretsCollection()\n        mock_secret_collection.data['junk'] = 'data'\n        mock_scan.return_value = mock_secret_collection\n\n        with mock.patch('detect_secrets_server.usage.ExternalHook') as hook, \\\n                mock.patch('detect_secrets_server.repos.base_tracked_repo.BaseTrackedRepo.update') as update, \\\n                mock.patch('detect_secrets.core.secrets_collection.SecretsCollection.json') as secrets_json:\n            assert main([\n                '--scan-repo',\n                'will-be-mocked',\n                '--output-hook',\n                'examples/standalone_hook.py',\n            ]) == 0\n\n            assert update.call_count == 0\n            assert hook().alert.call_count == 1\n            assert secrets_json.call_count == 1\n\n    def test_main_no_args(self):\n        # Needed for coverage\n        assert main([]) == 0\n\n\nclass TestSetAuthorsForFoundSecrets(object):\n\n    def test_success(self):\n        secrets = secrets_collection_factory([{\n            'filename': 'fileA',\n        }]).json()\n\n        with self.mock_repo() as repo:\n            set_authors_for_found_secrets(secrets, repo)\n\n        assert secrets['fileA'][0]['author'] == 'khock'\n\n    @contextmanager\n    def mock_repo(self):\n        mocked_git_blame_output = textwrap.dedent(\"\"\"\n            d39c008353447bbc1845812fcaf0a03b50af439f 177 174 1\n            author Kevin Hock\n            author-mail <khock@yelp.com>\n            author-time 1513196047\n            author-tz -0800\n            committer Foo\n            committer-mail <foo@example.com>\n            committer-time 1513196047\n            committer-tz -0800\n            summary mock\n            previous 23c630620c23843559485fd2ada02e9e7bc5a07e4 mock_output.java\n            filename some_file.java\n            \"super:secret f8616fefbo41fdc31960ehef078f85527\")));\n        \"\"\")[1:]\n\n        repo = mock_repo_factory()\n        with mock.patch.object(repo, 'get_blame', return_value=mocked_git_blame_output):\n            yield repo\n","sub_path":"tests/main_test.py","file_name":"main_test.py","file_ext":"py","file_size_in_byte":20883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"226853663","text":"import julia\nimport numpy as np\n\nj = julia.Julia()\nj.using(\"LowRankModels\")\n\n__all__ = ['QuadLoss','L1Loss','HuberLoss','ZeroReg','NonNegOneReg','QuadReg','NonNegConstraint','glrm', 'pca', 'nnmf', 'rpca']\n\n#Losses\ndef QuadLoss(scale=1.0, domain=j.RealDomain()):\n    if not isinstance(scale, float):\n        raise TypeError\n    return j.QuadLoss(scale, domain)\n\ndef L1Loss(scale=1.0, domain=j.RealDomain()):\n    if not isinstance(scale, float):\n        raise TypeError\n    return j.L1Loss(scale, domain)\n\ndef HuberLoss(scale=1.0, domain=j.RealDomain(), crossover=1.0):\n    if not isinstance(scale, float) or not isinstance(crossover, float):\n        raise TypeError\n    return j.HuberLoss(scale, domain, crossover)\n\n\n#Regularizers\ndef ZeroReg():\n    return j.ZeroReg()\n\ndef NonNegOneReg(scale=1):\n    return j.NonNegOneReg(scale)\n\ndef QuadReg(scale=1):\n    return j.QuadReg(scale)\n\ndef NonNegConstraint():\n    return j.NonNegConstraint()\n\n\n\nclass glrm:\n    \n    is_dimensionality_reduction = True\n    is_feature_selection = False\n    \n    #class constructor: default being PCA\n    def __init__(self, losses = QuadLoss(), rx = ZeroReg(), ry = ZeroReg(), n_components=2):\n        \n        self.losses = losses\n        self.rx = rx\n        self.ry = ry\n        self.k = n_components\n        self.fitted = False\n        self.hyperparameters = {'losses':losses, 'rx':rx, 'ry':ry}\n\n\n\n#fit the dimensionality reduction method to the input and then output dimensionality-reduced results\n\n    def fit(self):\n            if self.fitted:\n                return\n    \n            if self.training_inputs is None:\n                raise ValueError(\"Missing training data.\")\n\n            glrm_j = j.GLRM(self.training_inputs, self.losses, self.rx, self.ry, self.k)\n            X, Y, ch = j.fit_b(glrm_j)\n            self.X = X\n            self.Y = Y\n            self.fitted = True\n#            return np.dot(np.transpose(X), Y)\n\n    \n    #store training matrix as attribute of the class\n    def set_training_data(self, inputs):\n        \n            self.training_inputs = inputs\n\n    \n    \n    def produce(self, inputs): #calculate the output map; requires the input to be a numpy array\n        \n#        inputs = inputs.reshape(1, -1)\n\n        \n        #make sure dimension_reduce has already been executed beforehand, and Y and v match in terms of numbers of columns\n        try:\n            self.Y\n        except NameError:\n            raise Exception('Initial GLRM fitting not executed!')\n        else:\n            try:\n                if self.Y.shape[1] != inputs.shape[1]:\n                    raise ValueError\n            except ValueError:\n                raise Exception('Dimension of input vector does not match Y!')\n            else:\n                self.Y = self.Y.astype(float) #make sure column vectors finally have the datatype Array{float64,1} in Julia\n                num_cols = self.Y.shape[1]\n                ry = [j.FixedLatentFeaturesConstraint(self.Y[:, i]) for i in range(num_cols)]\n                glrm_new_j = j.GLRM(inputs, self.losses, self.rx, ry, self.k)\n                x, yp, ch = j.fit_b(glrm_new_j)\n                return x\n\n\nclass pca(glrm):\n    run_status = 0\n\nclass nnmf(glrm):\n    def __init__(self, losses = QuadLoss(), rx = NonNegConstraint(), ry = NonNegConstraint(), n_components=2):\n        self.losses = losses\n        self.rx = rx\n        self.ry = ry\n        self.k = n_components\n        self.fitted = False\n        self.hyperparameters = {'losses':losses, 'rx':rx, 'ry':ry}\n\n\nclass rpca(glrm):\n    def __init__(self, losses = HuberLoss(), rx = QuadReg(), ry = QuadReg(), n_components=2):\n        \n        self.losses = losses\n        self.rx = rx\n        self.ry = ry\n        self.k = n_components\n        self.fitted = False\n        self.hyperparameters = {'losses':losses, 'rx':rx, 'ry':ry}\n\n\n\n\n\n\n","sub_path":"pyglrm/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"485633286","text":"from constants import *\n\n# determine the mixing matrix\n\nH_traceless = [\n\t[Ve/2 - (delta_m*cos)/(4*E), -(delta_m*sin)/(4*E)],\n\t[-(delta_m*sin)/(4*E), -Ve/2 + (delta_m*cos)/(4*E)]\n]\n\nsome, V = la.eig(np.array(H_traceless))\nU = la.inv(V)\n\n# dispertions\n\ndef Dx(x):\n\tres = [[1, 1], [1, 1]]\n\n\tfor a in (0, 1):\n\t\tfor b in (0, 1):\n\t\t\tif a < b:\n\t\t\t\tres[a][b] = (2*sigma_x**2 - 2.0j*delta_m*c**2*hbar*cos/E**3 * x/c)\n\t\t\telif a > b:\n\t\t\t\tres[a][b] = (2*sigma_x**2 + 2.0j*delta_m*c**2*hbar*cos/E**3 * x/c)\n\t\t\telse:\n\t\t\t\tres[a][b] = 2*sigma_x**2\n\n\treturn np.array(res)\n\ndef Dyz(x):\n\tres = [[1, 1], [1, 1]]\n\n\tfor a in (0, 1):\n\t\tfor b in (0, 1):\n\t\t\tif a < b:\n\t\t\t\tres[a][b] = (2*sigma_x**2 + 1.0j*delta_m*c**2*hbar*cos/E**3 * x/c)\n\t\t\telif a > b:\n\t\t\t\tres[a][b] = (2*sigma_x**2 - 1.0j*delta_m*c**2*hbar*cos/E**3 * x/c)\n\t\t\telse:\n\t\t\t\tres[a][b] = 2*sigma_x**2\n\n\treturn np.array(res)\n\ndef phase(x):\n\tres = [[1, 1], [1, 1]]\n\n\tfor a in (0, 1):\n\t\tfor b in (0, 1):\n\t\t\tif a < b:\n\t\t\t\tres[a][b] = np.exp(-1.0j*Ve/(hbar*c) * x)\n\t\t\telif a > b:\n\t\t\t\tres[a][b] = np.exp(+1.0j*Ve/(hbar*c) * x)\n\t\t\telse:\n\t\t\t\tres[a][b] = 1.0\n\n\treturn np.array(res)\n\ndef decoherence(x):\n\tres = [[1, 0], [0, 1]]\n\n\tfor a in (0, 1):\n\t\tfor b in (0, 1):\n\t\t\tif a != b:\n\t\t\t\tres[a][b] = np.exp( - (0.5*delta_m/E**2)**2 * x**2 / Dx(x)[a][b])\n\t\t\telse:\n\t\t\t\tres[a][b] = 1.0\n\n\treturn np.array(res)\n\n# main functions of density operator\n\ndef rho_propagation(x):\n\tres = [[1, 1], [1, 1]]\n\n\tfor a in (0, 1):\n\t\tfor b in (0, 1):\n\t\t\telement = C**1.5\n\n\t\t\telement *= U[0][a].conjugate() * U[0][b]\n\t\t\telement *= phase(x)[a][b]\n\t\t\telement *= decoherence(x)[a][b]\n\t\t\telement *= Dx(x)[a][b]**(-0.5) * Dyz(x)[a][b]**(-1.0)\n\n\t\t\tres[a][b] = element\n\n\treturn np.array(res)\n\ndef rho_flavours(x):\n\treturn np.dot(np.dot(U, rho_propagation(x)), V)","sub_path":"weak.py","file_name":"weak.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"1588836","text":"# Libraries Included:\n# Numpy, Scipy, Scikit, Pandas\n\nprint(\"Hello, world!\")\nprint(1/4)\nimport numpy as np\n\nn_utilisateurs = 10000\n\nn_jours = 10\n\nn_periodes = 24\n\nn_plats = 3\n\n# tab pour savoir si un tuilsaeur a achete ou non\n# tab qui donne les indices des utilisateurs qui peuvent encore acheter\n\ntab_consom_potentiel = [i for i in range(n_utilisateurs)]\n\n\n# fonction qui à une période donnée retourne le paramètre de la loi de poisson\n# vérifier que ce paramètre ne déborde pas\ndef tiragePoisson(periode):\n   # coeff =  # je comprends pas ce que tu fais ....\n    # je suppose que lambda est tiré selon une loi uniforme entre des bornes variables\n    # pour que lambda soit plus grand à 22H qu'à 20H.\n    # OOK\n    a = -periode*(periode-24)\n    b= 1\n\n    pam_lambd = np.random.uniform(low=a , high=a + b)\n    return pam_lambd\n\n\n\n\n\n# tab_notes est composée d'une matrice pour chaque plat. Donc pour accéder à la matrice d'un plat on fera tab_notes[plat][utilisateurs][prix]\n\n# tab_prix sera la matrice qui pour un plat donné donne le vecteur ligne des prix pour un jour donné. Par exemple [[1 3 4 5 6 7 9] [10 10 10 10 10 10]] veut dire que pour le plat 1, on a mis les prix 1 à 20H, 3 à 20H10, 4 à 20H20. et 10 pour le plat 2\n\ndef genererNote(propension, prix):\n    # cette fonction sert à générer la note de l'utilsateurs à partir de la propension a payer\n    note = (propension - prix)  /propension * 5\n    return note\n\n\ndef argmaxCritere(propension, tab_prix, periode):\n    argmax = -1\n    maximu = -1\n    for plat in range(len(tab_prix)):\n        if propension[plat] - tab_prix[plat][periode] > maximu:\n            argmax = plat\n            maximu = propension[plat]-tab_prix[plat][periode]\n\n    return argmax\n\n\ndef f(i, n):\n    if i <= n / 3:\n        return 0, 0, n/3\n    elif i >= 2 * n / 3:\n        return 2, 2*n/3,n\n    else:\n        return 1, n/3, 2*n/3\n\n\ndef g(i, e_low, e_high,p):\n    i = i-e_low\n    e_high = e_high - e_low\n    e_low = 0\n    resultat = 0\n    if i <= e_high / 3:\n        resultat = 0\n    elif i >= 2 * e_high / 3:\n        resultat =  2\n    else:\n        resultat = 1\n\n    if p ==1:\n        resultat+=1\n        resultat %= 3\n    if p==2:\n        resultat +=2\n        resultat%=3\n    return resultat\n\n\ndef z(classe, gout_plat):\n\n\n    return classe + gout_plat\n\ndef remplir_prix(tab_prix,jr):\n    for i in range(len(tab_prix[0])):\n        tab_prix[0][i] = 1\n        tab_prix[1][i] = 2\n        tab_prix[2][i] = 3\n    return tab_prix\n\ntab_prix = [[0 for i in range(n_periodes)] for j in range(n_plats)]\n\nfor jr in range(n_jours):\n    tab_prix = remplir_prix(tab_prix, jr)\n    tab_ventes = [[0 for j in range(n_periodes)] for i in range(n_plats)]\n    n_util_restant = n_utilisateurs\n    tab_lambda = [0 for i in range(n_periodes)]\n    tab_consom_potentiel = [i for i in range(n_utilisateurs)]\n\n    for per in range(n_periodes):\n        n_util_restant = len(tab_consom_potentiel)\n        tab_lambda[per] = tiragePoisson(per)\n        # np = lambda\n        p = tab_lambda[per] / n_util_restant\n        temp = []\n        # pour chaque personne restante, tirer une loi de bernoulli et voir si elle consomme ou pas. Si c'est le cas, on l'enlève de la liste des consommateurs potentiels\n        for indice in tab_consom_potentiel:\n            if (np.random.rand() < p):\n                temp.append(indice)\n        tab_consom_potentiel = np.array(tab_consom_potentiel)\n        tab_consom_potentiel = np.delete(tab_consom_potentiel, temp)\n        # ici, on a la liste actualisée de tous les individus susceptibles de consommer\n        for i in temp:\n\n            propension = [0 for i in range(n_plats)]\n            # np.random.exponential(scale= parametre)\n            # maintenant on peut découper notre population selon la classe sociale et le gout pour chaque produit. L'attributino se fait en fonction de l'indice de la personne\n            classe, extremite_low, extremite_high = f(i, n_utilisateurs)\n            gout_plat = g(i,extremite_low,extremite_high,p)\n            for p in range(n_plats):\n                gout_plat = g(i, extremite_low, extremite_high, p)\n                propension[p] = np.random.exponential(scale=1/(z(classe, gout_plat+0.5)))\n            plat = argmaxCritere(propension, tab_prix, per)\n            tab_ventes[plat][per] += 1\n\n            # on a 2 possibilités: il achete le plat qui maximise un critère, ou bien il n'achète pas\n\n\n    print(\"Jour \",jr ,tab_ventes)\n\n\n","sub_path":"Scenario Simu .py","file_name":"Scenario Simu .py","file_ext":"py","file_size_in_byte":4444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"286091509","text":"#-*- coding:utf-8 -*-\nimport math\ndef radix_sort(input_list):\n\t\"\"\"\n\tinput: to be sorted list\n\treturn: ordered list\n\tmodify: 2018-3-1\n\tauthor: winter\n\t\"\"\"\n\tif len(input_list) == 0 :\n\t\treturn input_list\n\n\tn = len(input_list)\n\t'''\n\t此处是用来求最大位数的，因为求位数涉及到除10操作，因此我们可以借助对数\n\tmax_num = max(input_list)   #max value\n\tbits_num = 0\n\twhile(max_num):             #get max bits\n\t\tbits_num += 1\n\t\tmax_num /= 10\n\t'''\n\tbits_num = int(math.ceil(math.log10(max(input_list))))     #一句话解决是不是很easy，同样对于除2除3，除以任何正数的都可以用对数\n\n\tbucket = [[] for i in range(10)]   #set bucket\n\n\tfor i in range(1, bits_num+1):\n\t\tfor j in input_list:\n\t\t\tbucket[j % (10**(i)) // (10**(i-1))].append(j)     #取到某一位的数值\n\t\tdel input_list[:]                                      #销毁掉之前数组\n\t\tfor z in bucket:                                       #重新分配索引\n\t\t\tinput_list += z\n\t\t\tdel z[:]\n\n\treturn input_list\n\nif __name__ == \"__main__\":\n\tarray1 = [30, 40, 60, 10, 20, 50]\n\tprint(\"排序前：\", array1)\n\tarray2 = radix_sort(array1)\n\tprint(\"排序后：\", array2)\n","sub_path":"Sort/radixsort.py","file_name":"radixsort.py","file_ext":"py","file_size_in_byte":1173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"644771704","text":"import tensorflow as tf\n\nclass DDDQNNet:\n    def __init__(self, action_size, learning_rate, name='DQNetwork'):\n        self.state_size = [1, 4, 4]\n        self.action_size = action_size\n        self.learning_rate = float(learning_rate)\n\n        with tf.variable_scope(name):\n            # We create the placeholders\n            # *state_size means that we take each elements of state_size in tuple hence is like if we wrote\n            # [None, 84, 84, 4]\n            self.inputs_ = tf.placeholder(tf.float32, [None, 1, 4, 4], name=\"inputs\")\n            self.ISWeights_ = tf.placeholder(tf.float32, [None, 1], name='IS_weights')\n            self.actions_ = tf.placeholder(tf.float32, [None, action_size], name=\"actions_\")\n\n            # Remember that target_Q is the R(s,a) + ymax Qhat(s', a')\n            self.target_Q = tf.placeholder(tf.float32, [None], name=\"target\")\n\n            self.flatten = tf.layers.flatten(self.inputs_)\n\n            # Here we separate into two streams\n            # The one that calculate V(s)\n            self.value_fc = tf.layers.dense(inputs = self.flatten,\n                                  units = 64,\n                                  activation = tf.nn.elu,\n                                       kernel_initializer=tf.contrib.layers.xavier_initializer(),\n                                name=\"value_fc\")\n\n            self.value = tf.layers.dense(inputs=self.value_fc,\n                                         units=1,\n                                         activation=None,\n                                         kernel_initializer=tf.contrib.layers.xavier_initializer(),\n                                         name=\"value\")\n\n            # The one that calculate A(s,a)\n            self.advantage_fc = tf.layers.dense(inputs = self.flatten,\n                                  units = 64,\n                                  activation = tf.nn.elu,\n                                       kernel_initializer=tf.contrib.layers.xavier_initializer(),\n                                name=\"advantage_fc\")\n\n            self.advantage = tf.layers.dense(inputs = self.advantage_fc,\n                                        units = self.action_size,\n                                        activation = None,\n                                        kernel_initializer=tf.contrib.layers.xavier_initializer(),\n                                name=\"advantages\")\n\n\n\n            # Agregating layer\n            # Q(s,a) = V(s) + (A(s,a) - 1/|A| * sum A(s,a'))\n            self.output = self.value + tf.subtract(self.advantage, tf.reduce_mean(self.advantage,\n                                                                                  axis=1, keepdims=True))\n\n            # Q is our predicted Q value.\n            self.Q = tf.reduce_sum(tf.multiply(self.output, self.actions_), axis=1)\n\n            # The loss is modified because of PER\n            self.absolute_errors = tf.abs(self.target_Q - self.Q)# for updating Sumtree\n\n            self.loss = tf.reduce_mean(self.ISWeights_ * tf.squared_difference(self.target_Q, self.Q))\n\n            self.optimizer = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss)\n","sub_path":"script/lib/dddqnet.py","file_name":"dddqnet.py","file_ext":"py","file_size_in_byte":3153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"347191958","text":"#!/usr/bin/env python\n\nfrom scipy import sparse\nfrom sklearn.datasets import dump_svmlight_file\n\nimport argparse\nimport logging\nimport numpy as np\nimport os\nimport pandas as pd\n\nfrom kaggler.util import encode_categorical_features\n\n\nlogging.basicConfig(format='%(asctime)s   %(levelname)s   %(message)s',\n                    level=logging.DEBUG)\n\n\ndef generate_feature(train_file, label_file, test_file, feature_dir,\n                     feature_name):\n    # Load data files\n    logging.info('Loading training and test data')\n    trn = pd.read_csv(train_file, index_col=0)\n    tst = pd.read_csv(test_file, index_col=0)\n    label = pd.read_csv(label_file, index_col=0)\n    n_trn = trn.shape[0]\n    n_tst = tst.shape[0]\n\n    logging.info('Combining training and test data')\n    df = pd.concat([trn, tst], ignore_index=True)\n\n    cols = list(df.columns)\n    num_cols = [x for x in cols if x[0] == 'n']\n    cnt_cols = [x for x in cols if x[0] == 'o']\n    cat_cols = [x for x in cols if x[0] == 'c' or x[0] == 'r']\n\n    # no transformation for numerical variables\n    logging.info('Imputing missing values in numerical columns by 0')\n    X_num = df.ix[:, num_cols].values\n    X_num[np.isnan(X_num)] = 0.\n\n    # log(1 + x) transformation for ordinal variables\n    X_cnt = df.ix[:, cnt_cols].values\n    X_cnt[np.isnan(X_cnt)] = 0.\n    X_cnt = np.log1p(X_cnt)\n\n    # One-Hot-Encoding for categorical variables\n    logging.info('One-hot-encoding categorical columns')\n\n    X_col = encode_categorical_features(df[cat_cols], min_obs=3, n=n_trn,\n                                        nan_as_var=False)\n    X = sparse.hstack((X_num, X_cnt, X_col))\n    X = X.tocsr()\n\n    logging.info('Saving features into {}'.format(feature_dir))\n    for i in range(label.shape[1]):\n        train_feature_file = os.path.join(feature_dir, '{}.trn{:02d}.sps'.format(feature_name, i))\n        test_feature_file = os.path.join(feature_dir, '{}.tst{:02d}.sps'.format(feature_name, i))\n\n        dump_svmlight_file(X[:n_trn], label.ix[:, i], train_feature_file,\n                           zero_based=False)\n        dump_svmlight_file(X[n_trn:], np.zeros((n_tst,)), test_feature_file,\n                           zero_based=False)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--train-file', required=True, dest='train')\n    parser.add_argument('--label-file', required=True, dest='label')\n    parser.add_argument('--test-file', required=True, dest='test')\n    parser.add_argument('--feature-dir', required=True, dest='feature_dir')\n    parser.add_argument('--feature-name', required=True, dest='feature_name')\n\n    args = parser.parse_args()\n\n    generate_feature(train_file=args.train,\n                     label_file=args.label,\n                     test_file=args.test,\n                     feature_dir=args.feature_dir,\n                     feature_name=args.feature_name)\n","sub_path":"3rd-place/code/src/generate_feature3.py","file_name":"generate_feature3.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"371668698","text":"#!/usr/bin/python3\n# -*-coding:Utf-8 -*\n\n\"\"\"Contain constante use in the game.\"\"\"\n\nSCREEN_HEIGTH = 600\nSCREEN_WIDTH = 600\nELEMENT_HEIGTH = SCREEN_HEIGTH/15\nELEMENT_WIDTH = SCREEN_WIDTH/15\n\nCOLOR_FONT = (63, 34, 4)\nCOLOR_ELEMENT = (0, 0, 0)\nCOLOR_BLUE = (0, 0, 255)\n\nFPS = 30\n","sub_path":"constantes.py","file_name":"constantes.py","file_ext":"py","file_size_in_byte":275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"646713417","text":"import csv\n\nall_min_temp = 100\nall_min_date = ''\nall_max_temp = -100\nall_max_date = ''\n\n# CSV 파일열기\nwith open(\"data/sample.csv\") as input:\n    csv_reader = csv.reader(input)\n\n    # 메타정보, 헤더\n    for i in range(8):\n        next(csv_reader)\n        \n    min_temp = 0\n    max_temp = 0\n\n    for row in csv_reader:\n        min_temp = float(row[3])        \n        max_temp = float(row[4])\n\n        # 최저기온 처리\n        if(all_min_temp > min_temp):\n            all_min_temp = min_temp\n            all_min_date = row[0]\n\n        # 최고기온 처리\n        if(all_max_temp < max_temp):\n            all_max_temp = max_temp\n            all_max_date = row[0]\n                    \nprint(all_min_date, \"역대 최저기온 =\", all_min_temp)\nprint(all_max_date, \"역대 최고기온 =\", all_max_temp)","sub_path":"bk_all_data/p37.py","file_name":"p37.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"62267501","text":"# -*- coding: utf-8 -*-\nimport time\nimport logging\nimport threading\nfrom copy import copy\n\nimport pika\nfrom pika import credentials\n\nfrom .compat import Queue\nfrom .filters import FieldFilter\nfrom .formatters import JSONFormatter\nfrom .compat import ExceptionReporter\n\n\nclass RabbitMQHandlerOneWay(logging.Handler):\n    \"\"\"\n    Python/Django logging handler to ship logs to RabbitMQ.\n    Inspired by: https://github.com/ziXiong/MQHandler\n    \"\"\"\n    def __init__(self, level=logging.NOTSET, formatter=None,\n        host='localhost', port=5672, connection_params=None,\n        username=None, password=None,\n        exchange='log', declare_exchange=False,\n        routing_key_format=\"{name}.{level}\",\n        routing_key_formatter=None,\n        close_after_emit=False,\n        fields=None, fields_under_root=True, message_headers=None,\n        record_fields=None, exclude_record_fields=None,\n        heartbeat=60,\n        content_type='text/plain'):\n        # Initialize the handler.\n        #\n        # :param level:                 Logs level.\n        # :param formatter:             Use custom formatter for the logs.\n        # :param host:                  RabbitMQ host. Default localhost\n        # :param port:                  RabbitMQ Port. Default 5672\n        # :param connection_params:     Allow extra params to connect with RabbitMQ.\n        # :param message_headers:       A dictionary of headers to be published with the message. Optional.\n        # :param username:              Username in case of authentication.\n        # :param password:              Password for the username.\n        # :param exchange:              Send logs using this exchange.\n        # :param declare_exchange:      Whether or not to declare the exchange.\n        # :param routing_key_format:    Customize how messages will be routed to the queues.\n        # :param routing_key_formatter: Override how messages will be routed to the queues.\n        #                               Formatter is passed record object.\n        # :param close_after_emit:      Close connection after emit the record?\n        # :param fields:                Send these fields as part of all logs.\n        # :param fields_under_root:     Merge the fields in the root object.\n        # :record_fields:               A set of attributes that should be preserved from the record object.\n        # :exclude_record_fields:       A set of attributes that should be ignored from the record object.\n        # :heartbeat:                   Lower bound for heartbeat timeout.\n        # :content_type:                The format of the message sent to the queue.\n\n        super(RabbitMQHandlerOneWay, self).__init__(level=level)\n\n        # Important instances/properties.\n        self.exchange = exchange\n        self.connection = None\n        self.channel = None\n        self.exchange_declared = not declare_exchange\n        self.routing_key_format = routing_key_format\n        self.close_after_emit = close_after_emit\n\n        # Connection parameters.\n        # Allow extra params when connect to RabbitMQ.\n        # @see: http://pika.readthedocs.io/en/0.10.0/modules/parameters.html#pika.connection.ConnectionParameters\n        conn_params = connection_params if isinstance(connection_params, dict) else {}\n        self.connection_params = conn_params.copy()\n        self.connection_params.update(dict(host=host, port=port, heartbeat=heartbeat, blocked_connection_timeout=150))\n\n        if username and password:\n            self.connection_params['credentials'] = credentials.PlainCredentials(username, password)\n\n        # Extra params for message publication\n        self.message_headers = message_headers\n        self.content_type = content_type\n\n        # Save routing-key formatter.\n        self.routing_key_formatter = routing_key_formatter\n\n        # Logging.\n        self.formatter = formatter or JSONFormatter(\n            include=record_fields,\n            exclude=exclude_record_fields\n        )\n        self.fields = fields if isinstance(fields, dict) else {}\n        self.fields_under_root = fields_under_root\n\n        if len(self.fields) > 0:\n            self.addFilter(FieldFilter(self.fields, self.fields_under_root))\n\n        # Connect.\n        self.createLock()\n\n        # message queue\n        self.queue = Queue()\n        self.start_message_worker()\n\n    def open_connection(self):\n        \"\"\"\n        Connect to RabbitMQ.\n        \"\"\"\n        # Set logger for pika.\n        # See if something went wrong connecting to RabbitMQ.\n        if not self.connection or self.connection.is_closed or not self.channel or self.channel.is_closed:\n            handler = logging.StreamHandler()\n            handler.setFormatter(self.formatter)\n            rabbitmq_logger = logging.getLogger('pika')\n            rabbitmq_logger.addHandler(handler)\n            rabbitmq_logger.propagate = False\n            rabbitmq_logger.setLevel(logging.WARNING)\n\n            # Connect.\n            if not self.connection or self.connection.is_closed:\n                self.connection = pika.BlockingConnection(pika.ConnectionParameters(**self.connection_params))\n\n            if not self.channel or self.channel.is_closed:\n                self.channel = self.connection.channel()\n\n            if self.exchange_declared is False:\n                self.channel.exchange_declare(exchange=self.exchange, exchange_type='topic', durable=True, auto_delete=False)\n                self.exchange_declared = True\n\n            # Manually remove logger to avoid shutdown message.\n            rabbitmq_logger.removeHandler(handler)\n\n    def close_connection(self):\n        \"\"\"\n        Close active connection.\n        \"\"\"\n        self.stopping.set()\n        while not self.stopped.is_set():\n            time.sleep(1)\n        if self.channel:\n            del self.channel\n\n        if self.connection:\n            del self.connection\n\n        self.connection, self.channel = None, None\n\n    def start_message_worker(self):\n        self.stopping = threading.Event()\n        self.stopped = threading.Event()\n        worker = threading.Thread(target=self.message_worker)\n        worker.setDaemon(True)\n        worker.start()\n\n    def message_worker(self):\n        while not self.stopping.is_set():\n            try:\n                record, routing_key = self.queue.get(block=True, timeout=10)\n\n                if not self.connection or self.connection.is_closed or not self.channel or self.channel.is_closed:\n                    self.open_connection()\n\n                res = self.channel.basic_publish(\n                    exchange=self.exchange,\n                    routing_key=routing_key,\n                    body=record,\n                    properties=pika.BasicProperties(\n                        delivery_mode=2,\n                        headers=self.message_headers,\n                        content_type=self.content_type\n                    )\n                )\n\n            except Queue.Empty:\n                continue\n            except Exception:\n                self.channel, self.connection = None, None\n                self.handleError(record)\n            finally:\n                if self.stopping.is_set():\n                    self.stopped.set()\n                    break\n                self.queue.task_done()\n                if self.close_after_emit:\n                    self.close_connection()\n        self.stopped.set()\n\n    def emit(self, record):\n        if not hasattr(self, 'queue') or self.stopped.is_set():\n            return\n\n        try:\n            if self.routing_key_formatter:\n                routing_key = self.routing_key_formatter(record)\n            else:\n                routing_key = self.routing_key_format.format(\n                    name=record.name,\n                    level=record.levelname\n                )\n\n            if hasattr(record, 'request'):\n                no_exc_record = copy(record)\n                del no_exc_record.exc_info\n                del no_exc_record.exc_text\n                del no_exc_record.request\n\n                if record.exc_info:\n                    exc_info = record.exc_info\n                else:\n                    exc_info = (None, record.getMessage(), None)\n\n                if ExceptionReporter:\n                    reporter = ExceptionReporter(record.request, is_email=False, *exc_info)\n                    no_exc_record.traceback = reporter.get_traceback_text()\n\n                formatted = self.format(no_exc_record)\n            else:\n                formatted = self.format(record)\n\n            self.queue.put((formatted, routing_key))\n        except Exception:\n            self.channel, self.connection = None, None\n            self.handleError(record)\n\n    def close(self):\n        \"\"\"\n        Free resources.\n        \"\"\"\n        self.acquire()\n\n        if hasattr(self, 'queue'):\n            del self.queue\n\n        try:\n            self.close_connection()\n        finally:\n            self.release()\n","sub_path":"python_logging_rabbitmq/handlers_oneway.py","file_name":"handlers_oneway.py","file_ext":"py","file_size_in_byte":8919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"387830632","text":"if __name__ == '__main__':\n    students = []\n    for _ in range(int(input())):\n        name = input()\n        score = float(input())\n        students.append([name, score])\n\n    # Sort students by grade\n    sorted_students = sorted(students, key=lambda student: student[1])\n\n    # Exclude students with the worst grades\n    exclude_worst = [student for student in sorted_students\n                     if student[1] != sorted_students[0][1]]\n\n    # Select only the students with the second worst grades\n    second_worst = [student[0] for student in exclude_worst\n                    if student[1] == exclude_worst[0][1]]\n\n    # Sort and print names in alphabetical order\n    for name in sorted(second_worst):\n        print(name)\n","sub_path":"python/basic_data_types/nested_lists.py","file_name":"nested_lists.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"246824150","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nfrom wsgiref.simple_server import make_server\n\n\ndef index(url):\n    s =  '{1}{0}'.format(url, '我是').encode('utf-8')\n    print(s)\n    return s\n\n\ninfo = {'/index/': index}\n\n\ndef run(environ, start_response):\n    url = environ['PATH_INFO']\n    start_response('200 ok', [('Content-Type', 'text/html; charset=utf8'), ])\n    if info.get(url):\n        data = info[url](url)\n    else:\n        data = b'404 not found'\n    return [data, ]\n\n\nif __name__ == '__main__':\n    httpd = make_server('127.0.0.1', 8000, run)\n    httpd.serve_forever()\n","sub_path":"052Django框架/4.1 练习.py","file_name":"4.1 练习.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"171693690","text":"#!/usr/bin/python\n\ntry:\n    import cPickle as pickle\nexcept:\n    import pickle\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom fibte.trafficgen.flowGenerator import isElephant\nfrom fibte import LINK_BANDWIDTH\n\n\"\"\"\nThis module defines functions that extract statistics about the traffic files generated by the traffic generator.\n\"\"\"\n\nclass TrafficStats(object):\n    def __init__(self, filename):\n        self.filename = filename\n        self.params = self.get_traffic_params(self.filename)\n        self.traffic_per_host = self.load_traffic_file()\n        self.hosts = self.traffic_per_host.keys()\n        self.collectAllStats()\n\n    @staticmethod\n    def get_traffic_params(filename):\n        params = {'tg': ''}\n        slash_index = filename.find('/')\n        traffic_filename = filename[slash_index+1:]\n        traffic_filename = traffic_filename.split('.')[0]\n        traffic_filename = traffic_filename.replace('_to', '')\n        tg =  traffic_filename.split('_')[0]\n        params['tg'] = tg\n        if tg == 'tg1':\n            (senders, receivers, percents, rate, totaltime, timestep) = traffic_filename.split('_')[1:]\n            params['senders'] = senders.split(',')\n            params['receivers'] = receivers.split(',')\n            (pmice, pelephant) = percents.split('e')\n            params['pmice'] = float(pmice.replace('m', '').replace(',', '.'))\n            params['pelephant'] = float(pelephant.replace(',', '.'))\n            params['flow_rate'] = float(rate.strip('fr').replace(',', '.'))\n            params['total_time'] = int(totaltime.strip('t'))\n            params['time_step'] = int(timestep.strip('ts'))\n        elif tg == 'tg2':\n            (senders, receivers, percents, elephant_rate, mice_rate, target_load, totaltime, timestep) = traffic_filename.split('_')[1:]\n            params['senders'] = senders.split(',')\n            params['receivers'] = receivers.split(',')\n            (pmice, pelephant) = percents.split('e')\n            params['pmice'] = float(pmice.replace('m', '').replace(',', '.'))\n            params['pelephant'] = float(pelephant.replace(',', '.'))\n            params['elephant_rate'] = float(elephant_rate.strip('fre').replace(',', '.'))\n            params['mice_time'] = float(mice_rate.strip('frm').replace(',', '.'))\n            params['total_time'] = int(totaltime.strip('t'))\n            params['time_step'] = int(timestep.strip('ts'))\n        return params\n\n    def load_traffic_file(self):\n        traffic = pickle.load(open(self.filename,\"r\"))\n        return traffic\n\n    def get_last_flow_finish_time(self):\n        \"\"\"Returs the finish time of the last ending flow\"\"\"\n        max_flow_finish_time = 0\n        for (h, fl) in self.traffic_per_host.iteritems():\n            for f in fl:\n                start_time = f['start_time']\n                duration = f['duration']\n                end_time = start_time + duration\n                if end_time > max_flow_finish_time:\n                    max_flow_finish_time = end_time\n\n        return max_flow_finish_time\n\n    def flows_per_second(self):\n        \"\"\"\n        Returns\n        :param traffic:\n        :return:\n        \"\"\"\n        last_flow_end_time = int(self.get_last_flow_finish_time())\n        self.traffic_per_second = {i: [] for i in range(0, last_flow_end_time+1)}\n        for i in self.traffic_per_second.keys():\n            fws = [f for h, fl in self.traffic_per_host.iteritems() for f in fl if f['start_time'] <= i and (f['start_time']+f['duration']) >= i]\n            self.traffic_per_second[i] = fws\n\n    def collectAllStats(self):\n        self.flows_per_second()\n\n    def get_flows_over_time_and_ratio(self):\n        flows = []\n        mice = []\n        elephant = []\n        for i, fws in self.traffic_per_second.iteritems():\n            total_flows = len(fws)\n            if total_flows != 0:\n                flows.append(total_flows)\n                e_count = len([f for f in fws if isElephant(f)])\n                m_count = total_flows - e_count\n                elephant.append(e_count/float(total_flows))\n                mice.append(m_count/float(total_flows))\n            else:\n                flows.append(0)\n                mice.append(0)\n                elephant.append(0)\n        return (flows, mice, elephant)\n\n    def get_flows_over_time(self):\n        flows = []\n        mice = []\n        elephant = []\n        for i, fws in self.traffic_per_second.iteritems():\n            total_flows = sum([f['size'] for f in fws])\n            flows.append(total_flows)\n            e_count = sum([f['size'] for f in fws if isElephant(f)])\n            m_count = total_flows - e_count\n            elephant.append(e_count)\n            mice.append(m_count)\n\n        MAX_LOAD = len(self.hosts)*LINK_BANDWIDTH\n\n        flows = np.asarray(flows)/MAX_LOAD\n        mice = np.asarray(mice)/MAX_LOAD\n        elephant = np.asarray(elephant)/MAX_LOAD\n\n        return (flows, mice, elephant)\n\n    def plot_flows_over_time(self):\n        \"\"\"\n        Plots the number of flows per second, as well as the ratio of elephant and mice\n        :return:\n        \"\"\"\n        # Get data to plot\n        (flows, mice, elephant) = self.get_flows_over_time_and_ratio()\n        fig = plt.figure(figsize=(80, 20))\n        fig.suptitle(\"Number of active flows over time\", fontsize=20)\n\n\n        # Plot total number\n        sub = fig.add_subplot(2, 1, 1)\n        sub.set_xlabel(\"Time (s)\", fontsize=16)\n        sub.set_ylabel(\"Total number of flows\", fontsize=16)\n        sub.plot(flows, color='black', label=\"total\", linewidth=2.0)\n        sub.grid(True)\n\n        # Plot ratios\n        sub = fig.add_subplot(2, 1 ,2)\n        sub.set_xlabel(\"Time (s)\", fontsize=16)\n        sub.plot(mice, color='green', label='mice', linewidth=2.0)\n        sub.plot(elephant, color='blue', label='elephant', linewidth=2.0)\n        sub.set_ylabel('Elephant and Mice ratio', fontsize=16)\n        sub.grid(True)\n\n        # Write legend and plot\n        #plt.legend(loc='best')\n        plt.show()\n\n    def plot_traffic_over_time(self):\n        # Get data to plot\n        (flows, mice, elephant) = self.get_flows_over_time()\n        fig, ax1 = plt.subplots(figsize=(80, 20))\n        fig.suptitle(\"Total traffic in the network over time\", fontsize=20)\n        plt.xlabel(\"Time (s)\", fontsize=16)\n\n        # Plot total number\n        ax1.set_ylabel(\"DC traffic\", fontsize=16)\n        ax1.plot(flows, color='black', label=\"total\", linewidth=2.0)\n\n        # Plot ratios\n        ax1.plot(mice, color='green', label='mice', linewidth=2.0)\n        ax1.plot(elephant, color='blue', label='elephant', linewidth=2.0)\n\n        # Write legend and plot\n        plt.legend(loc='best')\n        # Set grid on\n        plt.grid(True)\n        plt.show()\n\n    def get_average_host_load(self):\n        \"\"\"\"\"\"\n        input_avg = []\n        output_avg = []\n        input_std = []\n        output_std = []\n        for (t, fws) in self.traffic_per_second.iteritems():\n            host_traffic_in = {h: 0 for h in self.hosts}\n            host_traffic_out = {h: 0 for h in self.hosts}\n            # Collect per host\n            for f in fws:\n                host_traffic_in[f['src']] += f['size']\n                host_traffic_out[f['dst']] += f['size']\n\n            # Compute averages\n            outavg = np.asarray(host_traffic_out.values())/LINK_BANDWIDTH\n            inavg = np.asarray(host_traffic_in.values())/LINK_BANDWIDTH\n            out_avg = outavg.mean()\n            in_avg = inavg.mean()\n            in_std = inavg.std()\n            out_std = outavg.std()\n\n            input_avg.append(in_avg)\n            output_avg.append(out_avg)\n            input_std.append(in_std)\n            output_std.append(out_std)\n\n        return ({'avg': input_avg, 'std': input_std},{'avg': output_avg, 'std': output_std})\n\n    def plot_average_host_load(self):\n        # Get data to plot\n        (input, output) = self.get_average_host_load()\n        fig = plt.figure(figsize=(80, 20))\n        fig.suptitle(\"Average host load over time\", fontsize=20)\n\n        sub = fig.add_subplot(2, 1, 1)\n        sub.set_xlabel(\"Time (s)\", fontsize=16)\n        sub.set_ylabel(\"Average host load\", fontsize=16)\n        sub.plot(input['avg'], color='black', label=\"Average traffic\", linewidth=2.0)\n        sub.grid(True)\n\n        sub = fig.add_subplot(2, 1, 2)\n        sub.set_xlabel(\"Time (s)\", fontsize=16)\n        sub.set_ylabel(\"Std host load\", fontsize=16)\n        sub.plot(input['std'], color='blue', label=\"std. host TX traffic\", linewidth=2.0)\n        sub.plot(output['std'], color='red', label=\"std. host RX traffic\", linewidth=2.0)\n        sub.grid(True)\n\n        # Write legend and plot\n        plt.legend(loc='best')\n        # Set grid on\n        plt.show()\n\n    def get_host_load(self, host):\n        input_load = []\n        output_load = []\n        for (t, fws) in self.traffic_per_second.iteritems():\n            tin_load = 0\n            tout_load = 0\n            # Collect per host\n            for f in fws:\n                if f['src'] == host:\n                    tin_load += f['size']\n                elif f['dst'] == host:\n                    tout_load += f['size']\n\n            input_load.append(tin_load)\n            output_load.append(tout_load)\n\n        input_load = np.asarray(input_load)\n        output_load = np.asarray(output_load)\n\n        return (input_load/LINK_BANDWIDTH, output_load/LINK_BANDWIDTH)\n\n    def plot_host_load(self, host):\n        # Get data to plot\n        (input, output) = self.get_host_load(host)\n        fig, ax1 = plt.subplots(figsize=(80, 20))\n        fig.suptitle(\"Host load over time\", fontsize=20)\n        plt.xlabel(\"Time (s)\", fontsize=16)\n        plt.ylim([0, 1.2])\n\n        # Plot total number\n        ax1.set_ylabel(\"Host traffic\", fontsize=16)\n        ax1.plot(input, color='green', label=\"Tx load\", linewidth=2.0)\n        ax1.plot(output, color='red', label='Rx load', linewidth=2.0)\n\n        # Write legend and plot\n        plt.legend(loc='best')\n        # Set grid on\n        plt.grid(True)\n        plt.show()\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('-f','--traffic_file',\n                           help='load traffic from a file so it can be repeated',\n                           default=\"\")\n\n    parser.add_argument('--node', help=\"Plot traffic observed at node links only. e.g: h_0_0\", type=str, default=None)\n\n    args = parser.parse_args()\n\n\n    if args.traffic_file:\n        ts = TrafficStats(args.traffic_file)\n        if args.node:\n            ts.plot_host_load(host=args.node)\n\n        ts.plot_flows_over_time()\n        ts.plot_traffic_over_time()\n        ts.plot_average_host_load()\n","sub_path":"fibte/trafficgen/trafficStats.py","file_name":"trafficStats.py","file_ext":"py","file_size_in_byte":10660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"429090977","text":"# NOTE Possible structure of try\n\"\"\"\ntry:\n    # Validation you want to run\nexcept:\n    # Command to run if it doesn't pass the validation. Except could have a type error\nelse:\n    # If succeeded, execute this\nfinally:\n    # This commands will be executed after Try finishes. Will be executed anyway.\n\"\"\"\ntry:\n    a = int(input('Numerador: '))\n    b = int(input('Denominador: '))\n    r = a / b\nexcept:\n    print('Erro!')\nelse:\n    print(f'O resultado é {r}')\nfinally:\n    print('Volte sempre...')\n\n\n# NOTE You can capture the error of the command\ntry:\n    a = int(input('Numerador: '))\n    b = int(input('Denominador: '))\n    r = a / b\nexcept Exception as erro:\n    print('Erro encontrado.')\n    print(f'Tipo: {erro.__class__}')\n    print(f'Causa: {erro.__cause__}')\nelse:\n    print(f'O resultado é {r}')\nfinally:\n    print('Volte sempre...')\n\n# NOTE We can have an exception specifically for a error type\ntry:\n    a = int(input('Numerador: '))\n    b = int(input('Denominador: '))\n    r = a / b\nexcept (ValueError, TypeError):\n    print('Tivemosum problema com os tipos de dados que você digitou.')\nexcept ZeroDivisionError:\n    print('Não é possível dividir por Zero.')\nexcept KeyboardInterrupt:\n    print('Operador encerrou o sistema.')\nexcept Exception as erro:\n    print(f'Erro encontrado. Tipo: {error.__cause__}')\nelse:\n    print(f'O resultado é {r}')\nfinally:\n    print('Volte sempre...')\n","sub_path":"Classes/World03/aula23-Error_Handling.py","file_name":"aula23-Error_Handling.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"194024295","text":"import subprocess\r\nimport argparse\r\nimport requests\r\nimport re\r\n\r\n\r\nMASTER_PAGE = \"https://spaces.telenav.com:8443/display/qa/Content+Licensing+Compliance+Dashboard\"\r\nCOUNTRIES = [\"ANZ\", \"MEA\", \"SA\", \"EU\", \"NA\", \"SEA\", \"TW\", \"TR\", \"CN\", \"IL\", \"AF\"]\r\nPRODUCTS = [\"GEN3\", \"GEN2\", \"GEN1\", \"DP2\", \"DENALI\", \"RAINIER\", \"FORD\", \"ICC\"]\r\n\r\ndef parse_args():\r\n    parser = argparse.ArgumentParser(description='Search Process License File.')\r\n    parser.add_argument('--Dev_Test_Report', required=False, help='Test Result')\r\n    args = parser.parse_args()\r\n    return args\r\n\r\ndef addToDebug(text):\r\n    print(text)\r\n    with open(\"Search_HTML_report_Log.txt\", 'a') as f:\r\n        f.write(text + \"\\n\\n\")\r\n\r\ndef callAllScripts(Dev_Test_Report_Log, License_Page, Prod, Region, Quarter, Date,Dev_Test_Report):\r\n    script1 = subprocess.call(\"python SearchLicensePage.py --url {} --outputFile Search_license.csv\".format(License_Page), shell=True)\r\n    if 0 != script1:\r\n        addToDebug(\"SearchLicensePage.py reports a script failure! Please check the console log for the error.\")\r\n        addToDebug(\"Please check the console log for the script error.\")\r\n        exit(1)\r\n        \r\n    script2 = subprocess.call(\"python SearchLicenseCheck.py --inputFile Search_license.csv --outputFile Search_result.csv --devReportpath {} --region {} --prod {}\".format(Dev_Test_Report_Log, Region, Prod), shell=True)\r\n    if(0!=script2):\r\n        addToDebug(\"LicenseCheck.py reports a script failure!\")\r\n        addToDebug(\"Please check the console log for the script error.\")\r\n        exit(1)\r\n\r\n    script3 = subprocess.call(\"python GenerateSearchLicenseReport.py --countCompareResultFile Search_result.csv --Url {} --Region {} --Product {} --Quarter {} --Date {} --LicenseReport {} --reportPath licensereport\".format(License_Page, Region, Prod, Quarter, Date, Dev_Test_Report), shell=True)\r\n    if(0!=script3):\r\n        addToDebug(\"GenerateLicenseReport.py reports a script failure!\")\r\n        addToDebug(\"Please check the console log for the script error.\")\r\n        exit(1)\r\n\r\n    exit(0)\r\n\r\n# Given a URL, returns all HTML found as a string.\r\ndef getStringFromURL(URL):\r\n    return requests.get(url=URL).text\r\n\r\n# Takes the region parameter and cuts off region, quarter and product so they can be used individually. \"\" is the default if keywords are not found.\r\ndef getKeywords(Region):\r\n    isolated_product = next((item for item in PRODUCTS if item in Region.upper()), \"\")\r\n    isolated_region = next((item for item in COUNTRIES if item in Region.upper().replace(\"DENALI\", \"\")), \"\") #DENALI causes false positive match to NA \r\n    isolated_quarter = next(iter(re.findall('[0-9][0-9]Q[0-9]', Region.upper())), \"\")\r\n\r\n    return {\"region\": isolated_region,\r\n            \"quarter\": isolated_quarter,\r\n            \"product\": isolated_product}\r\n\r\n# Some titles need modifications so that the script can make accurate matches.\r\ndef generalCleanUp(text):\r\n    text = text.replace(\"http://tact.telenav.com/\", \"\") # Telenav causes false positives to NA region\r\n    text = text.upper()\r\n    text = text.replace(\"NORTH AMERICA\", \"NA\")\r\n    text = text.replace(\"SOUTH AMERICA\", \"SA\")\r\n    text = text.replace(\"AFRICA\", \"AF\")\r\n    text = text.replace(\"TW\", \"TWN\")\r\n    return text\r\n\r\n# This returns a dictionary of {title : URL}\r\ndef getAllURLs():\r\n    full_html = requests.get(auth=('svcqauser01@telenav.com', 'QA@telenav'), url = MASTER_PAGE).text\r\n    pageID_matches = re.findall('pageId=([0-9]*)\">(.*?)</a>', full_html) # Confluence uses PageID in the URL if the title has illegal characters.\r\n    normal_matches = re.findall('href=\"/display/qa/(.*?)\"', full_html) # If the title has no strange characters, confluence using it for the URL.\r\n    URLs = {generalCleanUp(text): pageID for pageID, text in pageID_matches}\r\n    URLs_alternates = {generalCleanUp(text.replace(\"+\", \" \")): text for text in normal_matches}\r\n    URLs.update(URLs_alternates) # At this point, URL is a dictionary of {titles : information needed to generate a URL}\r\n    return convertValuesToURLs(URLs)\r\n\r\n# Given a dictionary of {titles : information needed to generate a URL}, returns a dictionary of {titles : fully working URLs}\r\ndef convertValuesToURLs(URLs):\r\n    dictionary = {}\r\n    for key, item in URLs.items():\r\n        if(item.isdigit()): dictionary[key] = \"https://spaces.telenav.com:8443/pages/viewpage.action?pageId=\" + URLs[key]\r\n        else: dictionary[key] = \"https://spaces.telenav.com:8443/display/qa/\" + URLs[key]\r\n    return dictionary\r\n\r\n# Given a dictionary of keywords, searches through all the URLs on the master page for a URL that contains all keywords.\r\ndef findURL(keywords):\r\n    URLs = getAllURLs()\r\n    list_of_matches = findLicensePage(URLs, keywords)\r\n    number_of_matches = len(list_of_matches)\r\n\r\n    if(number_of_matches == 0 and keywords[\"region\"] == \"MEA\"):\r\n        addToDebug(\"No matches with keywords. Changing region parameters from 'MEA' to 'ME'\")\r\n        temp_keywords = {\"region\": \"ME\", \"quarter\": keywords[\"quarter\"], \"product\": keywords[\"product\"]}\r\n        list_of_matches = findLicensePage(URLs, temp_keywords)\r\n        number_of_matches = len(list_of_matches)\r\n\r\n    if(number_of_matches == 1): return URLs[list_of_matches[0]]\r\n\r\n    if(number_of_matches >  1):\r\n        return_URLs = '\\n\\n'.join(URLs[item] for item in list_of_matches)\r\n        error_message = \"=== ERROR: ===\\n\\n Script matched with {} possible URLs:\\n\\n{}\".format(number_of_matches, return_URLs)\r\n        addToDebug(error_message)\r\n    else:\r\n        license_page_compilation = \"https://spaces.telenav.com:8443/display/qa/Content+Licensing+Compliance+Dashboard\"\r\n        error_message =  \"=== ERROR: ===\\n\\n Script could not find a matching URL!\\n\\nMaybe the license page hasn't been posted yet on {} ??\".format(license_page_compilation)\r\n        addToDebug(error_message)\r\n\r\n    return False\r\n\r\n# Returns a list of all license page URLS the script could find that matches the keywords.\r\ndef findLicensePage(URLs, keywords):\r\n    list_of_matches = [] # In normal conditions there should be just one URL in this array.\r\n    addToDebug(\"Searching main page for {}\".format(keywords.values()))\r\n\r\n    for url_title in URLs.keys():\r\n        if (checkAllKeywordsInString(keywords, url_title)):\r\n            list_of_matches.append(url_title)\r\n    return list_of_matches\r\n\r\n# Returns true if all keywords match in the URL\r\ndef checkAllKeywordsInString(keywords, url_title):\r\n    for keyword in keywords.values():\r\n        regex = '{} '.format(keyword)\r\n        if not re.search(regex, url_title): return False\r\n    return True\r\n\r\n# Jenkins will display HTML_report_Log.txt that is generated from here.\r\ndef generateLog(Dev_Test_Report_Log, License_Page, Prod, Region, Quarter, Date):\r\n    HTML_URL = \"http://tactaws.telenav.com/report/License/{}_{}_{}_{}/SearchLicenseReport.html\".format(Prod, Region, Quarter, Date)\r\n    License_Page = \"License Page modified: {}\".format(License_Page)\r\n    HTML_URL = \"HTML report URL: {}\".format(HTML_URL)\r\n    dev_report = \"Dev test report URL used: {}\".format(Dev_Test_Report_Log)\r\n\r\n    print(\"Calling scripts using these parameters:\")\r\n    print(dev_report)\r\n    print(License_Page)\r\n    print(HTML_URL)\r\n\r\n    addToDebug(\"\\n\\n\" + License_Page + \"\\n\\n\" + HTML_URL)\r\n\r\nif __name__ == '__main__':\r\n\r\n    with open(\"Search_HTML_report_Log.txt\", 'w') as f:\r\n        f.write(\"=== Search License check event log ===\\n\\n\")\r\n\r\n    args = parse_args()\r\n    Dev_Test_Report = args.Dev_Test_Report.strip()\r\n    Prod = Dev_Test_Report.split('/')[-1].split('_')[1]\r\n    Region = Dev_Test_Report.split('/')[-1].split('_')[2]\r\n    Quarter = Dev_Test_Report.split('/')[-1].split('_')[3]\r\n    Date = Dev_Test_Report.split('/')[-1].split('.')[0].split('_')[-1]\r\n\r\n    keywords = {\"region\": Region, \"quarter\": Quarter, \"product\": Prod}\r\n    License_Page = findURL(keywords)\r\n    # License_Page = \"https://spaces.telenav.com:8443/display/~jieli@telenavsoftware.com/Denali+Info3.4%2C+3.5%2C+3.6+South+America+19Q1+Content+Licensing+Compliance+Checklist\"\r\n\r\n    generateLog(Dev_Test_Report, License_Page, Prod, Region, Quarter, Date)\r\n    Dev_Test_Report_Log = Dev_Test_Report.split('/report/')[0] + \"/logs/CheckPoints_LicenceCheckPoint.log\"\r\n\r\n    callAllScripts(Dev_Test_Report_Log , License_Page, Prod, Region, Quarter, Date, Dev_Test_Report)\r\n\r\n'''\r\n\r\n--Dev_Test_Report X --License_Page X --Prod X --Region X\r\n\r\n--Dev_Test_Report http://hqa-ecreport-01.telenav.com/reports/Auto-Data-Automation/Dev_Data_Testing_MergeTraffic_4941778_201908060346 --License_Page http://tact.telenav.com/ec_latest_builds/DENALI_NGX/SA_HERE_19Q1/DENALI_NGX-SA_HERE_19Q1-TnMapDataAccess-20190805233711/DENALI_NGX-SA_HERE_19Q1-TnMapDataAccess-20190805233711_data.tar.gz --Prod MY18/MY19/MY20/MY21 --Region \"Called_from_EC_Commander\"\r\n\r\n--Dev_Test_Report http://hqa-ecreport-01.telenav.com/reports/Auto-Data-Automation/Dev_Data_Testing_MergeTraffic_4946390_201908080305 --License_Page http://tact.telenav.com/ec_latest_builds/DENALI_NGX/MEA_HERE_19Q1/DENALI_NGX-MEA_HERE_19Q1-TnMapDataAccess-20190808011137/DENALI_NGX-MEA_HERE_19Q1-TnMapDataAccess-20190808011137_data.tar.gz --Prod MY18/MY19/MY20/MY21 --Region \"Called_from_EC_Commander\"\r\n\r\n['MEA', '19Q1', 'MY21', 'RAINIER'] \r\n--Dev_Test_Report http://hqa-ecreport-01.telenav.com/reports/Auto-Data-Automation/Dev_Data_Testing_MergeTraffic_4945868_201908072205 --License_Page http://tact.telenav.com/ec_latest_builds/RAINIER_NGX/MEA_HERE_19Q1/RAINIER_NGX-MEA_HERE_19Q1-TnMapDataAccess-20190807184551/RAINIER_NGX-MEA_HERE_19Q1-TnMapDataAccess-20190807184551_data.tar.gz --Prod MY21 --Region \"Called_from_EC_Commander\"\r\n\r\n--Dev_Test_Report http://hqa-ecreport-01.telenav.com/reports/Auto-Data-Automation/Dev_Data_Testing_MergeTraffic_4945868_201908072205 --License_Page http://tact.telenav.com/ec_latest_builds/RAINIER_NGX/NA_HERE_19Q1/RAINIER_NGX-NA_HERE_19Q1-TnMapDataAccess-20190807184551/RAINIER_NGX-NA_HERE_19Q1-TnMapDataAccess-20190807184551_data.tar.gz --Prod MY21 --Region \"Called_from_EC_Commander\"\r\n\r\n--Dev_Test_Report http://hqa-ecreport-01.telenav.com/reports/Auto-Data-Automation/Dev_Data_Testing_MergeTraffic_4945990_201908072317 --License_Page http://tact.telenav.com/ec_latest_builds/GEN3_NGX/SA_HERE_19Q1/GEN3_NGX-SA_HERE_19Q1-TnMapDataAccess-20190807185851/GEN3_NGX-SA_HERE_19Q1-TnMapDataAccess-20190807185851_data.tar.gz --Prod MY18.5/MY19/MY20 --Region \"Called_from_EC_Commander\"\r\n\r\nTest EC  script parameters for ['SA', '19Q1', 'MY21', 'DENALI']\r\n--Dev_Test_Report http://hqa-ecreport-01.telenav.com/reports/Auto-Data-Automation/Dev_Data_Testing_MergeTraffic_4941778_201908060346 --License_Page http://tact.telenav.com/ec_latest_builds/DENALI_NGX/SA_HERE_19Q1/DENALI_NGX-SA_HERE_19Q1-TnMapDataAccess-20190805233711/DENALI_NGX-SA_HERE_19Q1-TnMapDataAccess-20190805233711_data.tar.gz --Prod MY18/MY19/MY20/MY21 --Region \"Called_from_EC_Commander\"\r\n\r\n['SA', '19Q1', 'MY20', 'GEN3']\r\n--Dev_Test_Report http://hqa-ecreport-01.telenav.com/reports/Auto-Data-Automation/Dev_Data_Testing_MergeTraffic_4945990_201908072317 --License_Page http://tact.telenav.com/ec_latest_builds/GEN3_NGX/SA_HERE_19Q1/GEN3_NGX-SA_HERE_19Q1-TnMapDataAccess-20190807185851/GEN3_NGX-SA_HERE_19Q1-TnMapDataAccess-20190807185851_data.tar.gz --Prod MY18.5/MY19/MY20 --Region \"Called_from_EC_Commander\"\r\n\r\n\r\n--Dev_Test_Report http://hqa-ecreport-01.telenav.com/reports/Auto-Data-Automation/Dev_Data_Testing_MergeTraffic_4990264_201908280249 \r\n--License_Page https://spaces.telenav.com:8443/pages/viewpage.action?spaceKey=qa&title=Denali+Info3.2%2C+3.4%2C+3.5%2C+3.6+ME+19Q2+Content+Licensing+Compliance+Checklist\r\n--Prod denali \r\n--Region MEA\r\n\r\n'''\r\n","sub_path":"trunk/RunSearchLicenseCheck.py","file_name":"RunSearchLicenseCheck.py","file_ext":"py","file_size_in_byte":11643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"393520926","text":"from django.shortcuts import render,redirect\nfrom django.core.exceptions import ValidationError\nfrom lists.models import Item,List\nfrom lists.forms import ItemForm,ExistingListItemForm\nfrom django.contrib.auth import get_user_model\n\nUser=get_user_model()\n# Create your views here.\ndef home_page(request):\n    return render(request,'home.html',{'form':ItemForm()})\ndef view_lists(request,list_id):\n    list_=List.objects.get(id=list_id)\n    form=ExistingListItemForm(for_list=list_)\n    if request.method == 'POST':\n        form=ExistingListItemForm(for_list=list_,data=request.POST)\n        if form.is_valid():\n            form.save()\n            return redirect(list_)\n    if request.user.is_authenticated:\n        return render(request, 'list.html', {'list': list_,'form':form})\n    return render(request,'my_lists.html',{'error_message':\"You can't get item with not log in\",'form':form})\ndef new_lists(request):\n    form=ItemForm(data=request.POST)\n    if form.is_valid():\n        list_=List()\n        if request.user.is_authenticated:\n            list_.owner=request.user\n        list_.save()\n        form.save(for_list=list_)\n        return redirect(list_)\n    else:\n        return render(request,'home.html',{\"form\":form})\ndef my_lists(request,email):\n    if request.user.is_authenticated:\n        owner=User.objects.get(email=email)\n        return render(request,'my_lists.html',{'owner':owner})\n    return render(request,'my_lists.html',{'error_message':f'{email} not logged in'})\n","sub_path":"lists/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"343781574","text":"#!/usr/bin/env python\nfrom setuptools import setup, find_packages\nfrom os.path import join, dirname\n\nhere = dirname(__file__)\n\nsetup(name='satoshi',\n      version='0.1.1',\n      description='Manipulate satoshi-related prices in Python 3, simple and sweet.',\n      long_description=open(join(here, 'README.md')).read(),\n      license='MIT',\n      long_description_content_type=\"text/markdown\",\n      author='canokaue',\n      author_email='kaue.cano@quan.digital',\n      url='https://github.com/quan-digital/satoshi/',\n      download_url = 'https://github.com/quan-digital/satoshi/dist/satoshi-0.1.1.tar.gz',\n      install_requires=[\n        'requests==2.23.0'\n      ],\n      packages=find_packages(),\n      keywords = ['satoshi', 'satoshi-nakamoto', 'bitcoin', 'crypto-api', 'bitcoin-price', 'bitcoin-api', \n      'satoshi-price', 'digital-currency', 'fiat'],\n      classifiers=[\n        'Development Status :: 5 - Production/Stable',\n        'Intended Audience :: Developers',\n        'Intended Audience :: Financial and Insurance Industry',\n        'Topic :: Software Development',\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.4',\n        'Programming Language :: Python :: 3.5',\n        'Programming Language :: Python :: 3.6',\n        'Programming Language :: Python :: 3.7',\n        'Operating System :: OS Independent',\n      ],\n      )","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"361345479","text":"#!/usr/bin/env python3\n\nimport numpy as np\nimport pandas as pd\nimport argparse, os, pathlib\nfrom functools import partial\nfrom itertools import product\nfrom utils.stability_utils import calc_rank\nfrom utils.eval_utils import calculate_kendall_tau_distance_quick,\\\n                             subgroup_kt,\\\n                             change_in_cond_exp_rank,\\\n                             cond_exp_rank,\\\n                             compute_rKL,\\\n                             change_in_rKL,\\\n                             compute_igf_ratio,\\\n                             num_retained_at_top_k,\\\n                             change_in_percent_at_top_k,\\\n                             percent_change_in_percent_at_top_k,\\\n                             ratio_of_percent_at_top_k,\\\n                             prob_lower,\\\n                             prob_lower_group,\\\n                             prob_lower_group_ratio\n                             \n\n\ndef calc_expected_distances(args):\n\n    '''\n    Can only handle 1 intervention (e.g. A<-bf)\n    IGF-Ratio is working on rank not Y -- needs to be fixed\n    '''\n\n    # Highest seed not yet used in data gen\n    seed = args.s_samples*((args.n_runs+1)*4+3)+args.initial_seed\n\n    # Initialize arrays to hold distances\n    noise_dists = np.zeros([len(args.metrics), args.s_samples, args.n_runs])\n    counter_dists_xres = np.zeros([args.s_samples, len(args.metrics)])\n    counter_dists_nonres = np.zeros([args.s_samples, len(args.metrics)])\n\n    # List of all intersectional groups\n    all_groups = ['wm', 'bm', 'am', 'wf', 'bf', 'af']\n                \n    # Loop through s_samples\n    for s in range(1, args.s_samples+1):\n\n        # Read in counterfactual data\n        counter = pd.read_pickle(args.base_repo_dir /'out'/\\\n                                'counterfactual_data'/args.output_dir/\n                                'counter_samp_{}.pkl'.format(s), compression='gzip')\n        \n        # Get list of counterfactual Y columns\n        # depends on 2-letter group (e.g. \"am\" or \"bf\")\n        counter_y_cols = [x for x in counter.columns if (('cf_y_' in x)&(x[-2:]==args.intervention_group))]\n\n        assert len(counter_y_cols)>0, \\\n            'Found no counterfactual data for intervention group {}'.format(args.intervention_group)\n        \n        # Calculate ranks for each counterfactual Y\n        for y in counter_y_cols:\n            counter['rank_'+y[5:]] = calc_rank(seed=seed, y=counter[y])\n            seed += 1\n        \n        # Read in noise distribution of ranks\n        noise = pd.read_pickle(args.base_repo_dir /'out'/\\\n                                'synthetic_data'/args.output_dir/\\\n                                'noise_rankings'/'samp_{}.pkl'.format(s), \n                                compression='gzip')\n        \n        # Get original rank\n        orig_rank = noise['rank']\n\n        # Set group column based on which_group\n        if args.which_group in all_groups:\n            # Intersectional\n            group_col = counter['original_a']\n        elif (args.which_group=='f') or (args.which_group=='m'):\n            # Sex column\n            group_col = counter['original_a'].map(lambda x: x[1])\n        elif ((args.which_group=='w') or (args.which_group=='b')) or (args.which_group=='a'):\n            # Race column\n            group_col = counter['original_a'].map(lambda x: x[0])\n        else:\n            # Throw error\n            assert 1==2, 'Invalid which_group value.'\n\n        # For \"change\" metrics, calculate original to measure against\n        if ('change_in_cond_exp_rank' in args.metrics) and (args.which_group in group_col.unique()):\n            orig_cond_exp_rank = cond_exp_rank(rank=orig_rank, groups=group_col, \n                                                    which_group=args.which_group, normalize=True)\n        else:\n            orig_cond_exp_rank = np.nan\n\n                            \n        if 'change_in_rkl' in args.metrics:\n            orig_rkl = compute_rKL(rank=orig_rank, groups=group_col)\n        else:\n            orig_rkl = np.nan\n\n        # Create list of partial functions for metrics\n        metrics_dict = {'kendalls_tau': partial(calculate_kendall_tau_distance_quick,\n                                                rank1=orig_rank), \n                        'subgroup_kt': partial(subgroup_kt, \n                                                rank1=orig_rank, \n                                                groups=group_col, \n                                                which_group=args.which_group),\n                        'change_in_cond_exp_rank': partial(change_in_cond_exp_rank, \n                                                            exp1 = orig_cond_exp_rank,\n                                                            groups=group_col, \n                                                            which_group=args.which_group,\n                                                            normalize=True),\n                        'change_in_rkl': partial(change_in_rKL, \n                                                    rKL1=orig_rkl, \n                                                    groups=group_col),\n                        'igf_ratio': partial(compute_igf_ratio,\n                                                groups=group_col,\n                                                which_group=args.which_group,\n                                                k=args.k),\n                        'igf_ratio_full': partial(compute_igf_ratio,\n                                                    k=args.k),\n                        'num_retained_at_top_k': partial(num_retained_at_top_k, \n                                                            rank1=orig_rank, \n                                                            k=args.k), \n                        'change_in_percent_at_top_k': \\\n                            partial(change_in_percent_at_top_k, \n                                        rank1=orig_rank, \n                                        k=args.k, \n                                        groups=group_col, \n                                        which_group=args.which_group),\n                        'percent_change_in_percent_at_top_k': \\\n                            partial(percent_change_in_percent_at_top_k, \n                                        rank1=orig_rank, \n                                        k=args.k, \n                                        groups=group_col, \n                                        which_group=args.which_group),\n                        'ratio_of_percent_at_top_k': \\\n                            partial(ratio_of_percent_at_top_k, \n                                        rank1=orig_rank, \n                                        k=args.k, \n                                        groups=group_col, \n                                        which_group=args.which_group),\n                        'prob_lower': partial(prob_lower, \n                                                rank1=orig_rank),\n                        'prob_lower_group': partial(prob_lower_group, \n                                                        rank1=orig_rank, \n                                                        groups=group_col,\n                                                        which_group=args.which_group),\n                        'prob_lower_group_ratio': partial(prob_lower_group_ratio,  \n                                                            rank1=orig_rank, \n                                                            groups=group_col, \n                                                            groupa=args.which_group, \n                                                            groupb=args.other_group)\n                        }\n        \n        # List of partial functions of metrics\n        metrics = [metrics_dict[m] for m in args.metrics]\n\n        # List of metric names to automatically fill with NA\n        na_metrics = []\n\n        # Confirm which_group exists in sample\n        if args.which_group not in group_col.unique():\n\n            # List of metrics which depend on \"which_group\"\n            which_group_metrics = ['subgroup_kt', 'change_in_cond_exp_rank', 'igf_ratio', \n                               'change_in_percent_at_top_k', 'percent_change_in_percent_at_top_k', \n                               'ratio_of_percent_at_top_k', 'prob_lower', 'prob_lower_group', \n                               'prob_lower_group_ratio']\n                               \n            # List of metrics to automatically fill with NA\n            na_metrics = [metrics_dict[m] for m in args.metrics if m in which_group_metrics]\n\n        # Confirm other_group exists in sample\n        if args.other_group not in group_col.unique():\n\n            # List of metrics which depend on \"other_group\"\n            other_group_metrics = ['prob_lower_group_ratio']\n\n            # List of metrics to automatically fill with NA\n            na_metrics = na_metrics + [metrics_dict[m] for m in args.metrics if m in other_group_metrics]\n\n        # Get distances between original rank and each rank from noise distribution\n        # Averages will give expected noise distances for this sample\n        for n in range(1, args.n_runs+1):\n            for m, metric in enumerate(metrics):\n                if metric in na_metrics:\n                    # Auto-fill with NaN if necessary\n                    noise_dists[m][s-1][n-1] = np.nan\n                else:\n                    noise_dists[m][s-1][n-1] = metric(rank2=noise['rank_{}'.format(n)])\n\n        # Get distances between original rank and counterfactual ranks\n        for m, metric in enumerate(metrics):\n            if metric in na_metrics:\n                # Auto-fill with NaN if necessary\n                counter_dists_nonres[s-1][m] = np.nan\n                counter_dists_xres[s-1][m] = np.nan\n            else:\n                # Get distance between original rank and counterfactual Y with resolving X\n                counter_dists_nonres[s-1][m] = metric(rank2=counter['rank_nonres_'+args.intervention_group])\n                \n                # Get distance between original rank and counterfactual Y with non-resolving X\n                counter_dists_xres[s-1][m] = metric(rank2=counter['rank_xres_'+args.intervention_group])\n\n        print('Calculating {} distances for group {}: {} of {} samples complete'\\\n                .format(args.output_dir, args.which_group, s, args.s_samples))\n\n    '''\n    Get expected distances between original rank and rank from re-sampled noise\n    Expectations taken over n_runs of noise distribution\n    E[ distance (original rank, rank from re-sampled noise) ]\n    1 value per metric for each sample\n    '''\n    exp_noise_dist = np.mean(noise_dists, axis=2)\n    \n    '''\n    Get expected value of expected distances between original rank and rank from re-sampled noise\n    Iterated expectation: expectation first taken over n_runs of noise distribution, then over s_samples\n    E[ E[ distance(original rank, rank from re-sampled noise) ] ]\n    1 value per metric for entire experiment trial\n    '''\n    exp_exp_noise_dist = np.mean(exp_noise_dist, axis=1)\n\n    '''\n    Get expected distance between original rank and counterfactual Y with non-resolving X\n    For intervention A<-intervention_group\n    Expectation taken over s_samples\n    E[ distance(original rank, counterfactual rank with non-resolving X for A<-intervention_group) ]\n    1 value per metric for entire experiment trial\n    '''\n    exp_cf_dist_nonres = np.nanmean(counter_dists_nonres, axis=0)\n\n\n    '''\n    Get expected distance between original rank and counterfactual Y with resolving X\n    For intervention For intervention A<-intervention_group\n    Expectation taken over s_samples\n    E[ distance(original rank, counterfactual rank with resolving X for A<-intervention_group) ]\n    1 value per metric for entire experiment trial\n    '''\n    exp_cf_dist_xres = np.nanmean(counter_dists_xres, axis=0)\n\n    # Bundle expected distances into list of dictionaries\n    exp_dicts = []\n    for m, metric in enumerate(metrics):\n        exp_dict = {'exp_exp_noise':exp_exp_noise_dist[m],\n                    'exp_nonres': exp_cf_dist_nonres[m],\n                    'exp_xres': exp_cf_dist_xres[m]}\n        exp_dicts.append(exp_dict)\n\n    # Bundle expected noise distances into 1d list of dataframes\n    exp_noise_dfs = []\n    for m, metric in enumerate(metrics):\n        noise_df = pd.DataFrame(noise_dists[m, :, :], columns=['orig_noise'+str(i+1) for i in range(args.n_runs)])\n        exp_noise_dfs.append(noise_df)\n    \n    # Bundle counterfactual distances into 1d list of dataframes\n    cf_dist_dfs = []\n    for m, metric in enumerate(metrics):\n        cf_dist_df = pd.concat([ pd.DataFrame(exp_noise_dist[m], columns=['exp_orig_noise']),\n                                pd.DataFrame(counter_dists_nonres[:,m], columns=['orig_nonres_'+args.intervention_group]), \n                                pd.DataFrame(counter_dists_xres[:,m], columns=['orig_xres_'+args.intervention_group]),\n                            ], axis=1)\n        cf_dist_dfs.append(cf_dist_df)\n\n    return (exp_noise_dfs, cf_dist_dfs, exp_dicts)\n\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser(description=\"Calculate Distances\")\n\n    # Required arguments\n    parser.add_argument(\"--s_samples\", type=int, help='Number of samples to draw for sampling distribution')\n    parser.add_argument(\"--n_runs\", type=int, help='Number of runs for error distribution')\n\n    # Optional arguments\n    parser.add_argument(\"--output_dir\", type=str, default='default')\n    parser.add_argument(\"--output_analysis_subdir\", type=str, default=None)\n    parser.add_argument(\"--sd_list\", nargs='+', type=float, default=None)\n    parser.add_argument(\"--corr_list\", nargs='+', type=float, default=None)\n    parser.add_argument(\"--initial_seed\", type=int, default=0, help='Initial seed for race sample passed to gen_stability_data.py')\n\n    parser.add_argument(\"--all_interventions\", action='store_true', \n                        help='Boolean flag indicating whether all intervention counterfactuals will be evaluated.\\\n                              If not set, only intervention to minority group will be evaluated.')\n\n    parser.add_argument(\"--all_metrics\", action='store_true', \n                            help='Boolean flag indicating whether to evaluate\\\n                                  all distance metrics.\\\n                                  If not set, only those Kendall\\'s Tau and \\\n                                  any others specifically set will be evaluated.')\n\n    parser.add_argument(\"--subgroup_kt\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric subgroup_kt.\\\n                              If not set, subgroup_kt will not be evaluated.')\n\n    parser.add_argument(\"--change_in_cond_exp_rank\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric change_in_cond_exp_rank.\\\n                              If not set, change_in_cond_exp_rank will not be evaluated.')\n\n    parser.add_argument(\"--num_retained_at_top_k\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric num_retained_at_top_k.\\\n                              If not set, num_retained_at_top_k will not be evaluated.')\n    \n    parser.add_argument(\"--change_in_percent_at_top_k\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric percent_change_in_percent_at_top_k.\\\n                              If not set, change_in_percent_at_top_k will not be evaluated.')\n\n    parser.add_argument(\"--percent_change_in_percent_at_top_k\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric change_in_percent_at_top_k.\\\n                              If not set, percent_change_in_percent_at_top_k will not be evaluated.')\n\n    parser.add_argument(\"--ratio_of_percent_at_top_k\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric ratio_of_percent_at_top_k.\\\n                              If not set, ratio_of_percent_at_top_k will not be evaluated.')\n\n    parser.add_argument(\"--prob_lower\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric prob_lower.\\\n                              If not set, prob_lower will not be evaluated.')\n\n    parser.add_argument(\"--prob_lower_group\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric prob_lower_group.\\\n                              If not set, prob_lower_group will not be evaluated.')\n\n    parser.add_argument(\"--prob_lower_group_ratio\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric prob_lower_group_ratio.\\\n                              If not set, prob_lower_group_ratio will not be evaluated.')\n\n    parser.add_argument(\"--change_in_rkl\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric change in rKL.\\\n                              If not set, change in rKL will not be evaluated.')\n\n    parser.add_argument(\"--igf_ratio\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric IGF-Ratio.\\\n                              If not set, IGF-Ratio will not be evaluated.')\n\n    parser.add_argument(\"--igf_ratio_full\", action='store_true', \n                        help='Boolean flag indicating whether to additionally evaluate\\\n                              the distance metric IGF-Ratio, where the group is the full dataset.\\\n                              If not set, full IGF-Ratio will not be evaluated.')\n\n    parser.add_argument(\"--no_kendalls_tau\", action='store_true', \n                        help='Boolean flag indicating whether to exclude Kendall\\'s Tau from evaluation.\\\n                            If not set, Kendall\\'s Tau will be evaluated.')\n    \n    parser.add_argument(\"--intervention_group\", type=str, default='bf',\n                        help='which group was used for counterfactual intervention A<-a')\n\n    parser.add_argument(\"--which_group\", type=str, default='bf',\n                        help='which group to use for group-specific metrics')\n\n    parser.add_argument(\"--other_group\", type=str, default='wm',\n                        help='which group to use as comparison group in group-specific metrics')\n    \n    parser.add_argument(\"--k\", type=int, default=0,\n                        help='Integer to use for \"top k\" evaluations.')            \n\n    # Parse arguments\n    args = parser.parse_args()\n\n    if args.prob_lower_group_ratio:\n        assert args.which_group!=args.other_group, \\\n            'Must provide 2 different groups for comparison in prob_lower_group_ratio.'\n\n    if args.k==0:\n        args.k=None\n\n    # Create list of names of distance functions\n    args.metrics = []\n    if not args.no_kendalls_tau:\n        args.metrics.append('kendalls_tau')\n    if args.subgroup_kt| args.all_metrics:\n        args.metrics.append('subgroup_kt')\n    if args.change_in_cond_exp_rank| args.all_metrics:\n        args.metrics.append('change_in_cond_exp_rank')\n    if args.change_in_rkl | args.all_metrics:\n        args.metrics.append('change_in_rkl')\n    if args.igf_ratio | args.all_metrics:\n        args.metrics.append('igf_ratio')\n    if args.igf_ratio_full | args.all_metrics:\n        args.metrics.append('igf_ratio_full')\n    if args.num_retained_at_top_k | args.all_metrics:\n        args.metrics.append('num_retained_at_top_k')\n    if args.change_in_percent_at_top_k | args.all_metrics:\n        args.metrics.append('change_in_percent_at_top_k')\n    if args.percent_change_in_percent_at_top_k | args.all_metrics:\n        args.metrics.append('percent_change_in_percent_at_top_k')\n    if args.ratio_of_percent_at_top_k | args.all_metrics:\n        args.metrics.append('ratio_of_percent_at_top_k')\n    if args.prob_lower | args.all_metrics:\n        args.metrics.append('prob_lower')\n    if args.prob_lower_group | args.all_metrics:\n        args.metrics.append('prob_lower_group')\n    if args.prob_lower_group_ratio | args.all_metrics:\n        args.metrics.append('prob_lower_group_ratio')\n    \n\n    # Save repo root directory\n    args.base_repo_dir = pathlib.Path(os.path.realpath(__file__)).parents[2]\n\n    # Save high-level output directory\n    main_output_dir = args.output_dir\n\n    # Directory to save metrics to\n    save_dir = main_output_dir\n    if args.output_analysis_subdir:\n        save_dir = save_dir+'/'+args.output_analysis_subdir\n\n    # Initialize list of lists to hold expected distances\n    exp_dicts_by_metric = [[] for m in range(len(args.metrics))]\n\n    # If specific corr_list and sd_list provided\n    # Loop through combinations in nested loop\n    if args.corr_list and args.sd_list:\n        for corr in args.corr_list:\n            for sd in args.sd_list:\n                args.output_dir = main_output_dir +'/corr_{}/sd_{}'.format(corr,sd)\n                exp_noise_dfs, cf_dist_dfs, exp_dicts = calc_expected_distances(args)\n\n                filename = 'corr_{}_sd_{}.pkl'.format(corr, sd)\n                for m, exp_noise_df in enumerate(exp_noise_dfs):\n                    exp_noise_df.to_pickle(args.base_repo_dir/'out'/'distance_metrics'/\\\n                                            save_dir/args.metrics[m]/'noise'/\\\n                                            filename, \n                                            compression='gzip')\n\n                for m, cf_dist_df in enumerate(cf_dist_dfs):\n                    cf_dist_df.to_pickle(args.base_repo_dir/'out'/'distance_metrics'/\\\n                                            save_dir/args.metrics[m]/'counterfactuals'/\\\n                                            filename, \n                                            compression='gzip')\n\n                for m, exp_dict in enumerate(exp_dicts):\n                    exp_dicts_by_metric[m].append(exp_dict)\n\n                print('Distance calculation for {} complete\\n'.format(args.output_dir))\n\n        for m, exp_dict in enumerate(exp_dicts):\n            # Create expected distance df with multi-index of subdirectories\n            exp_df = pd.DataFrame(list(np.array(exp_dicts_by_metric[m])), \n                                index=pd.MultiIndex.from_tuples(\\\n                                        list(product(['corr_{}'.format(x) for x in args.corr_list], \n                                                     ['sd_{}'.format(x) for x in args.sd_list]))))\n\n            # Save expected distance df to pickle\n            exp_df.to_pickle(args.base_repo_dir/'out'/'distance_metrics'/\\\n                                    save_dir/args.metrics[m]/'expected.pkl', \n                                compression='gzip')\n\n    else:\n        # Initialize list of lists of experiment subdirectories\n        sub_dirs = []\n\n        # Define path to traverse\n        output_path = args.base_repo_dir /'out'/'counterfactual_data'/ main_output_dir  \n\n        # Walk through experiment directory and subdirectories\n        for root, dirs, files in os.walk(output_path):\n\n            # Exclude hidden files\n            files = [f for f in files if not f[0] == '.']\n\n            # If directory contains files\n            if len(files)>0:\n\n                # Isolate experiment subdirectory\n                args.output_dir = main_output_dir+ root.split(main_output_dir)[1]\n\n                # Save subdirectory to list of lists\n                sub_dir = args.output_dir.split('/')[1:]\n                sub_dirs.append(sub_dir)\n\n                # Calculate distances from files in this subdir\n                exp_noise_dfs, cf_dist_dfs, exp_dicts = calc_expected_distances(args)\n\n                filename = '_'.join(sub_dir)+'.pkl'\n                for m, exp_noise_df in enumerate(exp_noise_dfs):\n                    exp_noise_df.to_pickle(args.base_repo_dir/'out'/'distance_metrics'/\\\n                                            save_dir/args.metrics[m]/'noise'/\\\n                                            filename, \n                                            compression='gzip')\n\n                for m, cf_dist_df in enumerate(cf_dist_dfs):\n                    cf_dist_df.to_pickle(args.base_repo_dir/'out'/'distance_metrics'/\\\n                                            save_dir/args.metrics[m]/'counterfactuals'/\\\n                                            filename, \n                                            compression='gzip')\n\n                for m, exp_dict in enumerate(exp_dicts):\n                    exp_dicts_by_metric[m].append(exp_dict)\n\n                print('Distance calculation for {} complete\\n'.format(args.output_dir))\n                \n        # Get list of indices which lex-sort list of lists of subdirectories\n        dirs_sort = np.lexsort(tuple([np.array(sub_dirs).T[i] \\\n                                    for i in np.flip(np.arange(np.array(sub_dirs).shape[1]))]))\n\n        for m, exp_dict in enumerate(exp_dicts):\n            # Create expected distance df with multi-index of lex-sorted subdirectories\n            exp_df = pd.DataFrame(list(np.array(exp_dicts_by_metric[m])[dirs_sort]), \n                                index=pd.MultiIndex.from_tuples(sorted(sub_dirs)))\n\n            # Save expected distance df to pickle\n            exp_df.to_pickle(args.base_repo_dir/'out'/'distance_metrics'/\\\n                                    save_dir/args.metrics[m]/'expected.pkl', \n                                compression='gzip')\n","sub_path":"src/pyscripts/calc_stability_distances.py","file_name":"calc_stability_distances.py","file_ext":"py","file_size_in_byte":26249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"466018183","text":"import boto3\nimport json\nimport os\nimport sharedutils\n\ndef lambda_handler(event, context):\n\n    state_machine_arn = os.getenv('StateMachineArn')\n\n    for record in event['Records']:\n\n        task_json = record['body']\n        taskResponse = \"State machine: \" + \\\n            str(sharedutils.execute_step_functions(state_machine_arn, task_json))        \n        print(taskResponse)\n\n    return {\n        \"statusCode\": 200,\n        \"body\": json.dumps({\n            \"message\": taskResponse,\n        }),\n    }","sub_path":"fixitycheck/steps.py","file_name":"steps.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"606786424","text":"import numpy as np\nimport pandas as pd \n\nfrom config import config\nfrom processing.data_management import load_pipeline\n\npipeline_file_name = 'lasso_regression_v1.pkl'\n\n_price_pipe = load_pipeline(pipeline_file_name)\n\ndef make_prediction(input_data):\n    data = pd.DataFrame(input_data)\n    prediction = _price_pipe.predict(data[config.KEEP])\n    output = np.exp(prediction)\n\n    results = {\n        'prediction': output,\n        'model_name': pipeline_file_name,\n        'version':'version1'\n    }\n\n    return results\n\n","sub_path":"07_CleanUp/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"551369655","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport math, random\nimport time\nimport gym\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.autograd as autograd \nimport torch.nn.functional as F\n\nfrom IPython.display import clear_output\nimport matplotlib.pyplot as plt\nfrom spinup.utils.logx import EpochLogger\n\nstart_time = time.time()\n\"\"\"\nthis version we use anneal eta instead of hard change \n\"\"\"\n\nclass StagePriorityReplayBuffer:\n    \"\"\"\n    weighted buffer that basically\n    gives more probability of sampling for more recent data\n    \"\"\"\n    def __init__(self, obs_dim, act_dim, size):\n        \"\"\"\n        :param obs_dim: size of observation\n        :param act_dim: size of the action\n        :param size: size of the buffer\n        \"\"\"\n        ## init buffers as numpy arrays\n        self.obs1_buf = np.zeros([size]+list(obs_dim), dtype=np.float32)\n        self.obs2_buf = np.zeros([size]+list(obs_dim), dtype=np.float32)\n        self.acts_buf = np.zeros([size], dtype=np.float32)\n        self.rews_buf = np.zeros(size, dtype=np.float32)\n        self.done_buf = np.zeros(size, dtype=np.float32)\n        self.ptr, self.size, self.max_size = 0, 0, size\n\n    def store(self, obs, act, rew, next_obs, done):\n        \"\"\"\n        data will get stored in the pointer's location\n        data should NOT be in tensor format.\n        it's easier if you get data from environment\n        then just store them with the given format\n        \"\"\"\n        self.obs1_buf[self.ptr] = obs\n        self.obs2_buf[self.ptr] = next_obs\n        self.acts_buf[self.ptr] = act\n        self.rews_buf[self.ptr] = rew\n        self.done_buf[self.ptr] = done\n        ## move the pointer to store in next location in buffer\n        self.ptr = (self.ptr+1) % self.max_size\n        ## keep track of the current buffer size\n        self.size = min(self.size+1, self.max_size)\n\n    def __len__(self):\n        return self.size\n\n    def sample_uniform_batch(self, batch_size=32):\n        ## sample with replacement from buffer\n        idxs = np.random.randint(0, self.size, size=batch_size)\n        return dict(obs1=self.obs1_buf[idxs],\n                    obs2=self.obs2_buf[idxs],\n                    acts=self.acts_buf[idxs],\n                    rews=self.rews_buf[idxs],\n                    done=self.done_buf[idxs])\n\n    def sample_priority_only_batch(self, force_priority_count, batch_size=32):\n        ## EVERYTHING IS PRIORITY ONLY\n        ## force priority count, when given, will override this replay buffer's default priority count\n        recent_data_size = batch_size\n        ## max index used for generate data indecies in a batch\n        max_index = min(int(force_priority_count),self.size)\n        ## relative indecies for selected recent data\n        recent_relative_idxs = -np.random.randint(0, max_index, size=recent_data_size)\n        recent_idxs = (self.ptr-1 + recent_relative_idxs)%self.size\n        return dict(obs1=self.obs1_buf[recent_idxs],\n                    obs2=self.obs2_buf[recent_idxs],\n                    acts=self.acts_buf[recent_idxs],\n                    rews=self.rews_buf[recent_idxs],\n                    done=self.done_buf[recent_idxs])\n\ndef compute_current_eta(eta_initial, eta_final, current_timestep, total_timestep):\n    ## linearly anneal eta as trained on more timesteps\n    current_eta = eta_initial + (eta_final - eta_initial) * current_timestep/total_timestep\n    return current_eta\n\ndef get_ck_list_exp(replay_size ,num_updates, eta_current, update_order):\n    ck_list = np.zeros(num_updates, dtype=int)\n    for k in range(num_updates):  ## compute ck for each k, using formula for old data first update\n        ck_list[k] = int(replay_size * eta_current ** (k * 1000 / num_updates))\n    if update_order == 'new_first':\n        ck_list = np.flip(ck_list, axis=0)\n    elif update_order == 'random':\n        ck_list = np.random.permutation(ck_list)\n    else:  ## 'old_first'\n        pass\n    return ck_list\n\ndef get_ck_list_linear(replay_size ,num_updates, m, update_order):\n    ck_list = np.zeros(num_updates, dtype=int)\n    for k in range(num_updates):  ## compute ck for each k, using formula for old data first update\n        ck_list[k] = int(replay_size - m * k * (1000 / num_updates))\n    if update_order == 'new_first':\n        ck_list = np.flip(ck_list)\n    elif update_order == 'random':\n        ck_list = np.random.permutation(ck_list)\n    else:  ## 'old_first'\n        pass\n    return ck_list\n\n\n\nclass CnnDQN(nn.Module):\n    def __init__(self, input_shape, num_actions):\n        super(CnnDQN, self).__init__()\n\n        self.input_shape = input_shape\n        self.num_actions = num_actions\n\n        self.features = nn.Sequential(\n            nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4),\n            nn.ReLU(),\n            nn.Conv2d(32, 64, kernel_size=4, stride=2),\n            nn.ReLU(),\n            nn.Conv2d(64, 64, kernel_size=3, stride=1),\n            nn.ReLU()\n        )\n\n        self.fc = nn.Sequential(\n            nn.Linear(self.feature_size(), 512),\n            nn.ReLU(),\n            nn.Linear(512, self.num_actions)\n        )\n\n    def forward(self, x):\n        x = self.features(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n\n    def feature_size(self):\n        return self.features(autograd.Variable(torch.zeros(1, *self.input_shape))).view(1, -1).size(1)\n\n    def act(self, state, epsilon, env):\n        if random.random() > epsilon:\n            with torch.no_grad():\n                state = Variable(torch.FloatTensor(np.float32(state)).unsqueeze(0))\n                q_value = self.forward(state)\n            action = q_value.max(1)[1].data[0]\n        else:\n            action = random.randrange(env.action_space.n)\n        return action\n\n\n# <h3>Synchronize current policy net and target net</h3>\n\ndef update_target(current_model, target_model):\n    target_model.load_state_dict(current_model.state_dict())\n\ndef test_agent(n, current_model, test_env):\n    ep_return_list = np.zeros(n)\n    for j in range(n):\n        o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0\n        while not d:\n            # Take deterministic actions at test time\n            a = current_model.act(state=o, epsilon=0, env=test_env)\n            o, r, d, _ = test_env.step(a)\n            ep_ret += r\n            ep_len += 1\n        ep_return_list[j] = ep_ret\n        logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)\n\n\n\n\n### Important: the whole trainning model\n\ndef Double_dqn(env_id = \"PongNoFrameskip-v4\", seed = 0, buffer_size = int(1e6), replay_initial = 10000,\n               num_frames = int(5e7), batch_size = 32, lr = 1e-4, gamma = 0.99, tau=10000, test_feq=int(1e6),\n               logger_kwargs=dict(),update_multiplier=1, hidden_activation_setting='relu',\n               use_linear_priority=False, update_order='old_first',\n               eta_0=0.994, m=900, c_min=5000, eta_final=1.0, no_eta_anneal=False):\n\n## update_order can be old_first, new_first, random\n## old first will first use data from all of buffer and then sample from a shrinking range of recent data\n## new first is update on recent data first then gradually become uniform sampling.\n\n    if no_eta_anneal:\n        eta_final = eta_0\n\n    \"\"\"check device\"\"\"\n    USE_CUDA = torch.cuda.is_available()\n    global Variable\n    Variable = lambda *args, **kwargs: autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else autograd.Variable(\n        *args, **kwargs)\n    \"\"\"set up logger\"\"\"\n    global logger\n    logger = EpochLogger(**logger_kwargs)\n    logger.save_config(locals())\n    \"\"\"wrap up atari environment\"\"\"\n    from common.wrappers import NoopResetEnv, wrap_deepmind, wrap_pytorch\n    env, test_env = gym.make(env_id), gym.make(env_id)\n    env, test_env = NoopResetEnv(env, noop_max=30), NoopResetEnv(test_env, noop_max=30)\n    env, test_env = wrap_deepmind(env, frame_stack=True), wrap_deepmind(test_env, frame_stack=True)\n    env, test_env = wrap_pytorch(env), wrap_pytorch(test_env)\n    ## seed torch and numpy\n    torch.manual_seed(seed)\n    np.random.seed(seed)\n    ## seed environment along with env action space so that everything about env is seeded\n    env.seed(seed)\n    env.action_space.np_random.seed(seed)\n    test_env.seed(seed)\n    test_env.action_space.np_random.seed(seed)\n\n    # Training setup\n    obs_dim = env.observation_space.shape\n    act_dim = env.action_space.n\n    print('Processed observation space', obs_dim, 'action space (%s)'%act_dim)\n    # Experience buffer with weighted/priority sampling\n    replay_buffer = StagePriorityReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=buffer_size)\n\n    current_model = CnnDQN(obs_dim, act_dim)\n    target_model = CnnDQN(obs_dim, act_dim)\n    if USE_CUDA:\n        current_model = current_model.cuda()\n        target_model = target_model.cuda()\n\n    optimizer = optim.Adam(current_model.parameters(), lr=lr)\n    mse_criterion = nn.MSELoss()\n\n    update_target(current_model, target_model)\n\n    # <h2>Epsilon greedy exploration</h2>\n\n    epsilon_start = 1.0\n    epsilon_final = 0.01\n    epsilon_decay = 30000\n\n    epsilon_by_frame = lambda frame_idx: epsilon_final + (epsilon_start - epsilon_final) * math.exp(\n        -1. * frame_idx / epsilon_decay)\n\n    epoch, episode_reward, ep_len = 0, 0, 0\n\n    state = env.reset()\n    for frame_idx in range(1, num_frames + 1):\n        # epsilon-greedy explore\n        epsilon = epsilon_by_frame(frame_idx)\n        action = current_model.act(state, epsilon, env)\n        next_state, reward, done, _ = env.step(action)\n        replay_buffer.store(state, action, reward, next_state, done)\n\n        state = next_state\n        episode_reward += reward\n        ep_len += 1\n\n        '''for ere we do updates together after each episode ends'''\n        if done and len(replay_buffer) > replay_initial:\n            ## first compute the current eta,\n            eta_current = compute_current_eta(eta_0, eta_final, frame_idx, num_frames)\n            num_updates = ep_len // 4  # ddqn(deepmind) update the network every 4 steps,\n                                       # num_updates should be aligned with this\n\n            ck_list = get_ck_list_exp(buffer_size, num_updates, eta_current, update_order)\n            for k in range(num_updates):\n                \"\"\"\n                first get priority count c_k, and we only uniformly sample from the most recent\n                c_k data points in the replay buffer\n                \"\"\"\n                c_k = ck_list[k]\n                if c_k < c_min:\n                    c_k = c_min\n                # get data from replay buffer\n                Batch  = replay_buffer.sample_priority_only_batch(c_k, batch_size)\n\n                S = Variable(torch.FloatTensor(Batch['obs1']))\n                S_prime = Variable(torch.FloatTensor(Batch['obs2']))\n                A = Variable(torch.LongTensor(Batch['acts']))\n                # unsqueeze is to make sure rewards and done tensors are of the shape nx1, instead of n\n                # to prevent problems later\n                R = Variable(torch.FloatTensor(Batch['rews']))\n                D = Variable(torch.FloatTensor(Batch['done']))\n\n                q_values = current_model(S)\n                next_q_values = current_model(S_prime)\n                next_q_state_values = target_model(S_prime)\n\n                q_value = q_values.gather(1, A.unsqueeze(1)).squeeze(1)\n                next_q_value = next_q_state_values.gather(1, torch.max(next_q_values, 1)[1].unsqueeze(1)).squeeze(1)\n                expected_q_value = R + gamma * next_q_value * (1 - D)\n\n                loss = mse_criterion(q_value, Variable(expected_q_value.data))\n\n                optimizer.zero_grad()\n                loss.backward()\n                optimizer.step()\n                # store diagnostic info to logger\n                logger.store(Loss=loss.cpu().item(),QVal=q_value.detach().cpu().numpy())\n\n        if done:\n            state = env.reset()\n            logger.store(EpRet=episode_reward, EpLen=ep_len)\n            episode_reward, ep_len = 0, 0\n\n        if frame_idx % tau == 0:\n            update_target(current_model, target_model)\n\n        if frame_idx % test_feq == 0 and len(replay_buffer) > replay_initial:\n            epoch += 1\n            print('epoch', epoch)\n            # Test the agent\n            test_agent(5, current_model, test_env)\n            # Log info about epoch\n            logger.log_tabular('Epoch', epoch)\n            logger.log_tabular('EpRet', with_min_and_max=True)\n            logger.log_tabular('TestEpRet', with_min_and_max=True)\n            logger.log_tabular('EpLen', average_only=True)\n            logger.log_tabular('TestEpLen', average_only=True)\n            logger.log_tabular('TotalEnvInteracts', frame_idx)\n            logger.log_tabular('QVal', with_min_and_max=True)\n            logger.log_tabular('Loss', average_only=True)\n            logger.log_tabular('Time', time.time() - start_time)\n            logger.dump_tabular()\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--env', type=str, default='PongNoFrameskip-v4')\n    parser.add_argument('--seed', '-s', type=int, default=0)\n    parser.add_argument('--t_feq', type=int, default=int(1e6))\n    parser.add_argument('--exp_name', type=str, default='ddqn_ere')\n    parser.add_argument('--data_dir', type=str, default='data/')\n    parser.add_argument('--steps', type=int, default=int(5e7))\n    args = parser.parse_args()\n\n    from spinup.utils.run_utils import setup_logger_kwargs\n    logger_kwargs = setup_logger_kwargs(args.exp_name, args.seed)\n\n    Double_dqn(env_id=args.env, seed=args.seed, buffer_size=int(1e6), replay_initial=10000, num_frames=args.steps,\n               batch_size=32, lr=0.00025, gamma=0.99, tau=10000, test_feq=args.t_feq, logger_kwargs=logger_kwargs)\n\n","sub_path":"algos/double_dqn_ere.py","file_name":"double_dqn_ere.py","file_ext":"py","file_size_in_byte":13805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"144647874","text":"import torch.nn as nn\r\nfrom torch.nn import Parameter\r\nimport torch.nn.functional as F\r\nimport torch\r\nimport math\r\nfrom torch.nn.modules.utils import _pair\r\nfrom modeling.gconv.make_gconv_indices import *\r\n\r\nmake_indices_functions = {(1, 4): make_c4_z2_indices,\r\n                          (4, 4): make_c4_p4_indices,\r\n                          (1, 8): make_d4_z2_indices,\r\n                          (8, 8): make_d4_p4m_indices}\r\n\r\n\r\ndef trans_filter(w, inds):\r\n    inds_reshape = inds.reshape((-1, inds.shape[-1])).astype(np.int64)\r\n    w_indexed = w[:, :, inds_reshape[:, 0].tolist(), inds_reshape[:, 1].tolist(), inds_reshape[:, 2].tolist()]\r\n    w_indexed = w_indexed.view(w_indexed.size()[0], w_indexed.size()[1],\r\n                                    inds.shape[0], inds.shape[1], inds.shape[2], inds.shape[3])\r\n    w_transformed = w_indexed.permute(0, 2, 1, 3, 4, 5)\r\n    return w_transformed.contiguous()\r\n\r\n\r\nclass SplitGConv2D(nn.Module):\r\n\r\n    def __init__(self, in_channels, out_channels, kernel_size, stride=1,\r\n                 dilation=1, padding=0, bias=True, input_stabilizer_size=1, output_stabilizer_size=4):\r\n        super(SplitGConv2D, self).__init__()\r\n        assert (input_stabilizer_size, output_stabilizer_size) in make_indices_functions.keys()\r\n        self.ksize = kernel_size\r\n\r\n        kernel_size = _pair(kernel_size)\r\n        stride = _pair(stride)\r\n        dilation= _pair(dilation)\r\n        padding = _pair(padding)\r\n        \r\n        self.in_channels = in_channels\r\n        self.out_channels = out_channels\r\n        self.kernel_size = kernel_size\r\n        self.stride = stride\r\n        self.dilation = dilation\r\n        self.padding = padding\r\n        self.input_stabilizer_size = input_stabilizer_size\r\n        self.output_stabilizer_size = output_stabilizer_size\r\n\r\n        self.weight = Parameter(torch.Tensor(\r\n            out_channels, in_channels, self.input_stabilizer_size, *kernel_size))\r\n        if bias:\r\n            self.bias = Parameter(torch.Tensor(out_channels))\r\n        else:\r\n            self.register_parameter('bias', None)\r\n        self.reset_parameters()\r\n\r\n        self.inds = self.make_transformation_indices()\r\n\r\n    def reset_parameters(self):\r\n        n = self.in_channels\r\n        for k in self.kernel_size:\r\n            n *= k\r\n        stdv = 1. / math.sqrt(n)\r\n        self.weight.data.uniform_(-stdv, stdv)\r\n        if self.bias is not None:\r\n            self.bias.data.uniform_(-stdv, stdv)\r\n\r\n    def make_transformation_indices(self):\r\n        return make_indices_functions[(self.input_stabilizer_size, self.output_stabilizer_size)](self.ksize)\r\n\r\n    def forward(self, input):\r\n        tw = trans_filter(self.weight, self.inds)\r\n        tw_shape = (self.out_channels * self.output_stabilizer_size,\r\n                    self.in_channels * self.input_stabilizer_size,\r\n                    self.ksize, self.ksize)\r\n        tw = tw.view(tw_shape)\r\n\r\n        input_shape = input.size()\r\n        input = input.view(input_shape[0], self.in_channels*self.input_stabilizer_size, input_shape[-2], input_shape[-1])\r\n\r\n        y = F.conv2d(input, weight=tw, bias=None, stride=self.stride, dilation=self.dilation,\r\n                        padding=self.padding)\r\n        batch_size, _, ny_out, nx_out = y.size()\r\n        y = y.view(batch_size, self.out_channels, self.output_stabilizer_size, ny_out, nx_out)\r\n\r\n        if self.bias is not None:\r\n            bias = self.bias.view(1, self.out_channels, 1, 1, 1)\r\n            y = y + bias\r\n        return y\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass P4ConvZ2(SplitGConv2D):\r\n\r\n    def __init__(self, *args, **kwargs):\r\n        super(P4ConvZ2, self).__init__(input_stabilizer_size=1, output_stabilizer_size=4, *args, **kwargs)\r\n\r\n\r\nclass P4ConvP4(SplitGConv2D):\r\n\r\n    def __init__(self, *args, **kwargs):\r\n        super(P4ConvP4, self).__init__(input_stabilizer_size=4, output_stabilizer_size=4, *args, **kwargs)\r\n\r\n\r\nclass P4MConvZ2(SplitGConv2D):\r\n\r\n    def __init__(self, *args, **kwargs):\r\n        super(P4MConvZ2, self).__init__(input_stabilizer_size=1, output_stabilizer_size=8, *args, **kwargs)\r\n\r\n\r\nclass P4MConvP4M(SplitGConv2D):\r\n\r\n    def __init__(self, *args, **kwargs):\r\n        super(P4MConvP4M, self).__init__(input_stabilizer_size=8, output_stabilizer_size=8, *args, **kwargs)\r\n","sub_path":"modeling/gconv/splitgconv2d.py","file_name":"splitgconv2d.py","file_ext":"py","file_size_in_byte":4273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"249447637","text":"from django.urls import path\n\n\nfrom . import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('profile', views.profile, name='profile'),\n    path('fav', views.fav, name='fav'),\n    path('audio/<int:id>', views.audio, name='audio'),\n    path('logout', views.logout_view, name='logout'),\n    path('add_song', views.add_song, name='add_song'),\n    path('my_songs', views.my_songs, name='my_songs'),\n    path('tag/<str:name>', views.tag, name='tag'),\n    path('download/<path:notes>', views.download, name='download'),\n    path('update_song/<int:id>', views.update_song, name='update_song'),\n    path('account', views.profile, name='account'),\n    path('edit_profile', views.edit_profile, name='edit_profile'),\n    path('view_profile/<int:id>', views.view_profile, name='view_profile'),\n]","sub_path":"app/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"376980631","text":"# Replace empty migration file data with below and uncomment model import\nfrom django.db import migrations\n\nimport requests\n\n# from ..models import Candle\n\ndef update_bitcoin_prices(apps, schema_editor):\n    \"\"\"Data load for Bitcoin daily price data.\n\n    Pulling data from coinbase sandbox api to seed database.\n    \"\"\"\n    coin_currency_sets = [\"BTC-USD\", \"BTC-EUR\"]\n    for coin_set in coin_currency_sets:\n        url = \"https://api-public.sandbox.pro.coinbase.com/products/{}/candles?granularity=86400\".format(coin_set)\n        r = requests.get(url)\n        response = r.json()\n        \n        # Seeding DB with Bitcoin response data and seperating coin_set info.\n        coin_curr = coin_set.split(\"-\")\n        for candle in response:\n            new_candle = Candle.objects.create(\n                item_id = 90, \n                base_currency = coin_curr[0],\n                quote_currency = coin_curr[1], \n                item_low = candle[1], \n                item_high = candle[2],\n                item_open = candle[3],\n                item_close = candle[4], \n                item_volume = candle[5],\n            ) \n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('prices', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.RunPython(update_bitcoin_prices),\n    ]\n\n","sub_path":"data_load.py","file_name":"data_load.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"611575525","text":"import requests\nimport json\nfrom datetime import datetime\nfrom pprint import pprint\nfrom pymongo import MongoClient\nimport bson\nimport uuid\nimport stripe\n\n\nclass product_object:\n    def __init__(self):\n        self.mycon = MongoClient(\"mongodb+srv://marijkevandonck:wHCAY6rM8pUai9Q@leonardodavinci-r3tnx.mongodb.net/test?retryWrites=true&w=majority\")\n        self.db = self.mycon.marijke_test\n        self.collection = self.db['test_stripe_prod_obj']\n\n        stripe.api_key = \"sk_test_wLe7RUNnV1RjVCan4HAZQwO900RkA4bV8S\"\n\n\n    def get_product_object_from_stripe(self, product_id):\n        product_object = stripe.Product.retrieve(\"{}\".format(product_id))\n        self.product = product_object\n\n    def get_plan_list_from_stripe(self, product_id):\n        plan_list = stripe.Plan.list(product=\"{}\".format(product_id))\n        self.plan_list = plan_list\n\n\n    def parse_product_object_from_stripe(self):\n        created_at_prod = datetime.utcfromtimestamp(self.product['created'])\n        product_updated = datetime.utcfromtimestamp(self.product['updated'])\n\n        self.product_obj = {\n            \"product_id\": self.product['id'],\n            \"attributes\": self.product['attributes'],\n            \"one_line_description\": self.product['caption'],\n            \"created_at\":  created_at_prod,\n            \"product_deactivate_on\": self.product['deactivate_on'],\n            \"product_info\": self.product['description'],\n            \"images\": self.product['images'],\n            \"product_name\": self.product['name'],\n            \"package_dimensions\": self.product['package_dimensions'],\n            \"shippable\": self.product['shippable'],\n            \"product_descriptor_on_creditcard\": self.product['statement_descriptor'],\n            \"type\": self.product['type'],\n            \"updated\": product_updated,\n            \"plans\": []\n        }\n\n\n        for plan in self.plan_list:\n          plan_created_at = datetime.utcfromtimestamp(plan['created'])\n\n          if not (plan['transform_usage']):\n            transform_usage = None\n          else:\n            transform_usage = {\n                \"divide_by\": plan['transform_usage']['divide_by'],\n                \"round\": plan['transform_usage']['round']\n              }\n\n          self.plan_obj = {\n             \"id\": plan['id'],\n              \"aggregate_usage\": plan['aggregate_usage'],\n              \"price\": plan['amount'],\n              \"billing_scheme\": plan['billing_scheme'],\n              \"plan_created_at\": plan_created_at,\n              \"currency\": plan['currency'],\n              \"interval\": plan['interval'],\n              \"interval_count\": plan['interval_count'],\n              \"plan_name\": plan['nickname'],\n              \"product_id\": plan['product'],\n              \"tiers\": plan['tiers'],\n              \"tiers_mode\": plan['tiers_mode'],\n              \"transform_usage\": {},\n              \"usage_type\": plan['usage_type']\n        }\n\n\n          self.product_obj['plans'].append(self.plan_obj)\n\n        # pprint(self.product_obj)\n\n\n    def create_product_obj(self, product_id):\n      self.get_product_object_from_stripe(product_id)\n      self.get_plan_list_from_stripe(product_id)\n      self.parse_product_object_from_stripe()\n      create_product_obj_in_mongo = self.collection.insert_one(self.product_obj)\n\n    def update_product_obj(self, product_id):\n      self.get_product_object_from_stripe(product_id)\n      self.get_plan_list_from_stripe(product_id)\n      self.parse_product_object_from_stripe()\n      print(self.product['id'])\n      update_product_obj_in_mongo = self.collection.replace_one({\"product_id\": self.product['id']}, self.product_obj)\n      # print('matched_count:' + str(update_product_obj_in_mongo.matched_count))\n      # print('modified_count:' + str(update_product_obj_in_mongo.modified_count))\n\n    def delete_product_obj(self, product_id):\n      delete_obj = {\"product_id\": product_id}\n      delete_product_obj_in_mongo = self.collection.delete_one(delete_obj)\n\n\n\n\n\n\nproduct = product_object()\n# product.get_product_object_from_stripe(\"prod_FwkcwDIofTCpyT\")\n# product.get_plan_list_from_stripe(\"prod_FwkcwDIofTCpyT\")\n# product.parse_product_object_from_stripe()\nproduct.create_product_obj(\"prod_FwkcwDIofTCpyT\")\n# product.delete_product_obj(\"prod_FwkcwDIofTCpyT\")\n# product.update_product_obj(\"prod_FwkcwDIofTCpyT\")\n","sub_path":"product_object/product_object.py","file_name":"product_object.py","file_ext":"py","file_size_in_byte":4299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"285915081","text":"\"\"\"\nCopyright 2020 The OneFlow Authors. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\n# ###################################################################\n# alexnet.py\n# Usage:\n#     Single Node: python alexnet.py -g 1\n#               -g gpu number\n#     Multi Nodes: python alexnet.py -g 8 -m -n \"192.168.1.15,192.168.1.16\"\n#               -g gpu number\n#               -m run on multi nodes\n#               -n IP addresses of nodes, seperated by comma\n# ###################################################################\n\nimport argparse\n\nimport oneflow as flow\n\nDATA_DIR = \"/dataset/imagenet_1k/oneflow/30/train\"\nparser = argparse.ArgumentParser(description=\"flags for multi-node and resource\")\nparser.add_argument(\"-i\", \"--iter_num\", type=int, default=10, required=False)\nparser.add_argument(\"-g\", \"--gpu_num_per_node\", type=int, default=1, required=False)\nparser.add_argument(\n    \"-m\", \"--multinode\", default=False, action=\"store_true\", required=False\n)\nparser.add_argument(\"-n\", \"--node_list\", type=str, default=None, required=False)\nparser.add_argument(\"-e\", \"--eval_dir\", type=str, default=DATA_DIR, required=False)\nparser.add_argument(\"-t\", \"--train_dir\", type=str, default=DATA_DIR, required=False)\nparser.add_argument(\"-load\", \"--model_load_dir\", type=str, default=\"\", required=False)\nparser.add_argument(\n    \"-save\", \"--model_save_dir\", type=str, default=\"./checkpoints\", required=False\n)\nargs = parser.parse_args()\n\n\ndef _data_load_layer(data_dir):\n    rgb_mean = [123.68, 116.78, 103.94]\n    ofrecord = flow.data.ofrecord_reader(\n        data_dir, batch_size=12, data_part_num=8, name=\"decode\",\n    )\n    image = flow.data.ofrecord_image_decoder(ofrecord, \"encoded\", color_space=\"RGB\")\n    label = flow.data.ofrecord_raw_decoder(\n        ofrecord, \"class/label\", shape=(), dtype=flow.int32\n    )\n    rsz = flow.image.resize(image, resize_x=227, resize_y=227, color_space=\"RGB\")\n    normal = flow.image.crop_mirror_normalize(\n        rsz,\n        color_space=\"RGB\",\n        output_layout=\"NCHW\",\n        mean=rgb_mean,\n        output_dtype=flow.float,\n    )\n    return label, normal\n\n\ndef _conv2d_layer(\n    name,\n    input,\n    filters,\n    kernel_size=3,\n    strides=1,\n    padding=\"SAME\",\n    data_format=\"NCHW\",\n    dilation_rate=1,\n    activation=\"Relu\",\n    use_bias=False,\n    weight_initializer=flow.random_uniform_initializer(),\n    bias_initializer=None,\n):\n    weight_shape = (filters, input.shape[1], kernel_size, kernel_size)\n    weight = flow.get_variable(\n        name + \"-weight\",\n        shape=weight_shape,\n        dtype=input.dtype,\n        initializer=weight_initializer,\n    )\n    output = flow.nn.conv2d(\n        input, weight, strides, padding, data_format, dilation_rate, name=name\n    )\n    if use_bias:\n        bias = flow.get_variable(\n            name + \"-bias\",\n            shape=(filters,),\n            dtype=input.dtype,\n            initializer=bias_initializer,\n        )\n        output = flow.nn.bias_add(output, bias, data_format)\n\n    if activation is not None:\n        if activation == \"Relu\":\n            output = flow.math.relu(output)\n        else:\n            raise NotImplementedError\n\n    return output\n\n\ndef alexnet(images, labels):\n    conv1 = _conv2d_layer(\n        \"conv1\", images, filters=64, kernel_size=11, strides=4, padding=\"VALID\"\n    )\n\n    pool1 = flow.nn.avg_pool2d(conv1, 3, 2, \"VALID\", \"NCHW\", name=\"pool1\")\n\n    conv2 = _conv2d_layer(\"conv2\", pool1, filters=192, kernel_size=5)\n\n    pool2 = flow.nn.avg_pool2d(conv2, 3, 2, \"VALID\", \"NCHW\", name=\"pool2\")\n\n    conv3 = _conv2d_layer(\"conv3\", pool2, filters=384)\n\n    conv4 = _conv2d_layer(\"conv4\", conv3, filters=384)\n\n    conv5 = _conv2d_layer(\"conv5\", conv4, filters=256)\n\n    pool5 = flow.nn.avg_pool2d(conv5, 3, 2, \"VALID\", \"NCHW\", name=\"pool5\")\n\n    if len(pool5.shape) > 2:\n        pool5 = flow.reshape(pool5, shape=(pool5.shape[0], -1))\n\n    fc1 = flow.layers.dense(\n        inputs=pool5,\n        units=4096,\n        activation=flow.math.relu,\n        use_bias=False,\n        kernel_initializer=flow.random_uniform_initializer(),\n        bias_initializer=False,\n        trainable=True,\n        name=\"fc1\",\n    )\n\n    dropout1 = flow.nn.dropout(fc1, rate=0.5)\n\n    fc2 = flow.layers.dense(\n        inputs=dropout1,\n        units=4096,\n        activation=flow.math.relu,\n        use_bias=False,\n        kernel_initializer=flow.random_uniform_initializer(),\n        bias_initializer=False,\n        trainable=True,\n        name=\"fc2\",\n    )\n\n    dropout2 = flow.nn.dropout(fc2, rate=0.5)\n\n    fc3 = flow.layers.dense(\n        inputs=dropout2,\n        units=1001,\n        activation=None,\n        use_bias=False,\n        kernel_initializer=flow.random_uniform_initializer(),\n        bias_initializer=False,\n        trainable=True,\n        name=\"fc3\",\n    )\n\n    # loss function\n    loss = flow.nn.sparse_softmax_cross_entropy_with_logits(\n        labels, fc3, name=\"softmax_loss\"\n    )\n\n    return loss\n\n\n# train job\n@flow.global_function\ndef alexnet_train_job():\n    # set hyper parameter\n    flow.config.train.primary_lr(0.00001)\n    flow.config.train.model_update_conf(dict(naive_conf={}))\n\n    # load data\n    (labels, images) = _data_load_layer(args.train_dir)\n\n    # construct network\n    loss = alexnet(images, labels)\n\n    # set loss\n    flow.losses.add_loss(loss)\n\n    return loss\n\n\n# inference job\n@flow.global_function\ndef alexnet_eval_job():\n    # load data\n    (labels, images) = _data_load_layer(args.eval_dir)\n\n    # construct inference network\n    loss = alexnet(images, labels)\n\n    return loss\n\n\ndef main():\n    # set running mode\n    flow.config.gpu_device_num(args.gpu_num_per_node)\n    flow.config.ctrl_port(9788)\n    flow.config.default_data_type(flow.float)\n\n    # set multi nodes mode port\n    if args.multinode:\n        flow.config.ctrl_port(12138)\n        nodes = []\n        for n in args.node_list.strip().split(\",\"):\n            addr_dict = {}\n            addr_dict[\"addr\"] = n\n            nodes.append(addr_dict)\n        flow.config.machine(nodes)\n\n    # load/initialize model\n    check_point = flow.train.CheckPoint()\n    if not args.model_load_dir:\n        check_point.init()\n    else:\n        check_point.load(args.model_load_dir)\n\n    # training iter\n    print(\"{:>12}  {:>12}  {:>12}\".format(\"iter\", \"loss type\", \"loss value\"))\n    for i in range(args.iter_num):\n        fmt_str = \"{:>12}  {:>12}  {:>12.10f}\"\n\n        # print training log\n        train_loss = alexnet_train_job().get().mean()\n        print(fmt_str.format(i, \"train loss:\", train_loss))\n\n        # print inference log\n        if (i + 1) % 10 == 0:\n            eval_loss = alexnet_eval_job().get().mean()\n            print(fmt_str.format(i, \"eval loss:\", eval_loss))\n\n        # save model\n        if (i + 1) % 100 == 0:\n            check_point.save(args.model_save_dir + str(i))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"oneflow/python/benchmarks/cnn_benchmark/alexnet.py","file_name":"alexnet.py","file_ext":"py","file_size_in_byte":7348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"597402008","text":"#\n# Copyright (c) 2017 nexB Inc. and others. All rights reserved.\n# http://nexb.com and https://github.com/nexB/vulnerablecode/\n# The VulnerableCode software is licensed under the Apache License version 2.0.\n# Data generated with VulnerableCode require an acknowledgment.\n#\n# You may not use this software except in compliance with the License.\n# You may obtain a copy of the License at: http://apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software distributed\n# under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n# CONDITIONS OF ANY KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations under the License.\n#\n# When you publish or redistribute any data created with VulnerableCode or any VulnerableCode\n# derivative work, you must accompany this data with the following acknowledgment:\n#\n#  Generated with VulnerableCode and provided on an \"AS IS\" BASIS, WITHOUT WARRANTIES\n#  OR CONDITIONS OF ANY KIND, either express or implied. No content created from\n#  VulnerableCode should be considered or used as legal advice. Consult an Attorney\n#  for any legal advice.\n#  VulnerableCode is a free software code scanning tool from nexB Inc. and others.\n#  Visit https://github.com/nexB/vulnerablecode/ for support and download.\nimport json\nimport os\nfrom unittest.mock import patch\n\nfrom django.test import TestCase\n\nfrom vulnerabilities import models\nfrom vulnerabilities.import_runner import ImportRunner\nfrom vulnerabilities.importers.safety_db import PypiVersionAPI\nfrom vulnerabilities.importers.safety_db import categorize_versions\nfrom vulnerabilities.data_source import Reference\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nTEST_DATA = os.path.join(BASE_DIR, 'test_data/')\n\nMOCK_VERSION_API = PypiVersionAPI(cache={\n    'ampache': {'2.0', '5.2.1'},\n    'django': {'1.8', '1.4.19', '1.4.22', '1.5.1', '1.6.9', '1.8.14'},\n    'zulip': {'2.0', '2.1.1', '2.1.2', '2.1.3'},\n})\n\n\n@patch('vulnerabilities.importers.SafetyDbDataSource.versions', new=MOCK_VERSION_API)\nclass SafetyDbImportTest(TestCase):\n    @classmethod\n    def setUpClass(cls) -> None:\n        fixture_path = os.path.join(TEST_DATA, 'safety_db', 'insecure_full.json')\n        with open(fixture_path) as f:\n            cls.mock_response = json.load(f)\n\n        cls.importer = models.Importer.objects.create(\n            name='safetydb_unittests',\n            license='CC-BY-NC 4.0',\n            last_run=None,\n            data_source='SafetyDbDataSource',\n            data_source_cfg={\n                'url': 'https://example.com',\n                'etags': {}\n            },\n        )\n\n    @classmethod\n    def tearDownClass(cls) -> None:\n        # Make sure no requests for unexpected package names have been made during the tests.\n        assert len(MOCK_VERSION_API.cache) == 3, MOCK_VERSION_API.cache\n\n    def test_import(self):\n        runner = ImportRunner(self.importer, 5)\n\n        with patch('vulnerabilities.importers.SafetyDbDataSource._fetch', return_value=self.mock_response):  # nopep8\n            with patch('vulnerabilities.importers.SafetyDbDataSource.set_api'):\n                runner.run()\n\n        assert models.Vulnerability.objects.count() == 9\n        assert models.VulnerabilityReference.objects.count() == 9\n        assert models.PackageRelatedVulnerability.objects.filter(\n            is_vulnerable=False).count() == 18\n        assert models.PackageRelatedVulnerability.objects.filter(\n            is_vulnerable=True).count() == 18\n\n        expected_package_count = sum([len(v) for v in MOCK_VERSION_API.cache.values()])\n        assert models.Package.objects.count() == expected_package_count\n\n        self.assert_by_vulnerability(\n            'pyup.io-37863',\n            'ampache',\n            {'2.0'},\n            {'5.2.1'},\n            cve_ids={'CVE-2019-12385', 'CVE-2019-12386'},\n        )\n\n        self.assert_by_vulnerability(\n            'pyup.io-25713',\n            'django',\n            {'1.8', '1.4.19', '1.5.1', '1.6.9'},\n            {'1.8.14', '1.4.22'},\n            cve_ids={'CVE-2015-2317'},\n        )\n\n        self.assert_by_vulnerability(\n            'pyup.io-25721',\n            'django',\n            {'1.8.14'},\n            {'1.8', '1.4.19', '1.5.1', '1.6.9', '1.4.22'},\n            cve_ids={'CVE-2016-6186'},\n        )\n\n        self.assert_by_vulnerability(\n            'pyup.io-38115',\n            'zulip',\n            {'2.0'},\n            {'2.1.1', '2.1.2', '2.1.3'},\n        )\n\n        self.assert_by_vulnerability(\n            'pyup.io-38114',\n            'zulip',\n            {'2.0', '2.1.1'},\n            {'2.1.2', '2.1.3'},\n            cve_ids={'CVE-2019-19775', 'CVE-2015-2104'},\n        )\n\n        self.assert_by_vulnerability(\n            'pyup.io-38200',\n            'zulip',\n            {'2.0', '2.1.1', '2.1.2'},\n            {'2.1.3'},\n            cve_ids={'CVE-2020-9444', 'CVE-2020-10935'},\n        )\n\n    def assert_by_vulnerability(\n            self,\n            vuln_ref,\n            pkg_name,\n            impacted_versions,\n            resolved_versions,\n            cve_ids=None,\n    ):\n        impacted_pkgs = set(models.Package.objects.filter(\n            name=pkg_name, version__in=impacted_versions))\n\n        assert len(impacted_pkgs) == len(impacted_versions)\n\n        resolved_pkgs = set(models.Package.objects.filter(\n            name=pkg_name, version__in=resolved_versions))\n\n        assert len(resolved_pkgs) == len(resolved_versions)\n\n        vuln_count = 1 if cve_ids is None else len(cve_ids)\n\n        vulns = {r.vulnerability for r in\n                 models.VulnerabilityReference.objects.filter(reference_id=vuln_ref)}\n\n        assert len(vulns) == vuln_count\n\n        for vuln in vulns:\n            assert {ip.package for ip in\n                    models.PackageRelatedVulnerability.objects.filter(\n                        vulnerability=vuln, is_vulnerable=True)\n                    } == impacted_pkgs\n\n            assert {rp.package for rp in\n                    models.PackageRelatedVulnerability.objects.filter(\n                        vulnerability=vuln, is_vulnerable=False)\n                    } == resolved_pkgs\n\n        if cve_ids:\n            assert {v.cve_id for v in vulns} == cve_ids\n\n\ndef test_categorize_versions():\n    all_versions = {'1.8', '1.4.19', '1.4.22', '1.5.1', '1.6.9', '1.8.14'}\n    version_specs = [\">=1.8,<1.8.3\", \"<1.4.20\", \">=1.5,<1.6\", \">=1.6,<1.6.11\", \">=1.7,<1.7.7\"]\n\n    impacted_purls, resolved_purls = categorize_versions('django', all_versions, version_specs)\n\n    assert len(impacted_purls) == 4\n    assert len(resolved_purls) == 2\n\n    impacted_versions = {p.version for p in impacted_purls}\n    resolved_versions = {p.version for p in resolved_purls}\n\n    assert impacted_versions == {'1.8', '1.4.19', '1.5.1', '1.6.9'}\n    assert resolved_versions == {'1.4.22', '1.8.14'}\n","sub_path":"vulnerabilities/tests/test_safety_db.py","file_name":"test_safety_db.py","file_ext":"py","file_size_in_byte":6904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"249341643","text":"# pyre-strict\n# misc utility functions...\n\nimport sys\nimport os\nimport json\nimport logging\nimport signal\nimport threading\nimport copy\nfrom time import sleep\nfrom datetime import datetime\nfrom contextlib import contextmanager\nfrom typing import (\n    Tuple,\n    Dict,\n    Set,\n    Iterable,\n    Iterator,\n    List,\n    TypeVar,\n    Generic,\n    Optional,\n    Callable,\n    Any,\n)\nfrom types import FrameType\nimport coloredlogs\nfrom pygtail import Pygtail\nimport docker\n\n__all__: List[str] = []\n\n\ndef export(obj) -> str:  # pyre-ignore\n    __all__.append(obj.__name__)\n    return obj\n\n\n@export\ndef strip_leading_whitespace(txt: str) -> Tuple[int, str]:\n    # Given a multi-line string, determine the largest w such that each line\n    # begins with at least w whitespace characters. Return w and the string\n    # with w characters removed from the beginning of each line.\n    lines = txt.split(\"\\n\")\n\n    to_strip = None\n    for line in lines:\n        lsl = len(line.lstrip())\n        if lsl:\n            c = len(line) - lsl\n            assert c >= 0\n            if to_strip is None or to_strip > c:\n                to_strip = c\n            # TODO: do something about mixed tabs & spaces\n\n    if not to_strip:\n        return (0, txt)\n\n    for i, line_i in enumerate(lines):\n        if line_i.lstrip():\n            lines[i] = line_i[to_strip:]\n\n    return (to_strip, \"\\n\".join(lines))\n\n\nT = TypeVar(\"T\")\n\n\n@export\nclass AdjM(Generic[T]):\n    # A sparse adjacency matrix for topological sorting\n    # which we should not have implemented ourselves\n    _forward: Dict[T, Set[T]]\n    _reverse: Dict[T, Set[T]]\n    _unconstrained: Set[T]\n\n    def __init__(self) -> None:\n        self._forward = dict()\n        self._reverse = dict()\n        self._unconstrained = set()\n\n    def sinks(self, source: T) -> Iterable[T]:\n        for sink in self._forward.get(source, []):\n            yield sink\n\n    def sources(self, sink: T) -> Iterable[T]:\n        for source in self._reverse.get(sink, []):\n            yield source\n\n    @property\n    def nodes(self) -> Iterable[T]:\n        for node in self._forward:\n            yield node\n\n    @property\n    def unconstrained(self) -> Iterable[T]:\n        for n in self._unconstrained:\n            assert not self._reverse[n]\n            yield n\n\n    def add_node(self, node: T) -> None:\n        if node not in self._forward:\n            assert node not in self._reverse\n            self._forward[node] = set()\n            self._reverse[node] = set()\n            self._unconstrained.add(node)\n        else:\n            assert node in self._reverse\n\n    def add_edge(self, source: T, sink: T) -> None:\n        self.add_node(source)\n        self.add_node(sink)\n        if sink not in self._forward[source]:\n            self._forward[source].add(sink)\n            self._reverse[sink].add(source)\n            if sink in self._unconstrained:\n                self._unconstrained.remove(sink)\n        else:\n            assert source in self._reverse[sink]\n            assert sink not in self._unconstrained\n\n    def remove_edge(self, source: T, sink: T) -> None:\n        if source in self._forward and sink in self._forward[source]:\n            self._forward[source].remove(sink)\n            self._reverse[sink].remove(source)\n            if not self._reverse[sink]:\n                self._unconstrained.add(sink)\n        else:\n            assert not (sink in self._reverse and source in self._reverse[sink])\n\n    def remove_node(self, node: T) -> None:\n        for source in list(self.sources(node)):\n            self.remove_edge(source, node)\n        for sink in list(self.sinks(node)):\n            self.remove_edge(node, sink)\n        del self._forward[node]\n        del self._reverse[node]\n        self._unconstrained.remove(node)\n\n\n@export\ndef topsort(adj: AdjM[T]) -> List[T]:\n    # topsort node IDs in adj (destroys adj)\n    # if there's a cycle, raises err: StopIteration with err.node = ID of a\n    # node involved in a cycle.\n    ans = []\n    node = next(adj.unconstrained, None)  # pyre-ignore\n    while node:\n        adj.remove_node(node)\n        ans.append(node)\n        node = next(adj.unconstrained, None)  # pyre-ignore\n    node = next(adj.nodes, None)  # pyre-ignore\n    if node:\n        err = StopIteration()\n        setattr(err, \"node\", node)\n        raise err\n    return ans\n\n\n@export\ndef write_values_json(\n    values_env: \"Env.Bindings[Value.Base]\", filename: str, namespace: str = \"\"\n) -> None:\n    from . import values_to_json\n\n    with open(filename, \"w\") as outfile:\n        print(\n            json.dumps(values_to_json(values_env, namespace=namespace), indent=2),  # pyre-ignore\n            file=outfile,\n        )\n\n\n@export\ndef provision_run_dir(name: str, run_dir: Optional[str] = None) -> str:\n    here = (\n        (run_dir in [\".\", \"./\"] or run_dir.endswith(\"/.\") or run_dir.endswith(\"/./\"))\n        if run_dir\n        else False\n    )\n    run_dir = os.path.abspath(run_dir or os.getcwd())\n\n    if here:\n        os.makedirs(run_dir, exist_ok=True)\n        return run_dir\n\n    now = datetime.today()\n    run_dir2 = os.path.join(run_dir, now.strftime(\"%Y%m%d_%H%M%S\") + \"_\" + name)\n    try:\n        os.makedirs(run_dir2, exist_ok=False)\n        return run_dir2\n    except FileExistsError:\n        pass\n\n    while True:\n        run_dir2 = os.path.join(\n            run_dir,\n            now.strftime(\"%Y%m%d_%H%M%S_\") + str(int(now.microsecond / 1000)).zfill(3) + \"_\" + name,\n        )\n        try:\n            os.makedirs(run_dir2, exist_ok=False)\n            return run_dir2\n        except FileExistsError:\n            sleep(1e-3)\n\n\n@export\nclass StructuredLogMessage:\n    message: str\n    kwargs: Dict[str, Any]\n\n    # from https://docs.python.org/3.8/howto/logging-cookbook.html#implementing-structured-logging\n    def __init__(self, _message: str, **kwargs) -> None:  # pyre-fixme\n        self.message = _message\n        self.kwargs = kwargs\n\n    def __str__(self) -> str:\n        return (\n            f\"{self.message} :: {', '.join(k+ ': ' + json.dumps(v) for k,v in self.kwargs.items())}\"\n        )\n\n\nVERBOSE_LEVEL = 15\n__all__.append(\"VERBOSE_LEVEL\")\nlogging.addLevelName(VERBOSE_LEVEL, \"VERBOSE\")\n\n\ndef verbose(self, message, *args, **kws):  # pyre-fixme\n    if self.isEnabledFor(VERBOSE_LEVEL):\n        self._log(VERBOSE_LEVEL, message, args, **kws)\n\n\nlogging.Logger.verbose = verbose\nNOTICE_LEVEL = 25\n__all__.append(\"NOTICE_LEVEL\")\nlogging.addLevelName(NOTICE_LEVEL, \"NOTICE\")\n\n\ndef notice(self, message, *args, **kws):  # pyre-fixme\n    if self.isEnabledFor(NOTICE_LEVEL):\n        self._log(NOTICE_LEVEL, message, args, **kws)\n\n\nlogging.Logger.notice = notice\n\nLOGGING_FORMAT = \"%(asctime)s,%(msecs)03d %(name)s %(levelname)s %(message)s\"\n__all__.append(\"LOGGING_FORMAT\")\n\n\n@export\ndef install_coloredlogs(logger: logging.Logger) -> None:\n    level_styles = {}\n    field_styles = {}\n\n    if sys.stderr.isatty() and \"NO_COLOR\" not in os.environ:\n        level_styles = dict(coloredlogs.DEFAULT_LEVEL_STYLES)\n        level_styles[\"debug\"][\"color\"] = 242\n        level_styles[\"notice\"] = {\"color\": \"magenta\"}\n        level_styles[\"info\"] = {}\n        field_styles = None\n\n    coloredlogs.install(\n        level=logger.getEffectiveLevel(),\n        logger=logger,\n        level_styles=level_styles,\n        field_styles=field_styles,\n        fmt=LOGGING_FORMAT,\n    )\n\n\n@export\n@contextmanager\ndef PygtailLogger(\n    logger: logging.Logger, filename: str, prefix: str = \"2| \"\n) -> Iterator[Callable[[], None]]:\n    \"\"\"\n    Helper for streaming task stderr into logger using pygtail. Context manager yielding a function\n    which reads the latest lines from the file and writes them into logger at verbose level. This\n    function also runs automatically on context exit.\n\n    Truncates lines at 4KB in case writer goes haywire.\n    \"\"\"\n    pygtail = Pygtail(filename, full_lines=True)\n    pygtail_ok = True\n\n    def poll() -> None:\n        nonlocal pygtail_ok\n        if pygtail_ok:\n            try:\n                for line in pygtail:\n                    logger.verbose((prefix + line.rstrip())[:4096])  # pyre-ignore\n            except:\n                pygtail_ok = False\n                # cf. https://github.com/bgreenlee/pygtail/issues/48\n                logger.verbose(  # pyre-ignore\n                    \"incomplete log stream due to the following exception; see %s\",\n                    filename,\n                    exc_info=sys.exc_info(),\n                )\n\n    try:\n        yield poll\n    finally:\n        poll()\n\n\n_terminating: Optional[bool] = None\n_terminating_lock: threading.Lock = threading.Lock()\n\n\n@export\n@contextmanager\ndef TerminationSignalFlag(logger: logging.Logger) -> Iterator[Callable[[], bool]]:\n    \"\"\"\n    Context manager installing termination signal handlers (SIGTERM, SIGQUIT, SIGINT, SIGHUP,\n    SIGPIPE) which set a global flag indicating whether such a signal has been received. Yields a\n    function which returns this flag.\n\n    Should be opened on the main thread wrapping all the desired operations. Once this is so, more\n    instances can be opened on any thread without interfering with each other, as long as they're\n    nested within the main one.\n    \"\"\"\n    signals = [\n        signal.SIGTERM,\n        signal.SIGQUIT,\n        signal.SIGINT,\n        signal.SIGHUP,\n        signal.SIGPIPE,\n        signal.SIGALRM,\n        signal.SIGUSR1,\n    ]\n\n    def handle_signal(sig: int, frame: FrameType) -> None:\n        global _terminating\n        if not _terminating:\n            if sig != signal.SIGUSR1:\n                logger.critical(StructuredLogMessage(\"ABORT\", signal=sig))\n            else:\n                # SIGUSR1 comes from ourselves, as the signal to abort after something else has\n                # already gone wrong\n                logger.notice(\"aborting workflow\")  # pyre-fixme\n        _terminating = True\n\n    global _terminating\n    global _terminating_lock\n    restore_signal_handlers = None\n    with _terminating_lock:\n        if _terminating is None:\n            restore_signal_handlers = dict(\n                (sig, signal.signal(sig, handle_signal)) for sig in signals\n            )\n            _terminating = False\n    try:\n        yield lambda: _terminating\n    finally:\n        if restore_signal_handlers:\n            with _terminating_lock:\n                for sig, handler in restore_signal_handlers.items():\n                    signal.signal(sig, handler)\n                _terminating = None\n\n\n@export\nclass CustomDeepCopyMixin:\n    \"\"\"\n    Mixin class overrides __deepcopy__ to consult an internal list of attribute names to be merely\n    shallow-copied when the time comes. Useful for attributes referencing large, immutable data\n    structures.\n\n    Override class variable _shallow_copy_attrs to a list of the attribute names to be\n    shallow-copied.\n    \"\"\"\n\n    _shallow_copy_attrs: Optional[List[str]] = None\n\n    def __deepcopy__(self, memo: Dict[int, Any]) -> Any:  # pyre-ignore\n        cls = self.__class__\n        cp = cls.__new__(cls)\n        memo[id(self)] = cp\n        for k in self._shallow_copy_attrs or []:\n            v = self.__dict__[k]\n            memo[id(v)] = v\n        for k, v in self.__dict__.items():\n            setattr(cp, k, copy.deepcopy(v, memo))\n        return cp\n\n\nbyte_size_units = {\n    \"K\": 1000,\n    \"KB\": 1000,\n    \"Ki\": 1024,\n    \"KiB\": 1024,\n    \"M\": 1000000,\n    \"MB\": 1000000,\n    \"Mi\": 1048576,\n    \"MiB\": 1048576,\n    \"G\": 1000000000,\n    \"GB\": 1000000000,\n    \"Gi\": 1073741824,\n    \"GiB\": 1073741824,\n    \"T\": 1000000000000,\n    \"TB\": 1000000000000,\n    \"Ti\": 1099511627776,\n    \"TiB\": 1099511627776,\n}\n\n\n@export\ndef parse_byte_size(s: str) -> int:\n    \"\"\"\n    convert strings like \"2000\", \"4G\", \"1.5 TiB\" to a positive number of bytes\n    \"\"\"\n\n    s = s.strip()\n    N = None\n    unit = None\n    for i in range(len(s)):\n        if s[i].isdigit() or s[i] == \".\":\n            N = float(s[: i + 1])\n            unit = s[i + 1 :].lstrip()\n        else:\n            break\n    if N and unit:\n        N *= byte_size_units.get(unit, 0)\n    if N is None or N <= 0:\n        raise ValueError(\"invalid byte size string, \" + s)\n    return int(N)\n\n\ndef splitall(path: str) -> List[str]:\n    \"\"\"\n    https://www.oreilly.com/library/view/python-cookbook/0596001673/ch04s16.html\n    \"\"\"\n    allparts = []\n    while 1:\n        parts = os.path.split(path)\n        if parts[0] == path:  # sentinel for absolute paths\n            allparts.insert(0, parts[0])\n            break\n        elif parts[1] == path:  # sentinel for relative paths\n            allparts.insert(0, parts[1])\n            break\n        else:\n            path = parts[0]\n            allparts.insert(0, parts[1])\n    return allparts\n\n\n@export\ndef path_really_within(lhs: str, rhs: str) -> bool:\n    \"\"\"\n    After resolving symlinks, is path lhs either equal to or nested within path rhs?\n    \"\"\"\n    lhs_cmp = splitall(os.path.realpath(lhs))\n    rhs_cmp = splitall(os.path.realpath(rhs))\n    return len(lhs_cmp) >= len(rhs_cmp) and lhs_cmp[: len(rhs_cmp)] == rhs_cmp\n\n\n@export\ndef chmod_R_plus(path: str, file_bits: int = 0, dir_bits: int = 0) -> None:\n    \"\"\"\n    recursive chmod to add permission bits (possibly different for files and subdirectiores)\n    does not follow symlinks\n    \"\"\"\n\n    def do1(path1: str, bits: int) -> None:\n        assert 0 <= bits < 0o10000\n        if path_really_within(path1, path):\n            os.chmod(path1, (os.stat(path1).st_mode & 0o7777) | bits)\n\n    if os.path.isdir(path):\n        for root, subdirs, files in os.walk(path, followlinks=False):\n            for dn in subdirs:\n                do1(os.path.join(root, dn), dir_bits)\n            for fn in files:\n                do1(os.path.join(root, fn), file_bits)\n    else:\n        do1(path, file_bits)\n\n\n@export\n@contextmanager\ndef LoggingFileHandler(logger: logging.Logger, filename: str) -> Iterator[logging.FileHandler]:\n    \"\"\"\n    Context manager which opens a logging.FileHandler and adds it to the logger; on exit, closes\n    the log file and removes the handler.\n    \"\"\"\n    fh = logging.FileHandler(filename)\n    try:\n        logger.addHandler(fh)\n        yield fh\n    finally:\n        fh.flush()\n        fh.close()\n        logger.removeHandler(fh)\n\n\n@export\nclass AtomicCounter:\n    _value: int\n    _lock: threading.Lock\n\n    def __init__(self) -> None:\n        self._value = 0\n        self._lock = threading.Lock()\n\n    def next(self) -> int:\n        with self._lock:\n            self._value += 1\n            return self._value\n\n\n@export\ndef initialize_local_docker(\n    logger: logging.Logger, client: Optional[docker.DockerClient] = None\n) -> Dict[str, int]:\n    client_in = client\n    client = client or docker.from_env()\n    detector = None\n    try:\n        # initialize docker swarm\n        state = \"(unknown)\"\n        while True:\n            info = client.info()\n            if \"Swarm\" in info and \"LocalNodeState\" in info[\"Swarm\"]:\n                state = info[\"Swarm\"][\"LocalNodeState\"]\n\n            # https://github.com/moby/moby/blob/e7b5f7dbe98c559b20c0c8c20c0b31a6b197d717/api/types/swarm/swarm.go#L185\n            if state == \"inactive\":\n                logger.warning(\n                    \"docker swarm is inactive on this host; performing `docker swarm init --advertise-addr 127.0.0.1 --listen-addr 127.0.0.1`\"\n                )\n                client.swarm.init(advertise_addr=\"127.0.0.1\", listen_addr=\"127.0.0.1\")\n            elif state == \"active\":\n                break\n            else:\n                logger.notice(  # pyre-fixme\n                    StructuredLogMessage(\"waiting for docker swarm to become active\", state=state)\n                )\n                sleep(2)\n\n        miniwdl_services = [\n            d\n            for d in [s.attrs for s in client.services.list()]\n            if \"Spec\" in d and \"Labels\" in d[\"Spec\"] and \"miniwdl_run_id\" in d[\"Spec\"][\"Labels\"]\n        ]\n        if miniwdl_services:\n            logger.warning(\n                \"docker swarm lists existing miniwdl-related services. This is normal if other miniwdl processes are running concurrently; otherwise, stale state could interfere with this run. To reset it, `docker swarm leave --force`\"\n            )\n\n        # Detect CPUs & memory available to Docker containers on the local host. These limits may\n        # differ from multiprocessing.cpu_count() and psutil.virtual_memory().total; in particular\n        # on macOS, where Docker containers run in a virtual machine with limited resources.\n        detector = client.containers.run(\n            \"alpine:3\",\n            name=f\"wdl-detector-{os.getpid()}\",\n            command=[\"/bin/ash\", \"-c\", \"nproc && free -b | awk '/^Mem:/{print $2}'\"],\n            log_config=docker.types.LogConfig(type=docker.types.LogConfig.types.JSON),\n            detach=True,\n        )\n        status = detector.wait()\n        assert isinstance(status, dict) and status.get(\"StatusCode\", -1) == 0, str(status)\n        stdout = detector.logs(stdout=True)\n        logger.debug(StructuredLogMessage(\"docker resource detection\", stdout=str(stdout)))\n        stdout = stdout.decode(\"utf-8\").strip().split(\"\\n\")\n        assert len(stdout) == 2\n        ans = (int(stdout[0]), int(stdout[1]))\n        logger.info(\n            StructuredLogMessage(\n                \"detected host resources available for Docker containers\",\n                cpu=ans[0],\n                mem_bytes=ans[1],\n            )\n        )\n        return {\"runtime_cpu_max\": ans[0], \"runtime_memory_max\": ans[1]}\n    finally:\n        if detector:\n            detector.remove()\n        if client and not client_in:\n            client.close()\n","sub_path":"WDL/_util.py","file_name":"_util.py","file_ext":"py","file_size_in_byte":17564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"590083492","text":"# -*- coding: utf-8 -*-\n# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\n\"\"\"\nTests for miscellaneous functionality in the `funcs` module\n\"\"\"\n\nfrom __future__ import (absolute_import, division, print_function,\n                        unicode_literals)\n\nimport numpy as np\nfrom numpy import testing as npt\n\nfrom ...tests.helper import pytest\nfrom ...extern import six\n\nfrom ... import units as u\nfrom ...time import Time\n\n\ndef test_sun():\n    \"\"\"\n    Test that `get_sun` works and it behaves roughly as it should (in GCRS)\n    \"\"\"\n    from ..funcs import get_sun\n\n    northern_summer_solstice = Time('2010-6-21')\n    northern_winter_solstice = Time('2010-12-21')\n    equinox_1 = Time('2010-3-21')\n    equinox_2 = Time('2010-9-21')\n\n    gcrs1 = get_sun(equinox_1)\n    assert np.abs(gcrs1.dec.deg) < 1\n\n    gcrs2 = get_sun(Time([northern_summer_solstice, equinox_2, northern_winter_solstice]))\n    assert np.all(np.abs(gcrs2.dec - [23.5, 0, -23.5]*u.deg) < 1*u.deg)\n\n\ndef test_concatenate():\n    from .. import FK5, SkyCoord\n    from ..funcs import concatenate\n\n    fk5 = FK5(1*u.deg, 2*u.deg)\n    sc = SkyCoord(3*u.deg, 4*u.deg, frame='fk5')\n\n    res = concatenate([fk5, sc])\n    np.testing.assert_allclose(res.ra, [1, 3]*u.deg)\n    np.testing.assert_allclose(res.dec, [2, 4]*u.deg)\n\n    with pytest.raises(TypeError):\n        concatenate(fk5)\n\n    with pytest.raises(TypeError):\n        concatenate(1*u.deg)\n\n\ndef test_constellations():\n    from .. import ICRS, FK5, SkyCoord\n    from ..funcs import get_constellation\n\n    inuma = ICRS(9*u.hour, 65*u.deg)\n    res = get_constellation(inuma)\n    res_short = get_constellation(inuma, short_name=True)\n    assert res == 'Ursa Major'\n    assert res_short == 'UMa'\n    assert isinstance(res, six.string_types) or getattr(res, 'shape', None) == tuple()\n\n    # these are taken from the ReadMe for Roman 1987\n    ras = [9, 23.5, 5.12, 9.4555, 12.8888, 15.6687, 19, 6.2222]\n    decs = [65, -20, 9.12, -19.9, 22, -12.1234, -40, -81.1234]\n    shortnames = ['UMa', 'Aqr', 'Ori', 'Hya', 'Com', 'Lib', 'CrA', 'Men']\n\n    testcoos = FK5(ras*u.hour, decs*u.deg, equinox='B1950')\n    npt.assert_equal(get_constellation(testcoos, short_name=True), shortnames)\n\n    # test on a SkyCoord, *and* test Boötes, which is special in that it has a\n    # non-ASCII character\n    bootest = SkyCoord(15*u.hour, 30*u.deg, frame='icrs')\n    boores = get_constellation(bootest)\n    assert boores == u'Boötes'\n    assert isinstance(boores, six.string_types) or getattr(boores, 'shape', None) == tuple()\n","sub_path":"lib/python2.7/site-packages/astropy/coordinates/tests/test_funcs.py","file_name":"test_funcs.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"364166866","text":"from django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom .models import Teacher, Course\n\n# Create your views here.\ndef index(request):\n    context = {\n        'classes':Course.objects.all()\n    }\n    return render(request, 'course/index.html', context)\n\ndef delete(request, id):\n    course = Course.objects.get(id=id)\n    context={'id':id,\n             'name': course.name,\n             'desc': course.description,\n             }\n    return render(request, 'course/delete.html', context)\n\ndef process(request):\n    errors = Course.objects.validate(request.POST)\n    if errors:\n        for error in errors:\n            messages.error(request, error)\n    else:\n        print(type(request.POST['teacher']))\n        cap_teacher = request.POST['teacher'].capitalize()\n        # request.POST['teacher'] = cap_teach\n        new_teacher = Teacher.objects.save_teacher(request.POST, cap_teacher)\n        new_course = Course.objects.save_course(request.POST, cap_teacher)\n\n    return redirect('/course/index/')\n\ndef deleted(request, id):\n    course_class = Course.objects.get(id=id)\n    course_class.delete()\n    return redirect('/course/index/')\n","sub_path":"apps/course/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"270556429","text":"\n\ndef string_merge(s):\n    dic, res = {}, []\n    for x in s:\n        t = ''\n        for i in x:\n            c = str((ord(i)-ord(x[0])+26)%26)\n            t = t+c\n        if t in dic:\n            dic[t].append(x)\n        else:\n            dic[t] = [x]\n    for x in dic:\n        res.append(dic[x])\n    return res\n\nif __name__ == \"__main__\":\n    s = [\"abc\", \"bcd\", \"acef\", \"xyz\", \"az\", \"ba\", \"a\", \"z\"]\n    res = string_merge(s)\n    print(s)\n    print(res)\n","sub_path":"exercise/lc/tag/string/249.py","file_name":"249.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"375319182","text":"import xadmin\nfrom django.utils.html import format_html\nfrom .models import MarriageBasic, MarriageCertification, MarriageExam, MarriageTextbook, \\\n    MarriageTuition, MarriageWechat, MarriageClass, MarriageOnduty, Total\nfrom import_export import resources, fields\nfrom import_export.widgets import ForeignKeyWidget, BooleanWidget\nfrom django.apps import apps\nfrom .layouts.detailLayouts import BasicLayout, TuitionLayout\n\n\n# TODO CODEREVIEW 外键后台inline显示的用法\n# class TuitionInline(object):\n#     model = MarriageTuition\n#     extra = 1\n#     style='one'\n#     # readonly_fields=['fee_material', 'fee_exam', 'fee_total',\n#                     'fee_exam_extra', 'fee_date', 'fee_method', 'fee_id']\n\n\n@xadmin.sites.register(MarriageBasic)\nclass BasicAdmin(object):\n    \"\"\"\n    婚姻基本信息\n    \"\"\"\n\n    class MarriageBasicResources(resources.ModelResource):\n        class ClassForeignWidget(ForeignKeyWidget):\n            def get_queryset(self, value, row, *args, **kwargs):\n                return MarriageClass.objects.filter(\n                    class_name__iexact=row[\"mar_class\"]\n                )\n\n        mar_class = fields.Field(\n            attribute='mar_class',\n            column_name='mar_class',\n            widget=ClassForeignWidget(MarriageClass, 'class_name')\n        )\n\n        class Meta:\n            model = MarriageBasic\n            import_id_fields = ('mar_number',)\n            # 导入数据时，如果该条数据未修改过，则会忽略\n            skip_unchanged = True\n            # 在导入预览页面中显示跳过的记录\n            report_skipped = True\n            fields = (\n                'mar_number', 'mar_name', 'mar_gender', 'mar_id_number',\n                'mar_loc', 'mar_deg', 'mar_major',\n                'mar_company', 'mar_duty',\n                'mar_status', 'mar_origin', 'mar_cellphone', 'mar_wechat', 'mar_email',\n                'mar_signup_date', 'mar_signup_people', 'mar_other', 'mar_class')\n\n    list_display = ['mar_number', 'tuition_state', 'mar_gender', 'mar_class', 'mar_class_num', 'mar_type', 'mar_group', 'mar_id_number',\n                    'mar_loc', 'mar_deg',\n                    'mar_major',\n                    'mar_company', 'mar_duty',\n                    'mar_status', 'mar_origin', 'mar_cellphone', 'mar_wechat', 'mar_email',\n                    'mar_signup_date', 'mar_signup_people', 'mar_other']\n    import_export_args = {'import_resource_class': MarriageBasicResources}\n    list_filter = ['mar_number', 'mar_name', 'mar_type', 'mar_group', 'mar_gender', 'mar_class', 'mar_class_num', 'mar_id_number',\n                   'mar_loc', 'mar_deg',\n                   'mar_major',\n                   'mar_company', 'mar_duty',\n                   'mar_status', 'mar_origin', 'mar_cellphone', 'mar_wechat', 'mar_email',\n                   'mar_signup_date', 'mar_signup_people', 'mar_other', 'mar_class__class_name', ]\n    list_editable = list_display\n    search_fields = ['mar_number', 'mar_name', 'mar_class__class_name']\n    show_bookmarks = False  \n\n    def tuition_state(self, obj):\n        info = obj.mar_name\n        if obj.marriagetuition.fee_date == '空':\n            color_code = 'red'\n            # info = '无交费信息'\n        else:\n            color_code = 'black'\n            # info = '已交费'\n        return format_html('<span style=\"color:{};\">{}</span>', color_code, info)\n\n    tuition_state.short_description = '姓名'\n    tuition_state.admin_order_field = 'mar_name'\n\n    # inlines = [TuitionInline]\n    def get_form_layout(self):\n        self.form_layout = BasicLayout\n        return super().get_form_layout()\n\n    # TODO CODEREVIEW Django默认生成的父键查询子键的方法名全部是小写\n    # TODO CODEREVIEW 重写save_models方法\n    # def save_models(self):\n    #     obj = self.new_obj\n    #     obj.save()\n    #     obj.marriagetuition_set.create()\n    #     obj.marriageexam_set.create()\n    #     obj.marriagewechat_set.create()\n    #     obj.marriageexamextra_set.create()\n    #     obj.marriagetextbook_set.create()\n    #     obj.marriagecertification_set.create()\n\n\n@xadmin.sites.register(MarriageClass)\nclass ClassAdmin(object):\n    '''\n    班级信息\n    '''\n\n    class ClassResources(resources.ModelResource):\n        # def __init__(self):\n        #     super().__init__()\n        #     field_list = apps.get_model('marriage', 'MarriageClass')._meta.fields\n        #\n        #     # 应用名与模型名\n        #     self.verbose_name_dict ={}\n        #     # 获取所有字段的verbose_name并存放在verbose_name_dict字典里\n        #     for i in field_list:\n        #         self.verbose_name_dict[i.name] = i.verbose_name\n        #\n        # def get_export_fields(self):\n        #     fields = self.get_fields()\n        #     # 默认导入导出field的column_name为字段的名称\n        #     # 这里修改为字段的verbose_name\n        #     for field in fields:\n        #         field_name = self.get_field_name(field)\n        #         if field_name in self.verbose_name_dict.keys():\n        #             field.column_name = self.verbose_name_dict[field_name]\n        #             # 如果设置过verbose_name，则将column_name替换为verbose_name\n        #             # 否则维持原有的字段名\n        #     return fields\n        class Meta:\n            model = MarriageClass\n            fields = ('class_name', 'class_teacher', 'class_date')\n            # 导入数据时，如果该条数据未修改过，则会忽略\n            skip_unchanged = True\n            # 在导入预览页面中显示跳过的记录\n            report_skipped = True\n            # 模型主键\n            import_id_fields = ('class_name',)\n\n    import_export_args = {'import_resource_class': ClassResources,\n                          }\n    list_display = ['class_name', 'class_index', 'class_id_example', 'class_teacher', 'class_recruit_teacher',\n                    'class_date']\n    list_filter = list_display\n    search_fields = list_display\n    show_bookmarks = False\n    ordering = ['-class_index']\n\n\n@xadmin.sites.register(MarriageTuition)\nclass TuitionAdmin(object):\n    \"\"\"\n    交费信息\n    \"\"\"\n\n    class TuitionResources(resources.ModelResource):\n        class TuitionForeignWidget(ForeignKeyWidget):\n            def get_queryset(self, value, row, *args, **kwargs):\n                return MarriageBasic.objects.filter(\n                    mar_number__iexact=row[\"relate_marriage\"]\n                )\n\n        relate_marriage = fields.Field(\n            attribute='relate_marriage',\n            column_name='relate_marriage',\n            widget=TuitionForeignWidget(MarriageBasic, 'mar_number')\n        )\n\n        class Meta:\n            model = MarriageTuition\n            import_id_fields = ('relate_marriage',)\n            # 导入数据时，如果该条数据未修改过，则会忽略\n            skip_unchanged = True\n            # 在导入预览页面中显示跳过的记录\n            report_skipped = True\n            fields = ('relate_marriage', 'fee_train', 'fee_material', 'fee_exam', 'fee_total', 'fee_date', 'fee_method', 'fee_tax', 'fee_invoice_header',\n                      'fee_invoice_id', 'fee_invoice_date', 'fee_other')\n\n    list_display = ['relate_marriage', 'get_mar_name', 'get_mar_class', \n                    'fee_train', 'fee_material', 'fee_exam', 'fee_total',\n                    'fee_date', 'fee_method', 'fee_tax',\n                    'fee_invoice_header',\n                    'fee_invoice_id', 'fee_invoice_date', 'fee_other'\n                    ]\n    # TODO CODEVIEW filter中外键的处理\n    list_filter = ['relate_marriage__mar_name','fee_train', 'fee_material', 'fee_exam', 'fee_total', 'fee_date', 'fee_method',\n                    'relate_marriage__mar_class__class_name', 'fee_tax', 'fee_invoice_header',\n                    'fee_invoice_id', 'fee_invoice_date', 'fee_other']\n    show_bookmarks = False\n    import_export_args = {'import_resource_class': TuitionResources,\n                          }\n    search_fields = ['relate_marriage__mar_name', 'relate_marriage__mar_number', 'relate_marriage__mar_number', 'relate_marriage__mar_class__class_name']\n    list_editable = ['fee_train', 'fee_material', 'fee_exam', 'fee_total', 'fee_date', 'fee_method', 'fee_id', 'fee_tax', 'fee_invoice_header',\n                     'fee_invoice_id', 'fee_invoice_date', 'fee_other']\n    # readonly_fields = ['relate_marriage']\n\n    def get_form_layout(self):\n        self.form_layout = TuitionLayout\n        return super().get_form_layout()\n\n\n@xadmin.sites.register(MarriageTextbook)\nclass TextbookAdmin(object):\n    \"\"\"\n    教材信息\n    \"\"\"\n\n    class TextbookResources(resources.ModelResource):\n        class TextbookForeignWidget(ForeignKeyWidget):\n            def get_queryset(self, value, row, *args, **kwargs):\n                return MarriageBasic.objects.filter(\n                    mar_number__iexact=row[\"relate_marriage\"]\n                )\n\n        relate_marriage = fields.Field(\n            attribute='relate_marriage',\n            column_name='relate_marriage',\n            widget=TextbookForeignWidget(MarriageBasic, 'mar_number')\n        )\n\n        class Meta:\n            model = MarriageTextbook\n            import_id_fields = ('relate_marriage',)\n            # 导入数据时，如果该条数据未修改过，则会忽略\n            skip_unchanged = True\n            # 在导入预览页面中显示跳过的记录\n            report_skipped = True\n            fields = ('relate_marriage', 'text_basic', 'text_skill', 'text_workbook', 'text_train', 'text_manual', 'text_other')\n\n    import_export_args = {'import_resource_class': TextbookResources, }\n    list_display = ['relate_marriage', 'get_mar_name', 'get_mar_class', 'text_basic', 'text_skill', 'text_workbook', 'text_train', 'text_manual', 'text_other']\n    list_filter = ['relate_marriage__mar_name', 'relate_marriage__mar_number','text_basic', 'text_skill', 'text_workbook', 'text_train', 'text_manual', 'text_other', 'relate_marriage__mar_class__class_name']\n    search_fields = ['relate_marriage__mar_name', 'relate_marriage__mar_number', 'relate_marriage__mar_class__class_name']\n    # readonly_fields = ['relate_marriage']\n    list_editable = ['text_basic', 'text_skill', 'text_workbook', 'text_train', 'text_manual', 'text_other']\n    show_bookmarks = False\n\n\n@xadmin.sites.register(MarriageWechat)\nclass WechatAdmin(object):\n    \"\"\"\n    365开通情况\n    \"\"\"\n\n    class WechatResources(resources.ModelResource):\n        class WechatForeignWidget(ForeignKeyWidget):\n            def get_queryset(self, value, row, *args, **kwargs):\n                return MarriageBasic.objects.filter(\n                    mar_number__iexact=row[\"relate_marriage\"]\n                )\n\n        relate_marriage = fields.Field(\n            attribute='relate_marriage',\n            column_name='relate_marriage',\n            widget=WechatForeignWidget(MarriageBasic, 'mar_number')\n        )\n\n        class Meta:\n            model = MarriageWechat\n            import_id_fields = ('relate_marriage',)\n            # 导入数据时，如果该条数据未修改过，则会忽略\n            skip_unchanged = True\n            # 在导入预览页面中显示跳过的记录\n            report_skipped = True\n            fields = ('relate_marriage', 'wechat_number', 'wechat_nickname', 'wechat_date', 'wechat_other')\n\n    import_export_args = {'import_resource_class': WechatResources, }\n    list_display = ['relate_marriage', 'get_mar_name', 'get_mar_class', 'wechat_number', 'wechat_nickname',\n                    'wechat_date', 'wechat_other']\n    list_filter = ['relate_marriage__mar_name', 'relate_marriage__mar_number','wechat_number', 'wechat_nickname', 'wechat_date', 'relate_marriage__mar_class__class_name', 'wechat_other']\n    search_fields = ['relate_marriage__mar_name', 'relate_marriage__mar_number', 'relate_marriage__mar_class__class_name']\n    # readonly_fields = ['relate_marriage']\n    list_editable = ['wechat_number', 'wechat_nickname', 'wechat_date', 'wechat_other']\n    show_bookmarks = False\n\n\n@xadmin.sites.register(MarriageExam)\nclass ExamAdmin(object):\n    \"\"\"\n    考试信息\n    \"\"\"\n\n    class ExamResources(resources.ModelResource):\n        class ExamForeignWidget(ForeignKeyWidget):\n            def get_queryset(self, value, row, *args, **kwargs):\n                return MarriageBasic.objects.filter(\n                    mar_number__iexact=row[\"relate_marriage\"]\n                )\n\n        relate_marriage = fields.Field(\n            attribute='relate_marriage',\n            column_name='relate_marriage',\n            widget=ExamForeignWidget(MarriageBasic, 'mar_number')\n        )\n\n        class Meta:\n            model = MarriageExam\n            import_id_fields = ('relate_marriage',)\n            # 导入数据时，如果该条数据未修改过，则会忽略\n            skip_unchanged = True\n            # 在导入预览页面中显示跳过的记录\n            report_skipped = True\n            fields = ('relate_marriage', 'batch', 'exam_total', 'exam_nation', 'exam_practice', 'result', 'other')\n\n    import_export_args = {'import_resource_class': ExamResources, }\n    list_display = ['relate_marriage', 'get_mar_name', 'get_mar_id_number', 'get_mar_class', 'batch',\n                    'exam_total', 'exam_nation', 'exam_practice', 'result', 'other']\n    list_filter = ['relate_marriage__mar_name', 'relate_marriage__mar_number', 'relate_marriage__mar_class__class_name',\n                   'batch', 'exam_total', 'exam_nation', 'exam_practice', 'result', 'other']\n    list_editable = ['batch', 'exam_total', 'exam_nation', 'exam_practice', 'result', 'other']\n    show_bookmarks = False\n    search_fields = ['relate_marriage__mar_name', 'relate_marriage__mar_number', 'relate_marriage__mar_class__class_name']\n    # readonly_fields = ['relate_marriage']\n\n\n@xadmin.sites.register(MarriageCertification)\nclass CertificationAdmin(object):\n    \"\"\"\n    证书信息\n    \"\"\"\n\n    class CertificationResources(resources.ModelResource):\n        class CertificationForeignWidget(ForeignKeyWidget):\n            def get_queryset(self, value, row, *args, **kwargs):\n                return MarriageBasic.objects.filter(\n                    mar_number__iexact=row[\"relate_marriage\"]\n                )\n\n        relate_marriage = fields.Field(\n            attribute='relate_marriage',\n            column_name='relate_marriage',\n            widget=CertificationForeignWidget(MarriageBasic, 'mar_number')\n        )\n\n        class Meta:\n            model = MarriageCertification\n            import_id_fields = ('relate_marriage',)\n            # 导入数据时，如果该条数据未修改过，则会忽略\n            skip_unchanged = True\n            # 在导入预览页面中显示跳过的记录\n            report_skipped = True\n            fields = ('relate_marriage', \"ass_cert_id\", \"ass_cert_date\", \"ass_cert_draw_people\", \"ass_cert_draw_date\",\n                \"nation_cert_id\", \"nation_cert_date\", \"nation_cert_draw_people\", \"nation_cert_draw_date\", \"other\" )\n\n    import_export_args = {'import_resource_class': CertificationResources, }\n    list_display = ['relate_marriage', 'get_mar_name', 'get_mar_id_number', 'get_mar_class', \"ass_cert_id\", \"ass_cert_date\", \"ass_cert_draw_people\", \"ass_cert_draw_date\",\n                    \"nation_cert_id\", \"nation_cert_date\", \"nation_cert_draw_people\", \"nation_cert_draw_date\", \"other\" ]\n    list_filter = ['relate_marriage__mar_name', 'relate_marriage__mar_number', \"ass_cert_id\", \"ass_cert_date\", \"ass_cert_draw_people\", \"ass_cert_draw_date\",\n                    \"nation_cert_id\", \"nation_cert_date\", \"nation_cert_draw_people\", \"nation_cert_draw_date\", \"other\", 'relate_marriage__mar_class__class_name']\n    list_editable = [\"ass_cert_id\", \"ass_cert_date\", \"ass_cert_draw_people\", \"ass_cert_draw_date\",\n                    \"nation_cert_id\", \"nation_cert_date\", \"nation_cert_draw_people\", \"nation_cert_draw_date\", \"other\" ]\n    show_bookmarks = False\n    search_fields = ['relate_marriage__mar_name', 'relate_marriage__mar_number', 'relate_marriage__mar_class__class_name']\n    # readonly_fields = ['relate_marriage']\n\n\n@xadmin.sites.register(MarriageOnduty)\nclass MarriageOndutyAdmin(object):\n    \"\"\"\n    出勤信息\n    \"\"\"\n\n    class OndutyResources(resources.ModelResource):\n        class OndutyForeignWidget(ForeignKeyWidget):\n            def get_queryset(self, value, row, *args, **kwargs):\n                return MarriageBasic.objects.filter(\n                    mar_number__iexact=row[\"relate_marriage\"]\n                )\n\n        relate_marriage = fields.Field(\n            attribute='relate_marriage',\n            column_name='relate_marriage',\n            widget=OndutyForeignWidget(MarriageBasic, 'mar_number')\n        )\n\n        class Meta:\n            model = MarriageOnduty\n            import_id_fields = ('relate_marriage',)\n            # 导入数据时，如果该条数据未修改过，则会忽略\n            skip_unchanged = True\n            # 在导入预览页面中显示跳过的记录\n            report_skipped = True\n            fields = ('relate_marriage', 'onduty', 'homework', 'other')\n\n    import_export_args = {'import_resource_class': OndutyResources}\n    list_display = ['relate_marriage', 'get_mar_name', 'get_mar_class', 'onduty', 'homework', 'other']\n    list_filter = ['relate_marriage__mar_name', 'relate_marriage__mar_number', 'relate_marriage__mar_class__class_name']\n    list_editable = ['onduty', 'homework', 'other']\n    show_bookmarks = False\n    search_fields = list_filter\n    reanonly_fields = ['relate_marriage']\n\n\n@xadmin.sites.register(Total)\nclass TotalAdmin(object):\n    \"\"\"\n    总览信息\n    \"\"\"\n    list_display_links = ('mar_name')\n    list_display = [\n        'mar_number', 'mar_name', 'mar_gender', 'mar_class', 'mar_class_num', 'mar_id_number',\n        'mar_type', 'mar_group',\n        'mar_loc', 'mar_deg', 'mar_major',\n        'mar_company', 'mar_duty',\n        'mar_status', 'mar_origin', 'mar_cellphone', 'mar_wechat', 'mar_email',\n        'mar_signup_date', 'mar_signup_people', 'mar_other',\n        'fee_train', 'fee_material', 'fee_exam', 'fee_total', 'fee_date', 'fee_method', 'fee_tax', 'fee_invoice_header',\n        'fee_invoice_id', 'fee_invoice_date', 'fee_other',\n        'text_basic', 'text_skill', 'text_workbook', 'text_train', 'text_manual', 'text_other',\n        'wechat_number', 'wechat_nickname', 'wechat_date', 'wechat_other',\n        'exam_batch', 'exam_total', 'exam_nation', 'exam_practice', 'exam_other',\n        'ass_cert_id', 'ass_cert_date', 'ass_cert_draw_people', 'ass_cert_draw_date',\n        'nation_cert_id', 'nation_cert_date', 'nation_cert_draw_people', 'nation_cert_draw_date', 'cert_other',\n        'ond_onduty', 'ond_homework', 'ond_other',\n    ]\n    show_bookmarks = False\n    list_filter = ['marriage__mar_name', 'marriage__mar_cellphone', 'marriage__mar_class__class_name',\n                   'marriage__marriagetuition__fee_date',\n                   'marriage__marriagewechat__wechat_number',\n                   'marriage__marriagecertification__ass_cert_id',\n                   'marriage__marriagecertification__nation_cert_id']\n\n","sub_path":"apps/marriage/adminx.py","file_name":"adminx.py","file_ext":"py","file_size_in_byte":19208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"474178614","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals, absolute_import\n\n# Create your views here.\n\nimport time\nimport json\n\nfrom decimal import Decimal\nfrom payApi.api.util import (PayUtil, WXQRPayUtil, OrderRequest, OJsonEncoder)\nfrom payApi.api.constants import Constants\nfrom payApi.api.decorator import (api_decorator, login_required, request_log)\nfrom django.utils import (timezone, dateparse)\nfrom django.conf import settings\nfrom django.http import (HttpResponse, HttpResponseBadRequest,\n                         HttpResponseServerError, JsonResponse)\nfrom django.views.decorators.http import require_POST\nfrom .lib import WXPay\nfrom .qrcode import generate_img\nfrom .util import WXPayUtil\nfrom .constants import WXConstants\nfrom .exceptions import SignInvalidException\n\n# 支付方式\n_PAY_FROM = Constants.PAY_FROM['WX']\n\n\ndef pay_qrcode(request):\n    '''\n    生成支付二维码\n    模式一 先生成二维码 接收扫同通知\n    模式二 直接扫码支付\n    '''\n    product_id = int(time.time())\n    wx_pay = WXPay(\n        app_id=settings.WX['APPID'],\n        mch_id=settings.WX['MCH_ID'],\n        key=settings.WX['PRODUCT_KEY'])\n    qrcode = wx_pay.get_qrcode(product_id)\n    qrpath = generate_img(qrcode)\n\n    return HttpResponse(qrpath)\n\n\n@api_decorator\n@request_log\n@require_POST\n@login_required\ndef qr_pay_mode_2(request):\n    '''扫码付款 模式二'''\n    # 交易类型限制\n    _TRADE_TYPE = Constants.WX_TRADE_TYPE['NATIVE']\n\n    # 验证请求参数合法\n    args = json.loads(request.body)\n    PayUtil.check_request_arg(args, _PAY_FROM)\n\n    # 订单号\n    out_trade_no = args.get('out_trade_no')\n\n    # 记录支付请求\n    PayUtil.save_pay_log(\n        out_trade_no=out_trade_no,\n        pay_from=_PAY_FROM,\n        trade_type=_TRADE_TYPE,\n        request=request)\n\n    # 订单信息\n    order_detail = PayUtil.check_order_info(out_trade_no,\n                                            args.get('pay_scene'))\n\n    # 订单号校验失败\n    if not order_detail:\n        return HttpResponseBadRequest(Constants.ORDER_INFO_ERR)\n\n    # 验证可支付订单状态\n    if order_detail.get(\n            'order_status') not in Constants.ORDER_STATUS_AVAILABLE:\n        return HttpResponseBadRequest(Constants.ORDER_STATUS_ERR)\n\n    body = order_detail.get('body')\n    detail = order_detail.get('detail')\n    attach = json.dumps(order_detail.get('attach'))\n    out_trade_no = order_detail.get('out_trade_no')\n    start_time = order_detail.get('start_time')\n    expire_time = dateparse.parse_datetime(order_detail.get('expire_time'))\n    total_fee = Decimal(order_detail.get('total_fee'))\n\n    # 检测订单号支付状态\n    pay_util = PayUtil(\n        app_id=settings.WX['APPID'],\n        out_trade_no=out_trade_no,\n        trade_type=_TRADE_TYPE,\n        pay_from=_PAY_FROM,\n        expire_time=expire_time,\n        order_fee=total_fee)\n\n    if pay_util.is_trade_no_paid():\n        return HttpResponseBadRequest(Constants.ORDER_PAID)\n\n    if pay_util.is_trade_expire():\n        return HttpResponseBadRequest(Constants.ORDER_CLOSED)\n\n    # 记录成功继续逻辑处理 save无返回值，不炸即ok\n    pay_util.save_pay_request()\n\n    # 预先根据订单号判断是否存在可用的二维码文件\n    qrpay_util = WXQRPayUtil(out_trade_no=out_trade_no, total_fee=total_fee)\n\n    qrpath = qrpay_util.get_qrpath()\n\n    if not qrpath:\n        wx_pay = WXPay(\n            app_id=settings.WX['APPID'],\n            mch_id=settings.WX['MCH_ID'],\n            key=settings.WX['PRODUCT_KEY'])\n\n        func = lambda x: timezone.localtime(x).strftime(\n                WXConstants.WX_DATETIME_FORMAT)\n\n        # 交易起始时间\n        time_start = func(dateparse.parse_datetime(start_time))\n\n        # 交易结束时间\n        time_expire = func(expire_time)\n\n        # 返回数据则验证签名成功\n        result = wx_pay.unifiedorder(\n            dict(\n                device_info='WEB-PC',\n                body=body,\n                detail=detail,\n                attach=attach,\n                out_trade_no=out_trade_no,\n                fee_type='CNY',\n                total_fee=PayUtil.unit2cent(total_fee),\n                spbill_create_ip=settings.WX['SPBILL_CREATE_IP'],\n                time_start=time_start,\n                time_expire=time_expire,\n                notify_url=settings.WX['NOTIFY_URL'],\n                trade_type='NATIVE'))\n\n        # 保存预支付订单相应信息\n        result['out_trade_no'] = out_trade_no\n        PayUtil.save_raw_response(result, 'WX_PREPAY')\n\n        # 预支付订单状态正确，只需验证最终result_code\n        if result.get('result_code') == WXConstants.SUCCESS:\n            # 生成二维码数据并记录\n            qrpath = generate_img(result['code_url'], 6)\n            qrpay_util.save_qrpath(\n                qrcode=result.get('code_url'), qrpath=qrpath)\n\n    if qrpath:\n        return JsonResponse({\"qr_path\": qrpath})\n\n    return HttpResponseServerError(Constants.PAY_FAILED)\n\n\n@api_decorator\n@login_required\n@require_POST\ndef qr_pay_mode_1(request):\n    '''扫码付款 接收扫码通知 模式一'''\n    wx_pay = WXPay(\n        app_id=settings.WX['APPID'],\n        mch_id=settings.WX['MCH_ID'],\n        key=settings.WX['PRODUCT_KEY'])\n    post_data = WXPayUtil.xml2dict(request.body)\n    # 验证数据签名有效性\n\n    if wx_pay.is_response_signature_valid(post_data):\n        total_fee = int(3.01 * 100)\n\n        # 返回数据则验证签名成功\n        prepay_result = wx_pay.unifiedorder(\n            dict(\n                device_info='WEB-PC',\n                body=u'十年有效期',\n                detail=u'康复品',\n                attach='',\n                out_trade_no=post_data['product_id'],\n                fee_type='CNY',\n                total_fee=total_fee,\n                spbill_create_ip='123.12.12.123',\n                time_start='',  # 交易起始时间\n                time_expire='',  # 交易结束时间\n                notify_url=settings.WX['NOTIFY_URL'],\n                trade_type='NATIVE',\n                product_id=post_data['product_id'],\n                openid=post_data['openid']))\n\n        # 发起交易\n        # 验证订单后，返回对应数据 result_code判断\n        response_data = dict(\n            return_code=WXConstants.SUCCESS,\n            return_msg='OK',\n            prepay_id=prepay_result['prepay_id'],\n            result_code=WXConstants.SUCCESS,\n            err_code_des='')\n        if False:\n            response_data['result_code'] = WXConstants.FAIL\n            response_data['err_code_des'] = WXConstants.FAIL\n        return HttpResponse(\n            WXPayUtil.dict2xml(wx_pay.fill_request_data(response_data)))\n\n    return HttpResponse('ok')\n\n\n@api_decorator\n@require_POST\ndef pay_notify(request):\n    '''接收异步通知信息'''\n\n    def success_response(msg=\"OK\"):\n        '''业务处理成功'''\n        return HttpResponse(\n            WXPayUtil.dict2xml({\n                \"return_code\": WXConstants.SUCCESS,\n                \"return_msg\": msg,\n            }))\n\n    def fail_response(msg=\"FAIL\"):\n        '''业务处理失败'''\n        return HttpResponse(\n            WXPayUtil.dict2xml({\n                \"return_code\": WXConstants.FAIL,\n                \"return_msg\": msg,\n            }))\n    wx_pay = WXPay(\n        app_id=settings.WX['APPID'],\n        mch_id=settings.WX['MCH_ID'],\n        key=settings.WX['PRODUCT_KEY'])\n    # 签名有效 xml数据验证及解析\n    try:\n        result_dict = wx_pay.process_response_xml(request.body)\n    except SignInvalidException:\n        return fail_response(Constants.SIGN_ERR)\n\n    attach = json.loads(result_dict.get('attach', ''))\n\n    # 回调数据记录\n    PayUtil.save_raw_response(result_dict, 'WX_NOTIFY')\n\n    # 保持通知订单中心的时间统一\n    pay_time = timezone.now()\n\n    # 微信的单位为分，类型为str，转换为decimal类型比较\n    total_fee = Decimal(result_dict.get('total_fee')) / Decimal('100')\n\n    # 预定成功通知内容\n    request_data = {\n        \"transaction_id\": result_dict.get('transaction_id'),\n        \"out_trade_no\": result_dict.get('out_trade_no'),\n        \"pay_status\": result_dict.get('result_code'),\n        \"total_fee\": total_fee,\n        \"pay_type\": _PAY_FROM,\n        \"attach\": attach,\n        \"pay_time\": pay_time,\n    }\n\n    # 验证业务结果 result_code\n    if result_dict.get('result_code') == WXConstants.SUCCESS:\n        status = 'SUCCESS'\n        pay_util = PayUtil(\n            app_id=settings.WX['APPID'],\n            trade_type=result_dict.get('trade_type', ''),\n            out_trade_no=result_dict.get('out_trade_no', ''),\n            pay_from=_PAY_FROM)\n\n        # 业务处理\n        last_unpaid_record = pay_util.get_last_unpaid_record('id', 'order_fee')\n\n        # 没有未支付订单\n        if not last_unpaid_record:\n            return fail_response(Constants.ORDER_INFO_ERR)\n\n        # 验证totalfee合法性\n        if last_unpaid_record.get('order_fee') != total_fee:\n            status = 'FAIL'\n\n        # 更新支付状态\n        save_result = pay_util.save_pay_result(\n            attach=attach,\n            total_fee=total_fee,\n            transaction_id=result_dict.get('transaction_id'),\n            pay_status=Constants.PAY_STATUS[status],\n            pay_time=pay_time)\n\n        # 通知订单中心\n        request_data['pay_status'] = status\n        OrderRequest.order_notify(data=request_data)\n\n        # 保存失败\n        if not save_result:\n            return fail_response(Constants.NOTIFY_FAILED)\n\n        if status == 'SUCCESS':\n            return success_response()\n    else:\n        # 通知订单中心，失败\n        request_data.update({\n            \"err_code\": result_dict.get('err_code'),\n            \"err_code_des\": result_dict.get('err_code_des'),\n        })\n        OrderRequest.order_notify(data=request_data)\n\n    return fail_response()\n","sub_path":"Python/pay_center/payApi/weixin_pay/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"512505981","text":"\nfrom module.converter.converter_module import Converter\nfrom module.editdata.edit_data_module import EditData\nfrom module.extract_data.extract_data_module import Extract_data\nfrom module.findnoise.find_noise_module import NoiseFinder\nfrom helper.find_field_csv import Find_field_csv\nfrom module.write_log.write_log_module import Write_log\nfrom module.fetch_files.fetch_files_module import Fetch_files\nfrom module.inspectdata.inspect_data_module import Inspect_data\nfrom module.rename_file.rename_file_module import Rename_file\n\nimport os\nimport sys\n\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import uic\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\nimport ctypes\n\n##############################\n#massage box 띄우는 건 model에 들어갈 예정\n###############################\ndef Mbox(title=\"알림\", text=\"\", style=0):\n    return ctypes.windll.user32.MessageBoxW(0, text, title, style)\n\n#리스트형태의 데이터를 comboBox에 넣을 수 있게 변형\nclass UserModel(QStandardItemModel):\n    def __init__(self, data=None, parent=None):\n        QStandardItemModel.__init__(self, parent)\n        for i, d in enumerate(data):\n            self.setItem(i, 0, QStandardItem(d))\n\n    def data(self, QModelIndex, role=None):\n        data = self.itemData(QModelIndex)\n        if role == Qt.DisplayRole:\n            return \"%s\" % (data[role])\n        elif role == Qt.UserRole:\n            print(data[role])\n        return QVariant()\n\n\nform_class = uic.loadUiType(\"module/controller/controller.ui\")[0]\nclass Controller(QMainWindow, form_class):\n\n    process_clicked_list = []\n\n    def __init__(self):\n        super().__init__()\n        self.setupUi(self) #버튼이름을 가져옴\n\n        self.get_ui()   # 리스트 멤버 변수에 위젯 할당 (관리하기 편하라고)\n\n        self.connect_function_and_widget() #버튼 담당\n        self.set_listview(\"data/0. data_gp_format\")  # 리스트 뷰 담당\n\n    #멤버 변수 리스트에 정리해두기\n    def get_ui(self):\n        self.activity_widget = [self.activity_listview]\n        self.button_widget_list = [self.convert_button, self.extract_button, self.filter_button, self.output_button, self.inspect_button,\n                                   self.all_process_button, self.refresh_listview_button]\n        self.checkbox_list = [\n            [self.gp_to_och_checkBox, self.och_to_csv_checkBox],\n            [self.min_average_checkBox, self.field_checkBox],\n            [self.find_noise_checkBox, self.edit_data_checkBox],\n            [self.och__checkBox, self.log_checkBox]\n        ]\n\n    def set_listview(self, path_activity_folder):\n\n        activity_list =  os.listdir(path_activity_folder)\n        self.item_model = QStandardItemModel(self.activity_widget[0])\n\n        for activity in activity_list:\n            item = QStandardItem(activity)\n            item.setCheckable(True)\n            self.item_model.appendRow(item)\n\n        self.activity_widget[0].setModel(self.item_model)\n        self.item_model.itemChanged.connect(self.on_listview_item_changed)\n\n    def on_listview_item_changed(self, item):\n\n        tmp_checked_list = []\n        i = 0\n        while self.item_model.item(i):\n            if self.item_model.item(i).checkState(): #check 되어있으면 2를 리턴 아니면 0\n                tmp_checked_list.append(self.item_model.item(i).text())\n            i += 1\n        self.process_clicked_list = tmp_checked_list\n\n    def connect_function_and_widget(self):\n\n        root_gp =  \"data/0. data_gp_format\"\n        root_och = 'data/1. data_och_format/'\n        root_csv = 'data/2. data_csv_format/'\n        root_summarized = 'data/3. data_csv_second_average/'\n        root_for_editted_file = 'data/5. data_csv_cut_error/'\n        root_for_log_excel = 'data/7. data_log_excel/'\n        root_for_field = 'data/8. data_field_find/'\n        root_for_log = 'data/9. data_log_csv/'\n        root_for_noise = 'data/30. data_noise/'\n        root_for_inspect = 'data/31. data_inspect/'\n        path_statu_player= 'data/40. data_statu_player/'\n        root_for_result_och = 'data/100. data_result'\n        path_player_num_info = 'helper/1. number_info/'\n        path_all_field_info = 'helper/output.csv'\n\n        address_ftp_list = [(\"fitogether.co\", 50021, \"kleaguejunior2018\", \"junior2018\")]\n        access_date = 0\n\n        self.button_widget_list[0].clicked.connect(lambda : self.convert_process(root_gp, root_och, root_csv,\n                                                                                 path_player_num_info, root_for_log_excel,\n                                                                                 self.process_clicked_list,\n                                                                                 self.checkbox_list[0][0].isChecked(),self.checkbox_list[0][1].isChecked()\n                                                                                 ))\n        self.button_widget_list[1].clicked.connect(lambda : self.extract_process(root_csv, root_summarized, path_all_field_info, root_for_field,\n                                                                                 self.process_clicked_list,\n                                                                                 self.checkbox_list[1][0].isChecked(),self.checkbox_list[1][1].isChecked()\n                                                                                 ))\n        self.button_widget_list[2].clicked.connect(lambda : self.filter_process(root_summarized, root_for_field, root_for_noise,\n                                                                                root_csv, root_for_editted_file,\n                                                                                self.process_clicked_list,\n                                                                                self.checkbox_list[2][0].isChecked(),self.checkbox_list[2][1].isChecked()\n                                                                                ))\n        self.button_widget_list[3].clicked.connect(lambda: self.output_process(root_for_result_och, root_for_editted_file,     #och 만드는데 필요한 변수\n                                                                               root_summarized, root_for_field, root_for_log,   #로그 만드는데 필요한 변수\n                                                                               path_statu_player,\n                                                                               self.process_clicked_list,\n                                                                               self.checkbox_list[3][0].isChecked(), self.checkbox_list[3][1].isChecked()\n                                                                                ))\n        self.button_widget_list[4].clicked.connect(lambda: self.inspect_process(root_csv, root_for_log_excel, root_for_log, root_for_inspect,\n                                                                                self.process_clicked_list\n                                                                                ))\n        self.button_widget_list[5].clicked.connect(lambda: self.all_process())\n\n        self.button_widget_list[6].clicked.connect(lambda: self.fetch_process(root_gp, address_ftp_list, access_date))\n\n    def convert_process(self, path_root_folder_gp, path_root_folder_och, path_root_folder_csv, path_root_num_info,\n                        root_for_log, name_folder_list, is_gp_to_och=True, is_och_to_csv=True):\n\n        for name_folder in name_folder_list:\n\n            if is_gp_to_och:\n                print(\"Gp_to_och_START\")\n\n                object_converter = Converter()\n                object_converter.convert_gp_to_och(path_root_folder_gp, path_root_folder_och,\n                                                   name_of_dir=name_folder)\n                print(\"Gp_to_och_END\")\n            if is_och_to_csv:\n                print(\"Och_to_csv_START\")\n                object_converter = Converter()\n                object_converter.convert_och_to_csv(path_root_folder_och, path_root_folder_csv,\n                                                    name_of_dir=name_folder)\n\n                object_rename = Rename_file()\n                object_rename.rename_csv_file(path_root_folder_csv, path_root_num_info, root_for_log, name_of_dir=name_folder)\n                print(\"Och_to_csv_END\")\n\n    def extract_process(self, path_root_folder_input, path_root_folder_for_min_average, path_field_info, path_root_folder_for_field,\n                        name_folder_list,\n                        is_min_average=True, is_field=True):\n\n        for name_folder in name_folder_list:\n            path_csv_folder = os.path.join(path_root_folder_input,name_folder).replace(\"\\\\\", \"/\")\n            path_output_folder_for_second_average = os.path.join(path_root_folder_for_min_average, name_folder).replace(\"\\\\\", \"/\")\n            path_output_folder_for_field = os.path.join(path_root_folder_for_field, name_folder).replace(\"\\\\\", \"/\")\n\n            if is_min_average:\n                print(\"Min_average_START\")\n                object_extract = Extract_data()\n                object_extract.summarize_csv(path_csv_folder+\"/\", path_output_folder_for_second_average+\"/\")\n                print(\"Min_average_END\")\n\n            if is_field:\n                print(\"Field_find_START\")\n                object_find_field = Find_field_csv()\n                object_find_field.find_field_csv_folder(path_csv_folder, path_field_info, path_output_folder_for_field)\n                print(\"Field_find_END\")\n\n    def filter_process(self, path_root_folder_summarized_data, path_read_field_folder, path_root_folder_noise,\n                       path_root_folder_to_cut, path_root_folder_for_eddited_files,\n                       name_folder_list,\n                       is_find_noise = True, is_edit_data = True):\n\n\n        for name_folder in name_folder_list:\n            if is_find_noise:\n                print(\"FIND_NOISE_START\")\n                object_noise = NoiseFinder()\n                object_noise.find_noise_in_data(path_root_folder_summarized_data, path_read_field_folder, path_root_folder_noise, name_folder)\n                print(\"FIND_NOISE_END\")\n\n            if is_edit_data:\n                print(\"Edit_Data_START\")\n                object_edit = EditData()\n                object_edit.cut_error(path_root_folder_to_cut, path_root_folder_noise, path_root_folder_for_eddited_files, name_folder)\n                print(\"Edit_Data_END\")\n\n    def output_process(self, path_root_folder_processed_och, path_root_folder_processed_csv,                    #och 만드는데 필요한 변수\n                       path_root_folder_for_min_average, path_root_folder_for_field, path_root_folder_for_log,       #로그 만드는데 필요한 변수\n                       path_write_statu_player,\n                       name_folder_list,\n                       is_och =True, is_log = True\n                       ):\n\n        for name_folder in name_folder_list:\n            if is_och:\n                print(\"OUTPUT_OCH_START\")\n                object_converter = Converter()\n                object_converter.convert_csv_to_och(path_root_folder_processed_csv,path_root_folder_processed_och,name_folder)\n                print(\"OUTPUT_OCH_END\")\n\n            if is_log:\n                print(\"OUTPUT_LOG_START\")\n                Write_log()\n                Object_write_log = Write_log()\n                Object_write_log.detect_playing(path_root_folder_for_min_average, path_root_folder_for_field,\n                                                path_root_folder_for_log, path_write_statu_player, name_folder)\n                print(\"OUTPUT_LOG_END\")\n\n    def inspect_process(self, root_csv, root_for_log_excel, root_for_log, root_for_inspect,\n                       name_folder_list\n                       ):\n\n        for name_folder in name_folder_list:\n            print(\"Inspect_data_START\")\n            object_inspect = Inspect_data()\n            object_inspect.do_inspection(root_for_log_excel, root_for_log, root_csv, root_for_inspect, name_folder)\n            print(\"Inspect_data_END\")\n\n    def fetch_process(self, path_destination_folder = \"data/0. data_gp_format\", address_ftp_list = [(\"fitogether.co\", 50021, \"kleaguejunior2018\", \"junior2018\")], access_date=0):\n\n        object_fetch_files = Fetch_files()\n        object_fetch_files.download_recent_file(path_destination_folder=path_destination_folder,address_ftp_list=address_ftp_list,access_date=access_date)\n\n        self.set_listview(path_destination_folder)  # 리스트 뷰 담당\n  \n        Mbox(\"fetch\", \"다운로드 완료\", 0)\n\n    def all_process(self):\n        print(\"all process start\")\n\n        root_gp = \"data/0. data_gp_format\"\n        root_och = 'data/1. data_och_format/'\n        root_csv = 'data/2. data_csv_format/'\n        root_summarized = 'data/3. data_csv_second_average/'\n        root_for_editted_file = 'data/5. data_csv_cut_error/'\n        root_for_log_excel = 'data/7. data_log_excel/'\n        root_for_field = 'data/8. data_field_find/'\n        root_for_log = 'data/9. data_log_csv/'\n        root_for_noise = 'data/30. data_noise/'\n        root_for_inspect = 'data/31. data_inspect/'\n        path_statu_player= 'data/40. data_statu_player/'\n        root_for_result_och = 'data/100. data_result'\n        path_player_num_info = 'helper/1. number_info/'\n        path_all_field_info = 'helper/output.csv'\n\n        self.convert_process(root_gp, root_och, root_csv,\n                             path_player_num_info, root_for_log_excel,\n                             self.process_clicked_list,\n                             self.checkbox_list[0][0].isChecked(),\n                             self.checkbox_list[0][1].isChecked()\n                             )\n        self.extract_process(root_csv, root_summarized, path_all_field_info, root_for_field,\n                                         self.process_clicked_list,\n                                         self.checkbox_list[1][0].isChecked(), self.checkbox_list[1][1].isChecked()\n                                         )\n        self.filter_process(root_summarized, root_for_field, root_for_noise,\n                                        root_csv, root_for_editted_file,\n                                        self.process_clicked_list,\n                                        self.checkbox_list[2][0].isChecked(), self.checkbox_list[2][1].isChecked()\n                                        )\n        self.output_process(root_for_result_och, root_for_editted_file,  # och 만드는데 필요한 변수\n                            root_summarized, root_for_field, root_for_log,  # 로그 만드는데 필요한 변수\n                            path_statu_player,\n                            self.process_clicked_list,\n                            self.checkbox_list[3][0].isChecked(), self.checkbox_list[3][1].isChecked()\n                            )\n        self. inspect_process(root_csv, root_for_log_excel, root_for_log, root_for_inspect,\n                              self.process_clicked_list\n                              )\n\n        print(\"all process end\\n\")\n\n        Mbox(\"All Process\", \"처리 완료\", 0)\n\n    def manage_token(self, path_root_folder_output, name_folder, process_handled, process_type):\n        pass\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    myWindow = Controller()\n    myWindow.show()\n    app.exec_()\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"module/controller/controller_module.py","file_name":"controller_module.py","file_ext":"py","file_size_in_byte":15424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"307905996","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nEXIF Manipulation\n=================\n\nExif data manipulation routines based on *exiftool*:\n\n-   :func:`parse_exif_data`\n-   :func:`read_exif_tags`\n-   :func:`copy_exif_tags`\n-   :func:`update_exif_tags`\n-   :func:`delete_exif_tags`\n-   :func:`read_exif_tag`\n-   :func:`write_exif_tag`\n\"\"\"\n\nfrom __future__ import division, unicode_literals\n\nimport logging\nimport re\nimport subprocess\n\nfrom colour_hdri.utilities import vivification\n\n__author__ = 'Colour Developers'\n__copyright__ = 'Copyright (C) 2015-2016 - Colour Developers'\n__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'\n__maintainer__ = 'Colour Developers'\n__email__ = 'colour-science@googlegroups.com'\n__status__ = 'Production'\n\n__all__ = ['EXIF_EXECUTABLE',\n           'parse_exif_data',\n           'read_exif_tags',\n           'copy_exif_tags',\n           'update_exif_tags',\n           'delete_exif_tags',\n           'read_exif_tag',\n           'write_exif_tag']\n\nLOGGER = logging.getLogger(__name__)\n\nEXIF_EXECUTABLE = 'exiftool'\n\n\ndef parse_exif_data(data):\n    \"\"\"\n    Parses given exif data output from *exiftool*.\n\n    Parameters\n    ----------\n    data : unicode\n        Exif data.\n\n    Returns\n    -------\n    list\n        Parsed exif data.\n    \"\"\"\n\n    search = re.search(\n        r'\\[(?P<group>\\w+)\\]\\s*(?P<id>(\\d+|-))?(?P<tag>.*?):(?P<value>.*$)',\n        data)\n\n    return map(lambda x: x.strip() if x is not None else x,\n               (search.group('group'),\n                search.group('id'),\n                search.group('tag'),\n                search.group('value')))\n\n\ndef read_exif_tags(image):\n    \"\"\"\n    Returns given image exif image tags.\n\n    Parameters\n    ----------\n    image : unicode\n        Image file.\n\n    Returns\n    -------\n    defaultdict\n        Exif tags.\n    \"\"\"\n\n    LOGGER.info(\"Reading '{0}' image exif data.\".format(image))\n\n    exif_tags = vivification()\n    lines = unicode(subprocess.check_output(\n        [EXIF_EXECUTABLE, '-D', '-G', '-a', '-u', '-n', image]),\n        'utf-8', 'ignore').split('\\n')\n\n    for line in lines:\n        if not line.strip():\n            continue\n\n        group, identifier, tag, value = parse_exif_data(line)\n        exif_tags[group][tag] = (value, identifier)\n\n    return exif_tags\n\n\ndef copy_exif_tags(source, target):\n    \"\"\"\n    Copies given source image file exif tag to given image target.\n\n    Parameters\n    ----------\n    source : unicode\n        Source image file.\n    target : unicode\n        Target image file.\n\n    Returns\n    -------\n    bool\n        Definition success.\n    \"\"\"\n\n    LOGGER.info(\"Copying '{0}' file exif data to '{1}' file.\".format(\n        source, target))\n\n    subprocess.check_output(\n        [EXIF_EXECUTABLE,\n         '-overwrite_original',\n         '-TagsFromFile',\n         '{0}'.format(source),\n         '{0}'.format(target)])\n\n    return True\n\n\ndef update_exif_tags(images):\n    \"\"\"\n    Updates given images siblings images pairs exif tags.\n\n    Parameters\n    ----------\n    images : list\n        Image files to update.\n\n    Returns\n    -------\n    bool\n        Definition success.\n    \"\"\"\n\n    success = True\n    for (source, target) in images:\n        success *= copy_exif_tags(source, target)\n\n    return success\n\n\ndef delete_exif_tags(image):\n    \"\"\"\n    Deletes all given image exif tags.\n\n    Parameters\n    ----------\n    image : unicode\n        Image file.\n\n    Returns\n    -------\n    bool\n        Definition success.\n    \"\"\"\n\n    LOGGER.info(\"Deleting '{0}' image exif tags.\".format(image))\n\n    subprocess.check_output(\n        [EXIF_EXECUTABLE, '-overwrite_original', '-all=', image])\n\n    return True\n\n\ndef read_exif_tag(image, tag):\n    \"\"\"\n    Returns given image exif tag value.\n\n    Parameters\n    ----------\n    image : unicode\n        Image file.\n    tag : unicode\n        Tag.\n\n    Returns\n    -------\n    unicode\n        Tag value.\n    \"\"\"\n\n    value = unicode(subprocess.check_output(\n        [EXIF_EXECUTABLE, '-{0}'.format(tag), image]),\n        'utf-8', 'ignore').split(':').pop().strip()\n\n    LOGGER.info(\"Reading '{0}' image '{1}' exif tag value: '{2}'\".format(\n        image, tag, value))\n\n    return value\n\n\ndef write_exif_tag(image, tag, value):\n    \"\"\"\n    Sets given image exif tag value.\n\n    Parameters\n    ----------\n    image : unicode\n        Image file.\n    tag : unicode\n        Tag.\n    value : unicode\n        Value.\n\n    Returns\n    -------\n    bool\n        Definition success.\n    \"\"\"\n\n    LOGGER.info(\"Writing '{0}' image '{1}' exif tag with '{2}' value.\".format(\n        image, tag, value))\n\n    subprocess.check_output(\n        [EXIF_EXECUTABLE, '-overwrite_original', '-{0}={1}'.format(tag, value),\n         image])\n\n    return True\n","sub_path":"colour_hdri/utilities/exif.py","file_name":"exif.py","file_ext":"py","file_size_in_byte":4737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"283455485","text":"import math\n\nfrom monad import Try\n\n\n# Gets number from console as float.\ndef input_number(number_name):\n\n\t# recover will be called in case of input_number failure,\n\t#   i.e. when user types non-numeric string.\n\tdef recover(exc):\n\t\tprint(exc)\n\t\treturn input_number(number_name)\n\n\tprint('Input {}: '.format(number_name), end='')\n\n\t# user input\n\treturn Try.of_failable(input).map(\n\t\tlambda val: float(val)    # cast string to float\n\t).recover(\n\t\tlambda exc: recover(exc)  # print exception if input string is not numeric\n\t)\n\n\nif __name__ == '__main__':\n\n\tnumber = input_number('number')\n\tbase = input_number('base')\n\tdenominator = input_number('denominator')\n\n\t# Calculates log(number, base) / denominator\n\tTry.of_failable(\n\n\t\t# number can be less or equal to zero which causes the domain error.\n\t\t# base can be less than zero which also causes the domain error.\n\t\tlambda: math.log(number, base)\n\t).map(\n\n\t\t# denominator can be 0, division by zero causes an exception.\n\t\tlambda log_res: log_res / denominator\n\t).map(\n\n\t\t# Print the result of above calculation.\n\t\tlambda result: print('Result is {}'.format(result))\n\t).recover(\n\n\t\t# Handle the generated exception.\n\t\tlambda exc: print(exc)\n\t)\n","sub_path":"task_2/try_task.py","file_name":"try_task.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"43740349","text":"import pandas as pd\n\n\nclassScore = pd.read_csv(\"../data/score_raw.csv\")\n\nemotion = classScore[['Climate', 'Sensitivity', 'Respect']].mean(axis=1)\norganization = classScore[['Behavior', 'Productivity', 'Instruction_Fromat']].mean(axis=1)\ninstruction = classScore[['Content_Understanding', 'Feedback', 'Language']].mean(axis=1)\nquality = classScore[['Climate', 'Sensitivity', 'Respect','Behavior', 'Productivity', 'Instruction_Fromat','Content_Understanding', 'Feedback', 'Language']].mean(axis=1)\n\n\nnew_set = pd.DataFrame({'quality':quality, 'emotion':emotion,'organization':organization,'instruction':instruction,'Whole_Class':classScore['Whole_Class'],'Individual':classScore['Individual'],'Multiple':classScore['Multiple'],'Peers':classScore['Peers'],'Task':classScore['Task']})\nnew_set_format = new_set.applymap(\"{0:.2f}\".format)\npearson_new_set = new_set.corr().applymap(\"{0:.2f}\".format)\nspearman_new_set = new_set.corr(method='spearman').applymap(\"{0:.2f}\".format)\n\nnew_set_format.to_csv(\"../data/new_set_quality.csv\", index=False)\npearson_new_set.to_csv(\"../data/pearson_new_set_quality.csv\", index=False)\nspearman_new_set.to_csv(\"../data/spearman_new_set_quality.csv\", index=False)\n","sub_path":"python/qualityAnalysis.py","file_name":"qualityAnalysis.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"130762615","text":"import numpy as np\n\n\ndef get_diff_forward(fxs):\n  ret = [fxs[0]]\n  while True:\n    fxs = (np.roll(fxs,-1) - fxs)[:-1]\n    if len(fxs) == 0:\n      break\n    ret.append(fxs[0])\n  return ret\n\ndef get_factorials(n):\n  ret = [1]\n  for i in range(1, n+1):\n    ret.append(ret[-1] * i)\n  return ret\n\ndef newton_diff_solver(t, h, n, diff):\n  fac = get_factorials(n)\n  def solver(i, sofar):\n    if i == 0:\n      return sofar + diff[0]\n    else:\n      temp = (sofar + diff[i]/fac[i])*(t-i+1)\n      print(temp)\n      return solver(i-1, temp)\n  return solver(n, 0)\n\n\nfxs = [1.0, 0.995, 0.98007, 0.95534, 0.92106, 0.87758]\ndiff = get_diff_forward(fxs)\nh = 0.1\nn = 5\nt = 0.48\nresult = newton_diff_solver(t, h, n, diff)\nprint(result)","sub_path":"newton-difference.py","file_name":"newton-difference.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"475356413","text":"# !/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# author: chinshin\r\n# datetime: 2020/4/20 15:28\r\nimport os\r\nimport tqdm\r\nimport torch\r\nimport pickle\r\nimport random\r\nimport numpy as np\r\nimport scipy.sparse as sp\r\nfrom typing import Dict, List\r\nfrom collections import OrderedDict\r\nfrom torch.utils.data import Dataset\r\nfrom rdkit import Chem\r\nfrom rdkit.Chem.rdmolops import GetAdjacencyMatrix\r\nimport networkx as nx\r\nfrom .masking import MolSpanMaskingScheme\r\n\r\n\r\nclass MolBertDataset(Dataset):\r\n    # add option 'adj_path' and 'with_adj'\r\n    def __init__(self, corpus_path, vocab, seq_len,\r\n                 with_span=False, with_mask=False, with_proximity=False, adj_path=None,\r\n                 geometric_p=0.2, span_lower=1, span_upper=5,\r\n                 encoding=\"utf-8\", corpus_lines=None, on_memory=True):\r\n\r\n        assert ((with_span or with_mask or with_proximity) and adj_path is not None) \\\r\n               or (not (with_span or with_mask or with_proximity) and adj_path is None)\r\n\r\n        self.corpus_path = corpus_path\r\n        self.vocab = vocab\r\n        self.seq_len = seq_len\r\n        self.with_span = with_span\r\n        self.with_mask = with_mask\r\n        self.with_proximity = with_proximity\r\n        self.adj_path = adj_path\r\n        self.geometric_p = geometric_p\r\n        self.span_lower = span_lower\r\n        self.span_upper = span_upper\r\n\r\n        self.encoding = encoding\r\n        self.corpus_lines = corpus_lines\r\n        self.on_memory = on_memory\r\n\r\n        with open(corpus_path, \"r\", encoding=encoding) as f:\r\n            if self.corpus_lines is None and not on_memory:\r\n                for _ in tqdm.tqdm(f, desc=\"Loading Dataset\", total=corpus_lines):\r\n                    self.corpus_lines += 1\r\n\r\n            if on_memory:\r\n                # -1 in order to strip '\\n'\r\n                # self.lines = [line[:-1].split(\"\\t\")\r\n                #               for line in tqdm.tqdm(f, desc=\"Loading Dataset\", total=corpus_lines)]\r\n                self.lines = [line[:-1]\r\n                              for line in tqdm.tqdm(f, desc=\"Loading Dataset\", total=corpus_lines)]\r\n                self.corpus_lines = len(self.lines)\r\n\r\n        if not on_memory:\r\n            self.file = open(corpus_path, \"r\", encoding=encoding)\r\n            self.random_file = open(corpus_path, \"r\", encoding=encoding)\r\n\r\n            for _ in range(random.randint(self.corpus_lines if self.corpus_lines < 1000 else 1000)):\r\n                self.random_file.__next__()\r\n\r\n        if adj_path is not None:\r\n            if os.path.getsize(adj_path) > 0:\r\n                with open(adj_path, \"rb\") as reader:\r\n                    unpickler = pickle.Unpickler(reader)\r\n                    self.adjs = unpickler.load()\r\n            else:\r\n                print('Empty file...')\r\n        else:\r\n            self.adjs = None\r\n\r\n    def __len__(self):\r\n        return self.corpus_lines\r\n\r\n    def __getitem__(self, item):\r\n        t = self.random_sent(item)\r\n        if self.adjs is not None:\r\n            adj = self.adjs[item]\r\n            adj = adj.todense()\r\n            if not self.with_span:\r\n                t_gen, t_label, masked_adj, candidate_mask = self.gen_word(t, adj)\r\n            else:\r\n                t_random, t_label, masked_adj = self.span_mask_word(t, adj, self.vocab)\r\n        else:\r\n            t_gen, t_label, candidate_mask = self.gen_word(t)\r\n\r\n        # add 'sos_index' and 'eos_index‘\r\n        # mask operation for masked language model\r\n        t = [self.vocab.sos_index] + t_gen + [self.vocab.eos_index]\r\n        t_label = [self.vocab.sos_index] + t_label + [self.vocab.eos_index]\r\n        candidate_mask = [0] + candidate_mask + [0]\r\n\r\n        # truncate\r\n        segment_label = ([1 for _ in range(len(t))])[:self.seq_len]\r\n        # print(t, t_label, segment_label)\r\n        bert_input = t[:self.seq_len]\r\n        bert_label = t_label[:self.seq_len]\r\n        bert_mask = candidate_mask[:self.seq_len]\r\n\r\n        padding = [self.vocab.pad_index for _ in range(self.seq_len - len(bert_input))]\r\n        padding_mask = [0 for _ in range(self.seq_len - len(bert_input))]\r\n        bert_input.extend(padding), bert_label.extend(padding), segment_label.extend(padding)\r\n        bert_mask.extend(padding_mask)\r\n\r\n        output = {\r\n            \"bert_input\": bert_input,\r\n            \"bert_label\": bert_label,\r\n            \"segment_label\": segment_label,\r\n            \"bert_mask\": bert_mask,\r\n        }\r\n\r\n        if self.with_mask or self.with_proximity or self.with_span:\r\n            # adj = self.adjs[item]\r\n            # version 1\r\n            # adj = self.process_adj(adj)\r\n            # version 2\r\n            # adj = self.process_adj_v2(adj)\r\n            # truncation operation for consistent length\r\n            masked_adj = self.process_adj_v2(masked_adj)\r\n            bert_adj = self.process_adj_v2(adj)\r\n            output.update({\"masked_adj\": masked_adj})\r\n            output.update({\"bert_adj\": bert_adj})\r\n\r\n        return {key: torch.tensor(value) for key, value in output.items()}\r\n\r\n    def gen_word(self, sentence, adj=None):\r\n        tokens = sentence.split()\r\n        output_label = []\r\n\r\n        masked_indices = []\r\n        candidate_mask = []\r\n        for i, token in enumerate(tokens):\r\n            prob = random.random()\r\n            if prob < 0.15:\r\n                prob /= 0.15\r\n\r\n                # 80% randomly change token to mask token\r\n                if prob < 0.8:\r\n                    tokens[i] = self.vocab.mask_index\r\n                    masked_indices.append(i)\r\n                    # masked_lm_labels.append(i)\r\n\r\n                # 10% randomly change token to random token\r\n                elif prob < 0.9:\r\n                    tokens[i] = random.randrange(len(self.vocab))\r\n                    masked_indices.append(i)\r\n                    # masked_lm_labels.append(i)\r\n\r\n                # 10% randomly change token to current token\r\n                else:\r\n                    tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)\r\n\r\n                output_label.append(self.vocab.stoi.get(token, self.vocab.unk_index))\r\n                candidate_mask.append(0)\r\n\r\n            else:\r\n                tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)\r\n                output_label.append(0)\r\n                candidate_mask.append(1)\r\n        # print(output_label)\r\n        if adj is not None:\r\n            for index in masked_indices:\r\n                # row\r\n                adj[index, :] = 0\r\n                # col\r\n                adj[:, index] = 0\r\n\r\n            return tokens, output_label, adj, candidate_mask\r\n\r\n        return tokens, output_label, candidate_mask\r\n\r\n    def span_mask_word(self, sentence, adj, vocab):\r\n        tokens = sentence.split(' ')\r\n        scheme = MolSpanMaskingScheme(self.geometric_p, 1, int(len(tokens) * 0.2))\r\n        tokens, output_label, masked_adj = scheme.mask(tokens, adj, vocab)\r\n        return tokens, output_label, masked_adj\r\n\r\n    def random_sent(self, index):\r\n        t = self.get_corpus_line(index)\r\n        return t\r\n\r\n    def get_corpus_line(self, item):\r\n\r\n        if self.on_memory:\r\n            return self.lines[item]\r\n        else:\r\n            line = self.file.__next__()\r\n            if line is None:\r\n                self.file.close()\r\n                self.file = open(self.corpus_path, \"r\", encoding=self.encoding)\r\n                line = self.file.__next__()\r\n\r\n            t = line[:-1]\r\n            return t\r\n\r\n    def process_adj(self, adj: Dict[str, List[str]]) -> np.ndarray:\r\n        \"\"\"\r\n        relative position matters.\r\n        :param adj:\r\n        :return:\r\n        \"\"\"\r\n        fps = list(adj.keys())\r\n        num_fps = len(fps)\r\n        fp_adj = np.zeros((self.seq_len, self.seq_len), dtype=np.float32)\r\n        # account for sos_index, we must '+1'\r\n        fp2idx = {fp: idx + 1 for idx, fp in enumerate(fps)}\r\n        # processing 'sos_index'\r\n        # first row\r\n        fp_adj[0, :(num_fps + 2)] = 1.0\r\n        # first column\r\n        fp_adj[:(num_fps + 2), 0] = 1.0\r\n        # processing 'eos_index'\r\n        # last row\r\n        fp_adj[(num_fps + 1), :(num_fps + 2)] = 1.0\r\n        # last column\r\n        fp_adj[:(num_fps + 2), (num_fps + 1)] = 1.0\r\n        # process indices except 'sos_index' and 'eos_index'\r\n        for fp in fps:\r\n            idx = fp2idx[fp]\r\n            # self-loop\r\n            fp_adj[idx, idx] = 1.0\r\n            adj_fps = adj[fp]\r\n            adj_indices = [fp2idx[adj_fp] for adj_fp in adj_fps]\r\n            for adj_idx in adj_indices:\r\n                fp_adj[idx, adj_idx] = 1.0\r\n                fp_adj[adj_idx, idx] = 1.0\r\n        return fp_adj\r\n\r\n    def process_adj_v2(self, adj: sp.coo_matrix) -> np.ndarray:\r\n        \"\"\"\r\n        relative position matters.\r\n        :param adj:\r\n        :return:\r\n        \"\"\"\r\n        if isinstance(adj, sp.coo_matrix):\r\n            adj = adj.todense()\r\n        num_fps = adj.shape[0]\r\n        fp_adj = np.zeros((self.seq_len, self.seq_len), dtype=np.float32)\r\n        # processing 'sos_index'\r\n        # first row\r\n        fp_adj[0, :(num_fps + 2)] = 1.0\r\n        # first column\r\n        fp_adj[:(num_fps + 2), 0] = 1.0\r\n        # processing 'eos_index'\r\n        # last row\r\n        fp_adj[(num_fps + 1), :(num_fps + 2)] = 1.0\r\n        # last column\r\n        fp_adj[:(num_fps + 2), (num_fps + 1)] = 1.0\r\n        # process indices except 'sos_index' and 'eos_index'\r\n        fp_adj[1: (1 + num_fps), 1: (1 + num_fps)] = adj\r\n\r\n        return fp_adj\r\n\r\n    # def convert_tokens_to_ids(self, tokens):\r\n    #     \"\"\"Converts a sequence of tokens into ids using the vocab.\"\"\"\r\n    #     ids = []\r\n    #     for token in tokens:\r\n    #         ids.append(self.vocab[token])\r\n    #     if len(ids) > self.seq_len:\r\n    #         logger.warning(\r\n    #             \"Token indices sequence length is longer than the specified maximum \"\r\n    #             \" sequence length for this BERT model ({} > {}). Running this\"\r\n    #             \" sequence through BERT will result in indexing errors\".format(len(ids), self.seq_len)\r\n    #         )\r\n    #     return ids","sub_path":"bert/dataset/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":10092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"491223012","text":"#!/usr/bin/python3\n\nfrom device import Device\n\nPCA9557_ADDRESS = 0x18\n\nclass GpioExtender(Device):\n  def __init__(self):\n    Device.__init__(self, PCA9557_ADDRESS)\n\n  def test(self):\n    self.write8(0x02, 0x08);\n    self.write8(0x03, 0xe1);\n    self.write8(0x01, 0x00);\n    self.write8(0x01, 0xff);\n\ng = GpioExtender()\ng.test()\n\n","sub_path":"pca9557.py","file_name":"pca9557.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"23166652","text":"#!/usr/bin/env python3\nimport csv\nimport sys\nimport re\nimport string\nimport collections\nfrom collections import Counter\nimport optparse \nimport time\nimport parseVersion\n\ncsv.field_size_limit(sys.maxsize)\ndef writeToShort(file, date, ipp, reason, reasons, clid, partner, brand, vers, os, email):\n    file.write('\"' + date+ '\";\"' + ipp + '\";\"' + reason + '\";\"' + reasons + '\";\"' + clid + '\";\"' + partner + '\";\"' + brand + '\";\"' + vers + '\";\"' + os + '\";\"' + email + '\"\\n')\n    \n#Парсер параметров\nparser = optparse.OptionParser()\nparser.add_option(\"-v\", \"--version\", dest = \"version\", action = \"store\",\n    help= \"selected version\")\nparser.add_option(\"-w\", \"--word\", dest = \"word\", action = \"store\",\n    help= \"selected version\")\nparser.add_option(\"-d\", \"--date\", dest = \"date\", action = \"store\",\n    help= \"date start\")\nparser.add_option(\"-a\", \"--answer\", dest = \"answer\", action = \"store\",\n    help= \"curtain answer\")\nparser.add_option(\"-e\", \"--equal\", dest = \"equal\", action = \"store_true\", default = False,\n    help= \"full equal version\")\nparser.add_option(\"-t\", \"--type\", dest = \"type\", action = \"store\", default = \"current\",\n    help= \"Type of survey to parse\")\nparser.add_option(\"-p\", \"--partner\", dest = \"partner\", action = \"store\",\n    help= \"Partner ID\")\n(options, args) = parser.parse_args()\nneedVersion=options.version\n\nimport ranges\n#Номера столбцов\nr = ranges.Ranges(options.type)\ndate=r.date\nreason=r.reason\nversion=r.version\nos=r.os\nip=r.ip\nemail=r.email\nclid=r.clid\npartner_id=r.partner_id\nbrand_id=r.brand_id\nreasonsRange=r.reasonsRange\n\nreasons=\"\" #Все причины в одной строке\n\n#Открытие и обнуление файлов\ngoodbye = csv.reader(open('Sheet_1.csv'), delimiter=',', quotechar='\"')\nreasonsLong = open('reasonsLong.csv', 'w')\nreasonsShort = open('reasonsShort.csv', 'w')\nnumbersF = open('numbers.csv', 'w')\nnumbersReasonF = open('ReasonNumbers.csv', 'w')\nreasonsLong.truncate()\nreasonsShort.truncate()\nnumbersF.truncate()\n\nnumbersF.write('Reasons:;\\n')\ni=0\ncnt = Counter()\nrsnCnt = {}\nreasonsShort.write('Date;IP;Reason;Reasons;Version;clid;partner;brand;OS;E-mail\\n')\nnames = []\nrsnCnt[needVersion] = Counter()\nrsnCnt['other'] = Counter()\nfor row in goodbye:\n    i+=1\n    if (i==2):\n        numbersReasonF.write('Version;')\n        for j in reasonsRange:\n            names.append(row[j])\n            numbersReasonF.write(row[j] + \";\")\n        numbersReasonF.write('All;')\n        numbersReasonF.write('\\n')\n    if (i>2):\n        ipp=row[ip]\n        #import urllib.request\n        #with urllib.request.urlopen('http://api.hostip.info/get_html.php?ip='+ipp+'&position=true') as url:\n        #    ipp = url.read()\n        vers=parseVersion.parseVersion(row[version])\n        flag=True;\n        if options.date:\n            if (time.strptime(options.date, \"%d.%m.%Y\") > time.strptime(row[date], \"%m/%d/%Y %H:%M:%S\")):\n                flag=False\n                \n        if flag:\n            if options.version:\n                flagNeedVersion=(not(options.equal) and (needVersion in vers)) or (options.equal and (needVersion==vers))\n                if flagNeedVersion:\n                    reasons=reasons + row[reason]+' '\n                    cnt[needVersion]+=1;\n                else:   \n                    cnt['other']+=1;\n                for j in reasonsRange:\n                    if (row[j]==str(j-8)):\n                        if flagNeedVersion:\n                            rsnCnt[needVersion][names[j-9]]+=1\n                        else:\n                            rsnCnt['other'][names[j-9]]+=1\n                        \n        flag=True\n        if (row[reason]==''): \n            flag=False\n        if options.answer:\n            if ((row[9-1+int(options.answer)])!=\"\"):\n                flag=True\n            else:\n                flag=False\n        if options.partner:\n            if not re.search(options.partner, row[partner_id]):\n                flag=False\n        if options.word:\n            if not re.search(options.word, row[reason]):\n                flag=False\n        if options.version:\n            if not re.search(options.version, vers):\n                flag=False\n        if options.date:\n            if (time.strptime(options.date, \"%d.%m.%Y\") > time.strptime(row[date], \"%m/%d/%Y %H:%M:%S\")):\n                flag=False\n        \n        if flag:\n            rr=\"\"\n            for kk in reasonsRange:\n                try:\n                    if str(kk-8)==row[kk]:\n                        rr=rr+row[kk]+\". \" + names[kk-9]+\"\\n\"\n                except ValueError:\n                    rr=rr\n            writeToShort(reasonsShort, row[date], str(ipp),row[reason],rr,vers,row[clid],row[partner_id],row[brand_id],row[os],row[email])\n        \n    reasonsLong.write(';'.join(row))\n    reasonsLong.write('\\n')\nif options.version:\n    for ke, val in rsnCnt.items():\n        ccc=0\n        numbersReasonF.write(str(ke) + ';')\n        for j in reasonsRange:\n            numbersReasonF.write(str(val[names[j-9]])+';')\n            ccc=ccc+val[names[j-9]];\n        numbersReasonF.write(str(ccc)+';')\n        numbersReasonF.write('\\n')\n        numbersReasonF.write(str(ke) + ';')\n        for j in reasonsRange:\n            if (ccc!=0):\n                numbersReasonF.write(str(val[names[j-9]]/ccc).replace('.',',')+';')\n            else:\n                numbersReasonF.write(';')\n        numbersReasonF.write('\\n')\n\nnumbersF.write('Count:;\\n')\nfor k,v in cnt.items():\n    numbersF.write(str(k) + ';' + str(v)+';' + str(v/(i-2)).replace('.',',') + '\\n')\n    #print(str(k) + ':   ' + str(int(1000*v/(i-2))/10)+'\\n')\n    ","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":5611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"201851599","text":"from .tests_base import TestsBase\n\nfrom nose.tools import assert_equal, assert_true, raises\n\nfrom arango.clients import Client\nfrom arango.collection import Collection, Collections\nfrom arango.utils import json\nfrom arango.exceptions import CollectionIdAlreadyExist, InvalidCollectionId, \\\n    InvalidCollection\n\n\nclass TestCollectionProxy(TestsBase):\n    def test_proxy(self):\n\n        assert_equal(\n            self.conn.collection.__class__,\n            Collections\n        )\n\n        assert_equal(\n            self.conn.collection.test.__class__,\n            Collection\n        )\n\n        assert_equal(\n            self.conn.collection.test.cid,\n            Collection(\n                connection=self.conn,\n                name=\"test\"\n            ).cid\n        )\n\n    def test_repr(self):\n        assert_equal(\n            str(self.conn.collection),\n            \"<Collections proxy for {0}>\".format(self.conn)\n        )\n\n    def test_collections_list(self):\n        collection = self.conn.collection()\n\n        assert_equal(\n            collection,\n            []\n        )\n\n\nclass TestCollection(TestsBase):\n    def setUp(self):\n        super(TestCollection, self).setUp()\n        self.c = self.conn.collection.test\n\n    def test_cid(self):\n        assert_equal(self.c.cid, \"test\")\n\n    def test_create(self):\n        self.c.create()\n        url = \"{0}{1}\".format(\n            self.conn.url,\n            self.c.CREATE_COLLECTION_PATH)\n\n        test_data = {\"name\": \"test\", \"waitForSync\": False}\n        test_args = {\"data\": json.dumps(test_data)}\n\n        assert_true(Client.post.called)\n        assert_equal(Client.post.call_args[0][0], url)\n        assert_equal(Client.post.call_args[1], test_args)\n\n    def test_load(self):\n        self.c.load()\n        url = \"{0}{1}\".format(\n            self.conn.url,\n            self.c.LOAD_COLLECTION_PATH.format(self.c.name)\n        )\n\n        assert_equal(Client.put.call_args[0][0], url)\n\n    def test_unload(self):\n        self.c.unload()\n        url = \"{0}{1}\".format(\n            self.conn.url,\n            self.c.UNLOAD_COLLECTION_PATH.format(self.c.name)\n        )\n\n        assert_equal(Client.put.call_args[0][0], url)\n\n    def test_delete(self):\n        self.c.delete()\n        url = \"{0}{1}\".format(\n            self.conn.url,\n            self.c.DELETE_COLLECTION_PATH.format(self.c.name)\n        )\n\n        assert_equal(Client.delete.call_args[0][0], url)\n\n    def test_truncate(self):\n        self.c.truncate()\n\n        url = \"{0}{1}\".format(\n            self.conn.url,\n            self.c.TRUNCATE_COLLECTION_PATH.format(self.c.name)\n        )\n\n        assert_equal(Client.put.call_args[0][0], url)\n\n    def test_info(self):\n        assert_equal(\n            self.c.info(resource=\"wrong\"),\n            self.c.info())\n\n    def test_properties(self):\n        self.c.properties(waitForSync=True)\n\n        url = \"{0}{1}\".format(\n            self.conn.url,\n            self.c.PROPERTIES_COLLECTION_PATH.format(self.c.name)\n        )\n\n        assert_equal(Client.put.call_args[0][0], url)\n\n        test_data = {\"waitForSync\": True}\n        test_args = {\"data\": json.dumps(test_data)}\n\n        self.c.properties(waitForSync=True)\n        assert_equal(Client.put.call_args[0][0], url)\n        assert_equal(Client.put.call_args[1], test_args)\n\n        self.c.properties()\n        assert_equal(Client.get.call_args[1], {})\n\n    def test_rename(self):\n        test_data = {\"name\": \"test1\"}\n        test_args = {\"data\": json.dumps(test_data)}\n\n        url = \"{0}{1}\".format(\n            self.conn.url,\n            self.c.RENAME_COLLECTION_PATH.format(self.c.name))\n\n        self.c.rename(name=\"test1\")\n\n        assert_equal(Client.post.call_args[0][0], url)\n        assert_equal(Client.post.call_args[1], test_args)\n\n    def test_rename_manual_collection(self):\n        c = Collection(connection=self.c.connection, name=\"manual\")\n        self.conn.collection.rename_collection(c, \"sample\")\n\n        assert_true(\"sample\" in self.conn.collection.collections)\n\n    @raises(CollectionIdAlreadyExist)\n    def test_rename_collection_with_exist_name(self):\n        self.conn.collection.rename_collection(self.c, \"test\")\n\n    @raises(InvalidCollection)\n    def test_rename_wrong_collection(self):\n        self.conn.collection.rename_collection(object(), \"test\")\n\n    @raises(InvalidCollectionId)\n    def test_rename_empty_collection(self):\n        self.conn.collection.test.rename(\"\")\n\n    def test_count(self):\n        assert_equal(self.c.count(), 0)\n        assert_equal(self.c.count(), len(self.c))\n\n    def test_repr(self):\n        exp_repr = \"<Collection '{0}' for {1}>\".format(\"test\", self.conn)\n        assert_equal(repr(self.c), exp_repr)\n","sub_path":"arango/tests/tests_collection.py","file_name":"tests_collection.py","file_ext":"py","file_size_in_byte":4687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"582111818","text":"#! coding:utf-8\n'''\niterable & iterator\n'''\n\na = [i*i for i in range(10)]  #列表生成式\nb = (i*i for i in range(10))  #生成器,可迭代对象\ndef func1(max):  #生成器,可迭代\n    n, a, b = 0, 0 ,1\n    while n < max:\n        yield b\n        a, b = b, a+b\n\nfrom collections.abc import Iterable,Iterator\nprint(isinstance([], Iterable))\n\nc = iter(a)\nprint(next(c))\nprint(next(c))\nprint(next(c))\n\n#生成器和迭代器都可以用next方法来调用,但是迭代器使用迭代的时候结束会报错.\n\n\n","sub_path":"for play example/iterator.py","file_name":"iterator.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"256863568","text":"import os\nimport sys\n\nsys.path.append(os.path.dirname(__file__))\nimport sourcegraph_lib\nimport sublime\nimport sublime_plugin\n\nfrom threading import Thread\n\nSETTINGS_FILENAME = 'Sourcegraph.sublime-settings'\nGOSUBLIME_SETTINGS_FILENAME = 'GoSublime.sublime-settings'\nSUBLIME_VERSION = int(sublime.version())\n\nSG_LIB_INSTANCE = {}\n\n\ndef find_gopath_from_gosublime():\n\tif sublime.load_settings(GOSUBLIME_SETTINGS_FILENAME).has('env'):\n\t\tgosubl_env = sublime.load_settings(GOSUBLIME_SETTINGS_FILENAME).get('env')\n\t\tif 'GOPATH' in gosubl_env:\n\t\t\treturn gosubl_env['GOPATH'].replace('$HOME', sourcegraph_lib.get_home_path()).replace(':$GS_GOPATH', '')\n\treturn None\n\nclass SfprintCommand(sublime_plugin.TextCommand):\n\tdef run(self, edit, error_message):\n\t\tself.view.insert(edit, self.view.size(), error_message)\n\ndef error_callback(error_message):\n\tsublime.active_window().destroy_output_panel('sg_errors')\n\terror_panel = sublime.active_window().find_output_panel('sg_errors')\n\tif not error_panel:\n\t\terror_panel = sublime.active_window().create_output_panel('sg_errors')\n\tsublime.active_window().run_command(\"show_panel\", {\"panel\": \"output.sg_errors\", \"toggle\": False})\n\terror_panel.run_command('sfprint', {\"error_message\": error_message})\n\treturn None\n\ndef success_callback():\n\tsublime.active_window().destroy_output_panel('sg_errors')\n\ndef load_settings(settings):\n\tsourcegraph_lib.SG_LOG_FILE = '/tmp/sourcegraph-sublime.log'\n\tsourcegraph_lib.ERROR_CALLBACK = error_callback\n\tsourcegraph_lib.SUCCESS_CALLBACK = success_callback\n\tsg_settings = sourcegraph_lib.Settings(EditorType=\"sublime\")\n\tif settings.has('LOG_LEVEL'):\n\t\tsourcegraph_lib.LOG_LEVEL = settings.get('LOG_LEVEL')\n\t\tsourcegraph_lib.log_output('[settings] Found logging setting in Settings file: %s' % sourcegraph_lib.LOG_LEVEL)\n\tif settings.has('ENABLE_LOOKBACK'):\n\t\tsg_settings.ENABLE_LOOKBACK = settings.get('ENABLE_LOOKBACK')\n\tif settings.has('SG_BASE_URL'):\n\t\tsg_settings.SG_BASE_URL = settings.get('SG_BASE_URL').rstrip('/')\n\tif settings.has('SG_SEND_URL'):\n\t\tsg_settings.SG_SEND_URL = settings.get('SG_SEND_URL').rstrip('/')\n\tif settings.has('SG_LOG_FILE'):\n\t\tsourcegraph_lib.SG_LOG_FILE = settings.get('SG_LOG_FILE')\n\tif settings.has('AUTO'):\n\t\tsg_settings.AUTO = settings.get('AUTO')\n\tif settings.has('GOBIN'):\n\t\tsg_settings.GOBIN = settings.get('GOBIN').rstrip(os.sep)\n\tshell_gopaths = sourcegraph_lib.find_gopath_from_shell(sg_settings.ENV.get('SHELL'))\n\tif settings.has('GOPATH'):\n\t\tsg_settings.ENV['GOPATH'] = str(settings.get('GOPATH').rstrip(os.sep)).strip()\n\t\tsourcegraph_lib.log_output('[settings] Using GOPATH found in Sublime settings file: %s' % sg_settings.ENV['GOPATH'])\n\telif shell_gopaths:\n\t\tsg_settings.ENV['GOPATH'] = os.pathsep.join(shell_gopaths)\n\t\tsourcegraph_lib.log_output('[settings] Using GOPATH from shell: %s' % sg_settings.ENV['GOPATH'])\n\telif find_gopath_from_gosublime():\n\t\tsg_settings.ENV['GOPATH'] = find_gopath_from_gosublime()\n\t\tsourcegraph_lib.log_output('[settings] Found GOPATH in GoSublime settings: %s' % sg_settings.ENV['GOPATH'])\n\tif 'GOPATH' in sg_settings.ENV and sg_settings.ENV.get('GOPATH') != '':\n\t\tsg_settings.ENV['GOPATH'] = sg_settings.ENV['GOPATH'].replace('~', sourcegraph_lib.get_home_path())\n\n\tglobal SG_LIB_INSTANCE\n\tSG_LIB_INSTANCE = sourcegraph_lib.Sourcegraph(sg_settings)\n\tSG_LIB_INSTANCE.post_load(False)        \n\tThread(target=SG_LIB_INSTANCE.post_load, args=[True]).start()\n\ndef reload_settings():\n\told_base_url = SG_LIB_INSTANCE.settings.SG_BASE_URL\n\tsettings = sublime.load_settings(SETTINGS_FILENAME)\n\tload_settings(settings)\n\tif SG_LIB_INSTANCE.settings.SG_BASE_URL != old_base_url and SG_LIB_INSTANCE.settings.AUTO:\n\t\tSG_LIB_INSTANCE.open_channel(hard_refresh=True)\n\n\ndef plugin_loaded():\n\tsettings = sublime.load_settings(SETTINGS_FILENAME)\n\tsettings.clear_on_change('sg-reload')\n\tsettings.add_on_change('sg-reload', reload_settings)\n\n\tgosublime_settings = sublime.load_settings(GOSUBLIME_SETTINGS_FILENAME)\n\tgosublime_settings.clear_on_change('sg-reload-gosubl')\n\tgosublime_settings.add_on_change('sg-reload-gosubl', reload_settings)\n\n\tload_settings(settings)\n\n\ndef cursor_offset(view):\n\trow, col = view.rowcol(view.sel()[0].begin())\n\tsymbol_start_offset = sourcegraph_lib.search_for_symbols(view.sel()[0].begin(), view.substr(view.line(view.sel()[0])), row, col, SG_LIB_INSTANCE.settings.ENABLE_LOOKBACK)\n\n\tstring_before = view.substr(sublime.Region(0, symbol_start_offset))\n\tstring_before.encode('utf-8')\n\tbuffer_before = bytearray(string_before, encoding='utf8')\n\treturn str(len(buffer_before))\n\n\ndef process_selection(view):\n\tif not sourcegraph_lib.check_filetype(view.file_name()):\n\t\treturn\n\tif len(view.sel()) == 0:\n\t\treturn\n\targs = sourcegraph_lib.LookupArgs(filename=view.file_name(), cursor_offset=cursor_offset(view), preceding_selection=str.encode(view.substr(sublime.Region(0, view.size()))), selected_token=view.substr(view.extract_scope(view.sel()[0].begin())))\n\tSG_LIB_INSTANCE.on_selection_modified_handler(args)\n\nclass SgOpenLogCommand(sublime_plugin.WindowCommand):\n\tdef run(self):\n\t\tself.window.open_file(sourcegraph_lib.SG_LOG_FILE)\n\n\nclass SgManualProcessCommand(sublime_plugin.TextCommand):\n\tdef run(self, edit):\n\t\tprocess_selection(self.view)\n\n\nclass SgAutoProcessCommand(sublime_plugin.EventListener):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\n\tdef on_selection_modified_async(self, view):\n\t\tif SG_LIB_INSTANCE.settings.AUTO:\n\t\t\tprocess_selection(view)\n","sub_path":"sourcegraph_sublime.py","file_name":"sourcegraph_sublime.py","file_ext":"py","file_size_in_byte":5413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"524944071","text":"# File: t (Python 2.2)\r\n\r\nfrom direct.gui.DirectGui import *\r\nfrom direct.fsm import StateData\r\nfrom toontown.toonbase import TTLocalizer\r\nfrom toontown.toontowngui import TTDialog\r\nfrom toontown.toonbase import ToontownGlobals\r\n\r\nclass DisplaySettingsDialog(DirectFrame, StateData.StateData):\r\n    ApplyTimeoutSeconds = 15\r\n    TimeoutCountdownTask = 'DisplaySettingsTimeoutCountdown'\r\n    \r\n    def __init__(self):\r\n        DirectFrame.__init__(self, pos = (0, 0, 0.29999999999999999), relief = None, image = getDefaultDialogGeom(), image_scale = (1.6000000000000001, 1, 1.2), image_pos = (0, 0, -0.050000000000000003), image_color = ToontownGlobals.GlobalDialogColor, text = TTLocalizer.DisplaySettingsTitle, text_scale = 0.12, text_pos = (0, 0.40000000000000002), borderWidth = (0.01, 0.01), sortOrder = NO_FADE_SORT_INDEX)\r\n        StateData.StateData.__init__(self, 'display-settings-done')\r\n        self.initialiseoptions(DisplaySettingsDialog)\r\n\r\n    \r\n    def unload(self):\r\n        if self.isLoaded == 0:\r\n            return None\r\n        \r\n        self.isLoaded = 0\r\n        self.exit()\r\n        DirectFrame.destroy(self)\r\n\r\n    \r\n    def load(self):\r\n        if self.isLoaded == 1:\r\n            return None\r\n        \r\n        self.isLoaded = 1\r\n        self.anyChanged = 0\r\n        self.apiChanged = 0\r\n        self.screenSizes = ((640, 480), (800, 600), (1024, 768), (1280, 1024), (1600, 1200))\r\n        guiButton = loader.loadModelOnce('phase_3/models/gui/quit_button')\r\n        gui = loader.loadModelOnce('phase_3.5/models/gui/friendslist_gui')\r\n        nameShopGui = loader.loadModelOnce('phase_3/models/gui/nameshop_gui')\r\n        circle = nameShopGui.find('**/namePanelCircle')\r\n        innerCircle = circle.copyTo(hidden)\r\n        innerCircle.setPos(0, 0, 0.20000000000000001)\r\n        self.c1b = circle.copyTo(self, -1)\r\n        self.c1b.setColor(0, 0, 0, 1)\r\n        self.c1b.setPos(0.043999999999999997, 0, -0.20999999999999999)\r\n        self.c1b.setScale(0.40000000000000002)\r\n        c1f = circle.copyTo(self.c1b)\r\n        c1f.setColor(1, 1, 1, 1)\r\n        c1f.setScale(0.80000000000000004)\r\n        self.c2b = circle.copyTo(self, -2)\r\n        self.c2b.setColor(0, 0, 0, 1)\r\n        self.c2b.setPos(0.043999999999999997, 0, -0.29999999999999999)\r\n        self.c2b.setScale(0.40000000000000002)\r\n        c2f = circle.copyTo(self.c2b)\r\n        c2f.setColor(1, 1, 1, 1)\r\n        c2f.setScale(0.80000000000000004)\r\n        self.introText = DirectLabel(parent = self, relief = None, scale = 0.059999999999999998, text = TTLocalizer.DisplaySettingsIntro, text_wordwrap = 25, text_align = TextNode.ALeft, pos = (-0.72499999999999998, 0, 0.29999999999999999))\r\n        self.introTextSimple = DirectLabel(parent = self, relief = None, scale = 0.059999999999999998, text = TTLocalizer.DisplaySettingsIntroSimple, text_wordwrap = 25, text_align = TextNode.ALeft, pos = (-0.72499999999999998, 0, 0.29999999999999999))\r\n        self.apiLabel = DirectLabel(parent = self, relief = None, scale = 0.059999999999999998, text = TTLocalizer.DisplaySettingsApi, text_align = TextNode.ARight, pos = (-0.080000000000000002, 0, 0))\r\n        self.apiMenu = DirectOptionMenu(parent = self, relief = RAISED, scale = 0.059999999999999998, items = [\r\n            'x'], pos = (0, 0, 0))\r\n        self.screenSizeLabel = DirectLabel(parent = self, relief = None, scale = 0.059999999999999998, text = TTLocalizer.DisplaySettingsResolution, text_align = TextNode.ARight, pos = (-0.080000000000000002, 0, -0.10000000000000001))\r\n        self.screenSizeLeftArrow = DirectButton(parent = self, relief = None, image = (gui.find('**/Horiz_Arrow_UP'), gui.find('**/Horiz_Arrow_DN'), gui.find('**/Horiz_Arrow_Rllvr'), gui.find('**/Horiz_Arrow_UP')), scale = (-1.0, 1.0, 1.0), pos = (0.040000000000000001, 0, -0.085000000000000006), command = self._DisplaySettingsDialog__doScreenSizeLeft)\r\n        self.screenSizeRightArrow = DirectButton(parent = self, relief = None, image = (gui.find('**/Horiz_Arrow_UP'), gui.find('**/Horiz_Arrow_DN'), gui.find('**/Horiz_Arrow_Rllvr'), gui.find('**/Horiz_Arrow_UP')), pos = (0.54000000000000004, 0, -0.085000000000000006), command = self._DisplaySettingsDialog__doScreenSizeRight)\r\n        self.screenSizeValueText = DirectLabel(parent = self, relief = None, text = 'x', text_align = TextNode.ACenter, text_scale = 0.059999999999999998, pos = (0.28999999999999998, 0, -0.10000000000000001))\r\n        self.windowedButton = DirectCheckButton(parent = self, relief = None, text = TTLocalizer.DisplaySettingsWindowed, text_align = TextNode.ALeft, text_scale = 0.59999999999999998, scale = 0.10000000000000001, boxImage = innerCircle, boxImageScale = 2.5, boxImageColor = VBase4(0, 0.25, 0.5, 1), boxRelief = None, pos = (0.096100000000000005, 0, -0.221), command = self._DisplaySettingsDialog__doWindowed)\r\n        self.fullscreenButton = DirectCheckButton(parent = self, relief = None, text = TTLocalizer.DisplaySettingsFullscreen, text_align = TextNode.ALeft, text_scale = 0.59999999999999998, scale = 0.10000000000000001, boxImage = innerCircle, boxImageScale = 2.5, boxImageColor = VBase4(0, 0.25, 0.5, 1), boxRelief = None, pos = (0.097000000000000003, 0, -0.311), command = self._DisplaySettingsDialog__doFullscreen)\r\n        self.apply = DirectButton(parent = self, relief = None, image = (guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.59999999999999998, 1, 1), text = TTLocalizer.DisplaySettingsApply, text_scale = 0.059999999999999998, text_pos = (0, -0.02), pos = (0.52000000000000002, 0, -0.53000000000000003), command = self._DisplaySettingsDialog__apply)\r\n        self.cancel = DirectButton(parent = self, relief = None, text = TTLocalizer.DisplaySettingsCancel, image = (guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.59999999999999998, 1, 1), text_scale = 0.059999999999999998, text_pos = (0, -0.02), pos = (0.20000000000000001, 0, -0.53000000000000003), command = self._DisplaySettingsDialog__cancel)\r\n        guiButton.removeNode()\r\n        gui.removeNode()\r\n        nameShopGui.removeNode()\r\n        innerCircle.removeNode()\r\n        self.hide()\r\n\r\n    \r\n    def enter(self, changeDisplaySettings, changeDisplayAPI):\r\n        if self.isEntered == 1:\r\n            return None\r\n        \r\n        self.isEntered = 1\r\n        if self.isLoaded == 0:\r\n            self.load()\r\n        \r\n        self.applyDialog = None\r\n        self.timeoutDialog = None\r\n        self.restoreDialog = None\r\n        self.revertDialog = None\r\n        base.transitions.fadeScreen(0.5)\r\n        properties = base.win.getProperties()\r\n        self.screenSizeIndex = self.chooseClosestScreenSize(properties.getXSize(), properties.getYSize())\r\n        self.fullscreen = properties.getFullscreen()\r\n        self.updateApiMenu(changeDisplaySettings, changeDisplayAPI)\r\n        self.updateWindowed()\r\n        self.updateScreenSize()\r\n        if changeDisplaySettings:\r\n            self.introText.show()\r\n            self.introTextSimple.hide()\r\n            if changeDisplayAPI and len(self.apis) > 1:\r\n                self.apiLabel.show()\r\n                self.apiMenu.show()\r\n            else:\r\n                self.apiLabel.hide()\r\n                self.apiMenu.hide()\r\n            self.windowedButton.show()\r\n            self.fullscreenButton.show()\r\n            self.c1b.show()\r\n            self.c2b.show()\r\n        else:\r\n            self.introText.hide()\r\n            self.introTextSimple.show()\r\n            self.apiLabel.hide()\r\n            self.apiMenu.hide()\r\n            self.windowedButton.hide()\r\n            self.fullscreenButton.hide()\r\n            self.c1b.hide()\r\n            self.c2b.hide()\r\n        self.anyChanged = 0\r\n        self.apiChanged = 0\r\n        self.show()\r\n        return None\r\n\r\n    \r\n    def exit(self):\r\n        if self.isEntered == 0:\r\n            return None\r\n        \r\n        self.isEntered = 0\r\n        self.cleanupDialogs()\r\n        base.transitions.noTransitions()\r\n        taskMgr.remove(self.TimeoutCountdownTask)\r\n        self.ignoreAll()\r\n        self.hide()\r\n        messenger.send(self.doneEvent, [\r\n            self.anyChanged,\r\n            self.apiChanged])\r\n        return None\r\n\r\n    \r\n    def cleanupDialogs(self):\r\n        if self.applyDialog != None:\r\n            self.applyDialog.cleanup()\r\n            self.applyDialog = None\r\n        \r\n        if self.timeoutDialog != None:\r\n            self.timeoutDialog.cleanup()\r\n            self.timeoutDialog = None\r\n        \r\n        if self.restoreDialog != None:\r\n            self.restoreDialog.cleanup()\r\n            self.restoreDialog = None\r\n        \r\n        if self.revertDialog != None:\r\n            self.revertDialog.cleanup()\r\n            self.revertDialog = None\r\n        \r\n\r\n    \r\n    def updateApiMenu(self, changeDisplaySettings, changeDisplayAPI):\r\n        self.apis = []\r\n        self.apiPipes = []\r\n        if changeDisplayAPI:\r\n            base.makeAllPipes()\r\n        \r\n        for pipe in base.pipeList:\r\n            if pipe.isValid():\r\n                self.apiPipes.append(pipe)\r\n                self.apis.append(pipe.getInterfaceName())\r\n            \r\n        \r\n        self.apiMenu['items'] = self.apis\r\n        self.apiMenu.set(base.pipe.getInterfaceName())\r\n\r\n    \r\n    def updateWindowed(self):\r\n        if self.fullscreen:\r\n            self.windowedButton['indicatorValue'] = 0\r\n            self.fullscreenButton['indicatorValue'] = 1\r\n        else:\r\n            self.windowedButton['indicatorValue'] = 1\r\n            self.fullscreenButton['indicatorValue'] = 0\r\n\r\n    \r\n    def updateScreenSize(self):\r\n        (xSize, ySize) = self.screenSizes[self.screenSizeIndex]\r\n        self.screenSizeValueText['text'] = '%s x %s' % (xSize, ySize)\r\n        if self.screenSizeIndex > 0:\r\n            self.screenSizeLeftArrow.show()\r\n        else:\r\n            self.screenSizeLeftArrow.hide()\r\n        if self.screenSizeIndex < len(self.screenSizes) - 1:\r\n            self.screenSizeRightArrow.show()\r\n        else:\r\n            self.screenSizeRightArrow.hide()\r\n\r\n    \r\n    def chooseClosestScreenSize(self, currentXSize, currentYSize):\r\n        for i in range(len(self.screenSizes)):\r\n            (xSize, ySize) = self.screenSizes[i]\r\n            if currentXSize == xSize and currentYSize == ySize:\r\n                return i\r\n            \r\n        \r\n        currentCount = currentXSize * currentYSize\r\n        bestDiff = None\r\n        bestI = None\r\n        for i in range(len(self.screenSizes)):\r\n            (xSize, ySize) = self.screenSizes[i]\r\n            diff = abs(xSize * ySize - currentCount)\r\n            if bestI == None or diff < bestDiff:\r\n                bestI = i\r\n                bestDiff = diff\r\n            \r\n        \r\n        return bestI\r\n\r\n    \r\n    def _DisplaySettingsDialog__doWindowed(self, value):\r\n        self.fullscreen = 0\r\n        self.updateWindowed()\r\n\r\n    \r\n    def _DisplaySettingsDialog__doFullscreen(self, value):\r\n        self.fullscreen = 1\r\n        self.updateWindowed()\r\n\r\n    \r\n    def _DisplaySettingsDialog__doScreenSizeLeft(self):\r\n        if self.screenSizeIndex > 0:\r\n            self.screenSizeIndex = self.screenSizeIndex - 1\r\n            self.updateScreenSize()\r\n        \r\n\r\n    \r\n    def _DisplaySettingsDialog__doScreenSizeRight(self):\r\n        if self.screenSizeIndex < len(self.screenSizes) - 1:\r\n            self.screenSizeIndex = self.screenSizeIndex + 1\r\n            self.updateScreenSize()\r\n        \r\n\r\n    \r\n    def _DisplaySettingsDialog__apply(self):\r\n        self.cleanupDialogs()\r\n        self.reparentTo(self.getParent(), 0)\r\n        self.applyDialog = TTDialog.TTDialog(dialogName = 'DisplaySettingsApply', style = TTDialog.TwoChoice, text = TTLocalizer.DisplaySettingsApplyWarning % self.ApplyTimeoutSeconds, text_wordwrap = 15, fadeScreen = 1, command = self._DisplaySettingsDialog__applyDone)\r\n\r\n    \r\n    def _DisplaySettingsDialog__applyDone(self, command):\r\n        self.applyDialog.cleanup()\r\n        self.applyDialog = None\r\n        self.reparentTo(self.getParent(), NO_FADE_SORT_INDEX)\r\n        base.transitions.fadeScreen(0.5)\r\n        if command != DIALOG_OK:\r\n            return None\r\n        \r\n        self.origPipe = base.pipe\r\n        self.origProperties = base.win.getProperties()\r\n        pipe = self.apiPipes[self.apiMenu.selectedIndex]\r\n        properties = WindowProperties()\r\n        (xSize, ySize) = self.screenSizes[self.screenSizeIndex]\r\n        properties.setSize(xSize, ySize)\r\n        properties.setFullscreen(self.fullscreen)\r\n        if not self.resetDisplayProperties(pipe, properties):\r\n            self._DisplaySettingsDialog__revertBack(1)\r\n            return None\r\n        \r\n        self.reparentTo(self.getParent(), 0)\r\n        self.timeoutDialog = TTDialog.TTDialog(dialogName = 'DisplaySettingsTimeout', style = TTDialog.TwoChoice, text = TTLocalizer.DisplaySettingsAccept % self.ApplyTimeoutSeconds, text_wordwrap = 15, fadeScreen = 1, command = self._DisplaySettingsDialog__timeoutDone)\r\n        self.timeoutRemaining = self.ApplyTimeoutSeconds\r\n        self.timeoutStart = None\r\n        taskMgr.add(self._DisplaySettingsDialog__timeoutCountdown, self.TimeoutCountdownTask)\r\n\r\n    \r\n    def _DisplaySettingsDialog__timeoutCountdown(self, task):\r\n        if self.timeoutStart == None:\r\n            self.timeoutStart = globalClock.getRealTime()\r\n        \r\n        elapsed = int(globalClock.getFrameTime() - self.timeoutStart)\r\n        remaining = max(self.ApplyTimeoutSeconds - elapsed, 0)\r\n        if remaining < self.timeoutRemaining:\r\n            self.timeoutRemaining = remaining\r\n            self.timeoutDialog['text'] = (TTLocalizer.DisplaySettingsAccept % remaining,)\r\n        \r\n        if remaining == 0:\r\n            self._DisplaySettingsDialog__timeoutDone('cancel')\r\n            return Task.done\r\n        \r\n        return Task.cont\r\n\r\n    \r\n    def _DisplaySettingsDialog__timeoutDone(self, command):\r\n        taskMgr.remove(self.TimeoutCountdownTask)\r\n        self.timeoutDialog.cleanup()\r\n        self.timeoutDialog = None\r\n        self.reparentTo(self.getParent(), NO_FADE_SORT_INDEX)\r\n        base.transitions.fadeScreen(0.5)\r\n        if command == DIALOG_OK:\r\n            self.anyChanged = 1\r\n            self.exit()\r\n            return None\r\n        \r\n        self._DisplaySettingsDialog__revertBack(0)\r\n        return None\r\n\r\n    \r\n    def _DisplaySettingsDialog__revertBack(self, reason):\r\n        if not self.resetDisplayProperties(self.origPipe, self.origProperties):\r\n            self.notify.warning(\"Couldn't restore original display settings!\")\r\n            base.panda3dRenderError()\r\n        \r\n        self.reparentTo(self.getParent(), 0)\r\n        if reason == 0:\r\n            revertText = TTLocalizer.DisplaySettingsRevertUser\r\n        else:\r\n            revertText = TTLocalizer.DisplaySettingsRevertFailed\r\n        self.revertDialog = TTDialog.TTDialog(dialogName = 'DisplaySettingsRevert', style = TTDialog.Acknowledge, text = revertText, text_wordwrap = 15, fadeScreen = 1, command = self._DisplaySettingsDialog__revertDone)\r\n\r\n    \r\n    def _DisplaySettingsDialog__revertDone(self, command):\r\n        self.revertDialog.cleanup()\r\n        self.revertDialog = None\r\n        self.reparentTo(self.getParent(), NO_FADE_SORT_INDEX)\r\n        base.transitions.fadeScreen(0.5)\r\n        return None\r\n\r\n    \r\n    def _DisplaySettingsDialog__cancel(self):\r\n        self.exit()\r\n\r\n    \r\n    def resetDisplayProperties(self, pipe, properties):\r\n        if base.win:\r\n            currentProperties = base.win.getProperties()\r\n        else:\r\n            currentProperties = WindowProperties()\r\n        newProperties = WindowProperties(currentProperties)\r\n        newProperties.addProperties(properties)\r\n        if base.pipe != pipe:\r\n            self.apiChanged = 1\r\n        \r\n        if base.pipe != pipe and base.win == None or currentProperties.getFullscreen() != newProperties.getFullscreen():\r\n            base.pipe = pipe\r\n            if not base.openMainWindow():\r\n                return 0\r\n            \r\n            base.win.requestProperties(properties)\r\n            base.disableShowbaseMouse()\r\n            NametagGlobals.setCamera(base.cam)\r\n            NametagGlobals.setMouseWatcher(base.mouseWatcherNode)\r\n            base.graphicsEngine.renderFrame()\r\n            base.graphicsEngine.renderFrame()\r\n            if base.win.isClosed():\r\n                self.notify.info('Window did not open, removing.')\r\n                base.closeWindow(base.win)\r\n                return 0\r\n            \r\n        else:\r\n            base.win.requestProperties(properties)\r\n        return 1\r\n\r\n\r\n","sub_path":"toontown/shtiker/DisplaySettingsDialog.py","file_name":"DisplaySettingsDialog.py","file_ext":"py","file_size_in_byte":16673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"298852290","text":"#Class DNA\nimport numpy as np\nimport string\nimport random as rnd\n\nclass DNA:\n\n\tletters = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz\"\n\t\n\tdef __init__(self, lenght):\n\t\tself.lenght = lenght\n\t\tself.DNACharacters = np.chararray(lenght)\n\n\n\tdef generateRandomDNA(self):\n\t\tfor i in range(0, self.lenght):\n\t\t\tself.DNACharacters[i] = rnd.choice(self.letters)\n\n\n\tdef toString(self):\n\t\tris = \"\"\n\n\t\tfor i in range(0, self.lenght):\n\t\t\tif self.DNACharacters[i] == \"\":\n\t\t\t\tris += \" \"\n\t\t\telse:\n\t\t\t\tris += self.DNACharacters[i]\n\t\treturn ris\n\n\tdef crossOver(self, dna):\n\t\tmidpoint = rnd.randint(0, self.lenght - 1)\n\n\t\tchild = DNA(self.lenght)\n\t\tfor i in range(0, self.lenght):\n\t\t\tif i <= midpoint:\n\t\t\t\tchild.DNACharacters[i] = self.DNACharacters[i]\n\t\t\telse:\n\t\t\t\tchild.DNACharacters[i] = dna.DNACharacters[i]\n\t\treturn self.mutation(child)\n\n\n\tdef mutation(self, x):\n\t\tok = rnd.randint(0, 99)\n\t\tif ok <= 1:\n\t\t\tpos = rnd.randint(0, self.lenght - 1)\n\t\t\tx.DNACharacters[pos] = rnd.choice(self.letters)\n\t\treturn x","sub_path":"Dna.py","file_name":"Dna.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"142195670","text":"import sys\nfrom selenium import webdriver\nfrom django.contrib.staticfiles.testing import StaticLiveServerTestCase\nimport unittest\n\n\nclass NewVisitorTest(StaticLiveServerTestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        for arg in sys.argv:\n            if 'liveserver' in arg:\n                cls.server_url = 'http://' + arg.split('=')[1]\n                return\n        super().setUpClass()\n        cls.server_url = cls.live_server_url\n\n    @classmethod\n    def tearDownClass(cls):\n        if cls.server_url == cls.live_server_url:\n            super().tearDownClass()\n\n    def setUp(self):\n        self.browser = webdriver.Firefox()\n\n    def tearDown(self):\n        self.browser.quit()\n\n\n    def test_can_start_a_list_and_retrive_it_later(self):\n        # Edith has heard about a cool new online to-do app.\n        # she goes to check out its homepage\n        self.browser.get(self.server_url)\n        self.assertIn('Todo', self.browser.title);\n        # self.fail('Finish the test!')\n\n\nif __name__ == '__main__':\n    unittest.main(warnings='ignore')\n","sub_path":"functional_tests.py","file_name":"functional_tests.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"496079303","text":"# -*- coding: utf-8 -*-\n\"\"\"\nTubデータ上のオブジェクトをあらわすクラス群。\npart.pyが使用する。\n\"\"\"\nimport os\nimport glob\nimport json\nfrom datetime import datetime\n\nimport donkeycar as dk\n\nclass Tubs:\n    \"\"\"\n    1つのインスタンスで1つのTubデータディレクトリをあらわすクラス。\n    連番の昇順に送信データを生成するジェネレータとして機能する。\n    \"\"\"\n    def __init__(self, tub_dir):\n        \"\"\"\n        引数指定先Tubデータディレクトリの妥当性を検査し、連番の昇順にファイルパスを\n        ならべなおし、recordファイル、imageファイル別のリスト（インスタンス変数）へ格納する。\n\n        引数\n            tub_dir     Tubデータディレクトリのパス\n        戻り値\n            なし\n        例外\n            妥当性検査に不合格の場合\n        \"\"\"\n        if tub_dir is None:\n            raise Exception('no tub_dir')\n        self.tub_dir = os.path.expanduser(tub_dir)\n        if not os.path.exists(self.tub_dir):\n            raise Exception('{} is not exists'.format(self.tub_dir))\n        if not os.path.isdir(self.tub_dir):\n            raise Exception('{} is not a directory'.format(self.tub_dir))\n        \n        records = glob.glob(os.path.join(self.tub_dir, 'record_*.json'))\n        images = glob.glob(os.path.join(self.tub_dir, '*_cam-image_array_.jpg'))\n\n        record_dict = {}\n        for record in records:\n            cnt = int(record.rsplit('record_')[-1].rsplit('.json')[0])\n            record_dict[cnt] = record\n        \n        self.sorted_records = []\n        sorted_record_keys = sorted(list(record_dict.keys()))\n        for sorted_record_key in sorted_record_keys:\n            self.sorted_records.append(record_dict[sorted_record_key])\n        \n        image_dict = {}\n        for image in images:\n            cnt = int(os.path.basename(image).rsplit('_cam-image_array_.jpg')[0])\n            image_dict[cnt] = image\n        \n        self.sorted_images = []\n        sorted_image_keys = sorted(list(image_dict.keys()))\n        for sorted_image_key in sorted_image_keys:\n            self.sorted_images.append(image_dict[sorted_image_key])\n\n        if sorted_record_keys != sorted_image_keys:\n            raise Exception('unmatch magic numner no_records={}, no_images={}'.format(\n                str(sorted_record_keys - sorted_image_keys), str(sorted_image_keys - sorted_record_keys)\n            ))\n    \n    def total(self):\n        return len(self.sorted_records)\n    \n    def indexOf(self, index):\n        record = TubRecord(self.sorted_records[index]).get()\n        image = TubImage(self.sorted_images[index]).get()\n        return record, image\n\nclass Tub:\n    \"\"\"\n    Tubデータ操作のユーティリティを提供する基底クラス。\n    \"\"\"\n    def eval_file(self, path):\n        \"\"\"\n        引数pathにて指定されたパスがファイルであるかどうかを確認する。\n\n        引数\n            path    検査対象のパス\n        戻り値\n            検査対象のパス（ユーザ短縮を使用している場合展開）\n        例外\n            ファイルではない場合、発生させる\n        \"\"\"\n        if path is None:\n            raise Exception('no record_path')\n        path = os.path.expanduser(path)\n        if not os.path.exists(path):\n            raise Exception('{} is not exists'.format(path))\n        if not os.path.isfile(path):\n            raise Exception('{} is not a file'.format(path))\n        return path\n\nclass TubRecord(Tub):\n    \"\"\"\n    １インスタンスで１つのrecordファイルをあらすクラス。\n    \"\"\"\n    def __init__(self, path):\n        \"\"\"\n        JSONファイルを読み込み辞書型に変換しインスタンス変数へ格納する。\n\n        引数\n            path    JSONファイルのパス\n        戻り値\n            なし\n        例外\n            引数指定パスがファイルではない場合\n        \"\"\"\n        with open(self.eval_file(path), 'r') as f:\n            self.record = json.load(f)\n\n    def get(self, use_timestamp = False):\n        \"\"\"\n        送信用JSONデータを作成する。\n\n        引数\n            use_timestamp   オリジナルのタイムスタンプを送信する：真、現在時刻を使用する：偽\n        戻り値\n            送信用JSONデータ（辞書型）\n        \"\"\"\n        if 'pilot/angle' not in self.record:\n            self.record['pilot/angle'] = 0.0\n        if 'pilot/throttle' not in self.record:\n            if self.record['user/mode'] == 'local_angle':\n                self.record['pilot/throttle'] = 0.5\n            else:\n                self.record['pilot/throttle'] = 0.0\n\n        if self.record['user/mode'] == 'user':\n            if 'angle' not in self.record:\n                self.record['angle'] = self.record['user/angle']\n            if 'throttle' not in self.record:\n                self.record['throttle'] = self.record['user/throttle']\n        elif self.record['user/mode'] == 'local_angle':\n            if 'angle' not in self.record:\n                self.record['angle'] = self.record['pilot/angle']\n            if 'throttle' not in self.record:\n                self.record['throttle'] = self.record['user/throttle']\n        else:\n            if 'angle' not in self.record:\n                self.record['angle'] = self.record['pilot/angle']\n            if 'throttle' not in self.record:\n                self.record['throttle'] = self.record['pilot/throttle']\n\n        if use_timestamp:\n            self.record['timestamp'] = str(datetime.now())\n\n        return self.record\n\nclass TubImage(Tub):\n    \"\"\"\n    １インスタンスで１つのイメージファイルをあらすクラス。\n    \"\"\"\n    def __init__(self, path):\n        \"\"\"\n        イメージファイルを読み込みPiCamera出力に合わせて型式を\n        np.ndarray型(120, 160,3)に変換しインスタンス変数へ格納する。\n\n        引数\n            path    イメージファイルのパス\n        戻り値\n            なし\n        例外\n            引数指定パスがファイルではない場合\n        \"\"\"\n        with open(self.eval_file(path), 'br') as f:\n            binary = f.read()\n        img = dk.util.img.binary_to_img(binary)\n        self.image_array = dk.util.img.img_to_arr(img)\n\n    def get(self):\n        \"\"\"\n        イメージデータ(np.ndarray, dtype=uint8, shape=(120,160,3))を返却する\n\n        引数\n            なし\n        戻り値\n            self.image_array    イメージデータ(np.ndarray, dtype=uint8, shape=(120,160,3))\n        \"\"\"\n        return self.image_array\n\n\n","sub_path":"donkeypart_tub_loader/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":6710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"629843025","text":"\"\"\"\n===================\nScatterer footprint\n===================\n\n\"\"\"\n\n\ndef run():\n    from PyMieSim.Scatterer import Sphere\n    from PyMieSim.Detector import LPmode\n    from PyMieSim.Source import PlaneWave\n    from PyOptik import ExpData\n\n    Detector = LPmode(Mode=\"2-1\", NA=0.3, sampling=200, gamma_offset=0, phi_offset=0, coupling_mode='Point')\n\n    source = PlaneWave(wavelength=450e-9,\n                       polarization=0,\n                       amplitude=1)\n\n    scatterer = Sphere(diameter=2000e-9,\n                       source=source,\n                       material=ExpData('BK7'))\n\n    footprint = Detector.get_footprint(scatterer)\n\n    footprint.plot().show()\n\n\nif __name__ == '__main__':\n    run()\n","sub_path":"PyMieSim/Examples/scatterer/plot_Footprint.py","file_name":"plot_Footprint.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"473731213","text":"#Faça um programa que receba o nome de um arquivo .csv e monte uma matriz de adjacências e uma lista de adjacências.\n#O arquivo irá conter N linhas, cada linha com a identificação de 2 vértices separados por \";\".\n\nimport csv\n\ndef popularLista(lista,indices):\n    for i in indices:  \n        conjunto = []\n        conjunto.append(i)\n        for valores in vertices:\n            if valores[0] == i:\n                conjunto.append(valores[1])\n            if valores[1] == i:\n                conjunto.append(valores[0])\n\n        lista.append(conjunto)\ndef mostrarLista(lista):\n    for i in lista:\n        x = 0\n        for cont in i:\n            if x == 0:\n                print(f'{cont:^3}', end='')\n                print(f'->', end='')\n            else:\n                print(f'{cont:^3}', end='')\n            x = +1\n        print()\ndef inicializarMatriz(obj,tamanho):\n    for i in range(0, tamanho):\n        l = []\n        for j in range(0, tamanho):\n            l.append(0)\n        obj.append(l)\ndef popularMatriz(tamanho, indices,matriz):\n    x = 0\n    for i in range(1, tamanho):\n        matriz[0][i] = indices[x]\n        matriz[i][0] = indices[x]\n        x += 1\n\n    for i in vertices:\n        valor1 = i[0]\n        valor2 = i[1]\n\n        for i in range(1, tamanho):\n            if matriz[i][0] == valor1:\n                for j in range(1, tamanho):\n                    if matriz[0][j] == valor2:\n                        matriz[i][j] = 1\n                        matriz[j][i] = 1\ndef mostrarMatriz(tamanho, matriz):\n    for i in range(0, tamanho):\n        for j in range(0, tamanho):\n            print(f'{matriz[i][j]:^3}', end='')\n        print()\n\narquivo = open('exercicio.csv')\n\nlinhas = csv.reader(arquivo)\nvertices = []\nfinal = []\n\nfor linha in linhas:\n    vertices.append(linha[0].split(\";\"))\n\nfor valores in vertices:\n    for i in valores:\n        final.append(i)\n\nindices = sorted((set(final)))\ntamanho = len(indices) + 1\nlista = []\npopularLista(lista,indices)\nprint(\"Lista:\")\nmostrarLista(lista)\nprint()\n\n\nmatriz = []\ninicializarMatriz(matriz, tamanho)\npopularMatriz(tamanho, indices, matriz)\nprint(\"Matriz:\")\nmostrarMatriz(tamanho, matriz)\nprint()\n\n\n\n\n","sub_path":"Grafos/Exercicios/Pratica1.py","file_name":"Pratica1.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"361713849","text":"# To run this code, first edit config.py with your configuration, then:\n#\n# mkdir data\n# USAGE : python3 collect_data.py\n# Author : Santhosh Nayak\n# It will produce the list of tweets for the user in the file For example : data/stream_apple.json\n# Note : According to the Twitter api, they'll allow you to extract a maximum of 3,200 tweets, 200 at a time,\n\nimport sys\nimport tweepy\nfrom tweepy import OAuthHandler\nimport time\nimport string\nimport config\nimport json\nimport os\n\nfrom openpyxl import load_workbook\n\ndef format_filename(fname):\n    \"\"\"Convert file name into a safe string.\n    Arguments:\n        fname -- the file name to convert\n    Return:\n        String -- converted file name\n    \"\"\"\n    return ''.join(convert_valid(one_char) for one_char in fname)\n\n\ndef convert_valid(one_char):\n    \"\"\"Convert a character into '_' if invalid.\n    Arguments:\n        one_char -- the char to convert\n    Return:\n        Character -- converted char\n    \"\"\"\n    valid_chars = \"-_.%s%s\" % (string.ascii_letters, string.digits)\n    if one_char in valid_chars:\n        return one_char\n    else:\n        return '_'\n\n\n# to get the time line of the user\ndef read_timeline(screen_name,outfile):\n\n    # Twitter API Authentication\n    auth = OAuthHandler(config.consumer_key, config.consumer_secret)\n    auth.set_access_token(config.access_token, config.access_token_secret)\n    api = tweepy.API(auth)\n\n    print(\"Initialising the Connection to Twitter API\")\n    time.sleep(2)\n    print(\"Connection established... :)\")\n\n    print(\" \")\n    print(\"Extracting the tweets from user : %s\" %screen_name)\n\n    try:\n        # initialize a list to hold all the tweepy Tweets\n        alltweets = []\n\n        # make initial request for most recent tweets (200 is the maximum allowed count)\n        new_tweets = api.user_timeline(screen_name=screen_name, count=200)\n\n        if len(new_tweets) == 0:\n            print(\"The user has not tweeted anything. Zero tweets found\")\n            return 3\n\n        # save most recent tweets\n        alltweets.extend(new_tweets)\n\n        # save the id of the oldest tweet less one\n        oldest = alltweets[-1].id - 1\n\n        #User display of status\n        print(\" \")\n        print(\"Tweets downloaded....%s\" %len(alltweets), end=' ',flush=True)\n\n        # keep grabbing tweets until there are no tweets left to grab\n        while len(new_tweets) > 0:\n\n            # all subsiquent requests use the max_id param to prevent duplicates\n            new_tweets = api.user_timeline(screen_name=screen_name, count=200, max_id=oldest)\n\n            # save most recent tweets\n            alltweets.extend(new_tweets)\n\n            # update the id of the oldest tweet less one\n            oldest = alltweets[-1].id - 1\n\n            print(\"...%s\" %len(alltweets), end=' ',flush=True)\n\n\n        print(\"\")\n        print(\"Downloaded %s tweets completed for the user %s \"% (len(alltweets),screen_name))\n\n        #transform the tweepy tweets into a json_strong and write into a json file\n        print(\"\")\n        print(\"Writing the downloaded tweets into the file : %s\" %outfile)\n        for tweet in alltweets:\n            json_str = json.dumps(tweet._json)\n            with open(outfile, 'a', encoding=\"utf-8\") as f:\n                f.write(json_str + \"\\n\")\n        return 1\n    except tweepy.TweepError as e:\n        z = e\n        print(z)\n        print(\"Error Response :\" + str(e.response))\n        print(\"Tweepy Error Code :\" + str(e.api_code))\n        print(\"tweepy error occured for user %s\" %screen_name)\n        return 2\n    except (ValueError, IndexError) as e1:\n        z = e1\n        print(z)\n        return 2\n    except Exception:# pass:  # catch *all* exceptions\n        print(\"Unkwown exception occured\")\n        return 2\n\n#Method to get the data from the XLS file given in config file.\ndef extract_data_from_excel():\n\n    # list to save all the twitter usernames of the Top Business Angels\n    business_angels_list = []\n\n    workbook = load_workbook(config.USERNAME_XLSX, use_iterators=True)\n    first_sheet = workbook.get_sheet_names()[0]\n    worksheet = workbook.get_sheet_by_name(first_sheet)\n\n    #row_count = 1897  #worksheet.get_highest_row() - 1\n\n    print(\"Fetching the username for %s from the Excel sheet\" % config.PERSON)\n    print(\"Please wait, this may take some time.\")\n    index = 0\n    for row in range(config.MIX_ROW_SIZE,config.MAX_ROW_SIZE):\n\n        '''for column in \"G\":\n            cell_name = \"{}{}\".format(column, row)\n\n            if worksheet[cell_name].value == config.PERSON:\n\n                for column in \"D\":\n                    cell_name = \"{}{}\".format(column, row)\n                    screen_name = worksheet[cell_name].value\n\n                    if screen_name[:5] == \"https\":\n                        business_angels_list.append(screen_name[20:])\n                    else:\n                        business_angels_list.append(screen_name[19:])'''\n        for column in \"D\":\n            cell_name = \"{}{}\".format(column, row)\n            screen_name = worksheet[cell_name].value\n\n            if screen_name[:5] == \"https\":\n                business_angels_list.append(screen_name[20:])\n            else:\n                business_angels_list.append(screen_name[19:])\n\n        index = index+1\n\n        #For status update\n        if index == 100:\n            print('*', end=\"\", flush=True)\n            index = 0\n\n    print(\" \")\n    print(\"Extracted all the usernames for %s\" % config.PERSON)\n    return business_angels_list\n\n\n#The main Program, entry point of the program\nif __name__ == '__main__':\n    print(\"Program Started...\")\n\n\n    #To fetch the data from the Excel Sheet\n    username_list = extract_data_from_excel()\n\n    if os.path.isfile(config.USERNAME_LIST_TXT):\n        print(\"Old execution file found.Extracting information on already downloaded data.\")\n        f = open(config.USERNAME_LIST_TXT, 'r')\n        read_userlist = f.readlines()\n\n    temp_list = list(username_list)\n\n    for user in username_list:\n        for saved_user in read_userlist:\n            saved_user = saved_user[:-1]\n            if saved_user == user:\n                temp_list.remove(saved_user)\n                break\n    print(\"Updated the file to remove redundant data\")\n    print(\"\")\n    print(\"Number of people remaining to be extracted :\" + str(len(temp_list)))\n    print(\"Total number of people to be extracted :\" + str(len(username_list)))\n    print(\"\")\n\n\n    #get the timeline of the user\n    for user in temp_list:\n\n        #include the data to the output json file\n        query_username = format_filename(user)\n        outfile = \"%s/stream_%s.json\" % (config.DATA_DIR, query_username)\n\n        status = read_timeline(user,outfile)\n\n        if status == 1:\n            with open(config.USERNAME_LIST_TXT, 'a') as f:\n                f.write(user + \"\\n\")\n                print(\"Updated the file %s\" %config.USERNAME_LIST_TXT)\n            print(\"Successfully written into the file for user : %s\" %user)\n            print(\"User %s data downloaded and written at Executed at : \" % user + str(time.ctime()))\n            print(\"system sleeps.. 5 minutes.\")\n            time.sleep(300)  # Arbitrary sleep time of 5 minutes to avoid the rate limit error\n            print(\"system wakes\")\n            print(\"=======================================================\")\n            print(\"\")\n        elif status == 3:\n            with open(config.WRITE_ERROR_LIST, 'a') as f:\n                f.write(user + \": No Tweets found \\n\")\n            print(\"No json file saved.Updated the error list with text : \\'No Tweets found\\'\")\n            print(\"\")\n            print(\"system sleeps.. 5 minutes.\")\n            time.sleep(300)  # Arbitrary sleep time of 5 minutes to avoid the rate limit error\n            print(\"system wakes\")\n            print(\"=======================================================\")\n        else:\n            with open(config.WRITE_ERROR_LIST, 'a') as f:\n                f.write(user + \"\\n\")\n            print(\"Some Error happened when writing for user : %s!\" % user)\n            print(\"Error occurred at : \" + str(time.ctime()))\n            print(\"system sleeps.. 5 minutes.\")\n            time.sleep(300)  # Arbitrary sleep time of 5 minutes to avoid the rate limit error\n            print(\"system wakes\")\n            print(\"=======================================================\")\n            print(\"\")\n\n    print(\"Program Completed\")\n    print(\"\")\n    print(\"****************************************************************\")\n    print(\"Program Developed by Santhosh Nayak (santhoshnayak0903@gmail.com)\")\n    print(\"****************************************************************\")\n\n# -------------------Program Ends here -------------------\n","sub_path":"collect_data.py","file_name":"collect_data.py","file_ext":"py","file_size_in_byte":8696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"545856172","text":"\"\"\"Group #16 --- Fichefet Pierrick --- Daubry Benjamin\"\"\"\n#! /usr/bin/env python3\n################################################################################\n#\n#\t\tImplementation of the wedding problem class\n#\n################################################################################\nfrom search import *\nimport sys\nimport time\n\n#################\n# Problem class #\n#################\n\nclass Wedding(Problem):\n\tdef __init__(self, init):\n\t\tself.affinitiesTable = [] # The affinity matrix\n\t\tself.numberOfPeople = 0 \n\t\tself.numberOfTable = 0 \n\t\tself.createTablesAssignment(init)\n\t\tself.allPossibleActions = self.allPossibleActions() # Set of all possible actions\n\t\tself.actionsAlreadyDone = [] # Set of all actions that have been done. \n\n\n\tdef successor(self, state):\n\t\tsuccessors = []\n\t\tactionList = self.allPossibleActions\n\t\tlastValue = state.value\n\t\tlastAction = state.m\n\t\tlastPeople = state.p\n\t\tlastState = state.tables\n\t\tfor action in actionList:\n\t\t\tpeopleLine1 = action[0][0]\n\t\t\tpeopleCol1 = action[0][1]\n\t\t\tpeopleLine2 = action[1][0]\n\t\t\tpeopleCol2 = action[1][1]\n\t\t\tswappedPeople1 = lastState[peopleLine1][peopleCol1]\n\t\t\tswappedPeople2 = lastState[peopleLine2][peopleCol2]\n\t\t\t#if(lastPeople != [swappedPeople1,swappedPeople2] and lastPeople != [swappedPeople2,swappedPeople1]):\n\t\t\t#if ([swappedPeople1,swappedPeople2] not in self.actionsAlreadyDone) and ([swappedPeople2,swappedPeople1] not in self.actionsAlreadyDone):\n\t\t\tnewState = cloneState(lastState)\n\t\t\tnewState[peopleLine1][peopleCol1] = swappedPeople2\n\t\t\tnewState[peopleLine2][peopleCol2] = swappedPeople1\n\t\t\tnewState[peopleLine1].sort()\n\t\t\tnewState[peopleLine2].sort()\n\t\t\tnewValue = quickValue(state,newState,action)\n\t\t\tyield (action,State(self.numberOfPeople,self.numberOfTable,action,[swappedPeople1,swappedPeople2],state,newState,newValue))\n\n\tdef value(self, state):\n\t\taffinitiesTable = self.affinitiesTable\n\t\ttableMaxPeople = self.numberOfPeople/self.numberOfTable\n\t\tvalue = 0\n\t\tfor table in state:\n\t\t\tfor people in table:\n\t\t\t\tpeopleIndex = 0\n\t\t\t\twhile(peopleIndex < tableMaxPeople):\n\t\t\t\t\tvalue += int(float(affinitiesTable[people][table[peopleIndex]]))\n\t\t\t\t\tpeopleIndex += 1\n\t\treturn value\n\n\t\"\"\"This function return a table representing the affinities between each people\"\"\"\n\tdef createAffinitiesTable(self, path):\n\t\taffinitiesTable = []\n\t\tf = open(path,'r')\n\t\tnumberOfLine = 0\n\t\tnumberOfColumn = 0\n\n\t\tfor line in f:\n\t\t\taffinitiesColTable = []\n\t\t\tminus = False # check Whether we have a negative value or not.\n\t\t\tif(numberOfLine == 0):\n\t\t\t\tself.numberOfPeople = line\n\t\t\telif(numberOfLine == 1):\n\t\t\t\tself.numberOfTable = line\n\t\t\telse:\n\t\t\t\tfor col in line:\n\t\t\t\t\tif(col != \" \" and col != '\\n' and col != \"-\"):\n\t\t\t\t\t\tif(minus):\n\t\t\t\t\t\t\taffinitiesColTable.append(\"-\"+col)\n\t\t\t\t\t\t\tminus = False\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\taffinitiesColTable.append(col)\n\t\t\t\t\telif(col == \"-\"):\n\t\t\t\t\t\tminus = True\n\t\t\t\t\tnumberOfColumn += 1\n\t\t\t\telse:\t\n\t\t\t\t\taffinitiesTable.append(affinitiesColTable)\n\t\t\tnumberOfLine += 1\n\t\tf.close\n\t\tself.numberOfPeople = int(float(self.numberOfPeople))\n\t\tself.numberOfTable = int(float(self.numberOfTable))\n\t\treturn affinitiesTable\n\n\t\"\"\"This function return the initial repartition of people at each table.\"\"\"\n\tdef createTablesAssignment(self, path):\n\t\tself.affinitiesTable = self.createAffinitiesTable(path)\n\t\ttableMaxPeople = self.numberOfPeople/self.numberOfTable # Maximum number of people et each table\n\t\ttablesAssignment = [] \n\t\ttableIndex = 0 \n\t\tpeopleLineIndex = 0\n\t\tlistOfSitPeople = [] # List of people who have been assigned to a table\n\t\tfor lineAffinities in self.affinitiesTable:\n\t\t\tif(tableIndex >= self.numberOfTable): # All table are full, so we stop\n\t\t\t\tbreak\n\t\t\tdicoOfNoSitPeople = {}\n\t\t\tkeysAffinity = list(dicoOfNoSitPeople.keys()) # List of all the value used as key represanting an affinity.\n\t\t\tpeopleColIndex = 0\n\t\t\tif (peopleLineIndex not in listOfSitPeople): # If peopleLineIndex have already been assigned don't need to assigne it again.\n\t\t\t\tfor Affinity in lineAffinities:\n\t\t\t\t\tif(peopleLineIndex != peopleColIndex and peopleColIndex not in listOfSitPeople):\n\t\t\t\t\t\tif(Affinity not in keysAffinity): \t\t\t\t\t# If this affinity already exist we need to add a new people\n\t\t\t\t\t\t\tdicoOfNoSitPeople[Affinity]=[peopleColIndex] \t# in the dictionnary at this key Otherwise we add this people at\n\t\t\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\t# this new key.\n\t\t\t\t\t\t\tdicoOfNoSitPeople[Affinity].append(peopleColIndex)\n\t\t\t\t\tpeopleColIndex+=1\n\t\t\t\t\tkeysAffinity = list(dicoOfNoSitPeople.keys())\n\n\t\t\t\torderedAffinities = []\n\t\t\t\tfor elem in keysAffinity:\n\t\t\t\t\torderedAffinities.append(int(float(elem))) # We want to order int not string.\n\t\t\t\torderedAffinities.sort(reverse=True) \n\t\t\t\ttablesAssignment.append([])\n\t\t\t\ttablesAssignment[tableIndex].append(peopleLineIndex)\n\t\t\t\tlistOfSitPeople.append(peopleLineIndex)\n\t\t\t\tnumberOfPeople = 1 # peopleLineIndex have already been assigned so just need to find tableMaxPeople-1 peoples. \n\t\t\t\tkey = 0\n\t\t\t\twhile (numberOfPeople < tableMaxPeople):\t\t\t\t\t\t# taking all people having best affinities \n\t\t\t\t\tfor elem in dicoOfNoSitPeople[str(orderedAffinities[key])]:\t# with peopleLineIndex\n\t\t\t\t\t\tif (numberOfPeople < tableMaxPeople):\n\t\t\t\t\t\t\ttablesAssignment[tableIndex].append(elem)\n\t\t\t\t\t\t\tlistOfSitPeople.append(elem)\n\t\t\t\t\t\tnumberOfPeople += 1\n\t\t\t\t\tkey += 1\n\t\t\t\t\t\n\t\t\t\ttablesAssignment[tableIndex].sort()\n\t\t\t\ttableIndex += 1\n\t\t\tpeopleLineIndex += 1\n\t\tself.initial = State(self.numberOfPeople,self.numberOfTable,[[0,0],[0,0]],[0,0],None,tablesAssignment, self.value(tablesAssignment))\n\n\t\"\"\"Return all possible actions, An action is a 2-uple containing the row and column of peoples who are going to be swapped\"\"\"\n\t\"\"\"action = [[row1,column1],[row2,column2]]\"\"\"\n\tdef allPossibleActions(self):\n\t\ttableMaxPeople = self.numberOfPeople/self.numberOfTable\n\t\tactionList = []\n\t\n\t\tline1 = 0\n\t\twhile(line1<self.numberOfTable-1):\n\t\t\tcol1 = 0\n\t\t\twhile (col1 < tableMaxPeople):\n\t\t\t\tline2 = line1+1\n\t\t\t\twhile(line2 < self.numberOfTable):\n\t\t\t\t\tcol2 = 0\n\t\t\t\t\twhile(col2 < tableMaxPeople):\n\t\t\t\t\t\taction = [[line1,col1],[line2,col2]]\n\t\t\t\t\t\tactionList.append(action)\n\t\t\t\t\t\tcol2 += 1\n\t\t\t\t\tline2 += 1\n\t\t\t\tcol1 += 1\n\t\t\tline1 += 1\n\t\treturn actionList\n\n###############\n# State class #\n###############\n\nclass State:\n\n\tdef __init__(self, n, t, m, p, oldState, tables, value):\n\t\tself.n = n\n\t\tself.t = t\n\t\tself.m = m\n\t\tself.p = p\n\t\tself.oldState = oldState\n\t\tself.tables = tables\n\t\tself.value = value\n\n######################\n# Auxiliary Function #\n######################\n\ndef concatAllTables(tables):\n\tcompareList = []\n\tfor table in tables:\n\t\tcompareList += table\n\treturn compareList\n\ndef quickValue(oldState,newTables,action):\n\toldValue = oldState.value\n\toldTable1 = oldState.tables[action[0][0]]\n\toldTable2 = oldState.tables[action[1][0]]\n\tnewTable1 = newTables[action[0][0]]\n\tnewTable2 = newTables[action[1][0]]\n\toldTableValue = singleTableValue(oldTable1) + singleTableValue(oldTable2) \n\tnewTableValue = singleTableValue(newTable1) + singleTableValue(newTable2)\n\treturn oldValue - oldTableValue + newTableValue\n\ndef singleTableValue(table):\n\taffinitiesTable = wedding.affinitiesTable\n\ttableMaxPeople = wedding.numberOfPeople/wedding.numberOfTable\n\tvalue = 0\n\tfor people in table:\n\t\tpeopleIndex = 0\n\t\twhile(peopleIndex < tableMaxPeople):\n\t\t\tvalue += int(float(affinitiesTable[people][table[peopleIndex]]))\n\t\t\tpeopleIndex += 1\n\treturn value\n\ndef cloneState(List):\n\tcloneList = []\n\tfor line in List:\n\t\tcloneList.append(list(line))\n\treturn cloneList\n\ndef maxValueTable(listNodes):\n\tlistNodes2 = sorted(listNodes,key=lambda a:a.state.value,reverse = True)\n\tfirstElem = listNodes2[0]\n\tcompareTables1 = concatAllTables(firstElem.state.tables)\n\tfor node in listNodes2:\n\t\tcompareTables2 = concatAllTables(node.state.tables)\n\t\tif( firstElem.state.value == node.state.value and compareTables1 > compareTables2):\n\t\t\tfirstElem = node\n\t\t\tcompareTables1 = compareTables2\n\t\telif(firstElem.state.value != node.state.value):\n\t\t\tbreak\n\treturn firstElem\n\ndef fiveMaxValueTables(listNodes):\n\tlistNodes2 = sorted(listNodes,key=lambda a:a.state.value,reverse = True)\n\tlistElem = []\n\ti = 0\n\tj = 0\n\tfor node in listNodes2:\n\t\tif(i < 5):\n\t\t\tlistElem.append(node)\n\t\t\tif(listElem[i].state.value != listElem[j].state.value):\n\t\t\t\tj = i\n\t\telif (listElem[4].state.value == node.state.value):\n\t\t\tlistElem.append(node)\n\t\telse:\n\t\t\tbreak\n\t\ti+=1\n\n\tif(len(listElem) == 5):\n\t\treturn listElem\n\t\n\tbestTable1 = listElem[j:len(listElem)]\n\tbestTable2 = sorted(bestTable1,key=lambda a:concatAllTables(a.state.tables),reverse = False)\n\treturn listElem[0:j]+bestTable2[0:5-j]\n\ndef printState(state):\n\tprint(state.value)\n\tfor table in state.tables:\n\t\ttable.sort()\n\t\tfor people in table:\n\t\t\tsys.stdout.write(str(people))\n\t\t\tsys.stdout.write(\" \")\n\t\tsys.stdout.write(\"\\n\")\n\n################\n# Local Search #\n################\n\ndef randomized_maxvalue(problem, limit=100, callback=None):\n    current = LSNode(problem, problem.initial, 0)\n    best = current\n    random.seed(42)\n    for step in range(limit):\n    \tif callback is not None:\n    \t\tcallback(current)\n    \toldNode = current.state.oldState\n    \t#print(\"SSSSSSSSSSTEEEEEEEEEEEEEEEPPPPPPPPPPPPP = \",step)\n    \t#for elem in fiveMaxValueTables(list(current.expand())):\n    \t#\tprint(elem.state.value)\n    \t#\tprint(concatAllTables(elem.state.tables))\n    \tcurrent = random.choice(fiveMaxValueTables(list(current.expand())))\n    \tif(step != 0):\n    \t\tif(current.state.tables == oldNode.tables):\n    \t\t\tbreak\n    \twedding.actionsAlreadyDone.append(current.state.p)\n    \twedding.actionsAlreadyDone.append(current.state.p.reverse())\n    \tif current.state.value > best.state.value:\n    \t\tbest = current\n    return best\n\ndef maxvalue(problem, limit=100, callback=None):\n    current = LSNode(problem, problem.initial, 0)\n    best = current\n    first = True\n    for step in range(limit):\n    \tif callback is not None:\n    \t\tcallback(current)\n    \toldNode = current.state.oldState\n    \tcurrent = maxValueTable(list(current.expand()))\n    \tif(step != 0):\n    \t\tif(current.state.tables == oldNode.tables):\n    \t\t\tprint(current.step)\n    \t\t\tbreak\n    \twedding.actionsAlreadyDone.append(current.state.p)\n    \twedding.actionsAlreadyDone.append(current.state.p.reverse())\n    \tif (current.state.value > best.state.value):\n    \t\tbest = current\n    return best\n\ndef random_walk(problem, limit=100, callback=None):\n    \"\"\"Perform a random walk in the search space and return the best solution\n    found. The returned value is a Node.\n    If callback is not None, it must be a one-argument function that will be\n    called at each step with the current node.\n    \"\"\"\n    current = LSNode(problem, problem.initial, 0)\n    best = current\n    for step in range(limit):\n        if callback is not None:\n            callback(current)\n        current = random.choice(list(current.expand()))\n        if current.state.value > best.state.value:\n            best = current\n    return best\n\nif __name__ == '__main__':\n\twedding = Wedding(sys.argv[1])\n\tprintState(wedding.initial)\n\n\tstart_time = time.time()\n\tnode2 = randomized_maxvalue(wedding, 100)\n\t#node2 = random_walk(wedding)\t\n\tinterval = time.time()-start_time\n\tprintState(node2.state)\n\tprint(\"step =\",node2.step)\n\tprint(interval)\n\n\t#node = maxvalue(wedding)\n\t#printState(node.state)\n\t#state = node.state\n\t#print(state)\n","sub_path":"template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":11210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"396130439","text":"\"\"\"\ndata generators for VoxelMorph\n\nfor the CVPR and MICCAI papers, we have data arranged in train/validate/test folders\ninside each folder is a /vols/ and a /asegs/ folder with the volumes\nand segmentations. All of our papers use npz formated data.\n\"\"\"\n\nimport os, sys\nimport numpy as np\nimport nibabel as nib\nimport glob\nimport itertools\nimport SimpleITK as sitk\n\n\ndef cvpr2018_gen(gen, atlas_vol_bs, batch_size=1):\n    \"\"\" generator used for cvpr 2018 model \"\"\"\n\n    volshape = atlas_vol_bs.shape[1:-1]\n    zeros = np.zeros((batch_size, *volshape, len(volshape)))\n    while True:\n        X = next(gen)[0]\n        yield ([X, atlas_vol_bs], [atlas_vol_bs, zeros])\n\n\ndef cvpr2018_gen_s2s(gen, batch_size=1):\n    \"\"\" generator used for cvpr 2018 model for subject 2 subject registration \"\"\"\n    zeros = None\n    while True:\n        X1 = next(gen)[0]\n        X2 = next(gen)[0]\n\n        if zeros is None:\n            volshape = X1.shape[1:-1]\n            zeros = np.zeros((batch_size, *volshape, len(volshape)))\n        yield ([X1, X2], [X2, zeros])\n\n\ndef miccai2018_gen(gen, atlas_vol_bs, batch_size=1, bidir=False):\n    \"\"\" generator used for miccai 2018 model \"\"\"\n    volshape = atlas_vol_bs.shape[1:-1]\n    zeros = np.zeros((batch_size, *volshape, len(volshape)))\n    while True:\n        X = next(gen)[0]\n        if bidir:\n            yield ([X, atlas_vol_bs], [atlas_vol_bs, X, zeros])\n        else:\n            yield ([X, atlas_vol_bs], [atlas_vol_bs, zeros])\n\n\ndef miccai2018_gen_s2s(gen, batch_size=1, bidir=False):\n    \"\"\" generator used for miccai 2018 model \"\"\"\n    zeros = None\n    while True:\n        X = next(gen)[0]\n        Y = next(gen)[0]\n        if zeros is None:\n            volshape = X.shape[1:-1]\n            zeros = np.zeros((batch_size, *volshape, len(volshape)))\n        if bidir:\n            yield ([X, Y], [Y, X, zeros])\n        else:\n            yield ([X, Y], [Y, zeros])\n            \ndef miccai2018_multimodel(gen,gen1, batch_size=1, bidir=False):\n    \"\"\" generator used for miccai 2018 model \"\"\"\n    zeros = None\n    while True:\n        X = next(gen1)[0]\n        Y = next(gen)[0]\n        if zeros is None:\n            volshape = X.shape[1:-1]\n            zeros = np.zeros((batch_size, *volshape, len(volshape)))\n        if bidir:\n            yield ([X, Y], [Y, X, zeros])\n        else:\n            yield ([X, Y], [Y, zeros])            \n\n\n\ndef example_gen(vol_names, batch_size=1, return_segs=False, seg_dir=None):\n    \"\"\"\n    generate examples\n\n    Parameters:\n        vol_names: a list or tuple of filenames\n        batch_size: the size of the batch (default: 1)\n\n        The following are fairly specific to our data structure, please change to your own\n        return_segs: logical on whether to return segmentations\n        seg_dir: the segmentations directory.\n        np_var: specify the name of the variable in numpy files, if your data is stored in \n            npz files. default to 'vol_data'\n    \"\"\"\n\n    while True:\n        idxes = np.random.randint(len(vol_names), size=batch_size)\n\n        X_data = []\n        for idx in idxes:\n            X = load_volfile(vol_names[idx])\n            X = X[np.newaxis, ..., np.newaxis]\n            X_data.append(X)\n\n        if batch_size > 1:\n            return_vals = [np.concatenate(X_data, 0)]\n        else:\n            return_vals = [X_data[0]]\n\n        # also return segmentations\n        if return_segs:\n            X_data = []\n            for idx in idxes:\n                X_seg = load_volfile(vol_names[idx].replace('norm', 'aseg'), np_var=np_var)\n                X_seg = X_seg[np.newaxis, ..., np.newaxis]\n                X_data.append(X_seg)\n            \n            if batch_size > 1:\n                return_vals.append(np.concatenate(X_data, 0))\n            else:\n                return_vals.append(X_data[0])\n\n        yield tuple(return_vals)\n\n\ndef load_example_by_name(vol_name, seg_name, np_var='vol_data'):\n    \"\"\"\n    load a specific volume and segmentation\n\n    np_var: specify the name of the variable in numpy files, if your data is stored in \n        npz files. default to 'vol_data'\n    \"\"\"\n    X = load_volfile(vol_name, np_var)\n    X = X[np.newaxis, ..., np.newaxis]\n\n    return_vals = [X]\n\n    X_seg = load_volfile(seg_name, np_var)\n    X_seg = X_seg[np.newaxis, ..., np.newaxis]\n\n    return_vals.append(X_seg)\n\n    return tuple(return_vals)\n\n\ndef load_volfile(datafile, np_var='vol_data'):\n    \"\"\"\n    load volume file\n    formats: nii, nii.gz, mgz, npz\n    if it's a npz (compressed numpy), variable names innp_var (default: 'vol_data')\n    \"\"\"\n    assert datafile.endswith(('.nrrd','.nii', '.nii.gz', '.mgz', '.npz')), 'Unknown data file'\n\n    if datafile.endswith(('.nii', '.nii.gz', '.mgz')):\n\n        X = nib.load(datafile).get_data()\n        \n    elif datafile.endswith(('.nrrd')):\n        X=sitk.GetArrayFromImage(sitk.ReadImage(datafile))\n        \n    else: # npz\n        if np_var is None:\n            np_var = 'vol_data'\n        X = np.load(datafile)[np_var]\n\n    return norm_data(X)\n    \ndef norm_data(data):\n    return (data-data.min())/(data.max()-data.min())\ndef imgnorm(N_I,index1=0.0001,index2=0.0001):\n    I_sort = np.sort(N_I.flatten())\n    I_min = I_sort[int(index1*len(I_sort))]\n    I_max = I_sort[-int(index2*len(I_sort))]\n    \n    N_I =1.0*(N_I-I_min)/(I_max-I_min)\n    N_I[N_I>1.0]=1.0\n    N_I[N_I<0.0]=0.0\n    N_I2 = N_I.astype(np.float32)\n    return N_I2\n    \n\ndef load_volume_liver(Dir):\n    f_3 = []\n    f_6 = []\n    extension='.nrrd'\n    #extension='.nrrd'\n    for f in os.listdir(Dir):\n        if(f!=[]):\n        #    f_3.append(glob.glob(os.path.join(Dir, f, f+'_11_3D_GRE_TRA_HELENA_W'+extension)))\n        #    F_img = list(itertools.chain(*f_3))\n            f_6.append(glob.glob(os.path.join(Dir, f, f+'_8_3D_GRE_TRA_HELENA_opp'+extension)))\n            F_img1 = list(itertools.chain(*f_6))\n    \n    return F_img1\n    \n    \n\n","sub_path":"voxelmorph_multi/src/.ipynb_checkpoints/datagenerators-checkpoint.py","file_name":"datagenerators-checkpoint.py","file_ext":"py","file_size_in_byte":5861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"515758020","text":"\n\nfrom xai.brain.wordbase.nouns._scintilla import _SCINTILLA\n\n#calss header\nclass _SCINTILLAS(_SCINTILLA, ):\n\tdef __init__(self,): \n\t\t_SCINTILLA.__init__(self)\n\t\tself.name = \"SCINTILLAS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"scintilla\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_scintillas.py","file_name":"_scintillas.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"150108913","text":"#!/usr/bin/env python3\n\n\"\"\"Problem 103: Special subset sums: optimum\"\"\"\n\nfrom itertools import combinations\n\n\ndef is_special_sum_set(s):\n    \"\"\"\n    Check if a set (represented as a list) is a special sum set.\n    \"\"\"\n    s.sort()\n\n    if any(sum(s[:i]) <= sum(s[-i+1:]) for i in range(2, (len(s) + 3)//2)):\n        return False\n\n    for i in range(2, len(s)//2+1):\n        sums = set()\n\n        for subset in combinations(s, i):\n            st_sum = sum(subset)\n            if st_sum in sums:\n                return False\n            sums.add(st_sum)\n    return True\n\n\ndef main():\n    candidates = []\n\n    # make an educated guess about limits, based on the problem definition\n    for s in combinations(range(30, 47), 6):\n        s = [20, *s]\n\n        if is_special_sum_set(s):\n            candidates.append(s)\n\n    return ''.join(map(str, min(candidates, key=sum)))\n\n\nif __name__ == '__main__':\n    print(main())\n","sub_path":"python/p103.py","file_name":"p103.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"224776098","text":"from __future__ import print_function\nimport os\nimport sys\nfrom skimage.transform import resize\nfrom skimage.io import imsave\nimport numpy as np\nfrom keras.models import Model\nfrom keras.layers import Input, concatenate, Conv1D, MaxPooling1D, Conv2DTranspose,Lambda\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint\nfrom keras import backend as K\nimport tensorflow as tf\nimport keras\n#import cv2\nimport scipy.io\n\nsys.path.append(\"../../model/\")\n\nimport cnn_240599 as cnn\nfrom keras.backend.tensorflow_backend import set_session\nconfig = tf.ConfigProto()\nconfig.gpu_options.per_process_gpu_memory_fraction = 0.45\nset_session(tf.Session(config=config))\n\nK.set_image_data_format('channels_last')  # TF dimension ordering in this code\n(size,fold, path1)=(sys.argv[1],sys.argv[2],sys.argv[3])\nsize=int(size)\n\nchannel=3\nbatch_size=2\nss = 1\n\nimport random\nmodel = cnn.cnn1d(size,3)\n\nall_ids=open('train_gs.dat','r')\nall_line=[]\nfor line in all_ids:\n    all_line.append(line.rstrip())\nall_ids.close()\n\nrandom.shuffle(all_line)\npartition_ratio=0.8\ntrain_line=all_line[0:int(len(all_line)*partition_ratio)]\ntest_line=all_line[int(len(all_line)*partition_ratio):len(all_line)]\n\ndef generate_data(train_line, batch_size, if_train):\n    \"\"\"Replaces Keras' native ImageDataGenerator.\"\"\"\n##### augmentation parameters ######\n    i = 0\n    while True:\n        image_batch = []\n        label_batch = []\n        for b in range(batch_size):\n            if i == len(train_line):\n                i = 0\n                random.shuffle(train_line)\n            sample = train_line[i]\n            i += 1\n            table=sample.split('\\t')\n            the_id=table[1]\n            image = np.loadtxt(path1 + the_id + '.csv')[:,1:4]\n            image_pad=np.zeros((size,3))\n            image_pad[0:image.shape[0],:]=image\n            label=int(table[0])\n            if (if_train==1):\n                #augmentation part\n                pass\n\n            image_batch.append(image_pad)\n            label_batch.append(label)\n        image_batch=np.array(image_batch)\n        label_batch=np.array(label_batch)\n#        print(image_batch.shape,label_batch.shape)\n        yield image_batch, label_batch\n\n#model_checkpoint = ModelCheckpoint('weights.h5', monitor='val_loss', save_best_only=False)\nname_model='weights_' + sys.argv[2] + '.h5'\ncallbacks = [\n#    keras.callbacks.TensorBoard(log_dir='./',\n#    histogram_freq=0, write_graph=True, write_images=False),\n    keras.callbacks.ModelCheckpoint(os.path.join('./', name_model),\n    verbose=0, save_weights_only=False,monitor='val_loss')\n    ]\n\nmodel.fit_generator(\n    generate_data(train_line, batch_size,True),\n    steps_per_epoch=int(len(train_line) // batch_size), nb_epoch=10,\n    validation_data=generate_data(test_line,batch_size,False),\n    validation_steps=int(len(test_line) // batch_size),callbacks=callbacks)\n\n","sub_path":"REAL_onoff/final_long_smartwatch_whole_sigmoid_leaky_center_seg512_shift128/oldtrain.py","file_name":"oldtrain.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"334803876","text":"# import sys\r\n# sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')\r\n\r\n\"\"\"\r\nmodule for detect the correct relationship between carWheel bbox and car bbox in a frame map. \r\n\"\"\"\r\n\r\nimport numpy as np \r\nimport pandas as pd\r\n\r\nimport cv2\r\n\r\n\r\n\r\n# car_wheel_BBox = (cls,id,pos)\r\n#carBBox = (cls,id,pos)\r\n#pos = (x1,y1,x2,y2)\r\n#frame = ()\r\n\r\n# frame_title=['frameid','channel','cls', \\\r\n#             'cb_x1','cb_y1','cb_x2''cb_y2', \\\r\n#             'wb_x1','wb_y1','wb_x2','wb_y2',\\\r\n#             'iou','cb_index','wb_index','carWheelCls','mode']\r\n# carPos_title = ['x1','y1','x2','y2']\r\n# carWheelPos_title = ['x1','y1','x2','y2']\r\n\r\n# \r\n# frame = pd.DataFrame([],columns=frame_title)\r\n# carPos = pd.Series([],index=carPos_title)\r\n# carWheelPos = pd.Series([],index=carWheelPos)\r\n\r\n#frame \r\nFRAME_WIDTH = 320\r\nFRAME_HIGHT = 192\r\n\r\n#object cls\r\nFRONT_WHEEL_CLS = 7\r\nREAR_WHEEL_CLS = 6\r\nCAR_CLS = 1\r\n\r\n\r\n\r\nclass CarAndCarWheel(object):\r\n    \"base bounding box object in one frame,frame is a pandas DataFrame\"\r\n    def __init__(self,frame): \r\n        self.frame = frame \r\n        self.frameid = frame.iloc[0].loc['frameid']\r\n        self.channel = frame.iloc[0].loc['channel']\r\n        self.car_BBoxes = self.gen_car_BBoxes() \r\n        self.car_wheel_BBoxes = self.gen_car_wheel_BBoxes()\r\n        self.pred_modes = self.gen_pred_modes()\r\n        self.relationshipMatrix = self.gen_relationship_matrix()\r\n        \r\n    def gen_car_BBoxes(self):\r\n        car_BBoxes = []\r\n        i = 0\r\n        while i < len(self.frame):\r\n            frame_data = self.frame.iloc[i]\r\n            carPos = (frame_data.loc['cb_x1'], frame_data.loc['cb_y1'],frame_data.loc['cb_x2'],frame_data.loc['cb_y2'])    \r\n            carWheelpos = (frame_data.loc['wb_x1'], frame_data.loc['wb_y1'],frame_data.loc['wb_x2'],frame_data.loc['wb_y2'])\r\n            car_BBox = (frame_data.loc['cb_index'],frame_data.loc['cls'],carPos)\r\n            car_BBoxes.append(car_BBox)\r\n            i += 1\r\n        return car_BBoxes\r\n\r\n    def gen_car_wheel_BBoxes(self):\r\n        car_wheel_BBoxes = []\r\n        i = 0\r\n        while i < len(self.frame):\r\n            frame_data = self.frame.iloc[i]\r\n            carPos = (frame_data.loc['cb_x1'], frame_data.loc['cb_y1'],frame_data.loc['cb_x2'],frame_data.loc['cb_y2'])    \r\n            carWheelpos = (frame_data.loc['wb_x1'], frame_data.loc['wb_y1'],frame_data.loc['wb_x2'],frame_data.loc['wb_y2'])\r\n            car_wheel_BBox = (frame_data.loc['wb_index'],frame_data.loc['carWheelCls'],carWheelpos)\r\n            car_wheel_BBoxes.append(car_wheel_BBox)\r\n            i += 1\r\n        return car_wheel_BBoxes        \r\n\r\n    def gen_pred_modes(self):\r\n        pred_modes = []\r\n        i = 0\r\n        while i < len(self.frame):\r\n            frame_data = self.frame.iloc[i]\r\n            carPos = (frame_data.loc['cb_x1'], frame_data.loc['cb_y1'],frame_data.loc['cb_x2'],frame_data.loc['cb_y2'])    \r\n            carWheelpos = (frame_data.loc['wb_x1'], frame_data.loc['wb_y1'],frame_data.loc['wb_x2'],frame_data.loc['wb_y2'])\r\n            \r\n            car_BBox = (frame_data.loc['cb_index'],frame_data.loc['cls'],carPos)\r\n            car_wheel_BBox = (frame_data.loc['wb_index'],frame_data.loc['carWheelCls'],carWheelpos)\r\n\r\n            predMode = (car_BBox[0],car_wheel_BBox[0],frame_data.loc['mode'])\r\n            pred_modes.append(predMode)\r\n            i += 1\r\n        return pred_modes         \r\n\r\n        \r\n\r\n    def gen_relationship_matrix(self):\r\n        # matrixRows = len(self.car_BBoxes)\r\n        # matrixCols = len(self.car_wheel_BBoxes)\r\n        # matrixRows = matrixCols = car_BBoxNum + car_wheel_BBoxNum\r\n\r\n        title_idx = []\r\n        title_col = []\r\n        for car in self.car_BBoxes:\r\n            if str(int(car[0])) not in title_idx:\r\n                title_idx.append(str(int(car[0])))\r\n        \r\n        for carWheel in self.car_wheel_BBoxes:\r\n            if str(int(carWheel[0])) not in title_col:\r\n                title_col.append(str(int(carWheel[0])))\r\n\r\n        matrixRows = len(title_idx)  \r\n        matrixCols = len(title_col)  \r\n\r\n        matrixValue = np.zeros((matrixRows,matrixCols))\r\n        relationship_map = pd.DataFrame(matrixValue,columns=title_col,index=title_idx)\r\n        \r\n\r\n        for mode in self.pred_modes:\r\n            if mode[2] == 1:\r\n                relationship_map.loc[str(int(mode[0])),str(int(mode[1]))] = 1\r\n\r\n        return relationship_map\r\n\r\n\r\n   \r\n    \r\n    \"\"\"\r\n    base matheds for check\r\n    \"\"\"\r\n    def calcu_area(self,bbox):\r\n        return abs(bbox[2][2] - bbox[2][0]) * abs(bbox[2][3] - bbox[2][1])\r\n\r\n\r\n    def calcu_iou(self,car_bbox,carwheel_bbox):\r\n        \"calculate the iou between car and carwheel\"\r\n        car_bbox_x1 = max(car_bbox[2][0],0)\r\n        car_bbox_y1 = max(car_bbox[2][1],0)\r\n        car_bbox_x2 = min(car_bbox[2][2],FRAME_WIDTH)\r\n        car_bbox_y2 = min(car_bbox[2][3],FRAME_HIGHT)\r\n\r\n        carwheel_bbox_x1 = max(carwheel_bbox[2][0],0)\r\n        carwheel_bbox_y1 = max(carwheel_bbox[2][1],0)\r\n        carwheel_bbox_x2 = min(carwheel_bbox[2][2],FRAME_WIDTH)\r\n        carwheel_bbox_y2 = min(carwheel_bbox[2][3],FRAME_HIGHT)\r\n \r\n        iou_x_min = max(car_bbox_x1,carwheel_bbox_x1)\r\n        iou_x_max = max(car_bbox_x2,carwheel_bbox_x2)\r\n        iou_y_min = min(car_bbox_y1,carwheel_bbox_y1)\r\n        iou_y_max = min(car_bbox_y2,carwheel_bbox_y2)\r\n\r\n        inter_size = (iou_x_max - iou_x_min + 1) * (iou_y_max - iou_y_min + 1)\r\n        whole_size = (carwheel_bbox_x2 - carwheel_bbox_x1 + 1) * (carwheel_bbox_y2 - carwheel_bbox_y1 + 1)\r\n\r\n        iou = inter_size/whole_size\r\n        if car_bbox_x1 > carwheel_bbox_x2 or car_bbox_x2 < carwheel_bbox_x1 or car_bbox_y1 >carwheel_bbox_y2 or car_bbox_y2 < carwheel_bbox_y1:\r\n            iou =  0.0\r\n\r\n        return iou\r\n\r\n\r\n    #modify carwheel cls value  front_cls = 7,rear_cls = 6\r\n    def modify_carwheel_cls(self,car_bbox,carwheel_bbox,cls_value):\r\n        i=0\r\n        while i < len(self.frame):\r\n            line = self.frame.iloc[i]\r\n            if line.loc['cb_index'] == car_bbox[0] and line.loc['wb_index'] == carwheel_bbox[0]:\r\n                line.loc['carWheelCls'] = cls_value\r\n                break\r\n            else:\r\n                i += 1\r\n\r\n    def get_carwheel_index_from_matrix(self,car,carwheel_list):\r\n        carwheel_id = []\r\n        for carwheel in carwheel_list:\r\n            if self.relationshipMatrix.loc[str(int(car[0])),str(int(carwheel[0]))] == 1:\r\n                carwheel_id.append(carwheel[0])\r\n        return carwheel_id\r\n\r\n\r\n\r\n#==============================================================================================================\r\n    \"\"\"\r\n    Screening strategy function\r\n    \"\"\"\r\n    #Screening strategy function\r\n    #1.travese carWheel to conform one carWheel only can match one car_BBox.\r\n    def carWheel_based_check(self):\r\n        \"closer to the ground id true.\"\r\n        for carWheel in self.car_wheel_BBoxes:\r\n            if self.relationshipMatrix.loc[:,str(int(carWheel[0]))].sum() <= 1:\r\n                continue\r\n\r\n            # there are 2 car_BBox iou >0.5 with car_wheel_BBox ,so it should be remove one.\r\n            bottomDist = []\r\n            minDist = []\r\n            for car in self.car_BBoxes:\r\n                if self.relationshipMatrix.loc[str(int(car[0])),str(int(carWheel[0]))] == 1.0:\r\n                    dist = [car[0],carWheel[0],abs(car[2][3]- carWheel[2][3])]\r\n                    bottomDist.append(dist)\r\n                    minDist.append(dist[2])\r\n\r\n            minIdx = minDist.index(min(minDist))\r\n            i=0\r\n            while i < len(bottomDist):\r\n                if i == minIdx:\r\n                    i += 1\r\n                    # continue\r\n                else:\r\n                    self.relationshipMatrix.loc[str(int(bottomDist[i][0])),str(int(bottomDist[i][1]))] = 0\r\n                    i += 1\r\n\r\n        # print(\"carWheel based Screening has been travese!!!\")\r\n\r\n    def one_carWheel_check(self,car_bbox,carwheel_bbox,channel):\r\n        \"one carbbox only has one car_wheel\"\r\n        if abs(car_bbox[2][3] - carwheel_bbox[2][3]) > 7:\r\n            self.relationshipMatrix.loc[str(int(car_bbox[0])),str(int(carwheel_bbox[0]))] = 0\r\n\r\n        if self.calcu_area(carwheel_bbox)/self.calcu_area(car_bbox) > 0.2 or self.calcu_area(carwheel_bbox)/self.calcu_area(car_bbox) < 0.01:\r\n            self.relationshipMatrix.loc[str(int(car_bbox[0])),str(int(carwheel_bbox[0]))] = 0\r\n        \r\n        if carwheel_bbox[2][0] - car_bbox[2][0] < 7 and channel == 3:\r\n                self.relationshipMatrix.loc[str(int(car_bbox[0])),str(int(carwheel_bbox[0]))] = 0\r\n\r\n    def two_carWheel_check(self,car_bbox,carwheel_bbox1,carwheel_bbox2,channel):\r\n        \"\"\"one carbbox only has two car_wheel\"\"\"\r\n        #0. all the carwheel_bbox should satify condition 1:one carbbox .\r\n        self.one_carWheel_check(car_bbox,carwheel_bbox1,channel)\r\n        self.one_carWheel_check(car_bbox,carwheel_bbox2,channel)\r\n        #1.the cls same and cross with eachother.\r\n        if carwheel_bbox1[1] == carwheel_bbox2[1] and self.calcu_iou(carwheel_bbox1,carwheel_bbox2) > 0:\r\n            dist1 = abs(car_bbox[2][3] - carwheel_bbox1[2][3])\r\n            dist2 = abs(car_bbox[2][3] - carwheel_bbox2[2][3])\r\n\r\n            if dist1 < dist2:\r\n                self.relationshipMatrix.loc[str(int(car_bbox[0])),str(int(carwheel_bbox2[0]))] = 0\r\n            if dist2 < dist1:\r\n                self.relationshipMatrix.loc[str(int(car_bbox[0])),str(int(carwheel_bbox1[0]))] = 0\r\n        \r\n        #2.the cls same and there is no cross with each other\r\n        if carwheel_bbox1[1] == carwheel_bbox2[1] and self.calcu_iou(carwheel_bbox1,carwheel_bbox2) == 0:\r\n            if self.calcu_area(carwheel_bbox1) > self.calcu_area(carwheel_bbox2):\r\n                self.modify_carwheel_cls(car_bbox,carwheel_bbox1,FRONT_WHEEL_CLS)\r\n                self.modify_carwheel_cls(car_bbox,carwheel_bbox2,REAR_WHEEL_CLS)\r\n            if self.calcu_area(carwheel_bbox1) < self.calcu_area(carwheel_bbox2):\r\n                self.modify_carwheel_cls(car_bbox,carwheel_bbox1,REAR_WHEEL_CLS)\r\n                self.modify_carwheel_cls(car_bbox,carwheel_bbox2,FRONT_WHEEL_CLS)\r\n                   \r\n    def multi_carWheel_check(self,car_bbox,carwheel_list,channel):\r\n        \"one carbbox have more than two carwheel bboxes\"\r\n        if self.relationshipMatrix.loc[str(int(car_bbox[0]))].sum() > 2.0:\r\n            for car_wheel in carwheel_list:\r\n                self.one_carWheel_check(car_bbox,car_wheel,channel)\r\n        \r\n        if self.relationshipMatrix.loc[str(int(car_bbox[0]))].sum() ==2.0:\r\n            carwheel_bbox_ids = self.get_carwheel_index_from_matrix(car_bbox,carwheel_list)\r\n            for carwheel in carwheel_list:\r\n                if carwheel[0] == carwheel_bbox_ids[0]:\r\n                    carwheel_bbox1 = carwheel\r\n                if carwheel[0] == carwheel_bbox_ids[1]:\r\n                    carwheel_bbox2 = carwheel\r\n\r\n            self.two_carWheel_check(car_bbox,carwheel_bbox1,carwheel_bbox2,int(channel))\r\n\r\n#==============================================================================================================\r\n    def check(self,channel):\r\n        #1. carWheel based check.\r\n        self.carWheel_based_check()\r\n\r\n        title_idx = []\r\n        for car in self.car_BBoxes:\r\n            title_idx.append(str(car[0]))\r\n        \r\n        #2. car based check\r\n        carwheel_bbox_two = []\r\n        carwheel_bbox_multi = []\r\n        for car_bbox in self.car_BBoxes:\r\n            if self.relationshipMatrix.loc[str(int(car_bbox[0]))].sum() == 0:\r\n                continue\r\n            if self.relationshipMatrix.loc[str(int(car_bbox[0]))].sum() == 1.0:\r\n                for carwheel_bbox in self.car_wheel_BBoxes:\r\n                    if self.relationshipMatrix.loc[str(int(car_bbox[0])),str(int(carwheel_bbox[0]))] == 1.0:\r\n                        self.one_carWheel_check(car_bbox,carwheel_bbox,int(channel))\r\n            \r\n            if self.relationshipMatrix.loc[str(int(car_bbox[0]))].sum() == 2.0:\r\n                for carwheel_bbox in self.car_wheel_BBoxes:\r\n                    if self.relationshipMatrix.loc[str(int(car_bbox[0])),str(int(carwheel_bbox[0]))] == 1.0:\r\n                        carwheel_bbox_two.append(carwheel_bbox)\r\n\r\n                carwheel_bbox1 = carwheel_bbox_two[0]\r\n                carwheel_bbox2 = carwheel_bbox_two[1]\r\n                self.two_carWheel_check(car_bbox,carwheel_bbox1,carwheel_bbox2,int(channel))\r\n\r\n            if self.relationshipMatrix.loc[str(int(car_bbox[0]))].sum() > 2.0:\r\n                for carwheel_bbox in self.car_wheel_BBoxes:\r\n                    if self.relationshipMatrix.loc[str(int(car_bbox[0])),str(int(carwheel_bbox[0]))] == 1.0:\r\n                        carwheel_bbox_multi.append(carwheel_bbox)\r\n\r\n                self.multi_carWheel_check(car_bbox,carwheel_bbox_multi,int(channel))\r\n        \r\n        #3. carWheel based check.\r\n        # self.carWheel_based_check()\r\n\r\n\r\n    def update_frame(self):\r\n        assert(len(self.car_BBoxes)>0 and len(self.car_wheel_BBoxes)>0)\r\n        for car_bbox in self.car_BBoxes:\r\n            for carwheel_bbox in self.car_wheel_BBoxes:\r\n                car_id,carwheel_id = car_bbox[0],carwheel_bbox[0]\r\n                i = 0\r\n                while i < len(self.frame):\r\n                    line = self.frame.iloc[i]\r\n                    if (line.loc['cb_index'],line.loc['wb_index']) == (car_id,carwheel_id):\r\n                        line.loc['mode'] = self.relationshipMatrix.loc[str(int(car_id)),str(int(carwheel_id))]\r\n                    i += 1\r\n    \r\n        return self.frame\r\n#=======================================================================================================================================\r\n\r\n\r\n\r\n\r\n\r\n \r\n\r\n        \r\n\r\n\r\n\r\n\r\n","sub_path":"code/src/relationship_0401/car_carwheel.py","file_name":"car_carwheel.py","file_ext":"py","file_size_in_byte":13785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"233199834","text":"from django import forms\nfrom django.contrib import admin\nfrom django.core.exceptions import ValidationError\n\nfrom .models import Collection, Individual, Node, Origin, Source, Tag\n\n\n@admin.register(Origin)\nclass OriginAdmin(admin.ModelAdmin):\n    readonly_fields = (\"date_created\", \"date_modified\")\n    search_fields = (\"name\",)\n\n\n@admin.register(Individual)\nclass IndividualAdmin(admin.ModelAdmin):\n    readonly_fields = (\"date_created\", \"date_modified\")\n    filter_horizontal = (\"aka\",)\n    search_fields = (\"name\",)\n\n\nclass SourceAdminForm(forms.ModelForm):\n    def clean(self):\n        \"\"\" See apps.nodes.models.Source \"\"\"\n\n        pk = self.instance.pk\n        user = self.cleaned_data.get(\"user\")\n        name = self.cleaned_data.get(\"name\")\n        individuals = self.cleaned_data.get(\"individuals\")\n\n        if not name and not individuals:\n            error = forms.ValidationError(\n                \"Either 'name' or 'individuals' must be defined.\"\n            )\n            self.add_error(\"name\", error)\n            self.add_error(\"individuals\", error)\n\n        try:\n            Source.validate_unique_together(user, name, individuals, source_pk=pk)\n        except ValidationError as error:\n            raise forms.ValidationError(error.message)\n\n        return self.cleaned_data\n\n\n@admin.register(Source)\nclass SourceAdmin(admin.ModelAdmin):\n\n    form = SourceAdminForm\n\n    readonly_fields = (\"date_created\", \"date_modified\")\n    filter_horizontal = (\"individuals\",)\n    search_fields = (\"name\",)\n\n\n@admin.register(Tag)\nclass TagAdmin(admin.ModelAdmin):\n    readonly_fields = (\"date_created\", \"date_modified\")\n\n\n@admin.register(Collection)\nclass CollectionAdmin(admin.ModelAdmin):\n    readonly_fields = (\"date_created\", \"date_modified\")\n\n\n@admin.register(Node)\nclass NodeAdmin(admin.ModelAdmin):\n    fieldsets = (\n        (\n            \"Node\",\n            {\n                \"fields\": (\n                    \"id\",\n                    \"text\",\n                    \"media\",\n                    \"link\",\n                    \"source\",\n                    \"notes\",\n                    \"tags\",\n                    \"collections\",\n                    \"origin\",\n                    \"is_starred\",\n                    \"in_trash\",\n                    \"related\",\n                    \"user\",\n                )\n            },\n        ),\n        (\n            \"Read-only\",\n            {\n                \"fields\": (\n                    \"date_created\",\n                    \"date_modified\",\n                    \"auto_ocr\",\n                    \"auto_tags\",\n                    \"auto_related\",\n                )\n            },\n        ),\n    )\n\n    list_display = (\"__str__\", \"source\", \"_tags\", \"_collections\", \"is_starred\")\n    readonly_fields = (\n        \"date_created\",\n        \"date_modified\",\n        \"auto_tags\",\n        \"auto_related\",\n        \"auto_ocr\",\n    )\n    filter_horizontal = (\"tags\", \"collections\")\n    autocomplete_fields = (\"origin\", \"source\")\n\n    def _tags(self, obj):\n        return \", \".join([str(t) for t in obj.tags.all()])\n\n    def _collections(self, obj):\n        return \", \".join([str(c) for c in obj.collections.all()])\n","sub_path":"apps/nodes/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":3137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"374292587","text":"\"\"\"There i am comparing 3 algs which finds Fibonacci Numbers\"\"\"\nfrom time import perf_counter\nfrom matplotlib import pyplot as plt\n\n\ndef fib1(n):\n    \"\"\"Recursive fibo\"\"\"\n    assert n >= 0\n    return n if n <= 1 else fib1(n - 1) + fib1(n - 2)\n\n\ndef fib2(nn):\n    \"\"\"Recursive fibo + Getting from cache founded Fibo number\"\"\"\n    cache = {}\n\n    def fibin(n):\n        assert n >= 0\n        if n not in cache:\n            cache[n] = n if n <= 1 else fibin(n - 1) + fibin(n - 2)\n        return cache[n]\n\n    return fibin(nn)\n\n\ndef fib3(n):\n    \"\"\"Iterative algo\"\"\"\n    assert n >= 0\n    f0, f1 = 0, 1\n    for i in range(n - 1):\n        f0, f1 = f1, f0 + f1\n    return f1\n\n\ndef timed(f, *args, n_iter=100):\n    \"\"\"Calculate the time of function's execution\"\"\"\n    acc = float('inf')\n    for i in range(n_iter):\n        t0 = perf_counter()\n        f(*args)\n        t1 = perf_counter()\n        acc = min(acc, t1 - t0)\n    return acc\n\n\ndef compare_timed(fs, args):\n    \"\"\"Plots the execution time for chosen fibo-functions and compares it\"\"\"\n    for f in fs:\n        plt.plot(args, [timed(f, arg) for arg in args], label=f.__name__)\n        plt.legend()\n        plt.grid(True)\n    plt.show()\n\n\ndef main():\n    \"\"\"main function\"\"\"\n    compare_timed([fib1, fib2, fib3], list(range(10)))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Stepik/Algorithms_theory_and_practice_course_217/3/3.5/compare_fibo_functions.py","file_name":"compare_fibo_functions.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"478549782","text":"\nfrom astropy.table import Table\nfrom astropy.io import fits\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pyM2FS.calibration_helper_funcs import air_to_vacuum\nfrom scipy.signal import medfilt\nfrom scipy.interpolate import CubicSpline\n\n\ncams = ['r','b']\n\n\nwaverange = np.arange(4274,6319,0.1)\ncalib_filnum = 1313\n\nsolar = Table.read(\"./lamp_linelists/SolarFluxAtlas.csv\",format='ascii.csv')\ntwifluxes = {}\ncalib_coefs = {}\n\n#filnums = range(1319,1330+1)\n# flat_loc = \"../data/B09/oneds/\"\n# for fil in os.listdir(flat_loc):\n#     if 'twiflat' in fil and int(fil.split('_')[2]) in filnums:\n#         flat = Table(fits.open('{}{}'.format(flat_loc,fil))['FLUX'].data)\n#         twifluxes[(fil[0],int(fil.split('_')[2]))] = flat\n\nfilnums = [None]\nflat_loc = \"../data/B09/final_oned/\"\nfor fil in os.listdir(flat_loc):\n    if 'twiflat' in fil:\n        flat = Table(fits.open('{}{}'.format(flat_loc,fil))['FLUX'].data)\n        twifluxes[(fil[0],None)] = flat\n\n\n\nmatches = {'r':[],'b':[]}\nfor fil in os.listdir('../data/B09/calibrations/'):\n    if 'calibration_full-ThAr' in fil and str(calib_filnum) in fil:\n        matches[fil[0]].append(int(fil.split('_')[-1].split('.')[0]))\nnewest_r = np.max(matches['r'])\nnewest_b = np.max(matches['b'])\ncalib_coefs['r'] = Table(fits.open('../data/B09/calibrations/{}'.format('{}_calibration_full-ThAr_11J_{}_{}.fits'.format('r',calib_filnum,newest_r)))['CALIB COEFS'].data)\ncalib_coefs['b'] = Table(fits.open('../data/B09/calibrations/{}'.format('{}_calibration_full-ThAr_11J_{}_{}.fits'.format('b',calib_filnum,newest_b)))['CALIB COEFS'].data)\n\n\n\n\n\n\n\npix1 = np.arange(2048).astype(np.float64)\npix2 = pix1*pix1\npix3 = pix2*pix1\npix4 = pix2*pix2\npix5 = pix3*pix2\ndef wave(calcoefs):\n    a,b,c,d,e,f = calcoefs\n    return a + pix1*b + pix2*c + pix3*d + \\\n            pix4*e + pix5*f\n\n\nall_subd_flux_tabs = {}\nsubd_flux_tabs = {}\nsubd_flux_uniform = []\nwave_calibs_tabs = {}\n\nfor cam in cams:\n    for filnum in filnums:\n        flx_tab = twifluxes[(cam,filnum)]\n        if filnum == filnums[0]:\n            subd_flux_tab = flx_tab.copy()\n            wave_calibs = flx_tab.copy()\n            calib_coef = calib_coefs[cam]\n            for fib in flx_tab.colnames:\n                subd_flux_tab[fib] = subd_flux_tab[fib]*0.\n                cal_ceof = calib_coef[fib]\n                wav = wave(cal_ceof)\n                wave_calibs[fib] = wav\n            wave_calibs_tabs[cam] = wave_calibs\n        cur_subd_flux_tab = flx_tab.copy()\n        for fib in flx_tab.colnames:\n            flx = flx_tab[fib]\n            subd = flx#/flx.max()\n            subd = subd - medfilt(subd,2*int(len(subd)/10) + 1)\n            subd_flux_tab[fib] += subd\n            cur_subd_flux_tab[fib] = subd\n            interpolator = CubicSpline(wave_calibs[fib],subd)\n            interpolated_fluxes = interpolator(waverange,extrapolate=False)\n            subd_flux_uniform.append(interpolated_fluxes)\n\n        all_subd_flux_tabs[(cam,filnum)] = cur_subd_flux_tab\n    subd_flux_tabs[cam] = subd_flux_tab\n\n\nplt.figure()\nfor cam in ['r','b']:\n    for fib in subd_flux_tabs[cam].colnames:\n        wav = wave_calibs_tabs[cam][fib]\n        subd = subd_flux_tabs[cam][fib]\n        plt.plot(wav,subd/np.max(np.abs(subd)),alpha=0.2)\n\nsolar_vac = air_to_vacuum(solar['Wave'])\nxmin,xmax = plt.xlim()\ncut = np.where((solar_vac > xmin) & (solar_vac<xmax))\ncut_vac = solar_vac[cut]\nsmoothing_scale = 2000\ncut_flx = np.convolve(solar['RawFlux'],np.ones(smoothing_scale)/smoothing_scale,mode='same')[cut]\n# cut_flx = np.array(solar['RawFlux'])[cut]\ndivisor = len(cut_flx) // (2*len(subd))\npadd = int((divisor * np.ceil(len(cut_flx)/divisor) ) - len(cut_flx))\ncut_flx = np.concatenate([np.array([cut_flx[0]]*padd),cut_flx])\ncut_vac = np.concatenate([np.array([cut_vac[0]]*padd),cut_vac])\ncut_flx = np.mean(cut_flx.reshape((-1,divisor)),axis=1)\ncut_vac = np.mean(cut_vac.reshape((-1,divisor)),axis=1)\ncut_subd = cut_flx - medfilt(cut_flx,2*int(len(cut_flx)/10) + 1)\nplt.plot(cut_vac,cut_subd/np.max(np.abs(cut_subd)),'k-',alpha=1.0)\nplt.show()","sub_path":"debug_scripts/compare_solarflux.py","file_name":"compare_solarflux.py","file_ext":"py","file_size_in_byte":4046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"300925771","text":"''' Super simple module to get basic random names and emails'''\nimport random\n\nrandom_first = ['John', 'Jane', 'Corey', 'Travis', 'Dave', 'Kurt', 'Neil', 'Sam', 'Mary', 'Maggie', 'Nicole', 'Steve', 'Tom']\nrandom_last = ['Smith', 'Doe', 'Jenkins', 'Robinson', 'Davis', 'Stuart', 'Jefferson', 'Jacobs', 'Wright', 'Patterson', 'Wilks', 'Arnold']\n\nfor num in range(20):\n    first = random.choice(random_first)\n    last = random.choice(random_last)\n    email = first.lower() + last.lower() + '@bogusemail.com'\n    print(f'{first} {last} - {email}')\n","sub_path":"random_names.py","file_name":"random_names.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"49687364","text":"# -*- coding: utf8 -*-\n#\n# Module NUMERIC\n#\n# Part of Nutils: open source numerical utilities for Python. Jointly developed\n# by HvZ Computational Engineering, TU/e Multiscale Engineering Fluid Dynamics,\n# and others. More info at http://nutils.org <info@nutils.org>. (c) 2014\n\n\"\"\"\nThe numeric module provides methods that are lacking from the numpy module.\n\"\"\"\n\nfrom __future__ import print_function, division\nimport numpy\n\n_abc = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' # indices for einsum\n\ndef grid( shape ):\n  shape = tuple(shape)\n  grid = numpy.empty( (len(shape),)+shape, dtype=int )\n  for i, sh in enumerate( shape ):\n    grid[i] = numpy.arange(sh)[(slice(None),)+(numpy.newaxis,)*(len(shape)-i-1)]\n  return grid\n\ndef round( arr ):\n  arr = numpy.asarray( arr )\n  return arr if arr.dtype == int \\\n    else ( arr - numpy.less(arr,0) + .5 ).astype( int )\n\ndef sign( arr ):\n  arr = numpy.asarray( arr )\n  return (arr>=0).astype(int) - (arr<=0).astype(int)\n\ndef floor( arr ):\n  ass = numpy.asarray( arr )\n  return arr if arr.dtype == int \\\n    else ( arr - numpy.less(arr,0) ).astype( int )\n\ndef overlapping( arr, axis=-1, n=2 ):\n  'reinterpret data with overlaps'\n\n  arr = numpy.asarray( arr )\n  if axis < 0:\n    axis += arr.ndim\n  assert 0 <= axis < arr.ndim\n  shape = arr.shape[:axis] + (arr.shape[axis]-n+1,n) + arr.shape[axis+1:]\n  strides = arr.strides[:axis] + (arr.strides[axis],arr.strides[axis]) + arr.strides[axis+1:]\n  overlapping = numpy.lib.stride_tricks.as_strided( arr, shape, strides )\n  overlapping.flags.writeable = False\n  return overlapping\n\ndef normdim( ndim, n ):\n  'check bounds and make positive'\n\n  assert isint(ndim) and ndim >= 0, 'ndim must be positive integer, got %s' % ndim\n  if n < 0:\n    n += ndim\n  assert 0 <= n < ndim, 'argument out of bounds: %s not in [0,%s)' % (n,ndim)\n  return n\n\ndef align( arr, trans, ndim ):\n  '''create new array of ndim from arr with axes moved accordin\n  to trans'''\n\n  arr = numpy.asarray( arr )\n  assert arr.ndim == len(trans)\n  if not len(trans):\n    return arr[(numpy.newaxis,)*ndim]\n  transpose = numpy.empty( ndim, dtype=int )\n  trans = numpy.asarray( trans )\n  nnew = ndim - len(trans)\n  if nnew > 0:\n    remaining = numpy.ones( ndim, dtype=bool )\n    remaining[trans] = False\n    inew, = remaining.nonzero()\n    trans = numpy.hstack([ inew, trans ])\n    arr = arr[(numpy.newaxis,)*nnew]\n  transpose[trans] = numpy.arange(ndim)\n  return arr.transpose( transpose )\n\ndef get( arr, axis, item ):\n  'take single item from array axis'\n\n  arr = numpy.asarray( arr )\n  axis = normdim( arr.ndim, axis )\n  return arr[ (slice(None),) * axis + (item,) ]\n\ndef expand( arr, *shape ):\n  'expand'\n\n  newshape = list( arr.shape )\n  for i, sh in enumerate( shape ):\n    if sh != None:\n      if newshape[i-len(shape)] == 1:\n        newshape[i-len(shape)] = sh\n      else:\n        assert newshape[i-len(shape)] == sh\n  expanded = numpy.empty( newshape )\n  expanded[:] = arr\n  return expanded\n\ndef linspace2d( start, stop, steps ):\n  'linspace & meshgrid combined'\n\n  start = tuple(start) if isinstance(start,(list,tuple)) else (start,start)\n  stop  = tuple(stop ) if isinstance(stop, (list,tuple)) else (stop, stop )\n  steps = tuple(steps) if isinstance(steps,(list,tuple)) else (steps,steps)\n  assert len(start) == len(stop) == len(steps) == 2\n  values = numpy.empty( (2,)+steps )\n  values[0] = numpy.linspace( start[0], stop[0], steps[0] )[:,numpy.newaxis]\n  values[1] = numpy.linspace( start[1], stop[1], steps[1] )[numpy.newaxis,:]\n  return values\n\ndef contract( A, B, axis=-1 ):\n  'contract'\n\n  A = numpy.asarray( A )\n  B = numpy.asarray( B )\n\n  maxdim = max( A.ndim, B.ndim )\n  m = _abc[maxdim-A.ndim:maxdim]\n  n = _abc[maxdim-B.ndim:maxdim]\n\n  axes = sorted( [ normdim(maxdim,axis) ] if isinstance(axis,int) else [ normdim(maxdim,ax) for ax in axis ] )\n  o = _abc[:maxdim-len(axes)] if axes == range( maxdim-len(axes), maxdim ) \\\n    else ''.join( _abc[a+1:b] for a, b in zip( [-1]+axes, axes+[maxdim] ) if a+1 != b )\n\n  return numpy.einsum( '%s,%s->%s' % (m,n,o), A, B )\n\ndef contract_fast( A, B, naxes ):\n  'contract last n axes'\n\n  assert naxes >= 0\n  A = numpy.asarray( A )\n  B = numpy.asarray( B )\n\n  maxdim = max( A.ndim, B.ndim )\n  m = _abc[maxdim-A.ndim:maxdim]\n  n = _abc[maxdim-B.ndim:maxdim]\n  o = _abc[:maxdim-naxes]\n\n  return numpy.einsum( '%s,%s->%s' % (m,n,o), A, B )\n\ndef dot( A, B, axis=-1 ):\n  '''Transform axis of A by contraction with first axis of B and inserting\n     remaining axes. Note: with default axis=-1 this leads to multiplication of\n     vectors and matrices following linear algebra conventions.'''\n\n  A = numpy.asarray( A )\n  B = numpy.asarray( B )\n\n  m = _abc[:A.ndim]\n  x = _abc[A.ndim:A.ndim+B.ndim-1]\n  n = m[axis] + x\n  o = m[:axis] + x\n  if axis != -1:\n    o += m[axis+1:]\n\n  return numpy.einsum( '%s,%s->%s' % (m,n,o), A, B )\n\ndef fastrepeat( A, nrepeat, axis=-1 ):\n  'repeat axis by 0stride'\n\n  A = numpy.asarray( A )\n  assert A.shape[axis] == 1\n  shape = list( A.shape )\n  shape[axis] = nrepeat\n  strides = list( A.strides )\n  strides[axis] = 0\n  return numpy.lib.stride_tricks.as_strided( A, shape, strides )\n\ndef fastmeshgrid( X, Y ):\n  'mesh grid based on fastrepeat'\n\n  return fastrepeat(X[numpy.newaxis,:],len(Y),axis=0), fastrepeat(Y[:,numpy.newaxis],len(X),axis=1)\n\ndef meshgrid( *args ):\n  'multi-dimensional meshgrid generalisation'\n\n  args = [ numpy.asarray(arg) for arg in args ]\n  shape = [ len(args) ] + [ arg.size for arg in args if arg.ndim ]\n  dtype = int if all( isintarray(a) for a in args ) else float\n  grid = numpy.empty( shape, dtype=dtype )\n  n = len(shape)-1\n  for i, arg in enumerate( args ):\n    if arg.ndim:\n      n -= 1\n      grid[i] = arg[(slice(None),)+(numpy.newaxis,)*n]\n    else:\n      grid[i] = arg\n  assert n == 0\n  return grid\n\ndef appendaxes( A, shape ):\n  'append axes by 0stride'\n\n  shape = (shape,) if isinstance(shape,int) else tuple(shape)\n  A = numpy.asarray( A )\n  return numpy.lib.stride_tricks.as_strided( A, A.shape + shape, A.strides + (0,)*len(shape) )\n\ndef takediag( A ):\n  diag = A[...,0] if A.shape[-1] == 1 \\\n    else A[...,0,:] if A.shape[-2] == 1 \\\n    else numpy.einsum( '...ii->...i', A )\n  diag.flags.writeable = False\n  return diag\n\ndef reshape( A, *shape ):\n  'more useful reshape'\n\n  newshape = []\n  i = 0\n  for s in shape:\n    if isinstance( s, (tuple,list) ):\n      assert numpy.product( s ) == A.shape[i]\n      newshape.extend( s )\n      i += 1\n    elif s == 1:\n      newshape.append( A.shape[i] )\n      i += 1\n    else:\n      assert s > 1\n      newshape.append( numpy.product( A.shape[i:i+s] ) )\n      i += s\n  assert i <= A.ndim\n  newshape.extend( A.shape[i:] )\n  return A.reshape( newshape )\n\ndef mean( A, weights=None, axis=-1 ):\n  'generalized mean'\n\n  return A.mean( axis ) if weights is None else dot( A, weights / weights.sum(), axis )\n\ndef normalize( A, axis=-1 ):\n  'devide by normal'\n\n  s = [ slice(None) ] * A.ndim\n  s[axis] = numpy.newaxis\n  return A / numpy.linlg.norm( A, axis )[ tuple(s) ]\n\ndef cross( v1, v2, axis ):\n  'cross product'\n\n  if v1.ndim < v2.ndim:\n    v1 = v1[ (numpy.newaxis,)*(v2.ndim-v1.ndim) ]\n  elif v2.ndim < v1.ndim:\n    v2 = v2[ (numpy.newaxis,)*(v1.ndim-v2.ndim) ]\n  return numpy.cross( v1, v2, axis=axis )\n\ndef times( A, B ):\n  \"\"\"Times\n  Multiply such that shapes are concatenated.\"\"\"\n  A = numpy.asarray( A )\n  B = numpy.asarray( B )\n\n  o = _abs[:A.ndim+B.ndim]\n  m = o[:A.ndim]\n  n = o[A.ndim:]\n\n  return numpy.einsum( '%s,%s->%s' % (m,n,o), A, B )\n\ndef stack( arrays, axis=0 ):\n  'powerful array stacker with singleton expansion'\n\n  arrays = [ numpy.asarray(array,dtype=float) for array in arrays ]\n  shape = [1] * max( array.ndim for array in arrays )\n  axis = normdim( len(shape)+1, axis )\n  for array in arrays:\n    for i in range(-array.ndim,0):\n      if shape[i] == 1:\n        shape[i] = array.shape[i]\n      else:\n        assert array.shape[i] in ( shape[i], 1 )\n  stacked = numpy.empty( shape[:axis]+[len(arrays)]+shape[axis:], dtype=float )\n  for i, arr in enumerate( arrays ):\n    stacked[(slice(None),)*axis+(i,)] = arr\n  return stacked\n\ndef bringforward( arg, axis ):\n  'bring axis forward'\n\n  arg = numpy.asarray(arg)\n  axis = normdim(arg.ndim,axis)\n  if axis == 0:\n    return arg\n  return arg.transpose( [axis] + range(axis) + range(axis+1,arg.ndim) )\n\ndef diagonalize( arg ):\n  'append axis, place last axis on diagonal of self and new'\n\n  diagonalized = numpy.zeros( arg.shape + (arg.shape[-1],) )\n  diag = numpy.einsum( '...ii->...i', diagonalized )\n  assert diag.base is diagonalized\n  diag.flags.writeable = True\n  diag[:] = arg\n  return diagonalized\n\ndef eig( A ):\n  '''If A has repeated eigenvalues, numpy.linalg.eig sometimes fails to produce\n  the complete eigenbasis. This function aims to fix that by identifying the\n  problem and completing the basis where necessary.'''\n\n  L, V = numpy.linalg.eig( A )\n\n  # check repeated eigenvalues\n  for index in numpy.ndindex( A.shape[:-2] ):\n    unique, inverse = numpy.unique( L[index], return_inverse=True )\n    if len(unique) < len(inverse): # have repeated eigenvalues\n      repeated, = numpy.where( numpy.bincount(inverse) > 1 )\n      vectors = V[index].T\n      for i in repeated: # indices pointing into unique corresponding to repeated eigenvalues\n        where, = numpy.where( inverse == i ) # corresponding eigenvectors\n        for j, n in enumerate(where):\n          W = vectors[where[:j]]\n          vectors[n] -= numpy.dot( numpy.dot( W, vectors[n] ), W ) # gram schmidt orthonormalization\n          scale = numpy.linalg.norm(vectors[n])\n          if scale < 1e-8: # vectors are near linearly dependent\n            u, s, vh = numpy.linalg.svd( A[index] - unique[i] * numpy.eye(len(inverse)) )\n            nnz = numpy.argsort( abs(s) )[:len(where)]\n            vectors[where] = vh[nnz].conj()\n            break\n          vectors[n] /= scale\n\n  return L, V\n\ndef isbool( a ):\n  return isboolarray( a ) and a.ndim == 0 or type(a) == bool\n\ndef isboolarray( a ):\n  return isinstance( a, numpy.ndarray ) and a.dtype == bool\n\ndef isint( a ):\n  return isintarray( a ) and a.ndim == 0 or numpy.issubdtype( type(a), numpy.integer )\n\ndef isintarray( a ):\n  return isinstance( a, numpy.ndarray ) and numpy.issubdtype( a.dtype, numpy.integer )\n\n# vim:shiftwidth=2:foldmethod=indent:foldnestmax=2\n","sub_path":"nutils/numeric.py","file_name":"numeric.py","file_ext":"py","file_size_in_byte":10284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"358394800","text":"\"\"\"\nGiven a string that consists of only uppercase English letters, you can replace any letter in the string with another letter at most k times. Find the length of a longest substring containing all repeating letters you can get after performing the above operations.\n\nNote:\nBoth the string's length and k will not exceed 104.\n\nExample 1:\n\nInput:\ns = \"ABAB\", k = 2\n\nOutput:\n4\n\nExplanation:\nReplace the two 'A's with two 'B's or vice versa.\nExample 2:\n\nInput:\ns = \"AABABBA\", k = 1\n\nOutput:\n4\n\nExplanation:\nReplace the one 'A' in the middle with 'B' and form \"AABBBBA\".\nThe substring \"BBBB\" has the longest repeating letters, which is 4.\n\"\"\"\n# Idea is to find (longest string length - longest char which occurs the most < k)\nclass Solution(object):\n    def characterReplacement(self, s, k):\n        \"\"\"\n        :type s: str\n        :type k: int\n        :rtype: int\n        \"\"\"\n        count = [0 for _ in range(26)]\n        start, end = 0, 0\n        max_char_cnt = 0\n        res = 0\n        while end < len(s):\n            count[ord(s[end]) - ord('A')] += 1\n            max_char_cnt = max(max_char_cnt, count[ord(s[end]) - ord('A')])\n            # key part\n            while end-start+1 - max_char_cnt > k:\n                count[ord(s[start]) - ord('A')] -= 1\n                start += 1\n                for i in range(26):\n                    max_char_cnt = max(max_char_cnt, count[i])\n            res = max(res, end-start+1)\n            end += 1\n        return res","sub_path":"leetcode/424_longest_repeating_character_replacement.py","file_name":"424_longest_repeating_character_replacement.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"649564462","text":"__date__ = \"10/25/2014\"\n__author__ = \"AlienOne\"\n__copyright__ = \"MIT\"\n__credits__ = [\"AlienOne\"]\n__license__ = \"MIT\"\n__version__ = \"0.0.1\"\n__maintainer__ = \"AlienOne\"\n__email__ = \"Justin@alienonesecurity.com\"\n__status__ = \"Development\"\n\n\nimport datetime\nimport requests\nfrom re import compile\nfrom ts_api_connector import get_query\n\n\ndef date_time(time_diff):\n    \"\"\"\n    UTC Offset Date & Time by hour diff\n    :param time_diff: Integer hour diff\n    :return: Date timestampt format Sun Apr 13 03:57:46 PM'\n    \"\"\"\n    now = datetime.datetime.utcnow()\n    time = now + datetime.timedelta(hours=time_diff)\n    format = \"%a %b %d %I:%M:%S %p\"\n    return time.strftime(format)\n\n\ndef object_check(string_obj):\n    \"\"\"\n    Type check IP or fully qualified domain name\n    :param string_obj: IP Address or FQDN\n    :return: Type string identifier IP or FQDN\n    \"\"\"\n    regex_ip = compile(r'^\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}$')\n    regex_fqdn = compile(r'(?=^.{4,255}$)(^((?!-)[a-zA-Z0-9-]{1,63}(?<!-)\\.)+[a-zA-Z]{2,63}$)')\n    if regex_ip.match(string_obj):\n        return 'IP'\n    elif regex_fqdn.match(string_obj):\n        return 'FQDN'\n    else:\n        pass\n\n\ndef passive_dns_check(string_obj, VTAPIKEY):\n    \"\"\"\n    VirusTotal Passive DNS check IP Addresses or FQDN's\n    :param string_obj: IP Address/FQDN\n    :param APIKEY: Virustotal API Key\n    :return:\n    \"\"\"\n\n    urls = {'IP': 'https://www.virustotal.com/vtapi/v2/ip-address/report',\n            'FQDN': 'https://www.virustotal.com/vtapi/v2/domain/report'}\n\n    parameters = [{'ip': string_obj, 'apikey': VTAPIKEY},\n                  {'domain': string_obj, 'apikey': VTAPIKEY}]\n\n    if 'IP' in object_check(string_obj):\n        ip_list = []\n        request_url = urls['IP']\n        r1 = requests.get(request_url, params=parameters[0])\n        data_response = r1.json()\n        data = [(key, value) for key, value in data_response.iteritems()]\n        if isinstance(data, list):\n            return data\n    elif 'FQDN' in object_check(string_obj):\n        fqdn_list = []\n        request_url = urls['FQDN']\n        r1 = requests.get(request_url, params=parameters[1])\n        data_response = r1.json()\n        data = [(key, value) for key, value in data_response.iteritems()]\n        if isinstance(data, list):\n            return data\n\n\ndef main():\n    \"\"\"\n    Main Function\n    :return: Ordered dictionary object comparing ThreatStream Data to VirusTotal Passive DNS Data\n    \"\"\"\n    TSAPIURL = 'https://api.threatstream.com/api/v2/'\n    TSAPIUSER = 'Justin@alienonesecurity.com'\n    TSAPIKEY = '4fce871f878b1e7584ec7a5e558e4e71e75cad61'\n    VTAPIKEY = \"5dff9055f2785dafbf43ef4ec02828130ec2b0ac10b218158b7c371f4e1ed5c9\"\n    resource = 'intelligence'\n    itype = 'c2_domain'\n    limit = '25'\n    api_format = 'json'\n    utc_time = 'UTC ' + date_time(0)\n    for results in get_query(TSAPIURL, TSAPIUSER, TSAPIKEY, resource, itype, limit, api_format):\n        print(results)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"GraphLab/TS/passive_dns_virustotal_and_threatstream.py","file_name":"passive_dns_virustotal_and_threatstream.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"84728103","text":"import random\nimport pytest\nfrom mage.graph_coloring_module import SDO\nfrom mage.graph_coloring_module import Graph\nfrom mage.graph_coloring_module import Parameter\n\n\n@pytest.fixture\ndef set_seed():\n    random.seed(42)\n\n\n@pytest.fixture\ndef graph_1():\n    return Graph(\n        [0, 1, 2, 3, 4],\n        {\n            0: [(1, 2), (2, 3)],\n            1: [(0, 2), (2, 2), (4, 5)],\n            2: [(0, 3), (1, 2), (3, 3)],\n            3: [(2, 3)],\n            4: [(1, 5)],\n        },\n    )\n\n\n@pytest.fixture\ndef graph_not_connected():\n    return Graph(\n        [0, 1, 2, 3, 4],\n        {\n            0: [(1, 2), (2, 3)],\n            1: [(0, 2), (2, 2)],\n            2: [(0, 3), (1, 2)],\n            3: [(4, 3)],\n            4: [(3, 3)],\n        },\n    )\n\n\ndef test_SDO(set_seed, graph_1):\n    algorithm = SDO()\n    individual = algorithm.run(graph_1, {Parameter.NO_OF_COLORS: 3})\n\n    expected_result = [2, 0, 1, 2, 1]\n    assert individual.chromosome == expected_result\n\n\ndef test_not_connected_graph(set_seed, graph_not_connected):\n    algorithm = SDO()\n    individual = algorithm.run(graph_not_connected, {Parameter.NO_OF_COLORS: 3})\n\n    expected_result = [2, 0, 1, 2, 1]\n    assert individual.chromosome == expected_result\n\n\ndef test_empty_graph(set_seed):\n    graph = Graph([], {})\n    algorithm = SDO()\n    individual = algorithm.run(graph, {Parameter.NO_OF_COLORS: 3})\n\n    expected_result = []\n    assert individual.chromosome == expected_result\n","sub_path":"python/tests/graph_coloring/test_SDO.py","file_name":"test_SDO.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"48919285","text":"# -*- coding: utf-8 -*-\n'''\nIntegration tests for Vultr\n'''\n\n# Import Python Libs\nfrom __future__ import absolute_import, print_function, unicode_literals\nimport os\nimport time\n\n# Import Salt Testing Libs\nfrom tests.support.case import ShellCase\nfrom tests.support.runtests import RUNTIME_VARS\nfrom tests.support.helpers import expensiveTest, generate_random_name\nfrom tests.support.unit import skipIf\n\n# Import Salt Libs\nfrom salt.config import cloud_providers_config\nfrom salt.ext import six\n\n# Create the cloud instance name to be used throughout the tests\nINSTANCE_NAME = generate_random_name('CLOUD-TEST-')\nPROVIDER_NAME = 'vultr'\nTIMEOUT = 500\n\n\n@expensiveTest\nclass VultrTest(ShellCase):\n    '''\n    Integration tests for the Vultr cloud provider in Salt-Cloud\n    '''\n\n    @expensiveTest\n    def setUp(self):\n        '''\n        Sets up the test requirements\n        '''\n        super(VultrTest, self).setUp()\n\n        # check if appropriate cloud provider and profile files are present\n        profile_str = 'vultr-config'\n        providers = self.run_cloud('--list-providers')\n        if profile_str + ':' not in providers:\n            self.skipTest(\n                'Configuration file for {0} was not found. Check {0}.conf files '\n                'in tests/integration/files/conf/cloud.*.d/ to run these tests.'\n                .format(PROVIDER_NAME)\n            )\n\n        # check if api_key, ssh_key_file, and ssh_key_names are present\n        config = cloud_providers_config(\n            os.path.join(\n                RUNTIME_VARS.FILES,\n                'conf',\n                'cloud.providers.d',\n                PROVIDER_NAME + '.conf'\n            )\n        )\n\n        api_key = config[profile_str][PROVIDER_NAME]['api_key']\n        ssh_file = config[profile_str][PROVIDER_NAME]['ssh_key_file']\n        ssh_name = config[profile_str][PROVIDER_NAME]['ssh_key_name']\n\n        if api_key == '' or ssh_file == '' or ssh_name == '':\n            self.skipTest(\n                'An API key, an ssh key file, and an ssh key name '\n                'must be provided to run these tests. Check '\n                'tests/integration/files/conf/cloud.providers.d/{0}.conf'\n                .format(PROVIDER_NAME)\n            )\n\n    def test_list_images(self):\n        '''\n        Tests the return of running the --list-images command for Vultr\n        '''\n        image_list = self.run_cloud('--list-images {0}'.format(PROVIDER_NAME))\n\n        self.assertIn(\n            'Debian 8 x64 (jessie)',\n            [i.strip() for i in image_list]\n        )\n\n    def test_list_locations(self):\n        '''\n        Tests the return of running the --list-locations command for Vultr\n        '''\n        location_list = self.run_cloud('--list-locations {0}'.format(PROVIDER_NAME))\n        self.assertIn(\n            'New Jersey',\n            [i.strip() for i in location_list]\n        )\n\n    def test_list_sizes(self):\n        '''\n        Tests the return of running the --list-sizes command for Vultr\n        '''\n        size_list = self.run_cloud('--list-sizes {0}'.format(PROVIDER_NAME))\n        self.assertIn(\n            '32768 MB RAM,110 GB SSD,40.00 TB BW',\n            [i.strip() for i in size_list]\n        )\n\n    # Commented for now, Vultr driver does not yet support key management\n#    def test_key_management(self):\n#        '''\n#        Test key management\n#        '''\n#        pub = 'ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAAAQQDDHr/jh2Jy4yALcK4JyWbVkPRaWmhck3IgCoeOO3z1e2dBowLh64QAM+Qb72pxekALga2oi4GvT+TlWNhzPH4V example'\n#        finger_print = '3b:16:bf:e4:8b:00:8b:b8:59:8c:a9:d3:f0:19:45:fa'\n#\n#        _key = self.run_cloud('-f create_key {0} name=\"MyPubKey\" public_key=\"{1}\"'.format(PROVIDER_NAME, pub))\n#\n#        # Upload public key\n#        self.assertIn(\n#            finger_print,\n#            [i.strip() for i in _key]\n#        )\n#\n#        try:\n#            # List all keys\n#            list_keypairs = self.run_cloud('-f list_keypairs {0}'.format(PROVIDER_NAME))\n#\n#            self.assertIn(\n#                finger_print,\n#                [i.strip() for i in list_keypairs]\n#            )\n#\n#            # List key\n#            show_keypair = self.run_cloud('-f show_keypair {0} keyname={1}'.format(PROVIDER_NAME, 'MyPubKey'))\n#\n#            self.assertIn(\n#                finger_print,\n#                [i.strip() for i in show_keypair]\n#            )\n#        except AssertionError:\n#            # Delete the public key if the above assertions fail\n#            self.run_cloud('-f remove_key {0} id={1}'.format(PROVIDER_NAME, finger_print))\n#            raise\n#\n#        # Delete public key\n#        self.assertTrue(self.run_cloud('-f remove_key {0} id={1}'.format(PROVIDER_NAME, finger_print)))\n\n    @skipIf(True, 'Skipped temporarily')\n    def test_instance(self):\n        '''\n        Test creating an instance on Vultr\n        '''\n        # check if instance with salt installed returned\n        try:\n            create_vm = self.run_cloud('-p vultr-test {0}'.format(INSTANCE_NAME), timeout=800)\n            self.assertIn(\n                INSTANCE_NAME,\n                [i.strip() for i in create_vm]\n            )\n            self.assertNotIn('Failed to start', six.text_type(create_vm))\n        except AssertionError:\n            self.run_cloud('-d {0} --assume-yes'.format(INSTANCE_NAME), timeout=TIMEOUT)\n            raise\n\n        # Vultr won't let us delete an instance less than 5 minutes old.\n        time.sleep(420)\n        # delete the instance\n        results = self.run_cloud('-d {0} --assume-yes'.format(INSTANCE_NAME), timeout=TIMEOUT)\n        try:\n            self.assertIn(\n                'True',\n                [i.strip() for i in results]\n            )\n        except AssertionError:\n            raise\n\n        # Final clean-up of created instance, in case something went wrong.\n        # This was originally in a tearDown function, but that didn't make sense\n        # To run this for each test when not all tests create instances.\n        # Also, Vultr won't let instances be deleted unless they have been alive for 5 minutes.\n        # If we exceed 6 minutes and the instance is still there, quit\n        ct = 0\n        while ct < 12 and INSTANCE_NAME in [i.strip() for i in self.run_cloud('--query')]:\n            self.run_cloud('-d {0} --assume-yes'.format(INSTANCE_NAME), timeout=TIMEOUT)\n            time.sleep(30)\n            ct = ct + 1\n","sub_path":"tests/integration/cloud/clouds/test_vultrpy.py","file_name":"test_vultrpy.py","file_ext":"py","file_size_in_byte":6392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"596406090","text":"from collections import deque\n'''\nLeetcode 42 接雨水\n很经典的题目\n最好的解法个人认为是使用单调栈\n\n解法一、维护一个单调递减的栈,栈中存的是下标\n'''\nclass Solution(object):\n    def trap(self, height):\n        if len(height) <= 2:\n            return 0\n        stack = []\n        res = 0\n        for i in range(len(height)):\n            if len(stack) == 0 and height[i] == 0:\n                continue\n            while len(stack) > 0 and height[i] >= height[stack[-1]]:\n                bottomIndex = stack.pop()\n                bottomHeight = height[bottomIndex]\n                if len(stack) == 0:\n                    break\n                leftIndex = stack[-1]\n                leftHeight = height[leftIndex]\n                topHeight = min(leftHeight, height[i])\n                res += (topHeight - bottomHeight) * (i - leftIndex-1)\n            stack.append(i)\n        return res\n\n'''\n方法二，算每一列接的雨水\n怎么算： 每列接的雨水是左边右边的最小高度减去现在的高度 * 1\n使用dp:可以维护两个左边右边的最小高度的数组，然后直接查一下就好\n比如左边的dp[i]记录的就是包括自己所在的0-i的最小高度\n'''\nclass Solution(object):\n    def trap(self, height):\n        if len(height) <= 2:\n            return 0\n        res = 0\n        dpLeft = [0] * len(height)\n        dpRight = [0] * len(height)\n        dpLeft[0] = height[0]\n        dpRight[-1] = height[-1]\n        for i in range(1,len(height)):\n            dpLeft[i] = max(dpLeft[i-1], height[i])\n        for i in range(len(height)-2,-1,-1):\n            dpRight[i] = max(dpRight[i+1], height[i])\n        for i in range(len(height)):\n            H = min(dpLeft[i], dpRight[i]) - height[i]\n            res += H * 1\n        return res\n\n'''\n如何用双指针来节约空间呢？\n如果maxLeft <= maxRight的话，那么左边第i个一定会用左边的maxLeft，那么每次只用更新左边然后移动就好\n反之，则用右边的指针\n'''\nclass Solution(object):\n    def trap(self, height):\n        if len(height) <= 2:\n            return 0\n        '''边界条件'''\n        maxLeft = height[0]\n        maxRight = height[-1]\n        left = 1\n        right = len(height)-2\n        res = 0\n        while left <= right:\n            if maxLeft <= maxRight:\n                #用左指针\n                maxLeft = max(maxLeft, height[left])\n                res += maxLeft - height[left]\n                left += 1\n            else:\n                maxRight = max(maxRight, height[right])\n                res += maxRight - height[right]\n                right -= 1\n        return res\n\n\n\na = Solution()\nh = [0,1,0,2,1,0,1,3,2,1,1,1]\nprint(a.trap(h))\nprint(\"Done\")\n","sub_path":"All_about_stack/Mono_stack/Leetcode_42/other_lc42.py","file_name":"other_lc42.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"382615212","text":"import numpy as np\nimport pandas as pd\nimport data_utils as utils\nimport osmnx as ox\nimport re\n\n\ndef process_aadt():\n    aadt_complete = pd.read_csv(\"intersection/raw/aadt.csv\", index_col=False)\n    aadt_complete = aadt_complete.drop_duplicates().dropna(subset=[\"Count\"])\n\n    aadt_2019_map = pd.read_csv(\n        \"intersection/raw/aadt_2019_map.csv\", index_col=0\n    )\n    aadt_2019_map = aadt_2019_map.rename(\n        columns={\n            \"RoadwayName\": \"Road Name\",\n            \"BeginDescription\": \"Beginning Description\",\n            \"EndDescription\": \"Ending Description\",\n        }\n    )\n\n    # We only care about the last 6 years\n    years = aadt_complete.Year.unique()\n    years_aadt = []\n\n    for year in years:\n        years_aadt.append(aadt_complete[:][aadt_complete.Year == year])\n    years = years[:6]\n    years_aadt = years_aadt[:6]\n\n    # Process each individual year dataframes\n    for i, year in enumerate(years):\n        years_aadt[i] = years_aadt[i].rename(\n            columns={\"Count\": \"Count_\" + str(year)}\n        )\n        columns_to_drop = [\"Year\"]\n        if i > 0:\n            columns_to_drop = [\n                \"Ramp\",\n                \"Bridge\",\n                \"Railroad Crossing\",\n                \"Year\",\n                \"Station ID\",\n                \"Municipality\",\n                \"One Way\",\n                \"Functional Class\",\n                \"Length\",\n                \"County\",\n                \"Signing\",\n                \"State Route\",\n                \"County Road\",\n            ]\n        years_aadt[i] = years_aadt[i].drop(columns_to_drop, axis=1)\n\n    # Join the dataframes\n    joined_aadt = years_aadt[0]\n    for year_aadt in years_aadt[1:]:\n        joined_aadt = pd.merge(\n            year_aadt.drop_duplicates(),\n            joined_aadt,\n            how=\"outer\",\n            on=[\"Road Name\", \"Beginning Description\", \"Ending Description\"],\n        )\n\n    joined_aadt = pd.merge(\n        joined_aadt,\n        aadt_2019_map,\n        how=\"inner\",\n        on=[\"Road Name\", \"Beginning Description\", \"Ending Description\"],\n    )\n    joined_aadt.to_csv(\n        \"intersection/intermediate/joined_aadt.csv\", index=False\n    )\n\n\ndef transform(x):\n    points = []\n    j = 0\n    point = []\n    for i in range(len(x)):\n        if j == 0:\n            point = [float(x[i])]\n            j += 1\n        elif j == 1:\n            point.append(float(x[i]))\n            points.append(point)\n            j = 0\n    return np.array(points)\n\n\ndef find_min_segment(point, segments, cutoff=True):\n    \"\"\"\n    returns index of closest segment.\n    Assumes point is np.array([x,y])\n    Segments is an array of road semgments each item in\n    the list represets one road.\n    \"\"\"\n\n    def return_min(segment):\n        return np.nanmin(utils.lineseg_dists(point, segment[:-1], segment[1:]))\n\n    # We are looking for all roads closer than 0.01 km.\n    # A unit in long-lat coordinates is equal to about 111 km\n    distances = np.array([return_min(segment) for segment in segments])\n    if cutoff:\n        return (distances < (0.01 / 111)).nonzero()[0]\n    else:\n        return np.nanargmin(distances)\n\n\ndef match_road_to_node():\n    data = pd.read_csv(\"intersection/intermediate/joined_aadt.csv\")\n    manhattan_accidents = pd.read_csv(\"accident/processed/manhattan.csv\")\n    unique_nodes, index_unique = np.unique(\n        manhattan_accidents[[\"node\"]].to_numpy(), return_index=True\n    )\n    graph_file = \"reference/newyork.graphml\"\n    graph = ox.io.load_graphml(graph_file)\n    unique_x = []\n    unique_y = []\n    num_connect = {}\n    for node in unique_nodes:\n        num_connect[node] = len(graph[node])\n        unique_x.append(graph.nodes[node][\"x\"])\n        unique_y.append(graph.nodes[node][\"y\"])\n    unique_x = np.expand_dims(np.array(unique_x), 1)\n    unique_y = np.expand_dims(np.array(unique_y), 1)\n    unique_points = np.concatenate((unique_x, unique_y), axis=1)\n\n    manhattan_roads = data[data[\"County\"].apply(lambda x: x == \"New York\")]\n    p = re.compile(r\"[-+]?[0-9]*\\.?[0-9]+\")\n    xy = manhattan_roads[\"geometry\"].apply(lambda x: p.findall(x))\n    segments = xy.apply(transform)\n    segments = segments.to_numpy()\n\n    minim = []\n    nodes_roads_data = {}\n\n    cols = []\n    for n in range(4, 10):\n        cols.append(\"Count_201\" + str(n))\n\n    data[\"Count_mean\"] = data[cols].mean(axis=1)\n    cols.append(\"Count_mean\")\n    for col in cols:\n        nodes_roads_data[col] = []\n\n    for i in range(len(unique_points)):\n        if i % 100 == 0:\n            print(i)\n        road_ids = list(\n            find_min_segment(unique_points[i], segments, cutoff=True)\n        )\n        for col in cols:\n            max_AADT = data.iloc[road_ids].dropna(subset=[col])[col].max()\n            nodes_roads_data[col].append(max_AADT)\n\n        minim.append(\",\".join(map(str, road_ids)))\n\n    num_connect_arr = []\n    for node in unique_nodes:\n        num_connect_arr.append(num_connect[node])\n\n    nodes_roads_data[\"nodes\"] = unique_nodes\n    nodes_roads_data[\"num_connect\"] = num_connect_arr\n    nodes_roads_data[\"roads\"] = minim\n\n    intersections = pd.DataFrame(data=nodes_roads_data)\n    intersections = intersections.dropna(subset=cols, how=\"all\")\n    intersections.to_csv(\"intersection/processed/data.csv\")\n","sub_path":"data/process_intersection.py","file_name":"process_intersection.py","file_ext":"py","file_size_in_byte":5227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"164553689","text":"from __future__ import print_function\r\nimport os\r\nimport keras\r\nimport numpy as np\r\nfrom keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\r\nfrom keras.models import Sequential, Model\r\nfrom keras.layers import Dropout, Flatten, Dense, Input, Activation\r\nfrom keras.utils import to_categorical, normalize\r\nfrom keras import applications\r\nfrom keras.optimizers import SGD\r\nimport matplotlib\r\nmatplotlib.use('Agg')\r\nimport matplotlib.pyplot as plt\r\n\r\n# dimensions of our images.\r\nimg_width, img_height = 224, 224\r\n\r\ntop_model_weights_path = '/home/dlcv/bottleneck_fc_model.h5'\r\ndir = \"/home/dlcv/TerrassaBuildings900/\"\r\nnb_train_samples = 450\r\nnb_validation_samples = 180\r\nepochs = 200\r\nbatch_size = 20\r\n\r\n# Load dict images\r\ndef load_image(dir, type):\r\n    full_dir = dir + type + 'images/'\r\n    dict_images = {}\r\n    for filename in os.listdir(full_dir):\r\n        name = os.path.splitext(filename)[0]\r\n        img = load_img(full_dir + filename, target_size=(img_width, img_height))\r\n        dict_images[name] = img_to_array(img, data_format='channels_last')\r\n\r\n    return dict_images\r\n\r\n# Load dict labels\r\ndef load_labels(dir, type):\r\n    dict_labels = {}\r\n    classes = set([])\r\n    full_dir = dir + type\r\n    with open(full_dir + 'annotation.txt', 'r') as f:\r\n        next(f)\r\n        for l in f:\r\n            x, y = l.split()\r\n            dict_labels[x] = y\r\n            classes.add(y)\r\n\r\n    return classes, dict_labels\r\n\r\ndef create_db(dict_img, dict_labels, classes):\r\n    x_train = []\r\n    labels = []\r\n    for key, value in dict_img.iteritems():\r\n        x_train.append(value)\r\n        name = dict_labels[key]\r\n        idx = classes.index(name)\r\n        labels.append(idx)\r\n\r\n    x_train = np.asarray(x_train)\r\n    y_train = np.asarray(labels)\r\n\r\n    return x_train, y_train\r\n\r\n\r\n\r\n\r\ndict_img = load_image(dir, 'train/')\r\nprint(\"# train samples:\", len(dict_img))\r\n\r\nclasses, dict_labels = load_labels(dir, 'train/')\r\nclasses = list(classes)\r\nprint('Classes:', classes)\r\nprint(\"# train labels:\", len(dict_labels))\r\n\r\nx_train, y_train = create_db(dict_img, dict_labels, classes)\r\nprint('x_train shape:', x_train.shape)\r\nprint('y_train shape:', y_train.shape)\r\n\r\ndict_img = load_image(dir, 'val/')\r\nprint(\"# val samples:\", len(dict_img))\r\n\r\nclasses, dict_labels = load_labels(dir, 'val/')\r\nclasses = list(classes)\r\nprint('Classes:', classes)\r\nprint(\"# val labels:\", len(dict_labels))\r\nnum_classes = len(classes)\r\nx_val, y_val = create_db(dict_img, dict_labels, classes)\r\nprint('x_val shape:', x_val.shape)\r\nprint('y_val shape:', y_val.shape)\r\n#y_train = np.array(y_train)\r\n#y_val = np.array(y_val)\r\n\r\nx_train = x_train.astype('float32')\r\nx_val = x_val.astype('float32')\r\nx_train /= 255\r\nx_val /= 255\r\n\r\ny_train = to_categorical(y_train, 13)\r\ny_val = to_categorical(y_val, 13)\r\n\r\ndef save_bottleneck_features():\r\n\r\n\r\n    # build the VGG16 network\r\n    model = applications.VGG19(include_top=False, weights='imagenet', input_shape=(img_width, img_height, 3))\r\n    print('model loaded')\r\n\r\n    bottleneck_features_train = model.predict(\r\n        x_train, batch_size=batch_size)\r\n    np.save(open(dir+'bottleneck_features_train_t4.npy', 'w'),\r\n            bottleneck_features_train)\r\n    print('pretrained features obtained')\r\n\r\n    bottleneck_features_validation = model.predict(\r\n        x_val, batch_size=batch_size)# nb_validation_samples // batch_size + 1)\r\n    np.save(open(dir+'bottleneck_features_validation_t4.npy', 'w'),\r\n            bottleneck_features_validation)\r\n    print('pretrained val features obtained')\r\n\r\ndef train_top_model():\r\n\r\n\r\n    train_data = np.load(open(dir+'bottleneck_features_train_t4.npy'))\r\n    train_labels = y_train\r\n\r\n    validation_data = np.load(open(dir+'bottleneck_features_validation_t4.npy'))\r\n    validation_labels = y_val\r\n\r\n    model = Sequential()\r\n    model.add(Flatten(input_shape=train_data.shape[1:]))\r\n    model.add(Dense(2048, activation='relu'))\r\n    model.add(Dense(256, activation='relu'))\r\n    model.add(Dense(13, activation='softmax'))\r\n\r\n    for layer in model.layers[:-1]:\r\n        layer.trainable = False\r\n\r\n    model.compile(optimizer='rmsprop',\r\n                  loss='categorical_crossentropy', metrics=['accuracy'])\r\n\r\n\r\n    print('top model compiled')\r\n    print(model.output_shape)\r\n    #print(model.layers_by_depth)\r\n    #print(model.)\r\n\r\n    print('ara venen les shapes')\r\n    print(train_data.shape)\r\n    print(train_labels.shape)\r\n    print(validation_data.shape)\r\n    print(validation_labels.shape)\r\n\r\n    history = model.fit(train_data, train_labels,\r\n              epochs=epochs,\r\n              batch_size=batch_size,\r\n              validation_data=(validation_data, validation_labels))\r\n    print('top model trained')\r\n\r\n    model.save_weights(top_model_weights_path)\r\n    # list all data in history\r\n    print(history.history.keys())\r\n    fig, axis = plt.subplots(1,2)#,figsize=(15,5))\r\n\r\n    # summarize history for accuracy\r\n\r\n    axis[0].plot(history.history['acc'])\r\n    axis[0].plot(history.history['val_acc'])\r\n    axis[0].set_title('model accuracy')\r\n    axis[0].set_ylabel('accuracy')\r\n    axis[0].set_xlabel('epoch')\r\n    axis[0].legend(['train', 'test'], loc='upper left')\r\n    #plt.show()\r\n    #axis axis[0].savefig('/imatge/mcata/Terrassa/accuracy_22.png')\r\n    # summarize history for loss\r\n\r\n    axis[1].plot(history.history['loss'])\r\n    axis[1].plot(history.history['val_loss'])\r\n    axis[1].set_title('model loss')\r\n    axis[1].set_ylabel('loss')\r\n    axis[1].set_xlabel('epoch')\r\n    axis[1].legend(['train', 'test'], loc='upper left')\r\n    #plt.show()\r\n    plt.savefig('/home/dlcv/task_4_vgg19.png')\r\n\r\nsave_bottleneck_features()\r\ntrain_top_model()\r\n","sub_path":"dlcv4.py","file_name":"dlcv4.py","file_ext":"py","file_size_in_byte":5688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"483915021","text":"orbits = {}\nwith open('6.txt') as f:\n   for line in f:\n       l = [x.strip() for x in line.split(\")\")]\n       orbits[l[1]] = l[0]\n\nyou = []\nsan = []\n\ndef createPath(key, s):\n    if key not in s:\n        return []\n    else:\n        list = createPath(s[key], s)\n        list.insert(0, key)\n        return list\n\ndef count(key, s):\n    if key not in s:\n        return 0\n    else:\n        return 1 + count(s[key], s)\n\n#Part 1\nsum = 0\nfor key in orbits:\n    sum += count(key, orbits)\nprint(\"Part 1: \" + str(sum))\n\n#Part 2\nyou = createPath(\"YOU\", orbits)[1:]\nsan = createPath(\"SAN\", orbits)[1:]\n\nmin = 1000000000\n\nfor key in you:\n    if key in san:\n        res = you.index(key) + san.index(key)\n        if res < min:\n            min = res\n\nprint(\"Part2: \" + str(min))\n","sub_path":"6/6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"476887878","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"Standard atlas for network nodes\n\n.. autosummary::\n   :nosignatures:\n\n   aal\n   power_264\n   dosenbach_39\n   harvard_oxford_112\n   mni_9\n   ekman_70\n   mni_mask\n   mni_gray_matter_mask\n   fox_seeds\n\n\"\"\"\nimport os\nimport numpy as np\nimport nibabel as nib\n\nfrom ..config import NetworktoolsConfig\n\n__all__ = ['aal', 'power_264', 'dosenbach_39', 'harvard_oxford_112', 'mni_9',\n           'ekman_70', 'mni_mask', 'mni_gray_matter_mask', 'fox_seeds']\n\n\ndef aal(n=116, space='2mm', labels_short=True):\n    \"\"\"Automated Anatomical Labeling (AAL) template atlas\n\n    Parameters\n    ----------\n    n : integer [116|490|1040|4040]\n        Number of nodes. (default=116). The versions >116 were created using\n        random, spatially constraint parcellation as descripted in [2]_.\n    space : string ['1mm'|'2mm'|'3mm']\n        space\n    label_short : boolean\n        Short label\n\n    Returns\n    -------\n    m : ndarray, shape(x,y,z)\n        3D array with unique values for each anatomically defined area.\n    path : string\n        Pointer to the atlas-file.\n    labels : list of strings\n        Anatomical labels corresonding to the nodes.\n    coords : ndarray, shape(116, 3)\n        MNI xyz-coordinates of the center of gravity for each node im mm space.\n\n    Notes\n    -----\n    This version has 116 nodes, whereas in the literature it has often only 90\n    nodes (without cerebellum). Note, the unique values for the 3mm vs 2mm\n    versions are randomly assigned, meaning that the coordinates are NOT\n    interchangeable! A systematic comparison of 8 different atlas was performed\n    in [3]_.\n\n    Note, that it has been argued that using functionally defined regions might\n    be superior to using anatomically or arbitrarily defined regions for\n    connectivity analysis since including 2 regions in a single node can be\n    detrimental to characterizing a graph [4]_ [5]_.\n\n    See also\n    --------\n    nt.roi.generation.random_parcellation: random, spatially constraint\n        parcellation\n    nt.roi.generation.functional_parcellation: functional parcellation\n\n    References\n    ----------\n    .. [1] Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F.,\n           Etard, O., Delcroix, N., Mazoyer, B., et al. (2002). Automated\n           anatomical labeling of activations in SPM using a macroscopic\n           anatomical parcellation of the MNI MRI single-subject brain.\n           NeuroImage, 15(1), 273–289. doi:10.1006/nimg.2001.0978\n    .. [2] Fornito, A., Zalesky, A., & Bullmore, E. T. (2010). Network scaling\n           effects in graph analytic studies of human resting-state FMRI data.\n           Frontiers in Systems Neuroscience, 4, 22.\n           doi:10.3389/fnsys.2010.00022\n    .. [3] Bohland, J. W., Bokil, H., Allen, C. B., & Mitra, P. P. (2009). The\n           brain atlas concordance problem: quantitative comparison of\n           anatomical parcellations. PloS one, 4(9), e7200.\n           doi:10.1371/journal.pone.0007200\n    .. [4] Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M.,\n           Beckmann, C. F., Nichols, T. E., et al. (2011). Network modelling\n           methods for FMRI. NeuroImage, 54(2), 875–891.\n           doi:10.1016/j.neuroimage.2010.08.063\n    .. [5] Wig, G. S., Schlaggar, B. L., & Petersen, S. E. (2011). Concepts and\n           principles in the analysis of brain networks. Annals of the New York\n           Academy of Sciences, 1224, 126–146.\n           doi:10.1111/j.1749-6632.2010.05947.x\n\n    Examples\n    --------\n    >>> _, p, labels, coords = aal(n=116, space='2mm')\n\n    Calculate the spatial distance matrix\n\n    >>> D = dist_matrix_spatial(coords)\n    >>> print D[0,1] # spatial distance between node 0 and node 1\n    80.0403270136\n    \"\"\"\n\n    # AAL 1mm created with:\n    # resample_image('AAL_2mm_490.nii.gz', 'out.nii.gz', interpolation='nn')\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates')\n    #TODO read: aal.nii.txt\n    data_file = 'AAL_%s_%s.nii.gz' %(space, n)\n    data_file2 = 'AAL_%s_labels.txt' %(n) # only 116 so far\n    #for 116 3 & 2 mm versions are the same\n    if not space is '1mm':\n        data_file3 = 'AAL_%s_%s_mm_coords.txt' %(space, n)\n        coords = np.loadtxt(os.path.join(data_dir, data_file3), dtype=np.float32)\n    else:\n        coords = 'not_impl_yet'\n\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n\n    d = nib.load(os.path.join(data_dir, data_file))\n    m = d.get_data()\n\n    if n == 116:\n        labels = np.loadtxt(os.path.join(data_dir, data_file2), dtype=str)\n        if labels_short:\n            labels = labels[:,0]\n        else:\n            labels = labels[:,1]\n    else:\n        labels = 'not_impl_yet'\n\n    return m, path, labels, coords\n\n\ndef power_264(space='2mm'):\n    \"\"\"264 nodes (radius=?) from Power et al. (2011)\n\n    Parameters\n    ----------\n    space : string ['2mm'|'3mm']\n        space\n\n    Returns\n    -------\n    m : ndarray, shape(n, )\n        array with unique values for each anatomically defined area.\n\n    path : string\n        Pointer to the atlas-file\n\n    labels : string\n        Anatomical label for the regions. #TODO\n\n    coords : ndarray, shape(264, 3)\n        MNI coordinates of the sphere centers.\n\n    References\n    ----------\n    .. [1] Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A.,\n           Church, J. A., Vogel, A. C., et al. (2011). Functional Network\n           Organization of the Human Brain. Neuron, 72(4), 665–678.\n           doi:10.1016/j.neuron.2011.09.006\n\n    Examples\n    --------\n    >>> _, p, labels, coords = power_264(space='2mm')\n    \"\"\"\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates')\n    data_file = 'Power_MNI%s.nii.gz' %space\n    data_file2 = 'Power_MNI_coordinates.txt'\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n\n    d = nib.load(path)\n    m = d.get_data()\n\n    coords = np.loadtxt(os.path.join(data_dir, data_file2))\n    labels = 'not_impl_yet'\n    return m, path, labels, coords\n\n\ndef dosenbach_39(space='2mm'):\n    \"\"\"39 nodes (radius=6 mm) from Dosenbach et al. (2007)\n\n    Parameters\n    ----------\n    space : string ['2mm'|'3mm']\n        space\n\n    Returns\n    -------\n    m : ndarray, shape(n, )\n        array with unique values for each anatomically defined area.\n\n    path : string\n        Pointer to the atlas-file\n\n    labels : string\n        Anatomical label for the regions.\n\n    coords : ndarray, shape(39, 3)\n        MNI coordinates of the sphere centers.\n\n    n2n : ndarray, shape(n, )\n        The nodes are considered as part of two networks [2]_, as either the\n        Cingulo-opercular (CO) or the Frontal-parietal (FP) network. This array\n        describes the node to network assignment (0: neither, 1: CO, 2: FP)\n\n    hemis : ndarray of strings, shape(n, )\n        The node is bilateral ('B'), or in the right ('R') or left ('L')\n        hemisphere.\n\n    References\n    ----------\n    .. [1] Dosenbach, N. U. F., Fair, D. A., Miezin, F. M., Cohen, A. L.,\n           Wenger, K. K., Dosenbach, R. A. T., Fox, M. D., et al. (2007).\n           Distinct brain networks for adaptive and stable task control in\n           humans. Proceedings of the National Academy of Sciences of the United\n           States of America, 104(26), 11073–11078. doi:10.1073/pnas.0704320104\n    .. [2] Nomura, E. M., Gratton, C., Visser, R. M., Kayser, A., Perez, F., &\n           D'esposito, M. (2010). Double dissociation of two cognitive control\n           networks in patients with focal brain lesions. Proceedings of the\n           National Academy of Sciences of the United States of America,\n           107(26), 12017–12022. doi:10.1073/pnas.1002431107\n\n    Examples\n    --------\n    >>> _, p, labels, coords, n2n, hemisphere = dosenbach_39(space='2mm')\n    \"\"\"\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates')\n    data_file = 'Dosenbach_MNI%s.nii.gz' %space\n    data_file2 = 'Dosenbach_MNI_coordinates.txt'\n    data_file3 = 'Dosenbach_MNI_labels.txt'\n    data_file4 = 'Dosenbach_hemisphere.txt'\n    data_file5 = 'Dosenbach_n2c.txt'\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n\n    d = nib.load(path)\n    m = d.get_data()\n\n    coords = np.loadtxt(os.path.join(data_dir, data_file2))\n    labels = np.loadtxt(os.path.join(data_dir, data_file3), dtype='str')\n    hemis = np.loadtxt(os.path.join(data_dir, data_file4), dtype='str')\n    n2n = np.loadtxt(os.path.join(data_dir, data_file5))\n    return m, path, labels, coords, n2n, hemis\n\n\ndef harvard_oxford_112(space='2mm', type='cortical'):\n    \"\"\"Harvard-Oxford Cortical Structural Atlas (HOCPA)\n\n    112 anatomically defined, cortical and subcortical nodes.\n\n    T1-weighted images of 21 healthy male and 16 healthy female subjects\n    (ages 18-50) were individually segmented by the CMA using semi-automated\n    tools developed in-house. The T1-weighted images were affine-registered to\n    MNI152 space using FLIRT (FSL), and the transforms then applied to the\n    individual labels. Finally, these were combined across subjects to form\n    population probability maps for each label.\n\n    #TODO subcortical part is missing\n\n    Parameters\n    ----------\n    space : string ['1mm'|'2mm']\n        space\n    type : string ['all'|'cortical'|'subcortical']\n        returns cortical, subcortical or all areas (default='cortical').\n\n    Returns\n    -------\n    m : ndarray, shape(n, )\n        array with unique values for each anatomically defined area.\n\n    path : string\n        Pointer to the atlas-file\n\n    labels : string\n        Anatomical label for the regions.\n\n    coords : ndarray, shape(112, 3)\n        MNI coordinates of the center of gravity.\n\n    References\n    ----------\n    .. [1] from FSL #TODO\n    .. [2] used e.g. in: Bassett, D. S., Wymbs, N. F., Porter, M. A.,\n           Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic\n           reconfiguration of human brain networks during learning. Proceedings\n           of the National Academy of Sciences, 108(18), 7641–7646.\n           doi:10.1073/pnas.1018985108\n\n    Examples\n    --------\n    >>> _, p, labels, coords = harvard_oxford_112(space='2mm')\n    \"\"\"\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates')\n    if type == 'cortical':\n        data_file = 'HarvardOxford-cort-maxprob-thr25-%s.nii.gz' %space\n        data_file2 = 'HarvardOxford-cort_MNI_coordinates.txt'\n        data_file3 = 'HarvardOxford-cort_MNI_labels.txt'\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n\n    d = nib.load(path)\n    m = d.get_data()\n\n    coords = np.loadtxt(os.path.join(data_dir, data_file2))\n    labels = np.loadtxt(os.path.join(data_dir, data_file3), dtype='str')\n    return m, path, labels, coords\n\n\ndef mni_9(space='2mm'):\n    \"\"\"9 cortical areas/lobes from the MNI Structural Atlas\n\n    A single subject’s structural image was hand segmented, and the labels were\n    then propagated to more than 50 subjects’ structural images using nonlinear\n    registration. Each resulting labelled brain was then transformed into MNI152\n    space using affine registration, before averaging segmentations across\n    subjects to produce the final probability images.\n\n    Parameters\n    ----------\n    space : string ['1mm'|'2mm']\n        space\n\n    Returns\n    -------\n    m : ndarray, shape(n, )\n        array with unique values for each anatomically defined area.\n\n    path : string\n        Pointer to the atlas-file\n\n    labels : string\n        Anatomical label for the regions.\n\n    coords : ndarray, shape(9, 3)\n        MNI coordinates of the center of gravity.\n\n    References\n    ----------\n    .. [1] J. Mazziotta, A. Toga, A. Evans, P. Fox, J. Lancaster, K. Zilles,\n           R. Woods, T. Paus, G. Simpson, B. Pike and others (2001). A\n           probabilistic atlas and reference system for the human brain:\n           International Consortium for Brain Mapping (ICBM). The Royal Society\n           Philosophical Transactions B, 356: 1293–1322.\n\n    Examples\n    --------\n    >>> _, p, labels, coords, = mni_9(space='2mm')\n    \"\"\"\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates')\n    data_file = 'MNI-maxprob-thr25-%s.nii.gz' %space\n    data_file2 = 'MNI_coordinates.txt'\n    data_file3 = 'MNI_labels.txt'\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n\n    d = nib.load(path)\n    m = d.get_data()\n\n    coords = np.loadtxt(os.path.join(data_dir, data_file2))\n    labels = np.loadtxt(os.path.join(data_dir, data_file3), dtype='str')\n    return m, path, labels, coords\n\n\ndef ekman_70(space='2mm'):\n    \"\"\"70 nodes (radius=12 mm) from Ekman et al. (2012)\n\n    Parameters\n    ----------\n    space : string ['2mm'|'3mm] #only 2mm supported so far\n        space\n\n    Returns\n    -------\n    m : ndarray, shape(n, )\n        array with unique values for each anatomically defined area.\n\n    path : string\n        Pointer to the atlas-file\n\n    labels : string\n        Anatomical label for the regions. #TODO\n\n    coords : ndarray, shape(70, 3)\n        MNI coordinates of the sphere centers.\n\n    n2n : ndarray, shape(n, )\n        The nodes are considered as part of two networks, as either the core or\n        the periphery of the network. This array describes the node to network\n        assignment (1: core, 2: periphery)\n\n    hemis : ndarray of strings, shape(n, )\n        The node is bilateral ('B'), or in the right ('R') or left ('L')\n        hemisphere.\n\n    References\n    ----------\n    .. [1] Ekman et al. (in prep)\n\n    Examples\n    --------\n    >>> _, p, labels, coords, n2n, hemis = ekman_70(space='2mm')\n    \"\"\"\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates')\n    data_file = 'Ekman_MNI%s.nii.gz' %space\n    data_file2 = 'Ekman_MNI%s_coordinates.txt' %space\n    data_file3 = 'Ekman_MNI%s_labels.txt' %space\n    data_file4 = 'Ekman_hemisphere.txt'\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n\n    d = nib.load(path)\n    m = d.get_data()\n\n    coords = np.loadtxt(os.path.join(data_dir, data_file2))\n    labels = np.loadtxt(os.path.join(data_dir, data_file3), dtype='str')\n    hemis = np.loadtxt(os.path.join(data_dir, data_file4), dtype='str')\n    # labels = 'not_impl_yet'\n    # coords = 'not_impl_yet'\n    # hemis = 'not_impl_yet'\n    n2n = np.zeros(70) # 'not_impl_yet'\n    return m, path, labels, coords, n2n, hemis\n\n\ndef mni_mask(space='2mm', no_white=False, subsamp=0, minimal=False):\n    \"\"\"MNI152 T1 brain mask template\n\n    Parameters\n    ----------\n    space : string ['1mm'|'2mm'|'3mm']\n        Spacial resolution.\n    no_white : boolean\n        The mask contains no white matter. This is defined by the tissue priors,\n        included in FSL.\n    minimal : boolean\n        The mask contains no white matter, no brain-stem and no cerebellum.\n    subsamp : integer [2|4]\n        Subsampled mask by factor `subsamp` (default=0).\n\n    Returns\n    -------\n    m : ndarray, shape(x, y, z)\n        Brain mask (1: brain tissue, 0: non-brain tissue).\n\n    path : string\n        Pointer to the atlas-file.\n\n    Notes\n    -----\n    Masks were subsampled using FSL::\n\n        # BASH code\n        input='MNI152_T1_3mm_brain_mask'\n        fslmaths $input -subsamp2 tmp\n        fslmaths tmp -thr 0.85 -bin ${input}_subsamp2\n        rm -f tmp.nii.gz\n\n    References\n    ----------\n    .. [1] from FSL\n\n    Examples\n    --------\n    >>> # 2 mm space\n    >>> mask, path = mni_mask(space='2mm')\n    >>> print mask.shape\n    (91, 109, 91)\n    >>> print mask[mask != 0].size\n    228483\n\n    >>> # 3 mm space\n    >>> mask, path = mni_mask(space='3mm')\n    >>> print mask.shape\n    (61, 73, 61)\n    >>> print mask[mask != 0].size\n    67749\n\n    >>> # 3 mm with no white matter\n    >>> mask, path = mni_mask(space='3mm', no_white=True)\n    >>> print mask[mask != 0].size\n    63310\n\n    >>> # 3 mm with no white matter, no brain-stem and no cerebellum\n    >>> mask, path = mni_mask(space='3mm', minimal=True)\n    >>> print mask[mask != 0].size\n    55490\n    \"\"\"\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates')\n\n    if subsamp:\n        if no_white:\n            data_file = 'MNI152_T1_%s_brain_mask_no_white_subsamp2.nii.gz' %space\n        else:\n            data_file = 'MNI152_T1_%s_brain_mask_subsamp2.nii.gz' %space\n    else:\n        if no_white:\n            data_file = 'MNI152_T1_%s_brain_mask_no_white.nii.gz' %space\n        else:\n            data_file = 'MNI152_T1_%s_brain_mask.nii.gz' %space\n\n    if minimal:\n        # only for 2mm & 3mm space so far\n        data_file = 'MNI152_T1_%s_brain_mask_no_white_no_brainstem_no_cerebellum_subsamp%s.nii.gz' %(space, subsamp)\n\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n    d = nib.load(os.path.join(data_dir, data_file))\n\n    return d.get_data(), path\n\n\ndef mni_gray_matter_mask(space='2mm'):\n    \"\"\"MNI152 T1 brain gray matter probability mask\n\n    Parameters\n    ----------\n    space : string ['1mm'|'2mm'|'3mm']\n        space\n\n    Returns\n    -------\n    m : ndarray, shape(x, y, z)\n        Gray matter probability values in range [0,1].\n\n    path : string\n        Pointer to the file\n\n    Notes\n    -----\n    The probability masks was subsampled using FSL FAST::\n\n        # BASH code\n        input='MNI152_T1_2mm_brain'\n        fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -o $input $input\n\n    .. warning::\n\n        The probability mask is for demonstration purposes only.\n        It is recommended to generate a subject specific mask using\n        individual T1 images.\n\n    Examples\n    --------\n    >>> mask, path = mni_gray_matter_mask(space='2mm')\n    >>> mask[mask!=0].size\n    136207\n    >>> plt.hist(mask[mask!=0], bins=20) # histogram of the GM probabilities\n    \"\"\"\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates', 'tissue_priors')\n    data_file = 'MNI152_T1_%s_brain_pve_1.nii.gz' %space\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n\n    d = nib.load(os.path.join(data_dir, data_file))\n    return d.get_data(), path\n\n\ndef fox_seeds(seed='all', space='3mm'):\n    \"\"\"Spherical seeds from Fox et al.\n\n    Parameters\n    ----------\n    seeds : string ['FEF'|'IPS'|'MT'|'LP'|'MPF'|'PCC'|'all']\n        Anatomical labels for the available seeds.\n    space : string ['1mm'|'2mm'|'3mm']\n        Anatomical space. #TODO: only 3mm supported so far\n\n    Returns\n    -------\n    m : ndarray, shape(x, y, z)\n        Gray matter probability values in range [0,1].\n    idx : ndarray, shape(x*y*z)\n        Boolean mask with seed Ids.\n    path : string\n        Pointer to the file.\n\n    Notes\n    -----\n    ::\n\n        Task negative = ['LP', 'MPF', 'PCC']\n        Task positive = ['FEF', 'IPS', 'MT']\n\n    References\n    ----------\n    .. [1] Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen,\n           D. C., & Raichle, M. E. (2005). The human brain is intrinsically\n           organized into dynamic, anticorrelated functional networks.\n           Proceedings of the National Academy of Sciences of the United States\n           of America, 102(27), 9673–9678. doi:10.1073/pnas.0504136102\n\n    Examples\n    --------\n    >>> seed_data, seed_idx, path = fox_seeds(seed='MT', space='3mm')\n    \"\"\"\n\n    # bash code to create the seeds from the FC1000 data\n    # fslmaths Fox_tn_LP_3mm -mul 1 1\n    # fslmaths Fox_tn_MPF_3mm -mul 2 2\n    # fslmaths Fox_tn_PCC_3mm -mul 3 3\n    # fslmaths Fox_tp_FEF_3mm -mul 4 4\n    # fslmaths Fox_tp_IPS_3mm -mul 5 5\n    # fslmaths Fox_tp_MT_3mm -mul 6 6\n    #\n    # fslmerge -t merge 1 2 3 4 5 6\n    # fslmaths merge -Tmax fox_seeds_all\n\n    data_dir = os.path.join(NetworktoolsConfig().get('paths', 'datadir'), 'MRI_templates', 'seeds')\n    # data_file = 'MNI152_T1_%s_brain_pve_1.nii.gz' %space\n\n    TN = ['LP', 'MPF', 'PCC']\n    TP = ['FEF', 'IPS', 'MT']\n\n    if seed is 'all':\n        data_file = 'fox_seeds_all_%s.nii.gz' %space\n    if seed in TN:\n        data_file = 'Fox_tn_%s_%s.nii.gz' %(seed, space)\n    elif seed in TP:\n        data_file = 'Fox_tp_%s_%s.nii.gz' %(seed, space)\n    else:\n        raise ValueError('Seed is not supported')\n\n    path = os.path.join(data_dir, data_file)\n    assert os.path.exists(path), 'Could not find the data in the networktools_data package.'\n\n    d = nib.load(os.path.join(data_dir, data_file)).get_data()\n\n    mask, _ = mni_mask(space=space)\n    idx = d[mask!=0] != 0  # Voxel ids of the seeds\n\n    return d, idx, path\n","sub_path":"nt/roi/atlas.py","file_name":"atlas.py","file_ext":"py","file_size_in_byte":21144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"14747584","text":"\"\"\"\n    WideResNet model definition\n    ported from https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py\n\"\"\"\n\n# import torchvision.transforms as transforms\n# import torch.nn as nn\n# import torch.nn.init as init\n# import torch.nn.functional as F\nimport tensorflow as tf \nimport tensorflow.keras.layers as tkl\nimport math\n\n__all__ = [\"WideResNet28x10Drop\"]\n\nP = 0.05\ninitializer = tf.keras.initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='uniform')\n#initializer = tf.keras.initializers.GlorotUniform()\nfc_init = tf.keras.initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='uniform')\n\n# def conv3x3(in_planes, out_planes, stride=1):\n#     return nn.Conv2d(\n#         in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True\n#     )\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    return tkl.Conv2D(\n        out_planes, kernel_size=3, strides=(stride, stride), padding='same', kernel_initializer=initializer\n    )\n\n\n# def conv_init(m):\n#     classname = m.__class__.__name__\n#     if classname.find(\"Conv\") != -1:\n#         init.xavier_uniform(m.weight, gain=math.sqrt(2))\n#         init.constant(m.bias, 0)\n#     elif classname.find(\"BatchNorm\") != -1:\n#         init.constant(m.weight, 1)\n#         init.constant(m.bias, 0)\n\n\nclass WideBasic(tf.keras.layers.Layer):\n    def __init__(self, in_planes, planes, stride=1):\n        super(WideBasic, self).__init__()\n\n        self.bn1 = tkl.BatchNormalization()\n        self.dropout = tkl.Dropout(rate=P)\n        self.conv1 = tkl.Conv2D(planes, kernel_size=3, padding='same', kernel_initializer=initializer)\n        #self.dropout = tkl.Dropout(rate=dropout_rate)\n        self.bn2 = tkl.BatchNormalization()\n        self.conv2 = tkl.Conv2D(\n            planes, kernel_size=3, strides=(stride, stride), padding='same', kernel_initializer=initializer\n        )\n\n        #self.shortcut = nn.Sequential()\n        # if stride != 1 or in_planes != planes:\n        #     self.shortcut = tkl.Conv2d(in_planes, planes, kernel_size=1, strides=stride)\n\n        self.stride = stride\n        self.in_planes = in_planes\n        self.planes = planes\n        if stride != 1 or in_planes != planes:\n            self.shortcut = tkl.Conv2D(planes, kernel_size=1, strides=(stride,stride), kernel_initializer=initializer)\n\n        self.relu = tkl.ReLU()\n\n    def forward(self, x):\n        out = self.dropout(self.conv1(self.relu(self.bn1(x))))\n        out = self.conv2(self.relu(self.bn2(out)))\n        if self.stride != 1 or self.in_planes != self.planes:\n            out += self.shortcut(x)\n\n        return out\n\n\nclass WideResNetDrop(tf.keras.Model):\n    def __init__(self, num_classes=10, depth=28, widen_factor=10, input_shape=None):\n        super(WideResNetDrop, self).__init__()\n        self.in_planes = 16\n\n        assert (depth - 4) % 6 == 0, \"Wide-resnet depth should be 6n+4\"\n        n = (depth - 4) / 6\n        k = widen_factor\n\n        nstages = [16, 16 * k, 32 * k, 64 * k]\n\n        self.conv1 = conv3x3(3, nstages[0])\n        self.layer1 = self._wide_layer(WideBasic, nstages[1], n, stride=1)\n        self.layer2 = self._wide_layer(WideBasic, nstages[2], n, stride=2)\n        self.layer3 = self._wide_layer(WideBasic, nstages[3], n, stride=2)\n        self.bn1 = tkl.BatchNormalization(nstages[3], momentum=0.9)\n        self.linear = tkl.Dense(num_classes, kernel_initializer=fc_init, bias_initializer=fc_init)\n        self.drop = tkl.Dropout(rate=P)\n\n        self.Input = tf.keras.Input(shape=input_shape)\n        out = self.conv1(self.Input)\n        out = self.layer1(out)\n        out = self.layer2(out)\n        out = self.layer3(out)\n        out = tkl.ReLU()(self.bn1(out))\n        out = tkl.AveragePooling2D((8,8))(out)\n        out = tkl.Flatten()\n        out = self.drop(out)\n        out = self.linear(out)\n\n        self.model = tf.keras.Model(inputs=self.Input, outputs=out)\n\n    def _wide_layer(self, block, planes, num_blocks, stride):\n        strides = [stride] + [1] * int(num_blocks - 1)\n        layers = []\n\n        for stride in strides:\n            layers.append(block(self.in_planes, planes, stride))\n            self.in_planes = planes\n\n        return tf.keras.Sequential(layers)\n\n    def forward(self, x):\n        out = self.model(x)\n\n        return out\n\n\nclass WideResNet28x10Drop:\n    base = WideResNetDrop\n    args = list()\n    kwargs = {\"depth\": 28, \"widen_factor\": 10}\n    # transform_train = transforms.Compose(\n    #     [\n    #         transforms.Resize(32),\n    #         transforms.RandomCrop(32, padding=4),\n    #         transforms.RandomHorizontalFlip(),\n    #         transforms.ToTensor(),\n    #         transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n    #     ]\n    # )\n    # transform_test = transforms.Compose(\n    #     [\n    #         transforms.Resize(32),\n    #         transforms.ToTensor(),\n    #         transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n    #     ]\n    # )\n    transform_train = [\n        lambda img, y: (tf.cast(tf.convert_to_tensor(img), dtype=tf.float32), y) ,\n        lambda img, y: (img/255.0, y),\n        lambda img, y: (tf.image.resize(img, [32, 32]), y),\n        lambda img, y: (tf.image.random_crop(img, [32, 32, 3]), y),\n        lambda img, y: (tf.image.random_flip_left_right(img), y),\n        lambda img, y: ((img-[0.4914, 0.4822, 0.4465])/[0.2023, 0.1994, 0.2010], y)\n    ]\n    transform_test = [\n        lambda img, y: (tf.cast(tf.convert_to_tensor(img), dtype=tf.float32), y) ,\n        lambda img, y: (img/255.0, y),\n        lambda img, y: (tf.image.resize(img, [32, 32]), y),\n        lambda img, y: ((img-[0.4914, 0.4822, 0.4465])/[0.2023, 0.1994, 0.2010], y)\n    ]\n","sub_path":"swag/models/wide_resnet_dropout.py","file_name":"wide_resnet_dropout.py","file_ext":"py","file_size_in_byte":5714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"411031293","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.15-x86_64/egg/metadata_client/tests/modules/sample_test.py\n# Compiled at: 2019-11-13 06:16:26\n# Size of source mod 2**32: 8761 bytes\n\"\"\"SampleTest class\"\"\"\nimport unittest\nfrom metadata_client.metadata_client import MetadataClient\nfrom .module_base import ModuleBase\nfrom ..common.config_test import *\nfrom common.generators import Generators\nfrom ..common.secrets import *\nfrom modules.sample import Sample\nMODULE_NAME = SAMPLE\n\nclass SampleTest(ModuleBase, unittest.TestCase):\n\n    def setUp(self):\n        self.mdc_client = MetadataClient(client_id=(CLIENT_OAUTH2_INFO['CLIENT_ID']),\n          client_secret=(CLIENT_OAUTH2_INFO['CLIENT_SECRET']),\n          token_url=(CLIENT_OAUTH2_INFO['TOKEN_URL']),\n          refresh_url=(CLIENT_OAUTH2_INFO['REFRESH_URL']),\n          auth_url=(CLIENT_OAUTH2_INFO['AUTH_URL']),\n          scope=(CLIENT_OAUTH2_INFO['SCOPE']),\n          user_email=(CLIENT_OAUTH2_INFO['EMAIL']),\n          base_api_url=BASE_API_URL)\n        _SampleTest__unique_name1 = Generators.generate_unique_name('Sample01')\n        self.sample_01 = {'name':_SampleTest__unique_name1, \n         'url':'https://in.xfel.eu/upex/sample/101', \n         'sample_type_id':'1', \n         'proposal_id':'-1', \n         'flg_proposal_system':'U', \n         'proposal_system_id':'-123', \n         'flg_available':'true', \n         'description':'desc 01'}\n        _SampleTest__unique_name_upd = Generators.generate_unique_name('SampleUpd1')\n        self.sample_01_upd = {'name':_SampleTest__unique_name_upd, \n         'url':'https://in.xfel.eu/upex/sample/202', \n         'sample_type_id':'1', \n         'proposal_id':'-1', \n         'flg_proposal_system':'U', \n         'proposal_system_id':'-321', \n         'flg_available':'false', \n         'description':'desc 01 updated!!!'}\n\n    def test_create_sample(self):\n        sample_01 = Sample(metadata_client=(self.mdc_client),\n          name=(self.sample_01['name']),\n          url=(self.sample_01['url']),\n          sample_type_id=(self.sample_01['sample_type_id']),\n          proposal_id=(self.sample_01['proposal_id']),\n          flg_proposal_system=(self.sample_01['flg_proposal_system']),\n          proposal_system_id=(self.sample_01['proposal_system_id']),\n          flg_available=(self.sample_01['flg_available']),\n          description=(self.sample_01['description']))\n        result1 = sample_01.create()\n        self.assert_create_success(MODULE_NAME, result1, self.sample_01)\n        sample = result1['data']\n        sample_id = result1['data']['id']\n        sample_name = result1['data']['name']\n        sample_proposal_id = result1['data']['proposal_id']\n        sample_01_dup = sample_01\n        result2 = sample_01_dup.create()\n        expect_app_info = {'name': ['has already been taken']}\n        self.assert_create_error(MODULE_NAME, result2, expect_app_info)\n        result3 = Sample.get_by_name_and_proposal_id(self.mdc_client, sample_name, sample_proposal_id)\n        self.assert_find_success(MODULE_NAME, result3, self.sample_01)\n        result4 = Sample.get_by_id(self.mdc_client, sample_id)\n        self.assert_find_success(MODULE_NAME, result4, self.sample_01)\n        result5 = Sample.get_by_id(self.mdc_client, -666)\n        self.assert_find_error(MODULE_NAME, result5, RESOURCE_NOT_FOUND)\n        sample_01.name = self.sample_01_upd['name']\n        sample_01.url = self.sample_01_upd['url']\n        sample_01.sample_type_id = self.sample_01_upd['sample_type_id']\n        sample_01.proposal_id = self.sample_01_upd['proposal_id']\n        sample_01.flg_proposal_system = self.sample_01_upd['flg_proposal_system']\n        sample_01.proposal_system_id = self.sample_01_upd['proposal_system_id']\n        sample_01.flg_available = self.sample_01_upd['flg_available']\n        sample_01.description = self.sample_01_upd['description']\n        result6 = sample_01.update()\n        self.assert_update_success(MODULE_NAME, result6, self.sample_01_upd)\n        sample_01.name = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\n        sample_01.flg_available = self.sample_01_upd['flg_available']\n        sample_01.description = self.sample_01_upd['description']\n        result7 = sample_01.update()\n        expect_app_info = {'name': ['is too long (maximum is 127 characters)']}\n        self.assert_update_error(MODULE_NAME, result7, expect_app_info)\n        result8 = sample_01.delete()\n        self.assert_delete_success(MODULE_NAME, result8)\n        result9 = sample_01.delete()\n        self.assert_delete_error(MODULE_NAME, result9, RESOURCE_NOT_FOUND)\n        result10 = Sample.get_all_by_proposal_id(self.mdc_client, sample_proposal_id)\n        expected_result = [\n         {'id':-1, \n          'name':'TestSample DO NOT DELETE!', \n          'flg_proposal_system':'', \n          'proposal_system_id':'', \n          'flg_available':True, \n          'proposal_id':-1, \n          'url':'https://www.google.com', \n          'sample_type_id':1, \n          'description':''}]\n        self.assert_find_success(MODULE_NAME, result10, expected_result)\n\n    def test_set_sample_by_name_and_proposal_id(self):\n        _SampleTest__unique_name = Generators.generate_unique_name('SampleSet01')\n        sample_set_01 = {'name':_SampleTest__unique_name, \n         'url':'https://in.xfel.eu/upex/sample/101', \n         'sample_type_id':'1', \n         'proposal_id':'-1', \n         'flg_proposal_system':'U', \n         'proposal_system_id':'456321', \n         'flg_available':'true', \n         'description':'desc 01'}\n        result1 = Sample.set_by_name_and_proposal_id(self.mdc_client, sample_set_01)\n        self.assert_create_success(MODULE_NAME, result1, sample_set_01)\n        sample_id = result1['data']['id']\n        sample_name = result1['data']['name']\n        sample_proposal_id = result1['data']['proposal_id']\n        result2 = Sample.set_by_name_and_proposal_id(self.mdc_client, sample_set_01)\n        self.assert_find_success(MODULE_NAME, result2, sample_set_01)\n        result3 = Sample.get_by_name_and_proposal_id(self.mdc_client, sample_name, sample_proposal_id)\n        self.assert_find_success(MODULE_NAME, result3, sample_set_01)\n        result8 = Sample.delete_by_id(self.mdc_client, sample_id)\n        self.assert_delete_success(MODULE_NAME, result8)\n        result9 = Sample.delete_by_id(self.mdc_client, sample_id)\n        self.assert_delete_error(MODULE_NAME, result9, RESOURCE_NOT_FOUND)\n\n    def fields_validation(self, receive, expect):\n        self.assert_eq_hfield(receive, expect, 'name', STRING)\n        self.assert_eq_hfield(receive, expect, 'url', STRING)\n        self.assert_eq_hfield(receive, expect, 'sample_type_id', STRING)\n        self.assert_eq_hfield(receive, expect, 'proposal_id', STRING)\n        self.assert_eq_hfield(receive, expect, 'flg_proposal_system', STRING)\n        self.assert_eq_hfield(receive, expect, 'proposal_system_id', STRING)\n        self.assert_eq_hfield(receive, expect, 'flg_available', BOOLEAN)\n        self.assert_eq_hfield(receive, expect, 'description', STRING)\n\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"pycfiles/metadata_client-3.0.5-py3.7/sample_test.cpython-37.py","file_name":"sample_test.cpython-37.py","file_ext":"py","file_size_in_byte":7298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"478702311","text":"\n# coding: utf-8\n\n# In[239]:\n\nimport numpy as np\nfrom matplotlib.colors import hsv_to_rgb\n\n\n# In[240]:\n\ndef colorizeArray(array2D, min_max=(-1, 1), colors=[(255,0,0), (255,255,255), (0,0,255)], \n                  preFunc=(lambda x: x)):\n    colorArray = np.array(colors)\n    minVal, maxVal = min_max\n    nColors = colorArray.shape[0]\n    array_F = preFunc(array2D)\n    rescaledX = np.clip((array_F-minVal)/(maxVal-minVal),0,1) # Puts array in range [0,1]\n    cPos = rescaledX*(nColors-1) # finds its position in the color array.\n    iLow = np.clip(np.floor(cPos).astype(np.uint8), 0, nColors - 2)\n    iFrac = cPos - iLow\n    lowColors = np.take(colorArray, iLow, axis=0)\n    highColors = np.take(colorArray, iLow+1, axis=0)\n    iFrac3D = np.expand_dims(iFrac, axis=2)\n    outColor = np.round(lowColors*(1 - iFrac3D) + highColors*(iFrac3D)).astype(np.uint8)\n    return outColor\n\n\n# In[241]:\n\ninArray = np.array([[-2, -0.1, 0.1, 1, 2],[-2, -0.1, 0.1, 1, 2]])\ncolorizeArray(inArray, min_max=(0,1), \n              colors = [(0,0,0), (255, 0, 0)], \n              preFunc = lambda x: np.abs(x))\n\n\n# In[242]:\n\ndims = (4,5)\ninArray = np.random.rand(*dims) + 1j*np.random.rand(*dims)\n\n\n# In[249]:\n\ndef colorizeComplexArray(inArray, maxRad=1, centerColor='white'):\n    angleNP = np.angle(inArray)/(2*np.pi) # on range [-.5, 0.5]\n    offset = np.sign(angleNP)/(-2) + 0.5\n    hue = angleNP + offset\n    mag = np.clip(np.abs(inArray)/maxRad, 0, 1)\n    ones = np.ones_like(hue, np.float)\n    if centerColor == 'black':\n        HSVChannel = np.array([hue, ones, mag])\n    elif centerColor == 'white':\n        HSVChannel = np.array([hue, mag, ones])\n    else:\n        print(\"centerColor must be in {'white', 'black'}\")\n    HSVFloat = np.dstack(HSVChannel)\n    rgbOut = np.floor(hsv_to_rgb(HSVFloat)*255.99).astype(np.uint8)\n    return rgbOut\n\n\n# In[248]:\n\ncolorizeComplexArray(inArray, maxRad=0.01, centerColor='white')\n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n","sub_path":"Helmholtz/colorize.py","file_name":"colorize.py","file_ext":"py","file_size_in_byte":1931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"297828417","text":"n, a = int(input()), list(map(int, input().split()))\n\nx = (2 * sum(a)) // n\n\nfor i in range(n // 2):\n\twhile not a[-1]:\n\t\ta.pop()\n\tal = a[-1]\n\tp2 = a.index(x - al)\n\tprint(len(a), p2 + 1)\n\ta.pop()\n\ta[p2] = 0\n","sub_path":"codeforces/701.A.py","file_name":"701.A.py","file_ext":"py","file_size_in_byte":206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"470898239","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Dec 22 21:20:27 2019\r\n\r\n\"\"\"\r\nimport csv\r\nimport statistics\r\nfrom random import seed\r\nfrom random import randint\r\n#function to load the csv file\r\ndef load_from_csv(fname):\r\n   #read the csv file in readonly mode\r\n   with open(fname, 'r') as f:\r\n       reader = csv.reader(f)\r\n       #create the data list\r\n       data_list = list(reader)\r\n       return data_list\r\n# get distance between two lists\r\ndef get_distance(list1,list2):\r\n    #get the Manhattan distance formula inplace\r\n    #process the first list\r\n    list1.sort()\r\n    n = len(list1) \r\n    res1 = 0\r\n    sum1 = 0\r\n    for i in range(n):\r\n        res1 += (float(list1[i]) * i - sum1)\r\n        sum1 += float(list1[i])\r\n    # process the second list\r\n    list2.sort()\r\n    n = len(list2) \r\n    res2 = 0\r\n    sum2 = 0\r\n    for i in range(n):\r\n        res2 += (float(list2[i]) * i - sum2)\r\n        sum2 += float(list2[i])\r\n    # return the final distance \r\n    return res1 + res2\r\n# get maximum from the specified coloumn  \r\ndef get_max(matrix,n):\r\n    #set the first element to max by default\r\n    max = matrix[0][n-1]\r\n    #iterate through array to find max element in coulumn\r\n    for i in range(len(matrix)):\r\n        for j in range(len(matrix)):\r\n            #check if its required coulumn number then look for maximum number\r\n            if j == n-1:\r\n                if matrix[i][j] > max:\r\n                    max = matrix[i][j]\r\n    #return the maximum number\r\n    return max\r\n#get minimum from the specified coloumn\r\ndef get_min(matrix,n):\r\n    #set the first element as minimum by default\r\n    min = matrix[0][n-1]\r\n    #iterate throgh array list \r\n    for i in range(len(matrix)):\r\n        for j in range(len(matrix)):\r\n            #if its required coumn number then find the min\r\n            if j == n-1:\r\n                if matrix[i][j] < min:\r\n                    min = matrix[i][j]\r\n    return min\r\n#get single column of matrix \r\ndef column(matrix, i):\r\n    return [row[i] for row in matrix]\r\n#convert the matrix to standard matrix\r\ndef get_standardised_matrix(matrix):\r\n    standardized_data = []\r\n    col_sum=0\r\n    col_max=0\r\n    col_min=0\r\n    #iterate through the array\r\n    for i in range(len(matrix)):\r\n        for j in range(len(matrix)):\r\n            #get the coulumn of the matrix\r\n            col_matrix = column(matrix,j)\r\n            #get the maximun value\r\n            col_max = get_max(matrix,j+1)\r\n            #get the minimum value\r\n            col_min = get_min(matrix,j+1)\r\n            #calculations for standarizaton of the matrix\r\n            for k in range(len(matrix)):\r\n                col_sum = col_sum+col_matrix[k]\r\n            col_avg = col_sum/3 \r\n            # taken from appendix start\r\n            standardized_data[i][j] = matrix[i][j] - col_avg*float(col_max) - col_min \r\n            col_sum = 0\r\n    return standardized_data\r\n#get the median of the coloumn matrix\r\ndef get_median(matrix, n):\r\n    #get the coulmn matrix \r\n    col_matrix = column(matrix,n-1)\r\n    #get the median of coloumn matrix\r\n    return statistics.median(col_matrix)\r\n#check if the key exist in list\r\ndef checkKey(dict, key): \r\n    if key in dict.keys(): \r\n        return 1 \r\n    else: \r\n        return 0\r\ndef get_groups(matrix, K):\r\n    seed(1)\r\n    c = [] # to hold the cluster\r\n    a = [] # temp variable\r\n    S = {} # to hold S list \r\n    #make the keys for cluster classes c0,c1,c2,c3 etc\r\n    for k in range(K):\r\n        a.append(\"c\"+str(k))\r\n    d = {el:0 for el in a}\r\n    for k in range(K):\r\n    \tvalue = randint(0, K)\r\n    \tc.append(matrix[value])\r\n    min_distance = 0\r\n    min_distance_index = 0\r\n    #compare the cluster class with all rows in data set\r\n    for i in range(len(matrix)):\r\n        for j in range(K):\r\n            #logic the check the distance of c1,c2..ck to Di\r\n            distance=get_distance(matrix[i],c[j])\r\n            if(min_distance == 0):\r\n                min_distance = distance\r\n                min_distance_index = 0\r\n            if(min_distance > distance):\r\n                min_distance = distance\r\n                min_distance_index = j\r\n            key = 'c'+str(j)\r\n            if(checkKey(d, key)):\r\n                c4 = {key:c[j]}\r\n                d.update(c4)\r\n        #update the S list \r\n        S.update({i:min_distance_index})\r\n        min_distance_index = 0\r\n        min_distance = 0\r\n    return S\r\n#get centriod function\r\ndef get_centroids(matrix,S, K):\r\n    cluster = S.values()\r\n    # print the S List\r\n    for i in cluster:\r\n        print(matrix[i-1])\r\n    return 1\r\n#run_test function to impliment test runs\r\ndef run_test():\r\n    matrix = load_from_csv(\"Data.csv\")\r\n    #K=5\r\n    #K=4\r\n    K=3\r\n    #create the groups \r\n    S=get_groups(matrix,K)\r\n    #get centriods \r\n    get_centroids(matrix,S,K)\r\n    return 1\r\n\r\nrun_test()","sub_path":"assignment.py","file_name":"assignment.py","file_ext":"py","file_size_in_byte":4808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"350717324","text":"import numpy as np\nfrom ctypes import *\nimport matplotlib.pyplot as plt\nfrom distpyinterface import *\ndef readtasfromdump(filename):\n\tfile = open(filename, \"r\")\n\tsplitlines = (file.readlines())\n\ttasstr = (str.split(splitlines[3]))\n\ttasstr = np.array(tasstr[3:])\n\ttas = tasstr.astype(np.float)\n\ttasm = tas.reshape(6,6)\n\treturn tasm\n\ndef settasmatrix(dist, tas):\n\tfor i in range(0,6):\n\t\tfor j in range(0,6):\n\t\t\tdist.settasmatrix_element(c_double(tas[i][j]), c_int(i), c_int(j))\n\ndef setParameters(dist, index, start, stop, numb, type):\n\tdist.setparameter_(byref(c_int(index)), byref(c_double(start)),byref(c_double(stop)),byref(c_int(numb)),byref(c_int(type)))\n\n'''\nSIMULATION\n#/                   mass, charge,   A,    Z, momentum (7Z TeV/c)\nREFERENCE 938.0      1  1    1  6500000.0 \n/      turns, total particles\nTRACK  100000   64\nNEXT\n/\nDISTRIBUTION\n//INPUT PARAMETERS\nEMITTANCE   1           2.5e-6    //Normalized [m]\nEMITTANCE   2           2.503e-6  //Normalized [m]\n/          BETAX           ALFX            DX            DPX       BETAY          ALFY            DY              DPY\nTWISS      156.921221293   2.11599999399   0.2973032402 -0.0030478 78.0330611129 -1.08169977617  -0.1155854323 -0.0011203\nCLOSEDORBIT  0.0   0.0   0.0   0.0  0.0   0.0\nDUMP FILENAME BINARY NORMALIZED\n// DISTRIBUTION TYPE\nSUBSAMPLE  1\nJX       LINEAR    2.0   4.0 / [sigma]\nPHIX     CONSTANT  0\nJY       LINEAR    2.0   4.0 / [sigma]\nPHIX     CONSTANT  0\nDELTA    CONSTANT  0.00027\nSIGMA    CONSTANT  0\nSUBSAMPLE  2 /second pair\nJX       LINEAR    2.0   4.0 / [sigma]\nPHIX     CONSTANT  0.001\nJY       LINEAR    2.0   4.0 / [sigma]\nPHIX     CONSTANT  0\nDELTA    CONSTANT  0.00027\nSIGMA    CONSTANT  0\nNEXT\nDUMP\nPOSTPR 1000 / produce fort.90 every 1000 turns\nNEXT\n'''\n\ndist = cdll.LoadLibrary(\"./buildDemo/libhello.so\")\ndist.initializedistribution_(byref(c_int(2)))\n\nmyfile = \"/home/tobias/codes/SixTrackTobias/test/orbit6d-element-dispersion/START_DUMP\"\ntas = readtasfromdump(myfile)\ntas[4][2]=0.002\ntas[5][2]=0.1\nsettasmatrix(dist, tas)\n\n\n\ndouble6 = c_double * 6\nphysical = double6(0,0,0,0,0,0)\npia2 = np.pi*2\nmomentum = c_double(6500000)\nmass = c_double(938.0)\neps = 2.0\ndim = c_int(6)\nzero = c_double(0)\ne1 = (1.0)\ne2 = (1.0)\n\nsetEmittance12(dist,e1,e2)\ndist.usedeltap_()\nsetmassmom(dist, mass, momentum)\nsetdisttype(dist,0)\n\n\nsetParameters(dist,1,2,4,4,1)\nsetParameters(dist,2,0.001,0,1,0)\nsetParameters(dist,3,2,4,4,1)\nsetParameters(dist,4,0,0,1,0)\nsetParameters(dist,5,0.2,2,1,0)\nsetParameters(dist,6,1,1,1,0)\n\ndist.printdistsettings_()\ndist.print2file()\n\nxd = []\npxd = []\nyd = []\nyxd = []\npyd = []\ndelta = []\ntime = []\nfor i in range(0,dist.getnumberdist_()):\n\tdist.getcoord_(physical,c_int(i))\n\txd.append(physical[0])\n\tpxd.append(physical[1])\n\tyd.append(physical[2])\n\tpyd.append(physical[3])\n\ttime.append(physical[4])\n\tdelta.append(physical[5])\n\n\tprint(physical[0], physical[1],physical[2],physical[3],physical[4], physical[5])\n\n\n#dist.printdistsettings_()\n#dist.print2fort13()\n\n","sub_path":"demo/example2.py","file_name":"example2.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"219692128","text":"import pandas as pd;\r\nfrom zipline.data import loader\r\nfrom zipline.algorithm import TradingAlgorithm\r\nfrom datetime import datetime\r\nimport pytz\r\nclass AlgoRunnerIterator(object):\r\n    '''\r\n    This class is scheduling Trading Algorithm to run multiple times \r\n    '''\r\n    \r\n    def __init__(self, timeConfigPath, initialize, handle_data, stockNames):\r\n        '''\r\n        Constructor\r\n        '''\r\n        self.timeConfigPath = timeConfigPath;\r\n        self.stockName = stockNames;\r\n        self.timeDF = self._getTimeScheduleTable();\r\n        self.initialize = initialize    \r\n        self.handle_data = handle_data\r\n        self.resetIndex();\r\n        \r\n    def hasNext(self):\r\n        return self.index < self.timeDF.shape[0];\r\n    \r\n    def algoRunNext(self):\r\n        if(self.hasNext()):\r\n            currentSeriesValue = self.timeDF.iloc[self.index];\r\n            self.index += 1;\r\n            start, end = self._getStartEndTimeFromCurrentSeries(currentSeriesValue);\r\n            algo = TradingAlgorithm(initialize=self.initialize, \r\n                        handle_data=self.handle_data, identifiers=self.stockName)\r\n            data = loader.load_bars_from_yahoo(stocks=self.stockName,\r\n                            start=start, end=end);\r\n            return algo.run(data);\r\n        else:\r\n            raise ValueError;\r\n        \r\n    def setInitialize(self, initialize):\r\n        self.initialize= initialize;\r\n        self.resetIndex();\r\n        \r\n    def setHandleData(self, handle_data):\r\n        self.handle_data= handle_data;\r\n        self.resetIndex();\r\n        \r\n    def resetIndex(self):\r\n        self.index = 0;\r\n        \r\n    def _getTimeScheduleTable(self):\r\n        return pd.read_csv(self.timeConfigPath, comment='#'); \r\n        \r\n    def _strToDatetime(self, dateStr):\r\n        utc=pytz.UTC\r\n        strAsDatetime = datetime.strptime(dateStr, '%Y-%m-%d')\r\n        return utc.localize(strAsDatetime)\r\n\r\n    def _getStartEndTimeFromCurrentSeries(self, currentSeriesValue):\r\n        startDateStr = currentSeriesValue['start']\r\n        endDateStr = currentSeriesValue['end']\r\n        startDate = self._strToDatetime(startDateStr)\r\n        endDate = self._strToDatetime(endDateStr)\r\n        return startDate, endDate","sub_path":"zipline/utils/AlgoRunnerIterator.py","file_name":"AlgoRunnerIterator.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"78956888","text":"from django.urls import path, include\n\nfrom .views import HomeView, AccTrendView, AccCountView, DeathCountView, InjuryCountView\n\n\nurlpatterns=[\n    path('', HomeView.as_view(), name='home'),\n    path('trend/<str:key>/<str:key2>', AccTrendView.as_view(), name='trend'),\n    path('acccount/<str:key>/<str:key2>', AccCountView.as_view(), name='acccount'),\n    path('deathcount/<str:key>/<str:key2>', DeathCountView.as_view(), name='deathcount'),\n    path('injurycount/<str:key>/<str:key2>', InjuryCountView.as_view(), name='injurycount'),\n    \n]","sub_path":"accident/acctype/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"461781990","text":"import json\nimport requests\nfrom StringIO import StringIO\n\nimport zipstream\nfrom mountbit.backend.configs import CONFIGS\nfrom mountbit.backend.users.service import UserService\nfrom mountbit.backend.background_task.apitask import api_task\nfrom mountbit.backend.rest import errorcode\nfrom mountbit.utils.timestamp import timestamp_now\nfrom mountbit.fs.versionsfs import disable_versions\nfrom mountbit.backend.request_context import request_context\nfrom mountbit.backend.background_task.apitask_progress import api_progress\nfrom mountbit.fs.utils import change_path_base\nfrom fs.path import pathjoin\n\nfrom ..fs_utils import get_auxiliary_fs\n\n\nclass Chunker(object):\n    def __init__(self, streamer):\n        self.zip_iter = iter(streamer)\n        self.chunk_size = 1024 * 512\n        self.buffer = StringIO()\n\n    def __iter__(self):\n        return self\n\n    def next(self):\n        while self.buffer.tell() < self.chunk_size:\n            try:\n                self.buffer.write(next(self.zip_iter))\n            except StopIteration:\n                break\n        self.buffer.seek(0)\n        chunk = self.buffer.read(self.chunk_size)\n        if not chunk:\n            raise StopIteration\n\n        new_buffer = StringIO()\n        new_buffer.write(self.buffer.read())\n        self.buffer = new_buffer\n        return chunk\n\n\ndef read_by_chunk(src_path):\n    chunk_size = 512*1024\n    src_file = requests.get(src_path, stream=True)\n    for chunk in src_file.iter_content(chunk_size=chunk_size):\n        yield chunk\n\n\ndef store_chunk(buf, fs, parent_dst, num):\n    dst = pathjoin(parent_dst, str(num))\n    if not fs.exists(parent_dst):\n        fs.makedir(parent_dst, recursive=True)\n    fs.setcontents(dst, buf)\n    return {'url': fs.get_download_url(dst), 'part': num}\n\n\n@api_task(ignore_result=True, bind=True)\ndef zipdir_stream(self, user_id, paths, public=False, agent=None, parent_folder='/'):\n    if not parent_folder:\n        parent_folder = '/'\n    user = UserService().get(user_id)\n    if user is None:\n        raise errorcode.UserNotFound\n    attempt_id = self.request.id or str(timestamp_now())\n    result_body = []\n    with api_progress(self) as progress_context, disable_versions(), request_context(user=user) as context:\n        home_fs = context.home_fs\n        z = zipstream.ZipFile()\n        for path in paths:\n            if home_fs.exists(path['path']):\n                if path['folder']:\n                    for root, files in home_fs.walk(path['path']):\n                        archive_root = root\n                        if public:\n                            archive_root = change_path_base(root, path['public_path'], '/')\n                        try:\n                            archive_root = change_path_base(archive_root, parent_folder, '/')\n                        except ValueError:\n                            pass\n\n                        for file_ in files:\n                            file_path = pathjoin(root, file_)\n                            if home_fs.exists(file_path):\n                                z.write_iter(iterable=read_by_chunk(home_fs.get_download_url(file_path)), arcname=pathjoin(archive_root, file_))\n                else:\n                    try:\n                        file_path = change_path_base(path['path'], parent_folder, '/')\n                    except ValueError:\n                        file_path = path['path']\n                    if public:\n                        file_path = change_path_base(file_path, path['public_path'], '/')\n\n                    z.write_iter(iterable=read_by_chunk(home_fs.get_download_url(path['path'])), arcname=file_path)\n\n        parent_dst = pathjoin(CONFIGS.mountbit.zipstream_path, attempt_id)\n        aux_fs = get_auxiliary_fs(context.user_fs)\n        for i, chunk in enumerate(Chunker(z)):\n            result_body.append(store_chunk(chunk, aux_fs, parent_dst, i))\n            self.task_result.part_body(json.dumps(result_body))\n        return json.dumps(result_body)\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"67511627","text":"import base64\nimport click\nfrom click_default_group import DefaultGroup  # type: ignore\nfrom datetime import datetime\nimport hashlib\nimport pathlib\nimport sqlite_utils\nfrom sqlite_utils.db import AlterError, BadMultiValues, DescIndex\nfrom sqlite_utils import recipes\nimport textwrap\nimport inspect\nimport io\nimport itertools\nimport json\nimport os\nimport sys\nimport csv as csv_std\nimport tabulate\nfrom .utils import (\n    file_progress,\n    find_spatialite,\n    sqlite3,\n    decode_base64_values,\n    progressbar,\n    rows_from_file,\n    Format,\n    TypeTracker,\n)\n\nCONTEXT_SETTINGS = dict(help_option_names=[\"-h\", \"--help\"])\n\nVALID_COLUMN_TYPES = (\"INTEGER\", \"TEXT\", \"FLOAT\", \"BLOB\")\n\nUNICODE_ERROR = \"\"\"\n{}\n\nThe input you provided uses a character encoding other than utf-8.\n\nYou can fix this by passing the --encoding= option with the encoding of the file.\n\nIf you do not know the encoding, running 'file filename.csv' may tell you.\n\nIt's often worth trying: --encoding=latin-1\n\"\"\".strip()\n\n\n# Increase CSV field size limit to maximum possible\n# https://stackoverflow.com/a/15063941\nfield_size_limit = sys.maxsize\n\nwhile True:\n    try:\n        csv_std.field_size_limit(field_size_limit)\n        break\n    except OverflowError:\n        field_size_limit = int(field_size_limit / 10)\n\n\ndef output_options(fn):\n    for decorator in reversed(\n        (\n            click.option(\n                \"--nl\",\n                help=\"Output newline-delimited JSON\",\n                is_flag=True,\n                default=False,\n            ),\n            click.option(\n                \"--arrays\",\n                help=\"Output rows as arrays instead of objects\",\n                is_flag=True,\n                default=False,\n            ),\n            click.option(\"--csv\", is_flag=True, help=\"Output CSV\"),\n            click.option(\"--tsv\", is_flag=True, help=\"Output TSV\"),\n            click.option(\"--no-headers\", is_flag=True, help=\"Omit CSV headers\"),\n            click.option(\"-t\", \"--table\", is_flag=True, help=\"Output as a table\"),\n            click.option(\n                \"--fmt\",\n                help=\"Table format - one of {}\".format(\n                    \", \".join(tabulate.tabulate_formats)\n                ),\n                default=\"simple\",\n            ),\n            click.option(\n                \"--json-cols\",\n                help=\"Detect JSON cols and output them as JSON, not escaped strings\",\n                is_flag=True,\n                default=False,\n            ),\n        )\n    ):\n        fn = decorator(fn)\n    return fn\n\n\ndef load_extension_option(fn):\n    return click.option(\n        \"--load-extension\",\n        multiple=True,\n        help=\"SQLite extensions to load\",\n    )(fn)\n\n\n@click.group(\n    cls=DefaultGroup,\n    default=\"query\",\n    default_if_no_args=True,\n    context_settings=CONTEXT_SETTINGS,\n)\n@click.version_option()\ndef cli():\n    \"Commands for interacting with a SQLite database\"\n    pass\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.option(\n    \"--fts4\", help=\"Just show FTS4 enabled tables\", default=False, is_flag=True\n)\n@click.option(\n    \"--fts5\", help=\"Just show FTS5 enabled tables\", default=False, is_flag=True\n)\n@click.option(\n    \"--counts\", help=\"Include row counts per table\", default=False, is_flag=True\n)\n@output_options\n@click.option(\n    \"--columns\",\n    help=\"Include list of columns for each table\",\n    is_flag=True,\n    default=False,\n)\n@click.option(\n    \"--schema\",\n    help=\"Include schema for each table\",\n    is_flag=True,\n    default=False,\n)\n@load_extension_option\ndef tables(\n    path,\n    fts4,\n    fts5,\n    counts,\n    nl,\n    arrays,\n    csv,\n    tsv,\n    no_headers,\n    table,\n    fmt,\n    json_cols,\n    columns,\n    schema,\n    load_extension,\n    views=False,\n):\n    \"\"\"List the tables in the database\"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    headers = [\"view\" if views else \"table\"]\n    if counts:\n        headers.append(\"count\")\n    if columns:\n        headers.append(\"columns\")\n    if schema:\n        headers.append(\"schema\")\n\n    def _iter():\n        if views:\n            items = db.view_names()\n        else:\n            items = db.table_names(fts4=fts4, fts5=fts5)\n        for name in items:\n            row = [name]\n            if counts:\n                row.append(db[name].count)\n            if columns:\n                cols = [c.name for c in db[name].columns]\n                if csv:\n                    row.append(\"\\n\".join(cols))\n                else:\n                    row.append(cols)\n            if schema:\n                row.append(db[name].schema)\n            yield row\n\n    if table:\n        print(tabulate.tabulate(_iter(), headers=headers, tablefmt=fmt))\n    elif csv or tsv:\n        writer = csv_std.writer(sys.stdout, dialect=\"excel-tab\" if tsv else \"excel\")\n        if not no_headers:\n            writer.writerow(headers)\n        for row in _iter():\n            writer.writerow(row)\n    else:\n        for line in output_rows(_iter(), headers, nl, arrays, json_cols):\n            click.echo(line)\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.option(\n    \"--counts\", help=\"Include row counts per view\", default=False, is_flag=True\n)\n@output_options\n@click.option(\n    \"--columns\",\n    help=\"Include list of columns for each view\",\n    is_flag=True,\n    default=False,\n)\n@click.option(\n    \"--schema\",\n    help=\"Include schema for each view\",\n    is_flag=True,\n    default=False,\n)\n@load_extension_option\ndef views(\n    path,\n    counts,\n    nl,\n    arrays,\n    csv,\n    tsv,\n    no_headers,\n    table,\n    fmt,\n    json_cols,\n    columns,\n    schema,\n    load_extension,\n):\n    \"\"\"List the views in the database\"\"\"\n    tables.callback(\n        path=path,\n        fts4=False,\n        fts5=False,\n        counts=counts,\n        nl=nl,\n        arrays=arrays,\n        csv=csv,\n        tsv=tsv,\n        no_headers=no_headers,\n        table=table,\n        fmt=fmt,\n        json_cols=json_cols,\n        columns=columns,\n        schema=schema,\n        load_extension=load_extension,\n        views=True,\n    )\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"tables\", nargs=-1)\n@click.option(\"--no-vacuum\", help=\"Don't run VACUUM\", default=False, is_flag=True)\n@load_extension_option\ndef optimize(path, tables, no_vacuum, load_extension):\n    \"\"\"Optimize all full-text search tables and then run VACUUM - should shrink the database file\"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    if not tables:\n        tables = db.table_names(fts4=True) + db.table_names(fts5=True)\n    with db.conn:\n        for table in tables:\n            db[table].optimize()\n    if not no_vacuum:\n        db.vacuum()\n\n\n@cli.command(name=\"rebuild-fts\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"tables\", nargs=-1)\n@load_extension_option\ndef rebuild_fts(path, tables, load_extension):\n    \"\"\"Rebuild all or specific full-text search tables\"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    if not tables:\n        tables = db.table_names(fts4=True) + db.table_names(fts5=True)\n    with db.conn:\n        for table in tables:\n            db[table].rebuild_fts()\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\ndef vacuum(path):\n    \"\"\"Run VACUUM against the database\"\"\"\n    sqlite_utils.Database(path).vacuum()\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@load_extension_option\ndef dump(path, load_extension):\n    \"\"\"Output a SQL dump of the schema and full contents of the database\"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    for line in db.conn.iterdump():\n        click.echo(line)\n\n\n@cli.command(name=\"add-column\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.argument(\"col_name\")\n@click.argument(\n    \"col_type\",\n    type=click.Choice(\n        [\"integer\", \"float\", \"blob\", \"text\", \"INTEGER\", \"FLOAT\", \"BLOB\", \"TEXT\"]\n    ),\n    required=False,\n)\n@click.option(\n    \"--fk\", type=str, required=False, help=\"Table to reference as a foreign key\"\n)\n@click.option(\n    \"--fk-col\",\n    type=str,\n    required=False,\n    help=\"Referenced column on that foreign key table - if omitted will automatically use the primary key\",\n)\n@click.option(\n    \"--not-null-default\",\n    type=str,\n    required=False,\n    help=\"Add NOT NULL DEFAULT 'TEXT' constraint\",\n)\n@load_extension_option\ndef add_column(\n    path, table, col_name, col_type, fk, fk_col, not_null_default, load_extension\n):\n    \"Add a column to the specified table\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    db[table].add_column(\n        col_name, col_type, fk=fk, fk_col=fk_col, not_null_default=not_null_default\n    )\n\n\n@cli.command(name=\"add-foreign-key\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.argument(\"column\")\n@click.argument(\"other_table\", required=False)\n@click.argument(\"other_column\", required=False)\n@click.option(\n    \"--ignore\",\n    is_flag=True,\n    help=\"If foreign key already exists, do nothing\",\n)\n@load_extension_option\ndef add_foreign_key(\n    path, table, column, other_table, other_column, ignore, load_extension\n):\n    \"\"\"\n    Add a new foreign key constraint to an existing table. Example usage:\n\n        $ sqlite-utils add-foreign-key my.db books author_id authors id\n\n    WARNING: Could corrupt your database! Back up your database file first.\n    \"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    try:\n        db[table].add_foreign_key(column, other_table, other_column, ignore=ignore)\n    except AlterError as e:\n        raise click.ClickException(e)\n\n\n@cli.command(name=\"add-foreign-keys\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"foreign_key\", nargs=-1)\n@load_extension_option\ndef add_foreign_keys(path, foreign_key, load_extension):\n    \"\"\"\n    Add multiple new foreign key constraints to a database. Example usage:\n\n    \\b\n    sqlite-utils add-foreign-keys my.db \\\\\n        books author_id authors id \\\\\n        authors country_id countries id\n    \"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    if len(foreign_key) % 4 != 0:\n        raise click.ClickException(\n            \"Each foreign key requires four values: table, column, other_table, other_column\"\n        )\n    tuples = []\n    for i in range(len(foreign_key) // 4):\n        tuples.append(tuple(foreign_key[i * 4 : (i * 4) + 4]))\n    try:\n        db.add_foreign_keys(tuples)\n    except AlterError as e:\n        raise click.ClickException(e)\n\n\n@cli.command(name=\"index-foreign-keys\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@load_extension_option\ndef index_foreign_keys(path, load_extension):\n    \"\"\"\n    Ensure every foreign key column has an index on it.\n    \"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    db.index_foreign_keys()\n\n\n@cli.command(name=\"create-index\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.argument(\"column\", nargs=-1, required=True)\n@click.option(\"--name\", help=\"Explicit name for the new index\")\n@click.option(\"--unique\", help=\"Make this a unique index\", default=False, is_flag=True)\n@click.option(\n    \"--if-not-exists\",\n    help=\"Ignore if index already exists\",\n    default=False,\n    is_flag=True,\n)\n@load_extension_option\ndef create_index(path, table, column, name, unique, if_not_exists, load_extension):\n    \"\"\"\n    Add an index to the specified table covering the specified columns.\n    Use \"sqlite-utils create-index mydb -- -column\" to specify descending\n    order for a column.\n    \"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    # Treat -prefix as descending for columns\n    columns = []\n    for col in column:\n        if col.startswith(\"-\"):\n            col = DescIndex(col[1:])\n        columns.append(col)\n    db[table].create_index(\n        columns, index_name=name, unique=unique, if_not_exists=if_not_exists\n    )\n\n\n@cli.command(name=\"enable-fts\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.argument(\"column\", nargs=-1, required=True)\n@click.option(\"--fts4\", help=\"Use FTS4\", default=False, is_flag=True)\n@click.option(\"--fts5\", help=\"Use FTS5\", default=False, is_flag=True)\n@click.option(\"--tokenize\", help=\"Tokenizer to use, e.g. porter\")\n@click.option(\n    \"--create-triggers\",\n    help=\"Create triggers to update the FTS tables when the parent table changes.\",\n    default=False,\n    is_flag=True,\n)\n@load_extension_option\ndef enable_fts(\n    path, table, column, fts4, fts5, tokenize, create_triggers, load_extension\n):\n    \"Enable full-text search for specific table and columns\"\n    fts_version = \"FTS5\"\n    if fts4 and fts5:\n        click.echo(\"Can only use one of --fts4 or --fts5\", err=True)\n        return\n    elif fts4:\n        fts_version = \"FTS4\"\n\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    db[table].enable_fts(\n        column,\n        fts_version=fts_version,\n        tokenize=tokenize,\n        create_triggers=create_triggers,\n    )\n\n\n@cli.command(name=\"populate-fts\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.argument(\"column\", nargs=-1, required=True)\n@load_extension_option\ndef populate_fts(path, table, column, load_extension):\n    \"Re-populate full-text search for specific table and columns\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    db[table].populate_fts(column)\n\n\n@cli.command(name=\"disable-fts\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@load_extension_option\ndef disable_fts(path, table, load_extension):\n    \"Disable full-text search for specific table\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    db[table].disable_fts()\n\n\n@cli.command(name=\"enable-wal\")\n@click.argument(\n    \"path\",\n    nargs=-1,\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@load_extension_option\ndef enable_wal(path, load_extension):\n    \"Enable WAL for database files\"\n    for path_ in path:\n        db = sqlite_utils.Database(path_)\n        _load_extensions(db, load_extension)\n        db.enable_wal()\n\n\n@cli.command(name=\"disable-wal\")\n@click.argument(\n    \"path\",\n    nargs=-1,\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@load_extension_option\ndef disable_wal(path, load_extension):\n    \"Disable WAL for database files\"\n    for path_ in path:\n        db = sqlite_utils.Database(path_)\n        _load_extensions(db, load_extension)\n        db.disable_wal()\n\n\n@cli.command(name=\"enable-counts\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"tables\", nargs=-1)\n@load_extension_option\ndef enable_counts(path, tables, load_extension):\n    \"Configure triggers to update a _counts table with row counts\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    if not tables:\n        db.enable_counts()\n    else:\n        # Check all tables exist\n        bad_tables = [table for table in tables if not db[table].exists()]\n        if bad_tables:\n            raise click.ClickException(\"Invalid tables: {}\".format(bad_tables))\n        for table in tables:\n            db[table].enable_counts()\n\n\n@cli.command(name=\"reset-counts\")\n@click.argument(\n    \"path\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@load_extension_option\ndef reset_counts(path, load_extension):\n    \"Reset calculated counts in the _counts table\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    db.reset_counts()\n\n\ndef insert_upsert_options(fn):\n    for decorator in reversed(\n        (\n            click.argument(\n                \"path\",\n                type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n                required=True,\n            ),\n            click.argument(\"table\"),\n            click.argument(\"json_file\", type=click.File(\"rb\"), required=True),\n            click.option(\n                \"--pk\", help=\"Columns to use as the primary key, e.g. id\", multiple=True\n            ),\n            click.option(\"--nl\", is_flag=True, help=\"Expect newline-delimited JSON\"),\n            click.option(\"--flatten\", is_flag=True, help=\"Flatten nested JSON objects\"),\n            click.option(\"-c\", \"--csv\", is_flag=True, help=\"Expect CSV\"),\n            click.option(\"--tsv\", is_flag=True, help=\"Expect TSV\"),\n            click.option(\"--delimiter\", help=\"Delimiter to use for CSV files\"),\n            click.option(\"--quotechar\", help=\"Quote character to use for CSV/TSV\"),\n            click.option(\n                \"--sniff\", is_flag=True, help=\"Detect delimiter and quote character\"\n            ),\n            click.option(\n                \"--no-headers\", is_flag=True, help=\"CSV file has no header row\"\n            ),\n            click.option(\n                \"--batch-size\", type=int, default=100, help=\"Commit every X records\"\n            ),\n            click.option(\n                \"--alter\",\n                is_flag=True,\n                help=\"Alter existing table to add any missing columns\",\n            ),\n            click.option(\n                \"--not-null\",\n                multiple=True,\n                help=\"Columns that should be created as NOT NULL\",\n            ),\n            click.option(\n                \"--default\",\n                multiple=True,\n                type=(str, str),\n                help=\"Default value that should be set for a column\",\n            ),\n            click.option(\n                \"--encoding\",\n                help=\"Character encoding for input, defaults to utf-8\",\n            ),\n            click.option(\n                \"-d\",\n                \"--detect-types\",\n                is_flag=True,\n                envvar=\"SQLITE_UTILS_DETECT_TYPES\",\n                help=\"Detect types for columns in CSV/TSV data\",\n            ),\n            load_extension_option,\n            click.option(\"--silent\", is_flag=True, help=\"Do not show progress bar\"),\n        )\n    ):\n        fn = decorator(fn)\n    return fn\n\n\ndef insert_upsert_implementation(\n    path,\n    table,\n    json_file,\n    pk,\n    nl,\n    flatten,\n    csv,\n    tsv,\n    delimiter,\n    quotechar,\n    sniff,\n    no_headers,\n    batch_size,\n    alter,\n    upsert,\n    ignore=False,\n    replace=False,\n    truncate=False,\n    not_null=None,\n    default=None,\n    encoding=None,\n    detect_types=None,\n    load_extension=None,\n    silent=False,\n):\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    if (delimiter or quotechar or sniff or no_headers) and not tsv:\n        csv = True\n    if (nl + csv + tsv) >= 2:\n        raise click.ClickException(\"Use just one of --nl, --csv or --tsv\")\n    if (csv or tsv) and flatten:\n        raise click.ClickException(\"--flatten cannot be used with --csv or --tsv\")\n    if encoding and not (csv or tsv):\n        raise click.ClickException(\"--encoding must be used with --csv or --tsv\")\n    if pk and len(pk) == 1:\n        pk = pk[0]\n    encoding = encoding or \"utf-8-sig\"\n    buffered = io.BufferedReader(json_file, buffer_size=4096)\n    decoded = io.TextIOWrapper(buffered, encoding=encoding)\n    tracker = None\n    if csv or tsv:\n        if sniff:\n            # Read first 2048 bytes and use that to detect\n            first_bytes = buffered.peek(2048)\n            dialect = csv_std.Sniffer().sniff(first_bytes.decode(encoding, \"ignore\"))\n        else:\n            dialect = \"excel-tab\" if tsv else \"excel\"\n        with file_progress(decoded, silent=silent) as decoded:\n            csv_reader_args = {\"dialect\": dialect}\n            if delimiter:\n                csv_reader_args[\"delimiter\"] = delimiter\n            if quotechar:\n                csv_reader_args[\"quotechar\"] = quotechar\n            reader = csv_std.reader(decoded, **csv_reader_args)\n            first_row = next(reader)\n            if no_headers:\n                headers = [\"untitled_{}\".format(i + 1) for i in range(len(first_row))]\n                reader = itertools.chain([first_row], reader)\n            else:\n                headers = first_row\n            docs = (dict(zip(headers, row)) for row in reader)\n            if detect_types:\n                tracker = TypeTracker()\n                docs = tracker.wrap(docs)\n    else:\n        try:\n            if nl:\n                docs = (json.loads(line) for line in decoded)\n            else:\n                docs = json.load(decoded)\n                if isinstance(docs, dict):\n                    docs = [docs]\n        except json.decoder.JSONDecodeError:\n            raise click.ClickException(\n                \"Invalid JSON - use --csv for CSV or --tsv for TSV files\"\n            )\n        if flatten:\n            docs = (dict(_flatten(doc)) for doc in docs)\n\n    extra_kwargs = {\"ignore\": ignore, \"replace\": replace, \"truncate\": truncate}\n    if not_null:\n        extra_kwargs[\"not_null\"] = set(not_null)\n    if default:\n        extra_kwargs[\"defaults\"] = dict(default)\n    if upsert:\n        extra_kwargs[\"upsert\"] = upsert\n    # Apply {\"$base64\": true, ...} decoding, if needed\n    docs = (decode_base64_values(doc) for doc in docs)\n    try:\n        db[table].insert_all(\n            docs, pk=pk, batch_size=batch_size, alter=alter, **extra_kwargs\n        )\n    except Exception as e:\n        if (\n            isinstance(e, sqlite3.OperationalError)\n            and e.args\n            and \"has no column named\" in e.args[0]\n        ):\n            raise click.ClickException(\n                \"{}\\n\\nTry using --alter to add additional columns\".format(e.args[0])\n            )\n        # If we can find sql= and parameters= arguments, show those\n        variables = _find_variables(e.__traceback__, [\"sql\", \"parameters\"])\n        if \"sql\" in variables and \"parameters\" in variables:\n            raise click.ClickException(\n                \"{}\\n\\nsql = {}\\nparameters = {}\".format(\n                    str(e), variables[\"sql\"], variables[\"parameters\"]\n                )\n            )\n        else:\n            raise\n    if tracker is not None:\n        db[table].transform(types=tracker.types)\n\n\ndef _flatten(d):\n    for key, value in d.items():\n        if isinstance(value, dict):\n            for key2, value2 in _flatten(value):\n                yield key + \"_\" + key2, value2\n        else:\n            yield key, value\n\n\ndef _find_variables(tb, vars):\n    to_find = list(vars)\n    found = {}\n    for var in to_find:\n        if var in tb.tb_frame.f_locals:\n            vars.remove(var)\n            found[var] = tb.tb_frame.f_locals[var]\n    if vars and tb.tb_next:\n        found.update(_find_variables(tb.tb_next, vars))\n    return found\n\n\n@cli.command()\n@insert_upsert_options\n@click.option(\n    \"--ignore\", is_flag=True, default=False, help=\"Ignore records if pk already exists\"\n)\n@click.option(\n    \"--replace\",\n    is_flag=True,\n    default=False,\n    help=\"Replace records if pk already exists\",\n)\n@click.option(\n    \"--truncate\",\n    is_flag=True,\n    default=False,\n    help=\"Truncate table before inserting records, if table already exists\",\n)\ndef insert(\n    path,\n    table,\n    json_file,\n    pk,\n    nl,\n    flatten,\n    csv,\n    tsv,\n    delimiter,\n    quotechar,\n    sniff,\n    no_headers,\n    batch_size,\n    alter,\n    encoding,\n    detect_types,\n    load_extension,\n    silent,\n    ignore,\n    replace,\n    truncate,\n    not_null,\n    default,\n):\n    \"\"\"\n    Insert records from JSON file into a table, creating the table if it\n    does not already exist.\n\n    Input should be a JSON array of objects, unless --nl or --csv is used.\n    \"\"\"\n    try:\n        insert_upsert_implementation(\n            path,\n            table,\n            json_file,\n            pk,\n            nl,\n            flatten,\n            csv,\n            tsv,\n            delimiter,\n            quotechar,\n            sniff,\n            no_headers,\n            batch_size,\n            alter=alter,\n            upsert=False,\n            ignore=ignore,\n            replace=replace,\n            truncate=truncate,\n            encoding=encoding,\n            detect_types=detect_types,\n            load_extension=load_extension,\n            silent=silent,\n            not_null=not_null,\n            default=default,\n        )\n    except UnicodeDecodeError as ex:\n        raise click.ClickException(UNICODE_ERROR.format(ex))\n\n\n@cli.command()\n@insert_upsert_options\ndef upsert(\n    path,\n    table,\n    json_file,\n    pk,\n    nl,\n    flatten,\n    csv,\n    tsv,\n    batch_size,\n    delimiter,\n    quotechar,\n    sniff,\n    no_headers,\n    alter,\n    not_null,\n    default,\n    encoding,\n    detect_types,\n    load_extension,\n    silent,\n):\n    \"\"\"\n    Upsert records based on their primary key. Works like 'insert' but if\n    an incoming record has a primary key that matches an existing record\n    the existing record will be updated.\n    \"\"\"\n    try:\n        insert_upsert_implementation(\n            path,\n            table,\n            json_file,\n            pk,\n            nl,\n            flatten,\n            csv,\n            tsv,\n            delimiter,\n            quotechar,\n            sniff,\n            no_headers,\n            batch_size,\n            alter=alter,\n            upsert=True,\n            not_null=not_null,\n            default=default,\n            encoding=encoding,\n            load_extension=load_extension,\n            silent=silent,\n        )\n    except UnicodeDecodeError as ex:\n        raise click.ClickException(UNICODE_ERROR.format(ex))\n\n\n@cli.command(name=\"create-table\")\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.argument(\"columns\", nargs=-1, required=True)\n@click.option(\"--pk\", help=\"Column to use as primary key\")\n@click.option(\n    \"--not-null\",\n    multiple=True,\n    help=\"Columns that should be created as NOT NULL\",\n)\n@click.option(\n    \"--default\",\n    multiple=True,\n    type=(str, str),\n    help=\"Default value that should be set for a column\",\n)\n@click.option(\n    \"--fk\",\n    multiple=True,\n    type=(str, str, str),\n    help=\"Column, other table, other column to set as a foreign key\",\n)\n@click.option(\n    \"--ignore\",\n    is_flag=True,\n    help=\"If table already exists, do nothing\",\n)\n@click.option(\n    \"--replace\",\n    is_flag=True,\n    help=\"If table already exists, replace it\",\n)\n@load_extension_option\ndef create_table(\n    path, table, columns, pk, not_null, default, fk, ignore, replace, load_extension\n):\n    \"\"\"\n    Add a table with the specified columns. Columns should be specified using\n    name, type pairs, for example:\n\n    \\b\n    sqlite-utils create-table my.db people \\\\\n        id integer \\\\\n        name text \\\\\n        height float \\\\\n        photo blob --pk id\n    \"\"\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    if len(columns) % 2 == 1:\n        raise click.ClickException(\n            \"columns must be an even number of 'name' 'type' pairs\"\n        )\n    coltypes = {}\n    columns = list(columns)\n    while columns:\n        name = columns.pop(0)\n        ctype = columns.pop(0)\n        if ctype.upper() not in VALID_COLUMN_TYPES:\n            raise click.ClickException(\n                \"column types must be one of {}\".format(VALID_COLUMN_TYPES)\n            )\n        coltypes[name] = ctype.upper()\n    # Does table already exist?\n    if table in db.table_names():\n        if ignore:\n            return\n        elif replace:\n            db[table].drop()\n        else:\n            raise click.ClickException(\n                'Table \"{}\" already exists. Use --replace to delete and replace it.'.format(\n                    table\n                )\n            )\n    db[table].create(\n        coltypes, pk=pk, not_null=not_null, defaults=dict(default), foreign_keys=fk\n    )\n\n\n@cli.command(name=\"drop-table\")\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.option(\"--ignore\", is_flag=True)\n@load_extension_option\ndef drop_table(path, table, ignore, load_extension):\n    \"Drop the specified table\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    try:\n        db[table].drop(ignore=ignore)\n    except sqlite3.OperationalError:\n        raise click.ClickException('Table \"{}\" does not exist'.format(table))\n\n\n@cli.command(name=\"create-view\")\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"view\")\n@click.argument(\"select\")\n@click.option(\n    \"--ignore\",\n    is_flag=True,\n    help=\"If view already exists, do nothing\",\n)\n@click.option(\n    \"--replace\",\n    is_flag=True,\n    help=\"If view already exists, replace it\",\n)\n@load_extension_option\ndef create_view(path, view, select, ignore, replace, load_extension):\n    \"Create a view for the provided SELECT query\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    # Does view already exist?\n    if view in db.view_names():\n        if ignore:\n            return\n        elif replace:\n            db[view].drop()\n        else:\n            raise click.ClickException(\n                'View \"{}\" already exists. Use --replace to delete and replace it.'.format(\n                    view\n                )\n            )\n    db.create_view(view, select)\n\n\n@cli.command(name=\"drop-view\")\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"view\")\n@click.option(\"--ignore\", is_flag=True)\n@load_extension_option\ndef drop_view(path, view, ignore, load_extension):\n    \"Drop the specified view\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    try:\n        db[view].drop(ignore=ignore)\n    except sqlite3.OperationalError:\n        raise click.ClickException('View \"{}\" does not exist'.format(view))\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"sql\")\n@click.option(\n    \"--attach\",\n    type=(str, click.Path(file_okay=True, dir_okay=False, allow_dash=False)),\n    multiple=True,\n    help=\"Additional databases to attach - specify alias and filepath\",\n)\n@output_options\n@click.option(\"-r\", \"--raw\", is_flag=True, help=\"Raw output, first column of first row\")\n@click.option(\n    \"-p\",\n    \"--param\",\n    multiple=True,\n    type=(str, str),\n    help=\"Named :parameters for SQL query\",\n)\n@load_extension_option\ndef query(\n    path,\n    sql,\n    attach,\n    nl,\n    arrays,\n    csv,\n    tsv,\n    no_headers,\n    table,\n    fmt,\n    json_cols,\n    raw,\n    param,\n    load_extension,\n):\n    \"Execute SQL query and return the results as JSON\"\n    db = sqlite_utils.Database(path)\n    for alias, attach_path in attach:\n        db.attach(alias, attach_path)\n    _load_extensions(db, load_extension)\n    db.register_fts4_bm25()\n\n    _execute_query(\n        db, sql, param, raw, table, csv, tsv, no_headers, fmt, nl, arrays, json_cols\n    )\n\n\n@cli.command()\n@click.argument(\n    \"paths\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=True),\n    required=False,\n    nargs=-1,\n)\n@click.argument(\"sql\")\n@click.option(\n    \"--attach\",\n    type=(str, click.Path(file_okay=True, dir_okay=False, allow_dash=False)),\n    multiple=True,\n    help=\"Additional databases to attach - specify alias and filepath\",\n)\n@output_options\n@click.option(\"-r\", \"--raw\", is_flag=True, help=\"Raw output, first column of first row\")\n@click.option(\n    \"-p\",\n    \"--param\",\n    multiple=True,\n    type=(str, str),\n    help=\"Named :parameters for SQL query\",\n)\n@click.option(\n    \"--encoding\",\n    help=\"Character encoding for CSV input, defaults to utf-8\",\n)\n@click.option(\n    \"-n\",\n    \"--no-detect-types\",\n    is_flag=True,\n    help=\"Treat all CSV/TSV columns as TEXT\",\n)\n@click.option(\"--schema\", is_flag=True, help=\"Show SQL schema for in-memory database\")\n@click.option(\"--dump\", is_flag=True, help=\"Dump SQL for in-memory database\")\n@click.option(\n    \"--save\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    help=\"Save in-memory database to this file\",\n)\n@click.option(\n    \"--analyze\",\n    is_flag=True,\n    help=\"Analyze resulting tables and output results\",\n)\n@load_extension_option\ndef memory(\n    paths,\n    sql,\n    attach,\n    nl,\n    arrays,\n    csv,\n    tsv,\n    no_headers,\n    table,\n    fmt,\n    json_cols,\n    raw,\n    param,\n    encoding,\n    no_detect_types,\n    schema,\n    dump,\n    save,\n    analyze,\n    load_extension,\n):\n    \"\"\"Execute SQL query against an in-memory database, optionally populated by imported data\n\n    To import data from CSV, TSV or JSON files pass them on the command-line:\n\n    \\b\n        sqlite-utils memory one.csv two.json \\\\\n            \"select * from one join two on one.two_id = two.id\"\n\n    For data piped into the tool from standard input, use \"-\" or \"stdin\":\n\n    \\b\n        cat animals.csv | sqlite-utils memory - \\\\\n            \"select * from stdin where species = 'dog'\"\n\n    The format of the data will be automatically detected. You can specify the format\n    explicitly using :json, :csv, :tsv or :nl (for newline-delimited JSON) - for example:\n\n    \\b\n        cat animals.csv | sqlite-utils memory stdin:csv places.dat:nl \\\\\n            \"select * from stdin where place_id in (select id from places)\"\n\n    Use --schema to view the SQL schema of any imported files:\n\n    \\b\n        sqlite-utils memory animals.csv --schema\n    \"\"\"\n    db = sqlite_utils.Database(memory=True)\n    # If --dump or --save or --analyze used but no paths detected, assume SQL query is a path:\n    if (dump or save or schema or analyze) and not paths:\n        paths = [sql]\n        sql = None\n    for i, path in enumerate(paths):\n        # Path may have a :format suffix\n        if \":\" in path and path.rsplit(\":\", 1)[-1].upper() in Format.__members__:\n            path, suffix = path.rsplit(\":\", 1)\n            format = Format[suffix.upper()]\n        else:\n            format = None\n        if path in (\"-\", \"stdin\"):\n            csv_fp = sys.stdin.buffer\n            csv_table = \"stdin\"\n        else:\n            csv_path = pathlib.Path(path)\n            csv_table = csv_path.stem\n            csv_fp = csv_path.open(\"rb\")\n        rows, format_used = rows_from_file(csv_fp, format=format, encoding=encoding)\n        tracker = None\n        if format_used in (Format.CSV, Format.TSV) and not no_detect_types:\n            tracker = TypeTracker()\n            rows = tracker.wrap(rows)\n        db[csv_table].insert_all(rows, alter=True)\n        if tracker is not None:\n            db[csv_table].transform(types=tracker.types)\n        # Add convenient t / t1 / t2 views\n        view_names = [\"t{}\".format(i + 1)]\n        if i == 0:\n            view_names.append(\"t\")\n        for view_name in view_names:\n            if not db[view_name].exists():\n                db.create_view(view_name, \"select * from [{}]\".format(csv_table))\n\n    if analyze:\n        _analyze(db, tables=None, columns=None, save=False)\n        return\n\n    if dump:\n        for line in db.conn.iterdump():\n            click.echo(line)\n        return\n\n    if schema:\n        click.echo(db.schema)\n        return\n\n    if save:\n        db2 = sqlite_utils.Database(save)\n        for line in db.conn.iterdump():\n            db2.execute(line)\n        return\n\n    for alias, attach_path in attach:\n        db.attach(alias, attach_path)\n    _load_extensions(db, load_extension)\n    db.register_fts4_bm25()\n\n    _execute_query(\n        db, sql, param, raw, table, csv, tsv, no_headers, fmt, nl, arrays, json_cols\n    )\n\n\ndef _execute_query(\n    db, sql, param, raw, table, csv, tsv, no_headers, fmt, nl, arrays, json_cols\n):\n    with db.conn:\n        try:\n            cursor = db.execute(sql, dict(param))\n        except sqlite3.OperationalError as e:\n            raise click.ClickException(str(e))\n        if cursor.description is None:\n            # This was an update/insert\n            headers = [\"rows_affected\"]\n            cursor = [[cursor.rowcount]]\n        else:\n            headers = [c[0] for c in cursor.description]\n        if raw:\n            data = cursor.fetchone()[0]\n            if isinstance(data, bytes):\n                sys.stdout.buffer.write(data)\n            else:\n                sys.stdout.write(str(data))\n        elif table:\n            print(tabulate.tabulate(list(cursor), headers=headers, tablefmt=fmt))\n        elif csv or tsv:\n            writer = csv_std.writer(sys.stdout, dialect=\"excel-tab\" if tsv else \"excel\")\n            if not no_headers:\n                writer.writerow(headers)\n            for row in cursor:\n                writer.writerow(row)\n        else:\n            for line in output_rows(cursor, headers, nl, arrays, json_cols):\n                click.echo(line)\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"dbtable\")\n@click.argument(\"q\")\n@click.option(\"-o\", \"--order\", type=str, help=\"Order by ('column' or 'column desc')\")\n@click.option(\"-c\", \"--column\", type=str, multiple=True, help=\"Columns to return\")\n@click.option(\n    \"--limit\",\n    type=int,\n    help=\"Number of rows to return - defaults to everything\",\n)\n@click.option(\n    \"--sql\", \"show_sql\", is_flag=True, help=\"Show SQL query that would be run\"\n)\n@click.option(\"--quote\", is_flag=True, help=\"Apply FTS quoting rules to search term\")\n@output_options\n@load_extension_option\n@click.pass_context\ndef search(\n    ctx,\n    path,\n    dbtable,\n    q,\n    order,\n    show_sql,\n    quote,\n    column,\n    limit,\n    nl,\n    arrays,\n    csv,\n    tsv,\n    no_headers,\n    table,\n    fmt,\n    json_cols,\n    load_extension,\n):\n    \"Execute a full-text search against this table\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    # Check table exists\n    table_obj = db[dbtable]\n    if not table_obj.exists():\n        raise click.ClickException(\"Table '{}' does not exist\".format(dbtable))\n    if not table_obj.detect_fts():\n        raise click.ClickException(\n            \"Table '{}' is not configured for full-text search\".format(dbtable)\n        )\n    if column:\n        # Check they all exist\n        table_columns = table_obj.columns_dict\n        for c in column:\n            if c not in table_columns:\n                raise click.ClickException(\n                    \"Table '{}' has no column '{}\".format(dbtable, c)\n                )\n    sql = table_obj.search_sql(columns=column, order_by=order, limit=limit)\n    if show_sql:\n        click.echo(sql)\n        return\n    if quote:\n        q = db.quote_fts(q)\n    try:\n        ctx.invoke(\n            query,\n            path=path,\n            sql=sql,\n            nl=nl,\n            arrays=arrays,\n            csv=csv,\n            tsv=tsv,\n            no_headers=no_headers,\n            table=table,\n            fmt=fmt,\n            json_cols=json_cols,\n            param=[(\"query\", q)],\n            load_extension=load_extension,\n        )\n    except click.ClickException as e:\n        if \"malformed MATCH expression\" in str(e) or \"unterminated string\" in str(e):\n            raise click.ClickException(\n                \"{}\\n\\nTry running this again with the --quote option\".format(str(e))\n            )\n        else:\n            raise\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"dbtable\")\n@click.option(\"-c\", \"--column\", type=str, multiple=True, help=\"Columns to return\")\n@output_options\n@load_extension_option\n@click.pass_context\ndef rows(\n    ctx,\n    path,\n    dbtable,\n    column,\n    nl,\n    arrays,\n    csv,\n    tsv,\n    no_headers,\n    table,\n    fmt,\n    json_cols,\n    load_extension,\n):\n    \"Output all rows in the specified table\"\n    columns = \"*\"\n    if column:\n        columns = \", \".join(\"[{}]\".format(c) for c in column)\n    ctx.invoke(\n        query,\n        path=path,\n        sql=\"select {} from [{}]\".format(columns, dbtable),\n        nl=nl,\n        arrays=arrays,\n        csv=csv,\n        tsv=tsv,\n        no_headers=no_headers,\n        table=table,\n        fmt=fmt,\n        json_cols=json_cols,\n        load_extension=load_extension,\n    )\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"tables\", nargs=-1)\n@output_options\n@load_extension_option\n@click.pass_context\ndef triggers(\n    ctx,\n    path,\n    tables,\n    nl,\n    arrays,\n    csv,\n    tsv,\n    no_headers,\n    table,\n    fmt,\n    json_cols,\n    load_extension,\n):\n    \"Show triggers configured in this database\"\n    sql = \"select name, tbl_name as [table], sql from sqlite_master where type = 'trigger'\"\n    if tables:\n        quote = sqlite_utils.Database(memory=True).quote\n        sql += \" and [table] in ({})\".format(\n            \", \".join(quote(table) for table in tables)\n        )\n    ctx.invoke(\n        query,\n        path=path,\n        sql=sql,\n        nl=nl,\n        arrays=arrays,\n        csv=csv,\n        tsv=tsv,\n        no_headers=no_headers,\n        table=table,\n        fmt=fmt,\n        json_cols=json_cols,\n        load_extension=load_extension,\n    )\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"tables\", nargs=-1)\n@click.option(\"--aux\", is_flag=True, help=\"Include auxiliary columns\")\n@output_options\n@load_extension_option\n@click.pass_context\ndef indexes(\n    ctx,\n    path,\n    tables,\n    aux,\n    nl,\n    arrays,\n    csv,\n    tsv,\n    no_headers,\n    table,\n    fmt,\n    json_cols,\n    load_extension,\n):\n    \"Show indexes for this database\"\n    sql = \"\"\"\n    select\n      sqlite_master.name as \"table\",\n      indexes.name as index_name,\n      xinfo.*\n    from sqlite_master\n      join pragma_index_list(sqlite_master.name) indexes\n      join pragma_index_xinfo(index_name) xinfo\n    where\n      sqlite_master.type = 'table'\n    \"\"\"\n    if tables:\n        quote = sqlite_utils.Database(memory=True).quote\n        sql += \" and sqlite_master.name in ({})\".format(\n            \", \".join(quote(table) for table in tables)\n        )\n    if not aux:\n        sql += \" and xinfo.key = 1\"\n    ctx.invoke(\n        query,\n        path=path,\n        sql=sql,\n        nl=nl,\n        arrays=arrays,\n        csv=csv,\n        tsv=tsv,\n        no_headers=no_headers,\n        table=table,\n        fmt=fmt,\n        json_cols=json_cols,\n        load_extension=load_extension,\n    )\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"tables\", nargs=-1, required=False)\n@load_extension_option\ndef schema(\n    path,\n    tables,\n    load_extension,\n):\n    \"Show full schema for this database or for specified tables\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    if tables:\n        for table in tables:\n            click.echo(db[table].schema)\n    else:\n        click.echo(db.schema)\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.option(\n    \"--type\",\n    type=(\n        str,\n        click.Choice([\"INTEGER\", \"TEXT\", \"FLOAT\", \"BLOB\"], case_sensitive=False),\n    ),\n    multiple=True,\n    help=\"Change column type to INTEGER, TEXT, FLOAT or BLOB\",\n)\n@click.option(\"--drop\", type=str, multiple=True, help=\"Drop this column\")\n@click.option(\n    \"--rename\", type=(str, str), multiple=True, help=\"Rename this column to X\"\n)\n@click.option(\"-o\", \"--column-order\", type=str, multiple=True, help=\"Reorder columns\")\n@click.option(\"--not-null\", type=str, multiple=True, help=\"Set this column to NOT NULL\")\n@click.option(\n    \"--not-null-false\", type=str, multiple=True, help=\"Remove NOT NULL from this column\"\n)\n@click.option(\"--pk\", type=str, multiple=True, help=\"Make this column the primary key\")\n@click.option(\n    \"--pk-none\", is_flag=True, help=\"Remove primary key (convert to rowid table)\"\n)\n@click.option(\n    \"--default\",\n    type=(str, str),\n    multiple=True,\n    help=\"Set default value for this column\",\n)\n@click.option(\n    \"--default-none\", type=str, multiple=True, help=\"Remove default from this column\"\n)\n@click.option(\n    \"--drop-foreign-key\",\n    type=str,\n    multiple=True,\n    help=\"Drop this foreign key constraint\",\n)\n@click.option(\"--sql\", is_flag=True, help=\"Output SQL without executing it\")\n@load_extension_option\ndef transform(\n    path,\n    table,\n    type,\n    drop,\n    rename,\n    column_order,\n    not_null,\n    not_null_false,\n    pk,\n    pk_none,\n    default,\n    default_none,\n    drop_foreign_key,\n    sql,\n    load_extension,\n):\n    \"Transform a table beyond the capabilities of ALTER TABLE\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    types = {}\n    kwargs = {}\n    for column, ctype in type:\n        if ctype.upper() not in VALID_COLUMN_TYPES:\n            raise click.ClickException(\n                \"column types must be one of {}\".format(VALID_COLUMN_TYPES)\n            )\n        types[column] = ctype.upper()\n\n    not_null_dict = {}\n    for column in not_null:\n        not_null_dict[column] = True\n    for column in not_null_false:\n        not_null_dict[column] = False\n\n    default_dict = {}\n    for column, value in default:\n        default_dict[column] = value\n    for column in default_none:\n        default_dict[column] = None\n\n    kwargs[\"types\"] = types\n    kwargs[\"drop\"] = set(drop)\n    kwargs[\"rename\"] = dict(rename)\n    kwargs[\"column_order\"] = column_order or None\n    kwargs[\"not_null\"] = not_null_dict\n    if pk:\n        if len(pk) == 1:\n            kwargs[\"pk\"] = pk[0]\n        else:\n            kwargs[\"pk\"] = pk\n    elif pk_none:\n        kwargs[\"pk\"] = None\n    kwargs[\"defaults\"] = default_dict\n    if drop_foreign_key:\n        kwargs[\"drop_foreign_keys\"] = drop_foreign_key\n\n    if sql:\n        for line in db[table].transform_sql(**kwargs):\n            click.echo(line)\n    else:\n        db[table].transform(**kwargs)\n\n\n@cli.command()\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.argument(\"columns\", nargs=-1, required=True)\n@click.option(\n    \"--table\", \"other_table\", help=\"Name of the other table to extract columns to\"\n)\n@click.option(\"--fk-column\", help=\"Name of the foreign key column to add to the table\")\n@click.option(\n    \"--rename\",\n    type=(str, str),\n    multiple=True,\n    help=\"Rename this column in extracted table\",\n)\n@load_extension_option\ndef extract(\n    path,\n    table,\n    columns,\n    other_table,\n    fk_column,\n    rename,\n    load_extension,\n):\n    \"Extract one or more columns into a separate table\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    kwargs = dict(\n        columns=columns,\n        table=other_table,\n        fk_column=fk_column,\n        rename=dict(rename),\n    )\n    db[table].extract(**kwargs)\n\n\n@cli.command(name=\"insert-files\")\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\")\n@click.argument(\n    \"file_or_dir\",\n    nargs=-1,\n    required=True,\n    type=click.Path(file_okay=True, dir_okay=True, allow_dash=True),\n)\n@click.option(\n    \"-c\",\n    \"--column\",\n    type=str,\n    multiple=True,\n    help=\"Column definitions for the table\",\n)\n@click.option(\"--pk\", type=str, help=\"Column to use as primary key\")\n@click.option(\"--alter\", is_flag=True, help=\"Alter table to add missing columns\")\n@click.option(\"--replace\", is_flag=True, help=\"Replace files with matching primary key\")\n@click.option(\"--upsert\", is_flag=True, help=\"Upsert files with matching primary key\")\n@click.option(\"--name\", type=str, help=\"File name to use\")\n@click.option(\"--text\", is_flag=True, help=\"Store file content as TEXT, not BLOB\")\n@click.option(\n    \"--encoding\",\n    help=\"Character encoding for input, defaults to utf-8\",\n)\n@click.option(\"-s\", \"--silent\", is_flag=True, help=\"Don't show a progress bar\")\n@load_extension_option\ndef insert_files(\n    path,\n    table,\n    file_or_dir,\n    column,\n    pk,\n    alter,\n    replace,\n    upsert,\n    name,\n    text,\n    encoding,\n    silent,\n    load_extension,\n):\n    \"\"\"\n    Insert one or more files using BLOB columns in the specified table\n\n    Example usage:\n\n    \\b\n    sqlite-utils insert-files pics.db images *.gif \\\\\n        -c name:name \\\\\n        -c content:content \\\\\n        -c content_hash:sha256 \\\\\n        -c created:ctime_iso \\\\\n        -c modified:mtime_iso \\\\\n        -c size:size \\\\\n        --pk name\n    \"\"\"\n    if not column:\n        if text:\n            column = [\"path:path\", \"content_text:content_text\", \"size:size\"]\n        else:\n            column = [\"path:path\", \"content:content\", \"size:size\"]\n        if not pk:\n            pk = \"path\"\n\n    def yield_paths_and_relative_paths():\n        for f_or_d in file_or_dir:\n            path = pathlib.Path(f_or_d)\n            if f_or_d == \"-\":\n                yield \"-\", \"-\"\n            elif path.is_dir():\n                for subpath in path.rglob(\"*\"):\n                    if subpath.is_file():\n                        yield subpath, subpath.relative_to(path)\n            elif path.is_file():\n                yield path, path\n\n    # Load all paths so we can show a progress bar\n    paths_and_relative_paths = list(yield_paths_and_relative_paths())\n\n    with progressbar(paths_and_relative_paths, silent=silent) as bar:\n\n        def to_insert():\n            for path, relative_path in bar:\n                row = {}\n                # content_text is special case as it considers 'encoding'\n\n                def _content_text(p):\n                    resolved = p.resolve()\n                    try:\n                        return resolved.read_text(encoding=encoding)\n                    except UnicodeDecodeError as e:\n                        raise UnicodeDecodeErrorForPath(e, resolved)\n\n                lookups = dict(FILE_COLUMNS, content_text=_content_text)\n                if path == \"-\":\n                    stdin_data = sys.stdin.buffer.read()\n                    # We only support a subset of columns for this case\n                    lookups = {\n                        \"name\": lambda p: name or \"-\",\n                        \"path\": lambda p: name or \"-\",\n                        \"content\": lambda p: stdin_data,\n                        \"content_text\": lambda p: stdin_data.decode(\n                            encoding or \"utf-8\"\n                        ),\n                        \"sha256\": lambda p: hashlib.sha256(stdin_data).hexdigest(),\n                        \"md5\": lambda p: hashlib.md5(stdin_data).hexdigest(),\n                        \"size\": lambda p: len(stdin_data),\n                    }\n                for coldef in column:\n                    if \":\" in coldef:\n                        colname, coltype = coldef.rsplit(\":\", 1)\n                    else:\n                        colname, coltype = coldef, coldef\n                    try:\n                        value = lookups[coltype](path)\n                        row[colname] = value\n                    except KeyError:\n                        raise click.ClickException(\n                            \"'{}' is not a valid column definition - options are {}\".format(\n                                coltype, \", \".join(lookups.keys())\n                            )\n                        )\n                    # Special case for --name\n                    if coltype == \"name\" and name:\n                        row[colname] = name\n                yield row\n\n        db = sqlite_utils.Database(path)\n        _load_extensions(db, load_extension)\n        try:\n            with db.conn:\n                db[table].insert_all(\n                    to_insert(), pk=pk, alter=alter, replace=replace, upsert=upsert\n                )\n        except UnicodeDecodeErrorForPath as e:\n            raise click.ClickException(\n                UNICODE_ERROR.format(\n                    \"Could not read file '{}' as text\\n\\n{}\".format(e.path, e.exception)\n                )\n            )\n\n\n@cli.command(name=\"analyze-tables\")\n@click.argument(\n    \"path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False, exists=True),\n    required=True,\n)\n@click.argument(\"tables\", nargs=-1)\n@click.option(\n    \"-c\",\n    \"--column\",\n    \"columns\",\n    type=str,\n    multiple=True,\n    help=\"Specific columns to analyze\",\n)\n@click.option(\"--save\", is_flag=True, help=\"Save results to _analyze_tables table\")\n@load_extension_option\ndef analyze_tables(\n    path,\n    tables,\n    columns,\n    save,\n    load_extension,\n):\n    \"Analyze the columns in one or more tables\"\n    db = sqlite_utils.Database(path)\n    _load_extensions(db, load_extension)\n    _analyze(db, tables, columns, save)\n\n\ndef _analyze(db, tables, columns, save):\n    if not tables:\n        tables = db.table_names()\n    todo = []\n    table_counts = {}\n    for table in tables:\n        table_counts[table] = db[table].count\n        for column in db[table].columns:\n            if not columns or column.name in columns:\n                todo.append((table, column.name))\n    # Now we now how many we need to do\n    for i, (table, column) in enumerate(todo):\n        column_details = db[table].analyze_column(\n            column, total_rows=table_counts[table], value_truncate=80\n        )\n        if save:\n            db[\"_analyze_tables_\"].insert(\n                column_details._asdict(), pk=(\"table\", \"column\"), replace=True\n            )\n        most_common_rendered = _render_common(\n            \"\\n\\n  Most common:\", column_details.most_common\n        )\n        least_common_rendered = _render_common(\n            \"\\n\\n  Least common:\", column_details.least_common\n        )\n        details = (\n            (\n                textwrap.dedent(\n                    \"\"\"\n        {table}.{column}: ({i}/{total})\n\n          Total rows: {total_rows}\n          Null rows: {num_null}\n          Blank rows: {num_blank}\n\n          Distinct values: {num_distinct}{most_common_rendered}{least_common_rendered}\n        \"\"\"\n                )\n                .strip()\n                .format(\n                    i=i + 1,\n                    total=len(todo),\n                    most_common_rendered=most_common_rendered,\n                    least_common_rendered=least_common_rendered,\n                    **column_details._asdict()\n                )\n            )\n            + \"\\n\"\n        )\n        click.echo(details)\n\n\ndef _generate_convert_help():\n    help = textwrap.dedent(\n        \"\"\"\n    Convert columns using Python code you supply. For example:\n\n    \\b\n    $ sqlite-utils convert my.db mytable mycolumn \\\\\n        '\"\\\\n\".join(textwrap.wrap(value, 10))' \\\\\n        --import=textwrap\n\n    \"value\" is a variable with the column value to be converted.\n\n    The following common operations are available as recipe functions:\n    \"\"\"\n    ).strip()\n    recipe_names = [\n        n for n in dir(recipes) if not n.startswith(\"_\") and n not in (\"json\", \"parser\")\n    ]\n    for name in recipe_names:\n        fn = getattr(recipes, name)\n        help += \"\\n\\nr.{}{}\\n\\n  {}\".format(\n            name, str(inspect.signature(fn)), fn.__doc__\n        )\n    help += \"\\n\\n\"\n    help += textwrap.dedent(\n        \"\"\"\n    You can use these recipes like so:\n\n    \\b\n    $ sqlite-utils convert my.db mytable mycolumn \\\\\n        'r.jsonsplit(value, delimiter=\":\")'\n    \"\"\"\n    ).strip()\n    return help\n\n\n@cli.command(help=_generate_convert_help())\n@click.argument(\n    \"db_path\",\n    type=click.Path(file_okay=True, dir_okay=False, allow_dash=False),\n    required=True,\n)\n@click.argument(\"table\", type=str)\n@click.argument(\"columns\", type=str, nargs=-1, required=True)\n@click.argument(\"code\", type=str)\n@click.option(\n    \"--import\", \"imports\", type=str, multiple=True, help=\"Python modules to import\"\n)\n@click.option(\n    \"--dry-run\", is_flag=True, help=\"Show results of running this against first 10 rows\"\n)\n@click.option(\n    \"--multi\", is_flag=True, help=\"Populate columns for keys in returned dictionary\"\n)\n@click.option(\"--where\", help=\"Optional where clause\")\n@click.option(\n    \"-p\",\n    \"--param\",\n    multiple=True,\n    type=(str, str),\n    help=\"Named :parameters for where clause\",\n)\n@click.option(\"--output\", help=\"Optional separate column to populate with the output\")\n@click.option(\n    \"--output-type\",\n    help=\"Column type to use for the output column\",\n    default=\"text\",\n    type=click.Choice([\"integer\", \"float\", \"blob\", \"text\"]),\n)\n@click.option(\"--drop\", is_flag=True, help=\"Drop original column afterwards\")\n@click.option(\"-s\", \"--silent\", is_flag=True, help=\"Don't show a progress bar\")\ndef convert(\n    db_path,\n    table,\n    columns,\n    code,\n    imports,\n    dry_run,\n    multi,\n    where,\n    param,\n    output,\n    output_type,\n    drop,\n    silent,\n):\n    sqlite3.enable_callback_tracebacks(True)\n    db = sqlite_utils.Database(db_path)\n    if output is not None and len(columns) > 1:\n        raise click.ClickException(\"Cannot use --output with more than one column\")\n    if multi and len(columns) > 1:\n        raise click.ClickException(\"Cannot use --multi with more than one column\")\n    if drop and not (output or multi):\n        raise click.ClickException(\"--drop can only be used with --output or --multi\")\n    # If single line and no 'return', add the return\n    if \"\\n\" not in code and not code.strip().startswith(\"return \"):\n        code = \"return {}\".format(code)\n    where_args = dict(param) if param else []\n    # Compile the code into a function body called fn(value)\n    new_code = [\"def fn(value):\"]\n    for line in code.split(\"\\n\"):\n        new_code.append(\"    {}\".format(line))\n    code_o = compile(\"\\n\".join(new_code), \"<string>\", \"exec\")\n    locals = {}\n    globals = {\"r\": recipes, \"recipes\": recipes}\n    for import_ in imports:\n        globals[import_] = __import__(import_)\n    exec(code_o, globals, locals)\n    fn = locals[\"fn\"]\n    if dry_run:\n        # Pull first 20 values for first column and preview them\n        db.conn.create_function(\"preview_transform\", 1, lambda v: fn(v) if v else v)\n        sql = \"\"\"\n            select\n                [{column}] as value,\n                preview_transform([{column}]) as preview\n            from [{table}]{where} limit 10\n        \"\"\".format(\n            column=columns[0],\n            table=table,\n            where=\" where {}\".format(where) if where is not None else \"\",\n        )\n        for row in db.conn.execute(sql, where_args).fetchall():\n            click.echo(str(row[0]))\n            click.echo(\" --- becomes:\")\n            click.echo(str(row[1]))\n            click.echo()\n        count = db[table].count_where(\n            where=where,\n            where_args=where_args,\n        )\n        click.echo(\"Would affect {} row{}\".format(count, \"\" if count == 1 else \"s\"))\n    else:\n        try:\n            db[table].convert(\n                columns,\n                fn,\n                where=where,\n                where_args=where_args,\n                output=output,\n                output_type=output_type,\n                drop=drop,\n                multi=multi,\n                show_progress=not silent,\n            )\n        except BadMultiValues as e:\n            raise click.ClickException(\n                \"When using --multi code must return a Python dictionary - returned: {}\".format(\n                    repr(e.values)\n                )\n            )\n\n\ndef _render_common(title, values):\n    if values is None:\n        return \"\"\n    lines = [title]\n    for value, count in values:\n        lines.append(\"    {}: {}\".format(count, value))\n    return \"\\n\".join(lines)\n\n\nclass UnicodeDecodeErrorForPath(Exception):\n    def __init__(self, exception, path):\n        self.exception = exception\n        self.path = path\n\n\nFILE_COLUMNS = {\n    \"name\": lambda p: p.name,\n    \"path\": lambda p: str(p),\n    \"fullpath\": lambda p: str(p.resolve()),\n    \"sha256\": lambda p: hashlib.sha256(p.resolve().read_bytes()).hexdigest(),\n    \"md5\": lambda p: hashlib.md5(p.resolve().read_bytes()).hexdigest(),\n    \"mode\": lambda p: p.stat().st_mode,\n    \"content\": lambda p: p.resolve().read_bytes(),\n    \"mtime\": lambda p: p.stat().st_mtime,\n    \"ctime\": lambda p: p.stat().st_ctime,\n    \"mtime_int\": lambda p: int(p.stat().st_mtime),\n    \"ctime_int\": lambda p: int(p.stat().st_ctime),\n    \"mtime_iso\": lambda p: datetime.utcfromtimestamp(p.stat().st_mtime).isoformat(),\n    \"ctime_iso\": lambda p: datetime.utcfromtimestamp(p.stat().st_ctime).isoformat(),\n    \"size\": lambda p: p.stat().st_size,\n}\n\n\ndef output_rows(iterator, headers, nl, arrays, json_cols):\n    # We have to iterate two-at-a-time so we can know if we\n    # should output a trailing comma or if we have reached\n    # the last row.\n    current_iter, next_iter = itertools.tee(iterator, 2)\n    next(next_iter, None)\n    first = True\n    for row, next_row in itertools.zip_longest(current_iter, next_iter):\n        is_last = next_row is None\n        data = row\n        if json_cols:\n            # Any value that is a valid JSON string should be treated as JSON\n            data = [maybe_json(value) for value in data]\n        if not arrays:\n            data = dict(zip(headers, data))\n        line = \"{firstchar}{serialized}{maybecomma}{lastchar}\".format(\n            firstchar=(\"[\" if first else \" \") if not nl else \"\",\n            serialized=json.dumps(data, default=json_binary),\n            maybecomma=\",\" if (not nl and not is_last) else \"\",\n            lastchar=\"]\" if (is_last and not nl) else \"\",\n        )\n        yield line\n        first = False\n\n\ndef maybe_json(value):\n    if not isinstance(value, str):\n        return value\n    stripped = value.strip()\n    if not (stripped.startswith(\"{\") or stripped.startswith(\"[\")):\n        return value\n    try:\n        return json.loads(stripped)\n    except ValueError:\n        return value\n\n\ndef json_binary(value):\n    if isinstance(value, bytes):\n        return {\"$base64\": True, \"encoded\": base64.b64encode(value).decode(\"latin-1\")}\n    else:\n        raise TypeError\n\n\ndef _load_extensions(db, load_extension):\n    if load_extension:\n        db.conn.enable_load_extension(True)\n        for ext in load_extension:\n            if ext == \"spatialite\" and not os.path.exists(ext):\n                ext = find_spatialite()\n            db.conn.load_extension(ext)\n","sub_path":"scenarios/vulnerable_lambda/terraform/lambda_source_code/policy_applier_lambda1_src/sqlite_utils/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":64081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"181940717","text":"\"\"\"\nxrit-demux.py\nhttps://github.com/sam210723/COMS-1\n\nFrontend for CCSDS demultiplexer\n\"\"\"\n\nfrom argparse import ArgumentParser\nfrom configparser import ConfigParser\nfrom demuxer import Demuxer\nfrom os import mkdir, path\nimport socket\nfrom time import time\n\n# Globals\nargs = None\nconfig = None\nstime = None\nsource = None\ndownlink = None\noutput = None\npacketf = None\nsck = None\nbuflen = 892\ndemux = None\n\ndef init():\n    print(\"┌───────────────────────────────────┐\")\n    print(\"│        COMS-1 xRIT Demuxer        │\")\n    print(\"│    github.com/sam210723/COMS-1    │\")\n    print(\"└───────────────────────────────────┘\\n\")\n    \n    global args\n    global config\n    global stime\n    global demux\n\n    # Handle arguments and config file\n    args = parse_args()\n    config = parse_config(args.config)\n    print_config()\n\n    # Configure directories and input source\n    dirs()\n    config_input()\n\n    # Create demuxer instance\n    demux = Demuxer(downlink)\n\n    # Check demuxer thread is ready\n    if not demux.coreReady:\n        print(\"DEMUXER CORE THREAD FAILED TO START\\nExiting...\")\n        exit()\n\n    print(\"──────────────────────────────────────────────────────────────────────────────────\\n\")\n\n    # Get processing start time\n    stime = time()\n\n    # Enter main loop\n    loop()\n\n\ndef loop():\n    \"\"\"\n    Handles data from the selected input source\n    \"\"\"\n    global demux\n    global source\n    global sck\n    global buflen\n\n    while True:\n        if source == \"OSP\":\n            data = sck.recv(buflen)\n            demux.push(data)\n        \n        elif source == \"GOESRECV\":\n            data = sck.recv(buflen + 8)\n\n            if len(data) == buflen + 8:\n                demux.push(data[8:])\n\n        elif source == \"FILE\":\n            global packetf\n            global stime\n\n            if not packetf.closed:\n                # Read VCDU from file\n                data = packetf.read(buflen)\n\n                # No more data to read from file\n                if data == b'':\n                    print(\"INPUT FILE LOADED\")\n                    packetf.close()\n                    continue\n                \n                # Push VCDU to demuxer\n                demux.push(data)\n            else:\n                # Demuxer has all VCDUs from file, wait for processing\n                if demux.complete():\n                    runTime = round(time() - stime, 3)\n                    print(\"FINISHED PROCESSING FILE ({}s)\\nExiting...\".format(runTime))\n                    \n                    # Stop core thread\n                    demux.stop()\n                    exit()\n\n\ndef config_input():\n    \"\"\"\n    Configures the selected input source\n    \"\"\"\n\n    global source\n    global sck\n\n    if source == \"OSP\":\n        sck = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n        ip = config.get('osp', 'ip')\n        port = int(config.get('osp', 'vchan'))\n        addr = (ip, port)\n\n        print(\"Connecting to Open Satellite Project ({})...\".format(ip), end='')\n        connect_socket(addr)\n\n    elif source == \"GOESRECV\":\n        sck = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n        ip = config.get('goesrecv', 'ip')\n        port = int(config.get('goesrecv', 'vchan'))\n        addr = (ip, port)\n\n        print(\"Connecting to goesrecv ({})...\".format(ip), end='')\n        connect_socket(addr)\n        nanomsg_init()\n\n    elif source == \"FILE\":\n        global packetf\n        packetf = open(args.file, 'rb')\n        print(\"Opened file: \\\"{}\\\"\".format(args.file))\n\n    else:\n        print(\"UNKNOWN INPUT MODE: \\\"{}\\\"\".format(source))\n        print(\"Exiting...\")\n        exit()\n\n\ndef connect_socket(addr):\n    \"\"\"\n    Connects TCP socket to address and handle exceptions\n    \"\"\"\n\n    try:\n        sck.connect(addr)\n        print(\"CONNECTED\")\n    except socket.error as e:\n        if e.errno == 10061:\n            print(\"CONNECTION REFUSED\")\n        else:\n            print(e)\n    \n        print(\"\\nExiting...\")\n        exit()\n\n\ndef nanomsg_init():\n    \"\"\"\n    Sets up nanomsg publisher in goesrecv to send VCDUs over TCP\n    \"\"\"\n\n    global sck\n\n    sck.send(b'\\x00\\x53\\x50\\x00\\x00\\x21\\x00\\x00')\n    nmres = sck.recv(8)\n\n    # Check nanomsg response\n    if nmres != b'\\x00\\x53\\x50\\x00\\x00\\x20\\x00\\x00':\n        print(\"  ERROR CONFIGURING NANOMSG (BAD RESPONSE)\\n  Exiting...\\n\")\n        exit()\n\n\ndef dirs():\n    \"\"\"\n    Configures directories for demuxed files\n    \"\"\"\n\n    paths = [\n        output,\n        output + \"/LRIT\",\n        output + \"/LRIT/IMG\",\n        output + \"/LRIT/IMG/FD\",\n        output + \"/LRIT/IMG/ENH\",\n        output + \"/LRIT/IMG/LSH\",\n        output + \"/LRIT/ADD\",\n        output + \"/LRIT/ADD/ANT\",\n        output + \"/LRIT/ADD/GOCI\",\n        output + \"/LRIT/ADD/NWP\",\n        output + \"/LRIT/ADD/TYP\",\n    ]\n\n    print()\n    # Loop through paths in list\n    for p in paths:\n        absp = path.abspath(p)\n        \n        # Create new directory if it doesn't exist already\n        if not path.isdir(absp):\n            try:\n                mkdir(absp)\n                print(\"Created: {}\".format(p))\n            except OSError as e:\n                print(\"Error creating output folders\\n{}\\n\\nExiting...\".format(e))\n                exit()\n\n\ndef parse_args():\n    \"\"\"\n    Parses command line arguments\n    \"\"\"\n\n    argp = ArgumentParser()\n    argp.description = \"Frontend for CCSDS demultiplexer\"\n    argp.add_argument(\"--config\", action=\"store\", help=\"Configuration file path (.ini)\", default=\"xrit-demux.ini\")\n    argp.add_argument(\"--file\", action=\"store\", help=\"Path to VCDU packet file\", default=None)\n    \n    return argp.parse_args()\n\n\ndef parse_config(path):\n    \"\"\"\n    Parses configuration file\n    \"\"\"\n\n    global source\n    global downlink\n    global output\n\n    cfgp = ConfigParser()\n    cfgp.read(path)\n\n    if args.file == None:\n        source = cfgp.get('demuxer', 'input').upper()\n    else:\n        source = \"FILE\"\n    \n    downlink = cfgp.get('demuxer', 'mode').upper()\n    output = cfgp.get('demuxer', 'output')\n\n    return cfgp\n\n\ndef print_config():\n    \"\"\"\n    Prints configuration information\n    \"\"\"\n\n    global downlink\n    global output\n\n    print(\"SPACECRAFT:       COMS-1\")\n\n    if downlink == \"LRIT\":\n        rate = \"64 kbps\"\n    elif downlink == \"HRIT\":\n        rate = \"3 Mbps\"\n    print(\"DOWNLINK:         {} ({})\".format(downlink, rate))\n\n    if source == \"OSP\":\n        s = \"Open Satellite Project (github.com/opensatelliteproject/xritdemod)\"\n    elif source == \"GOESRECV\":\n        s = \"goesrecv (github.com/pietern/goestools)\"\n    elif source == \"FILE\":\n        s = \"File ({})\".format(args.file)\n    else:\n        s = \"UNKNOWN\"\n\n    print(\"INPUT SOURCE:     {}\".format(s))\n    \n    absp = path.abspath(output)\n    absp = absp[0].upper() + absp[1:]  # Fix lowercase drive letter\n    print(\"OUTPUT PATH:      {}\".format(absp))\n\n\ntry:\n    init()\nexcept KeyboardInterrupt:\n    demux.stop()\n    print(\"Exiting...\")\n    exit()\n","sub_path":"demux/xrit-demux.py","file_name":"xrit-demux.py","file_ext":"py","file_size_in_byte":7212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"353769056","text":"'''\nGiven the root of a binary search tree, rearrange the tree in in-order so that the leftmost node in the tree is now the root of the tree, and every node has no left child and only one right child.\n\n \n\nExample 1:\n\n\nInput: root = [5,3,6,2,4,null,8,1,null,null,null,7,9]\nOutput: [1,null,2,null,3,null,4,null,5,null,6,null,7,null,8,null,9]\nExample 2:\n\n\nInput: root = [5,1,7]\nOutput: [1,null,5,null,7]\n \n\nConstraints:\n\nThe number of nodes in the given tree will be in the range [1, 100].\n0 <= Node.val <= 1000\n'''\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def increasingBST(self, root: TreeNode) -> TreeNode:\n        def inorder(node):\n            if node:\n                yield from inorder(node.left)\n                yield node.val\n                yield from inorder(node.right)\n        \n        ans = curr = TreeNode(None)\n        for v in inorder(root):\n            curr.right = TreeNode(v)\n            curr = curr.right\n        \n        return ans.right\n        \n","sub_path":"problems/897_increasing_order_search_tree.py","file_name":"897_increasing_order_search_tree.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"223907970","text":"# pylint: disable=W0401, W0614, R0913\n\"\"\"functions for interacting with customer database\"\"\"\nfrom peewee import *\nfrom customer_model import *\n\n\ndef add_customer(cust_id, firstname, lastname, address, phone, email, is_active, credit):\n    \"\"\"adds a new customer to the database\"\"\"\n    new_customer = Customer.create(customer_id=cust_id,\n                                   name=firstname,\n                                   last_name=lastname,\n                                   home_address=address,\n                                   phone_number=phone,\n                                   email_address=email,\n                                   status=is_active,\n                                   credit_limit=credit)\n    new_customer.save()\n\ndef search_customer(cust_id):\n    \"\"\"searchs for a customer and returns either customer details\n       or, if the customer doesn't exist, a blank dictionary\"\"\"\n    try:\n        query = Customer.get(Customer.customer_id == cust_id)\n        result = {'name' : query.name, 'last name' : query.last_name,\n                  'email address' : query.email_address,\n                  'phone number' : query.phone_number}\n    except DoesNotExist:\n        result = {}\n    return result\n\ndef delete_customer(cust_id):\n    \"\"\"deletes a customer\"\"\"\n    try:\n        query = Customer.get(Customer.customer_id == cust_id)\n        query.delete_instance()\n    except DoesNotExist:\n        raise ValueError\n\ndef update_customer_credit(cust_id, new_credit_limit):\n    \"\"\"updates credit limit of customer\"\"\"\n    try:\n        query = Customer.get(Customer.customer_id == cust_id)\n        query.credit_limit = new_credit_limit\n        query.save()\n    except DoesNotExist:\n        raise ValueError\n\ndef list_active_customers():\n    \"\"\"returns the number of customers whose status equals True\"\"\"\n    number_active_customers = Customer.select().where(Customer.status).count()\n    return number_active_customers\n\nif __name__ == \"__main__\":\n    DB.init('customers.db')\n    DB.create_tables([Customer])\n","sub_path":"students/chelseamsmith/Lesson03/basic_operations.py","file_name":"basic_operations.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"461188999","text":"#!/usr/bin/env python\n# coding: utf8\n\n\"\"\"\n    Entrypoint provider for tcp server.\n\n    USAGE: python -m spleeter.server\n\"\"\"\n\nimport signal\nfrom .separator import Separator\n\nfrom .ladspa_server import LADSPA_TCPServer\n\nimport numpy as np\n\n__email__ = 'mohamed@elawadi.net'\n__author__ = 'Mohamed Elawadi'\n__license__ = 'MIT License'\n\nshould_process = True\n\ndef handler(signum, frame):\n    global should_process\n    should_process = not should_process\n    if should_process:\n        print(\"converting\")\n    else:\n        print(\"not converting\")\n\n\nsignal.signal(signal.SIGUSR1, handler)\n\nsep = Separator(\n    'spleeter:2stem-finetune-realtime',\n    MWF=False,\n    stft_backend='tensorflow',\n    multiprocess=False)\n\nclass Spleeter_Server(LADSPA_TCPServer):\n    def process(self, channel, sample_rate, data):\n        if np.max(data) == np.min(data) == 0:\n            return data\n        if should_process:\n            return sep.separate(data.astype('float64').reshape((-1, 1)))['vocals'].astype('float32')[:,0]\n        else:\n            return data\n\n\nprint(\"warming up\")\nsep.separate(np.zeros((1024, 2)))\nprint(\"serving on :8083\")\nSpleeter_Server.serve_forever(8083)\n","sub_path":"spleeter/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"418774273","text":"# import the necessary packages\nimport argparse\n\n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-n\", \"--name\", required=True,\n\thelp=\"name of the user\")\n\n#args = vars(ap.parse_args())\n#ap = argparse.ArgumentParser()\n\nap.add_argument(\"-i\", \"--idade\", required= True,\n    help=\"age of the user\")\n\nargs = vars(ap.parse_args())\n\n\nprint(args)\n# display a friendly message to the user\nprint(\"Hi there {}, it's nice to meet you!\".format(args[\"name\"]))\n\nprint(\"Seu nome é {0} e sua idade é {1}\".format(args[\"name\"],args[\"idade\"]) )\n\n\n\n","sub_path":"simple-example.py","file_name":"simple-example.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"509786586","text":"import pandas as pd\nimport numpy as np\nimport pickle\n\nfrom preprocessing.preprocess import split\nfrom utils.save_to_csv import save_data_to_csv\nfrom preprocessing.preprocess import DataPreprocessor \nfrom preprocessing.transformations import fix_label_type \n\nfrom sklearn.model_selection import GridSearchCV, cross_val_score\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import svm\nfrom sklearn.multioutput import MultiOutputClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.multiclass import OneVsRestClassifier\n\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import precision_score\n\npreprocess = pickle.load(open('dumps/preprocessor.pkl', 'rb'))\n\n\n##################################################\n## Load train dataset\n\ndf_train = pd.read_csv('results/train_after.csv')\nX_train, y_train = df_train.drop(labels=['TestResultsCode'], axis=1), df_train[['TestResultsCode']]\ny_train = fix_label_type(y_train)\n\nX_train = X_train.drop(labels=['PatientID'], axis=1)\n\n##################################################\n## Training various models\n\n## We use grid search to optimize our parameters\ndef grid_search(model, X, y, param_grid):\n    clf = GridSearchCV(estimator=model, param_grid=param_grid,\n                    n_jobs=-1)\n    clf.fit(X, y)\n    return clf \n\nprint('### Training ###')\nmodels = [('SVC', svm.SVC(), dict(C= np.logspace(-10, 10, 10), kernel=['rbf','poly'])),\n          ('KNN', KNeighborsClassifier(), dict(n_neighbors=np.linspace(2,10,9, dtype=int))),\n          ('RandomForest', RandomForestClassifier(max_depth=10, random_state=0), dict(max_depth=np.linspace(2,16,15))),\n          ('LogisticRegression', LogisticRegression(random_state=0), dict()),\n          ('PolynomialLogisticRegression',  Pipeline([('poly', PolynomialFeatures(degree=2)),\n                  ('linear', LogisticRegression())]), dict())]\n\nmodels_per_column = dict()\n\nfor column in y_train.columns:\n    print(f'# Fitting for column {column}')\n    fitted_models = []\n    for name, model, param_grid in models:\n        print(f'## Fitting model {name}')\n        clf = grid_search(model, X_train, y_train[column], param_grid)\n        fitted_models.append((name, clf))\n    models_per_column[column] = fitted_models\n\n##################################################\n## Choosing best model using validation dataset\n\n\ndf_val = pd.read_csv('results/val_after.csv')\nX_val, y_val = df_val.drop(labels=['TestResultsCode'], axis=1), df_val[['TestResultsCode']]\ny_val = fix_label_type(y_val)\nX_val = X_val.drop(labels=['PatientID'], axis=1)\n\n\n## Each target column has a different wanted score (with it's params)\ncolumn_score_map = {'Virus' : (precision_score, {'average':'weighted'}), \n    'Spreader': (recall_score, {'average': \"binary\", 'pos_label': \"Spreader\"}), \n    'AtRisk': (recall_score, {'average': \"binary\", 'pos_label': \"atRisk\"})}\n\nbest_models = dict()\n\nfor column in ['Virus', 'Spreader', 'AtRisk']:\n    fitted_models = models_per_column[column]\n    best_model, best_score = None, -1\n    for name, model in fitted_models:\n        y_hat = model.predict(X_val)\n        score_function, params =  column_score_map[column]\n        score = score_function(y_val[column], y_hat, **params)\n        if score > best_score:\n            best_model, best_score = model, score\n    \n    best_models[column] = (best_model, best_score)\n    \n\n##################################################\n## Testing our model on the dataset\n\ndf_test = pd.read_csv('results/test_after.csv')\nX_test, y_test = df_test.drop(labels=['TestResultsCode'], axis=1), df_test[['TestResultsCode']]\ny_test = fix_label_type(y_test)\nX_test = X_test.drop(labels=['PatientID'], axis=1)\n\nprint('### Testing Scores ###')\n\nprint('#########################################')\nprint(f'# checking for column Virus')\ncolumn = 'Virus'\nmodel, _  = best_models['Virus']\ny_hat = model.predict(X_test)\naccuracy = accuracy_score(y_test[column], y_hat)\nprint(f'accuracy: {accuracy}')\npercision = precision_score(y_test[column],y_hat, average='weighted')\nprint(f'percision: {percision}')\nrecall = recall_score(y_test[column],y_hat, average='macro')\nprint(f'recall score: {recall}')\nf1 = f1_score(y_test[column],y_hat, average='macro')\nprint(f'f1 score: {f1}')\n\nprint('#########################################')\nprint(f'# checking for column Spreader')\ncolumn = 'Spreader'\nmodel,_ = best_models['Spreader']\ny_hat = model.predict(X_test)\naccuracy = accuracy_score(y_test[column], y_hat)\nprint(f'accuracy: {accuracy}')\npercision = precision_score(y_test[column],y_hat, average=\"binary\", pos_label=\"Spreader\")\nprint(f'percision: {percision}')\nrecall = recall_score(y_test[column],y_hat, average=\"binary\", pos_label=\"Spreader\")\nprint(f'recall score: {recall} (PREFERED)')\nf1 = f1_score(y_test[column],y_hat, average=\"binary\", pos_label=\"Spreader\")\nprint(f'f1 score: {f1}')\n\n\nprint('#########################################')\nprint(f'# checking for column AtRisk')\ncolumn = 'AtRisk'\nmodel, _ = best_models['AtRisk']\n\ny_hat = model.predict(X_test)\naccuracy = accuracy_score(y_test[column], y_hat)\nprint(f'accuracy: {accuracy}')\npercision = precision_score(y_test[column],y_hat , average=\"binary\", pos_label=\"atRisk\")\nprint(f'percision: {percision}')\nrecall = recall_score(y_test[column],y_hat, average=\"binary\", pos_label=\"atRisk\")\nprint(f'recall score: {recall} (PREFERED)')\nf1 = f1_score(y_test[column],y_hat, average=\"binary\", pos_label=\"atRisk\")\nprint(f'f1 score: {f1}')\n\n\n##################################################\n## Predicting unkown values\n\ndf_unknown = pd.read_csv('data/virus_hw3_unlabeled.csv')\n\nX_unknown = df_unknown.drop(labels=['TestResultsCode'], axis=1) \nX_unknown = preprocess.transform(X_unknown)\n\n\npatientIds = X_unknown['PatientID']\nX_unknown = X_unknown.drop(labels=['PatientID'], axis=1)\n\n\ny_pred = pd.DataFrame()\ny_pred['Virus'] = pd.Series(best_models['Virus'][0].predict(X_unknown))\ny_pred['Spreader'] = pd.Series(best_models['Spreader'][0].predict(X_unknown))\ny_pred['AtRisk'] = pd.Series(best_models['AtRisk'][0].predict(X_unknown))\n\ny_pred['TestResultsCode'] = y_pred[['Virus', 'Spreader', 'AtRisk']].agg('_'.join, axis=1)\n\nResult =  pd.concat([patientIds, y_pred['TestResultsCode']], axis=1)\n\nResult.to_csv('results/predicted.csv', index=False)\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"371854060","text":"# Julia Suter, 2020\n# Project: Employing the Scene Graph for Phrase Grounding\n# Master Thesis, University of Heidelberg\n\n# Scene_Graph.py\n# ----------------\n# Classes/data management for scene graphs, object nodes \n# and phrase entities and concepts and their attributes.\n\n\n# Imports\nimport re\nimport os\nfrom shutil import copyfile\nimport collections\n\nimport networkx as nx\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport matplotlib.font_manager as fm\n\nfrom PIL import Image, ImageDraw, ImageFont\nimport xml.etree.ElementTree as ET\n\n# Import Scene Graph modules\nimport config as cfg\nimport util.graph_functions as graph_funct\nimport box_geometry as box\n\n\n\n# Scene graph labels (150) divided into Flickr30k categories\n\nbodypart_set = ['hair','face','hand','ear','head','eye','mouth','arm','finger',\n\t\t\t\t'foot','head','leg','neck','nose','paw']\nclothes_set = ['shirt','pant','hat','jacket','shoe','jean','glove','short',\n\t\t\t   'cap','coat','helmet','sneaker','sock']\nperson_nodes = ['woman','man','girl','boy','person','child','kid','lady','guy']\nanimal_nodes = ['animal','dog','cat','zebra','bear','bird','giraffe','elephant',\n\t\t\t\t'horse','sheep','cow']\nvehicle_nodes = ['airplane','bike','boat','car','truck','bus','motorcycle',\n\t\t\t\t 'plane','truck','vehicle']\n\n\n# Scene graph relations\nrelations = [\"on\", \"has\", \"near\", \"behind\", \"of\", \"wearing\", \"in\", \"made of\",\n\t\t\t\"holding\", \"with\", \"under\", \"in front of\", \"sitting on\", \"at\",\n\t\t\t\"above\", \"watching\", \"walking on\", \"riding\", \"standing on\",\n\t\t\t\"over\",\"attached to\", \"carrying\", \"covered in\", \"looking at\",\n\t\t\t\"for\", \"hanging from\", \"eating\", \"laying on\", \"and\", \"wears\",\n\t\t\t\"belonging to\", \"using\", \"between\", \"along\", \"covering\", \"parked on\",\n\t\t\t\"part of\", \"on back of\", \"against\", \"lying on\", \"painted on\",\n\t\t\t\"to\", \"from\"]\n\n\n\ndef get_ent_category(cat):\n\t\"\"\"Get Flickr30k category for Scene Graph label.\"\"\"\n\n\tif cat in bodypart_set:\n\t\treturn 'bodyparts'\n\telif cat in clothes_set:\n\t\treturn 'clothing'\n\telif cat in person_nodes:\n\t\treturn 'people'\n\telif cat in animal_nodes:\n\t\treturn 'animals'\n\telif cat in vehicle_nodes:\n\t\treturn 'vehicles'\n\telse:\n\t\treturn 'other'\n\n\nclass Node:\n\t\"\"\"Class for Scene Graph Nodes\"\"\"\n\n\tdef __init__(self, label, coordinates, center, rank, SceneGraph):\n\n\t\t# Get label, category and ID\n\t\tself.label = label\n\t\tself.category, self.ID = label.split('-')\n\n\t\t# Flickr30k Entity category corresponding to label\n\t\tself.ent_category = get_ent_category(self.category)\n\n\t\t# Rank by ID (not used)\n\t\tself.rank = int(rank)\n\t\tif self.rank == 'pl':\n\t\t\trank = 10000\n\n\t\t# Get coordinates and area size\n\t\tself.coordinates = coordinates\n\t\tself.center = center\n\t\tself.xmin, self.ymin, self.xmax, self.ymax = coordinates\n\t\tself.area = graph_funct.get_box_size(self.coordinates)\n\n\t\t# Initialize neighbors and edges dicts/lists\n\t\tself.neighbors = []\n\t\tself.out_edges = {}\n\t\tself.in_edges = {}\n\t\tself.node_triples = []\n\n\t\t# For each triple\n\t\tfor n1, rel, n2 in SceneGraph.all_triples:\n\n\t\t\tif n1 == label or n2 == label:\n\t\t\t\tself.node_triples.append((n1, rel, n2))\n\n\t\t\t# If n1 is current node, save as out edge\n\t\t\tif n1 == label:\n\t\t\t\tif rel in self.out_edges.keys():\n\t\t\t\t\tself.out_edges[rel].append(n2)\n\t\t\t\telse:\n\t\t\t\t\tself.out_edges[rel] = [n2]\n\t\t\t\tself.neighbors.append(n2)\n\n\t\t\t# If n2 is current node, save as in edge\n\t\t\telif n2 == label:\n\t\t\t\tif rel in self.in_edges.keys():\n\t\t\t\t\tself.in_edges[rel].append(n1)\n\t\t\t\telse:\n\t\t\t\t\tself.in_edges[rel] = [n1]\n\t\t\t\tself.neighbors.append(n1)\n\n\n\t\t# Set bodypart and clothes lists\n\t\tbodypart_set = ['hair','face','hand','ear','head','eye','mouth','arm','finger','foot','head','leg','neck','nose']\n\t\tclothes_set = ['shirt','pants','hat','jacket','shoe','jeans','glove','shorts','cap','coat','helmet','sneaker','sock','trunks','trousers']\n\n\t\tself.bodyparts = []\n\t\tself.clothes = []\n\n\t\t# If node is person node, save \"has\" and \"wearing\" relations and nodes\n\t\tif self.category in ['woman','man','girl','boy','person','child','kid','lady','guy']:\n\n\t\t\tif 'has' in self.out_edges.keys():\n\t\t\t\tself.bodyparts = sorted([n for n in self.out_edges['has'] if n.split('-')[0] in bodypart_set])\n\t\t\tif 'wearing' in self.out_edges.keys():\n\t\t\t\tself.clothes = sorted([n for n in self.out_edges['wearing'] if n.split('-')[0] in clothes_set])\n\n\tdef __str__(self):\n\t\t\"\"\"Print label.\"\"\"\n\t\treturn self.label\n\nclass SceneGraph:\n\t\"\"\"Class for Scene Graph.\"\"\"\n\n\tdef __init__(self, file_ID, is_flickr=True, flickr_version='all', sg_data_dir=None):\n\n\t\tself.file_ID = file_ID\n\n\t\t# Set paths (if Flickr data)\n\t\tif is_flickr:\n\t\t\tself.path = cfg.SCENE_GRAPH_FLICKR_DIR + \"/qualitative_sg_out_flickr_\" + flickr_version + \"/\"\n\t\t\tself.bboxes_file = self.path + self.file_ID + '_bboxes.txt'\n\t\t\tself.res_file = self.path + self.file_ID + '_res.txt'\n\t\t\tself.original_image = cfg.FLICKR_IMAGES + file_ID + '.jpg'\n\n\t\t# Set directories by arguments\n\t\tif sg_data_dir:\n\t\t\tself.bboxes_file = sg_data_dir + self.file_ID + '_bboxes.txt'\n\t\t\tself.res_file = sg_data_dir + self.file_ID + '_res.txt'\n\n\t\t# Initialize\n\t\tself.all_triples = []\n\t\tself.all_nodes = []\n\t\tself.all_edges = []\n\t\tself.all_edge_labels = {}\n\t\tself.all_relations = []\n\n\t\tself.node_centers = {}\n\t\tself.node_coordinates = []\n\n\t\tself.label_dict = {}\n\n\t\tself.nodes_dict = {}\n\t\tself.nodes = []\n\t\tself.triples = []\n\n\n\tdef get_triples(self):\n\t\t\"\"\"Get all triples for Scene Graph/image.\"\"\"\n\n\t\t# Open file\n\t\twith open(self.res_file) as infile:\n\t\t\tdata = infile.read()\n\n\t\t# Prepare data\n\t\tdata = data.strip()\n\t\tdata = data.split('\\n')\n\n\t\t# For each line, get nodes and relation\n\t\tfor i, line in enumerate(data):\n\t\t\tif line.strip() == '':\n\t\t\t\tcontinue\n\n\t\t\t# Get nodes and relation\n\t\t\telements = line.split(' - ')\n\t\t\tnode_1 = elements[0].strip()\n\t\t\trelation = elements[1].strip()\n\t\t\tnode_2 = elements[2].strip()\n\n\t\t\t# Save nodes\n\t\t\tself.all_nodes.append(node_1)\n\t\t\tself.all_nodes.append(node_2)\n\n\t\t\t# Save edge and edge label (relation)\n\t\t\tself.all_edges.append((node_1, node_2))\n\t\t\tself.all_edge_labels[(node_1, node_2)] = relation\n\t\t\tself.all_relations.append(relation)\n\n\t\t\t# Save triples\n\t\t\tself.all_triples.append((node_1, relation, node_2))\n\n\t\t# Set nodes\n\t\tself.all_nodes = list(set(self.all_nodes))\n\n\t\t# Get node types = labels\n\t\tself.node_types = [node.split('-')[0] for node in self.all_nodes]\n\n\tdef get_bboxes(self):\n\t\t\"\"\"Get all bounding boxes for Scene Graph/image.\"\"\"\n\n\t\tnode_coordinates = []\n\n\t\t# Open corresponding bounding box file by ID\n\t\twith open(self.bboxes_file) as infile:\n\n\t\t\t# Prepare data\n\t\t\tdata = infile.read().strip()\n\t\t\tnode_coordinates = data.split('\\n')\n\t\t\tnode_coordinates = [n for n in node_coordinates if n.strip() != '']\n\n\t\t\t# For each node\n\t\t\tfor node in node_coordinates:\n\n\t\t\t\t# Save and clean name and coordinates\n\t\t\t\tnode_name, coordinates = node.split(';')\n\t\t\t\tnode_name = node_name.strip()\n\t\t\t\tcoordinates = coordinates.strip()[1:-1]\n\n\t\t\t\t# Get boundary coordinates as floats\n\t\t\t\txmin, ymin, xmax, ymax = coordinates.split(', ')\n\t\t\t\txmin = float(xmin)\n\t\t\t\txmax = float(xmax)\n\t\t\t\tymin = float(ymin)\n\t\t\t\tymax = float(ymax)\n\n\t\t\t\tcoordinates = (xmin, ymin, xmax, ymax)\n\n\t\t\t\t# Compute center position for x and y\n\t\t\t\tx_position = xmin + (xmax - xmin)/2\n\t\t\t\ty_position = ymin + (ymax - ymin)/2\n\n\t\t\t\t# Get center\n\t\t\t\tcenter = (x_position, y_position)\n\n\t\t\t\t# Save node center and coordinates\n\t\t\t\tself.node_centers[node_name] = center\n\t\t\t\tself.node_coordinates.append((node_name, coordinates, center))\n\n\tdef get_nodes(self):\n\t\t\"\"\"Get all nodes and their information for Scene Graph/image.\"\"\"\n\n\t\t# Get coordinates\n\t\tif not self.node_coordinates:\n\t\t\tself.get_bboxes()\n\n\t\t# Get triples\n\t\tif not self.all_triples:\n\t\t\tself.get_triples()\n\n\t\tprocessed_nodes = []\n\n\t\t# Process each node\n\t\tfor i, (node_name, coordinates, center) in enumerate(self.node_coordinates):\n\n\t\t\t# Skip if already processed\n\t\t\tif node_name in processed_nodes:\n\t\t\t\tcontinue\n\n\t\t\tprocessed_nodes.append(node_name)\n\n\t\t\t# Get node as Node class\n\t\t\tnew_node = Node(node_name, coordinates, center, i, self)\n\n\t\t\t# Save Node\n\t\t\tself.nodes.append(new_node)\n\t\t\tself.nodes_dict[node_name] = new_node\n\n\n\t\t# Save all triples\n\t\tfor n1, rel, n2 in self.all_triples:\n\t\t\ttry:\n\t\t\t\tself.triples = (self.nodes_dict[n1], rel, self.nodes_dict[n2])\n\t\t\texcept KeyError:\n\t\t\t\tcontinue\n\n\n\tdef get_label_dict(self):\n\t\t\"\"\"Get category/label dict for Scene Graph/image.\"\"\"\n\n\t\t# Set node sets\n\t\tperson_nodes = ['woman','man','girl','boy','person','child','kid','lady','guy']\n\n\t\tsimilar_object_nodes = ['truck','car','motorcycle','bike','book','paper','glass','cup',\n\t\t\t\t\t\t\t\t'table','desk','stand']\n\n\t\t# Get nodes\n\t\tif not self.nodes:\n\t\t\tself.get_nodes()\n\n\t\t# Initialize label dict\n\t\tself.label_dict = {}\n\t\tself.label_dict['person_'] = []\n\t\tself.label_dict['car_'] = []\n\t\tself.label_dict['bike_'] = []\n\t\tself.label_dict['book_'] = []\n\t\tself.label_dict['glass_'] = []\n\t\tself.label_dict['table_'] = []\n\n\t\t# For each node, check whether they are part of label set\n\t\tfor node in self.nodes:\n\n\t\t\t# Save person nodes with special person_ key\n\t\t\tif node.category in person_nodes:\n\t\t\t\tself.label_dict['person_'].append(node)\n\n\t\t\tif node.category in ['truck','car']:\n\t\t\t\tself.label_dict['car_'].append(node)\n\n\t\t\tif node.category in ['motorcycle','bike']:\n\t\t\t\tself.label_dict['bike_'].append(node)\n\n\t\t\tif node.category in ['book','paper']:\n\t\t\t\tself.label_dict['book_'].append(node)\n\n\t\t\tif node.category in ['glass','cup']:\n\t\t\t\tself.label_dict['glass_'].append(node)\n\n\t\t\tif node.category in ['table','desk','stand']:\n\t\t\t\tself.label_dict['table_'].append(node)\n\n\t\t\t# Save node and label in dict\n\t\t\tif node.category in self.label_dict.keys():\n\t\t\t\tself.label_dict[node.category].append(node)\n\t\t\telse:\n\t\t\t\tself.label_dict[node.category] = [node]\n\n\t\treturn self.label_dict\n\n\n\tdef get_image_ratio(self):\n\t\t\"\"\"Get image ratio.\"\"\"\n\n\t\t# Get original image data\n\t\timage_file = self.original_image\n\t\timg = mpimg.imread(image_file,0)\n\n\t\t# Compute ratio of original image to downsampled image\n\t\tmax_axis = max(img.shape[:2])\n\t\tself.ratio = max_axis/1024\n\t\tself.area = img.shape[0]*img.shape[1]\n\t\treturn self.ratio, self.area\n\n\n\tdef overlay_on_image(self, image_file_path, outpath='', format=\"jpg\", save_original=True):\n\t\t\"\"\"Draw Scene Graph and lay over original image.\"\"\"\n\n\t\t# Set output filename\n\t\tfilename = 'SG_' + self.file_ID + '.png'\n\n\t\t# Get original image path\n\t\timage_file = image_file_path + self.file_ID + \".\"+format\n\n\t\tif save_original:\n\t\t\t# Copy original file in output folder for comparison\n\t\t\tcopyfile(image_file, outpath+'SG_'+self.file_ID+'_original.jpg')\n\n\n\t\t# Plot original image\n\t\timg = mpimg.imread(image_file,0)\n\t\t# plt.imshow(img)\n\t\tax = plt.gca()\n\n\t\t# Compute ratio of original image to downsampled image (1024, 768, 3)\n\t\tmax_axis = max(img.shape[:2])\n\t\tself.ratio = max_axis/1024\n\n\t\t# Create Graph\n\t\tG=nx.Graph()\n\n\t\t# Add edges\n\t\tG.add_edges_from(self.all_edges)\n\n\t\t# Apply ratio to node positions in order to adjust to image\n\t\tself.node_centers = {node:(pos[0]*self.ratio, pos[1]*self.ratio) for node,pos in self.node_centers.items()}\n\n\t\t# Add positions\n\t\tpos = self.node_centers\n\n\t\t# Draw\n\t\tnx.draw(G,pos,edge_color='black',width=0.5,linewidths=0.8,\\\n\t\tnode_size=1250,node_color='white',shape='o', node_angle=0, font_size=8, font_color='black',alpha=0.6,\\\n\t\tlabels={node:node for node in G.nodes()}, ax=ax)\n\n\t\t# Draw labels\n\t\tnx.draw_networkx_edge_labels(G,pos,edge_labels=self.all_edge_labels,font_color='red', font_size=8)\n\n\t\t# Clean and save\n\t\tplt.axis('off')\n\t\tplt.tight_layout()\n\t\tplt.savefig(outpath+filename, format=\"PNG\")\n\n\t\t# Close\n\t\tplt.close()\n\n\t\tself.nodes_by_degree = sorted(G.degree, key=lambda x: x[1], reverse=True)\n\t\tself.degree_dict = {k:v for k,v in G.degree}\n\n\tdef save_captions_as_image(self, dataset, outpath=''):\n\t\t\"\"\"Save captions as image.\"\"\"\n\n\t\t# Get captions by file ID\n\t\tcaptions = dataset.captions_dict[self.file_ID]\n\n\t\t# Join to string\n\t\tcaptions_to_print = \"\\n\".join(captions)\n\n\t\t# Load font (arial)\n\t\tfont = ImageFont.truetype(\"/home/jsuter/repositories/arial.ttf\", 15)\n\n\t\t# Set image settings\n\t\timg = Image.new('RGB', (600, 150), color = (73, 109, 137))\n\t\td = ImageDraw.Draw(img)\n\t\td.text((10,10), captions_to_print, fill=(255,255,0), font=font)\n\n\t\t# Save image\n\t\timg.save(outpath+'SG_'+self.file_ID+'_captions.png')\n\n\n\tdef create_network(self, outpath='', format=\"jpg\"):\n\t\t\"\"\"Draw Scene Graph and lay over original image.\"\"\"\n\n\t\t# Get original image path\n\t\timage_file = self.original_image\n\n\t\t# Plot original image\n\t\timg = mpimg.imread(image_file,0)\n\t\tplt.imshow(img)\n\t\tax = plt.gca()\n\n\t\t# Compute ratio of original image to downsampled image (1024, 768, 3)\n\t\tmax_axis = max(img.shape[:2])\n\t\tself.ratio = max_axis/1024\n\t\tratio = self.ratio\n\n\t\t# Create Graph\n\t\tG=nx.Graph()\n\n\t\t# Add edges\n\t\tG.add_edges_from(self.all_edges, label=self.all_edge_labels)\n\n\t\t# Apply ratio to node positions in order to adjust to image\n\t\tself.node_centers = {node:(pos[0]*ratio, pos[1]*ratio) for node,pos in self.node_centers.items()}\n\n\t\t# Add positions\n\t\tpos = self.node_centers\n\n\t\t# Draw\n\t\tnx.draw(G,pos,edge_color='black',width=0.5,linewidths=0.8,\\\n\t\tnode_size=1250,node_color='white',shape='o', node_angle=0, font_size=8, font_color='black',alpha=0.6,\\\n\t\tlabels={node:node for node in G.nodes()}, ax=ax)\n\n\t\t# Draw labels\n\t\t#nx.draw_networkx_edge_labels(G,pos,edge_labels=self.all_edge_labels,font_color='red', font_size=8)\n\n\t\t# Clean and save\n\t\tplt.axis('off')\n\t\tplt.tight_layout()\n\t\tplt.show()\n\t\t#plt.savefig(outpath+filename, format=\"PNG\")\n\n\t\t# Close\n\t\tplt.close()\n\n\t\t# Save degrees\n\t\tself.nodes_by_degree = sorted(G.degree, key=lambda x: x[1], reverse=True)\n\t\tself.degree_dict = {k:v for k,v in G.degree}\n\n\nclass Entity():\n\t\"\"\"Class for entities in captions.\"\"\"\n\n\tdef __init__(self, data):\n\n\t\t# Match parts of entity string\n\t\tmatches = re.search(r'EN#(.*?)\\/(.*?)\\s(.*)', data)\n\n\t\t# Save full data string\n\t\tself.data = data\n\n\t\t# Get ID\n\t\tself.ID = matches.group(1)\n\n\t\t# Get category\n\t\tself.category = matches.group(2)\n\n\t\t# Get string\n\t\tself.string = matches.group(3).lower()\n\n\t\t# Get object (final word in string)\n\t\tself.object = self.string.split(' ')[-1]\n\n\t\t# Is entity scene?\n\t\tself.is_scene = False\n\n\t\t# Initialize coordinates\n\t\tself.xmax = None\n\t\tself.xmin = None\n\t\tself.ymax = None\n\t\tself.ymin = None\n\n\t\tself.coordinates = None\n\n\t\tself.area = None\n\n\n\tdef set_bbox(self, coordinates):\n\t\t\"\"\"Set bounding box coordinates.\"\"\"\n\n\t\tif coordinates == None:\n\t\t\tself.is_scene = True\n\t\telse:\n\t\t\tself.xmin, self.ymin, self.xmax, self.ymax = coordinates\n\t\t\tself.coordinates = (self.xmin, self.ymin, self.xmax, self.ymax)\n\t\t\tself.area = graph_funct.get_box_size(self.coordinates)\n\n\nclass Dataset():\n\t\"\"\"Dataset class for loading Flickr datasets.\"\"\"\n\n\tdef __init__(self, path):\n\n\t\t# Set paths and file names\n\t\tself.path = path\n\t\tself.capation_path = self.path + \"/flickr30k-captions/\"\n\t\tself.captions_file = self.capation_path+\"results_20130124.token\"\n\n\t\tself.entities_sents_path = self.path + \"/flickr30k_entities/Sentences/\"\n\t\tself.entites_bboxes_path = self.path + \"/flickr30k_entities/Annotations/\"\n\n\t\t# Set dicts and lists\n\t\tself.captions_dict = {}\n\t\tself.entities_dict = {}\n\t\tself.all_entities = []\n\n\n\t\tself.captions_with_ent = {}\n\t\tself.word_ent_ID_dict = {}\n\t\tself.ent_ID_dict = {}\n\n\t\tself.caption_phrase_dict = {}\n\t\tself.all_words = []\n\t\tself.all_relations = []\n\n\n\tdef get_captions(self):\n\t\t\"\"\"Get all captions.\"\"\"\n\n\t\t# Open caption file\n\t\twith open(self.captions_file) as infile:\n\t\t\tdata = infile.read()\n\n\t\t# Clean captions\n\t\tcaptions = data.split('\\n')\n\t\tcaptions = [cap for cap in captions if cap.strip() != '']\n\n\t\tfor cap in captions:\n\t\t\tfor rel in relations:\n\t\t\t\tif rel in cap:\n\t\t\t\t\tself.all_relations.append(rel)\n\n\t\t# Get captions sets (5 each)\n\t\tcaptions_per_image = [captions[i:i+5] for i in range(0, len(captions), 5)]\n\n\t\t# For each caption set\n\t\tfor caption_set in captions_per_image:\n\n\t\t\t# Get file\n\t\t\tfile_ID  = caption_set[0].split('\\t')[0]\n\t\t\tfile_ID = file_ID[:-6]\n\n\t\t\t# Get captions\n\t\t\textracted_captions = [cap.split('\\t')[1] for cap in caption_set]\n\n\t\t\t# Save captions for file ID\n\t\t\tself.captions_dict[file_ID] = extracted_captions\n\n\n\tdef get_entities(self):\n\t\t\"\"\"Get all entities.\"\"\"\n\n\t\t# Get all files\n\t\tfiles = [f for f in os.listdir(self.entities_sents_path) if f.endswith('.txt')]\n\n\t\t# Process each file\n\t\tfor file in files:\n\n\t\t\t# Get file ID\n\t\t\tfile_ID = file[:-4]\n\n\t\t\tentity_dict = {}\n\t\t\tbbox_dict = {}\n\n\t\t\t# Open entity file and read\n\t\t\twith open(self.entities_sents_path+file) as infile:\n\t\t\t\tdata = infile.read()\n\t\t\t\tdata = data.strip()\n\n\t\t\t# Load bounding box file as XML\n\t\t\troot = ET.parse(self.entites_bboxes_path+file_ID+'.xml').getroot()\n\n\t\t\tfor object_xml in root.findall('size'):\n\t\t\t\twidth = int(object_xml.findall('width')[0].text)\n\t\t\t\theight = int(object_xml.findall('height')[0].text)\n\n\t\t\t# neural-motifs scaled such that the greatest dim is 1024\n\t\t\tif width > height:\n\t\t\t\tnewWidth = 1024\n\t\t\t\tnewHeight = newWidth * height / width\n\t\t\telse:\n\t\t\t\tnewHeight = 1024\n\t\t\t\tnewWidth = newHeight * width / height\n\n\t\t\t# For each object\n\t\t\tfor xml_object in root.findall('object'):\n\n\t\t\t\t# Find names and scene\n\t\t\t\tnames = xml_object.findall('name')\n\t\t\t\tscene = xml_object.find('scene')\n\n\t\t\t\t# Check whether object is scene\n\t\t\t\tis_scene = True if scene is not None else False\n\n\t\t\t\t# If not, load bounding box cordinates\n\t\t\t\tif not is_scene:\n\n\t\t\t\t\tymin = int(xml_object.find('bndbox/ymin').text) * newWidth/width\n\t\t\t\t\tymax = int(xml_object.find('bndbox/ymax').text) * newWidth/width\n\t\t\t\t\txmax = int(xml_object.find('bndbox/xmax').text) * newHeight/height\n\t\t\t\t\txmin = int(xml_object.find('bndbox/xmin').text) * newHeight/height\n\n\t\t\t\t# Save coordinates for object (None for scene or non-visual object)\n\t\t\t\tif is_scene:\n\t\t\t\t\tfor name in names:\n\t\t\t\t\t\tbbox_dict[name.text]  = None\n\t\t\t\telse:\n\t\t\t\t\tfor name in names:\n\t\t\t\t\t\tif name.text in bbox_dict.keys() and bbox_dict[name.text] !=0:\n\t\t\t\t\t\t\tbbox_dict[name.text].append((xmin, ymin, xmax, ymax))\n\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tbbox_dict[name.text] = [(xmin, ymin, xmax, ymax)]\n\n\t\t\t# Merge coordinates for gold entity bounding boxes\n\t\t\tfor key in bbox_dict.keys():\n\t\t\t\tall_coords = bbox_dict[key]\n\t\t\t\tif all_coords != None:\n\t\t\t\t\tmerged_coords = (graph_funct.multiple_box_union(all_coords))\n\t\t\t\t\tbbox_dict[key] = merged_coords\n\n\t\t\t# Get captions\n\t\t\tcaptions = data.split('\\n')\n\n\t\t\t# For each cap\n\t\t\tfor i, cap in enumerate(captions):\n\n\t\t\t\t# Save caption for image\n\t\t\t\tself.captions_with_ent[(file_ID, str(i))] = cap\n\n\t\t\t\tword_ID_dict = {}\n\t\t\t\tcap_entities = []\n\t\t\t\tcaption_phrases = []\n\n\t\t\t\t# Parse out phrases\n\t\t\t\tcap_raw = re.sub(r'(EN#\\d+\\/\\w+.*?)(\\s+)([\\w\\\"\\']+)','\\g<1>$$$\\g<3>',cap)\n\t\t\t\tcap_words_raw = cap_raw.split(' ')\n\n\t\t\t\tcurrent_ID = None\n\t\t\t\tcurrent_phrase = ''\n\n\t\t\t\t# Divide up caption into phrases and assign entity ID to each phrase word\n\t\t\t\tfor word_id, word in enumerate(cap_words_raw):\n\t\t\t\t\tif word.startswith('['):\n\t\t\t\t\t\t\n\t\t\t\t\t\t# Get ID and word\n\t\t\t\t\t\tID = re.search(r'EN#(\\d+)\\/.*', word).group(1)\n\t\t\t\t\t\tword = word.split('$$$')[-1]\n\t\t\t\t\t\tcurrent_ID = str(ID)\n\n\t\t\t\t\t\tsingle = False\n\n\t\t\t\t\t\t# If entity = 0, don't save\n\t\t\t\t\t\tif current_ID == '0':\n\t\t\t\t\t\t\tcurrent_ID = None\n\n\t\t\t\t\t\t# If end of phrase (single word phrase), save as caption phrase\n\t\t\t\t\t\tif word.endswith(']'):\n\t\t\t\t\t\t\tword = word[:-1]\n\t\t\t\t\t\t\tsingle = True\n\t\t\t\t\t\t\tcurrent_phrase += word\n\t\t\t\t\t\t\tcaption_phrases.append((current_phrase, current_ID))\n\t\t\t\t\t\t\tcurrent_phrase = ''\n\t\t\t\t\t\t \n\t\t\t\t\t\t# If not yet end of phrase, keep appending\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tcurrent_phrase += '_'+word\n\t\t\t\t\t\t\n\t\t\t\t\t\t# Save word ID and word\n\t\t\t\t\t\tword_ID_dict[(word_id, word)] = current_ID\n\n\t\t\t\t\t\tif single:\n\t\t\t\t\t\t\tcurrent_ID = None\n\t\t\t\t\t\t\tsingle = False\n\n\t\t\t\t\telse:\n\t\t\t\t\t\t# If end of non-single word phrase\n\t\t\t\t\t\tif word.endswith(']'):\n\t\t\t\t\t\t\tsingle = False\n\n\t\t\t\t\t\t\tif current_ID == '0':\n\t\t\t\t\t\t\t\tcurrent_ID = None\n\n\t\t\t\t\t\t\tif word.endswith(']'):\n\t\t\t\t\t\t\t\tword = word[:-1]\n\t\t\t\t\t\t\t\tsingle = True\n\t\n\t\t\t\t\t\t\t# Save phrase\n\t\t\t\t\t\t\tcurrent_phrase += '_'+word\n\t\t\t\t\t\t\tcaption_phrases.append((current_phrase, current_ID))\n\t\t\t\t\t\t\tcurrent_phrase = ''\n\n\t\t\t\t\t\t\t# Save word ID and word\n\t\t\t\t\t\t\tword_ID_dict[(word_id, word)] = current_ID\n\n\t\t\t\t\t\t\tif single:\n\t\t\t\t\t\t\t\tcurrent_ID = None\n\t\t\t\t\t\t\t\tsingle = False\n\t\n\t\t\t\t\t\t# Print word ID and word\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tword_ID_dict[(word_id, word)] = current_ID\n\t\t\t\t\t\t\tcurrent_phrase += '_'+word\n\n\t\t\t\tentity_words = {}\n\t\t\t\tent_IDs = {}\n\n\t\t\t\t# Find and clean all entity strings\n\t\t\t\tentities = re.findall(r'\\[\\/.*?\\]', cap)\n\t\t\t\tentities = [ent[1:-1] for ent in entities]\n\n\t\t\t\t# For each entity\n\t\t\t\tfor ent in entities:\n\n\t\t\t\t\t# Entity instance\n\t\t\t\t\tent = Entity(ent)\n\n\t\t\t\t\t# If ID does not indicate \"non-visual\"\n\t\t\t\t\tif ent.ID != '0':\n\n\t\t\t\t\t\t# Save bounding box\n\t\t\t\t\t\tent.set_bbox(bbox_dict[ent.ID])\n\n\t\t\t\t\t\t# Append entities\n\t\t\t\t\t\tcap_entities.append(ent)\n\t\t\t\t\t\tself.all_entities.append(ent)\n\n\t\t\t\t\t# If there are coordinates, save entity for entity ID, otherwise None\n\t\t\t\t\tif ent.coordinates == None:\n\t\t\t\t\t\tent_IDs[str(ent.ID)] = None\n\t\t\t\t\telse:\n\t\t\t\t\t\tent_IDs[str(ent.ID)] = ent\n\n\t\t\t\t# Save caption entities\n\t\t\t\tentity_dict[i] = cap_entities\n\n\t\t\t\t# Save caption phrases and entity ID dicts\n\t\t\t\tself.caption_phrase_dict[(file_ID),str(i)] = caption_phrases\n\t\t\t\tself.word_ent_ID_dict[(file_ID, str(i))] = word_ID_dict\n\t\t\t\tself.ent_ID_dict[(file_ID, str(i))] = ent_IDs\n\n\t\t\t# Save entity by file ID\n\t\t\tself.entities_dict[file_ID] = entity_dict\n\n\n\nclass PhraseConcept:\n\t\"\"\"Class for Phrase Concepts used for ConceptNet extension.\"\"\"\n\n\tdef __init__(self, data, image_ID, caption_ID, concept_text, ent_ID, entity, is_plural):\n\t\t\n\t\t# Data\n\t\tself.line = data\n\t\tself.caption_ID = caption_ID\n\t\tself.image_ID = image_ID\n\n\t\tself.concept_text = concept_text\n\t\tself.ent_ID = ent_ID\n\t\tself.entity = entity\n\t\tself.is_plural = is_plural\n","sub_path":"t2sga/Scene_Graph.py","file_name":"Scene_Graph.py","file_ext":"py","file_size_in_byte":21556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"575581672","text":"import pandas as pd\nimport numpy as np\n\ndf = pd.DataFrame(np.random.randn(10,4),\n                  columns=list('ABCD'))\n\ndays = pd.date_range('20191223',periods=10)\ndf.index = days\n#print(df.iloc[1:10,1:2)\n\ndf.describe()\nprint(df.sort_values('A', axis=0))\nprint(df.describe())\n\n\n\n","sub_path":"python_machine_learning/DataFrame in Pandas/temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"581480854","text":"# Minecraft Turtle Example\nfrom mcturtle import minecraftturtle\nfrom mcpi import minecraft\nfrom mcpi import block\n\n# create connection to minecraft\nmc = minecraft.Minecraft.create()\n\n# get players position\npos = mc.player.getPos()\n\n# create minecraft turtle\nsteve = minecraftturtle.MinecraftTurtle(mc, pos)\n\nsteve.speed(0)\nsteve.penblock(block.WOOL.id, 14)\n\n# steve.forward(5)\nfor i in range(4):\n    steve.up(90)\n    steve.forward(5)","sub_path":"minecrafty.py","file_name":"minecrafty.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"400941136","text":"\"\"\"\nEvolutionHR spider created on the top of ATSSpider\n\nscrapy crawl evolution_hr -a mining_job_id=9999 -a iteration=1 -a extract=1 -a url=\"http://www.evolution-hr.com/en/category/job-ads/\"\n\nSample URL:\n    http://www.evolution-hr.com/en/category/job-ads/\n\"\"\"\n\nfrom re import compile\nfrom scrapy.http import Request\nfrom scrapy.selector import Selector\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import ConvertDateString, Prefix, Replace\n\npattern = {\n    'ref_number': compile(r'en\\/([^\\/]*)'),\n}\n\n\nclass EvolutionHR(ATSSpider):\n\n    name = 'evolution_hr'\n\n    def parse(self, response):\n        sel = Selector(response)\n        for href in sel.xpath(\n            '//div[@id=\"section_content\"]//div[@id=\"job_list_title\"]/a/@href'\n        ).extract():\n            yield Request(\n                callback=self.parse_job_callback(),\n                url=href\n            )\n\n        next_page = sel.xpath(\n            '//div[@class=\"wp-pagenavi\"]/a[@class=\"nextpostslink\"]/@href'\n        ).extract()\n        if next_page:\n            yield Request(\n                callback=self.parse,\n                url=next_page[0]\n            )\n\n    def parse_job(self, response):\n        \"\"\"\n        Extract all required information.\n        \"\"\"\n        sel = Selector(response)\n\n        loader = BrightcorpItemLoader(selector=sel)\n\n        loader.add_xpath(\n            'title',\n            '//div/div[@id=\"job_ad_title\"]/text()'\n        )\n        loader.add_xpath(\n            'date',\n            '//div[@id=\"hot_job_details\"]/div[contains(text(), \"Post Date:\")]/text()',\n            Replace('Post Date:'),\n            ConvertDateString('%Y/%m/%d')\n        )\n        loader.add_value(\n            'referencenumber',\n            response.url,\n            Prefix('%s-' % self.name),\n            re=pattern['ref_number']\n        )\n        loader.add_value('url', response.url)\n        loader.add_xpath(\n            'description',\n            '//div[@id=\"hot_job_details\"]/div[@id=\"job_list_content\"]/node()[following-sibling::div[contains(@class, \"fb-social-plugin\")]]'\n        )\n        loader.add_value('apply_url', response.url)\n\n        yield loader.load_item()\n","sub_path":"brightcorp/brightcorp/spiders/evolution_hr.py","file_name":"evolution_hr.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"602991676","text":"def write_to_file(data):\n    f = open('my_file.txt','w')\n    f.write(data)\n    f.close()\n\ndef read_from_file(filename):\n    f = open(filename,'r')\n    innhold = f.read()\n    print(innhold)\n\n\n\ndef number_of_lines(filename):\n    num_lines = sum(1 for line in open(filename))\n    return num_lines\n\nprint(number_of_lines('numbers.txt'))\n\ndef number_frequency(filename):\n\n    number_list = []\n    f = open(filename,'r')\n    innhold = f.read()\n    number_freq = {x:innhold.count(x) for x in innhold}\n    number_freq.pop('\\n',None)\n    return number_freq\n\nnumber_freq = number_frequency('numbers.txt')\nprint(number_frequency('numbers.txt'))\n\n\nfor i in number_freq:\n    print(i,':',number_freq[i])\n\n\n\n\n\n","sub_path":"øvinger 9/tallforekomster.py","file_name":"tallforekomster.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"432542394","text":"from pico2d import *\n\nimport game_framework\nimport main_state\n\nname = \"MainState\"\n\nTIME_PER_ACTION = 1.0\nACTION_PER_TIME = 1.0 / TIME_PER_ACTION\nFRAMES_PER_ACTION = 8\n\nknife=None\nsword=None\nrapier=None\nsound=None\nbackground=None\nshopkeeper=None\nshopkeeper_frame=0\n\ndef enter():\n    global knife,sword,rapier,sound,shopkeeper,background\n    knife = load_image('knife.png')\n    sword = load_image('sword.png')\n    rapier = load_image('rapier.png')\n    shopkeeper=load_image('shopkeeper.png')\n    background=load_image('background.png')\n    sound=load_music('lobby.mp3')\n    sound.set_volume(100)\n    sound.repeat_play()\n\n\ndef exit():\n    global knife, sword, rapier,sound,shopkeeper,background\n    del(sword)\n    del(rapier)\n    del(knife)\n    del(sound)\n    del(shopkeeper)\n    del(background)\n\n\n\ndef pause():\n    pass\n\n\ndef resume():\n    pass\n\n\ndef handle_events():\n    events = get_events()\n    for event in events:\n        if event.type == SDL_QUIT:\n            game_framework.quit()\n        elif event.type == SDL_KEYDOWN and event.key == SDLK_ESCAPE:\n            game_framework.quit()\n        elif event.type==SDL_KEYDOWN and event.key==SDLK_1:\n            main_state.cadence_weapon = 0\n            game_framework.change_state(main_state)\n\n\n        elif event.type==SDL_KEYDOWN and event.key==SDLK_2:\n            main_state.cadence_weapon = 1\n            game_framework.change_state(main_state)\n\n\n        elif event.type==SDL_KEYDOWN and event.key==SDLK_3:\n            main_state.cadence_weapon = 2\n            game_framework.change_state(main_state)\n\n\ndef update():\n    global shopkeeper_frame\n    shopkeeper_frame = (shopkeeper_frame + FRAMES_PER_ACTION * ACTION_PER_TIME * game_framework.frame_time) % 8\n\n\n\ndef draw():\n    clear_canvas()\n    background.clip_composite_draw(0, 0, 477, 322, 0, ' ', 400, 300, 800, 600)\n    shopkeeper.clip_composite_draw(int(shopkeeper_frame)*47,38,47,38,0,' ',400,500,47*2,38*2)\n    knife.clip_composite_draw(0, 20, 19, 20, 0, ' ', 200 ,300, 48, 48)\n    sword.clip_composite_draw(0, 24, 24, 24, 0, ' ', 400, 300, 48, 48)\n    rapier.clip_composite_draw(0, 27, 26, 27, 0, ' ', 600, 300, 48, 48)\n\n    update_canvas()\n","sub_path":"2DGP Project-NecroDancer/weapon_state.py","file_name":"weapon_state.py","file_ext":"py","file_size_in_byte":2153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"639679486","text":"import re\nimport sys\nfrom collections import Counter\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nsys.path.append(\".\")\nimport config\n\n\ndef create_event(event_name: str, dates: str, early_time: int, later_time: int):\n    \"\"\"建立 when2meet 投票\n\n    Args:\n        event_name (str): 投票名稱\n        dates (str): 投票日期 (%Y-%m-%d), 多日期之間以 \"|\" 連結\n        early_time (int): ��早時間(24小時制)\n        later_time (int): 最晚時間(24小時制)\n\n    Returns:\n        [type]: [description]\n    \"\"\"\n    data = {\n        \"NewEventName\": event_name,\n        \"DateTypes\": \"SpecificDates\",\n        \"PossibleDates\": dates,\n        \"NoEarlierThan\": early_time,\n        \"NoLaterThan\": later_time,\n        \"TimeZone\": \"Asia/Taipei\",\n    }\n\n    response = requests.post(\"https://www.when2meet.com/SaveNewEvent.php\", data=data)\n    response.encoding = \"utf-8\"\n    soup = BeautifulSoup(response.text, \"html.parser\")\n    attrs = soup.find(\"body\").attrs\n    event_id = attrs[\"onload\"].replace(\"window.location='/\", \"\")\n    event_id.strip(\"'\")\n    if event_id[-1] == \"'\":\n        event_id = event_id[:-1]\n    return f\"https://www.when2meet.com/{event_id}\"\n\n\ndef gettime_attendant(event_id: str, url: str) -> str:\n    \"\"\"取得 when2meet 結果\n\n    Args:\n        event_id (str): vote id (EATender)\n        url (str): when2meet url\n\n    Returns:\n        (str): LINE Notify message text\n    \"\"\"\n    response = requests.get(url, timeout=10)\n    soup = BeautifulSoup(response.text, \"html.parser\")\n\n    raw_date = soup.find_all(\n        \"div\",\n        attrs={\n            \"style\": [\n                \"display:inline-block;*display:inline;zoom:1;text-align:center;font-size:10px;width:44px;padding-right:1px;\",\n                \"display:inline-block;*display:inline;zoom:1;text-align:center;font-size:10px;width:44px;padding-right:1px;margin-right:10px;\",\n            ]\n        },\n    )\n    dates = []\n    for each in raw_date:\n        dates.append(\n            str(each)\n            .replace(\n                '<div style=\"display:inline-block;*display:inline;zoom:1;text-align:center;font-size:10px;width:44px;padding-right:1px;margin-right:10px;\">',\n                \"\",\n            )\n            .replace(\n                '<div style=\"display:inline-block;*display:inline;zoom:1;text-align:center;font-size:10px;width:44px;padding-right:1px;\">',\n                \"\",\n            )\n            .replace(\n                '<br/><div style=\"display:inline-block;*display:inline;zoom:1;font-size:16px;\">',\n                \"\",\n            )\n            .replace(\"</div>\", \"\")\n        )\n    dates = list(dict.fromkeys(dates))\n    for i in range(len(dates)):\n        dates[i] = dates[i].replace(\"  \", \" \")\n        dates[i] = f\"{dates[i][:len(dates[i]) - 3]}, {dates[i][len(dates[i]) - 3:]}\"\n\n    time_template = {\n        \"Midnight\": 0,\n        \"1 AM\": 1,\n        \"2 AM\": 2,\n        \"3 AM\": 3,\n        \"4 AM\": 4,\n        \"5 AM\": 5,\n        \"6 AM\": 6,\n        \"7 AM\": 7,\n        \"8 AM\": 8,\n        \"9 AM\": 9,\n        \"10 AM\": 10,\n        \"11 AM\": 11,\n        \"Noon\": 12,\n        \"1 PM\": 13,\n        \"2 PM\": 14,\n        \"3 PM\": 15,\n        \"4 PM\": 16,\n        \"5 PM\": 17,\n        \"6 PM\": 18,\n        \"7 PM\": 19,\n        \"8 PM\": 20,\n        \"9 PM\": 21,\n        \"10 PM\": 22,\n        \"11 PM\": 23,\n    }\n    raw_times = soup.find_all(\n        \"div\",\n        attrs={\n            \"style\": \"text-align:right;width:44px;font-size:10px;margin:4px 4px 0px 0px;\"\n        },\n    )\n    times = []\n    for each in raw_times:\n        each = re.findall(\n            r\"<div style=\\\"text-align:right;width:44px;font-size:10px;margin:4px 4px 0px 0px;\\\">(.*?)</div>\",\n            str(each),\n        )\n        if not each:\n            continue\n        this = each[0].strip(\"\\xa0\")\n        if this in time_template.keys():\n            this = time_template[this]\n            times.append(this)\n    times = list(dict.fromkeys(times))\n\n    group_grid = []\n    for i in range(len(dates)):\n        group_grid.append([[] for j in range((len(times) * 4))])\n\n    raw_attendant = re.findall(\n        r\"PeopleNames\\[\\d\\] = '(.*?)';PeopleIDs\\[\\d\\] = (.*?);\", str(response.text)\n    )\n    raw_attendant = list(dict.fromkeys(raw_attendant))\n    attendant = dict()\n    for each in raw_attendant:\n        attendant[each[1]] = each[0]\n\n    availables = re.findall(\n        r\"AvailableAtSlot\\[(\\d*?)\\]\\.push\\((\\d*?)\\);\", str(response.text)\n    )\n    availables = list(dict.fromkeys(availables))\n\n    for each in availables:\n        time_slot = int(each[0]) % (len(times) * 4)\n        date_slot = int(each[0]) // (len(times) * 4)\n        group_grid[date_slot][time_slot].append(str(each[1]))\n\n    grid_count = []\n    for each in group_grid:\n        grid_count.append(list(map(len, each)))\n\n    datetimes = dict()\n    for i in range(len(group_grid)):\n        for j in range(len(group_grid[i])):\n            attendant_name = tuple(map(lambda s: attendant[s], group_grid[i][j]))\n            if attendant_name not in datetimes.keys() and attendant_name:\n                datetimes[attendant_name] = dict()\n                datetimes[attendant_name][dates[i]] = list()\n            elif attendant_name:\n                datetimes[attendant_name][dates[i]] = list()\n\n    for i in range(len(group_grid)):\n        for j in range(len(group_grid[i])):\n            attendant_name = tuple(map(lambda s: attendant[s], group_grid[i][j]))\n            try:\n                if attendant_name:\n                    datetimes[attendant_name][dates[i]].append(j)\n            except:\n                pass\n    try:\n        group_amount = list(map(len, dict.fromkeys(datetimes)))\n        max_people = max(group_amount)\n    except ValueError:\n        return \"還沒有人投票哦！\"\n\n    result = dict()\n    for i, amount in enumerate(group_amount):\n        if amount == max_people:\n            for j, datetime in enumerate(datetimes):\n                if i == j:\n                    result[datetime] = datetimes[datetime]\n\n    def time_segment(raw_time: list):\n        time_dict = {\n            0: \"00:00\",\n            1: \"00:15\",\n            2: \"00:30\",\n            3: \"00:45\",\n            4: \"01:00\",\n            5: \"01:15\",\n            6: \"01:30\",\n            7: \"01:45\",\n            8: \"02:00\",\n            9: \"02:15\",\n            10: \"02:30\",\n            11: \"02:45\",\n            12: \"03:00\",\n            13: \"03:15\",\n            14: \"03:30\",\n            15: \"03:45\",\n            16: \"04:00\",\n            17: \"04:15\",\n            18: \"04:30\",\n            19: \"04:45\",\n            20: \"05:00\",\n            21: \"05:15\",\n            22: \"05:30\",\n            23: \"05:45\",\n            24: \"06:00\",\n            25: \"06:15\",\n            26: \"06:30\",\n            27: \"06:45\",\n            28: \"07:00\",\n            29: \"07:15\",\n            30: \"07:30\",\n            31: \"07:45\",\n            32: \"08:00\",\n            33: \"08:15\",\n            34: \"08:30\",\n            35: \"08:45\",\n            36: \"09:00\",\n            37: \"09:15\",\n            38: \"09:30\",\n            39: \"09:45\",\n            40: \"10:00\",\n            41: \"10:15\",\n            42: \"10:30\",\n            43: \"10:45\",\n            44: \"11:00\",\n            45: \"11:15\",\n            46: \"11:30\",\n            47: \"11:45\",\n            48: \"12:00\",\n            49: \"12:15\",\n            50: \"12:30\",\n            51: \"12:45\",\n            52: \"13:00\",\n            53: \"13:15\",\n            54: \"13:30\",\n            55: \"13:45\",\n            56: \"14:00\",\n            57: \"14:15\",\n            58: \"14:30\",\n            59: \"14:45\",\n            60: \"15:00\",\n            61: \"15:15\",\n            62: \"15:30\",\n            63: \"15:45\",\n            64: \"16:00\",\n            65: \"16:15\",\n            66: \"16:30\",\n            67: \"16:45\",\n            68: \"17:00\",\n            69: \"17:15\",\n            70: \"17:30\",\n            71: \"17:45\",\n            72: \"18:00\",\n            73: \"18:15\",\n            74: \"18:30\",\n            75: \"18:45\",\n            76: \"19:00\",\n            77: \"19:15\",\n            78: \"19:30\",\n            79: \"19:45\",\n            80: \"20:00\",\n            81: \"20:15\",\n            82: \"20:30\",\n            83: \"20:45\",\n            84: \"21:00\",\n            85: \"21:15\",\n            86: \"21:30\",\n            87: \"21:45\",\n            88: \"22:00\",\n            89: \"22:15\",\n            90: \"22:30\",\n            91: \"22:45\",\n            92: \"23:00\",\n            93: \"23:15\",\n            94: \"23:30\",\n            95: \"23:45\",\n        }\n\n        base_time = times[0] * 4\n        all_day_time = []\n        time_session = f\"{time_dict[base_time + raw_time[0]]}\"\n\n        for i in range(len(raw_time)):\n            this_time = base_time + raw_time[i]\n            if i < len(raw_time) - 1 and raw_time[i] + 1 == raw_time[i + 1]:\n                continue\n            else:\n                time_session += f\"~{time_dict[this_time + 1]}\"\n                all_day_time.append(time_session)\n                if i < len(raw_time) - 1:\n                    time_session = f\"{time_dict[base_time + raw_time[i + 1]]}\"\n\n        return all_day_time\n\n    wrapped_result = {\n        \"event_name\": str(\n            re.findall(r\"<title>(.*?) - When2meet</title>\", str(soup))[0]\n        ),\n        \"candidates\": [],\n    }\n\n    for key_1, item_1 in result.items():\n        candidate = {\n            \"participants\": key_1,\n            \"available_time\": list(),\n        }\n        for key_2, item_2 in item_1.items():\n            this_date = dict()\n            this_date[\"date\"] = key_2\n            this_date[\"time\"] = time_segment(result[key_1][key_2])\n            candidate[\"available_time\"].append(this_date)\n\n        wrapped_result[\"candidates\"].append(candidate)\n\n    # 餐廳選擇結果\n    choose_result = \"\"\n    like_list = []\n    event_data = config.db.vote_pull.find_one({\"_id\": event_id})\n    for each_participant, like in event_data[\"participants\"].items():\n        like_list += like\n    occurence_count = Counter(like_list)\n    for restaurant_index, count in occurence_count.most_common(3):\n        restaurant_name = event_data[\"restaurants\"][restaurant_index][\"name\"]\n        choose_result += f\"{restaurant_name} ： {count}票\\n\"\n\n    message = f'投票結果出爐啦！\\n\\n【{wrapped_result[\"event_name\"]}】\\n------------------\\n{choose_result}'\n    for each in wrapped_result[\"candidates\"]:\n        message += f'參與者：{each[\"participants\"]}\\n'\n        for each_available in each[\"available_time\"]:\n            message += f'日期：{each_available[\"date\"]}\\n'\n            message += f\"時間：\"\n            for i, each_time in enumerate(each_available[\"time\"]):\n                if i == 0:\n                    message += f\"{each_time}\\n\"\n                else:\n                    message += f\"------{each_time}\\n\"\n            message += \"------------------\\n\"\n\n    return message\n","sub_path":"project/vote/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"653376785","text":"# -*- coding: utf-8 -*-\n\nfrom logging import getLogger\n\nfrom ..web.models import InfoSite\n\nlog = getLogger('django')\n\n\ndef get_info():\n\t# Obtiene o crea una instancia de InfoSite en caso de que no exista.\n\n\tinfo = InfoSite.objects.all()\n\tif info:\n\t\treturn info[0]\n\telse:\n\t\tinfo = InfoSite(email='chevarrias@gmail.com',\n\t\t\ttelefono='5555555', direccion='Av. Sin Nombre #123',\n\t\t\tfacebook='http://facebook.com/', site='http://127.0.0.1:8000')\n\t\tinfo.save()\n\t\treturn info\n\ndef get_coors():\n\timport random, decimal\n\tnum_1_min = -11.942884\n\tnum_1_max = -12.112792\n\tnum_2_min = -76.835941\n\tnum_2_max = -77.042962\n\treturn str(round(random.random()*(num_1_min-num_1_max)+num_1_max, 12))+\",\"+str(round(random.random()*(num_2_min-num_2_max)+num_2_max, 12))","sub_path":"src/apps/cursos/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"119397949","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\nimport skimage\nfrom skimage.segmentation import slic,watershed, quickshift, mark_boundaries\nimport itertools\nimport logging\nimport json\nimport re\nimport random\nfrom collections import OrderedDict\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport matplotlib.lines as lines\nfrom matplotlib.patches import Polygon\n\n\n# import ssd\nfrom ssd_tf.model import Model\n\n\nclass TomatoProcessing():\n    \"\"\"Class that does the post processing\"\"\"\n    def __init__(self, image=None, r=None, depth_image=None, intrinsics=None):\n        \"\"\"Initialize parameters for post processing\"\"\"\n        self.size_image = (480, 640)\n\n        # max number of tomato returned\n        self.n_tomato_max = 10\n\n        # workspace setting\n        self.l_lim = self.size_image[1] / 3\n        self.r_lim = 2 * self.size_image[1] /3\n\n        # color for sub_segmentation\n        self.red_level = 200\n        self.proportion_min_red = 50\n        self.distance_max_r = 3400 # deltaE_ciede94 distance\n\n        # class input\n        self.image = image\n        self.results = r\n        self.depth_image = depth_image\n        self.intrinsics = intrinsics\n\n\n        # class output\n        self.data = None\n\n\n        # result sorting variables\n        self.detection_thresh = 0.8\n\n    def set_input(self, image=None, results=None, depth_image=None, intrinsics=None, size_image=None):\n        \"\"\"setter for the input of the class\"\"\"\n        if image is not None: self.image = image\n        if results is not None: self.results = results\n        if depth_image is not None:\n            self.depth_image = depth_image\n            self.depth_image_3d = np.dstack((self.depth_image, self.depth_image, self.depth_image))\n        if intrinsics is not None: self.intrinsics = intrinsics\n        if size_image is not None :\n            self.height = size_image[0]\n            self.width = size_image[1]\n        return 0\n\n    def get_output(self):\n        \"\"\"getter for the output of the class\"\"\"\n        return {'data' : self.data, 'scores': self.scores}\n\n\n    def processed_image(self, t0=None):\n        \"\"\"Compute an image usefull for visualization, can print the fps preprocess time is given\"\"\"\n\n        if self.image is None or self.results is None: return None\n        initial_img = np.copy(self.image)\n        image_masks = np.zeros((self.height, self.width, 3), dtype=np.uint8)\n\n\n\n\n        in_ws_scores = self.in_ws_data['scores']\n        in_ws_n_tomato = min(len(in_ws_scores), self.n_tomato_max)\n\n        print_height = 20\n        if in_ws_n_tomato == 0:\n            cv2.putText(initial_img, 'NO TOMATO DETECTED IN WORKSPACE', (10, print_height), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), thickness=2)\n            print_height += 20\n\n        out_ws_scores = self.out_ws_data['scores']\n        out_ws_n_tomato = min(len(out_ws_scores), self.n_tomato_max)\n\n\n        if self.depth_image is None:\n            final_img = np.hstack([(initial_img).astype(np.uint8), image_masks])\n        else:\n            depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(self.depth_image, alpha=255 / self.depth_image.max()), cv2.COLORMAP_JET)\n            final_img = np.hstack([(initial_img).astype(np.uint8), image_masks, (depth_colormap * 255).astype(np.uint8)])\n\n\n        if self.depth_image is not None:\n            depth_colormap = (cv2.applyColorMap(cv2.convertScaleAbs(self.depth_image, alpha=255 / self.depth_image.max()), cv2.COLORMAP_JET)*255).astype(np.uint8)\n\n        in_ws_rois = self.in_ws_data['rois']\n        in_ws_ids_pixels = self.in_ws_data['id_pixel_masks']\n        in_ws_centers = self.in_ws_data['centers']\n\n        color1 = (255, 0, 0)\n\n        # inside workspace\n        for n in range(in_ws_n_tomato-1, -1, -1):\n\n\n            [y1, x1, y2, x2] = in_ws_rois[n]\n            id = in_ws_ids_pixels[n]\n            center = in_ws_centers[n]\n            score = in_ws_scores[n]\n            image_masks[y1:y2 ,x1:x2, :][id == 1] = (0, 0, 255)\n            image_masks[y1:y2, x1:x2, :][id == 2] = (0, 255, 0)\n\n            # draw roi\n            if n == 0:\n                line_width = 6\n            else:\n                line_width = 2\n\n            cv2.rectangle(initial_img, tuple((x1, y1)), tuple((x2, y2)), color1, line_width, lineType=4)\n            cv2.rectangle(image_masks, tuple((x1, y1)), tuple((x2, y2)), color1, line_width, lineType=4)\n            if self.depth_image is not None:\n                cv2.rectangle(depth_colormap, tuple((x1, y1)), tuple((x2, y2)), color1, line_width, lineType=4)\n\n\n            # draw detection score\n            cv2.putText(initial_img, '{:6d}'.format(int(score)), tuple((x1, y1)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))\n\n            # draw depth\n            if self.in_ws_data['depths'] is not None:\n                depth = self.in_ws_data['depths'][n]\n                cv2.putText(initial_img, 'd={:1.3f}m'.format(depth), tuple((x1, y2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))\n\n            # draw center\n            cv2.circle(initial_img, tuple(center), 5, (255, 0, 0), -1)\n\n        out_ws_rois = self.out_ws_data['rois']\n        out_ws_ids_pixels = self.out_ws_data['id_pixel_masks']\n        out_ws_centers = self.out_ws_data['centers']\n\n        color1 = (0, 0, 255)\n\n        # outside workspace\n        for n in range(out_ws_n_tomato-1, -1, -1):\n\n\n            [y1, x1, y2, x2] = out_ws_rois[n]\n            id = out_ws_ids_pixels[n]\n            center = out_ws_centers[n]\n            score = out_ws_scores[n]\n            image_masks[y1:y2 ,x1:x2, :][id == 1] = (0, 0, 255)\n            image_masks[y1:y2, x1:x2, :][id == 2] = (0, 255, 0)\n\n\n            line_width = 1\n\n            cv2.rectangle(initial_img, tuple((x1, y1)), tuple((x2, y2)), color1, line_width, lineType=4)\n            cv2.rectangle(image_masks, tuple((x1, y1)), tuple((x2, y2)), color1, line_width, lineType=4)\n            if self.depth_image is not None:\n                cv2.rectangle(depth_colormap, tuple((x1, y1)), tuple((x2, y2)), color1, line_width, lineType=4)\n\n\n            # draw detection score\n            cv2.putText(initial_img, '{:6d}'.format(int(score)), tuple((x1, y1)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))\n\n            # draw depth\n            if self.out_ws_data['depths'] is not None:\n                depth = self.out_ws_data['depths'][n]\n                cv2.putText(initial_img, 'd={:1.3f}m'.format(depth), tuple((x1, y2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))\n\n            # draw center\n            cv2.circle(initial_img, tuple(center), 5, (255, 0, 0), -1)\n\n        g_scores = self.g_data['scores']\n        g_n_tomato = min(len(g_scores), self.n_tomato_max)\n        g_rois = self.g_data['rois']\n        g_ids_pixels = self.g_data['id_pixel_masks']\n        g_centers = self.g_data['centers']\n\n        color1 = (255, 255, 255)\n\n        # green tomato\n        for n in range(g_n_tomato - 1, -1, -1):\n\n            [y1, x1, y2, x2] = g_rois[n]\n            id = g_ids_pixels[n]\n            center = g_centers[n]\n            score = g_scores[n]\n            image_masks[y1:y2, x1:x2, :][id == 1] = (0, 0, 255)\n            image_masks[y1:y2, x1:x2, :][id == 2] = (0, 255, 0)\n\n\n\n            cv2.rectangle(initial_img, tuple((x1, y1)), tuple((x2, y2)), color1, 2, lineType=4)\n            cv2.line(initial_img, tuple((x1, y2)), tuple((x2, y1)), color1, 1, lineType=4)\n            cv2.line(initial_img, tuple((x1, y1)), tuple((x2, y2)), color1, 1, lineType=4)\n            cv2.rectangle(image_masks, tuple((x1, y1)), tuple((x2, y2)), color1, 2, lineType=4)\n            cv2.line(image_masks, tuple((x1, y2)), tuple((x2, y1)), color1, 1, lineType=4)\n            cv2.line(image_masks, tuple((x1, y1)), tuple((x2, y2)), color1, 1, lineType=4)\n            if self.depth_image is not None:\n                cv2.rectangle(depth_colormap, tuple((x1, y1)), tuple((x2, y2)), color1, 2, lineType=4)\n                cv2.line(depth_colormap, tuple((x1, y2)), tuple((x2, y1)), color1, 1, lineType=4)\n                cv2.line(depth_colormap, tuple((x1, y1)), tuple((x2, y2)), color1, 1, lineType=4)\n\n            # draw detection score\n            cv2.putText(initial_img, '{:6d}'.format(int(score)), tuple((x1, y1)), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n                        (255, 255, 255))\n\n            # draw depth\n            if self.out_ws_data['depths'] is not None:\n                depth = self.g_data['depths'][n]\n                cv2.putText(initial_img, 'd={:1.3f}m'.format(depth), tuple((x1, y2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n                            (255, 255, 255))\n\n            # draw center\n            cv2.circle(initial_img, tuple(center), 5, color1, -1)\n\n\n        if self.depth_image is None:\n            final_img = np.hstack([initial_img, image_masks])\n        else:\n            final_img = np.hstack([initial_img, image_masks, depth_colormap])\n\n        t = time.time() - t0\n        cv2.putText(final_img, 'FPS : {:2.0f}'.format(1/t), (10, print_height), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), thickness=2)\n        if print_height != 20:print_height-=15\n        else: print_height=0\n\n        color1 = (255,0,0)\n        cv2.line(final_img, (int(self.l_lim), print_height), (int(self.l_lim), self.height), color1, 2)\n        cv2.line(final_img, (int(self.r_lim), print_height), (int(self.r_lim), self.height), color1, 2)\n\n        line_height_min = int(self.size_image[0] / 2 + 10)\n        line_height_max = int(self.size_image[0] / 2 - 10)\n        cv2.line(final_img, (int(self.l_lim), line_height_min), (int(self.l_lim) - 20, line_height_min), color1, 2)\n        cv2.line(final_img, (int(self.l_lim), line_height_max), (int(self.l_lim) - 20, line_height_max), color1, 2)\n\n        cv2.line(final_img, (int(self.r_lim), line_height_min), (int(self.r_lim) + 20, line_height_min), color1, 2)\n        cv2.line(final_img, (int(self.r_lim), line_height_max), (int(self.r_lim) + 20, line_height_max), color1, 2)\n\n        return final_img\n\n\n    def pixel_id(self, sub_img):\n        # matrix of boolean, telling if it is red but not green\n        red_mat = np.zeros((sub_img.shape))\n        red_mat[:,:,0] = self.red_level\n        d_r =  skimage.color.deltaE_ciede94(skimage.color.rgb2lab(sub_img), skimage.color.rgb2lab(red_mat)) < self.distance_max_r\n        # range between 2000 and 4000\n        return d_r\n\n    def sub_segmentation(self):\n        \"\"\"Does the complet post processing\n        :param image: initial image\n        :param r: output of the mrcnn\n        :return n: number of tomato detected\n        :return score_detection: given by the network for each tomato\n        :return rois: rois given by the network\"\"\"\n\n        if self.image is None or self.results is None: return None\n\n\n        scores = self.results['scores']\n        n_tomato = min(len(scores), self.n_tomato_max)\n        if n_tomato == 0: return None\n        in_ws_n_tomato_out = 0\n        out_ws_n_tomato_out = 0\n        g_n_tomato_out = 0\n\n        rois = self.results['boxes']\n        in_ws_rois_out = []\n        in_ws_centers = []\n        in_ws_red_scores = []\n        in_ws_ids_pixels = []\n        if self.depth_image is not None: in_ws_depths = []\n        else: in_ws_depths = None\n\n        out_ws_rois_out = []\n        out_ws_centers = []\n        out_ws_red_scores = []\n        out_ws_ids_pixels = []\n        if self.depth_image is not None:\n            out_ws_depths = []\n        else:\n            out_ws_depths = None\n\n        g_rois_out = []\n        g_centers = []\n        g_red_scores = []\n        g_ids_pixels = []\n        if self.depth_image is not None:\n            g_depths = []\n        else:\n            g_depths = None\n\n\n        for n in range(n_tomato):\n            if scores[n]<self.detection_thresh:\n                break\n\n\n\n            number_r_pixel = sys.float_info.epsilon\n            [y1, x1, y2, x2] = [int(rois[n][0]*480), int(rois[n][1]*640), int(rois[n][2]*480), int(rois[n][3]*640)]\n\n            # take the sub image in the roi\n            sub_image = self.image[y1:y2,x1:x2]\n            id = self.pixel_id(sub_image)\n            number_r_pixel = np.count_nonzero(id==1)\n            centertmp = (np.sum(np.argwhere(id==1), axis=0) / (number_r_pixel + sys.float_info.epsilon) + np.array([y1,x1]))\n\n            # green tomatoes\n            proportion_red_pixel = number_r_pixel / ((y2-y1) * (x2-x1))\n            if proportion_red_pixel < self.proportion_min_red:\n                g_rois_out.append([y1, x1, y2, x2])\n                g_n_tomato_out += 1\n                g_centers.append((int(x1 + (x2-x1)/2), (int(y1 + (y2-y1)/2))))\n                g_red_scores.append(int(number_r_pixel))\n                g_ids_pixels.append(id)\n                if self.depth_image is not None:\n                    sub_depth = self.depth_image[y1:y2, x1:x2]\n                    depth = np.sum(sub_depth[np.nonzero(id)]) / (number_r_pixel + sys.float_info.epsilon)\n                    g_depths.append(depth)\n\n            else:\n\n                # outside workspace\n                if centertmp[1] < self.l_lim or centertmp[1] > self.r_lim:\n\n                    out_ws_rois_out.append([y1, x1, y2, x2])\n                    out_ws_n_tomato_out += 1\n                    out_ws_centers.append((int(centertmp[1]), int(centertmp[0])))\n                    out_ws_red_scores.append(int(number_r_pixel))\n                    out_ws_ids_pixels.append(id)\n                    if self.depth_image is not None :\n                        sub_depth = self.depth_image[y1:y2, x1:x2]\n                        depth = np.sum(sub_depth[np.nonzero(id)]) / (number_r_pixel + sys.float_info.epsilon)\n                        out_ws_depths.append(depth)\n\n                # inside workspace\n                else:\n                    in_ws_rois_out.append([y1, x1, y2, x2])\n                    in_ws_n_tomato_out += 1\n                    in_ws_centers.append((int(centertmp[1]), int(centertmp[0])))\n                    in_ws_red_scores.append(int(number_r_pixel))\n                    in_ws_ids_pixels.append(id)\n                    if self.depth_image is not None:\n                        sub_depth = self.depth_image[y1:y2, x1:x2]\n                        depth = np.sum(sub_depth[np.nonzero(id)]) / (number_r_pixel + sys.float_info.epsilon)\n                        in_ws_depths.append(depth)\n\n\n\n        self.g_data = {'tomato_count' : g_n_tomato_out, 'scores' : g_red_scores, 'rois' : g_rois_out, 'centers' : g_centers,\\\n                     'depths': g_depths, 'id_pixel_masks' : g_ids_pixels}\n\n        self.in_ws_data = {'tomato_count' : in_ws_n_tomato_out, 'scores' : in_ws_red_scores, 'rois' : in_ws_rois_out, 'centers' : in_ws_centers,\\\n                     'depths': in_ws_depths, 'id_pixel_masks' : in_ws_ids_pixels}\n\n        self.out_ws_data = {'tomato_count': out_ws_n_tomato_out, 'scores': out_ws_red_scores, 'rois': out_ws_rois_out, 'centers': out_ws_centers, \\\n                       'depths': out_ws_depths, 'id_pixel_masks': out_ws_ids_pixels}\n\n\n\n        return self.in_ws_data, self.out_ws_data, self.g_data\n\n    def deprojection(self):\n        \"\"\"Compute 3D data\"\"\"\n\n        if self.depth_image is None:\n            self.data.update({'points_3d' : None})\n            return None\n\n        if self.in_ws_data is None:\n            self.sub_segmentation()\n\n        in_ws_n_tomato = self.in_ws_data['tomato_count']\n        out_ws_n_tomato = self.out_ws_data['tomato_count']\n        g_n_tomato = self.g_data['tomato_count']\n        if in_ws_n_tomato == 0 and out_ws_n_tomato == 0 and g_n_tomato == 0: return -1\n\n        ppx    = self.intrinsics.ppx\n        ppy    = self.intrinsics.ppy\n        fx     = self.intrinsics.fx\n        fy     = self.intrinsics.fy\n\n        in_ws_points = []\n        out_ws_points = []\n        g_points = []\n\n        for n in range(in_ws_n_tomato):\n            depth = self.in_ws_data['depths'][n]\n            center = self.in_ws_data['centers'][n]\n\n            in_ws_points.append([depth * (center[0] - ppx) / fx, depth * (center[1] - ppy) / fy, depth])\n\n        for n in range(out_ws_n_tomato):\n            depth = self.out_ws_data['depths'][n]\n            center = self.out_ws_data['centers'][n]\n\n            out_ws_points.append([depth * (center[0] - ppx) / fx, depth * (center[1] - ppy) / fy, depth])\n\n        for n in range(g_n_tomato):\n            depth = self.g_data['depths'][n]\n            center = self.g_data['centers'][n]\n\n            g_points.append([depth * (center[0] - ppx) / fx, depth * (center[1] - ppy) / fy, depth])\n\n        self.in_ws_data.update({'points_3d': in_ws_points})\n        self.out_ws_data.update({'points_3d': out_ws_points})\n        self.g_data.update({'points_3d': g_points})\n\n        return 0\n\n\n    def sort_by_depth(self):\n        \"\"\"sort the data by depth\"\"\"\n        if self.out_ws_data is not None and self.out_ws_data['tomato_count'] != 0:\n            depth = self.out_ws_data['depths']\n            index_list = [i for i in range(len(depth))]\n            combined = zip(index_list, depth)\n            zipped_sorted = sorted(combined, key=lambda x: x[1])\n            index_list, depth = map(list, zip(*zipped_sorted))\n            data_sorted = {name: [] for name in self.out_ws_data if name != 'tomato_count'}\n            for name in data_sorted:\n                for ind in index_list:\n                    data_sorted[name].append(self.out_ws_data[name][ind])\n            data_sorted.update({'tomato_count': self.out_ws_data['tomato_count']})\n            self.out_ws_data = data_sorted\n\n\n        if self.in_ws_data is not None and self.in_ws_data['tomato_count'] != 0:\n            depth = self.in_ws_data['depths']\n            index_list = [i for i in range(len(depth))]\n            combined = zip(index_list, depth)\n            zipped_sorted = sorted(combined, key=lambda x: x[1])\n            index_list, depth = map(list, zip(*zipped_sorted))\n            data_sorted = {name: [] for name in self.in_ws_data if name != 'tomato_count'}\n            for name in data_sorted:\n                for ind in index_list:\n                    data_sorted[name].append(self.in_ws_data[name][ind])\n            data_sorted.update({'tomato_count': self.in_ws_data['tomato_count']})\n            self.in_ws_data = data_sorted\n\n\n\n","sub_path":"Tomato/Tomato_ssd.py","file_name":"Tomato_ssd.py","file_ext":"py","file_size_in_byte":18253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"640795462","text":"from django.shortcuts import redirect, get_object_or_404,render_to_response\nfrom django.template import RequestContext\nfrom django.core.urlresolvers import reverse\nfrom django.views.generic.simple import direct_to_template\nfrom django.contrib.auth.models import User\n\n\nfrom accounts.forms import SignupForm\n\n\ndef signup(request, signup_form=SignupForm, template_name='accounts/signup.html', success_url=None, extra_context=None):\n\n    form = signup_form()\n    if request.method == 'POST':\n        form = signup_form(request.POST)\n        if form.is_valid():\n            user = form.save()\n\n            if success_url: redirect_to = success_url\n            else: redirect_to = reverse('accounts_signup_complete',\n                                        kwargs={'username': user.username})\n            return redirect(redirect_to)\n\n    data = { 'form': form, }\n    return render_to_response(template_name, data, context_instance=RequestContext(request))\n\n\ndef direct_to_user_template(request, username, template_name, extra_context=None):\n\n    user = get_object_or_404(User, username__iexact=username)\n\n    if not extra_context: extra_context = dict()\n    extra_context['viewed_user'] = user\n    return direct_to_template(request,\n                              template_name,\n                              extra_context=extra_context)\n","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"449015732","text":"from django.contrib.auth.models import AbstractUser\nfrom django.db import models\n\n\nclass User(AbstractUser):\n    follow = models.ManyToManyField(\n        'self', symmetrical=False, related_name='follower',\n        blank=True, verbose_name='на кого подписан'\n    )\n\n    class Meta:\n        verbose_name = 'пользователь'\n        verbose_name_plural = 'пользователи'\n","sub_path":"src/users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"492855067","text":"\"\"\"\nTODO: brief description of filters.py\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom backtester.common_algos import swingpoints\n\n\nclass SwarmFilter(object):\n    @staticmethod\n    def filter_equity(equity, gf_function, gf_context):\n\n        is_picked, gf_data = gf_function(equity, gf_context)\n\n        # Litte bit future referencing but without entry point bug\n        # Unusable in production (real-time environments)\n        eqty_chg = equity.shift(-1) - equity\n        return eqty_chg[is_picked].cumsum().ffill(), gf_data\n\n\n    @staticmethod\n    def rolling_mean(avg_swarm_eqty, context):\n        period = context['ma_period']\n        ma = avg_swarm_eqty.rolling(period).mean()\n        return avg_swarm_eqty > ma, {'values': ma, 'name': 'GF: Moving average ({0} periods)'.format(period)}\n\n\n    @staticmethod\n    def volatility_chandelier(avg_swarm_eqty, context):\n        period = context['period']\n        down_factor = context['down_factor']\n        up_factor = context['up_factor']\n\n        vola = avg_swarm_eqty.diff(periods=period).abs().rolling(60).median()\n\n        swing_point = pd.Series(np.nan, index=avg_swarm_eqty.index)\n        swing_point_regime = pd.Series(0, index=avg_swarm_eqty.index)\n\n        # Swing point bullish regime\n        swing_switch = 1\n\n        # Swing point start index\n        sw_i = -1\n\n        # Min/Max prices for swings\n        sw_h_max = avg_swarm_eqty[0]\n        sw_l_min = avg_swarm_eqty[0]\n\n        for i in range(len(avg_swarm_eqty)):\n            if i == 0:\n                continue\n            if np.isnan(avg_swarm_eqty[i]):\n                continue\n            if np.isnan(vola.values[i]):\n                continue\n            elif sw_i == -1 and vola.values[i] > 0:\n                sw_h_max = sw_l_min = avg_swarm_eqty[i]\n                sw_i = i\n\n            if swing_switch == 1:\n                #\n                #  We have a bullish swing\n                #\n                sw_h_max = max(sw_h_max, avg_swarm_eqty[i])\n\n                # Check for reversion\n                if avg_swarm_eqty[i] <= sw_h_max - vola[sw_i] * down_factor:\n                    # Reverse swing\n                    swing_switch = -1\n                    sw_l_min = avg_swarm_eqty.values[i]\n                    sw_h_max = avg_swarm_eqty.values[i]\n                    swing_point.values[i] = sw_l_min + vola[sw_i] * up_factor\n\n                    sw_i = i\n                else:\n                    swing_point.values[i] = sw_h_max - vola[sw_i] * down_factor\n\n\n            else:\n                #\n                #  We have a bearish swing\n                #\n                sw_l_min = min(sw_l_min, avg_swarm_eqty.values[i])\n\n                # Check for reversion\n                if avg_swarm_eqty.values[i] >= sw_l_min + vola[sw_i] * up_factor:\n                    # Reverse swing\n                    swing_switch = 1\n                    sw_l_min = avg_swarm_eqty.values[i]\n                    sw_h_max = avg_swarm_eqty.values[i]\n                    sw_i = i\n                    swing_point.values[i] = sw_h_max - vola[sw_i] * down_factor\n                else:\n                    swing_point.values[i] = sw_l_min + vola[sw_i] * up_factor\n\n            swing_point_regime.values[i] = swing_switch\n\n        return swing_point_regime == 1, {'values': swing_point,\n                                         'input_equity': avg_swarm_eqty,\n                                         'name': 'GF: SwingPoint ({0} chg-periods, {1} up, {2} down)'.format(period, up_factor, down_factor)}\n\n\n    @staticmethod\n    def swingpoint_daily(avg_swarm_eqty, context):\n        down_factor = context['down_factor']\n        up_factor = context['up_factor']\n\n\n        spreadSeries = avg_swarm_eqty\n\n        data = pd.DataFrame({'exo': spreadSeries, 'volume': pd.Series(0, index=spreadSeries.index) })\n        sp_df = swingpoints(up_factor, down_factor, data)\n\n\n        BULLISH = 1\n        BEARISH = -1\n        UNDEFINED = 0\n        lastSWPHigh = np.inf  # intmax('int32');\n        lastSWPLow = -np.inf  # intmin('int32');\n        EPSILON = 0.000000000001\n\n        nDays = len(sp_df)\n        currentState = UNDEFINED\n        nBase = 0  # nDays - length(spreadSeries);\n        marketState = np.zeros(nDays, dtype=np.int8)  # zeros(1, nDays);\n\n        sphIndicator = sp_df['sphIndicator'].values\n        splIndicator = sp_df['splIndicator'].values\n        sphLevel = sp_df['sphLevel'].values\n        splLevel = sp_df['splLevel'].values\n\n        for dd in range(1, nDays):\n            if dd <= nBase:\n                continue\n            if sphIndicator[dd - nBase]:\n                lastSWPHigh = sphLevel[dd - nBase]\n            if splIndicator[dd - nBase]:\n                lastSWPLow = splLevel[dd - nBase]\n\n            if currentState == BULLISH:\n                pass\n                if (spreadSeries[dd - nBase] - lastSWPLow <= -EPSILON and\n                                spreadSeries[dd - nBase - 1] - lastSWPLow > -EPSILON) \\\n                        or \\\n                        (lastSWPLow - lastSWPHigh >= EPSILON and\n                                     spreadSeries[dd - nBase] - lastSWPHigh <= -EPSILON and\n                                     spreadSeries[dd - nBase - 1] - lastSWPHigh > -EPSILON):\n                    currentState = BEARISH\n\n            elif currentState == BEARISH:\n                if (spreadSeries[dd - nBase] - lastSWPHigh >= EPSILON and\n                                spreadSeries[dd - nBase - 1] - lastSWPHigh < EPSILON) \\\n                        or \\\n                        (lastSWPLow - lastSWPHigh >= EPSILON and\n                                     spreadSeries[dd - nBase] - lastSWPLow >= EPSILON and\n                                     spreadSeries[dd - nBase - 1] - lastSWPLow < EPSILON):\n                    currentState = BULLISH\n\n            else:  # currentState == UNDEFINED\n                if spreadSeries[dd] - lastSWPHigh >= EPSILON:\n                    currentState = BULLISH\n                if spreadSeries[dd] - lastSWPLow <= -EPSILON:\n                    currentState = BEARISH\n\n            marketState[dd] = currentState\n\n        return pd.Series(marketState == BULLISH, index=spreadSeries.index, dtype=np.uint8), \\\n               {\n                   'values': pd.Series(marketState, index=spreadSeries.index),\n                    'input_equity': avg_swarm_eqty,\n                    'name': 'GF: SwingPoint Daily ({0} up, {1} down)'.format(up_factor,\n                                                                                              down_factor)\n               }","sub_path":"backtester/swarms/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":6534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"448211781","text":"from auto_test.YH.common.base import get_response\nfrom auto_test.YH.common.logger import Log\nfrom auto_test.YH.case.fresh_purchase.apis_test.app_api.get_key import getKey\nfrom auto_test.YH.test_data.read_data import get_test_data\nfrom auto_test.YH.case.fresh_purchase.apis_test.app_api.app_login import getAppToken\n\n\n\nclass makeOrder(object):\n    def __init__(self):\n        self.log = Log()  # 初始化日志\n        self.token = getAppToken().get_token()  # 获取token\n        self.key = getKey().get_key()  # 获取key\n        self.test_data = get_test_data('/fresh_data.xlsx', 'app', 2)  # 初始化测试数据\n\n    def get_order(self):\n        \"\"\"\n        制单\n        :return:\n        \"\"\"\n        headers = eval(self.test_data.get('header'))\n        headers['login-token'] = getAppToken().get_token()\n        url = self.test_data.get('route') + self.key\n        try:\n            res = get_response(url, self.test_data.get('method'),\n                               data=self.test_data.get('data').encode('utf-8'),\n                               headers=headers)\n            return res\n        except Exception as e:\n            self.log.error('请求异常%s' % e)\n\n\n","sub_path":"auto_test/YH/case/fresh_purchase/apis_test/app_api/make_order.py","file_name":"make_order.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"272215018","text":"#!/usr/bin/python3\n\n\nimport sys\nfrom time import sleep\ndata =sys.argv[1:]\nsum = 0\nfor i in data:\n    sum = sum +int(i)\n\nprint (\" The sum of the numbers is \",sum)\n\n","sub_path":"Day1_P5.py","file_name":"Day1_P5.py","file_ext":"py","file_size_in_byte":163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"187494168","text":"\n\nfrom xai.brain.wordbase.nouns._librarian import _LIBRARIAN\n\n#calss header\nclass _LIBRARIANS(_LIBRARIAN, ):\n\tdef __init__(self,): \n\t\t_LIBRARIAN.__init__(self)\n\t\tself.name = \"LIBRARIANS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"librarian\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_librarians.py","file_name":"_librarians.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"412404456","text":"# -*- coding: utf-8 -*-\n# Part of Odoo. See LICENSE file for full copyright and licensing details.\n\nfrom ast import literal_eval\nfrom copy import deepcopy\nfrom lxml import etree\n\nimport odoo\nfrom odoo import http, _\nfrom odoo.http import content_disposition, request\nfrom odoo.exceptions import UserError, AccessError, ValidationError\nfrom odoo.addons.web_studio.controllers import export\nfrom odoo.tools import ustr\n\n\nclass WebStudioController(http.Controller):\n\n    @http.route('/web_studio/chatter_allowed', type='json', auth='user')\n    def is_chatter_allowed(self, model):\n        \"\"\" Returns True iff a chatter can be activated on the model's form views, i.e. if\n            - it is a custom model (since we can make it inherit from mail.thread), or\n            - it already inherits from mail.thread.\n        \"\"\"\n        Model = request.env[model]\n        return Model._custom or isinstance(Model, type(request.env['mail.thread']))\n\n    @http.route('/web_studio/get_studio_action', type='json', auth='user')\n    def get_studio_action(self, action_name, model, view_id=None, view_type=None):\n        view_type = 'tree' if view_type == 'list' else view_type  # list is stored as tree in db\n        model = request.env['ir.model'].search([('model', '=', model)], limit=1)\n\n        action = None\n        if hasattr(self, '_get_studio_action_' + action_name):\n            action = getattr(self, '_get_studio_action_' + action_name)(model, view_id=view_id, view_type=view_type)\n\n        return action\n\n    def _get_studio_action_acl(self, model, **kwargs):\n        return {\n            'name': _('Access Control Lists'),\n            'type': 'ir.actions.act_window',\n            'res_model': 'ir.model.access',\n            'views': [[False, 'list'], [False, 'form']],\n            'target': 'current',\n            'domain': [],\n            'context': {\n                'default_model_id': model.id,\n                'search_default_model_id': model.id,\n            },\n            'help': _(\"\"\"<p class=\"o_view_nocontent_smiling_face\">\n                Add a new access control list\n            </p>\n            \"\"\"),\n        }\n\n    def _get_studio_action_automations(self, model, **kwargs):\n        return {\n            'name': _('Automated Actions'),\n            'type': 'ir.actions.act_window',\n            'res_model': 'base.automation',\n            'views': [[False, 'list'], [False, 'form']],\n            'target': 'current',\n            'domain': [],\n            'context': {\n                'default_model_id': model.id,\n                'search_default_model_id': model.id,\n            },\n            'help': _(\"\"\" <p class=\"o_view_nocontent_smiling_face\">\n                Add a new automated action\n            </p>\n            \"\"\"),\n        }\n\n    def _get_studio_action_filters(self, model, **kwargs):\n        return {\n            'name': _('Filter Rules'),\n            'type': 'ir.actions.act_window',\n            'res_model': 'ir.filters',\n            'views': [[False, 'list'], [False, 'form']],\n            'target': 'current',\n            'domain': [],\n            'context': {  # model_id is a Selection on ir.filters\n                'default_model_id': model.model,\n                'search_default_model_id': model.model,\n            },\n            'help': _(\"\"\" <p class=\"o_view_nocontent_smiling_face\">\n                Add a new filter\n            </p>\n            \"\"\"),\n        }\n\n    def _get_studio_action_reports(self, model, **kwargs):\n        return {\n            'name': _('Reports'),\n            'type': 'ir.actions.act_window',\n            'res_model': 'ir.actions.report',\n            'views': [[False, 'kanban'], [False, 'form']],\n            'target': 'current',\n            'domain': [(\"binding_model_id.transient\",\"=\",False)],\n            'context': {\n                'default_model': model.model,\n                'search_default_model': model.model,\n            },\n            'help': _(\"\"\" <p class=\"o_view_nocontent_empty_report\">\n                Add a new report\n            </p>\n            \"\"\"),\n        }\n\n    def _get_studio_action_translations(self, model, **kwargs):\n        \"\"\" Open a view for translating the field(s) of the record (model, id). \"\"\"\n        domain = ['|', ('name', '=', model.model), ('name', 'ilike', model.model + ',')]\n\n        # search view + its inheritancies\n        views = request.env['ir.ui.view'].search([('model', '=', model.model)])\n        domain = ['|', '&', ('name', '=', 'ir.ui.view,arch_db'), ('res_id', 'in', views.ids)] + domain\n\n        def make_domain(fld, rec):\n            name = \"%s,%s\" % (fld.model_name, fld.name)\n            return ['&', ('res_id', '=', rec.id), ('name', '=', name)]\n\n        def insert_missing(fld, rec):\n            if not fld.translate:\n                return []\n\n            if fld.related:\n                try:\n                    # traverse related fields up to their data source\n                    while fld.related:\n                        rec, fld = fld.traverse_related(rec)\n                    if rec:\n                        return ['|'] + domain + make_domain(fld, rec)\n                except AccessError:\n                    return []\n\n            assert fld.translate and rec._name == fld.model_name\n            request.env['ir.translation'].insert_missing(fld, rec)\n            return []\n\n        # insert missing translations of views\n        for view in views:\n            for name, fld in view._fields.items():\n                domain += insert_missing(fld, view)\n\n        # insert missing translations of model, and extend domain for related fields\n        record = request.env[model.model].search([], limit=1)\n        if record:\n            for name, fld in record._fields.items():\n                domain += insert_missing(fld, record)\n\n        action = {\n            'name': _('Translate view'),\n            'type': 'ir.actions.act_window',\n            'res_model': 'ir.translation',\n            'view_mode': 'tree',\n            'views': [[request.env.ref('base.view_translation_dialog_tree').id, 'list']],\n            'target': 'current',\n            'domain': domain,\n        }\n\n        return action\n\n    @http.route('/web_studio/create_new_menu', type='json', auth='user')\n    def create_new_menu(self, app_name=False, menu_name=False, model_id=False, is_app=False, parent_id=None, icon=None):\n        \"\"\" Create a new menu @menu_name, linked to a new action associated to the model_id\n            @param model_id: if not set, the action associated to this menu is the home menu\n                except if @is_app is True that will create a new model\n            @param is_app: if True, create an extra menu (app, without parent)\n            @param parent_id: the parent of the new menu.\n                To be set if is_app is False.\n            @param icon: the icon of the new app.\n                It can either be:\n                 - the ir.attachment id of the uploaded image\n                 - if the icon has been created, an array containing: [icon_class, color, background_color]\n                To be set if is_app is True.\n        \"\"\"\n        model = None\n        if model_id:\n            model = request.env['ir.model'].browse(model_id)\n        elif is_app:\n            # create a new model\n            model = request.env['ir.model'].studio_name_create(menu_name)\n\n        # create the action\n        if model:\n            action = request.env['ir.actions.act_window'].create({\n                'name': menu_name,\n                'res_model': model.model,\n                'help': \"\"\"\n                    <p class=\"o_view_nocontent_smiling_face\">\n                        This is your new action ; by default, it contains a list view and a form view.\n                    </p>\n                    <p>\n                        You can start customizing these screens by clicking on the Studio icon on the\n                        top right corner (you can also customize this help message there).\n                    </p>\n                \"\"\",\n            })\n            action_ref = 'ir.actions.act_window,' + str(action.id)\n        else:\n            action = request.env.ref('base.action_open_website')\n            action_ref = 'ir.actions.act_url,' + str(action.id)\n\n        if is_app:\n            # create the menus (app menu + first submenu)\n            menu_values = {\n                'name': app_name,\n                'child_id': [(0, 0, {\n                    'name': menu_name,\n                    'action': action_ref,\n                })]\n            }\n\n            menu_values.update(self._get_icon_fields(icon))\n\n            new_context = dict(request.context)\n            new_context.update({'ir.ui.menu.full_list': True})  # allows to create a menu without action\n            new_menu = request.env['ir.ui.menu'].with_context(new_context).create(menu_values)\n\n        else:\n            # create the submenu\n            new_menu = request.env['ir.ui.menu'].create({\n                'name': menu_name,\n                'action': action_ref,\n                'parent_id': parent_id,\n            })\n\n        return {\n            'menu_id': new_menu.id,\n            'action_id': action.id,\n        }\n\n    @http.route('/web_studio/edit_menu_icon', type='json', auth='user')\n    def edit_menu_icon(self, menu_id, icon):\n        values = self._get_icon_fields(icon)\n        request.env['ir.ui.menu'].browse(menu_id).write(values)\n\n    def _get_icon_fields(self, icon):\n        \"\"\" Get icon related fields (depending on the @icon received). \"\"\"\n        if isinstance(icon, int):\n            icon_id = request.env['ir.attachment'].browse(icon)\n            if not icon_id:\n                raise UserError(_('The icon is not linked to an attachment'))\n            return {'web_icon_data': icon_id.datas}\n        elif isinstance(icon, list) and len(icon) == 3:\n            return {'web_icon': ','.join(icon)}\n        else:\n            raise UserError(_('The icon has not a correct format'))\n\n    @http.route('/web_studio/set_background_image', type='json', auth='user')\n    def set_background_image(self, attachment_id):\n        attachment = request.env['ir.attachment'].browse(attachment_id)\n        if attachment:\n            request.env.user.sudo(request.uid).company_id.background_image = attachment.datas\n\n    @http.route('/web_studio/reset_background_image', type='json', auth='user')\n    def reset_background_image(self):\n        request.env.user.sudo(request.uid).company_id.background_image = None\n\n    def create_new_field(self, values):\n        \"\"\" Create a new field with given values.\n            In some cases we have to convert \"id\" to \"name\" or \"name\" to \"id\"\n            - \"model\" is the current model we are working on. In js, we only have his name.\n              but we need his id to create the field of this model.\n            - The relational widget doesn't provide any name, we only have the id of the record.\n              This is why we need to search the name depending of the given id.\n        \"\"\"\n        # Get current model\n        model = request.env['ir.model'].search([('model', '=', values.pop('model_name'))])\n        values['model_id'] = model.id\n\n        # Field type is called ttype in the database\n        if values.get('type'):\n            values['ttype'] = values.pop('type')\n\n        # For many2one and many2many fields\n        if values.get('relation_id'):\n            values['relation'] = request.env['ir.model'].browse(values.pop('relation_id')).model\n        # For related one2many fields\n        if values.get('related') and values.get('ttype') == 'one2many':\n            field_name = values.get('related').split('.')[-1]\n            field = request.env['ir.model.fields'].search([\n                ('name', '=', field_name),\n                ('model', '=', values.pop('relational_model')),\n            ])\n            field.ensure_one()\n            values.update(\n                relation=field.relation,\n                relation_field=field.relation_field,\n            )\n        # For one2many fields\n        if values.get('relation_field_id'):\n            field = request.env['ir.model.fields'].browse(values.pop('relation_field_id'))\n            values.update(\n                relation=field.model_id.model,\n                relation_field=field.name,\n            )\n        # For selection fields\n        if values.get('selection'):\n            values['selection'] = ustr(values['selection'])\n\n        # Optional default value at creation\n        default_value = values.pop('default_value', False)\n\n        # Create new field\n        new_field = request.env['ir.model.fields'].create(values)\n\n        if default_value:\n            if new_field.ttype == 'selection':\n                if default_value is True:\n                    selection_values = literal_eval(new_field.selection)\n                    # take the first selection value as default one in this case\n                    default_value = len(selection_values) and selection_values[0][0]\n            self.set_default_value(new_field.model, new_field.name, default_value)\n\n        return new_field\n\n    @http.route('/web_studio/add_view_type', type='json', auth='user')\n    def add_view_type(self, action_type, action_id, res_model, view_type, args):\n        view_type = 'tree' if view_type == 'list' else view_type  # list is stored as tree in db\n        try:\n            request.env[res_model].fields_view_get(view_type=view_type)\n        except UserError:\n            return False\n        self.edit_action(action_type, action_id, args)\n        return True\n\n    @http.route('/web_studio/edit_action', type='json', auth='user')\n    def edit_action(self, action_type, action_id, args):\n\n        action_id = request.env[action_type].browse(action_id)\n        if action_id:\n            if 'groups_id' in args:\n                args['groups_id'] = [(6, 0, args['groups_id'])]\n\n            if 'view_mode' in args:\n                args['view_mode'] = args['view_mode'].replace('list', 'tree')  # list is stored as tree in db\n\n                # As view_id and view_ids have precedence on view_mode, we need to correctly set them\n                if action_id.view_id or action_id.view_ids:\n                    view_modes = args['view_mode'].split(',')\n\n                    # add new view_mode\n                    missing_view_modes = [x for x in view_modes if x not in [y.view_mode for y in action_id.view_ids]]\n                    for view_mode in missing_view_modes:\n                        vals = {\n                            'act_window_id': action_id.id,\n                            'view_mode': view_mode,\n                        }\n                        if action_id.view_id and action_id.view_id.type == view_mode:\n                            # reuse the same view_id in the corresponding view_ids record\n                            vals['view_id'] = action_id.view_id.id\n\n                        request.env['ir.actions.act_window.view'].create(vals)\n\n                    for view_id in action_id.view_ids:\n                        if view_id.view_mode in view_modes:\n                            # resequence according to new view_modes\n                            view_id.sequence = view_modes.index(view_id.view_mode)\n                        else:\n                            # remove old view_mode\n                            view_id.unlink()\n\n            action_id.write(args)\n\n        return True\n\n    @http.route('/web_studio/set_another_view', type='json', auth='user')\n    def set_another_view(self, action_id, view_mode, view_id):\n\n        action_id = request.env['ir.actions.act_window'].browse(action_id)\n        window_view = request.env['ir.actions.act_window.view'].search([('view_mode', '=', view_mode), ('act_window_id', '=', action_id.id)])\n        if not window_view:\n            window_view = request.env['ir.actions.act_window.view'].create({'view_mode': view_mode, 'act_window_id': action_id.id})\n\n        window_view.view_id = view_id\n\n        return True\n\n    def _get_studio_view(self, view):\n        domain = [('inherit_id', '=', view.id), ('name', '=', self._generate_studio_view_name(view))]\n        return view.search(domain, order='priority desc, name desc, id desc', limit=1)\n\n    def _set_studio_view(self, view, arch):\n        studio_view = self._get_studio_view(view)\n        if studio_view and len(arch):\n            studio_view.arch_db = arch\n        elif studio_view:\n            studio_view.unlink()\n        elif len(arch):\n            self._create_studio_view(view, arch)\n\n    def _generate_studio_view_name(self, view):\n        return \"Odoo Studio: %s customization\" % (view.name)\n\n    @http.route('/web_studio/get_studio_view_arch', type='json', auth='user')\n    def get_studio_view_arch(self, model, view_type, view_id=False):\n        view_type = 'tree' if view_type == 'list' else view_type  # list is stored as tree in db\n\n        if not view_id:\n            # TOFIX: it's possibly not the used view ; see fields_get_view\n            # try to find the lowest priority matching ir.ui.view\n            view_id = request.env['ir.ui.view'].default_view(request.env[model]._name, view_type)\n        # We have to create a view with the default view if we want to customize it.\n        view = self._get_or_create_default_view(model, view_type, view_id)\n        studio_view = self._get_studio_view(view)\n\n        return {\n            'studio_view_id': studio_view and studio_view.id or False,\n            'studio_view_arch': studio_view and studio_view.arch_db or \"<data/>\",\n        }\n\n    @http.route('/web_studio/edit_view', type='json', auth='user')\n    def edit_view(self, view_id, studio_view_arch, operations=None):\n        IrModelFields = request.env['ir.model.fields']\n        view = request.env['ir.ui.view'].browse(view_id)\n\n        parser = etree.XMLParser(remove_blank_text=True)\n        arch = etree.fromstring(studio_view_arch, parser=parser)\n        model = view.model\n\n        # Determine whether an operation is associated with\n        # the creation of a binary field\n        def create_binary_field(op):\n            node = op.get('node')\n            if node and node.get('tag') == 'field' and node.get('field_description'):\n                ttype = node['field_description'].get('type')\n                is_image = node['attrs'].get('widget') == 'image'\n                return ttype == 'binary' and not is_image\n            return False\n\n        # Every time the creation of a binary field is requested,\n        # we also create an invisible char field meant to contain the filename.\n        # The char field is then associated with the binary field\n        # via the 'filename' attribute of the latter.\n        for op in [op for op in operations if create_binary_field(op)]:\n            filename = op['node']['field_description']['name'] + '_filename'\n\n            # Create an operation adding an additional char field\n            char_op = deepcopy(op)\n            char_op['node']['field_description'].update({\n                'name': filename,\n                'type': 'char',\n                'field_description': _('Filename for %s') % op['node']['field_description']['name'],\n            })\n            char_op['node']['attrs']['invisible'] = '1'\n            operations.append(char_op)\n\n            op['node']['attrs']['filename'] = filename\n\n        for op in operations:\n            # create a new field if it does not exist\n            if 'node' in op:\n                if op['node'].get('tag') == 'field' and op['node'].get('field_description'):\n                    model = op['node']['field_description']['model_name']\n                    # Check if field exists before creation\n                    field = IrModelFields.search([\n                        ('name', '=', op['node']['field_description']['name']),\n                        ('model', '=', model),\n                    ], limit=1)\n                    if not field:\n                        field = self.create_new_field(op['node']['field_description'])\n                    op['node']['attrs']['name'] = field.name\n                if op['node'].get('tag') == 'filter' and op['target']['tag'] == 'group' and op['node']['attrs'].get('create_group'):\n                    op['node']['attrs'].pop('create_group')\n                    create_group_op = {\n                        'node': {\n                            'tag': 'group',\n                            'attrs': {\n                                'name': 'studio_group_by',\n                            }\n                        },\n                        'empty': True,\n                        'target': {\n                            'tag': 'search',\n                        },\n                        'position': 'inside',\n                    }\n                    self._operation_add(arch, create_group_op, model)\n            # set a more specific xpath (with templates//) for the kanban view\n            if view.type == 'kanban':\n                if op.get('target') and op['target'].get('tag') == 'field':\n                    op['target']['tag'] = 'templates//field'\n\n            # call the right operation handler\n            getattr(self, '_operation_%s' % (op['type']))(arch, op, model)\n\n        # Save or create changes into studio view, identifiable by xmlid\n        # Example for view id 42 of model crm.lead: web-studio_crm.lead-42\n        new_arch = etree.tostring(arch, encoding='unicode', pretty_print=True)\n        self._set_studio_view(view, new_arch)\n\n        # Normalize the view\n        studio_view = self._get_studio_view(view)\n        try:\n            normalized_view = studio_view.normalize()\n            self._set_studio_view(view, normalized_view)\n        except ValidationError:  # Element '<...>' cannot be located in parent view\n            # If the studio view is not applicable after normalization, let's\n            # just ignore the normalization step, it's better to have a studio\n            # view that is not optimized than to prevent the user from making\n            # the change he would like to make.\n            self._set_studio_view(view, new_arch)\n\n        ViewModel = request.env[view.model]\n        fields_view = ViewModel.with_context({'studio': True}).fields_view_get(view.id, view.type)\n        view_type = 'list' if view.type == 'tree' else view.type\n\n        return {\n            'fields_views': {view_type: fields_view},\n            'fields': ViewModel.fields_get(),\n            'studio_view_id': studio_view.id,\n        }\n\n    @http.route('/web_studio/rename_field', type='json', auth='user')\n    def rename_field(self, studio_view_id, studio_view_arch, model, old_name, new_name):\n        studio_view = request.env['ir.ui.view'].browse(studio_view_id)\n\n        # a field cannot be renamed if it appears in a view ; we thus reset the\n        # studio view before all operations to be able to rename the field\n        studio_view.arch_db = studio_view_arch\n\n        field_id = request.env['ir.model.fields']._get(model, old_name)\n        field_id.write({'name': new_name})\n\n    def _create_studio_view(self, view, arch):\n        # We have to play with priorities. Consider the following:\n        # View Base: <field name=\"x\"/><field name=\"y\"/>\n        # View Standard inherits Base: <field name=\"x\" position=\"after\"><field name=\"z\"/></field>\n        # View Custo inherits Base: <field name=\"x\" position=\"after\"><field name=\"x2\"/></field>\n        # We want x,x2,z,y, because that's what we did in studio, but the order of xpath\n        # resolution is sequence,name, not sequence,id. Because \"Custo\" < \"Standard\", it\n        # would first resolve in x,x2,y, then resolve \"Standard\" with x,z,x2,y as result.\n        return request.env['ir.ui.view'].create({\n            'type': view.type,\n            'model': view.model,\n            'inherit_id': view.id,\n            'mode': 'extension',\n            'priority': 99,\n            'arch': arch,\n            'name': self._generate_studio_view_name(view),\n        })\n\n    @http.route('/web_studio/edit_field', type='json', auth='user')\n    def edit_field(self, model_name, field_name, values):\n        field = request.env['ir.model.fields'].search([('model', '=', model_name), ('name', '=', field_name)])\n        field.write(values)\n\n        # remove default value if the value is not acceptable anymore\n        if field.ttype == 'selection':\n            current_default = request.env['ir.default'].get(model_name, field_name, company_id=True)\n            if current_default:\n                selection_values = literal_eval(field.selection)\n                if current_default not in [x[0] for x in selection_values]:\n                    request.env['ir.default'].discard_values(model_name, field_name, [current_default])\n\n    @http.route('/web_studio/edit_view_arch', type='json', auth='user')\n    def edit_view_arch(self, view_id, view_arch):\n        view = request.env['ir.ui.view'].browse(view_id)\n\n        if view:\n            view.write({'arch': view_arch})\n            ViewModel = request.env[view.model]\n            try:\n                fields_view = ViewModel.with_context({'studio': True}).fields_view_get(view.id, view.type)\n                view_type = 'list' if view.type == 'tree' else view.type\n                return {\n                    'fields_views': {\n                        view_type: fields_view,\n                    },\n                    'fields': ViewModel.fields_get(),\n                }\n            except Exception:\n                return False\n\n    @http.route('/web_studio/export', type='http', auth='user')\n    def export(self, token):\n        \"\"\" Exports a zip file containing the 'studio_customization' module\n            gathering all customizations done with Studio (customizations of\n            existing apps and freshly created apps).\n        \"\"\"\n        studio_module = request.env['ir.module.module'].get_studio_module()\n        data = request.env['ir.model.data'].search([('studio', '=', True)])\n        content = export.generate_archive(studio_module, data)\n\n        return request.make_response(content, headers=[\n            ('Content-Disposition', content_disposition('customizations.zip')),\n            ('Content-Type', 'application/zip'),\n            ('Content-Length', len(content)),\n        ], cookies={'fileToken': token})\n\n    @http.route('/web_studio/create_default_view', type='json', auth='user')\n    def create_default_view(self, model, view_type, attrs):\n        attrs['string'] = \"Default %s view for %s\" % (view_type, model)\n        # The grid view arch has the attributes set as children nodes and not in\n        # the view node\n        if view_type == 'grid':\n            arch = self._get_default_grid_view(attrs)\n        else:\n            arch = self._get_default_view(view_type, attrs)\n        request.env['ir.ui.view'].create({\n            'type': view_type,\n            'model': model,\n            'arch': arch,\n            'name': attrs['string'],\n        })\n\n    def _get_default_view(self, view_type, attrs):\n        arch = etree.Element(view_type, attrs)\n        return etree.tostring(arch, encoding='unicode', pretty_print=True, method='html')\n\n    def _get_or_create_default_view(self, model, view_type, view_id=False):\n        View = request.env['ir.ui.view']\n        # If we have no view_id to inherit from, it's because we are adding\n        # fields to the default view of a new model. We will materialize the\n        # default view as a true view so we can keep using our xpath mechanism.\n        if view_id:\n            view = View.browse(view_id)\n        else:\n            arch = request.env[model].fields_view_get(view_id, view_type)['arch']\n            view = View.create({\n                'type': view_type,\n                'model': model,\n                'arch': arch,\n                'name': \"Default %s view for %s\" % (view_type, model),\n            })\n        return view\n\n    def _node_to_expr(self, node):\n        if not node.get('attrs') and node.get('xpath_info'):\n            # Format of expr is /form/tag1[]/tag2[]/[...]/tag[]\n            expr = ''.join(['/%s[%s]' % (parent['tag'], parent['indice']) for parent in node.get('xpath_info')])\n        else:\n            # Format of expr is //tag[@attr1_name=attr1_value][@attr2_name=attr2_value][...]\n            expr = '//' + node['tag']\n            for k, v in node.get('attrs', {}).items():\n                if k == 'class':\n                    # Special case for classes which usually contain multiple values\n                    expr += '[contains(@%s,\\'%s\\')]' % (k, v)\n                else:\n                    expr += '[@%s=\\'%s\\']' % (k, v)\n\n            # Avoid matching nodes in sub views.\n            # Example with field as node:\n            # A field should be defined only once in a view but in some cases,\n            # a view can be composed by some other views where a field with\n            # the same name may exist.\n            # Here, we want to generate xpath based on the nodes in the parent view only.\n            if not node.get('subview_xpath'):\n                expr = expr + '[not(ancestor::field)]'\n\n        # If we receive a more specific xpath because we are editing an inline\n        # view, we add it in front of the generated xpath.\n        if node.get('subview_xpath'):\n            xpath = node.get('subview_xpath')\n            if node.get('isSubviewAttr'):\n                expr = xpath\n            # Hack to check if the last subview xpath element is not the same than expr\n            # E.g when we add a field in an empty subview list the expr computed\n            # by studio will be only '/tree' but this is useless since the\n            # subview xpath already specify this element. So in this case,\n            # we don't add the expr computed by studio.\n            elif len(xpath) - len(expr) != xpath.find(expr):\n                expr = xpath + expr\n        return expr\n\n    # Create a new xpath node based on an operation\n    # TODO: rename it in master\n    def _get_xpath_node(self, arch, operation):\n        expr = self._node_to_expr(operation['target'])\n        position = operation['position']\n\n        return etree.SubElement(arch, 'xpath', {\n            'expr': expr,\n            'position': position\n        })\n\n    def _operation_remove(self, arch, operation, model=None):\n        expr = self._node_to_expr(operation['target'])\n\n        # We have to create a brand new xpath to remove this field from the view.\n        # TODO: Sometimes, we have to delete more stuff than just a single tag !\n        etree.SubElement(arch, 'xpath', {\n            'expr': expr,\n            'position': 'replace'\n        })\n\n    def _operation_add(self, arch, operation, model):\n        node = operation['node']\n        xpath_node = self._get_xpath_node(arch, operation)\n\n        # Take a xml_node and put columns on it:\n        # If the xml_node is not a group, this function will create a group node\n        # to add two columns on it.\n        def add_columns(xml_node, title=False):\n            # Get the random key generated is JS.\n            # Expected value: 'studio_<tag_name>_<random_key>\n            name = 'studio_group_' + xml_node.get('name').split('_')[2]\n\n            if xml_node.tag != 'group':\n                xml_node_group = etree.SubElement(xml_node, 'group', {'name': name})\n            else:\n                xml_node_group = xml_node\n\n            xml_node_page_left = etree.SubElement(xml_node_group, 'group', {'name': name + '_left'})\n            xml_node_page_right = etree.SubElement(xml_node_group, 'group', {'name': name + '_right'})\n            if title:\n                xml_node_page_left.attrib['string'] = _('Left Title')\n                xml_node_page_right.attrib['string'] = _('Right Title')\n\n        # Create the actual node inside the xpath. It needs to be the first\n        # child of the xpath to respect the order in which they were added.\n        xml_node = etree.Element(node['tag'], node.get('attrs'))\n        if node['tag'] == 'notebook':\n            name = 'studio_page_' + node['attrs']['name'].split('_')[2]\n            xml_node_page = etree.Element('page', {'string': 'New Page', 'name': name})\n            add_columns(xml_node_page)\n            xml_node.insert(0, xml_node_page)\n        elif node['tag'] == 'page':\n            add_columns(xml_node)\n        elif node['tag'] == 'group':\n            if 'empty' not in operation:\n                add_columns(xml_node, title=True)\n        elif node['tag'] == 'button':\n            # To create a stat button, we need\n            #   - a many2one field (1) that points to this model\n            #   - a field (2) that counts the number of records associated with the current record\n            #   - an action to jump in (3) with the many2one field (1) as domain/context\n            #\n            # (1) [button_field] the many2one field\n            # (2) [button_count_field] is a non-stored computed field (to always have the good value in the stat button, if access rights)\n            # (3) [button_action] an act_window action to jump in the related model\n            button_field = request.env['ir.model.fields'].browse(node['field'])\n            button_count_field, button_action = self._get_or_create_fields_for_button(model, button_field, node['string'])\n\n            # the XML looks like <button> <field/> </button : a element `field` needs to be inserted inside the button\n            xml_node_field = etree.Element('field', {'widget': 'statinfo', 'name': button_count_field.name, 'string': node['string'] or button_count_field.field_description})\n            xml_node.insert(0, xml_node_field)\n\n            xml_node.attrib['type'] = 'action'\n            xml_node.attrib['name'] = str(button_action.id)\n        else:\n            xml_node.text = node.get('text')\n        xpath_node.insert(0, xml_node)\n\n    def _get_or_create_fields_for_button(self, model, field, button_name):\n        \"\"\" Returns the button_count_field and the button_action link to a stat button.\n            @param field: a many2one field\n        \"\"\"\n\n        if field.ttype != 'many2one' or field.relation != model:\n            raise UserError(_('The related field of a button has to be a many2one to %s.' % model))\n\n        model = request.env['ir.model'].search([('model', '=', model)], limit=1)\n\n        # There is a counter on the button ; as the related field is a many2one, we need\n        # to create a new computed field that counts the number of records in the one2many\n        button_count_field_name = 'x_%s__%s_count' % (field.name, field.model.replace('.', '_'))[0:63]\n        button_count_field = request.env['ir.model.fields'].search([('name', '=', button_count_field_name), ('model_id', '=', model.id)])\n        if not button_count_field:\n            compute_function = \"\"\"\n                    results = self.env['%(model)s'].read_group([('%(field)s', 'in', self.ids)], '%(field)s', '%(field)s')\n                    dic = {}\n                    for x in results: dic[x['%(field)s'][0]] = x['%(field)s_count']\n                    for record in self: record['%(count_field)s'] = dic.get(record.id, 0)\n                \"\"\" % {\n                    'model': field.model,\n                    'field': field.name,\n                    'count_field': button_count_field_name,\n                }\n            button_count_field = request.env['ir.model.fields'].create({\n                'name': button_count_field_name,\n                'field_description': '%s count' % field.field_description,\n                'model': model.model,\n                'model_id': model.id,\n                'ttype': 'integer',\n                'store': False,\n                'compute': compute_function.replace('    ', ''),  # remove indentation for safe_eval\n            })\n\n        # The action could already exist but we don't want to recreate one each time\n        button_action_domain = \"[('%s', '=', active_id)]\" % (field.name)\n        button_action_context = \"{'search_default_%s': active_id,'default_%s': active_id}\" % (field.name, field.name)\n        button_action = request.env['ir.actions.act_window'].search([\n            ('name', '=', button_name), ('res_model', '=', field.model),\n            ('domain', '=', button_action_domain), ('context', '=', button_action_context),\n        ])\n        if not button_action:\n            # Link the button with an associated act_window\n            button_action = request.env['ir.actions.act_window'].create({\n                'name': button_name,\n                'res_model': field.model,\n                'view_mode': 'tree,form',\n                'view_type': 'form',\n                'domain': button_action_domain,\n                'context': button_action_context,\n            })\n\n        return button_count_field, button_action\n\n    def _operation_move(self, arch, operation, model=None):\n        xpath_node = self._get_xpath_node(arch, operation)\n        xml_node = etree.Element('xpath', {\n            'expr': self._node_to_expr(operation['node']),\n            'position': 'move',\n        })\n        xpath_node.append(xml_node)\n\n    # Create or update node for each attribute\n    def _operation_attributes(self, arch, operation, model=None):\n        ir_model_data = request.env['ir.model.data']\n        new_attrs = operation['new_attrs']\n\n        if 'groups' in new_attrs:\n            eval_attr = []\n            for many2many_value in new_attrs['groups']:\n                group_xmlid = ir_model_data.search([\n                    ('model', '=', 'res.groups'),\n                    ('res_id', '=', many2many_value)])\n                if not group_xmlid:\n                    raise UserError(_(\n                        \"Only groups with an external ID can be used here. Please choose another \" +\n                        \"group or assign manually an external ID to this group.\"\n                    ))\n                eval_attr.append(group_xmlid.complete_name)\n            eval_attr = \",\".join(eval_attr)\n            new_attrs['groups'] = eval_attr\n\n        xpath_node = self._get_xpath_node(arch, operation)\n\n        for key, new_attr in new_attrs.items():\n            xml_node = etree.Element('attribute', {'name': key})\n            xml_node.text = str(new_attr)\n            xpath_node.insert(0, xml_node)\n\n            # change the field description when changing the field label (for custom fields)\n            if key == 'string' and operation.get('node', {}).get('tag') == 'field':\n                field_name = operation.get('node', {}).get('attrs', {}).get('name')\n                field_id = request.env['ir.model.fields'].search([('model', '=', model), ('name', '=', field_name)])\n                if field_name.startswith('x_') and field_id and field_id.field_description != new_attr:\n                    field_id.write({'field_description': new_attr})\n\n    def _operation_buttonbox(self, arch, operation, model=None):\n        studio_view_arch = arch  # The actual arch is the studio view arch\n        # Get the arch of the form view with inherited views applied\n        arch = request.env[model].fields_view_get(view_type='form')['arch']\n        parser = etree.XMLParser(remove_blank_text=True)\n        arch = etree.fromstring(arch, parser=parser)\n\n        # Create xpath to put the buttonbox as the first child of the sheet\n        if arch.find('sheet'):\n            sheet_node = arch.find('sheet')\n            if list(sheet_node):\n                # Check if children exists\n                xpath_node = etree.SubElement(studio_view_arch, 'xpath', {\n                    'expr': '//sheet/*[1]',\n                    'position': 'before'\n                })\n            else:\n                xpath_node = etree.SubElement(studio_view_arch, 'xpath', {\n                    'expr': '//sheet',\n                    'position': 'inside'\n                })\n            # Create and insert the buttonbox node inside the xpath node\n            buttonbox_node = etree.Element('div', {'name': 'button_box', 'class': 'oe_button_box'})\n            xpath_node.append(buttonbox_node)\n\n    def _operation_chatter(self, arch, operation, model=None):\n        def _get_remove_field_op(arch, field_name):\n            return {\n                'type': 'remove',\n                'target': {\n                    'tag': 'field',\n                    'attrs': {\n                        'name': field_name,\n                    },\n                }\n            }\n\n        if not self.is_chatter_allowed(operation['model']):\n            # Chatter can only be activated form models that (can) inherit from mail.thread\n            return\n\n        # From this point, the model is either a custom model or inherits from mail.thread\n        model = request.env['ir.model'].search([('model', '=', operation['model'])])\n        if model.state == 'manual' and not model.is_mail_thread:\n            # Activate mail.thread inheritance on the custom model\n            model.write({'is_mail_thread': True})\n\n        # Remove message_ids and message_follower_ids if already defined in form view\n        if operation['remove_message_ids']:\n            self._operation_remove(arch, _get_remove_field_op(arch, 'message_ids'))\n        if operation['remove_follower_ids']:\n            self._operation_remove(arch, _get_remove_field_op(arch, 'message_follower_ids'))\n\n        xpath_node = etree.SubElement(arch, 'xpath', {\n            'expr': '//sheet',\n            'position': 'after',\n        })\n        chatter_node = etree.Element('div', {'class': 'oe_chatter'})\n        thread_node = etree.Element('field', {'name': 'message_ids', 'widget': 'mail_thread'})\n        follower_node = etree.Element('field', {'name': 'message_follower_ids', 'widget': 'mail_followers'})\n        chatter_node.append(follower_node)\n        chatter_node.append(thread_node)\n        xpath_node.append(chatter_node)\n\n    def _operation_kanban_dropdown(self, arch, operation, model):\n        \"\"\" Insert a dropdown and its corresponding needs in an kanban view arch.\n            Implied modifications:\n                - create an integer field x_color in the model if it doesn't exist\n                - add the field x_color in the view\n                - add a dropdown section in the view\n                - modify the kanban class to use `oe_kanban_color_`\n        \"\"\"\n        model_id = request.env['ir.model'].search([('model', '=', model)])\n        if not model_id:\n            return\n\n        color_field_name = 'x_color'\n        if not request.env['ir.model.fields'].search([('model_id', '=', model_id.id), ('name', '=', color_field_name), ('ttype', '=', 'integer')]):\n            # create a field if it doesn't exist in the model\n            request.env['ir.model.fields'].create({\n                'model': model,\n                'model_id': model_id.id,\n                'name': color_field_name,\n                'field_description': 'Color',\n                'ttype': 'integer',\n            })\n\n        # add the field at the beginning\n        etree.SubElement(arch, 'xpath', {\n            'expr': 'templates',\n            'position': 'before',\n        }).append(etree.Element('field', {'name': color_field_name}))\n\n        # add the dropdown before the rest\n        dropdown_node = etree.fromstring(\"\"\"\n            <div class=\"o_dropdown_kanban dropdown\" name=\"kanban_dropdown\">\n                <a class=\"dropdown-toggle o-no-caret btn\" data-toggle=\"dropdown\" href=\"#\" >\n                    <span class=\"fa fa-bars fa-lg\"/>\n                </a>\n                <div class=\"dropdown-menu\" role=\"menu\">\n                    <t t-if=\"widget.editable\"><a type=\"edit\" class=\"dropdown-item\">Edit</a></t>\n                    <t t-if=\"widget.deletable\"><a type=\"delete\" class=\"dropdown-item\">Delete</a></t>\n                    <ul class=\"oe_kanban_colorpicker\" data-field=\"%(field)s\"/>\n                </div>\n            </div>\n        \"\"\" % {'field': color_field_name})\n        etree.SubElement(arch, 'xpath', {\n            'expr': '//div/*[1]',\n            'position': 'before',\n        }).append(dropdown_node)\n\n        # set the corresponding color attribute on the kanban record\n        xpath_node = etree.SubElement(arch, 'xpath', {\n            'expr': '//div',\n            'position': 'attributes',\n        })\n        xml_node = xpath_node.find('attribute[@name=\"%s\"]' % ('color'))\n        if xml_node is None:\n            xml_node = etree.Element('attribute', {'name': 'color'})\n            xml_node.text = color_field_name\n            xpath_node.insert(0, xml_node)\n        else:\n            xml_node.text = color_field_name\n\n    def _operation_kanban_image(self, arch, operation, model):\n        \"\"\" Insert a image and its corresponding needs in an kanban view arch\n            Implied modifications:\n                - add the field in the view\n                - add a section (kanban_right) in the view\n                - add the field with `kanban_image` in this section\n        \"\"\"\n        model_id = request.env['ir.model'].search([('model', '=', model)])\n        if not model_id:\n            raise UserError(_('The model %s does not exist.') % model)\n\n        if not operation.get('field'):\n            raise UserError(_('Please specify a field.'))\n\n        field_id = request.env['ir.model.fields'].search([\n            ('model', '=', model),\n            ('name', '=', operation['field'])\n        ])\n        if not field_id:\n            raise UserError(_('The field %s does not exist.') % operation['field'])\n\n        # add field at the beginning\n        etree.SubElement(arch, 'xpath', {\n            'expr': 'templates',\n            'position': 'before',\n        }).append(etree.Element('field', {'name': field_id.name}))\n\n        # add the image inside the view\n        etree.SubElement(arch, 'xpath', {\n            'expr': '//div',\n            'position': 'inside',\n        }).append(\n            etree.fromstring(\"\"\"\n                <div class=\"oe_kanban_bottom_right\">\n                    <img\n                        t-att-src=\"kanban_image('%(model)s', 'image_small', record.%(field)s.raw_value)\"\n                        t-att-title=\"record.%(field)s.value\"\n                        width=\"24\" height=\"24\" class=\"oe_kanban_avatar float-right\"\n                    />\n                </div>\n            \"\"\" % {'model': field_id.relation, 'field': field_id.name})\n        )\n\n    def _operation_kanban_priority(self, arch, operation, model):\n        \"\"\" Insert a priority and its corresponding needs in an kanban view arch\n            Implied modifications:\n                - create a selection field x_priority in the model if it doesn't exist\n                - add a section (kanban_left) in the view\n                - add the field x_priority with the widget priority in this section\n        \"\"\"\n        model_id = request.env['ir.model'].search([('model', '=', model)])\n        if not model_id:\n            raise UserError(_('The model %s does not exist.') % model)\n\n        if operation.get('field'):\n            field_id = request.env['ir.model.fields'].search([\n                ('model', '=', model),\n                ('name', '=', operation['field'])\n            ])\n            if not field_id:\n                raise UserError(_('The field %s does not exist.') % operation['field'])\n\n        else:\n            field_id = request.env['ir.model.fields'].search([\n                ('model_id', '=', model_id.id),\n                ('name', '=', 'x_priority'),\n                ('ttype', '=', 'selection')\n            ])\n            # create a field selection x_priority if it doesn't exist in the model\n            if not field_id:\n                field_id = request.env['ir.model.fields'].create({\n                    'model': model,\n                    'model_id': model_id.id,\n                    'name': 'x_priority',\n                    'field_description': 'Priority',\n                    'ttype': 'selection',\n                    'selection': \"[('0', 'Low'), ('1', 'Normal'), ('2', 'High')]\",\n                })\n\n        # add priority inside the view\n        etree.SubElement(arch, 'xpath', {\n            'expr': '//div',\n            'position': 'inside',\n        }).append(\n            etree.fromstring(\"\"\"\n                <div class=\"oe_kanban_bottom_left\">\n                    <field name=\"%s\" widget=\"priority\"/>\n                </div>\n            \"\"\" % (field_id.name))\n        )\n\n    def _operation_statusbar(self, arch, operation, model=None):\n        \"\"\" Create and insert a header as the first child of the form. \"\"\"\n        xpath_node = etree.SubElement(arch, 'xpath', {\n            'expr': '//form/*[1]',\n            'position': 'before'\n        })\n        xpath_node.append(etree.Element('header'))\n\n    @http.route('/web_studio/get_email_alias', type='json', auth='user')\n    def get_email_alias(self, model_name):\n        \"\"\" Returns the email alias associated to the model @model_name if both exist\n        \"\"\"\n        result = {'alias_domain': request.env['ir.config_parameter'].get_param('mail.catchall.domain')}\n        model = request.env['ir.model'].search([('model', '=', model_name)], limit=1)\n        if model:\n            email_alias = request.env['mail.alias'].search([('alias_model_id', '=', model.id)], limit=1)\n            if email_alias:\n                result['email_alias'] = email_alias.alias_name\n        return result\n\n    @http.route('/web_studio/set_email_alias', type='json', auth='user')\n    def set_email_alias(self, model_name, value):\n        \"\"\" Set the email alias associated to the model @model_name\n             - if there is no email alias, it will be created\n             - if there is one and the value is empty, it will be unlinked\n        \"\"\"\n        model = request.env['ir.model'].search([('model', '=', model_name)], limit=1)\n        if model:\n            email_alias = request.env['mail.alias'].search([('alias_model_id', '=', model.id)], limit=1)\n            if email_alias:\n                if value:\n                    email_alias.alias_name = value\n                else:\n                    email_alias.unlink()\n            else:\n                request.env['mail.alias'].create({\n                    'alias_model_id': model.id,\n                    'alias_name': value,\n                })\n\n    @http.route('/web_studio/get_default_value', type='json', auth='user')\n    def get_default_value(self, model_name, field_name):\n        \"\"\" Return the default value associated to the given field. \"\"\"\n        return {\n            'default_value': request.env['ir.default'].get(model_name, field_name, company_id=True)\n        }\n\n    @http.route('/web_studio/set_default_value', type='json', auth='user')\n    def set_default_value(self, model_name, field_name, value):\n        \"\"\" Set the default value associated to the given field. \"\"\"\n        request.env['ir.default'].set(model_name, field_name, value, company_id=True)\n\n    @http.route('/web_studio/create_stages_model', type='json', auth='user')\n    def create_stages_model(self, model_name):\n        \"\"\" Create a new model if it does not exist\n        \"\"\"\n        stages_model = model_name + '_stage'\n        if not model_name.startswith('x_'):\n            stages_model = 'x_' + stages_model\n        model_id = request.env['ir.model'].search([\n            ('model', '=', stages_model),\n        ])\n        if not model_id:\n            model = request.env['ir.model'].search([('model', '=', model_name)])\n            model_id = request.env['ir.model'].create({\n                'model': stages_model,\n                'name': model.name + ' ' + _('stages'),\n            })\n        return model_id.id\n\n    @http.route('/web_studio/create_inline_view', type='json', auth='user')\n    def create_inline_view(self, model, view_id, field_name, subview_type, subview_xpath):\n        inline_view = request.env[model].load_views([[False, subview_type]])\n        view = request.env['ir.ui.view'].browse(view_id)\n        studio_view = self._get_studio_view(view)\n        if not studio_view:\n            studio_view = self._create_studio_view(view, '<data/>')\n        parser = etree.XMLParser(remove_blank_text=True)\n        arch = etree.fromstring(studio_view.arch_db, parser=parser)\n        expr = \"//field[@name='%s']\" % field_name\n        if subview_xpath:\n            expr = subview_xpath + expr\n        position = 'inside'\n        xpath_node = arch.find('xpath[@expr=\"%s\"][@position=\"%s\"]' % (expr, position))\n        if xpath_node is None:  # bool(node) == False if node has no children\n            xpath_node = etree.SubElement(arch, 'xpath', {\n                'expr': expr,\n                'position': position\n            })\n        view_arch = inline_view['fields_views'][subview_type]['arch']\n        xml_node = etree.fromstring(view_arch)\n        xpath_node.insert(0, xml_node)\n        studio_view.arch_db = etree.tostring(arch, encoding='utf-8', pretty_print=True)\n        return studio_view.arch_db\n","sub_path":"addons13/web_studio/controllers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":52625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"35559200","text":"import sys\nsys.path.append('src/util')\nimport requests\nimport pandas as pd\nimport numpy as np\nimport os\nfrom pytz import timezone\nfrom datetime import datetime, date, timedelta\nfrom dateutil.relativedelta import *\nfrom dotenv import load_dotenv\nimport logging\nimport logging.config\nimport math\nfrom utils import *\nimport json\nimport schedule\nimport time\n\nnp.seterr(divide='ignore', invalid='ignore')\nload_dotenv(dotenv_path='stock.env')\n\nlogging.config.fileConfig(fname='log.conf', disable_existing_loggers=False)\nlogger = logging.getLogger()\n\ndata_location = os.getenv(\"data\")\ndata_realtime = os.getenv(\"data\") + os.getenv(\"data_realtime\")\ntz = os.getenv(\"timezone\")\ndate_format = os.getenv(\"date_format\")\ndatetime_format = os.getenv(\"datetime_format\")\n\ndef isDown(df):\n    fiveRedCandle = True\n    for i in range(0, 5):\n        # print(df.iloc[i].Close, df.iloc[i].Open)\n        if df.iloc[i].Close >= df.iloc[i].Open:\n            fiveRedCandle = False\n            break\n    maxHigh = max(list(df.loc[0:15].High))\n    changedPercent = (maxHigh - df.iloc[0].Low)/df.iloc[0].Low\n    reducedTwentyPercent = changedPercent > 20\n    # print(fiveRedCandle, changedPercent, reducedTwentyPercent)\n    return fiveRedCandle or reducedTwentyPercent\n\ndef scan(stocks):\n    downStocks = []\n    downBelowMA200Stocks = []\n    for stock in stocks:\n        df = pd.read_csv(\"{}{}.csv\".format(data_realtime, stock))\n        getIndicators(df)\n        if isDown(df):\n            if df.iloc[0].Close < df.iloc[0].MA200:\n                downBelowMA200Stocks.append(stock)\n            else:\n                downStocks.append(stock)\n    if len(downStocks) > 0:\n        print(\"Stock on down trend above MA200:\")\n        print(\",\".join(downStocks))\n    if len(downBelowMA200Stocks) > 0:\n        print(\"Stock on down trend under MA200:\")\n        print(\",\".join(downBelowMA200Stocks))\n\nif __name__ == \"__main__\":\n    stocks = list(pd.read_csv(data_location + os.getenv('all_stocks'), header=None)[0])\n    # stocks = ['MST']\n    if len(sys.argv) == 2:\n        if sys.argv[1] == 'down':\n            scan(stocks)\n","sub_path":"src/scan/trends.py","file_name":"trends.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"650459773","text":"# Dangling comment placement in empty lists\n# Regression test for https://github.com/python/cpython/blob/03160630319ca26dcbbad65225da4248e54c45ec/Tools/c-analyzer/c_analyzer/datafiles.py#L14-L16\na1 = [  # a\n]\na2 = [  # a\n    # b\n]\na3 = [\n    # b\n]\n\n# Add magic trailing comma only if there is more than one entry, but respect it if it's\n# already there\nb1 = [\n    aksjdhflsakhdflkjsadlfajkslhfdkjsaldajlahflashdfljahlfksajlhfajfjfsaahflakjslhdfkjalhdskjfa\n]\nb2 = [\n    aksjdhflsakhdflkjsadlfajkslhfdkjsaldajlahflashdfljahlfksajlhfajfjfsaahflakjslhdfkjalhdskjfa,\n]\nb3 = [\n    aksjdhflsakhdflkjsadlfajkslhfdkjsaldajlahflashdfljahlfksajlhfajfjfsaahflakjslhdfkjalhdskjfa,\n    aksjdhflsakhdflkjsadlfajkslhfdkjsaldajlahflashdfljahlfksajlhfajfjfsaahflakjslhdfkjalhdskjfa\n]\n\n# Comment placement in non-empty lists\nc1 = [ # trailing open bracket\n    # leading item\n    1,\n\n    # between\n\n    2, # trailing item\n    # leading close bracket\n] # trailing close bracket\n","sub_path":"crates/ruff_python_formatter/resources/test/fixtures/ruff/expression/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"211128178","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n'''\n@author: Tengwei Yuan\n'''\n\nimport re\nfrom urllib import request\nfrom urllib.request import urlopen\n\nfrom bs4 import BeautifulSoup\n\n\ndef httpCrawler(url):\n    '''\n    @summary: 网页抓取\n    '''\n    content = httpRequest(url)\n    title = parseHtml(content)\n    saveData(title)\n\n\ndef httpRequest(url):\n    '''\n    @summary: 网络请求\n    '''\n    try:\n        ret = None\n        SockFile = None\n        req = request.Request(url)\n        req.add_header('User-Agent', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.2; SV1; .NET CLR 1.1.4322)')\n        req.add_header('Pragma', 'no-cache')\n\n        SockFile = urlopen(req)\n        ret = SockFile.read()\n    finally:\n        if SockFile:\n            SockFile.close()\n\n    return ret\n\n\ndef saveHtml(source, file_name):\n    f = open(file_name, 'w', encoding='utf8')\n    f.write(BeautifulSoup(source, 'lxml').prettify())\n    f.close()\n\n\ndef parseHtml(html):\n    '''\n    @summary: 抓取结构化数据\n    '''\n\n    content = None\n    pattern = '<title>([^<]*?)</title>'\n    temp = re.findall(pattern, html)\n    if temp:\n        content = temp[0]\n\n    print(content)\n    return content\n\n\n### get all needed urls from current search page and also url to next page\ndef get_urls(start_url):\n    html = urlopen(start_url)\n\n    base_url = 'http://place.qyer.com'\n    bsObj = BeautifulSoup(html, 'lxml')\n\n    for link in bsObj.findAll(\"a\", attrs={'class': 'ui_page_item ui_page_next'}):\n        if 'href' in link.attrs:\n            next_page_link = base_url + link.attrs['href']\n            print(next_page_link)\n            get_urls(next_page_link)\n    return\n\n\n### get all needed items in current page with required methods like selenium\ndef get_items():\n    return\n\n\nfrom lxml import html\nimport requests\n\n\ndef clean(item):\n    return '. '.join(item).lstrip('\\n').lstrip(' ').rstrip(' ').rstrip('\\n')\n\n\ndef getInfo():\n    page = requests.get('http://place.qyer.com/poi/V2YJY1FiBzBTZVI3/')\n    tree = html.fromstring(page.content)\n    title_xpath = '//*[@class=\"poiDet-largeTit\"]/h1[@class=\"en\"]/a/text()'\n    body_xpath = '//*[@class=\"poiDet-detail\"]/text()'\n    rank_title_xpath = '//div[@class=\"infos\"]/div[1]/ul/li[@class=\"rank\"]/text()'\n    rank_info_xpath = '//div[@class=\"infos\"]/div[1]/ul/li[@class=\"rank\"]/span/text()'\n\n    tips_xpath = '//*[@class=\"poiDet-tips\"]/li[*]/div[@class=\"content\"]/p/text()'\n\n    rank_info = ''\n\n    title = tree.xpath(title_xpath)\n    body = tree.xpath(body_xpath)\n    rank_title = tree.xpath(rank_title_xpath)\n    rank_info = tree.xpath(rank_info_xpath)\n\n    tips = tree.xpath(tips_xpath)\n\n    title = clean(title)\n    body = clean(body)\n    rank_title = clean(rank_title)\n    rank_info = clean(rank_info)\n    tips = clean(tips)\n\n    print(title)\n    print(rank_title, rank_info)\n    print(body)\n\n    print(tips)\n\n\ndef getReview():\n    # url = 'http://place.qyer.com/poi/V2UJYlFkBzBTZFI-/'\n    #\n    # review_title_xpath = \"//*[@class='poiDet-largeTit']/h1[@class='en']/a/text()\"\n    # review_info_xpath = '//*[@id=\"commentTar\"]/div[2]/div[*]/text()'\n    # page = requests.get('http://place.qyer.com/poi/V2UJYlFkBzBTZFI-/')\n\n    url = 'https://cn.tripadvisor.com/Restaurant_Review-g154943-d4041836-Reviews-Cactus_Club_Cafe-Vancouver_British_Columbia.html'\n    source = httpRequest(url)\n    saveHtml(source, 'test.html')\n\n    review_title_xpath = '//*[@id=\"HEADING\"]/text()'\n    review_info_xpath = '//*[@id=\"review_*\"]/div/div[2]/div/div/div[4]/p/text()'\n    page = requests.get(url)\n\n    tree = html.fromstring(page.content)\n\n    review_title = tree.xpath(review_title_xpath)\n    review_info = tree.xpath(review_info_xpath)\n\n    print(review_title)\n    print(review_info)\n\n\nfrom selenium import webdriver\nimport time\n\n\ndef getReview_JS():\n    driver = webdriver.PhantomJS(executable_path='/usr/local/bin/phantomjs')\n    # driver.set_window_size(1120, 550)\n\n    # driver = webdriver.Chrome(executable_path='/usr/local/bin/chromedriver')\n    # driver.get(\"http://pythonscraping.com/pages/javascript/ajaxDemo.html\")\n    driver.get(\n            \"https://cn.tripadvisor.com/Restaurant_Review-g154943-d4041836-Reviews-Cactus_Club_Cafe-Vancouver_British_Columbia.html\")\n    time.sleep(1)\n    # review_xpath = '//*[@id=\"review_338052777\"]//p'\n    # review_xpath = '//p[@class=\"partial_entry\"]'\n    review_xpath = '//*[@id=\"REVIEWS\"]//p[@class=\"partial_entry\"]'\n    reviews = driver.find_elements_by_xpath(review_xpath)\n    for review in reviews:\n        print(review.text)\n    driver.close()\n\n\ndef get_review_pages():\n    start_url = 'https://cn.tripadvisor.com/Restaurant_Review-g154943-d4041836-Reviews-Cactus_Club_Cafe-Vancouver_British_Columbia.html#REVIEWS'\n    driver = webdriver.PhantomJS(executable_path='/usr/local/bin/phantomjs')\n    driver.get(start_url)\n    links = driver.find_elements_by_link_text(u'//a[contains(text(), \"下一页\")]')\n    for link in links:\n        # link.click()\n        print(link.text)\n\n    return\n\n\ndef saveData(data):\n    '''\n    @summary: 数据存储\n    '''\n    f = open('test', 'w', encoding='utf-16')\n    f.write(data)\n    f.close()\n\n\nif __name__ == '__main__':\n    # url = 'http://www.amazon.com'\n    # #httpCrawler(url)\n    # source = httpRequest(url)\n    # saveHtml(source, 'test1.html')\n\n    # getInfo()\n    # getReview_JS()\n    # get_review_pages()\n\n    # test_1()\n\n    start_url = 'http://place.qyer.com/vancouver/food/?page=1'\n    get_urls(start_url)\n","sub_path":"spider/SpiderTripAdvisor.py","file_name":"SpiderTripAdvisor.py","file_ext":"py","file_size_in_byte":5422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"189253598","text":"from pyramid.config import Configurator\n\n\ndef main(global_config, **settings):\n    config = Configurator(settings=settings)\n    config.include('pyramid_chameleon')\n    config.add_route('index', '/')\n    config.add_route('chat', '/chat')\n    config.scan()\n    return config.make_wsgi_app()\n\n\ndef meinheld_server_runner(wsgi_app, global_config, **settings):\n    from meinheld import server, middleware\n    host = settings.get('host', '0.0.0.0')\n    port = int(settings.get('port', 8080))\n\n    server.listen((host, port))\n    server.set_access_logger(None)\n    server.set_error_logger(None)\n\n    print('Starting HTTP server on http://%s:%s' % (host, port))\n    server.run(middleware.WebSocketMiddleware(wsgi_app))\n","sub_path":"wsocket/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"22081783","text":"import inspect, numpy, os, random, sys, transforms\nfrom baseforms import MoebiusBase, SphericalBase\nfrom click import progressbar\nfrom image import Image\nfrom math import log10, isnan\n\n\ndef safelog10(x):\n    try:\n        return log10(x)\n    except ValueError:\n        return 0.0\n\ndef test_seed(num_transforms, moebius_chance, spherical_chance, seed):\n    \"\"\"\n    Test whether the IFS seed will be both non-degenerate and interesting by\n    rendering a low-resolution version\n    \"\"\"\n    lowres = IFSI(150, 150, 1000, 1000, num_transforms, moebius_chance, spherical_chance, seed)\n    if lowres.iterate(range(1000)):\n        if lowres.im.interest_factor() >= 120:\n            return True\n    return False\n\n\ndef get_seed(num_transforms, moebius_chance, spherical_chance):\n    while True:\n        seed = random.randrange(sys.maxsize)\n        if test_seed(num_transforms, moebius_chance, spherical_chance, seed):\n            return seed\n\n\nclass IFSI: # IFS Image\n    def __init__(self, width, height, iterations, num_points, num_transforms, moebius_chance, spherical_chance, seed, exclude=[], include=[], filename=None):\n        self.seed = seed\n        self.rng = random.Random(seed)\n        self.ifs = IFS(self.rng, num_transforms, moebius_chance, spherical_chance, exclude, include)\n        self.im = Image(width, height, max(1, (num_points * iterations) / (width * height)))\n        self.iterations = iterations\n        self.num_points = num_points\n        self.name = \"-\".join([t.get_name() for w,t in self.ifs.transforms])\n        if filename == None:\n            self.filename = os.path.join(\"im\", self.name + \"_\" + str(self.seed))\n            self.filename += \"_\" + str(self.im.width) + \"x\" + str(self.im.height)\n            self.filename += \".png\"\n        else:\n            self.filename = filename\n\n    def render(self, bar=True):\n        if bar is True:\n            label = \"Rendering \" + self.name\n            with progressbar(length=self.num_points, label=label, width=0) as iter:\n                if self.iterate(iter):\n                    return self\n        else:\n            if hasattr(bar, \"UpdateBar\"):\n                guibar = bar\n            else:\n                guibar = None\n            if self.iterate(range(self.num_points), guibar=guibar):\n                return self\n        return False\n\n    def iterate(self, iterator, guibar=None):\n        for i in iterator:\n\n            # Start with a random point, and the color black\n            px = self.rng.uniform(-1, 1)\n            py = self.rng.uniform(-1, 1)\n            r, g, b = 0.0, 0.0, 0.0\n            zero_count, cont_count = 0, 0\n\n            # Run the starting point through the system repeatedly\n            for j in range(self.iterations):\n                t = self.ifs.choose_transform()\n                try:\n                    px, py = t.transform(px, py)\n                except ZeroDivisionError:\n                    zero_count += 1\n                    if zero_count >= 10:\n                        cont_count +=1\n                        if cont_count >= 10:\n                            # Degenerate form. Abort render.\n                            return False\n                        continue\n                r, g, b = t.transform_colour(r, g, b)\n\n                # Apply final transform for every iteration\n                fx, fy = self.ifs.final_transform(px, py)\n                x = int((fx + 1) * self.im.width / 2)\n                y = int((fy + 1) * self.im.height / 2)\n\n                # Plot the point in the image buffer\n                self.im.add_radiance(x, y, [r, g, b])\n\n            if guibar:\n                guibar.UpdateBar(i+1)\n        return self\n\n    def save_image(self):\n        if not os.path.exists(\"im\"):\n            os.makedirs(\"im\")\n        self.im.save(self.filename)\n        return self\n\n    def get_image(self):\n        a = numpy.array([safelog10(x) for x in self.im.data])\n        return numpy.reshape(a / max(a), (self.im.width, self.im.height, 3))\n\nclass IFS:\n    def __init__(self, rng, num_transforms, moebius_chance, spherical_chance, exclude, include):\n        self.transforms = []\n        self.total_weight = 0\n        self.rng = rng\n\n        transform_choices = []\n        transform_list = inspect.getmembers(sys.modules[\"transforms\"], inspect.isclass)\n        name_list = [name for (name, obj) in transform_list]\n        name_list = list(set(name_list) - set(exclude))\n\n        if include != []:\n            name_list = list(set(name_list) & set(include))\n        for (name, obj) in transform_list:\n            if name in name_list:\n                transform_choices.append(obj)\n\n        for n in range(num_transforms):\n            # Pick a transform, and possibly either a Moebius and/or Spherical baseform\n            xform = self.rng.choice(transform_choices)(self.rng)\n            if self.rng.random() < moebius_chance:\n                xform = MoebiusBase(self.rng, xform)\n            if self.rng.random() < spherical_chance:\n                xform = SphericalBase(self.rng, xform)\n            self.add_transform(xform)\n\n    def add_transform(self, transform):\n        weight = self.rng.gauss(1, 0.2) * self.rng.gauss(1, 0.2)\n        self.total_weight += weight\n        self.transforms.append((weight, transform))\n\n    def choose_transform(self):\n        w = self.rng.random() * self.total_weight\n        running_total = 0\n        for weight, transform in self.transforms:\n            running_total += weight\n            if w <= running_total:\n                return transform\n\n    def final_transform(self, px, py):\n        \"\"\"\n        Final transform to be applied after each iteration\n        \"\"\"\n        a = 0.5\n        b = 0\n        c = 0\n        d = 1\n        z = complex(px, py)\n        z2 = (a * z + b) / (c * z + d)\n        return z2.real, z2.imag\n","sub_path":"ifs.py","file_name":"ifs.py","file_ext":"py","file_size_in_byte":5781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"264628367","text":"#  Make a dictionary where the key is a worker’s name, and the value is a list\n#  where the first element is the clock in time, second element is the clock\n#  out time, and the third element is the total hours worked that day. Each\n#  worker’s list starts at [0, 0, 0]. Create functions for clock_in and\n#  clock_out.\n#\n#     clock_in takes the dictionary of workers, the name of the worker, and the\n#     clock in time as parameters. When the worker clocks in, enter and save\n#     their clock in time as the first element in the associated list value.\n#\n#     clock_out takes the same parameters, but with a clock out time instead\n#     of clock in time. When the worker clocks out, enter and save their clock\n#     out time and calculate the hours worked for that day and store it as the\n#     third element in the list.\n#\n#  To make this program a little easier, we’re entering the clock in and\n#  clock out times as integers. As a bonus mission, try adding the times as\n#  strings representing the 24 hour clock (e.g., \"08:00\"), and then figure out\n#  how to calculate the time worked. And you can do this exercise either by\n#  aliasing or copying the dictionary.\n\n\ndef clock_in(dict, worker_name, clock_in_time):\n    dict[worker_name] = [clock_in_time, 0, 0]\n\n\ndef clock_out(dict, worker_name, clock_out_time):\n    dict[worker_name][1] = clock_out_time\n    dict[worker_name][2] = clock_out_time - dict[worker_name][0]\n\n\ndef main():\n    workers = {\n        \"George Spelvin\": [0, 0, 0],\n        \"Jane Doe\": [0, 0, 0],\n        \"John Smith\": [0, 0, 0]}\n    print(workers.get(\"George Spelvin\"))   # should print [0,0,0]\n    clock_in(workers, \"George Spelvin\", 8)\n    clock_out(workers, \"George Spelvin\", 17)\n    print(workers.get(\"George Spelvin\"))   # should print [8, 17, 9]\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"unit1/Chapter 11 - Dictionaries and Tuples/Chapter 11 - Exercise 04.py","file_name":"Chapter 11 - Exercise 04.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"58475339","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\ntry:\n    from email.MIMEMultipart import MIMEMultipart\n    from email.MIMEText import MIMEText\nexcept ImportError: #Python3\n    from email.mime.multipart import MIMEMultipart\n    from email.mime.text import MIMEText\nimport smtplib\nimport os\nfrom webbreaker.notifiers.notifier import Notifier\nfrom webbreaker.webbreakerlogger import Logger\nfrom subprocess import CalledProcessError\ntry:\n    import ConfigParser as configparser\nexcept ImportError: #Python3\n    import configparser\n\ntry:  # Python 2\n    config = configparser.SafeConfigParser()\nexcept NameError:  # Python 3\n    config = configparser.ConfigParser()\n\nclass EmailNotifier(Notifier):\n    def __init__(self, emailer_settings=None):\n        if emailer_settings:\n            self.emailer_settings = emailer_settings\n        else:\n            self.is_agent = True\n            self.__read_agent_settings__()\n        Notifier.__init__(self, \"EmailNotifier\")\n\n    def notify(self, event):\n        try:\n            msg = MIMEMultipart()\n            msg['From'] = self.emailer_settings['from_address']\n            msg['To'] = self.emailer_settings['to_address']\n            msg['Subject'] = \"{0} {1}\".format(event['subject'], event['scanname'])\n\n            html = str(self.emailer_settings['email_template']).format(event['server'],\n                                                                      event['scanname'],\n                                                                      event['scanid'],\n                                                                      event['subject'],\n                                                                      \"\".join(\n                                                                          [\"<li>{0}</li>\".format(t) for t in event['targets']]))\n            msg.attach(MIMEText(html, 'html'))\n\n            mail_server = smtplib.SMTP(self.emailer_settings['smtp_host'], self.emailer_settings['smtp_port'])\n            mail_server.sendmail(msg['From'], msg['To'], msg.as_string())\n\n            mail_server.quit()\n        except (Exception, AttributeError) as e:  # we don't want email failure to stop us, just log that it happened\n            Logger.app.error(\"Error sending email. {}\".format(e.message))\n            Logger.console.error(\"Error sending email, see log: {}!\".format(Logger.app_logfile))\n\n    def cloudscan_notify(self, recipient, subject, git_url, ssc_url, state, scan_id, scan_name):\n        try:\n            msg = MIMEMultipart()\n            msg['From'] = self.from_address\n            msg['To'] = recipient\n            msg['Subject'] = subject\n\n            html = str(self.email_template).format(git_url, ssc_url, scan_name, scan_id, state, self.chatroom)\n            msg.attach(MIMEText(html, 'html'))\n\n            mail_server = smtplib.SMTP(self.smtp_host, self.smtp_port)\n            mail_server.sendmail(msg['From'], msg['To'], msg.as_string())\n\n            mail_server.quit()\n        except (Exception, AttributeError) as e:  # we don't want email failure to stop us, just log that it happened\n            Logger.app.error(\"Error sending email. {}\".format(e.message))\n            Logger.console.error(\"Error sending email, see log: {}!\".format(Logger.app_logfile))\n\n\n    def __read_agent_settings__(self):\n        settings_file = os.path.abspath(os.path.join('webbreaker', 'etc', 'email.ini'))\n        try:\n            config.read(settings_file)\n            self.smtp_host = config.get(\"agent_emailer\", \"smtp_host\")\n            self.smtp_port = config.get(\"agent_emailer\", \"smtp_port\")\n            self.from_address = config.get(\"agent_emailer\", \"from_address\")\n            self.email_template = config.get(\"agent_emailer\", \"email_template\")\n            self.default_to_address = config.get(\"agent_emailer\", \"default_to_address\")\n            self.chatroom = config.get(\"agent_emailer\", \"chatroom\")\n\n        except (configparser.NoOptionError, CalledProcessError) as noe:\n            Logger.console.error(\"{} has incorrect or missing values {}\".format(settings_file, noe))\n        except configparser.Error as e:\n            Logger.app.error(\"Error reading {} {}\".format(settings_file, e))\n\n    def __str__(self):\n        return \"EmailNotifier\"\n","sub_path":"webbreaker/notifiers/emailer.py","file_name":"emailer.py","file_ext":"py","file_size_in_byte":4225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"384162572","text":"from __future__ import division\nimport numpy as np\n\nclass Invest(object):\n    \"\"\" Investment instrument class \"\"\"\n    def __init__(self, asset):\n        \"\"\" Constructor of the class. \n\n        Args:\n            asset (int): Total asset possessed. \n\n        \"\"\"\n        self.asset = asset\n\n    def simulate(self, ntrials, position):\n        \"\"\" Simulate the investment given number of shares to purchase.\n\n        Args:\n            ntrials (int): number of trials to simulate.\n            position (int): number of shares to purchase.\n\n        Returns:\n            (float): daily return rate calculated from the simulation results.\n\n        \"\"\"\n        position_value = self.asset / position    # Value per share given position\n        cumu_ret = []\n        for trial in xrange(ntrials):\n            gain = np.random.uniform(size=position) > 0.49    # Gain money?\n            cumu_ret.append(gain.sum() * position_value * 2)\n\n        daily_ret = [ret/1000-1 for ret in cumu_ret]\n        return daily_ret\n\n    \n","sub_path":"yx887/invest/investment.py","file_name":"investment.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"50926739","text":"\n# Import Libraries\nimport sys\nimport getopt\nfrom time import strftime, gmtime\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Convolution1D, Flatten, MaxPooling1D, Dropout, PReLU\nfrom keras.optimizers import RMSprop, Adam, Nadam\nfrom keras.regularizers import l2\nfrom keras.models import load_model, Model\n\nfrom learning_utils import *\n\n\nPHOVECT_COLS = ['consonant', 'speaker', 'vowel', 'token']\nNAMES = ['Jonny', 'Ira', 'Anna', 'Dani', 'Theresa']\nCONS = ['g', 'b']\nVOWS = ['I', 'o', 'a', 'ae', 'e', 'u']\nPHONEME_DIR = '/Users/Jonny/Github/SVN-ComboPack/ratrixSounds/phonemes/'\n\nMAPBACK = {'g':1,'b':2,\n           'Jonny':1,'Ira':2,'Anna':3,'Dani':4,'Theresa':5,\n           'I':1,'o':2,'a':3,'ae':4,'e':5,'u':6}\n\nSPEC_TYPE = \"mel\" # or \"spec\"\n\nMEL_PARAMS = {\n    \"n_fft\"      : 960,\n    \"hop_length\" : 960,\n    \"n_mels\"     : 64,\n    \"fmin\"       : 1000,\n    \"htk\"        : True\n}\n\nSPEC_PARAMS = {\n    \"window\"          : \"hamming\",\n    \"nperseg\"         : 960,\n    \"noverlap\"        : 480,\n    \"return_onesided\" : True,\n    \"scaling\"         : \"spectrum\"\n}\n\nLOAD_PARAMS = {\n    \"scale\"      : True,\n    \"prop_train\" : 0.9,\n    \"as_row\"     : False,\n\n    # What form should the data be in?\n    # possible vals:\n        #   \"regression\" - fit spectrograms to continuous predictions\n        #   \"trials\"     - fit spectrograms to record of trials\n    \"net_type\"   : \"regression\",\n\n    # Weight training by what?\n    # possible vals:\n        #   None - don't weight\n        #   \"correct\" - weight by accuracy\n    \"weight_by\"  : None\n}\n\n\nDATA_DIR       = '/Users/Jonny/Documents/fixSpeechData/'\nN_CONV_FILTERS = 64\nFILT_LEN       = 32\nLEARN_RATE     = 0.005\nL2_WEIGHT      = 0.001\nOPTIMIZER_TYPE = \"adam\" # nadam, adam, or rmsprop\nNET_TYPE       = LOAD_PARAMS[\"net_type\"]\n\n# Jitter audio so all don't start at same time\nN_JIT = 1\nJIT_AMT = 0.1 # In seconds\n\n\n##########################################\n\nphovect_idx, phovect_xdi, file_idx, file_loc = (\n    make_phoneme_iterators(NAMES, CONS, VOWS, MAPBACK, PHONEME_DIR)\n    )\n\nX_train,cons_train,speak_train,vow_train = audio_from_files(file_loc, phovect_idx, N_JIT, JIT_AMT)\n\n\n\n\n##########################################\n# Check if we were passed a model string or if we are training fresh\ntry:\n    opts,args = getopt.getopt(sys.argv[1:],\"p:\")\nexcept:\n    opts = None\n    args = None\n\nloaded = False\nfor opt, arg in opts:\n    if opt == '-p':\n        print('Loading model {}'.format(arg))\n        model = load_model(arg)\n        loaded = True\n\nif not loaded:\n    print('Building model')\n\n    if OPTIMIZER_TYPE == \"nadam\":\n        optimizer = Nadam(lr=LEARN_RATE)\n    elif OPTIMIZER_TYPE == \"adam\":\n        optimizer = Adam(lr=LEARN_RATE)\n    else:\n        optimizer = RMSprop(lr=LEARN_RATE, clipnorm=1.)\n\n    #Build model\n    #Convolution layers\n    #As per https://arxiv.org/pdf/1412.6806v3.pdf\n    model = Sequential()\n    # FIRST 2 CONV LAYERS\n    model.add(Convolution1D(N_CONV_FILTERS, FILT_LEN,\n                            init='he_normal',\n                            border_mode='same',\n                            W_regularizer=l2(L2_WEIGHT),\n                            activation='relu',\n                            #activity_regularizer=activity_l2(L2_WEIGHT),\n                            batch_input_shape=X_train.shape))\n    # model.add(PReLU())\n    model.add(Dropout(0.1))\n    model.add(Convolution1D(N_CONV_FILTERS, FILT_LEN,\n                            init='he_normal',\n                            border_mode='same',\n                            activation='relu',\n                            W_regularizer=l2(L2_WEIGHT)))\n                            #activity_regularizer=activity_l2(L2_WEIGHT),))\n    # model.add(PReLU())\n    model.add(Dropout(0.1))\n    # model.add(Convolution1D(N_CONV_FILTERS, FILT_LEN,\n    #                         init='he_normal',\n    #                         border_mode='same',\n    #                         # activation=PReLU(),\n    #                         W_regularizer=l2(L2_WEIGHT)))\n    #                         #activity_regularizer=activity_l2(L2_WEIGHT),\n    # model.add(Dropout(0.1))\n    model.add(MaxPooling1D(pool_length=4))\n\n    model.add(Convolution1D(N_CONV_FILTERS*2, FILT_LEN,\n                            init='he_normal',\n                            border_mode='same',\n                            activation='relu',\n                            #activity_regularizer=activity_l2(L2_WEIGHT),\n                            W_regularizer=l2(L2_WEIGHT)))\n    # model.add(PReLU())\n    model.add(Dropout(0.1))\n    model.add(Convolution1D(N_CONV_FILTERS*2, FILT_LEN,\n                            init='he_normal',\n                            border_mode='same',\n                            W_regularizer=l2(L2_WEIGHT),\n                            #activity_regularizer=activity_l2(L2_WEIGHT),\n                            activation='relu'))\n    # model.add(PReLU())\n    model.add(Dropout(0.1))\n    model.add(MaxPooling1D(pool_length=4))\n\n    model.add(Convolution1D(N_CONV_FILTERS*2, FILT_LEN,\n                            init='he_normal',\n                            border_mode='same',\n                            W_regularizer=l2(L2_WEIGHT),\n                            activation='relu'))\n    # model.add(PReLU())\n    #model.add(Dropout(0.1))\n    model.add(Convolution1D(N_CONV_FILTERS*2, FILT_LEN,\n                            init='he_normal',\n                            border_mode='same',\n                            W_regularizer=l2(L2_WEIGHT),\n                            activation='relu'))\n    # model.add(PReLU())\n    model.add(Dropout(0.1))\n    model.add(MaxPooling1D(pool_length=4))\n\n    # model.add(Convolution1D(N_CONV_FILTERS*4, 1,\n    #                         init='he_normal',\n    #                         border_mode='same',\n    #                         W_regularizer=l2(L2_WEIGHT),\n    #                         #activity_regularizer=activity_l2(L2_WEIGHT),\n    #                         # activation=PReLU()))\n    model.add(Dropout(0.1))\n\n    model.add(Flatten())\n    model.add(Dense(2,\n                    activation='softmax',\n                    init=\"he_uniform\",\n                    W_regularizer=l2(L2_WEIGHT)\n                    ))\n    model.add(Dropout(0.1))\n    model.compile(loss='binary_crossentropy',\n                  optimizer=optimizer,\n                  metrics=['binary_accuracy'])\n\n    for i in range(100):\n        X_train,y_train = audio_from_files(file_loc,phovect_idx,N_JIT,JIT_AMT)\n        model.fit(X_train, y_train, batch_size=2, nb_epoch=1)\n        model_str = '/Users/Jonny/Documents/Speech_Models/raw_conv_{}_N{}'.format(strftime(\"%m%d%H%M\", gmtime()),N_CONV_FILTERS)\n        model.save(model_str)\n\n\n","sub_path":"analysis/speech/stim_properties/raw_conv_classifier_standalone.py","file_name":"raw_conv_classifier_standalone.py","file_ext":"py","file_size_in_byte":6678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"121308670","text":"from django.contrib.auth.models import User\nfrom django.test import TestCase\n\nfrom wiki.models import Page\n\n\n\n\nclass PageListViewTests(TestCase):\n    def test_multiple_pages(self):\n        # Make some test data to be displayed on the page.\n        user = User.objects.create()\n\n        Page.objects.create(title=\"My Test Page\", content=\"test\", author=user)\n        Page.objects.create(title=\"Another Test Page\", content=\"test\", author=user)\n\n        # Issue a GET request to the MakeWiki homepage.\n        # When we make a request, we get a response back.\n        response = self.client.get('/')\n\n        # Check that the response is 200 OK.\n        self.assertEqual(response.status_code, 200)\n\n        # Check that the number of pages passed to the template\n        # matches the number of pages we have in the database.\n        responses = response.context['pages']\n        self.assertEqual(len(responses), 2)\n\n        self.assertQuerysetEqual(\n            responses,\n            ['<Page: My Test Page>', '<Page: Another Test Page>'],\n            ordered=False\n        )\n\n","sub_path":"wiki/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"391880845","text":"#!/usr/bin/env python3\n\nimport argparse\nimport datetime\nimport os\nimport sys\nimport threading\nimport urllib.request\nimport math\nimport json\n\n\ndef record_interval(stop_rec, stream_url, target_dir, station, interval):\n    file_extension = \".mp3\"\n\n    conn = urllib.request.urlopen(stream_url)\n    content_type = conn.getheader('Content-Type')\n    if content_type == 'application/ogg' or content_type == 'audio/ogg':\n        file_extension = '.ogg'\n    elif content_type == 'audio/x-mpegurl':\n        print('Sorry, M3U playlists are currently not supported')\n        sys.exit()\n    elif content_type != \"audio/mpeg\":\n        print('Unknown content type \"' + content_type + '\". Assuming mp3.')\n\n    # Ignoring buffered content\n    timestamp = datetime.datetime.now().timestamp()\n    timestamp = math.floor(timestamp + 0.5)\n\n    while not conn.close and datetime.datetime.now().timestamp() - timestamp < 2:\n        conn.read(1024)\n\n    # Start recording\n    timestamp = datetime.datetime.now().timestamp()\n    timestamp = math.floor(timestamp + 0.5)\n\n    filename = os.path.join(target_dir, station.lower().replace(\" \", \"\").replace(\"-\", \"\") + '-')\n    tmp_filename = filename + \".downloading\"\n    with open(tmp_filename, \"wb\") as target:\n        while not stop_rec.is_set() and not conn.closed and datetime.datetime.now().timestamp() - timestamp < interval:\n            target.write(conn.read(1024))\n\n    conn.close()\n    filename = filename + str(timestamp) + file_extension\n    os.rename(tmp_filename, filename)\n    return filename\n\n\ndef record_worker(stop_rec, stream_url, target_dir, station, interval):\n    while not stop_rec.is_set():\n        try:\n            record_interval(stop_rec, stream_url, target_dir, station, interval)\n        except Exception as e:\n            print(e, file=sys.stderr)\n\n\ndef record(stations, target_dir, interval):\n    stop_rec = threading.Event()\n\n    recording_threads = []\n    for station in stations:\n        record_thread = threading.Thread(target=record_worker,\n                                         args=(stop_rec, station['endpoint'], target_dir, station['name'], interval))\n        recording_threads.append(record_thread)\n\n    for record_thread in recording_threads:\n        record_thread.setDaemon(True)\n        record_thread.start()\n\n    for record_thread in recording_threads:\n        record_thread.join()\n\n    stop_rec.set()\n\n\ndef parse_arguments():\n    parser = argparse.ArgumentParser(description='This script record internet streaming live stations.')\n\n    parser.add_argument('-df', \"--data_folder\", type=str, help=\"Destination folder for recorded audios.\",\n                        default=\"data\")\n    parser.add_argument('-i', \"--interval\", type=int, help=\"Interval time to reset recording\", default=10 * 60)\n    parser.add_argument('-f', \"--stations\", type=str, help=\"Json file with stations list\", default=\"stations.json\")\n\n    return parser.parse_args()\n\n\ndef main():\n    args = parse_arguments()\n\n    stations = []\n    try:\n        with open(args.stations) as target:\n            stations.extend(json.loads(target.read()))\n    except Exception as e:\n        print(\"Error loading config file: \", e, file=sys.stderr)\n\n    if not os.path.isdir(args.data_folder):\n        print('Folder to save data does not exist', file=sys.stderr)\n        exit(0)\n\n    record(stations, args.data_folder, args.interval)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"s1-record-stream.py","file_name":"s1-record-stream.py","file_ext":"py","file_size_in_byte":3395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"589768133","text":"import copy\nfrom typing import Dict, Union, List\nfrom pathlib import Path\n\nfrom typeguard import typechecked\nfrom zsvision.zs_utils import memcache, concat_features\n\nfrom utils import memory_summary\nfrom base.base_dataset import BaseDataset\n\n\nclass DiDeMo(BaseDataset):\n\n    @staticmethod\n    @typechecked\n    def dataset_paths(training_file=None) -> Dict[str, Union[Path, str, Dict, List[str]]]:\n        subset_paths = {}\n        test_splits = {\n            \"val\": \"val_list.txt\",\n            \"test\": \"test_list.txt\",\n            \"public_server_val\": \"public_server_val.txt\",\n            \"public_server_test\": \"public_server_test.txt\",\n        }\n        for split_name, fname in test_splits.items():\n            if training_file is None:\n                subset_paths[split_name] = {\"train\": \"train_list.txt\", \"val\": fname}\n            else:\n                subset_paths[split_name] = {\"train\": training_file, \"val\": fname}\n\n        feature_names = BaseDataset.common_feat_names()\n        custom_paths = {\n            \"audio\": [\"aggregated_audio_feats/vggish-audio-raw.pickle\"],\n            \"ocr\": [\"aggregated_ocr_feats/ocr-feats.pkl\"],\n            \"speech\": [\"aggregated_speech_feats/stt_w2v.pickle\"],\n            \"face\": [\"aggregated_facefeats_25fps_256px_stride1/face-avg.pickle\"],\n        }\n\n        text_feat_paths = BaseDataset.common_text_feat_paths()\n        # include non-standard text features\n        text_feat_paths[\"openai\"] = \"openai-feats.pkl\"\n        text_feat_paths = {key: Path(\"aggregated_text_feats\") / fname\n                           for key, fname in text_feat_paths.items()}\n\n        challenge_text_feat_paths = {\n            key: Path(\"aggregated_text_feats\") / f\"{key}{fname.suffix}\"\n            for key, fname in text_feat_paths.items()\n        }\n        feature_info = {\n            \"custom_paths\": custom_paths,\n            \"feature_names\": feature_names,\n            \"subset_list_paths\": subset_paths,\n            \"text_feat_paths\": text_feat_paths,\n            \"challenge_text_feat_paths\": challenge_text_feat_paths,\n            \"raw_captions_path\": \"raw-captions.pkl\",\n        }\n        return feature_info\n\n    def load_features(self):\n        root_feat = Path(self.root_feat)\n        if self.distil_params is not None:\n            self.distil_features = {}\n            d_base_path = self.distil_params['base_path']\n\n            teachers = list(map(lambda x: root_feat / Path(d_base_path + x), self.distil_params['teachers']))\n\n            for i, f_name in enumerate(teachers):\n                self.distil_features[i] = memcache(f_name)\n\n        feat_names = {key: self.visual_feat_paths(key) for key in\n                      self.paths[\"feature_names\"]}\n        feat_names.update(self.paths[\"custom_paths\"])\n        features = {}\n        for expert, rel_names in feat_names.items():\n            if expert not in self.ordered_experts:\n                continue\n            feat_paths = tuple([root_feat / rel_name for rel_name in rel_names])\n            if len(feat_paths) == 1:\n                features[expert] = memcache(feat_paths[0])\n            else:\n                # support multiple forms of feature (e.g. max and avg pooling). For\n                # now, we only support direct concatenation\n                msg = f\"{expert}: Only direct concatenation of muliple feats is possible\"\n                print(f\"Concatenating aggregates for {expert}....\")\n                assert self.feat_aggregation[expert][\"aggregate\"] == \"concat\", msg\n                axis = self.feat_aggregation[expert][\"aggregate-axis\"]\n                x = concat_features.cache_info()  # pylint: disable=no-value-for-parameter\n                print(f\"concat cache info: {x}\")\n                features_ = concat_features(feat_paths, axis=axis)\n                memory_summary()\n\n                # Make separate feature copies for each split to allow in-place filtering\n                features[expert] = copy.deepcopy(features_)\n\n        self.features = features\n        if self.challenge_mode:\n            self.load_challenge_text_features()\n        else:\n            self.raw_captions = memcache(root_feat / self.paths[\"raw_captions_path\"])\n            text_feat_path = root_feat / self.paths[\"text_feat_paths\"][self.text_feat]\n            self.text_features = memcache(text_feat_path)\n\n    def sanity_checks(self):\n        msg = (f\"Expected to have single test caption for DiDemo, since we assume\"\n               f\"that the captions are fused (but using {self.num_test_captions})\")\n        assert self.num_test_captions == 1, msg\n","sub_path":"data_loader/DiDeMo_dataset.py","file_name":"DiDeMo_dataset.py","file_ext":"py","file_size_in_byte":4540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"613931041","text":"import bluetooth\nfrom bluetooth import lookup_name\nimport time\nimport RPi.GPIO as GPIO\nfrom Arduino import arduino\n\nboard = arduino('9600')\n\nwhile True:\n    GPIO.setmode(GPIO.BOARD)\n    GPIO.setup(40, GPIO.OUT)\n\n    def RPIbluetooth():\n        print(\"Checking \" + time.strftime(\"%a, %d %b %Y %H:%M:%S\", time.gmtime()))\n        result = bluetooth.lookup_name('A0:10:81:91:E8:EF', timeout=1)\n        if (result is not None):\n            print(\"User present\")\n            GPIO.output(40, True)\n            time.sleep(3)\n            GPIO.output(40, False)\n        else: \n            GPIO.output(40, False)\n            print(\"User out of range\")\n            time.sleep(2)\n            RPIbluetooth()\n\n    if arduino.digitalRead(12) == \"HIGH\":\n        print('High')\n        RPIbluetooth()\n        time.sleep(1)\n    else:\n        print('fail')\n        time.sleep(2)\n","sub_path":"RPiBluetooth.py","file_name":"RPiBluetooth.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"51981583","text":"from bbfreeze import Freezer #@UnresolvedImport\n\nfolder_name =\"SysInterceptor\" \n\nf = Freezer(folder_name, includes=())\nf.addScript(\"..\\\\interceptor_tray.py\", gui_only=True)\nf.use_compression = 0\nf.include_py = True\nf()    # starts the freezing proces\n\nimport os;\nos.system(\"copy ..\\\\CARRIER.ICO .\\\\SysInterceptor\")\nos.system(\"copy .\\\\SysInterceptor\\\\*.* ..\\\\..\\\\..\\\\..\\\\output\\\\final_release\")\nos.system(\"xcopy /E/Y ..\\\\calculator\\\\*.* ..\\\\..\\\\..\\\\..\\\\output\\\\final_release\\\\calculator\\\\\")\n","sub_path":"kb_codes/eclipse/py/syscarrier/src/interceptor/exe_generator/bbfreeze_exec_generator.py","file_name":"bbfreeze_exec_generator.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"632441767","text":"# Simple Linear Regression\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport os\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\n# Importing the dataset\ndataset = pd.read_csv('../data/Salary_Data.csv')\nX = dataset.loc[:, 'YearsExperience'].values.reshape(-1, 1)\ny = dataset.loc[:, 'Salary'].values.reshape(-1, 1)\n\n# Splitting the dataset into the Training set and Test set\nX_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                    test_size=1/3,\n                                                    random_state=0)\n\n# Feature Scaling\n\"\"\"from sklearn.preprocessing import StandardScaler\nsc_X = StandardScaler()\nX_train = sc_X.fit_transform(X_train)\nX_test = sc_X.transform(X_test)\nsc_y = StandardScaler()\ny_train = sc_y.fit_transform(y_train)\"\"\"\n\n# Fitting Simple Linear Regression to the Training set\nregressor = LinearRegression(fit_intercept=True,\n                             normalize=False,\n                             copy_X=True,\n                             n_jobs=None)\nregressor.fit(X_train, y_train)\n\n# Predicting the Test set results\ny_pred = regressor.predict(X_test)\n\n# Visualising the Training set results\nfig = plt.figure(figsize=(16, 8))\nplt.scatter(X_train, y_train, color='red')\nplt.plot(X_train, regressor.predict(X_train), color='blue')\nplt.title('Salary vs Experience (Training set)')\nplt.xlabel('Years of Experience')\nplt.ylabel('Salary')\n# plt.show()\nplt.savefig('../img/linreg_training_set.png')\n\n# Visualising the Test set results\nfig = plt.figure(figsize=(16, 8))\nplt.scatter(X_test, y_test, color='red')\nplt.plot(X_train, regressor.predict(X_train), color='blue')\nplt.title('Salary vs Experience (Test set)')\nplt.xlabel('Years of Experience')\nplt.ylabel('Salary')\n# plt.show()\nplt.savefig('../img/linreg_test_set.png')\n","sub_path":"src/simple_linear_regression.py","file_name":"simple_linear_regression.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"219806495","text":"import numpy as np\nimport pandas as pd\nfrom time import time\nimport shutil\nimport os\nimport sys\nimport inspect\nimport unittest\n\ncurrentdir = os.path.dirname(\n    os.path.abspath(\n        inspect.getfile(\n            inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\nsys.path.insert(0, parentdir)\n\nfrom src.lr.models.transformers.processor import filter_df_by_label, clean_df  # noqa\nfrom src.lr.models.transformers.XLNetWrapper import XLNetWrapper  # noqa\n\n\nbase_path = parentdir + \"/src/data/toy/\"\n\n\nclass BasicXLNetTraining(unittest.TestCase):\n\n    @classmethod\n    def tearDown(cls):\n        if os.path.exists(\"example.log\"):\n            os.remove(\"example.log\")\n\n        if os.path.exists(\"xlnet\"):\n            shutil.rmtree(\"xlnet\")\n\n        paths = [\"cached_dev_to_eval_200\", \"cached_test_200\", \"cached_train_200\",\n                 \"cached_train_to_eval_200\"]\n\n        for path in paths:\n            path = parentdir + path\n            if os.path.exists(path):\n                os.remove(path)\n\n    def test_xlnet_training(self):\n\n        hyperparams = {\"local_rank\": -1,\n                       \"max_seq_length\": 200,\n                       \"overwrite_cache\": False,\n                       \"num_train_epochs\": 1.0,\n                       \"per_gpu_train_batch_size\": 32,\n                       \"per_gpu_eval_batch_size\": 32,\n                       \"gradient_accumulation_steps\": 1,\n                       \"learning_rate\": 5e-5,\n                       \"weight_decay\": 0.0,\n                       \"adam_epsilon\": 1e-8,\n                       \"max_grad_norm\": 1.0,\n                       \"max_steps\": 7,\n                       \"warmup_steps\": 0,\n                       \"save_steps\": 6,\n                       \"no_cuda\": False,\n                       \"n_gpu\": 1,\n                       \"model_name_or_path\": \"xlnet\",\n                       \"output_dir\": \"xlnet\",\n                       \"random_state\": 42,\n                       \"fp16\": False,\n                       \"fp16_opt_level\": \"01\",\n                       \"device\": \"cpu\",\n                       \"verbose\": False,\n                       \"model_type\": \"xlnet\",\n                       \"pad_on_left\": False,\n                       \"pad_token\": 0,\n                       \"pad_token_segment_id\": 0,\n                       \"mask_padding_with_zero\": True,\n                       \"eval_sample_size\": 100,\n                       \"n_cores\": 7,\n                       \"base_path\": base_path + \"cached_\",\n                       \"pretrained_weights\": 'xlnet-base-cased'}\n\n        # ## loading base model\n\n        my_xlnet = XLNetWrapper(hyperparams)\n\n        # ## Loading DFs\n\n        train_path = base_path + \"train.csv\"\n        test_path = base_path + \"dev.csv\"\n        df = pd.read_csv(train_path)\n        test = pd.read_csv(test_path)\n        test = test.sample(100, random_state=hyperparams[\"random_state\"])\n        df = clean_df(df, 7)\n        test = clean_df(test, 7)\n\n        # ### Eval 1\n\n        pred = my_xlnet.predict(test, transform=True, mode=\"test\")\n        lmap = my_xlnet.processor.get_label_map()\n        filtered = filter_df_by_label(test.dropna()).reset_index(drop=True)\n        before_acc = np.mean(filtered.label.map(lambda x: lmap[x]) == pred)\n\n        # ### Fit\n\n        global_step, tr_loss, train_time = my_xlnet.fit(df)\n\n        # ### Eval 2\n\n        eval_path = base_path + \"cached_test_200\"\n        pred = my_xlnet.predict(None, transform=False, path=eval_path)\n        lmap = my_xlnet.processor.get_label_map()\n        filtered = filter_df_by_label(test.dropna()).reset_index(drop=True)\n        after_acc = np.mean(filtered.label.map(lambda x: lmap[x]) == pred)\n\n        self.assertTrue(np.round(tr_loss, 3) == 1.134)\n        self.assertTrue(before_acc == 0.25)\n        self.assertTrue(after_acc == 0.32)\n\n\nif __name__ == '__main__':\n    unittest.main(verbosity=2)\n","sub_path":"tests/BasicXLNetTraining.py","file_name":"BasicXLNetTraining.py","file_ext":"py","file_size_in_byte":3856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"621586988","text":"#Frontend\n\nfrom tkinter import *\nimport tkinter.messagebox\nimport pshop_backend\nimport mysql.connector\n\n\nroot = Tk()\nroot.title(\"Petshop Database Management System\")\nroot.geometry(\"1200x700\")\n#root.config(bg=\"grey\")\n\nTitleFrame = Frame(root, bd=2, padx=5, pady=8, relief=RIDGE)\nTitleFrame.pack(side = TOP)\n\nLabel(TitleFrame ,font=('arial', 20, 'bold'), text=\"Petshop Database Management\").grid()\n\n\n#===============================================================================\ndef est_conn():\n    pshop_backend.connect_to_database(hostname.get(), username.get(), password.get(), databasename.get())\n    tkinter.messagebox.showinfo(\"Register\", \"Connected to database\")\n    ConnectFrame.destroy()\n    DisplayData()\n\ndef connect_to_database():\n    global ConnectFrame\n    ConnectFrame = Toplevel(root)\n    ConnectFrame.title(\"Connect to database\")\n    ConnectFrame.geometry(\"300x300\")\n\n    global hostname, username, password, databasename\n\n    hostname = StringVar()\n    username = StringVar()\n    password = StringVar()\n    databasename = StringVar()\n\n    hostname.set(\"localhost\")\n    databasename.set(\"my_petshop_gui2\")   # sets the default value\n\n\n    Label(ConnectFrame, text = \"Please enter details below\").pack()\n    Label(ConnectFrame, text =\"\").pack()\n    Label(ConnectFrame, text =\"Mysql server address\").pack()\n    mysql_add = Entry(ConnectFrame, textvariable = hostname)\n    mysql_add.pack()\n    Label(ConnectFrame, text =\" Mysql Username\").pack()\n    mysql_uname = Entry(ConnectFrame, textvariable = username)\n    mysql_uname.pack()\n    Label(ConnectFrame, text =\"Mysql Password\").pack()\n    mysql_pass = Entry(ConnectFrame, textvariable = password, show=\"*\")\n    mysql_pass.pack()\n    Label(ConnectFrame, text =\"Database Name\").pack()\n    mysql_pass = Entry(ConnectFrame, textvariable = databasename)\n    mysql_pass.pack()\n    Label(ConnectFrame, text =\"\").pack()\n    Button(ConnectFrame, text=\"Connect\", command = est_conn).pack()\n\n#=========================Functions======================================\ndef DisplayData():\n    info_list.delete(0,END)\n    #info_list.insert(0, \"PetID     PetType            NumInStock      NumSold     Price                 PetSubType\")\n    rows = pshop_backend.viewDataAll()\n    #info_list.insert(END, rows[0][1], rows[2])\n    for row in rows:\n        info_list.insert(END, str(row[0]) + '%15s' % str(row[1]) + '%15s' % str(row[2])\\\n                         + '%15s' % str(row[3]) + '%18s' % str(row[4]) + '%15s' % str(row[5]) + '%25s' % str(row[6]) + '%15s' % str(row[7])\\\n                         + '%15s' % str(row[8]) + '%15s' % str(row[9]), str(\"\"))\n\n\ndef m_exit():\n    root.destroy()\n\n\n#====================================================================================\n\nMesgFrame = LabelFrame(root, font=('arial', 20, 'bold'), text=\"Pet & Client Info\\n\")\nMesgFrame.pack(side = TOP)\n\ninfo_list = Listbox(MesgFrame, width = 100, height = 20)\ninfo_list.grid(row = 0, column = 0, padx = 20)\n\n#================================Pet Table=============================================\n#========================================================================================================\n#========================================================================================================\n#========================================================================================================\ndef pet_table():\n\n    PetFrame = Toplevel(root)\n    PetFrame.title(\"Pet Info\")\n    PetFrame.geometry(\"600x700\")\n\n    Label(PetFrame).grid(row=0, column=1)\n    Label(PetFrame).grid(row=1, column=1)\n    Label(PetFrame, text = \"Pet Input\").grid(row=2, column=2)\n    lblPetType = Label(PetFrame, text = \"Pet Type\")\n    lblPetType.grid(row=3, column = 0, columnspan = 2)\n    txtPetType = Entry(PetFrame, width = 20)\n    txtPetType.grid(row=3, column = 2, columnspan = 2)\n\n    lblPetSubType = Label(PetFrame, text = \"Pet Sub Type\")\n    lblPetSubType.grid(row=4, column = 0, columnspan = 2)\n    txtPetSubType = Entry(PetFrame, width = 20)\n    txtPetSubType.grid(row=4, column = 2, columnspan = 2)\n\n    lblNumStock = Label(PetFrame, text = \"Number In Stock\")\n    lblNumStock.grid(row=5, column = 0, columnspan = 2)\n    txtNumStock = Entry(PetFrame, width = 20)\n    txtNumStock.grid(row=5, column = 2, columnspan = 2)\n\n    lblNumSold = Label(PetFrame, text = \"Number Sold\")\n    lblNumSold.grid(row=6, column = 0, columnspan = 2)\n    txtNumSold = Entry(PetFrame, width = 20)\n    txtNumSold.grid(row=6, column = 2, columnspan = 2)\n\n    lblPetPrice = Label(PetFrame, text = \"Price in USD\")\n    lblPetPrice.grid(row=7, column = 0, columnspan = 2)\n    txtPetPrice = Entry(PetFrame, width = 20)\n    txtPetPrice.grid(row=7, column = 2, columnspan = 2)\n\n    Label(PetFrame).grid(row=8, column=1)\n    Label(PetFrame).grid(row=9, column=1)\n    Label(PetFrame, text = \"Pet Table\").grid(row=10, column=2)\n\n    #=======Functions for the Function===============\n    def display_pet():\n        pet_list.delete(0,END)\n    #info_list.insert(0, \"PetID     PetType            NumInStock      NumSold     Price                 PetSubType\")\n        rows = pshop_backend.viewData('pet_info')\n    #info_list.insert(END, rows[0][1], rows[2])\n        for row in rows:\n            #pet_list.insert(END, str(row[0]) + '%15s' % str(row[1]) + '%15s' % str(row[2])\\\n                       #  + '%15s' % str(row[3]) + '%18s' % str(row[4]) + '%15s' % str(row[5]), str(\"\"))\n            pet_list.insert(END, row)\n\n\n    def clearData():\n     txtPetType.delete(0,END)\n     txtPetSubType.delete(0,END)\n     txtNumStock.delete(0,END)\n     txtNumSold.delete(0,END)\n     txtPetPrice.delete(0,END)\n\n     def add_data():\n         pass\n\n    def pet_Rec(event):\n            global psd\n            searchStd = pet_list.curselection()[0]\n            psd = pet_list.get(searchStd)\n\n            txtPetType.delete(0,END)\n            txtPetType.insert(END, psd[1])\n            txtPetSubType.delete(0,END)\n            txtPetSubType.insert(END,psd[2])\n            txtNumStock.delete(0,END)\n            txtNumStock.insert(END,psd[3])\n            txtNumSold.delete(0,END)\n            txtNumSold.insert(END,psd[4])\n            txtPetPrice.delete(0,END)\n            txtPetPrice.insert(END,psd[5])\n\n    def addData():\n        if(len(txtPetType.get())!=0):\n            pshop_backend.addPetRec(txtPetType.get(), txtPetSubType.get(), txtNumStock.get(), txtNumSold.get(), txtPetPrice.get())\n            pet_list.delete(0,END)\n            pet_list.insert(END, (txtPetType.get(), txtPetSubType.get(), txtNumStock.get(), txtNumSold.get(), txtPetPrice.get()))\n        print(txtPetType.get())\n\n    def DeleteData():\n        if(len(txtPetSubType.get())!=0):\n            pshop_backend.deleteRec('pet_info', 'pet_id', psd[0])\n            clearData()\n            display_pet()\n\n\n    def searchDatabase():\n        pet_list.delete(0,END)\n        for row in pshop_backend.searchData(txtPetType.get(), txtPetSubType.get(), txtNumStock.get(), txtNumSold.get(), txtPetPrice.get()):\n            pet_list.insert(END, row, str(\"\"))\n\n\n    def update():\n        if len(txtPetType.get()) != 0:\n            pshop_backend.dataUpdate(psd[0], txtPetType.get(), txtPetSubType.get(), txtNumStock.get(), txtNumSold.get(), txtPetPrice.get())\n            display_pet()\n\n\n\n#===========Listbox & ScrollBar=======================\n\n    scrollbar = Scrollbar(PetFrame)\n    scrollbar.grid(row=11, column=9, sticky='ns')\n\n    pet_list = Listbox(PetFrame, width = 60, height = 20, yscrollcommand = scrollbar.set)\n    pet_list.bind('<<ListboxSelect>>', pet_Rec)\n    pet_list.grid(row=11, column=0, columnspan=9, padx=15)\n    scrollbar.config(command = pet_list.yview)\n\n\n    #=========Buttons=============================\n    petAddData = Button(PetFrame, text=\"Add New\", command = addData)\n    petAddData.grid(row=12, column=0)\n\n    petDisplayData = Button(PetFrame, text=\"Display\", command = display_pet)\n    petDisplayData.grid(row=12, column=1)\n\n    petClearData = Button(PetFrame, text=\"Clear\", command = clearData)\n    petClearData.grid(row=12, column=2)\n\n    petDeleteData = Button(PetFrame, text=\"Delete\", command = DeleteData)\n    petDeleteData.grid(row=12, column=3)\n\n    petSearchData = Button(PetFrame, text=\"Search\", command = searchDatabase)\n    petSearchData.grid(row=12, column=4)\n\n    petUpdateData = Button(PetFrame, text=\"Update\", command = update)\n    petUpdateData.grid(row=12, column=5)\n\n    petExit = Button(PetFrame, text=\"Exit\", command = lambda: pshop_backend.close_win(PetFrame))\n    petExit.grid(row=12, column=6)\n\n\n#================================Transaction Table=============================================\n#========================================================================================================\n#========================================================================================================\n#========================================================================================================\ndef tran_table():\n\n    tranFrame = Toplevel(root)\n    tranFrame.title(\"Transaction Info\")\n    tranFrame.geometry(\"600x700\")\n\n    Label(tranFrame).grid(row=0, column=1)\n    Label(tranFrame).grid(row=1, column=1)\n    Label(tranFrame, text = \"Transaction Input\").grid(row=2, column=2)\n    lblDate = Label(tranFrame, text = \"Date\")\n    lblDate.grid(row=3, column = 0, columnspan = 2)\n    txtDate = Entry(tranFrame, width = 20)\n    txtDate.grid(row=3, column = 2, columnspan = 2)\n\n    lblCustomer = Label(tranFrame, text = \"Customer Name\")\n    lblCustomer.grid(row=4, column = 0, columnspan = 2)\n    txtCustomer = Entry(tranFrame, width = 20)\n    txtCustomer.grid(row=4, column = 2, columnspan = 2)\n\n    lblPetId = Label(tranFrame, text = \"PetId\")\n    lblPetId.grid(row=5, column = 0, columnspan = 2)\n    txtPetId = Entry(tranFrame, width = 20)\n    txtPetId.grid(row=5, column = 2, columnspan = 2)\n\n    lblQuantity = Label(tranFrame, text = \"Quantity\")\n    lblQuantity.grid(row=6, column = 0, columnspan = 2)\n    txtQuantity = Entry(tranFrame, width = 20)\n    txtQuantity.grid(row=6, column = 2, columnspan = 2)\n\n    lblTotalPrice = Label(tranFrame, text = \"Total Price in USD\")\n    lblTotalPrice.grid(row=7, column = 0, columnspan = 2)\n    txtTotalPrice = Entry(tranFrame, width = 20)\n    txtTotalPrice.grid(row=7, column = 2, columnspan = 2)\n\n    Label(tranFrame).grid(row=8, column=1)\n    Label(tranFrame).grid(row=9, column=1)\n    Label(tranFrame, text = \"Transaction Table\").grid(row=10, column=2)\n\n\n    #=============================Functions============================\n    #=======Functions for the Function===============\n    def display_tran():\n        tran_list.delete(0,END)\n    #info_list.insert(0, \"PetID     PetType            NumInStock      NumSold     Price                 PetSubType\")\n        rows = pshop_backend.viewData('transaction_info')\n    #info_list.insert(END, rows[0][1], rows[2])\n        for row in rows:\n            #tran_list.insert(END, str(row[0]) + '%15s' % str(row[1]) + '%15s' % str(row[2])\\\n            #             + '%15s' % str(row[3]) + '%18s' % str(row[4]) + '%15s' % str(row[5]), str(\"\"))\n            tran_list.insert(END, row)\n\n\n    def clearData():\n        txtDate.delete(0,END)\n        txtCustomer.delete(0,END)\n        txtPetId.delete(0,END)\n        txtQuantity.delete(0,END)\n        txtTotalPrice.delete(0,END)\n\n\n\n    def tran_Rec(event):\n        global sd\n        searchStd = tran_list.curselection()[0]\n        sd = tran_list.get(searchStd)\n\n        txtDate.delete(0,END)\n        txtDate.insert(END, sd[1])\n        txtCustomer.delete(0,END)\n        txtCustomer.insert(END,sd[2])\n        txtPetId.delete(0,END)\n        txtPetId.insert(END,sd[3])\n        txtQuantity.delete(0,END)\n        txtQuantity.insert(END,sd[4])\n        txtTotalPrice.delete(0,END)\n        txtTotalPrice.insert(END,sd[5])\n\n\n    def addData():\n        if(len(txtCustomer.get())!=0):\n            pshop_backend.addTranRec(str(txtDate.get()), txtCustomer.get(), txtPetId.get(), txtQuantity.get(), txtTotalPrice.get())\n            tran_list.delete(0,END)\n            tran_list.insert(END, (txtDate.get(), txtCustomer.get(), txtPetId.get(), txtQuantity.get(), txtTotalPrice.get()))\n\n\n    def DeleteData():\n        if(len(txtCustomer.get())!=0):\n            pshop_backend.deleteRec('transaction_info', 'tran_id', sd[0])\n            clearData()\n            display_tran()\n\n    def searchDatabase():\n        tran_list.delete(0,END)\n        for row in pshop_backend.searchTranData(txtDate.get(), txtCustomer.get(), txtPetId.get(), txtQuantity.get(), txtTotalPrice.get()):\n            tran_list.insert(END,row,str(\"\"))\n\n    def update():\n        if len(txtDate.get()) != 0:\n            pshop_backend.tran_dataUpdate(sd[0], txtDate.get(), txtCustomer.get(), txtPetId.get(), txtQuantity.get(), txtTotalPrice.get())\n            display_tran()\n\n\n    #===========Listbox & ScrollBar=======================\n\n    scrollbar = Scrollbar(tranFrame)\n    scrollbar.grid(row=11, column=9, sticky='ns')\n\n    tran_list = Listbox(tranFrame, width = 60, height = 20, yscrollcommand = scrollbar.set)\n    tran_list.bind('<<ListboxSelect>>', tran_Rec)\n    tran_list.grid(row=11, column=0, columnspan=9, padx=15)\n    scrollbar.config(command = tran_list.yview)\n\n    #===================Buttons=========================\n    tranAddData = Button(tranFrame, text=\"Add New\", command = addData)\n    tranAddData.grid(row=12, column=0)\n\n    tranDisplayData = Button(tranFrame, text=\"Display\", command = display_tran)\n    tranDisplayData.grid(row=12, column=1)\n\n    tranClearData = Button(tranFrame, text=\"Clear\", command = clearData)\n    tranClearData.grid(row=12, column=2)\n\n    tranDeleteData = Button(tranFrame, text=\"Delete\", command = DeleteData)\n    tranDeleteData.grid(row=12, column=3)\n\n    tranSearchData = Button(tranFrame, text=\"Search\", command = searchDatabase)\n    tranSearchData.grid(row=12, column=4)\n\n    tranUpdateData = Button(tranFrame, text=\"Update\", command = update)\n    tranUpdateData.grid(row=12, column=5)\n\n    tranExit = Button(tranFrame, text=\"Exit\", command = lambda: pshop_backend.close_win(tranFrame))\n    tranExit.grid(row=12, column=6)\n    #========================================================================================================\n    #========================================================================================================\n    #========================================================================================================\n\n\n\n\n\n#===========================================================================\nBotFrame = Frame(root)\nBotFrame.pack(side=TOP)\nbot0 = Button(BotFrame, text=\"Connect to Database\", command = connect_to_database)\nbot0.grid(row=0, column=0)\nbot4 = Button(BotFrame, text=\"Display Database\", command = DisplayData)\nbot4.grid(row=0, column=1)\nbot1 = Button(BotFrame, text=\"Show Pet Database\", command = pet_table)\nbot1.grid(row=0, column=2)\nbot2 = Button(BotFrame, text=\"Show Customer Database\", command = tran_table)\nbot2.grid(row=0, column=3)\nbot3 = Button(BotFrame, text=\"Exit\", command = m_exit)\nbot3.grid(row=0, column=4)\n\n\n\nroot.mainloop()\n","sub_path":"petshop_gui/pshop_frontend.py","file_name":"pshop_frontend.py","file_ext":"py","file_size_in_byte":15021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"283188327","text":"from django import forms\nfrom django.contrib.auth.models import User\nfrom mainApp.models import Customers, CustomerBankAccount, CustomerCreditCard, CustomerAndroidPay, CustomerApplePay, CustomerPaypal\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.core.exceptions import ValidationError\n\nclass UserForm(forms.ModelForm):\n    password = forms.CharField(widget=forms.PasswordInput())\n\n    class Meta:\n        model = User\n        # The username should match the Customer ID\n        fields = ('username', 'email', 'password')\n\nclass CustomersForm(forms.ModelForm):\n    class Meta:\n        model = Customers\n        fields = (\n                'customerid',\n                'companyname',\n                'contactname',\n                'contacttitle',\n                'address',\n                'city',\n                'region',\n                'postalcode',\n                'country',\n                'phone',\n                'fax'\n        )\n\n        labels = {\n                'customerid' :  _('CustomerID'),\n                'companyname' : _('Company Name'),\n                'contactname' : _('Contact Name'),\n                'contacttitle' : _('Contact Title'),\n                'address' : _('Address'),\n                'city' : _('City'),\n                'region' : _('Region'),\n                'postalcode' : _('Postal Code'),\n                'country' : _('Country'),\n                'phone' : _('Phone'),\n                'fax' : _('Fax')\n        }\n\nclass AdvancedSearchForm(forms.Form):\n    productname = forms.CharField(label='Product Name', max_length=100, required=False)\n    suppliername = forms.CharField(label='Supplier Name', max_length=100, required=False)\n\n    CATEGORIES = (\n        (None, '------------'),\n        (1, 'Beverages'),\n        (2, 'Condiments'),\n        (3, 'Confections'),\n        (4, 'Dairy Products'),\n        (5, 'Grains/Cereals'),\n        (6, 'Meat/Poultry'),\n        (7, 'Produce'),\n        (8, 'Seafood')\n    )\n\n    categoryid = forms.ChoiceField(label='Category', choices=CATEGORIES, required=False)\n\n    SUPPLIERS = (\n        (None, '------------'),\n        (1, 'Exotic Liquids'),\n        (2, 'New Orleans Cajun Delights'),\n        (3, \"Grandma Kelly's Homestead\"),\n        (4, 'Tokyo Traders'),\n        (5, \"Cooperativa de Quesos 'Las Cabras'\"),\n        (6, \"Mayumi's\"),\n        (7, 'Pavlova, Ltd.'),\n        (8, 'Specialty Biscuits, Ltd.'),\n        (9, 'PB Knckebrd AB'),\n        (10, 'Refrescos Americanas LTDA'),\n        (11, 'Heli Swaren GmbH & Co. KG'),\n        (12, 'Plutzer Lebensmittelgromrkte AG'),\n        (13, 'Nord-Ost-Fisch Handelsgesellschaft mbH'),\n        (14, 'Formaggi Fortini s.r.l.'),\n        (15, 'Norske Meierier'),\n        (16, 'Bigfoot Breweries'),\n        (17, 'Svensk Sjfda AB'),\n        (18, 'Aux joyeux ecclsiastiques'),\n        (19, 'New England Seafood Cannery'),\n        (20, 'Leka Trading'),\n        (21, 'Lyngbysild'),\n        (22, 'Zaanse Snoepfabriek'),\n        (23, 'Karkki Oy'),\n        (24, \"G'day, Mate\"),\n        (25, 'Ma Maison'),\n        (26, 'Pasta Buttini s.r.l.'),\n        (27, 'Escargots Nouveaux'),\n        (28, 'Gai pturage'),\n        (29, \"Forts d'rables\"),\n    )\n\n    supplierid = forms.ChoiceField(label='Supplier', choices=SUPPLIERS, required=False)\n\n    def clean(self):\n        cleaned_data = super(AdvancedSearchForm, self).clean()\n        productname = cleaned_data.get(\"productname\")\n        suppliername = cleaned_data.get(\"suppliername\")\n        categoryid = cleaned_data.get(\"categoryid\")\n        supplierid = cleaned_data.get(\"supplierid\")\n\n        if not (productname or suppliername or categoryid or supplierid):\n            raise ValidationError(\"You must specify at least one field\")\n\n        # This list holds the keys with null values from cleaned_data\n        validKeys = [k for k,v in cleaned_data.items() if v is not '']\n\n        cleaned_data[\"validKeys\"] = validKeys\n\n        return cleaned_data\n\nclass QuickSearchForm(forms.Form):\n    productname = forms.CharField(label='Product Name', max_length=100, required=False)\n\n    CATEGORIES = (\n        (None, '------------'),\n        (1, 'Beverages'),\n        (2, 'Condiments'),\n        (3, 'Confections'),\n        (4, 'Dairy Products'),\n        (5, 'Grains/Cereals'),\n        (6, 'Meat/Poultry'),\n        (7, 'Produce'),\n        (8, 'Seafood')\n    )\n\n    categoryid = forms.ChoiceField(label='Category', choices=CATEGORIES, required=False)\n\n    def clean(self):\n        cleaned_data = super(QuickSearchForm, self).clean()\n        productname = cleaned_data.get(\"productname\")\n        categoryid = cleaned_data.get(\"categoryid\")\n\n        if not (productname or categoryid):\n            raise ValidationError(\"You must specify at least one field\")\n\n        # This list holds the keys with null values from cleaned_data\n        validKeys = [k for k,v in cleaned_data.items() if v is not '']\n\n        cleaned_data[\"validKeys\"] = validKeys\n\n        return cleaned_data\n\nclass CustomerBankAccountForm(forms.ModelForm):\n    class Meta:\n        model = CustomerBankAccount\n        fields = (\n                'customerid',\n                'routingno',\n                'accountno',\n        )\n\n        labels = {\n                'customerid' :  _('CustomerID'),\n                'routingno' : _('Routing Number'),\n                'accountno' : _('Account Number'),\n        }\n\nclass CustomerCreditCardForm(forms.ModelForm):\n    CARD_TYPE_CHOICES = (\n        ('visa', 'Visa'),\n        ('mastercard', 'Mastercard'),\n    )\n    cardtype = forms.ChoiceField(label='Category', choices=CARD_TYPE_CHOICES)\n    cardno = forms.IntegerField(label='Card Number')\n    cvv = forms.IntegerField(label='CVV')\n    expiration = forms.DateField(widget=forms.SelectDateWidget())\n\n    def clean(self):\n        cleaned_data = super(CustomerCreditCardForm, self).clean()\n        cardtype = cleaned_data.get(\"cardtype\")\n        cardno = cleaned_data.get(\"cardno\")\n        cvv = cleaned_data.get(\"cvv\")\n        expiration = cleaned_data.get(\"expiration\")\n\n        # List of validation errors\n        errors = []\n\n        # Add the CVV and CardNo length errors\n        if cvv and cardno:\n            cardno_length = len(str(cardno))\n            cvv_length = len(str(cvv))\n            if cardno_length > 19 or cardno_length < 16:\n                errors.append(_(\"Card number must be between 16 and 19 digits\"))\n            if cvv_length != 3:\n                errors.append(_(\"CVV must be 3 digits\"))\n\n        # Raise all validation errors\n        if len(errors) > 0:\n            raise ValidationError(errors)\n\n        return cleaned_data\n\n    class Meta:\n        model = CustomerCreditCard\n        fields = (\n                'customerid',\n                'cardtype',\n                'cardno',\n                'cvv',\n                'expiration',\n        )\n\n        labels = {\n                'customerid' :  _('CustomerID'),\n                'cardtype' : _('Card Type'),\n                'cardno' : _('Card Number'),\n                'cvv' : _('CVV'),\n                'expiration' : _('Expiration'),\n        }\n\nclass CustomerAndroidPayForm(forms.ModelForm):\n    email = forms.EmailField(label='Email address')\n\n    class Meta:\n        model = CustomerAndroidPay\n        fields = (\n                'customerid',\n                'email',\n        )\n\n        labels = {\n                'customerid' :  _('CustomerID'),\n                'email' : _('Email address'),\n        }\n\nclass CustomerApplePayForm(forms.ModelForm):\n    email = forms.EmailField(label='Email address')\n\n    class Meta:\n        model = CustomerApplePay\n        fields = (\n                'customerid',\n                'email',\n        )\n\n        labels = {\n                'customerid' :  _('CustomerID'),\n                'email' : _('Email address'),\n        }\n\nclass CustomerPaypalForm(forms.ModelForm):\n    email = forms.EmailField(label='Email address')\n\n    class Meta:\n        model = CustomerPaypal\n        fields = (\n                'customerid',\n                'email',\n        )\n\n        labels = {\n                'customerid' :  _('CustomerID'),\n                'email' : _('Email address'),\n        }\n\nclass ShipmentTypeForm(forms.Form):\n    shipment_choices = (\n        (1, 'Ground'),\n        (2, 'Air'),\n        (3, 'Sea'),\n    )\n\n    shipmentType = forms.ChoiceField(label='Shipment Method', choices=shipment_choices)\n\ndef getAvailablePaymentTypes(customerid):\n    # Get a list of the payment options that a given user has\n    # CustomerBankAccount, CustomerCreditCard, CustomerAndroidPay, CustomerApplePay, CustomerPaypal\n    bank_account_exists = CustomerBankAccount.objects.filter(customerid=customerid).exists()\n    credit_card_exists = CustomerCreditCard.objects.filter(customerid=customerid).exists()\n    android_pay_exists = CustomerAndroidPay.objects.filter(customerid=customerid).exists()\n    apple_pay_exists = CustomerApplePay.objects.filter(customerid=customerid).exists()\n    paypal_exists = CustomerPaypal.objects.filter(customerid=customerid).exists()\n\n    bank_account_choice = ('bank_account', 'Bank Account')\n    credit_card_choice = ('credit_card', 'Credit Card')\n    android_pay_choice = ('android_pay', 'Android Pay')\n    apple_pay_choice = ('apple_pay', 'Apple Pay')\n    paypal_choice = ('paypal', 'Paypal')\n\n    # Pair up the boolean that checks if the payment type exists with its choice tuple\n    exists_choices_pairs = [\n        [bank_account_exists, bank_account_choice],\n        [credit_card_exists, credit_card_choice],\n        [android_pay_exists, android_pay_choice],\n        [apple_pay_exists, apple_pay_choice],\n        [paypal_exists, paypal_choice],\n    ]\n\n    choices_list = []\n\n    for pair in exists_choices_pairs:\n        if pair[0]:\n            # If the payment type exists, add the tuple to the choices list\n            choices_list.append(pair[1])\n\n    # Cast the choices list to a proper choices tuple\n    choices = tuple(choices_list)\n\n    return choices\n\nclass PaymentTypeForm(forms.Form):\n    def __init__(self, customerid, *args, **kwargs):\n        super(PaymentTypeForm, self).__init__(*args, **kwargs)\n        self.fields['paymentType'] = forms.ChoiceField(label='Payment Type', choices=getAvailablePaymentTypes(customerid))\n\nclass ShippingAddressForm(forms.Form):\n    address = forms.CharField(label='Address', widget=forms.TextInput(attrs={'placeholder': 'Street Address'}), max_length=60, required=True)\n    city = forms.CharField(label='City', widget=forms.TextInput(attrs={'placeholder': 'City'}), max_length=15, required=True)\n    region = forms.CharField(label='Region', widget=forms.TextInput(attrs={'placeholder': 'Region'}), max_length=15, required=True)\n    postalcode = forms.CharField(label='Postal Code', widget=forms.TextInput(attrs={'placeholder': 'Postal Code'}), max_length=10, required=True)\n    country = forms.CharField(label='Country', widget=forms.TextInput(attrs={'placeholder': 'Country'}), max_length=15, required=True)\n\n    def clean(self):\n        cleaned_data = super(ShippingAddressForm, self).clean()\n\n        address = cleaned_data.get(\"address\")\n        city = cleaned_data.get(\"city\")\n        region = cleaned_data.get(\"region\")\n        postalcode = cleaned_data.get(\"postalcode\")\n        country = cleaned_data.get(\"country\")\n\n        if not (address and city and region and postalcode and country):\n            raise ValidationError(\"You must specify all address fields\")\n\n        return cleaned_data\n\nclass InventoryReportingForm(forms.Form):\n    productname = forms.CharField(label='Product Name', max_length=100, required=False)\n    suppliername = forms.CharField(label='Supplier Name', max_length=100, required=False)\n\n    CATEGORIES = (\n        (None, '------------'),\n        (1, 'Beverages'),\n        (2, 'Condiments'),\n        (3, 'Confections'),\n        (4, 'Dairy Products'),\n        (5, 'Grains/Cereals'),\n        (6, 'Meat/Poultry'),\n        (7, 'Produce'),\n        (8, 'Seafood')\n    )\n\n    categoryid = forms.ChoiceField(label='Category', choices=CATEGORIES, required=False)\n    unitprice = forms.DecimalField(label='Unit Price', max_digits=10, decimal_places=4, required=False)\n    unitsinstock__lte = forms.IntegerField(label='In Stock (Upper)', required=False)\n    unitsinstock__gte = forms.IntegerField(label='In Stock (Lower)', required=False)\n    reorderlevel__lte = forms.IntegerField(label='ReorderLevel (Upper)', required=False)\n    reorderlevel__gte = forms.IntegerField(label='ReorderLevel (Lower)', required=False)\n    discontinued = forms.CharField(label='Discontinued', required=False)\n\n    def clean(self):\n        cleaned_data = super(InventoryReportingForm, self).clean()\n        productname = cleaned_data.get(\"productname\")\n        suppliername = cleaned_data.get(\"suppliername\")\n        categoryid = cleaned_data.get(\"categoryid\")\n        unitprice = cleaned_data.get(\"unitprice\")\n        unitsinstock__lte = cleaned_data.get(\"unitsinstock__lte\")\n        unitsinstock__gte = cleaned_data.get(\"unitsinstock__gte\")\n        reorderlevel__lte = cleaned_data.get(\"reorderlevel__lte\")\n        reorderlevel__gte = cleaned_data.get(\"reorderlevel__gte\")\n        discontinued = cleaned_data.get(\"discontinued\")\n        if discontinued:\n            discontinued = ord(discontinued)\n\n        if not (productname or suppliername or categoryid or unitprice or (unitsinstock__lte and unitsinstock__gte) or (reorderlevel__lte and reorderlevel__gte) or discontinued):\n            raise ValidationError(\"You must specify at least one field\")\n        if unitsinstock__lte or unitsinstock__gte:\n            if unitsinstock__lte and unitsinstock__gte:\n                if unitsinstock__lte > unitsinstock__gte:\n                    raise ValidationError(\"Upper means the field must be the upper bound\")\n            else:\n                # One is there but not the other\n                raise ValidationError(\"You must specify both in stock fields if you specify one\")\n        if reorderlevel__lte or reorderlevel__gte:\n            if reorderlevel__lte and reorderlevel__gte:\n                if reorderlevel__lte > reorderlevel__gte:\n                    raise ValidationError(\"Upper means the field must be the upper bound\")\n            else:\n                raise ValidationError(\"You must specify both reorder level fields if you specify one\")\n\n        # This list holds the keys with null values from cleaned_data\n        validKeys = [k for k,v in cleaned_data.items() if (v is not '') and (v is not None)]\n\n        cleaned_data[\"validKeys\"] = validKeys\n\n        return cleaned_data\n","sub_path":"mainApp/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":14498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"332252852","text":"import numpy as np\nimport torch\nimport collections\nimport elf\nimport wandb\nimport matplotlib.pyplot as plt\nfrom elf.segmentation.multicut import multicut_kernighan_lin\nfrom elf.segmentation.features import project_node_labels_to_pixels\nfrom rag_utils import find_dense_subgraphs\n\nimport rewards\nfrom utils.graphs import collate_edges, get_edge_indices\nfrom utils.general import random_label_cmap\n\nState = collections.namedtuple(\"State\", [\"raw\", \"sp_seg\", \"edge_ids\", \"edge_feat\", \"node_feat\", \"subgraph_indices\",\n                                         \"sep_subgraphs\",  \"gt_edge_weights\"])\n\n\nclass MulticutEmbeddingsEnv:\n\n    def __init__(self, cfg, device):\n        super(MulticutEmbeddingsEnv, self).__init__()\n\n        self.reset()\n        self.cfg = cfg\n        self.device = device\n        self.max_p = torch.nn.MaxPool2d(3, padding=1, stride=1)\n        self.reward_function = eval(\"rewards.\" + self.cfg.reward_function)(cfg.s_subgraph)\n\n\n    def execute_action(self, actions, logg_vals=None, post_stats=False, post_images=False, tau=None, train=True):\n        self.current_edge_weights = actions.squeeze()\n        self.current_soln = self.get_current_soln(self.current_edge_weights)\n\n        self.sg_current_edge_weights = []\n        for i, sz in enumerate(self.cfg.s_subgraph):\n            self.sg_current_edge_weights.append(\n                self.current_edge_weights[self.subgraph_indices[i].view(-1, sz)])\n\n            reward = self.reward_function(prediction_segmentation=self.current_soln.long(),\n                                                                      gt=self.sg_gt_edges, dir_edges=self.dir_edge_ids,\n                                                                      superpixel_segmentation=self.init_sp_seg.long(),\n                                                                      actions=actions,\n                                                                      subgraph_indices=self.subgraph_indices)\n\n        if post_stats:\n            tag = \"train/\" if train else \"validation/\"\n            wandb.log({tag + \"avg_return\": reward[-1].item()})\n            if post_images:\n                mc_soln = self.gt_soln[-1].cpu() if self.gt_edge_weights is not None else torch.zeros(self.raw.shape[-2:])\n                wandb.log({tag + \"pred_mean\": wandb.Histogram(self.current_edge_weights.view(-1).cpu().numpy())})\n                fig, axes = plt.subplots(2, 3, sharex='col', sharey='row', gridspec_kw={'hspace': 0, 'wspace': 0})\n                axes[0, 0].imshow(self.gt_seg[-1].cpu().squeeze(), cmap=random_label_cmap(), interpolation=\"none\")\n                axes[0, 0].set_title('gt')\n                if self.raw.ndim == 3:\n                    axes[0, 1].imshow(self.raw[-1, 0])\n                else:\n                    axes[0, 1].imshow(self.raw[-1])\n                axes[0, 1].set_title('raw image')\n                axes[0, 2].imshow(self.init_sp_seg[-1].cpu(), cmap=random_label_cmap(), interpolation=\"none\")\n                axes[0, 2].set_title('superpixels')\n                axes[1, 0].imshow(mc_soln, cmap=random_label_cmap(), interpolation=\"none\")\n                axes[1, 0].set_title('mc_gt')\n                axes[1, 1].imshow(self.init_sp_seg[-1].cpu())\n                axes[1, 1].set_title('embed')\n                axes[1, 2].imshow(self.current_soln[-1].cpu(), cmap=random_label_cmap(), interpolation=\"none\")\n                axes[1, 2].set_title('prediction')\n                wandb.log({tag: [wandb.Image(fig, caption=\"state\")]})\n                plt.close('all')\n            if logg_vals is not None:\n                for key, val in logg_vals.items():\n                    wandb.log({tag + key: val})\n\n        self.acc_reward.append(reward[-1].item())\n        return reward\n\n    def get_state(self):\n        return State(self.raw, self.batched_sp_seg, self.edge_ids, self.edge_feat, self.node_feat,\n                     self.subgraph_indices, self.sep_subgraphs, self.gt_edge_weights)\n\n    def update_data(self, raw, gt, edge_ids, gt_edges, sp_seg, rags, edge_feat, node_feat, *args, **kwargs):\n        bs = raw.shape[0]\n        dev = raw.device\n        for _sp_seg in sp_seg:\n            assert all(_sp_seg.unique() == torch.arange(_sp_seg.max() + 1, device=dev))\n\n        self.rags = rags\n        self.gt_seg, self.init_sp_seg = gt.squeeze(1), sp_seg.squeeze(1)\n        self.raw = raw\n        self.node_feat = node_feat if node_feat is None else torch.cat(node_feat, 0)\n        self.edge_feat = edge_feat if edge_feat is None else torch.cat(edge_feat, 0)\n\n        subgraphs, self.sep_subgraphs = [], []\n        _subgraphs, _sep_subgraphs = find_dense_subgraphs([eids.transpose(0, 1).cpu().numpy() for eids in edge_ids], self.cfg.s_subgraph)\n        _subgraphs = [torch.from_numpy(sg.astype(np.int64)).to(dev).permute(2, 0, 1) for sg in _subgraphs]\n        _sep_subgraphs = [torch.from_numpy(sg.astype(np.int64)).to(dev).permute(2, 0, 1) for sg in _sep_subgraphs]\n\n        self.dir_edge_ids = [torch.cat([_edge_ids, torch.stack([_edge_ids[1], _edge_ids[0]], dim=0)], dim=1) for _edge_ids in edge_ids]\n        self.n_nodes = [eids.max() + 1 for eids in edge_ids]\n        self.edge_ids, (self.n_offs, self.e_offs) = collate_edges(edge_ids)\n        for i in range(len(self.cfg.s_subgraph)):\n            subgraphs.append(torch.cat([sg + self.n_offs[i] for i, sg in enumerate(_subgraphs[i*bs:(i+1)*bs])], -2).flatten(-2, -1))\n            self.sep_subgraphs.append(torch.cat(_sep_subgraphs[i*bs:(i+1)*bs], -2).flatten(-2, -1))\n\n        self.subgraphs = subgraphs\n        self.subgraph_indices = get_edge_indices(self.edge_ids, subgraphs)\n\n        batched_sp = []\n        for sp, off in zip(self.init_sp_seg, self.n_offs):\n            batched_sp.append(sp + off)\n        self.batched_sp_seg = torch.stack(batched_sp, 0)\n\n        self.gt_edge_weights = gt_edges\n        if self.gt_edge_weights is not None:\n            self.gt_edge_weights = torch.cat(self.gt_edge_weights)\n            self.gt_soln = self.get_current_soln(self.gt_edge_weights)\n            self.sg_gt_edges = [self.gt_edge_weights[sg].view(-1, sz) for sz, sg in\n                                zip(self.cfg.s_subgraph, self.subgraph_indices)]\n\n        self.current_edge_weights = torch.ones(self.edge_ids.shape[1], device=self.edge_ids.device) / 2\n\n        return\n\n    def get_current_soln(self, edge_weights):\n        p_min = 0.001\n        p_max = 1.\n        segmentations = []\n        for i in range(1, len(self.e_offs)):\n            probs = edge_weights[self.e_offs[i-1]:self.e_offs[i]]\n            probs -= probs.min()\n            probs /= probs.max()\n            costs = (p_max - p_min) * probs + p_min\n            costs = (torch.log((1. - costs) / costs)).detach().cpu().numpy()\n            node_labels = elf.segmentation.multicut.multicut_decomposition(self.rags[i-1], costs, internal_solver='greedy-additive', n_threads=4)\n            mc_seg = project_node_labels_to_pixels(self.rags[i-1], node_labels).squeeze()\n\n            mc_seg = torch.from_numpy(mc_seg.astype(np.long)).to(self.device)\n            # mask = mc_seg[None] == torch.unique(mc_seg)[:, None, None]\n            # mc_seg = (mask * (torch.arange(len(torch.unique(mc_seg)), device=mc_seg.device)[:, None, None] + 1)).sum(0) - 1\n\n            segmentations.append(mc_seg)\n        return torch.stack(segmentations, dim=0)\n\n    def reset(self):\n        self.acc_reward = []\n","sub_path":"environments/multicut.py","file_name":"multicut.py","file_ext":"py","file_size_in_byte":7354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"244394334","text":"\nimport random\ndef main():\n  print('***********************************')\n  print('** Welcome to BlackJack! **')\n  print('***********************************')\n\n\n\n  player()\n  dealer()\n\ndef hit():\n  first = random.randint(1,13)\n  if first == 11 or first == 12 or first == 13:\n    first = 10\n  return first\n\ndef player():\n  player_hand = hit()\n  print('Your first card is: %d' % player_hand)\n  \n  print()\n  answer = 'y'\n  while player_hand < 21 and answer == 'y':\n   print('Do you want another card?')\n   answer = input('Type y for Yes, n for No: ')\n   print()\n   if answer == 'y':\n     draw = random.randint(1,13)\n     if draw == 11 or draw == 12 or draw == 13:\n       draw = 10\n     player_hand = player_hand + draw\n     print('Your next card is: %d' % draw)\n     print('Your combined value is %d' % player_hand)\n     print()\n   if answer == 'n':\n     player_hand = player_hand\n     print('Your combined value is %d' % player_hand)\n     break\n   if player_hand > 21:\n     print('Bust')\n     break\n   if player_hand == 21:\n     print('21!')\n     break\n  return player_hand\n\ndef dealer():\n  dealer_hand = 0\n  if dealer_hand < 17:\n    while dealer_hand < 17:\n      print('Dealer draws another card.')\n      dealer_hand = hit()\n      print('Dealer\\'s card is: %d' % dealer_hand)\n      dealer_hand = dealer_hand + hit()\n      print('Dealer\\'s value is %d, you have %d' % (dealer_hand,dealer_hand)) #how to get player_hand here\n  if dealer_hand >= 17:\n    print('Stand')\n  return dealer_hand\nmain()\n","sub_path":"Blackjack.py","file_name":"Blackjack.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"522604784","text":"from twisted.internet import reactor,protocol\nfrom twisted.protocols import basic\nclass EchoProtocol(basic.LineReceiver):\n    def lineReceived(self, line):\n        if line.decode() == 'quit':\n            self.sendLine(\"Goodbye.\".encode())\n            self.transport.loseConnection()\n        else:\n            say=\"You said: \" + line.decode()\n            self.sendLine(say.encode())\n\nclass EchoServerFactory(protocol.ServerFactory):\n    protocol = EchoProtocol\n\nif __name__==\"__main__\":\n    port = 5001\n    reactor.listenTCP(port, EchoServerFactory())\n    reactor.run()\n","sub_path":"base_python/t_1019.py","file_name":"t_1019.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"306516885","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import validation_curve, cross_val_score\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.feature_selection import SelectKBest, chi2\nfrom sklearn.pipeline import make_pipeline\n\n# Separate dataset into training/test set\ndf = pd.read_csv(\"code/diabetic_retinopathy.csv\", header=None)\n\nX = df.iloc[:, :-1]\ny = df.iloc[:,-1]\nX_train, X_test = np.split(X, [900], axis = 0)\ny_train, y_test = np.split(y, [900], axis = 0)\n\nX_train.shape\n\n\n# Part (a)\nprint(\"Part (a)\")\nprint(\"Training set - Label count\")\nprint(y_train.value_counts())\n\nmajority_label = y_train.mode()[0] # Find majority label\ny_test_pred = pd.Series(majority_label, index=np.arange(len(y_test)))\ny_train_pred = pd.Series(majority_label, index=np.arange(len(y_train)))\n\nprint(\"Accuracy score (train): \",accuracy_score(y_train, y_train_pred))\nprint(\"Accuracy score (test): \",accuracy_score(y_test, y_test_pred))\n\n# Part (b)\nprint(\"Part (b)\")\n\nclassifier = DecisionTreeClassifier(random_state = 347)\nclassifier.fit(X_train, y_train)\n\ny_test_pred = classifier.predict(X_test)\ny_train_pred = classifier.predict(X_train)\n\nprint(\"Accuracy score (train): \",accuracy_score(y_train, y_train_pred))\nprint(\"Accuracy score (test): \",accuracy_score(y_test, y_test_pred))\n\n# Part (c)\nprint(\"Part (c)\")\nprint(\"Max depth: \", classifier.tree_.max_depth)\n\nmax_depth = range(1,11)\n\nval_score = []\ntrain_score = []\n\nfor i in max_depth:\n    classifier = DecisionTreeClassifier(max_depth = i, random_state = 347).fit(X_train, y_train)\n    y_test_pred, y_train_pred = classifier.predict(X_test), classifier.predict(X_train)\n    train_score.append(accuracy_score(y_train, y_train_pred))\n    val_score.append(accuracy_score(y_test, y_test_pred))\n\nfig1 = plt.figure()\nax1 = plt.axes()\nax1.plot(max_depth, train_score, color ='blue', label = 'training score')\nax1.plot(max_depth, val_score, color ='red', label = 'prediction score')\nax1.set_xlabel('max depth')\nax1.set_ylabel('score')\nax1.legend(loc='best');\n\nfig1.savefig('fig_1.png')\n\nplt.plot(max_depth, train_score, color ='blue', label = 'training score')\nplt.plot(max_depth, val_score, color ='red', label = 'training score')\n\n# Part (d)\nprint(\"Part (d)\")\n\n# Part (e)\nprint(\"Part (e)\")\ntrain_score, val_score = validation_curve(classifier, X_train, y_train, 'max_depth', max_depth, cv = 4)\n\nfig2 = plt.figure()\nax2 = plt.axes()\nax2.plot(max_depth, np.mean(train_score,1), color = 'blue', label = 'training score')\nax2.plot(max_depth, np.mean(val_score,1), color = 'red', label = 'validation score')\nax2.legend(loc='best')\nax2.set_xlabel('max depth')\nax2.set_ylabel('score');\nfig2.savefig('fig_2.png')\n\nmax_depth\nval_score\nnp.mean(val_score,1)\n\nmax_depth_best = max_depth[np.argmax(np.mean(val_score,1))]\nprint(\"Max depth with best performance: \", max_depth_best)\n\nclassifier = DecisionTreeClassifier(max_depth = max_depth_best, random_state = 347).fit(X_train, y_train)\ny_test_pred, y_train_pred = classifier.predict(X_test), classifier.predict(X_train)\nprint(\"Accuracy score (train): \",accuracy_score(y_train, y_train_pred))\nprint(\"Accuracy score (test): \",accuracy_score(y_test, y_test_pred))\n\n\n# Part (f)\nprint(\"Part (f)\")\n\nmodel = make_pipeline(SelectKBest(chi2),\n                      DecisionTreeClassifier(max_depth = 10, random_state = 347))\nfeatures = range(1,11)\n\ntrain_score, val_score = validation_curve(model, X_train, y_train, param_name = \"selectkbest__k\", param_range = features, cv = 4)\nfig3 = plt.figure()\nax3 = plt.axes()\nax3.plot(features, np.mean(train_score,1), color = 'blue', label = 'training score')\nax3.plot(features, np.mean(val_score,1), color = 'red', label = 'validation score')\nax3.legend(loc='best')\nax3.set_xlabel('features')\nax3.set_ylabel('score');\nfig3.savefig('fig_3.png')\n\n\nfeatures_best = features[np.argmax(np.mean(val_score,1))]\nprint(\"Number features with best performance: \", features_best)\n\nmodel.set_params(selectkbest__k = features_best)\nmodel.fit(X_train, y_train)\ny_test_pred, y_train_pred = model.predict(X_test), model.predict(X_train)\nprint(\"Accuracy score (train): \",accuracy_score(y_train, y_train_pred))\nprint(\"Accuracy score (test): \",accuracy_score(y_test, y_test_pred))\n","sub_path":"Problem Set 1/code/question_2.py","file_name":"question_2.py","file_ext":"py","file_size_in_byte":4270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"33710092","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom . models import Book\n\nfrom django.contrib.auth import login\nfrom django.contrib.auth.decorators import login_required\n\nfrom django.urls import resolve\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\n\nfrom django.core.mail import send_mail\nfrom django.template.loader import render_to_string\nfrom django.views.decorators.cache import cache_control\n\n\n\n\n\n# Create your views here.\n\n@login_required\ndef addBook(request):\n    return render(request,\"list.html\")\n\n@login_required\ndef book(request):\n    return render(request,\"addBookButton.html\")\n\n\n    \n@login_required\ndef sell(request):\n    return render(request,\"book_registration.html\")\n# def donate(request):\n#     return HttpResponseRedirect(\"/addbook\")\n\n@login_required\ndef donate(request):\n    return render(request,\"donate_book_register.html\")\n\n\n\n\n@cache_control(no_cache=True, must_revalidate=True)\n@login_required\ndef added(request):\n    book_name=request.POST['book_name']\n    bookowner=request.user.id\n    category=request.POST['category']\n    writer=request.POST['writer']\n    desc=request.POST['description']\n    image=request.FILES['book_photo']\n    condition=request.POST['condition']\n    actualPrice=int(request.POST['actual_price'])\n    sellingPrice=int(request.POST['selling_price'])\n    publication=request.POST['publication']\n\n    displaySellingPrice=sellingPrice+(10*sellingPrice/100)\n\n    # url_name=resolve(request.path).url_name\n    # if(url_name == 'added_sell'):\n    #     donation=request.POST['false']\n    # else:\n    #     donation=request.POST['true']\n\n    \n\n    book=Book(bname=book_name, bookowner=bookowner, category=category, writer=writer, description=desc, image=image, condition=condition, actual_price=actualPrice, selling_price=sellingPrice, publication=publication, display_selling_price=displaySellingPrice)\n    book.save()\n    return render(request,\"book_registration.html\")\n\n\n\n\n@login_required\ndef added(request):\n    book_name=request.POST['book_name']\n    bookowner=request.user.id\n    category=request.POST['category']\n    writer=request.POST['writer']\n    desc=request.POST['description']\n    image=request.FILES['book_photo']\n    condition=request.POST['condition']\n    actualPrice=int(request.POST['actual_price'])\n    sellingPrice=int(request.POST['selling_price'])\n    publication=request.POST['publication']\n    user=request.user.username\n\n\n    displaySellingPrice=sellingPrice+(10*sellingPrice/100)\n\n    # url_name=resolve(request.path).url_name\n    # if(url_name == 'added_sell'):\n    #     donation=request.POST['false']\n    # else:\n    #     donation=request.POST['true']\n    \n    book=Book(bookowner=bookowner,bname=book_name,  category=category, writer=writer, description=desc, image=image, condition=condition, actual_price=actualPrice, selling_price=sellingPrice, publication=publication, display_selling_price=displaySellingPrice)\n    book.save()\n\n    # subject = 'Book Uploaded'\n    # message = render_to_string('bookaddedmail.html', {\n    #             'user': user,\n    #             'Book_Name' : book_name,\n    #             'Author' : writer,\n    #             'Price' : sellingPrice,\n    #             'Publication' : publication,\n    #  })\n    # from_email=[settings.EMAIL_HOST_USER]\n    # to_email=[request.user.email]\n    # send_mail(subject=subject,from_email=from_email,recipient_list=to_email,message=message,fail_silently=False)\n    return render(request,\"book_registration.html\",{'msg':'success'})\n    \n\n    \n\n\n\n\n\n@login_required\ndef addeddonate(request):\n    book_name=request.POST['book_name']\n    bookowner=request.user.id\n    category=request.POST['category']\n    writer=request.POST['writer']\n    desc=request.POST['description']\n    image=request.FILES['book_photo']\n    condition=request.POST['condition']\n    publication=request.POST['publication']\n\n    donation=True\n\n\n\n\n    book=Book(bname=book_name, bookowner=bookowner, category=category, writer=writer,donation=donation, description=desc, image=image, condition=condition, actual_price=0, selling_price=0, publication=publication, display_selling_price=0)\n    book.save()\n    return render(request,\"donate_book_register.html\")\n\n\n\n\n\n","sub_path":"last_this_is_last_final_testing_code-master/addBook/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"443915970","text":"# from bs4 import BeautifulSoup\nimport requests\nimport json\nimport csv\nfrom utils import convert_epoch, hash_file, check_duplicate\n\n# Must use API becuse we cannot extract data through HTMl\nsource = requests.get(\"https://www.nyse.com/api/ipo-center/calendar\").text\n\n# Open a CSV file to save the entries\ncsv_file = open('IPO_Filings.csv', 'w')\ncsv_output = csv.writer(csv_file)\ncsv_output.writerow([\"Date Filed\", \"Company\", \"Ticker\", \"Industry\", \"Price\"])\n\n# Traverse JSON payload to get information\ndata_dict = json.loads(source)\ncompanies_list = data_dict['calendarList']\nfor company in companies_list:\n    # Get useful information from the filings\n    issuer = company['issuer_nm']\n    ticker = company['symbol']\n    industry = company['custom_group_industry_nm']\n    price_range = company['current_file_price_range_usd']\n    date_filed = convert_epoch(company['init_file_dt']) # Change this to expected?\n\n    if company['deal_status_desc'] == 'Filed': # This will need to be 'Expected' I think\n        csv_output.writerow([date_filed, issuer, ticker, industry, price_range])\n\n# Close the file\ncsv_file.close()\n\n# Call function to hash current file\nhash_number = hash_file()\n\n# Call function to check if this hash already exists\ncheck_duplicate(hash_number)\n","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"52620936","text":"# Copyright 2021 The Feast Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport logging\nimport multiprocessing\nimport socket\nfrom contextlib import closing\nfrom datetime import datetime, timedelta\nfrom multiprocessing import Process\nfrom sys import platform\nfrom typing import Any, Dict, List\n\nimport pandas as pd\nimport pytest\nfrom _pytest.nodes import Item\n\nfrom feast import FeatureStore\nfrom feast.wait import wait_retry_backoff\nfrom tests.data.data_creator import create_dataset\nfrom tests.integration.feature_repos.integration_test_repo_config import (\n    IntegrationTestRepoConfig,\n)\nfrom tests.integration.feature_repos.repo_configuration import (\n    AVAILABLE_OFFLINE_STORES,\n    AVAILABLE_ONLINE_STORES,\n    Environment,\n    TestData,\n    construct_test_environment,\n    construct_universal_test_data,\n)\nfrom tests.integration.feature_repos.universal.data_sources.file import (\n    FileDataSourceCreator,\n)\n\nlogger = logging.getLogger(__name__)\n\n\ndef pytest_configure(config):\n    if platform in [\"darwin\", \"windows\"]:\n        multiprocessing.set_start_method(\"spawn\")\n    else:\n        multiprocessing.set_start_method(\"fork\")\n    config.addinivalue_line(\n        \"markers\", \"integration: mark test that has external dependencies\"\n    )\n    config.addinivalue_line(\"markers\", \"benchmark: mark benchmarking tests\")\n    config.addinivalue_line(\n        \"markers\", \"goserver: mark tests that use the go feature server\"\n    )\n\n\ndef pytest_addoption(parser):\n    parser.addoption(\n        \"--integration\",\n        action=\"store_true\",\n        default=False,\n        help=\"Run tests with external dependencies\",\n    )\n    parser.addoption(\n        \"--benchmark\", action=\"store_true\", default=False, help=\"Run benchmark tests\",\n    )\n    parser.addoption(\n        \"--goserver\",\n        action=\"store_true\",\n        default=False,\n        help=\"Run tests that use the go feature server\",\n    )\n\n\ndef pytest_collection_modifyitems(config, items: List[Item]):\n    should_run_integration = config.getoption(\"--integration\") is True\n    should_run_benchmark = config.getoption(\"--benchmark\") is True\n    should_run_goserver = config.getoption(\"--goserver\") is True\n\n    integration_tests = [t for t in items if \"integration\" in t.keywords]\n    if not should_run_integration:\n        for t in integration_tests:\n            items.remove(t)\n    else:\n        items.clear()\n        for t in integration_tests:\n            items.append(t)\n\n    benchmark_tests = [t for t in items if \"benchmark\" in t.keywords]\n    if not should_run_benchmark:\n        for t in benchmark_tests:\n            items.remove(t)\n    else:\n        items.clear()\n        for t in benchmark_tests:\n            items.append(t)\n\n    goserver_tests = [t for t in items if \"goserver\" in t.keywords]\n    if should_run_goserver:\n        items.clear()\n        for t in goserver_tests:\n            items.append(t)\n\n\n@pytest.fixture\ndef simple_dataset_1() -> pd.DataFrame:\n    now = datetime.utcnow()\n    ts = pd.Timestamp(now).round(\"ms\")\n    data = {\n        \"id_join_key\": [1, 2, 1, 3, 3],\n        \"float_col\": [0.1, 0.2, 0.3, 4, 5],\n        \"int64_col\": [1, 2, 3, 4, 5],\n        \"string_col\": [\"a\", \"b\", \"c\", \"d\", \"e\"],\n        \"ts_1\": [\n            ts,\n            ts - timedelta(hours=4),\n            ts - timedelta(hours=3),\n            ts - timedelta(hours=2),\n            ts - timedelta(hours=1),\n        ],\n    }\n    return pd.DataFrame.from_dict(data)\n\n\n@pytest.fixture\ndef simple_dataset_2() -> pd.DataFrame:\n    now = datetime.utcnow()\n    ts = pd.Timestamp(now).round(\"ms\")\n    data = {\n        \"id_join_key\": [\"a\", \"b\", \"c\", \"d\", \"e\"],\n        \"float_col\": [0.1, 0.2, 0.3, 4, 5],\n        \"int64_col\": [1, 2, 3, 4, 5],\n        \"string_col\": [\"a\", \"b\", \"c\", \"d\", \"e\"],\n        \"ts_1\": [\n            ts,\n            ts - timedelta(hours=4),\n            ts - timedelta(hours=3),\n            ts - timedelta(hours=2),\n            ts - timedelta(hours=1),\n        ],\n    }\n    return pd.DataFrame.from_dict(data)\n\n\ndef start_test_local_server(repo_path: str, port: int):\n    fs = FeatureStore(repo_path)\n    fs.serve(\"localhost\", port, no_access_log=True)\n\n\n@pytest.fixture(scope=\"session\")\ndef environment(request, worker_id):\n    e = construct_test_environment(\n        request.param, worker_id=worker_id, fixture_request=request\n    )\n\n    yield e\n\n    e.feature_store.teardown()\n    e.data_source_creator.teardown()\n    if e.online_store_creator:\n        e.online_store_creator.teardown()\n\n\n_config_cache = {}\n\n\ndef pytest_generate_tests(metafunc: pytest.Metafunc):\n    \"\"\"\n    This function receives each test function (wrapped in Metafunc)\n    at the collection stage (before tests started).\n    Here we can access all fixture requests made by the test as well as its markers.\n    That allows us to dynamically parametrize the test based on markers and fixtures\n    by calling metafunc.parametrize(...).\n\n    See more examples at https://docs.pytest.org/en/6.2.x/example/parametrize.html#paramexamples\n\n    We also utilize indirect parametrization here. Since `environment` is a fixture,\n    when we call metafunc.parametrize(\"environment\", ..., indirect=True) we actually\n    parametrizing this \"environment\" fixture and not the test itself.\n    Moreover, by utilizing `_config_cache` we are able to share `environment` fixture between different tests.\n    In order for pytest to group tests together (and share environment fixture)\n    parameter should point to the same Python object (hence, we use _config_cache dict to store those objects).\n    \"\"\"\n    if \"environment\" in metafunc.fixturenames:\n        markers = {m.name: m for m in metafunc.definition.own_markers}\n\n        if \"universal_offline_stores\" in markers:\n            offline_stores = AVAILABLE_OFFLINE_STORES\n        else:\n            # default offline store for testing online store dimension\n            offline_stores = [(\"local\", FileDataSourceCreator)]\n\n        online_stores = None\n        if \"universal_online_stores\" in markers:\n            # Online stores are explicitly requested\n            if \"only\" in markers[\"universal_online_stores\"].kwargs:\n                online_stores = [\n                    AVAILABLE_ONLINE_STORES.get(store_name)\n                    for store_name in markers[\"universal_online_stores\"].kwargs[\"only\"]\n                    if store_name in AVAILABLE_ONLINE_STORES\n                ]\n            else:\n                online_stores = AVAILABLE_ONLINE_STORES.values()\n\n        if online_stores is None:\n            # No online stores requested -> setting the default or first available\n            online_stores = [\n                AVAILABLE_ONLINE_STORES.get(\n                    \"redis\",\n                    AVAILABLE_ONLINE_STORES.get(\n                        \"sqlite\", next(iter(AVAILABLE_ONLINE_STORES.values()))\n                    ),\n                )\n            ]\n\n        extra_dimensions: List[Dict[str, Any]] = [{}]\n\n        if \"python_server\" in metafunc.fixturenames:\n            extra_dimensions.extend(\n                [\n                    {\"python_feature_server\": True},\n                    {\"python_feature_server\": True, \"provider\": \"aws\"},\n                ]\n            )\n\n        if \"goserver\" in markers:\n            extra_dimensions.append({\"go_feature_retrieval\": True})\n\n        configs = []\n        for provider, offline_store_creator in offline_stores:\n            for online_store, online_store_creator in online_stores:\n                for dim in extra_dimensions:\n                    config = {\n                        \"provider\": provider,\n                        \"offline_store_creator\": offline_store_creator,\n                        \"online_store\": online_store,\n                        \"online_store_creator\": online_store_creator,\n                        **dim,\n                    }\n                    # temporary Go works only with redis\n                    if config.get(\"go_feature_retrieval\") and (\n                        not isinstance(online_store, dict)\n                        or online_store[\"type\"] != \"redis\"\n                    ):\n                        continue\n\n                    # aws lambda works only with dynamo\n                    if (\n                        config.get(\"python_feature_server\")\n                        and config.get(\"provider\") == \"aws\"\n                        and (\n                            not isinstance(online_store, dict)\n                            or online_store[\"type\"] != \"dynamodb\"\n                        )\n                    ):\n                        continue\n\n                    c = IntegrationTestRepoConfig(**config)\n\n                    if c not in _config_cache:\n                        _config_cache[c] = c\n\n                    configs.append(_config_cache[c])\n\n        metafunc.parametrize(\n            \"environment\", configs, indirect=True, ids=[str(c) for c in configs]\n        )\n\n\n@pytest.fixture(scope=\"session\")\ndef python_server(environment):\n    proc = Process(\n        target=start_test_local_server,\n        args=(environment.feature_store.repo_path, environment.get_local_server_port()),\n        daemon=True,\n    )\n    if (\n        environment.python_feature_server\n        and environment.test_repo_config.provider == \"local\"\n    ):\n        proc.start()\n        # Wait for server to start\n        wait_retry_backoff(\n            lambda: (\n                None,\n                _check_port_open(\"localhost\", environment.get_local_server_port()),\n            ),\n            timeout_secs=10,\n        )\n\n    yield\n\n    if proc.is_alive():\n        proc.kill()\n\n\ndef _check_port_open(host, port) -> bool:\n    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:\n        return sock.connect_ex((host, port)) == 0\n\n\n@pytest.fixture(scope=\"session\")\ndef universal_data_sources(environment) -> TestData:\n    return construct_universal_test_data(environment)\n\n\n@pytest.fixture(scope=\"session\")\ndef e2e_data_sources(environment: Environment):\n    df = create_dataset()\n    data_source = environment.data_source_creator.create_data_source(\n        df, environment.feature_store.project, field_mapping={\"ts_1\": \"ts\"},\n    )\n\n    return df, data_source\n","sub_path":"sdk/python/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":10663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"50219982","text":"# -*- coding : utf-8 -*-\r\n\r\nfrom pymongo import MongoClient\r\nimport pymongo\r\nimport copy\r\n\r\nclient =  MongoClient(\"mongodb://localhost\")\r\nmongo_db = client.zhihu_spider\r\n\r\ncols = ['user', 'answer']\r\n\r\ndef set_data_to_db(col,data):\r\n    if col in cols is False:\r\n        return \"Invalid collections name\"\r\n\r\n    users = mongo_db[col]\r\n    _d = {}\r\n\r\n    if col == 'user' and data:\r\n        _d['username'] = data.get('username','0')\r\n    else:\r\n        _d['url'] = data.get('url','0')\r\n\r\n    if users.find(_d).count():\r\n        return \"data has exists in database\"\r\n\r\n    users.insert_one(data)\r\n\r\ndef get_data_from_db(col):\r\n    if col in cols is False:\r\n        return \"Invalid collections name\"\r\n\r\n    users = mongo_db[col]\r\n\r\n    datas = users.find({}).sort('agree_num',pymongo.DESCENDING)\r\n\r\n    result = []\r\n\r\n    for data in datas :\r\n\r\n        del data['_id']\r\n        result.append(data)\r\n\r\n    return result\r\n","sub_path":"dbtool.py","file_name":"dbtool.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"169012598","text":"from ftplib import FTP\nimport os\n\nclass FTPService:\n\n    def __init__(self, report, server, user, pswd):\n        self.server = server\n        self.user = user\n        self.pswd = pswd\n        self.report = report\n        self.ftp = FTP(server)\n\n    def download_files(self, local_path, path_in_ftp_server):\n        if not os.path.isdir(local_path):\n            os.makedirs(local_path)\n        os.chdir(local_path)\n\n        r = self.ftp.login(self.user, self.pswd)\n        self.report.write(r, True, False, True)\n        \n        downloaded_files = self.download_files_of_subdir(local_path, path_in_ftp_server, False)\n\n        r = self.ftp.close()\n        self.report.write('ftp finished', True, False, True)\n\n        print(';\\n'.join(downloaded_files))\n        return downloaded_files\n    \n    def download_files_of_subdir(self, local_path, path_in_ftp_server, delete_path_in_ftp_server = False):\n        levels = len(path_in_ftp_server.split('/'))\n        downloaded_files = []\n        # go to ftp directory\n        self.report.write('go to ' + path_in_ftp_server, True, False, True)\n        r = self.ftp.cwd(path_in_ftp_server)\n        self.report.write(r, True, False, True)\n\n        # download files\n        files_or_folders = self.ftp.nlst()\n        self.report.write('Files/Folders to download:\\n' + '\\n'.join(files_or_folders) + '\\n' + str(len(files_or_folders)) + ' files/folders', True, False, True)\n        \n        for folder_or_file in files_or_folders:\n            if os.path.isfile( path_in_ftp_server + '/' + folder_or_file):\n                # must be a file\n                print('test file')\n                downloaded_file = self.download_and_delete_file(local_path, folder_or_file)\n                if len(downloaded_file)>0:\n                    downloaded_files.append(downloaded_file)\n            elif os.path.isdir( path_in_ftp_server + '/' + folder_or_file):\n                print('test dir')\n                # supposed to be a folder\n                downloaded_files += self.download_files_of_subdir(local_path, folder_or_file, False)\n            elif folder_or_file.endswith('.zip') or folder_or_file.endswith('.tgz'):\n                print('test .zip')\n                # must be a file\n                downloaded_file = self.download_and_delete_file(local_path, folder_or_file)\n                if len(downloaded_file)>0:\n                    downloaded_files.append(downloaded_file)\n            else:\n                # supposed to be a folder\n                print('test nothing')\n                downloaded_files += self.download_files_of_subdir(local_path, folder_or_file, False)\n\n\n        # up level\n        for i in range(0,levels):\n            r = self.ftp.cwd('..')\n            self.report.write(r, True, False, True)\n\n        if delete_path_in_ftp_server:\n            r = self.ftp.rmd(path_in_ftp_server)\n        self.report.write(r, True, False, True)\n        return downloaded_files\n        \n        \n    def download_and_delete_file(self, local_path, file):\n        downloaded_file = ''\n        r = self.ftp.retrbinary('RETR ' + file, open(file, 'wb').write)\n        self.report.write(r, True, False, True)\n        if os.path.exists(local_path + '/' + file):\n\n            statinfo = os.stat(local_path + '/' + file)\n            self.report.write(str(statinfo.st_size), True, False, True)\n\n            downloaded_file = file\n            r = self.ftp.delete(file)\n            self.report.write(r, True, False, True)\n        return downloaded_file\n\n    def list_content(self, path_in_ftp_server):\n        \n        r = self.ftp.login(self.user, self.pswd)\n        self.report.write(r, True, False, True)\n        \n        files_list = self.list_content_of_files_of_subdir(path_in_ftp_server)\n\n        r = self.ftp.close()\n        self.report.write('ftp finished', True, False, True)\n\n        print(';\\n'.join(files_list))\n        return files_list\n    \n    def list_content_of_files_of_subdir(self, path_in_ftp_server):\n        levels = len(path_in_ftp_server.split('/'))\n        items_in_ftp = []\n        # go to ftp directory\n        self.report.write('go to ' + path_in_ftp_server, True, False, True)\n        r = self.ftp.cwd(path_in_ftp_server)\n        self.report.write(r, True, False, True)\n\n        # download files\n        files_or_folders = self.ftp.nlst()\n        self.report.write('Files/Folders in FTP:' + '\\n'.join(files_or_folders) + '\\n' + str(len(files_or_folders)) + ' files/folders', True, False, True)\n        \n        for folder_or_file in files_or_folders:\n            if os.path.isdir(folder_or_file):\n                items_in_ftp += self.list_content_of_files_of_subdir(folder_or_file)\n            else:\n                items_in_ftp.append(folder_or_file)\n            \n        # up level\n        for i in range(0,levels):\n            r = self.ftp.cwd('..')\n            self.report.write(r, True, False, True)\n\n        \n        self.report.write(r, True, False, True)\n        return items_in_ftp\n","sub_path":"src/xml_converter/src/reuse/services/ftp_service/ftp_service.py","file_name":"ftp_service.py","file_ext":"py","file_size_in_byte":4947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"128705952","text":"\"\"\"\nCode was copied from https://github.com/VHellendoorn/ICLR20-Great\nAuthor: https://github.com/VHellendoorn\n\nMIT License\n\nCopyright (c) 2020 Vincent Hellendoorn\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\n\nimport tensorflow as tf\n\nfrom . import util\n\n\nclass AttentionLayer(tf.keras.layers.Layer):\n    \"\"\"Implementation of multi-headed attention with optional edge-bias.\n\n        This class supports self-attention and key-value attention, with (non-optional) masks. If bias_dim is not None, the attention computation(s) assumes that a (sparse) bias vector is provided, formatted like: (edge_type, batch_index, key_index, query_index). Bias edge types are embedded in the same dimension as each head's attention and projected to a scalar before being inserted into the attention computation as (q + b) * k.\n    \"\"\"\n\n    def __init__(self, attention_dim, num_heads=8, hidden_dim=None, bias_dim=None):\n        super(AttentionLayer, self).__init__()\n        self.attention_dim = attention_dim\n        self.hidden_dim = hidden_dim if hidden_dim is not None else self.attention_dim\n        self.num_heads = 1 if num_heads is None else num_heads\n        self.attention_dim_per_head = self.attention_dim // self.num_heads\n        self.bias_dim = bias_dim\n\n    def build(self, _):\n        self.attn_query = self.add_weight(name='q',\n                                          shape=(self.hidden_dim, self.num_heads, self.attention_dim_per_head),\n                                          initializer='glorot_uniform')\n        self.attn_keys = self.add_weight(name='k', shape=(self.hidden_dim, self.num_heads, self.attention_dim_per_head),\n                                         initializer='glorot_uniform')\n        self.attn_values = self.add_weight(name='v',\n                                           shape=(self.hidden_dim, self.num_heads, self.attention_dim_per_head),\n                                           initializer='glorot_uniform')\n        self.weight_out = self.add_weight(name='o',\n                                          shape=(self.num_heads, self.attention_dim_per_head, self.hidden_dim),\n                                          initializer='glorot_uniform')\n        if self.bias_dim is not None:\n            self.bias_embs = self.add_weight(name='e1', shape=(self.bias_dim, self.attention_dim_per_head),\n                                             initializer='glorot_uniform')\n            self.bias_scalar = self.add_weight(name='e2', shape=(self.attention_dim_per_head, 1),\n                                               initializer='glorot_uniform')\n\n    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, 4), dtype=tf.int32)])\n    def call(self, states, key_states, masks, attention_bias):\n        # Compute key, query and value vectors, reshaped to [Batch, Heads, Time, Dim] where Dim is attention_dim//num_heads.\n        query, keys, values = self.compute_qkv(states, key_states)\n\n        # Compute attention weights, and context from these.\n        alpha = self.get_attention_weights(query, keys, masks, attention_bias)\n\n        # Compute weigthed context and project out.\n        context = tf.einsum('bhqk,bkha->bqha', alpha, values)\n        context = tf.einsum('btha,had->btd', context, self.weight_out)\n        return context\n\n    # Compute key, query and value vectors.\n    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None), dtype=tf.float32)])\n    def compute_qkv(self, states, key_states):\n        query = tf.einsum('btd,dha->btha', states, self.attn_query)  # Queries are always computed on states\n        keys = tf.einsum('btd,dha->btha', key_states, self.attn_keys)\n        values = tf.einsum('btd,dha->btha', key_states, self.attn_values)\n        return query, keys, values\n\n    # Compute attention weights from cross-product between keys and queries (scaled, masked, softmaxed).\n    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, 4), dtype=tf.int32)])\n    def get_attention_weights(self, query, keys, masks, attention_bias):\n        alpha = tf.einsum('bkha,bqha->bhqk', keys, query)\n\n        # If bias_dim is set, assume that a bias vector is provided.\n        if self.bias_dim is not None:\n            # Embed edge types in per-head-attention dimension. Experimentally, mat-mul tends to be faster here, but regular embedding is equally valid.\n            bias = tf.matmul(tf.one_hot(attention_bias[:, 0], self.bias_dim), self.bias_embs)\n            # Project down to a scalar.\n            bias = tf.squeeze(tf.matmul(bias, self.bias_scalar), -1)\n            alpha_shape = tf.shape(alpha)\n            bias_shape = tf.stack([alpha_shape[0], alpha_shape[2], alpha_shape[3]])\n            # Scatter edge biases to their [batch_index, key_index, query_index] positions.\n            bias = tf.scatter_nd(attention_bias[:, 1:], bias, bias_shape)\n\n            # Since bias is a scalar, we can reduce memory cost by rewriting the attention from (q + b) * k to q*k + b*reduce_sum(k, -1)\n            summed_keys = tf.reduce_sum(keys, -1)  # bkh\n            bias = tf.einsum('bqk,bkh->bhqk', bias, summed_keys)\n            # Accordingly, simply add the bias as a residual to standard dot-product attention.\n            alpha += bias\n\n        # Scale and apply mask\n        alpha *= tf.math.rsqrt(tf.cast(self.attention_dim_per_head, 'float32'))\n        alpha = alpha * masks + (1.0 - tf.math.ceil(masks)) * tf.float32.min\n        alpha = tf.nn.softmax(alpha)\n        alpha *= masks\n        return alpha\n\n\nclass LayerNormalization(tf.keras.layers.Layer):\n    def __init__(self, hidden_dim):\n        super(LayerNormalization, self).__init__()\n        self.hidden_dim = hidden_dim\n\n    def build(self, _):\n        self.scale = tf.Variable(tf.ones(self.hidden_dim))\n        self.bias = tf.Variable(tf.zeros(self.hidden_dim))\n        self.build = True\n\n    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(), dtype=tf.float32)])\n    def call(self, x, epsilon=1e-3):\n        mean, variance = tf.nn.moments(x, -1, keepdims=True)\n        norm_x = (x - mean) * tf.math.rsqrt(variance + epsilon)\n        return norm_x * self.scale + self.bias\n\n\nclass Transformer(tf.keras.layers.Layer):\n    \"\"\"Transformer language model: converts indices into hidden states through layers of multi-headed attention and feed-forward dense layers.\n\n        Augments a generic Transformer with attentional bias, if bias_dim is provided. See documentation on AttentionLayer for more details.\n        To generate language from the resulting states, pass the states to the 'predict' function. Note that it assumes that the input vocabulary is output vocabulary (i.e., it reuses the model's embedding table).\n    \"\"\"\n    NOOP_BIAS = tf.zeros((0, 4), 'int32')\n\n    default_config = {\n        'hidden_dim': 128,\n        'dropout_rate': 0.1,\n        'num_edge_types': 9,\n        'ff_dim': 2048,\n        'num_layers': 6,\n        'attention_dim': 128,\n        'num_heads': 8,\n    }\n\n    def __init__(self, model_config=None, shared_embedding=None, vocab_dim=None, is_encoder_decoder=False):\n        super(Transformer, self).__init__()\n        if model_config is None:\n            model_config = Transformer.default_config\n        self.is_encoder_decoder = is_encoder_decoder\n        self.bias_dim = model_config['num_edge_types']\n        self.hidden_dim = model_config['hidden_dim']\n        self.ff_dim = model_config['ff_dim']\n        self.attention_dim = model_config['attention_dim']\n        self.num_layers = model_config['num_layers']\n        self.num_heads = model_config['num_heads']\n        self.dropout_rate = model_config['dropout_rate']\n\n        # Initialize embedding variable in constructor to allow reuse by other models\n        if shared_embedding is not None:\n            self.embed = shared_embedding\n        elif vocab_dim is None:\n            raise ValueError('Pass either a vocabulary dimension or an embedding Variable')\n        else:\n            random_init = tf.random_normal_initializer(stddev=self.hidden_dim ** -0.5)\n            self.embed = tf.Variable(random_init([vocab_dim, self.hidden_dim]), dtype=tf.float32)\n\n        # Initialize default positional encoding for very long sequences. Can make this a parameter if necessary.\n        self.pos_enc = util.positional_encoding(self.hidden_dim, 5000)\n\n    def build(self, _):\n        # Set up multi-headed attention, and feed-forward layers.\n        make_att = lambda: AttentionLayer(self.attention_dim, self.num_heads, self.hidden_dim, self.bias_dim)\n        self.attention = [make_att() for _ in range(self.num_layers)]  # make_att_deprecated\n        if self.is_encoder_decoder:\n            self.enc_attention = [make_att() for _ in range(self.num_layers)]\n\n        # Layer normalization for every residual layer\n        self.ln = [[LayerNormalization(self.hidden_dim) for _ in range(3 if self.is_encoder_decoder else 2)] for _ in\n                   range(self.num_layers)]\n        self.ln_out = LayerNormalization(self.hidden_dim)\n\n        # Two-layer feed-forward with wide layer in the middle\n        self.ff_1 = [tf.keras.layers.Dense(self.ff_dim, activation='relu') for _ in range(self.num_layers)]\n        self.ff_2 = [tf.keras.layers.Dense(self.hidden_dim) for _ in range(self.num_layers)]\n\n    # Default 'call' applies standard self-attention, with dropout if training=True.\n    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, 4), dtype=tf.int32),\n                                  tf.TensorSpec(shape=(), dtype=tf.bool)])\n    def call(self, states, masks, attention_bias, training):\n        real_dropout_rate = self.dropout_rate * tf.cast(training,\n                                                        'float32')  # Easier for distributed training than an explicit conditional\n        for ix in range(self.num_layers):\n            new_states = self.ln[ix][0](states)\n            new_states = self.attention[ix](new_states, new_states, masks, attention_bias)\n            new_states = tf.nn.dropout(new_states, rate=real_dropout_rate)\n            states += new_states\n\n            new_states = self.ff_1[ix](self.ln[ix][1](states))\n            new_states = tf.nn.dropout(new_states, rate=real_dropout_rate)\n            new_states = self.ff_2[ix](new_states)\n            new_states = tf.nn.dropout(new_states, rate=real_dropout_rate)\n            states += new_states\n        return self.ln_out(states)\n\n    # Standard encoder-decoder attention, with dropout if training=True.\n    # NOTE: tentatively does not support attention bias within the query itself; extending this should be straightforward.\n    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, None, None, None), dtype=tf.float32),\n                                  tf.TensorSpec(shape=(None, 4), dtype=tf.int32),\n                                  tf.TensorSpec(shape=(), dtype=tf.bool)])\n    def enc_dec_attention(self, states, masks, key_states, key_masks, attention_bias, training):\n        real_dropout_rate = self.dropout_rate * tf.cast(training,\n                                                        'float32')  # Easier for distributed training than an explicit conditional\n        for ix in range(self.num_layers):\n            new_states = self.ln[ix][0](states)\n            new_states = self.attention[ix](new_states, new_states, masks, tf.zeros((0, 4), dtype='int32'))\n            new_states = tf.nn.dropout(new_states, rate=real_dropout_rate)\n            states += new_states\n\n            new_states = self.ln[ix][2](states)\n            new_states = self.enc_attention[ix](new_states, key_states, key_masks, attention_bias)\n            new_states = tf.nn.dropout(new_states, rate=real_dropout_rate)\n            states += new_states\n\n            new_states = self.ff_1[ix](self.ln[ix][1](states))\n            new_states = tf.nn.dropout(new_states, rate=real_dropout_rate)\n            new_states = self.ff_2[ix](new_states)\n            new_states = tf.nn.dropout(new_states, rate=real_dropout_rate)\n            states += new_states\n        return self.ln_out(states)\n\n    # Embed inputs. Note: applies scaling before positional encoding.\n    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None), dtype=tf.int32)])\n    def embed_inputs(self, inputs):\n        states = tf.nn.embedding_lookup(self.embed, inputs)\n        states *= tf.math.sqrt(tf.cast(tf.shape(states)[-1], 'float32'))\n        states += self.pos_enc[:tf.shape(states)[1]]\n        return states\n\n    # Generates tokens from transformer states using the transposed embedding layer.\n    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None, None), dtype=tf.float32)])\n    def predict(self, states):\n        return tf.matmul(states, self.embed, transpose_b=True)\n\n    # Convenience function: returns a sequence mask in which each token can only see states up to its own position. Useful for generative language modeling (e.g. decoding).\n    @tf.function\n    def get_sequence_mask(self, seq_len):\n        return tf.sequence_mask(lengths=tf.range(1, seq_len + 1), maxlen=seq_len, dtype=tf.float32)\n","sub_path":"src/encoders/utils/great_transformer_network.py","file_name":"great_transformer_network.py","file_ext":"py","file_size_in_byte":15202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"573120225","text":"# -*-coding:utf-8 -*-\n\"\"\"统计维修单\"\"\"\n\nfrom pymongo import MongoClient, ReturnDocument\nfrom datetime import datetime, timedelta\nfrom app.base_class import CodeTable\nfrom app.commontools import create_day_list, in_date\n\n\ndef statistics_maintenaces(citycode, startdate='2016-01-01'):\n    \"\"\"\n\n    :param startdate: 统计开始的日期 '2016-01-01'\n    :param citycode: 城市代码\n    :return: None\n    \"\"\"\n    mongo_client = MongoClient(host='127.0.0.1', port=27017)\n    today = datetime.today().strftime('%Y-%m-%d')\n\n    # 获取某个城市的维修单数据\n    maintenaces = mongo_client.spv1.maintenaces.aggregate(\n        [\n            {'$lookup': {'from': 'companies', 'localField': 'companyId', 'foreignField': '_id', 'as': 'company'}},\n            {'$project': {'_id': 0, 'company': 1}}\n        ])\n    target_maintenaces = []\n    for maintenace in maintenaces:\n        if str(int(maintenace.get('company')[0].get('cityCode'))) == citycode:\n            target_maintenaces.append(maintenace)\n\n    # 获取辖区列表\n    province_code_abbr = citycode[0:2]\n    city_code_abbr = citycode[2:4]\n    codetable = CodeTable()\n    countylist = codetable.get_belong_county_info(province_code_abbr, city_code_abbr)  # 获取市内区域代码列表\n\n    # 按照辖区分类维修数据\n    area_relatived_maintenaces = {}\n    for county_info in countylist:\n        temp_maintenace_list = []\n        for tmaintenace in target_maintenaces:\n            if str(int(tmaintenace.get('company')[0].get('countyCode'))) == county_info[1]:\n                temp_maintenace_list.append(tmaintenace)\n        area_relatived_maintenaces[(county_info[0], county_info[1])] = temp_maintenace_list\n\n    # 按照每天统计\n    for k, v in area_relatived_maintenaces.items():\n        # 生成按照天的时间段\n        days = create_day_list(startdate, today)\n        if len(v) == 0:  # 如果某个地区的投诉为0,则全部置为0\n            for day in days:\n                daily_data = {\n                    'provinceCode': citycode[0:2] + '0000',\n                    'cityCode': citycode,\n                    'countyCode': k[1],  # 辖区代码\n                    'date': day[0],\n                    'maintenaceQty': 0,  # 维修量\n                }\n\n                updated_data = mongo_client.statistics.maintenacesstatistics.find_one_and_update(\n                    {'cityCode': citycode, 'provinceCode': citycode[0:2] + '0000', 'date': day[0], 'countyCode': k[1]},\n                    {\n                        '$set': daily_data}, upsert=True, return_document=ReturnDocument.AFTER)\n                print(updated_data)\n        else:\n            for day in days:\n                count = 0  # 记数\n                for maintenace in v:\n                    if in_date(day, maintenace.get('created')):\n                        count += 1\n                if count > 0:\n                    daily_data = {\n                        'provinceCode': citycode[0:2] + '0000',\n                        'cityCode': citycode,\n                        'countyCode': k[1],  # 辖区代码\n                        'date': day[0],\n                        'maintenaceQty': count,  # 维修单量\n                    }\n                else:\n                    daily_data = {\n                        'provinceCode': citycode[0:2] + '0000',\n                        'cityCode': citycode,\n                        'countyCode': k[1],  # 辖区代码\n                        'date': day[0],\n                        'maintenaceQty': 0,  # 维修单量\n                    }\n                updated_data = mongo_client.statistics.maintenacesstatistics.find_one_and_update(\n                    {'cityCode': citycode, 'provinceCode': citycode[0:2] + '0000', 'date': day[0], 'countyCode': k[1]},\n                    {\n                        '$set': daily_data}, upsert=True, return_document=ReturnDocument.AFTER)\n                print(updated_data)\n\nif __name__ == '__main__':\n    statistics_maintenaces('371100')\n","sub_path":"statistics/statistics_maintenaces.py","file_name":"statistics_maintenaces.py","file_ext":"py","file_size_in_byte":3987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"540822715","text":"import sklearn.utils.linear_assignment_ as su\nimport numpy as np\nimport sys\nimport os\nfrom nltk.parse import stanford\nimport nltk\nfrom nltk.tree import ParentedTree\nfrom zss import simple_distance, Node\nimport random\nnumnodes =0\n\nclass Cassim:\n    def __init__(self):\n        self.sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')\n        os.environ['STANFORD_PARSER'] = 'jars/stanford-parser.jar'\n        os.environ['STANFORD_MODELS'] = 'jars/stanford-parser-3.5.2-models.jar'\n        self.parser = stanford.StanfordParser(model_path=\"jars/englishPCFG.ser.gz\")\n\n    def convert_mytree(self, nltktree,pnode):\n        global numnodes\n        for node in nltktree:\n            numnodes+=1\n            if type(node) is nltk.ParentedTree:\n                tempnode = Node(node.label())\n                pnode.addkid(tempnode)\n                self.convert_mytree(node,tempnode)\n        return pnode\n\n    def minweight_edit_distance(self, doc1, doc2):\n        global numnodes\n        doc1sents = self.sent_detector.tokenize(doc1.strip())\n        doc2sents = self.sent_detector.tokenize(doc2.strip())\n        doc1parsed = self.parser.raw_parse_sents((doc1sents))\n        doc2parsed = self.parser.raw_parse_sents((doc2sents))\n        costMatrix = []\n        doc1parsed = list(doc1parsed)\n        for i in range(len(doc1parsed)):\n            doc1parsed[i] = list(doc1parsed[i])[0]\n        doc2parsed = list(doc2parsed)\n        for i in range(len(doc2parsed)):\n                doc2parsed[i] = list(doc2parsed[i])[0]\n        for i in range(len(doc1parsed)):\n            numnodes = 0\n            sentencedoc1 = ParentedTree.convert(doc1parsed[i])\n            tempnode = Node(sentencedoc1.root().label())\n            new_sentencedoc1 = self.convert_mytree(sentencedoc1,tempnode)\n            temp_costMatrix = []\n            sen1nodes = numnodes\n            for j in range(len(doc2parsed)):\n                numnodes=0.0\n                sentencedoc2 = ParentedTree.convert(doc2parsed[j])\n                tempnode = Node(sentencedoc2.root().label())\n                new_sentencedoc2 = self.convert_mytree(sentencedoc2,tempnode)\n                ED = simple_distance(new_sentencedoc1, new_sentencedoc2)\n                ED = ED / (numnodes + sen1nodes)\n                temp_costMatrix.append(ED)\n            costMatrix.append(temp_costMatrix)\n        costMatrix = np.array(costMatrix)\n        rownum= costMatrix.shape[0]\n        colnum = costMatrix.shape[1]\n        if rownum > colnum:\n            costMatrixRandom = costMatrix[np.random.randint(rownum, size=colnum),:]\n        else:\n            costMatrixRandom = costMatrix[:,np.random.randint(colnum, size=rownum)]\n    \n        indexes = su.linear_assignment(costMatrix)\n        total = 0\n        minWeight = 0\n        rowMarked = [0] * len(doc1parsed)\n        colMarked = [0] * len(doc2parsed)\n        for row, column in indexes:\n            total += costMatrix[row][column]\n            rowMarked[row] = 1\n            colMarked [column] = 1\n        minWeight = total\n    \n        for k in range(len(rowMarked)):\n            if rowMarked[k]==0:\n                total+= np.min(costMatrix[k])\n        for c in range(len(colMarked)):\n            if colMarked[c]==0:\n                total+= np.min(costMatrix[:,c])\n        maxlengraph = max(len(doc1parsed),len(doc2parsed))\n        minlengraph = min(len(doc1parsed),len(doc2parsed))\n    \n        indexes = su.linear_assignment(costMatrixRandom)\n        randtotal = 0\n        for row, column in indexes:\n            randtotal +=costMatrixRandom[row][column]\n        lengraph = costMatrixRandom.shape[0]\n    \n        return total/maxlengraph#, minWeight/minlengraph, randtotal/lengraph\n","sub_path":"Cassim.py","file_name":"Cassim.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"219408442","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        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Branch',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(max_length=40)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Course',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(max_length=40)),\n                ('course_id', models.CharField(unique=True, max_length=10)),\n                ('branch', models.ForeignKey(to='basic.Branch')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Membership',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('course', models.ForeignKey(to='basic.Course')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Notification',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('type', models.CharField(max_length=100)),\n                ('object_id', models.IntegerField(blank=True)),\n                ('user_name', models.CharField(max_length=50)),\n                ('link', models.CharField(max_length=100)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Register',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('code', models.CharField(unique=True, max_length=32)),\n                ('email', models.EmailField(unique=True, max_length=75)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='SetNotification',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('keyword', models.CharField(max_length=100)),\n                ('notification', models.ForeignKey(to='basic.Notification')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='UserProfile',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('reg', models.CharField(unique=True, max_length=12, blank=True)),\n                ('website', models.URLField(blank=True)),\n                ('picture', models.ImageField(upload_to=b'profile_images', blank=True)),\n                ('role', models.CharField(default=b'Student', max_length=20)),\n                ('branch', models.ForeignKey(to='basic.Branch', blank=True)),\n                ('courses', models.ManyToManyField(to='basic.Course', through='basic.Membership')),\n                ('notifications', models.ManyToManyField(to='basic.Notification', through='basic.SetNotification')),\n                ('user', models.OneToOneField(to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.AddField(\n            model_name='setnotification',\n            name='user',\n            field=models.ForeignKey(to='basic.UserProfile'),\n            preserve_default=True,\n        ),\n        migrations.AddField(\n            model_name='membership',\n            name='member',\n            field=models.ForeignKey(to='basic.UserProfile'),\n            preserve_default=True,\n        ),\n        migrations.AddField(\n            model_name='course',\n            name='students',\n            field=models.ManyToManyField(to='basic.UserProfile', through='basic.Membership'),\n            preserve_default=True,\n        ),\n    ]\n","sub_path":"src/basic/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":4555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"316903959","text":"import requests\nimport json\nimport PythonTokenGenerator\n\n# Test environment URL\nurl = 'https://biblio-qa1.dev.rosettastone.com'\nactivity_set = '839ca07b-d2bb-4d2b-aa79-215c8f201e28'\n\n# Set service end point, user, environment\nend_point = '/content/activity_set/' + activity_set + '/aggregate/'\nuser = 'mhotchkiss-qa1@rosettastone.com'\nenvironment = 'qa1'\n\n# Get the generated userId and auth_token\nresponse = PythonTokenGenerator.get_token(environment, user)\nuser_id = response[0]\nauth_token = response[1]\nprint('auth_token = ' + auth_token + '\\n')\n\nheaders = {\n    'Authorization': 'Bearer ' + auth_token\n}\n\n# Make the request\nr = requests.get(url + end_point + user_id, headers=headers)\n\n# Process and format the response\nresults = json.loads(r.text)\npretty = json.dumps(results, indent=2)\nprint(pretty)\n\n#{\n#  \"error_description\": \"not_found: missing:https://db-host-5/biblio/839ca07b-d2bb-4d2b-aa79-215c8f201e28%2Faggregate%2Fc6e3f3dd-b9dd-4c98-96a1-126190ec4927\",\n#  \"error\": \"Entity Not Found\"\n#}\n","sub_path":"RoStoneIssues/AEB6286/AEB6286.py","file_name":"AEB6286.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"412472803","text":"from enum import Enum\n\nfrom sqlalchemy import create_engine, Column, DateTime, func, Integer, BigInteger, Text\nfrom sqlalchemy.dialects.postgresql import JSON, JSONB\nfrom sqlalchemy.exc import DatabaseError\nfrom flask_sqlalchemy import Model\nfrom sqlalchemy.orm import sessionmaker\n\nfrom dukepy.config import Config\nfrom dukepy.traces import print_exception_traces\n\n\nclass DBType(Enum):\n    sqlite = 1\n    mysql = 2\n    postgres = 3\n\n\ndb_type = DBType.sqlite  # Default\nif \"sqlite\" == Config()[\"database\"][\"active\"]:\n    db_type = DBType.sqlite\nif \"mysql\" == Config()[\"database\"][\"active\"]:\n    db_type = DBType.mysql\nif \"postgres\" == Config()[\"database\"][\"active\"]:\n    db_type = DBType.postgres\n\n\n## Init session\nclass DBSession():\n    def uri(self, type):\n        db_uri = None\n\n        if type == DBType.sqlite:\n            db_uri = \"sqlite:///\" + Config()[\"database\"][\"sqlite\"][\"path\"]\n\n        if type == DBType.mysql:\n            config = Config()[\"database\"][\"mysql\"]\n            db_uri = \"mysql+pymysql://{0}:{1}@{2}:3306/{3}\".format(config[\"user\"], config[\"password\"], config[\"host\"],\n                                                                   config[\"db\"])\n\n        if type == DBType.postgres:\n            config = Config()[\"database\"][\"postgres\"]\n            db_uri = \"postgresql://{0}:{1}@{2}:{3}/{4}\".format(config[\"user\"], config[\"password\"], config[\"host\"],\n                                                               config[\"port\"], config[\"db\"])\n\n        return db_uri\n\n    def create_session(self, db_uri):\n        engine = create_engine(db_uri)\n        Session = sessionmaker(bind=engine)\n        session = Session()\n        session._model_changes = {}\n        return session\n\n\ndb_session_helper = DBSession()\ndb_uri = db_session_helper.uri(db_type)\ndb_session = db_session_helper.create_session(db_uri)\n\n\n## Declare base\nclass AlchemyBase(Model):\n    \"\"\"\n    ref: https://chase-seibert.github.io/blog/2016/03/31/flask-sqlalchemy-sessionless.html\n    \"\"\"\n\n    id = Column(Integer, primary_key=True, autoincrement=True, nullable=False)\n    if db_type == DBType.sqlite:\n        json_data = Column(Text, nullable=True)\n    if db_type == DBType.mysql:\n        json_data = Column(Text, nullable=True)\n    if db_type == DBType.postgres:\n        json_data = Column(JSONB, nullable=True)\n\n    # https://stackoverflow.com/a/12155686/973425\n    created_at = Column(DateTime, nullable=False,\n                        server_default=func.now())\n    updated_at = Column(DateTime, nullable=False,\n                        server_default=func.now(),\n                        server_onupdate=func.now())\n\n    def save(self):\n        local_object = db_session.merge(self)\n        db_session.add(local_object)\n        # try:\n        self._flush()\n        db_session.commit()\n        # except DatabaseError as e:\n        #     code = e.orig.args[0]\n        #     if code == 1062:\n        #         raise\n        #     return None\n        return local_object\n\n    def update(self, **kwargs):\n        for attr, value in kwargs.items():\n            setattr(self, attr, value)\n        return self.save()\n\n    def delete(self):\n        local_object = db_session.merge(self)  # https://stackoverflow.com/a/47663833/973425\n        db_session.delete(local_object)\n        self._flush()\n        db_session.commit()\n\n    def _flush(self):\n        try:\n            db_session.flush()\n            # db_session.refresh(self)\n        except DatabaseError as e:\n            db_session.rollback()\n            print_exception_traces(e)\n            raise\n\n    @staticmethod\n    def session():\n        return db_session\n","sub_path":"src/dukepy/sql_alchemy/tables/alchemy_base.py","file_name":"alchemy_base.py","file_ext":"py","file_size_in_byte":3610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"245831410","text":"import re # for regular expressions\nimport nltk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import PorterStemmer\nfrom spellchecker import SpellChecker\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nimport pickle\n\nnltk.download('punkt')\nnltk.download('stopwords')\n\nwords_removed= ['collect', 'part', 'promote', 'review', 'really', 'product']\n\nstemmer = PorterStemmer()\n\nspell = SpellChecker()\n\nwith open('logisticReg_final_model.pkl', 'rb') as file:\n    model = pickle.load(file)\n    \nwith open('tf_idf_vectorizer_new.pkl', 'rb') as file:\n    tf_idf = pickle.load(file)\n    \n\nclass get_sentiment():\n    \n    def __init__(self):\n        \n        return None\n    \n    def clean_review(self, review_text):\n        review_text = re.sub(r\"http\\S+|www.\\S+\", \"\", review_text)                        \n        review_text = re.sub(\"[^a-zA-Z]\", \" \", review_text)                       \n        review_text = str(review_text).lower()                                    \n        review_text = word_tokenize(review_text)                                  \n        review_text = [i for i in review_text if len(i)>2]                        \n        review_text = [i for i in review_text if i not in stopwords.words('english')]  \n        review_text = [i for i in review_text if i not in words_removed]\n        review_text = [stemmer.stem(i) for i in review_text]                           \n        review_text = [spell.correction(i) for i in review_text]                       \n        review_text = ' '.join(review_text)\n        \n        return(review_text)\n    \n    def sentiment (self, review_text):\n        \n        review_text = self.clean_review(review_text)\n        \n        X = tf_idf.transform([review_text])\n        \n        y= model.predict(X)\n        \n        return(int(y))\n        \n\n        \nimport pandas as pd\nimport numpy as np\nimport random\n\nitem_based_reco = pd.read_csv('item_based_reco.csv')\nitem_based_reco.set_index('reviews_username', inplace=True)\n\ndataset = pd.read_csv('sample30.csv')\n\nid_name= pd.read_csv('id_name.csv')\n\nclass pdt_recommendation(get_sentiment):\n    \n    def __init__(self):\n        \n        #self.username= username\n        return None\n    \n    \n    def recom_using_item_based(self, username): ##takes username as input and returns 20 reco pdts\n        \n        d = item_based_reco.loc[username].sort_values(ascending=False)[0:20]\n        top_20 = list(d.index)\n        \n        return top_20\n    \n    def pdt_overall_sentiment(self, pdt_id): ##takes a pdt id as input serach the reviews and give the sentiment_score\n        \n        filt_df= dataset.loc[dataset['id']== pdt_id, ['reviews_title', 'reviews_text']]\n        filt_df['merge']= filt_df['reviews_title']+ str(' ')+ filt_df['reviews_text']\n        all_reviews = list(filt_df['merge'])\n        \n        if len(all_reviews)>300:\n            all_reviews= random.sample(all_reviews, 300)\n        \n        #print(len(all_reviews))\n        \n        sentiment_score= sum([self.sentiment(str(review)) for review in all_reviews])\n        \n        return (round(sentiment_score/len(all_reviews),2))\n    \n    def predict(self, username):\n        \n        top_20_pdt_id= self.recom_using_item_based(username)\n        top_20_pdt_sent={}\n        for id in top_20_pdt_id:\n            top_20_pdt_sent[id] = self.pdt_overall_sentiment(id)\n            \n        top_20_pdt_sent= dict(sorted(top_20_pdt_sent.items(), key= (lambda x: -x[1])))\n\n        top_5_pdt_id= list(top_20_pdt_sent.keys())[0:5]\n        \n        \n        top_5_pdt_name= [id_name.loc[id_name['id']==x , 'name_by_brand'].values[0] for x in top_5_pdt_id]\n        \n        \n        return(top_5_pdt_name)\n            \n\n        ","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"509933111","text":"import pygame\nimport random\n\nfrom os import path\n\nimg_dir=path.join(path.dirname(__file__),'img')\n\nwidth=600\nheight=600\n\n\nwhite=(255,255,255)\nblack=(0,0,0)\nred=(255,0,0)\ngreen=(0,255,0)\nblue=(0,255,0)\n\n\n#initialize pygame and create window\n\npygame.init()\npygame.mixer.init()\nscreen=pygame.display.set_mode((width,height))\npygame.display.set_caption(\"my game\")\n\nclass Player(pygame.sprite.Sprite):\n    def __init__(self):\n        pygame.sprite.Sprite.__init__(self)\n        self.image=pygame.transform.scale(player_img,(40,40))\n        self.image.set_colorkey(black)\n        #self.image.fill(green)\n        self.rect=self.image.get_rect()\n        self.rect.centerx=width/2\n        self.rect.bottom=height-10\n        self.speedx=0\n\n    def update(self):\n        self.speedx=0\n        keystate=pygame.key.get_pressed()\n        if keystate[pygame.K_LEFT]:\n            self.speedx=-5\n        if keystate[pygame.K_RIGHT]:\n            self.speedx=5\n        self.rect.x+=self.speedx\n        if self.rect.right>width:\n            self.rect.right=width\n        if self.rect.left<0:\n            self.rect.left=0\n        #print(self.speedx,self.rect.x)\n\n    def shoot(self):\n        bullet=Bullet(self.rect.centerx,self.rect.top)\n        all_sprites.add(bullet)\n        bullets.add(bullet)\n\nclass Mob(pygame.sprite.Sprite):\n    def __init__(self):\n        pygame.sprite.Sprite.__init__(self)\n        self.image=pygame.transform.scale(monstor_img,(40,30))\n        self.image.set_colorkey(black)\n        self.rect=self.image.get_rect()\n        self.rect.x=random.randrange(width-self.rect.width)\n        self.rect.y=random.randrange(-100,-40)\n        self.speedy=random.randrange(1,4)\n\n    def update(self):\n        self.rect.y+=self.speedy\n        if self.rect.top>height+10:\n            self.rect.x=random.randrange(width-self.rect.width)\n            self.rect.y=random.randrange(-100,-40)\n            self.speedy=random.randrange(1,4)\n\nclass Bullet(pygame.sprite.Sprite):\n    def __init__(self,x,y):\n        pygame.sprite.Sprite.__init__(self)\n        self.image=bullet_img\n        self.image.set_colorkey(black)\n        \n        self.rect=self.image.get_rect()\n        self.rect.bottom=y\n        self.rect.centerx=x\n        self.speedy=-5\n\n    def update(self):\n        self.rect.y+=self.speedy\n        if self.rect.bottom<0:\n            self.kill()\n            \n\n#load all game images\n\nplayer_img=pygame.image.load(path.join(img_dir,'ship-medium.png')).convert()\nbullet_img=pygame.image.load(path.join(img_dir,'laserRed16.png')).convert()\nmonstor_img=pygame.image.load(path.join(img_dir,'monstor.png')).convert()\n\nall_sprites=pygame.sprite.Group()\n\nmobs=pygame.sprite.Group()\n\nbullets=pygame.sprite.Group()\n\nplayer=Player()\nall_sprites.add(player)\n\nfor i in range(10):\n    m=Mob()\n    all_sprites.add(m)\n    mobs.add(m)\n    \ndone=True\n\nwhile done:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            done = False\n        elif event.type==pygame.KEYDOWN:\n            if event.key==pygame.K_SPACE:\n                player.shoot()\n\n    all_sprites.update()\n\n    hits=pygame.sprite.groupcollide(mobs,bullets,True,True)\n    for hit in hits:\n        m=Mob()\n        all_sprites.add(m)\n        mobs.add(m)\n\n    hits=pygame.sprite.spritecollide(player,mobs,False)\n    if hits:\n        print('lost game')\n        done=False\n    \n    screen.fill(black)\n    all_sprites.draw(screen)\n    pygame.display.flip()\n    pygame.time.delay(10)\n    \n","sub_path":"player_shoot.py","file_name":"player_shoot.py","file_ext":"py","file_size_in_byte":3454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"122363554","text":"from mwclient import Site\nimport pandas as pd\nimport re\nimport src.local  # create a file called local.py with your credentials\n\n\ndef site_login():\n    \"\"\"\n    Log-in to the Semantic MediaWiki using a USERNAME and PASSWORD\n    Create a file called local.py with your credentials\n    \"\"\"\n    site = Site(('http', 'beta.floranorthamerica.org'))\n    site.login(src.local.USERNAME, src.local.PASSWORD)\n    return site\n\n\ndef extract_property_text(printouts, property):\n    \"\"\"\n    Extract property data from the data returned by the API (=\"printouts\") for a property\n\n    Parameters\n    ----------\n    printouts: query results returned by the site.ask query function\n    property: property name (e.g., Illustrator, Distribution, etc.)\n\n    Returns\n    -------\n    property_text: isolated property text string for the property in question\n\n    \"\"\"\n    property_text = printouts[property]\n    if len(property_text) == 1:\n        property_text = property_text[0] # property_text comes as a list, even with one value\n        if isinstance(property_text, dict): # If the property is a page type, it returns a dictionary\n            property_text = property_text['fulltext'] # with namespace, url, etc. 'fulltext' is what we want\n    # if property_text contains more than one value, it is useful to simply return it as a list!\n    if len(property_text) > 1 and isinstance(property_text[0], dict): # UNLESS property_text is a dictionary, in the\n        # case of page properties. In this case we need to extract the 'fulltext' property values and return them as a list,\n        # similar to string type properties TODO: return Series instead of list here\n        prop_value_list = list()\n        prop_value_list.append([prop_value['fulltext'] for prop_value in property_text]) #\n        property_text = prop_value_list[0]\n    return property_text\n\n\ndef extract_taxon_properties(site, query_string, properties):\n    \"\"\"\n    Return taxa names matched by a query string, as well as the property data asked for in a list\n    using the mwclient function \"ask\".\n\n    Parameters\n    ----------\n    site: a logged-in Site object of the flora\n    query_string: a query string to run ask SMW ask query API\n    properties: a list of the properties you'd like to return\n\n    Returns\n    -------\n    taxon_names: a list of the taxon name returned to match to the property data returned\n    property_texts: a list of property text data asked for by the query string\n    \"\"\"\n    properties_texts = []\n    taxon_names = []\n\n    for answer in site.ask(query_string):  # use a for loop to collect properties and other things we want to store!\n        for title, data in answer.items():\n            if title == 'printouts':\n                properties_texts.append([extract_property_text(data, property) for property in properties])\n            if title == 'fulltext':\n                taxon_names.append(data)\n                print(\"Appending {} data\".format(data))\n\n    return taxon_names, properties_texts\n\n\ndef ask_query(query_string, output_file_name):\n    \"\"\"\n    Run an ask query against the API module \"ask\" using the mwclient function \"ask\".\n\n    Parameters\n    ----------\n    output_file_name: output file name\n    query_string: a query string to run ask SMW ask query API, e.g.:\n\n    Examples:\n        query_string = \"[[Authority::Linnaeus]][[Distribution::Nunavut]]|?Taxon family\"\n        query_string = \"[[Authority::Miller]]|?Taxon family|?Volume\"\n        query_string = \"[[Distribution::Ont.]][[Author::Geoffrey A. Levin]]|?Taxon family|?Volume|?Illustration|?Distribution\"\n        query_string = \"[[Distribution::Ont.]][[Author::Geoffrey A. Levin]]|?Taxon family|?Volume|?Distribution\"\n        query_string = \"[[Illustrator::+]][[Illustration::Present]]\"\n\n    Returns\n    -------\n    properties_texts_data_frame: a pandas dataframe with matched taxa names and properties\n\n    \"\"\"\n    site = site_login()\n    properties = re.split(r'\\|\\?', query_string)[1:]\n    taxon_names, properties_texts = extract_taxon_properties(site, query_string, properties)\n    properties_texts = pd.DataFrame(properties_texts, columns=properties)\n    properties_texts_data_frame = pd.concat([pd.Series(taxon_names), properties_texts], axis=1)\n    properties_texts_data_frame = properties_texts_data_frame.rename(columns={0: \"Taxon name\"})\n    properties_texts_data_frame.to_csv(output_file_name, index=False)\n    return properties_texts_data_frame","sub_path":"python/src/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":4407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"178535603","text":"from django.shortcuts import render\nfrom django.http import Http404\nfrom .models import Shows,Reservation,Category\nfrom datetime import datetime\nfrom django.contrib.auth.decorators import login_required\n\nfrom django.contrib.auth.forms import UserCreationForm\n\n# Create your views here.\n\n\ndef index(request):\n    return render( request,'reservations/index.html')\n\n\n\ndef detail(request,id_spectacle):\n    try:\n        spectacle = Shows.objects.get(pk=id_spectacle)\n    except Shows.DoesNotExist:\n        raise Http404(\"Ce spectacle n existe pas !\")\n    if request.method=='POST':\n        nombre_place=int(request.POST.get(\"nombre_place\"))\n        place_dispo= spectacle.location.get_available_places()\n        if (place_dispo >= nombre_place):\n            total= nombre_place*spectacle.get_price()\n            current_user=request.user\n            location=spectacle.location\n            booking=Reservation.objects.create(\n                user=current_user,\n                nombre_place=nombre_place,\n                prix_total=total,\n                spectacle=spectacle,\n                location=location\n            )\n\n\n\n    return render(request,'reservations/detail.html', {'spectacle': spectacle})\n\n\ndef spectacles(request):\n    spectacle_dispo = Shows.objects.filter(bookable=True)\n    return render(request,'reservations/spectacles.html',{'spectacle_dispo':spectacle_dispo})\n\n\n\n@login_required(login_url='/utilisateur/connexion/')\ndef reservation(request):\n    spectacle_dispo=Shows.objects.filter(bookable=True)\n    return render(request,'reservations/reservations.html',{'spectacle_dispo':spectacle_dispo})\n\ndef categorie(request):\n    categories=Category.objects.all()\n    return render(request,'reservations/categories.html',{'categories':categories})\n\ndef spectacle_categu(request,id_categorie):\n    spectacles=Shows.objects.filter(category=id_categorie)\n    return render(request, 'reservations/spectacle_category.html', {'spectacles': spectacles})\n\ndef modif_bookable(request,data):\n\n    pass","sub_path":"reservations/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"387202412","text":"# -*- coding: utf-8 -*-\n\"\"\"\n===============================================================================\nNanoGen view controllers (:mod:`sknano.apps.nanogen_gui._nanogen_controllers`)\n===============================================================================\n\n.. currentmodule:: sknano.apps.nanogen_gui._nanogen_controllers\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\nfrom __future__ import unicode_literals\n__docformat__ = 'restructuredtext en'\n\n\ntry:\n    from PyQt5.QtWidgets import QApplication\nexcept ImportError:\n    from PyQt4.QtGui import QApplication\n\nfrom ._nanogen_views import NanoGenView, SWNTGeneratorView, \\\n    MWNTGeneratorView, GrapheneGeneratorView, \\\n    FullereneGeneratorView, BulkStructureGeneratorView\n\n__all__ = ['NanoGenController', 'SWNTGeneratorController',\n           'MWNTGeneratorController', 'GrapheneGeneratorController',\n           'BulkStructureGeneratorController', 'ViewControllerMixin',\n           'GeneratorViewController']\n\n\nclass ViewControllerMixin:\n    def refresh_view(self):\n        \"\"\"Refresh `NanoGenView`.\"\"\"\n        self.view.update_app_view()\n\n\nclass NanoGenController(ViewControllerMixin):\n    \"\"\":mod:`~sknano.apps.nanogen_gui` MVC controller class.\n\n    Parameters\n    ----------\n    args : :attr:`python:sys.argv`\n    model : :class:`~sknano.apps.nanogen_gui._nanogen_model.NanoGenModel`\n        An instance of\n        :class:`~sknano.apps.nanogen_gui._nanogen_model.NanoGenModel`.\n\n    \"\"\"\n    def __init__(self, args, model=None):\n        app = QApplication(args)\n        self.model = model\n        self.view = NanoGenView(self, self.model)\n        self.model.notify_observers()\n        self.view.show()\n        app.exec_()\n\n\nclass GeneratorViewController(ViewControllerMixin):\n    def __init__(self, model=None, view=None, **kwargs):\n        self.model = model\n        self.view = view(self, self.model, **kwargs)\n        self.model.notify_observers()\n        self.view.show()\n\n    def get_generator_parameters(self):\n        return self.view.get_generator_parameters()\n\n\nclass SWNTGeneratorController(GeneratorViewController):\n    def __init__(self, model=None, **kwargs):\n        super().__init__(model=model, view=SWNTGeneratorView, **kwargs)\n\n\nclass MWNTGeneratorController(GeneratorViewController):\n    def __init__(self, model=None, **kwargs):\n        super().__init__(model=model, view=MWNTGeneratorView, **kwargs)\n\n\nclass GrapheneGeneratorController(GeneratorViewController):\n    def __init__(self, model=None, **kwargs):\n        super().__init__(model=model, view=GrapheneGeneratorView, **kwargs)\n\n\nclass FullereneGeneratorController(GeneratorViewController):\n    def __init__(self, model=None, **kwargs):\n        super().__init__(model=model, view=FullereneGeneratorView, **kwargs)\n\n\nclass BulkStructureGeneratorController(GeneratorViewController):\n    def __init__(self, model=None, **kwargs):\n        super().__init__(model=model, view=BulkStructureGeneratorView,\n                         **kwargs)\n","sub_path":"sknano/apps/nanogen_gui/_nanogen_controllers.py","file_name":"_nanogen_controllers.py","file_ext":"py","file_size_in_byte":3001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"235323483","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nfrom sys import version_info\nif version_info.major == 3:\n    pass\nelif version_info.major == 2:\n    input = raw_input\nelse:\n    print (\"Unknown python version - input function not safe\")\n\nfrom os import environ\n#from collections import deque\nfrom sys import stdin\n#from statistics import median\n#from sys import setrecursionlimit\n#setrecursionlimit (11000)\n\n\"\"\"\ntc 1\nanswer: 633\n9 5\n2 3 4 2 3 6 8 4 5\nanswer: 2\n6 3\n2 2 2 9 1 15\nanswer: 2\n10 3\n4 5 6 2 7 1 8 0 9 3\nanswer: 2\n14 4\n5 5 5 5 10 1 1 1 4 0 2 8 3 14\nanswer: 5\n50 8\n34 26 83 26 66 26 34 83 56 88 1 5 8 8 5 1 2 35 65 33 31 30 0 200 156 22 22 22 22 33 33 33 44 4 44 44 12 82 48 25 22 22 21 12 12 33 1 1 1 9\nanswer: 7\n\n\"\"\"\n\ndef countSort (n, l):\n    mxVal = 201\n    c = [0] * mxVal\n    for i in range (n):\n        c [l [i]] += 1\n    res = []\n    for i in range (mxVal):\n        if c [i]:\n            res += [i] * c [i]\n    return res\n\nclass CSM: # count sort median\n    def __init__(self, lst, n):\n        mxVal = 201\n        self.lst_size = n\n        self.odd = n % 2\n        self.nh = n // 2\n        self.csm_size = mxVal\n        self.data = [0] * mxVal\n        self.fill (lst, n)\n        self.init_csm ()\n    def fill (self, lst, n):\n        for i in range (n):\n            self.data [lst [i]] += 1\n    def init_csm (self):\n        hc = 0\n        brk = False\n        for i in range (self.csm_size):\n            if self.data [i]:\n                if hc + self.data [i] <= self.nh:\n                    hc += self.data [i]\n                    continue\n                hc += self.data [i]\n                for ix in range (self.data [i],0,-1):\n                    hc -= 1\n                    if hc <= self.nh:   # hc half counter\n                        self.nhdf = ix  # diff2nh\n                        self.nhnr = i   # nh number (in half of list)\n                        brk = True\n                        break\n            if brk: break\n    def csm (self):\n        if self.odd:\n            return self.nhnr\n        else:\n            if self.nhdf > 1: nh_1 = self.nhnr\n            else:   # nh_1 is number in nh - 1\n                nh_1 = self.nhnr - 1\n                while not self.data [nh_1]: nh_1 -= 1\n            return (nh_1 + self.nhnr) / 2\n    def qu_csm (self, fst, new):   # queue - drop fst, add new\n        self.data [fst] -= 1\n        self.data [new] += 1\n        if (fst == new or fst < self.nhnr and new < self.nhnr or\n            fst > self.nhnr and new >= self.nhnr or\n            fst == self.nhnr and new > self.nhnr \n                and self.data [self.nhnr] >= self.nhdf): pass\n        elif (fst < self.nhnr and new >= self.nhnr or\n            fst == self.nhnr and new > self.nhnr):\n            if self.nhdf < self.data [self.nhnr]: self.nhdf += 1\n            else:\n                self.nhnr += 1\n                while not self.data [self.nhnr]: self.nhnr += 1\n                self.nhdf = 1\n        elif (fst >= self.nhnr and new < self.nhnr):\n            if self.nhdf > 1: self.nhdf -= 1\n            else:\n                self.nhnr -= 1\n                while not self.data [self.nhnr]: self.nhnr -= 1\n                self.nhdf = self.data [self.nhnr]\n    def median (self):  # via creating sorted list (O (n))\n        l = self.srt_lst ()\n        nh = self.nh\n        odd = self.odd\n        return l [nh] if odd else (l [nh - 1] + l [nh]) / 2\n    # fill a list to 'count array'\n    def srt_lst (self):\n        res = []\n        for i in range (self.csm_size):\n            if self.data [i]:\n                res += [i] * self.data [i]\n        return res\n\ndef activityNotifications (exptr, n, d):\n    odd = d % 2\n    dh = d // 2\n    def get_med (seq):\n        return seq [dh] if odd else (seq [dh - 1] + seq [dh]) / 2\n    act = 0\n    if n < 100 and d < 100:\n        for i in range (d, n):\n            md = get_med (sorted (exptr [i - d : i]))\n            if exptr [i] >= md * 2:\n                act += 1\n        return act\n    else:\n        lst = CSM (exptr [ : d], d)\n        for i in range (d, n):\n            md = lst.csm ()\n            if exptr [i] >= md * 2:  #lst.median () * 2: # md * 2:\n                act += 1\n            lst.qu_csm (exptr [i - d], exptr [i])\n        return act\n\ndef main ():\n    fptr = open (environ ['OUTPUT_PATH'], 'w')\n    [n, d] = map (int, input ().split ())\n    #expenditure = list (map (int, stdin.readline ().rstrip ().split ()))\n    expenditure = [int (x) for x in stdin.readline ().split ()]\n    result = activityNotifications (expenditure, n, d)\n    print (result)\n    fptr.write (str (result) + '\\n')\n    fptr.close ()\n\nif __name__ == '__main__':\n    main ()\n","sub_path":"2017/hackerrank/activityNotifications.py","file_name":"activityNotifications.py","file_ext":"py","file_size_in_byte":4679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"62717339","text":"import logging\nimport models.backbone.resnet_cifar as resnet\nlogger = logging.getLogger('base')\n\ndef define_net(arch, block_name, num_classes=100, pretrained=False):\n    if arch.startswith(\"resnet\"):\n        depth = int(arch.split('-')[-1])\n        net = resnet.ResNet(depth=depth, num_classes=num_classes, block_name=block_name)\n    else:\n        raise NotImplementedError('The model [{}] is not implemented.'.format(arch))\n    logger.info('The network [{}] has created.'.format(arch))\n    return net","sub_path":"models/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"438008892","text":"##import rospy\nimport atexit\nimport ray\n#from ray.rllib.agents.ddpg.apex import ApexDDPGTrainer\nfrom ray import tune\nfrom ray.tune import run_experiments, grid_search\nimport gym\n\n\nnum_of_workers = 12\nhorizon = 40\n\n#ray.init()\n#ray.init(redis_address=\"localhost:48842\")\n#ray.init(redis_address=\"localhost:14458\",huge_pages= True, plasma_directory='/mnt/hugepages')\n#ray.init(plasma_directory='/tmp/plasma')\nray.init(object_store_memory=175000000000, redis_max_memory=20000000000)\n\n\ndef on_episode_start(info):\n    episode = info[\"episode\"]\n    print(\"episode {} started\".format(episode.episode_id))\n\n\n\ndef on_episode_end(info):\n    episode = info[\"episode\"]\n    print(episode.episode_id, episode.length)\n\n\ndef on_sample_end(info):\n    print(\"returned sample batch of size {}\".format(info[\"samples\"].count))\n\n\ndef on_train_result(info):\n    print(\"trainer.train() result: {} -> {} episodes\".format(\n        info[\"trainer\"], info[\"result\"][\"episodes_this_iter\"]))\n    # you can mutate the result dict to add new fields to return\n    info[\"result\"][\"callback_ok\"] = True\n\n\ndef on_postprocess_traj(info):\n    episode = info[\"episode\"]\n    batch = info[\"batch\"]\n    print(\"postprocessed {} steps\".format(batch.count))\n    if \"num_batches\" not in episode.custom_metrics:\n        episode.custom_metrics[\"num_batches\"] = 0\n    episode.custom_metrics[\"num_batches\"] += 1\n\n\nrun_experiments({\n        \"Our_results\": {\n            \"run\": \"APEX_DDPG\",\n            \"env\": 'FetchPickAndPlace-v1',\n            \"stop\": {\"episode_reward_mean\": 20,},\n            #\"resources_per_trial\":{\"cpu\": 10, \"gpu\": 1},\n            \"config\": {\n                \"n_step\":2, \n                \"compress_observations\": True, \"prioritized_replay\": False,\n                \"timesteps_per_iteration\": 1500, \"min_iter_time_s\":30,\n                \"horizon\":horizon, \"parameter_noise\": True,\n                \"num_workers\": 4, \"collect_metrics_timeout\":1600,\n                \"buffer_size\":200000, \"lr\": 1e-3,  # to try different learning rates #\"per_worker_exploration\": True,\n                \"num_cpus_per_worker\": 1.5,\n                \"num_cpus_for_driver\":1,\n                \"batch_mode\": \"complete_episodes\",\n                \"per_worker_exploration\" : True,\n                \"callbacks\": {\n                #\"on_episode_start\": tune.function(on_episode_start),\n                #\"on_episode_step\": None,\n                #\"on_episode_end\": tune.function(on_episode_end),\n                #\"on_sample_end\": tune.function(on_sample_end),\n                \"on_train_result\": tune.function(on_train_result),\n                #\"on_postprocess_traj\": tune.function(on_postprocess_traj),\n                }\n                },\n        },\n    })\n","sub_path":"train_ee.py","file_name":"train_ee.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"634090987","text":"\"\"\" Compiled: 2020-09-18 10:38:49 \"\"\"\n\n#__src_file__ = \"extensions/advanced_corporate_actions/./etc/FCorpActionSetup.py\"\n\"\"\"----------------------------------------------------------------------------\n MODULE\n     FCorpActionSetup - Module which create the additional infos needed for the corporate\n    action processing. \n\n DESCRIPTION\n     This module set up all the necessary additional infos for the advanced \n    corporate action processing.\n ---------------------------------------------------------------------------\"\"\"\nimport FBDPCommon\nimport acm\nimport FBDPCurrentContext\nimport FCorpActionElectionStatesSetup\n    \ndef CreateAdditionalInfo():\n    FBDPCommon.CreateAdditionalInfo('Position', 'CorpActionElection',\n    'Standard', 'Integer', description='Reference to Position', subTypes=[])\n    FBDPCommon.CreateAdditionalInfo('PositionInstance', 'CorpActionElection',\n    'Standard', 'Integer', description='Position', subTypes=[])\n    FBDPCommon.CreateAdditionalInfo('RollbackElection', 'CorpActionElection',\n    'Standard', 'String', description='RollbackElection', subTypes=[])\n    FBDPCommon.CreateAdditionalInfo('TaxRate', 'CorpActionElection',\n    'Standard', 'Double', description='Tax Rate Applicable', \n    subTypes=[], defaultValue=1.0)\n    FBDPCommon.CreateAdditionalInfo('SuggestedBy', 'CorpActionElection',\n    'Standard', 'String', description='Suggested By Trader', subTypes=[])\n    FBDPCommon.CreateAdditionalInfo('TraderElection', 'CorpActionElection',\n    'Standard', 'String', description='Elected By Trader', subTypes=[], defaultValue='N/A')\n    FBDPCommon.CreateAdditionalInfo('TraderInterestedIn', 'CorpAction',\n    'Standard', 'String', description='Intererted By Trader', subTypes=[])\n    FBDPCommon.CreateAdditionalInfo('MarkitCAStatus', 'CorpAction',\n    'Standard', 'String', description='Workflow Status', subTypes=[])\n    FBDPCommon.CreateAdditionalInfo('CADefinitionFile', 'CorpAction',\n    'Standard', 'String', description='File defines the Corporate Action', subTypes=[])\n\ndef CreateMarketPlaces():\n    MarketNames = ['ExDate', 'RecordDate']\n    for mktName in MarketNames:\n        if not acm.FMarketPlace[mktName]:\n            mkt = acm.FMarketPlace()\n            mkt.Name(mktName)\n            mkt.Commit()\n\ndef CreatePositionSepcification():\n    spec = acm.FPositionSpecification[\"Corporate Actions\"]\n    if spec:\n        return\n\n    specification= acm.FPositionSpecification()\n    specification.Name(\"Corporate Actions\")\n    specification.Commit()\n    attributes = ['Counterparty.Name', 'Portfolio.Name', 'Instrument.Name', 'Instrument.Underlying.Name']\n    for attr in attributes:\n        definition = acm.FPositionAttributeDefinition()\n        definition.Definition(attr)\n        definition.Specification(specification)\n        definition.Commit()\n\ndef Setup():\n    FBDPCurrentContext.CreateLog(\"FCorpActionSetup\", 1, 1, 0, 0,\n        False, \"\", \"\")\n    CreateAdditionalInfo()\n    CreatePositionSepcification()\n    CreateMarketPlaces()\n    FCorpActionElectionStatesSetup.CreateCorporateActionElectionStateChart()\n\nSetup()","sub_path":"Extensions/Advanced Corporate Actions/FPythonCode/FCorpActionSetup.py","file_name":"FCorpActionSetup.py","file_ext":"py","file_size_in_byte":3069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"347250356","text":"import traceback\n\n\"\"\"######\nGrades\n\n* Try not to worry.\n* I don't normalize grades until the end.\n* And I will be \"curving\" things at that point.\n\n* On assignment 3: 75 is good, 65 is OK. You are \n  all writing reasonabl code. But almost all of \n  you could improve.\n\nI grade hard so you can learn. I will not be\nassigning letter grades in the same way.\nI'm aware this is a one credit hour course.\n\n\"\"\"\n\n\n\n\n\n\n\n\"\"\"######\nExceptions\n\"\"\"\n\ndef f():\n    return \"str\" + 1\n\n#print(int(f()))\n\n\"\"\"\nExceptions propogate up the call stack.\n\"Unroll\" the stack.\n\"\"\"\n\n\n\n\n\n\"\"\"######\nRaising exceptions\n\nMany operations can raise exceptions.\nAnd we can do it explicitly.\n\"\"\"\n\n#raise TypeError(\"...\")\n#raise KeyError(\"test\")\n#raise RuntimeError()\n#raise RuntimeError\n\n\n\n\n\n\n\"\"\"######\nCatching exceptions\n\"\"\"\ntry:\n    raise RuntimeError(\"test\")\nexcept RuntimeError:\n    # Handle error\n    traceback.print_exc()\n\n\ntry:\n    d = {}\n    d[101]\nexcept LookupError as e:\n    print(type(e), e.args)\n\n\n\n\n\"\"\"######\nFinally\n\"\"\"\n#del code\n#def code():\n#    pass\ntry:\n    code()\nexcept NameError:\n    print(\"Error\")\nfinally:\n    print(\"Done\")\n\n\n\n\n\n\"\"\"######\nBuilt-in exceptions\n\nBaseException\n +-- SystemExit\n +-- KeyboardInterrupt\n +-- GeneratorExit\n +-- Exception\n      +-- StopIteration\n      +-- ArithmeticError\n      |    +-- FloatingPointError\n      |    +-- OverflowError\n      |    +-- ZeroDivisionError\n      +-- AssertionError\n      +-- AttributeError\n      +-- BufferError\n      +-- EOFError\n      +-- ImportError\n      +-- LookupError\n      |    +-- IndexError\n      |    +-- KeyError\n      +-- MemoryError\n      +-- NameError\n      |    +-- UnboundLocalError\n      +-- OSError\n      |    +-- BlockingIOError\n      |    +-- ChildProcessError\n      |    +-- ConnectionError\n      |    |    +-- BrokenPipeError\n      |    |    +-- ConnectionAbortedError\n      |    |    +-- ConnectionRefusedError\n      |    |    +-- ConnectionResetError\n      |    +-- FileExistsError\n      |    +-- FileNotFoundError\n      |    +-- InterruptedError\n      |    +-- IsADirectoryError\n      |    +-- NotADirectoryError\n      |    +-- PermissionError\n      |    +-- ProcessLookupError\n      |    +-- TimeoutError\n      +-- ReferenceError\n      +-- RuntimeError\n      |    +-- NotImplementedError\n      +-- SyntaxError\n      |    +-- IndentationError\n      |         +-- TabError\n      +-- SystemError\n      +-- TypeError\n      +-- ValueError\n      |    +-- UnicodeError\n      |         +-- UnicodeDecodeError\n      |         +-- UnicodeEncodeError\n      |         +-- UnicodeTranslateError\n      +-- Warning\n           +-- DeprecationWarning\n           +-- PendingDeprecationWarning\n           +-- RuntimeWarning\n           +-- SyntaxWarning\n           +-- UserWarning\n           +-- FutureWarning\n           +-- ImportWarning\n           +-- UnicodeWarning\n           +-- BytesWarning\n           +-- ResourceWarning\n\"\"\"\n\n\n\"\"\"######\nUser-defined exceptions\n\"\"\"\n\nclass MyError(Exception):\n    def __init__(self, error_code, msg):\n        self.code = error_code\n        self.msg = msg\n\n    def __repr__(self):\n        return \"{}({!r}, {!r})\".format(type(self).__name__, self.code, self.msg)\n    \n    def __str__(self):\n        return \"{} ({})\".format(self.msg, self.code)\n\ndef func():\n    raise MyError(500, \"Server error\")\n\n#func()\n\ntry:\n    func()\nexcept MyError as e:\n    print(e)\n\n\ntry:\n    func()\nexcept Exception:\n    print(\"error\")\n\n\n\n\n\n\"\"\"######\nAdditional control constructs that python has:\n* Else on try/except\n* Else on loops\n\"\"\"\n\ntry:\n    #raise Exception\n    pass\nexcept RuntimeError:\n    print(\"Raise\")\nelse:\n    print(\"No raise\")\nfinally:\n    print(\"Always\")\n\n\n# Example from Python Manual (https://docs.python.org/3.4/tutorial/controlflow.html)\nfor n in range(2, 10):\n    for x in range(2, n):\n        if n % x == 0:\n            print(n, 'equals', x, '*', n//x)\n            break\n    else:\n        # loop fell through without finding a factor\n        print(n, 'is a prime number')\n\n\ndone = False\nwhile not done:\n    #break\n    done = True\nelse:\n    print(\"Done normally\")\n\n\n\n\"\"\"######\nGenerators with input\n\"\"\"\n\ndef generateSets():\n    s = set()\n    while True:\n        s.add((yield frozenset(s)))\n\ng = generateSets()\ng.send(None)\nprint(g.send(1))\nprint(g.send(\"a\"))\nprint(g.send(42))\ng.close()\n\n\n\"\"\"######\nyield from\n\nA way to yield everything that would be yielded from another\ngenerator. It totally integrates the sub interator into this one\nincluding if the user of the iterator uses .send(v).\n\"\"\"\n\ndef g(iterable):\n    yield from iterable\n    # Roughly the same as\n    # for v in iterable: yield v\n\n\"\"\"We can use this to build something odd.  A generator with multiple\nlevels that takes input somewhere deep inside.\n\nThis is a form of coroutine.\n\"\"\"\n\ndef collect():\n    acc = 0\n    while True:\n        v = yield\n        if v is None:\n            break\n        acc += v\n    return acc\n\ndef g():\n    print((yield from collect()), (yield from collect()))\n    # To prevent exception\n    yield \n\nx = g()\nx.send(None)\nx.send(1)\nx.send(2)\nx.send(None)\nx.send(30)\nx.send(1)\nx.send(None)\nx.close()\n\n# What is x? What does it represent?\n\n\n\n\"\"\"######\nCoroutines\n\nCooperative concurrency.\n\"\"\"\n\ndef reader(filename, sink):\n    with open(filename, \"r\") as fi:\n        sink.send(None)\n        for line in fi:\n            if sink.send(line.strip()):\n                print(\"Matched!\")\n        sink.close()\n\ndef matcher(pat):\n    isLastMatch = False\n    while True:\n        v = (yield isLastMatch)\n        if pat in v:\n            print(v)\n            isLastMatch = True\n        else:\n            isLastMatch = False\n\nreader(\"07-AdvFlowControl.py\", matcher(\"matcher\"))\n\n\"\"\"\nGenerators and coroutines are hard to wrap your head around.\nThis is just a taste so if you run into them you at least know \nwhat you are looking at.\n\"\"\"\n\n\n\"\"\"######\nPython 3.5 introduced actual coroutine syntax.\nThis is used for asynchronous programming and \"tasks\".\n\nhttps://docs.python.org/3.5/library/asyncio-task.html\n\"\"\"\n\nimport asyncio\n\nasync def compute(x, y):\n    print(\"Compute %s + %s ...\" % (x, y))\n    await asyncio.sleep(1.0)\n    return x + y\n\nasync def print_sum(x, y):\n    result = await compute(x, y)\n    print(\"%s + %s = %s\" % (x, y, result))\n\nloop = asyncio.get_event_loop()\nloop.run_until_complete(print_sum(1, 2))\nloop.close()\n\n\"\"\"This is mostly just syntactically different.\n\nawait is the same as yield from. And there are libraries that do this\nkind of scheduling for generators.\n\nI think there are other differences, but honestly I didn't know about\nthis until yesterday.\n\n\"\"\"\n","sub_path":"Python/07-AdvFlowControl (1).py","file_name":"07-AdvFlowControl (1).py","file_ext":"py","file_size_in_byte":6536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"418965486","text":"import csv\r\nimport numpy as np\r\n\r\ndef getData(filename):\r\n    with open(filename, \"rb\") as csvfile:\r\n        datareader = csv.reader(csvfile)\r\n        count = 0\r\n        for row in datareader:\r\n            \r\n            \r\n            if len(row) < 4096 :\r\n                maxValue = max(row)\r\n                minValue = min(row)\r\n                row = np.asarray(row)\r\n                interm = np.zeros(4096)\r\n                interm[:row.size] = row\r\n                row = interm\r\n            if len(row) > 4096:\r\n                maxValue = max(row[:4096])\r\n                minValue = min(row[:4096])\r\n                row = np.asarray(row)\r\n                row =row[:4096]\r\n                \r\n                count = count + 1\r\n            yield (row, maxValue, minValue)\r\n            \r\n\r\nif __name__ == \"__main__\":\r\n    data = []\r\n    path = \"data/test.csv\"\r\n    count =0\r\n    min_num = 0\r\n    max_num = 0\r\n\r\n    for row, maxValue , minValue in getData(path):\r\n        # print(row.max(axis=0))\r\n        if (maxValue > max_num):\r\n            max_num = maxValue\r\n        if (minValue < min_num):\r\n            min_num = minValue\r\n        count = count +1\r\n    print(min_num, max_num, count)\r\n        # count = count +1\r\n        # if count <10:\r\n        #     print(row)\r\n    #     data.append(row)\r\n    # data = np.asarray(data)\r\n            # print(b)\r\n   \r\n","sub_path":"project_malware_detection/inputProcess.py","file_name":"inputProcess.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"420590511","text":"import torch\nfrom torch import nn\nfrom torchsummary import summary\n\ndef DoubleConV(in_channels, out_channels):\n    mid_channels = out_channels\n    return nn.Sequential(\n        nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), # padding = 1, giu nguyen size anh\n        nn.BatchNorm2d(mid_channels),\n        nn.LeakyReLU(0.2),\n        nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),\n        nn.BatchNorm2d(out_channels),\n        nn.LeakyReLU(0.2)\n    )\n# 0 0 0 0 0 0 0\n# 0 1 1 1 1 1 0\n# 0 1 1 1 1 1 0 \n# 0 1 1 1 1 1 0\n# 0 0 0 0 0 0 0\n\ndef Down(in_channels, out_channels):\n    return nn.Sequential(\n        nn.MaxPool2d(2),\n        DoubleConV(in_channels, out_channels)\n    )\n\ndef Up(in_channels, out_channels):\n    return nn.Sequential(\n        nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)\n    )\n\ndef OutConv(in_channels, out_channels):\n    return nn.Conv2d(in_channels, out_channels, kernel_size=1)\n\nclass UNet(nn.Module):\n    def __init__(self, n_channels, n_classes):\n        super().__init__()\n        self.n_channels = n_channels\n        self.n_classes = n_classes\n        self.inc = DoubleConV(n_channels, 64) #floor 1\n        self.down1 = Down(64,128)\n        self.down2 = Down(128,256)\n        self.down3 = Down(256,512)\n        self.center = Down(512,1024)\n        self.up1 = Up(1024,512)\n        self.tmp1 = DoubleConV(1024,512)\n        self.up2 = Up(512,256)\n        self.tmp2 = DoubleConV(512,256)\n        self.up3 = Up(256,128)\n        self.tmp3 = DoubleConV(256,128)\n        self.up4 = Up(128,64)\n        self.tmp4 = DoubleConV(128,64)\n        self.output = nn.Sequential(\n            OutConv(64, n_classes),\n            # nn.Tanh()\n        )\n    def forward(self, x):\n        f1L = self.inc(x) #64\n        f2L = self.down1(f1L) #128\n        f3L = self.down2(f2L) #256\n        f4L = self.down3(f3L) #512\n        cent = self.center(f4L) #1024\n\n        f4R = self.up1(cent) #512\n        f4RT = self.tmp1(torch.cat((f4L, f4R), dim=1)) #512->1024\n        f3R = self.up2(f4RT) #256\n        f3RT = self.tmp2(torch.cat((f3L, f3R), dim=1)) #256->512\n        f2R = self.up3(f3RT) #128\n        f2RT = self.tmp3(torch.cat((f2L, f2R), dim=1)) #128->256\n        f1R = self.up4(f2RT) #64\n        f1RT = self.tmp4(torch.cat((f1L, f1R), dim=1)) #512->1024\n        logits = self.output(f1RT)\n        return logits\n        \nif __name__ == \"__main__\":\n    model = UNet(3,1)\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    model = model.to(device)\n    summary(model, input_size=(3, 240, 320))\n    ","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"299316179","text":"import streamlit as vAR_st\r\nimport statistics as sts\r\nimport source.title_1 as head\r\n\r\ndef to_clr():\r\n    vAR_st.session_state['sdvr']=\"Standard Deviation\"\r\n    vAR_st.session_state['sd_in']=\"\"\r\n\r\ndef sd(x):\r\n    w1,col1,col2,w2=vAR_st.columns((1,2,2,1)) \r\n    us1,bc1,bc2,us2=vAR_st.columns((4,3,3,6))   \r\n    vAR_c=[]\r\n    vAR_b=x.split(\",\")\r\n    with bc1:\r\n        vAR_st.write(\"\")  \r\n        if vAR_st.button(\"submit\"):\r\n            try:\r\n                if x==\"\":\r\n                    with col2:\r\n                        vAR_st.info(\"Enter the values separated by comma\")\r\n                else:\r\n                    for i in vAR_b:\r\n                        vAR_c.append(float(i))\r\n                    standard_deviation = sts.stdev(vAR_c)\r\n                    with col1:\r\n                        standard_deviation=round(standard_deviation,4)\r\n                        vAR_st.write(\"\")\r\n                        vAR_st.markdown(\"### Result\")\r\n                    with col2:\r\n                        vAR_st.write(\"\")\r\n                        vAR_st.success(standard_deviation)\r\n            except BaseException as er:\r\n                with col2:\r\n                    vAR_st.info(\"Invalid input\")\r\n        with bc2:\r\n            vAR_st.write(\"\")\r\n            vAR_st.button(\"Clear\", on_click=to_clr)\r\n                    \r\ndef var(x):\r\n    w1,col1,col2,w2=vAR_st.columns((1,2,2,1)) \r\n    us1,bc1,bc2,us2=vAR_st.columns((4,3,3,6))   \r\n    vAR_c=[]\r\n    \r\n    \r\n    vAR_b=x.split(\",\")\r\n    with bc1:\r\n        vAR_st.write(\"\")  \r\n        if vAR_st.button(\"submit\"):\r\n            try:\r\n                if x==\"\":\r\n                    with col2:\r\n                        vAR_st.info(\"Enter the values separated by comma\")\r\n                else:\r\n                    for i in vAR_b:\r\n                        vAR_c.append(float(i))\r\n                    vari=sts.variance(vAR_c)\r\n                    with col1:\r\n                        vAR_st.write(\"\")\r\n                        vAR_st.markdown(\"### Varience\")\r\n                    with col2:\r\n                        vari=round(vari,4)\r\n                        vAR_st.write(\"\")\r\n                        vAR_st.success(vari)\r\n            except BaseException as er:\r\n                with col2:\r\n                    vAR_st.info(\"Invalid input\")\r\n        with bc2:\r\n            vAR_st.write(\"\")\r\n            vAR_st.button(\"Clear\", on_click=to_clr)\r\n\r\ndef stats():\r\n    head.title()\r\n    vAR_st.markdown(\"<p style='text-align: center; color: black; font-size:20px;'><span style='font-weight: bold'>Problem Statement: </span>Application to find the Standard deviation and Varience</p>\", unsafe_allow_html=True)\r\n    vAR_st.markdown(\"<hr style=height:2px;background-color:gray>\",unsafe_allow_html=True)\r\n    w1,c1,c2,w2=vAR_st.columns((1,2,2,1))\r\n    with c1:\r\n        \r\n        vAR_st.markdown(\"### \")\r\n        vAR_st.markdown(\"### Enter the data\")\r\n        \r\n        vAR_st.markdown(\"## \")\r\n        vAR_st.markdown(\"### Select\")\r\n    with c2:\r\n        vAR_a=vAR_st.text_input(\"\",key=\"sd_in\",placeholder=\"eg. 21,25,27,20,24,21,46\")\r\n        vAR_r=vAR_st.selectbox(\"\",[\"Standard Deviation\",\"Varience\"],key=\"sdvr\")\r\n    if vAR_r==\"Standard Deviation\":\r\n        sd(vAR_a)\r\n    if vAR_r==\"Varience\":\r\n        var(vAR_a)\r\n#stats()","sub_path":"Streamlitapp/Grade-10/source/sd_var.py","file_name":"sd_var.py","file_ext":"py","file_size_in_byte":3261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"618691669","text":"from flask import Flask,render_template,redirect, url_for, request\nimport os,json,math\n\napp = Flask(__name__)\n\ndef getLeaves():\n    metadata=getMetadata(1)\n    last=math.ceil(int(metadata['hashes'])/int(metadata['perfile']))\n    lines=[]\n    for x in range(1,last+1):\n        file=\"lvl_1_\"+str(x)+\".txt\"\n        path=os.path.join(os.getcwd(),\"data\",file)\n        try:\n            with open(path,'r') as f:\n                lines=lines+f.read().splitlines()\n        except:\n            print(\"error\")\n    return lines\n\ndef getMetadata(level):\n    file=\"metadata_\"+str(level)+\".json\"\n    path=os.path.join(os.getcwd(),\"data\",file)\n    try:\n        with open(path,'r') as f:\n            json_data=f.read()\n    except:\n        print(\"metadata not found\")\n    parsed_json = json.loads(json_data)\n    metadata={}\n    metadata['hashes']=parsed_json['hashes']\n    metadata['perfile']=parsed_json['perfile']\n    metadata['isLastLevel']=parsed_json['isLastLevel']\n    return metadata\n\ndef updateMetadata(level):\n    file=\"metadata_\"+str(level)+\".json\"\n    path=os.path.join(os.getcwd(),\"data\",file)\n    try:\n        with open(path,'r') as f:\n            json_data=f.read()\n    except:\n        print(\"metadata not found\")\n    parsed_json = json.loads(json_data)\n    parsed_json['hashes']=str(int(parsed_json['hashes'])+1)\n    with open(path,'w') as f:\n            f.write(json.dumps(parsed_json))\n\ndef insertLeaf(leafHash):\n    metadata=getMetadata(1)\n    if int(metadata['hashes'])==0:\n        file=\"lvl_1_1.txt\"\n        path=os.path.join(os.getcwd(),\"data\",file)\n        try:\n            with open(path, 'w') as f:\n                f.write(str(leafHash)+\"\\n\")\n            updateMetadata(1)\n        except:\n            print(\"error\")\n            return \"error\"\n    else:\n        file=\"lvl_1_\"+str(int(int(metadata['hashes'])/int(metadata['perfile']))+1)+\".txt\"\n        path=os.path.join(os.getcwd(),\"data\",file)\n        try:\n            with open(path, 'a') as f:\n                f.write(str(leafHash)+\"\\n\")\n                updateMetadata(1)\n        except:\n            print(error)\n            return \"error\"\n    return json.dumps(str(leafHash))\n\ndef getProof(leafHash):\n    return \"proof\"\n\ndef checkDuplicate(leafHash):\n    return \"no duplicate\"\n\n@app.route(\"/\")\ndef listApis():\n    return \"list of all apis\"\n\n@app.route(\"/tree\", methods = ['GET'])\ndef hashes():\n    if request.method == 'GET':\n        return json.dumps(getLeaves())\n\n@app.route(\"/add\",  methods = ['GET', 'POST'])\ndef insert():\n    if request.method == 'POST':\n      leafHash = request.form['leafHash']\n      print(insertLeaf(leafHash))\n      return redirect(url_for('success',leafHash = leafHash))\n    else:\n        return render_template('insertLeafHelper.html')\n\n@app.route(\"/proof\" ,  methods = ['GET', 'POST'])\ndef showProof():\n    return \"return audit proof of a leaf\"\n\n@app.route('/success/<leafHash>')\ndef success(leafHash):\n   return 'inserted %s' % leafHash\n\nif __name__ == \"__main__\":\n    app.run()","sub_path":"python/merkle_tree_python/merkle_leaves_working/merkle.py","file_name":"merkle.py","file_ext":"py","file_size_in_byte":2967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"508086973","text":"######################################################################################################################\n#  Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.                                           #\n#                                                                                                                    #\n#  Licensed under the Apache License Version 2.0 (the \"License\"). You may not use this file except in compliance     #\n#  with the License. A copy of the License is located at                                                             #\n#                                                                                                                    #\n#      http://www.apache.org/licenses/                                                                               #\n#                                                                                                                    #\n#  or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES #\n#  OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions    #\n#  and limitations under the License.                                                                                #\n######################################################################################################################\n\nimport os\nfrom boto3.dynamodb.conditions import Key\nfrom util.dynamodb_utils import DynamoDBUtils\n\nclass ScheduleState:\n\n    def __init__(self, logger, service):\n        self._logger = logger\n        self.schedule_state_table_name = os.getenv(\"SCHEDULE_STATES_TABLE\", None)\n        self.dynamodb_table = DynamoDBUtils.get_dynamodb_table_resource_ref(self.schedule_state_table_name)\n        self.service = service\n\n    def get_schedule_state(self, schedule_name):\n        self._logger.debug(f\"Get scheduled state, for schedule name {schedule_name} for service {self.service}.\")\n        if schedule_name is not None:\n            resp = self.dynamodb_table.get_item(\n                Key={\"name\": schedule_name, 'service': self.service}, ConsistentRead=True)\n            return resp.get(\"Item\", {})\n        else:\n            self._logger.debug(f\"Schedule unavailable for schedule name {schedule_name} for service {self.service}.\")\n            return {\"name\": schedule_name, 'service': self.service , \"state\": \"unknown\"}\n\n    def save_schedule_state(self, schedule_name, state, localized_time, execution_id=None):\n        self._logger.debug(f\"Update state {state}, for schedule name {schedule_name} for service {self.service} at time {localized_time} to save schedule state.\")\n        get_schedule = self.get_schedule_state(schedule_name=schedule_name)\n        if bool(get_schedule) is False:\n            self._logger.debug(f\"Previous state {state}, for schedule name {schedule_name}, at time {localized_time} is unavailable creating a new entry\")\n            try:\n                resp = self.dynamodb_table.put_item(\n                    Item={\n                        'name': schedule_name,\n                        'state': state,\n                        'ssm-execution-id': execution_id,\n                        'service': self.service,\n                        'time': localized_time\n                    }\n                )\n                return resp\n            except Exception as error:\n                self._logger.error(f\"Error saving state {state}, for schedule name {schedule_name}, service {self.service}, at time {localized_time}\")\n                self._logger.error(error)\n        else:\n            try:\n                return self.dynamodb_table.update_item(TableName=self.schedule_state_table_name, \n                                                        Key={\"name\": get_schedule[\"name\"], 'service': self.service},\n                                                        UpdateExpression=\"set #st =:s, #tm =:t, #ssmexecutionid =:e\",\n                                                        ExpressionAttributeNames={\n                                                            \"#ssmexecutionid\": 'ssm-execution-id',\n                                                            '#st': 'state',\n                                                            '#tm': 'time'\n                                                        },\n                                                        ExpressionAttributeValues={\n                                                            \":s\": str(state),\n                                                            \":t\": str(localized_time),\n                                                            \":e\": str(execution_id)\n                                                        },ReturnValues=\"UPDATED_NEW\")\n            except Exception as error:\n                self._logger.error(f\"Error updating state {state}, for schedule name {schedule_name} for service {self.service} at time {localized_time}\")\n                self._logger.error(error)\n\n    def get_all_schedules(self):\n        self._logger.debug(\"Retrieve all the schedules with previous states\")\n        schedules = []\n        scan_kwargs = {\n            'FilterExpression': Key('service').eq(self.service),\n        }\n        resp = self.dynamodb_table.scan(**scan_kwargs)\n        schedules.extend(resp.get(\"Items\"))\n        while True:\n            token = resp.get('LastEvaluatedKey', None)\n            if token is not None:\n                scan_kwargs = {\n                    'ExclusiveStartKey': token,\n                    'FilterExpression': Key('service').eq(self.service),\n                }\n                resp = self.dynamodb_table.scan(**scan_kwargs)\n                schedules.extend(resp.get(\"Items\"))\n            else:\n                break\n        return schedules\n\n    def delete_schedule(self, schedule_name):\n        self._logger.debug(f\"Remove the schedule {schedule_name} for service {self.service} and state.\")\n        if schedule_name is not None:\n            try:\n                self.dynamodb_table.delete_item(Key={\"name\": schedule_name, \"service\": self.service})\n            except Exception as error:\n                self._logger.error(f\"Error removing schedule name {schedule_name} for service {self.service} and state.\")\n                self._logger.error(error)\n","sub_path":"source/lambda/util/schedule_state.py","file_name":"schedule_state.py","file_ext":"py","file_size_in_byte":6325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"527917830","text":"from django.test import TestCase\nfrom django.contrib.auth import get_user_model\nfrom .models import Doctor\nfrom .forms import DoctorForm\nimport json\n\n\nclass DoctorFormTestCase(TestCase):\n\n\t# def setup(self):\n\n\t# \tuser = get_user_model().object.create_user('Amy')\n\n\n\tdef test_valid_data(self):\n\n\t\tform = DoctorForm({\n\n\t\t\t'first_name': \"Amy\",\n\t\t\t'last_name': \"Anderson\",\n\t\t\t'email': \"Aanderson@gmail.com\",\n\t\t\t})\n\n\t\tself.assertTrue(form.is_valid())\n\t\tdoctor = form.save()\n\n\t\tself.assertEqual(doctor.first_name, \"Amy\")\n\t\tself.assertEqual(doctor.last_name, \"Anderson\")\n\t\tself.assertEqual(doctor.email, \"Aanderson@gmail.com\")\n\n\n\n\tdef test_get_doctors(self):\n\t\t\"\"\" Getting doctors from a populated database \"\"\"\n\n\t\tdoctorA = Doctor(first_name=\"Joe\", last_name= \"Alpha\", email= \"Janderson@gmail.com\")\n\n\t\tdoctorB = Doctor(first_name=\"Dan\", last_name= \"Beta\", email= \"Janderson@gmail.com\")\n\n\t\tdoctorA.save()\n\t\tdoctorB.save()\n\n\t\tresponse = self.client.get(\"/doctors/\")\n\n\t\tself.assertEqual(response.status_code, 200)\n\n\t\tdata = json.loads(response.content)\n\n\t\tprint(data)\n\n\t\tself.assertEqual(len(data), 2)\n\n\n\tdef test_get_doctor(self):\n\t\t\"\"\" Get 1 doctor from a database \"\"\"\n\n\t\tdoctorA = Doctor(first_name=\"Joe\", last_name= \"Alpha\", email= \"Janderson@gmail.com\")\n\n\t\tdoctorB = Doctor(first_name=\"Dan\", last_name= \"Beta\", email= \"Janderson@gmail.com\")\n\n\t\tdoctorA.save()\n\t\tdoctorB.save()\n\n\t\tresponse = self.client.get(\"/doctors/%s/\" % doctorA.id)\n\n\t\tself.assertEqual(response.status_code, 200)\n\n\t\tdata = json.loads(response.content.decode(\"utf-8\"))\n\n\t\tprint(data['first_name'])\n\n\t\tself.assertEqual(len(data), 3)\n\n\t\tself.assertEqual(data['first_name'], \"Joe\")\n\n\n\tdef test_doctor_does_not_exist(self):\n\t\t\"\"\" Return 404 iff no doctor \"\"\"\n\n\t\tdoctorA = Doctor(first_name=\"Joe\", last_name= \"Alpha\", email= \"Janderson@gmail.com\")\n\n\t\tdoctorB = Doctor(first_name=\"Dan\", last_name= \"Beta\", email= \"Janderson@gmail.com\")\n\n\t\tdoctorA.save()\n\t\tdoctorB.save()\n\n\t\tresponse = self.client.get(\"/doctors/4975948/\")\n\n\t\tself.assertEqual(response.status_code, 404)\n\n\n\n\n\n\t# def test_delete_doctor(self):\n\t# \t\"\"\" Delete doctor \"\"\"\n\n\t# \tdoctorA = Doctor(first_name=\"Joe\", last_name= \"Alpha\", email= \"Janderson@gmail.com\")\n\n\t# \tdoctorB = Doctor(first_name=\"Dan\", last_name= \"Beta\", email= \"Janderson@gmail.com\")\n\n\t# \tdoctorA.save()\n\t# \tdoctorB.save()\n\n\t# \tresponse = self.client.delete(\"/doctors/1/\")\n\n\t# \tself.assertEqual(response.status_code, 204)\n\n\t# \t# data = json.loads(response.content)\n\n\t# \t# print(data['first_name'])\n\n\t# \t# self.assertEqual(len(data), 3)\n\n\t# \t# self.assertEqual(data['first_name'], \"Joe\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\n\n\n","sub_path":"doctor/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"626883613","text":"import requests\r\nimport boto3\r\n\r\n#Define ENV variables\r\ncompany_code = ''\r\nphone_number = ''\r\nsender_name = ''\r\nexchange_api_key = ''\r\n\r\n#Sanity Check\r\nif len(company_code) < 1 or len(phone_number) < 1 or len(sender_name) < 1 or len(exchange_api_key) < 1:\r\n    print('Please set the parameters correctly')\r\n    exit()\r\n\r\n#Get share price data and convert to JSON for dict operations\r\nsymbols = {'symbols': company_code}\r\nshare_data = requests.get('https://query1.finance.yahoo.com/v7/finance/quote?lang=en-US&region=US&corsDomain=finance.yahoo.com&fields=regularMarketPrice', params=symbols)\r\npricedata = share_data.json()\r\n\r\n#Get exchange-rate list and convert to JSON for dict operations\r\napikey = {'app_id': exchange_api_key}\r\ncurr_exchange = requests.get('https://openexchangerates.org/api/latest.json', params=apikey)\r\nexdata = curr_exchange.json()\r\n\r\n#Extract the USD price and AUD exchange-rate from the JSONs and calculate the price in AUD\r\nusdprice = pricedata[\"quoteResponse\"]['result'][0]['regularMarketPrice']\r\nexrate = exdata['rates']['AUD']\r\nshareprice = str(round(usdprice * exrate, 2))\r\n\r\n#SMS the price\r\nclient = boto3.client('sns')\r\nclient.publish(PhoneNumber=phone_number, Message=shareprice, MessageAttributes={'AWS.SNS.SMS.SenderID': {'DataType': 'String', 'StringValue': sender_name}})","sub_path":"stock-price-sms.py","file_name":"stock-price-sms.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"183317115","text":"#! /usr/bin/env python3\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives.cmac import CMAC\nfrom cryptography.hazmat.primitives.ciphers import algorithms\n\n##################################################################\n###                                                            ###\n###   NOTE: THIS FEATURE IS ONLY SUPPORTED ON IMX8 DXL B0      ###\n###                                                            ###\n##################################################################\n\n\n###############  USE OF THIS SCRIPT  ############################################\n#\n#  1. Fill in customer_otp variable below to correspond to OEM secret\n#\n#  2. Run this python script\n#\n#  3. Console will give SECO and V2X KEK to use for open and closed lifecycles\n#\n#################################################################################\n\n\n\n# Customer to fill in securely provisioned OEM closed otp secret with a valid AES 256 key\ncustomer_otp = \"000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f\"\n\n# OEM open always uses facsimile otp secret - do not change this value\nfacsimile_otp = \"693f25bd6a299107cf7220b9ab504111fd3003bfe9aa38294262c3abab372790\"\n\n\ndef run_kdf(otp_val, label):\n\n# FIPS compliant key derivation (SP 800-108)\n#   - CMAC based KDF in counter mode\n#   - two loops of algorithm (256 bits generated)\n#   - label set by caller as \"SECO HSM\" or \"V2X HSM\"\n#   - context is \"NXP IMX8 DXL B0 OTP ROOT KEK\"\n\n    label_hex = label.encode(\"utf_8\").hex()\n\n    zero_byte = \"00\"\n\n    context = \"NXP IMX8 DXL B0 OTP ROOT KEK\"\n    context_hex = context.encode(\"utf_8\").hex()\n\n    L = \"0100\"  # 256 bits = 0x100\n\n    fixed_info = label_hex + zero_byte + context_hex + L\n\n    # First loop\n    counter = \"00000001\"\n    kdf_input = counter + fixed_info\n\n    cmac = CMAC(algorithms.AES(bytes.fromhex(otp_val)), default_backend())\n    cmac.update(bytes.fromhex(kdf_input))\n    key_val = cmac.finalize()\n\n    # Second loop\n    counter = \"00000002\"\n    kdf_input = counter + fixed_info\n\n    cmac = CMAC(algorithms.AES(bytes.fromhex(otp_val)), default_backend())\n    cmac.update(bytes.fromhex(kdf_input))\n    key_val += cmac.finalize()\n\n    print(key_val.hex())\n\ndef calc_seco_key(otp_val):\n    run_kdf(otp_val, \"SECO HSM\")\n\ndef calc_v2x_key(otp_val):\n    run_kdf(otp_val, \"V2X HSM\")\n\n\nif __name__ == \"__main__\":\n    print(\"OEM closed SECO KEK:\")\n    calc_seco_key(customer_otp)\n    print(\"OEM closed V2X KEK:\")\n    calc_v2x_key(customer_otp)\n    print(\"OEM open SECO KEK:\")\n    calc_seco_key(facsimile_otp)\n    print(\"OEM open V2X KEK:\")\n    calc_v2x_key(facsimile_otp)\n\n","sub_path":"tools/oem_kek_derivation.py","file_name":"oem_kek_derivation.py","file_ext":"py","file_size_in_byte":2628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"637047596","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.6 (62161)\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/coils/core/vcard/render_team.py\n# Compiled at: 2012-10-12 07:02:39\nimport vobject\n\ndef render_team(team, ctx, **params):\n    card = vobject.vCard()\n    card.add('n')\n    card.n.value = vobject.vcard.Name(family='OpenGroupware', given=team.name)\n    card.add('fn').value = team.name\n    card.add('uid').value = 'coils://Team/%d' % team.object_id\n    return str(card.serialize())","sub_path":"pycfiles/OpenGroupware-0.1.48-py2.6/render_team.py","file_name":"render_team.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"557465097","text":"array = list(map(int,input().split()))\narray.sort()\n\nresult = 0\ncount = 0\nfor i in range(len(array)):\n  count+=1\n  if array[i]<=count:\n    count=0\n    result+=1\n\nprint(result)","sub_path":"ch_2/모험가길드.py","file_name":"모험가길드.py","file_ext":"py","file_size_in_byte":175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"354536837","text":"# ITP Week 3 Day 3 Solution\n\nimport requests\nimport json\nimport openpyxl\nfrom openpyxl.styles import Font\n\n# using the requests package, we can make API calls to retrieve JSON\n# and storing it into a variable here called \"response\"\nresponse = requests.get(\"https://rickandmortyapi.com/api/character\")\n\n# verify the response status as 200\n# print(response)\n\n# verify the raw string data of the response\n# print(response.text)\n\n# load as a python json object and store into a variable called \"clean_data\"\nclean_data = json.loads(response.text)\n\n# print(clean_data)\n\n# go through the results,\nresult = clean_data[\"results\"]\n\n# print(result[0])\n# print(result[0]['location']['name'])\n\nwb = openpyxl.load_workbook(\"./input.xlsx\")\nsheet = wb['Sheet1']\n\nsheet['A1'] = \"Name\"\n# sheet['A1'].font = Font(bold = True)\nsheet['B1'] = \"Species\"\n# sheet['B1'].font = Font(bold = True)\nsheet['C1'] = \"Gender\"\n# sheet['C1'].font = Font(bold = True)\nsheet['D1'] = \"Location\"\n# sheet['D1'].font = Font(bold = True)\n\nheader_list = [sheet['A1'], sheet['B1'], sheet['C1'], sheet['D1']]\n\nfor header in header_list:\n    header.font = Font(bold = True)\n\ncounter = 2\n# for each row in an excel spreadsheet\nfor char in result:\n    # print(\"Name: \" + char['name'])\n    sheet['A' + str(counter)] = char['name']\n\n    # print(\"Species: \" + char['species'])\n    sheet['B' + str(counter)] = char['species']\n\n    # print(\"Gender: \" + char[\"gender\"])\n    sheet['C' + str(counter)] = char['gender']\n\n    # print(\"Location: \" + char['location']['name'])\n    sheet['D' + str(counter)] = char['location']['name']\n    counter+=1\n# grab the name, species, gender, location name\n\n\nwb.save('./output.xlsx')","sub_path":"day_1/w3d3_solution.py","file_name":"w3d3_solution.py","file_ext":"py","file_size_in_byte":1663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"418875569","text":"from selenium import webdriver\nimport time\nimport requests\nfrom bs4 import BeautifulSoup\nimport xlwt  # 电子表格操作模块\n\nbrowser = webdriver.Chrome(r'C:\\Users\\guo\\Desktop\\renshe\\chromedriver.exe')\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',\n}\n\nbrowser.get('https://网址/register/#/login?_k=ax56bx')\nbrowser.find_element_by_xpath(\n    '/html/body/div/div/div/div[2]/div/div[2]/div[1]/div[2]/form/div[3]/span[2]/input').send_keys('用户名')\nbrowser.find_element_by_xpath(\n    '/html/body/div/div/div/div[2]/div/div[2]/div[1]/div[2]/form/div[4]/span[2]/input').send_keys('，密码')\ntime.sleep(1)\nbrowser.find_element_by_xpath('/html/body/div/div/div/div[2]/div/div[2]/div[1]/div[2]/form/div[6]/button[1]').click()\ntime.sleep(1)\n\n\ndef getData():  # 获取数据函数\n    datalist = []  # 总的数据列表\n    for i in range(1, 65):\n        url = 'https://网址/ApplyCollegeNew?page=' + str(i)\n        page_text = requests.get(url=url, headers=headers, timeout=10).text\n        # 实例化bs对象，加载页面源码\n        soup = BeautifulSoup(page_text, 'lxml')\n        # 数据解析，返回列表[]\n        li_list = soup.select('#collegesLists > li')\n        # 循环列表\n        for li in li_list:\n            data = []  # 定义列表，用于保存每一行的数据\n            title = li.select('.collegeFeature >h3>a')[0].string\n            data.append(title)\n            detail = li.select('.collegeFeature')[0].text\n            data.append(detail)\n            datalist.append(data)  # 将每行列表添加到总列表\n\n    return datalist\n\n\ndef saveData(datalist, savepath):\n    print('save....')\n    book = xlwt.Workbook(encoding='utf-8')\n    sheet = book.add_sheet('大学列表', cell_overwrite_ok=True)\n    col = ('学校名称', '其他说明')\n    # 表头字段名的写入\n    for i in range(0, len(col)):  # 元组是不可变的，len取长度\n        sheet.write(0, i, col[i])  # 列名\n    # 数据记录的写入\n    for i in range(0, len(datalist)):  # 使用len(列表）获得长度\n        data = datalist[i]\n        for j in range(0, len(data)):\n            sheet.write(i + 1, j, data[j])\n    book.save(savepath)\n    print('save ok....')\n\n\nif __name__ == \"__main__\":\n    savepath = '大学数据.xls'\n    datalist = getData()\n    saveData(datalist, savepath)\n\n","sub_path":"python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"416052592","text":"import requests\r\nfrom tkinter import *\r\n\r\ndef weather():\r\n    city = city_listbox.get()\r\n    url = 'https://openweathermap.org/data/2.5/weather?q={}&appid=---paste your appid here---'.format(city)\r\n    res = requests.get(url)\r\n    output = res.json()\r\n\r\n    weather_status = output['weather'][0]['description']\r\n    temperature = output['main']['temp']\r\n    humidity = output['main']['humidity']\r\n    wind_speed = output['wind']['speed']\r\n\r\n    weather_status_label.configure(text=\"Weather status : \" + weather_status)\r\n    temperature_label.configure(text=\"Temperature : \" + str(temperature))\r\n    humidity_label.configure(text=\"Humidity : \" + str(humidity))\r\n    wind_speed_label.configure(text=\"Wind speed  : \" + str(wind_speed))\r\n\r\n\r\nwindow = Tk()\r\nwindow.geometry(\"400x350\")\r\n\r\ncity_name_list = [\"Lucknow\", \"Delhi\", \"Bangalore\", \"Pune\", \"Mumbai\", \"Akola\", 'Amravati']\r\n\r\ncity_listbox = StringVar(window)\r\ncity_listbox.set(\"Select the city\")\r\noption = OptionMenu(window, city_listbox, *city_name_list)\r\noption.grid(row=2, column=2, padx=150, pady=10)\r\n\r\nb1 = Button(window, text=\"GO\", width=15, command=weather)\r\nb1.grid(row=5, column=2, padx=150)\r\n\r\nweather_status_label = Label(window, font=(\"times\", 10, \"bold\"))\r\nweather_status_label.grid(row=10, column=2)\r\n\r\ntemperature_label = Label(window, font=(\"times\", 10, \"bold\"))\r\ntemperature_label.grid(row=12, column=2)\r\n\r\nhumidity_label = Label(window, font=(\"times\", 10, \"bold\"))\r\nhumidity_label.grid(row=14, column=2)\r\n\r\nwind_speed_label = Label(window, font=(\"times\", 10, \"bold\"))\r\nwind_speed_label.grid(row=16, column=2)\r\n\r\nwindow.mainloop()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"9.Current Weather.py","file_name":"9.Current Weather.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"31473550","text":"import pandas as pd\nfrom sklearn import svm, metrics\nfrom sklearn.model_selection import train_test_split\n\nbmi = pd.read_csv('bmi.csv', header=0)\n\nlabel = bmi['label']\n\nnormal_weight = bmi['weight'] / 180\nnormal_height = bmi['height'] / 80\n\nnormal_data = pd.concat([normal_weight,normal_height], axis=1)\n\ndata_train, data_test, label_train, label_test = train_test_split(normal_data, label)\n\nclf = svm.SVC(gamma='auto')\nclf.fit(data_train, label_train)\n\npredict = clf.predict(data_test)\n\nscore = metrics.accuracy_score(label_test, predict)\nreport = metrics.classification_report(label_test, predict)\n\nprint('정답률: %s%% '%round(score*100,2))\nprint(report)","sub_path":"03. AI/01. Machine Learning/머신러닝_학생평가/민현기_bmi_test.py","file_name":"민현기_bmi_test.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"631446489","text":"#!/usr/bin/env python  \nimport roslib\nroslib.load_manifest('projection')\nimport rospy\nimport math\nimport tf\nimport geometry_msgs.msg\nimport tests_affichages\nfrom geometry_msgs.msg import WrenchStamped\n\n#def callback(data):\n    #rospy.loginfo(rospy.get_caller_id() + \"I heard %s\", data.data)\n    #print(\"Valeur\")\n    #print(data.force[0])\n\n\n\ndef listener():\n    print(\"je me lance\")\n    rospy.init_node('tf_listener')\n    listener_tf = tf.TransformListener()\n    rate = rospy.Rate(1.0)\n    L = []\n    #rospy.Subscriber(\"optoforce_1\", WrenchStamped, callback)\n    print(\"J'ai souscrit\")\n    #while not rospy.is_shutdown():\n        #try:\n    if listener_tf.frameExists('/base_link'):\n        print(\"Existe\")\n        (trans,rot) = listener_tf.lookupTransform('base_link', '/world', rospy.Time(0)) #le temps de ce noeud bug ac le ros bag...\n        print(\"Force\")\n        x =tests_affichages.fois_dix(trans[0])\n        print(trans[0],x)\n    else:\n        print(\"NotExist\")\n#except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):\n    #print(\"Exception\")\n    #continue\n\n    #rate.sleep()\n    rospy.spin()\n    \nif __name__ == '__main__':\n    listener()\n","sub_path":"nodes/listerner2.py","file_name":"listerner2.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"55361543","text":"import math\nimport numpy as np\nimport pandas as pd\nimport random\nimport re\nfrom scipy.special import digamma, gammaln, psi # gamma function utils\nfrom scipy.special import polygamma\nfrom sklearn import svm\nimport time\n\n\ndef normalize(row):\n    return row / (row.sum() + 0.0000001)\n\n\ndef normalize_rows(input_matrix):\n    \"\"\"\n    Normalizes the rows of a 2d input_matrix so they sum to 1\n    \"\"\"\n    row_sums = input_matrix.sum(axis=1) + 0.0000001\n    new_matrix = input_matrix / row_sums[:, np.newaxis]\n    return new_matrix\n\n\ndef normalize_columns(input_matrix):\n    \"\"\"\n    Normalizes the columns of a 2d input_matrix so they sum to 1\n    \"\"\"\n    col_sums = input_matrix.sum(axis=0) + 0.0000001\n    new_matrix = input_matrix / col_sums[np.newaxis :]\n    return new_matrix\n\n\ndef evaluate_embeddings(training_data, training_labels, testing_data, testing_labels):\n    clf = svm.SVC()\n    clf.fit(training_data, training_labels)\n    predictions = clf.predict(testing_data)\n    # print(predictions)\n    # print(testing_labels)\n\n    true_positives = 0\n    false_positives = 0\n    false_negatives = 0\n    for i in range(len(predictions)):\n        if predictions[i] == 1 and testing_labels[i] == 1:\n            true_positives += 1\n        elif predictions[i] == 1 and testing_labels[i] == 0:\n            false_positives += 1\n        elif predictions[i] == 0 and testing_labels[i] == 1:\n            false_negatives += 1\n    recall = true_positives/(true_positives+false_negatives)\n    if true_positives+false_positives > 0:\n        precision = true_positives/(true_positives+false_positives)\n        print(\"  F1 = {0:.3f}\".format(2*precision*recall/(precision+recall)))\n        print(\"  Precision = {0:.3f} ({1}/{2})\".format(precision, true_positives, true_positives+false_positives))\n    print(\"  Recall = {0:.3f} ({1}/{2})\".format(recall, true_positives, true_positives+false_negatives))\n\n\ndef load_csv(input_path, test_set_size, training_set_size, num_stop_words, min_word_freq, text_column, label_column, label_dict):\n    labels = []\n    documents = []\n    df = pd.read_csv(input_path,\n                     converters={\n                         label_column: lambda x: (label_dict[x]),\n                         text_column: lambda line: (re.sub('[^0-9a-zA-Z]+', ' ', line).lower().split()),\n                                    # replace non-alphanumeric characters with spaces\n                     },\n                     encoding='unicode_escape',\n                     nrows=test_set_size+training_set_size)\n    testing_df = df[:test_set_size]\n    training_df = df[test_set_size:]\n\n    print('Training data size = {}'.format(training_df.shape[0]))\n    print('Testing data size = {}'.format(testing_df.shape[0]))\n\n    # Remove the stop words\n    word_freq = {}\n    for i, row in training_df.iterrows():\n        for word in row[text_column]:\n            if word in word_freq.keys():\n                word_freq[word] += 1\n            else:\n                word_freq[word] = 1\n    words_sorted_by_freq = sorted(word_freq.items(), key=lambda kv: kv[1])[:-num_stop_words]\n    words_sorted_by_freq = list(filter(lambda kv: kv[1] >= min_word_freq, words_sorted_by_freq))\n\n    vocabulary = sorted([kv[0] for kv in words_sorted_by_freq])\n    words = set(vocabulary)\n    vocabulary_size = len(vocabulary)\n    idx = dict(zip(vocabulary, range(len(vocabulary))))\n    print('Vocabulary size = {}'.format(len(words)))\n    # print(idx)\n\n    testing_term_doc_matrix = np.zeros([testing_df.shape[0], vocabulary_size], dtype=np.float)\n    for i, row in testing_df.iterrows():\n        for word in row[text_column]:\n            if word in words:\n                testing_term_doc_matrix[i][idx[word]] += 1\n\n    training_term_doc_matrix = np.zeros([training_df.shape[0], vocabulary_size], dtype=np.float)\n    for i, row in training_df.iterrows():\n        for word in row[text_column]:\n            if word in words:\n                training_term_doc_matrix[i-test_set_size][idx[word]] += 1\n\n    return (vocabulary_size,\n            training_term_doc_matrix,\n            training_df[label_column],\n            testing_term_doc_matrix,\n            testing_df[label_column],\n            vocabulary,)\n\n# From gensim.  \n# https://github.com/RaRe-Technologies/gensim/blob/6c80294ad8df16a878cb6df586c797184b39564a/gensim/models/ldamodel.py#L434\ndef dirichlet_expectation(alpha):\n    \"\"\"\n    For a vector `theta~Dir(alpha)`, compute `E[log(theta)]`.\n    \"\"\"\n    if (len(alpha.shape) == 1):\n        result = psi(alpha) - psi(np.sum(alpha))\n    else:\n        result = psi(alpha) - psi(np.sum(alpha, 1))[:, np.newaxis]\n    return result.astype(alpha.dtype) # keep the same precision as input\n\n\nclass LDA(object):\n    def __init__(self, vocabulary_size):\n        self.vocabulary_size = vocabulary_size\n        self.num_topics = None\n        self.alpha = None\n        self.phi = None\n\n    def get_variable_length_docs(self, term_doc_matrix):\n        variable_length_docs = []\n        for row in term_doc_matrix:\n            variable_length_doc = []\n            for word, count in enumerate(row.tolist()):\n                for i in range(int(count)):\n                    variable_length_doc.append(word)\n            variable_length_docs.append(variable_length_doc)\n        return variable_length_docs\n\n\n    def train(self, num_topics, term_doc_matrix, iterations, e_iterations, e_epsilon, alpha_learning_rate=0.2, alpha_learning_rate_decay=0.01, initial_training_set_size=None, initial_training_iterations=None):\n        print('Training an LDA model with {} topics...'.format(num_topics))\n        docs = self.get_variable_length_docs(term_doc_matrix)\n\n        # Initialize the model\n        self.num_topics = num_topics\n        self.alpha = np.repeat(1.0 / num_topics, num_topics)\n        self.phi = normalize_rows(np.random.random((self.num_topics, self.vocabulary_size)))\n            # word distribution of each topic\n            # dim = num_topics * vocabulary_size\n\n        if initial_training_set_size is not None and initial_training_iterations is not None:\n            print('Initialize the model using {} documents'.format(initial_training_set_size))\n\n            initial_docs = docs[:initial_training_set_size]\n\n            # Initialize the hidden variables\n            pi = [normalize_rows(np.random.random((len(doc), self.num_topics))) for doc in initial_docs]\n                # topic assignment of each word in each doc\n                # dim = num_docs * |doc| * num_topics\n\n            gamma = normalize_rows(np.random.random((len(initial_docs), self.num_topics)))\n                # topic assignment of each doc\n                # dim = num_docs * num_topics\n\n            for iteration in range(initial_training_iterations):\n                print('  E-M iteration {}'.format(iteration))\n                # E-step\n                (pi, gamma) = self.e_step(initial_docs, pi, gamma, e_iterations, e_epsilon)\n\n                # M-step\n                self.m_step(initial_docs, pi, gamma, alpha_learning_rate)\n                print(self.alpha)\n                alpha_learning_rate = alpha_learning_rate * (1-alpha_learning_rate_decay)\n\n        print('Train the model using {} documents'.format(len(docs)))\n\n        # Reset the hidden variables\n        pi = [normalize_rows(np.random.random((len(doc), self.num_topics))) for doc in docs]\n            # topic assignment of each word in each doc\n            # dim = num_docs * |doc| * num_topics\n        gamma = normalize_rows(np.random.random((len(docs), self.num_topics)))\n            # topic assignment of each doc\n            # dim = num_docs * num_topics\n        (pi, gamma) = self.e_step(docs, pi, gamma, e_iterations, e_epsilon)\n\n        for iteration in range(iterations):\n            print('  E-M iteration {}'.format(iteration))\n            # E-step\n            (pi, gamma) = self.e_step(docs, pi, gamma, e_iterations, e_epsilon)\n\n            # M-step\n            self.m_step(docs, pi, gamma, alpha_learning_rate)\n            print(self.alpha)\n            alpha_learning_rate = alpha_learning_rate * (1-alpha_learning_rate_decay)\n\n\n    def e_step(self, docs, pi, gamma, e_iterations, e_epsilon):\n        for j, doc in enumerate(docs):\n            for e_iteration in range(e_iterations):\n                previous_gamma = gamma[j].copy()\n                for t, word in enumerate(doc):\n                    x = digamma(gamma[j].sum())\n                    for i in range(self.num_topics):\n                        pi[j][t][i] = self.phi[i][word] * np.exp(digamma(gamma[j][i]) - x)\n                    pi[j] = normalize_rows(pi[j])\n\n                for i in range(self.num_topics):\n                    gamma[j][i] = self.alpha[i] + pi[j][:,i].sum()\n\n                gamma_diff = np.absolute(gamma[j] - previous_gamma).sum()\n                if gamma_diff < e_epsilon:\n                    break\n\n        return (pi, gamma)\n\n\n    def m_step(self, docs, pi, gamma, alpha_learning_rate):\n        for i in range(self.num_topics):\n            for v in range(self.vocabulary_size):\n                self.phi[i][v] = 0.0\n                for j, doc in enumerate(docs):\n                    for t, word in enumerate(doc):\n                        if word == v:\n                            self.phi[i][v] += pi[j][t][i]\n        self.phi = normalize_rows(self.phi)\n        # print(self.phi)\n        self.alpha = self.alpha * (1.0-alpha_learning_rate) + normalize(np.average(normalize_rows(gamma), axis=0)) * alpha_learning_rate\n\n\n    def print_model(self, vocabulary, print_freq_threshold=0.02):\n        for topic, words in enumerate(self.phi):\n            print('Topic {0}: {1:.3f}'.format(topic, self.alpha[topic]))\n            for w, p in enumerate(words / (np.sum(words) + 0.0000001)):\n                if p > print_freq_threshold:\n                    print(' {0} : {1}'.format(vocabulary[w], p))\n\n\n    def get_topic_distributions(self, term_doc_matrix, e_iterations, e_epsilon):\n        print(\"Predict topic distributions for the new documents using the existing LDA model...\")\n\n        docs = self.get_variable_length_docs(term_doc_matrix)\n        num_docs = term_doc_matrix.shape[0]\n\n        # Initialize the hidden variables\n        pi = [normalize_rows(np.random.random((len(doc), self.num_topics))) for doc in docs]\n            # topic assignment of each word in each doc\n            # dim = num_docs * |doc| * num_topics\n\n        gamma = normalize_rows(np.random.random((num_docs, self.num_topics)))\n            # topic assignment of each doc\n            # dim = num_docs * num_topics\n\n        (pi, gamma) = self.e_step(docs, pi, gamma, e_iterations, e_epsilon)\n        return normalize_rows(gamma)\n\n\ndef main():\n    np.random.seed(0)\n\n    '''\n    (vocabulary_size,\n     training_term_doc_matrix,\n     training_labels,\n     testing_term_doc_matrix,\n     testing_labels,\n     vocabulary) = load_csv(input_path = 'spam.csv',\n                            test_set_size=1000,\n                            training_set_size=2000,\n                            num_stop_words=20,\n                            min_word_freq=5,\n                            text_column='Message',\n                            label_column='Category',\n                            label_dict = {'spam': 1, 'ham': 0})\n    '''\n    (vocabulary_size,\n     training_term_doc_matrix,\n     training_labels,\n     testing_term_doc_matrix,\n     testing_labels,\n     vocabulary) = load_csv(input_path = 'FA-KES-Dataset.csv',\n                            test_set_size=200,\n                            training_set_size=200,\n                            num_stop_words=100,\n                            min_word_freq=3,\n                            text_column='article_content',\n                            label_column='labels',\n                            label_dict = {'1': 1, '0': 0})\n\n    print(\"== SVM with word frequencies ==\")\n    evaluate_embeddings(normalize_rows(training_term_doc_matrix),\n                        training_labels,\n                        normalize_rows(testing_term_doc_matrix),\n                        testing_labels)\n\n    print(\"== SVM with topic distributions from LDA ==\")\n    lda = LDA(vocabulary_size)\n\n    lda.train(num_topics=15, term_doc_matrix=training_term_doc_matrix, iterations=10, e_iterations=10, e_epsilon=0.001, initial_training_set_size=50, initial_training_iterations=50, alpha_learning_rate=0.4, alpha_learning_rate_decay=0.005)\n    lda.print_model(vocabulary, print_freq_threshold=0.01)\n\n    evaluate_embeddings(lda.get_topic_distributions(term_doc_matrix=training_term_doc_matrix, e_iterations=100, e_epsilon=0.001),\n                        training_labels,\n                        lda.get_topic_distributions(term_doc_matrix=testing_term_doc_matrix, e_iterations=100, e_epsilon=0.001),\n                        testing_labels)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"lda_var_inf_without_smoothing_v2.py","file_name":"lda_var_inf_without_smoothing_v2.py","file_ext":"py","file_size_in_byte":12775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"285828216","text":"#全排列\n\nclass Solution:\n    def permute(self, nums) :\n        #self.nums=nums\n        res=[]\n        lenth = len(nums)\n        self.down(0,res,lenth,nums)\n        return res\n\n\n    def down(self,first,res,lenth,nums):\n        if first==lenth:\n            res.append(nums[:])\n\n        for i in range(first,lenth):\n            nums[first], nums[i] = nums[i], nums[first]\n            self.down(first+1,res,lenth,nums)\n            nums[first], nums[i] = nums[i], nums[first]","sub_path":"Week_03/permute.py","file_name":"permute.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"184921276","text":"\"\"\"\nModule for Proper Orthogonal Decomposition (POD).\nThree different methods can be employed: Truncated Singular Value Decomposition,\nTruncated Randomized Singular Value Decomposition, Truncated Singular Value \nDecomposition via correlation matrix.\n\"\"\"\nimport numpy as np\n\nfrom .reduction import Reduction\n\n\nclass POD(Reduction):\n    def __init__(self, method='svd', **kwargs):\n        \"\"\"\n        \"\"\"\n        available_methods = {\n            'svd': (self._svd, {\n                'rank': -1\n            }),\n            'randomized_svd': (self._rsvd, {\n                'rank': -1\n            }),\n            'correlation_matrix': (self._corrm, {\n                'rank': -1,\n                'save_memory': False\n            }),\n        }\n\n        self._modes = None\n        self._singular_values = None\n\n        method = available_methods.get(method)\n        if method is None:\n            raise RuntimeError(\n                \"Invalid method for POD. Please chose one among {}\".format(\n                    ', '.join(available_methods)))\n\n        self.__method, args = method\n        args.update(kwargs)\n\n        for hyperparam, value in args.items():\n            setattr(self, hyperparam, value)\n\n    @property\n    def modes(self):\n        \"\"\"\n        The POD modes.\n\n        :type: numpy.ndarray\n        \"\"\"\n        return self._modes\n\n    @property\n    def singular_values(self):\n        \"\"\"\n        The singular values\n\n        :type: numpy.ndarray\n        \"\"\"\n        return self._singular_values\n\n    def fit(self, X):\n        \"\"\"\n        Create the reduced space for the given snapshots `X` using the\n        specified method\n\n        :param numpy.ndarray X: the input snapshots matrix (stored by column)\n        \"\"\"\n        self._modes, self._singular_values = self.__method(X)\n        return self\n\n    def reduce(self, X):\n        \"\"\"\n        Reduces the given snapshots.\n\n        :param numpy.ndarray X: the input snapshots matrix (stored by column).\n        \"\"\"\n        return self.modes.T.conj().dot(X)\n\n    def expand(self, X):\n        \"\"\"\n        Projects a reduced to full order solution.\n\n        :type: numpy.ndarray\n        \"\"\"\n        return self.modes.dot(X).T\n\n    def _truncation(self, X, s):\n        \"\"\"\n        Return the number of modes to select according to the `rank` parameter.\n        See POD.__init__ for further info.\n\n        :param numpy.ndarray X: the matrix to decompose.\n        :param numpy.ndarray s: the singular values of X.\n\n        :return: the number of modes\n        :rtype: int\n        \"\"\"\n        if self.rank == 0:\n            omega = lambda x: 0.56 * x**3 - 0.95 * x**2 + 1.82 * x + 1.43\n            beta = np.divide(*sorted(X.shape))\n            tau = np.median(s) * omega(beta)\n            rank = np.sum(s > tau)\n        elif self.rank > 0 and self.rank < 1:\n            cumulative_energy = np.cumsum(s**2 / (s**2).sum())\n            rank = np.searchsorted(cumulative_energy, self.rank) + 1\n        elif self.rank >= 1 and isinstance(self.rank, int):\n            rank = self.rank #min(self.rank, U.shape[1])\n        else:\n            rank = X.shape[1]\n\n        return rank\n\n    def _svd(self, X):\n        \"\"\"\n        Truncated Singular Value Decomposition.\n\n        :param numpy.ndarray X: the matrix to decompose.\n        :return: the truncated left-singular vectors matrix, the truncated\n            singular values array, the truncated right-singular vectors matrix.\n        :rtype: numpy.ndarray, numpy.ndarray, numpy.ndarray\n\n        References:\n        Gavish, Matan, and David L. Donoho, The optimal hard threshold for\n        singular values is, IEEE Transactions on Information Theory 60.8\n        (2014): 5040-5053.\n        \"\"\"\n        U, s = np.linalg.svd(X, full_matrices=False)[:2]\n\n        rank = self._truncation(X, s)\n        return U[:, :rank], s[:rank]\n\n    def _rsvd(self, X):\n        \"\"\"\n        Truncated randomized Singular Value Decomposition.\n\n        :param numpy.ndarray X: the matrix to decompose.\n        :return: the truncated left-singular vectors matrix, the truncated\n            singular values array, the truncated right-singular vectors matrix.\n        :rtype: numpy.ndarray, numpy.ndarray, numpy.ndarray\n        \"\"\"\n\n        P = np.random.rand(X.shape[1], X.shape[0])\n        Q = np.linalg.qr(X.dot(P))[0]\n\n        Y = Q.T.conj().dot(X)\n\n        Uy, s = np.linalg.svd(Y, full_matrices=False)[:2]\n        U = Q.dot(Uy)\n\n        rank = self._truncation(X, s)\n        return U[:, :rank], s[:rank]\n\n    def _corrm(self, X):\n        \"\"\"\n        Truncated Singular Value Decomposition. calculated with correlation matrix.\n\n        :param numpy.ndarray X: the matrix to decompose.\n        :return: the truncated left-singular vectors matrix, the truncated\n            singular values array, the truncated right-singular vectors matrix.\n        :rtype: numpy.ndarray, numpy.ndarray, numpy.ndarray\n        \"\"\"\n\n        if self.save_memory:\n            corr = np.empty(shape=(X.shape[1], X.shape[1]))\n            for i, i_snap in enumerate(X.T):\n                for j, k_snap in enumerate(X.T):\n                    corr[i, j] = np.inner(i_snap, k_snap)\n\n        else:\n            corr = X.T.dot(X)\n\n        s, U = np.linalg.eig(corr)\n        U = X.dot(U) / np.sqrt(s)\n        rank = self._truncation(X, s)\n\n        return U[:, :rank], s[:rank]\n","sub_path":"ezyrb/pod.py","file_name":"pod.py","file_ext":"py","file_size_in_byte":5320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"211790187","text":"#!/usr/bin/env python\n#coding=utf8\n\"\"\"\n====================================\n :mod:`main` Main\n====================================\n.. moduleauthor:: Daewoo Kim\n.. note:: note...\n\n설명\n=====\n\nThis is for analyzing energy consumption of drone,\nand propose the neural network model for drone's energy consumption\n\n참고\n====\n * https://github.com/rhoowd/energy_drone\n\n관련 작업자\n===========\n\n본 모듈은 다음과 같은 사람들이 관여했습니다:\n * Daewoo Kim\n * Se-eun Yoon\n\n CUDA_VISIBLE_DEVICES=0\n\n\"\"\"\nimport config\nimport log_result as log\nimport models\nimport graph\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nimport pandas as pd\nimport numpy as np\nfrom keras import backend as K\n\nimport time\n\nimport input_preprocess as ip\n\nFLAGS = config.flags.FLAGS\n\n\nif __name__ == '__main__':\n\n\tdataframe = pd.read_csv(FLAGS.f_dir+FLAGS.f_n)\n\n\tHISTORY_NUM = 0\n\thistory_labels = ['vel_x', 'vel_y', 'vel_z', 'acc_x', 'acc_y', 'acc_z', 'roll', 'pitch', 'yaw', 'act_vx', 'act_vy']\n\t# x_labels: 'vel_x', 'vel_y', 'vel_z', 'acc_x', 'acc_y', 'acc_z', 'roll', 'pitch', 'yaw', 'act_vx', 'act_vy'\n\tx_labels = ['vel_x', 'vel_y', 'vel_z', 'acc_x', 'acc_y', 'acc_z', 'roll', 'pitch', 'yaw', 'act_vx', 'act_vy']\n\ty_label = ['power']\n\n\t# make history columns\n\tfor l in history_labels:\n\t\tfor n in range(HISTORY_NUM):\n\t\t\tx_labels.append(l+\"_\" + str(n+1))\t\n\n\t# input normalization\n\tsc = StandardScaler()\n\tx_data = sc.fit_transform(dataframe[x_labels])\n\t# x_data = dataframe[x_labels].values\n\n\ty_data = dataframe[y_label].values\n\n\n\tlog.logger.info(\"x_shape: \" + str(x_data.shape) + \", y_shape:\" + str(y_data.shape))\n\n\n\tx_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=FLAGS.test_size, random_state=FLAGS.seed)\n\n\t# Create model\n\tmodel = models.flexible_model_koo(input_dim=x_data.shape[1], output_dim=1)\n\n\t# Start training\n\tmodel.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=FLAGS.n_e,\n\t\t\t  batch_size=FLAGS.b_s, verbose=FLAGS.verbose)\n\n\t# Evaluate the model\n\tscores = model.evaluate(x_test, y_test)\n\tlog.logger.info(\"ACC(test):\\t\" + str(scores[2] * 100) + \"%\\t\" + log.filename + \" s\" + str(FLAGS.seed) + \"\\t\")\n\tlog.logger.info(\"MSE(test):\\t\" + str(scores[1]) + \"\\t\" + log.filename + \" s\"+ str(FLAGS.seed) + \"\\t\")\n\tscores = model.evaluate(x_data, y_data)\n\tlog.logger.info(\"ACC(all):\\t\" + str(scores[2] * 100) + \"%\\t\" + log.filename + \" s\" + str(FLAGS.seed) + \"\\t\")\n\tlog.logger.info(\"MSE(all):\\t\" + str(scores[1]) + \"\\t\" + log.filename + \" s\" + str(FLAGS.seed) + \"\\t\")\n\n\n\t# save prediction result\n\tpredictions = model.predict(x_test)\n\ty_test_t = y_test.reshape((-1, 1))\n\tpredictions_train = model.predict(x_train)\n\ty_train_t = y_train.reshape((-1, 1))\n\tresult = np.concatenate((y_test_t,predictions),axis=1)\n\tresult_train = np.concatenate((y_train_t, predictions_train), axis=1)\n\tnow = time.localtime()\n\ts_time = \"%02d%02d-%02d%02d%02d\" % (now.tm_mon, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec)\n\tresult_file_name = \"pred_result/\"+str(FLAGS.depth)+ \"-\" + str(FLAGS.h_size)+\"-\"+s_time+\".csv\"\n\ttrain_result_file_name = \"pred_result/\"+str(FLAGS.depth)+ \"-\" + str(FLAGS.h_size)+\"-\"+s_time+\"-train.csv\"\n\tnp.savetxt(result_file_name, result, delimiter=\",\")\n\tnp.savetxt(train_result_file_name, result_train, delimiter=\",\")\n\n\n\t# Save model\n\tmodel_json = model.to_json()\n\twith open(\"result/model/\"+log.filename+\".json\", \"w\") as json_file:\n\t\tjson_file.write(model_json)  # serialize model to JSON\n\tmodel.save_weights(\"result/model/\"+log.filename+\".h5\")  # weight\n\tprint(\"Save model ... done\")\n\n\t# Make graph\n\tif FLAGS.graph == 1:\n\t\test = model.predict(x_data)\n\t\tgraph.draw_graph(x_data[:, -1], y_data, est)\n\t\tprint(\"Save graph ... done\")\n\n\tK.clear_session()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"154403648","text":"from django.test import TestCase, RequestFactory\nfrom django.urls import reverse\nfrom .models import List\nfrom .views import delete\n\n\nclass ListTests(TestCase):\n\n    def setUp(self):\n        self.item = List.objects.create(item='Testing')\n\n    # def create_model(self, item='Testing'):\n    #     return List.objects.create(item=item)\n\n    # def create_model2(self, item='Testing delete'):\n    #     return List.objects.create(item=item)\n\n    # def create_model3(self, item='Testing cross'):\n    #     return List.objects.create(item=item)\n\n    def test_content(self):\n        # items = self.create_model()\n        items = List.objects.get(pk=1)\n        result_item = f'{items.item}'\n        assert result_item == 'Testing'\n\n    def test_correct_view_used(self):\n        response = self.client.get(reverse('home'))\n        assert response.status_code == 200\n\n    def test_delete_views(self):\n        self.factory = RequestFactory()\n        request = self.factory.get('/delete/')\n        request.item = self.item\n        response = delete(request, list_id=1)\n        assert response.status_code == 302\n","sub_path":"build/lib/models/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"126564456","text":"\"\"\"Handles most database transactions. It has knowledge of the primary classes\nand their relationships and saves them accordingly.\n\"\"\"\n\nimport sqlalchemy as sa\n\nfrom substrate import BioCategory, Curator, GeneList, GeneSignature, \\\n    GeoDataset, Report, SoftFile, Tag\n\nfrom gen3va.database.utils import session_scope\n\n\ndef count(class_):\n    \"\"\"Returns the number of rows in an class's associated table.\n    \"\"\"\n    with session_scope() as session:\n        return session.query(class_).count()\n\n\ndef get(class_, value, key='id'):\n    \"\"\"Gets entity by comparing column to value.\n    \"\"\"\n    with session_scope() as session:\n        return session\\\n            .query(class_)\\\n            .filter(getattr(class_, key) == value)\\\n            .first()\n\n\ndef get_many(klass, values, key='id'):\n    \"\"\"Gets entities whose column values are in values.\n    \"\"\"\n    with session_scope() as session:\n        return session\\\n            .query(klass)\\\n            .filter(getattr(klass, key).in_(values))\\\n            .all()\n\n\ndef get_all(klass):\n    \"\"\"Gets all entities of a specific class.\n    \"\"\"\n    with session_scope() as session:\n        return session.query(klass).all()\n\n\ndef get_signatures_by_ids(extraction_ids):\n    \"\"\"Returns all gene signatures with matching extraction IDs.\n    \"\"\"\n    with session_scope() as session:\n        return session\\\n            .query(GeneSignature)\\\n            .filter(GeneSignature.extraction_id.in_(extraction_ids))\\\n            .all()\n\n\ndef add_object(obj):\n    \"\"\"Create new object.\n    \"\"\"\n    with session_scope() as session:\n        session.add(obj)\n\n\ndef update_object(obj):\n    \"\"\"Update object, i.e. saves any edits.\n    \"\"\"\n    with session_scope() as session:\n        session.merge(obj)\n\n\ndef delete_object(obj):\n    \"\"\"Deletes object provided.\n    \"\"\"\n    with session_scope() as session:\n        session.delete(obj)\n\n\ndef get_tags_by_curator(curator):\n    \"\"\"Returns all tags by a particular curator\n    \"\"\"\n    with session_scope() as session:\n        return session\\\n            .query(Tag)\\\n            .filter(Tag.curator_fk == Curator.id)\\\n            .filter(Curator.name == curator)\\\n            .all()\n\n\ndef get_bio_categories():\n    \"\"\"Returns all bio categories which have tags from particular curators.\n    \"\"\"\n    with session_scope() as session:\n        return session\\\n            .query(BioCategory)\\\n            .order_by(BioCategory.order)\\\n            .all()\n\n\ndef get_bio_categories_by_curator(curator):\n    \"\"\"Returns all bio categories which have tags from particular curators.\n    \"\"\"\n    with session_scope() as session:\n        return session\\\n            .query(BioCategory)\\\n            .filter(BioCategory.id == Tag.bio_category_fk)\\\n            .filter(Curator.id == Tag.curator_fk)\\\n            .filter(Curator.name == curator)\\\n            .all()\n\n\ndef get_statistics():\n    \"\"\"Returns object with DB statistics for about page.\n    \"\"\"\n    with session_scope() as session:\n        meta_counts = session\\\n            .execute(\"\"\"SELECT `name`, COUNT(DISTINCT `value`) AS `count`\n                FROM `optional_metadata` \n                WHERE `name` IN ('cell', 'dose', 'time') \n                GROUP BY `name`\"\"\")\\\n            .fetchall()\n        meta_counts = dict(meta_counts)    \n\n        cell_counts = session\\\n            .execute(\"\"\"SELECT `value`, COUNT(`value`) AS `count` \n                FROM `optional_metadata` \n                WHERE `name`='cell' \n                GROUP BY `value`\n                \"\"\")\\\n            .fetchall()\n        cell_counts = [{'cell': item[0], 'count': item[1]} for item in cell_counts]\n\n        return {\n            'num_gene_signatures': count(GeneSignature),\n            'num_reports': count(Report),\n            'num_cells': meta_counts['cell'],\n            'num_doses': meta_counts['dose'],\n            'num_times': meta_counts['time'],\n            'cell_counts': cell_counts\n        }\n\ndef get_all_drug_meta():\n    \"\"\"Returns a dict {pert_id: pert_iname} for all the drugs.\n    \"\"\"\n    with session_scope() as session:\n        d_pert_name = session\\\n            .execute(\"\"\"SELECT `pert_id`, `pert_iname` \n                FROM `drug`\n                \"\"\")\\\n            .fetchall()\n        d_pert_name = dict(d_pert_name)\n\n        return d_pert_name\n","sub_path":"gen3va/database/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"334592937","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.5 (3350)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/kieffer/workspace/fabio/build/lib.macosx-10.6-intel-3.5/fabio/test/codecs/test_bruker100image.py\n# Compiled at: 2020-04-03 09:02:03\n# Size of source mod 2**32: 3955 bytes\n\"\"\"\n#bruker100 Unit tests\n\n19/01/2015\n\"\"\"\nfrom __future__ import print_function, with_statement, division, absolute_import\nimport unittest, os, logging\nlogger = logging.getLogger(__name__)\nfrom fabio.bruker100image import Bruker100Image\nfrom fabio.openimage import openimage\nfrom ..utilstest import UtilsTest\nTESTIMAGES = 'NaCl_10_01_0009.sfrm         512 512 -30 5912 34.4626 26.189\\n                NaCl_10_01_0009.sfrm.gz      512 512 -30 5912 34.4626 26.189\\n                NaCl_10_01_0009.sfrm.bz2     512 512 -30 5912 34.4626 26.189'\nREFIMAGE = 'NaCl_10_01_0009.npy.bz2'\n\nclass TestBruker100(unittest.TestCase):\n    __doc__ = ' check some read data from bruker version100 detector'\n\n    def setUp(self):\n        \"\"\"\n        download images\n        \"\"\"\n        UtilsTest.getimage(REFIMAGE)\n        self.im_dir = os.path.dirname(UtilsTest.getimage(TESTIMAGES.split()[0] + '.bz2'))\n\n    def test_read(self):\n        \"\"\" check we can read bruker100 images\"\"\"\n        for line in TESTIMAGES.split('\\n'):\n            vals = line.split()\n            name = vals[0]\n            dim1, dim2 = [int(x) for x in vals[1:3]]\n            shape = (dim2, dim1)\n            mini, maxi, mean, stddev = [float(x) for x in vals[3:]]\n            obj = Bruker100Image()\n            obj.read(os.path.join(self.im_dir, name))\n            self.assertAlmostEqual(mini, obj.getmin(), 2, 'getmin')\n            self.assertAlmostEqual(maxi, obj.getmax(), 2, 'getmax')\n            self.assertAlmostEqual(mean, obj.getmean(), 2, 'getmean')\n            self.assertAlmostEqual(stddev, obj.getstddev(), 2, 'getstddev')\n            self.assertEqual(shape, obj.shape)\n\n    def test_same(self):\n        \"\"\" check we can read bruker100 images\"\"\"\n        ref = openimage(os.path.join(self.im_dir, REFIMAGE))\n        for line in TESTIMAGES.split('\\n'):\n            obt = openimage(os.path.join(self.im_dir, line.split()[0]))\n            self.assertTrue(abs(ref.data - obt.data).max() == 0, 'data are the same')\n\n    def test_write(self):\n        fname = TESTIMAGES.split()[0]\n        obt = openimage(os.path.join(self.im_dir, fname))\n        name = os.path.basename(fname)\n        obj = Bruker100Image(data=obt.data, header=obt.header)\n        obj.write(os.path.join(UtilsTest.tempdir, name))\n        other = openimage(os.path.join(UtilsTest.tempdir, name))\n        self.assertEqual(abs(obt.data - other.data).max(), 0, 'data are the same')\n        for key in obt.header:\n            self.assertTrue(key in other.header, 'Key %s is in header' % key)\n            self.assertEqual(obt.header[key], other.header[key], 'value are the same for key %s' % key)\n\n        os.unlink(os.path.join(UtilsTest.tempdir, name))\n\n\ndef suite():\n    loadTests = unittest.defaultTestLoader.loadTestsFromTestCase\n    testsuite = unittest.TestSuite()\n    testsuite.addTest(loadTests(TestBruker100))\n    return testsuite\n\n\nif __name__ == '__main__':\n    runner = unittest.TextTestRunner()\n    runner.run(suite())","sub_path":"pycfiles/fabio-0.10.0-cp35-cp35m-macosx_10_6_intel/test_bruker100image.cpython-35.py","file_name":"test_bruker100image.cpython-35.py","file_ext":"py","file_size_in_byte":3290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"516532672","text":"#coding=utf-8\n\n\nclass Article:\n    def __init__(self,\n                 volume,\n                 number,\n                 index,\n                 title,\n                 text,\n                 keywords,\n                 text_zh):\n        self.volume = volume\n        self.number = number\n        self.index = index\n        self.title = title\n        self.text = text\n        self.keywords = keywords\n        self.text_zh = text_zh\n","sub_path":"article.py","file_name":"article.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"135024033","text":"import numpy as np\nimport pickle\nimport PIL.Image as Image\n\nimport pylot.utils\nfrom pylot.perception.segmentation.utils import transform_to_cityscapes_palette\n\nfrom erdos.op import Op\nfrom erdos.utils import setup_csv_logging, setup_logging\n\n\nclass CameraLoggerOp(Op):\n    \"\"\" Logs frames for different types of cameras.\"\"\"\n\n    def __init__(self, name, flags, log_file_name=None, csv_file_name=None):\n        super(CameraLoggerOp, self).__init__(name)\n        self._flags = flags\n        self._logger = setup_logging(self.name, log_file_name)\n        self._csv_logger = setup_csv_logging(self.name + '-csv', csv_file_name)\n        self._bgr_frame_cnt = 0\n        self._segmented_frame_cnt = 0\n        self._top_down_segmented_frame_cnt = 0\n        self._depth_frame_cnt = 0\n        self._left_bgr_frame_cnt = 0\n        self._right_bgr_frame_cnt = 0\n\n    @staticmethod\n    def setup_streams(input_streams):\n        input_streams.filter(pylot.utils.is_center_camera_stream).add_callback(\n            CameraLoggerOp.on_bgr_frame)\n        input_streams.filter(pylot.utils.is_left_camera_stream).add_callback(\n            CameraLoggerOp.on_bgr_frame_left)\n        input_streams.filter(pylot.utils.is_right_camera_stream).add_callback(\n            CameraLoggerOp.on_bgr_frame_right)\n        input_streams.filter(\n            pylot.utils.is_front_segmented_camera_stream).add_callback(\n                CameraLoggerOp.on_front_segmented_frame)\n        input_streams.filter(\n            pylot.utils.is_top_down_segmented_camera_stream).add_callback(\n                CameraLoggerOp.on_top_down_segmented_frame)\n        input_streams.filter(\n            pylot.utils.is_depth_camera_stream).add_callback(\n                CameraLoggerOp.on_depth_frame)\n        return []\n\n    def on_bgr_frame(self, msg):\n        self._bgr_frame_cnt += 1\n        if self._bgr_frame_cnt % self._flags.log_every_nth_frame != 0:\n            return\n        # Write the image.\n        assert msg.encoding == 'BGR', 'Expects BGR frames'\n        rgb_array = pylot.utils.bgr_to_rgb(msg.frame)\n        file_name = '{}carla-center-{}.png'.format(\n            self._flags.data_path, msg.timestamp.coordinates[0])\n        rgb_img = Image.fromarray(np.uint8(rgb_array))\n        rgb_img.save(file_name)\n\n    def on_bgr_frame_left(self, msg):\n        self._left_bgr_frame_cnt += 1\n        if self._left_bgr_frame_cnt % self._flags.log_every_nth_frame != 0:\n            return\n        # Write the image.\n        assert msg.encoding == 'BGR', 'Expects BGR frames'\n        rgb_array = pylot.utils.bgr_to_rgb(msg.frame)\n        file_name = '{}carla-left-{}.png'.format(\n            self._flags.data_path, msg.timestamp.coordinates[0])\n        rgb_img = Image.fromarray(np.uint8(rgb_array))\n        rgb_img.save(file_name)\n\n    def on_bgr_frame_right(self, msg):\n        self._right_bgr_frame_cnt += 1\n        if self._right_bgr_frame_cnt % self._flags.log_every_nth_frame != 0:\n            return\n        # Write the image.\n        assert msg.encoding == 'BGR', 'Expects BGR frames'\n        rgb_array = pylot.utils.bgr_to_rgb(msg.frame)\n        file_name = '{}carla-right-{}.png'.format(\n            self._flags.data_path, msg.timestamp.coordinates[0])\n        rgb_img = Image.fromarray(np.uint8(rgb_array))\n        rgb_img.save(file_name)\n\n    def on_front_segmented_frame(self, msg):\n        self._segmented_frame_cnt += 1\n        if self._segmented_frame_cnt % self._flags.log_every_nth_frame != 0:\n            return\n        frame = transform_to_cityscapes_palette(msg.frame)\n        # Write the segmented image.\n        img = Image.fromarray(np.uint8(frame))\n        file_name = '{}carla-segmented-{}.png'.format(\n            self._flags.data_path, msg.timestamp.coordinates[0])\n        img.save(file_name)\n\n    def on_top_down_segmented_frame(self, msg):\n        self._top_down_segmented_frame_cnt += 1\n        if self._top_down_segmented_frame_cnt % self._flags.log_every_nth_frame != 0:\n            return\n        frame = transform_to_cityscapes_palette(msg.frame)\n        # Write the segmented image.\n        img = Image.fromarray(np.uint8(frame))\n        file_name = '{}carla-top-down-segmented-{}.png'.format(\n            self._flags.data_path, msg.timestamp.coordinates[0])\n        img.save(file_name)\n\n    def on_depth_frame(self, msg):\n        self._depth_frame_cnt += 1\n        if self._depth_frame_cnt % self._flags.log_every_nth_frame != 0:\n            return\n        # Write the depth information.\n        file_name = '{}carla-depth-{}.pkl'.format(\n            self._flags.data_path, msg.timestamp.coordinates[0])\n        pickle.dump(msg.frame,\n                    open(file_name, 'wb'),\n                    protocol=pickle.HIGHEST_PROTOCOL)\n","sub_path":"pylot/loggers/camera_logger_operator.py","file_name":"camera_logger_operator.py","file_ext":"py","file_size_in_byte":4712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"277175596","text":"#coding:utf-8\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport math\nimport cv2\n\ndef cal_conv_out_img_size(img_size, filter_size, padding=None, strides=1):\n    if not padding:\n        padding = [0]*4\n    if not strides:\n        strides = 1\n    if len(filter_size) == 1:\n        filter_size = [filter_size, filter_size]\n    h = int(np.ceil((img_size[0] + padding[0] + padding[1] - filter_size[0])*1.0 / strides[0]) + 1)\n    w = int(np.ceil((img_size[1] + padding[2] + padding[3] - filter_size[1])*1.0 / strides[1]) + 1)\n    return [h, w]\n\ndef convolve(filter, mat, padding, strides, gray_flag=False):\n    '''\n    图像二维卷积\n    :param filter:卷积核，必须为二维(2 x 1也算二维) 否则返回None\n    :param mat:图片 [h,w] || [h,w,3]\n    :param padding:对齐 [4] : [0,0,5,5]\n    :param strides: int\n    :return:返回卷积后的图片。(灰度图，彩图都适用)\n    '''\n    shape = cal_conv_out_img_size(mat.shape, filter.shape, padding, strides)\n    result = np.zeros(shape)\n    filter_size = filter.shape\n    mat_size = mat.shape\n    assert len(filter_size) == 2\n    # 灰度图像\n    if len(mat_size) == 2:\n        mat = mat.reshape([mat_size[0], mat_size[1], 1])\n        result = result.reshape(shape[0], shape[1], 1)\n        mat_size = mat.shape\n        gray_flag = True\n    # 先遍历图像的行\n    for c in range(mat_size[-1]):\n        # pad: [0,0,5,5]   [h,w]=>[h, 5+w+5] :在图片的左右填充\n        # pad: [5,5,0,0]   [h,w]=>[5+h+5, w] :在图片的上下填充\n        pad_mat = np.pad(mat[:, :,  c], ((padding[0], padding[1]), (padding[2], padding[3])), 'constant')\n        # 遍历图像的列\n        for i in range(0, mat_size[0], strides[1]):\n            # 以卷积核的左上角为原点，拿卷积核和图像的相应位置相乘然后相加，就是做卷积操作\n            for j in range(0, mat_size[1], strides[0]):\n                val = (filter*pad_mat[i:i+filter_size[0], j:j+filter_size[1]]).sum()\n                result[i, j, c] = val\n    return result.reshape([result.shape[0], result.shape[1]]) if gray_flag else result\n\n\ndef row_convolve(filter, mat, padding=None, strides=[1, 1]):\n    '''\n    线性卷积就是先用一维的卷积横向卷积\n    :param filter:线性卷积核 [1, n]\n    :param mat:图片 [h,w] || [h,w,3]\n    :param padding:对齐\n    :param strides:移动步长\n    :return:返回卷积后的图片。(灰度图，彩图都适用) 若不是线性卷积核，返回None\n    '''\n    filter_size = filter.shape\n    assert len(filter_size) <= 2\n    if len(filter_size) == 1:\n        filter = filter.reshape([1, -1])\n        filter_size = filter.shape\n    if padding == None or len(padding) < 2:\n        padding = [filter_size[1]//2, filter_size[1]//2]\n    result = convolve(filter, mat, [0, 0, padding[0], padding[1]], strides)\n    return result\n\n\ndef col_convolve(filter, mat, padding=None, strides=[1, 1]):\n    '''\n    线性卷积就是先用一维的卷积横向卷积\n    :param filter:线性卷积核 [n, 1]\n    :param mat:图片 [h,w] || [h,w,3]\n    :param padding:对齐\n    :param strides:移动步长\n    :return:返回卷积后的图片。(灰度图，彩图都适用) 若不是线性卷积核，返回None\n    '''\n\n    # 卷积核标准化为[n,1]\n    filter_size = filter.shape\n    assert len(filter_size) <= 2\n    if len(filter_size) == 1:\n        filter = filter.reshape([-1, 1])\n        filter_size = filter.shape\n    elif filter_size[0] == 1:\n        filter = filter.reshape([-1, 1])\n        filter_size = filter.shape\n    if padding == None or len(padding) < 2:\n        padding = [filter_size[0]//2, filter_size[0]//2]\n    result = convolve(filter, mat, [padding[0], padding[1], 0, 0], strides)\n    return result\n\n\ndef divided_convolve(filter, img):\n    '''\n    将二维卷积分解为横向线性卷积和纵向线性卷积的叠加，先横向线性卷积，再进行纵向线性卷积\n    :param filter: 线性卷积核 touple: ([n], [n])  n为卷积核的大小\n    :param mat: 图片 [h, w, 3]  ||  [h, w]\n    :return: 卷积后的图片,(灰度图，彩图都适用)\n    '''\n    (row_filter, col_filter) = filter if len(filter) == 2 else (filter, filter)\n    result = row_convolve(row_filter, img)\n    result = col_convolve(col_filter, result)\n    return result\n\n\ndef judgeConnect(m2, threshold):\n    '''\n    极大值过于稀疏，将极大值点的八邻域的模糊点也设为255，设置为边界，这样边界会更加明显\n    :param m2:[h,w] 图像\n    :param threshold: 阈值是上下限\n    :return: 图像[h,w]\n    '''\n\n    e = 0.01\n    # 存所有极大值点的坐标，假设有n个极大值点，最后s为[n, 2]\n    s = []\n    # 存所有点的坐标，遍历完后cood为[h,w,2]  2表示[x, y]\n    cood = []\n    # 遍历h\n    for i in range(m2.shape[0]):\n        cood.append([])\n        # 遍历w\n        for j in range(m2.shape[1]):\n            cood[-1].append([i, j])\n            if abs(m2[i, j] - 255) < e:\n                s.append([i, j])\n    cood = np.array(cood)\n    # 如果栈没有空，就一直查找\n    while not len(s) == 0:\n        # 从栈中弹出一个值来判断其八邻域内是否有极大值点\n        index = s.pop()\n        # 得到index点的八邻域的像素值窗口\n        jud = m2[max(0, index[0] - 1):min(index[0] + 2, m2.shape[1]), max(0, index[1] - 1):min(index[1] + 2, m2.shape[0])]\n        # 取得index坐标的八邻域坐标点\n        jud_i = cood[max(0, index[0] - 1):min(index[0] + 2, cood.shape[1]), max(0, index[1] - 1):min(index[1] + 2, cood.shape[0])]\n        # 取出在八邻域内像素值在模糊区间的点的掩码mask\n        jud = (jud > threshold[0]) & (jud < threshold[1])\n        # 将这些点的坐标取出来\n        jud_i = jud_i[jud]\n        # 将这些点的坐标入栈，并将这些点的八领域的模糊点的像素置255，然后等待下一步继续判断，也就是将所有极大值点的八邻域的模糊点也作为边界\n        for i in range(jud_i.shape[0]):\n            s.append(list(jud_i[i]))\n            m2[jud_i[i][0], jud_i[i][1]] = 255\n    return m2\n\n\ndef DecideAndConnectEdge(g_l, g_t, threshold=None):\n    '''\n    非极大值抑制函数+连接函数\n    :param g_l:图像每一点梯度的幅值 [h, w]\n    :param g_t:图像每一点梯度的相角[h, w]\n    :param threshold: 上下限\n    :return:图片\n    '''\n    if threshold == None:\n        lower_boundary = g_l.mean()*0.5\n        threshold = [lower_boundary, lower_boundary*3]\n    result = np.zeros(g_l.shape)\n    for i in range(g_l.shape[0]):\n        for j in range(g_l.shape[1]):\n            isLocalExtreme = True\n            # 得到一个像素点的八邻域，并限制不会超出图像的尺度\n            eight_neiborhood = g_l[max(0, i-1): min(i+2, g_l.shape[0]), max(0, j-1): min(j+2, g_l.shape[1])]\n            # 在图像内部可以双线性插值的点才进行非极大值抑制，图像边缘点直接设为极大值\n            if eight_neiborhood.shape == (3, 3):\n                # 梯度的正方向为dy向上，dx向左\n                # 如果梯度的tanθ∈[0, 1], 角度在0到45度之间  abs(tanθ)=abs(dy/dx) = d/1 => d=abs(dy/dx)\n                if 0 <= g_t[i, j] < 1:\n                    d = abs(g_t[i, j])\n                    first = eight_neiborhood[1, 2] + (eight_neiborhood[0, 2] - eight_neiborhood[1, 2]) * d\n                    second = eight_neiborhood[1, 0] + (eight_neiborhood[2, 0] - eight_neiborhood[1, 0]) * d\n                    if not (g_l[i, j] > first and g_l[i, j] > second):\n                        isLocalExtreme = False\n\n                # 如果梯度的tanθ > 1 角度在45度到90度之间， abs(tanθ)=abs(dy/dx) = 1/d => d=abs(dx/dy)\n                elif g_t[i, j] >= 1:\n                    d = abs(1 / g_t[i, j])\n                    first = eight_neiborhood[0, 1] + (eight_neiborhood[0, 2] - eight_neiborhood[0, 1]) * d\n                    second = eight_neiborhood[2, 1] + (eight_neiborhood[2, 0] - eight_neiborhood[2, 1]) * d\n                    if not (g_l[i, j] > first and g_l[i, j] > second):\n                        isLocalExtreme = False\n\n                # 如果梯度的tanθ < -1 角度在90度到145度之间， abs(tanθ)=abs(dy/dx) = 1/d => d=abs(dx/dy)\n                elif g_t[i, j] <= -1:\n                    d = abs(1 / g_t[i, j])\n                    first = eight_neiborhood[0, 1] + (eight_neiborhood[0, 0] - eight_neiborhood[0, 1]) * d\n                    second = eight_neiborhood[2, 1] + (eight_neiborhood[2, 2] - eight_neiborhood[2, 1]) * d\n                    if not (g_l[i, j] > first and g_l[i, j] > second):\n                        isLocalExtreme = False\n\n                # 如果梯度的tanθ∈[-1, 0] 角度在145度到180度之间 abs(tanθ)=abs(dy/dx) = d/1 => d=abs(dy/dx)\n                elif -1 < g_t[i, j] < 0:\n                    d = abs(g_t[i, j])\n                    first = eight_neiborhood[1, 0] + (eight_neiborhood[0, 0] - eight_neiborhood[1, 0]) * d\n                    second = eight_neiborhood[1, 2] + (eight_neiborhood[2, 2] - eight_neiborhood[1, 2]) * d\n                    if not (g_l[i, j] > first and g_l[i, j] > second):\n                        isLocalExtreme = False\n            if isLocalExtreme:\n                result[i, j] = g_l[i, j]       #非极大值抑制\n    # 将大于阈值的点设置为255，也就是梯度大于上阈值的点，直接就可以判定为边界，对处于阈值之间的模糊点进行保留，最后在连接函数来筛选这些模糊点\n    result[result >= threshold[1]] = 255\n    # 将小于最小阈值的点设置为0，也就是很小的极值当做是局部的最大，但是幅值太低了，直接设置为0，舍弃掉这些\n    result[result <= threshold[0]] = 0\n\n    # 进行非极大值抑制后连接所有可能连接的边\n    result = judgeConnect(result, threshold)\n    result[result != 255] = 0\n    return result\n\n\ndef OneDimensionStandardNormalDistribution(x, sigma):\n    '''\n    计算一维高斯核的工具函数\n    :param x:坐标，也就是离中心的距离\n    :param sigma: 模糊参数\n    :return: 该点的高斯核的参数\n    '''\n    E = -0.5 / (sigma*sigma)\n    return 1/ (math.sqrt(2*math.pi)*sigma)*math.exp(x*x*E)\n\n\nif __name__ == '__main__':\n\n    # Gaussian_filter_3 = 1.0/16*np.array([(1,2,1),(2,4,2),(1,2,1)]) #Gaussian smoothing kernel when sigma = 0.8, size: 3x3\n    # Gaussian_filter_5 = 1.0/159*np.array([\n    #     [2,4,5,4,2],\n    #     [4,9,12,9,4],\n    #     [5,12,15,12,5],\n    #     [4,9,12,9,4],\n    #     [2,4,5,4,2]\n    # ])  #Gaussian smoothing kernel when sigma = 1.4, size: 5x5\n\n\n\n    pic_path = 'img'\n    pics = os.listdir(pic_path)\n\n    # 读取图片\n    for i in pics:\n        # 因为png的像素值是0-1所以乘以255，标准化到0-255的范围，和jpg一致\n\n        filename = os.path.join(pic_path, i)\n        # [h, w, 3]\n        img = plt.imread(filename)\n        if i.split('.')[-1] == 'png':\n            img = img * 255\n        # 在rgb通道上取均值，灰度化\n        img = img.mean(axis=-1)\n\n        # 高斯核函数的参数\n        sigma = 1.52\n        # 卷积核的大小, 11\n        dim = int(np.round(6*sigma+1))\n        # 将卷积核限定为奇数\n        dim = dim + 1 if dim % 2 == 0 else dim\n        # 算出一维的高斯核的坐标取值，坐标表示离当前点的距离\n        # [5,4,3,2,1,0,1,2,3,4,5]\n        linear_Gaussian_filter = [np.abs(t - (dim//2)) for t in range(dim)]\n\n        # 将坐标送入高斯核产生函数中产生高斯核数值\n        # [11]\n        linear_Gaussian_filter = np.array([OneDimensionStandardNormalDistribution(t, sigma) for t in linear_Gaussian_filter])\n        # 高斯核归一化\n        linear_Gaussian_filter = linear_Gaussian_filter / linear_Gaussian_filter.sum()\n\n        # 拆分的线性卷积代替二维卷积,进行高斯滤波\n        img2 = divided_convolve(linear_Gaussian_filter, img)\n        # 也可以直接用二维卷积来做\n        # img2 = convolve(Gaussian_filter_5, img, [2, 2, 2, 2], [1, 1])\n\n        # 显示灰度化后的图片\n        plt.imshow(img2.astype(np.uint8), cmap='gray')\n        plt.axis('off')\n        plt.show()\n\n        # 两个sobel矩阵求梯度。sobel算子的正负隐含梯度的正方向为dy向上，dx向左，这里会影响后面的线性插值\n        sobel_kernel_y = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])\n        sobel_kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])\n        # 对灰度化后的图片求导，也就是用sobel核来卷积,x方向求导\n        img_x_grad = convolve(sobel_kernel_x, img2, [1,1,1,1], [1,1])\n        plt.imshow(img_x_grad.astype(np.uint8), cmap='gray')\n        plt.axis('off')\n        plt.show()\n        # y方向求导\n        img_y_grad = convolve(sobel_kernel_y, img2, [1,1,1,1], [1,1])\n        plt.imshow(img_y_grad.astype(np.uint8), cmap='gray')\n        plt.axis('off')\n        plt.show()\n        # 平方开根号得到梯度的幅值\n        gradiant_length = (img_x_grad**2 + img_y_grad**2)**(1.0/2)\n\n        # 展示梯度幅值的图像\n        img_x_grad = img_x_grad.astype(np.float64)\n        img_y_grad = img_y_grad.astype(np.float64)\n        # 防止求比值的时候出现Nan\n        img_x_grad[img_x_grad == 0] = 0.00000001\n        # 梯度的角度\n        gradiant_tangent = img_y_grad / img_x_grad\n\n        plt.imshow(gradiant_length.astype(np.uint8), cmap='gray')\n        plt.axis('off')\n        plt.show()\n\n        #lower_boundary = 50\n        # 后处理，非极大值抑制和连接极大值的像素点，形成边界\n        final_img = DecideAndConnectEdge(gradiant_length, gradiant_tangent)\n        cv2.imshow('edge', final_img.astype(np.uint8))\n        cv2.waitKey(0)","sub_path":"1、canny edge detection/CannyDetection.py","file_name":"CannyDetection.py","file_ext":"py","file_size_in_byte":13709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"338323980","text":"#problem21\r\nA=float(input())\r\nN=A\r\nprint(\"NOTAS:\")\r\nprint(\"%d nota(s) de R$ 100.00\"%(N/100))\r\nb=N%100\r\nprint(\"%d nota(s) de R$ 50.00\"%(b/50))\r\nd=b%50\r\nprint(\"%d nota(s) de R$ 20.00\"%(d/20))\r\nf=d%20\r\nprint(\"%d nota(s) de R$ 10.00\"%(f/10))\r\nh=f%10\r\nprint(\"%d nota(s) de R$ 5.00\"%(h/5))\r\nj=h%5\r\nprint(\"%d nota(s) de R$ 2.00\"%(j/2))\r\nl=j%2\r\nprint(\"MOEDAS:\")\r\nE=A*100\r\nB=(int(E))\r\nprint(\"%d moeda(s) de R$ 1.00\"%l)\r\nm=B%100\r\nprint(\"%d moeda(s) de R$ 0.50\"%(m/50))\r\no=m%50\r\nprint(\"%d moeda(s) de R$ 0.25\"%(o/25))\r\nq=o%25\r\nprint(\"%d moeda(s) de R$ 0.10\"%(q/10))\r\ns=q%10\r\nprint(\"%d moeda(s) de R$ 0.05\"%(s/5))\r\nu=s%5\r\nprint(\"%d moeda(s) de R$ 0.01\"%u)\r\n#problem22\r\nA,B,C,D=map(int,input().split())\r\nif B>C and D>A and (C+D)>(A+B) and C>=0 and D>=0 and A%2==0:\r\n                print(\"Valores aceitos\")\r\nelse:\r\n    print(\"Valores nao aceitos\")\r\n# problem23\r\nA,B,C =map(float,input().split())\r\nd = B * B - 4 * A * C\r\ne = pow(d,0.5)\r\nif (d < 0 or A == 0):\r\n    print(\"Impossivel calcular\")\r\nelse:\r\n    f = (-B + e) / (2 * A)\r\n    g = (-B - e) / (2 * A)\r\n    print(\"R1 = %.5f\"%f)\r\n    print(\"R2 = %.5f\"%g)\r\n# problem24\r\nn=float(input())\r\nif n<0.00 or n>100.00:\r\n    print(\"Fora de intervalo\")\r\nelif n>=0.00 and n<=25.00:\r\n    print(\"Intervalo [0,25]\")\r\nelif n>25.00 and n<=50.00:\r\n    print(\"Intervalo (25,50]\")\r\nelif n>50.00 and n<=75.00:\r\n    print(\"Intervalo (50,75]\")\r\nelif n>75.00 and n<=100.00:\r\n    print(\"Intervalo (75,100]\")\r\n#problem24\r\nsnack={\r\n    1:4.00,\r\n    2:4.50,\r\n    3:5.00,\r\n    4:2.00,\r\n    5:1.50\r\n}\r\nX,Y=map(int,input().split())\r\nTotal=snack[X]*Y\r\nprint(\"Total: R$ %.2f\"%Total)\r\n# problem25\r\nN1,N2,N3,N4=map(float,input().split())\r\nMedia=((N1*2)+(N2*3)+(N3*4)+(N4*1))/10\r\nprint(\"Media: %.1f\"%Media)\r\nif Media>=7.0:\r\n    print(\"Aluno aprovado.\")\r\nelif Media<5.0:\r\n    print(\"Aluno reprovado.\")\r\nelif Media >=5.0 and Media<=6.9:\r\n    print(\"Aluno em exame.\")\r\n    N5=float(input())\r\n    print(\"Nota do exame:\",N5)\r\n    Media2=(N5+Media)/2\r\n    if Media2>=5.0:\r\n        print(\"Aluno aprovado.\")\r\n        print(\"Media final: %.1f\"%Media2)\r\n    elif Media2<=4.9:\r\n        print(\"Aluno reprovado.\")\r\n        print(\"Media final: %.1f\" % Media2)\r\n# problem26\r\nX,Y=map(float,input().split())\r\nif(X==0 and Y==0):\r\n    print(\"Origem\")\r\nelif(X==0):\r\n    print(\"Eixo Y\")\r\nelif(Y==0):\r\n    print(\"Eixo X\")\r\nelif(X>0 and Y>0):\r\n    print(\"Q1\")\r\nelif(X<0 and Y>0):\r\n    print(\"Q2\")\r\nelif(X<0 and Y<0):\r\n    print(\"Q3\")\r\nelif(X>0 and Y<0):\r\n    print(\"Q4\")\r\n# problem27\r\nA,B,C=map(int,input().split())\r\nlist=[A,B,C]\r\nlist.sort()\r\nfor x in list:\r\n    print(x)\r\nprint()\r\nprint(A)\r\nprint(B)\r\nprint(C)\r\n#another\r\na,b,c=map(float,input().split())\r\nif(a < b):\r\n    temp = a\r\n    a = b\r\n    b = temp\r\nif(b < c):\r\n    temp = b\r\n    b = c\r\n    c = temp\r\nif(a < b):\r\n    temp = a\r\n    a = b\r\n    b = temp\r\nprint(a)\r\nprint(b)\r\nprint(c)\r\n\r\n#problem28\r\nA,B,C=map(float,input().split())\r\nif (B+C)>A and (A+B)>C and (A+C)>B:\r\n    Perimetro=A+B+C\r\n    print(\"Perimetro = %.1f\"%Perimetro)\r\nelse:\r\n    Area=((1/2)*(A+B))*C\r\n    print(\"Area = %.1f\"%Area)\r\n#problem29\r\nA,B=map(int,input().split())\r\nif A%B==0 or B%A==0:\r\n    print(\"Sao Multiplos\")\r\nelse:\r\n    print(\"Nao Sao Multiplos\")\r\n#problem30\r\nA,B,C=map(float,input().split())\r\nlist=[A,B,C]\r\nlist.sort(reverse=True)\r\nprint(list)\r\nA=list[0]\r\nB=list[1]\r\nC=list[2]\r\nif (A >= B + C):\r\n    print(\"NAO FORMA TRIANGULO\")\r\nelif ((A*A)==((B*B) + (C*C))):\r\n    print(\"TRIANGULO RETANGULO\")\r\nelif ((A*A)>((B*B) + (C*C))):\r\n    print(\"TRIANGULO OBTUSANGULO\")\r\nelif ((A*A)<((B*B) + (C*C))):\r\n    print(\"TRIANGULO ACUTANGULO\")\r\nif (A==B==C):\r\n    print(\"TRIANGULO EQUILATERO\")\r\nelif (A==B or A==C or B==C):\r\n    print(\"TRIANGULO ISOSCELES\")","sub_path":"uri-problem21-30.py","file_name":"uri-problem21-30.py","file_ext":"py","file_size_in_byte":3656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"16706015","text":"# -*- coding: future_annotations -*-\nimport logging\nfrom typing import TYPE_CHECKING\n\nfrom nbsafety.data_model.timestamp import Timestamp\nfrom nbsafety.singletons import nbs\n\nif TYPE_CHECKING:\n    from typing import Iterable, Set\n\n    # avoid circular imports\n    from nbsafety.data_model.data_symbol import DataSymbol\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.ERROR)\n\n\nclass UpdateProtocol:\n    def __init__(self, updated_sym: DataSymbol) -> None:\n        self.updated_sym = updated_sym\n        self.seen: Set[DataSymbol] = set()\n\n    def __call__(self, new_deps: Set[DataSymbol], mutated: bool, propagate_to_namespace_descendents: bool) -> None:\n        # in most cases, mutated implies that we should propagate to namespace descendents, since we\n        # do not know how the mutation affects the namespace members. The exception is for specific\n        # known events such as 'list.append()' or 'list.extend()' since we know these do not update\n        # the namespace members.\n        logger.warning(\n            \"updated sym %s (containing scope %s) with children %s\",\n            self.updated_sym,\n            self.updated_sym.containing_scope,\n            self.updated_sym.children_by_cell_position.values(),\n        )\n        directly_updated_symbols = nbs().aliases[self.updated_sym.obj_id] if mutated else {self.updated_sym}\n        self._collect_updated_symbols_and_refresh_namespaces(\n            directly_updated_symbols, propagate_to_namespace_descendents\n        )\n        logger.warning(\n            'for symbol %s: mutated=%s; updated_symbols=%s', self.updated_sym, mutated, directly_updated_symbols\n        )\n        updated_symbols_with_ancestors = set(self.seen)\n        logger.warning('all updated symbols for symbol %s: %s', self.updated_sym, updated_symbols_with_ancestors)\n        nbs().updated_symbols |= self.seen\n        for updated_sym in directly_updated_symbols:\n            if not updated_sym.is_stale and updated_sym is not self.updated_sym:\n                updated_sym.refresh()\n        self.seen |= new_deps  # don't propagate to stuff on RHS\n        for dsym in updated_symbols_with_ancestors:\n            self._propagate_staleness_to_deps(dsym, skip_seen_check=True)\n\n    def _collect_updated_symbols_and_refresh_namespaces(\n        self, updated_symbols: Iterable[DataSymbol], refresh_descendent_namespaces: bool\n    ) -> None:\n        logger.warning('collecting updated symbols and namespaces for %s', updated_symbols)\n        for dsym in updated_symbols:\n            if dsym.is_import or dsym in self.seen:\n                continue\n            dsym.updated_timestamps.add(Timestamp.current())\n            self.seen.add(dsym)\n            containing_ns = dsym.containing_namespace\n            if containing_ns is not None:\n                logger.warning('containing scope for %s: %s; ids %s, %s', dsym, containing_ns, dsym.obj_id, containing_ns.obj_id)\n                containing_ns.namespace_stale_symbols.discard(dsym)\n                containing_ns.max_descendent_timestamp = Timestamp.current()\n                self._collect_updated_symbols_and_refresh_namespaces(\n                    nbs().aliases[containing_ns.obj_id], refresh_descendent_namespaces\n                )\n            if refresh_descendent_namespaces:\n                dsym_ns = dsym.namespace\n                if dsym_ns is not None:\n                    self._collect_updated_symbols_and_refresh_namespaces(\n                        dsym_ns.all_data_symbols_this_indentation(), refresh_descendent_namespaces\n                    )\n\n    def _propagate_staleness_to_namespace_parents(self, dsym: DataSymbol, skip_seen_check=False):\n        if not skip_seen_check and dsym in self.seen:\n            return\n        self.seen.add(dsym)\n        containing_ns = dsym.containing_namespace\n        if containing_ns is None:\n            return\n        logger.warning(\"add %s to namespace stale symbols of %s\", dsym, containing_ns)\n        containing_ns.namespace_stale_symbols.add(dsym)\n        for containing_alias in nbs().aliases[containing_ns.obj_id]:\n            self._propagate_staleness_to_namespace_parents(containing_alias)\n\n        for containing_alias in nbs().aliases[containing_ns.obj_id]:\n            # do this in 2 separate loops to make sure all containing_alias are added to 'seen'\n            # works around the issue when one alias depends on another\n            for child in self._non_class_to_instance_children(containing_alias):\n                logger.warning('propagate from namespace parent of %s to child %s', dsym, child)\n                self._propagate_staleness_to_deps(child)\n\n    def _non_class_to_instance_children(self, dsym):\n        if self.updated_sym is dsym:\n            for dep_introduced_pos, dsym_children in dsym.children_by_cell_position.items():\n                if not nbs().settings.backwards_cell_staleness_propagation and dep_introduced_pos <= nbs().active_cell_position_idx:\n                    continue\n                yield from dsym_children\n            return\n        for dep_introduced_pos, dsym_children in dsym.children_by_cell_position.items():\n            if not nbs().settings.backwards_cell_staleness_propagation and dep_introduced_pos <= nbs().active_cell_position_idx:\n                continue\n            for child in dsym_children:\n                # Next, complicated check to avoid propagating along a class -> instance edge.\n                # The only time this is OK is when we changed the class, which will not be the case here.\n                child_namespace = child.namespace\n                if child_namespace is not None and child_namespace.cloned_from is not None:\n                    if child_namespace.cloned_from.obj_id == dsym.obj_id:\n                        continue\n                yield child\n\n    def _propagate_staleness_to_namespace_children(self, dsym: DataSymbol, skip_seen_check=False):\n        if not skip_seen_check and dsym in self.seen:\n            return\n        self.seen.add(dsym)\n        self_ns = nbs().namespaces.get(dsym.obj_id, None)\n        if self_ns is None:\n            return\n        for ns_child in self_ns.all_data_symbols_this_indentation(exclude_class=True):\n            logger.warning('propagate from %s to namespace child %s', dsym, ns_child)\n            self._propagate_staleness_to_deps(ns_child)\n\n    def _propagate_staleness_to_deps(self, dsym: DataSymbol, skip_seen_check=False):\n        if not skip_seen_check and dsym in self.seen:\n            return\n        self.seen.add(dsym)\n        if dsym not in nbs().updated_symbols:\n            if dsym.should_mark_stale(self.updated_sym):\n                dsym.fresher_ancestors.add(self.updated_sym)\n                dsym.required_timestamp = Timestamp.current()\n                self._propagate_staleness_to_namespace_parents(dsym, skip_seen_check=True)\n                self._propagate_staleness_to_namespace_children(dsym, skip_seen_check=True)\n        for child in self._non_class_to_instance_children(dsym):\n            logger.warning('propagate %s %s to %s', dsym, dsym.obj_id, child)\n            self._propagate_staleness_to_deps(child)\n","sub_path":"nbsafety/data_model/update_protocol.py","file_name":"update_protocol.py","file_ext":"py","file_size_in_byte":7111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"389697842","text":"import time\nimport os, sys\nimport argparse\nfrom argparse import Namespace\nfrom multiprocessing import Process\nfrom typing import Dict, Any, Optional\nimport random\nimport logging\nimport json\n\nimport torch\nfrom torch import nn\nimport numpy as np\n\nimport dopt\nfrom dopt import NEIOptimizer, Trainer, Server\nfrom dopt.utils import get_output_shape\nfrom test_objective_function import run_train_net_kfold # The objective function\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# The configurations\nCONFIG = {}\nCONFIG[\"computer_list\"] = {\n    \"acet_update\": ['tst008@acet116-lnx-11.bucknell.edu'], # To git pull\n    \"acet\": [\n#         'tst008@acet116-lnx-11.bucknell.edu',\n        'tst008@acet116-lnx-12.bucknell.edu',\n#         'tst008@acet116-lnx-13.bucknell.edu',\n#         'tst008@acet116-lnx-14.bucknell.edu',\n#         'tst008@acet116-lnx-15.bucknell.edu',\n#         'tst008@acet116-lnx-16.bucknell.edu',\n#         'tst008@acet116-lnx-17.bucknell.edu',\n#         'tst008@acet116-lnx-18.bucknell.edu',\n#         'tst008@acet116-lnx-19.bucknell.edu',\n#         'tst008@acet116-lnx-1.bucknell.edu',\n#         'tst008@acet116-lnx-20.bucknell.edu',\n#         'tst008@acet116-lnx-21.bucknell.edu',\n    ],\n#     \"tung-torch\": ['tung@jvs008-r1.bucknell.edu']\n}\n# Commands to run on target machines here\nCONFIG[\"commands\"] = {\n    \"acet_update\": \"cd PycharmProjects/distributed-optimizer/ && git pull\" + \\\n                   \" && module switch python/3.7-2020-05-28\" + \\\n                   \" && export LD_LIBRARY_PATH=/usr/remote/lib:/usr/remote/anaconda-3.7-2020-05-28/lib\" + \\\n                   \" && python3 ~/PycharmProjects/distributed-optimizer/distributed_optimizer_bird.py --run_as trainer\",\n    \"acet\": \"sleep 10 && module switch python/3.7-2020-05-28\" + \\\n                   \" && export LD_LIBRARY_PATH=/usr/remote/lib:/usr/remote/anaconda-3.7-2020-05-28/lib\" + \\\n                   \" && python3 ~/PycharmProjects/distributed-optimizer/distributed_optimizer_bird-Copy1.py --run_as trainer\",\n    \"tung-torch\": \"/opt/anaconda/envs/jupyter37/bin/python ~/pj/dopt_v2/distributed_optimizer_bird.py --run_as trainer --data_folder ~/pj/dopt_v2/data/CroppedYale/\"\n}\n#\n# CONFIG[\"commands\"] = {\n#     \"acet\": \"sleep 20 && echo 'Hey'\",\n#     \"tung-torch\": \"sleep 20 && echo 'Hey'\"\n# }\nCONFIG[\"server\"] = {\n    \"host\": \"jvs008-r1.bucknell.edu\",\n    \"port\": 15556,\n    \"logging_level\": logging.WARNING\n}\nCONFIG[\"trainer\"] = {\n    \"username\": \"tst008\",\n    \"num_constraints\": 1\n}\nCONFIG[\"optimizer\"] = {\n    \"bounds\": {\n        \"x1\": [0, 10],\n        \"x2\": [0, 10]\n    },\n    \"initial_candidates\": [\n        {\"x1\": 3, \"x2\": 6},\n        {\"x1\": 4, \"x2\": 7}\n    ],\n    \"device\": \"cpu\",\n    \"seed\": 0,\n    \"filename\": \"test2.dopt\"\n}\n\n\ndef get_feasibility(candidate) -> float:\n    x1, x2, _ = candidate.values()\n    return -(x1 - x2 + 1.5)\n    \ndef objective_function_torch_input(X):\n    BASE_VAR = 0.1\n    \n    X = -X.view(-1, 2)\n    mask_constraint = X[...,0] - X[...,1] + 1.5 > 0\n    cos_x = torch.cos(X[...,0])\n    sin_y = torch.sin(X[...,1])\n    first_term = sin_y * torch.exp((1-cos_x)**2)\n    second_term = cos_x * torch.exp((1-sin_y)**2)\n    third_term = (X[...,0] - X[...,1])**2\n    result = -(first_term + second_term + third_term) \n    Y = ((result + 120) / 230) #* mask_constraint \n    # Add noise\n    se = torch.norm(X, dim=-1, keepdim=True) * 0.02\n    Yvar = BASE_VAR + se * torch.rand_like(se)\n    true_var = BASE_VAR + se\n    Y = Y.view(-1, 1, 1) + torch.rand_like(se) * Yvar\n    return Y, Yvar.view_as(Y)**2, true_var.view_as(Y)**2\n    \n# Plug in the objective function here\ndef objective_function(candidate, logger):\n    time.sleep(random.randint(60, 90))\n    logger.info(json.dumps(candidate))\n    feasibility = get_feasibility(candidate)\n    if feasibility > 0:\n        logger.info(\"Infeasible!\")\n        observation = {\n            \"objective\": [0.001, 0.001],\n            \"constraints\": [feasibility]\n        }\n        return observation\n    logger.info(\"Returning the observation\")\n    \n    # Simulate input\n    X = torch.tensor([candidate[\"x1\"], candidate[\"x2\"]], dtype=float)\n    Y, Yvar, _ = objective_function_torch_input(X)\n    mean, variance = Y.item(), Yvar.item()\n    observation = {\n        \"objective\": [mean, variance],\n        \"constraints\": [feasibility]\n    }\n    return observation\n\n    \n# Calls start_optimizer and start_trainers simultaneously\ndef start_server():\n    optimizer = NEIOptimizer(\n        CONFIG[\"optimizer\"][\"filename\"], \n        CONFIG[\"optimizer\"][\"bounds\"], \n        initial_candidates=CONFIG[\"optimizer\"][\"initial_candidates\"],\n        device=CONFIG[\"optimizer\"][\"device\"],\n        seed=CONFIG[\"optimizer\"][\"seed\"]\n    )\n    server = Server(optimizer, CONFIG, logging_level=CONFIG[\"server\"][\"logging_level\"])\n    server.run()\n    \ndef start_trainers():\n    trainer = Trainer(\n        objective_function, \n        CONFIG[\"trainer\"][\"username\"],\n        CONFIG[\"server\"][\"host\"],\n        CONFIG[\"server\"][\"port\"],\n        num_constraints=CONFIG[\"trainer\"][\"num_constraints\"]\n    )\n    trainer.run()\n    \n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(prog='distributed_optimizer.py',\n                                     description='''Optimize objective function of specified by a `Trainer`''')\n    parser.add_argument('--run_as', action='store', dest='run_as',\n                           help='Specify the role of the machine (server or trainer). Defaults to server',\n                           type=str, required=False,\n                           default=\"server\")\n    parser.add_argument('--data_folder', action='store', dest='data_folder',\n                           help='Specify the directory to the data folder (for clients only)',\n                           type=str, required=False,\n                           default=\"~/PycharmProjects/summer/data/CroppedYale/\")\n    args = parser.parse_args()\n    \n    # Can modify these code to accomodate more options\n    # E.g. Run different Trainers on same task\n    if args.run_as == \"server\":\n        start_server()\n    elif args.run_as == \"trainer\":\n        start_trainers()\n    \n    \n    \n","sub_path":"distributed_optimizer_bird-Copy1.py","file_name":"distributed_optimizer_bird-Copy1.py","file_ext":"py","file_size_in_byte":6104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"629606794","text":"from os.path import join\n\nimport numpy as np\nimport pandas as pd\n\n\ndef return_filtered_and_normed(signal, delta, type='min'):\n    # TODO: comment\n    l_smooth = signal.rolling(window=delta, center=True).mean().dropna()\n    if type == 'min':\n        l_norm = (l_smooth - l_smooth.min()) / (l_smooth.max() - l_smooth.min())\n    else:\n        l_norm = l_smooth / l_smooth.max()\n\n    return l_norm\n\n\n# TODO: to be removed?\ndef retrieve_load_data_partitions(path_load_data, date_slice, alpha, delta, regions, norm_type):\n    \"\"\"Allows to get load for a whole region or per subregion\"\"\"\n    dict_regions = {'EU': ['AT', 'BE', 'CH', 'DE', 'DK', 'ES',\n                           'FR', 'UK', 'IE', 'IT', 'LU',\n                           'NL', 'PT', 'SE', 'CZ',\n                           'BG', 'CH', 'EE',\n                           'FI', 'EL', 'HR', 'HU', 'LT', 'LV', 'PL', 'RO', 'SI', 'SK'],\n                    'CWE': ['FR', 'BE', 'LU', 'NL', 'DE'],\n                    'BL': ['BE', 'LU', 'NL']}  # TODO: BL is actually also the code of country\n\n    load_data = pd.read_csv(join(path_load_data, 'load_opsd_2015_2018.csv'), index_col=0)\n    # load_data.index = pd.date_range('2015-01-01T00:00', '2018-12-31T23:00', freq='H')\n    load_data.index = pd.to_datetime(load_data.index)\n\n    print(load_data)\n    exit()\n\n    load_data_sliced = load_data.loc[date_slice[0]:date_slice[1]]\n\n    # Adding the stand-alone regions to load dict.\n    standalone_regions = list(load_data.columns)\n    for region in standalone_regions:\n        dict_regions.update({str(region): str(region)})\n\n    if alpha == 'load_central':\n\n        # Extract lists of load subdivisions from load_dict.\n        # e.g.: for regions ['BL', 'DE'] => ['BE', 'NL', 'LU', 'DE']\n        regions_list = []\n        for key in regions:\n            if isinstance(dict_regions[key], str):\n                regions_list.append(str(dict_regions[key]))\n            elif isinstance(dict_regions[key], list):\n                regions_list.extend(dict_regions[key])\n            else:\n                raise TypeError('Check again the type. Should be str or list.')\n\n        load_vector = load_data_sliced[regions_list].sum(axis=1)\n\n    elif alpha == 'load_partition':\n\n        if regions in standalone_regions:\n            load_vector = load_data_sliced[dict_regions[regions]]\n        else:\n            load_vector = load_data_sliced[dict_regions[regions]].sum(axis=1)\n\n    load_vector_norm = return_filtered_and_normed(load_vector, delta, norm_type)\n\n    return load_vector_norm\n\n\ndef resource_quality_mapping(cap_factor_df, delta, measure):\n    # TODO: comment\n\n    cap_factor_rolling = cap_factor_df.rolling(delta, center=True)\n\n    if measure == 'mean':\n        cap_factor_per_window_df = cap_factor_rolling.mean().dropna()\n    elif measure == 'median':\n        cap_factor_per_window_df = cap_factor_rolling.median().dropna()\n\n    else:\n        raise ValueError(' Measure {} is not available.'.format(str(measure)))\n\n    return cap_factor_per_window_df\n\n\ndef critical_window_mapping(cap_factor_per_window_df, alpha, delta, regions, load_df, norm_type):\n    # TODO: comment\n\n    if alpha == 'load_central':\n\n        load_df_sum = load_df.sum(axis=1)\n        alpha_reference = return_filtered_and_normed(load_df_sum, delta, norm_type).to_frame()\n        critical_windows = cap_factor_per_window_df.gt(alpha_reference.values).astype(int)\n\n        return critical_windows\n\n    elif alpha == 'load_partition':\n\n        # TODO: update or remove\n        for region, tech in key_list:\n            l_norm = retrieve_load_data_partitions(path_load_data, date_slice, alpha, delta, region, norm_type)\n            # Flip axes.\n            alpha_reference = l_norm[:, np.newaxis]\n\n            # Select region of interest within the dict value with 'tech' key.\n            critical_windows = (input_dict[region][tech] > alpha_reference).astype(int)\n            output_dict[region][tech] = critical_windows\n\n    else:\n        raise ValueError('No such alpha rule. Retry.')\n\n    return output_dict\n","sub_path":"resite/models/complementarity/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"318156729","text":"import sys\nimport numpy as np\nfrom collections import Counter\nfrom statistics import stdev\nfrom scipy.spatial.distance import cityblock\n\ndef normalise(data, test_data):\n    max = np.max(data[:,:-1])\n    data[:,:-1] = data[:,:-1]/max\n    max = np.max(test_data[:,:-1])\n    test_data[:,:-1] = test_data[:,:-1]/max\n    return data, test_data\n\ndef nearest_neighbor(train_data, test_data, k):\n    object_id = 1\n    accuracy = []\n    for predict in test_data:\n        distance = []\n        for data in train_data:\n            temp_dist =cityblock(np.array(data[:-1]) , np.array(predict[:-1]))\n            distance.append((temp_dist,data[-1]))\n        votes = [i[1] for i in sorted(distance)[:k]]\n        predicted_class = Counter(votes).most_common(1)[0][0]\n        votes = np.asarray(votes)\n        print(object_id, votes)\n        if len(np.unique(votes)) == k:\n            if predict[-1] in votes:\n                accuracy.append(1/k)\n            else:\n                accuracy.append(0)\n        else:\n            accuracy.append(1) if predicted_class == predict[-1] else accuracy.append(0)\n        #print('ID=%5d, predicted=%3d, true=%3d, accuracy=%4.2f'% (object_id, predicted_class, predict[-1], accuracy[-1]));\n        object_id += 1\n    print('classification accuracy=%6.2f'%(sum(accuracy)/len(accuracy)*100))\n\nif __name__ == '__main__':\n    train_data,test_data  = normalise(np.loadtxt(sys.argv[1]),np.loadtxt(sys.argv[2]) )\n    data = np.loadtxt(sys.argv[1])\n    k = int(sys.argv[3])\n    print(len(train_data[0])-1)\n    print(train_data.shape)\n    nearest_neighbor(train_data, test_data, k)\n","sub_path":"kNN/knn_classift_opt.py","file_name":"knn_classift_opt.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"109379075","text":"from pygameJoystick import pyGameJoystick\nfrom roboSocketCom import RoboSocketCom\nfrom jsonController import jsonController\nfrom webSocketsOpencvServer import WebSocketsOpencvServer\nimport json,pygame,time,asyncio,websockets,threading\n\n\ndef SocketsOpencv(ipAdress,port):\n    WebSocketsOpencvServer(serverHost=ipAdress,serverPort=port)\n\nif __name__ == '__main__':\n    try:\n        socketsOpencv =threading.Thread(target=SocketsOpencv,args=(\"0.0.0.0\",5001,))\n        socketsOpencv.run()\n    except threading.ThreadError as exp:\n        print(\"WebSocketsOpencvServer Thread ta bir sorun ile karşılaşıldı...\",exp)\n    try:\n        pygame.init()\n        joystcik = pyGameJoystick(pyGame=pygame)\n    except Exception as exp:\n        print(\"joytcik veya pygame başlatılırken bir sorun çıktı sorun... : \", exp)\n    try:\n        roboSocketCom = RoboSocketCom(clientHost=\"172.19.96.227\", clientPort=5000)\n    except Exception as exp:\n        print(\"RoboSocketCom sunucuya bağlanma işlemi başlatılırken bir sorun çıktı sorun... hata kodu : \", exp)\n\n    try:\n        json_Controller = jsonController(joyStick=joystcik, roboSocketCom=roboSocketCom)\n    except Exception as exp:\n        print(\"jsonController başlatılırken bir sorun çıktı sorun... hata kodu : \", exp)\n\n","sub_path":"Rover - ver2.3/Controller-PC/loopPC.py","file_name":"loopPC.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"217511436","text":"# -*- coding: utf-8 -*-\n\nimport logging\nimport os\nimport sys\nfrom datetime import datetime\nimport requests\n\n\nlogging.basicConfig()\nlogger = logging.getLogger(\"task\")\nfile_handler = logging.FileHandler(\"log/task.log\")\nfile_handler.setFormatter(logging.Formatter(\"%(asctime)s %(levelname)s %(message)s\"))\nlogger.addHandler(file_handler)\nlogger.setLevel(logging.DEBUG)\n\n\ndef run_task(func, task_name='task', **kwargs):\n    try:\n        d1 = datetime.now()\n        func(**kwargs)\n        d2 = datetime.now()\n        logger.debug('task[%s] cost %ss' % (task_name, (d2 - d1).__str__()))\n    except Exception as ex:\n        logger.debug('task[%s] error' % (task_name, ))\n        logger.exception(ex)\n\n\nif __name__ == '__main__':\n\n    logger.debug('start task %s %s' % (sys.argv[1:].__str__(), datetime.now().__str__()))\n    if len(sys.argv) > 1:\n        t = __import__('apps.%s.sub_task' % sys.argv[1], fromlist=[''])\n        t.run(sys.argv[1:])","sub_path":"task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"147440068","text":"import os\nimport shutil\nimport subprocess\nimport math\n\nfrom datetime import datetime\nfrom PIL import Image, ImageDraw\nfrom dataclasses import dataclass\nfrom typing import Tuple, List\nfrom random import randint, uniform, choice, random\n\nCOLORS = ['#F0CA4D',\n          '#324d5c', \n          '#f2a336',\n          '#BD9E20',\n          '#0A1C28',\n          '#EEE9D1',\n          '#333741',\n          '#F1EAD1',\n          '#424142',\n          '#BFB7A8',\n          '#111826',\n          '#F0EBEF',\n          '#1477B2',\n          '#0C3F60',\n          '#12537E' ]\n\nBLUES = [ '#060B23',\n          '#162137',\n          '#27374B',\n          '#374D5F',\n          '#486373',\n          '#597987',\n          '#698F9B',\n          '#7AA5AF',\n          '#8ABBC3',\n          '#9BD1D7',\n          '#ACE8EC',]\n\nRED_ORANGES = [\n    '#E36D60',\n    '#DB6860',\n    '#D46461',\n    '#CD6062',\n    '#C65C63',\n    '#BF5864',\n    '#B85364',\n    '#B14F65',\n    '#AA4B66',\n    '#A34767',\n    '#9C4368',\n]\n\nEARTH_WORM = [\n'#CA6671',\n'#C46771',\n'#BF6972',\n'#B96B73',\n'#B46C74',\n'#AE6E75',\n'#A97076',\n'#A37177',\n'#9E7378',\n'#987579',\n'#93777A',\n'#7E4047', \n'#80454C', \n'#824B51', \n'#845056', \n'#86565B', \n'#885B60', \n'#8A6165', \n'#8C666A', \n'#8E6C6F', \n'#907174', \n'#93777A', \n'#7E4047',\n'#854950',\n'#8D535A',\n'#945D64',\n'#9C676D',\n'#A47177',\n'#AB7B81',\n'#B3858A',\n'#BA8F94',\n'#C2999E',\n'#CAA3A8',\n'#4B262A',\n'#573236',\n'#643F43',\n'#714B4F',\n'#7D585C',\n'#8A6469',\n'#977175',\n'#A37D82',\n'#B08A8E',\n'#BD969B',\n'#CAA3A8',\n]\n\nBLUE_WITH_A_TASTE_OF_YELLOW = BLUES + [ '#f48b00', '#c98a2d', 'black', 'white']\n\ndef random_color(): \n    return (randint(0, 255), randint(0, 255), randint(0, 255))\n\ndef random_curve():\n    return uniform(-20, 20)\n\ndef random_fill():\n    return randint(0, len(COLORS)-1)\n\ndef random_grey():\n    thing = randint(0, 255)\n    return (thing, thing, thing)\n\ndef random_green():\n    return (randint(0, 100), randint(150, 255), randint(0, 100))\n\ndef random_angle(): \n    return randint(180, 360)\n\ndef whites(num):\n    return [\"white\" for _ in range(num)]\n\ndef random_blue():\n    return choice(BLUES + RED_ORANGES+COLORS+whites(10))\n\ndef yellowish():\n    base = randint(0,168)\n    return (base + 87, base + 56, base)\n\nSO_MANY_CHOICES = BLUES + RED_ORANGES + [random_grey() for _ in range(30)] + COLORS\nR_N_B = BLUES + whites(100)\n\n@dataclass\nclass Circle: \n    radius: float\n    location: Tuple[int, int]\n    angle: int = 0\n    fill = 0\n    active: bool = True\n    curve: int = 0\n    oob_deactiavte = False\n    history = []\n\n    def __post_init__(self):\n        self.angle = random_angle()\n        self.curve = random_curve()\n        self.fill = random_fill()\n        self.history = []\n\n    @property\n    def x(self):\n        return self.location[0]\n\n    @property\n    def y(self):\n        return self.location[1]\n\n    def draw(self): \n        if not self.active:\n            return\n        #DRAW.ellipse((self.x - self.radius, \n                     #self.y - self.radius,\n                     #self.x + self.radius,\n                     #self.y + self.radius), fill=random_blue())\n        self.history.append(((self.x - self.radius, self.y - self.radius, self.x + self.radius, self.y + self.radius), 'white'))#choice(R_N_B)))\n\n    def really_draw(self): \n        if self.oob_deactiavte:\n            return\n        out = yellowish()\n        for thing in self.history:\n            DRAW.ellipse(thing[0], fill=thing[1], outline=out)\n\n    def maybe_deactivate(self):\n        # If we are above 10% of the max we have a chance to deactivate\n        if self.x > IMAGE_WIDTH*.97 or self.x < IMAGE_WIDTH * .03:\n            if random() > .9:\n                self.active = False\n                self.oob_deactiavte = True\n        if self.y > IMAGE_HEIGHT*.9 or self.y < IMAGE_HEIGHT * .1:\n            if random() > .9:\n                self.active = False\n                self.oob_deactiavte = True\n\n\n    def step(self):\n        if not self.active:\n            return\n        step = self.radius/2\n        rad_angle = math.radians(self.angle)\n        self.location = (self.x + step*math.cos(rad_angle), \n                         self.y + step*math.sin(rad_angle))\n        self.angle += self.curve\n\n        # Maybe decrease radius\n        if randint(0, 100) > 70:\n            self.radius -= 1\n        #if randint(0, 100) > 99:\n            #self.radius += 5\n        #Maybe change our rotation\n        if randint(0, 100) > 79:\n            self.curve = random_curve()\n\n        self.maybe_deactivate()\n        if self.x + self.radius >= IMAGE_WIDTH  or self.x - self.radius <= 0:\n            self.active = False\n            self.oob_deactiavte = True\n        if self.y + self.radius >= IMAGE_HEIGHT  or self.y - self.radius <= 0:\n            self.active = False\n            self.oob_deactiavte = True\n        if self.radius <= 0:\n            self.active = False\n\n# Get the current time, use it as the directory we save everything in\nSAVE_DIR = datetime.now().strftime(\"%Y-%m-%d %H.%M.%S\")\nos.mkdir(SAVE_DIR)\nshutil.copy(__file__, SAVE_DIR)  # Save a copy of this script so that we can recreate stuff\n\n# The size of the image\nIMAGE_WIDTH = 2000\nIMAGE_HEIGHT = 2000\n# Create a new image\nCURRENT_IMAGE = Image.new('RGB', (IMAGE_WIDTH, IMAGE_HEIGHT), color=random_grey())\nDRAW = ImageDraw.Draw(CURRENT_IMAGE)\n\n# Missing: initial setup\ncircles = []\nfor _ in range(100):\n    rad = randint(10, 30)\n    #x = randint(2*rad, IMAGE_WIDTH-2*rad)\n    #y = randint(2*rad, IMAGE_HEIGHT-2*rad)\n    x = IMAGE_WIDTH/2\n    y = IMAGE_HEIGHT/2\n    circles.append(Circle(rad, (x, y)))\n\nfor c in circles:\n    while c.active:\n        c.draw()\n        c.step()\n    c.really_draw()\n\nCURRENT_IMAGE.show()\nCURRENT_IMAGE.save(os.path.join(SAVE_DIR, \"{}.png\".format(\"final\")))","sub_path":"2018-05-14 23.45.01/circles.py","file_name":"circles.py","file_ext":"py","file_size_in_byte":5705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"645497635","text":"# Copyright (C) 2010,2011 Linaro Limited\n#\n# Author: Zygmunt Krynicki <zygmunt.krynicki@linaro.org>\n#\n# This file is part of lava-dashboard-tool.\n#\n# lava-dashboard-tool is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser General Public License version 3\n# as published by the Free Software Foundation\n#\n# lava-dashboard-tool 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 Lesser General Public License\n# along with lava-dashboard-tool.  If not, see <http://www.gnu.org/licenses/>.\n\n\"\"\"\nUnit tests for the launch_control.commands package\n\"\"\"\n\nfrom unittest import TestCase\n\nfrom lava_dashboard_tool.commands import XMLRPCCommand\n\n\nclass XMLRPCCommandTestCase(TestCase):\n\n    def test_construct_xml_rpc_url_preserves_path(self):\n        self.assertEqual(\n            XMLRPCCommand._construct_xml_rpc_url(\"http://domain/path\"),\n            \"http://domain/path/xml-rpc/\")\n        self.assertEqual(\n            XMLRPCCommand._construct_xml_rpc_url(\"http://domain/path/\"),\n            \"http://domain/path/xml-rpc/\")\n\n    def test_construct_xml_rpc_url_adds_proper_suffix(self):\n        self.assertEqual(\n            XMLRPCCommand._construct_xml_rpc_url(\"http://domain/\"),\n            \"http://domain/xml-rpc/\")\n        self.assertEqual(\n            XMLRPCCommand._construct_xml_rpc_url(\"http://domain\"),\n            \"http://domain/xml-rpc/\")\n","sub_path":"lava_dashboard_tool/tests/test_commands.py","file_name":"test_commands.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"318074611","text":"# coding=utf-8\nfrom __future__ import division, absolute_import, print_function, unicode_literals\nimport unittest\nfrom .core import get_app\n\nURLS = [\n    '/',\n    '/about',\n    '/cfp',\n    '/login',\n    '/metrics'\n]\n\n\nclass BasicTestCase(unittest.TestCase):\n\n    def setUp(self):\n        self.client, self.app, self.db = get_app()\n\n    def test_url(self):\n        for url in URLS:\n            rv = self.client.get(url)\n            assert rv.status_code == 200, \"Fetching %s results in HTTP 200\" % url\n","sub_path":"tests/test_basic.py","file_name":"test_basic.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"328167833","text":"# IMPORTS ####################################################\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nplt.rcParams.update({'figure.max_open_warning': 0})\n\nimport pandas as pd\nimport seaborn as sns\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom IPython import display\nfrom sklearn import metrics\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom torch.utils.data import TensorDataset\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\nfrom sklearn.cluster import KMeans\n\nfrom sklearn import metrics\nfrom sklearn.preprocessing import LabelBinarizer\nimport statistics\nfrom statistics import mean\nfrom scipy.stats import entropy\n\nimport pdb\n\nimport argparse\n\ntorch.manual_seed(1)\nnp.random.seed(7)\nsns.set(style=\"white\", palette=\"muted\", color_codes=True, context=\"talk\")\n\nimport warnings\nfrom sklearn.exceptions import DataConversionWarning\nwarnings.filterwarnings(action='ignore', category=DataConversionWarning)\nwarnings.filterwarnings(action='ignore', category=UserWarning)\n\n# hyperparameters and parser\nlambdas = 100\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--outputfile', required=True, type=int, help='number for c1 trial')\nparser.add_argument('--n', required=True, type=int, help='number of clusters')\n\nparams = parser.parse_args()\n\nmy_n = params['n']\nmy_trial = params['outputfile']\n\nprint(f\"my_trail #: {my_trial}\")\n\n# open output file\nfile1 = open(f\"output/clustering/clustering_{my_trial}/output_c1.txt\",\"w\")\n\n# LOAD DATA ####################################################\n\ndef load_ICU_data(path, n):\n    column_names = ['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n                    'martial_status', 'occupation', 'relationship', 'race', 'sex',\n                    'capital_gain', 'capital_loss', 'hours_per_week', 'country', 'target']\n    input_data = (pd.read_csv(path, names=column_names,\n                              na_values=\"?\", sep=r'\\s*,\\s*', engine='python'))\n    \n    # targets: 1 when someone makes over 50k , otherwise 0\n    y = (input_data['target'] == '>50K').astype(int)\n\n    # x featues: including protected classes\n    X = (input_data\n         .drop(columns=['target', 'fnlwgt'])\n         .fillna('Unknown')\n         .pipe(pd.get_dummies))\n\n    # z features:\n    Z_race = X[['race_Amer-Indian-Eskimo',\n          'race_Asian-Pac-Islander',\n          'race_Black',\n          'race_Other',\n          'race_White']]\n    Z_race = Z_race.rename(columns = {'race_Amer-Indian-Eskimo':0,\n                              'race_Asian-Pac-Islander':1,\n                              'race_Black':2,\n                              'race_Other':3,\n                              'race_White':4})\n\n    Z_sex = X[['sex_Female','sex_Male']]\n    Z_sex = Z_sex.rename(columns = {'sex_Female':0,'sex_Male':1})\n\n    X = X.drop(columns = ['race_Amer-Indian-Eskimo','race_Asian-Pac-Islander','race_Black','race_Other','race_White', 'sex_Female','sex_Male'])\n\n    n_clusters = n\n    Kmean1 = KMeans(n_clusters=n_clusters)\n    Kmean1.fit(X)\n    b = np.zeros((X.shape[0],n_clusters))\n    b[np.arange(X.shape[0]),Kmean1.labels_] = 1\n    Z_c1 = pd.DataFrame(b)\n\n    n_clusters = 4\n    Kmean2 = KMeans(n_clusters=n_clusters)\n    Kmean2.fit(X)\n    c = np.zeros((X.shape[0],n_clusters))\n    c[np.arange(X.shape[0]),Kmean2.labels_] = 1\n    Z_c2 = pd.DataFrame(c)\n    \n    return X, y, Z_race, Z_sex, Z_c1, Z_c2\n\n# load ICU data set\nX, y, Z_race, Z_sex, Z_c1, Z_c2 = load_ICU_data('data/adult.data', my_n)\n\n# split into train/test set\n(X_train, X_test, y_train, y_test, _, Z_test_race, _, Z_test_sex, Z_train_c1, Z_test_c1, _, Z_test_c2) = train_test_split(X, y, Z_race, Z_sex, Z_c1, Z_c2, test_size=0.5, stratify=y, random_state=7)\n                                                                                                                         \nZ_sets = [Z_test_sex, Z_test_race, Z_test_c1, Z_test_c2]\nZ_sets_names = ['Z_test_sex', 'Z_test_race', 'Z_test_c1', 'Z_test_c2']                                                                                                                         \n\nfile1.writelines([f\"features X: {X.shape[0]} samples, {X.shape[1]} attributes \\n\",\n                  f\"targets y: {y.shape} samples \\n\",\n                  f\"sensitives Z_train_c1: {Z_train_c1.shape[0]} samples, {Z_train_c1.shape[1]} attributes \\n\",\n                  f\"sensitives Z_test_race: {Z_test_race.shape[0]} samples, {Z_test_race.shape[1]} attributes \\n\",\n                  f\"sensitives Z_test_sex: {Z_test_sex.shape[0]} samples, {Z_test_sex.shape[1]} attributes \\n\",\n                  f\"sensitives Z_test_c1: {Z_test_c1.shape[0]} samples, {Z_test_c1.shape[1]} attributes \\n\",\n                  f\"sensitives Z_test_c2: {Z_test_c2.shape[0]} samples, {Z_test_c2.shape[1]} attributes \\n\"])\n\n# standardize the data\nscaler = StandardScaler().fit(X_train)\nscale_df = lambda df, scaler: pd.DataFrame(scaler.transform(df), \n                                           columns=df.columns, index=df.index)\nX_train = X_train.pipe(scale_df, scaler) \nX_test = X_test.pipe(scale_df, scaler)\n\nclass PandasDataSet(TensorDataset):\n\n    def __init__(self, *dataframes):\n        tensors = (self._df_to_tensor(df) for df in dataframes)\n        super(PandasDataSet, self).__init__(*tensors)\n\n    def _df_to_tensor(self, df):\n        if isinstance(df, pd.Series):\n            df = df.to_frame('dummy')\n        return torch.from_numpy(df.values).float()\n\ntrain_data = PandasDataSet(X_train, y_train, Z_train_c1)\ntest_data = PandasDataSet(X_test, y_test, Z_test_c1, Z_test_c2, Z_test_race, Z_test_sex)\n\ntrain_loader = DataLoader(train_data, batch_size=32, shuffle=True, drop_last=True)\n\nfile1.writelines([f\"# training samples: {len(train_data)} \\n\",f\"# batches: {len(train_loader)} \\n\"])\nfile1.writelines(\"\\n\")\n\n# HELPER FUNCTIONS ####################################################\ndef p_rule(y_pred, protected, Z_test, threshold = 0.5):\n    p_rule_out = []\n    for i in range(Z_test.shape[1]):\n        with warnings.catch_warnings():\n            warnings.simplefilter(action='ignore', category=FutureWarning)\n            y_z_i = y_pred[np.array(protected) == i] > threshold if threshold else y_pred[np.array(protected) == i]\n            y_z_all = y_pred[np.array(protected) != i] > threshold if threshold else y_pred[np.array(protected) != i]\n        odds = y_z_i.mean() / y_z_all.mean()\n        p_rule_out.append(min(odds, 1./odds) * 100)\n    return  p_rule_out\n\ndef plot_distributions(y_true, Z_true, y_pred, name, image_name, Z_pred = None, epoch=None):\n    fig, axes = plt.subplots(figsize=(8, 5))\n    \n    protected = []\n    for _, row in Z_true.iterrows():\n        protected.append(row[row == 1].index[0])\n    \n    subplot_df = (\n        Z_true\n        .assign(protected=protected)\n        .assign(y_pred=y_pred))\n    _subplot(subplot_df, protected,\n             name, ax = axes)\n    _performance_text(fig, y_true, Z_true, y_pred, name, image_name, protected, Z_pred, epoch)\n    fig.tight_layout()\n    return fig\n\ndef _subplot(subplot_df, col, name, ax):\n    for label, df in subplot_df.groupby(col):\n        sns.kdeplot(df['y_pred'].fillna(0), ax=ax, label=df['protected'].iloc[0], shade=True)\n    ax.set_title(f'Sensitive attribute: {name}')\n    ax.set_xlim(0, 1)\n    ax.set_ylim(0, 7)\n    ax.set_yticks([])\n    ax.set_ylabel('Prediction distribution')\n    ax.set_xlabel(r'$P({{income>50K}}|z_{{{}}})$'.format(name))\n\ndef _performance_text(fig, y_test, Z_test, y_pred, name, image_name, protected, Z_pred=None, epoch=None):\n\n    file1.writelines(f\"{image_name}_{name} \\n\")\n    file1.writelines(\"\\n\")\n    \n    clf_roc_auc = metrics.roc_auc_score(y_test, y_pred)\n    clf_accuracy = metrics.accuracy_score(y_test, y_pred > 0.5) * 100\n    p_rule_out = p_rule(y_pred, protected, Z_test)\n    for i in range(Z_test.shape[1]):\n        file1.writelines(f\"Class {i}: {round(p_rule_out[i],2)} \\n\")\n        \n    file1.writelines([f\"overall_min: {round(np.array(p_rule_out).min(),2)} \\n\", f\"overall_mean: {round(np.array(p_rule_out).mean(),2)} \\n\"])\n\n    file1.writelines(\"\\n\")\n    file1.writelines([\"Classifier performance: \\n\",f\"- ROC AUC: {clf_roc_auc:.2f} \\n\", f\"- Accuracy: {clf_accuracy:.1f} \\n\"])\n    file1.writelines(\"\\n\")\n    \n    if Z_pred is not None:\n        adv_roc_auc = multiclass_roc_auc_score(Z_test, Z_pred)\n        file1.writelines([\"Adversary performance: \\n\",f\"- ROC AUC: {adv_roc_auc:.2f} \\n\"])\n        file1.writelines(\"\\n\")\n\ndef multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n    lb = LabelBinarizer()\n    lb.fit(y_test)\n    y_test = lb.transform(y_test)\n    y_pred = lb.transform(classify(y_pred))\n    return metrics.roc_auc_score(y_test, y_pred, average=average)\n\ndef classify(y_pred):\n    y_pred_out = y_pred\n    r = 0\n    for _, row in y_pred.iterrows():\n        row_max = row.max()\n        for c in range(len(row)):\n            y_pred_out.at[r,c] = (int(0) if row[c] < row_max else int(1))\n        r += 1\n    return y_pred_out\n\ndef test(image_name):\n    i = 0\n    for Z_test, Z_test_name in zip(Z_sets, Z_sets_names):\n        print(i)\n        adv = Adversary(n_features = 1, n_sensitive = Z_test.shape[1])\n        \n        with torch.no_grad():\n            pre_clf_test = clf(test_data.tensors[0])\n            pre_adv_test = adv(pre_clf_test)\n\n        y_pre_clf = pd.Series(pre_clf_test.data.numpy().ravel(),\n                              index=y_test.index)\n        y_pre_adv = pd.DataFrame(pre_adv_test.numpy())\n        \n        fig = plot_distributions(y_test, Z_test, y_pre_clf, Z_test_name, image_name, y_pre_adv)\n        fig.savefig('output/clustering/clustering_{}/{}_{}.png'.format(my_trial, image_name, Z_test_name))\n\ndef average(my_list):\n    total = 0\n    for i in my_list: total += i\n    return float(total)/float(len(my_list))\n                                                                                                                          \n                                                                                                                          \n# PRETRAIN CLASSIFIER ####################################################\n\nclass Classifier(nn.Module):\n\n    def __init__(self, n_features, n_hidden=32, p_dropout=0.2):\n        super(Classifier, self).__init__()\n        self.network = nn.Sequential(\n            nn.Linear(n_features, n_hidden),\n            nn.ReLU(),\n            nn.Dropout(p_dropout),\n            nn.Linear(n_hidden, n_hidden),\n            nn.ReLU(),\n            nn.Dropout(p_dropout),\n            nn.Linear(n_hidden, n_hidden),\n            nn.ReLU(),\n            nn.Dropout(p_dropout),\n            nn.Linear(n_hidden, 1),\n        )\n\n    def forward(self, x):\n        return torch.sigmoid(self.network(x))\n\nclf = Classifier(n_features=X.shape[1])\nclf_criterion = nn.BCELoss()\nclf_optimizer = optim.Adam(clf.parameters())\n\ndef pretrain_classifier(clf, data_loader, optimizer, criterion):\n    for x, y, _ in data_loader:\n        clf.zero_grad()\n        p_y = clf(x)\n        loss = criterion(p_y, y)\n        loss.backward()\n        optimizer.step()\n    return clf\n\nN_CLF_EPOCHS = 2\n\nfor epoch in range(N_CLF_EPOCHS):\n    clf = pretrain_classifier(clf, train_loader, clf_optimizer, clf_criterion)\n\n# PRETRAIN ADVERSARY ####################################################\n\nclass Adversary(nn.Module):\n\n    def __init__(self, n_features, n_sensitive, n_hidden=32):\n        super(Adversary, self).__init__()\n        self.network = nn.Sequential(\n            nn.Linear(n_features, n_hidden),\n            nn.ReLU(),\n            nn.Linear(n_hidden, n_hidden),\n            nn.ReLU(),\n            nn.Linear(n_hidden, n_hidden),\n            nn.ReLU(),\n            nn.Linear(n_hidden, n_sensitive),\n        )\n\n    def forward(self, x):\n        return torch.sigmoid(self.network(x)) \n\n\ndef pretrain_adversary1(adv, clf, data_loader, optimizer, criterion, lambdas):\n    for x, _, z1 in data_loader:\n        p_y = clf(x).detach()\n        adv.zero_grad()\n        p_z = adv(p_y)                                                                                                                 \n        loss = (criterion(p_z, z1) * lambdas).mean() \n        loss.backward()\n        optimizer.step()\n    return adv\n\nadv1 = Adversary(n_features = 1, n_sensitive = Z_train_c1.shape[1])\nadv_criterion = nn.BCELoss(reduce=False)\nadv1_optimizer = optim.Adam(adv1.parameters(), lr = 0.02, eps = 10e-9)\n\nN_ADV_EPOCHS = 5\n\nfor epoch in range(N_ADV_EPOCHS):\n    adv1 = pretrain_adversary1(adv1, clf, train_loader, adv1_optimizer, adv_criterion, lambdas)\n\n# TRAIN AND TEST ####################################################\n\n# before training: baseline\ntest('baseline')\n\ndef train(clf, adv1, data_loader, clf_criterion, adv_criterion,\n          clf_optimizer, adv1_optimizer, lambdas):\n    \n    # Train adversary\n    adv_losses = []\n    for x, y, z1 in data_loader:\n        p_y = clf(x)\n        adv1.zero_grad()\n        p_z1 = adv1(p_y)\n        loss_adv = (adv_criterion(p_z1, z1) * lambdas).mean()\n        adv_losses.append(loss_adv)\n        loss_adv.backward()\n        adv1_optimizer.step()\n \n    # Train classifier on a single batch\n    clf_losses = []\n    entropies = []\n    for x, y, z1 in data_loader:\n        pass\n    p_y = clf(x)\n    p_z1 = adv1(p_y)\n    for p_z1_i in p_z1:\n        entropies.append(entropy(p_z1_i.detach()))\n    clf.zero_grad()\n    loss_adv = (adv_criterion(p_z1, z1) * lambdas).mean()\n    clf_loss = clf_criterion(p_y, y) - loss_adv # minimax loss\n    clf_losses.append(clf_loss)\n    clf_loss.backward()\n    clf_optimizer.step()\n\n    return clf, adv1, average(adv_losses), average(clf_losses), average(entropies)\n\nN_EPOCH_COMBINED = 165\n\nplt_adv_loss = []\nplt_clf_loss = []\nz_entropies = []\n\nfor epoch in range(1, N_EPOCH_COMBINED):\n    \n    clf, adv1, adv_loss, clf_loss, z_entropy = train(clf, adv1, train_loader, clf_criterion, adv_criterion,\n                     clf_optimizer, adv1_optimizer, lambdas)\n\n    plt_adv_loss.append(adv_loss)\n    plt_clf_loss.append(clf_loss)\n    z_entropies.append(z_entropy)\n    \n    with torch.no_grad():\n        clf_pred = clf(test_data.tensors[0])\n        adv_pred1 = adv1(clf_pred)\n\n    print(epoch)\n \nplt.figure()\nplt.savefig(f'output/clustering/clustering_{my_trial}/entropy_fig.png')\n\nplt.figure()\nplt.savefig(f'output/clustering/clustering_{my_trial}/clf_loss_fig.png')\n\nplt.figure()\nplt.savefig(f'output/clustering/clustering_{my_trial}/adv_loss_fig.png')\n\n# after training: results\ntest('output')\n\nfile1.close()\n","sub_path":"fairness-c1-v1.py","file_name":"fairness-c1-v1.py","file_ext":"py","file_size_in_byte":14538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"211371975","text":"from queue import Queue\nfrom GraphAL import GraphAL, DisjointSetForest\n\n\ndef topological(graph):\n    all_in_degrees = []\n    sorted_results = []\n    q = Queue()\n    for i in range(GraphAL.get_num_vertices(graph)):\n        all_in_degrees.append(GraphAL.get_vertex_in_degree(graph, i))\n    # print(\"in >>>>>>\", all_in_degrees)\n\n    for i in range(len(all_in_degrees)):\n        if all_in_degrees[i] == 0:\n            q.put(i)\n\n    while not q.empty():\n        u = q.get()\n        sorted_results.append(u)\n        for i in range(len(graph.adj_list)):\n            all_in_degrees[i] -= 1\n            if all_in_degrees[i] == 0:\n                q.put(i)\n    if len(sorted_results) != graph.get_num_vertices():\n            return None\n\n    print(\" Topological: \", sorted_results)\n\n\ndef k(graph):\n    dsf = DisjointSetForest\n\n    if graph.adj_list is None:\n        return None\n    edges = []\n    for i in range(len(graph.adj_list)):\n        temp = graph.adj_list[i]\n        while temp is not None:\n            edges.append([i, temp.item, temp.weight])\n            temp = temp.next\n    edges.sort(key=lambda tup: tup[2])\n\n    print(\" Kruskal: \", edges)\n\n\ndef main():\n    graph = GraphAL(initial_num_vertices=6, is_directed=True)\n    graph_2 = GraphAL(initial_num_vertices=4, is_directed=False)\n    # graph 1\n    graph.add_edge(0, 1, 1.0)\n    graph.add_edge(0, 4, 1.0)\n    graph.add_edge(1, 2, 1.0)\n    graph.add_edge(2, 3, 1.0)\n    graph.add_edge(4, 5, 1.0)\n    graph.add_edge(5, 2, 1.0)\n    graph.add_edge(5, 3, 1.0)\n\n    # graph_2\n    graph_2.add_edge(0, 1, 6.0)\n    graph_2.add_edge(0, 2, 5.0)\n    graph_2.add_edge(2, 3, 1.0)\n    graph_2.add_edge(1, 3, 2.0)\n\n    # Algorithms\n    # print(graph_2)\n    topological(graph)\n    k(graph_2)\n\n\nmain()\n","sub_path":"Lab6.py","file_name":"Lab6.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"549488530","text":"from modules import php\nfrom utils import DictTree\nfrom utils import torch_utils\nfrom utils import torch_wrapper as torch\nfrom . import vi_agent\n\n\nclass PHPAgent(vi_agent.VIAgent):\n    def __init__(self, env, config):\n        super().__init__(env, config)\n        self._make_phps(env)\n\n    def _make_phps(self, env):\n        php_configs = self._php_configs(env)\n        ctx_size = self.posterior_rnn_config.features_out_size * (1 + self.posterior_rnn_config.bidirectional)\n        self.phps = {}\n        for name, config in php_configs.items():\n            if self.config.teacher:\n                php_maker = config.get('teacher', php.PHP)\n            else:\n                php_maker = php.catalog\n            self.phps[name] = php_maker(config.model | DictTree(\n                sub_names=config.sub_names,\n                obs_size=env.obs_size,\n                ctx_size=ctx_size,\n            ))\n        for name, p in self.phps.items():\n            p.finalize(self.phps, env.actions)\n            if not self.config.teacher:\n                self.add_module(name, p)\n\n    def reset(self, init_arg):\n        return DictTree(stack=[DictTree(\n            name=self.root_php_name,\n            arg=init_arg,\n            cnt=torch.zeros(1, device=self._get_value(init_arg).device)),\n        ])\n\n    async def forward(self, iput):\n        stack = iput.mem_in.stack.copy()\n        ret_name = iput.ret_name\n        ret_val = iput.ret_val\n        steps = []\n        loss = []\n        log_p = []\n        log_q = []\n        step_idx = 0\n        while True:\n            top = stack[-1]\n            top_php = self.phps[top.name]\n            step_iput = DictTree(\n                is_root=(len(stack) == 1),\n                arg=self._get_value(top.arg),\n                cnt=top.cnt,\n                ret_name=ret_name,\n                ret_val=self._get_value(ret_val),\n                obs=self._get_value(iput.obs),\n            )\n            if 'ctx' in iput:\n                step_iput.ctx = iput.ctx\n                if 'mem_out' in iput:\n                    step = iput.mem_out.steps[step_idx]\n                    step_iput.sub_name = step.sub_name\n                    step_iput.sub_arg = step.sub_arg\n                    step_idx += 1\n                step_iput.act_name = iput.act_name\n                step_iput.act_arg = iput.act_arg\n            elif 'act_name' in iput:\n                step_iput.act_name = iput.act_name\n                step_iput.act_arg = iput.act_arg\n            step_oput = await top_php(step_iput)\n            steps.append(DictTree(\n                name=top.name,\n                arg=self._get_value(top.arg, teacher=False),\n                cnt=top.cnt,\n                ret_name=ret_name,\n                ret_val=self._get_value(ret_val, teacher=False),\n                sub_name=step_oput.sub_name,\n                sub_arg=step_oput.sub_arg,\n            ))\n            if 'ctx' in iput:\n                loss.extend(step_oput.loss)\n                log_p.extend(step_oput.log_p)\n                log_q.extend(step_oput.log_q)\n            if step_oput.sub_name is None:\n                # terminate php\n                assert top.cnt > 0\n                stack.pop()\n                if stack:\n                    ret_name = top.name\n                    ret_val = step_oput.sub_arg\n                else:\n                    # terminate agent\n                    act_name = None\n                    act_arg = step_oput.sub_arg\n                    break\n            elif step_oput.sub_name in self.act_names:\n                # take action\n                stack[-1] = DictTree(top, cnt=top.cnt + 1)\n                act_name = step_oput.sub_name\n                act_arg = step_oput.sub_arg\n                break\n            else:\n                # call php\n                stack[-1] = DictTree(top, cnt=top.cnt + 1)\n                ret_name = None\n                ret_val = ret_val.new_empty(0)\n                stack.append(DictTree(name=step_oput.sub_name, arg=step_oput.sub_arg, cnt=ret_val.new_zeros(1)))\n        oput = DictTree(\n            mem_out=DictTree(steps=steps, stack=stack),\n        )\n        if 'mem_out' in iput:\n            assert torch_utils.eq(iput.mem_out, oput.mem_out)\n        if 'ctx' in iput:\n            oput.loss = loss\n            oput.log_p = log_p\n            oput.log_q = log_q\n        elif 'act_name' in iput:\n            oput.error = step_oput.error\n        else:\n            oput.act_name = act_name\n            oput.act_arg = act_arg\n        return oput\n","sub_path":"agents/php_agent.py","file_name":"php_agent.py","file_ext":"py","file_size_in_byte":4484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"445737780","text":"# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Implementations of different data feeders to provide data for TF trainer.\"\"\"\n\n# TODO(ipolosukhin): Replace this module with feed-dict queue runners & queues.\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport itertools\nimport math\n\nimport numpy as np\nimport six\nfrom six.moves import xrange  # pylint: disable=redefined-builtin\n\nfrom tensorflow.python.framework import dtypes\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.platform import tf_logging as logging\n\n# pylint: disable=g-multiple-import,g-bad-import-order\nfrom .pandas_io import HAS_PANDAS, extract_pandas_data, extract_pandas_matrix, extract_pandas_labels\nfrom .dask_io import HAS_DASK, extract_dask_data, extract_dask_labels\n\n# pylint: enable=g-multiple-import,g-bad-import-order\n\n\ndef _get_in_out_shape(x_shape, y_shape, n_classes, batch_size=None):\n  \"\"\"Returns shape for input and output of the data feeder.\"\"\"\n  x_is_dict, y_is_dict = isinstance(\n      x_shape, dict), y_shape is not None and isinstance(y_shape, dict)\n  if y_is_dict and n_classes is not None:\n    assert isinstance(n_classes, dict)\n\n  if batch_size is None:\n    batch_size = list(x_shape.values())[0][0] if x_is_dict else x_shape[0]\n  elif batch_size <= 0:\n    raise ValueError('Invalid batch_size %d.' % batch_size)\n\n  if x_is_dict:\n    input_shape = {}\n    for k, v in list(x_shape.items()):\n      input_shape[k] = [batch_size] + (list(v[1:]) if len(v) > 1 else [1])\n  else:\n    x_shape = list(x_shape[1:]) if len(x_shape) > 1 else [1]\n    input_shape = [batch_size] + x_shape\n\n  if y_shape is None:\n    return input_shape, None, batch_size\n\n  def out_el_shape(out_shape, num_classes):\n    out_shape = list(out_shape[1:]) if len(out_shape) > 1 else []\n    # Skip first dimension if it is 1.\n    if out_shape and out_shape[0] == 1:\n      out_shape = out_shape[1:]\n    if num_classes is not None and num_classes > 1:\n      return [batch_size] + out_shape + [num_classes]\n    else:\n      return [batch_size] + out_shape\n\n  if not y_is_dict:\n    output_shape = out_el_shape(y_shape, n_classes)\n  else:\n    output_shape = dict([\n        (k, out_el_shape(v, n_classes[k]\n                         if n_classes is not None and k in n_classes else None))\n        for k, v in list(y_shape.items())\n    ])\n\n  return input_shape, output_shape, batch_size\n\n\ndef _data_type_filter(x, y):\n  \"\"\"Filter data types into acceptable format.\"\"\"\n  if HAS_DASK:\n    x = extract_dask_data(x)\n    if y is not None:\n      y = extract_dask_labels(y)\n  if HAS_PANDAS:\n    x = extract_pandas_data(x)\n    if y is not None:\n      y = extract_pandas_labels(y)\n  return x, y\n\n\ndef _is_iterable(x):\n  return hasattr(x, 'next') or hasattr(x, '__next__')\n\n\ndef setup_train_data_feeder(x,\n                            y,\n                            n_classes,\n                            batch_size=None,\n                            shuffle=True,\n                            epochs=None):\n  \"\"\"Create data feeder, to sample inputs from dataset.\n\n  If `x` and `y` are iterators, use `StreamingDataFeeder`.\n\n  Args:\n    x: numpy, pandas or Dask matrix or dictionary of aforementioned. Also\n      supports iterables.\n    y: numpy, pandas or Dask array or dictionary of aforementioned. Also\n      supports\n      iterables.\n    n_classes: number of classes. Must be None or same type as y. In case, `y`\n      is `dict`\n      (or iterable which returns dict) such that `n_classes[key] = n_classes for\n        y[key]`\n    batch_size: size to split data into parts. Must be >= 1.\n    shuffle: Whether to shuffle the inputs.\n    epochs: Number of epochs to run.\n\n  Returns:\n    DataFeeder object that returns training data.\n\n  Raises:\n    ValueError: if one of `x` and `y` is iterable and the other is not.\n  \"\"\"\n  x, y = _data_type_filter(x, y)\n  if HAS_DASK:\n    # pylint: disable=g-import-not-at-top\n    import dask.dataframe as dd\n    if (isinstance(x, (dd.Series, dd.DataFrame)) and\n        (y is None or isinstance(y, (dd.Series, dd.DataFrame)))):\n      data_feeder_cls = DaskDataFeeder\n    else:\n      data_feeder_cls = DataFeeder\n  else:\n    data_feeder_cls = DataFeeder\n\n  if _is_iterable(x):\n    if y is not None and not _is_iterable(y):\n      raise ValueError('Both x and y should be iterators for '\n                       'streaming learning to work.')\n    return StreamingDataFeeder(x, y, n_classes, batch_size)\n  return data_feeder_cls(\n      x, y, n_classes, batch_size, shuffle=shuffle, epochs=epochs)\n\n\ndef _batch_data(x, batch_size=None):\n  if (batch_size is not None) and (batch_size <= 0):\n    raise ValueError('Invalid batch_size %d.' % batch_size)\n\n  x_first_el = six.next(x)\n  x = itertools.chain([x_first_el], x)\n\n  chunk = dict([(k, []) for k in list(x_first_el.keys())]) if isinstance(\n      x_first_el, dict) else []\n  chunk_filled = False\n  for data in x:\n    if isinstance(data, dict):\n      for k, v in list(data.items()):\n        chunk[k].append(v)\n        if (batch_size is not None) and (len(chunk[k]) >= batch_size):\n          chunk[k] = np.matrix(chunk[k])\n          chunk_filled = True\n      if chunk_filled:\n        yield chunk\n        chunk = dict([(k, []) for k in list(x_first_el.keys())]) if isinstance(\n            x_first_el, dict) else []\n        chunk_filled = False\n    else:\n      chunk.append(data)\n      if (batch_size is not None) and (len(chunk) >= batch_size):\n        yield np.matrix(chunk)\n        chunk = []\n\n  if isinstance(x_first_el, dict):\n    for k, v in list(data.items()):\n      chunk[k] = np.matrix(chunk[k])\n    yield chunk\n  else:\n    yield np.matrix(chunk)\n\n\ndef setup_predict_data_feeder(x, batch_size=None):\n  \"\"\"Returns an iterable for feeding into predict step.\n\n  Args:\n    x: numpy, pandas, Dask array or dictionary of aforementioned. Also supports\n      iterable.\n    batch_size: Size of batches to split data into. If `None`, returns one\n      batch of full size.\n\n  Returns:\n    List or iterator (or dictionary thereof) of parts of data to predict on.\n\n  Raises:\n    ValueError: if `batch_size` <= 0.\n  \"\"\"\n  if HAS_DASK:\n    x = extract_dask_data(x)\n  if HAS_PANDAS:\n    x = extract_pandas_data(x)\n  if _is_iterable(x):\n    return _batch_data(x, batch_size)\n  if len(x.shape) == 1:\n    x = np.reshape(x, (-1, 1))\n  if batch_size is not None:\n    if batch_size <= 0:\n      raise ValueError('Invalid batch_size %d.' % batch_size)\n    n_batches = int(math.ceil(float(len(x)) / batch_size))\n    return [x[i * batch_size:(i + 1) * batch_size] for i in xrange(n_batches)]\n  return [x]\n\n\ndef setup_processor_data_feeder(x):\n  \"\"\"Sets up processor iterable.\n\n  Args:\n    x: numpy, pandas or iterable.\n\n  Returns:\n    Iterable of data to process.\n  \"\"\"\n  if HAS_PANDAS:\n    x = extract_pandas_matrix(x)\n  return x\n\n\ndef check_array(array, dtype):\n  \"\"\"Checks array on dtype and converts it if different.\n\n  Args:\n    array: Input array.\n    dtype: Expected dtype.\n\n  Returns:\n    Original array or converted.\n  \"\"\"\n  # skip check if array is instance of other classes, e.g. h5py.Dataset\n  # to avoid copying array and loading whole data into memory\n  if isinstance(array, (np.ndarray, list)):\n    array = np.array(array, dtype=dtype, order=None, copy=False)\n  return array\n\n\ndef _access(data, iloc):\n  \"\"\"Accesses an element from collection, using integer location based indexing.\n\n  Args:\n    data: array-like. The collection to access\n    iloc: `int` or `list` of `int`s. Location(s) to access in `collection`\n\n  Returns:\n    The element of `a` found at location(s) `iloc`.\n  \"\"\"\n  if HAS_PANDAS:\n    import pandas as pd  # pylint: disable=g-import-not-at-top\n    if isinstance(data, pd.Series) or isinstance(data, pd.DataFrame):\n      return data.iloc[iloc]\n  return data[iloc]\n\n\ndef _check_dtype(dtype):\n  if dtypes.as_dtype(dtype) == dtypes.float64:\n    logging.warn(\n        'float64 is not supported by many models, consider casting to float32.')\n  return dtype\n\n\nclass DataFeeder(object):\n  \"\"\"Data feeder is an example class to sample data for TF trainer.\"\"\"\n\n  def __init__(self,\n               x,\n               y,\n               n_classes,\n               batch_size=None,\n               shuffle=True,\n               random_state=None,\n               epochs=None):\n    \"\"\"Initializes a DataFeeder instance.\n\n    Args:\n      x: One feature sample which can either Nd numpy matrix of shape\n        `[n_samples, n_features, ...]` or dictionary of Nd numpy matrix.\n      y: label vector, either floats for regression or class id for\n        classification. If matrix, will consider as a sequence of labels.\n        Can be `None` for unsupervised setting. Also supports dictionary of\n        labels.\n      n_classes: Number of classes, 0 and 1 are considered regression, `None`\n        will pass through the input labels without one-hot conversion. Also, if\n        `y` is `dict`, then `n_classes` must be `dict` such that\n        `n_classes[key] = n_classes for label y[key]`, `None` otherwise.\n      batch_size: Mini-batch size to accumulate samples in one mini batch.\n      shuffle: Whether to shuffle `x`.\n      random_state: Numpy `RandomState` object to reproduce sampling.\n      epochs: Number of times to iterate over input data before raising\n        `StopIteration` exception.\n\n    Attributes:\n      x: Input features (ndarray or dictionary of ndarrays).\n      y: Input label (ndarray or dictionary of ndarrays).\n      n_classes: Number of classes (if `None`, pass through indices without\n        one-hot conversion).\n      batch_size: Mini-batch size to accumulate.\n      input_shape: Shape of the input (or dictionary of shapes).\n      output_shape: Shape of the output (or dictionary of shapes).\n      input_dtype: DType of input (or dictionary of shapes).\n      output_dtype: DType of output (or dictionary of shapes.\n    \"\"\"\n    x_is_dict, y_is_dict = isinstance(x, dict), y is not None and isinstance(\n        y, dict)\n    if isinstance(y, list):\n      y = np.array(y)\n\n    self._x = dict([(k, check_array(v, v.dtype)) for k, v in list(x.items())\n                   ]) if x_is_dict else check_array(x, x.dtype)\n    self._y = None if y is None else (\n        dict([(k, check_array(v, v.dtype)) for k, v in list(y.items())])\n        if y_is_dict else check_array(y, y.dtype))\n\n    # self.n_classes is not None means we're converting raw target indices\n    # to one-hot.\n    if n_classes is not None:\n      if not y_is_dict:\n        y_dtype = (np.int64\n                   if n_classes is not None and n_classes > 1 else np.float32)\n        self._y = (None if y is None else check_array(y, dtype=y_dtype))\n\n    self.n_classes = n_classes\n    self.max_epochs = epochs\n\n    x_shape = dict([(k, v.shape) for k, v in list(self._x.items())\n                   ]) if x_is_dict else self._x.shape\n    y_shape = dict([(k, v.shape) for k, v in list(self._y.items())\n                   ]) if y_is_dict else None if y is None else self._y.shape\n\n    self.input_shape, self.output_shape, self._batch_size = _get_in_out_shape(\n        x_shape, y_shape, n_classes, batch_size)\n\n    # Input dtype matches dtype of x.\n    self._input_dtype = (\n        dict([(k, _check_dtype(v.dtype)) for k, v in list(self._x.items())])\n        if x_is_dict else _check_dtype(self._x.dtype))\n\n    # self._output_dtype == np.float32 when y is None\n    self._output_dtype = (\n        dict([(k, _check_dtype(v.dtype)) for k, v in list(self._y.items())])\n        if y_is_dict else (\n            _check_dtype(self._y.dtype) if y is not None else np.float32))\n\n    # self.n_classes is None means we're passing in raw target indices\n    if n_classes is not None and y_is_dict:\n      for key in list(n_classes.keys()):\n        if key in self._output_dtype:\n          self._output_dtype[key] = np.float32\n\n    self._shuffle = shuffle\n    self.random_state = np.random.RandomState(\n        42) if random_state is None else random_state\n\n    num_samples = list(self._x.values())[0].shape[\n        0] if x_is_dict else self._x.shape[0]\n    if self._shuffle:\n      self.indices = self.random_state.permutation(num_samples)\n    else:\n      self.indices = np.array(range(num_samples))\n    self.offset = 0\n    self.epoch = 0\n    self._epoch_placeholder = None\n\n  @property\n  def x(self):\n    return self._x\n\n  @property\n  def y(self):\n    return self._y\n\n  @property\n  def shuffle(self):\n    return self._shuffle\n\n  @property\n  def input_dtype(self):\n    return self._input_dtype\n\n  @property\n  def output_dtype(self):\n    return self._output_dtype\n\n  @property\n  def batch_size(self):\n    return self._batch_size\n\n  def make_epoch_variable(self):\n    \"\"\"Adds a placeholder variable for the epoch to the graph.\n\n    Returns:\n      The epoch placeholder.\n    \"\"\"\n    self._epoch_placeholder = array_ops.placeholder(\n        dtypes.int32, [1], name='epoch')\n    return self._epoch_placeholder\n\n  def input_builder(self):\n    \"\"\"Builds inputs in the graph.\n\n    Returns:\n      Two placeholders for inputs and outputs.\n    \"\"\"\n\n    def get_placeholder(shape, dtype, name_prepend):\n      if shape is None:\n        return None\n      if isinstance(shape, dict):\n        placeholder = {}\n        for key in list(shape.keys()):\n          placeholder[key] = array_ops.placeholder(\n              dtypes.as_dtype(dtype[key]), [None] + shape[key][1:],\n              name=name_prepend + '_' + key)\n      else:\n        placeholder = array_ops.placeholder(\n            dtypes.as_dtype(dtype), [None] + shape[1:], name=name_prepend)\n      return placeholder\n\n    self._input_placeholder = get_placeholder(self.input_shape,\n                                              self._input_dtype, 'input')\n    self._output_placeholder = get_placeholder(self.output_shape,\n                                               self._output_dtype, 'output')\n    return self._input_placeholder, self._output_placeholder\n\n  def set_placeholders(self, input_placeholder, output_placeholder):\n    \"\"\"Sets placeholders for this data feeder.\n\n    Args:\n      input_placeholder: Placeholder for `x` variable. Should match shape\n        of the examples in the x dataset.\n      output_placeholder: Placeholder for `y` variable. Should match\n        shape of the examples in the y dataset. Can be `None`.\n    \"\"\"\n    self._input_placeholder = input_placeholder\n    self._output_placeholder = output_placeholder\n\n  def get_feed_params(self):\n    \"\"\"Function returns a `dict` with data feed params while training.\n\n    Returns:\n      A `dict` with data feed params while training.\n    \"\"\"\n    return {\n        'epoch': self.epoch,\n        'offset': self.offset,\n        'batch_size': self._batch_size\n    }\n\n  def get_feed_dict_fn(self):\n    \"\"\"Returns a function that samples data into given placeholders.\n\n    Returns:\n      A function that when called samples a random subset of batch size\n      from `x` and `y`.\n    \"\"\"\n    x_is_dict, y_is_dict = isinstance(\n        self._x, dict), self._y is not None and isinstance(self._y, dict)\n\n    # Assign input features from random indices.\n    def extract(data, indices):\n      return (np.array(_access(data, indices)).reshape((indices.shape[0], 1)) if\n              len(data.shape) == 1 else _access(data, indices))\n\n    # assign labels from random indices\n    def assign_label(data, shape, dtype, n_classes, indices):\n      shape[0] = indices.shape[0]\n      out = np.zeros(shape, dtype=dtype)\n      for i in xrange(out.shape[0]):\n        sample = indices[i]\n        # self.n_classes is None means we're passing in raw target indices\n        if n_classes is None:\n          out[i] = _access(data, sample)\n        else:\n          if n_classes > 1:\n            if len(shape) == 2:\n              out.itemset((i, int(_access(data, sample))), 1.0)\n            else:\n              for idx, value in enumerate(_access(data, sample)):\n                out.itemset(tuple([i, idx, value]), 1.0)\n          else:\n            out[i] = _access(data, sample)\n      return out\n\n    def _feed_dict_fn():\n      \"\"\"Function that samples data into given placeholders.\"\"\"\n      if self.max_epochs is not None and self.epoch + 1 > self.max_epochs:\n        raise StopIteration\n      assert self._input_placeholder is not None\n      feed_dict = {}\n      if self._epoch_placeholder is not None:\n        feed_dict[self._epoch_placeholder.name] = [self.epoch]\n\n      # Take next batch of indices.\n      x_len = list(self._x.values())[0].shape[\n          0] if x_is_dict else self._x.shape[0]\n      end = min(x_len, self.offset + self._batch_size)\n      batch_indices = self.indices[self.offset:end]\n\n      # adding input placeholder\n      feed_dict.update(\n          dict([(self._input_placeholder[k].name, extract(v, batch_indices))\n                for k, v in list(self._x.items())]) if x_is_dict else\n          {self._input_placeholder.name: extract(self._x, batch_indices)})\n\n      # move offset and reset it if necessary\n      self.offset += self._batch_size\n      if self.offset >= x_len:\n        self.indices = self.random_state.permutation(\n            x_len) if self._shuffle else np.array(range(x_len))\n        self.offset = 0\n        self.epoch += 1\n\n      # return early if there are no labels\n      if self._output_placeholder is None:\n        return feed_dict\n\n      # adding output placeholders\n      if y_is_dict:\n        for k, v in list(self._y.items()):\n          n_classes = (self.n_classes[k] if k in self.n_classes else\n                       None) if self.n_classes is not None else None\n          shape, dtype = self.output_shape[k], self._output_dtype[k]\n          feed_dict.update({\n              self._output_placeholder[k].name:\n                  assign_label(v, shape, dtype, n_classes, batch_indices)\n          })\n      else:\n        shape, dtype, n_classes = self.output_shape, self._output_dtype, self.n_classes\n        feed_dict.update({\n            self._output_placeholder.name:\n                assign_label(self._y, shape, dtype, n_classes, batch_indices)\n        })\n\n      return feed_dict\n\n    return _feed_dict_fn\n\n\nclass StreamingDataFeeder(DataFeeder):\n  \"\"\"Data feeder for TF trainer that reads data from iterator.\n\n  Streaming data feeder allows to read data as it comes it from disk or\n  somewhere else. It's custom to have this iterators rotate infinetly over\n  the dataset, to allow control of how much to learn on the trainer side.\n  \"\"\"\n\n  def __init__(self, x, y, n_classes, batch_size):\n    \"\"\"Initializes a StreamingDataFeeder instance.\n\n    Args:\n      x: iterator each element of which returns one feature sample. Sample can\n        be a Nd numpy matrix or dictionary of Nd numpy matrices.\n      y: iterator each element of which returns one label sample. Sample can be\n        a Nd numpy matrix or dictionary of Nd numpy matrices with 1 or many\n        classes regression values.\n      n_classes: indicator of how many classes the corresponding label sample\n        has for the purposes of one-hot conversion of label. In case where `y`\n        is a dictionary, `n_classes` must be dictionary (with same keys as `y`)\n        of how many classes there are in each label in `y`. If key is\n        present in `y` and missing in `n_classes`, the value is assumed `None`\n        and no one-hot conversion will be applied to the label with that key.\n      batch_size: Mini batch size to accumulate samples in one batch. If set\n        `None`, then assumes that iterator to return already batched element.\n\n    Attributes:\n      x: input features (or dictionary of input features).\n      y: input label (or dictionary of output features).\n      n_classes: number of classes.\n      batch_size: mini batch size to accumulate.\n      input_shape: shape of the input (can be dictionary depending on `x`).\n      output_shape: shape of the output (can be dictionary depending on `y`).\n      input_dtype: dtype of input (can be dictionary depending on `x`).\n      output_dtype: dtype of output (can be dictionary depending on `y`).\n    \"\"\"\n    # pylint: disable=invalid-name,super-init-not-called\n    x_first_el = six.next(x)\n    self._x = itertools.chain([x_first_el], x)\n    if y is not None:\n      y_first_el = six.next(y)\n      self._y = itertools.chain([y_first_el], y)\n    else:\n      y_first_el = None\n      self._y = None\n    self.n_classes = n_classes\n\n    x_is_dict = isinstance(x_first_el, dict)\n    y_is_dict = y is not None and isinstance(y_first_el, dict)\n    if y_is_dict and n_classes is not None:\n      assert isinstance(n_classes, dict)\n\n    # extract shapes for first_elements\n    if x_is_dict:\n      x_first_el_shape = dict(\n          [(k, [1] + list(v.shape)) for k, v in list(x_first_el.items())])\n    else:\n      x_first_el_shape = [1] + list(x_first_el.shape)\n\n    if y_is_dict:\n      y_first_el_shape = dict(\n          [(k, [1] + list(v.shape)) for k, v in list(y_first_el.items())])\n    elif y is None:\n      y_first_el_shape = None\n    else:\n      y_first_el_shape = ([1] + list(y_first_el[0].shape if isinstance(\n          y_first_el, list) else y_first_el.shape))\n\n    self.input_shape, self.output_shape, self._batch_size = _get_in_out_shape(\n        x_first_el_shape, y_first_el_shape, n_classes, batch_size)\n\n    # Input dtype of x_first_el.\n    if x_is_dict:\n      self._input_dtype = dict(\n          [(k, _check_dtype(v.dtype)) for k, v in list(x_first_el.items())])\n    else:\n      self._input_dtype = _check_dtype(x_first_el.dtype)\n\n    # Output dtype of y_first_el.\n    def check_y_dtype(el):\n      if isinstance(el, np.ndarray):\n        return el.dtype\n      elif isinstance(el, list):\n        return check_y_dtype(el[0])\n      else:\n        return _check_dtype(np.dtype(type(el)))\n\n    # Output types are floats, due to both softmaxes and regression req.\n    if n_classes is not None and (y is None or not y_is_dict) and n_classes > 0:\n      self._output_dtype = np.float32\n    elif y_is_dict:\n      self._output_dtype = dict(\n          [(k, check_y_dtype(v)) for k, v in list(y_first_el.items())])\n    elif y is None:\n      self._output_dtype = None\n    else:\n      self._output_dtype = check_y_dtype(y_first_el)\n\n  def get_feed_params(self):\n    \"\"\"Function returns a `dict` with data feed params while training.\n\n    Returns:\n      A `dict` with data feed params while training.\n    \"\"\"\n    return {'batch_size': self._batch_size}\n\n  def get_feed_dict_fn(self):\n    \"\"\"Returns a function, that will sample data and provide it to placeholders.\n\n    Returns:\n      A function that when called samples a random subset of batch size\n      from x and y.\n    \"\"\"\n    self.stopped = False\n\n    def _feed_dict_fn():\n      \"\"\"Samples data and provides it to placeholders.\n\n      Returns:\n        `dict` of input and output tensors.\n      \"\"\"\n\n      def init_array(shape, dtype):\n        \"\"\"Initialize array of given shape or dict of shapes and dtype.\"\"\"\n        if shape is None:\n          return None\n        elif isinstance(shape, dict):\n          return dict([(k, np.zeros(shape[k], dtype[k]))\n                       for k in list(shape.keys())])\n        else:\n          return np.zeros(shape, dtype=dtype)\n\n      def put_data_array(dest, index, source=None, n_classes=None):\n        \"\"\"Puts data array into container.\"\"\"\n        if source is None:\n          dest = dest[:index]\n        elif n_classes is not None and n_classes > 1:\n          if len(self.output_shape) == 2:\n            dest.itemset((index, source), 1.0)\n          else:\n            for idx, value in enumerate(source):\n              dest.itemset(tuple([index, idx, value]), 1.0)\n        else:\n          if len(dest.shape) > 1:\n            dest[index, :] = source\n          else:\n            dest[index] = source[0] if isinstance(source, list) else source\n        return dest\n\n      def put_data_array_or_dict(holder, index, data=None, n_classes=None):\n        \"\"\"Puts data array or data dictionary into container.\"\"\"\n        if holder is None:\n          return None\n        if isinstance(holder, dict):\n          if data is None:\n            data = {k: None for k in holder.keys()}\n          assert isinstance(data, dict)\n          for k in holder.keys():\n            num_classes = n_classes[k] if (n_classes is not None and\n                                           k in n_classes) else None\n            holder[k] = put_data_array(holder[k], index, data[k], num_classes)\n        else:\n          holder = put_data_array(holder, index, data, n_classes)\n        return holder\n\n      if self.stopped:\n        raise StopIteration\n\n      inp = init_array(self.input_shape, self._input_dtype)\n      out = init_array(self.output_shape, self._output_dtype)\n\n      for i in xrange(self._batch_size):\n        # Add handling when queue ends.\n        try:\n          next_inp = six.next(self._x)\n          inp = put_data_array_or_dict(inp, i, next_inp, None)\n        except StopIteration:\n          self.stopped = True\n          if i == 0:\n            raise\n          inp = put_data_array_or_dict(inp, i, None, None)\n          out = put_data_array_or_dict(out, i, None, None)\n          break\n\n        if self._y is not None:\n          next_out = six.next(self._y)\n          out = put_data_array_or_dict(out, i, next_out, self.n_classes)\n\n      # creating feed_dict\n      if isinstance(inp, dict):\n        feed_dict = dict([(self._input_placeholder[k].name, inp[k])\n                          for k in list(self._input_placeholder.keys())])\n      else:\n        feed_dict = {self._input_placeholder.name: inp}\n      if self._y is not None:\n        if isinstance(out, dict):\n          feed_dict.update(\n              dict([(self._output_placeholder[k].name, out[k])\n                    for k in list(self._output_placeholder.keys())]))\n        else:\n          feed_dict.update({self._output_placeholder.name: out})\n\n      return feed_dict\n\n    return _feed_dict_fn\n\n\nclass DaskDataFeeder(object):\n  \"\"\"Data feeder for that reads data from dask.Series and dask.DataFrame.\n\n  Numpy arrays can be serialized to disk and it's possible to do random seeks\n  into them. DaskDataFeeder will remove requirement to have full dataset in the\n  memory and still do random seeks for sampling of batches.\n  \"\"\"\n\n  def __init__(self,\n               x,\n               y,\n               n_classes,\n               batch_size,\n               shuffle=True,\n               random_state=None,\n               epochs=None):\n    \"\"\"Initializes a DaskDataFeeder instance.\n\n    Args:\n      x: iterator that returns for each element, returns features.\n      y: iterator that returns for each element, returns 1 or many classes /\n        regression values.\n      n_classes: indicator of how many classes the label has.\n      batch_size: Mini batch size to accumulate.\n      shuffle: Whether to shuffle the inputs.\n      random_state: random state for RNG. Note that it will mutate so use a\n        int value for this if you want consistent sized batches.\n      epochs: Number of epochs to run.\n\n    Attributes:\n      x: input features.\n      y: input label.\n      n_classes: number of classes.\n      batch_size: mini batch size to accumulate.\n      input_shape: shape of the input.\n      output_shape: shape of the output.\n      input_dtype: dtype of input.\n      output_dtype: dtype of output.\n\n    Raises:\n      ValueError: if `x` or `y` are `dict`, as they are not supported currently.\n    \"\"\"\n\n    if isinstance(x, dict) or isinstance(y, dict):\n      raise ValueError(\n          'DaskDataFeeder does not support dictionaries at the moment.')\n\n    # pylint: disable=invalid-name,super-init-not-called\n    import dask.dataframe as dd  # pylint: disable=g-import-not-at-top\n    # TODO(terrytangyuan): check x and y dtypes in dask_io like pandas\n    self._x = x\n    self._y = y\n    # save column names\n    self._x_columns = list(x.columns)\n    if isinstance(y.columns[0], str):\n      self._y_columns = list(y.columns)\n    else:\n      # deal with cases where two DFs have overlapped default numeric colnames\n      self._y_columns = len(self._x_columns) + 1\n      self._y = self._y.rename(columns={y.columns[0]: self._y_columns})\n\n    # TODO(terrytangyuan): deal with unsupervised cases\n    # combine into a data frame\n    self.df = dd.multi.concat([self._x, self._y], axis=1)\n    self.n_classes = n_classes\n\n    x_count = x.count().compute()[0]\n    x_shape = (x_count, len(self._x.columns))\n    y_shape = (x_count, len(self._y.columns))\n    # TODO(terrytangyuan): Add support for shuffle and epochs.\n    self._shuffle = shuffle\n    self.epochs = epochs\n    self.input_shape, self.output_shape, self._batch_size = _get_in_out_shape(\n        x_shape, y_shape, n_classes, batch_size)\n    self.sample_fraction = self._batch_size / float(x_count)\n    self._input_dtype = _check_dtype(self._x.dtypes[0])\n    self._output_dtype = _check_dtype(self._y.dtypes[self._y_columns])\n    if random_state is None:\n      self.random_state = 66\n    else:\n      self.random_state = random_state\n\n  def get_feed_params(self):\n    \"\"\"Function returns a `dict` with data feed params while training.\n\n    Returns:\n      A `dict` with data feed params while training.\n    \"\"\"\n    return {'batch_size': self._batch_size}\n\n  def get_feed_dict_fn(self, input_placeholder, output_placeholder):\n    \"\"\"Returns a function, that will sample data and provide it to placeholders.\n\n    Args:\n      input_placeholder: tf.Placeholder for input features mini batch.\n      output_placeholder: tf.Placeholder for output labels.\n\n    Returns:\n      A function that when called samples a random subset of batch size\n      from x and y.\n    \"\"\"\n\n    def _feed_dict_fn():\n      \"\"\"Samples data and provides it to placeholders.\"\"\"\n      # TODO(ipolosukhin): option for with/without replacement (dev version of\n      # dask)\n      sample = self.df.random_split(\n          [self.sample_fraction, 1 - self.sample_fraction],\n          random_state=self.random_state)\n      inp = extract_pandas_matrix(sample[0][self._x_columns].compute()).tolist()\n      out = extract_pandas_matrix(sample[0][self._y_columns].compute())\n      # convert to correct dtype\n      inp = np.array(inp, dtype=self._input_dtype)\n      # one-hot encode out for each class for cross entropy loss\n      if HAS_PANDAS:\n        import pandas as pd  # pylint: disable=g-import-not-at-top\n        if not isinstance(out, pd.Series):\n          out = out.flatten()\n      out_max = self._y.max().compute().values[0]\n      encoded_out = np.zeros((out.size, out_max + 1), dtype=self._output_dtype)\n      encoded_out[np.arange(out.size), out] = 1\n      return {input_placeholder.name: inp, output_placeholder.name: encoded_out}\n\n    return _feed_dict_fn\n","sub_path":"Tensorflow/source/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py","file_name":"data_feeder.py","file_ext":"py","file_size_in_byte":31142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"482884304","text":"\nimport os\nfrom flask import Flask\nfrom flask import render_template\napp = Flask(__name__, static_folder=\"static\")\n\n@app.route('/')\ndef index():\n  images = [x for x in os.listdir(app.static_folder)]\n  return render_template('index.html', images=images)\n  #return \"Image hosting for I/O Bot. I'm currently seeing these images:<br/><br/>{0}\".format(\", \".join(images))\n  #return page\n\nif __name__ == '__main__':\n    app.run()","sub_path":"pics.py","file_name":"pics.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"30235196","text":"import numpy as np\nimport pandas as pd\n\n\nclass Model:\n\n\n    def place_bets(self, opps, summary, inc):\n        N = len(opps)\n        min_bet = summary.iloc[0].to_dict()['Min_bet']\n        bets = np.zeros((N,3))\n        bets[np.arange(N), np.random.choice([0,1,2], N)] = min_bet \n        return pd.DataFrame(data=bets, columns=['BetH', 'BetD', 'BetA'], index=opps.index)\n\n","sub_path":"src/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"439479228","text":"#!/usr/bin/env python\n# vim: set fileencoding=utf-8 :\n# Created by Andre Anjos <andre.dos.anjos@cern.ch>\n# Sun 28 Feb 19:52:16 2010 \n\n\"\"\"A bunch of methods that will be added to the boostrap script. Please refer\nto the virtualenv homepage for the explanation on those.\n\"\"\"\n\nimport os, sys, subprocess\n\nSWURL = 'http://sw.andreanjos.org/git/simple/'\nPACKAGES = [\n    'django-rosetta',\n    ]\n\ndef after_install(options, home_dir):\n  \"\"\"After everything is installed, this function is called.\n  \n  At this point, we populate our environment with all our goods.\n  \"\"\"\n  if sys.platform == 'win32': bin = 'Scripts'\n  else: bin = 'bin'\n\n  # we first install pip, which is easier to use\n  installer = [os.path.join(home_dir, bin, 'easy_install'), '--quiet']\n  subprocess.call(installer + ['pip'])\n  \n  installer = [os.path.join(home_dir, bin, 'pip'), 'install']\n  installer.append('--find-links=%s' % SWURL)\n\n  # installs our current development package\n  subprocess.call(installer + ['--editable=.'])\n\n  # a sequence of installs\n  if options.upgrade: installer.append('--upgrade')\n  if PACKAGES: subprocess.call(installer + PACKAGES)\n\ndef extend_parser(parser):\n  \"\"\"Adds an upgrade option.\"\"\"\n  parser.add_option('-U', '--upgrade', action='store_true', dest='upgrade', \n    help='Use this if you want to upgrade instead of installing (default)')\n","sub_path":"scripts/extra_text.py","file_name":"extra_text.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"390927324","text":"import os\nimport inspect\nimport numpy as np\nimport math\nfrom NNModels.data_source import DataSource\nfrom helpers.read import readDataAndLabelsFromDiskFQ, readSelectedDataAndLabelsFromDiskFQ\nfrom helpers.data import selectCases\n\n\nclass DataLoader(DataSource):\n    TRAINING_DATA = 0\n    VALIDATION_DATA = 1\n    TESTING_DATA = 2\n    ALL_DATA = 3\n    DATA_SPLIT = [0.7, 0.9]\n    DATA_LOADER_ROOT = os.path.dirname(os.path.abspath(inspect.stack()[0][1])) + \"/../data_loaders\"\n\n    # Constructor\n\n    def __init__(self, balanced=True):\n        self._data_source = None\n        self._year = None\n        self._save_file_path = None\n        self._numpy_data_path = None\n        self._numpy_labels_path = None\n        self._headers_path = None\n        self._headers = []\n        self._data_train = None\n        self._labels_train = None\n        self._data_val = None\n        self._labels_val = None\n        self._data_test = None\n        self._labels_test = None\n        self._data_loaded = False\n        self._full_data_set = None\n        self._full_data_set_loaded = False\n        self._label_name = \"\"\n        self._balanced = balanced\n\n        if not os.path.exists(self.DATA_LOADER_ROOT):\n            os.makedirs(self.DATA_LOADER_ROOT)\n\n    # Private functions\n\n    def _package_data(self, data_array, headers_list=None, labels_array=None, combine=False):\n        # Check that if headers_list is specified, that its size matches the given data\n        if headers_list is not None:\n            assert len(headers_list) == data_array.shape[1],  \"Length mismatch\"\n        else:\n            headers_list = self._headers\n\n        ret_dict = dict()\n        for header in headers_list:\n            header_ind = headers_list.index(header)\n            ret_dict[header] = data_array[:, header_ind]\n\n        if combine:\n            ret_dict[self._label_name] = labels_array\n            return ret_dict\n        else:\n            return (ret_dict, labels_array)\n\n    def _saved_file_exists(self):\n        return os.path.isfile(self._save_file_path)\n\n    def _save_selected_indices(self, indices):\n        np.save(self._save_file_path, indices)\n\n    def _load_selected_indices(self):\n        return np.load(self._save_file_path)\n\n    # Public functions\n\n    def load_data(self, load_full=False):\n        if not self._data_loaded:\n            data_and_labels = None\n\n            # There is no save file for this loader, we need to load the data from disk and shuffle it\n            if not self._saved_file_exists():\n                [data_all, labels_all] = readDataAndLabelsFromDiskFQ(self._numpy_data_path, self._numpy_labels_path)\n\n                if self._balanced:\n                    [data, labels, selected_indices] = selectCases(data_all, labels_all, shuffle=True)\n                else:\n                    data = data_all\n                    labels = labels_all\n                    selected_indices = np.arange(0, data_all.shape[0])\n\n                # Save the selected indices so that this exact data split can be reproduced\n                self._save_selected_indices(selected_indices)\n\n                data_and_labels = np.hstack([data, labels.reshape(labels.shape[0], 1)])\n\n            # There is a save file for this loader, so we need to load it to reproduce the data split\n            else:\n                selected_indices = self._load_selected_indices()\n                data_and_labels = readSelectedDataAndLabelsFromDiskFQ(self._numpy_data_path, self._numpy_labels_path,\n                                                                      selected_indices)\n\n            # Split the data\n            num_samples = data_and_labels.shape[0]\n\n            # Training data split\n            train_split = data_and_labels[0:int(np.round(self.DATA_SPLIT[0] * num_samples)), :]\n            self._data_train = train_split[:, :-1]\n            self._labels_train = train_split[:, -1]\n\n            # Evaluation data split\n            val_split = data_and_labels[int(np.round(self.DATA_SPLIT[0] * num_samples)):int(np.round(self.DATA_SPLIT[1]\n                                                                                                     * num_samples)), :]\n            self._data_val = val_split[:, :-1]\n            self._labels_val = val_split[:, -1]\n\n            # Test data split\n            # Test data split\n            test_split = data_and_labels[int(np.round(self.DATA_SPLIT[1] * num_samples)):, :]\n            self._data_test = test_split[:, :-1]\n            self._labels_test = test_split[:, -1]\n\n            self._data_loaded = True\n\n            # Also Load the full data set if requested\n            if load_full:\n                self._full_data_set = data_and_labels\n                self._full_data_set_loaded = True\n\n    def steps_per_epoch(self, data_type, batch_size):\n        if data_type is self.TRAINING_DATA:\n            return int(math.ceil(self._data_train.shape[0] / batch_size))\n        elif data_type is self.VALIDATION_DATA:\n            return int(math.ceil(self._data_val.shape[0] / batch_size))\n        elif data_type is self.TESTING_DATA:\n            return int(math.ceil(self._data_test.shape[0] / batch_size))\n        elif data_type is self.ALL_DATA and self._full_data_set_loaded:\n            return int(math.ceil(self._full_data_set.shape[0] / batch_size))\n        else:\n            raise KeyError(\"Invalid data type\" + str(data_type))\n\n    def get_all_data(self):\n        # Format the data as expected by the prebuilt estimators:\n        #   A dictionary of arrays by column name\n\n        dict_train = self._package_data(data_array=self._data_train)\n        dict_val = self._package_data(data_array=self._data_val)\n        dict_test = self._package_data(data_array=self._data_test)\n\n        return (dict_train, self._labels_train), (dict_val, self._labels_val), (dict_test, self._labels_test)\n\n    def get_raw_data_by_type(self, data_type):\n            if data_type is self.TRAINING_DATA:\n                return self._data_train, self._labels_train\n            elif data_type is self.VALIDATION_DATA:\n                return self._data_val, self._labels_val\n            elif data_type is self.TESTING_DATA:\n                return self._data_test, self._labels_test\n            elif data_type is self.ALL_DATA and self._full_data_set_loaded:\n                return self._full_data_set, None\n            else:\n                raise KeyError(\"Invalid data type\" + str(data_type))\n\n    def get_packaged_data_by_type(self, data_type, combine=False):\n        if data_type is self.TRAINING_DATA:\n            return self._package_data(data_array=self._data_train, labels_array=self._labels_train, combine=combine)\n        elif data_type is self.VALIDATION_DATA:\n            return self._package_data(data_array=self._data_val, labels_array=self._labels_val, combine=combine)\n        elif data_type is self.TESTING_DATA:\n            return self._package_data(data_array=self._data_test, labels_array=self._labels_test, combine=combine)\n        else:\n            raise KeyError(\"Invalid data type\" + str(data_type))\n\n    def get_total_rows_for_data_type(self, data_type):\n        if data_type is self.TRAINING_DATA:\n            return self._data_train.shape[0]\n        elif data_type is self.VALIDATION_DATA:\n            return self._data_val.shape[0]\n        elif data_type is self.TESTING_DATA:\n            return self._data_test.shape[0]\n        else:\n            raise KeyError(\"Invalid data type\" + str(data_type))\n\n    def get_data_for_header(self, header):\n        if not self._full_data_set_loaded:\n            raise AttributeError(\"Full data set must be loaded\")\n\n        if header == self._label_name:\n            return self._full_data_set[:, -1]\n        else:\n            header_ind = self._headers.index(header)\n            return self._full_data_set[:, header_ind]\n\n    def get_data_name(self):\n        return self._data_source\n\n    # TF Enumerator data functions\n    def train_input_fn(self, features, labels, batch_size, epochs):\n        raise NotImplementedError(\"Not Implemented\")\n\n    def eval_input_fn(self, features, labels, batch_size):\n        raise NotImplementedError(\"Not Implemented\")\n\n    def get_feature_columns(self):\n        raise NotImplementedError(\"Not Implemented\")\n\n    def get_feature_columns_for_headers(self, header_list):\n        raise NotImplementedError(\"Not Implemented\")\n\n    def get_header_list(self):\n        if self._full_data_set_loaded:\n            return self._headers + [self._label_name]\n        else:\n            return self._headers\n\n    def get_header_list_with_label(self):\n        return self._headers + [self._label_name]\n\n    def get_label_name(self):\n        return self._label_name\n\n    def get_diagnosis_headers(self):\n        raise NotImplementedError(\"Not Implemented\")\n\n    def get_procedure_headers(self):\n        raise NotImplementedError(\"Not Implemented\")\n\n    def get_rounding_exception_columns(self):\n        raise NotImplementedError(\"Not Implemented\")\n\n    def get_raw_data(self):\n        raw_data, _ = self.get_raw_data_by_type(self.ALL_DATA)\n        return raw_data\n\n    def get_all_data_and_labels(self):\n        raw_data, _ = self.get_raw_data_by_type(self.ALL_DATA)\n\n        label_index = self._headers.index(self._label_name)\n        labels = raw_data[:, label_index]\n\n        return np.delete(raw_data, label_index, axis=1), labels\n\n    def get_indices_for_headers(self, header_list):\n        full_header_list = self.get_header_list()\n        return list(map(full_header_list.index, header_list))\n\n    def get_diagnosis_data(self, data_type, packaged=False, num_rows=None):\n        diag_headers = self.get_diagnosis_headers()\n        diag_header_indices = self.get_indices_for_headers(diag_headers)\n        raw_data, _ = self.get_raw_data_by_type(data_type)\n        raw_data = raw_data[0:num_rows, diag_header_indices]\n\n        if packaged:\n            packaged_data, _ = self._package_data(data_array=raw_data, headers_list=diag_headers)\n            return packaged_data\n        else:\n            return raw_data\n\n    def get_procedure_data(self, data_type, packaged=False, num_rows=None):\n        proc_headers = self.get_procedure_headers()\n        proc_header_indices = self.get_indices_for_headers(proc_headers)\n        raw_data, _ = self.get_raw_data_by_type(data_type)\n        raw_data = raw_data[0:num_rows, proc_header_indices]\n\n        if packaged:\n            packaged_data, _ = self._package_data(data_array=raw_data, headers_list=proc_headers)\n            return packaged_data\n        else:\n            return raw_data\n","sub_path":"NNModels/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":10528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"313458971","text":"# coding=utf-8\nimport datetime\n\nimport geopandas as gp\nimport numpy as np\nfrom shapely.geometry import Point\n\nfrom src import constants as C\n\n\n# some helper function for Bower.pred()\ndef get_distance(row):\n    a = np.array(row.geometry.coords[0])\n    b = np.array(row.cen_coords)\n    return np.linalg.norm(a - b)\n\n\ndef calc_risk(df, grid_size, now_date):\n    distance = df.apply(get_distance, axis=1) // (grid_size / 2) + 1\n    n_weeks = df[C.COL.datetime].apply(lambda x: (now_date - x).days // 7 + 1)\n    return (1 / distance * 1 / n_weeks).sum()\n\n\nclass Bower:\n    \"\"\"developed in:\n    Bowers, K.J. et al. 2004. Prospective Hot-SpottingThe Future of Crime Mapping?\n    The British journal of criminology. 44, 5 (Sep. 2004), 641–658.\n\n    Attributes\n    ----------\n    last_date: the last date of the events, set after self.fit(), used in self.has_fit()\n    events\n    \"\"\"\n\n    def __str__(self):\n        return (\n            'Bower method: weighted by distance and time, '\n            'bandwidth={}, time window={}, verbose={}'\n            ''.format(self.bw, self.tw, self.verbose))\n\n    def __init__(self, grid_size, bw=400, tw=60, verbose=0):\n        \"\"\"\n        :param grid_size: bower normalize distance by 1/2 of grid_size\n        :param bw: bandwidth, int, default = 400\n        :param tw: time window, int, default = 60, number of days in the past to be considered\n        :param verbose: level of verbosity\n        \"\"\"\n        self.bw = bw\n        self.tw = tw\n        self.verbose = verbose\n        self.grid_size = grid_size\n        # attributes set after self.fit\n        self.last_date = None\n        self.events = None\n\n    def has_fit(self):\n        return self.last_date is not None\n\n    def fit(self, x_coords, y_coords=None, last_date=None):\n        \"\"\"\n        :param x_coords: pd.Series\n            Indexed and sorted by Date, with values = coords\n\n            For compatibility with inputs containing names of coords, such as those for RTM,\n            coords can be dict. In this case, only len(coords)=1 (1 key) is allowed.\n\n        :param y_coords: not used in bower, for compatibility purpose\n\n        :param last_date: string (format='%Y-%m-%d') or DateTime, default None\n            the last date of the time window. If None, the last date of coords is used\n        \"\"\"\n\n        # for compatibility\n        if isinstance(x_coords, dict):\n            if len(x_coords) != 1: raise ValueError('input coords is dict, but len!=1')\n            if self.verbose > 0: print('coords is a dictionary, len==1, keep its value only')\n            x_coords = list(x_coords.values())[0]\n\n        if self.tw is not None:\n            if last_date is None:\n                last_date = x_coords.index.max().normalize() + datetime.timedelta(days=1, seconds=-1)\n            elif isinstance(last_date, str):\n                last_date = datetime.datetime.strptime(last_date, '%Y-%m-%d') + datetime.timedelta(seconds=-1)\n            if self.verbose > 0:\n                print('last_date = %s' % last_date)\n\n            # pandas time index slice include both begin and last date,\n            # to have a time window=tw, the difference should be tw-1\n            # the above is wrong, since we're using datetime. down to the last sec\n            begin_date = last_date - datetime.timedelta(days=self.tw)\n            x_coords = x_coords.loc[begin_date:last_date]\n            self.last_date = last_date\n\n        events = gp.GeoDataFrame(x_coords.apply(lambda x: Point(*x))).rename(\n            columns={C.COL.coords: 'geometry'}).reset_index()\n        self.events = events\n\n    def predict(self, spatial_units, now_date=None):\n        \"\"\"\n\n        :param spatial_units: assuming coords of the centers. pd.Series([coord], index=Date)\n        :param now_date: Datetime-like object, default None\n            now_date for the prediction. If None, now_date=self.last_date+1sec\n        :return: pd.Series, index=data.index, value=risk score\n        \"\"\"\n        # TODO spatial_units can be shapes other than grids\n\n        if now_date is None:\n            now_date = self.last_date + datetime.timedelta(seconds=1)\n            if self.verbose > 0:\n                print('now_date is None, using self.last_date+1sec=%s as now_date' % now_date)\n\n        # grids_center has same index as coords\n        grids = gp.GeoDataFrame(spatial_units.values)\n        grids.columns = ['cen_coords']\n        grids['geometry'] = grids.cen_coords.apply(lambda x: Point(*x))\n        grids['geometry'] = grids.buffer(self.bw)\n        joined = gp.sjoin(self.events, grids)\n        pred = joined.groupby('index_right') \\\n            .apply(lambda df: calc_risk(df, self.grid_size, now_date)) \\\n            .reindex(grids.index).fillna(0)\n        return pred\n\n    def tune(self, bw=None):\n        \"\"\"\n        Bowers' paper doesn't have bw tuning, this method is for consistency\n        \"\"\"\n        if bw is not None:\n            self.bw = bw\n\n\ndef main():\n    return\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"src/model/bsln_bower.py","file_name":"bsln_bower.py","file_ext":"py","file_size_in_byte":4978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"134459836","text":"#!/usr/bin/env python\n\"\"\"GUI for Experiments.\"\"\"\nfrom __future__ import division, print_function\n\nimport elaps.defines as defines\nimport elaps.io\nimport elaps.symbolic as symbolic\nimport elaps.signature as signature\n\nfrom elaps.qt import QCall, QJobProgress\n\nimport sys\nimport os\nimport string\nimport itertools\nfrom PyQt4 import QtGui, QtCore\nfrom PyQt4.QtCore import pyqtSlot\n\n\nclass PlayMat(QtGui.QMainWindow):\n\n    \"\"\"GUI for Experiment.\"\"\"\n\n    def __init__(self, app=None, load=None, reset=False):\n        \"\"\"Initilialize the PlayMat.\"\"\"\n        if app:\n            self.Qapp = app\n        else:\n            self.Qapp = QtGui.QApplication(sys.argv)\n            self.Qapp.viewer = None\n        self.Qapp.playmat = self\n        QtGui.QMainWindow.__init__(self)\n        self.samplers = elaps.io.load_all_samplers()\n        if not self.samplers:\n            self.alert(\"ERROR: No Samplers found!\")\n            sys.exit()\n        self.docs = {}\n        self.sigs = {}\n        self.papi_names = elaps.io.load_papinames()\n        self.last_filebase = None\n\n        # set up UI\n        self.UI_init()\n        self.hideargs = set([signature.Ld, signature.Inc, signature.Work,\n                             signature.Lwork, signature.Info])\n        if not reset:\n            try:\n                self.UI_settings_load()\n                self.UI_jobprogress.hide()\n            except:\n                pass\n\n        # set experiment\n        self.experiment = None\n        if load:\n            try:\n                self.experiment_load(load)\n            except:\n                self.alert(\"ERROR: Can't load %r\" % load)\n        if not self.experiment and not reset:\n            try:\n                self.experiment_qt_load()\n            except:\n                pass\n        if not self.experiment:\n            self.experiment_reset()\n\n        self.UI_setall()\n\n    def UI_init(self):\n        \"\"\"Initialize all GUI elements.\"\"\"\n        self.UI_setting = 1\n\n        # window\n        self.pyqtConfigure(\n            windowTitle=\"ELAPS:PlayMat\", unifiedTitleAndToolBarOnMac=True\n        )\n        self.setCorner(QtCore.Qt.TopRightCorner, QtCore.Qt.TopDockWidgetArea)\n        self.statusBar()\n\n        # DEBUG: print experiment\n        QtGui.QShortcut(\n            QtGui.QKeySequence.Print, self, lambda: print(self.experiment)\n        )\n\n        def create_menus():\n            \"\"\"Create all menus.\"\"\"\n            menu = self.menuBar()\n\n            # file\n            fileM = menu.addMenu(\"File\")\n\n            # file > submit\n            self.UI_submitA = QtGui.QAction(\n                \"Run\", self, shortcut=QtGui.QKeySequence(\"Ctrl+R\"),\n                triggered=self.on_submit\n            )\n            fileM.addAction(self.UI_submitA)\n\n            # submit last shortcut\n            QtGui.QShortcut(\n                QtGui.QKeySequence(\"Ctrl+Shift+R\"), self, self.on_submit_last\n            )\n\n            # file\n            fileM.addSeparator()\n\n            # file > reset\n            fileM.addAction(QtGui.QAction(\n                \"Reset Experiment\", self, triggered=self.on_experiment_reset\n            ))\n\n            # file > load\n            fileM.addAction(QtGui.QAction(\n                \"Load Experiment ...\", self,\n                shortcut=QtGui.QKeySequence.Open,\n                triggered=self.on_experiment_load\n            ))\n\n            # load report shortcut\n            QtGui.QShortcut(\n                QtGui.QKeySequence(\"Ctrl+Shift+O\"), self,\n                self.on_experiment_load_report\n            )\n\n            # fie > save\n            fileM.addAction(QtGui.QAction(\n                \"Save Experiment ...\", self,\n                shortcut=QtGui.QKeySequence.Save,\n                triggered=self.on_experiment_save\n            ))\n\n            # file\n            fileM.addSeparator()\n\n            fileM.addAction(QtGui.QAction(\n                \"Start Viewer\", self, triggered=self.on_viewer_start\n            ))\n\n            # view\n            self.UI_viewM = menu.addMenu(\"View\")\n\n            # view > hideargs\n            self.UI_hideargs = []\n            for desc, classes in (\n                (\"hide flags\", (signature.Flag,)),\n                (\"hide scalars\", (signature.Scalar,)),\n                (\"hide leading dimensions\", (signature.Ld, signature.Inc)),\n                (\"hide work spaces\", (signature.Work, signature.Lwork)),\n                (\"hide infos\", (signature.Info,))\n            ):\n                action = QtGui.QAction(\n                    desc, self, checkable=True, toggled=self.on_hideargs_toggle\n                )\n                action.classes = set(classes)\n                self.UI_viewM.addAction(action)\n                self.UI_hideargs.append((action, set(classes)))\n\n        def create_toolbar():\n            \"\"\"Create all toolbars.\"\"\"\n            # sampler\n            self.UI_sampler = QtGui.QComboBox()\n            self.UI_sampler.addItems(sorted(self.samplers.keys()))\n            self.UI_sampler.currentIndexChanged[str].connect(\n                self.on_sampler_change\n            )\n\n            samplerT = self.addToolBar(\"Sampler\")\n            samplerT.pyqtConfigure(movable=False, objectName=\"Sampler\")\n            samplerT.addWidget(QtGui.QLabel(\"Sampler:\"))\n            samplerT.addWidget(self.UI_sampler)\n\n            # #threads\n            self.UI_nthreads = QtGui.QComboBox()\n            self.UI_nthreads.currentIndexChanged[str].connect(\n                self.on_nthreads_change\n            )\n\n            nthreadsT = self.addToolBar(\"#threads\")\n            nthreadsT.pyqtConfigure(movable=False, objectName=\"#threads\")\n            nthreadsT.addWidget(QtGui.QLabel(\"#threads:\"))\n            nthreadsT.addWidget(self.UI_nthreads)\n\n            # submit\n            samplerT = self.addToolBar(\"Submit\")\n            samplerT.pyqtConfigure(movable=False, objectName=\"Submit\")\n\n            # spacer\n            spacer = QtGui.QWidget()\n            spacer.setSizePolicy(QtGui.QSizePolicy.Expanding,\n                                 QtGui.QSizePolicy.Expanding)\n            samplerT.addWidget(spacer)\n\n            # submit\n            self.UI_submit = QtGui.QAction(self.style().standardIcon(\n                QtGui.QStyle.SP_DialogOkButton\n            ), \"Run\", self, triggered=self.on_submit)\n            samplerT.addAction(self.UI_submit)\n\n        def create_ranges():\n            \"\"\"Create the ranges dock widget.\"\"\"\n            # checkboxes\n            self.UI_userange = QtGui.QCheckBox(\n                \" \", toggled=self.on_userange_toggle\n            )\n\n            self.UI_usesumrange = QtGui.QCheckBox(\n                \" \", toggled=self.on_usesumrange_toggle\n            )\n\n            self.UI_calls_parallel = QtGui.QCheckBox(\n                \" \", toggled=self.on_calls_parallel_toggle\n            )\n\n            # range\n            self.UI_rangevar = QtGui.QLineEdit(\n                textEdited=self.on_rangevar_change,\n                editingFinished=self.UI_range_set\n            )\n            self.UI_rangevar.setValidator(QtGui.QRegExpValidator(\n                QtCore.QRegExp(\"[a-zA-Z]+\"), self.UI_rangevar\n            ))\n            self.UI_rangevar.setFixedWidth(32)\n            self.UI_rangevals = QtGui.QLineEdit(\n                minimumWidth=32,\n                textEdited=self.on_rangevals_change,\n                editingFinished=self.UI_range_set\n            )\n\n            rangeL = QtGui.QHBoxLayout(spacing=0)\n            rangeL.setContentsMargins(0, 0, 0, 0)\n            rangeL.addWidget(QtGui.QLabel(\"for \"))\n            rangeL.addWidget(self.UI_rangevar)\n            rangeL.addWidget(QtGui.QLabel(\" = \"))\n            rangeL.addWidget(self.UI_rangevals)\n            rangeL.addWidget(QtGui.QLabel(\":\"))\n            rangeL.addStretch(1)\n            self.UI_rangeW = QtGui.QWidget()\n            self.UI_rangeW.setLayout(rangeL)\n\n            # reps\n            self.UI_nreps = QtGui.QLineEdit(\n                textEdited=self.on_nreps_change,\n                editingFinished=self.UI_nreps_set\n            )\n            # self.UI_nreps.setValidator(QtGui.QIntValidator(bottom=1))\n            self.UI_nreps.setValidator(QtGui.QIntValidator(1, 1000000, self))\n            self.UI_nreps.setFixedWidth(32)\n\n            nrepsL = QtGui.QHBoxLayout(spacing=0)\n            nrepsL.setContentsMargins(16, 4, 0, 0)\n            nrepsL.addWidget(QtGui.QLabel(\"repeat \"))\n            nrepsL.addWidget(self.UI_nreps)\n            nrepsL.addWidget(QtGui.QLabel(\" times:\"))\n            nrepsL.addStretch(1)\n            self.UI_nrepsW = QtGui.QWidget()\n            self.UI_nrepsW.setLayout(nrepsL)\n\n            # sumrange\n            self.UI_sumrange_parallel = QtGui.QComboBox()\n            self.UI_sumrange_parallel.addItems([\"sum over\", \"#omp for\"])\n            self.UI_sumrange_parallel.currentIndexChanged[int].connect(\n                self.on_sumrange_parallel_change\n            )\n            self.UI_sumrangevar = QtGui.QLineEdit(\n                textEdited=self.on_sumrangevar_change,\n                editingFinished=self.UI_sumrange_set\n            )\n            self.UI_sumrangevar.setValidator(QtGui.QRegExpValidator(\n                QtCore.QRegExp(\"[a-zA-Z]+\"), self.UI_sumrangevar\n            ))\n            self.UI_sumrangevar.setFixedWidth(32)\n            self.UI_sumrangevals = QtGui.QLineEdit(\n                minimumWidth=32,\n                textEdited=self.on_sumrangevals_change,\n                editingFinished=self.UI_sumrange_set\n            )\n\n            sumrangeL = QtGui.QHBoxLayout(spacing=0)\n            sumrangeL.setContentsMargins(32, 0, 0, 0)\n            sumrangeL.addWidget(self.UI_sumrange_parallel)\n            sumrangeL.addWidget(QtGui.QLabel(\" \"))\n            sumrangeL.addWidget(self.UI_sumrangevar)\n            sumrangeL.addWidget(QtGui.QLabel(\" = \"))\n            sumrangeL.addWidget(self.UI_sumrangevals)\n            sumrangeL.addWidget(QtGui.QLabel(\":\"))\n            sumrangeL.addStretch(1)\n            self.UI_sumrangeW = QtGui.QWidget()\n            self.UI_sumrangeW.setLayout(sumrangeL)\n\n            # calls_parallel\n            calls_parallelL = QtGui.QHBoxLayout()\n            calls_parallelL.setContentsMargins(48, 0, 0, 0)\n            calls_parallelL.addWidget(QtGui.QLabel(\"in parallel:\"))\n            calls_parallelL.addStretch(1)\n            self.UI_calls_parallelW = QtGui.QWidget()\n            self.UI_calls_parallelW.setLayout(calls_parallelL)\n\n            rangesL = QtGui.QGridLayout(spacing=0)\n            rangesL.addWidget(self.UI_userange, 0, 0)\n            rangesL.addWidget(self.UI_rangeW, 0, 1)\n            rangesL.addWidget(self.UI_nrepsW, 1, 1)\n            rangesL.addWidget(self.UI_usesumrange, 2, 0)\n            rangesL.addWidget(self.UI_sumrangeW, 2, 1)\n            rangesL.addWidget(self.UI_calls_parallel, 3, 0)\n            rangesL.addWidget(self.UI_calls_parallelW, 3, 1)\n            rangesL.setRowStretch(4, 1)\n\n            rangesW = QtGui.QWidget()\n            rangesW.setLayout(rangesL)\n\n            rangesD = QtGui.QDockWidget(\n                \"Ranges\", objectName=\"Ranges\",\n                features=QtGui.QDockWidget.DockWidgetVerticalTitleBar,\n            )\n            rangesD.setWidget(rangesW)\n\n            self.addDockWidget(QtCore.Qt.TopDockWidgetArea, rangesD)\n\n        def create_note():\n            \"\"\"Create the note input.\"\"\"\n            self.UI_note = QtGui.QTextEdit(\n                textChanged=self.on_note_change\n            )\n\n            noteD = QtGui.QDockWidget(\n                \"Note\", objectName=\"Note\",\n                features=(QtGui.QDockWidget.DockWidgetVerticalTitleBar |\n                          QtGui.QDockWidget.DockWidgetMovable)\n            )\n            noteD.setWidget(self.UI_note)\n\n            self.addDockWidget(QtCore.Qt.TopDockWidgetArea, noteD)\n\n        def create_calls():\n            \"\"\"Create the calls list and add button (central widget).\"\"\"\n            self.UI_calls = QtGui.QListWidget(\n                verticalScrollMode=QtGui.QListWidget.ScrollPerPixel,\n                selectionMode=QtGui.QListWidget.ExtendedSelection,\n                dragDropMode=QtGui.QListWidget.InternalMove,\n                contextMenuPolicy=QtCore.Qt.CustomContextMenu,\n                customContextMenuRequested=self.on_calls_rightclick\n            )\n            self.UI_calls.model().layoutChanged.connect(self.on_calls_reorder)\n\n            self.setCentralWidget(self.UI_calls)\n\n            # shortcuts\n            QtGui.QShortcut(\n                QtGui.QKeySequence.New, self.UI_calls,\n                activated=self.on_call_add\n            )\n            QtGui.QShortcut(\n                QtGui.QKeySequence.Close, self.UI_calls,\n                activated=self.on_call_remove\n            )\n\n            # context menus\n            self.UI_call_contextmenu = QtGui.QMenu()\n            self.UI_calls_contextmenu = QtGui.QMenu()\n\n            # add\n            add = QtGui.QAction(\n                \"Add call\", self, shortcut=QtGui.QKeySequence.New,\n                triggered=self.on_call_add\n\n            )\n            self.UI_call_contextmenu.addAction(add)\n            self.UI_calls_contextmenu.addAction(add)\n\n            # remove\n            self.UI_call_contextmenu.addAction(QtGui.QAction(\n                \"Remove call\", self,\n                shortcut=QtGui.QKeySequence.Close,\n                triggered=self.on_call_remove\n            ))\n\n            # clone\n            self.UI_call_contextmenu.addAction(QtGui.QAction(\n                \"Clone call\", self, triggered=self.on_call_clone\n            ))\n\n            self.UI_call_contextmenu.addSeparator()\n            self.UI_calls_contextmenu.addSeparator()\n\n            self.UI_call_contextmenu.addMenu(self.UI_viewM)\n            self.UI_calls_contextmenu.addMenu(self.UI_viewM)\n\n        def create_counters():\n            \"\"\"Create the counters widget.\"\"\"\n            countersW = QtGui.QWidget()\n            countersW.setLayout(QtGui.QVBoxLayout())\n            countersW.layout().insertStretch(100, 1)\n\n            self.UI_countersD = QtGui.QDockWidget(\n                \"PAPI counters\", objectName=\"PAPI counters\",\n                features=(QtGui.QDockWidget.DockWidgetVerticalTitleBar |\n                          QtGui.QDockWidget.DockWidgetMovable)\n            )\n            self.UI_countersD.setWidget(countersW)\n\n            self.addDockWidget(QtCore.Qt.TopDockWidgetArea, self.UI_countersD)\n            self.UI_countersD.hide()\n\n            self.UI_countersD.widgets = []\n\n        def create_style():\n            \"\"\"Set style options.\"\"\"\n            # stylesheet\n            self.setStyleSheet(\"\"\"\n                QLineEdit[invalid=\"true\"],\n                *[invalid=\"true\"] QLineEdit {\n                    background: #FFDDDD;\n                }\n                QLabel:disabled,\n                QLineEdit:disabled {\n                    color: #888888;\n                }\n            \"\"\")\n\n            palette = self.Qapp.palette()\n            dark = palette.text().color()\n            darka = palette.text().color()\n            darka.setAlpha(63)\n            # pens and brushes (for dataargs)\n            self.pens = {\n                None: QtGui.QColor(0, 0, 255, 0),\n                \"maxfront\": darka,\n                \"maxback\": QtGui.QPen(darka, 0, QtCore.Qt.DashLine),\n                \"minfront\": dark,\n                \"minback\": QtGui.QPen(dark, 0, QtCore.Qt.DashLine)\n            }\n            windowcolor = palette.window().color()\n            windowcoloralpha = palette.window().color()\n            windowcoloralpha.setAlpha(63)\n            self.brushes = {\n                \"max\": windowcoloralpha,\n                \"min\": windowcolor\n            }\n\n        create_menus()\n        create_toolbar()\n        create_ranges()\n        create_note()\n        create_calls()\n        create_counters()\n        create_style()\n\n        self.UI_jobprogress = QJobProgress(self)\n\n        self.show()\n\n        self.UI_setting -= 1\n\n    def UI_settings_load(self):\n        \"\"\"Load Qt settings.\"\"\"\n        settings = QtCore.QSettings(\"HPAC\", \"ELAPS:PlayMat\")\n        self.hideargs = eval(str(settings.value(\"hideargs\", type=str)),\n                             signature.__dict__)\n        self.UI_setting += 1\n        self.restoreGeometry(settings.value(\"geometry\",\n                                            type=QtCore.QByteArray))\n        self.restoreState(settings.value(\"windowState\",\n                                         type=QtCore.QByteArray))\n        self.UI_setting -= 1\n\n    def start(self):\n        \"\"\"Start the Mat (enter the main loop).\"\"\"\n        import signal\n        signal.signal(signal.SIGINT, self.on_console_quit)\n\n        # make sure python handles signals every 500ms\n        timer = QtCore.QTimer(timeout=lambda: None)\n        timer.start(500)\n\n        sys.exit(self.Qapp.exec_())\n\n    # utility\n    def log(self, *args):\n        \"\"\"Log a message to stdout and statusbar.\"\"\"\n        msg = \" \".join(map(str, args))\n        self.statusBar().showMessage(msg, 2000)\n        print(msg)\n\n    def alert(self, *args):\n        \"\"\"Log a message to stderr and statusbar.\"\"\"\n        msg = \" \".join(map(str, args))\n        self.statusBar().showMessage(msg)\n        print(\"\\033[31m%s\\033[0m\" % msg, file=sys.stderr)\n\n    def UI_set_invalid(self, widget, state=True):\n        \"\"\"Set a widget's \"invalid\" property.\"\"\"\n        widget.setProperty(\"invalid\", state)\n        widget.style().unpolish(widget)\n        widget.style().polish(widget)\n        widget.update()\n\n    # experiment routines\n    def experiment_set(self, ex):\n        \"\"\"Insert own/new objects into loaded experiment.\"\"\"\n        # own Sampler\n        if ex.sampler is None or ex.sampler[\"name\"] not in self.samplers:\n            sampler = self.samplers[min(self.samplers)]\n        else:\n            sampler = self.samplers[ex.sampler[\"name\"]]\n        ex.set_sampler(sampler, force=True)\n\n        # own Signatures\n        newcalls = []\n        for call in ex.calls:\n            sig = self.sig_get(call[0])\n            if sig:\n                newcalls.append(sig(*call[1:]))\n            else:\n                newcalls.append(call)\n        ex.calls = newcalls\n\n        self.experiment = ex\n\n    def experiment_reset(self):\n        \"\"\"Reset experiment to default.\"\"\"\n        self.experiment_set(elaps.io.load_experiment_string(\n            defines.default_experiment_str\n        ))\n        self.log(\"Loaded default Experiment\")\n\n    def experiment_qt_load(self):\n        \"\"\"Load Experiment from Qt setting.\"\"\"\n        ex = elaps.io.load_experiment_string(str(\n            QtCore.QSettings(\"HPAC\", \"ELAPS:PlayMat\").value(\"Experiment\",\n                                                            type=str)\n        ))\n        self.experiment_set(ex)\n        self.log(\"Loaded last Experiment\")\n\n    def experiment_load(self, filename):\n        \"\"\"Load Experiment from a file.\"\"\"\n        ex = elaps.io.load_experiment(filename)\n        self.experiment_set(ex)\n        self.log(\"Loaded Experiment from %r.\" % os.path.relpath(filename))\n\n    def experiment_write(self, filename):\n        \"\"\"Write Experiment to a file.\"\"\"\n        elaps.io.wrte_experiment(self.experiment, filename)\n        self.log(\"Written Experiment to %r.\" % os.path.relpath(filename))\n\n    def experiment_infer_update_set(self, callid=None):\n        \"\"\"Infer Ld and Lwork (if not showing).\"\"\"\n        ex = self.experiment\n        if signature.Ld in self.hideargs:\n            ex.infer_lds(callid)\n        if signature.Lwork in self.hideargs:\n            ex.infer_lworks(callid)\n        self.UI_submit_setenabled()\n        self.UI_calls_set()\n\n    def experiment_submit(self, filebase):\n        \"\"\"Submit a job.\"\"\"\n        ex = self.experiment\n        backend = ex.sampler[\"backend\"]\n        jobid = ex.submit(filebase)\n        self.last_filebase = filebase\n        self.UI_jobprogress.add_job(filebase, jobid, ex)\n        self.log(\"Submitted job for %r to %r.\" % (filebase, backend.name))\n\n    # loaders\n    def sig_get(self, routine):\n        \"\"\"(Try to) get the Signature for a routine.\"\"\"\n        if routine not in self.sigs:\n            try:\n                sig = elaps.io.load_signature(routine)\n                sig()  # try the signature\n                self.sigs[routine] = sig\n                self.log(\"Loaded Signature for %r.\" % routine)\n            except:\n                self.sigs[routine] = None\n                self.log(\"Can't load Signature for %r.\" % routine)\n        return self.sigs[routine]\n\n    def docs_get(self, routine):\n        \"\"\"(Try to) get the documentation for a routine.\"\"\"\n        if routine not in self.docs:\n            try:\n                self.docs[routine] = elaps.io.load_doc(routine)\n                self.log(\"Loaded documentation for %r.\" % routine)\n            except:\n                self.docs[routine] = None\n                self.log(\"Can't load documentation for %r.\" % routine)\n        return self.docs[routine]\n\n    # viewer\n    def viewer_start(self, *args):\n        \"\"\"Start the Viewer.\"\"\"\n        from viewer import Viewer\n        Viewer(*args, app=self.Qapp)\n\n    # UI utility\n    def UI_dialog(self, msgtype, title, text, callbacks=None):\n        \"\"\"Show a simple user dialog with multiple options.\"\"\"\n        if callbacks is None:\n            callbacks = {\"Ok\": None}\n        msgtypes = {\n            \"information\": QtGui.QMessageBox.information,\n            \"warning\": QtGui.QMessageBox.warning,\n            \"question\": QtGui.QMessageBox.question,\n            \"critical\": QtGui.QMessageBox.question\n        }\n        buttontypes = {\n            \"Ok\": QtGui.QMessageBox.Ok,\n            \"Cancel\": QtGui.QMessageBox.Cancel\n        }\n\n        callbackmap = {}\n        buttons = 0\n        for key, callback in callbacks.iteritems():\n            callbackmap[buttontypes[key]] = callback\n            buttons |= buttontypes[key]\n\n        ret = msgtypes[msgtype](self, title, text, buttons)\n        if callbackmap[ret] is not None:\n            callbackmap[ret][0](*callbackmap[ret][1])\n\n    def UI_alert(self, *args, **kwargs):\n        \"\"\"Alert a messagebox.\"\"\"\n        msg = \" \".join(map(str, args))\n        title = kwargs.get(\"title\", \"\")\n        self.UI_dialog(\"information\", title, msg)\n\n    def UI_varyactions(self, name):\n        \"\"\"Generate vary menu for Operand.\"\"\"\n        ex = self.experiment\n        data = ex.get_operand(name)\n        vary = ex.vary[name]\n\n        actions = []\n\n        withrep = QtGui.QAction(\n            \"Vary with repetitions\", self,\n            checkable=True, checked=\"rep\" in vary[\"with\"],\n            toggled=self.on_vary_with_toggle\n        )\n        withrep.name = name\n        withrep.with_ = \"rep\"\n        actions.append(withrep)\n\n        if ex.sumrange:\n            withsumrange = QtGui.QAction(\n                \"Vary with %s\" % ex.sumrange_var, self,\n                checkable=True, checked=ex.sumrange_var in vary[\"with\"],\n                toggled=self.on_vary_with_toggle\n            )\n\n            withsumrange.name = name\n            withsumrange.with_ = ex.sumrange_var\n            actions.append(withsumrange)\n\n        if not vary[\"with\"]:\n            return actions\n\n        if len(data[\"dims\"]) > 1:\n            actions.append(None)\n            alongG = QtGui.QActionGroup(self, exclusive=True)\n            for along in range(len(data[\"dims\"])):\n                if along < 3:\n                    text = \"Vary along %s (dim %d)\" % (\n                        u\"\\u2193\\u2192\\u2197\"[along],\n                        along + 1\n                    )\n                else:\n                    text = \"Vary along dim %d\" % (along + 1)\n\n                alongA = QtGui.QAction(\n                    text, self,\n                    checkable=True,\n                    checked=vary[\"along\"] == along,\n                    toggled=self.on_vary_along_toggle,\n                )\n                alongA.name = name\n                alongA.along = along\n                alongG.addAction(alongA)\n                actions.append(alongA)\n\n        actions.append(None)\n        text = \"Change vary offset (%s)\" % vary[\"offset\"]\n        offset = QtGui.QAction(\n            text, self, triggered=self.on_vary_offset\n        )\n        offset.name = name\n        actions.append(offset)\n\n        actions.append(None)\n\n        return actions\n\n    # UI setters\n    def UI_setall(self):\n        \"\"\"Set all UI elements.\"\"\"\n        # sampler\n        self.UI_hideargs_set()\n        self.UI_sampler_set()\n        self.UI_nthreads_set()\n        self.UI_range_set()\n        self.UI_nreps_set()\n        self.UI_sumrange_set()\n        self.UI_calls_parallel_set()\n        self.UI_submit_setenabled()\n        self.UI_note_set()\n        self.UI_counters_set()\n        self.UI_calls_set()\n\n    def UI_hideargs_set(self):\n        \"\"\"Set UI element: hideargs options.\"\"\"\n        self.UI_setting += 1\n        for UI_showarg, classes in self.UI_hideargs:\n            UI_showarg.setChecked(self.hideargs >= classes)\n        self.UI_setting -= 1\n\n    def UI_sampler_set(self):\n        \"\"\"Set UI element: sampler.\"\"\"\n        self.UI_setting += 1\n        self.UI_sampler.setCurrentIndex(\n            self.UI_sampler.findText(self.experiment.sampler[\"name\"])\n        )\n        self.UI_setting -= 1\n\n    def UI_nthreads_set(self):\n        \"\"\"Set UI element: #threads.\"\"\"\n        self.UI_setting += 1\n        self.UI_nthreads.clear()\n        self.UI_nthreads.addItems(\n            map(str, range(1, self.experiment.sampler[\"nt_max\"] + 1))\n        )\n        if self.experiment.range:\n            self.UI_nthreads.addItem(str(self.experiment.range_var))\n        self.UI_nthreads.setCurrentIndex(\n            self.UI_nthreads.findText(str(self.experiment.nthreads))\n        )\n        self.UI_setting -= 1\n\n    @pyqtSlot()\n    def UI_range_set(self):\n        \"\"\"Set UI element: range.\"\"\"\n        self.UI_setting += 1\n        ex = self.experiment\n        userange = bool(ex.range)\n        self.UI_userange.setChecked(userange)\n        self.UI_rangeW.setEnabled(userange)\n        if userange:\n            self.UI_rangevar.setText(str(ex.range_var))\n            self.UI_set_invalid(self.UI_rangevar, False)\n            self.UI_rangevals.setText(str(ex.range_vals))\n            self.UI_set_invalid(self.UI_rangevals, False)\n        self.UI_setting -= 1\n\n    @pyqtSlot()\n    def UI_nreps_set(self):\n        \"\"\"Set UI element: nreps.\"\"\"\n        self.UI_setting += 1\n        self.UI_nreps.setText(str(self.experiment.nreps))\n        self.UI_set_invalid(self.UI_nreps, False)\n        self.UI_setting -= 1\n\n    @pyqtSlot()\n    def UI_sumrange_set(self):\n        \"\"\"Set UI element: sumrange.\"\"\"\n        self.UI_setting += 1\n        ex = self.experiment\n        usesumrange = bool(ex.sumrange)\n        self.UI_usesumrange.setChecked(usesumrange)\n        self.UI_sumrangeW.setEnabled(usesumrange)\n        if usesumrange:\n            self.UI_sumrange_parallel.setEnabled(ex.sampler[\"omp_enabled\"])\n            self.UI_sumrange_parallel.setCurrentIndex(int(ex.sumrange_parallel))\n            self.UI_sumrangevar.setText(str(ex.sumrange_var))\n            self.UI_set_invalid(self.UI_sumrangevar, False)\n            self.UI_sumrangevals.setText(str(ex.sumrange_vals))\n            self.UI_set_invalid(self.UI_sumrangevals, False)\n        self.UI_setting -= 1\n\n    def UI_calls_parallel_set(self):\n        \"\"\"Set UI element: calls_parallel.\"\"\"\n        self.UI_setting += 1\n        ex = self.experiment\n        calls_parallel = ex.calls_parallel\n        self.UI_calls_parallel.setEnabled(ex.sampler[\"omp_enabled\"])\n        self.UI_calls_parallel.setChecked(calls_parallel)\n        self.UI_calls_parallelW.setEnabled(calls_parallel)\n        self.UI_setting -= 1\n\n    def UI_calls_set(self):\n        \"\"\"Set UI element: calls.\"\"\"\n        self.UI_setting += 1\n        for callid, call in enumerate(self.experiment.calls):\n            if callid >= self.UI_calls.count():\n                UI_call = QCall(self, callid)\n                self.UI_calls.addItem(UI_call)\n                self.UI_calls.setItemWidget(UI_call, UI_call.widget)\n            self.UI_calls.item(callid).setall()\n        while self.UI_calls.count() > len(self.experiment.calls):\n            self.UI_calls.takeItem(len(self.experiment.calls))\n        self.UI_setting -= 1\n\n    def UI_note_set(self):\n        \"\"\"Set UI element: note.\"\"\"\n        self.UI_setting += 1\n        self.UI_note.setPlainText(self.experiment.note or \"\")\n        self.UI_setting -= 1\n\n    def UI_counters_set(self):\n        \"\"\"Set UI element: counters.\"\"\"\n        self.UI_setting += 1\n        ex = self.experiment\n        if not ex.sampler[\"papi_enabled\"]:\n            self.UI_countersD.hide()\n            self.UI_setting -= 1\n            return\n\n        # PAPI is available\n        counterWs = self.UI_countersD.widgets\n        for counterid in range(ex.sampler[\"papi_counters_max\"]):\n            if counterid >= len(counterWs):\n                # create widgets as needed\n                counterW = QtGui.QComboBox(\n                    currentIndexChanged=self.on_counter_change\n                )\n                counterW.addItem(\"\", QtCore.QVariant(\"\"))\n                for i, name in enumerate(ex.sampler[\"papi_counters_avail\"]):\n                    event = self.papi_names[name]\n                    counterW.addItem(event[\"short\"], QtCore.QVariant(name))\n                    counterW.setItemData(i + 1, name + \"\\n\" + event[\"long\"],\n                                         QtCore.Qt.ToolTipRole)\n                self.UI_countersD.widget().layout().insertWidget(counterid,\n                                                                 counterW)\n                counterWs.append(counterW)\n            else:\n                counterW = counterWs[counterid]\n            # set values\n            if counterid < len(ex.papi_counters):\n                counterW.setCurrentIndex(counterW.findData(\n                    QtCore.QVariant(ex.papi_counters[counterid])\n                ))\n            else:\n                counterW.setCurrentIndex(0)\n            counterW.setVisible(counterid <= len(ex.papi_counters))\n        # remove additional widgets\n        while len(counterWs) > ex.sampler[\"papi_counters_max\"]:\n            counterWs[-1].deleteLater()\n            del counterWs[-1]\n        self.UI_countersD.show()\n        self.UI_setting -= 1\n\n    def UI_submit_setenabled(self):\n        \"\"\"En/Disable the submit Action.\"\"\"\n        try:\n            self.experiment.check_sanity(True)\n            enabled = True\n            tooltip = \"Run\"\n        except Exception as e:\n            enabled = False\n            tooltip = str(e)\n        self.UI_submitA.setEnabled(enabled)\n        self.UI_submit.setEnabled(enabled)\n        self.UI_submit.setToolTip(tooltip)\n\n    # UI events\n    def on_console_quit(self, *args):\n        \"\"\"Event: Ctrl-C from the console.\"\"\"\n        print(\"\\r\", end=\"\")\n        self.close()\n        if self.Qapp.viewer:\n            self.Qapp.viewer.close()\n        self.Qapp.quit()\n\n    def closeEvent(self, event):\n        \"\"\"Event: close main window.\"\"\"\n        settings = QtCore.QSettings(\"HPAC\", \"ELAPS:PlayMat\")\n        settings.setValue(\"geometry\", self.saveGeometry())\n        settings.setValue(\"windowState\", self.saveState())\n        settings.setValue(\"Experiment\", repr(self.experiment))\n        settings.setValue(\"hideargs\", repr(self.hideargs))\n        self.log(\"Experiment saved.\")\n\n    @pyqtSlot()\n    def on_submit(self):\n        \"\"\"Event: submit.\"\"\"\n        if self.Qapp.keyboardModifiers() & QtCore.Qt.ShiftModifier:\n            if self.last_filebase:\n                self.on_submit_last()\n                return\n\n        reportpath = elaps.io.reportpath\n        if self.last_filebase:\n            reportpath = \"%s.%s\" % (self.last_filebase,\n                                    defines.report_extension)\n        filename = QtGui.QFileDialog.getSaveFileName(\n            self, \"Generate Report\", reportpath,\n            \"*.\" + defines.report_extension\n        )\n        if not filename:\n            return\n        filebase = str(filename)\n        if filebase[-4:] == \".\" + defines.report_extension:\n            filebase = filebase[:-4]\n        self.experiment_submit(filebase)\n\n    @pyqtSlot()\n    def on_submit_last(self):\n        \"\"\"Event: resubmit last job.\"\"\"\n        if not self.last_filebase:\n            self.on_submit()\n            return\n        self.experiment_submit(self.last_filebase)\n\n    @pyqtSlot()\n    def on_experiment_reset(self):\n        \"\"\"Event: reset experiment.\"\"\"\n        self.experiment_reset()\n        self.UI_setall()\n\n    @pyqtSlot()\n    def on_experiment_load(self, report=False):\n        \"\"\"Event: load experiment.\"\"\"\n        filename = QtGui.QFileDialog.getOpenFileName(\n            self, \"Load Experiment\",\n            elaps.io.reportpath if report else elaps.io.experimentpath,\n            \" \".join(\"*.\" + ext for ext in defines.experiment_extensions)\n        )\n        if not filename:\n            return\n        self.experiment_load(str(filename))\n        self.UI_setall()\n\n    @pyqtSlot()\n    def on_experiment_load_report(self):\n        \"\"\"Event: load experiment from report.\"\"\"\n        self.on_experiment_load(True)\n\n    @pyqtSlot()\n    def on_experiment_save(self):\n        \"\"\"Event: save experiment.\"\"\"\n        filename = QtGui.QFileDialog.getSaveFileName(\n            self,\n            \"Save Setup\",\n            elaps.io.experimentpath,\n            \"*.\" + defines.experiment_extension\n        )\n        if not filename:\n            return\n        filebase = str(filename)\n        if filebase[-4:] == \".\" + defines.experiment_extension:\n            filebase = filebase[:-4]\n        filename = \"%s.%s\" % (filebase, defines.experiment_extension)\n        elaps.io.write_experiment(self.experiment, filename)\n\n    @pyqtSlot()\n    def on_viewer_start(self):\n        \"\"\"Event: start Viewer.\"\"\"\n        if not self.Qapp.viewer:\n            self.viewer_start()\n        self.Qapp.viewer.show()\n\n    # @pyqtSlot(bool)  # sender() pyqt bug\n    def on_hideargs_toggle(self, checked):\n        \"\"\"Event: toggle showarg.\"\"\"\n        if self.UI_setting:\n            return\n        classes = self.Qapp.sender().classes\n        if checked:\n            self.hideargs |= classes\n        else:\n            self.hideargs -= classes\n        self.UI_hideargs_set()\n        self.UI_calls_set()\n\n    @pyqtSlot(str)\n    def on_note_change(self):\n        \"\"\"Event: changed note.\"\"\"\n        if self.UI_setting:\n            return\n        self.experiment.note = self.UI_note.toPlainText()\n\n    @pyqtSlot(str)\n    def on_sampler_change(self, value, force=False):\n        \"\"\"Event: change sampler.\"\"\"\n        if self.UI_setting:\n            return\n        try:\n            sampler = self.samplers[str(value)]\n            self.experiment.set_sampler(sampler, force=force)\n            self.UI_setall()\n        except Exception as e:\n            self.UI_dialog(\n                \"question\", \"Incompatible sampler\",\n                \"Sampler %s is not compatible with the current experiment\\n\"\n                \"(%s)\\nAdjust the experiment?\" % (value, e),\n                {\"Ok\": (self.on_sampler_change, (value, True)), \"Cancel\": None}\n            )\n\n    @pyqtSlot(str)\n    def on_nthreads_change(self, value):\n        \"\"\"Event: change #threads.\"\"\"\n        if self.UI_setting:\n            return\n        self.experiment.set_nthreads(str(value), force=True)\n\n    @pyqtSlot(bool)\n    def on_userange_toggle(self, checked):\n        \"\"\"Event: change if range is used.\"\"\"\n        if self.UI_setting:\n            return\n        ex = self.experiment\n        if checked:\n            range_var = str(self.UI_rangevar.text())\n            range_vals = str(self.UI_rangevals.text())\n            ex.set_range((range_var, range_vals), force=True)\n        else:\n            ex.set_range(None)\n            self.UI_sumrange_set()\n        self.UI_nthreads_set()\n        self.UI_range_set()\n        self.UI_calls_set()\n        self.UI_submit_setenabled()\n\n    @pyqtSlot(str)\n    def on_rangevar_change(self, value):\n        \"\"\"Event: change range variable.\"\"\"\n        try:\n            self.experiment.set_range_var(str(value))\n            self.UI_set_invalid(self.UI_rangevar, False)\n            self.UI_nthreads_set()\n            self.UI_sumrange_set()\n            self.UI_calls_set()\n            self.UI_submit_setenabled()\n        except:\n            self.UI_set_invalid(self.UI_rangevar)\n\n    @pyqtSlot(str)\n    def on_rangevals_change(self, value):\n        \"\"\"Event: change range.\"\"\"\n        try:\n            self.experiment.set_range_vals(str(value))\n            self.UI_set_invalid(self.UI_rangevals, False)\n            self.experiment_infer_update_set()\n        except:\n            self.UI_set_invalid(self.UI_rangevals)\n\n    @pyqtSlot(str)\n    def on_nreps_change(self, value):\n        \"\"\"Event: change #repetitions.\"\"\"\n        try:\n            self.experiment.set_nreps(str(value))\n            self.UI_set_invalid(self.UI_nreps, False)\n            self.experiment_infer_update_set()\n        except:\n            self.UI_set_invalid(self.UI_nreps)\n\n    @pyqtSlot(bool)\n    def on_usesumrange_toggle(self, checked):\n        \"\"\"Event: change if sumrange is used.\"\"\"\n        if self.UI_setting:\n            return\n        ex = self.experiment\n        if checked:\n            sumrange_var = str(self.UI_sumrangevar.text())\n            sumrange_vals = str(self.UI_sumrangevals.text())\n            sumrange_parallel = bool(int(\n                self.UI_sumrange_parallel.currentIndex()\n            ))\n            ex.set_sumrange((sumrange_var, sumrange_vals), force=True)\n            ex.set_sumrange_parallel(sumrange_parallel, force=True)\n        else:\n            ex.set_sumrange(None)\n        self.UI_sumrange_set()\n        self.UI_calls_set()\n        self.UI_submit_setenabled()\n\n    @pyqtSlot(int)\n    def on_sumrange_parallel_change(self, value):\n        \"\"\"Event: change if sumrange is in parallel.\"\"\"\n        if self.UI_setting:\n            return\n        self.experiment.set_sumrange_parallel(bool(value))\n        self.UI_sumrange_set()\n\n    @pyqtSlot(str)\n    def on_sumrangevar_change(self, value):\n        \"\"\"Event: change sumrange variable.\"\"\"\n        try:\n            self.experiment.set_sumrange_var(str(value))\n            self.UI_set_invalid(self.UI_sumrangevar, False)\n            self.UI_calls_set()\n            self.UI_submit_setenabled()\n        except:\n            self.UI_set_invalid(self.UI_sumrangevar)\n\n    @pyqtSlot(str)\n    def on_sumrangevals_change(self, value):\n        \"\"\"Event: change sumrange.\"\"\"\n        try:\n            self.experiment.set_sumrange_vals(str(value))\n            self.UI_set_invalid(self.UI_sumrangevals, False)\n            self.experiment_infer_update_set()\n        except:\n            self.UI_set_invalid(self.UI_sumrangevals)\n\n    @pyqtSlot(bool)\n    def on_calls_parallel_toggle(self, value):\n        \"\"\"Event: change if calls are in parallel.\"\"\"\n        if self.UI_setting:\n            return\n        self.experiment.set_calls_parallel(bool(value))\n        self.UI_calls_parallel_set()\n\n    @pyqtSlot()\n    def on_calls_reorder(self):\n        \"\"\"Event: change call order.\"\"\"\n        calls = self.experiment.calls\n        self.experiment.calls = []\n        for idx in range(self.UI_calls.count()):\n            self.experiment.calls.append(\n                calls[self.UI_calls.item(idx).fixcallid]\n            )\n        self.UI_calls_set()\n\n    def on_routine_set(self, callid, value):\n        \"\"\"Event: Set routine value.\"\"\"\n        ex = self.experiment\n        if value not in ex.sampler[\"kernels\"]:\n            self.UI_set_invalid(self.UI_calls.item(callid).UI_args[0])\n            return\n        sig = self.sig_get(value)\n        if not sig:\n            try:\n                # prepare call\n                call = [value]\n\n                # set call\n                ex.set_call(callid, call)\n                self.UI_set_invalid(self.UI_calls.item(callid).UI_args[0],\n                                    False)\n                self.experiment_infer_update_set()\n            except:\n                self.UI_set_invalid(self.UI_calls.item(callid).UI_args[0])\n        else:\n            try:\n                # prepare call\n                call = sig()\n                operands = ex.operands\n                varnames = [name for name in string.ascii_uppercase\n                            if name not in operands]\n                for argid, arg in enumerate(sig):\n                    if isinstance(arg, (signature.Dim, signature.Ld)):\n                        call[argid] = defines.default_dim\n                    if isinstance(arg, signature.Data):\n                        call[argid] = varnames.pop(0)\n\n                # set call\n                ex.set_call(callid, call, force=True)\n                ex.infer_lds(callid)\n                ex.infer_lworks(callid)\n                self.UI_set_invalid(self.UI_calls.item(callid).UI_args[0],\n                                    False)\n                self.experiment_infer_update_set()\n            except Exception as e:\n                import traceback\n                traceback.print_exc()\n                self.UI_set_invalid(self.UI_calls.item(callid).UI_args[0])\n\n    def on_arg_set(self, callid, argid, value):\n        \"\"\"Event: Set argument value.\"\"\"\n        if self.UI_setting:\n            return\n        if argid == 0:\n            self.on_routine_set(callid, value)\n            return\n        try:\n            self.experiment.set_arg(callid, argid, value)\n            self.UI_set_invalid(self.UI_calls.item(callid).UI_args[argid],\n                                False)\n            self.experiment_infer_update_set()\n        except Exception as e:\n            if \"Incompatible operand sizes\" in str(e):\n                ret = QtGui.QMessageBox.question(\n                    self, str(e), \"Adjust dimensions automatically?\",\n                    QtGui.QMessageBox.Ok | QtGui.QMessageBox.Cancel\n                )\n                if ret == QtGui.QMessageBox.Ok:\n                    self.experiment.set_arg(callid, argid, value, force=True)\n                    self.UI_set_invalid(\n                        self.UI_calls.item(callid).UI_args[argid], False\n                    )\n                    self.experiment_infer_update_set()\n                return\n            self.UI_set_invalid(self.UI_calls.item(callid).UI_args[argid])\n            if \"Incompatible operand types:\" in str(e):\n                self.UI_alert(e)\n\n    @pyqtSlot(QtCore.QPoint)\n    def on_calls_rightclick(self, pos):\n        \"\"\"Event: right click in calls.\"\"\"\n        globalpos = self.UI_calls.viewport().mapToGlobal(pos)\n        item = self.UI_calls.itemAt(pos)\n        if item:\n            self.UI_call_contextmenu.item = item\n            self.UI_call_contextmenu.exec_(globalpos)\n        else:\n            self.UI_calls_contextmenu.exec_(globalpos)\n\n    @pyqtSlot()\n    def on_call_add(self):\n        \"\"\"Event: add call.\"\"\"\n        self.experiment.calls.append([\"\"])\n        self.UI_submit_setenabled()\n        self.UI_calls_set()\n        callid = len(self.experiment.calls) - 1\n        self.UI_calls.item(callid).UI_args[0].setFocus()\n        selected_callid = self.UI_calls.currentRow()\n        if selected_callid != -1:\n            self.UI_calls.setItemSelected(self.UI_calls.item(selected_callid),\n                                          False)\n            self.UI_calls.setCurrentRow(callid)\n\n    @pyqtSlot()\n    def on_call_remove(self):\n        \"\"\"Event: remove call.\"\"\"\n        callid = self.UI_calls.currentRow()\n        if callid == -1:\n            return\n        self.experiment.calls.pop(callid)\n        self.UI_submit_setenabled()\n        self.UI_calls_set()\n        if callid:\n            self.UI_calls.setCurrentRow(callid - 1)\n\n    @pyqtSlot()\n    def on_call_clone(self):\n        \"\"\"Event: clone call.\"\"\"\n        self.experiment.calls.append(\n            self.experiment.calls[self.UI_call_contextmenu.item.callid].copy()\n        )\n        self.UI_submit_setenabled()\n        self.UI_calls_set()\n\n    def on_infer_ld(self, callid, argid):\n        \"\"\"Event: infer ld.\"\"\"\n        self.experiment.infer_ld(callid, argid)\n        self.UI_submit_setenabled()\n        self.UI_calls_set()\n\n    @pyqtSlot()\n    def on_infer_lwork(self, callid, argid):\n        \"\"\"Event: infer lwork.\"\"\"\n        self.experiment.infer_lwork(callid, argid)\n        self.UI_submit_setenabled()\n        self.UI_calls_set()\n\n    # @pyqtSlot(bool)  # sender() pyqt bug\n    def on_vary_with_toggle(self, checked):\n        \"\"\"Event: changed vary with.\"\"\"\n        sender = self.Qapp.sender()\n        ex = self.experiment\n        name = sender.name\n        with_ = sender.with_\n        if checked:\n            ex.add_vary_with(name, with_)\n        else:\n            ex.remove_vary_with(name, with_)\n        self.experiment_infer_update_set()\n\n    # @pyqtSlot(bool)  # sender() pyqt bug\n    def on_vary_along_toggle(self, checked):\n        \"\"\"Event: changed vary along.\"\"\"\n        sender = self.Qapp.sender()\n        self.experiment.set_vary_along(sender.name, sender.along)\n        self.experiment_infer_update_set()\n\n    # @pyqtSlot()  # sender() pyqt bug\n    def on_vary_offset(self):\n        \"\"\"Event: set vary offset.\"\"\"\n        sender = self.Qapp.sender()\n        ex = self.experiment\n        name = sender.name\n        offset = ex.vary[name][\"offset\"]\n        value, ok = QtGui.QInputDialog.getText(\n            self, \"Vary offset for %s\" % name,\n            \"Vary offset for %s:\" % name, text=str(offset)\n        )\n        if not ok:\n            return\n        value = str(value) or \"0\"\n        try:\n            ex.set_vary_offset(name, value)\n            self.experiment_infer_update_set()\n        except:\n            self.UI_alert(\"Invalid offset:\\n%s\" % value)\n\n    @pyqtSlot()\n    def on_counter_change(self):\n        \"\"\"Event: changed PAPI counter.\"\"\"\n        if self.UI_setting:\n            return\n        counternames = [\n            str(widget.itemData(widget.currentIndex()).toString())\n            for widget in self.UI_countersD.widgets\n        ]\n        self.experiment.papi_counters = [name for name in counternames if name]\n        self.UI_counters_set()\n\n    def on_open_viewer(self, filename):\n        \"\"\"Event: open report in Viewer.\"\"\"\n        if not self.Qapp.viewer:\n            self.viewer_start(filename)\n            return\n        self.Qapp.viewer.report_load(filename, UI_alert=True)\n        self.Qapp.viewer.UI_setall()\n        self.Qapp.viewer.show()\n        self.Qapp.viewer.raise_()\n","sub_path":"elaps/qt/playmat.py","file_name":"playmat.py","file_ext":"py","file_size_in_byte":46271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"617381781","text":"import logging\nfrom datetime import datetime, timedelta, date\nfrom model.option_data_details import OptionDataDetails\nfrom model.option_data import OptionData\nfrom service.nse_options_service import NseOptionsService\nfrom parsers.date_parser import DatesParser\n\nlog = logging.getLogger(__name__)\n\n\nclass NSEOptionsServiceAdapter:\n\n    def __init__(self):\n        self.nse_options_service = NseOptionsService()\n        self.dates_parser = DatesParser()\n        self.put_option_data = OptionDataDetails()\n        self.call_option_data = OptionDataDetails()\n\n    # this assumes only 1 expiry per week.\n    def actual_next_weekly_expiry_date(self, option_chain, next_expiry_date):\n        proposed_next_expiry = None\n        expiry_on_thursday = False\n\n        # expiry_dates = option_chain['records']['expiryDates']\n        # expiry_dates[1] = '15-Sep-2021'\n        for expiry_date in option_chain['records']['expiryDates']:\n            if next_expiry_date == expiry_date:\n                expiry_on_thursday = True  # 90% of time it will be the case (expiry on Thursday)\n                break\n\n        # if expiry is not on Thursday, assuming it will be either on Monday, Tuesday, Wednesday or Friday\n        if not expiry_on_thursday:\n            proposed_next_expiry = self.dates_parser.date_parser_init.change_date_format_2(next_expiry_date)\n            formatted_date = proposed_next_expiry.split('-')\n            proposed_next_expiry = date(int(formatted_date[0]), int(formatted_date[1]), int(formatted_date[2]))\n\n            for expiry_date in option_chain['records']['expiryDates']:\n                expiry_date = self.dates_parser.date_parser_init.change_date_format_2(expiry_date)\n                formatted_date = expiry_date.split('-')\n                expiry_date = date(int(formatted_date[0]), int(formatted_date[1]), int(formatted_date[2]))\n                if datetime.today().date() >= expiry_date:\n                    continue\n                else:\n                    if proposed_next_expiry - timedelta(1) == expiry_date:\n                        proposed_next_expiry = expiry_date\n                        break\n                    if proposed_next_expiry - timedelta(2) == expiry_date:\n                        proposed_next_expiry = expiry_date\n                        break\n\n                    if proposed_next_expiry - timedelta(3) == expiry_date:\n                        proposed_next_expiry = expiry_date\n                        break\n                    if proposed_next_expiry + timedelta(1) == expiry_date:\n                        proposed_next_expiry = expiry_date\n                        break\n                    else:\n                        print(\"No expiry this week.\")\n                        print()\n                        exit(0)\n            proposed_next_expiry = self.dates_parser.date_parser_init.change_date_format_1(proposed_next_expiry)\n\n        if expiry_on_thursday:\n            return next_expiry_date\n        else:\n            return proposed_next_expiry\n\n    def get_reqd_fields(self, security):\n        option_chain = self.nse_options_service.get_option_chain(security)\n\n        # only for testing purposes, in real scenario API will be called as above\n        # with open(f\"../test_data/option_chain_BANKNIFTY_2021_09_11 02_36_25_562800\", \"r\") as f:\n        #     option_chain = json.loads(f.read())\n\n        next_expiry_date = self.dates_parser.proposed_next_weekly_expiry_date(datetime.today().date())\n        actual_next_weekly_expiry = self.actual_next_weekly_expiry_date(option_chain, next_expiry_date)\n        print(actual_next_weekly_expiry)\n        print()\n\n        put_option_data = dict()\n        call_option_data = dict()\n        start_time = datetime.now()\n        for option_data in option_chain['records']['data']:\n            if option_data['expiryDate'] == actual_next_weekly_expiry:\n                self.put_option_data.strike_price = option_data['strikePrice']\n                self.put_option_data.option_data = OptionData(option_data['PE'])\n                put_option_data[self.put_option_data.strike_price] = self.put_option_data.option_data\n                self.call_option_data.strike_price = option_data['strikePrice']\n                self.call_option_data.option_data = OptionData(option_data['CE'])\n                call_option_data[self.call_option_data.strike_price] = self.call_option_data.option_data\n\n        end_time = datetime.now()\n        total_time = end_time - start_time\n        print(f\"Total time: {total_time}\")\n        print()\n\n        return put_option_data, call_option_data, actual_next_weekly_expiry\n\n\n# nse_option_adapter = NSEOptionsServiceAdapter()\n# print(nse_option_adapter.get_reqd_fields(\"BANKNIFTY\"))\n","sub_path":"adapter/nse_options_service_adapter.py","file_name":"nse_options_service_adapter.py","file_ext":"py","file_size_in_byte":4695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"211315638","text":"# Time Complexity : O(n)\n# Space Complexity : O(n)\n# Did this code successfully run on Leetcode : Yes\n# Any problem you faced while coding this : No\n'''\n1. Maintain a stack containing active function call. Every time a new func call occurs update the prev log's execution time by subtracting prev end time and curr time\n2. When an end time of a func call is encountered, pop the lement from stack and update the prev logs execution time\nby subtracting prev end time and curr time + 1\n\n'''\n\nfrom collections import deque\nclass Solution:\n    def exclusiveTime(self, n: int, logs: List[str]) -> List[int]:\n        \n        if len(logs) < 2:\n            return\n        \n        log_map = [0]*n\n        \n        stack = deque()\n        prev_end = 0\n        for i in range(len(logs)):\n            \n            log_id, SE, time = tuple(logs[i].split(\":\"))\n            \n            if SE == \"start\":\n                if stack:\n                    peek_id = stack[-1]\n                    log_map[peek_id] += int(time) - prev_end\n                    \n                stack.append(int(log_id))\n                prev_end = int(time)\n            else:\n                log_map[stack.pop()] += int(time) - prev_end + 1\n                prev_end = int(time) + 1\n                \n            \n        return log_map\n                \n                \n            \n        ","sub_path":"execlusive_time_of_functions.py","file_name":"execlusive_time_of_functions.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"190724831","text":"'''\n@author:lvming\n@time:2021/6/28\n'''\nfrom time import sleep\n\n'''\n    selenium grid模块的分布式部署\n    Grid:\n       Selenium Grid。是selenium家族存在时间特别有就，但是又鲜有人使用的组件。使用有难度限制\n       是一个分布式部署的组件。是分布式形态中最为典型的一种，叫做MS的形态。\n       M表示主节点，不干活只下发任务\n       S表示从节点，只干活\n       核心环境：\n        1.M环境\n        2.S环境\n           两者环境保持一致\n           1.JDK：百度安装指南\n           2.Selenium Stand alone.jar：在selenium官网下载。\n           3.webdriver.exe：浏览器驱动，版本对应即可\n        3.部署M节点\n           1.java -jar ./Selenium Stand alone.jar -role hub -port 4000\n        4.部署S节点\n           1.java -jar ./Selenium Stand alone.jar -role node -hub http://192.168.xx.xx:4000/grid/register -port 5000\n    Remote创建grid的浏览器对象，webdriver创建selenium的浏览器对象\n    这个技术在面试的时候用来装逼，以及对于现有自动化测试框架体系技术做一个效率提升。和自动化测试精进很有帮助\n    课后练手：\n\t1.部署selenium grid\n\t2.结合多线程的用例并发，将原有的关键字驱动+Excel驱动实现一个excel一个线程的形态。\n\t3.尝试基于UnitTest来实现多线程用例并发处理。\n'''\n\n# 通过grid模块创建浏览器驱动对象\nfrom selenium.webdriver import Remote\nimport threading\n\n# 引入多线程实现自动化测试效果\ndef open(driver):\n    driver.get('http://www.baidu.com')\n    sleep(3)\n    driver.quit()\n\n# 定义所有Node节点信息:M节点是不干活的,只需要写入所有的子节点信息即可\nhotsts = {'http://172.21.194.56:4000/wd/hub':'chrome',\n          'http://172.21.194.56:5000/wd/hub':'chrome',\n          'http://172.21.194.56:6000/wd/hub':'chrome'}\n\n# 定义线程组\nth = []\n# 基于节点创建浏览器对象\nfor host,browser in hotsts.items():\n\n    driver = Remote(command_executor=host,\n                    desired_capabilities={\n                        \"platform\":\"Windows\",# mac的是OS\n                        \"browserName\":browser\n                    })\n    # 将创建好的driver对象，创建对应的线程任务。\n    th.append(threading.Thread(target=open,args=[driver]))\n\n# 启动所有的已存线程\nfor t in th:\n    t.start()\n\n\n\n","sub_path":"class11/threading_demo/demo01.py","file_name":"demo01.py","file_ext":"py","file_size_in_byte":2416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"286801","text":"import tessif.frused.namedtuples as nts\nimport numpy as np\nimport pandas as pd\n\n# gradient_costs_negative\n\n# one source, three sinks\n# due to flow costs starting flow: source to sink_1\n# due to change flow costs there will be a change in flow from source to sinks\n# difference between sink_2 and sink_3 are gradient costs\n# expected behaviour:source feed sink_1 until rise of flow costs, afterwards the sink with cheaper gradient costs will be fed\n\nmapping = {\n    'sources': {\n        'sourceA': {\n            'name': 'source1',\n            'outputs': ('electricity',),\n            'flow_rates': {'electricity': nts.MinMax(min=10, max=10)},  \t\t\t# force a flow rate of exactly 10\n        },\n    },\n    'transformers': {},\n    'sinks': {\n        'sinkA': {\n            'name': 'sink1',\n            'inputs': ('electricity',),\n            'flow_rates': {'electricity': nts.MinMax(min=0, max=10)},  \t\t\t\t# flow_rate between 0 and 10 (same as source)\n            'flow_costs': {'electricity': [0, 0, 0, 0, 0, 10, 10, 10, 10, 10]},\t\t# flow_costs force a change in terms fo the active sink\n            'gradient_costs': {\n                'electricity': nts.PositiveNegative(positive=0, negative=0), },  \t# zero gradient costs (default)\n        },\n        'sinkB': {\n            'name': 'sink2',\n            'inputs': ('electricity',),\n            'flow_rates': {'electricity': nts.MinMax(min=0, max=10)},  \t\t\t\t# flow_rate between 0 and 10 (same as source)\n            'flow_costs': {'electricity': [10, 10, 10, 10, 10, 0, 0, 0, 0, 0]},\t\t# flow_costs force a change in terms fo the active sink\n            'gradient_costs': {\n                'electricity': nts.PositiveNegative(positive=2, negative=2)},  \t\t# high gradient costs\n        },\n        'sinkC': {\n            'name': 'sink3',\n            'inputs': ('electricity',),\n            'flow_rates': {'electricity': nts.MinMax(min=0, max=10)},  \t\t\t\t# flow_rate between 0 and 10 (same as source)\n            'flow_costs': {'electricity': [10, 10, 10, 10, 10, 0, 0, 0, 0, 0]},\t\t# flow_costs force a change in terms fo the active sink\n            'gradient_costs': {\n                'electricity': nts.PositiveNegative(positive=1, negative=1)},  \t\t# low gradient costs\n        },\n    },\n    'storages': {},\n    'busses': {\n        'busA': {\n            'name': 'centralbus',\n            'inputs': ('source1.electricity',),\n            'outputs': ('sink1.electricity', 'sink2.electricity', 'sink3.electricity',),\n        },\n    },\n    'timeframe': {\n        'primary': pd.date_range('01/01/2022', periods=10, freq='H'),\n    },\n    'global_constraints': {\n        'primary': {\n            'name': 'default',\n            'emissions': float('+inf'),\n            'material': float('+inf')},\n    },\n}  # minimum working example sink - linear - gradient_costs_negative\n","sub_path":"src/tessif/examples/application/verification_scenarios/sink/linear/gradient_costs_negative.py","file_name":"gradient_costs_negative.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"568974283","text":"load(\":cc_toolchain_util.bzl\", \"absolutize_path_in_str\")\nload(\":framework.bzl\", \"ForeignCcDeps\", \"get_foreign_cc_dep\")\n\ndef create_configure_script(\n        workspace_name,\n        target_os,\n        tools,\n        flags,\n        root,\n        user_options,\n        user_vars,\n        is_debug,\n        configure_command,\n        deps,\n        inputs,\n        configure_in_place):\n    env_vars_string = get_env_vars(workspace_name, tools, flags, user_vars, deps, inputs)\n\n    script = []\n    for ext_dir in inputs.ext_build_dirs:\n        script += [\"##increment_pkg_config_path## $$EXT_BUILD_ROOT$$/\" + ext_dir.path]\n\n    script += [\"echo \\\"PKG_CONFIG_PATH=$$PKG_CONFIG_PATH$$\\\"\"]\n\n    configure_path = \"$$EXT_BUILD_ROOT$$/{root}/{configure}\".format(\n        root = root,\n        configure = configure_command,\n    )\n    if (configure_in_place):\n        script += [\"##symlink_contents_to_dir## $$EXT_BUILD_ROOT$$/{} $$BUILD_TMPDIR$$\".format(root)]\n        configure_path = \"$$BUILD_TMPDIR$$/{}\".format(configure_command)\n\n    script += [\"{env_vars} \\\"{configure}\\\" --prefix=$$BUILD_TMPDIR$$/$$INSTALL_PREFIX$$ {user_options}\".format(\n        env_vars = env_vars_string,\n        configure = configure_path,\n        user_options = \" \".join(user_options),\n    )]\n    return \"\\n\".join(script)\n\ndef create_make_script(\n        workspace_name,\n        tools,\n        flags,\n        root,\n        user_vars,\n        deps,\n        inputs,\n        make_commands,\n        prefix):\n    env_vars_string = get_env_vars(workspace_name, tools, flags, user_vars, deps, inputs)\n    script = []\n    for ext_dir in inputs.ext_build_dirs:\n        script += [\"##increment_pkg_config_path## $$EXT_BUILD_ROOT$$/\" + ext_dir.path]\n\n    script += [\"echo \\\"PKG_CONFIG_PATH=$$PKG_CONFIG_PATH$$\\\"\"]\n\n    script += [\"##symlink_contents_to_dir## $$EXT_BUILD_ROOT$$/{} $$BUILD_TMPDIR$$\".format(root)]\n    script += [\"\" + \" && \".join(make_commands)]\n    return \"\\n\".join(script)\n\ndef get_env_vars(\n        workspace_name,\n        tools,\n        flags,\n        user_vars,\n        deps,\n        inputs):\n    vars = _get_configure_variables(tools, flags, user_vars)\n    deps_flags = _define_deps_flags(deps, inputs)\n\n    vars[\"LDFLAGS\"] = vars[\"LDFLAGS\"] + deps_flags.libs\n\n    # -I flags should be put into preprocessor flags, CPPFLAGS\n    # https://www.gnu.org/software/autoconf/manual/autoconf-2.63/html_node/Preset-Output-Variables.html\n    vars[\"CPPFLAGS\"] = deps_flags.flags\n\n    return \" \".join([\"{}=\\\"{}\\\"\"\n        .format(key, _join_flags_list(workspace_name, vars[key])) for key in vars])\n\ndef _define_deps_flags(deps, inputs):\n    # It is very important to keep the order for the linker => put them into list\n    lib_dirs = []\n\n    # Here go libraries built with Bazel\n    gen_dirs_set = {}\n    for lib in inputs.libs:\n        dir_ = lib.dirname\n        if not gen_dirs_set.get(dir_):\n            gen_dirs_set[dir_] = 1\n            lib_dirs += [\"-L$$EXT_BUILD_ROOT$$/\" + dir_]\n\n    include_dirs_set = {}\n    for include_dir in inputs.include_dirs:\n        include_dirs_set[include_dir] = \"-I$$EXT_BUILD_ROOT$$/\" + include_dir\n    for header in inputs.headers:\n        include_dir = header.dirname\n        if not include_dirs_set.get(include_dir):\n            include_dirs_set[include_dir] = \"-I$$EXT_BUILD_ROOT$$/\" + include_dir\n    include_dirs = include_dirs_set.values()\n\n    # For the external libraries, we need to refer to the places where\n    # we copied the dependencies ($EXT_BUILD_DEPS/<lib_name>), because\n    # we also want configure to find that same files with pkg-config\n    # -config or other mechanics.\n    # Since we need the names of include and lib directories under\n    # the $EXT_BUILD_DEPS/<lib_name>, we ask the provider.\n    gen_dirs_set = {}\n    for dep in deps:\n        external_deps = get_foreign_cc_dep(dep)\n        if external_deps:\n            for artifact in external_deps.artifacts.to_list():\n                if not gen_dirs_set.get(artifact.gen_dir):\n                    gen_dirs_set[artifact.gen_dir] = 1\n\n                    dir_name = artifact.gen_dir.basename\n                    include_dirs += [\"-I$$EXT_BUILD_DEPS$$/{}/{}\".format(dir_name, artifact.include_dir_name)]\n                    lib_dirs += [\"-L$$EXT_BUILD_DEPS$$/{}/{}\".format(dir_name, artifact.lib_dir_name)]\n\n    return struct(\n        libs = lib_dirs,\n        flags = include_dirs,\n    )\n\n# See https://www.gnu.org/software/make/manual/html_node/Implicit-Variables.html\n_CONFIGURE_FLAGS = {\n    \"CFLAGS\": \"cc\",\n    \"CXXFLAGS\": \"cxx\",\n    \"ARFLAGS\": \"cxx_linker_static\",\n    \"ASFLAGS\": \"assemble\",\n    \"LDFLAGS\": \"cxx_linker_executable\",\n    # missing: cxx_linker_shared\n}\n\n_CONFIGURE_TOOLS = {\n    \"CC\": \"cc\",\n    \"CXX\": \"cxx\",\n    \"AR\": \"cxx_linker_static\",\n    # missing: cxx_linker_executable\n}\n\ndef _get_configure_variables(tools, flags, user_env_vars):\n    vars = {}\n\n    for flag in _CONFIGURE_FLAGS:\n        flag_value = getattr(flags, _CONFIGURE_FLAGS[flag])\n        if flag_value:\n            vars[flag] = flag_value\n\n    # Merge flags lists\n    for user_var in user_env_vars:\n        toolchain_val = vars.get(user_var)\n        if toolchain_val:\n            vars[user_var] = toolchain_val + [user_env_vars[user_var]]\n\n    tools_dict = {}\n    for tool in _CONFIGURE_TOOLS:\n        tool_value = getattr(tools, _CONFIGURE_TOOLS[tool])\n        if tool_value:\n            tools_dict[tool] = [tool_value]\n\n    # Replace tools paths if user passed other values\n    for user_var in user_env_vars:\n        toolchain_val = tools_dict.get(user_var)\n        if toolchain_val:\n            tools_dict[user_var] = [user_env_vars[user_var]]\n\n    vars.update(tools_dict)\n\n    # Put all other environment variables, passed by the user\n    for user_var in user_env_vars:\n        if not vars.get(user_var):\n            vars[user_var] = [user_env_vars[user_var]]\n\n    return vars\n\ndef _absolutize(workspace_name, text):\n    return absolutize_path_in_str(workspace_name, \"$$EXT_BUILD_ROOT$$/\", text)\n\ndef _join_flags_list(workspace_name, flags):\n    return \" \".join([_absolutize(workspace_name, flag) for flag in flags])\n","sub_path":"tools/build_defs/configure_script.bzl","file_name":"configure_script.bzl","file_ext":"bzl","file_size_in_byte":6088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"513350482","text":"\r\nimport numpy as np\r\nimport os\r\nimport time\r\n#from resnet50 import ResNet50\r\nfrom keras.preprocessing import image\r\nfrom keras.layers import GlobalAveragePooling2D, Dense, Dropout,Activation,Flatten\r\nfrom tensorflow.keras.applications import EfficientNetB5\r\nfrom tensorflow.keras import layers\r\nimport tensorflow as tf\r\n#from imagenet_utils import preprocess_input\r\n\r\nfrom keras.layers import Input\r\nfrom keras.models import Model\r\nfrom keras.utils import np_utils\r\nfrom sklearn.utils import shuffle\r\nfrom sklearn.model_selection import train_test_split\r\nfrom keras.optimizers import Adam, SGD\r\nfrom keras.callbacks import ModelCheckpoint, LearningRateScheduler, TerminateOnNaN, CSVLogger, TensorBoard\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef lr_schedule(epoch,lr):\r\n  if epoch < 25:   \r\n      return 0.01\r\n  elif epoch < 75:\r\n      return 0.00001\r\n  #else:\r\n  #  return lr * tf.math.exp(-0.1)\r\n #if epoch < 80:\r\n   # elif epoch < 100:\r\n   #     return 0.0001\r\n   # else:\r\n   #     return 0.00001\r\n\r\n\r\n# Loading the training data\r\nPATH = os.getcwd()\r\n# Define data path\r\ndata_path = PATH + '/data'\r\ndata_dir_list = os.listdir(data_path)\r\n\r\nimg_data_list=[]\r\n\r\nfor dataset in data_dir_list:\r\n\timg_list=os.listdir(data_path+'/'+ dataset)\r\n\tprint ('Loaded the images of dataset-'+'{}\\n'.format(dataset))\r\n\tfor img in img_list:\r\n\t\timg_path = data_path + '/'+ dataset + '/'+ img \r\n\t\timg = image.load_img(img_path, target_size=(456, 456))\r\n\t\tx = image.img_to_array(img)\r\n\t\tx = np.expand_dims(x, axis=0)\r\n\t\tprint('Input image shape:', x.shape)\r\n\t\timg_data_list.append(x)\r\n\r\nimg_data = np.array(img_data_list)\r\n#img_data = img_data.astype('float32')\r\nprint (img_data.shape)\r\nimg_data=np.rollaxis(img_data,1,0)\r\nprint (img_data.shape)\r\nimg_data=img_data[0]\r\nprint (img_data.shape)\r\n\r\n\r\n# Define the number of classes\r\nnum_classes = 2\r\nnum_of_samples = img_data.shape[0]\r\nlabels = np.ones((num_of_samples,),dtype='int64')\r\n\r\nlabels[0:575]=0\r\nlabels[576:1151]=1\r\n#labels[404:606]=2\r\n#labels[606:]=3\r\n\r\nnames = ['defect','no defect']\r\n# convert class labels to on-hot encoding\r\nY = np_utils.to_categorical(labels, num_classes)\r\n\r\n#Shuffle the dataset\r\nx,y = shuffle(img_data,Y, random_state=2)\r\n# Split the dataset\r\nX_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=2)\r\n\r\ntraining_datagen = ImageDataGenerator(rotation_range=10,brightness_range=(1.1, 0.9),shear_range=10.0,zoom_range=[0.8, 1.2],vertical_flip=True,fill_mode='wrap',\r\n    width_shift_range=0.2,\r\n    height_shift_range=0.2,\r\n    horizontal_flip=True)\r\n\r\nvalid_datagen = ImageDataGenerator()\r\n\r\nt_gen = training_datagen.flow(X_train, y_train, batch_size=4)\r\nv_gen = valid_datagen.flow(X_test, y_test, batch_size=4)\r\n#t_gen = training_datagen.flow(X_train, y_train)\r\n#v_gen = valid_datagen.flow(X_test, y_test)\r\n\r\nimage_input = tf.keras.Input(shape=(456, 456, 3))\r\n#model = ResNet50(weights='imagenet')\r\nmodel = EfficientNetB5(input_tensor=image_input, include_top=False,weights=\"imagenet\")\r\nmodel.summary()\r\nmodel.trainable = False\r\nlast_layer = model.output\r\nlast_layer = tf.keras.layers.GlobalAveragePooling2D(name= 'avg_pool_last')(last_layer)\r\n#x= tf.keras.layers.Flatten(name='flatten')(last_layer)\r\nlast_layer = tf.keras.layers.BatchNormalization(name = 'batch_last')(last_layer)\r\ntop_dropout_rate = 0.2\r\nlast_layer = tf.keras.layers.Dropout(top_dropout_rate, name = 'top_dropout')(last_layer)\r\nout = tf.keras.layers.Dense(num_classes, activation='softmax', name='output_layer')(last_layer)\r\n#image_input = Input(shape=(500, 500, 3))\r\ncustom_resnet_model = tf.keras.Model(inputs=image_input,outputs= out)\r\ncustom_resnet_model.summary()\r\n\r\n#for layer in custom_resnet_model.layers[:-4]:\r\n#\tlayer.trainable = False\r\n\r\n\r\n\r\n\r\n\r\n#custom_resnet_model.layers[-6].trainable\r\n#custom_resnet_model.trainable=True\r\n\r\nadam = tf.keras.optimizers.Adam()\r\n\r\ncustom_resnet_model.compile(loss='categorical_crossentropy',optimizer=adam ,metrics=['accuracy'])\r\n\r\nt=time.time()\r\ncallback = tf.keras.callbacks.LearningRateScheduler(lr_schedule)\r\n#hist = custom_resnet_model.fit_generator(generator=t_gen, steps_per_epoch=1036//4, epochs=120, verbose = 1, validation_data= v_gen, validation_steps=116//4, shuffle = True, initial_epoch=0 )\r\nhist = custom_resnet_model.fit(t_gen,steps_per_epoch=1036//4, epochs=25, callbacks=[callback],verbose = 1, validation_data= v_gen, validation_steps=116//4, shuffle = True, initial_epoch=0 )\r\n\r\ntrain_loss=hist.history['loss']\r\nval_loss=hist.history['val_loss']\r\ntrain_acc=hist.history['accuracy']\r\nval_acc=hist.history['val_accuracy']\r\n\r\nfor layer in custom_resnet_model.layers:\r\n    if not isinstance(layer, layers.BatchNormalization):\r\n        layer.trainable = True\r\n\r\n\r\ncustom_resnet_model.compile(loss='categorical_crossentropy',optimizer=adam ,metrics=['accuracy'])\r\nhist = custom_resnet_model.fit(t_gen,steps_per_epoch=1036//4, epochs=75, callbacks=[callback],verbose = 1, validation_data= v_gen, validation_steps=116//4, shuffle = True, initial_epoch=25 )\r\n\r\ntrain_loss1=hist.history['loss']\r\nval_loss1=hist.history['val_loss']\r\ntrain_acc1=hist.history['accuracy']\r\nval_acc1=hist.history['val_accuracy']\r\n\r\ntrain_loss.append(train_loss1)\r\nval_loss.append(val_loss1)\r\ntrain_acc.append(train_acc1)\r\nval_acc.append(val_acc1)\r\n\r\nprint('Training time: %s' % (t - time.time()))\r\n(loss, accuracy) = custom_resnet_model.evaluate(X_test, y_test, batch_size=4, verbose=1)\r\n\r\nprint(\"[INFO] loss={:.4f}, accuracy: {:.4f}%\".format(loss,accuracy * 100))\r\n\r\n\r\ndata = train_loss + val_loss + train_acc + val_acc \r\n# opening the csv file in 'a+' mode \r\n#print(data.dtype)\r\nfile = open('data.csv', 'w+', newline ='') \r\n  \r\n# writing the data into the file \r\nwith file:     \r\n    out = csv.writer(file)\r\n    out.writerows(map(lambda x: [x], data))\r\n    #write.writerows(data)\r\nfile.close()\r\n\r\nxc=range(0,75,1)\r\nprint(train_loss)\r\nplt.figure(1,figsize=(7,5))\r\nplt.plot(xc,train_loss)\r\nplt.plot(xc,val_loss)\r\nplt.xlabel('num of Epochs')\r\nplt.ylabel('loss')\r\nplt.title('train_loss vs test_loss')\r\nplt.grid(True)\r\nplt.legend(['train','test'])\r\n#print plt.style.available # use bmh, classic,ggplot for big pictures\r\nplt.style.use(['classic'])\r\nplt.savefig('loss_graph.png')\r\n\r\nplt.figure(2,figsize=(7,5))\r\nplt.plot(xc,train_acc)\r\nplt.plot(xc,val_acc)\r\nplt.xlabel('num of Epochs')\r\nplt.ylabel('accuracy')\r\nplt.title('train_acc vs test_acc')\r\nplt.grid(True)\r\nplt.legend(['train','test'],loc=4)\r\n#print plt.style.available # use bmh, classic,ggplot for big pictures\r\nplt.style.use(['classic'])\r\nplt.savefig('Accuracy_graph.png')","sub_path":"Encoder Models/EfficientNetB5_binary.py","file_name":"EfficientNetB5_binary.py","file_ext":"py","file_size_in_byte":6591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"116515166","text":"#!/usr/bin/python\n'''\nAuthor mark@espressive.com\nContributor: cesar@espressive.com\nTest the cards API\n'''\nimport json\nimport logging\nimport unittest\nimport xmlrunner\nfrom lib import APIUtils\nfrom lib import UIUtils as ui\n\nlogging.basicConfig(\n    filename = 'csvAPITest.log',\n    level    = logging.INFO,\n    format   = '%(asctime)s %(message)s',\n    filemode = 'w' # To overwrite the log\n)\n\nclass cardTest(unittest.TestCase):\n\n    def setUp(self):\n        self.headers1 = {\n            'Media-Type': 'multipart/form-data'\n        }\n        self.headers2 = {\n            'Content-Type': 'application/json'\n        }\n        self.version  = 'v0.1'\n        self.host     = 'qa.dev.espressive.com'\n        self.head     = '/api/csv/'\n        self.username = 'espressive@acme.com'\n        self.protocol = 'https://'\n        self.password = 'itautomation'\n        self.url = self.protocol + self.host\n        self.url = self.url + self.head \n        self.url = self.url + self.version\n        self.url = self.url + '/csvfile/'\n\n        self.key = APIUtils.getAuthToken(\n            username = self.username,\n            password = self.password,\n            host     = self.host,\n        )\n        auth = \"Token \" + self.key\n        self.headers1['Authorization'] = auth\n        self.headers2['Authorization'] = auth\n\n    def tearDown(self):\n        APIUtils.killAuthToken(\n            host = self.host, \n            headers=self.headers1\n        )\n\n    def test_csv(self):\n\n        #--------------------------------------------------\n        #\n        # Upload file and get the ID\n        #\n        #--------------------------------------------------\n        file  = ui.getCSV()\n        url   = self.url\n        files = {'csv_file': (file)}\n        data  = {'business_object': '8'}\n        response = APIUtils.APIPostCall(\n            url     = url,\n            headers = self.headers1,\n            files   = files,\n            data    = data\n        )\n        logging.info(response.text)\n        response = response.json()\n        logging.info(response['id'])\n\n        #--------------------------------------------------\n        #\n        # Update or create users\n        #\n        #--------------------------------------------------\n        url = self.url + str(response['id']) \n        url = url + '/parse_csv/'\n        logging.info(url)\n        response = APIUtils.APIPostCall(\n            url     = url,\n            headers = self.headers2,\n            data    = None\n        )\n        logging.info(response.text)\n\n        #--------------------------------------------------\n        #\n        # Verify HTTP Status\n        #\n        #--------------------------------------------------\n        if( 200 != response.status_code ):\n            status = str(response.status_code)\n            self.fail( 'HTTP Status: ' + status )\n\nif __name__ == '__main__':\n    unittest.main(testRunner=xmlrunner.XMLTestRunner(output='reports'))\n","sub_path":"tests/csvAPITest.py","file_name":"csvAPITest.py","file_ext":"py","file_size_in_byte":2925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"560416999","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport utils\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport xarray as xr\n\n\nif __name__ == '__main__':\n    # set dirs\n    # -------------------------------------------------------------------------\n    project_dir = os.path.dirname(os.path.realpath(__file__))\n    data_dir = os.path.join(project_dir, 'data')\n    out_dir = os.path.join(project_dir, 'results')\n\n    # read input data\n    # -------------------------------------------------------------------------\n    fname = os.path.join(data_dir, 'ESA_CCI_SM_monthly_anomalies_0d25.nc')\n    da_sm = utils.read_nc(fname, 'SM_anomaly')\n\n    fname = os.path.join(data_dir, 'GRACE_CSR_monthly_0d25.nc')\n    da_tws = utils.read_nc(fname, 'lwe_thickness')\n\n    # SPI and SPEI\n    # -------------------------------------------------------------------------\n    aggs = [3, 12, 48]\n\n    spis = []\n    speis = []\n\n    for agg in aggs:\n        spi = utils.read_csv(\n            infile=os.path.join(out_dir, 'taskB', 'methode_leander',\n                                'SPI_time_scale_{}.csv'.format(agg)),\n            parse_col='time').rename(\n            columns={'Agg-{}'.format(agg): 'SPI-{}'.format(agg)})\n        spis.append(spi)\n\n        spei = utils.read_csv(\n            infile=os.path.join(out_dir, 'taskB', 'methode_leander',\n                                'SPEI_time_scale_{}.csv'.format(agg)),\n            parse_col='time').rename(\n            columns={'Agg-{}'.format(agg): 'SPEI-{}'.format(agg)})\n        speis.append(spei)\n\n    df_spis = pd.concat(spis, axis=1)\n    df_speis = pd.concat(speis, axis=1)\n\n    # discharge anomaly\n    # -------------------------------------------------------------------------\n    df_discharge = utils.read_grdc_discharge(data_dir)\n    df_discharge.index.name = 'time'\n\n    ds_discharge = df_discharge.to_xarray()\n    climatology = ds_discharge.groupby('time.month').mean('time')\n\n    anomalies = ds_discharge.groupby('time.month') - climatology\n    print(anomalies)\n\n    df_discharge_anoms = anomalies.to_dataframe()\n    df_discharge_anoms = (df_discharge_anoms\n                          .drop('month', axis=1)\n                          .rename(columns={'harsova': 'Q_harsova',\n                                           'ceatal_izmail': 'Q_ceatal_izmail'}))\n\n    # aggregate spatial data sets\n    # -------------------------------------------------------------------------\n    sm_basin = da_sm.groupby('time').mean().to_series()\n    sm_basin.name = 'SM'\n    tws_basin = da_tws.groupby('time').mean().to_series()\n    tws_basin.name = 'TWS'\n\n    # merge everything\n    # -------------------------------------------------------------------------\n    df_merged = pd.concat([sm_basin, tws_basin,\n                           df_spis, df_speis,\n                           df_discharge_anoms], axis=1)\n\n    # calc correlations and plot\n    # -------------------------------------------------------------------------\n    corrs = df_merged.corr(method='pearson')\n    corrs.to_csv(os.path.join(out_dir, 'taskC', 'correlation_matrix.csv'))\n\n    # create triangular mask for heatmap\n    mask = np.zeros_like(corrs)\n    mask[np.triu_indices_from(mask)] = True\n\n    # plot heatmap of pairwise correlations\n    f, ax = plt.subplots(figsize=(8, 8))\n\n    # define correct cbar height and pass to sns.heatmap function\n    cbar_kws = {\"fraction\": 0.046, \"pad\": 0.04}\n    sns.heatmap(corrs, mask=mask, cmap='coolwarm_r', square=True,\n                vmin=-1, vmax=1, annot=True,\n                cbar_kws=cbar_kws, ax=ax)\n    # save\n    plt.savefig(\n        os.path.join(out_dir, 'taskC', 'correlation_matrix.png'),\n        bbox_inches=\"tight\")\n    plt.close()\n","sub_path":"taskC.py","file_name":"taskC.py","file_ext":"py","file_size_in_byte":3712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"378385630","text":"import spacy\r\nimport torch\r\nimport socket\r\nimport os\r\nimport glob\r\n\r\nfrom algorithm import Coref\r\nfrom utils import (encode_distance, BATCH_SIZE_PATH, SIZE_FP,\r\n                               SIZE_FP_COMPRESSED, SIZE_FS, SIZE_FS_COMPRESSED,\r\n                               SIZE_GENRE, SIZE_PAIR_IN, SIZE_SINGLE_IN,SIZE_EMBEDDING)\r\nfrom dataset import (NCDataset, NCBatchSampler,\r\n    load_embeddings_from_file, padder_collate,\r\n    SIZE_PAIR_IN, SIZE_SINGLE_IN, SIZE_EMBEDDING)\r\nfrom model import Model\r\n\r\n# Load datasets and embeddings\r\nlist_of_files = glob.glob('checkpoints/*modelranking') # * means all if need specific format then *.csv\r\nlatest_file = max(list_of_files, key=os.path.getctime)\r\n#save_path = os.path.join('checkpoints', current_time + '_' + socket.gethostname() + '_')\r\n#save_name = \"ranking\"\r\n#best_model_path = save_path + \"best_model\" + save_name\r\nweights = 'weights/'\r\ncheckpoint_file = latest_file\r\nembed_path = weights\r\ntensor_embeddings, voc = load_embeddings_from_file(embed_path + \"tuned_word\")\r\n\r\n# Construct model\r\nprint(\"🏝 Build model\")\r\nh1 = 1000\r\nh2 = 500\r\nh3 = 500\r\nmodel = Model(len(voc), SIZE_EMBEDDING, h1,h2,h3, SIZE_PAIR_IN, SIZE_SINGLE_IN)\r\nmodel.load_embeddings(tensor_embeddings)\r\n\r\n#print(model.state_dict)\r\n#cklmvb = input(\"state dict printed\")\r\ncuda = torch.cuda.is_available()\r\n\r\nif cuda:\r\n\tmodel.cuda()\r\n#if weights is not None:\r\n#\tprint(\"🏝 Loading pre-trained weights\")\r\n#\tmodel.load_weights(weights)\r\nif checkpoint_file is not None:\r\n\tprint(\"⛄️ Loading model from\", checkpoint_file)\r\n\tmodel.load_state_dict(torch.load(checkpoint_file) if cuda else torch.load(checkpoint_file, map_location=lambda storage, loc: storage))\r\n\tmodel.eval()\r\n\r\nmy_utterances = ['My Sister has a dog.','She loves him.']\r\ncoref0 = Coref()\r\nprint(\"Single Pair Inp,\",SIZE_SINGLE_IN)\r\nprint(\"Pair Inp,\",SIZE_PAIR_IN)\r\n#lel = input()\r\ncoref_clusters = coref0.one_shot_coref(utterances=my_utterances)\r\nprint(\"Coref Clusters are\")\r\nprint(coref_clusters)","sub_path":"neuralcoref-memnet/test_coref_algo.py","file_name":"test_coref_algo.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"73149393","text":"import time\nimport sys\n\nimport json\nimport requests\n\nimport tornado.escape\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.locks\nimport tornado.options\nimport tornado.web\nimport tornado.gen\nimport logging\n\n\nfrom tornado.options import define, options\n\nimport tornado.queues\n\n\n\n\ndefine(\"port\", default=8888, help=\"run on the given port\", type=int)\n\nq = tornado.queues.Queue(maxsize=4)\n\nclass NoResultError(Exception):\n    pass\n\n\n\nclass Application(tornado.web.Application):\n    def __init__(self):\n        handlers = [\n            (r\"/result/*(\\d+)*/*\", RESULT),\n            (r\"/send\", SEND),\n        ]\n\n        super(Application, self).__init__(handlers)\n\n\nclass Quotes():\n    def __init__(self):\n\n        self.tasks = {}\n        self.id_counter = 1\n        self.worked_task = []\n        self.need_work_task = []\n        tornado.ioloop.IOLoop.current().spawn_callback(worker)\n\n\n\n\n    async def new_task(self,URL):\n        if URL not in list([url_[\"url\"] for url_ in self.tasks.values()]):\n            self.tasks[str(self.id_counter)] = \\\n                {\n                \"url\":URL,\n                \"Status\":\"New\",\n                \"RESP C Len\": 0,\n                \"RESP Status\": \"None\",\n                \"RESP Body\" : \"None\"\n                    }\n\n            logger.info(\"save new task {0}\".format(self.tasks[str(self.id_counter)]))\n\n            await q.put(self.id_counter)\n            # await q.join()\n            self.need_work_task.append(self.id_counter)\n\n            self.id_counter += 1\n\n            return\n\n\n    def info(self,num_q):\n        logger.info(\"Info of task {0} {1}\".format(num_q,self.tasks[str(num_q)]))\n        if str(num_q) in self.tasks.keys():\n            return self.tasks[str(num_q)]\n        else:\n            logger.info(\"Info of task {0} id not founded\".format(num_q))\n            return {\"info\":\"id not founded\"}\n\n    async def get_url(self,count_id):\n\n        try:\n            logger.info(\"Get page ingormation\")\n            self.tasks[str(count_id)][\"Status\"] = \"Pending\"\n            logger.info(\"Task id: {0} status is Pending\".format(count_id))\n            res = requests.get(self.tasks[str(count_id)][\"url\"])\n            self.tasks[str(count_id)][\"RESP Status\"] = res.status_code\n            self.tasks[str(count_id)][\"RESP Body\"] = res.text\n            self.tasks[str(count_id)][\"RESP C Len\"] = len(res.text)\n\n            self.tasks[str(count_id)][\"Status\"] = \"Completed\"\n            logger.info(\"Task id: {0} status is Completed\".format(count_id))\n        except:\n            logger.info(\"Task id: {0} status is Error\".format(count_id))\n            self.tasks[str(count_id)][\"Status\"] = \"Error\"\n\n\n\nasync def worker():\n\n    async for count_id in q:\n        if count_id is None:\n            return\n\n        try:\n            await Q.get_url(count_id)\n        except:\n            logger.info(\"Worker error get_url\")\n        finally:\n            q.task_done()\n            q.join()\n\n\nclass RESULT(tornado.web.RequestHandler):\n\n    def get(self, *args, **kwargs):\n\n        resp = {}\n        if args[0] ==None:\n            logger.info(\"Get info of latt 10 task\")\n            resp[\"tasks\"] =list()\n            for task_id in list(Q.tasks.keys())[-10:]:\n                try:\n\n                    info_task = Q.tasks[task_id]\n                    info_task[\"id\"] = task_id\n                    resp[\"tasks\"].append(info_task)\n                except:\n                    print(\"except\")\n\n            resp[\"Error\"] = \"None\"\n\n\n        else:\n            logger.info(\"Get info of task id: {0}\".format(args[0]))\n\n\n            resp[\"id\"] = args[0]\n\n            if args[0] in list(Q.tasks.keys()):\n                resp.update(Q.info(args[0]))\n                resp[\"Error\"] = \"None\"\n            else:\n                resp[\"Error\"] = \"Task ID not defined\"\n        return self.write(json.dumps(resp))\n\nclass SEND(tornado.web.RequestHandler):\n    async def post(self, *args, **kwargs):\n\n\n        data = json.loads(self.request.body.decode().rstrip())\n\n        logger.info(\"post {0}\".format(data))\n        await Q.new_task(data['url'])\n        resp = data\n\n\n        id_ = Q.id_counter-1\n        if id_:\n\n            logger.info(\"Creat new task id: {1} url: {0}\".format(data['url'],id_))\n\n            resp[\"task id\"] = str(id_)\n            resp[\"Error\"] = \"None\"\n\n        else:\n            logger.info(\"Url: {0} waiting of Quotes\".format(data['url']))\n            resp[\"Error\"] = \"Url in Quotes\"\n\n        try:\n            self.write(json.dumps(resp))\n\n\n        except Exception as e:\n            resp[\"Error\"] = \"\"\n\n            self.write(json.dumps(resp))\n            logger.warning(e)\n\n        finally:\n            self.finish()\n\n\ndef setup_custom_logger(name):\n    formatter = logging.Formatter(fmt='%(asctime)s %(levelname)-8s %(message)s',\n                                  datefmt='%Y-%m-%d %H:%M:%S')\n    handler = logging.FileHandler('log.txt', mode='a')\n    handler.setFormatter(formatter)\n    screen_handler = logging.StreamHandler(stream=sys.stdout)\n    screen_handler.setFormatter(formatter)\n    logger = logging.getLogger(name)\n    logger.setLevel(logging.DEBUG)\n    logger.addHandler(handler)\n    logger.addHandler(screen_handler)\n    return logger\n\n\n\nasync def main():\n\n\n\n        app = Application()\n        app.listen(options.port)\n        logger.info(\"Run server\")\n        shutdown_event = tornado.locks.Event()\n        await shutdown_event.wait()\n\n\nif __name__ == \"__main__\":\n\n    Q = Quotes()\n    logger = setup_custom_logger('Quotes')\n    logger.info(\"Start app\")\n    tornado.ioloop.IOLoop.current().run_sync(main)\n","sub_path":"async_tornado.py","file_name":"async_tornado.py","file_ext":"py","file_size_in_byte":5558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"222546325","text":"\"\"\"\n\n@author: ruben\n\n\"\"\"\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nfrom pastas.read.knmi import KnmiStation\n\n# How to use it?\n# data from a meteorological station\ndownload = False\n\nif not download:\n    # use a file with locations:\n    knmi = KnmiStation.fromfile('../data/KNMI_Bilt.txt')\n\n    # without locations\n    knmi = KnmiStation.fromfile('../data/KNMI_NoLocation.txt')\n\n    # hourly data without locations\n    knmi = KnmiStation.fromfile('../data/KNMI_Hourly.txt')\nelse:\n    # or download it directly from\n    # https://www.knmi.nl/nederland-nu/klimatologie/daggegevens\n    knmi = KnmiStation(stns=260, start=datetime(1970, 1, 1),\n                       end=datetime(1971, 1, 1))  # de bilt\n    knmi.download() # for now only works for meteorological stations\n                    # and daily data (so not hourly)\n\n# plot\nf1, axarr = plt.subplots(2, sharex=True)\nknmi.data['RH'].plot(ax=axarr[0])\naxarr[0].set_title(knmi.variables['RH'])\nif 'EV24' in knmi.data.keys():\n    knmi.data['EV24'].plot(ax=axarr[1])\n    axarr[1].set_title(knmi.variables['EV24'])\n\nif True:\n    # use a file of a rainfall station:\n    knmi = KnmiStation.fromfile('../data/KNMI_Akkrum.txt')\n    # plot\n    f2 = plt.figure()\n    ax = f2.add_subplot(111)\n    knmi.data['RD'].plot(ax=ax)\n    ax.set_title(knmi.variables['RD'], fontsize=10)\n\nplt.show()\n\n","sub_path":"examples/reads/test_knmidata.py","file_name":"test_knmidata.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"554749847","text":"from __future__ import absolute_import\nfrom builtins import object\nfrom proteus import *\nfrom proteus.default_p import *\nfrom proteus.ctransportCoefficients import smoothedHeaviside\ntry:\n    from .risingBubble import *\nexcept:\n    from risingBubble import *\nfrom proteus.mprans import VOF3P\n\nLevelModelType = VOF3P.LevelModel\ncoefficients = VOF3P.Coefficients(LS_model=LS_model,\n                                  V_model=V_model,\n                                  RD_model=RD_model,\n                                  ME_model=VOF_model,\n                                  VOS_model=VOS_model,\n                                  checkMass=True,\n                                  useMetrics=useMetrics,\n                                  epsFact=epsFact_vof,\n                                  sc_uref=vof_sc_uref,\n                                  sc_beta=vof_sc_beta,\n                                  movingDomain=movingDomain,\n                                  STABILIZATION_TYPE=1 if EXPLICIT_VOF is True else 0)\n                                  #EXPLICIT_METHOD=EXPLICIT_VOF)\n\ndef getDBC_vof(x,flag):\n    if flag == boundaryTags['top'] and openTop:\n        return lambda x,t: 1.0\n\ndirichletConditions = {0:getDBC_vof}\n\ndef getAFBC_vof(x,flag):\n    if flag != boundaryTags['top'] or not openTop:\n        return lambda x,t: 0.0\n\nadvectiveFluxBoundaryConditions = {0:getAFBC_vof}\ndiffusiveFluxBoundaryConditions = {0:{}}\n\nclass VOF_IC(object):\n    def uOfXT(self,x,t):\n        return smoothedHeaviside(epsFact_consrv_heaviside*he,signedDistanceToBubble(x))\n\ninitialConditions  = {0:VOF_IC()}\n","sub_path":"proteus/tests/surface_tension/rising_bubble_rans3p/vof_p.py","file_name":"vof_p.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"279086050","text":"from codigos.No import *\n\nclass Arvore:\n\n    def __init__(self):\n        self.raiz = No(None)\n        self.raiz = None\n\n    def InserirNo(self, chave):\n\n        novo = No(chave)\n\n        if self.raiz == None:\n            self.raiz = novo\n        else:\n            atual = self.raiz\n            while True:\n                anterior = atual\n                if chave <= atual.chave:\n                    atual = atual.esq\n                    if atual == None:\n                        anterior.esq = novo\n                        return\n\n                else:\n                    atual = atual.dir\n                    if atual == None:\n                        anterior.dir = novo\n                        return\n\n\n\n    def BuscarNo(self, chave):\n\n        if self.raiz == None:\n            return None\n\n        noAtual = self.raiz\n\n        while noAtual.chave != chave:\n\n            if chave < noAtual.chave:\n                noAtual = noAtual.esq\n\n            else:\n                noAtual = noAtual.dir\n\n            if noAtual == None:\n                return None\n\n        # Retorna o no com a chave\n        return noAtual\n\n    def GetNoSucessor(self, apaga):\n        paidosucessor = apaga\n        sucessor = apaga\n        noAtual = apaga.dir\n\n        while noAtual != None:\n            paidosucessor = sucessor\n            sucessor = noAtual\n            noAtual = noAtual.esq\n\n        #Se o sucessor não for o filho a direita do nó que se quer apagar\n        if sucessor != apaga.dir:\n            #O pai do sucessor herda a subarvore a direita do sucessor\n            paidosucessor.esq = sucessor.dir\n            #A nova subarvore a direita do sucessor será a subarvore a direita do nó que se quer apagar\n            sucessor.dir = apaga.dir\n\n        return sucessor\n\n    def ExcluirNo(self, chave):\n\n        if self.raiz is None:\n            return False\n\n        noAtual = self.raiz\n        pai = self.raiz\n        filho_esq = True\n\n        while noAtual.item != chave:\n            pai = noAtual\n            if chave < noAtual.chave:\n                noAtual = noAtual.esq\n                filho_esq = True\n            else:\n                noAtual = noAtual.dir\n                filho_esq = False\n\n            if noAtual == None:\n                return False\n\n        if noAtual.dir is None and noAtual.esq is None:\n            if noAtual == self.raiz:\n                self.raiz = None\n\n            else:\n                if filho_esq:\n                    pai.esq = None\n                else:\n                    pai.dir = None\n\n        elif noAtual.dir == None:\n            if noAtual == self.raiz:\n                self.raiz = noAtual.esq\n\n            else:\n                if filho_esq:\n                    pai.esq = noAtual.esq\n                else:\n                    pai.dir = noAtual.esq\n\n        elif noAtual.esq is None:\n            if noAtual == self.raiz:\n                self.raiz = noAtual.dir\n            else:\n                if filho_esq:\n                    pai.dir = noAtual.dir\n                else:\n                    pai.esq = noAtual.dir\n\n        else:\n            sucessor = self.noSucessor(noAtual)\n\n            if noAtual == self.raiz:\n                self.raiz = sucessor\n            else:\n                if filho_esq:\n                    pai.esq = sucessor\n                else:\n                    pai.dir = sucessor\n\n            sucessor.esq = noAtual.esq\n\n        return True\n\n    def ImprimirEmOrdem(self, no):\n        if no is not None:\n            self.ImprimirEmOrdem(no.esq)\n            print(no.chave, end=\" \")\n            self.ImprimirEmOrdem(no.dir)\n\n    def ImprimirPosOrdem(self, no):\n        if no is not None:\n            self.ImprimirPosOrdem(no.esq)\n            self.ImprimirPosOrdem(no.dir)\n            print(no.chave, end=\" \")\n\n    def ImprimirPreOrdem(self, no):\n        if no is not None:\n            print(no.chave, end=\" \")\n            self.ImprimirPreOrdem(no.esq)\n            self.ImprimirPreOrdem(no.dir)\n\n    def getRaiz(self):\n        return self.raiz\n\n    # def ImprimirEmOrdem2(self):\n    #\n    #         self.ImprimirEmOrdem(no.esq)\n    #         print(no.item, end=\" \")\n    #         self.ImprimirEmOrdem(no.dir)\n","sub_path":"codigos/AVLTree_2.py","file_name":"AVLTree_2.py","file_ext":"py","file_size_in_byte":4171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"212193734","text":"import Nodes as nodes\nimport pygame\nimport math\n\nwidth = 700 # screen\nheight = 700 # screen\n\n#Display init\npygame.init()\ngameDisplay = pygame.display.set_mode((width, height))\npygame.display.set_caption('FTC')\nclock = pygame.time.Clock()\ntime = 0\ndone = False\n\n#Font/Image init\ncomic = pygame.font.SysFont('Comic Sans MS', 30)\ncomicS = pygame.font.SysFont('Comic Sans MS', 15)\nfield = pygame.image.load('field.png').convert()\nfield = pygame.transform.scale(field, (width, height))\n\n#Starting positions (inches)\nstartx = 12*11.25\nstarty = 12*9\nstarth = -90\n\n#Robot Dimensions\nrobotWidth = 18\nrobotHeight = 18\n\n#Simulation Update Speed (mills)\nspeedUp = 1 # keep < 5\n\n#Velocitys of Robot\nmaxForVel = 30 #inch/s\nmaxStaVel = 20 #inch/s\nmaxTurVel = 90 #deg/s\n\n#Robot Classes\nrc = nodes.RobotConstraints(startx,starty,starth,robotWidth,robotHeight,maxForVel,maxStaVel,maxTurVel)\nrp = nodes.RobotPos(rc)\npygame.mixer.music.load('zing.wav')\npath = nodes.Path(rc, 0.5, pygame)\n\n#Track\ntrack = []\n\n#Path Constraints\n\npath.addPose(-10,0,0)\npath.addPose(-22, -3, 45)\npath.addPose(-4, 4, 0)\n# path.addPose(20,20,45)\n# path.addPose(-10,60,90)\n# path.addPose(-4,0,0)x\n\n\n\n#Drawing Classes\ndef drawRobot():\n    c = rp.getCorners()\n    for i in range(3):\n        pygame.draw.line(gameDisplay,(0,0,0),c[i],c[i+1],3)\n    pygame.draw.line(gameDisplay, (0, 0, 0), c[3], c[0], 3)\n    v = rp.getVect()\n    pygame.draw.line(gameDisplay,(204,102,0),(v[0],v[1]),(v[2],v[3]),3)\n    track.append((rp.inchToPix(rp.x),rp.inchToPix(rp.y)))\ndef drawTimer():\n    ts = comic.render(str(getTime()), False, (0, 0, 0))\n    pygame.draw.rect(gameDisplay,(204,204,255),(10,8,100,50))\n    pygame.draw.rect(gameDisplay,(51,0,102),(10,8,100,50),5)\n    gameDisplay.blit(ts, (15, 10))\ndef drawPathTimes():\n    if len(path.times) != 0:\n        pygame.draw.rect(gameDisplay, (255,255,200), (10, 70, 80, 25*len(path.times)+10))\n        pygame.draw.rect(gameDisplay, (199, 184, 133), (10, 70, 80, 25*len(path.times)+10), 5)\n    i = 0\n    for t in path.times:\n        i+=1\n        pt = comicS.render(str(i)+':  '+str(t), False, (255, 0, 0))\n        gameDisplay.blit(pt, (15, 25*i+50))\ndef drawTrack():\n    for (x,y) in track:\n        pygame.draw.circle(gameDisplay, (0, 102, 0), (x, y), 1)\ndef drawPath():\n    for (x,y,h) in path.getAllPoses():\n        px = rp.inchToPix(x)\n        py = rp.inchToPix(y)\n        v = nodes.Vec2d(0,30)\n        rv = v.getRotatedVec(-h)\n        pygame.draw.line(gameDisplay,(255,255,200),(px,py),(px+rv.x,py-rv.y),3)\n        pygame.draw.circle(gameDisplay, (100, 255, 0), (px, py), 3)\n\n#Time\ndef getTime():\n    return (math.trunc(time/10)/100)*speedUp\n\n#Robot moving by path\ndef updateRobotPos():\n    pows = path.update(rp.getPose(), getTime())\n    rp.move(pows[0],pows[1],pows[2])\n\n#Updating the display\ndef updateDisplay():\n    gameDisplay.blit(field, (0, 0))\n    drawRobot()\n    drawTrack()\n    drawTimer()\n    drawPath()\n    drawPathTimes()\n    if(getTime() > 1):\n        updateRobotPos()\n\n#Main Display Loop\nwhile not done:\n    updateDisplay()\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            done = True\n    pygame.display.update()\n    clock.tick(100*speedUp)\n    time = pygame.time.get_ticks()\n","sub_path":"Display.py","file_name":"Display.py","file_ext":"py","file_size_in_byte":3217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"332952867","text":"import os\r\nimport librosa\r\nimport librosa.display\r\nimport matplotlib.pyplot as plt\r\nimport pickle\r\nfrom tqdm.notebook import tqdm\r\nfrom sklearn.datasets import load_files       \r\nfrom keras.utils import np_utils\r\nimport numpy as np\r\nfrom glob import glob\r\nimport torch\r\nimport numpy as np\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom torch import nn\r\nfrom torchvision import transforms\r\nimport torchvision\r\nfrom torch import optim\r\ninputPath = input()\r\n\r\nclass CNN(nn.Module):\r\n    def __init__(self):\r\n        super(CNN, self).__init__()\r\n        \r\n        self.Convolution1 = nn.Sequential(\r\n            \r\n            # convolutional layer\r\n            nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1),\r\n            \r\n            # Batch Normalization layer\r\n            nn.BatchNorm2d(64),\r\n            \r\n            # Activation Function\r\n            nn.ReLU(),\r\n            \r\n            nn.Dropout(p=0.2),\r\n            \r\n            # max pooling layer\r\n            nn.MaxPool2d(kernel_size=2),\r\n            \r\n            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),\r\n            \r\n            nn.BatchNorm2d(128),\r\n            \r\n            nn.ReLU(),\r\n            \r\n            nn.Dropout(p=0.2),\r\n            \r\n            nn.MaxPool2d(kernel_size=2),\r\n            \r\n            \r\n            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),\r\n            \r\n            nn.BatchNorm2d(256),\r\n            \r\n            nn.ReLU()\r\n        \r\n          \r\n            )\r\n                \r\n        #Fully Connected 1\r\n        \r\n        self.fc = nn.Linear(10*35*256, 512)\r\n        self.fc2 = nn.Linear(512, 64)\r\n        self.fc3 = nn.Linear(64, 5)\r\n\r\n\r\n        \r\n   # forward function\r\n    def forward(self, x):\r\n        \r\n        output = self.Convolution1(x)\r\n                \r\n        output = output.view(output.size(0), -1)\r\n        \r\n        output = self.fc(output)\r\n        output = self.fc2(output)\r\n        output = self.fc3(output)\r\n\r\n        return output\r\n\r\n# We will train model on GPU. So we need to move the model first to the GPU.\r\nmodel = CNN()\r\nif torch.cuda.is_available():\r\n    model.cuda()\r\nmodel.eval()\r\nmodel.load_state_dict(torch.load(r'C:\\Users\\Tarun\\Desktop\\MIDAS project\\MY_MODEL1.pt'))\r\n\r\npath = os.path.normpath(inputPath)\r\ncount = 0\r\nfor i in os.listdir(path):\r\n    audio, sr = librosa.load(os.path.join(path,i))\r\n    #fixing lenght and processing audio file\r\n    y = librosa.util.fix_length(audio, 66150)\r\n    S = np.abs(librosa.stft(y))\r\n    D_short = librosa.feature.chroma_stft(S=S, n_chroma=40)\r\n\r\n    #applying transformations on the data\r\n    D_short = (D_short-D_short.min())/(D_short.max()-D_short.min())\r\n    trans = transforms.ToPILImage()\r\n    a = trans(D_short)\r\n    trans = transforms.Resize((40,140))\r\n    a = trans(a)\r\n    trans = transforms.ToTensor()\r\n    a = trans(a)\r\n    a= a.unsqueeze(0)\r\n    count+=1\r\n    \r\n    a = a.to('cuda')\r\n    output = model(a)\r\n    result = torch.argmax(output)\r\n    if(result == 0):\r\n        res = \"a\"\r\n    elif(result == 1):\r\n        res = \"b\"\r\n    elif(result == 2):\r\n        res = \"c\"\r\n    elif(result == 3):\r\n        res = \"d\"\r\n    elif(result == 4):\r\n        res = \"e\"\r\n    with open('myfile.txt', 'a') as f_out:\r\n        f_out.write(i)\r\n        f_out.write(',')\r\n        f_out.write(res)\r\n        f_out.write('\\n')\r\nf_out.close()\r\n","sub_path":"SPEECH EMOTION PROBLEM/Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":3428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"445193613","text":"# -*- coding: utf-8 -*-\n\n##############################################################################\n#\n#    OpenERP, Open Source Management Solution\n#    Copyright (C) 2012 - INECO PARTNERSHIP LIMITED (<http://www.ineco.co.th>).\n#\n#    This program is free software: you can redistribute it and/or modify\n#    it under the terms of the GNU Affero General Public License as\n#    published by the Free Software Foundation, either version 3 of the\n#    License, or (at your option) any later version.\n#\n#    This program is distributed in the hope that it will be useful,\n#    but WITHOUT ANY WARRANTY; without even the implied warranty of\n#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#    GNU Affero General Public License for more details.\n#\n#    You should have received a copy of the GNU Affero General Public License\n#    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n#\n##############################################################################\n\nfrom openerp.osv import osv, fields\n\nclass account_journal(osv.osv):\n\n    _inherit=\"account.journal\"\n\n    def get_account_journal(self,cr,uid,model,type=False,subtype=False,context=None):\n\n        if not context:\n            context={}\n        res =False\n\n        company_obj=self.pool.get('res.company')\n        company_id = context.get('company_id',False)\n        if not company_id:\n            company_id = self.pool.get('res.users')._get_company(cr, uid, context=context)\n            #company_obj.get_company(cr,uid,context).id\n\n        company=company_obj.browse(cr,uid,company_id)\n\n        if model=='account.invoice':\n            if type==\"in_invoice\":\n                res=company.in_invoice_journal_id and company.in_invoice_journal_id.id or False\n            if type==\"in_cash\":\n                res=company.in_cash_journal_id and company.in_cash_journal_id.id or False\n            if type==\"in_refund\":\n                res=company.in_refund_journal_id and company.in_refund_journal_id.id or False\n            if type==\"in_charge\":\n                res=company.in_charge_journal_id and company.in_charge_journal_id.id or False\n            if type==\"in_deposit\":\n                res=company.in_deposit_journal_id and company.in_deposit_journal_id.id or False\n\n            if type==\"out_invoice\":\n                res=company.out_invoice_journal_id and company.out_invoice_journal_id.id or False\n            if type==\"out_cash\":\n                res=company.out_cash_journal_id and company.out_cash_journal_id.id or False\n            if type==\"out_refund\":\n                res=company.out_refund_journal_id and company.out_refund_journal_id.id or False\n            if type==\"out_charge\":\n                res=company.out_charge_journal_id and company.out_charge_journal_id.id or False\n            if type==\"out_deposit\":\n                res=company.out_deposit_journal_id and company.out_deposit_journal_id.id or False\n\n        if model=='account.payment':\n            if type==\"in\":\n                res=company.in_payment_journal_id and company.in_payment_journal_id.id or False\n            if type==\"out\":\n                res=company.out_payment_journal_id and company.out_payment_journal_id.id or False\n\n        if model=='account.advance' or model=='account.advance.clear':\n            res=company.advance_journal_id and company.advance_journal_id.id or False\n\n        if model=='account.petty.payment':\n            res=company.out_petty_journal_id and company.out_petty_journal_id.id or False\n\n        if model=='account.cash.move': # and type=\"in\":\n            res=company.in_petty_journal_id and company.in_petty_journal_id.id or False\n\n        if model=='account.bank.move' or model=='account.bank.transfer':\n            res=company.bank_journal_id and company.bank_journal_id.id or False\n\n        if model=='account.cheque.move' and type in ('RP','RR','RC','RS'):\n            res=company.in_cheque_journal_id and company.in_cheque_journal_id.id or False\n\n        if model=='account.cheque.move' and type in ('PP','PR','PC'):\n            res=company.out_cheque_journal_id and company.out_cheque_journal_id.id or False\n\n        return res\n\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4\n","sub_path":"ineco_thai_account/account_journal.py","file_name":"account_journal.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"28247150","text":"\"\"\"Utilities for downloading from the web.\n\"\"\"\n\nimport chardet\nimport glob\nimport logging\nimport os\nimport shutil\nimport tarfile\nimport urllib\nimport zipfile\nimport warnings\n\nfrom tqdm import tqdm\nfrom smart_open import open, parse_uri\n\nfrom mirdata.validate import md5\n\nlogging.basicConfig(format=\"%(levelname)s: %(message)s\", level=logging.INFO)\n\n\nclass RemoteFileMetadata(object):\n    \"\"\"The metadata for a remote file\n\n    Attributes:\n        filename (str): the remote file's basename\n        url (str): the remote file's url\n        checksum (str): the remote file's md5 checksum\n        destination_dir (str or None): the relative path for where to save the file\n        unpack_directories (list or None): list of relative directories. For each directory\n            the contents will be moved to destination_dir (or data_home if not provieds)\n\n    \"\"\"\n\n    def __init__(\n        self, filename, url, checksum, destination_dir=None, unpack_directories=None\n    ):\n        self.filename = filename\n        self.url = url\n        self.checksum = checksum\n        self.destination_dir = destination_dir\n        self.unpack_directories = unpack_directories\n\n\ndef downloader(\n    save_dir,\n    remotes=None,\n    index=None,\n    partial_download=None,\n    info_message=None,\n    force_overwrite=False,\n    cleanup=False,\n    allow_invalid_checksum=False,\n):\n    \"\"\"Download data to `save_dir` and optionally log a message.\n\n    Args:\n        save_dir (str):\n            The directory to download the data\n        remotes (dict or None):\n            A dictionary of RemoteFileMetadata tuples of data in zip format.\n            If None, there is no data to download\n        index (core.Index):\n            A mirdata Index class, which may contain a remote index to be downloaded\n            or a subset of remotes to download by default.\n        partial_download (list or None):\n            A list of keys to partially download the remote objects of the download dict.\n            If None, all data specified by the index is downloaded\n        info_message (str or None):\n            A string of info to log when this function is called.\n            If None, no string is logged.\n        force_overwrite (bool):\n            If True, existing files are overwritten by the downloaded files.\n        cleanup (bool):\n            Whether to delete the zip/tar file after extracting.\n        allow_invalid_checksum (bool):\n            Allow having an invalid checksum, and whenever this happens prompt a\n            warning instead of deleting the files.\n\n    \"\"\"\n    if not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n\n    if not index:\n        raise ValueError(\"Index must be specified.\")\n\n    if allow_invalid_checksum:\n        cleanup = True\n\n    if cleanup:\n        logging.warning(\n            \"Zip and tar files will be deleted after they are uncompressed. \"\n            + \"If you download this dataset again, it will overwrite existing files, even if force_overwrite=False\"\n        )\n\n    if index.remote:\n        # add index to remotes\n        if not remotes:\n            remotes = {}\n        remotes[\"index\"] = index.remote\n\n    # if partial download is specified, use it. Otherwise, use the\n    # partial download specified by the index.\n    partial_download = partial_download if partial_download else index.partial_download\n\n    if remotes:\n        if partial_download:\n            # check the keys in partial_download are in the download dict\n            if not isinstance(partial_download, list) or any(\n                [k not in remotes for k in partial_download]\n            ):\n                raise ValueError(\n                    \"partial_download must be a list which is a subset of {}, but got {}\".format(\n                        list(remotes.keys()), partial_download\n                    )\n                )\n            objs_to_download = partial_download\n        else:\n            objs_to_download = list(remotes.keys())\n\n        logging.info(\"Downloading {} to {}\".format(objs_to_download, save_dir))\n\n        for k in objs_to_download:\n            logging.info(\"[{}] downloading {}\".format(k, remotes[k].filename))\n            extension = os.path.splitext(remotes[k].filename)[-1]\n            if \".zip\" in extension:\n                download_zip_file(\n                    remotes[k],\n                    save_dir,\n                    force_overwrite,\n                    cleanup,\n                    allow_invalid_checksum,\n                )\n            elif \".gz\" in extension or \".tar\" in extension or \".bz2\" in extension:\n                download_tar_file(\n                    remotes[k],\n                    save_dir,\n                    force_overwrite,\n                    cleanup,\n                    allow_invalid_checksum,\n                )\n            else:\n                download_from_remote(\n                    remotes[k], save_dir, force_overwrite, allow_invalid_checksum\n                )\n\n            if remotes[k].unpack_directories:\n                for src_dir in remotes[k].unpack_directories:\n                    # path to destination directory\n                    destination_dir = (\n                        os.path.join(save_dir, remotes[k].destination_dir)\n                        if remotes[k].destination_dir\n                        else save_dir\n                    )\n                    # path to directory to unpack\n                    source_dir = os.path.join(destination_dir, src_dir)\n\n                    if not os.path.exists(source_dir):\n                        logging.info(\n                            \"Data not downloaded, because it probably already exists on your computer. \"\n                            + \"Run .validate() to check, or rerun with force_overwrite=True to delete any \"\n                            + \"existing files and download from scratch\"\n                        )\n                        return\n\n                    move_directory_contents(source_dir, destination_dir)\n\n    if info_message is not None:\n        logging.info(info_message.format(save_dir))\n\n\nclass DownloadProgressBar(tqdm):\n    \"\"\"\n    Wrap `tqdm` to show download progress\n    \"\"\"\n\n    def update_to(self, b=1, bsize=1, tsize=None):\n        if tsize is not None:\n            self.total = tsize\n        self.update(b * bsize - self.n)\n\n\ndef download_from_remote(remote, save_dir, force_overwrite, allow_invalid_checksum):\n    \"\"\"Download a remote dataset into path\n    Fetch a dataset pointed by remote's url, save into path using remote's\n    filename and ensure its integrity based on the MD5 Checksum of the\n    downloaded file.\n\n    Adapted from scikit-learn's sklearn.datasets.base._fetch_remote.\n\n    Args:\n        remote (RemoteFileMetadata): Named tuple containing remote dataset\n            meta information: url, filename and checksum\n        save_dir (str): Directory to save the file to. Usually `data_home`\n        force_overwrite  (bool):\n            If True, overwrite existing file with the downloaded file.\n            If False, does not overwrite, but checks that checksum is consistent.\n\n    Returns:\n        str: Full path of the created file.\n\n    \"\"\"\n    file_uri = parse_uri(save_dir)\n    if file_uri.scheme != \"file\":\n        raise NotImplementedError(\n            \"mirdata only supports downloading to a local filesystem. \"\n            \"To use mirdata with a remote filesystem, download to a local filesytem, \"\n            \"and transfer the data to your remote filesystem, setting data_home appropriately.\"\n        )\n    if remote.destination_dir is None:\n        download_dir = save_dir\n    else:\n        download_dir = os.path.join(save_dir, remote.destination_dir)\n\n    if not os.path.exists(download_dir):\n        os.makedirs(download_dir)\n\n    download_path = os.path.join(download_dir, remote.filename)\n\n    if not os.path.exists(download_path) or force_overwrite:\n        # if we got here, we want to overwrite any existing file\n        if os.path.exists(download_path):\n            os.remove(download_path)\n\n        # If file doesn't exist or we want to overwrite, download it\n        with DownloadProgressBar(\n            unit=\"B\", unit_scale=True, unit_divisor=1024, miniters=1\n        ) as t:\n            try:\n                urllib.request.urlretrieve(\n                    remote.url,\n                    filename=download_path,\n                    reporthook=t.update_to,\n                    data=None,\n                )\n            except Exception as exc:\n                error_msg = \"\"\"\n                            mirdata failed to download the dataset from {}!\n                            Please try again in a few minutes.\n                            If this error persists, please raise an issue at\n                            https://github.com/mir-dataset-loaders/mirdata,\n                            and tag it with 'broken-link'.\n                            \"\"\".format(\n                    remote.url\n                )\n                logging.error(error_msg)\n                raise exc\n    else:\n        logging.info(\n            \"{} already exists and will not be downloaded. \".format(download_path)\n            + \"Rerun with force_overwrite=True to delete this file and force the download.\"\n        )\n\n    checksum = md5(download_path)\n    if remote.checksum != checksum:\n        if allow_invalid_checksum:\n            warnings.warn(\n                \"{} has an MD5 checksum ({}) \"\n                \"differing from expected ({}), \"\n                \"file may be corrupted.\".format(\n                    download_path, checksum, remote.checksum\n                ),\n                UserWarning,\n            )\n        else:\n            raise IOError(\n                \"{} has an MD5 checksum ({}) \"\n                \"differing from expected ({}), \"\n                \"file may be corrupted.\".format(\n                    download_path, checksum, remote.checksum\n                )\n            )\n    return download_path\n\n\ndef download_zip_file(\n    zip_remote, save_dir, force_overwrite, cleanup, allow_invalid_checksum\n):\n    \"\"\"Download and unzip a zip file.\n\n    Args:\n        zip_remote (RemoteFileMetadata):\n            Object containing download information\n        save_dir (str):\n            Path to save downloaded file\n        force_overwrite (bool):\n            If True, overwrites existing files\n        cleanup (bool):\n            If True, remove zipfile after unziping\n\n    \"\"\"\n    zip_download_path = download_from_remote(\n        zip_remote, save_dir, force_overwrite, allow_invalid_checksum\n    )\n    unzip(zip_download_path, cleanup=cleanup)\n\n\ndef extractall_unicode(zfile, out_dir):\n    \"\"\"Extract all files inside a zip archive to a output directory.\n\n    In comparison to the zipfile, it checks for correct file name encoding\n\n    Args:\n        zfile (obj): Zip file object created with zipfile.ZipFile\n        out_dir (str): Output folder\n\n    \"\"\"\n    ZIP_FILENAME_UTF8_FLAG = 0x800\n\n    for m in zfile.infolist():\n        data = zfile.read(m)  # extract zipped data into memory\n\n        filename = m.filename\n\n        # if block to deal with irmas and good-sounds archives\n        # check if the zip archive does not have the encoding info set\n        # encode-decode filename only if it's different than the original name\n        if (m.flag_bits & ZIP_FILENAME_UTF8_FLAG == 0) and filename.encode(\n            \"cp437\"\n        ).decode(errors=\"ignore\") != filename:\n            filename_bytes = filename.encode(\"cp437\")\n            if filename_bytes.decode(\"utf-8\", \"replace\") != filename_bytes.decode(\n                errors=\"ignore\"\n            ):\n                guessed_encoding = chardet.detect(filename_bytes)[\"encoding\"] or \"utf8\"\n                filename = filename_bytes.decode(guessed_encoding, \"replace\")\n            else:\n                filename = filename_bytes.decode(\"utf-8\", \"replace\")\n\n        disk_file_name = os.path.join(out_dir, filename)\n\n        dir_name = os.path.dirname(disk_file_name)\n        if not os.path.exists(dir_name):\n            os.makedirs(dir_name)\n\n        if not os.path.isdir(disk_file_name):\n            with open(disk_file_name, \"wb\") as fd:\n                fd.write(data)\n\n\ndef unzip(zip_path, cleanup):\n    \"\"\"Unzip a zip file inside it's current directory.\n\n    Args:\n        zip_path (str): Path to zip file\n        cleanup (bool): If True, remove zipfile after unzipping\n\n    \"\"\"\n    zfile = zipfile.ZipFile(zip_path, \"r\")\n    extractall_unicode(zfile, os.path.dirname(zip_path))\n    zfile.close()\n    if cleanup:\n        os.remove(zip_path)\n\n\ndef download_tar_file(\n    tar_remote, save_dir, force_overwrite, cleanup, allow_invalid_checksum\n):\n    \"\"\"Download and untar a tar file.\n\n    Args:\n        tar_remote (RemoteFileMetadata): Object containing download information\n        save_dir (str): Path to save downloaded file\n        force_overwrite (bool): If True, overwrites existing files\n        cleanup (bool): If True, remove tarfile after untarring\n\n    \"\"\"\n    tar_download_path = download_from_remote(\n        tar_remote, save_dir, force_overwrite, allow_invalid_checksum\n    )\n    untar(tar_download_path, cleanup=cleanup)\n\n\ndef untar(tar_path, cleanup):\n    \"\"\"Untar a tar file inside it's current directory.\n\n    Args:\n        tar_path (str): Path to tar file\n        cleanup (bool): If True, remove tarfile after untarring\n\n    \"\"\"\n    tfile = tarfile.open(tar_path, \"r\")\n    tfile.extractall(os.path.dirname(tar_path))\n    tfile.close()\n    if cleanup:\n        os.remove(tar_path)\n\n\ndef move_directory_contents(source_dir, target_dir):\n    \"\"\"Move the contents of source_dir into target_dir, and delete source_dir\n\n    Args:\n        source_dir (str): path to source directory\n        target_dir (str): path to target directory\n\n    \"\"\"\n    directory_contents = glob.glob(os.path.join(source_dir, \"*\"))\n    for fpath in directory_contents:\n        target_path = os.path.join(target_dir, os.path.basename(fpath))\n        if os.path.exists(target_path):\n            logging.info(\n                \"{} already exists. Run with force_overwrite=True to download from scratch\".format(\n                    target_path\n                )\n            )\n            continue\n        shutil.move(fpath, target_dir)\n\n    shutil.rmtree(source_dir)\n","sub_path":"mirdata/download_utils.py","file_name":"download_utils.py","file_ext":"py","file_size_in_byte":14213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"109202165","text":"import os, sys\n\nfrom django.db import models\n\nclass BackupTask(models.Model):\n    backup_directory = models.ForeignKey(\"chiropractor.BackupDirectory\")\n    backup_destination_directory = models.CharField(max_length=500)  \n    created_date = models.DateTimeField(auto_now=True, auto_now_add=True)\n    \n    class Meta:\n        ordering = ['-created_date']\n        \n    def __unicode__(self, *args, **kwargs):\n        return \"{id}{bd}{cd}\".format(\n            id=self.id, \n            bd=self.backup_directory,\n            cd=self.created_date)\n\nclass BackupDirectory(models.Model):\n    backup_directory = models.CharField(blank=False, null=True, max_length=500)\n    excluded_files = models.CharField(max_length=100, blank=True, null=True)\n    \n    class Meta:\n        ordering = ['backup_directory']\n    \n    def __unicode__(self, *args, **kwargs):\n        return \"{bd}\".format(bd=self.backup_directory)\n    \n    @property\n    def backup_directory_contents(self):\n        \"\"\"\n        return contents of the BackupDirectory object attribute backup_directory.\n        \"\"\"\n        contents_l = []\n        \n        # walk contents of root direcotry\n        for root, sub_directories, files in os.walk(self.backup_directory):\n            tmp_d = {'root': root, \n                'sub_directories': sub_directories, \n                'files': files}\n            contents_l.append(tmp_d)\n        return contents_l        \n        \n    @staticmethod\n    def directory_contents(directory=None):\n        \"\"\"\n        return contents of a passed in directory.\n        \"\"\"\n        contents_l = []\n        if not directory:\n            return contents_l\n        \n        # need to add beginning slash, it is not present.        \n        directory = \"/{}\".format(directory)\n        \n        # walk contents of root direcotry\n        for root, sub_directories, files in os.walk(directory):\n            tmp_d = {'root': root, \n                'sub_directories': sub_directories, \n                'files': files}\n            contents_l.append(tmp_d)\n        return contents_l\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"325373517","text":"import importlib\nimport pkgutil\n\n_builders = {}\n_tools = {}\n_loaded_tools = False\n\ndef _load_tools():\n    global _loaded_tools\n    if not _loaded_tools:\n        # Lazily load the tools so we don't get cyclic imports.\n        # XXX: There's probably a better way to do this.\n        for _, name, _ in pkgutil.walk_packages(__path__, '.'):\n            importlib.import_module(name, __package__)\n        _loaded_tools = True\n\ndef builder(*args):\n    if len(args) == 0:\n        raise TypeError('must provide at least one language')\n    multi = len(args) > 1\n\n    def wrapper(fn):\n        for i in args:\n            _builders[i] = (fn, multi)\n        return fn\n    return wrapper\n\ndef get_builder(lang, env):\n    _load_tools()\n    try:\n        fn, multi = _builders[lang]\n        return fn(env, lang) if multi else fn(env)\n    except KeyError:\n        raise ValueError('unknown language \"{}\"'.format(lang))\n\ndef tool(name):\n    def wrapper(fn):\n        _tools[name] = fn\n        return fn\n    return wrapper\n\ndef get_tool(name, env):\n    _load_tools()\n    try:\n        return _tools[name](env)\n    except KeyError:\n        raise ValueError('unknown tool \"{}\"'.format(name))\n","sub_path":"bfg9000/tools/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"627041941","text":"import sys\r\nimport socket\r\n\r\n# Create a TCP/IP socket\r\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\r\n# Connect the socket to the port where the server is listening\r\nserver_address = ('localhost', 31001)\r\nprint(f\"connecting to {server_address}\")\r\nsock.connect(server_address)\r\n\r\ntry:\r\n    # Send data\r\n    name=\"test.jpg\"\r\n    file = open(name,'rb')\r\n    content =file.read()\r\n    print ('sending data...')\r\n    sock.sendall(content)\r\nfinally:\r\n    print(\"closing\")\r\n    sock.close()","sub_path":"tugas1/Tugas1 B/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"461856394","text":"from django.shortcuts import redirect\nfrom django.urls import re_path\nfrom django.utils.translation import gettext_lazy as _\n\nfrom workbench import generic\nfrom workbench.logbook.forms import (\n    BreakForm,\n    BreakSearchForm,\n    LoggedCostForm,\n    LoggedCostMoveForm,\n    LoggedCostSearchForm,\n    LoggedHoursForm,\n    LoggedHoursMoveForm,\n    LoggedHoursSearchForm,\n)\nfrom workbench.logbook.models import Break, LoggedCost, LoggedHours\nfrom workbench.logbook.views import create\n\n\nurlpatterns = [\n    re_path(\n        r\"^$\", lambda request: redirect(\"logbook_loggedhours_list\"), name=\"logbook\"\n    ),\n    re_path(\n        r\"^hours/create/$\",\n        create,\n        {\"viewname\": \"createhours\"},\n        name=\"logbook_loggedhours_create\",\n    ),\n    re_path(\n        r\"^hours/$\",\n        generic.ListView.as_view(\n            model=LoggedHours,\n            search_form_class=LoggedHoursSearchForm,\n            show_create_button=False,\n        ),\n        name=\"logbook_loggedhours_list\",\n    ),\n    re_path(\n        r\"^hours/(?P<pk>\\d+)/$\",\n        generic.DetailView.as_view(model=LoggedHours),\n        name=\"logbook_loggedhours_detail\",\n    ),\n    re_path(\n        r\"^hours/(?P<pk>\\d+)/update/$\",\n        generic.UpdateView.as_view(model=LoggedHours, form_class=LoggedHoursForm),\n        name=\"logbook_loggedhours_update\",\n    ),\n    re_path(\n        r\"^hours/(?P<pk>\\d+)/move/$\",\n        generic.UpdateView.as_view(\n            model=LoggedHours,\n            form_class=LoggedHoursMoveForm,\n            template_name=\"modalform.html\",\n            title=_(\"Move %(object)s\"),\n        ),\n        name=\"logbook_loggedhours_move\",\n    ),\n    re_path(\n        r\"^hours/(?P<pk>\\d+)/delete/$\",\n        generic.DeleteView.as_view(\n            model=LoggedHours, template_name=\"modal_confirm_delete.html\"\n        ),\n        name=\"logbook_loggedhours_delete\",\n    ),\n    re_path(\n        r\"^costs/create/$\",\n        create,\n        {\"viewname\": \"createcost\"},\n        name=\"logbook_loggedcost_create\",\n    ),\n    re_path(\n        r\"^costs/$\",\n        generic.ListView.as_view(\n            model=LoggedCost,\n            search_form_class=LoggedCostSearchForm,\n            show_create_button=False,\n        ),\n        name=\"logbook_loggedcost_list\",\n    ),\n    re_path(\n        r\"^costs/(?P<pk>\\d+)/$\",\n        generic.DetailView.as_view(model=LoggedCost),\n        name=\"logbook_loggedcost_detail\",\n    ),\n    re_path(\n        r\"^costs/(?P<pk>\\d+)/update/$\",\n        generic.UpdateView.as_view(model=LoggedCost, form_class=LoggedCostForm),\n        name=\"logbook_loggedcost_update\",\n    ),\n    re_path(\n        r\"^hours/(?P<pk>\\d+)/move/$\",\n        generic.UpdateView.as_view(\n            model=LoggedCost,\n            form_class=LoggedCostMoveForm,\n            template_name=\"modalform.html\",\n            title=_(\"Move %(object)s\"),\n        ),\n        name=\"logbook_loggedcost_move\",\n    ),\n    re_path(\n        r\"^costs/(?P<pk>\\d+)/delete/$\",\n        generic.DeleteView.as_view(\n            model=LoggedCost, template_name=\"modal_confirm_delete.html\"\n        ),\n        name=\"logbook_loggedcost_delete\",\n    ),\n    re_path(\n        r\"^breaks/$\",\n        generic.ListView.as_view(\n            model=Break,\n            search_form_class=BreakSearchForm,\n            # show_create_button=False,\n        ),\n        name=\"logbook_break_list\",\n    ),\n    re_path(\n        r\"^breaks/(?P<pk>\\d+)/$\",\n        generic.DetailView.as_view(model=Break),\n        name=\"logbook_break_detail\",\n    ),\n    re_path(\n        r\"^breaks/create/$\",\n        generic.CreateView.as_view(\n            model=Break, form_class=BreakForm, template_name=\"modalform.html\"\n        ),\n        name=\"logbook_break_create\",\n    ),\n    re_path(\n        r\"^breaks/(?P<pk>\\d+)/update/$\",\n        generic.UpdateView.as_view(\n            model=Break, form_class=BreakForm, template_name=\"modalform.html\"\n        ),\n        name=\"logbook_break_update\",\n    ),\n    re_path(\n        r\"^breaks/(?P<pk>\\d+)/delete/$\",\n        generic.DeleteView.as_view(\n            model=Break, template_name=\"modal_confirm_delete.html\"\n        ),\n        name=\"logbook_break_delete\",\n    ),\n]\n","sub_path":"workbench/logbook/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":4131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"576023903","text":"# %% [markdown]\n# # Imports\nimport json\nimport os\nimport pickle\nimport random\nimport warnings\nfrom operator import itemgetter\nfrom pathlib import Path\nfrom timeit import default_timer as timer\n\nimport colorcet as cc\nimport matplotlib.colors as mplc\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom joblib import Parallel, delayed\nfrom matplotlib.cm import ScalarMappable\nfrom matplotlib.colors import LogNorm\nfrom sklearn.metrics import adjusted_rand_score\nfrom sklearn.model_selection import ParameterGrid\nfrom sklearn.neighbors import NearestNeighbors\n\nfrom graspy.cluster import AutoGMMCluster, GaussianCluster\nfrom graspy.embed import AdjacencySpectralEmbed, LaplacianSpectralEmbed\nfrom graspy.plot import gridplot, heatmap, pairplot\nfrom graspy.utils import symmetrize\nfrom src.cluster import DivisiveCluster\nfrom src.data import load_everything, load_metagraph\nfrom src.embed import ase, lse, preprocess_graph\nfrom src.graph import MetaGraph\nfrom src.hierarchy import signal_flow\nfrom src.io import savefig, saveobj, saveskels\nfrom src.utils import get_blockmodel_df, get_sbm_prob, invert_permutation\nfrom src.visualization import (\n    bartreeplot,\n    get_color_dict,\n    get_colors,\n    remove_spines,\n    sankey,\n    screeplot,\n)\n\nFNAME = os.path.basename(__file__)[:-3]\nprint(FNAME)\n\nSAVESKELS = True\nSAVEFIGS = True\nBRAIN_VERSION = \"2020-01-16\"\n\nsns.set_context(\"talk\")\n\nbase_path = Path(\"maggot_models/data/raw/Maggot-Brain-Connectome/\")\n\n\ndef stashfig(name, **kws):\n    savefig(name, foldername=FNAME, save_on=SAVEFIGS, **kws)\n\n\ndef stashskel(name, ids, labels, colors=None, palette=None, **kws):\n    saveskels(\n        name,\n        ids,\n        labels,\n        colors=colors,\n        palette=None,\n        foldername=FNAME,\n        save_on=SAVESKELS,\n        **kws,\n    )\n\n\ndef r():\n    return random.randint(0, 255)\n\n\ndef extract_ids(lod):\n    out_list = []\n    for d in lod:\n        skel_id = d[\"skeleton_id\"]\n        out_list.append(skel_id)\n    return out_list\n\n\ndef get_edges(adj):\n    adj = adj.copy()\n    all_edges = []\n    for i in range(adj.shape[0]):\n        row = adj[i, :]\n        col = adj[:, i]\n        col = np.delete(col, i)\n        edges = np.concatenate((row, col))\n        all_edges.append(edges)\n    return all_edges\n\n\n# %% [markdown]\n# # Play with getting edge df representatino\ngraph_type = \"Gadn\"\nmg = load_metagraph(graph_type, version=BRAIN_VERSION)\n\ng = mg.g\nmeta = mg.meta\nedgelist_df = mg.to_edgelist()\n\nmax_pair_edge_df = edgelist_df.groupby(\"edge pair ID\").max()\nedge_max_weight_map = dict(\n    zip(max_pair_edge_df.index.values, max_pair_edge_df[\"weight\"])\n)\nedgelist_df[\"max_weight\"] = itemgetter(*edgelist_df[\"edge pair ID\"])(\n    edge_max_weight_map\n)\n\n# %% [markdown]\n# # Try thresholding in this new format\nprops = []\nprop_edges = []\nprop_syns = []\nthreshs = np.linspace(0, 0.3, 20)\nfor threshold in threshs:\n    thresh_df = max_pair_edge_df[max_pair_edge_df[\"weight\"] > threshold]\n    prop = len(thresh_df[thresh_df[\"edge pair counts\"] == 2]) / len(thresh_df)\n    props.append(prop)\n    prop_edges_left = (\n        thresh_df[\"edge pair counts\"].sum() / max_pair_edge_df[\"edge pair counts\"].sum()\n    )\n    prop_edges.append(prop_edges_left)\n    temp_df = edgelist_df[edgelist_df[\"max_weight\"] > threshold]\n    p_syns = temp_df[\"weight\"].sum() / edgelist_df[\"weight\"].sum()\n    prop_syns.append(p_syns)\n# %% [markdown]\n# #\n\nplot_df = pd.DataFrame()\nplot_df[\"Threshold\"] = threshs\nplot_df[\"P(mirror edge present)\"] = props\nplot_df[\"Proportion of edges left\"] = prop_edges\nplot_df[\"Proportion of synapses left\"] = prop_syns\n\nfig, ax = plt.subplots(1, 1, figsize=(10, 5))\n\nsns.lineplot(data=plot_df, x=\"Threshold\", y=\"P(mirror edge present)\", ax=ax)\nax_right = ax.twinx()\nsns.lineplot(\n    data=plot_df,\n    x=\"Threshold\",\n    y=\"Proportion of synapses left\",\n    ax=ax_right,\n    color=\"orange\",\n)\nremove_spines(ax_right)\nremove_spines(ax)\nax.set_ylim((0, 1))\nax_right.set_ylim((0, 1))\nax.set_title(f\"{graph_type}\")\nstashfig(f\"thresh-sweep-{graph_type}-brain-syns\")\n\n# %% [markdown]\n# # Plot these against each other\nfig, ax = plt.subplots(1, 1, figsize=(10, 10))\nsns.scatterplot(data=plot_df, x=\"P(mirror edge present)\", y=\"Proportion of edges left\")\nax.set_xlim((0, 1.1))\nax.set_ylim((0, 1.1))\nremove_spines(ax)\nstashfig(f\"thresh-sweep-paired-{graph_type}-brain\")\n# %% [markdown]\n# # Look at IOU\nthreshold = 0.05\nthresh_df = max_pair_edge_df[max_pair_edge_df[\"weight\"] > threshold]\n\nsource_pair_ids = np.unique(max_pair_edge_df[\"source Pair ID\"])\ntarget_pair_ids = np.unique(max_pair_edge_df[\"target Pair ID\"])\npair_ids = np.union1d(source_pair_ids, target_pair_ids)\n\nious = []\nkept_pairs = []\nremoved_pairs = []\nedge_dfs = []\nkeep_cols = [\n    \"source\",\n    \"target\",\n    \"weight\",\n    \"source Pair ID\",\n    \"target Pair ID\",\n    \"edge pair counts\",\n]\n\nfor pid in pair_ids:\n    temp_df = thresh_df[\n        (thresh_df[\"source Pair ID\"] == pid) | (thresh_df[\"target Pair ID\"] == pid)\n    ]\n\n    if len(temp_df) > 0:\n        iou = len(temp_df[temp_df[\"edge pair counts\"] == 2]) / len(temp_df)\n        ious.append(iou)\n        kept_pairs.append(pid)\n        edge_dfs.append(temp_df[keep_cols])\n    else:\n        removed_pairs.append(pid)\n\n# inds = np.argsort(ious)\n\n# edge_dfs[inds]\n\n# %% [markdown]\n# #\nplot_df = pd.DataFrame()\n\nfig, ax = plt.subplots(1, 1, figsize=(10, 5))\nsns.distplot(ious, norm_hist=False, kde=False)\nremove_spines(ax)\nax.set_xlabel(\"IOU Score\")\nax.set_xlim((0, 1))\nax.set_title(f\"{graph_type}, threshold = {threshold:0.2f}\")\nax.set_ylabel(\"Pair count\")\n\n# %% [markdown]\n# #\niou_df = pd.DataFrame()\niou_df[\"IOU Score\"] = ious\niou_df[\"Pair ID\"] = kept_pairs\niou_df[\"Left ID\"] = -1\niou_df[\"Right ID\"] = -1\nfor i, pid in enumerate(kept_pairs):\n    temp_df = meta[meta[\"Pair ID\"] == pid]\n    iou_df.loc[i, \"Left ID\"] = temp_df[temp_df[\"Hemisphere\"] == \"L\"].index[0]\n    iou_df.loc[i, \"Right ID\"] = temp_df[temp_df[\"Hemisphere\"] == \"R\"].index[0]\n\n# %% [markdown]\n# #\niou_df.sort_values(\"IOU Score\", ascending=True, inplace=True)\niou_df.index = range(len(iou_df))\n\ncolors = []\nids = []\nfor i in range(100):\n    left_id = iou_df.loc[i, \"Left ID\"]\n    right_id = iou_df.loc[i, \"Right ID\"]\n    iou = iou_df.loc[i, \"IOU Score\"]\n    c = int(iou * 255.0)\n    print(c)\n    print(iou)\n    hex_color = \"#%02X%02X%02X\" % (c, c, c)\n    colors.append(hex_color)\n    colors.append(hex_color)\n    ids.append(left_id)\n    ids.append(right_id)\n\nstashskel(\"low-iou-sorted-pairs\", ids, colors, colors=colors, palette=None)\n\n#%%\nleft_id = 12121795\npair_id = meta.loc[left_id, \"Pair ID\"]\nind = np.where(kept_pairs == pair_id)[0][0]\nedge_dfs[ind]\n# %% [markdown]\n# #\n\niou_df.sort_values(\"IOU Score\", ascending=False, inplace=True)\niou_df.index = range(len(iou_df))\n\ncolors = []\nids = []\nfor i in range(100):\n    left_id = iou_df.loc[i, \"Left ID\"]\n    right_id = iou_df.loc[i, \"Right ID\"]\n    iou = iou_df.loc[i, \"IOU Score\"]\n    c = int(iou * 255.0)\n    print(c)\n    print(iou)\n    hex_color = \"#%02X%02X%02X\" % (c, c, c)\n    colors.append(hex_color)\n    colors.append(hex_color)\n    ids.append(left_id)\n    ids.append(right_id)\n\nstashskel(\"high-iou-sorted-pairs\", ids, colors, colors=colors, palette=None)\n\n# %% [markdown]\n# #\n\n# Get rid of unpaired cells for now\n\nmg.meta[\"is_paired\"] = False\npairs = mg[\"Pair\"]\nis_paired = pairs != -1\nmg.reindex(is_paired)\nmg.sort_values([\"Hemisphere\", \"Pair ID\"])\n\nplot_adj = False\nif plot_adj:\n    heatmap(\n        mg.adj,\n        sort_nodes=False,\n        inner_hier_labels=mg[\"Class 1\"],\n        outer_hier_labels=mg[\"Hemisphere\"],\n        transform=\"binarize\",\n        cbar=False,\n        figsize=(30, 30),\n        hier_label_fontsize=10,\n    )\n    heatmap(\n        mg.adj, sort_nodes=False, transform=\"binarize\", cbar=False, figsize=(30, 30)\n    )\n\nn_pairs = mg.adj.shape[0] // 2\n\nmg.verify()\n\n\n# %% [markdown]\n# # Extract subgraphs\n\n\nextract_fb = True\nif extract_fb:\n    from_classes = [\"MBON\", \"FAN\", \"FBN\", \"FB2N\"]\n    to_classes = [\"MBIN\"]\n    sub_mg = MetaGraph(mg.adj, mg.meta)\n    from_class_inds = sub_mg.meta[\"Class 1\"].isin(from_classes)\n    to_class_inds = sub_mg.meta[\"Class 1\"].isin(to_classes)\n    any_inds = np.logical_or(from_class_inds, to_class_inds)\n    sub_mg.reindex(any_inds)\n    sub_mg.sort_values([\"Hemisphere\", \"Class 1\", \"Pair ID\"])\n    # meta = sub_mg.meta\n    # meta[\"Original index\"] = range(meta.shape[0])\n    # meta.sort_values(\n    #     [\"Hemisphere\", \"Class 1\", \"Pair ID\"],\n    #     inplace=True,\n    #     kind=\"mergesort\",\n    #     ascending=False,\n    # )\n    # temp_inds = meta[\"Original index\"]\n    # sub_mg = sub_mg.reindex(temp_inds)\n    # sub_mg = MetaGraph(sub_mg.adj, meta)\n\n    adj = sub_mg.adj.copy()\n    from_class_inds = np.where(sub_mg.meta[\"Class 1\"].isin(from_classes).values)[0]\n    left_inds = np.where(sub_mg.meta[\"Hemisphere\"] == \"L\")[0]\n    right_inds = np.where(sub_mg.meta[\"Hemisphere\"] == \"R\")[0]\n    to_class_inds = np.where(sub_mg.meta[\"Class 1\"].isin(to_classes).values)[0]\n    from_left = np.intersect1d(left_inds, from_class_inds)\n    from_right = np.intersect1d(right_inds, from_class_inds)\n    to_left = np.intersect1d(left_inds, to_class_inds)\n    to_right = np.intersect1d(right_inds, to_class_inds)\n\n    left_left_adj = adj[np.ix_(from_left, to_left)]\n    left_right_adj = adj[np.ix_(from_left, to_right)]\n    right_right_adj = adj[np.ix_(from_right, to_right)]\n    right_left_adj = adj[np.ix_(from_right, to_left)]\nelse:\n    adj = mg.adj.copy()\n    left_left_adj = adj[:n_pairs, :n_pairs]\n    left_right_adj = adj[:n_pairs, n_pairs : 2 * n_pairs]\n    right_right_adj = adj[n_pairs : 2 * n_pairs, n_pairs : 2 * n_pairs]\n    right_left_adj = adj[n_pairs : 2 * n_pairs, :n_pairs]\n\nif plot_adj:\n    heatmap(\n        mg.adj,\n        inner_hier_labels=mg[\"Class 1\"],\n        outer_hier_labels=mg[\"Hemisphere\"],\n        transform=\"binarize\",\n        figsize=(10, 10),\n    )\n\n# %% [markdown]\n# # Plot all edges, weights\n\nll_edges = left_left_adj.ravel()\nlr_edges = left_right_adj.ravel()\nrr_edges = right_right_adj.ravel()\nrl_edges = right_left_adj.ravel()\n\n\nleft_edges = np.concatenate((ll_edges, lr_edges))\nright_edges = np.concatenate((rr_edges, rl_edges))\n\n\nprops = []\nthreshs = np.linspace(0, 0.3, 200)\nfor threshold in threshs:\n    keep_mask = np.logical_or(left_edges >= threshold, right_edges >= threshold)\n    thresh_left_edges = left_edges.copy()[keep_mask]\n    thresh_right_edges = right_edges.copy()[keep_mask]\n    n_consistent = np.logical_and(thresh_left_edges != 0, thresh_right_edges != 0).sum()\n    prop_consistent = n_consistent / len(thresh_left_edges)\n    props.append(prop_consistent)\n\nif extract_fb:\n    first_ind = np.where(np.array(props) > 0.95)[0][0]\n\nplot_df = pd.DataFrame()\nplot_df[\"Threshold\"] = threshs\nplot_df[\"P(mirror edge present)\"] = props\n\nfig, ax = plt.subplots(1, 1, figsize=(10, 5))\n\n# if extract_fb:\n#     ax.axvline(threshs[first_ind], c=\"r\")\n\nsns.lineplot(data=plot_df, x=\"Threshold\", y=\"P(mirror edge present)\", ax=ax)\nremove_spines(ax)\n\nif extract_fb:\n    ax.set_title(f\"{graph_type}, 0.95 at thresh = {threshs[first_ind]:0.2f}\")\n    stashfig(f\"thresh-sweep-{graph_type}-fbpaper\")\nelse:\n    ax.set_title(f\"{graph_type}\")\n    stashfig(f\"thresh-sweep-{graph_type}-brain\")\n\n# %% [markdown]\n# #\n# left_edges = ll_edges + lr_edges\n# right_edges = rr_edges + rl_edges\n\nnonzero_inds = np.where(np.logical_or(left_edges > 0, right_edges > 0))[0]\nleft_edges = left_edges[nonzero_inds]\nright_edges = right_edges[nonzero_inds]\n\nsns.set_context(\"talk\", font_scale=1.25)\nplot_log = False\nif plot_log:\n    left_edges = np.log10(left_edges + 0.01)\n    right_edges = np.log10(right_edges + 0.01)\n    xlabel = \"Log10(Left edge weight)\"\n    ylabel = \"Log10(Right edge weight)\"\nelse:\n    xlabel = \"Left edge weight\"\n    ylabel = \"Right edge weight\"\n\npad = 1\nxmin = left_edges.min() - pad\nymin = right_edges.min() - pad\nxmax = left_edges.max() + pad\nymax = right_edges.max() + pad\n\nedge_plot_df = pd.DataFrame()\nscale = 0.15\nedge_plot_df[\"y\"] = left_edges + np.random.normal(scale=scale, size=left_edges.size)\nedge_plot_df[\"x\"] = right_edges + np.random.normal(scale=scale, size=right_edges.size)\nfig, axs = plt.subplots(1, 2, figsize=(16, 8), sharex=True, sharey=True)\nsns.scatterplot(\n    data=edge_plot_df, x=\"x\", y=\"y\", ax=axs[0], s=1, alpha=0.2, linewidth=False\n)\nmax_val = max(xmax, ymax)\naxs[0].plot((0, max_val), (0, max_val), color=\"red\", linewidth=1, linestyle=\"--\")\naxs[0].set_xlabel(xlabel)\naxs[0].set_ylabel(ylabel)\naxs[0].axis(\"square\")\n\naxs[1].hexbin(\n    left_edges,\n    right_edges,\n    bins=\"log\",\n    gridsize=(int(left_edges.max()), int(right_edges.max())),\n    cmap=\"Blues\",\n)\naxs[1].axis(\"square\")\naxs[1].set_xlabel(xlabel)\nfor ax in axs:\n    remove_spines(ax, keep_corner=False)\n    ax.set_xticks([])\n    ax.set_yticks([])\n    ax.yaxis.set_major_locator(plt.MaxNLocator(3))\n    ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n\naxs[1].set_ylim((ymin, ymax))\naxs[1].set_xlim((xmin, xmax))\n\nplt.tight_layout()\n\ncorr = np.corrcoef(left_edges, right_edges)[0, 1]\nplt.suptitle(f\"{graph_type}, all paired edges, correlation = {corr:0.2f}\", y=0.99)\nstashfig(f\"all-paired-edge-corr-{graph_type}\")\naxs[1].set_ylim((ymin, 30))\naxs[1].set_xlim((xmin, 30))\nstashfig(f\"all-paired-edge-corr-zoom-{graph_type}\")\n\n# %% [markdown]\n# # Get pairs into proper format\nll_edges = get_edges(left_left_adj)\nlr_edges = get_edges(left_right_adj)\nrr_edges = get_edges(right_right_adj)\nrl_edges = get_edges(right_left_adj)\n\nleft_edge_vecs = []\nright_edge_vecs = []\nfor i in range(n_pairs):\n    left_edge_vecs.append(np.concatenate((ll_edges[i], lr_edges[i])))\n    right_edge_vecs.append(np.concatenate((rr_edges[i], rl_edges[i])))\n\n# %% [markdown]\n# # Now add back in the super nodes - Janky but ok for now\n\ngraph_type = \"G\"\nmg_for_unpaired = load_metagraph(graph_type, version=BRAIN_VERSION)\nmg_for_unpaired.meta.head()\n\nkeep_classes = [\"KC\", \"sens\"]\n\n\nclass_label = mg_for_unpaired[\"Class 1\"]\nis_keep_class = [c in keep_classes for c in class_label]\n\nis_keep = np.logical_or(is_paired, is_keep_class)\n\nmg_for_unpaired.reindex(is_keep)\n# print(np.unique(mg[\"Pair\"]))\nmeta = mg_for_unpaired.meta\nmeta[\"Original index\"] = range(meta.shape[0])\nmeta.sort_values(\n    [\"Hemisphere\", \"Pair ID\", \"Merge Class\"],\n    inplace=True,\n    kind=\"mergesort\",\n    ascending=False,\n)\ntemp_inds = meta[\"Original index\"]\nmg_for_unpaired = mg_for_unpaired.reindex(temp_inds)\nmg_for_unpaired = MetaGraph(mg_for_unpaired.adj, meta)\n\nadj = mg_for_unpaired.adj\nfor c in keep_classes:\n    for side in [\"L\", \"R\"]:  # this is the side of the paired neurons\n        for direction in [\"ipsi\", \"contra\"]:\n            if direction == \"ipsi\":\n                class_inds = np.where(\n                    np.logical_and(\n                        mg_for_unpaired[\"Hemisphere\"] == side,\n                        mg_for_unpaired[\"Class 1\"] == c,\n                    )\n                )[0]\n                unpaired_inds = np.where(\n                    np.logical_and(\n                        mg_for_unpaired[\"Hemisphere\"] == side,\n                        mg_for_unpaired[\"Pair ID\"] == -1,\n                    )\n                )[0]\n            elif direction == \"contra\":\n                class_inds = np.where(\n                    np.logical_and(\n                        mg_for_unpaired[\"Hemisphere\"] != side,\n                        mg_for_unpaired[\"Class 1\"] == c,\n                    )\n                )[0]\n                unpaired_inds = np.where(\n                    np.logical_and(\n                        mg_for_unpaired[\"Hemisphere\"] != side,\n                        mg_for_unpaired[\"Pair ID\"] == -1,\n                    )\n                )[0]\n            unpaired_inds = np.intersect1d(class_inds, unpaired_inds)\n            paired_inds = np.where(\n                np.logical_and(\n                    mg_for_unpaired[\"Hemisphere\"] == side,\n                    mg_for_unpaired[\"Pair ID\"] != -1,\n                )\n            )[0]\n\n            # from\n            from_edges = adj[np.ix_(unpaired_inds, paired_inds)]\n            pair_super_edges = from_edges.sum(axis=0)\n\n            # to\n            to_edges = adj[np.ix_(paired_inds, unpaired_inds)]\n            pair_super_edges = to_edges.sum(axis=1)\n\n\n# %% [markdown]\n# # Plot, by pair, the IOU score\n\nthreshold = 8\n\niou_scores = []\nrand_iou_scores = []\nfor i in range(n_pairs):\n    left_edge_vec = left_edge_vecs[i].copy()\n    left_edge_vec[left_edge_vec <= threshold] = 0\n    right_edge_vec = right_edge_vecs[i].copy()\n    right_edge_vec[right_edge_vec <= threshold] = 0\n    ind = np.random.choice(len(right_edge_vecs))\n    rand_vec = right_edge_vecs[ind].copy()\n    rand_vec[rand_vec <= threshold] = 0\n\n    left_mask = left_edge_vec > 0\n    right_mask = right_edge_vec > 0\n    or_mask = np.logical_or(left_mask, right_mask)  # anything that is an edge in either\n    and_mask = np.logical_and(left_mask, right_mask)  # edge in both\n    iou_score = and_mask.sum() / or_mask.sum()\n    if not np.isnan(iou_score):\n        iou_scores.append(iou_score)\n    else:\n        left_id = mg.meta.index[i]\n        right_id = mg.meta.index[i + n_pairs]\n        print(f\"Nan IOU score for pair {left_id}, {right_id}\")\n    rand_mask = rand_vec > 0\n    or_mask = np.logical_or(left_mask, rand_mask)  # anything that is an edge in either\n    and_mask = np.logical_and(left_mask, rand_mask)  # edge in both\n    iou_score = and_mask.sum() / or_mask.sum()\n    if not np.isnan(iou_score):\n        rand_iou_scores.append(iou_score)\n\nfig, ax = plt.subplots(1, 1, figsize=(10, 5))\nsns.distplot(iou_scores, ax=ax, norm_hist=True, label=\"True pair\")\nsns.distplot(rand_iou_scores, ax=ax, norm_hist=True, label=\"Random pair\")\nax.set_xlabel(\"IOU score (binary edges)\")\nax.set_title(f\"Pair edge agreement, {graph_type}, theshold = {threshold}\")\nax.yaxis.set_major_locator(plt.NullLocator())\nremove_spines(ax)\nax.set_xlim((0, 1))\nstashfig(f\"pair-IOU-random-{graph_type}-t{threshold}\")\n\nfig, ax = plt.subplots(1, 1, figsize=(10, 5))\nsns.distplot(iou_scores, ax=ax, norm_hist=True)\nax.set_xlabel(\"IOU score (binary edges)\")\nax.set_title(f\"Pair edge agreement, {graph_type}, theshold = {threshold}\")\nax.yaxis.set_major_locator(plt.NullLocator())\nremove_spines(ax)\nax.set_xlim((0, 1))\nstashfig(f\"pair-IOU-{graph_type}-t{threshold}\")\n\n# %% [markdown]\n# # Plot, for a subset of random pairs, the edge weights\nn_plots = 5\nfor i in range(n_plots):\n    fig, axs = plt.subplots(4, 6, figsize=(30, 20))\n    axs = axs.ravel()\n    rand = np.random.randint(1e8)\n    np.random.seed(rand)\n    for ax in axs:\n        ind = np.random.choice(len(right_edge_vecs))\n        left_edge_vec = left_edge_vecs[ind].copy()\n        right_edge_vec = right_edge_vecs[ind].copy()\n        left_mask = left_edge_vec > 0\n        right_mask = right_edge_vec > 0\n        or_mask = np.logical_or(left_mask, right_mask)\n        remove_spines(ax)\n        if or_mask.sum() > 0:\n            left_id = mg.meta.index[ind]\n            right_id = mg.meta.index[ind + n_pairs]\n            left_edge_vec = left_edge_vec[or_mask]\n            right_edge_vec = right_edge_vec[or_mask]\n            corr = np.corrcoef(left_edge_vec, right_edge_vec)[0, 1]\n            sns.scatterplot(left_edge_vec, right_edge_vec, ax=ax)\n            ax.axis(\"equal\")\n            ax.set_title(f\"Corr = {corr:0.2f}\")\n            max_val = max(left_edge_vec.max(), right_edge_vec.max())\n            ax.plot(\n                (0, max_val), (0, max_val), color=\"red\", linewidth=1, linestyle=\"--\"\n            )\n            ax.text(max_val, max_val, max_val)\n            # ax.xaxis.set_major_locator(plt.MaxNLocator(1))\n            # xticks = ax.get_xticks()\n            # print(xticks)\n            # ax.set_yticks(xticks)\n            ax.xaxis.set_major_locator(plt.NullLocator())\n            ax.yaxis.set_major_locator(plt.NullLocator())\n            ax.set_xlabel(left_id)\n            ax.set_ylabel(right_id)\n\n    plt.suptitle(f\"{graph_type}, edge weights by pair\", y=1.02)\n    plt.tight_layout()\n    stashfig(f\"edge-weights-by-pair-{graph_type}-r{rand}\")\n# %% [markdown]\n# # For some random cells plot the pair correlations\n\n# %% [markdown]\n# # Look into a few of the low x or y edges\n\n# %% [markdown]\n# #\n# Extract edges\nll_edges = get_edges(left_left_adj)\nlr_edges = get_edges(left_right_adj)\nrr_edges = get_edges(right_right_adj)\nrl_edges = get_edges(right_left_adj)\n\npair_corrs = []\nfor i in range(n_pairs):\n    left_edge_vec = np.concatenate((ll_edges[i], lr_edges[i]))\n    right_edge_vec = np.concatenate((rr_edges[i], rl_edges[i]))\n    both_edges = np.stack((left_edge_vec, right_edge_vec), axis=-1)\n    avg_edges = np.mean(both_edges, axis=-1)\n    # inds = np.where(avg_edges > threshold)[0]\n\n    # check that left and right edge both have\n    inds = np.where(\n        np.logical_and(left_edge_vec > threshold, right_edge_vec > threshold)\n    )[0]\n    if len(inds) > 0:\n        left_edge_vec = left_edge_vec[inds]\n        # left_edge_vec[left_edge_vec > 0] = 1\n        right_edge_vec = right_edge_vec[inds]\n        # print(left_edge_vec)\n        # print(right_edge_vec)\n        # right_edge_vec[right_edge_vec > 0] = 1\n        R = np.corrcoef(left_edge_vec, right_edge_vec)\n        corr = R[0, 1]\n        # print(corr)\n        # print()\n        if i < 40:\n            plt.figure()\n            plt.scatter(left_edge_vec, right_edge_vec)\n            plt.title(corr)\n            plt.axis(\"square\")\n        # corr = np.count_nonzero(left_edge_vec - right_edge_vec) / len(left_edge_vec)\n    else:\n        corr = 0\n    if np.isnan(corr):\n        corr = 0\n    pair_corrs.append(corr)\npair_corrs = np.array(pair_corrs)\n\n\nground_truth_file = (\n    base_path / \"neuron-groups/GroundTruth_NeuronPairs_Brain-2019-07-29.csv\"\n)\n\nground_truth_df = pd.read_csv(ground_truth_file)\nground_truth_df.set_index(\"ID\", inplace=True)\nground_truth_df.head()\nground_truth_ids = ground_truth_df.index.values\nground_truth_sides = ground_truth_df[\"Hemisphere\"].values\n\nknown_inds = []\nfor cell_id, side in zip(ground_truth_ids, ground_truth_sides):\n    if side == \" left\":\n        ind = np.where(mg.meta.index.values == cell_id)[0]\n        if len(ind) > 0:\n            known_inds.append(ind[0])\n\nnot_known_inds = np.setdiff1d(range(n_pairs), known_inds)\nnew_pair_corrs = pair_corrs[not_known_inds]\ntruth_pair_corrs = pair_corrs[known_inds]\n\nsns.set_context(\"talk\")\nplt.figure(figsize=(10, 5))\nsns.distplot(new_pair_corrs, label=\"New pairs\")\nsns.distplot(truth_pair_corrs, label=\"Ground truth\")\nplt.legend()\nplt.title(threshold)\nstashfig(f\"both-t{threshold}-corr-nodewise\")\n\nout_pair_df = pd.DataFrame()\nout_pair_df\n\n# %% Look at correlation vs degree\ndeg_df = mg.calculate_degrees()\nplot_df = pd.DataFrame()\ntotal_degree = deg_df[\"Total degree\"].values\nplot_df[\"Mean total degree\"] = (\n    total_degree[:n_pairs] + total_degree[n_pairs : 2 * n_pairs]\n) / 2\nplot_df[\"Correlation\"] = pair_corrs\nplt.figure(figsize=(10, 5))\nsns.scatterplot(data=plot_df, x=\"Mean total degree\", y=\"Correlation\")\nstashfig(\"corr-vs-degree\")\n\nsns.jointplot(\n    data=plot_df, x=\"Mean total degree\", y=\"Correlation\", kind=\"hex\", height=10\n)\nstashfig(\"corr-vs-degree-hex\")\n\n# %% [markdown]\n# # Find the cells where correlation is < 0\nskeleton_labels = mg.meta.index.values[: 2 * n_pairs]\nside_labels = mg[\"Hemisphere\"][: 2 * n_pairs]\nleft_right_pairs = zip(\n    skeleton_labels[:n_pairs], skeleton_labels[n_pairs : 2 * n_pairs]\n)\n\ncolors = []\nids = []\n\nfor i, (left, right) in enumerate(left_right_pairs):\n    if pair_corrs[i] < 0:\n        hex_color = \"#%02X%02X%02X\" % (r(), r(), r())\n        colors.append(hex_color)\n        colors.append(hex_color)\n        ids.append(left)\n        ids.append(right)\n\nstashskel(\"pairs-low-corr\", ids, colors, colors=colors, palette=None)\n\n# %% [markdown]\n# # Look at number of disagreements vs degree\nprop_disagreements = []\nfor i in range(n_pairs):\n    left_edge_vec = np.concatenate((ll_edges[i], lr_edges[i]))\n    right_edge_vec = np.concatenate((rr_edges[i], rl_edges[i]))\n    left_edge_vec[left_edge_vec > 0] = 1\n    right_edge_vec[right_edge_vec > 0] = 1\n    n_disagreement = np.count_nonzero(left_edge_vec - right_edge_vec)\n    prop_disagreement = n_disagreement / len(left_edge_vec)\n    prop_disagreements.append(prop_disagreement)\nprop_disagreements = np.array(prop_disagreements)\nplot_df[\"Prop. disagreements\"] = prop_disagreements\n\nsns.jointplot(\n    data=plot_df, x=\"Mean total degree\", y=\"Prop. disagreements\", kind=\"hex\", height=10\n)\n#%%\npair_corrs = []\nfor i in range(n_pairs):\n    left_edge_vec = np.concatenate((ll_edges[i], lr_edges[i]))\n    right_edge_vec = np.concatenate((rr_edges[i], rl_edges[i]))\n    both_edges = np.stack((left_edge_vec, right_edge_vec), axis=-1)\n    avg_edges = np.mean(both_edges, axis=-1)\n\n#%%\nleft_edges = np.concatenate((left_left_adj.ravel(), left_right_adj.ravel()))\nright_edges = np.concatenate((right_right_adj.ravel(), right_left_adj.ravel()))\nall_edges = np.stack((left_edges, right_edges), axis=1)\nall_edges_sum = np.sum(all_edges, axis=1)\nedge_mask = all_edges_sum > 0\nall_edges = all_edges[edge_mask]\nleft_edges = left_edges[edge_mask]\nright_edges = right_edges[edge_mask]\nmean_edges = np.mean(all_edges, axis=-1)\ndiff_edges = np.abs(left_edges - right_edges)\nplot_df = pd.DataFrame()\nplot_df[\"Mean (L/R) edge\"] = mean_edges\nplot_df[\"Diff (L/R) edge\"] = diff_edges\nplt.figure(figsize=(10, 10))\nsns.scatterplot(mean_edges, diff_edges)\nsns.jointplot(data=plot_df, x=\"Mean (L/R) edge\", y=\"Diff (L/R) edge\", kind=\"hex\")\nplt.figure(figsize=(10, 5))\nbins = np.linspace(-1, 40, 41)\nsns.distplot(diff_edges, kde=False, norm_hist=False, bins=bins)\nsns.jointplot(\n    data=plot_df,\n    x=\"Mean (L/R) edge\",\n    y=\"Diff (L/R) edge\",\n    kind=\"hex\",\n    xlim=(0, 5),\n    ylim=(0, 5),\n)\n\n# %% [markdown]\n# # this works, would be to plot a heatmap\n\nfig, ax = plt.subplots(1, 1, figsize=(10, 10))\nedge_plot_df = pd.DataFrame()\nedge_plot_df[\"y\"] = left_edges\nedge_plot_df[\"x\"] = right_edges\ncount_df = edge_plot_df.groupby([\"x\", \"y\"]).size().unstack(fill_value=0)\ncount_df = count_df.combine_first(count_df.T).fillna(0.0)\nsns.heatmap(\n    count_df + 1,\n    cmap=\"Blues\",\n    cbar=True,\n    ax=ax,\n    square=True,\n    norm=LogNorm(vmin=count_df.values.min(), vmax=count_df.values.max()),\n)\nylims = ax.get_ylim()\nax.set_ylim((ylims[1], ylims[0]))\nax.yaxis.set_major_locator(plt.MaxNLocator(3))\nax.xaxis.set_major_locator(plt.MaxNLocator(3))\n","sub_path":"notebooks/58.4-BDP-analyze-pairs.py","file_name":"58.4-BDP-analyze-pairs.py","file_ext":"py","file_size_in_byte":26324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"413477723","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nfrom os.path import join, dirname, abspath, isfile, isdir\nimport argparse\n\nfrom dotenv import load_dotenv\n\ndir_scr = os.path.abspath(os.path.dirname(__file__))\nsys.path.append(join(dir_scr, \"..\"))\nimport helper as fn\n\nos.chdir(dir_scr)\nfile_env = os.path.join(dir_scr, \".env\")\n\ndef main(_args):\n    \"\"\"\n    initialize container\n    \"\"\"\n\n    envs = fn.getenv(file_env)\n    node = envs['NODE']\n    if not _args.node is None:\n        node = _args.node\n    if node == \"\":\n        print(f\"[error] node type not set.\")\n        sys.exit()\n\n    target = \"\"\n    if node != \"all\":\n        target = f\"rds-{node}\"\n\n    # サービス削除\n    cmd_down = \"docker-compose down\"\n    for line in fn.cmdlines(_cmd=cmd_down):\n        sys.stdout.write(line)\n\n    fn.rmdir(join(dir_scr, \"vol\", node))\n\n    # サービス作成\n    for line in fn.cmdlines(_cmd=f\"docker-compose up -d {target}\"):\n        sys.stdout.write(line)\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser(description='set env params')\n    parser.add_argument('--node', '-n', help=\"(option) set generate node type. 'master' or 'slave' or 'all'\")\n    args = parser.parse_args()\n\n    _input = input(\"initialize container. ok? (y/*) :\").lower()\n    if not _input in [\"y\", \"yes\"]:\n        print(\"[info] initialize canceled.\")\n        sys.exit()\n\n    print(\"[info] initialize start.\")\n    main(args)\n    print(\"[info] initialize end.\")\n","sub_path":"init.py","file_name":"init.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"316666253","text":"def ui_config():\n    dataset = [\n        'Stackexchange',\n        'Ionesoft'\n    ]\n    algorithm = [\n        'Plain',\n        'Stemming',\n        'Lemmatization',\n        'Stopwords',\n        'Stopwords & stemming',\n        'Stopwords & lemmatization'\n    ]\n    domain_limit = [\n        'max_df 100%',\n        'max_df  75%',\n        'max_df  50%'\n    ]\n    return dataset, algorithm, domain_limit\n","sub_path":"backend/lib/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"452606384","text":"maior = soma = 0\r\nmenor = 9999999999\r\nproduto = 1\r\nqtde = int(input(\"Quantos números você deseja informar: \"))\r\nwhile qtde > 0:\r\n   numero = int(input(\"Digite um número: \"))\r\n   if numero > maior:\r\n       maior = numero\r\n   if numero < menor:\r\n       menor = numero\r\n   soma = soma + numero\r\n   produto = produto * numero\r\n   qtde = qtde - 1\r\nprint('-='*30)\r\nprint(f'O menor valor digitado foi {menor}.')\r\nprint(f'O maior valor digitado foi {maior}.')\r\nprint(f'A soma entre os valores digitados é {soma}.')\r\nprint(f'O produto entre os valores digitados é {produto}.')\r\nprint('-='*30)\r\n\r\n","sub_path":"Exercícios 01/Quest 20.py","file_name":"Quest 20.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"324095585","text":"def mul(a,c):\n\treturn a*c\ndef tables (a,b):\n\tc = 0\n\twhile c!=b+1: \n\t\tprint (mul(a,c))\n\t\tc+=1\n\nif __name__ == '__main__':\n\ta = float(input(\"Enter the value of the number: \"))\n\tb = int(input(\"Enter the maximum number of table lines you need: \"))\n\tprint ('The tables of ',a,'to the number',b,)\n\ttables(a,b)","sub_path":"tables.py","file_name":"tables.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"379375575","text":"from coffea import processor\nfrom coffea.nanoevents import NanoAODSchema\nimport uproot3\nimport numpy as np\nimport sys\nimport os\nimport time\nimport argparse\nfrom collections import OrderedDict\n\nsys.path.append(\"../utilities/\")\nimport uproot3Utilities as uproot3utl\nimport processorPreSelection\n\n\ndef print_cutflow(cutflow):\n    \"\"\"Print cut efficiencies for cuts needed for defining some of the variables.\n\n    For instance, to compute deltaR between leading 2 jets, events needs\n    to have at least 2 jets.\n\n    Args:\n        cutflow (dict[coffea.processor.defaultdict_accumulator]):\n            Keys are cut names\n            Values are numbers of processed events (sum of gen weights)\n \n    Returns:\n        dict[float]\n            Keys are cut names\n            Values are absolute efficiencies\n    \"\"\"\n\n    len_column1 = max([ len(k) for k in cutflow.keys() ])\n    efficiencies = {}\n\n    print(\"\\nCutflow:\")\n    print(\"\\tCut\" + (len_column1-3)*\" \" + \"  Abs. eff. [%]   Rel. eff. [%]\")\n    n_all = cutflow[\"all\"]\n    for cut, n in cutflow.items():\n        if cut != \"all\":\n            absolute_efficiency = 100*n/n_all\n            if n_previous_cut > 0.:\n                relative_efficiency = 100*n/n_previous_cut\n            else:\n                relative_efficiency = np.nan\n            spaces = (len_column1-len(cut)+(absolute_efficiency<10))*\" \"\n            print(\"\\t%s%s  %.2f           %.2f\" %(cut, spaces, absolute_efficiency, relative_efficiency))\n            efficiencies[cut] = absolute_efficiency/100\n        n_previous_cut = n\n    \n    efficiencies[\"totalEfficiency\"] = absolute_efficiency\n \n    return efficiencies\n\n\ndef write_root_file(output_file, events, efficiencies, debug):\n    \"\"\"Write ROOT file with Events and Efficiencies TTrees.\n\n    Args:\n        output_file (str)\n        events (coffea.processor.accumulator.dict_accumulator)\n            Keys are Events branch names\n            Values are branches (coffea.processor.accumulator.column_accumulator)\n        efficiencies (dict[float])\n            Keys are Efficicies branch names\n            Values are efficiencies (float)\n        debug (bool)\n\n    Returns:\n        None\n    \"\"\"\n\n    # Making branches to write to Events tree\n    branches_events, branches_init_events = uproot3utl.make_branches(events, debug)\n    branches_efficiencies, branches_init_efficiencies = uproot3utl.make_branches(efficiencies, debug)\n\n    if debug:\n        print(\"\\nEvents branches keys:\")\n        print(branches_events.keys())\n        print(\"\\nEvents branchesInit keys:\")\n        print(branches_init_events.keys())\n\n\n    with uproot3.recreate(output_file) as root_file:\n        # Save events to output ROOT file\n        uproot3utl.write_tree_to_root_file(root_file, \"Events\", branches_events, branches_init_events)\n        print(\"\\nTTree Events saved to output file %s\" %output_file)\n\n        # Save cut efficiencies to output ROOT file\n        uproot3utl.write_tree_to_root_file(root_file, \"Efficiencies\", branches_efficiencies, branches_init_efficiencies)\n        print(\"TTree Efficiencies saved to output file %s\" %output_file)\n\n    return\n\n\ndef main(input_files, output_file, file_type, processor_name, chunksize, maxchunks, nworkers, debug):\n    \"\"\"Produce ROOT file with pre-selected events.\n    \n    Args:\n        input_files (list[str])\n        output_file (str)\n        file_type (str)\n        processor_name (str)\n        chunksize (int)\n        maxchunks (int or None)\n        nworkers (int)\n        debug (bool)\n\n    Returns:\n        None\n    \"\"\"\n\n    print(\"Input files:\")\n    print(input_files)\n\n    ## Fileset\n    fileset = { \"fileset\": input_files }\n\n    ## Make pre-selections\n    accumulator = processor.run_uproot_job(\n        fileset,\n        treename = \"Events\",\n        processor_instance = getattr(processorPreSelection, processor_name)(file_type),\n        executor = processor.iterative_executor,\n        executor_args = {\"schema\": NanoAODSchema, \"workers\": nworkers},\n        chunksize = chunksize,\n        maxchunks = maxchunks\n        )\n\n    ## Print out cutflow\n    cutflow = accumulator.pop(\"cutflow\")\n    efficiencies = print_cutflow(cutflow)\n\n    ## Making output ROOT file\n    if efficiencies[\"totalEfficiency\"] == 0.:\n        print(\"\\nWARNING: 0 event passed pre-selections. Will not write an empty ROOT file.\")\n    else:\n        write_root_file(output_file, accumulator, efficiencies, debug)\n\n    return\n\n\nif __name__ == \"__main__\":\n\n    tstart = time.time()\n\n    ## Parse arguments\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"-i\", \"--inputFiles\",\n        help=\"Input ROOT files to skim. Format: Comma separated list of files \"\\\n             +\"or txt file with list of file (1 file name per line)\",\n        # Example for the format:\n        # Ex1: file1.root,file2.root\n        # Ex2: $ cat input.txt\n        #      file1.root\n        #      file2.root\n        required=True\n        )\n    parser.add_argument(\n        \"-o\", \"--output\",\n        help=\"Output ROOT file\",\n        required=True\n        )\n    parser.add_argument(\n        \"-t\", \"--fileType\",\n        help=\"Input file type, mandatory argument\",\n        choices=[\"PFNanoAOD_102X\", \"PFNanoAOD_106X_v01\", \"PFNanoAOD_106X_v02\"],\n        required=True\n        )\n    parser.add_argument(\n        \"-p\", \"--processor\",\n        help=\"Coffea processor to be used, as defined in processorPreSelection.py\",\n        required=True\n        )\n    parser.add_argument(\n        \"-c\", \"--chunksize\",\n        help=\"Size of the data chunks (default=100000)\",\n        default=100000, type=int\n        )\n    parser.add_argument(\n        \"-m\", \"--maxchunks\",\n        help=\"Maximum number of chunks to process, no flag means no maximum\",\n        type=int\n        )\n    parser.add_argument(\n        \"-n\", \"--nworkers\",\n        help=\"Number of worker nodes (default=4)\",\n        default=4, type=int\n        )\n    parser.add_argument(\n        \"-debug\", \"--debug\",\n        help=\"Debug mode\",\n        action=\"store_true\",\n        )\n    \n    \n    args = parser.parse_args()\n\n    ## Create output directory if it does not exist\n    split = args.output.split(\"/\")\n    if len(split) > 1:\n        if len(split) == 2:\n            outputDirectory = (args.output[0]==\"/\")*\"/\" + split[0]\n        elif len(split) > 1:\n            outputDirectory = (args.output[0]==\"/\")*\"/\" + os.path.join(*args.output.split(\"/\")[:-1])\n        if not os.path.exists(outputDirectory):\n            os.makedirs(outputDirectory)\n\n    ## Get list of input ROOT files\n    # If coma separated list of ROOT files (which ends with the .root of the last ROOT file)\n    if args.inputFiles.endswith(\".root\"):\n        inputFiles = args.inputFiles.split(\",\")\n    # Else list of ROOT files in txt file\n    else:\n        with open (args.inputFiles, \"r\") as txtfile:\n            inputFiles = txtfile.readlines()\n        inputFiles = [ x.replace(\"\\n\", \"\") for x in inputFiles ]\n        inputFiles = [ x for x in inputFiles if x.endswith(\".root\") ]\n\n    ## Make pre-selection\n    main(inputFiles, args.output, args.fileType, args.processor, args.chunksize, args.maxchunks, args.nworkers, args.debug)\n\n\n    elapsed = time.time() - tstart\n    print(\"\\nTotal elapsed time: %d s\" %elapsed)\n\n\n","sub_path":"preSelection/makePreSelection.py","file_name":"makePreSelection.py","file_ext":"py","file_size_in_byte":7196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"344699077","text":"import os\nimport json\n\n# Gestion des logs\nclass TrainLogging(object):\n\n  def __init__(self, current_results_path, train_id):\n    # set paths\n    self._current_results_path = current_results_path\n    self._train_id = train_id\n    self._settings_file_name = 'settings.csv'\n    self._epochs_file_name = 'epochs.csv'\n    self._results_file_name = 'results.csv'\n    # init setting file\n    log_path = self.get_settings_path()\n    if not os.path.exists(log_path):\n      file_handler = open(log_path, \"w\")\n      header = TrainLogging._get_settings_header()\n      file_handler.write(header+\"\\n\")\n      file_handler.close()\n    # init log file\n    log_path = self.get_epochs_path()\n    if not os.path.exists(log_path):\n      file_handler = open(log_path, \"w\")\n      header = TrainLogging._get_epochs_header()\n      file_handler.write(header+\"\\n\")\n      file_handler.close()\n    # init results file\n    log_path = self.get_results_path()\n    if not os.path.exists(log_path):\n      file_handler = open(log_path, \"w\")\n      header = TrainLogging._get_results_header()\n      file_handler.write(header+\"\\n\")\n      file_handler.close()\n  \n  @staticmethod\n  def _get_settings_header():\n    return \"train_id;classes_count;classes;validation_size;img_size;num_channels;learning_rate;num_iteration;batch_size;input_layer_dropout;flatten_layer_dropout;conv_layers_count;conv_layers_params;fc_layers_count;fc_layers_params;transformations_count;transformations;training_data;validation_data\"\n  \n  @staticmethod\n  def _get_epochs_header():\n    return \"training_epoch;training_accuracy;validation_accuracy;validation_loss\"\n    \n  @staticmethod\n  def _get_results_header():\n    return \"train_id;class_count;data_count;correct_predictions\"\n\n  def get_epochs_path(self):\n    return os.path.join(self._current_results_path, self._train_id, self._epochs_file_name)\n\n  def get_settings_path(self):\n    return os.path.join(self._current_results_path, self._settings_file_name)\n\n  def get_results_path(self):\n    return os.path.join(self._current_results_path, self._results_file_name)\n  \n  def log_settings(self, classes, settings, conv_layers_params, fc_layers_params, transformations, data):\n    log_path = self.get_settings_path()\n    file_handler = open(log_path, \"a\")\n    line = \"{};{};{};{};{};{};{};{};{};{};{};{};{};{};{};{};{};{};{}\".format(\n      self._train_id,\n      len(classes),\n      json.dumps(classes),\n      settings['validation_size'],\n      settings['img_size'],\n      settings['num_channels'],\n      settings['learning_rate'],\n      settings['num_iteration'],\n      settings['batch_size'],\n      settings['input_layer_dropout'],\n      settings['flatten_layer_dropout'],\n      len(conv_layers_params),\n      json.dumps(conv_layers_params),\n      len(fc_layers_params),\n      json.dumps(fc_layers_params),\n      len(transformations),\n      json.dumps(transformations),\n      len(data.train.labels),\n      len(data.valid.labels)\n    )\n    file_handler.write(line+\"\\n\")\n    file_handler.close()\n    \n  def log_epoch(self, epoch, acc, val_acc, val_loss):\n    log_path = self.get_epochs_path()\n    file_handler = open(log_path, \"a\")\n    line = \"{};{};{};{}\".format(\n      epoch+1,\n      acc,\n      val_acc,\n      val_loss,\n    )\n    file_handler.write(line+\"\\n\")\n    file_handler.close()\n\n  def log_results(self,classes, data_counts, results):\n    log_path = self.get_results_path()\n    \n    file_handler = open(log_path, \"a\")\n    \n    for index, class_name in enumerate(classes) :\n      line = \"{};{};{};{}\".format(\n        self._train_id,\n        class_name,\n        data_counts[index],\n        results[index]\n      )\n      file_handler.write(line+\"\\n\")\n    \n    file_handler.close()","sub_path":"train_logging.py","file_name":"train_logging.py","file_ext":"py","file_size_in_byte":3670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"216592035","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        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n        ('server', '0002_move_user'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='game',\n            name='players',\n            field=models.ManyToManyField(to=settings.AUTH_USER_MODEL),\n        ),\n        migrations.AddField(\n            model_name='move',\n            name='date',\n            field=models.DateTimeField(auto_now_add=True, null=True),\n        ),\n    ]\n","sub_path":"anranserver/server/migrations/0003_auto_20150808_2136.py","file_name":"0003_auto_20150808_2136.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"499929949","text":"from collections import Counter\nstr1= \"pale\"\nstr2 = \"pile\"\n\ndef compare(str1,str2):\n    a = len(str1)\n    b = len(str2)\n    va1 = 0\n    if a == b:\n        for i in range(a):\n            if str1[i]==str2[i]:\n                continue\n            if str1[i]!=str2[i]:\n                val += 1\n        if val == 1:\n            return True\n    if a > b:\n        if a == b+1:\n            print(\"yes\")\n    return 0\n\n\nanswer = compare(list(str1),list(str2))","sub_path":"string/string_compare.py","file_name":"string_compare.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"225913564","text":"print('=-='*10)\nprint('    TRANSAÇÕES EFETUADAS')\nprint('=-='*10)\n\ntotal = 0\nqnt = int(input(\"Informe a quantidade de transações efetuadas hoje: \"))\n\nfor n in range (qnt):\n    valor = float(input(\"Informe o valor da transação: \"))\n    total = total + valor\n\nmedia = total / qnt\n\nprint(f\"O valor total gasto foi de R$ {round(total,2)} e a média de gasto foi de R$ {round(media,2)}.\")\n\n\n\n\n\n\n\n\n","sub_path":"FIAP/Fase2_cap3/RM86567_Ex02.py","file_name":"RM86567_Ex02.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"103384520","text":"import torch\nimport pytorch_lightning as pl\nimport pandas as pd\nimport numpy as np\nimport cv2\n\n# from scipy import ndimage\n# from albumentations import CLAHE\n\nfrom .augmentation import get_augmentation_v2\n\n\ndef yolo2voc(image_height, image_width, bboxes):\n    \"\"\"\n    yolo => [xmid, ymid, w, h] (normalized)\n    voc  => [x1, y1, x2, y1]\n\n    \"\"\"\n    bboxes = bboxes.copy().astype(\n        float\n    )  # otherwise all value will be 0 as voc_pascal dtype is np.int\n\n    bboxes[..., [0, 2]] = bboxes[..., [0, 2]] * image_width\n    bboxes[..., [1, 3]] = bboxes[..., [1, 3]] * image_height\n\n    bboxes[..., [0, 1]] = bboxes[..., [0, 1]] - bboxes[..., [2, 3]] / 2\n    bboxes[..., [2, 3]] = bboxes[..., [0, 1]] + bboxes[..., [2, 3]]\n\n    return bboxes\n\n\nclass CXRDataset(torch.utils.data.Dataset):\n    def __init__(\n        self, data_dir, df, size=1024, mode=\"train\", transform=None, smooth=None\n    ):\n        self.data_dir = data_dir\n        self.df = df\n        self.size = size\n        self.mode = mode\n        self.training = self.mode == \"train\"\n        self.transform = transform\n        self.smooth = smooth\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, index):\n        img_id = self.df.loc[index, \"id\"].split(\"_image\")[0]\n        img_path = f\"{self.data_dir}/train/{img_id}_image.png\"\n\n        label = np.array(\n            list(\n                self.df.loc[\n                    index,\n                    [\n                        \"Negative for Pneumonia\",\n                        \"Typical Appearance\",\n                        \"Indeterminate Appearance\",\n                        \"Atypical Appearance\",\n                    ],\n                ]\n            )\n        )\n        if self.smooth:\n            label = np.clip(label, self.smooth, 1 - self.smooth)\n\n        label = label.astype(\"float32\")\n        img = cv2.imread(img_path, -1).astype(\"float32\")\n        img = np.concatenate((img[:, :, np.newaxis],) * 3, axis=-1)\n\n        mask_txt = img_path.replace(\".png\", \".txt\")\n        mask = np.zeros(img.shape[:2])\n        h, w = img.shape[:2]\n\n        try:\n            with open(mask_txt, \"r\") as f:\n                data = (\n                    np.array(f.read().replace(\"\\n\", \" \").strip().split(\" \"))\n                    .astype(np.float32)\n                    .reshape(-1, 5)\n                )\n            bdata = data[:, 1:]\n            bboxes = yolo2voc(h, w, bdata)\n            for bbox in bboxes:\n                x1, y1, x2, y2 = [int(bi) for bi in bbox]\n                mask[y1:y2, x1:x2] = 1.0  # TODO: Check dimension\n        except ValueError:  # No opacity - empty\n            pass\n\n        aug = self.transform(image=img, mask=mask)\n        img = aug[\"image\"]\n        mask = aug[\"mask\"]\n\n        return img, label, mask, img_path\n\n\nclass CXRDataModule(pl.LightningDataModule):\n    def __init__(self, cfg):\n        super().__init__()\n        self.cfg = cfg\n        self.batch_size = self.cfg.batch_size  # For auto_scale_batch_size\n        self.setup()\n\n    def setup(self, stage=None):\n\n        train_study_level = pd.read_csv(self.cfg.data_dir + \"/train_study_level.csv\")\n        train_image_level = pd.read_csv(self.cfg.data_dir + \"/train_image_level.csv\")\n\n        more_than_2_ids = []\n        for i in range(len(train_image_level)):\n            row = train_image_level.iloc[i]\n            sid = row[\"StudyInstanceUID\"]\n            sid_df = train_image_level[train_image_level[\"StudyInstanceUID\"] == sid]\n            if len(sid_df) >= 2:\n                more_than_2_ids.append(sid)\n\n        # Cleansing\n        train_image_level = train_image_level[\n            ~train_image_level[\"StudyInstanceUID\"].isin(more_than_2_ids)\n        ]\n        train_image_level.reset_index(inplace=True)\n\n        train_study_level[\"StudyInstanceUID\"] = train_study_level[\"id\"].apply(\n            lambda x: x.replace(\"_study\", \"\")\n        )\n        del train_study_level[\"id\"]\n        df = train_image_level.merge(train_study_level, on=\"StudyInstanceUID\")\n\n        # Apply fold\n        df = df.sample(frac=1).reset_index(drop=True)\n\n        df[\"fold\"] = df.index % 5\n\n        df_train = df[(df[\"fold\"] != self.cfg.fold_index)].reset_index(drop=True)\n        df_valid = df[(df[\"fold\"] == self.cfg.fold_index)].reset_index(drop=True)\n        # df_test = df[(df[\"fold\"] == self.cfg.fold_index)].reset_index(drop=True)\n\n        if self.cfg.augment_class:\n            negative = df_train[df_train[\"Negative for Pneumonia\"] == 1]\n            typical = df_train[df_train[\"Typical Appearance\"] == 1]\n            indeterminate = df_train[df_train[\"Indeterminate Appearance\"] == 1]\n            atypical = df_train[df_train[\"Atypical Appearance\"] == 1]\n\n            df_aug_train = pd.concat(\n                (\n                    negative,\n                    negative,\n                    typical,\n                    indeterminate,\n                    indeterminate,\n                    indeterminate,\n                    atypical,\n                    atypical,\n                    atypical,\n                    atypical,\n                    atypical,\n                    atypical,\n                ),\n                axis=0,\n            ).reset_index(drop=True)\n            df_train = df_aug_train\n\n        print(\"Training :: \", len(df_train))\n        print(\"Validation :: \", len(df_valid))\n\n        train_aug, val_aug = get_augmentation_v2(self.cfg.image_size)\n\n        self.train_dataset = CXRDataset(\n            data_dir=self.cfg.data_dir,\n            df=df_train,\n            size=self.cfg.image_size,\n            mode=\"train\",\n            transform=train_aug,\n            smooth=self.cfg.label_smoothing,\n        )\n\n        self.val_dataset = CXRDataset(\n            data_dir=self.cfg.data_dir,\n            df=df_valid,\n            size=self.cfg.image_size,\n            mode=\"val\",\n            transform=val_aug,\n        )\n\n        # self.test_dataset = CXRDataset(\n        #     data_dir=self.cfg.data_dir,\n        #     df=df_test,\n        #     size=self.cfg.image_size,\n        #     mode=\"test\",\n        #     transform=va,\n        # )\n\n    def train_dataloader(self):\n        train_dataloader = torch.utils.data.DataLoader(\n            self.train_dataset,\n            batch_size=self.batch_size,\n            shuffle=False,\n            num_workers=self.cfg.num_workers,\n            pin_memory=True,\n        )\n\n        return train_dataloader\n\n    # FIXME: shuffle=True: for various viz, doesn't matter at performance right?\n    def val_dataloader(self):\n        val_dataloader = torch.utils.data.DataLoader(\n            self.val_dataset,\n            batch_size=self.batch_size * 2,\n            shuffle=True,\n            num_workers=self.cfg.num_workers,\n            pin_memory=True,\n        )\n\n        return val_dataloader\n\n    def test_dataloader(self):\n        test_dataloader = torch.utils.data.DataLoader(\n            self.test_dataset,\n            batch_size=self.batch_size,\n            shuffle=False,\n            num_workers=self.cfg.num_workers,\n            pin_memory=False,\n        )\n\n        return test_dataloader\n","sub_path":"lightning/inputs/cxr_dm_aux.py","file_name":"cxr_dm_aux.py","file_ext":"py","file_size_in_byte":7062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"641160697","text":"import unittest\nimport copy\nfrom unittest.mock import Mock\n\nfrom shisell import AnalyticsDispatcher, AnalyticsContext\nfrom shisell.extenders import create_scoped, with_context, with_extra, with_extras, with_filter, with_identity, with_identities, with_meta\n\nRETURN_VALUE = 'some_return_value'\nEVENT_NAME = 'some_event_name'\n\n\nclass ExtendersTestSuite(unittest.TestCase):\n    def setUp(self):\n        dispatch_mock = Mock(name='dispatch')\n        dispatch_mock.return_value = RETURN_VALUE\n        self.dispatch_mock = dispatch_mock\n\n        self.dispatcher = AnalyticsDispatcher(dispatch_mock)\n        self.original_context = copy.deepcopy(self.dispatcher.Context)\n\n    def test_with_context(self):\n        \"\"\"\n        should call dispatch with passed context\n        \"\"\"\n\n        context = AnalyticsContext()\n        context.ExtraData = {'extra_key': 'some other data'}\n        context.MetaData = {'meta_key': 'some other data'}\n        context.Scopes = ['other', 'scope']\n\n        self.dispatcher.extend(with_context(context)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_create_scoped(self):\n        \"\"\"\n        should call dispatch with passed scope\n        \"\"\"\n\n        scope = \"some_scope\"\n        context = AnalyticsContext()\n        context.Scopes.append(scope)\n\n        self.dispatcher.extend(create_scoped(scope)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_with_extra(self):\n        \"\"\"\n        should call dispatch with passed extra data\n        \"\"\"\n\n        key = 'some_key'\n        value = 'some_value'\n        context = AnalyticsContext()\n        context.ExtraData[key] = value\n\n        self.dispatcher.extend(with_extra(key, value)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_with_extras(self):\n        \"\"\"\n        should call dispatch with all extras\n        \"\"\"\n\n        extras = {'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}\n        context = AnalyticsContext()\n        context.ExtraData = extras\n\n        self.dispatcher.extend(with_extras(extras)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_with_extras_doesnt_throw(self):\n        \"\"\"\n        should return same dispatcher if invalid input\n        \"\"\"\n\n        dispatcher = self.dispatcher.extend(with_extras(None))\n\n        self.assertIs(dispatcher, self.dispatcher)\n\n    def test_with_filter(self):\n        \"\"\"\n        should call dispatch with passed filter\n        \"\"\"\n\n        filter = 'some_filter'\n        context = AnalyticsContext()\n        context.Filters.append(filter)\n\n        self.dispatcher.extend(with_filter(filter)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_with_filters(self):\n        \"\"\"\n        should call dispatch with all filters\n        \"\"\"\n\n        filters = ['filter1', 'filter2']\n        context = AnalyticsContext()\n        context.Filters = filters\n\n        self.dispatcher.extend(with_filter(*filters)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_with_metadata(self):\n        \"\"\"\n        should call dispatch with passed meta data\n        \"\"\"\n\n        key = 'some_key'\n        value = 'some_value'\n        context = AnalyticsContext()\n        context.MetaData[key] = value\n\n        self.dispatcher.extend(with_meta(key, value)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_with_identity(self):\n        \"\"\"\n        should call dispatch with passed identity\n        \"\"\"\n\n        key = 'some_key'\n        value = 'some_value'\n        context = AnalyticsContext()\n        context.Identities[key] = value\n\n        self.dispatcher.extend(with_identity(key, value)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_with_identities(self):\n        \"\"\"\n        should call dispatch with all passed identities\n        \"\"\"\n\n        identities = {'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}\n        context = AnalyticsContext()\n        context.Identities = identities\n\n        self.dispatcher.extend(with_identities(identities)).dispatch(EVENT_NAME)\n\n        self.dispatch_mock.assert_called_once_with(EVENT_NAME, context)\n        self.assertEqual(self.dispatcher.Context, self.original_context)\n\n    def test_with_identities_doesnt_throw(self):\n        \"\"\"\n        should return same dispatcher if invalid input\n        \"\"\"\n\n        dispatcher = self.dispatcher.extend(with_identities(None))\n\n        self.assertIs(dispatcher, self.dispatcher)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/test_extenders.py","file_name":"test_extenders.py","file_ext":"py","file_size_in_byte":5394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"196574049","text":"x=input()\nm=int(input())\nd=ans=0\nr=int(1e20)\nfor i in x:\n\td=max(d,ord(i)-ord('0'))\nl=d+1\nwhile l<=r:\n\tmid=(l+r)//2\n\tcurr=0\n\tfor i in x:\n\t\tcurr*=mid\n\t\tcurr+=ord(i)-ord('0')\n\tif curr<=m:\n\t\tans=max(ans,mid)\n\t\tl=mid+1\n\telse:\n\t\tr=mid-1\nprint(ans-d)","sub_path":"atcoder/abc192/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"33805996","text":"# import required libraries\nimport os\nimport telebot\nimport datetime\nimport requests\nimport urllib.request\nimport subprocess\nimport config\n\n# setup bot with Telegram token from config\nbot = telebot.TeleBot(config.TELEGRAM_TOKEN)\n\nbot_text = '''\nBip-bop human,\nI classify images using neural networks 🚀\nSend me pictures, and I will classify them for you 🤟\n'''\n\n# store files in /tmp so storage does not get complete  \nresult_storage_path = 'tmp'\n\n# Handles all text messages that contains the command '/start' \n@bot.message_handler(commands=['start'])\ndef send_welcome(message):\n    bot.send_message(message.chat.id, bot_text)\n\n# Handles all photo messages\n@bot.message_handler(content_types=['photo'])\ndef handle(message):\n    log_request(message)\n    image_name = save_image_from_message(message)\n  \n    # object recognition\n    object_recognition_image(image_name)\n    bot.send_photo(message.chat.id, open('data/darknet/predictions.jpg','rb'), 'Identified objects, if any! 👻')\n  \n    # image classification\n    classification_list_result = classify_image(image_name)\n  \n    # send classification results\n    output = 'The image classifies as:\\n'\n    for result in classification_list_result:\n        output += result\n    output += '\\n🚀 Gimme more pics! 🚀'\n    \n    bot.reply_to(message, output)\n    cleanup_remove_image(image_name);  \n  \n# ----------- Helper functions ---------------\ndef log_request(message):\n    file = open('data/logs.txt', 'a') #append to file\n    file.write(\"{0} - {1} {2} [{3}]\\n\".format(datetime.datetime.now(), message.from_user.first_name, message.from_user.last_name, message.from_user.id)) \n    print(\"{0} - {1} {2} [{3}]\".format(datetime.datetime.now(), message.from_user.first_name, message.from_user.last_name, message.from_user.id))\n    file.close() \n  \ndef get_image_id_from_message(message):\n    # there are multiple array of images, check the biggest\n    return message.photo[len(message.photo)-1].file_id\n\ndef save_image_from_message(message):\n    cid = message.chat.id  \n    image_id = get_image_id_from_message(message)\n    bot.send_message(cid, '🔥 Analyzing image, be patient ! 🔥')\n  \n    # prepare image for downlading\n    file_path = bot.get_file(image_id).file_path\n\n    # generate image download url\n    image_url = \"https://api.telegram.org/file/bot{0}/{1}\".format(config.TELEGRAM_TOKEN, file_path)\n    print(image_url)\n  \n    # create folder to store pic temporary, if it doesnt exist\n    if not os.path.exists(result_storage_path):\n        os.makedirs(result_storage_path)\n  \n    # retrieve and save image\n    image_name = \"{0}.jpg\".format(image_id)\n    urllib.request.urlretrieve(image_url, \"{0}/{1}\".format(result_storage_path,image_name))\n  \n    return image_name;\n\ndef classify_image(image_name):\n    # classify image -> https://pjreddie.com/darknet/imagenet/\n    os.system('cd data/darknet && ./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights ../../{0}/{1} > ../../{0}/results.txt'.format(result_storage_path, image_name)) \n  \n    # retrieve classification results\n    results_file = open(\"{0}/results.txt\".format(result_storage_path),\"r\") \n    results = results_file.readlines()\n    results_file.close()  \n    return results\n  \ndef object_recognition_image(image_name):\n    # object recognition -> https://pjreddie.com/darknet/yolo/\n    os.system('cd data/darknet && ./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights ../../{0}/{1}'.format(result_storage_path, image_name)) \n  \ndef cleanup_remove_image(image_name):\n    os.remove('{0}/{1}'.format(result_storage_path, image_name))\n\nif __name__ == '__main__':\n    # run bot in loop\n    bot.polling(none_stop=True)\n","sub_path":"imageClassificationBot/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":3679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"8958764","text":"import gmpy2\nfrom Crypto.Cipher import AES\nfrom Crypto.Util.number import long_to_bytes\n\nc = list(map(int, open('enc.txt').read().split()))\n\nq = int(gmpy2.gcd(c[0]-c[1] + 1, c[1] - c[2] + 1))\nfor i in range(1,len(c) - 1):\n    q = int(gmpy2.gcd(q,c[i] - c[i+1] + 1))\n\nassert gmpy2.is_prime(q)\nprint(\"p: found!\")\n\nrand = c[0] % q - 1\nprint(\"rand: found!\")\n\np = (q - 1) // 2\np2 = int(gmpy2.iroot(p, 2)[0])\np1 = p // p2\n\nwhile p2 - p1 < 10 ** 9:\n    if p == p1 * p2 and gmpy2.is_prime(p1) and gmpy2.is_prime(p2):\n        break\n    p2 += 1\n    p1 = p // p2\n\nassert p == p1 * p2 and gmpy2.is_prime(p1) and gmpy2.is_prime(p2)\nprint(\"p1, p2: found!\")\n\nd = []\n\nfor i, c_i in enumerate(c):\n    d.append((c_i - rand - i - 1) // (rand + i) // q)\n\nres = \"\"\nfor i in d:\n    if pow(i, 2 * p2, q) == 1 :\n        res += '0'\n    elif pow(i, 2 * p1, q) == 1:\n        res += '1'\n    else:\n        print('err')\n        break\n\nres = int(res,2)\n\nprint('flag_enc: found!')\n\nc = long_to_bytes(res)\nkey = long_to_bytes(rand)\naes = AES.new(key[:16], AES.MODE_CBC, key[16:32])\nprint(aes.decrypt(c).decode('utf-8'))\n","sub_path":"sharif-ctf-2016/crypto/unterscheide-200/my_solve.py","file_name":"my_solve.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"43097633","text":"# Copyright (c) 2021, IAC Electricals and contributors\n# For license information, please see license.txt\n\nimport frappe\nfrom frappe import _\nfrom frappe.model.document import Document\nfrom frappe.model.mapper import get_mapped_doc\n\nclass PriceSchedule(Document):\n\tdef validate(self):\n\t\tfor i in self.items:\n\t\t\tif i.freight_charges_type == \"Percent\" or i.freight_charges_type == \"Amount\":\n\t\t\t\tif i.freight_charges == 0 or i.freight_charges == None:\n\t\t\t\t\tfrappe.throw(\"Please Enter First Freight Charges for row \"+ str(i.idx)+\" in Item Table\")\n\n\t\t\t# if i.freight_charges != 0 or i.freight_charges != None:\n\t\t\t# \tprint(\"########################\")\n\t\t\t# \tif not i.freight_charges_type:\n\t\t\t# \t\tfrappe.throw(\"Please Enter First Freight Charges type.........................for row \"+ str(i.idx))\t\n\n\t\t\tif i.freight_charges_type_ == \"Percent\" or i.freight_charges_type_ == \"Amount\":\n\t\t\t\tif i.freight_charges_ == 0 or i.freight_charges_ == None:\n\t\t\t\t\tfrappe.throw(\"Please Enter Secound Freight Charges for row \"+ str(i.idx)+\" in Item Table\")\n\n\t\t\t# if i.freight_charges_ != 0 or i.freight_charges_ != None:\n\t\t\t# \tif not i.freight_charges_type_:\n\t\t\t# \t\tfrappe.throw(\"Please Enter Secound Freight Charges type.........................for row \"+ str(i.idx))\t\t\t\n\n\n@frappe.whitelist()\ndef address_query(name):\n\tif name:\n\t\taddress_lists = frappe.db.get_all(\"Address\",{'name':name},[\"name\",\"address_line1\",\"address_line2\",\"city\",\"state\",\"country\",\"pincode\"])\n\t\tif address_lists:\n\t\t\treturn address_lists\n\n@frappe.whitelist()\ndef contact_query(name):\n\tif name:\n\t\tcontact_info = {\n\t\t\t'mobile_no' :'',\n\t\t\t'email' :'',\n\t\t\t'first_name':'',\n\t\t\t'middle_name':'',\n\t\t\t'last_name':'' \n\t\t}\n\t\tcontact_doc = frappe.get_doc(\"Contact\",name)\n\t\tif contact_doc:\t\t\n\t\t\tcontact_info['first_name'] = contact_doc.get('first_name')\n\t\t\tcontact_info['middle_name'] = contact_doc.get('middle_name')\n\t\t\tcontact_info['last_name'] = contact_doc.get('last_name')\n\t\t\tfor phone in contact_doc.phone_nos:\n\t\t\t\tif phone.get('is_primary_mobile_no'):\n\t\t\t\t\tcontact_info['mobile_no'] = phone.get('phone')\n\t\t\tfor email in contact_doc.email_ids:\n\t\t\t\tif email.get('is_primary'):\n\t\t\t\t\tcontact_info['email'] = email.get('email_id')\t\t\n\t\t\treturn contact_info\t\t\n\n\n@frappe.whitelist()\ndef fetch_address_contact_name(name):\n\tif name:\n\t\taddress_contact_name = {\n\t\t\t'address_name' :'',\n\t\t\t'contact_name' :'' \n\t\t}\n\t\tadd_name = frappe.get_all('Dynamic Link', filters={'link_doctype': 'Customer', 'link_name': name, 'parenttype': 'Address'}, fields=['parent'])\n\t\tcon_name = frappe.get_all('Dynamic Link', filters={'link_doctype': 'Customer', 'link_name': name, 'parenttype': 'Contact'}, fields=['parent'])\n\t\tif add_name:\n\t\t\taddress_contact_name['address_name'] = add_name[0].get('parent')\n\t\tif con_name:\n\t\t\taddress_contact_name['contact_name'] = con_name[0].get('parent')\n\t\treturn address_contact_name\n\n\n@frappe.whitelist()\ndef number_to_word(amount):\n\tdef get_word(n):\n\t\twords={ 0:\"\", 1:\"One\", 2:\"Two\", 3:\"Three\", 4:\"Four\", 5:\"Five\", 6:\"Six\", 7:\"Seven\", 8:\"Eight\", 9:\"Nine\", 10:\"Ten\", 11:\"Eleven\", 12:\"Twelve\", 13:\"Thirteen\", 14:\"Fourteen\", 15:\"Fifteen\", 16:\"Sixteen\", 17:\"Seventeen\", 18:\"Eighteen\", 19:\"Nineteen\", 20:\"Twenty\", 30:\"Thirty\", 40:\"Forty\", 50:\"Fifty\", 60:\"Sixty\", 70:\"Seventy\", 80:\"Eighty\", 90:\"Ninty\" }\n\t\tif n<=20:\n\t\t\treturn words[n]\n\t\telse:\n\t\t\tones=n%10\n\t\t\ttens=n-ones\n\t\t\treturn words[tens]+\" \"+words[ones]\n\n\tdef get_all_word(n):\n\t\td=[100,10,100,100]\n\t\tv=[\"\",\"Hundred And\",\"Thousand\",\"lakh\"]\n\t\tw=[]\n\t\tfor i,x in zip(d,v):\n\t\t\tt=get_word(n%i)\n\t\t\tif t!=\"\":\n\t\t\t\tt+=\" \"+x\n\t\t\tw.append(t.rstrip(\" \"))\n\t\t\tn=n//i\n\t\tw.reverse()\n\t\tw=' '.join(w).strip()\n\t\tif w.endswith(\"And\"):\n\t\t\tw=w[:-3]\n\t\treturn w\n\n\tarr=str(amount).split(\".\")\n\tamount=int(arr[0])\n\tcrore=amount//10000000\n\tamount=amount%10000000\n\tword=\"\"\n\tif crore>0:\n\t\tword+=get_all_word(crore)\n\t\tword+=\" crore \"\n\tword+=get_all_word(amount).strip()+\" only.\"\n\tif len(arr)>1:\n\t\tif len(arr[1])==1:\n\t\t\tarr[1]+=\"0\"\n\t\tword+=\" and \"+get_all_word(int(arr[1]))+\" paisa\"\n\t\n\treturn word\n\n\n@frappe.whitelist()\ndef calculate_taxes(tax_temlet_name,total_amount,unit_price_1_total_amount):\n\ttry:\n\t\ttax_items = []\n\t\ttx_calculation = 0.0\n\t\ttotal_tax_amount =0.0\n\t\ttax_details = frappe.get_doc(\"Sales Taxes and Charges Template\", tax_temlet_name).taxes\n\t\tfor taxes in tax_details:\n\t\t\ttx_calculation = float(total_amount)/100*taxes.rate\n\t\t\tif taxes.idx == 1:\n\t\t\t\ttotal_tax_amount =float(total_amount) + tx_calculation\n\t\t\telse:\n\t\t\t\ttotal_tax_amount = total_tax_amount + tx_calculation\n\n\n\t\t\tunit_price_1_tx_calculation = float(unit_price_1_total_amount)/100*taxes.rate\n\t\t\tif taxes.idx == 1:\n\t\t\t\tunit_price_1_total_tax_amount =float(unit_price_1_total_amount) + unit_price_1_tx_calculation\n\t\t\telse:\n\t\t\t\tunit_price_1_total_tax_amount = unit_price_1_total_tax_amount + unit_price_1_tx_calculation\t\n\n\t\t\ttemp = {\n\t\t\t\t'charge_type' : taxes.charge_type,\n\t\t\t\t'account_head' : taxes.account_head,\n\t\t\t\t'description' : taxes.description,\n\t\t\t\t'rate' : taxes.rate,\n\t\t\t\t'unit_price_2_tax_amount' : tx_calculation,\n\t\t\t\t'unit_price_2_total':total_tax_amount,\n\t\t\t\t'unit_price_1_tax_amount' : unit_price_1_tx_calculation,\n\t\t\t\t'unit_price_1_total':unit_price_1_total_tax_amount\n\t\t\t}\n\t\t\ttax_items.append(temp)\n\t\treturn tax_items\n\texcept Exception as e:\n\t\traise e\n\n\n@frappe.whitelist()\ndef make_blanket_order(source_name, target_doc=None, ignore_permissions=False):\n\tfrappe.log_error(frappe.get_traceback(), _(\"Blanket order Button clicked....(Error_log)\"))\n\tdoclist = get_mapped_doc(\"Price Schedule\", source_name, {\n\t\t\"Price Schedule\": {\n\t\t\t\"doctype\": \"Blanket Order\",\n\t\t\t\"field_map\": {\n\t\t\t\t\t\"name\": \"Price Schedule\",\n\t\t\t\t\t\"name\":\"price_schedule_no\",\n\t\t\t\t\t\"terms\":\"tc_name\",\n\t\t\t\t\t\"term_details\":\"terms\"\n\t\t\t\t},\n\t\t\t\"validation\": {\n\t\t\t\t\t\"docstatus\": [\"=\", 1]\n\t\t\t\t}\n\t\t\t},\n\t\t\t\"Price Schedule Items\": {\n\t\t\t\t\"doctype\": \"Blanket Order Item\",\n\t\t\t\t\"field_map\": {\n\t\t\t\t\t\"total_quantity\": \"qty\"\n\t\t\t\t},\n\t\t\t},\n\t\t}, target_doc)\n\n\treturn doclist\n\n\n@frappe.whitelist()\ndef make_sales_order(source_name, target_doc=None, ignore_permissions=False):\n\tdef set_missing_values(source, target):\n\t\ttarget.against_price_schedule = 1\n\tdef update_item(source_doc, target_doc, source_parent):\n\t\ttarget_doc.against_price_schedule = 1\n\t\ttarget_doc.price_schedule = source_name\n\n\tdoclist = get_mapped_doc(\"Price Schedule\", source_name, {\n\t\t\"Price Schedule\": {\n\t\t\t\"doctype\": \"Sales Order\",\n\t\t\t\"field_map\": {\n\t\t\t\t\t\"name\":\"price_schedule_no\",\n\t\t\t\t\t\"sales_taxes_and_charges_template\":\"taxes_and_charges\",\n\t\t\t\t\t\"contact_person_mobile_no\":\"contact_mobile\",\n\t\t\t\t\t\"terms\":\"tc_name\",\n\t\t\t\t\t\"term_details\":\"terms\"\n\t\t\t\t},\n\t\t\t\"validation\": {\n\t\t\t\t\t\"docstatus\": [\"=\", 1]\n\t\t\t\t}\n\t\t\t},\n\t\t\t\"Price Schedule Items\": {\n\t\t\t\t\"doctype\": \"Sales Order Item\",\n\t\t\t\t\"field_map\": {\n\t\t\t\t\t\"total_quantity\": \"qty\"\n\t\t\t\t},\n\t\t\t\t\"postprocess\": update_item\n\t\t\t},\n\t\t\t\"Sales Taxes and Charges Table\": {\n\t\t\t\t\"doctype\": \"Sales Taxes and Charges\"\n\t\t\t},\n\t\t}, target_doc, set_missing_values)\n\n\treturn doclist\t\n\n\n","sub_path":"iac_electricals/iac_electricals/doctype/price_schedule/price_schedule.py","file_name":"price_schedule.py","file_ext":"py","file_size_in_byte":6875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"287122655","text":"import sys\nimport tensorflow as tf\nimport coloredlogs, logging\nimport os, ipdb\nimport glob\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport re\n\n\ncoloredlogs.install()\nanomaly_classification_feature_selection_folder='/home/user/baxter_ws/src/SPAI/smach_based_introspection_framework/introspection_data_folder.AC_offline_test/anomaly_classification_feature_selection_folder'\n\n\ndef get_anomalous_samples():\n    logger = logging.getLogger()\n    logger.info(\"load_csv_data_from_filtered_scheme\")\n    \n    folders = glob.glob(os.path.join(\n        anomaly_classification_feature_selection_folder,\n        'No.* filtering scheme',\n        'anomalies_grouped_by_type',\n        'anomaly_type_(*)',\n    )) \n\n    data_by_type = []\n    types = []\n    for folder in folders: # for each anomaly_type\n        samples = []\n        logger.info(folder)\n        path_postfix = os.path.relpath(folder, anomaly_classification_feature_selection_folder).replace(\"anomalies_grouped_by_type\"+os.sep, \"\")\n\n        prog = re.compile(r'anomaly_type_\\(?([^\\(\\)]+)\\)?')\n        anomaly_type = prog.search(path_postfix).group(1)\n        csvs = glob.glob(os.path.join(folder, '*', '*.csv'))\n        for j in csvs:\n            df =  pd.read_csv(j, sep = ',')\n            # delete the 1st column with is time index\n            df = df.drop(['Unnamed: 0'], axis = 1)\n            samples.append(df.values.T)\n        data = np.stack(samples)\n        logger.info('Successfully! load the data of %s'%anomaly_type)\n        # np.save(\"./anomalies/\"+anomaly_type+\".npy\", data)\n        \n        data_by_type.append(data)\n        types.append(anomaly_type)\n    return data_by_type, types\n\ndef feed_dict(batch_size, samples_type=None):\n    if samples_type == 'train':\n        indeces = np.random.randint(num_train_samples, size=batch_size)\n        samples = train_samples\n        \n    elif samples_type == 'valid':\n        indeces = np.random.randint(num_valid_samples, size=batch_size)\n        samples = valid_samples\n        \n    elif samples_type == 'test':\n        indeces = np.random.randint(num_test_samples, size=batch_size)\n        samples = test_samples\n        \n    return np.take(samples, indeces, axis=0)\n\n\nif __name__ == '__main__':\n\n    x_in_dim = 6\n    beta_1 = 0.05 # adam parameters\n    beta_2 = 0.001 # adam parameters\n    num_epochs = 3 # i.e. iterations 40000\n    batch_size = 1\n    num_hidden_units = 500\n    learning_rate_1 = 2e-5\n    learning_rate_2 = 1e-5\n    num_epochs_to_diff_learn_rate = int(num_epochs/2) # change the learning rate after half of the epochs\n    num_epochs_to_save_model = 1000 # save model after each 1000 iterations\n    #decay_rate = .7\n\n    z_dim = 20\n    \n    data_by_type, types = get_anomalous_samples()\n\n    for i, samples in enumerate(data_by_type):\n        anomaly_type = types[i]\n        time_steps = samples.shape[1]\n        train_samples = samples\n        valid_samples = samples\n        test_samples  = samples\n        num_train_samples = train_samples.shape[0] \n        num_valid_samples = valid_samples.shape[0] \n        num_test_samples = test_samples.shape[0] \n\n        network_params =  ''.join([\n                          'time_steps={}-'.format(time_steps),          \n                          'latent_dim={}-'.format(z_dim),          \n                          'dataset={}'.format(anomaly_type)])\n\n        # Dir structure : /base_dir/network_params/run_xx/train_or_test/\n        log_root = './anomalies_%s'%anomaly_type\n        log_base_dir = os.path.join(log_root, network_params)\n    \n        # Check for previous runs\n        if not os.path.isdir(log_base_dir):\n            os.makedirs(log_base_dir)\n\n        previous_runs = os.listdir(log_base_dir)\n\n        if len(previous_runs) == 0:\n            run_number = 1\n        else:\n            run_number = max([int(str.split(s,'run_')[1]) for s in previous_runs if 'run' in s]) + 1\n\n        log_dir = os.path.join(log_base_dir,'run_{0:02d}'.format(run_number))\n\n        train_summary_writer = tf.summary.FileWriter(log_dir + '/train')\n        valid_summary_writer = tf.summary.FileWriter(log_dir + '/valid')\n        test_summary_writer = tf.summary.FileWriter(log_dir + '/test')\n        model_save_path = log_dir + '/models'\n        figure_save_path = log_dir + '/figures'\n\n        #############################\n        # Setup graph\n        #############################\n        tf.reset_default_graph()\n        X = tf.placeholder(tf.float32, shape=(batch_size, x_in_dim, time_steps))\n\n        # time_slices containts input x at time t across batches.\n        x_in = time_steps * [None]\n        x_out = time_steps * [None]\n        h_enc = time_steps * [None]\n        h_dec = (time_steps + 1) * [None]\n\n        for t in range(time_steps):\n            x_in[t] = tf.squeeze(tf.slice(X,begin=[0,0,t],size=[-1,-1,1]),axis=2)\n\n\n        ###### Encoder network ###########\n        with tf.variable_scope('encoder_rnn'):\n            cell_enc = tf.nn.rnn_cell.BasicRNNCell(num_hidden_units,activation=tf.nn.tanh)\n            h_enc[0] = tf.zeros([batch_size,num_hidden_units], dtype=tf.float32) # Initial state is 0\n\n            # h_t+1 = tanh(Wenc*h_t + Win*x_t+1 + b )\n            #Most basic RNN: output = new_state = act(W * input + U * state + B).\n            #https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/python/ops/rnn_cell_impl.py\n            for t in range(time_steps-1):\n                _ , h_enc[t+1] = cell_enc(inputs=x_in[t+1], state=h_enc[t])\n\n\n        mu_enc = tf.layers.dense(h_enc[-1], z_dim, activation=None, name='mu_enc')\n        log_sigma_enc = tf.layers.dense(h_enc[-1], z_dim, activation=None, name='log_sigma_enc')\n\n        ###### Reparametrize ##############\n        eps = tf.random_normal(tf.shape(log_sigma_enc))\n        z = mu_enc + tf.exp(log_sigma_enc) * eps\n\n        ##### Decoder network ############\n        with tf.variable_scope('decoder_rnn'):\n            W_out = tf.get_variable('W_out',shape=[num_hidden_units, x_in_dim])\n            b_out = tf.get_variable('b_out',shape=[x_in_dim])\n\n            cell_dec = tf.nn.rnn_cell.BasicRNNCell(num_hidden_units,activation=tf.nn.tanh)\n            h_dec[0] = tf.layers.dense(z, num_hidden_units, activation=tf.nn.tanh)\n\n            for t in range(time_steps):\n                x_out[t] = tf.nn.sigmoid(tf.matmul(h_dec[t], W_out) + b_out)\n                if t < time_steps - 1:\n                    _, h_dec[t+1] = cell_dec(inputs=x_out[t], state=h_dec[t])\n\n        ##### Loss #####################\n        with tf.variable_scope('loss'):\n            # Latent loss: -KL[q(z|x)|p(z)]\n            with tf.variable_scope('latent_loss'):\n                sigma_sq_enc = tf.square(tf.exp(log_sigma_enc))\n                latent_loss = -.5 * tf.reduce_mean(tf.reduce_sum((1 + tf.log(1e-10 + sigma_sq_enc)) - tf.square(mu_enc) - sigma_sq_enc, axis=1),axis=0)\n                latent_loss_summ = tf.summary.scalar('latent_loss',latent_loss)\n\n            # Reconstruction Loss: log(p(x|z))    \n            with tf.variable_scope('recon_loss'):    \n                for i in range(time_steps):\n                    if i == 0:\n                        recon_loss_ = x_in[i] * tf.log(1e-10 + x_out[i]) + (1 - x_in[i]) * tf.log(1e-10+1-x_out[i])\n                    else:\n                        recon_loss_ += x_in[i] * tf.log(1e-10 + x_out[i]) + (1 - x_in[i]) * tf.log(1e-10+1-x_out[i])\n\n                #collapse the loss, mean across a sample across all x_dim and time points, mean over batches\n                recon_loss = -tf.reduce_mean(tf.reduce_mean(recon_loss_/(time_steps),axis=1),axis=0)\n\n\n            recon_loss_summ = tf.summary.scalar('recon_loss', recon_loss)\n\n            with tf.variable_scope('total_loss'):\n                total_loss = latent_loss + recon_loss\n\n            total_loss_summ = tf.summary.scalar('total_loss', total_loss)\n\n        global_step = tf.Variable(0,name='global_step') \n\n        #learning_rate = tf.train.exponential_decay(initial_learning_rate, epoch_num, num_epochs, decay_rate, staircase=False)\n        learning_rate = tf.Variable(learning_rate_1,name='learning_rate')\n        train_step = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta_1, beta2=beta_2).minimize(total_loss,global_step=global_step)    \n        scalar_summaries = tf.summary.merge([latent_loss_summ, recon_loss_summ, total_loss_summ])\n        #image_summaries = tf.summary.merge()\n\n        train_summary_writer.add_graph(tf.get_default_graph())\n\n\n        #############################\n        # Training/Logging\n        #############################\n        num_batches = int(num_train_samples/batch_size)\n        global_step_op = tf.train.get_global_step()\n        saver = tf.train.Saver()\n\n        avg_loss_epoch = []\n        avg_latent_loss_epoch = []\n        with tf.Session() as sess:\n            sess.run(tf.global_variables_initializer())\n            for epoch in range(num_epochs):\n                epoch_loss = 0.\n                epoch_latent_loss = 0.\n                for batch in range(num_batches):\n                    batch_num = sess.run(global_step_op)\n\n                    if epoch < num_epochs_to_diff_learn_rate:\n                        curr_learning_rate = learning_rate_1\n                    else:\n                        curr_learning_rate = learning_rate_2\n\n                    #_ , loss, scalar_train_summaries, x_out_, x_in_,learning_rate_,latent_loss_ = \\\n                    #sess.run([train_step, total_loss, scalar_summaries, x_out, x_in,learning_rate, latent_loss],feed_dict={X: feed_dict(batch_size,'train'), learning_rate: curr_learning_rate})\n\n                    _ , loss, scalar_train_summaries, learning_rate_, latent_loss_ = \\\n                    sess.run([train_step, total_loss, scalar_summaries, learning_rate, latent_loss],feed_dict={X: feed_dict(batch_size,'train'), learning_rate: curr_learning_rate})\n\n                    # Check for NaN\n                    if np.isnan(loss):\n                        sys.exit(\"Loss during training at epoch: {}\".format(epoch))\n\n                    epoch_loss += loss\n                    epoch_latent_loss += latent_loss_\n\n                print('Average loss epoch {0}: {1}'.format(epoch, epoch_loss/num_batches)) \n                print('Average latent loss epoch {0}: {1}'.format(epoch, epoch_latent_loss/num_batches)) \n                print('Learning Rate {}'.format(learning_rate_))\n                print('')\n                avg_loss_epoch.append(epoch_loss/num_batches)\n                avg_latent_loss_epoch.append(epoch_latent_loss/num_batches)\n\n                # Write train summaries once a epoch\n                scalar_train_summaries = sess.run(scalar_summaries,feed_dict={X: feed_dict(batch_size,'train')})\n                train_summary_writer.add_summary(scalar_train_summaries, global_step=batch_num)\n\n                # Write validation summaries\n                scalar_valid_summaries = sess.run(scalar_summaries,feed_dict={X: feed_dict(batch_size,'valid')})\n                valid_summary_writer.add_summary(scalar_valid_summaries, global_step=batch_num)\n\n                # Write test summaries\n                scalar_test_summaries = sess.run(scalar_summaries,feed_dict={X: feed_dict(batch_size,'test')})\n                test_summary_writer.add_summary(scalar_test_summaries, global_step=batch_num)\n\n                # Save the models\n                if epoch % num_epochs_to_save_model == 0:\n                    save_path = saver.save(sess, model_save_path + '/epoch_{}.ckpt'.format(epoch))\n\n        # plotting\n        fig, ax = plt.subplots()\n        ax.plot(avg_loss_epoch, label='avg_loss_epoch')\n        ax.plot(avg_latent_loss_epoch, label='avg_latent_loss_epoch')\n        plt.legend()\n\n        if not os.path.isdir(figure_save_path):\n            os.makedirs(figure_save_path)\n        plt.savefig(figure_save_path +'/loss.eps', format='eps', dpi=300)\n        plt.show()\n","sub_path":"processing_anomalies_with_vrae.py","file_name":"processing_anomalies_with_vrae.py","file_ext":"py","file_size_in_byte":11855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"416598516","text":"\n\nfrom xai.brain.wordbase.nouns._beagle import _BEAGLE\n\n#calss header\nclass _BEAGLES(_BEAGLE, ):\n\tdef __init__(self,): \n\t\t_BEAGLE.__init__(self)\n\t\tself.name = \"BEAGLES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"beagle\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_beagles.py","file_name":"_beagles.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"564673352","text":"#!/usr/bin/python\n\nimport argparse\nimport time\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"targetTerm\", help=\"The number of terms to print\", type=int)\nparser.add_argument(\"-a\", \"--printAll\", help=\"Print all of the terms\", type=bool)\nargs = parser.parse_args()\n\ncurrentTerm = 0\na = 0\nb = 1\n\nstrings = []\nstart = time.time()\nwhile(currentTerm < args.targetTerm):\n strings.append(\"Term %s = %s\" % (currentTerm + 1, a))\n new = a + b\n a = b\n b = new\n \n currentTerm = currentTerm + 1\n \nif(args.printAll):\n print(\"\\n\".join(strings), \"\\nCalculated in : \", time.time() - start)\n print(\"Printed in : \", time.time() - start)\nelse:\n print(\"Term \", args.targetTerm, \" = \", b - a, \"\\nCalculated in : \", time.time() - start)\n","sub_path":"printterms.py","file_name":"printterms.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"72"}
+{"seq_id":"94063071","text":"from typing import Optional, Union, Sequence, Any\n\nimport torch\nfrom torch.nn import Module\n\nfrom tqdm import tqdm\n\nfrom torch_kalman.design import Design\nfrom torch_kalman.process import Process\nfrom torch_kalman.state_belief import Gaussian, StateBelief\nfrom torch_kalman.state_belief.over_time import StateBeliefOverTime\nfrom torch_kalman.internals.utils import identity\n\n\nclass KalmanFilter(Module):\n    \"\"\"\n    TODO\n    \"\"\"\n    family = Gaussian\n    design_cls = Design\n\n    def __init__(self, measures: Sequence[str], processes: Sequence[Process], **kwargs):\n\n        super().__init__()\n        self.design = self.design_cls(measures=measures, processes=processes, **kwargs)\n\n        # parameters from design:\n        self.design_parameters = self.design.param_dict()\n\n    def predict_initial_state(self, design_for_batch: Design) -> 'Gaussian':\n        return self.family(\n            means=design_for_batch.initial_mean,\n            covs=design_for_batch.initial_covariance,\n            # we consider this a one-step-ahead prediction, so last measured one step ago:\n            last_measured=torch.ones(design_for_batch.num_groups, dtype=torch.int)\n        )\n\n    def forward(self,\n                input: Any,\n                initial_prediction: Optional[StateBelief] = None,\n                forecast_horizon: int = 0,\n                progress: Union[tqdm, bool] = False,\n                **kwargs) -> StateBeliefOverTime:\n        \"\"\"\n        :param input: The multivariate time-series to be fit by the kalman-filter. The exact structure depends on the\n          kalman-filter `family`; for most, it is a tensor where the first dimension represents the groups, the second\n          dimension represents the time-points, and the third dimension represents the measures.\n        :param initial_prediction: If a StateBelief, this is used as the prediction for time=0; if None then each\n          process generates initial values.\n        :param forecast_horizon: Number of timesteps past the end of the input to continue making predictions\n        :param progress: Should progress-bar be generated?\n        :param kwargs: Other kwargs that will be passed to the `design_for_batch` method.\n        :return: A StateBeliefOverTime consisting of one-step-ahead predictions.\n        \"\"\"\n\n        num_groups, num_timesteps, num_measures, *_ = self.family.get_input_dim(input)\n        if num_measures != len(self.design.measures):\n            raise ValueError(\n                f\"This KalmanFilter has {len(self.design.measures)} measures; but the input shape is \"\n                f\"{(num_groups, num_timesteps, num_measures)} (3rd dim should == measure-size).\"\n            )\n\n        assert forecast_horizon >= 0\n        num_timesteps += forecast_horizon\n\n        design_for_batch = self.design.for_batch(num_groups=num_groups, num_timesteps=num_timesteps, **kwargs)\n\n        # initial state of the system:\n        if initial_prediction is None:\n            state_prediction = self.predict_initial_state(design_for_batch)\n        else:\n            state_prediction = initial_prediction.copy()\n\n        progress = progress or identity\n        if progress is True:\n            progress = tqdm\n        times = progress(range(num_timesteps))\n\n        # generate one-step-ahead predictions:\n        state_predictions = []\n        for t in times:\n            if t > 0:\n                # take state-pred of previous t (now t-1), correct it according to what was actually measured at t-1\n                state_belief = state_prediction.update_from_input(input, time=t - 1)\n\n                # predict the state for t, from information from t-1\n                # F at t-1 is transition *from* t-1 *to* t\n                F = design_for_batch.F(t - 1)\n                Q = design_for_batch.Q(t - 1)\n                state_prediction = state_belief.predict(F=F, Q=Q)\n\n            # compute how state-prediction at t translates into measurement-prediction at t\n            H = design_for_batch.H(t)\n            R = design_for_batch.R(t)\n            state_prediction.compute_measurement(H=H, R=R)\n\n            # append to output:\n            state_predictions.append(state_prediction)\n\n        return self.family.concatenate_over_time(state_beliefs=state_predictions, design=self.design)\n\n    def smooth(self, states: StateBeliefOverTime):\n        raise NotImplementedError\n","sub_path":"torch_kalman/kalman_filter.py","file_name":"kalman_filter.py","file_ext":"py","file_size_in_byte":4354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"601513820","text":"#!/usr/bin/env python\n'''\n########################################################################\nwrap up widget construction in fuctions for easier use, base open some\nassuptions (e.g., expansion); use **extras fkw args for width, font/color,\ne.t.c., and repark result manually later to override defaults if needed;\n#########################################################################\n'''\nfrom tkinter import *\n\ndef frame(root,side=TOP, **extras):\n    widget = Frame(root)\n    widget.pack(side=side, expand=NO, fill=X)\n    if extras: widget.config(**extras)\n    return widget\n\ndef label(root,side,text,**extras):\n    widget = Label(root,text=text,relief=RIDGE)\n    widget.pack(side=side,expand=YES,fill=BOTH)\n    if extras: widget.config(**extras)\n    return widget\n\ndef button(root,text,command,side=TOP,**extras):\n    widget = Button(root,text=text,command=command)\n    widget.pack(side=side,expand=YES,fill=BOTH)\n    if extras: widget.config(**extras)\n    return widget\n \ndef entry(root,side,linkvar,**extras):\n    widget = Entry(root,relief=SUNKEN,textvariable=linkvar)\n    widget.pack(side=side,expand=YES,fill=BOTH)\n    if extras: widget.config(**extras)\n    return widget\n\nif __name__ == '__main__':\n    app = Tk()\n    frm = frame(app)\n    label(frm,LEFT,'SPAM')\n    button(frm,BOTTOM,'Press', lambda : print('pushed'))\n    mainloop()\n    ","sub_path":"widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"91364155","text":"\n\n# for doing system calls to the command line\nfrom subprocess import call\nimport os\n# import the Methods file so I can call the functinons from there\nimport Methods as Methods\nimport sys\n\n\n# Create a Liat (Array)\nnames=[\"Troy\",\"Liz\",\"Sam\"]\n\n\n# Step through the array using (foreach)\nfor i in names:\n    if i==\"Liz\":\n        print(\"We found Liz\")\n    else:\n        print(\"There was another user\")\n\n\n\n1\n# call something from the command line\ncall('dir /w /a', shell=True)\n\n# define a function\n# must be defined before it is called\ndef doForLoop():\n    for x in range(0, 3):\n        print(\"We're on iteration %d\" % (x+1))\n\n\n# Call a local method, previously defined\ndoForLoop()\n\n# Call a remote method\nMethods.doForLoopAgain()","sub_path":"TroyHello/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"356752082","text":"from Neuron import Neuron\n\n\nclass Layer:\n\n    # Constructor\n    def __init__(self, inputs, amountNeurons, _random):\n        self._neurons = []\n        self._random = _random\n        self._output = []\n\n        for i in range(amountNeurons):\n            self._neurons.append(Neuron(inputs, self._random))\n\n    # Behavior\n    def outputs(self, inputs):\n        f = [None] * len(self._neurons)\n\n        for i in range(len(self._neurons)):\n            f[i] = self._neurons[i].output(inputs)\n        \n        self._output = f\n\n    # Getters & Setters\n    def getOutput(self):\n        return self._output","sub_path":"Perceptron/Layer.py","file_name":"Layer.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"586882356","text":"# A regular polygon has n number of sides. Each side has length s.\r\n\r\nimport math\r\n\r\ndef polysum(n, s):\r\n\tarea = (0.25 * n * (s**2)) / (math.tan(math.pi / n))\r\n\tperimeter = s * n\r\n\r\n\tresult = area + perimeter**2\r\n\treturn round(result, 4)\r\n\r\n# test case\r\nprint(polysum(24, 19))","sub_path":"MIT6001x/Week2/polysum.py","file_name":"polysum.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"135697812","text":"from discord.ext import commands, tasks\nimport os\nimport crawler\n\nbot = commands.Bot(\"!\")\nTOKEN = os.getenv(\"DISCORD_TOKEN\")\n\n\n@bot.event\nasync def on_ready():\n    send_so.start()\n    print(f\"Logged in as {bot.user.name}({bot.user.id})\")\n\n\n@tasks.loop(seconds=300)\nasync def send_so():\n    ch_id = 781729030491209762\n    url = 'https://www.coupang.com/vp/products/2378328151'\n    if not crawler.isSoldOut(url):\n        channel = bot.get_channel(ch_id)\n        await channel.send('품절 풀림!')\n\n\n@bot.command(pass_context=True)\nasync def hi(ctx):\n    await ctx.send('Hi, ' + ctx.author.mention)\n\nif __name__ == \"__main__\":\n    bot.run(TOKEN)\n","sub_path":"bot/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"134066326","text":"import os\nimport allel\nimport numpy as np\nimport json\nimport multiprocessing\nimport pickle\nimport re\nimport zipfile\nfrom wsgiref.util import FileWrapper\nfrom functools import partial\nimport time\nimport sys\nimport gzip\nimport copy\nif sys.platform.startswith('linux'):\n    import fcntl\nelse:\n    from lockfile import LockFile\n\n\nclass MyEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,\n                            np.int16, np.int32, np.int64, np.uint8,\n                            np.uint16, np.uint32, np.uint64)):\n            return int(obj)\n        elif isinstance(obj, (np.float_, np.float16, np.float32,\n                              np.float64)):\n            return float(obj)\n        elif isinstance(obj, np.ndarray):\n            return list(obj)\n        elif isinstance(obj, np.bool_):\n            return bool(obj)\n        else:\n            return json.JSONEncoder.default(self, obj)\n\n\ndef RenameJsonKey(strJson):\n    if isinstance(strJson,dict):\n        strJson = json.dumps(strJson)\n    #先默认json的key中没有特殊符号\n    pattern = re.compile(r\"\\\"([\\w.$:]+)\\\":\")\n    strJson = pattern.sub(lambda m: m.group(0).replace('.', \"_\").replace('$', \"^\"), strJson)\n    return strJson\n\n\nclass TransformV2J(object):\n    def addhead(self, header, filepath_json):\n        record_head = []\n        for line in header:\n            line = line.strip('\\n')\n            record_head.append(line)\n        record = {\n           \"Header\": record_head\n        }\n        with open(filepath_json, 'a') as fp:\n            recordstring = json.dumps(record, cls=MyEncoder)\n            recordstring = recordstring[:-1] + ',' + '\\n'\n            fp.write(recordstring)\n            fp.write('\"Data\":[')\n        return\n\n    #delete the last comma, and add the bracket\n    def addEnd(self, filepath_json):\n        # in win platform is \\r\\n; in linux is \\n; in os is \\r\n        if sys.platform.startswith('win'):\n            offsize = -3\n        else:\n            offsize = -2\n        with open(filepath_json, 'rb+') as filehandle:\n            #delete \\n and ,\n            filehandle.seek(offsize, os.SEEK_END)\n            filehandle.truncate()\n        with open(filepath_json, 'a') as fp:\n            fp.write(']}')\n\n    def chunker2string(self, chunker, fields, samples, mode='MergeSamples'):\n        li = []\n        # 把NaN转换成-1\n        for i in range(chunker[1]):\n            for field in fields:\n                if isinstance(chunker[0][field][i], np.ndarray) and not isinstance(chunker[0][field][i][0], np.str):\n                    nanpos = np.isnan(chunker[0][field][i])\n                    chunker[0][field][i][nanpos] = -1.0\n\n        if mode == 'MergeAll':\n            for i in range(chunker[1]):\n                #basic\n                recorddict1 = {\n                    \"CHROM\": chunker[0]['variants/CHROM'][i],\n                    \"POS\" : chunker[0]['variants/POS'][i],\n                    \"ID\": chunker[0]['variants/ID'][i],\n                    \"REF\": chunker[0]['variants/REF'][i],\n                    \"ALT\": chunker[0]['variants/ALT'][i],\n                    \"QUAL\": chunker[0]['variants/QUAL'][i],\n                }\n                #filter\n                recorddict2 = {\n                    \"FILTER\": {\n                        k_filter[9:] : chunker[0][k_filter][i] for k_filter in fields if 'variants/FILTER' in k_filter\n                    }\n                }\n                #Info\n                recorddict3 = {\n                    \"Info\": {\n                        k_Info[9:] : chunker[0][k_Info][i] for k_Info in fields if k_Info not in ['variants/CHROM', 'variants/POS', 'variants/ID', 'variants/REF', 'variants/ALT', 'variants/QUAL', 'variants/numalt', 'variants/svlen', 'variants/is_snp']\n                        and 'variants/FILTER' not in k_Info and 'calldata/' not in k_Info\n                    }\n                }\n                #Samples\n                recordsamples = []\n                for k_sample, j in zip(samples, range(samples.size)):\n                    recordsample1 = {\n                        \"SampleNo\": k_sample\n                    }\n                    recordsample2 = {\n                        k_field[9:]: [chunker[0][k_field][i][j][n] for n in\n                                      range(chunker[0][k_field][i][j].size)] if isinstance(\n                            chunker[0][k_field][i][j], np.ndarray) else chunker[0][k_field][i][j] for k_field in\n                        fields if \"calldata/\" in k_field\n                    }\n                    recordsample = dict(recordsample1, **recordsample2)\n                    recordsamples.append(recordsample)\n                recorddict4 = {\n                    \"Samples\": recordsamples\n                }\n                recorddictMerge = dict(recorddict1, **recorddict2, **recorddict3, **recorddict4)\n                li.append(recorddictMerge)\n\n        elif mode == 'MergeSamples':\n            for i in range(chunker[1]):\n                recorddict1 = {\n                    k_field[9:]: [chunker[0][k_field][i][m] for m in range(chunker[0][k_field][i].size)] if isinstance(\n                        chunker[0][k_field][i], np.ndarray) else chunker[0][k_field][i] for k_field in fields if\n                    'variants/' in k_field and k_field not in  ['variants/numalt', 'variants/svlen', 'variants/is_snp']\n                }\n                recordsamples = []\n                for k_sample, j in zip(samples, range(samples.size)):\n                    recordsample1 = {\n                        \"SampleNo\": k_sample\n                    }\n                    recordsample2 = {\n                        k_field[9:]: [chunker[0][k_field][i][j][n] for n in\n                                      range(chunker[0][k_field][i][j].size)] if isinstance(\n                            chunker[0][k_field][i][j], np.ndarray) else chunker[0][k_field][i][j] for k_field in\n                        fields if \"calldata/\" in k_field\n                    }\n                    recordsample = dict(recordsample1, **recordsample2)\n                    recordsamples.append(recordsample)\n                recorddict2 = {\n                    \"Samples\": recordsamples\n                }\n\n                recorddict = dict(recorddict1, **recorddict2)\n                li.append(recorddict)\n\n        recordstring = json.dumps(li, cls=MyEncoder)\n        recordstring = recordstring[1:-1]   #delete first and last brackets.  \"[...]\" ----> \"...\"\n        recordstring = recordstring + ','+ '\\n'\n        return recordstring\n\n\n    def IoOperat_multi(self, tmpfile, mode, chunker):\n        # tmpfile = \"value_\" + md5 + \".dat\"\n        with open(tmpfile, \"rb\") as f:\n            fields = pickle.load(f)\n            samples = pickle.load(f)\n            headers = pickle.load(f)\n            filepath_json = pickle.load(f)\n        recordstring = self.chunker2string(chunker, fields, samples, mode)\n        if sys.platform.startswith('linux'):\n            with open(filepath_json, \"a\") as fp:\n                fcntl.flock(fp.fileno(), fcntl.LOCK_EX)\n                fp.write(recordstring)\n        else:\n            lock = LockFile(filepath_json)\n            lock.acquire()\n            with open(filepath_json, \"a\") as fp:\n                fp.write(recordstring)\n            lock.release()\n        return\n\n    #useless function. just for test\n    def vcf2json_Single(self, filepath_vcf, filepath_json, mode):\n        fields, samples, headers, chunks = allel.iter_vcf_chunks(filepath_vcf, fields=['*'], chunk_length=50)\n\n        if os.path.exists(filepath_json):\n            os.remove(filepath_json)\n        self.addhead(headers[0], filepath_json)\n\n        for chunker in chunks:\n            with open(filepath_json, 'a') as fp:\n                recordstring = self.chunker2string(chunker, fields, samples, mode)\n                fp.write(recordstring)\n\n        return\n\n    def vcf2json_multi2(self, filepath_vcf, filepath_json, md5, mode):\n        fields, samples, headers, chunks = allel.iter_vcf_chunks(filepath_vcf, fields=['variants/*', 'calldata/*'],chunk_length=500)\n\n        if os.path.exists(filepath_json):\n            os.remove(filepath_json)\n        #增加原vcf文件的头部信息, 用于逆向转换\n        self.addhead(headers[0], filepath_json)\n\n        tmpfile = \"value_\" + md5 + \".dat\"\n        with open(tmpfile, \"wb\") as f:\n            pickle.dump(fields, f)\n            pickle.dump(samples, f)\n            pickle.dump(headers, f)\n            pickle.dump(filepath_json, f)\n\n        cores = multiprocessing.cpu_count()\n        #processnum = int(cores / 2)\n        processnum = max(int(cores / 2), 2)\n\n        # 自己调度迭代器 防止内存溢出\n        pool = multiprocessing.Pool(processes=processnum)\n        index = 0\n        tmpchunks = []\n        first = True\n        realchunks = []\n        for chunker in chunks:\n            index += 1\n            tmpchunks.append(chunker)\n            if index % (processnum * 10) == 0:\n                if not first:\n                    AppResult.get()\n                    realchunks.clear()\n                realchunks = copy.deepcopy(tmpchunks)\n                tmpchunks.clear()\n                first = False\n                AppResult = pool.map_async(partial(self.IoOperat_multi, tmpfile, mode), realchunks)\n\n        if \"AppResult\" in locals().keys():\n            AppResult.get()\n\n        pool.map(partial(self.IoOperat_multi, tmpfile, mode), tmpchunks)\n        tmpchunks.clear()\n        if realchunks:\n            realchunks.clear()\n        pool.close()\n        pool.join()  # 主进程阻塞等待子进程的退出\n        #delete two last character '\\n' and ',' and add '}'\n        self.addEnd(filepath_json)\n        os.remove(tmpfile)  # 删除临时文件,节约空间\n\n\n    def dotranform(self, filepath_vcf, mode):\n        file_json = os.path.splitext(filepath_vcf)[0] + \".json\"\n        self.vcf2json_multi2(filepath_vcf, file_json, \"tmpdat\", mode)\n\n\n    #with output path\n    def dotransformWithOutPath(self, filepath_vcf, filepath_json, mode):\n        self.vcf2json_multi2(filepath_vcf, filepath_json, \"tmpdat\", mode)\n\n\n    def preview(self, filepath_vcf, mode):\n        fields, samples, headers, chunks = allel.iter_vcf_chunks(filepath_vcf, fields=['*'], chunk_length=2)\n        #get first 2 lines for example\n        #get json\n        for chunker in chunks:\n            recordstring = self.chunker2string(chunker, fields, samples, mode)\n            recordstring = RenameJsonKey(recordstring)\n            break\n\n        #get vcf\n        if filepath_vcf.endswith('gz'): #.vcf.gz\n            linenum = 0\n            vcfline = str()\n            with gzip.open(filepath_vcf, 'rb') as file:\n                for line in file:\n                    if not line:\n                        break\n                    else:\n                        strline = bytes.decode(line)\n                        if strline[1] != '#':\n                            vcfline += strline\n                            linenum += 1\n                            if linenum == 3:\n                                break\n            result = {\"vcf\": vcfline, \"json\": recordstring}\n        else:   #.vcf\n            linenum = 0\n            vcfline = str()\n            with open(filepath_vcf, 'rb') as file:\n                while True:\n                    line = file.readline()\n                    if not line:\n                        break\n                    else:\n                        if line[1] != '#':\n                            vcfline += line\n                            linenum += 1\n                            if linenum == 3:\n                                break\n\n            result = {\"vcf\": vcfline, \"json\": recordstring}\n        return result\n","sub_path":"transform_core.py","file_name":"transform_core.py","file_ext":"py","file_size_in_byte":11704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"545309268","text":"from datetime import datetime\nimport numpy as np\nimport networkx as nx\nimport torch\nimport random\n\ndef print_with_datetime(*args):\n    now = datetime.now().strftime(\"[%H:%M:%S]\")\n    print(now, *args)\n\ndef compute_eff_res(graph):\n    num_nodes = graph.number_of_nodes()\n    adj = np.array(nx.to_numpy_matrix(graph))\n    laplacian_matrix = np.diag(np.array(adj.sum(1)).squeeze()) - adj\n    pinv_laplacian_matrix = np.linalg.pinv(laplacian_matrix)\n    Lp_dot_1 = np.matmul(\n        np.matrix(pinv_laplacian_matrix.diagonal()).T,\n        np.ones((1, num_nodes))\n        )\n    eff_res = torch.from_numpy(\n        Lp_dot_1+Lp_dot_1.T-pinv_laplacian_matrix-pinv_laplacian_matrix.T\n        )\n\n    return eff_res\n\ndef set_random_seed(seed):\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n    torch.cuda.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    torch.manual_seed(seed)\n    random.seed(seed)\n    np.random.seed(seed)\n","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"582386362","text":"import sys\nsys.path.append('..')\n\nimport unittest\nfrom model import Field, Model, field_assembler\n\nclass TestModel( unittest.TestCase ):\n    def test_field( self ):\n        fname = 'login'\n        ftype = 'String(100)'\n        field = Field( fname, ftype )\n        self.assertEqual( \"    login = db.Column( db.String(100), nullable=True )\", field.definition() )\n\n        funique = True\n        fnull = False\n        field = Field( fname, ftype, unique=funique, nullable=fnull )\n        self.assertEqual( \"    login = db.Column( db.String(100), unique=True, nullable=False )\", field.definition() )\n\n        fpk = True\n        field = Field( fname, ftype, pkey=fpk )\n        res1 = field.definition()\n        self.assertEqual( \"    login = db.Column( db.String(100), primary_key=True, autoincrement=True )\", res1 )\n        field = Field( fname, ftype, unique=funique, nullable=fnull, pkey=fpk )\n        res2 = field.definition()\n        self.assertEqual( res1, res2 )\n\n        fautoinc = False\n        field = Field( fname, ftype, pkey=fpk, autoinc=fautoinc )\n        self.assertEqual( \"    login = db.Column( db.String(100), primary_key=True, autoincrement=False )\", field.definition() )\n\n        f = field_assembler( ('login', 'String(100)') )\n        self.assertEqual( \"    login = db.Column( db.String(100), nullable=True )\", f.definition() )\n\n        f = field_assembler( ('login', 'String(100)', ['unique']) )\n        self.assertEqual( \"    login = db.Column( db.String(100), unique=True, nullable=True )\", f.definition() )\n\n        f = field_assembler( ('login', 'String(100)', ['unique','notnull']) )\n        self.assertEqual( \"    login = db.Column( db.String(100), unique=True, nullable=False )\", f.definition() )\n\n        f = field_assembler( ('login', 'String(100)', ['pkey','autoinc']) )\n        self.assertEqual( \"    login = db.Column( db.String(100), primary_key=True, autoincrement=True )\", f.definition() )\n\n        f = field_assembler( ('login', 'String(100)', ['pkey']) )\n        self.assertEqual( \"    login = db.Column( db.String(100), primary_key=True, autoincrement=False )\", f.definition() )\n\n    def test_model( self ):\n        name = 'company'\n        fields =  [ \n            ('id', 'Integer', ['pkey', 'autoinc']),\n            ('name', 'String(200)', ['notnull']),\n            ('email', 'String(200)', ['unique', 'notnull']),\n            ('active', 'Boolean'),\n        ]\n        model = Model( name, fields )\n        value = model.value()\n        self.assertIn( 'class Company( db.Model ):', value )\n        self.assertIn( '    id = db.Column( db.Integer, primary_key=True, autoincrement=True )', value )\n        self.assertIn( '    name = db.Column( db.String(200), nullable=False )', value )\n        self.assertIn( '    email = db.Column( db.String(200), unique=True, nullable=False )', value )\n        self.assertIn( '    active = db.Column( db.Boolean, nullable=True )', value )\n\n        name = 'user'\n        fields = [\n            ('id', 'Integer', ['pkey', 'autoinc']),\n            ('username', 'String(80)', ['unique', 'notnull']),\n            ('email', 'String(100)', ['notnull']),\n            ('company_id', 'Integer', ['fkey'], 'company.id')\n        ]\n        model = Model( name, fields )\n        value = model.value()\n        self.assertIn( \"    company_id = db.Column( db.Integer, db.ForeignKey( 'company.id' ) )\", value )\n        self.assertIn( \"    company = db.relationship( 'Company' ) )\", value )\n\n        uniques = [\n            ['username', 'company_id'], \n            ['email']\n        ]\n        model = Model( name, fields, uniques=uniques )\n        value = model.value()\n        self.assertIn( \"    db.UniqueConstraint( 'username', 'company_id', name='ukey_1' )\", value )\n        self.assertIn( \"    db.UniqueConstraint( 'email', name='ukey_2' )\", value )\n        self.assertIn( \"def __init__( self, id, username, email, company_id )\", value )\n        self.assertIn( \"self.username = username\", value )\n        self.assertIn( \"self.company_id = company_id\", value )\n        self.assertNotIn( \"self.company = company\", value )\n        \n        model.remove_field( 'email' )\n        self.assertNotIn( \"'name': 'email'\", model.show_fields() )\n        model.add_field( ('email', 'String(200)', ['unique', 'notnull']) )\n\n        value = model.value()\n        self.assertIn( '    email = db.Column( db.String(200), unique=True, nullable=False )', value )\n        \n        model.clear_fields()\n        self.assertEqual( 0, len (model.fields ) )\n        model.add_fields( fields )\n        self.assertEqual( len( fields ), len (model.fields ) )\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"flute/tests/model_test.py","file_name":"model_test.py","file_ext":"py","file_size_in_byte":4635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"307370965","text":"# Auction scraper - looking for info from an auctions site to identify more into on govt sell offs of property to plug budget\n# This is a template for a Python scraper on morph.io (https://morph.io)\n# including some code snippets below that you should find helpful\nimport scraperwiki\nimport lxml.html\n#\n# # Read in a page\nhtml=scraperwiki.scrape(\"https://www.sdlauctions.co.uk/property-list/\")\n# Print the variable html containing the webpage\n##commented out the print link so running the page doesn't take ages!\n#print(html)\n# # Find something on the page using css selectors\nroot=lxml.html.fromstring(html)\n# inspecting the all the info in the <a tag within the <p> and <li> tags (P and LI are parents of A)\nroot.cssselect(\"li p a\")\n# then store the matched links in 'matchedlinks'\nmatchedlinks=root.cssselect(\"li p a\")\n##commented out the print link so running the page doesn't take ages!\n# print(matchedlinks)\n# create a dictionary called record\nrecord = {}\n# Set a counter\ncounter = 0\n# It's time to loop through the confusing codes from the page <Element a at 0x7f3fb1c536d8> calling each one li\n# We're calling it li because it relates to the tag we've grabbed it from\nfor li in matchedlinks:\n  #increase counter by 1\n  counter= counter+1\n  #store the text elements of li in a new variable called 'listtext'\n  listtext=li.text_content()\n  # print that\n  print(listtext).encode('utf-8')\n  # store it in the 'record' dictionary under the key 'address'\n  record[\"address\"] = listtext\n  #store the link in the data, the html reference or href\n  record['link'] = li.attrib['href']\n  # save the text that's been stored in the dictionary 'record' and save it to a table\n  scraperwiki.sqlite.save(['address'],record)\n# # Write out to the sqlite database using scraperwiki library\n# scraperwiki.sqlite.save(unique_keys=['name'], data={\"name\": \"susan\", \"occupation\": \"software developer\"})\n#\n# # An arbitrary query against the database\n# scraperwiki.sql.select(\"* from data where 'name'='peter'\")\n\n# You don't have to do things with the ScraperWiki and lxml libraries.\n# You can use whatever libraries you want: https://morph.io/documentation/python\n# All that matters is that your final data is written to an SQLite database\n# called \"data.sqlite\" in the current working directory which has at least a table\n# called \"data\".\n","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"474760248","text":"\"\"\"NPUPA_workbench URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.0/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import path, include\nfrom . import views\n\napp_name = 'downloader'\nurlpatterns = [\n    path('', views.home, name='home'),\n    path('base/', views.base, name='base'),\n    path('upload/', views.upload, name='upload'),\n    path('downloads/', views.downloads, name='downloads'),\n    path('contents/', views.download_file, name='contents'),\n\n    # url(r'^download/',views.download,name=\"download\"),\n]\n","sub_path":"downloader/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"627448523","text":"\nimport sys\nimport os\nimport time\nimport re\nimport math\n\n\n\n#Parses a file with the regex pattern \\w+\\t\\w+\\t\\d\ndef parse_emote_file_readin(some_file_path):\n    myfile = open(some_file_path)\n    liststr = myfile.read().lower()\n    myfile.close()\n    return re.findall(r\"\\w+\\t\\w+\\t\\d\", liststr)\n\n#Parses a file with the regex pattern [a-zA-Z]{3,}\ndef parse_file_readin(some_file_path):\n    myfile = open(some_file_path)\n    liststr = myfile.read().lower()\n    myfile.close()\n    return re.findall(r\"[a-zA-Z]{3,}\", liststr)\n\ndef print_ranked( emote_path, doc_path ):\n    emotereg = []\n    emotelist = []\n    docreg = []\n    emoteDict = {}\n    docDict = {}\n\n##    anger = 0\n##    anticipation = 0\n##    disgust = 0\n##    fear = 0\n##    joy\t= 0\n##    negative = 0\n##    positive = 0\n##    sadness = 0\n##    surprise = 0\n##    trust = 0\n    emote_vec = [0,0,0,0,0,0,0,0,0,0]\n    emote_word =['anger', 'anticipation', 'disgust', 'fear',\n                 'joy','negative','positive', 'sadness', 'surprise', 'trust']\n    emotereg = parse_emote_file_readin(emote_path)\n\n    for elem in emotereg:\n        emotelist = elem.split('\\t')\n        if not emoteDict.has_key(emotelist[0]) :\n            emoteDict[emotelist[0]] = []\n            emoteDict[emotelist[0]].append(int(emotelist[2]))\n        else:\n            emoteDict[emotelist[0]].append(int(emotelist[2]))\n\n    docreg = parse_file_readin(doc_path)\n    not_in_emote = 0\n    for elem in docreg:\n        if emoteDict.has_key(elem) :\n            emote_vec[0]    += emoteDict[elem][0]\n            emote_vec[1]    += emoteDict[elem][1]\n            emote_vec[2]    += emoteDict[elem][2]\n            emote_vec[3]    += emoteDict[elem][3]\n            emote_vec[4]    += emoteDict[elem][4]\n            emote_vec[5]    += emoteDict[elem][5]\n            emote_vec[6]    += emoteDict[elem][6]\n            emote_vec[7]    += emoteDict[elem][7]\n            emote_vec[8]    += emoteDict[elem][8]\n            emote_vec[9]    += emoteDict[elem][9]\n        else:\n            not_in_emote += 1\n            \n    return emote_vec\n\n\ndef main():\n\n#https://en.wikipedia.org/wiki/Contrasting_and_categorization_of_emotions\n\n    e_path = \"NRC_wordlevel_alphabetized_v0.92.txt\"\n    d_path = \"hfy_4c3y0v.txt\"\n\n    cos_vec = []\n      \n    cos_vec.append(print_ranked(e_path, d_path))\n\nif __name__ == '__main__':\n  main()\n\n\n","sub_path":"Multiversee/word_emotion_lexicon/word_emote_test.py","file_name":"word_emote_test.py","file_ext":"py","file_size_in_byte":2337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"69930122","text":"import os\nimport base64\n\nfrom flask import Flask, render_template, request, redirect, url_for, session\n\nfrom model import Donation, Donor, DoesNotExist\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n    return redirect(url_for('all'))\n\n@app.route('/donations/')\ndef all():\n    donations = Donation.select()\n    return render_template('donations.jinja2', donations=donations)\n\n@app.route('/add_donation/', methods=['GET','POST'])\ndef add_donation():\n    if request.method == 'POST':\n        donor = request.form['donor']\n        if donor == '':\n            return render_template(\"add_donation.jinja2\", error=\"The Donor Name cannot be blank\")\n        try:\n            add_donation = float(request.form['donation'])\n        except:\n            return render_template(\"add_donation.jinja2\", error=\"Please enter a valid amount\")\n        if add_donation <= 0:\n            return render_template(\"add_donation.jinja2\", error=\"Please enter a valid amount\")\n        try:\n            donor_id = Donor.get(Donor.name == donor).id\n        except DoesNotExist:\n            donor_id = Donor.create(name=donor).id\n        finally:\n            Donation.create(value=add_donation, donor=donor_id)\n            return redirect(url_for('all'))\n\n    else:\n        return render_template(\"add_donation.jinja2\")\n\nif __name__ == \"__main__\":\n    port = int(os.environ.get(\"PORT\", 6738))\n    app.run(host='0.0.0.0', port=port)\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"405423693","text":"import torch\nimport torch.utils.data\nfrom torchvision import datasets, transforms\nimport torchvision.datasets as dataset\nfrom random import randint\nimport numpy\nimport matplotlib\n# @yuchen\nmatplotlib.use(\"AGG\")\nimport matplotlib.pyplot as plt\nimport pickle\nfrom torch.autograd import grad, Variable\nfrom os import path, makedirs\nimport torch.nn.functional as F\nfrom tqdm import tqdm\n\ndef resize_image(mnist_images):\n    # We wish to resize the mnist images from 28x28 to 32x32\n    n, w, h = mnist_images.size()\n    data = torch.zeros(n, 32, 32)\n    data[:,2:30,2:30] = mnist_images\n    return data\n\ndef get_data_synthetic():\n    '''\n    \n    Generates random, uniform 2D Gaussians centered at:\n    \n    +-----+-----+-----+\n    |     |     |     |\n    |  1  |  2  |  3  |\n    |     |     |     |\n    +-----+-----+-----+\n    |     |     |     |\n    |  4  |  5  |  6  |\n    |     |     |     |\n    +-----+-----+-----+\n    |     |     |     |\n    |  7  |  8  |  9  |\n    |     |     |     |\n    +-----+-----+-----+\n    \n    Each image is (32, 32), with means of Gaussians at \n    5, 15 and 25 of the intersections of both axis.\n    \n    '''\n    TOTAL = 20000\n    DENSITY = 2000 # number of points to sample\n    \n    NUM_CLASSES = 9\n    CHANNELS = 1\n    \n    means = [(j, i) for i in range(5, 26, 10) for j in range(5, 26, 10)]\n    covar = [[2, 0], [0, 2]] # 1 -> radius of 3, 2 -> radius of 5\n    \n    train_data = [None]*TOTAL\n    train_labels = [None]*TOTAL\n    \n    outdir = \"synthetic\"\n    if not path.isdir(outdir):\n        makedirs(outdir)\n    \n    # Note: pointer assignments are faster in Python than Numpy/Pytorch through Python.\n    for i in tqdm(range(TOTAL), desc=\"Generating Gaussians\", ncols=80):\n        label = randint(0, NUM_CLASSES-1)\n        train_labels[i] = label\n        mean = means[label]\n        data = numpy.zeros((32, 32))\n        x, y = numpy.random.multivariate_normal(mean, covar, DENSITY).T\n        keep = (x >= 0) & (x <= 31) & (y >= 0) & (y <= 31)\n        x = numpy.rint(x)[keep].astype(numpy.int32)\n        y = numpy.rint(y)[keep].astype(numpy.int32)\n        data[x, y] = 1.0\n        train_data[i] = torch.from_numpy(data).float()\n        \n        #save_image(data, \"{:0>6}\".format(i), outdir, CHANNELS)\n        \n    train_data = torch.cat(train_data).view(TOTAL, CHANNELS, 32, 32)\n    train_labels = onehot(torch.LongTensor(train_labels), NUM_CLASSES).view(TOTAL, 1, 1, NUM_CLASSES)\n    \n    test_data = torch.FloatTensor([])\n    test_labels = torch.FloatTensor([])\n    \n    return train_data, train_labels\n\ndef onehot(indices, d):\n    base = torch.zeros((indices.size(0), d))\n    return base.scatter_(1, indices.view(-1, 1), 1)\n\ndef get_data():\n    CIFAR10_train = dataset.CIFAR10(root=\"./data\", train=True, transform=transforms.Compose(\n        [transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))]), download=True)\n    CIFAR10_train_data = [data[0].numpy() for data in CIFAR10_train]\n    \n    CIFAR10_train_data = torch.FloatTensor(CIFAR10_train_data)\n    CIFAR10_train_labels = torch.LongTensor(CIFAR10_train.train_labels)\n    return CIFAR10_train_data, CIFAR10_train_labels\n\ndef create_dataset(mnist_train_data, mnist_train_labels, batch_size):\n    n = len(mnist_train_labels)\n    # one hot encoding\n    labels = torch.zeros(n, 10).scatter_(1, mnist_train_labels.view(n, 1), 1)\n    dataset = torch.utils.data.TensorDataset(mnist_train_data, labels)\n    return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, pin_memory = True)\n\ndef create_dataset_yuchen(data, labels, batch_size):\n    dataset = torch.utils.data.TensorDataset(data, labels)\n    return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, pin_memory=True)\n\ndef combine_to_tensor(gen_list):\n    return torch.cat(gen_list, dim=0)\n\ndef exp_decay_loss(scores, constant, batchSize, windowSize, gpu_mode):\n    n, d = scores.size()\n    assert d == 1\n    assert n % batchSize == 0\n    numEditors = n // batchSize\n    chunks = scores.view(numEditors, batchSize)\n    factor = -constant/float(windowSize)\n    decay = torch.exp(torch.arange(numEditors)*factor)\n    \n    if gpu_mode:\n        decay = decay.cuda()\n    \n    means = chunks.mean(dim=1)\n    assert means.size() == decay.size()\n    \n    return (means*decay).sum()\n\n#def grad_penalty(real_data, fake_data, penalty, discriminator, labels = None):\n#    alpha = torch.cuda.FloatTensor(fake_data.shape[0], 1, 1, 1).uniform_(0, 1).expand(fake_data.shape)\n#    interpolates = alpha * fake_data + (1 - alpha) * real_data\n#    #interpolates = Variable(interpolates, requires_grad=True)\n#    disc_interpolates = discriminator(interpolates, labels)\n\n#    grad_out = torch.ones(disc_interpolates.size()).cuda()\n#        \n#    gradients = grad(\n#        outputs=disc_interpolates,\n#        inputs=interpolates,\n#        grad_outputs = grad_out\n#    )[0]\n#                              #create_graph=True, retain_graph=True, only_inputs=True)[0]\n#    gradients = gradients.view(gradients.size(0), -1)\n#    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n#    return penalty * gradient_penalty\n\n# @yuchen unoptimized code below\n\ndef grad_penalty(real_data, fake_data, penalty, discriminator, update_index):\n    alpha = torch.cuda.FloatTensor(fake_data.shape[0], 1, 1, 1).uniform_(0, 1).expand(fake_data.shape)\n    interpolates = alpha * fake_data + (1 - alpha) * real_data\n    interpolates = Variable(interpolates, requires_grad=True)\n    disc_interpolates = discriminator(interpolates, update_index)\n\n    grad_out = torch.ones(disc_interpolates.size()).cuda()\n    gradients = grad(outputs=disc_interpolates, inputs=interpolates,\n                              grad_outputs = grad_out,\n                              create_graph=True, retain_graph=True, only_inputs=True)[0]\n    gradients = gradients.view(gradients.size(0), -1)\n    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n    return penalty * gradient_penalty\n\n\ndef create_noisy_dataset(mnist_train_data, mnist_train_labels):\n    noisy_to_real = {}\n    def add_noise_to_image(image, prob):\n        output = numpy.zeros(image.shape)\n        thres = 1 - prob \n        for i in range(image.shape[0]):\n            for j in range(image.shape[1]):\n                rdn = numpy.random.uniform(0,1)\n                output[i][j] = rdn\n                if rdn > prob:\n                    output[i][j] = image[i][j]\n        return output\n\n    noisy_dataset = []\n    for i, image in enumerate(mnist_train_data):\n        noisy_image = add_noise_to_image(image, 0.5)\n        combined = combine_image_and_onehot_label(noisy_image, mnist_train_labels[i])\n        noisy_dataset.append(combined)\n        noisy_to_real[noisy_image.tostring()] = image\n        print(i)\n\n    noisy_dataset = torch.tensor(numpy.array(noisy_dataset))\n    my_dataset = torch.utils.data.DataLoader(noisy_dataset, batch_size=100, shuffle=True, drop_last=True)\n    pickle.dump(my_dataset, open(\"noisy_data\", \"wb\"))\n    pickle.dump(noisy_to_real, open(\"noisy_to_real\", \"wb\"))\n    return noisy_dataset\n\ndef show_image(data, label):\n    # Plot\n    data = data.permute(1,2,0)\n    print(data.shape)\n    plt.title(\"Label is \" + str(label)) \n    plt.imshow(data)\n    plt.show()\n\ndef save_image(data, label, directory):\n    # Plot\n    data = data.transpose((1,2,0))\n    filename = directory + \"/\" + label + \".png\"\n    matplotlib.image.imsave(filename, data)    \n\ndef combine_image_and_onehot_label(data, label):\n    id_matrix = numpy.identity(10)\n    result = numpy.zeros((33, 32))\n    label_result = numpy.zeros(32)\n    \n    label_onehot = id_matrix[label]\n    label_result[0:10] = label_onehot\n    \n    result[:-1,:] = data\n    result[-1,:] = label_result\n    \n    return result\n    \ndef extract_label_and_image(combined):\n    images = numpy.zeros((len(combined), 32, 32))\n    labels = numpy.zeros((len(combined), 10))\n    \n    for i, element in enumerate(combined):\n        image = element[:-1,:]\n        images[i] = image\n        \n        onehot = element[-1,:10]\n        labels[i] = onehot\n        \n    return torch.from_numpy(images).float(), torch.from_numpy(labels).float() \n\ndef normal_init(m, mean, std):\n    if isinstance(m, torch.nn.ConvTranspose2d) or isinstance(m, torch.nn.Conv2d):\n        m.weight.data.normal_(mean, std)\n        m.bias.data.zero_()\n\ndef print_network(net):\n    num_params = 0\n    for param in net.parameters():\n        num_params += param.numel()\n    print(net)\n    print('Total number of parameters: %d' % num_params)\n\ndef histo(network, name, epoch, directory):\n    params = torch.FloatTensor([]).cuda()\n    for p in network.parameters():\n        p1 = p.view(-1)\n        params = torch.cat((params, p1), 0)\n    params = params.detach().cpu().numpy()\n    plt.hist(params, bins = 1000)\n    plt.title('Histogram of ' + name + ' network, ep: ' + str(epoch))\n    label = name + '_hist_ep_' + str(epoch)\n    plt.savefig(directory + \"/\" + label + \".png\")\n\ndef plot_editor_scores(generator, discriminator, gpu_mode, num_edits, folder, epoch, wlabels=False):\n    batch = 64\n    z = torch.randn((batch, 128))\n    if gpu_mode:\n        z = z.cuda().float()\n    if wlabels:\n        labels = numpy.random.choice(10,100)\n        onehot_labels = numpy.eye(10)[labels]\n        onehot_labels = torch.from_numpy(onehot_labels)\n        onehot_labels = onehot_labels.view(-1,1,1,10).float()\n        onehot_labels = onehot_labels.cuda() \n    \n    if wlabels:\n        gen_images = generator(z, onehot_labels)\n    else:\n        gen_images = generator.forwardGenerateDetached(z, 1+num_edits)\n    \n    G_tensor = gen_images\n    editor_scores = [discriminator(G_tensor).mean().item()]\n    for i in range(num_edits):\n        editor_scores.append(discriminator(generator.forwardGenerateDetached(z, 1+i)).mean().item())\n    edit_id = numpy.arange(0, len(editor_scores), 1)\n    \n    fig, ax = plt.subplots()\n    ax.plot(edit_id, editor_scores)\n    ax.set(xlabel='Editor #', ylabel='D scores',\n                   title='Wish to see concave upward')\n    ax.grid()\n    plt.savefig(folder + \"/\" + \"Epoch\" + str(epoch) + \".png\")\n    fig.clear()\n    \n    print(\"Edit losses:\" + str(editor_scores))\n\ndef compute_wass_distance(images, discriminator, generator, folder, epoch, num_editors):\n    num_samples = 100\n    z = torch.randn((num_samples, 128)).cuda()\n    real = torch.mean(discriminator(images))\n  \n    generated = generator.forwardGenerate(z, num_editors+1)\n    wass_distance = []\n    for edit_images in generated:\n        edit_images = edit_images.detach()\n        edit_mean = torch.mean(discriminator(edit_images))\n        wass_distance.append(real - edit_mean)\n\n    edit_id = numpy.arange(0, len(wass_distance), 1)\n    fig, ax = plt.subplots()\n    ax.plot(edit_id, wass_distance)\n    ax.set(xlabel='Editor #', ylabel='Wass distance',\n                   title='Wish to see convex downward')\n    ax.grid()\n    plt.savefig(folder + \"/\" + \"Epoch\" + str(epoch) + \".png\")\n    fig.clear()\n\ndef wrong_label_gen(label, wrong_num):\n    if label.is_cuda:\n        ones = torch.ones(label.size()).cuda()\n        generated_sample = torch.zeros(label.size()).cuda()\n        generated_sample = torch.FloatTensor([]).cuda()\n    else:\n        ones = torch.ones(label.size())\n        generated_sample = torch.zeros(label.size())\n        generated_sample = torch.FloatTensor([])\n    \n    dim = label.shape[3]\n    wrong_labels = ones - label\n    \n    for j in range(wrong_labels.shape[0]):\n        wrongs = []\n        for i in range(wrong_labels.shape[3]):\n            if wrong_labels[j, 0, 0, i].item() != 0:\n                wrongs.append(i)\n        shuffle(wrongs)\n        t = 0\n        while t < wrong_num:\n            onehot_labels = numpy.eye(dim)[wrongs[t]]\n            onehot_labels = torch.from_numpy(onehot_labels)\n            onehot_labels = onehot_labels.view(-1,1,1,dim).float()\n            if label.is_cuda:\n                onehot_labels = onehot_labels.cuda()\n            generated_sample = torch.cat((generated_sample, onehot_labels), 0)\n            t += 1\n                \n    return generated_sample\n\ndef get_var_of_data(data):\n    std = torch.std(data, dim=0)\n    return std \n\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":12153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"249422333","text":"#!/usr/bin/env python3\n\n# pylint: disable=invalid-name\n\n# based on https://github.com/bamos/zsh-history-analysis\nimport argparse\nimport os\nimport sys\nimport time\nfrom itertools import groupby\nfrom collections import Counter, defaultdict\nfrom typing import Dict, Tuple, List\n# import statistics\n\ndef group_by_key(input_value):\n    \"\"\"\n    group dictionary by key\n    \"\"\"\n    grouped_m = defaultdict(list)\n    for k, value in input_value:\n        grouped_m[k].append(value)\n    return grouped_m\n\nclass Command:\n    \"\"\"\n    class Command\n    \"\"\"\n    def __init__(self, raw):\n        tup = raw.split(\";\")\n        # TODO: Should this be hard-coded?\n        self.timestamp_epoch = int(tup[0][2:-2].split(':')[0])\n        self.timestamp_struct = time.gmtime(self.timestamp_epoch)\n        self.full_command = tup[1].strip()\n        self.base_command = tup[1].split()[0]\n\nclass HistoryData:\n    \"\"\"\n    class HistoryData\n    \"\"\"\n    def __init__(self, filenames):\n        if isinstance(filenames, str):\n            filenames = [filenames]\n        commands = []\n        for filename in filenames:\n            with open(filename, 'rb') as f:\n                it = iter(f)\n                for line in it:\n                    try:\n                        full_line = line.decode()\n                        while full_line.strip()[-1] == '\\\\':\n                            full_line += next(it).decode()\n                        commands.append(Command(full_line))\n                    except Exception as ex:\n                        # print(\"Warning: Exception parsing.\")i\n                        # print(ex)\n                        pass\n\n        self.commands = commands\n\n    def get_commands(self) -> List[Command]:\n        return self.commands\n\n    def get_full_commands(self) -> List[str]:\n        \"\"\"\n        gets all the base commands\n        \"\"\"\n        return [cmd.full_command for cmd in self.commands]\n\n    def get_base_commands(self) -> List[str]:\n        \"\"\"\n        gets all the base commands\n        \"\"\"\n        return [cmd.base_command for cmd in self.commands]\n\n    def get_top_commands(self, amount=10, full=False) -> List[Tuple[str, int]]:\n        \"\"\"\n        get all the top commands\n        \"\"\"\n        if full:\n            cmds = self.get_base_commands()\n        else:\n            cmds = self.get_full_commands()\n        return Counter(cmds).most_common(amount, )\n\n\n\n\nhistory_file = \"/home/mandy/.zhistory\"\n\nhistory_data = HistoryData(history_file)\n\ncmd = history_data.get_commands()[0]\n\n\nfor (command, amount) in history_data.get_top_commands():\n    print(\"command %s amount %s\" % (command, amount))\n\nprint(history_data.get_top_commands())\n\nprint(history_data.get_top_commands(full=True))\n","sub_path":"bin/zsh_history.py","file_name":"zsh_history.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"277089548","text":"from mint import RegridEdges\nimport numpy\nimport sys\nfrom pathlib import Path\n\n\ndef test_compute_weights():\n\n    data_dir = Path(__file__).absolute().parent / '../../data'\n\n    # create a regridder\n    rg = RegridEdges()\n\n    # src is lon-lat grid so set the flags to 0, 0. This will \n    # not apply any transformations to the grid\n    rg.setSrcGridFlags(0, 0)\n\n    # src grid file\n    src_file = str(data_dir / Path('latlon4x2.nc'))\n    # load the src grid, latlon is the name of the mesh stored in the netcdf file\n    rg.loadSrcGrid(f'{src_file}:latlon')\n\n    # dst is cubed-sphere. Cells at the poles need to be fixed to\n    # make them as compact as possible. Use flags 1, 1\n    rg.setDstGridFlags(1, 1)\n\n    # dst grid file\n    dst_file = str(data_dir / Path('cs_4.nc'))\n    # load the dst grid, physics is the name of the mesh stored in the netcdf file\n    rg.loadDstGrid(f'{dst_file}:physics')\n\n    # compute the regridding weights. numCellsPerBucket is used to accelerate the cell\n    # search. The smaller numCellPerBucket the faster the search. However, numCellPerBucket\n    # there are edge cases where the search fails when numCellsPerBucket is too small.\n    # periodX is the periodicity length to add/subtract to make the cells well behaved. \n    # (periodX can be 0 if a regional model)\n    rg.build(numCellsPerBucket=128, periodX=360., debug=2)\n\n    # save the weights in a netCDF file\n    rg.dumpWeights('test_regrid_edges_py.nc')\n\n\ndef test_apply_weights():\n\n    data_dir = Path(__file__).absolute().parent / '../../data'\n    \n    # create a regridder\n    rg = RegridEdges()\n\n    # src is lon-lat grid\n    rg.setSrcGridFlags(0, 0)\n    rg.loadSrcGrid(f'{data_dir}/latlon4x2.nc:latlon')\n\n    # dst is cubed-sphere\n    rg.setDstGridFlags(1, 1)\n    rg.loadDstGrid(f'{data_dir}/cs_4.nc:physics')\n\n    # load the weights \n    rg.loadWeights('test_regrid_edges_py.nc')\n\n    num_src_edges = rg.getNumSrcEdges()\n    num_dst_edges = rg.getNumDstEdges()\n    print(f'number of edges (src/dst): {num_src_edges}/{num_dst_edges}')\n\n    # create some mock field\n    src_data = numpy.array(range(0, num_src_edges), numpy.float64)\n\n    # allocate some space to receive the data\n    dst_data = numpy.empty((num_dst_edges,), numpy.float64)\n\n    # apply the weights to the src field, will fill in dst_data\n    rg.apply(src_data, dst_data)\n\n    print(f'dst_data = {dst_data}')\n\n\nif __name__ == '__main__':\n\n    test_compute_weights()\n    test_apply_weights()\n\n\n","sub_path":"mint/tests/test_regrid_edges.py","file_name":"test_regrid_edges.py","file_ext":"py","file_size_in_byte":2458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"160037311","text":"import torch\nfrom typing import Tuple, Optional\nimport warnings\nimport nvtx\nfrom sptrans.sddmm import bsddmm\nfrom sptrans.spmm import bspmm\nfrom sptrans.softmax import bcsr_softmax\n\n\ndef sp_multi_head_attention_forward(\n    query: torch.Tensor,                                # query (seq_len, bs, embed_dim)\n    key: torch.Tensor,                                  # key   (seq_len, bs, embed_dim)\n    value: torch.Tensor,                                # value (seq_len, bs, embed_dim)\n    embed_dim_to_check: int,                            # This equals to embed_dim\n    num_heads: int,                                     # number of attention heads\n    in_proj_weight: torch.Tensor,                       # Project the input q, k, v, (3 * embed_dim, embed_dim)\n    in_proj_bias: torch.Tensor,                         # Bias for the above projection. (3 * embed_dim)\n    bias_k: Optional[torch.Tensor],                     # bias for key, (1, 1, embed_dim)\n    bias_v: Optional[torch.Tensor],                     # bias for value, (1, 1, embed_dim)\n    add_zero_attn: bool,                                # TODO\n    dropout_p: float,                                   # probability of an element to be zeros\n    out_proj_weight: torch.Tensor,                      # output projection weight (embed_dim, embed_dim)\n    out_proj_bias: torch.Tensor,                        # output projection bias (embed_dim)\n    training: bool = True,                              # training or inference\n    key_padding_mask: Optional[torch.Tensor] = None,    # TODO:\n    need_weights: bool = True,                          # If True, returns the attention weight\n    row_indices: Optional[torch.Tensor] = None,         # indices to the csr mask rows\n    row_offsets: Optional[torch.Tensor] = None,         # the row indices of the csr mask\n    column_indices: Optional[torch.Tensor] = None,      # the column indices of the csr mask\n    vec_length: Optional[int] = 2,                      # the vector length of column vector sparsity\n    use_separate_proj_weight: bool = False,             # if True, the q/k/v_proj_weight will be used rather than in_proj_weight\n    q_proj_weight: Optional[torch.Tensor] = None,\n    k_proj_weight: Optional[torch.Tensor] = None,\n    v_proj_weight: Optional[torch.Tensor] = None,\n    static_k: Optional[torch.Tensor] = None,            # TODO\n    static_v: Optional[torch.Tensor] = None,            # TODO\n) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n\n    # Get problem size\n    tgt_len, bsz, embed_dim = query.size()\n    assert embed_dim == embed_dim_to_check\n    # allow MHA to have different sizes for the feature dimension\n    assert key.size(0) == value.size(0) and key.size(1) == value.size(1)\n\n    # Get feature dimension of each head\n    head_dim = embed_dim // num_heads\n    assert head_dim * num_heads == embed_dim, \"embed_dim must be divisible by num_heads\"\n\n    # This computes the 1/sqrt{d} in the attention score before softmax\n    scaling = float(head_dim) ** -0.5\n\n    with nvtx.annotate(\"sp Input Linear Projection\"):\n    # If using the in_proj_weight for projection\n        if not use_separate_proj_weight:\n            # For self-attention whose query, key, and value are the same\n            if (query is key or torch.equal(query, key)) and (key is value or torch.equal(key, value)):\n                q, k, v = torch.nn.functional.linear(query, in_proj_weight, in_proj_bias).chunk(3, dim=-1)\n\n            elif key is value or torch.equal(key, value):\n                # encoder-decoder attention\n                # This is inline in_proj function with in_proj_weight and in_proj_bias\n                _b = in_proj_bias\n                _start = 0\n                _end = embed_dim\n                _w = in_proj_weight[_start:_end, :]\n                if _b is not None:\n                    _b = _b[_start:_end]\n                q = torch.nn.functional.linear(query, _w, _b)\n\n                if key is None:\n                    assert value is None\n                    k = None\n                    v = None\n                else:\n\n                    # This is inline in_proj function with in_proj_weight and in_proj_bias\n                    _b = in_proj_bias\n                    _start = embed_dim\n                    _end = None\n                    _w = in_proj_weight[_start:, :]\n                    if _b is not None:\n                        _b = _b[_start:]\n                    k, v = torch.nn.functional.linear(key, _w, _b).chunk(2, dim=-1)\n\n            else:\n                # This is inline in_proj function with in_proj_weight and in_proj_bias\n                _b = in_proj_bias\n                _start = 0\n                _end = embed_dim\n                _w = in_proj_weight[_start:_end, :]\n                if _b is not None:\n                    _b = _b[_start:_end]\n                q = torch.nn.funtional.linear(query, _w, _b)\n\n                # This is inline in_proj function with in_proj_weight and in_proj_bias\n                _b = in_proj_bias\n                _start = embed_dim\n                _end = embed_dim * 2\n                _w = in_proj_weight[_start:_end, :]\n                if _b is not None:\n                    _b = _b[_start:_end]\n                k = torch.nn.functional.linear(key, _w, _b)\n\n                # This is inline in_proj function with in_proj_weight and in_proj_bias\n                _b = in_proj_bias\n                _start = embed_dim * 2\n                _end = None\n                _w = in_proj_weight[_start:, :]\n                if _b is not None:\n                    _b = _b[_start:]\n                v = torch.nn.functional.linear(value, _w, _b)\n        else:\n            q_proj_weight_non_opt = torch.jit._unwrap_optional(q_proj_weight)\n            len1, len2 = q_proj_weight_non_opt.size()\n            assert len1 == embed_dim and len2 == query.size(-1)\n\n            k_proj_weight_non_opt = torch.jit._unwrap_optional(k_proj_weight)\n            len1, len2 = k_proj_weight_non_opt.size()\n            assert len1 == embed_dim and len2 == key.size(-1)\n\n            v_proj_weight_non_opt = torch.jit._unwrap_optional(v_proj_weight)\n            len1, len2 = v_proj_weight_non_opt.size()\n            assert len1 == embed_dim and len2 == value.size(-1)\n\n            if in_proj_bias is not None:\n                q = torch.nn.functional.linear(query, q_proj_weight_non_opt, in_proj_bias[0:embed_dim])\n                k = torch.nn.functional.linear(key, k_proj_weight_non_opt, in_proj_bias[embed_dim : (embed_dim * 2)])\n                v = torch.nn.functional.linear(value, v_proj_weight_non_opt, in_proj_bias[(embed_dim * 2) :])\n            else:\n                q = torch.nn.functional.linear(query, q_proj_weight_non_opt, in_proj_bias)\n                k = torch.nn.functional.linear(key, k_proj_weight_non_opt, in_proj_bias)\n                v = torch.nn.functional.linear(value, v_proj_weight_non_opt, in_proj_bias)\n\n    batch_size = bsz * num_heads\n    with nvtx.annotate(\"sp Input transpose\"):\n        # Transpose the batch*head and sequence length dimensions\n        q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)\n        if k is not None:\n            k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)\n        if v is not None:\n            v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)\n\n        if static_k is not None:\n            assert static_k.size(0) == bsz * num_heads\n            assert static_k.size(2) == head_dim\n            k = static_k\n\n        if static_v is not None:\n            assert static_v.size(0) == bsz * num_heads\n            assert static_v.size(2) == head_dim\n            v = static_v\n\n        src_len = k.size(1)\n\n        if key_padding_mask is not None:\n            assert key_padding_mask.size(0) == bsz\n            assert key_padding_mask.size(1) == src_len\n\n        if add_zero_attn:\n            src_len += 1\n            k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1)\n            v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1)\n            if key_padding_mask is not None:\n                key_padding_mask = torch.nn.functional.pad(key_padding_mask, (0, 1))\n    \n    # batched matrix multiplication\n    with nvtx.annotate(\"sp QK^T\"):\n        attn_output_weights = bsddmm(row_indices, row_offsets, column_indices, q, k, vec_length)\n    \n    with nvtx.annotate(\"sp Softmax\"):\n        attn_output_weights = bcsr_softmax(row_indices, row_offsets, attn_output_weights, scaling, vec_length, batch_size)\n    \n    # Apply dropout\n    with nvtx.annotate(\"sp dropout\"):\n        attn_output_weights = torch.nn.functional.dropout(attn_output_weights, p=dropout_p, training=training)\n    \n    # batch multiplication with the value\n    with nvtx.annotate(\"sp AV\"):\n        attn_output = bspmm(row_indices, row_offsets, column_indices, attn_output_weights, v, vec_length)\n    \n    # transpose the output and concatenate the heads\n    with nvtx.annotate(\"sp Output transpose\"):\n        attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)\n\n    with nvtx.annotate(\"sp Output Projection\"):\n        attn_output = torch.nn.functional.linear(attn_output, out_proj_weight, out_proj_bias)\n\n    if need_weights:\n        # average attention weights over heads\n        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)\n        return attn_output, attn_output_weights.sum(dim=1) / num_heads\n    else:\n        return attn_output, None\n    \n\nclass spMultiheadAttention(torch.nn.MultiheadAttention):\n    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None):\n        super(spMultiheadAttention, self).__init__(embed_dim, num_heads, dropout, bias, add_bias_kv, add_zero_attn, kdim, vdim)\n    \n    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, key_padding_mask: Optional[torch.Tensor] = None,\n                need_weights: bool = True, row_indices: Optional[torch.Tensor] = None, row_offsets: Optional[torch.Tensor] = None,\n                column_indices: Optional[torch.Tensor] = None, vec_length: int = 2) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n        return sp_multi_head_attention_forward(\n            query, key, value, self.embed_dim, self.num_heads,\n            self.in_proj_weight, self.in_proj_bias,\n            self.bias_k, self.bias_v, self.add_zero_attn,\n            self.dropout, self.out_proj.weight, self.out_proj.bias,\n            training=self.training,\n            key_padding_mask=key_padding_mask, need_weights=need_weights,\n            row_indices=row_indices, row_offsets=row_offsets, column_indices=column_indices,\n            vec_length=vec_length)\n","sub_path":"spattention.py","file_name":"spattention.py","file_ext":"py","file_size_in_byte":10809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"512942396","text":"# python wcount.py http://mail.ru word1 [word2, ...]\nimport sys\nimport urllib2\nimport re\n\nurl = sys.argv[1]\nwords = sys.argv[2:]\n\nprint(url)\ncontent = urllib2.urlopen(url).read()\n\nwords_from_page = re.split('[\\s\\.,; : \\{\\} \\| = \\(\\) \\d 0123456789 \\[\\] \\%\\^\\& <\\\\/> \\t]', content)\n\nwords_dict = {}\n\nfor word in words_from_page:\n    if word in words_dict:\n        words_dict[word] += 1\n    else:\n        words_dict[word] = 1\n\nfrom pprint import pprint\n# pprint(words_dict)\n\nfor word in words:\n    if word in words_dict:\n        print(word, words_dict[word])\n\n\n\n\n","sub_path":"wcount.py","file_name":"wcount.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"277902752","text":"# revenue.py\n# Update the revenue data on Socrata.\n# It is presumed that the new data have already been converted from a pdf to a csv. Actually, two csv files: one for GF and SF revenues, another for available balances. The available balances will be added to revenues.\n# Adam Scherling April 18, 2018. Updated June 11, 2018\n# Converted/Updated July 2021, Calvin Chan\n\n# NOTE: NEED TO RUN parse_revenues.py FIRST. RUN THAT IMMEDIATELY PRIOR TO RUNNING revenue.py\n\n### This script will be able to creating new revenue entries in Socrata from the created CSV's via `parse_revenues.py`.\n\n## SETUP\n\nimport datetime\nfrom typing import final\nimport pandas as pd\nimport credentials\nfrom sodapy import Socrata\n\n# Instantiating variables\nfy = '2021-2022'\nfy_shorthand = 2022\nfilepath_prefix = '../../data/approved_budget/FY21-22/'\n\n# API endpoint for the dataset\nsocrata_endpoint = \"https://data.lacity.org/resource/ih6g-qkwz.json\"\nsocrata_id = 'ih6g-qkwz'\n\n# Get current timestamp\ntimestamp = str(datetime.datetime.now())\n\n## EXTRACTING + CLEANING DATA\n\n\n# set up Socrata client\n# Don't need credentials to read in public data.\nclient = Socrata('data.lacity.org', None)\n\n# uncomment if you are going to log in / push to the data portal\n# username = credentials.lahub_user\n# password = credentials.lahub_pass\n# apptoken = credentials.lahub_auth\n# client = Socrata('data.lacity.org', apptoken, username=username, password=password)\n\n# read in the data on Socrata and save as backup\nold_revenues = pd.DataFrame(client.get(socrata_id))\nold_revenues.to_csv(f'{filepath_prefix}old_revenues_{timestamp}.csv', index=False)\n\n# Filtering out current year\nold_revenues = old_revenues[old_revenues['fiscal_year_2'] != 2022]\n\n# Read in new data -- these spreadsheets are from parse_revenues.py\nnew_revenues = pd.read_csv('../../data/approved_budget/FY21-22/new_revenues.csv')\navailable_balances = pd.read_csv('../../data/approved_budget/FY21-22/new_available_balances.csv')\n\n### Cleaning dataframes\n\n# Remove percent columns\nnew_revenues.drop(columns=['Percent'], inplace=True)\navailable_balances.drop(columns=['Percent'], inplace=True)\n\n# Remove rows without an available balance\navailable_balances = available_balances[available_balances['Available.Balance'] != 0]\n\n# Cleaning new_revenues\nnew_revenues = new_revenues.merge(available_balances, how='outer', on='Revenue.Source')\nnew_revenues['Available.Balance'] = new_revenues['Available.Balance'].fillna(0)\nnew_revenues['Amount'] += new_revenues['Available.Balance']\nnew_revenues.drop(columns=['Available.Balance'], inplace=True)\nnew_revenues['Fiscal.Year.Shorthand'] = fy_shorthand\nnew_revenues['Fiscal.Year'] = fy\n\n### Dropping rows that still have null values because this could mean these rows are just slightly misnamed in `Revenue.Source` w.r.t. the `Available Balances` table.\n### The above is not necessarily true from year to year. Quality check of the data is still required.\nnew_revenues.dropna(axis=0, how='any', inplace=True)\n\n# Renaming columns\nnew_revenues.rename(columns={\n    'Revenue.Source' : 'revenue_source',\n    'Amount': 'amount',\n    'Fund.Type' : 'fund_type',\n    'Fiscal.Year' : 'fiscal_year',\n    'Fiscal.Year.Shorthand': 'fiscal_year_2'\n}, inplace=True)\n\n### Dropping rows that still have null values from `old_revenues`. These entries are most likely continuously listed under similar names\n### Quality check before this row would still be best practice.\nold_revenues.dropna(axis=0, how='any', inplace=True)\n\n## CREATING FINAL DATAFRAME AND UPLOADING TO SOCRATA\n\n# Creating the `final_revenues` table and exporting it\nfinal_revenues = pd.concat([new_revenues, old_revenues])\nfinal_revenues = final_revenues[old_revenues.columns]\nfinal_revenues.to_csv(f'{filepath_prefix}new_revenues.csv', index=False)\n\n# Upload to Socrata here\n# client.replace(socrata_id, final_revenues)\n","sub_path":"scripts/python-scripts/revenue.py","file_name":"revenue.py","file_ext":"py","file_size_in_byte":3834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"638396967","text":"# !/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\ntests.py is our unittest file\n\"\"\"\n\nimport django.core.urlresolvers\nfrom django.http import HttpRequest\nfrom django.template.loader import render_to_string\nimport django.test\nimport db.models\nimport db.views\n\n# bump\n\n\nclass HomePageTest(django.test.TestCase):\n\n    def test_root_url_resolves_to_home_page_view(self):\n        found = django.core.urlresolvers.resolve('/')\n\n        self.assertEqual(found.func, db.views.index)\n\n    def test_home_page_returns_correct_html(self):\n        request = HttpRequest()\n        response = db.views.index(request)\n\n        expected_html = render_to_string('index.html')\n\n        self.assertEqual(response.content.decode(), expected_html)\n\n\nclass ModelsTest(django.test.TestCase):\n\n    def test_saving_and_retrieving_building(self):\n        first_building = db.models.Building(\n            title=\"Test Building\", street=\"Test Street 123\", zipcode=\"12345\",\n            city=\"Test City\", category=\"Universität\")\n        first_building.save()\n        second_building = db.models.Building(\n            title=\"Test Building 2\", street=\"Test Street 124\", zipcode=\"12346\",\n            city=\"Test City 2\", category=\"Krankenhaus\")\n        second_building.save()\n\n        saved_building = db.models.Building.objects.filter(\n            title=\"Test Building\")\n\n        self.assertEqual(saved_building[0], first_building)\n\n    def test_saving_and_retrieving_department(self):\n        first_department = db.models.Department(\n            title=\"Test Department\", number=1)\n        first_department.save()\n        second_department = db.models.Department(\n            title=\"Test Department\", number=2)\n        second_department.save()\n\n        saved_department = db.models.Department.objects.filter(number=1)\n\n        self.assertEqual(saved_department[0], first_department)\n\n    def test_saving_and_retrieving_employee(self):\n        first_department = db.models.Department(\n            title=\"Test Department\", number=1)\n        first_department.save()\n        first_employee = db.models.Employee(\n            department=first_department, empID=\"ID-1\",\n            firstname=\"Firstname\", lastname=\"Lastname\")\n        first_employee.save()\n        second_employee = db.models.Employee(\n            department=first_department, empID=\"ID-2\",\n            firstname=\"Firstname 2\", lastname=\"Lastname 2\")\n        second_employee.save()\n\n        saved_employee = db.models.Employee.objects.filter(empID=\"ID-2\")\n\n        self.assertEqual(saved_employee[0], second_employee)\n\n    def test_saving_and_retrieving_contractor(self):\n        first_contractor = db.models.Contractor(\n            name=\"Test Contractor\", street=\"Test Street 123\",\n            zipcode=\"12345\", city=\"Test City\", phone=\"238457923057920384\",\n            fax=\"8349572093475897435\")\n        first_contractor.save()\n        second_contractor = db.models.Contractor(\n            name=\"Test Contractor 2\", street=\"Test Street 124\",\n            zipcode=\"12346\", city=\"Test City 2\", phone=\"9999457923057920384\",\n            fax=\"8349572093475811111\")\n        second_contractor.save()\n\n        saved_contractor = db.models.Contractor.objects.filter(zipcode=\"12346\")\n\n        self.assertEqual(saved_contractor.first(), second_contractor)\n\n\nclass ViewsTest(django.test.TestCase):\n\n    def test_prognosis_retrieving_and_matching_newest_with_project(self):\n\n        first_project = db.models.Project.objects.create()\n\n        first_budgetplan = db.models.Budgetplan.objects.create(\n            project=first_project)\n\n        first_budgetyear_first_project = db.models.Budgetyear.objects.create(\n            budgetplan=first_budgetplan)\n        second_budgetyear_first_project = db.models.Budgetyear.objects.create(\n            budgetplan=first_budgetplan)\n        third_budgetyear_first_project = db.models.Budgetyear.objects.create(\n            budgetplan=first_budgetplan)\n\n        first_employee = db.models.Employee.objects.create()\n\n        newest_prognosis = db.models.Prognosis.objects.create(\n            budgetyear=first_budgetyear_first_project,\n            employee=first_employee,\n            text=\"dummy\")\n\n        first_prognosis_first_bugegetyear_first_project = db.models.Prognosis.objects.create(\n            budgetyear=first_budgetyear_first_project,\n            employee=first_employee,\n            text=\"new\")\n        second_prognosis_first_bugegetyear_first_project = db.models.Prognosis.objects.create(\n            budgetyear=first_budgetyear_first_project,\n            employee=first_employee,\n            text=\"old\")\n        first_prognosis_second_bugegetyear_first_project = db.models.Prognosis.objects.create(\n            budgetyear=second_budgetyear_first_project,\n            employee=first_employee,\n            text=\"new\")\n        second_prognosis_second_bugegetyear_first_project = db.models.Prognosis.objects.create(\n            budgetyear=second_budgetyear_first_project,\n            employee=first_employee,\n            text=\"old\")\n\n        wanted_prognosiss = db.models.Prognosis.objects.filter(\n            budgetyear__budgetplan__project__id=first_project.id)\n\n        for prognosis in wanted_prognosiss:\n            if newest_prognosis.timestamp < prognosis.timestamp:\n                newest_prognosis = prognosis\n\n        print(newest_prognosis.timestamp, newest_prognosis.text,\n              newest_prognosis.budgetyear.id)\n","sub_path":"db/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"401766451","text":"\"\"\"Столбцовая диаграмма самых активных обсуждений на базе hn_submissions.py\"\"\"\nimport requests\nfrom operator import itemgetter\nimport pygal\nfrom pygal.style import LightColorizedStyle as LCS, LightenStyle as LS\nimport sys\n\n\ndef get_pygal_config():\n    my_config = pygal.Config()\n    my_config.x_label_rotation = 45\n    my_config.show_legend = False\n    my_config.title_font_size = 24\n    my_config.label_font_size = 14\n    my_config.major_label_font_size = 18\n    my_config.truncate_label = 15\n    my_config.show_y_guides = False\n    my_config.width = 1000\n    return my_config\n\n\ndef get_article_information(url):\n    \"\"\"Возвращает словарь с информацией об отдельной статье.\"\"\"\n    submission_r = requests.get(url)\n    if submission_r.status_code != 200:\n        print('Submission status code: ', submission_r.status_code)\n        print('Bad url: ', url)\n    return submission_r.json()\n\n\nnon_bmp_map = dict.fromkeys(range(0x10000, sys.maxunicode + 1), 0xfffd)\n# Создание вызова API и сохранение ответа.\nurl = 'https://hacker-news.firebaseio.com/v0/topstories.json'\nr = requests.get(url)\nprint('Status code: ', r.status_code, '\\n')\n\n# Обработка информации о каждой статье.\nsubmission_ids = r.json()\nsubmission_dicts, names = [], []\nfor submission_id in submission_ids[:50]:\n    # Создание отдельного вызова API Для каждой статьи.\n    url = ''.join(['https://hacker-news.firebaseio.com/v0/item/', str(submission_id), '.json'])\n    response_dict = get_article_information(url)\n    # Обработка информации о текущей статье.\n    submission_dict = {\n        'label': response_dict['title'].translate(non_bmp_map),\n        'xlink': 'https://news.ycombinator.com/item?id=' + str(submission_id),\n        'value': response_dict.get('descendants', 0)\n                      }\n    submission_dicts.append(submission_dict)\n\nsubmission_dicts = sorted(submission_dicts, key=itemgetter('value'), reverse=True)\nfor submission_dict in submission_dicts:\n    names.append(submission_dict['label'])\n\nmy_style = LS('#333366', base_style=LCS)\nmy_config = get_pygal_config()\nchart = pygal.Bar(my_config, style=my_style)\nchart.title = 'Top comment articles'\nchart.x_labels = names\nchart.add('', submission_dicts)\nchart.render_to_file('top_articles.svg')\n","sub_path":"Part_2_Chapter_17/task17-2.py","file_name":"task17-2.py","file_ext":"py","file_size_in_byte":2456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"310026572","text":"#!/usr/bin/env python3\n\nimport cs50\n\ndef main():\n    \n    while True:\n        print(\"Height: \", end = \"\")\n        height = cs50.get_int()\n        if height >= 0 and height <23:\n            break\n        \n    for row in range(height):\n        \n        for space in range (height - row - 1):\n            print(\" \", end = \"\")\n            \n        for hash in range (row + 2):\n            print(\"#\", end = \"\")\n        \n        print(\"\")\n\nif __name__ == \"__main__\":\n    main()","sub_path":"pset6/mario.py","file_name":"mario.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"20756328","text":"from django.contrib import admin\nfrom django.contrib.admin import SimpleListFilter\n\nfrom accounts.admin import MyBaseModelAdmin\nfrom .forms import ApparatusImportForm, LaboratoryImportForm\nfrom .models import Apparatus, Laboratory\n\n\nclass StaffFilter(SimpleListFilter):\n    title = 'staff'\n    parameter_name = 'staff'\n\n    def lookups(self, request, model_admin):\n        staffs = set([f.staff for f in model_admin.model.objects.all()])\n        return [(s.id, s.name) for s in staffs]\n\n    def queryset(self, request, queryset):\n        if self.value():\n            return queryset.filter(staff__id__exact=self.value())\n\n\nclass FacilityAdmin(MyBaseModelAdmin):\n    list_filter = ('school', StaffFilter,)\n    exclude = ('staff',)\n\n    # Only set staff field during the first save.\n    def save_model(self, request, obj, form, change):\n        if not obj.pk:\n            obj.staff = request.user\n        super().save_model(request, obj, form, change)\n\n    def has_change_permission(self, request, obj=None):\n        return self.permission_control(request, obj)\n\n    def has_delete_permission(self, request, obj=None):\n        return self.permission_control(request, obj)\n\n    def permission_control(self, request, obj=None):\n        if request.user.is_superuser:\n            return True\n        if obj and obj.staff == request.user:\n            return True\n        return False\n\n\nclass ApparatusAdmin(FacilityAdmin):\n    list_display = ('name', 'model_no', 'cost', 'purchased', 'school', 'staff',)\n    ordering = ('name', 'model_no',)\n    search_fields = ('name', 'model_no',)\n    date_hierarchy = 'purchased'\n    list_per_page = 50\n    model_import_form = ApparatusImportForm\n\n\nclass LaboratoryAdmin(FacilityAdmin):\n    list_display = ('name', 'location', 'capacity', 'school', 'staff',)\n    ordering = ('school', 'location',)\n    search_fields = ('name', 'location',)\n    list_per_page = 25\n    model_import_form = LaboratoryImportForm\n\n\nadmin.site.register(Apparatus, ApparatusAdmin)\nadmin.site.register(Laboratory, LaboratoryAdmin)\n","sub_path":"lims_root/inventory/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"599123581","text":"# -*- coding: utf-8 -*-\n__author__ = 'liudong'\n__date__ = '2019/10/18 4:18 PM'\nimport re\n\n# 拆分字符串\ndef main():\n    poem = '窗前明月光，疑是地上霜。举头望明月，低头思故乡。'\n    sentence_list = re.split(r'[, 。，.]', poem)\n    while '' in sentence_list:\n        sentence_list.remove('')\n    print(sentence_list)\n\ndef main1():\n    sentence = '你Y是傻叉吗？我操你大爷的。Fuck you.'\n    purified = re.sub('[[操肏艹]|fuck|shit|傻[比屄逼叉缺吊屌]|煞笔]]', '*', sentence, flags=re.IGNORECASE)\n    print(purified)\n\nif __name__ == '__main__':\n    main1()","sub_path":"Python/Python_Basic/split_string.py","file_name":"split_string.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"6218572","text":"\"\"\"\nCopyright (c) 2013 Samuel B. Fries\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\n\nimport sublime\nimport sublime_plugin\nimport re\n\nclass SnakeToCamelCase(sublime_plugin.TextCommand):\n    def run(self, edit):\n        view = self.view\n        regions = view.sel()\n\n        for region in regions:\n            val = view.substr(region)\n            strings = [x.capitalize() for x in val.split(\"_\")[1:]]\n            val = val.split(\"_\")[0] + \"\".join(strings)\n            view.replace(edit, region, val)\n\nclass CamelCaseToSnake(sublime_plugin.TextCommand):\n    def run(self, edit):\n        view = self.view\n        regions = view.sel()\n\n        self.split_re = re.compile('([A-Z]+)')\n\n        for region in regions:\n            val       = view.substr(region)\n            lines     = val.split('\\n')\n            lines_sub = [self.process_line(line) for line in lines]\n            sub       = \"\\n\".join(lines_sub)\n\n            view.replace(edit, region, sub)\n\n    def process_line(self, line):\n\n        def process_token(tkn):\n            char_first = tkn[0]\n            return '_' + tkn.lower() if char_first.isupper() else tkn\n\n        matches = self.split_re.split(line)\n        matches = matches[1:] if matches[0] == '' else matches\n        matches = matches[0:-1] if matches[-1] == '' else matches\n\n        if len(matches) > 0:\n            head     = [matches[0].lower()]\n            rest     = [process_token(tkn) for tkn in matches[1:]]\n            words    = head + rest\n            sentence = \"\".join(words)\n            return sentence\n        else:\n            return '';\n\nclass UnderscoreToSpacesCommand(sublime_plugin.TextCommand):\n    def run(self, edit):\n        view = self.view\n        regions = view.sel()\n\n        for region in regions:\n\n            val = view.substr(region)\n\n            val = \" \".join(val.split(\"_\"))\n\n            self.view.replace(edit, region, val)\n\n\nclass SpacesToUnderscoresCommand(sublime_plugin.TextCommand):\n    def run(self, edit):\n        view = self.view\n        regions = view.sel()\n\n        for region in regions:\n            val = view.substr(region)\n            val = \"_\".join(val.split(\" \"))\n            self.view.replace(edit, region, val)\n","sub_path":"text_convert.py","file_name":"text_convert.py","file_ext":"py","file_size_in_byte":3171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"142243392","text":"# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Types.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nfrom __future__ import print_function\n\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_model_analysis.types_compat import Any, Dict, List, Text, Tuple, Union, NamedTuple\n\n# pylint: disable=invalid-name\nTensorType = Union[tf.Tensor, tf.SparseTensor]\nDictOfTensorType = Dict[bytes, TensorType]\nTensorTypeMaybeDict = Union[TensorType, DictOfTensorType]\n\n# Type of keys we support for prediction, label and features dictionaries.\nKeyType = Union[Union[bytes, Text], Tuple[Union[bytes, Text], Ellipsis]]\n\n# Value of a Scalar fetched during session.run.\nFetchedScalarValue = Union[np.float32, np.float64, np.int32, np.int64, bytes]\n\n# Value of a Tensor fetched using session.run.\nFetchedTensorValue = Union[tf.SparseTensorValue, np.ndarray]\n\n# Value fechted using session.run.\nFetchedValue = Union[FetchedScalarValue, FetchedTensorValue]\n\n# Dictionary of Tensor values fetched.\n# The dictionary maps original dictionary keys => ('node' => value).\nDictOfFetchedTensorValues = Dict[KeyType, Dict[bytes, FetchedTensorValue]]\n\nListOfFetchedTensorValues = List[FetchedTensorValue]\n\n\ndef is_tensor(obj):\n  return isinstance(obj, tf.Tensor) or isinstance(obj, tf.SparseTensor)\n\n\n# Used in building the model diagnostics table, a MatrializedColumn is a value\n# inside of ExampleAndExtract that will be emitted to file.\nMaterializedColumn = NamedTuple(\n    'MaterializedColumn',\n    [('name', str), ('value',\n                     Union[List[str], List[int], List[float]])])\n\n# Used in building model diagnostics table, the ExampleAndExtracts holds an\n# example and all its \"extractions.\" Extractions that should be emitted to file.\n# Each Extract has a name, stored as the key of the DictOfExtractedValues.\nDictOfExtractedValues = Dict[Text, Any]\nExampleAndExtracts = NamedTuple(  # pylint: disable=invalid-name\n    'ExampleAndExtracts',\n    [('example', bytes),\n     ('extracts', DictOfExtractedValues)])\n","sub_path":"tensorflow_model_analysis/types.py","file_name":"types.py","file_ext":"py","file_size_in_byte":2565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"489518752","text":"import operator\nimport sys\n\n\ndef mark(answer_path, output_path):\n    answer_dict = {}\n    output_dict = {}\n\n    min_count = 0x7fffffff\n    with open(answer_path, 'r') as answer:\n        for line in answer:\n            params = line.split()\n            count = int(params[0])\n            word = params[1]\n            min_count = min(min_count, count)\n            if count in answer_dict:\n                answer_dict[count].add(word)\n            else:\n                answer_dict[count] = {word}\n\n    count_list = []\n    with open(output_path, 'r') as output:\n        for line in output:\n            params = line.split()\n            count = int(params[0])\n            word = params[1]\n            count_list.append(count)\n            if count in output_dict:\n                output_dict[count].add(word)\n            else:\n                output_dict[count] = {word}\n\n    # check correctness\n    if count_list != list(sorted(count_list)[::-1]):\n        return -1\n\n    for count in answer_dict.keys():\n        if count != min_count:\n            if output_dict[count] != answer_dict[count]:\n                return -2\n            del output_dict[count]\n        if count == min_count:\n            if not output_dict[count].issubset(answer_dict[count]):\n                return -3\n            del output_dict[count]\n\n    if output_dict:\n        return -4\n    else:\n        return 1\n\n\nif __name__ == '__main__':\n    result = mark(sys.argv[1], sys.argv[2])\n    if result == 1:\n        print(\"Test Passed\")\n    elif result == -1:\n        print(\"Wrong Answer: incorrect order\")\n    elif result == -4:\n        print(\"Wrong Answer: output exceeds top 20\")\n    elif result <= 0:\n        print(\"Wrong Answer\")\n","sub_path":"TopK_CommonWords/check_common.py","file_name":"check_common.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"491080763","text":"# Copyright (C) 2017 O.S. Systems Software LTDA.\n# This software is released under the MIT License\n\nimport base64\nimport json\n\nfrom Crypto.PublicKey import RSA\n\nfrom .base import HydraManager\nfrom .exceptions import HydraResponseError, HydraRequestError\n\n\nclass JWK:\n\n    PUBLIC = 'public'\n    PRIVATE = 'private'\n\n    def __init__(self, **jwk):\n        self.jwk = jwk\n        self.kty = jwk.get('kty')\n        self.alg = jwk.get('alg')\n        self.kid = jwk.get('kid')\n\n        self.n = jwk.get('n')\n        self.e = jwk.get('e')\n        self.d = jwk.get('d')\n\n        self.type = None\n        if all([self.n, self.e, self.d]):\n            self.type = self.PRIVATE\n        elif all([self.n, self.e]):\n            self.type = self.PUBLIC\n\n    @staticmethod\n    def b64_to_int(data):\n        return int.from_bytes(base64.urlsafe_b64decode(data + '=='), 'big')\n\n    def to_rsa(self):\n        \"\"\"Converts a JWK to RSA.\"\"\"\n        numbers = [self.n, self.e]\n        if self.type == self.PRIVATE:\n            numbers.append(self.d)\n        return RSA.construct(tuple(self.b64_to_int(n) for n in numbers))\n\n\nclass JWKManager(HydraManager):\n\n    def get(self, key, type_):\n        path = '/keys/{}/{}'.format(key, type_)\n        response = self.hydra.request('GET', path, scope='hydra.keys.get')\n        if not response.ok:\n            raise HydraRequestError('Request to get JWK failed.')\n        try:\n            jwk = response.json()['keys'][0]\n        except json.decoder.JSONDecodeError:\n            raise HydraResponseError(\n                'Bad response content type (expected JSON)')\n        except KeyError:\n            raise HydraResponseError(\n                'Bad response content (expected to have a keys key)')\n        except IndexError:\n            raise HydraResponseError('Bad response (empty key set)')\n        else:\n            return JWK(**jwk)\n","sub_path":"hydra/jwk.py","file_name":"jwk.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"286258490","text":"import pandas as pd\nimport random\nimport os\nfrom bert.tokenization import FullTokenizer, convert_to_unicode\nfrom bert.run_classifier import file_based_convert_examples_to_features, DataProcessor, InputExample\nfrom multiprocessing import Process, current_process, cpu_count\n\ndef createFinetuningData():\n    \n    _createBalancedData('./data/multiline_report_index_train.csv', './data/fine_tuning_data_train.csv')\n    _createBalancedData('./data/multiline_report_index_validate.csv', './data/fine_tuning_data_validate.csv')\n    _createBalancedData('./data/multiline_report_index_test.csv', './data/fine_tuning_data_test.csv')\n\n    processor = ReportProcessor()\n\n    train_examples = processor.get_train_examples('./data')\n    f = open('./data/fine_tuning_examples_number.txt', 'w+')\n    f.write(f'Number of train examples: {len(train_examples)}\\n')\n    f.close()\n\n    validate_examples = processor.get_validate_examples('./data')\n    f = open('./data/fine_tuning_examples_number.txt', 'a')\n    f.write(f'Number of validate examples: {len(validate_examples)}\\n')\n    f.close()\n\n    test_examples = processor.get_test_examples('./data')\n    f = open('./data/fine_tuning_examples_number.txt', 'a')\n    f.write(f'Number of test examples: {len(test_examples)}\\n')\n    f.close()\n\n    vocab_file = './data/BERT/uncased_L-12_H-768_A-12/vocab.txt'\n\n    train_process = Process(target=_convertToTfrecord, args=(train_examples, vocab_file, './data/bert_finetuning_data/train', 'train.tf_record'))\n    train_process.start()\n    validate_process = Process(target=_convertToTfrecord, args=(validate_examples, vocab_file, './data/bert_finetuning_data/validate', 'validate.tf_record'))\n    validate_process.start()\n    test_process = Process(target=_convertToTfrecord, args=(test_examples, vocab_file, './data/bert_finetuning_data/test', 'test.tf_record'))\n    test_process.start()\n\n    train_process.join()\n    validate_process.join()\n    test_process.join()\n\n\ndef _convertToTfrecord(examples, vocab_file, output_dir, output_file):\n    max_seq_length = 128\n    processor = ReportProcessor()\n    label_list = processor.get_labels()\n    tokenizer = FullTokenizer(vocab_file=vocab_file, do_lower_case=True)\n\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n\n    output_file_path = os.path.join(output_dir, output_file)\n\n    print('Converting files to tfrecord.')\n    file_based_convert_examples_to_features(\n            examples, label_list, max_seq_length, tokenizer, output_file_path)\n    print(f'Finished tfrecord creation at {output_file_path}')\n\n\n\ndef _createBalancedData(index_file, output_file_path):\n\n    reports_index_df = pd.read_csv(index_file, sep='\\t')\n    reports_index = reports_index_df.to_dict('records')\n\n    pos_sequences = []\n    neg_sequences = []\n    for report in reports_index:\n        file_path = report['File_Path']\n        print(f'Processing {file_path}')\n\n        price_change = report['Change_Nominal']\n\n        with open(file_path) as f:\n            lines = [line.rstrip('\\n') for line in f]\n\n        if price_change == 'positive':\n            pos_sequences.extend(lines)\n        else:\n            neg_sequences.extend(lines)\n\n    print('Shuffling sequences...')\n    random.shuffle(pos_sequences)\n    random.shuffle(neg_sequences)\n\n    print('Balancing data...')\n    if len(pos_sequences) > len(neg_sequences):\n        pos_sequences = pos_sequences[:len(neg_sequences)]\n    else:\n        neg_sequences = neg_sequences[:len(pos_sequences)]\n\n    print('Merging positive and negative sequences...')\n    labeled_sequences = []\n\n    for pos_sequence in pos_sequences:\n        labeled_sequence = {\n            'label': 1,\n            'sequence': pos_sequence\n        }\n\n        labeled_sequences.append(labeled_sequence)\n\n    for neg_sequence in neg_sequences:\n        labeled_sequence = {\n            'label': 0,\n            'sequence': neg_sequence\n        }\n\n        labeled_sequences.append(labeled_sequence)\n\n    print('Shuffling sequences...')\n    random.shuffle(labeled_sequences)\n\n    print(f'Writing to file {output_file_path}')\n    labeled_sequences_df = pd.DataFrame(labeled_sequences)\n    labeled_sequences_df.to_csv(output_file_path, sep='\\t', index=False)\n\n\n\nclass ReportProcessor(DataProcessor):\n\n  def get_train_examples(self, data_dir):\n    \"\"\"See base class.\"\"\"\n    return self._create_examples(\n        self._read_tsv(os.path.join(data_dir, \"fine_tuning_data_train.csv\")), \"train\")\n\n  def get_validate_examples(self, data_dir):\n    \"\"\"See base class.\"\"\"\n    return self._create_examples(\n        self._read_tsv(os.path.join(data_dir, \"fine_tuning_data_validate.csv\")), \"validate\")\n\n  def get_test_examples(self, data_dir):\n    \"\"\"See base class.\"\"\"\n    return self._create_examples(\n        self._read_tsv(os.path.join(data_dir, \"fine_tuning_data_test.csv\")), \"test\")\n\n  def get_labels(self):\n    \"\"\"See base class.\"\"\"\n    return [\"0\", \"1\"]\n\n  def _create_examples(self, lines, set_type):\n    \"\"\"Creates examples for the training and dev sets.\"\"\"\n    examples = []\n    for (i, line) in enumerate(lines):\n        if i == 0:\n            continue\n        guid = \"%s-%s\" % (set_type, i)\n        \n        text_a = convert_to_unicode(line[1])\n        label = convert_to_unicode(line[0])\n        examples.append(\n            InputExample(guid=guid, text_a=text_a, text_b=None, label=label))\n    return examples\n\nif __name__ == '__main__':\n    createFinetuningData()\n","sub_path":"create_finetuning_data.py","file_name":"create_finetuning_data.py","file_ext":"py","file_size_in_byte":5403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"47066010","text":"\"\"\"\nTesting visa connection to talk to three instruments\nToDo: Split object classes into independent files.\n\"\"\"\nfrom datetime import datetime\nfrom time import sleep\nfrom E8267D import E8267D\nfrom N9000B import N9000B\nfrom N1914A import N1914A\nimport pathlib\nimport csv\n\n\ndef run_pout_pin_test(frequencies=None, sig_gen_min=-30, sig_gen_max=-5, power_step=1, single_excel=False):\n    # check for list type argument and\n    if not isinstance(frequencies, list) or not frequencies:\n        print(\"Not frequencies provided\")\n        return False\n    # Setting limits for current setup.\n    min_freq = 1  # GHz\n    max_freq = 12  # GHz\n    if not all([(min_freq <= freq) and (freq <= max_freq) for freq in frequencies]):\n        print(\"Frequencies not withing range\")\n        return False\n    # Instrument settings\n    sig_gen = 'TCPIP::192.168.10.102::INSTR'  # E8267D PSG signal generator\n    cxa_x_series = 'TCPIP::192.168.10.103::INSTR'  # N9000B signal analyzer\n    power_supply = 'TCPIP::192.168.10.104::INSTR'  # E3634A power supply\n    power_sensor = 'TCPIP::192.168.10.101::INSTR'  # N1914A power sensor\n\n    # Constants\n    wait_response = 2  # seconds\n\n    # Initialize instruments\n    psg = E8267D(sig_gen)\n    cxa = N9000B(cxa_x_series)\n    ps = N1914A(power_sensor)\n\n    # set rf output off\n    psg.rf_off()\n    sleep(wait_response)\n\n    # Set RBW\n    #cxa.set_resbwid(100, 'kHz')\n    #cxa.set_freq_span(2)  # Default unit GHz\n\n    # Labels\n    labels_list = ['FREQ_SET(GHz)', 'Pin(dBm)', 'Pout(dBm)', 'Pref(dBm)',\n                   'POUT_PSG(dBm)', 'Delta_CXA-PSG(dB)',\n                   'Delta_PS-PSG(dB)', 'DIFF_PSG_CXA(dBm)', 'CAL_FACTOR_A', 'CAL_FACTOR_B',\n                   'Pin(W)', 'Pout(W)', 'Pref(W)', 'Pdis(W)']\n\n    rsp_array = []\n    if single_excel:\n        rsp_array = [labels_list]\n    test_results = []\n    for freq_start in frequencies:\n        # Labels for measurements\n        if not single_excel:\n            rsp_array = [labels_list]\n\n        # Start setup.\n        print('Setting Freq to:', freq_start)\n        psg.set_freq(freq_start)\n        ps.set_freq(freq_start, 1)\n        ps.set_freq(freq_start, 2)\n        # Change CXA if outside START and STOP frequencies.\n        if cxa.freq_start() > freq_start * 1e9 or cxa.freq_stop() < freq_start * 1e9:\n            cxa.set_freq_cent(freq_start)\n        cxa.set_marker_x(freq_start)\n        sleep(wait_response)\n        out_min, out_max, out_step = (sig_gen_min, sig_gen_max, power_step)  # start and stop values\n        pow_bool = True\n        while out_min <= out_max:\n            psg.set_power(out_min)\n            if pow_bool:\n                psg.rf_on()\n                pow_bool = False\n            sleep(wait_response)\n            pow_sens_in = ps.read_power(1)\n            pow_sens_in_watts = (10 ** (pow_sens_in/10))/1000\n            pow_sens_circu = ps.read_power(2)\n            pow_refl_watts = (10 ** (pow_sens_circu/10))/1000\n            pin_dbm = cxa.marker_y()\n            pout_watts = (10 ** (pin_dbm/10))/1000\n            p_dissipated = pow_sens_in_watts - (pow_refl_watts + pout_watts)\n            freq_in = cxa.marker_x()\n            print(pin_dbm, freq_in, pow_sens_in)\n            rsp_array.append([freq_start, pow_sens_in, pin_dbm, pow_sens_circu, out_min, pin_dbm - out_min,\n                              pow_sens_in - out_min, pow_sens_in - pin_dbm, ps.read_cal(1), ps.read_cal(2),\n                              pow_sens_in_watts, pout_watts, pow_refl_watts, p_dissipated])\n            out_min += out_step\n        psg.rf_off()\n        sleep(wait_response)\n        if not single_excel:\n            test_results.append(rsp_array)\n    # safety needs to be added to the close method.\n    psg.set_power(-40)\n    psg.close()\n    cxa.close()\n    ps.close()\n    if single_excel:\n        test_results = rsp_array\n    return test_results\n\n\ndef sweep(freq_start=1, freq_stop=12, step=1):\n    \"\"\"\n    Returns list of frequencies with specified parameters.\n    :param freq_start:\n    :param freq_stop:\n    :param step:\n    :return:\n    \"\"\"\n    freqs = []\n    while freq_start <= freq_stop:\n        freqs.append(freq_start)\n        freq_start = round(freq_start + round(step, 1), 1)\n    return freqs\n\n\ndef save_csv(timestamp, prefix, data):\n    \"\"\"\n    Creates csv files with a list of arrays, using timestamp creates a unique directory.\n    :param timestamp:\n    :param name:\n    :param data:\n    :return: True\n    \"\"\"\n    if not data or not isinstance(data, list):\n        print(\"Data in wrong format\")\n        return False\n    # Create directory\n    pathlib.Path(str(timestamp)).mkdir(exist_ok=True)\n    # Write CSV file\n    for index, rows in enumerate(data):\n        file_name = timestamp + '/' + str(prefix) + '_' + timestamp \\\n                    + '_' + str(index) + '.csv'\n        with open(file_name, 'w', newline='') as results:\n            csvw = csv.writer(results, delimiter=',')\n            csvw.writerows(rows)\n\n\nif __name__ == '__main__':\n    frequencies = sweep(1, 12, 1)\n    print(frequencies)\n    # TIMESTAMP = datetime.now().strftime(\"%Y%m%d_%H%M\")\n    # NAME = 'all_freqs'\n    # # data = [[[3, 2, 1], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]\n    # run_pout_pin_test([1, 1.227, 1.575])\n    # save_csv('output_test', NAME, data)\n\n","sub_path":"Capstone/JosueExamples/test_limi.py","file_name":"test_limi.py","file_ext":"py","file_size_in_byte":5240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"221165116","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom collections import Counter\nfrom copy import deepcopy\nfrom decimal import Decimal\nfrom random import choice\nfrom time import time\n\nfrom typing import Callable\nfrom typing import Iterable\nfrom typing import List\n\nfrom stvpoll.exceptions import BallotException\nfrom stvpoll.exceptions import CandidateDoesNotExist\nfrom stvpoll.exceptions import IncompleteResult\nfrom stvpoll.exceptions import STVException\n\n\ndef _minmax(iterable, key=None, lowest=False):\n    method = lowest and min or max\n    return method(iterable, key=key)\n\n\nclass PreferenceBallot(object):\n    def __init__(self, preferences, count):\n        # type: (List[Candidate],int) -> None\n        self.preferences = preferences\n        self.count = count\n        self.multiplier = Decimal(1)\n\n    @property\n    def value(self):\n        return self.multiplier * self.count\n\n    def decrease_value(self, multiplier):\n        self.multiplier *= multiplier\n\n    @property\n    def current_preference(self):\n        try:\n            return self.preferences[0]\n        except IndexError:\n            pass\n\n    @property\n    def exhausted(self):\n        return len(self.preferences) == 0\n\n    def get_transfer_preference(self, standing_candidates):\n        while not self.exhausted:\n            self.preferences.pop(0)\n            if self.current_preference in standing_candidates:\n                return self.current_preference\n\n\nclass Candidate(object):\n    EXCLUDED = 0\n    HOPEFUL = 1\n    ELECTED = 2\n    status = HOPEFUL\n    votes = Decimal(0)\n    votes_transferred = False\n\n    def __init__(self, obj):\n        # type: (object) -> None\n        self.obj = obj\n\n    def __repr__(self):  # pragma: no coverage\n        # type: () -> basestring\n        return '<Candidate: {}>'.format(str(self.obj))\n\n    @property\n    def standing(self):\n        return self.status == self.HOPEFUL\n\n    def __eq__(self, o):\n        # type: (Candidate) -> bool\n        if isinstance(o, Candidate):\n            return self.obj == o.obj\n        return self.obj == o\n\n    def __hash__(self):\n        # type: () -> int\n        return self.obj.__hash__()\n\n\nclass ElectionRound(object):\n    SELECTION_METHOD_DIRECT = 0\n    SELECTION_METHOD_HISTORY = 1\n    SELECTION_METHOD_RANDOM = 2\n    SELECTION_METHOD_NO_COMPETITION = 3\n    SELECTION_METHOD_CPO = 4\n    SELECTION_METHODS = (\n        'Direct',\n        'Tiebreak (history)',\n        'Tiebreak (Random)',\n        'No competition left',\n        'Comparison of Pairs of Outcomes',\n    )\n    status = None\n    selection_method = None\n\n    def __init__(self, _id):\n        # type: (int) -> None\n        self._id = _id\n        self.selected = []\n\n    def status_display(self):\n        # type: () -> basestring\n        return self.status == Candidate.ELECTED and 'Elected' or 'Excluded'\n\n    def select(self, candidate, votes, method, status):\n        # type: (Candidate, List[Candidate], int, int) -> None\n        self.selected.append(candidate)\n        self.status = status\n        self.votes = deepcopy(votes)\n        self.selection_method = method\n\n    @property\n    def method_str(self):\n        # type: () -> basestring\n        if isinstance(self.selection_method, int):\n            return self.SELECTION_METHODS[self.selection_method]\n\n    def __repr__(self):  # pragma: no coverage\n        # type: () -> basestring\n        return '<ElectionRound {}: {} {}{}>'.format(\n            self._id,\n            self.status_display(),\n            ', '.join([c.obj for c in self.selected]),\n            self.selection_method and ' ({})'.format(self.method_str))\n\n    def as_dict(self):\n        # type: () -> dict\n        return {\n            'status': self.status_display(),\n            'selected': tuple(s.obj for s in self.selected),\n            'method': self.method_str,\n            'vote_count': tuple({c.obj: c.votes} for c in self.votes),\n        }\n\n\nclass ElectionResult(object):\n    exhausted = Decimal(0)\n    runtime = .0\n    randomized = False\n    empty_ballot_count = 0\n\n    def __init__(self, poll):\n        # type: (STVPollBase) -> None\n        self.poll = poll\n        self.rounds = []\n        self.elected = []\n        self.start_time = time()\n        self.transfer_log = []\n\n    def __repr__(self):  # pragma: no coverage\n        # type: () -> basestring\n        return '<ElectionResult in {} round(s): {}>'.format(len(self.rounds),  ', '.join(map(str, self.elected)))\n\n    def new_round(self):\n        self.rounds.append(ElectionRound(\n            _id=len(self.rounds)+1))\n\n    def finish(self):\n        self.runtime = time() - self.start_time\n\n    @property\n    def current_round(self):\n        # type: () -> ElectionRound\n        return self.rounds[-1]\n\n    def select(self, candidate, votes, method, status=Candidate.ELECTED):\n        # type: (Candidate, List[Candidate], int, int) -> None\n        candidate.status = status\n        if status == Candidate.ELECTED:\n            self.elected.append(candidate)\n        self.current_round.select(candidate, votes, method, status)\n\n    @property\n    def complete(self):\n        # type: () -> bool\n        return len(self.elected) == self.poll.seats\n\n    def elected_as_tuple(self):\n        # type: () -> tuple\n        return tuple(map(lambda x: x.obj, self.elected))\n\n    def elected_as_set(self):\n        # type: () -> set\n        return set(self.elected_as_tuple())\n\n    def as_dict(self):\n        # type: () -> dict\n        return {\n            'winners': self.elected_as_tuple(),\n            'candidates': tuple([c.obj for c in self.poll.candidates]),\n            'complete': self.complete,\n            'rounds': tuple([r.as_dict() for r in self.rounds]),\n            'randomized': self.randomized,\n            'quota': self.poll.quota,\n            'runtime': self.runtime,\n            'empty_ballot_count': self.empty_ballot_count,\n        }\n\n\nclass STVPollBase(object):\n    _quota = None\n\n    def __init__(self, seats, candidates, quota=None, random_in_tiebreaks=True):\n        # type: (int, Iterable, Callable, bool) -> None\n        self.candidates = [Candidate(c) for c in candidates]\n        self.ballots = []\n        self._quota_function = quota\n        self.seats = seats\n        self.errors = []\n        self.random_in_tiebreaks = random_in_tiebreaks\n        self.result = ElectionResult(self)\n        if len(self.candidates) < self.seats:\n            raise STVException('Not enough candidates to fill seats')\n\n    def get_existing_candidate(self, obj):\n        # type: (object) -> Candidate\n        for candidate in self.candidates:\n            if candidate == obj:\n                return candidate\n        raise CandidateDoesNotExist()\n\n    @property\n    def quota(self):\n        # type: () -> int\n        if not self._quota:\n            self._quota = self._quota_function(self)\n        return self._quota\n\n    @property\n    def ballot_count(self):\n        # type: () -> int\n        return sum([b.count for b in self.ballots])\n\n    def add_ballot(self, ballot, num=1):\n        # type: (List, int) -> None\n        candidates = []\n        for obj in ballot:\n            candidates.append(self.get_existing_candidate(obj))\n\n        # Empty votes will not affect quota, but will be accounted for in result.\n        if candidates:\n            self.ballots.append(PreferenceBallot(candidates, num))\n        else:\n            self.result.empty_ballot_count += num\n\n    def get_candidate(self, most_votes=True, sample=None):\n        # type: (bool, List(Candidate)) -> (Candidate, int)\n        if sample is None:\n            sample = self.standing_candidates\n        candidate = _minmax(sample, key=lambda c: c.votes, lowest=not most_votes)\n        ties = self.get_ties(candidate)\n        if ties:\n            return self.resolve_tie(ties, most_votes)\n        return candidate, ElectionRound.SELECTION_METHOD_DIRECT\n\n    def choice(self, candidates):\n        if self.random_in_tiebreaks:\n            self.result.randomized = True\n            return choice(candidates)\n        raise IncompleteResult('Unresolved tiebreak (random disallowed)')\n\n    def resolve_tie(self, candidates, most_votes=True):\n        # type: (List[Candidate], bool) -> (Candidate, int)\n        for stage in self.result.transfer_log[::-1]:\n            stage_votes = [v for v in stage['current_votes'] if v in candidates]\n            primary_candidate = _minmax(stage_votes, key=lambda c: c.votes, lowest=not most_votes)\n            ties = self.get_ties(primary_candidate, stage_votes)\n            if ties:\n                candidates = [c for c in candidates if c in ties]\n            else:\n                winner = candidates[candidates.index(primary_candidate)]  # Get correct Candidate instance\n                return winner, ElectionRound.SELECTION_METHOD_HISTORY\n        return self.choice(candidates), ElectionRound.SELECTION_METHOD_RANDOM\n\n    def transfer_votes(self, candidate, transfer_quota=Decimal(1)):\n        # type: (Candidate, Decimal) -> None\n        transfers = Counter()\n        for ballot in self.ballots:\n            if candidate == ballot.current_preference:\n                ballot.decrease_value(transfer_quota)\n                target_candidate = ballot.get_transfer_preference(self.standing_candidates)\n                if target_candidate:\n                    target_candidate.votes += ballot.value\n                    transfers[(candidate, target_candidate)] += ballot.value\n                else:\n                    self.result.exhausted += ballot.value\n\n        self.result.transfer_log.append({\n            'transfers': transfers,\n            'current_votes': self.current_votes,\n            'exhausted_votes': self.result.exhausted,\n        })\n        candidate.votes_transferred = True\n\n    def initial_votes(self):\n        # type () -> None\n        for ballot in self.ballots:\n            assert ballot.current_preference, 'Initial votes called with an empty vote'\n            ballot.current_preference.votes += ballot.value\n\n        self.result.transfer_log.append({\n            'transfers': None,\n            'current_votes': self.current_votes,\n            'exhausted_votes': self.result.exhausted,\n        })\n\n    def get_ties(self, candidate, sample=None):\n        # type (Candidate, List[Candidate]) -> List[Candidate]\n        if not sample:\n            sample = self.standing_candidates\n        ties = [c for c in sample if c.votes == candidate.votes]\n        if len(ties) > 1:\n            return ties\n\n    @property\n    def standing_candidates(self):\n        # type: () -> List[Candidate]\n        return [c for c in self.candidates if c.standing]\n\n    @property\n    def current_votes(self):\n        # type: () -> List[Candidate]\n        return deepcopy(self.standing_candidates)\n\n    @property\n    def seats_to_fill(self):\n        # type () -> int\n        return self.seats - len(self.result.elected)\n\n    @property\n    def complete(self):\n        # type: () -> bool\n        return self.result.complete\n\n    def select(self, candidate, method, status=Candidate.ELECTED):\n        # type: (Candidate, int, int) -> None\n        self.result.new_round()\n        self.result.select(candidate, self.standing_candidates, method, status)\n\n    def select_multiple(self, candidates, method, status=Candidate.ELECTED, resolve_ties=False):\n        # type: (List[Candidate], int, int) -> None\n        if candidates:\n            self.result.new_round()\n            while candidates:\n                # Select candidates in order. If requested, resolve ties.\n                index = 0\n                _method = method\n                if resolve_ties:\n                    candidate, _method = self.get_candidate(\n                        most_votes=status == Candidate.ELECTED,\n                        sample=candidates)\n                    index = candidates.index(candidate)\n\n                self.result.select(\n                    candidates.pop(index),\n                    self.standing_candidates,\n                    _method, status)\n\n    def calculate(self):\n        # type: () -> ElectionResult\n        # if not self.ballots:  # pragma: no coverage\n        #     raise STVException('No ballots registered.')\n        self.initial_votes()\n        try:\n            self.do_rounds()\n        except IncompleteResult:\n            pass\n        self.result.finish()\n        return self.result\n\n    def do_rounds(self):\n        # type: () -> None\n        while self.seats_to_fill:\n            self.calculate_round()\n\n    def calculate_round(self):  # pragma: no coverage\n        # type: (int) -> None\n        raise NotImplementedError()\n","sub_path":"stvpoll/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":12538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"361694036","text":"from confluent_kafka import Consumer, KafkaError\nimport logging\nimport traceback\nimport time\nfrom pyhive import hive\nimport threading\nfrom concurrent.futures import ThreadPoolExecutor, wait\nimport string\n\nlogdir = \"/tmp\"\nfiledir = \"/home/liupeng/logindata\"\n\nhandler = logging.FileHandler(logdir + '/kafka-to-file.log', encoding='UTF-8')\nlogging_format = logging.Formatter(\n    '%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(lineno)s - %(message)s')\nlogger = logging.getLogger(__name__)\nhandler.setFormatter(logging_format)\nlogger.setLevel(logging.DEBUG)\nlogger.addHandler(handler)\n\n\ndef writeToFile(topic, msg):\n    # print(\"topic: {}, message:{}\".format(msg.topic(), msg.value()))\n    cols = msg.value().decode().split(\"|\")\n    filedate = cols[-1].split()[0]\n    # print(\"mesage date: {}\".format(filedate))\n    sequence = (topic, filedate, \"txt\")\n    filename = \".\".join(sequence)\n    try:\n        with open(filedir + '/' + filename, \"a\") as myfile:\n            myfile.write(msg.value().decode())\n            myfile.write(\"\\n\")\n    except Exception as e:\n        logger.exception(traceback.format_exc())\n        print(e)\n\n\ndef consume(topic):\n\n    try:\n\n        c = Consumer({\n            'bootstrap.servers': '10.158.61.170:9092,10.158.61.171:9092,10.158.61.90:9092,10.158.61.95:9092',\n            'group.id': 'python-to-file',\n            'auto.offset.reset': 'earliest'\n        })\n        c.subscribe([topic])\n\n        while True:\n            msg = c.poll(1.0)\n\n            if msg is None:\n                continue\n            if msg.error():\n                logger.info(\"Consumer error: {}\".format(msg.error()))\n                continue\n\n            writeToFile(topic, msg)\n\n    except Exception as e:\n        logger.exception(traceback.format_exc())\n        print(e)\n    finally:\n        c.close()\n\n\nexecutor = ThreadPoolExecutor(4)\nfs = []\nfor i, v in enumerate(['login-receive', 'login-response', 'verify-receive', 'verify-response']):\n    fs.append(executor.submit(consume, v))\n\nwait(fs)\n\nexecutor.shutdown()\n","sub_path":"python/bigdata/kafka-to-file.py","file_name":"kafka-to-file.py","file_ext":"py","file_size_in_byte":2034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"237197492","text":"__author__ = 'Jakehp'\nfrom random import randint\nimport qkPhoton\nimport qkCommChannel\nimport qkComm\n\n#will extend qkComm in the future\n#Receivers *cannot* access any photons - except via measurement\nclass qkReceiver(qkComm.qkComm):\n\n    PHOTON_PULSE_SIZE = 128\n    MIN_REQ_OF_SHARED = 25\n\n    def __init__(self):\n        self.recordedPolarizations = []\n        self.randomBasis = []\n        self.otherBasis = []\n        self.sharedKey = []\n        self.subSharedKey = []\n        self.decision = -1\n        self.otherDecision = -1\n        self.validKey = -1\n\n    #Computes random basis's for randomBasis list\n    def setRandomBasis(self):\n        self.randomBasis.clear()\n        i = 0\n        while i < self.PHOTON_PULSE_SIZE:\n            x = randint(0, 1)\n            if x == 0:\n                self.randomBasis.append(\"R\")\n            else:\n                self.randomBasis.append(\"D\")\n            i += 1\n        return\n\n    #Simply exists for ease of understanding\n    def receive(self, arg1):\n        self.measurePolarizations(arg1)\n        return\n\n    #Measure and record all polarizations from photon pulse in the comm channel\n    def measurePolarizations(self, insecureCommChannel):\n        assert isinstance(insecureCommChannel, qkCommChannel.qkCommChannel), 'Invalid Arg'\n        if len(insecureCommChannel.photonPulse) != self.PHOTON_PULSE_SIZE:\n            print(\"CommChannel has no photons for receiver || CommChannel # of pulses != receivers photon_pulse_size\")\n            return -1\n        self.setRandomBasis()\n        i = 0\n        while i < self.PHOTON_PULSE_SIZE:\n            tempPho = insecureCommChannel.photonPulse[i]\n            assert isinstance(tempPho, qkPhoton.qkPhoton), 'Not a qkPhoton object - Error'\n            self.recordedPolarizations.append(tempPho.measure(self.randomBasis[i]))\n            i += 1\n        insecureCommChannel.photonPulse.clear()\n        return\n\n    #Prints All Data\n    def printAll(self):\n        print(\"qkReceiver recordedPolars:      \", self.recordedPolarizations)\n        print(\"qkReceiver Random Basis:        \", self.randomBasis)\n        print(\"Sender's Basis:                 \", self.otherBasis)\n        print(\"qkReceiver's sharedKey:         \", self.sharedKey)\n        print(\"qkReceiver's subSharedKey:      \", self.subSharedKey)\n\n    #Prints Lengths Of Data\n    def printDetails(self):\n        print(\"qkSender Photon Polarizations:  \", len(self.recordedPolarizations))\n        print(\"qkSender Random Basis:          \", len(self.randomBasis))\n        print(\"Receiver's Basis:               \", len(self.otherBasis))","sub_path":"qkReceiver.py","file_name":"qkReceiver.py","file_ext":"py","file_size_in_byte":2575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"26461080","text":"# -*- coding: utf-8 -*-\n###########################################################################################\n#\n#    author:Qdodoo suifeng\n#    module name for Qdodoo\n#    Copyright (C) 2015 qdodoo Technology CO.,LTD. (<http://www.qdodoo.com/>).\n#\n###########################################################################################\n\nfrom openerp import models, _\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nimport calendar\nimport time\n\n\nclass qdodoo_account_asset(models.Model):\n    \"\"\"\n        继承修改固定资产模块功能\n    \"\"\"\n    _inherit = 'account.asset.asset'\n\n    # 生成折旧板中数据的时候，按照采购日期的下月初开始\n    def compute_depreciation_board(self, cr, uid, ids, context=None):\n        # 折旧板模型\n        depreciation_lin_obj = self.pool.get('account.asset.depreciation.line')\n        for asset in self.browse(cr, uid, ids, context=context):\n            # 如果剩余价值为空，跳出循环\n            if asset.value_residual == 0.0:\n                continue\n            # 查询是否有已登账的折旧板\n            posted_depreciation_line_ids = depreciation_lin_obj.search(cr, uid, [('asset_id', '=', asset.id), ('move_check', '=', True)],order='depreciation_date desc')\n            # 查询是否有未登账的折旧板\n            old_depreciation_line_ids = depreciation_lin_obj.search(cr, uid, [('asset_id', '=', asset.id), ('move_id', '=', False)])\n            # 删除未登账的明细\n            if old_depreciation_line_ids:\n                depreciation_lin_obj.unlink(cr, uid, old_depreciation_line_ids, context=context)\n            # 获取剩余价格\n            amount_to_depr = residual_amount = asset.value_residual\n            # 如果是年限百分比\n            if asset.prorata:\n                depreciation_date = datetime.strptime(self._get_last_depreciation_date(cr, uid, [asset.id], context)[asset.id], '%Y-%m-%d')\n            else:\n                # depreciation_date = 1st January of purchase year\n                purchase_date = datetime.strptime(asset.purchase_date, '%Y-%m-%d')\n                #if we already have some previous validated entries, starting date isn't 1st January but last entry + method period\n                if (len(posted_depreciation_line_ids)>0):\n                    last_depreciation_date = datetime.strptime(depreciation_lin_obj.browse(cr,uid,posted_depreciation_line_ids[0],context=context).depreciation_date, '%Y-%m-%d')\n                    depreciation_date = (last_depreciation_date+relativedelta(months=+asset.method_period))\n                else:\n                    # -----odoo\n                    # depreciation_date = datetime(purchase_date.year, 1, 1)\n                    # ----suifeng\n                    if purchase_date.month == 12:\n                        depreciation_date = datetime(purchase_date.year+1, 1, 1)\n                    else:\n                        depreciation_date = datetime(purchase_date.year, purchase_date.month+1, 1)\n                    # -------\n            # 获取年月日、时分秒\n            day = depreciation_date.day\n            month = depreciation_date.month\n            year = depreciation_date.year\n            total_days = (year % 4) and 365 or 366\n\n            undone_dotation_number = self._compute_board_undone_dotation_nb(cr, uid, asset, depreciation_date, total_days, context=context)\n            for x in range(len(posted_depreciation_line_ids), undone_dotation_number):\n                i = x + 1\n                amount = self._compute_board_amount(cr, uid, asset, i, residual_amount, amount_to_depr, undone_dotation_number, posted_depreciation_line_ids, total_days, depreciation_date, context=context)\n                residual_amount -= amount\n                vals = {\n                     'amount': amount,\n                     'asset_id': asset.id,\n                     'sequence': i,\n                     'name': str(asset.id) +'/' + str(i),\n                     'remaining_value': residual_amount,\n                     'depreciated_value': (asset.purchase_value - asset.salvage_value) - (residual_amount + amount),\n                     'depreciation_date': depreciation_date.strftime('%Y-%m-%d'),\n                }\n                depreciation_lin_obj.create(cr, uid, vals, context=context)\n                # Considering Depr. Period as months\n                depreciation_date = (datetime(year, month, day) + relativedelta(months=+asset.method_period))\n                day = depreciation_date.day\n                month = depreciation_date.month\n                year = depreciation_date.year\n        return True\n\nclass qdodoo_account_asset_depreciation_line(models.Model):\n    _inherit = 'account.asset.depreciation.line'\n\n    # 修改生成会计凭证时日期为当月最后一天\n    # 重新组织数据格式，合并生成凭证\n    def create_move(self, cr, uid, ids, context=None):\n        context = dict(context or {})\n        asset_obj = self.pool.get('account.asset.asset')\n        period_obj = self.pool.get('account.period')\n        move_obj = self.pool.get('account.move')\n        move_line_obj = self.pool.get('account.move.line')\n        currency_obj = self.pool.get('res.currency')\n        created_move_ids = []\n        asset_ids = []\n        # 组织数据\n        all_dict = {} # {日期(账期):{分类账：{资产obj:[line_obj]}}}\n        for line in self.browse(cr, uid, ids, context=context):\n            depreciation_date = context.get('depreciation_date') or line.depreciation_date or time.strftime('%Y-%m-%d')\n            depreciation_date = depreciation_date[:8] + str(calendar.monthrange(int(depreciation_date[:4]),int(depreciation_date[5:7]))[1])\n            context.update({'date': depreciation_date})\n            if depreciation_date in all_dict:\n                if line.asset_id.category_id.journal_id in all_dict[depreciation_date]:\n                    if line.asset_id in all_dict[depreciation_date][line.asset_id.category_id.journal_id]:\n                        all_dict[depreciation_date][line.asset_id.category_id.journal_id][line.asset_id].append(line)\n                    else:\n                        all_dict[depreciation_date][line.asset_id.category_id.journal_id][line.asset_id] = [line]\n                else:\n                    all_dict[depreciation_date][line.asset_id.category_id.journal_id] = {line.asset_id:[line]}\n            else:\n                all_dict[depreciation_date] = {line.asset_id.category_id.journal_id:{line.asset_id:[line]}}\n        for key,value in all_dict.items():\n            # 获取对应的账期\n            period_ids = period_obj.find(cr, uid, key, context=context)\n            for key1,value1 in value.items():\n                # 根据账期、资产类别合并凭证明细\n                journal_id = key1.id\n                move_vals = {\n                    'name': '/',\n                    'date': key,\n                    'ref': '合并凭证',\n                    'period_id': period_ids and period_ids[0] or False,\n                    'journal_id': journal_id,\n                    }\n                move_id = move_obj.create(cr, uid, move_vals, context=context)\n                sign = (key1.type == 'purchase' and 1) or -1\n                for key2,value2 in value1.items():\n                    company_currency = key2.company_id.currency_id.id\n                    current_currency = key2.currency_id.id\n                    asset_name = key2.name\n                    for line_obj in value2:\n                        # 创建凭证明细行\n                        reference = line_obj.name\n                        amount = currency_obj.compute(cr, uid, current_currency, company_currency, line_obj.amount, context=context)\n                        move_line_obj.create(cr, uid, {\n                            'name': asset_name,\n                            'ref': reference,\n                            'move_id': move_id,\n                            'account_id': key2.category_id.account_depreciation_id.id,\n                            'debit': 0.0,\n                            'credit': amount,\n                            'period_id': period_ids and period_ids[0] or False,\n                            'journal_id': journal_id,\n                            'currency_id': company_currency != current_currency and current_currency or False,\n                            'amount_currency': company_currency != current_currency and - sign * line_obj.amount or 0.0,\n                            'date': key,\n                        })\n                        move_line_obj.create(cr, uid, {\n                            'name': asset_name,\n                            'ref': reference,\n                            'move_id': move_id,\n                            'account_id': key2.category_id.account_expense_depreciation_id.id,\n                            'credit': 0.0,\n                            'debit': amount,\n                            'period_id': period_ids and period_ids[0] or False,\n                            'journal_id': journal_id,\n                            'currency_id': company_currency != current_currency and current_currency or False,\n                            'amount_currency': company_currency != current_currency and sign * line_obj.amount or 0.0,\n                            'analytic_account_id': key2.department.id,\n                            'date': key,\n                            'asset_id': key2.id\n                        })\n                        self.write(cr, uid, line_obj.id, {'move_id': move_id}, context=context)\n                    asset_ids.append(key2.id)\n                created_move_ids.append(move_id)\n\n        # we re-evaluate the assets to determine whether we can close them\n        for asset in asset_obj.browse(cr, uid, list(set(asset_ids)), context=context):\n            if currency_obj.is_zero(cr, uid, asset.currency_id, asset.value_residual):\n                asset.write({'state': 'close'})\n        return created_move_ids","sub_path":"qdodoo_account_asset/qdodoo_account_asset.py","file_name":"qdodoo_account_asset.py","file_ext":"py","file_size_in_byte":10004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"560707689","text":"import argparse\nimport csv\nimport glob\nimport os\nimport shutil\nimport sys\n\nimport datasets\nimport torch.distributed as dist\nfrom typing import Callable, Dict, Tuple, Optional\nimport evaluate\nimport numpy as np\nimport torch\nfrom datasets import Dataset, load_dataset\nfrom peft import LoraConfig, TaskType, get_peft_model, prepare_model_for_kbit_training, PeftConfig, PeftModel, \\\n    prepare_model_for_int8_training\nfrom transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase, \\\n    EvalPrediction, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, \\\n    EarlyStoppingCallback, BitsAndBytesConfig, set_seed, AutoModelForSeq2SeqLM, Seq2SeqTrainer, GenerationConfig\nfrom transformers.trainer_utils import get_last_checkpoint\nfrom transformers.utils import logging\n\nfrom causlTrainer import CausalTrainer\n\n\n# region utils\n\ndef find_all_linear_names(model_id, bits=4):\n    if bits == 4:\n        bnb_config = BitsAndBytesConfig(\n            load_in_4bit=True,\n            # bnb_4bit_use_double_quant=True,\n            bnb_4bit_quant_type=\"fp4\",\n            bnb_4bit_compute_dtype=torch.bfloat16\n        )\n    else:\n        bnb_config = None\n\n    model_loading_args = {\n        \"pretrained_model_name_or_path\": model_id,\n        \"quantization_config\": bnb_config,\n        \"cache_dir\": cache_dir,\n        \"trust_remote_code\": True\n    }\n\n    model = AutoModelForCausalLM.from_pretrained(**model_loading_args)\n\n    import bitsandbytes as bnb\n    cls = bnb.nn.Linear4bit if bits == 4 else (bnb.nn.Linear8bitLt if bits == 8 else torch.nn.Linear)\n    lora_module_names = set()\n    for name, module in model.named_modules():\n        if isinstance(module, cls):\n            names = name.split('.')\n            lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n\n    if 'lm_head' in lora_module_names:  # needed for 16-bit\n        lora_module_names.remove('lm_head')\n    return list(lora_module_names)\n\n\ndef print_trainable_parameters(model):\n    \"\"\"\n    Prints the number of trainable parameters in the model.\n    \"\"\"\n    trainable_params = 0\n    all_param = 0\n    for _, param in model.named_parameters():\n        all_param += param.numel()\n        if param.requires_grad:\n            trainable_params += param.numel()\n    print(\n        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n    )\n\n\ndef bytes_to_human_readable_str(size: int) -> str:\n    \"\"\"\n    Format a size (in bytes) for humans.\n    Code taken from: https://github.com/allenai/allennlp/blob/main/allennlp/common/util.py\n    \"\"\"\n    GBs = size / (1024 * 1024 * 1024)\n    if GBs >= 10:\n        return f\"{int(round(GBs, 0))}G\"\n    if GBs >= 1:\n        return f\"{round(GBs, 1):.1f}G\"\n    MBs = size / (1024 * 1024)\n    if MBs >= 10:\n        return f\"{int(round(MBs, 0))}M\"\n    if MBs >= 1:\n        return f\"{round(MBs, 1):.1f}M\"\n    KBs = size / 1024\n    if KBs >= 10:\n        return f\"{int(round(KBs, 0))}K\"\n    if KBs >= 1:\n        return f\"{round(KBs, 1):.1f}K\"\n    return f\"{size}B\"\n\n\ndef is_distributed() -> bool:\n    \"\"\"\n    Checks if the distributed process group is available and has been initialized\n    Code taken from: https://github.com/allenai/allennlp/blob/main/allennlp/common/util.py\n    \"\"\"\n    return dist.is_available() and dist.is_initialized()\n\n\ndef peak_cpu_memory() -> Dict[str, str]:\n    \"\"\"\n    Get peak memory usage for each worker, as measured by max-resident-set size:\n    https://unix.stackexchange.com/questions/30940/getrusage-system-call-what-is-maximum-resident-set-size\n    Only works on OSX and Linux, otherwise the result will be 0.0 for every worker.\n    Code taken from: https://github.com/allenai/allennlp/blob/main/allennlp/common/util.py\n    \"\"\"\n    if sys.platform not in (\"linux\", \"darwin\"):\n        peak_bytes = 0\n    else:\n        import resource\n        peak = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n        if sys.platform == \"darwin\":\n            # On OSX the result is in bytes.\n            peak_bytes = peak\n        else:\n            # On Linux the result is in kilobytes.\n            peak_bytes = peak * 1_024\n\n    if is_distributed():\n        global_rank = dist.get_rank()\n        world_size = dist.get_world_size()\n\n        peak_bytes_tensor = torch.tensor([global_rank, peak_bytes])\n        # All of these tensors will be gathered into this list.\n        gather_results = [torch.tensor([0, 0]) for _ in range(world_size)]\n\n        # If the backend is 'nccl', this means we're training on GPUs, so these tensors\n        # need to be on GPU.\n        if dist.get_backend() == \"nccl\":\n            peak_bytes_tensor = peak_bytes_tensor.cuda()\n            gather_results = [x.cuda() for x in gather_results]\n\n        dist.all_gather(gather_results, peak_bytes_tensor)\n\n        results_dict: Dict[int, int] = {}\n        for peak_bytes_tensor in gather_results:\n            results_dict[int(peak_bytes_tensor[0])] = int(peak_bytes_tensor[1])\n\n    else:\n        results_dict = {0: peak_bytes}\n\n    formatted_results_dict = {}\n    for cpu, memory in results_dict.items():\n        formatted_results_dict[f'cpu_{cpu}_memory_usage'] = bytes_to_human_readable_str(memory)\n\n    return formatted_results_dict\n\n\ndef peak_gpu_memory() -> Dict[str, str]:\n    \"\"\"\n    Get the peak GPU memory usage in bytes by device.\n    # Returns\n    `Dict[int, int]`\n        Keys are device ids as integers.\n        Values are memory usage as integers in bytes.\n        Returns an empty `dict` if GPUs are not available.\n\n    Code taken from: https://github.com/allenai/allennlp/blob/main/allennlp/common/util.py\n    \"\"\"\n    if not torch.cuda.is_available():\n        return {}\n\n    device = torch.cuda.current_device()\n\n    results_dict: Dict[int, int] = {}\n    if is_distributed():\n        # If the backend is not 'nccl', we're training on CPU.\n        if dist.get_backend() != \"nccl\":\n            return {}\n\n        global_rank = dist.get_rank()\n        world_size = dist.get_world_size()\n        peak_bytes = torch.cuda.max_memory_allocated(device)\n        peak_bytes_tensor = torch.tensor([global_rank, peak_bytes], device=device)\n        # All of these tensors will be gathered into this list.\n        gather_results = [torch.tensor([0, 0], device=device) for _ in range(world_size)]\n\n        dist.all_gather(gather_results, peak_bytes_tensor)\n\n        for peak_bytes_tensor in gather_results:\n            results_dict[int(peak_bytes_tensor[0])] = int(peak_bytes_tensor[1])\n    else:\n        results_dict = {0: torch.cuda.max_memory_allocated()}\n\n    # Reset peak stats.\n    torch.cuda.reset_max_memory_allocated(device)\n\n    formatted_results_dict = {}\n    for gpu, memory in results_dict.items():\n        formatted_results_dict[f'gpu_{gpu}_memory_usage'] = bytes_to_human_readable_str(memory)\n\n    return formatted_results_dict\n\n\ndef get_memory_metrics(split: str) -> Dict[str, str]:\n    res = {}\n    mem_metrics = peak_cpu_memory()\n    mem_metrics.update(peak_gpu_memory())\n    for k, v in mem_metrics.items():\n        res[f'{split}_{k}'] = v\n    return res\n\n\n# endregion\n\n# region dataset processing\ndef tokenize_dataset(examples: Dataset, tokenizer: PreTrainedTokenizerBase,\n                     text_column: str, label_column: str, max_length: int):\n    batch_size = len(examples[text_column])\n    model_inputs = {\n        \"input_ids\": [],\n        \"attention_mask\": [],\n        \"labels\": []\n    }\n\n    tokenized_inputs = tokenizer(examples[text_column])\n    with tokenizer.as_target_tokenizer():\n        tokenized_labels = tokenizer(examples[label_column])\n\n    for i in range(batch_size):\n        tok_inpt_ids = tokenized_inputs['input_ids'][i]\n        inpt_mask = tokenized_inputs['attention_mask'][i]\n        tok_lbl_ids = tokenized_labels['input_ids'][i]\n\n        if len(tok_inpt_ids) < max_length and len(tok_lbl_ids) < max_length:\n            model_inputs['input_ids'].append(tok_inpt_ids)\n            model_inputs['attention_mask'].append(inpt_mask)\n            model_inputs['labels'].append(tok_lbl_ids)\n\n    return model_inputs\n\n\ndef preprocess_dataset_for_causal_lm(examples: Dataset, tokenizer: PreTrainedTokenizerBase,\n                                     text_column: str, label_column: str, max_length: int):\n    if tokenizer.pad_token_id is None:\n        tokenizer.pad_token_id = tokenizer.eos_token_id\n    batch_size = len(examples[text_column])\n    inputs = [f'{x}' for x in examples[text_column]]\n    targets = [f'{x}' for x in examples[label_column]]\n    tokenized_inputs = tokenizer(inputs)\n    tokenized_labels = tokenizer(targets)\n\n    model_inputs = {\n        \"input_ids\": [],\n        \"attention_mask\": [],\n        \"labels\": []\n    }\n    for i in range(batch_size):\n        sample_input_ids = tokenized_inputs[\"input_ids\"][i]\n        # happens in xglm for some reason\n        if sample_input_ids[0] == tokenizer.eos_token_id:\n            sample_input_ids = sample_input_ids[1:]\n\n        label_input_ids = tokenized_labels[\"input_ids\"][i]\n        if label_input_ids[0] == tokenizer.eos_token_id:\n            label_input_ids = label_input_ids[1:]\n\n        # The original code appended  [tokenizer.pad_token_id], and gpt2 did converge with this. But bloom did not.\n        # With  [tokenizer.eos_token_id] it does, and it seems a more natural choice.\n        # TODO: Why we add the padding? Is it suppose to be eos token?\n        label_input_ids = label_input_ids + [tokenizer.eos_token_id]  # [tokenizer.pad_token_id]\n        # TODO: This is so the label and input will be differnet tokens. I think?\n\n        if len(sample_input_ids + label_input_ids) < max_length:\n            inpt = sample_input_ids + label_input_ids\n            model_inputs[\"input_ids\"].append(inpt)\n            model_inputs[\"attention_mask\"].append([1] * len(inpt))\n            model_inputs[\"labels\"].append([-100] * len(sample_input_ids) + label_input_ids)\n    return model_inputs\n\n\n# endregion\n\n\ndef get_eval_func(tokenizer: PreTrainedTokenizerBase, metric_id: str) -> Callable:\n    def eval_func(eval_preds: EvalPrediction):\n        preds = eval_preds.predictions\n        labels = eval_preds.label_ids\n        preds = np.where(preds != -100, preds, tokenizer.pad_token_id)\n        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n        decoded_labels = tokenizer.batch_decode(labels.tolist(), skip_special_tokens=True)\n        metric = evaluate.load(metric_id)\n        res = metric.compute(references=decoded_labels, predictions=decoded_preds)\n\n        logger = logging.get_logger()\n        logger.debug(f'_debug_  decoded labels: \\n{decoded_labels}\\n')\n        logger.debug(f'_debug_  decoded preds: \\n{decoded_preds}\\n')\n\n        memory_metrics = get_memory_metrics('train')\n        res.update(memory_metrics)\n\n        return res\n\n    return eval_func\n\n\ndef _delete_checkpoints(output_dir: str):\n    logger = logging.get_logger()\n    checkpoint_files = glob.glob(f'{output_dir}/checkpoint*/*')\n    for f in checkpoint_files:\n        logger.warning(f'deleting file {f}!')\n        if not f.endswith(\"json\"):\n            os.remove(f)\n\n\ndef train_model(model_id: str,\n                is_seq2seq_model: bool,\n                train_dataset: Dataset,\n                eval_dataset: Optional[Dataset],\n                test_dataset: Optional[Dataset],\n                max_length: int,\n                output_dir: str,\n                train_in_4_bit: bool,\n                train_in_8_bit: bool,\n                train_with_lora: bool,\n                cache_dir: str\n                ):\n    # TODO: revisit\n    if eval_dataset and len(eval_dataset) > 200:\n        eval_dataset = eval_dataset.select(range(200))\n\n    logger = logging.get_logger()\n    device = torch.device(\"mps\" if torch.backends.mps.is_available() else 0 if torch.cuda.is_available() else \"cpu\")\n\n    logger.debug(f'_debug_ device: {device}')\n\n    if train_in_4_bit:\n        assert train_with_lora\n        assert not train_in_8_bit\n\n    # region tokenizer and dataset preparation\n    tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir)\n    tokenizer.bos_token_id = 1\n    if tokenizer.pad_token_id is None:\n        tokenizer.pad_token_id = tokenizer.eos_token_id\n\n    if is_seq2seq_model:\n        preprocess_func = tokenize_dataset\n    else:\n        preprocess_func = preprocess_dataset_for_causal_lm\n\n    train_dataset = train_dataset.map(\n        lambda examples: preprocess_func(examples, tokenizer, \"text\", \"label\", max_length),\n        batched=True, remove_columns=train_dataset.column_names)\n    if eval_dataset:\n        eval_dataset = eval_dataset.map(\n            lambda examples: preprocess_func(examples, tokenizer, \"text\", \"label\", max_length),\n            batched=True, remove_columns=eval_dataset.column_names)\n    if test_dataset:\n        test_dataset = test_dataset.map(\n            lambda examples: preprocess_func(examples, tokenizer, \"text\", \"label\", max_length),\n            batched=True, remove_columns=test_dataset.column_names)\n\n    # endregion\n\n    # region model preparation\n    if train_in_4_bit:\n        bnb_config = BitsAndBytesConfig(\n            load_in_4bit=True,\n            bnb_4bit_use_double_quant=True,\n            bnb_4bit_quant_type=\"fp4\",\n            bnb_4bit_compute_dtype=torch.bfloat16\n        )\n    else:\n        bnb_config = None\n\n    model_loading_args = {\n        \"pretrained_model_name_or_path\": model_id,\n        \"quantization_config\": bnb_config,\n        \"cache_dir\": cache_dir,\n        \"trust_remote_code\": True,\n        # \"torch_dtype\": torch.bfloat16 if (train_in_4_bit or train_in_8_bit) else None,\n        # \"load_in_8bit\": train_in_8_bit,\n        # \"device_map\": \"auto\"\n    }\n\n    if is_seq2seq_model:\n        model = AutoModelForSeq2SeqLM.from_pretrained(**model_loading_args)\n    else:\n        model = AutoModelForCausalLM.from_pretrained(**model_loading_args)\n\n    if train_in_4_bit or train_in_8_bit:\n        model.gradient_checkpointing_enable()\n        model = prepare_model_for_kbit_training(model)\n\n    if train_with_lora:\n        task_type = TaskType.SEQ_2_SEQ_LM if is_seq2seq_model else TaskType.CAUSAL_LM\n\n        config = LoraConfig(\n            r=16,\n            lora_alpha=32,\n            target_modules=[\"q_proj\", \"v_proj\"] if \"xglm\" in model_id else None,\n            lora_dropout=0.05,\n            bias=\"none\",\n            task_type=task_type\n        )\n\n        model = get_peft_model(model, config)\n        print_trainable_parameters(model)\n\n    # endregion\n\n    model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n\n    data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer,\n                                           model=model,\n                                           padding=True,\n                                           label_pad_token_id=tokenizer.eos_token_id if is_seq2seq_model else -100\n                                           )\n\n    training_args = Seq2SeqTrainingArguments(\n        output_dir=output_dir,\n        num_train_epochs=5 if train_in_8_bit else 20,\n        per_device_train_batch_size= 1 if train_in_8_bit else 4,  # if \"base\" in model_id else 1,\n        per_device_eval_batch_size= 1 if train_in_8_bit else 4,  # if  \"base\" in model_id else 1,\n\n        logging_strategy='steps',\n        evaluation_strategy='steps' if eval_dataset else \"no\",\n        save_strategy='steps',\n        logging_steps=500,\n        save_total_limit=2,\n        load_best_model_at_end=eval_dataset is not None,\n        metric_for_best_model=\"exact_match\" if eval_dataset else None,\n\n        predict_with_generate=True,\n        generation_max_length=max_length + 10,\n        generation_num_beams=1,\n\n        # TODO: Why we cannot do fp16 with Lora? (The loss is 0)\n        fp16=device != \"mps\",\n        gradient_accumulation_steps=16 if train_in_8_bit else 4,  # if \"base\" in model_id else 16,\n        eval_accumulation_steps=1,\n        optim=\"paged_adamw_8bit\" if (train_in_4_bit or train_in_8_bit) else \"adamw_hf\",\n        lr_scheduler_type=\"linear\",\n        learning_rate=2e-4,\n        warmup_steps=2,\n        use_mps_device=device.type == \"mps\",\n        report_to=\"none\"\n    )\n\n    trainer_cls = Seq2SeqTrainer if is_seq2seq_model else CausalTrainer\n    trainer = trainer_cls(\n        model=model,\n        args=training_args,\n        train_dataset=train_dataset,\n        eval_dataset=eval_dataset,\n        tokenizer=tokenizer,\n        data_collator=data_collator,\n        compute_metrics=get_eval_func(tokenizer, \"exact_match\"),\n        callbacks=[EarlyStoppingCallback(early_stopping_patience=5)] if eval_dataset and not train_in_4_bit else [],\n    )\n\n    checkpoint = get_last_checkpoint(output_dir)\n    if checkpoint is not None:\n        logger.info(f'_debug_ Resuming training from checkpoint: {checkpoint}')\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\n\n    # this needs to be before the save_metrics. As otherwise it will overwrite them\n    trainer.save_model()\n    trainer.save_state()\n\n    metrics = train_result.metrics\n    metrics.update(get_memory_metrics('train'))\n\n    if test_dataset:\n        test_metrics = trainer.evaluate(test_dataset, metric_key_prefix=\"test\")\n        metrics.update(test_metrics)\n\n    # TODO: Why do we need that?\n    trainer.log_metrics(\"train\", metrics)\n    trainer.save_metrics(\"train\", metrics)\n\n    #  TODO: Why do we need that? It saves the best model and so we can delete the checkpoint\n    #     trainer.save_model()  # Saves the tokenizer too for easy upload\n    # TODO: Why do we need that?\n\n    _delete_checkpoints(output_dir)\n\n\ndef evaluate_model(model_id: str,\n                   is_seq2seq_model: bool,\n                   eval_dataset: Dataset,\n                   max_length: int,\n                   label: str,\n                   output_dir: str,\n                   train_in_4_bit: bool,\n                   train_with_lora: bool,\n                   cache_dir: str,\n                   ):\n    device = torch.device(\"mps\" if torch.backends.mps.is_available() else 0 if torch.cuda.is_available() else \"cpu\")\n    logger = logging.get_logger()\n    # region model preparation\n    if train_in_4_bit:\n        assert train_with_lora\n\n    # TODO: remove in the future and just save the best model to the main dir\n    # model_id = get_last_checkpoint(model_id)\n    #     assert model_id is not None\n\n    if train_with_lora:\n        peft_model_name_or_path = model_id\n        peft_config = PeftConfig.from_pretrained(peft_model_name_or_path)\n        model_id = peft_config.base_model_name_or_path\n\n    if train_in_4_bit:\n        bnb_config = BitsAndBytesConfig(\n            load_in_4bit=True,\n            # bnb_4bit_use_double_quant=True,\n            bnb_4bit_quant_type=\"nf4\",\n            bnb_4bit_compute_dtype=torch.bfloat16\n        )\n    else:\n        bnb_config = None\n\n    model_loading_args = {\n        \"pretrained_model_name_or_path\": model_id,\n        \"quantization_config\": bnb_config,\n        \"cache_dir\": cache_dir,\n        \"trust_remote_code\": True\n    }\n\n    if is_seq2seq_model:\n        model = AutoModelForSeq2SeqLM.from_pretrained(**model_loading_args)\n    else:\n        model = AutoModelForCausalLM.from_pretrained(**model_loading_args)\n\n    if train_with_lora:\n        model = PeftModel.from_pretrained(model, peft_model_name_or_path)\n\n    # endregion\n\n    # region tokenizer and dataset preparation\n\n    tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir)\n    if tokenizer.pad_token_id is None:\n        tokenizer.pad_token_id = tokenizer.eos_token_id\n\n    if is_seq2seq_model:\n        preprocess_func = tokenize_dataset\n    else:\n        preprocess_func = preprocess_dataset_for_causal_lm\n\n    eval_dataset = eval_dataset.map(\n        lambda examples: preprocess_func(examples, tokenizer, \"text\", \"label\", max_length),\n        batched=True, remove_columns=eval_dataset.column_names)\n\n    # endregion\n\n    data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer,\n                                           model=model,\n                                           padding=True,\n                                           # label_pad_token_id=tokenizer.eos_token_id,\n                                           label_pad_token_id=-100)\n\n    training_args = Seq2SeqTrainingArguments(\n        output_dir=output_dir,\n        per_device_eval_batch_size=4,\n        predict_with_generate=True,\n        generation_max_length=max_length + 10,\n        # TODO: Why we cannot do fp16 with Lora? (The loss is 0)\n        # fp16=device != \"mps\",\n        eval_accumulation_steps=1,\n        use_mps_device=device.type == \"mps\",\n        report_to=\"none\"\n    )\n\n    trainer_cls = Seq2SeqTrainer if is_seq2seq_model else CausalTrainer\n    trainer = trainer_cls(\n        model=model,\n        args=training_args,\n        eval_dataset=eval_dataset,\n        tokenizer=tokenizer,\n        data_collator=data_collator,\n        compute_metrics=get_eval_func(tokenizer, \"exact_match\")\n    )\n\n    pred_output = trainer.predict(eval_dataset)\n    metrics = pred_output.metrics\n    metrics.update(get_memory_metrics(f'test_{label}'))\n\n    predictions = pred_output.predictions\n    # decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n    # logger.info(decoded_predictions)\n\n    # TODO: Why do we need that?\n    trainer.log_metrics(f'test_{label}', metrics)\n    trainer.save_metrics(f'test_{label}', metrics)\n\n\n# region dataset loading\ndef load_mtop_dataset(dataset_path: str, add_instruction: bool):\n    with open(dataset_path, \"r\", encoding='utf-8') as f:\n        rows = list(csv.reader(f, delimiter='\\t', quoting=csv.QUOTE_MINIMAL))\n        dataset_dict = {\n            \"text\": [f'Parse the following sentence: {r[0]} Answer: ' if add_instruction else r[0] for r in rows],\n            \"label\": [r[1] for r in rows]\n        }\n        ds = Dataset.from_dict(dataset_dict)\n        return ds\n\n\ndef load_nli_dataset(dataset_path: str, add_instruction: bool):\n    def get_prompt(premise: str, hypothesis: str) -> str:\n        prompt_str = f'Premise: {premise}\\n\\n' \\\n                     f'Hypothesis: {hypothesis}\\n\\n' \\\n                     f'Label: '\n        if add_instruction:\n            instruct_str = f'In the Natural Language Inference (NLI) task, given a premise and an hypothesis, ' \\\n                           f'the goal is to determine the logical relationship between the premise and the hypothesis. ' \\\n                           f'Please label the logical relationship between the premise and the hypothesis as ' \\\n                           f'either \"entailment\", \"contradiction\", or \"neutral\".\\n\\n'\n            prompt_str = f'{instruct_str}{prompt_str}'\n        return prompt_str\n\n    label_id_to_str = {\n        0: \"entailment\",\n        1: \"neutral\",\n        2: \"contradiction\"\n    }\n\n    dataset = datasets.load_dataset(\"csv\", data_files=dataset_path)['train']\n\n    dataset = dataset.map(\n        lambda x: {\n            \"text\": [get_prompt(premise, hypothesis) for premise, hypothesis in zip(x[\"premise\"], x[\"hypothesis\"])],\n            \"label\": [label_id_to_str[label] for label in x[\"label\"]]},\n        batched=True,\n        num_proc=1, remove_columns=[\"premise\", \"hypothesis\"]\n    )\n\n    return dataset\n\n\n# endregion\n\ndef debug_run(model_id: str, is_seq2seq: bool, cache_dir: str):\n    dataset_name = \"cardiffnlp/tweet_sentiment_multilingual\"\n    dataset = load_dataset(dataset_name, \"english\")\n    classes = [k.replace(\"_\", \" \") for k in dataset[\"train\"].features[\"label\"].names]\n    dataset = dataset.rename_column(\"label\", \"temp\")\n    dataset = dataset.map(\n        lambda x: {\"label\": [classes[label] for label in x[\"temp\"]]},\n        batched=True,\n        num_proc=1,\n    )\n    train_dataset = dataset['train'].select(range(1000))\n    dev_dataset = dataset['train'].select(range(1000))\n\n    output_dir = \"hf_try_temp\"\n\n    train_model(model_id,\n                is_seq2seq,\n                train_dataset,\n                dev_dataset,\n                output_dir,\n                train_with_lora=True,\n                train_in_4_bit=False,\n                cache_dir=cache_dir)\n\n    exit()\n\n\ndef load_custom_dataset(dataset_path: str, add_instruction: bool):\n    if \"mtop\" in dataset_path:\n        dataset = load_mtop_dataset(dataset_path, add_instruction)\n    elif \"xnli\" in dataset_path:\n        dataset = load_nli_dataset(dataset_path, add_instruction)\n    elif \"amazon\" in dataset_path:\n        dataset = datasets.load_dataset(\"csv\", data_files=dataset_path)['train']\n    else:\n        raise NotImplementedError(dataset_path)\n\n    return dataset\n\n\ndef train_model_sub_command(args):\n    set_seed(args.seed)\n\n    model_id = args.model_id\n    assert model_id in (model_list_seq2seq + model_list_causal)\n    is_seq2seq_model = model_id in model_list_seq2seq\n\n    train_dataset = load_custom_dataset(args.train_dataset_path, args.add_instruction)\n    eval_dataset = load_custom_dataset(args.eval_dataset_path, args.add_instruction) if args.eval_dataset_path else None\n    test_dataset = load_custom_dataset(args.test_dataset_path, args.add_instruction) if args.test_dataset_path else None\n\n    logger.info(f'\\n\\n!!!!!!!!!! Training model !!!!!!!!!!\\n\\n'\n                f'{args}\\n\\n'\n                f'!!!!!!!!!!!!!!!!!\\n\\n')\n\n    train_model(model_id=model_id,\n                is_seq2seq_model=is_seq2seq_model,\n                train_dataset=train_dataset,\n                eval_dataset=eval_dataset,\n                test_dataset=test_dataset,\n                max_length=args.max_length,\n                output_dir=args.output_dir,\n                train_with_lora=args.lora,\n                train_in_4_bit=args.qlora,\n                train_in_8_bit=args.train_8_bits,\n                cache_dir=cache_dir,\n                )\n\n\ndef evaluate_model_sub_command(args):\n    eval_dataset = load_custom_dataset(args.eval_dataset_path, args.add_instruction)\n    label = f'{os.path.basename(args.eval_dataset_path)}'\n\n    logger.info(f'\\n\\n!!!!!!!!!! Evaluating model !!!!!!!!!!\\n\\n'\n                f'{args}\\n\\n'\n                f'!!!!!!!!!!!!!!!!!\\n\\n')\n\n    evaluate_model(model_id=args.model_id,\n                   is_seq2seq_model=args.seq2seq,\n                   eval_dataset=eval_dataset,\n                   max_length=args.max_length,\n                   label=label,\n                   output_dir=args.output_dir,\n                   train_with_lora=args.lora,\n                   train_in_4_bit=args.qlora,\n                   cache_dir=cache_dir)\n\n\nif __name__ == '__main__':\n    logger = logging.get_logger()\n    logger.setLevel(logging.INFO)\n\n    if os.path.exists('/dccstor'):\n        cache_dir = '/dccstor/gmc/users/ofir.arviv/transformers_cache'\n    if os.path.exists('/cs/labs/oabend'):\n        cache_dir = '/cs/labs/oabend/ofir.arviv/transformers_cache'\n    else:\n        cache_dir = None\n\n\n    # region argparser\n\n    def add_common_args(input_parser):\n        input_parser.add_argument('--model-id', required=True, type=str)\n        input_parser.add_argument('--output-dir', required=True, type=str)\n        input_parser.add_argument(\"--lora\", action=\"store_true\", default=False)\n        input_parser.add_argument(\"--qlora\", action=\"store_true\", default=False)\n        input_parser.add_argument(\"--train-8-bits\", action=\"store_true\", default=False)\n        input_parser.add_argument(\"--add-instruction\", action=\"store_true\", default=False)\n        input_parser.add_argument('--max-length', required=False, type=int, default=1024)\n        input_parser.add_argument('--cache-dir', required=False, type=str, default=None)\n\n\n    parser = argparse.ArgumentParser()\n    subparsers = parser.add_subparsers(help='sub-command help')\n    # region Train argparser\n    parser_train = subparsers.add_parser('train')\n    parser_train.set_defaults(which='train')\n    parser_train.add_argument('--train-dataset-path', required=True, type=str)\n    parser_train.add_argument('--eval-dataset-path', required=False, type=str)\n    parser_train.add_argument('--test-dataset-path', required=False, type=str)\n    parser_train.add_argument('--seed', required=True, type=int)\n    add_common_args(parser_train)\n    # endregion\n\n    # region Eval argparser\n    parser_eval = subparsers.add_parser('evaluate')\n    parser_eval.set_defaults(which='evaluate')\n    parser_eval.add_argument('--eval-dataset-path', required=True, type=str)\n    parser_eval.add_argument('--seq2seq', action=\"store_true\", default=False)\n    add_common_args(parser_eval)\n\n    # endregion\n    args = parser.parse_args()\n\n    model_list_causal = [\"decapoda-research/llama-65b-hf\",\n                         \"facebook/xglm-7.5B\",\n                         \"facebook/xglm-564M\",\n                         \"bigscience/bloom-7b1\",\n                         \"facebook/xglm-564M\"\n                         \"tiiuae/falcon-7b-instruct\"]\n    model_list_seq2seq = [\"google/flan-t5-xxl\",\n                          \"google/mt5-base\",\n                          \"google/flan-ul2\"]\n\n    if args.which == \"train\":\n        train_model_sub_command(args)\n\n    if args.which == \"evaluate\":\n        evaluate_model_sub_command(args)\n","sub_path":"experiments/hugginface_experiments/src/run_4bit_exp_2_fp4_std_lr_2e4.py","file_name":"run_4bit_exp_2_fp4_std_lr_2e4.py","file_ext":"py","file_size_in_byte":29073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"589657728","text":"from threading import Thread\nfrom time import sleep\nfrom EngineWrapper import EngineWrapper\nimport importlib\n\nclass EngineManager():\n\tEntities = {}\n\n\t_engines = {}\n\n\t_running = False\n\n\tdef AddEngine(self, engineName):\n\t\tengineClass = getattr(importlib.import_module(engineName), engineName)\n\t\tengine = engineClass(self.Entities)\n\t\tengineWrapper = EngineWrapper(engine)\n\t\tt = Thread(target = engineWrapper.Start)\n\t\tself._engines[engineName] = { \"EngineWrapper\" : engineWrapper, \"Thread\" : t }\n\t\tt.start()\n\t\tprint(\"Engine loaded : {0}\".format(engineName))\n\n\tdef Stop(self):\n\t\tfor engineName, engineData in self._engines.items():\n\t\t\tengineData[\"EngineWrapper\"].Stop()\n\n\tdef Transfert(self, command):\n\t\ttry:\n\t\t\tcommandSplit = command.split(' ')\n\t\t\tengineName = commandSplit[0]\n\t\t\tcommandSplit = commandSplit[1:]\n\n\t\t\tengineWrapper = self._engines[engineName][\"EngineWrapper\"]\n\t\t\tcontext = engineWrapper.Engine\n\t\t\tcontextPosition = 0\n\t\texcept Exception:\n\t\t\tprint(\"No engine of this name\")\n\t\t\treturn\n\t\ttry:\n\t\t\twhile(not callable(context) and contextPosition < len(commandSplit)):\n\t\t\t\tcontext = getattr(context, commandSplit[contextPosition])\n\t\t\t\tcontextPosition += 1\n\t\t\targs = []\n\t\t\twhile(contextPosition < len(commandSplit)):\n\t\t\t\targs.append(commandSplit[contextPosition])\n\t\t\t\tcontextPosition += 1\n\t\t\tcontext(*args)\n\n\t\texcept Exception as e:\n\t\t\t\tprint(\"Engine run error : \" + type(engineWrapper.Engine).__name__)\n\t\t\t\tprint(e)\n\t\t\t\tengineWrapper.Stop()\n\t\t","sub_path":"EngineManager.py","file_name":"EngineManager.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"372457520","text":"\"\"\"\nLinking to java main method\n\"\"\"\nimport cast_upgrade_1_5_25 # @UnusedImport\nfrom cast.application import ApplicationLevelExtension, create_link\nimport logging\n\n\n\nclass CallToJava:\n    \n    def __init__(self, class_full_name, call_object):\n\n        self.class_full_name = class_full_name\n        self.call_object = call_object\n\n\nclass JavaMain:\n    \n    def __init__(self, method_full_name, method_object):\n\n        self.method_full_name = method_full_name\n        self.method_object = method_object\n\n\nclass ExtensionApplication(ApplicationLevelExtension):\n\n    def end_application(self, application):\n\n        logging.info('Linking calls to java programs')\n\n        # 1. loading\n        calls = [CallToJava(e.get_property('CAST_CallToJavaProgram.javaClassFullName'), e) for e in application.objects().has_type('CAST_CallToJavaProgram').load_property('CAST_CallToJavaProgram.javaClassFullName')]\n\n        if not calls:\n            logging.info('No call to java programs : nothing to do')\n            return\n        \n        # load the main Java methods       \n        mains = {m.get_fullname():JavaMain(m.get_fullname(), m) for m in application.search_objects(name='main', category='JV_METHOD')}\n\n        if not mains:\n            logging.info('No main java methods : nothing to do')\n            return\n        \n        for call in calls:\n            \n            try:\n                logging.debug('searching ' + call.class_full_name + '.main')\n                main = mains[call.class_full_name + '.main']\n                logging.debug('creating link between ' + str(call.call_object) + ' and ' + str(main.method_object))\n                create_link('callLink', call.call_object, main.method_object)                    \n            except KeyError:\n                pass            \n","sub_path":"analyze/extensions/com.castsoftware.wbslinker.1.6.9/java_main_linking.py","file_name":"java_main_linking.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"116900470","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport common\nimport re\nimport six\nfrom IPython import embed\n\n\nre_flags = re.M | re.U | re.S | re.I\nJ = os.path.join\nC = os.path.abspath(os.getcwd())\nuniquify = common.uniquify\n\n\ndef cliparse(argv=None):\n    def callback(parser, **kw):\n        parser.add_argument('--no-followers',\n                            default=False, action='store_true')\n        parser.add_argument('--no-notifs',\n                            default=False, action='store_true')\n        parser.add_argument('--pages', default=1, type=int)\n    return common.cliparse(\n        argv=argv,\n        callback=callback,\n        description='userids from usernames')\n\n\ndef is_ham(item):\n    if item['screen_name'].lower() in [\n        'kiorky', 'valk', 'res1854006', 'res1854006b',\n        'res122188800', 'valkphotos',\n    ]:\n        return True\n    return False\n\ndef append_ret(usersd, user):\n    ret = {}\n    for n in ['description', 'id', 'name', 'screen_name']:\n        ret[n] = getattr(user, n)\n    if ret not in usersd:\n        usersd.append(ret)\n    return usersd\n\n\ndef main():\n    parser, pargs = cliparse()\n    wrapper = common.Wrapper.load(pargs.config)\n    accounts = wrapper.accounts.values()\n    i = [a for a in accounts if a['user'] == wrapper.config['main']][0]\n    api = i['api']\n    tws = wrapper.get_timeline(uid=wrapper.config['main'], api=i['api'], page=1)\n    embed()           \n    #for t in tws[1:20]:api.destroy_status(t.id); \n\n\n\nif __name__ == '__main__':\n    main()\n# vim:set et sts=4 ts=4 tw=0:\n","sub_path":"src/tdelete.py","file_name":"tdelete.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"387260579","text":"#coding: utf-8\n# +-------------------------------------------------------------------\n# | 宝塔Linux面板\n# +-------------------------------------------------------------------\n# | Copyright (c) 2015-2099 宝塔软件(http://bt.cn) All rights reserved.\n# +-------------------------------------------------------------------\n# | Author: 黄文良 <2879625666@qq.com>\n# +-------------------------------------------------------------------\nimport os,sys,public,json\nclass downloadFile:\n    logPath = 'data/speed.json'\n    timeoutCount = 0;\n    oldTime = 0;\n    #下载文件\n    def DownloadFile(self,url,filename):\n        try:\n            path = os.path.dirname(filename)\n            if not os.path.exists(path): os.makedirs(path)\n            import urllib,socket,ssl\n            try:\n                ssl._create_default_https_context = ssl._create_unverified_context\n            except:pass\n            socket.setdefaulttimeout(10)\n            self.pre = 0;\n            self.oldTime = time.time();\n            if sys.version_info[0] == 2:\n                urllib.urlretrieve(url,filename=filename,reporthook= self.DownloadHook)\n            else:\n                urllib.request.urlretrieve(url,filename=filename,reporthook= self.DownloadHook)\n            self.WriteLogs(json.dumps({'name':'下载文件','total':0,'used':0,'pre':0,'speed':0}));\n        except:\n            if self.timeoutCount > 5: return;\n            self.timeoutCount += 1\n            time.sleep(5)\n            self.DownloadFile(url,filename)\n            \n    #下载文件进度回调  \n    def DownloadHook(self,count, blockSize, totalSize):\n        used = count * blockSize\n        pre1 = int((100.0 * used / totalSize))\n        if self.pre != pre1:\n            dspeed = used / (time.time() - self.oldTime);\n            speed = {'name':'下载文件','total':totalSize,'used':used,'pre':self.pre,'speed':dspeed}\n            self.WriteLogs(json.dumps(speed))\n            self.pre = pre1\n\n    #取下载进度\n    def GetSpeed(self):\n        speedLog = public.ReadFile(self.logPath)\n        if not speedLog: return  {'name':'下载文件','total':0,'used':0,'pre':0,'speed':0}\n        return json.loads(speedLog)\n    \n    #写输出日志\n    def WriteLogs(self,logMsg):\n        public.WriteFile(self.logPath,logMsg)","sub_path":"www/server/panel/class/downloadFile.py","file_name":"downloadFile.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"122210638","text":"# coding=utf-8\n'''\nВычислительная математика\nЛабораторная работа №5\nВариант 2\n'''\nfrom random import uniform\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom lagrange import lagrange\n\ndef model_function(x):\n    return np.cos(x)\n\n\ndef build_graph(points, i, title):\n    t1 = np.arange(points[0][0], points[-1][0], 0.01)\n    plt.subplot(220 + i)\n    plt.plot(t1, model_function(t1))\n    plt.plot(t1, [lagrange( points, r) for r in t1])\n    for point in points:\n        plt.plot(point[0], point[1], 'ro')\n    plt.title(title)\n\n\ndef generate_point(func, min, max, amount):\n    res = sorted([uniform(min, max) for x in range(amount)])\n    return list(map(lambda x: (x, func(x)), res))\n\ndef chebyshev_nodes (func,k):\n   res = []\n   m = 0\n   for m in range(k):\n        res.append(0)\n        res[m] = np.cos((2*m + 1) * np.pi / (2 * k))\n   res.sort()\n   return list(map(lambda x: (x, func(x)), res))\n\n\n# ============ 3-4 points F(x) from 0 to 2Pi\npivot_points = chebyshev_nodes(func=model_function,k=2)\n#lagrange_solution = lagrange(pivot_points)\nbuild_graph(pivot_points,  1, \"2 points\")\n# ============ 8-10 from 0 to 2Pi\npivot_points = chebyshev_nodes(model_function, 5)\n#lagrange_solution  = lagrange (pivot_points)\nbuild_graph(pivot_points,  2, \"5 points\")\n# ============ Shuffle 1 dot\npivot_points = chebyshev_nodes(model_function, 10)\n#lagrange_solution  = lagrange (pivot_points)\nbuild_graph(pivot_points,  3, \"10 points\")\n# ============\npivot_points = chebyshev_nodes(model_function, 50)\n#lagrange_solution  = lagrange (pivot_points)\nbuild_graph(pivot_points,  4, \"50 points\")\n\nplt.show()\n","sub_path":"Course 2/Computational Mathematics/CompMathLab5/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"385644971","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, print_function, unicode_literals\nfrom django.conf.urls import url\nfrom django.contrib.auth.decorators import login_required\nfrom account.views.pc import alatting_logout, LoginView, ProfileView, \\\n    PosterServerIndexView, PosterConsumerIndexView, PosterIndexView\n\n__author__ = 'lyhapple'\n\n\nurlpatterns = [\n    url(r'^login/$', LoginView.as_view(), name='login'),\n    url(r'^logout/$', alatting_logout, name='logout'),\n\n    url(r'^profile.html$',\n        login_required(ProfileView.as_view()), name='profile'),\n\n]\n\n\n# 海报服务\nurlpatterns += [\n    url(r'^posters/(?P<poster_pk>[\\d]+).html$',\n        login_required(PosterIndexView.as_view()),\n        name='posters_index'),\n\n    url(r'^posters/(?P<poster_pk>[\\d]+)/server.html$',\n        login_required(PosterServerIndexView.as_view()),\n        name='posters_server'),\n\n    url(r'^posters/(?P<poster_pk>[\\d]+)/consumer.html$',\n        login_required(PosterConsumerIndexView.as_view()),\n        name='posters_consumer'),\n]\n","sub_path":"account/urls/pc.py","file_name":"pc.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"561055759","text":"import numpy as np\n\n\nclass Grid():\n    def __init__(self, nx, ny, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0):\n        self.xmin, self.xmax, self.ymin, self.ymax = xmin, xmax, ymin, ymax\n        self.nx, self.ny = nx, ny\n        self.hx = 1. / (nx - 1)\n        if ny > 1:\n            self.hy = 1. / (ny - 1)\n            self.x, self.y = np.mgrid[self.xmin:self.xmax:(self.nx * 1j), self.ymin:self.ymax:(self.ny * 1j)]\n        else:\n            self.x = np.linspace(self.xmin, self.xmax, self.nx)\n\n\n    def setBC(self, l=0, r=0, b=0, t=1):\n        \"\"\"Set boundary conditions left, right, bottom, top\"\"\"\n        self.l, self.r, self.b, self.t = l, r, b, t\n\n    def setBCFunc(self, func):\n        xmin, ymin = self.xmin, self.ymin\n        xmax, ymax = self.xmax, self.ymax\n        x = np.arange(xmin, xmax + self.dx * 0.5, self.dx)\n        y = np.arange(ymin, ymax + self.dy * 0.5, self.dy)\n\n        self.l = func(xmin, y)\n        self.r = func(xmax, y)\n        self.b = func(x, ymin)\n        self.t = func(x, ymax)\n\n    def dx1(self, X, bc=\"False\"):\n\n        dx = np.zeros_like(X)\n        dx[1:-1] = (X[2:] - X[:-2]) / 2. / self.hx\n        if bc == \"False\":\n            dx[0] = (X[1] - X[0]) / self.hx\n            dx[-1] = (X[-1] - X[-2]) / self.hx\n        else:\n            dx[0] = (X[0] - self.l) / self.hx\n            dx[-1] = (self.r - X[-1]) / self.hx\n        return dx\n\n    def dx2(self, X, bc=\"False\"):\n        d2x = np.zeros_like(X)\n        d2x[1:-1] = (X[2:] - 2 * X[1:-1] + X[:-2]) / self.hx / self.hx\n        if bc == \"False\":\n            d2x[0] = (X[2] - 2 * X[1] + X[0]) / self.hx / self.hx\n            d2x[-1] = (X[-1] - 2 * X[-2] + X[-3]) / self.hx / self.hx\n        else:\n            d2x[0] = (X[1] - 2 * X[0] + self.l) / self.hx / self.hx\n            d2x[-1] = (self.r - 2 * X[-1] + X[-2]) / self.hx / self.hx\n        return d2x\n\n    def dy1(self, X, bc=\"False\"):\n        dy = np.zeros((self.nx, self.ny))\n        dy[:, 1:-1] = (X[:, 2:] - X[:, :-2]) / 2 / self.hy\n        if bc == \"False\":\n            dy[:, 0] = (X[:, 1] - X[:, 0]) / self.hy\n            dy[:, -1] = (X[:, -1] - X[:, -2]) / self.hy\n        else:\n            dy[:, 0] = (X[:, 0] - self.b) / self.hy\n            dy[-1] = (self.t - X[:, -1]) / self.hy\n        return dy\n\n    def dy2(self, X, bc=\"False\"):\n        d2y = np.zeros((self.nx, self.ny))\n        d2y[:, 1:-1] = (X[:, 2:] - 2 * X[:, 1:-1] + X[:, :-2]) / self.hy / self.hy\n        if bc == \"False\":\n            d2y[:, 0] = (X[:, 2] - 2 * X[:, 1] + X[:, 0]) / self.hy / self.hy\n            d2y[:, -1] = (X[:, -1] - 2 * X[:, -2] + X[:, -3]) / self.hy\n        else:\n            d2y[:, 0] = (X[:, 1] - 2 * X[:, 0] + self.b) / self.hy / self.hy\n            d2y[:, -1] = (self.t - 2 * X[:, -1] + X[:, -2]) / self.hy / self.hy\n        return d2y\n","sub_path":"Python/DiffEqPractice/deriv.py","file_name":"deriv.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"126915577","text":"from ext.db import db\n\n\nclass TerminalDnsServers(db.Model):\n    __tablename__ = 'terminal_dns_servers'\n\n    dns_server = db.Column(db.VARCHAR(80), primary_key=True)\n    terminal_id = db.Column(db.BIGINT(), unique=False)\n\n    def __init__(self, terminal_id, dns_server):\n        self.terminal_id = terminal_id\n        self.credential_id = dns_server\n\n    def __repr__(self):\n        dct = self.__dict__\n        lst = ['{}: {}'.format(k, dct[k]) for k in dct]\n        return '< {}\\n{} >\\n'.format(self.__class__.__name__, '\\n'.join(lst))\n\n","sub_path":"sdwan/hive_guider/src/module/terminal_dns_servers.py","file_name":"terminal_dns_servers.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"492596843","text":"#618\nimport itertools\n\n# Alice would lose 57 happiness units by sitting next to Bob.\n\ndef readFileToList():\n\twith open('input.txt', 'r') as filehandle:  \n\t\tinLine = []\n\t\tfor line in filehandle:\n\t\t\tinLineList = []\n\t\t\tinLineRow = line.split(' ')\n\t\t\tinLineList.append(inLineRow[0])\t\t\t# 0 - Alice\n\t\t\tif (inLineRow[2] == 'gain'):\t\t\t# + gain\n\t\t\t\tinLineList.append(int(inLineRow[3]))\n\t\t\telse:\t\t\t\t\t\t\t\t\t# lose\n\t\t\t\tinLineList.append(-1*int(inLineRow[3]))\n\t\t\tinLineList.append((inLineRow[10].strip())[:-1])\t\t# 2 - Bob\n\t\t\tinLine.append(inLineList)\n\t\treturn inLine\n\ndef makeListOfNames(inList):\n\tnameList = []\n\tfor row in inList:\n\t\tif row[0] not in nameList:\n\t\t\tnameList.append(row[0])\n\t\tif row[2] not in nameList:\n\t\t\tnameList.append(row[2])\n\treturn nameList\n\ndef evalNamePairVal(name1,name2,inList):\n\tfor row in inList:\n\t\tif (row[0] == name1) and (row[2] == name2):\n\t\t\t#print(name1,name2,row[1])\n\t\t\treturn row[1]\n\tassert False,\"huh\"\n\ndef computeHappinessScore(listOfNames,inList):\n\thappinessScore = 0\n\tfor nameOffset in range(len(listOfNames)-1):\n\t\thappinessScore += evalNamePairVal(listOfNames[nameOffset],listOfNames[nameOffset+1],inList)\n\t\thappinessScore += evalNamePairVal(listOfNames[nameOffset+1],listOfNames[nameOffset],inList)\n\thappinessScore += evalNamePairVal(listOfNames[-1],listOfNames[0],inList)\n\thappinessScore += evalNamePairVal(listOfNames[0],listOfNames[-1],inList)\n\treturn happinessScore\n\ninList = readFileToList()\nnames = makeListOfNames(inList)\n\nprint(inList)\nprint(names)\nnamesLists = list(itertools.permutations(names))\n#print(namesLists)\nhappinessValue = 0\nfor listOfNames in namesLists:\n\thappy = computeHappinessScore(listOfNames,inList)\n\tif happy > happinessValue:\n\t\thappinessValue = happy\nprint(happinessValue)\n","sub_path":"AoC/AoC-2015/Day13/D13P2.py","file_name":"D13P2.py","file_ext":"py","file_size_in_byte":1725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"238321926","text":"\"\"\"Memory lock module.\"\"\"\nfrom threading import RLock\nfrom typing import Dict  # noqa\n\nfrom tuco.exceptions import TucoAlreadyLockedError, TucoDoNotLockError\n\nfrom .base import BaseLock\n\n\nclass MemoryLock(BaseLock):\n    \"\"\"Simple memory lock implementation.\"\"\"\n\n    global_lock = RLock()\n    locks = {}  # type: Dict[str, str]\n\n    def lock(self) -> bool:\n        \"\"\"Lock an object.\"\"\"\n        try:\n            hash_key = self.hash_key\n        except TucoDoNotLockError:\n            return True\n\n        with self.global_lock:\n            if hash_key in self.locks:\n                raise TucoAlreadyLockedError()\n            self.locks[hash_key] = \"locked\"\n            return True\n\n    def unlock(self) -> bool:\n        \"\"\"Unlock an object.\"\"\"\n        try:\n            hash_key = self.hash_key\n        except TucoDoNotLockError:\n            return True\n\n        with self.global_lock:\n            try:\n                del self.locks[hash_key]\n            except KeyError:\n                pass\n            return True\n","sub_path":"src/tuco/locks/memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"455410998","text":"import os\ndef rename_file(name):\n    file_list = os.listdir(name)\n    #print(file_list)\n    saved_path = os.getcwd()\n    print(\"current working directory is\"+saved_path)\n    os.chdir(name)\n    for file_name in file_list:\n        print(\"old name - \"+file_name)\n        print(\"new name - \"+file_name.translate(None,\"0123456789\"))\n        os.rename(file_name,file_name.translate(None,\"0123456789\"))\n    os.chdir(saved_path)\n\n\nrename_file(\"r'c:\\oop\\prank'\")","sub_path":"day_file/py/New_Study/os操作.py","file_name":"os操作.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"561399775","text":"# https://www.hackerrank.com/challenges/simple-array-sum/problem?isFullScreen=true\r\n\r\n# Input Format\r\n# The first line contains an integer, n, denoting the size of the array.\r\n# The second line contains n space-separated integers representing the array's elements.\r\n\r\n# Constraints\r\n# 0 < n, ar[i] <= 1000\r\n\r\n# Output Format\r\n# Print the sum of the array's elements as a single integer.\r\n\r\n# Sample Input\r\n# 6\r\n# 1 2 3 4 10 11\r\n\r\n# Sample Output\r\n# 31\r\n\r\n\r\n\r\n#!/bin/python3\r\n\r\nimport os\r\nimport sys\r\n\r\n#\r\n# Complete the simpleArraySum function below.\r\n#\r\ndef simpleArraySum(ar):\r\n    #\r\n    # Write your code here.\r\n    return sum(ar)\r\n\r\nif __name__ == '__main__':\r\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\r\n\r\n    ar_count = int(input())\r\n\r\n    ar = list(map(int, input().rstrip().split()))\r\n\r\n    result = simpleArraySum(ar)\r\n\r\n    fptr.write(str(result) + '\\n')\r\n\r\n    fptr.close()","sub_path":"0_reference/Hackerrank/Practice Problem Solving/Warmup/Simple Array Sum.py","file_name":"Simple Array Sum.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"552545462","text":"\"\"\"mixns_demo URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.conf.urls import url, include\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url,include\nfrom django.contrib import admin\nfrom app01 import views\n\n#第三种url简写\n#简写第一步\nfrom rest_framework import routers\n\nrouters = routers.DefaultRouter()\nrouters.register(r\"books001\",views.BookViewSet) #books001为URL前缀\n\n\n\nurlpatterns = [\n    url(r'^admin/', admin.site.urls),\n    url(r'^publisher/$', views.PublsherMixView.as_view()),\n    # url(r'^publisher/(\\d+)/$', views.PublisherDetailView.as_view()), #不加超链接\n    url(r'^publisher/(?P<pk>\\d+)/', views.PublisherDetailMix.as_view(), name=\"publisher_detail\"),\n\n\n    url(r'^book/$', views.BookMixView.as_view()),\n    url(r'^book/(?P<pk>\\d+)/$', views.BookDetailMix.as_view()),\n\n    ##**********第二种*******\n    url(r'^publishers/$', views.PublsherGenView.as_view()),\n    url(r'^publishers/(?P<pk>\\d+)/$', views.PublisherDetailGen.as_view()),\n\n    url(r'^books/$', views.BookGenView.as_view(),name=\"book_list\"),\n    url(r'^books/(?P<pk>\\d+)/$', views.BookDetailGen.as_view(),name=\"book_detail\"),\n\n    ##*****第三种 这种还可以简写\n    # url(r'^books1/$', views.BookViewSet.as_view({\"get\":\"list\",\"post\":\"create\"}),name=\"book_list\"),\n    # url(r'^books1/(?P<pk>\\d+)/$', views.BookViewSet.as_view({\n    #             'get': 'retrieve',\n    #             'put': 'update',\n    #             'patch': 'partial_update',\n    #             'delete': 'destroy'\n    #         }),name=\"book_detail\"),\n\n\n    ##简写第二步\n    url(r'^', include(routers.urls)),\n\n\n]\n\n#简写默认生成如下url\n# # ^books001/$ [name='book-list']\n# ^ ^books001\\.(?P<format>[a-z0-9]+)/?$ [name='book-list']\n# ^ ^books001/(?P<pk>[^/.]+)/$ [name='book-detail']\n# ^ ^books001/(?P<pk>[^/.]+)\\.(?P<format>[a-z0-9]+)/?$ [name='book-detail']","sub_path":"mixns_demo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"173105705","text":"#Send data to web server using urllib\n\nimport urllib.request\nimport urllib.parse\n\ndef main():\n    url = \"http://httpbin.org/get\"\n\n    #Create data to pass to the GET request\n    args = {\n        \"name\": \"Diego Carrillo\",\n        \"is_author\": True\n    }\n\n    #Data needs to be url-encoded before passing as arguments\n    data = urllib.parse.urlencode(args)\n\n    #Issue the request with the data params as part of the URL\n    #result = urllib.request.urlopen(url + \"?\" + data)\n\n    #Issue the request with a data parameter to use POST\n    url = \"http://httpbin.org/post\"\n    data_encoded = data.encode()\n    result = urllib.request.urlopen(url, data=data_encoded)\n\n    print(\"Result code {0}\".format(result.status))\n    print(\"Returned Data: -----------------------\")\n    print(result.read().decode('utf-8'))\n\nif __name__ == \"__main__\":\n    main()","sub_path":"python_web/send_dataurllib.py","file_name":"send_dataurllib.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"560047510","text":"import nltk\nimport jieba.analyse\n\nfrom src.service import bigram_tagger, cfg\nfrom src.util.commonutils import stringNotContainsChinese\n\n\nclass NPExtractor(object):\n    def __init__(self, sentence):\n        self.sentence = sentence\n\n    # Split the sentence into singlw words/tokens\n    def tokenize_sentence(self, sentence):\n        tokens = nltk.word_tokenize(sentence)\n        return tokens\n\n    # Normalize brown corpus' tags (\"NN\", \"NN-PL\", \"NNS\" > \"NN\")\n    def normalize_tags(self, tagged):\n        n_tagged = []\n        for t in tagged:\n            if t[1] == \"NP-TL\" or t[1] == \"NP\":\n                n_tagged.append((t[0], \"NNP\"))\n                continue\n            if t[1].endswith(\"-TL\"):\n                n_tagged.append((t[0], t[1][:-3]))\n                continue\n            if t[1].endswith(\"S\"):\n                n_tagged.append((t[0], t[1][:-1]))\n                continue\n            n_tagged.append((t[0], t[1]))\n        return n_tagged\n\n    # Extract the main topics from the sentence\n    def extract(self):\n\n        tokens = self.tokenize_sentence(self.sentence)\n        tags = self.normalize_tags(bigram_tagger.tag(tokens))\n\n        merge = True\n        while merge:\n            merge = False\n            for x in range(0, len(tags) - 1):\n                t1 = tags[x]\n                t2 = tags[x + 1]\n                key = \"%s+%s\" % (t1[1], t2[1])\n                value = cfg.get(key, '')\n                if value:\n                    merge = True\n                    tags.pop(x)\n                    tags.pop(x)\n                    match = \"%s %s\" % (t1[0], t2[0])\n                    pos = value\n                    tags.insert(x, (match, pos))\n                    break\n\n        matches = []\n        for t in tags:\n            if t[1] == \"NNP\" or t[1] == \"NNI\":\n                # if t[1] == \"NNP\" or t[1] == \"NNI\" or t[1] == \"NN\":\n                matches.append(t[0])\n        return matches\n\n    @staticmethod\n    def extractKeywords(sentence: str, top=3, filterwords=None) -> set:\n        \"\"\"\n        提取一段不包含中文字符串的关键词，返回前 top 个词\n        filterwords 为过滤词，通过 nltk 提取的关键词中如果\n        含有过滤词，则不返回这个词\n        :type top: int\n        \"\"\"\n        np_extractor = NPExtractor(sentence)\n        result = np_extractor.extract()  # result 为 list集合\n        keywords = set()\n        number = 0\n\n        for word in result:\n            if number > top:\n                break\n            if filterwords:\n                isAdd = True\n                for filterword in filterwords:\n                    if str(word).find(filterword) == -1:\n                        isAdd = False\n                        break\n                if isAdd:\n                    keywords.add(word)\n                    number += 1\n            else:\n                keywords.add(word)\n                number += 1\n        return keywords\n\n\ndef extractTagFiles(tagkeyword: dict, keydic: dict, filterwords=None):\n    \"\"\"\n    提取 会议 Tag 字段算法\n    :param tagkeyword: 关键词映射，关键词 → Tag\n    :param keydic: 会议信息字典\n    :param filterwords: 会议 Tag 过滤词\n    \"\"\"\n    tagString = keydic.get(\"tag\")\n    if tagString is None:\n        # 会议信息未归类尝试获取会议中文名\n        conferenceName = keydic.get(\"cnName\")\n        # 没有中文名测试获取英文名\n        if conferenceName is None:\n            conferenceName = keydic.get(\"enName\")\n\n        # 会议名称不为None\n        if conferenceName is not None:\n            isFind = False\n            # 检查会议名称是否包含关键词集中某个关键词，\n            # 如果包含可以根据这个关键词对会议进行归类\n            # 如果不包含关键词集中的任一关键词则判断会议名称是中文还是英文\n            # 如果是中文采用结巴分词进行关键词提取\n            # 如果是英文则采用NLTK生成关键词\n            for k, v in tagkeyword.items():\n                if str(conferenceName).find(k) != -1:\n                    isFind = True\n                    keydic[\"tag\"] = v\n                    break\n            if not isFind:\n                # 会议名称为英文\n                if stringNotContainsChinese(conferenceName):\n                    keywords = NPExtractor.extractKeywords(conferenceName, filterwords=filterwords)\n                    print(\"使用 NLTK 提取Tag\")\n                    keydic[\"tag\"] = \",\".join(keywords)\n\n                else:\n                    jieba.analyse.set_stop_words(\"file/txt/stop_words.txt\")\n                    keywords = jieba.analyse.extract_tags(conferenceName, 5)\n                    keydic[\"tag\"] = \",\".join(keywords)\n","sub_path":"src/crawler/service/taghanldler.py","file_name":"taghanldler.py","file_ext":"py","file_size_in_byte":4728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"604661934","text":"# import necessary libraries\nimport pandas as pd\nimport re\nimport torch\nimport collections\nimport numpy as np\nimport ujson as json\nimport time\nimport torch.nn as nn\nimport os\nimport argparse\nfrom pathlib import Path\nfrom torch.utils.tensorboard import SummaryWriter\nimport matplotlib.pyplot as plt\nfrom tokenizers import BertWordPieceTokenizer\nimport random\nfrom tqdm import tqdm\nimport numpy as np\nimport arguments.train_arguments as arguments\nfrom data_processing.articles import Articles\nfrom models.models import InnerProduct\nimport data_processing.dictionaries as dictionary\nimport sampling.sampler_util as sampler_util\nimport training.eval_util as eval_util\n\nnp.random.seed(0)\n\nparser = argparse.ArgumentParser(\n    description=\"Train model on article data and test evaluation\"\n)\narguments.add_data(parser)\narguments.add_training(parser)\narguments.add_model(parser)\narguments.add_optimization(parser)\nargs = parser.parse_args()\n\n# set device\nif torch.cuda.is_available() and args.use_gpu:\n    device = \"cuda\"\nelif not args.use_gpu:\n    device = \"cpu\"\nelse:\n    device = \"cpu\"\n    print(\"Cannot use GPU. Using CPU instead.\")\nprint(f\"Device: {device}\")\n\n# set output directory path\noutput_path = Path(args.output_dir)\n\n# tensboard log and graph output folder declaration\nlog_tensorboard_dir = output_path / \"runs\" / args.word_embedding_type\nwriter = SummaryWriter(log_tensorboard_dir)\n\n# load datasets\ntrain_path = Path(args.train_path)\ntest_path = Path(args.test_path)\neval_path = Path(args.eval_path)\n\ntrain_data = Articles(train_path)\ntest_data = Articles(test_path)\neval_data = Articles(eval_path, index_file=args.index_file_path)\nprint(\"Data Loaded\")\n\n# initialize tokenizer from BERT library\ntokenizer = BertWordPieceTokenizer(args.tokenizer_file, lowercase=True)\nprint(\"Tokenizer Initialized!\")\n\n# create and save or load dictionaries based on arguments\nif args.create_dicts:\n    (\n        final_word_ids,\n        final_url_ids,\n        final_publication_ids,\n    ) = dictionary.create_merged_dictionaries(\n        train_data.examples, \"target\", args.tokenizer_file\n    )\n    print(\"Dictionaries Created\")\n\n    dict_path = Path(args.data_dir) / \"dictionaries\"\n    if not dict_path.is_dir():\n        dict_path.mkdir()\n\n    dictionary.save_dictionaries(\n        final_word_ids, final_url_ids, final_publication_ids, dict_path\n    )\nelse:\n    dictionary_dir = Path(args.dict_dir)\n    final_word_ids, final_url_ids, final_publication_ids = dictionary.load_dictionaries(\n        dictionary_dir\n    )\n    print(\"Dictionaries loaded.\")\n\n# map items in dataset using dictionary keys (convert words and urls to numbers for the model)\nif args.map_items:\n    badtokens = []\n    if args.bad_token_path.is_file():\n        bad_token_path = Path(args.bad_token_path)\n        with open(bad_token_path, \"r\") as f:\n            badTokens = [int(line.rstrip()) for line in f]\n\n        for dataset in [train_data, test_data, eval_data]:\n            dataset.map_items(\n                tokenizer, final_url_ids, final_publication_ids, filter=False,\n            )\n    else:\n        for dataset in [train_data, test_data, eval_data]:\n            dataset.map_items(\n                tokenizer, final_url_ids, final_publication_ids, filter=False\n            )\n    print(\"Items mapped\")\n    mapped_data_path = Path(args.data_dir) / \"mapped-data\"\n    if not mapped_data_path.is_dir():\n        mapped_data_path.mkdir()\n\n    train_mapped_path = mapped_data_path / \"train.json\"\n    test_mapped_path = mapped_data_path / \"test.json\"\n    eval_mapped_path = mapped_data_path / \"evaluation.json\"\n    with open(train_mapped_path, \"w\") as file:\n        json.dump(train_data.examples, file)\n    with open(test_mapped_path, \"w\") as file:\n        json.dump(test_data.examples, file)\n    with open(eval_mapped_path, \"w\") as file:\n        json.dump(eval_data.examples, file)\n    print(f\"Mapped Data saved to {mapped_data_path} directory\")\n\n# create weights for dataset samples to ensure only positive and negative examples are chosen in respective samples\npos_sampler = train_data.create_positive_sampler(args.target_publication)\nneg_sampler = train_data.create_negative_sampler(args.target_publication)\n\ntrain_batch_sampler = sampler_util.BatchSamplerWithNegativeSamples(\n    pos_sampler=pos_sampler,\n    neg_sampler=neg_sampler,\n    items=train_data.examples,\n    batch_size=args.batch_size,\n)\n\n\n# define function to return necessary data for dataloader to pass into model\ndef collate_fn(examples):\n    words = []\n    articles = []\n    labels = []\n    publications = []\n    for example in examples:\n        if args.use_all_words:\n            words.append(list(set(example[\"text\"])))\n        else:\n            if len(list(set(example[\"text\"]))) > args.words_to_use:\n                words.append(\n                    random.sample(list(set(example[\"text\"])), args.words_to_use)\n                )\n            else:\n                words.append(list(set(example[\"text\"])))\n        articles.append(example[\"url\"])\n        publications.append(example[\"model_publication\"])\n        labels.append(example[\"model_publication\"])\n    num_words = [len(x) for x in words]\n    words = np.concatenate(words, axis=0)\n    word_attributes = torch.tensor(words, dtype=torch.long)\n    articles = torch.tensor(articles, dtype=torch.long)\n    num_words.insert(0, 0)\n    num_words.pop(-1)\n    attribute_offsets = torch.tensor(np.cumsum(num_words), dtype=torch.long)\n    publications = torch.tensor(publications, dtype=torch.long)\n    real_labels = torch.tensor(labels, dtype=torch.long)\n    return publications, articles, word_attributes, attribute_offsets, real_labels\n\n\n# change negative example publication ids to the ids of the first half for predictions\ndef collate_with_neg_fn(examples):\n    (\n        publications,\n        articles,\n        word_attributes,\n        attribute_offsets,\n        real_labels,\n    ) = collate_fn(examples)\n    publications[len(publications) // 2 :] = publications[: len(publications) // 2]\n    return publications, articles, word_attributes, attribute_offsets, real_labels\n\n\n# pin memory if using GPU for high efficiency\nif args.use_gpu:\n    pin_mem = True\nelse:\n    pin_mem = False\n\n# create dataloaders for iterable data when training and testing recall\ntrain_loader = torch.utils.data.DataLoader(\n    train_data,\n    batch_sampler=train_batch_sampler,\n    collate_fn=collate_with_neg_fn,\n    pin_memory=pin_mem,\n)\neval_loader = torch.utils.data.DataLoader(\n    eval_data, batch_size=10000, collate_fn=collate_fn, pin_memory=pin_mem\n)\ntest_loader = torch.utils.data.DataLoader(\n    test_data, batch_size=10000, collate_fn=collate_fn, pin_memory=pin_mem\n)\n\n\n# function that allows for infinite iteration over training batches\ndef cycle(iterable):\n    while True:\n        for x in iterable:\n            yield x\n\n\n# initialize model, loss, and optimizer\nkwargs = dict(\n    n_publications=len(final_publication_ids),\n    n_articles=len(final_url_ids),\n    n_attributes=len(final_word_ids),\n    emb_size=args.emb_size,\n    sparse=args.use_sparse,\n    use_article_emb=args.use_article_emb,\n    mode=args.word_embedding_type,\n)\nmodel = InnerProduct(**kwargs)\nmodel.reset_parameters()\nmodel.to(device)\n\nloss = torch.nn.BCEWithLogitsLoss()\n\nif args.optimizer_type == \"RMS\":\n    optimizer = torch.optim.RMSprop(\n        model.parameters(), lr=args.learning_rate, momentum=args.momentum\n    )\nelse:\n    optimizer = torch.optim.SGD(\n        model.parameters(), lr=args.learning_rate, momentum=args.momentum\n    )\n\nprint(model)\nprint(optimizer)\nmodel.train()  # turn on training mode\ncheck = True\nrunning_loss = 0\n\nlabels = torch.Tensor(\n    (np.arange(args.batch_size) < args.batch_size // 2).astype(np.float32)\n)\nlabels = labels.to(device)\n\nvalidation_recall_list = []\nprint(\"Beginning Training\")\nprint(\"--------------------\")\n\n# training loop with validation checks\nfor step, batch in enumerate(cycle(train_loader)):\n    # calculate test and evaluation performance based on user intended frequency\n    if step % args.frequency == 0 and step != args.training_steps:\n        # output loss\n        model.eval()\n        torch.no_grad()\n        writer.add_scalar(\"Loss/train\", running_loss / args.frequency, step)\n        print(f\"Training Loss: {running_loss/args.frequency}\")\n        running_loss = 0.0\n        logit_list = []\n        for eval_batch in tqdm(eval_loader):\n            current_logits = eval_util.calculate_batched_predictions(\n                eval_batch, model, device, args.target_publication\n            )\n            logit_list = logit_list + list(current_logits)\n        converted_list = np.array(logit_list)\n        sorted_preds = np.sort(converted_list)\n        indices = np.argsort(converted_list)\n        calc_recall = eval_util.calculate_recall(\n            eval_data,\n            indices,\n            args.eval_recall_max,\n            args.target_publication,\n            \"Eval\",\n            writer,\n            step,\n        )\n        validation_recall_list.append(calc_recall)\n\n        # save model for easy reloading\n        if max(validation_recall_list) == validation_recall_list[-1]:\n            model_path = output_path / \"model\"\n            if not model_path.is_dir():\n                model_path.mkdir()\n            model_string = (\n                str(step) + args.word_embedding_type + \"-inner-product-model.pt\"\n            )\n            model_path = model_path / model_string\n            torch.save(model.state_dict(), model_path)\n\n        # check if validation recall is increasing\n        if len(validation_recall_list) > 3:\n            full_length = len(validation_recall_list)\n            if (\n                validation_recall_list[-1] < validation_recall_list[-2]\n                and validation_recall_list[-2] < validation_recall_list[-3]\n                and validation_recall_list[-3] < validation_recall_list[-4]\n            ):\n                print(\"Validation Recall Decreased For Two Successive Iterations!\")\n                break\n\n    # turn to training mode and calculate loss for backpropagation\n    torch.enable_grad()\n    model.train()\n    optimizer.zero_grad()\n    publications, articles, word_attributes, attribute_offsets, real_labels = batch\n    publication_set = [args.target_publication] * len(real_labels)\n    publication_set = torch.tensor(publication_set, dtype=torch.long)\n    publication_set = publication_set.to(device)\n    articles = articles.to(device)\n    word_attributes = word_attributes.to(device)\n    attribute_offsets = attribute_offsets.to(device)\n    logits = model(publication_set, articles, word_attributes, attribute_offsets)\n    L = loss(logits, labels)\n    L.backward()\n    optimizer.step()\n    running_loss += L.item()\n    if step != 0 and step % args.training_steps == 0:\n        writer.add_scalar(\"Loss/train\", running_loss / args.frequency, step)\n        print(f\"Training Loss: {running_loss/100}\")\n        print(\"Getting Final Evaluation Results\")\n        print(\"--------------------\")\n        break\n\n# load best model for final performance metrics and data saving\nproper_step_model = (\n    str(np.argmax(validation_recall_list) * args.frequency)\n    + args.word_embedding_type\n    + \"-inner-product-model.pt\"\n)\n\nwriter.add_scalar(\"Peaked_Steps\", np.argmax(validation_recall_list) * args.frequency)\nwriter.add_scalar(\"Max_Evaluation_Recall\", np.max(validation_recall_list))\n\nabs_model_path = output_path / \"model\" / proper_step_model\nkwargs = dict(\n    n_publications=len(final_publication_ids),\n    n_articles=len(final_url_ids),\n    n_attributes=len(final_word_ids),\n    emb_size=args.emb_size,\n    sparse=args.use_sparse,\n    use_article_emb=args.use_article_emb,\n    mode=args.word_embedding_type,\n)\nmodel = InnerProduct(**kwargs)\nmodel.load_state_dict(torch.load(abs_model_path))\nmodel.to(device)\nmodel.eval()\ntorch.no_grad()\n\n# get final evaluation results and create a basic csv of top articles\neval_logit_list = []\nfor batch in tqdm(eval_loader):\n    current_logits = eval_util.calculate_batched_predictions(\n        batch, model, device, args.target_publication\n    )\n    eval_logit_list = eval_logit_list + list(current_logits)\nconverted_list = np.array(eval_logit_list)\nsorted_preds = np.sort(converted_list)\nindices = np.argsort(converted_list)\ncalc_recall = eval_util.calculate_recall(\n    eval_data,\n    indices,\n    args.eval_recall_max,\n    args.target_publication,\n    \"Eval\",\n    writer,\n    step,\n)\nranked_df = eval_util.create_ranked_results_list(\n    final_word_ids, sorted_preds, indices, eval_data\n)\neval_util.save_ranked_df(output_path, \"evaluation\", ranked_df, args.word_embedding_type)\n\n# get final test results and create a basic csv of top articles\ntest_logit_list = []\nfor batch in tqdm(test_loader):\n    current_logits = eval_util.calculate_batched_predictions(\n        batch, model, device, args.target_publication\n    )\n    test_logit_list = test_logit_list + list(current_logits)\nconverted_list = np.array(test_logit_list)\nsorted_preds = np.sort(converted_list)\nindices = np.argsort(converted_list)\ncalc_recall = eval_util.calculate_recall(\n    test_data,\n    indices,\n    args.test_recall_max,\n    args.target_publication,\n    \"Test\",\n    writer,\n    step,\n)\nranked_df = eval_util.create_ranked_results_list(\n    final_word_ids, sorted_preds, indices, test_data\n)\neval_util.save_ranked_df(output_path, \"test\", ranked_df, args.word_embedding_type)\n\n# close writer and exit\nwriter.close()\nprint(f\"Ranked Data Saved to {output_path / 'results'}!\")\nprint(\"Done!\")\n","sub_path":"rankfromsets/train-rankfromsets.py","file_name":"train-rankfromsets.py","file_ext":"py","file_size_in_byte":13449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"79443470","text":"from django import forms\nfrom django.core.validators import MinValueValidator, MaxValueValidator\n\n__author__ = 'Ruben'\n\n\nclass EditProfesionForm(forms.Form):\n    professionId = forms.IntegerField()\n    nivel = forms.IntegerField(validators=[MinValueValidator(0), MaxValueValidator(460)], label='Nivel')\n\n    def __init__(self, *args, **kwargs):\n        professionId = kwargs.pop('professionId', -1)\n\n        super(EditProfesionForm, self).__init__(*args, **kwargs)\n        self.fields['professionId'].widget = forms.HiddenInput()\n\n        if professionId != -1:\n            self.fields['professionId'].initial = professionId","sub_path":"giants/giantsapp/forms/EditProfesionForm.py","file_name":"EditProfesionForm.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"649432171","text":"import csv\nfrom smbus2 import SMBus\nimport time\n\n# setup csv file\nfile = \"si7021_log.csv\"\nfields = ['time', 'humidity', 'temperature']\ncountdown = 1 * 10\n\n# setup bus connection\nbus = SMBus(1)\n\nwith open(file, 'w') as csvfile:\n    csvwriter = csv.writer(csvfile, delimiter=',')\n    csvwriter.writerow(fields)\n    count = 0\n    \n    while count < countdown:\n        now = time.localtime()\n        current_time = time.strftime(\"%H:%M:%S\", now)\n        bus.write_byte(0x40, 0xE5)\n        data0 = bus.read_byte(0x40, 0x40)\n        data1 = bus.read_byte(0x40, 0x40)\n        humidity = ((data0 * 256 + data1) * 125 / 65536.0) - 6\n        bus.write_byte(0x40, 0xE0)\n        data0 = bus.read_byte(0x40, 0x40)\n        data1 = bus.read_byte(0x40, 0x40)\n        tempc = ((data0 * 256 + data1) * 175.72 / 65536.0) - 46.85\n        csvwriter.writerow([current_time, \"{:.4f}\".format(humidity), \"{:.4f}\".format(tempc)])\n        count = count + 1\n        time.sleep(0.1); # frequency of data acquisition is 0.1 seconds (100 milliseconds)\n","sub_path":"si7021.py","file_name":"si7021.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"88892956","text":"from ftw.testbrowser import browsing\nfrom opengever.testing import IntegrationTestCase\n\n\nclass TestUserDetails(IntegrationTestCase):\n\n    @browsing\n    def test_user_details(self, browser):\n        self.login(self.regular_user, browser)\n\n        browser.open(self.portal, view='@@user-details/kathi.barfuss')\n\n        self.assertEquals([u'B\\xe4rfuss K\\xe4thi (kathi.barfuss)'],\n                          browser.css('h1.documentFirstHeading').text)\n\n        metadata = browser.css('.vertical').first.lists()\n\n        self.assertEquals(\n            ['Name', u'B\\xe4rfuss K\\xe4thi (kathi.barfuss)'], metadata[0])\n        self.assertEquals(['Active', 'Yes'], metadata[1])\n        self.assertEquals(['Email', 'kathi.barfuss@gever.local'], metadata[2])\n\n    @browsing\n    def test_user_details_return_not_found_for_not_exisiting_user(self, browser):\n        with browser.expect_http_error(code=404):\n            browser.login().open(self.portal, view='@@user-details/not.found')\n\n    @browsing\n    def test_list_all_team_memberships(self, browser):\n        self.login(self.regular_user, browser)\n        browser.open(self.portal, view='@@user-details/kathi.barfuss')\n\n        self.assertEquals(\n            [u'Projekt \\xdcberbaung Dorfmatte'], browser.css('.teams li').text)\n        self.assertEquals('http://nohost/plone/@@team-details/1',\n                          browser.css('.teams a').first.get('href'))\n","sub_path":"opengever/ogds/base/tests/test_userdetails.py","file_name":"test_userdetails.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"144691410","text":"import os\nimport telebot\nimport mysql.connector\nimport pandas as pd\nimport numpy as np\nfrom zipfile import ZipFile\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\nfrom mysql.connector import Error\n\n# Load TensorFlow Decision Forests\n\n# Load the training dataset using pandas\nimport pandas\n#import mysql.connector\nfrom replit import db\nfrom telebot import types\n\nmy_secret = os.getenv('API_KEY')\n#my_secret =\"1676961222:AAF7kZ_rf9olinM3ScqA2WqgdoqAo3APgws\"\nprint(my_secret)\nbot=telebot.TeleBot('1676961222:AAF7kZ_rf9olinM3ScqA2WqgdoqAo3APgws')\n#@bot.message_handler(commands=['hello'])\n#def hello(message):\n#  bot.reply_to(message,'Bonjour pour utiliser l\\'assistant veuillez entrer les informations suivantes:')\n # bot.send_message(message.chat.id,'nom(s) et prenom(s):')\n\n#bot.polling()\n\n#154.72.167.172\ndef insert(connection,message,ind):\n    mySql_insert_query = \"\"\"INSERT INTO Users (Chat_Id, Nom, Sexe,Age, Poids,Taille) \n                              VALUES \n                              (%s, %s,%s,%s,%s,%s) \"\"\"\n    record = (message.chat.id, ind.name, ind.sex, ind.age,ind.poids,ind.taille)\n    cursor = connection.cursor()\n    cursor.execute(mySql_insert_query,record)\n    connection.commit()\n    print(cursor.rowcount, \"Record inserted successfully into Laptop table\")\n    cursor.close()\ndef create_table(connection):\n    mySql_Create_Table_Query = \"\"\"CREATE TABLE Users ( \n                                            Id int(11) NOT NULL,\n                                            Chat_Id int(11) NOT NULL,\n                                            Nom varchar(250) NOT NULL,\n                                            Sexe varchar(250) NOT NULL,\n                                            Age int(11) NOT NULL,\n                                            Poids int(11) NOT NULL,\n                                            Taille int(11) NOT NULL,\n                                            PRIMARY KEY (Id)) \"\"\"\n\n    cursor = connection.cursor()\n    result = cursor.execute(mySql_Create_Table_Query)\n    # result = cursor.execute(mySql_Create_Table_Query)\n    print(\"Laptop Table created successfully \")\n\n\ndef connect_sql():\n    try:\n        connection = mysql.connector.connect(host='localhost',\n                                             database='tensor',\n                                             user='root',\n                                             password='')\n\n\n    except mysql.connector.Error as error:\n        print(\"Failed to create table in MySQL: {}\".format(error))\n\n    return connection\ndef close_conn(connection):\n    if connection.is_connected():\n        cursor = connection.cursor()\n        cursor.close()\n        connection.close()\n        print(\"MySQL connection is closed\")\n\n\n\"\"\"\n## First, load the data and apply preprocessing\n\"\"\"\n\n# Download the actual data from http://files.grouplens.org/datasets/foodlens/ml-latest-small.zip\"\n# Use the ratings.csv file\nfoodlens_data_file_url = (\n    \"http://files.grouplens.org/datasets/foodlens/ml-latest-small.zip\"\n)\nfoodlens_zipped_file = keras.utils.get_file(\n    \"ml-latest-small.zip\", foodlens_data_file_url, extract=False\n)\nkeras_datasets_path = Path(foodlens_zipped_file).parents[0]\nprint('keras_dataset_path', keras_datasets_path)\nfoodlens_dir = keras_datasets_path / \"ml-latest-small\"\n\n# Only extract the data the first time the script is run.\nif not foodlens_dir.exists():\n    with ZipFile(foodlens_zipped_file, \"r\") as zip:\n        # Extract files\n        print(\"Extracting all the files now...\")\n        zip.extractall(path=keras_datasets_path)\n        print(\"Done!\")\n\n# ratings_file = foodlens_dir / \"ratings.csv\"\nratings_file = \"ratings.csv\"\n\ndf = pd.read_csv(ratings_file)\n\n\"\"\"\nFirst, need to perform some preprocessing to encode users and foods as integer indices.\n\"\"\"\nuser_ids = df[\"userId\"].unique().tolist()\nuser2user_encoded = {x: i for i, x in enumerate(user_ids)}\nuserencoded2user = {i: x for i, x in enumerate(user_ids)}\nfood_ids = df[\"foodId\"].unique().tolist()\nfood2food_encoded = {x: i for i, x in enumerate(food_ids)}\nfood_encoded2food = {i: x for i, x in enumerate(food_ids)}\ndf[\"user\"] = df[\"userId\"].map(user2user_encoded)\ndf[\"food\"] = df[\"foodId\"].map(food2food_encoded)\n\nnum_users = len(user2user_encoded)\nnum_foods = len(food_encoded2food)\ndf[\"rating\"] = df[\"rating\"].values.astype(np.float32)\n# min and max ratings will be used to normalize the ratings later\nmin_rating = min(df[\"rating\"])\nmax_rating = max(df[\"rating\"])\n\nprint(\n    \"Number of users: {}, Number of foods: {}, Min rating: {}, Max rating: {}\".format(\n        num_users, num_foods, min_rating, max_rating\n    )\n)\n\n\"\"\"\n## Prepare training and validation data\n\"\"\"\ndf = df.sample(frac=1, random_state=42)\nx = df[[\"user\", \"food\"]].values\n#print('valeur de x', x)\n\n# Normalize the targets between 0 and 1. Makes it easy to train.\ny = df[\"rating\"].apply(lambda x: (x - min_rating) / (max_rating - min_rating)).values\n#print('valeur de y', y)\n# Assuming training on 90% of the data and validating on 10%.\ntrain_indices = int(0.9 * df.shape[0])\nx_train, x_val, y_train, y_val = (\n    x[:train_indices],\n    x[train_indices:],\n    y[:train_indices],\n    y[train_indices:],\n)\n\n\"\"\"\n## Create the model\nWe embed both users and foods in to 50-dimensional vectors.\nThe model computes a match score between user and food embeddings via a dot product,\nand adds a per-food and per-user bias. The match score is scaled to the `[0, 1]`\ninterval via a sigmoid (since our ratings are normalized to this range).\n\"\"\"\nEMBEDDING_SIZE = 50\n\nclass RecommenderNet(keras.Model):\n    def __init__(self, num_users, num_foods, embedding_size, **kwargs):\n        super(RecommenderNet, self).__init__(**kwargs)\n        self.num_users = num_users\n        self.num_foods = num_foods\n        self.embedding_size = embedding_size\n        self.user_embedding = layers.Embedding(\n            num_users,\n            embedding_size,\n            embeddings_initializer=\"he_normal\",\n            embeddings_regularizer=keras.regularizers.l2(1e-6),\n        )\n        self.user_bias = layers.Embedding(num_users, 1)\n        self.food_embedding = layers.Embedding(\n            num_foods,\n            embedding_size,\n            embeddings_initializer=\"he_normal\",\n            embeddings_regularizer=keras.regularizers.l2(1e-6),\n        )\n        self.food_bias = layers.Embedding(num_foods, 1)\n\n    def call(self, inputs):\n        user_vector = self.user_embedding(inputs[:, 0])\n        user_bias = self.user_bias(inputs[:, 0])\n        food_vector = self.food_embedding(inputs[:, 1])\n        food_bias = self.food_bias(inputs[:, 1])\n        dot_user_food = tf.tensordot(user_vector, food_vector, 2)\n        # Add all the components (including bias)\n        x = dot_user_food + user_bias + food_bias\n        # The sigmoid activation forces the rating to between 0 and 1\n        return tf.nn.sigmoid(x)\n\n\n\nuser_dict = {}\n\n\nclass User:\n    def __init__(self, name):\n        self.name = name\n        self.age = None\n        self.sex = None\n        self.taille = None\n        self.poids = None\n\n\n# Handle '/start' and '/help'\n@bot.message_handler(commands=['help', 'start'])\ndef send_welcome(message):\n    msg = bot.reply_to(message, \"\"\"\\\nBonjour, pour utiliser l'assistant, veuillez entrer vos parametres.\nQuel est votre nom complet?\n\"\"\")\n    bot.register_next_step_handler(msg, process_name_step)\n\n\ndef process_name_step(message):\n    try:\n        chat_id = message.chat.id\n        name = message.text\n        user = User(name)\n        user_dict[chat_id] = user\n        msg = bot.reply_to(message, 'Quel est votre age?')\n        bot.register_next_step_handler(msg, process_age_step)\n    except Exception as e:\n        bot.reply_to(message, 'oooops')\n\n\ndef process_age_step(message):\n    try:\n        chat_id = message.chat.id\n        age = message.text\n        if not age.isdigit():\n            msg = bot.reply_to(message, 'Vous devez saisir un nombre quel est votre age?')\n            bot.register_next_step_handler(msg, process_age_step)\n            return\n        user = user_dict[chat_id]\n        user.age = age\n        msg = bot.reply_to(message, 'Quelle est votre taille en cm?')\n        bot.register_next_step_handler(msg, process_taille_step)\n    except Exception as e:\n        bot.reply_to(message, 'oooops')\n\ndef process_taille_step(message):\n  try:\n    chat_id=message.chat.id\n    taille=message.text\n    if not taille.isdigit():\n       msg = bot.reply_to(message, 'Vous devez saisir un nombre quel est votre taille en cm?')\n       bot.register_next_step_handler(msg, process_taille_step)\n       return\n    user=user_dict[chat_id]\n    user.taille=taille\n    msg = bot.reply_to(message, 'Quel est votre poids en Kg?')\n    bot.register_next_step_handler(msg, process_poids_step)\n  except Exception as e:\n        bot.reply_to(message, 'oooops')\n\ndef process_poids_step(message):\n  try:\n    chat_id=message.chat.id\n    poids=message.text\n    if not poids.isdigit():\n       msg = bot.reply_to(message, 'Vous devez saisir un nombre quel est votre poids en Kg?')\n       bot.register_next_step_handler(msg, process_poids_step)\n       return\n    user=user_dict[chat_id]\n    user.poids=poids\n    markup = types.ReplyKeyboardMarkup(one_time_keyboard=True)\n    markup.add('Homme', 'Femme')\n    msg = bot.reply_to(message, 'Quel est votre genre?', reply_markup=markup)\n    bot.register_next_step_handler(msg, process_sex_step)\n  except Exception as e:\n        bot.reply_to(message, 'oooops')\n\ndef process_sex_step(message):\n    try:\n        chat_id = message.chat.id\n        sex = message.text\n        user = user_dict[chat_id]\n        if (sex == u'Homme') or (sex == u'Femme'):\n            user.sex = sex\n        else:\n            raise Exception(\"sexe inconnu\")\n        bot.send_message(chat_id, 'Merci d\\'avoir suivi la procedure votre profil est le suivant: \\n' 'Nom : ' + user.name + '\\n Age : ' + str(user.age)+' ans' +'\\n Taille : ' + user.taille+' cm' +'\\n Poids : ' + user.poids+' Kg' +'\\n Sexe : ' + user.sex)\n\n        con=connect_sql()\n        insert(con,message,user)\n        close_conn(con)\n        #bot.send_message(user)\n    except Exception as e:\n        bot.reply_to(message, 'oooopse'+e)\n\n\n\n# Enable saving next step handlers to file \"./.handlers-saves/step.save\".\n# Delay=2 means that after any change in next step handlers (e.g. calling register_next_step_handler())\n# saving will hapen after delay 2 seconds.\nbot.enable_save_next_step_handlers(delay=2)\n@bot.message_handler(commands=['infos'])\ndef infos(message):\n    chat_id=message.chat.id\n    #user = user_dict[chat_id]\n    connection = connect_sql()\n    cursor = connection.cursor()\n    sql_select_Query = \"\"\"select * from Users where Chat_Id = %s\"\"\"\n    #record=message.chat.id\n    cursor.execute(sql_select_Query,(message.chat.id,))\n    # get all records\n    records = cursor.fetchall()\n    print(\"Total number of rows in table: \", cursor.rowcount)\n    print(\"\\nSending it to telegram\")\n    for row in records:\n        user={\n            \"name\":row[2],\n            \"age\":row[4],\n            \"sex\":row[3],\n            \"poids\":row[5],\n            \"taille\":row[6]\n        }\n\n        # print(\"Id = \", row[0], )\n        # print(\"Chat_id = \", row[1])\n        # print(\"name  = \", row[2])\n        # print(\"age  = \", row[3])\n        # print(\"sex  = \", row[4])\n        # print(\"poids  = \", row[5])\n        # print(\"taille  = \", row[6])\n        # print(\"Purchase date  = \", row[3], \"\\n\")\n    bot.send_message(message.chat.id,'Votre profil est le suivant: \\n' +'Nom : ' + user[\"name\"] + '\\n Age : ' + str(user[\"age\"]) + ' ans' + '\\n Taille : ' + str(user[\"taille\"]) + ' cm\\n' + ' Poids : ' + str(user[\"poids\"]) + ' Kg' + '\\n Sexe : ' + user[\"sex\"])\n\n@bot.message_handler(commands=['food'])\ndef food(message):\n    print(\"Bonjour\")\n    chat_id = message.chat.id\n    # user = user_dict[chat_id]\n    connection = connect_sql()\n    cursor = connection.cursor()\n    sql_select_Query = \"\"\"select * from Users where Chat_Id = %s\"\"\"\n    # record=message.chat.id\n    cursor.execute(sql_select_Query, (message.chat.id,))\n    # get all records\n    records = cursor.fetchall()\n    print(\"Total number of rows in table: \", cursor.rowcount)\n    print(\"\\nSending it to telegram\")\n    for row in records:\n        user = {\n            \"name\": row[2],\n            \"age\": row[4],\n            \"sex\": row[3],\n            \"poids\": row[5],\n            \"taille\": row[6]\n        }\n    if user['age']>30 & user['age']<50 & user['poids']>60 & user['poids']<100:\n        userId_=1\n    elif user['age']>10 & user['age']<20 & user['poids']>60 & user['poids']<100:\n        userId_=5\n    elif user['age']>50 & user['age']<60 & user['poids']>60 & user['poids']<100:\n        userId_=3\n    elif user['age']>20 & user['age']<30 & user['poids']>60 & user['poids']<100:\n        userId_=9\n    elif user['age']>60 & user['age']<70 & user['poids']>60 & user['poids']<100:\n        userId_=11\n    elif user['age']>70 & user['age']<100 & user['poids']>60 & user['poids']<100:\n        userId_=15\n    else:\n        userId_=10\n\n\n\n\n    model = RecommenderNet(num_users, num_foods, EMBEDDING_SIZE)\n    model.compile(\n        loss=tf.keras.losses.BinaryCrossentropy(), optimizer=keras.optimizers.Adam(lr=0.001)\n    )\n\n    \"\"\"\n    ## Train the model based on the data split\n    \"\"\"\n    history = model.fit(\n        x=x_train,\n        y=y_train,\n        batch_size=64,\n        epochs=50,\n        verbose=1,\n        validation_data=(x_val, y_val),\n    )\n\n    \"\"\"\n    ## Plot training and validation loss\n    \"\"\"\n    plt.plot(history.history[\"loss\"])\n    plt.plot(history.history[\"val_loss\"])\n    plt.title(\"model loss\")\n    plt.ylabel(\"loss\")\n    plt.xlabel(\"epoch\")\n    plt.legend([\"train\", \"test\"], loc=\"upper left\")\n    plt.show()\n\n    \"\"\"\n    ## Show top 10 food recommendations to a user\n    \"\"\"\n\n    # food_df = pd.read_csv(foodlens_dir / \"foods.csv\")\n    food_df = pd.read_csv(\"data.csv\")\n    # Let us get a user and see the top recommendations.\n    #user_id = df.userId.sample(1).iloc[0]\n    user_id=userId_\n    print('user_id', user_id)\n    foods_watched_by_user = df[df.userId == user_id]\n    foods_not_watched = food_df[\n        ~food_df[\"foodId\"].isin(foods_watched_by_user.foodId.values)\n    ][\"foodId\"]\n    foods_not_watched = list(\n        set(foods_not_watched).intersection(set(food2food_encoded.keys()))\n    )\n    foods_not_watched = [[food2food_encoded.get(x)] for x in foods_not_watched]\n    user_encoder = user2user_encoded.get(user_id)\n    user_food_array = np.hstack(\n        ([[user_encoder]] * len(foods_not_watched), foods_not_watched)\n    )\n    ratings = model.predict(user_food_array).flatten()\n    top_ratings_indices = ratings.argsort()[-10:][::-1]\n    recommended_food_ids = [\n        food_encoded2food.get(foods_not_watched[x][0]) for x in top_ratings_indices\n    ]\n\n    bot.send_message(message.chat.id,\"Showing recommendations for user: {}\".format(user_id))\n    bot.send_message(message.chat.id,\"====\" * 12)\n    bot.send_message(message.chat.id,\"foods with high ratings from ciqual\")\n    bot.send_message(message.chat.id,\"----\" * 20)\n\n    print(\"Showing recommendations for user: {}\".format(user_id))\n    print(\"====\" * 9)\n    print(\"foods with high ratings from user\")\n    print(\"----\" * 8)\n    top_foods_user = (\n        foods_watched_by_user.sort_values(by=\"rating\", ascending=False)\n            .head(5)\n            .foodId.values\n    )\n    food_df_rows = food_df[food_df[\"foodId\"].isin(top_foods_user)]\n    rec=\"\"\n    for row in food_df_rows.itertuples():\n        print(row.alim_nom_fr, \": Qte sucre\", row.sucres)\n        rec+=\"\\n\"+\"----\"+\"\\n\"+row.alim_nom_fr+ \" : Qte sucre \"+ str(row.sucres)\n    bot.send_message(message.chat.id, rec)\n\n\n    bot.send_message(message.chat.id,\"----\" * 20)\n    bot.send_message(message.chat.id,\"Top 10 food recommendations\")\n    bot.send_message(message.chat.id,\"----\" * 20)\n\n    print(\"----\" * 8)\n    print(\"Top 10 food recommendations\")\n    print(\"----\" * 8)\n    recommended_foods = food_df[food_df[\"foodId\"].isin(recommended_food_ids)]\n    eat=\"\"\n    for row in recommended_foods.itertuples():\n        print(row.alim_nom_fr, \": Qte sucre\", row.sucres)\n        eat+=\"\\n\"+\"----------------------------------------------------------------------------------------\"+\"\\n\"+row.alim_nom_fr+ \" : Qte sucre \"+str(row.sucres)\n    bot.send_message(message.chat.id,eat)\n\n    #bot.send_message(message.chat.id,'Votre profil est le suivant: \\n' + 'Nom : ' + user[\"name\"] + '\\n Age : ' + str(user[\"age\"]) + ' ans' + '\\n Taille : ' + str(user[\"taille\"]) + ' cm\\n' + ' Poids : ' + str(user[\"poids\"]) + ' Kg' + '\\n Sexe : ' + user[\"sex\"])\n# Load next_step_handlers from save file (default \"./.handlers-saves/step.save\")\n# WARNING It will work only if enable_save_next_step_handlers was called!\nbot.load_next_step_handlers()\n\nbot.polling()","sub_path":"telebot_final_code.py","file_name":"telebot_final_code.py","file_ext":"py","file_size_in_byte":16838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"497841409","text":"import socket\nfrom time import sleep\nfrom torch.cuda.memory import empty_cache\nimport tqdm\nimport yaml\nimport os\nimport pickle\n\nimport re\nimport traceback\n\nimport select\n\nimport sys\n\nsys.path.append(\"../\")\nimport values\nfrom values import TOKEN_BUFFER_SIZE, BUFFER_SIZE, SEPARATOR\n\n# QUERIES\n# MODEL - get latest model\n# CONFIG - get latest config\n# UPDATE_MODEL - send updated model\n\n# TOKEN_BUFFER_SIZE = 16\n# BUFFER_SIZE = 4096\n# SEPARATOR = '&'\n\n# MODEL_NAME = 'model.pth'\n# CONFIG_NAME = 'config.yaml'\nMAX_RETRY = 5\n\n\nclass Client:\n    def __init__(self, config):\n        super().__init__()\n        print(\"\\nINITIALISING CLIENT...\")\n        print(\"CONFIG:\")\n        print(config)\n        self.config = config\n        self.HOST = self.config[\"HOST\"]\n        self.PORT = self.config[\"PORT\"]\n\n        # MODEL FOLDER\n        try:\n            self.model_folder = self.config[\"model_folder\"]\n            if not os.path.isdir(self.model_folder):\n                print(\"\\nFOLDER UNAVAILABLE, CREATING FOLDER...\")\n                try:\n                    os.makedirs(self.model_folder)\n                except:\n                    print(\"\\nUNABLE TO CREATE FOLDER\")\n                    if not os.path.isdir(\"MODEL\"):\n                        print(\"\\nCREATING DEFAULT PATH /MODEL...\")\n                        os.mkdir(\"MODEL\")\n                    else:\n                        print(\"\\nUSING DEFAULT PATH: /MODEL\")\n                    self.model_folder = \"MODEL\"\n\n        except Exception as e:\n            print(e)\n            # print('\\nPATH TO MODEL NOT FOUND...')\n            if not os.path.isdir(\"MODEL\"):\n                print(\"\\nCREATING DEFAULT PATH /MODEL...\")\n                os.mkdir(\"MODEL\")\n            else:\n                print(\"\\nUSING DEFAULT PATH: /MODEL\")\n            self.model_folder = \"MODEL\"\n\n        # MODEL NAME\n        try:\n            self.model_name = self.config[\"model_name\"]\n            print(\"MODEL NAME: \", self.model_name)\n        except Exception as e:\n            print(\"\\nMODEL NAME NOT FOUND, USING DEFAULT NAME model.pth\")\n            self.model_name = \"model.pth\"\n\n        # CREATE SOCKET\n        self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        print('\\nCLIENT INITIALISED')\n    \n    def empty_socket(self):\n        while True:\n            ready_to_read, write, error = select.select([self.s],[self.s],[], 1)\n            if len(ready_to_read) == 0:\n                break\n            for sock in ready_to_read:\n                sock.recv(BUFFER_SIZE)\n\n    def is_token(self, token):\n        check = re.fullmatch(values.TOKEN_REGEX, token)\n        if check:\n            return True\n        return False\n\n    def request_token(self):\n        self.empty_socket()\n        self.s.send(values.REQUIRES_TOKEN.encode())\n        response = self.s.recv(TOKEN_BUFFER_SIZE).decode() # receive response from server\n        if self.is_token(response): #RECEIVED TOKEN\n            self.save_token(response)\n            return True\n        return False\n\n    def send_token(self, token):\n        self.empty_socket()\n        self.s.send(token.encode())\n        response = self.s.recv(TOKEN_BUFFER_SIZE).decode() # receive response from server\n        if response == values.receive_token_invalid:\n            try:\n                os.remove(\"token.pkl\")\n                print(values.client_invalid_token)\n            except:\n                traceback.print_exc()\n            return False\n        elif response == values.receive_token_valid: # TOKEN AUTHENTICATED\n            self.empty_socket()\n            self.s.send(values.client_received_message.encode())\n            return True\n\n    def save_token(self, token):\n        with open(\"token.pkl\", \"wb\") as file:\n            pickle.dump(token, file)  # SAVE TOKEN\n        file.close()\n\n    def handle_token(self):\n        try:\n            if os.path.isfile(\"token.pkl\"):  # TOKEN IS PRESENT\n                with open(\"token.pkl\", \"rb\") as file:\n                    token = pickle.load(file)\n                result = self.send_token(token)\n            else:  # NEW CLIENT, REQUIRES TOKEN\n                result = self.request_token()\n            return result\n        except:\n            traceback.print_exc()\n        return False\n\n    def connect(self):\n        result = True\n        for i in range(MAX_RETRY):\n            try:\n                self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n                self.s.bind(('',self.config[\"CLIENT_PORT\"]))\n                self.s.connect((self.HOST, self.PORT))\n                result = self.handle_token()\n                if result:\n                    print(values.client_connection_success)\n                    break\n                else:\n                    print(values.client_connection_fail_retry)\n            except Exception as e:\n                print(\"\\nEXCEPTION IN CONNECT: \", e)\n                traceback.print_exc()\n                result = False\n        if result == False:\n            print(values.client_connection_fail)\n\n        return result\n\n    def get(self):\n        result = False\n        for i in range(MAX_RETRY):\n            try:\n                # OK RESPONSE\n                data = self.s.recv(BUFFER_SIZE).decode()\n                print(\"\\nRESPONSE FROM SERVER: \", data)\n                if data == values.no_update_available:\n                    print(values.no_update_available)\n                    self.empty_socket()\n                    self.s.send(values.client_received_message.encode()) # SEND OK TO SERVER\n                    return False\n\n                else: # TODO: maybe change to a proper condition\n                    # UPDATE AVAILABLE\n                    try:\n                        filename, filesize = data.split(SEPARATOR)\n                        filesize = int(filesize)\n                        path = os.path.join(self.model_folder, self.model_name)\n\n                        self.empty_socket()\n                        self.s.sendall(values.metadata_valid.encode())\n                        result = True\n                    except: # INVALID METADATA\n                        self.empty_socket()\n                        self.s.sendall(values.metadata_invalid.encode()) \n                        self.s.close()\n                        result = False\n\n                    if result == True:\n                        print(\"\\nRECEIVED INFO: \", filename, \" SIZE: \", filesize)\n                        # RECEIVE FILE\n                        progress = tqdm.tqdm(range(filesize), desc=\"RECEIVING \" + filename,position=0,leave=True)\n                        p=0\n                        with open(path, 'wb') as file:\n                            while p < filesize:\n                                data = self.s.recv(BUFFER_SIZE)\n                                if not data:\n                                    break\n                                file.write(data)\n                                p += len(data)\n                                progress.update(len(data))\n                            \n                        if p == filesize: # SEND OK \n                            self.empty_socket()\n                            self.s.sendall(values.client_receive_model_success.encode())\n                            print(values.client_receive_model_success)\n                        else:\n                            result = False\n\n            except Exception as e:\n                print(\"\\nEXCEPTION IN get: \", e)\n                traceback.print_exc()\n                result = False\n            self.s.close()\n            if result == True:\n                break\n            else:\n                print(values.get_failed_retry)\n                if i+1 != MAX_RETRY:\n                    sleep(10)\n                    self.connect()\n\n        if result == False:\n            print(values.get_failed)\n        return result\n\n    def send(self):  # SEND UPDATED MODEL\n        result = False\n        for i in range(MAX_RETRY):\n            try:\n                filename = self.model_name\n                path = os.path.join(self.model_folder, self.model_name)\n                filesize = os.path.getsize(path)\n                print('CLIENT MODEL PATH: ', path)\n\n                self.empty_socket()\n                self.s.send(f\"{filename}{SEPARATOR}{filesize}\".encode())\n\n                response = self.s.recv(TOKEN_BUFFER_SIZE).decode() # GET METADATA RESPONSE FROM SERVER\n\n                if response == values.metadata_valid: # VALID METADATA\n                    progress = tqdm.tqdm(range(filesize), desc=\"SENDING UPDATED MODEL TO SERVER\",position=0,leave=True)\n                    p = 0\n                    self.empty_socket()\n                    with open(path, 'rb') as file:\n                        while True:\n                            read = file.read(BUFFER_SIZE)\n                            if not read:\n                                break\n                            self.s.sendall(read)\n                            p += len(read)\n                            progress.update(len(read))\n\n                    if (\n                        p != filesize\n                    ):  # ERROR IN SEND, FULL FILE HAS NOT BEEN SENT, RETRY\n                        result = False\n                        print(values.send_model_fail)\n                    \n                    else:\n                        result = True\n                        print('\\nSENT MODEL TO SERVER')\n                        \n                \n                elif response == values.metadata_invalid:\n                    print(values.metadata_invalid)\n                    result = False\n                else:  # INVALID RESPONSE FROM CLIENT\n                    print(values.client_invalid_response)\n                    result = False\n\n                if result == True:  # RECEIVE OK\n                    response = self.s.recv(TOKEN_BUFFER_SIZE).decode()\n                    print('\\nRESPONSE: ', response)\n                \n\n            except Exception as e:\n                print(\"\\nEXCEPTION in send: \", e)\n                traceback.print_exc()\n                # self.s.close()\n                result = False\n\n            # self.s.close()\n            if result == True:\n                break\n            else:\n                print(values.send_failed_retry)\n                if i+1 != MAX_RETRY:\n                    sleep(10)\n                    self.connect()\n\n        if result == False:\n            print(values.send_failed)\n        return result\n","sub_path":"client/Client.py","file_name":"Client.py","file_ext":"py","file_size_in_byte":10403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"542919550","text":"import komand\nfrom .schema import SubmitDescriptorsInput, SubmitDescriptorsOutput\n# Custom imports below\nimport requests\nfrom urllib.parse import urlencode\n\n\nclass SubmitDescriptors(komand.Action):\n    def __init__(self):\n        super(self.__class__, self).__init__(\n                name='submit_descriptors',\n                description='Submit data to Facebook\\'s graph API.',\n                input=SubmitDescriptorsInput(),\n                output=SubmitDescriptorsOutput())\n\n    def run(self, params={}):\n        query_params = {}\n        # filter through key:values to get non-empty pairs\n        for param in params:\n            if params.get(param):\n                query_params[param] = params[param]\n        self.logger.info(query_params)\n        payload = urlencode(query_params)\n        try:\n            auth_url = \"https://graph.facebook.com/v2.8/threat_descriptors?access_token={}|{}\".format(self.connection.appid,\n                                                                                                       self.connection.appsecret)\n            response = requests.post(url=auth_url, data=payload)\n            data = response.json()\n            return {\"success\": data[\"success\"], \"id\": str(data[\"id\"])}\n        except:\n            self.logger.error(\"An error has occurred while retrieving submit descriptor\")\n            raise\n\n    def test(self):\n        try:\n            app_id = self.connection.appid\n            app_secret = self.connection.appsecret\n            type_ = 'CMD_LINE'\n            text = \"''\"\n            query_params = urlencode({\n                'access_token': app_id + '|' + app_secret,\n                'type': type_,\n                'text': text\n            })\n            url = \"https://graph.facebook.com/v2.8/threat_descriptors?\"\n            response = requests.get(url + query_params)\n            self.logger.info(\"Status Code: \" + str(response.status_code))\n            self.logger.info(\"Response: \" + response.content.decode())\n            return {\"status\": str(response.status_code)}\n        except:\n            self.logger.error(\"An error has occurred while retrieving submit descriptor\")\n            raise\n","sub_path":"facebook_threat_exchange/komand_facebook_threat_exchange/actions/submit_descriptors/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":2163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"125623242","text":"import scrapy\nfrom ..items import JavaDescriptionItem\n\n\nclass DocsSpider(scrapy.Spider):\n    # scrapy crawl docs -o docs.json\n    name = \"docs\"\n\n    # start urls\n    start_urls = [\"https://docs.oracle.com/javase/8/docs/api/\"]\n\n    # parse\n    def parse(self, response):\n        # get the all classes frame url\n        all_classes_frame_url = response.xpath(\"/html/frameset/frameset/frame[position()=2]/@src\").get()\n        if all_classes_frame_url:\n            all_classes_frame_url = response.urljoin(all_classes_frame_url)\n            yield scrapy.Request(all_classes_frame_url, callback=self.parse_class_url)\n\n    # parse all classes' url\n    def parse_class_url(self, response):\n        for class_url in response.xpath(\"/html/body/div//a/@href\"):\n            class_url = response.urljoin(class_url.get())\n            yield scrapy.Request(class_url, callback=self.parse_explanation)\n\n    # extract explanation from class html\n    def parse_explanation(self, response):\n        # use the string() method of xpath to extract all the description of this class\n        keyword = response.xpath(\"string(/html/body/div[4]/div[1]/ul/li/pre/span)\")\n        description = response.xpath(\"string(/html/body/div[4]/div[1]/ul/li/div)\")\n        if keyword.get() and description.get():\n            item = JavaDescriptionItem()\n            item['keyword'] = keyword.get()\n            item['description'] = description.get()\n            yield item\n","sub_path":"collector/collector/spiders/docs.py","file_name":"docs.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"100976109","text":"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\r\nfrom sys import argv\r\nimport requests\r\nimport math\r\nfrom bs4 import BeautifulSoup\r\nimport os\r\nimport re\r\nfrom time import sleep\r\nimport random\r\n\r\n\r\n# scrape process flow:\r\n#1.search the job w/o location\r\n#2.get the location li: location+job count + job_location_url\r\n#3.jump to job_location_url\r\n#4.for each div class \"row result\" find href\r\n#    results_col ->3 blocks -> row result/ organicJob -> jobtitle & company\r\n#    for each div row result/ organicJob find the job_detail_url:\r\n#        collect all li values\r\n#        handle expections\r\n#    flip page job_location_url + &start=10x(page_no - 1)\r\n\r\n\r\n# initialize the scrape\r\n# input: job_keyword\r\n# output: a set of top locations for the job w/. url by location\r\ndef job_search_setup(job_keyword):\r\n\r\n    print('job title:' + job_keyword.replace(' ', '+'))\r\n\r\n    job_url = \"https://www.indeed.com/jobs?q={0}&l=\".format(job_keyword.replace(' ', '+'))\r\n    base_txt = \"https://www.indeed.com\"\r\n    location_set = list()\r\n    results_page = BeautifulSoup(requests.get(job_url).content, 'lxml')\r\n#    the keyword for top location block is onmousedown = \"rbptk('rb', 'loc', ....)\r\n    loc_tags_all = results_page.find_all('li', onmousedown = re.compile(\"^rbptk\\('rb', 'loc', \") )\r\n\r\n    for tag in loc_tags_all:\r\n#        create a list with 3 elements to store one location entry\r\n        location_info = list()\r\n#        use regular expression to remove comma and brackets\r\n        p = re.compile('\\(|\\)|,')\r\n        location_info.append(str(tag.a.contents[0]))\r\n        location_info.append(float(p.sub('', tag.contents[-1].strip())))\r\n        location_info.append( base_txt + tag.a.get('href'))\r\n        location_set.append(location_info)\r\n    return location_set\r\n\r\n\r\n# start search by location\r\ndef get_result_by_location(location_set):\r\n# this is a rough estimate of number of job posting to scrape\r\n# as the number will always be a multiplier of ten in each location\r\n    print('total number of job to scrape:', int(sum([loc[1] for loc in location_set]) * scope))\r\n    \r\n    all_jobs_in_keywords = list()\r\n    for loc in location_set:\r\n#        make_folder(base_directory + '\\\\' + loc[0] )\r\n        print('current location:', loc[0], 'with job count:', str(loc[1]))\r\n#        use extend method to keep the flatten list structure\r\n#        number of rows will be number of job postings\r\n#        last element of each row [:][-1] is a list of job requirements \r\n        all_jobs_in_keywords.extend(get_job_detail(*loc))\r\n        \r\n# add an element of job keyword for each record\r\n    for entry in all_jobs_in_keywords:\r\n        entry.insert(0, job_keyword)\r\n    \r\n    return all_jobs_in_keywords\r\n\r\ndef get_job_detail(location_txt, job_quantity, url):\r\n#    location_txt, job_quantity, url = loc\r\n    scrape_stop_count = math.ceil(job_quantity * scope/10)\r\n    all_jobs_in_location = list()\r\n#    search job start with page = 1\r\n    for i in range(scrape_stop_count):\r\n        url_suffix = '&start={0}'.format(i*10)\r\n        print('current page:' + url_suffix)\r\n        \r\n#        get all job posting from the page and append to the list\r\n        all_jobs_in_location.extend(get_all_jobs_from_page(url+url_suffix))\r\n#    add one element of location to the records\r\n    for entry in all_jobs_in_location:\r\n        entry.insert(0, location_txt)\r\n     \r\n    return all_jobs_in_location\r\n\r\ndef get_all_jobs_from_page(url):\r\n    results_page = BeautifulSoup(requests.get(url).content, 'lxml')\r\n    #\"class_\":\"  row  result\", \r\n    loc_tags_all = results_page.find_all( 'div', {\"data-tn-component\":\"organicJob\"})\r\n    job_posting = list()\r\n    all_job_in_page = list()\r\n    for loc_tag in loc_tags_all:\r\n        print(loc_tag.h2.a.get('title'), loc_tag.span.text.strip())\r\n#        print(loc_tag.h2.a.get('href'))\r\n        job_posting = list()\r\n        job_posting.append(loc_tag.h2.a.get('title'))\r\n        job_posting.append(loc_tag.span.text.strip())\r\n#        sleep(random.random())\r\n#        retrive the job details as list and save the list as last element of job posting list\r\n        job_posting.append(retrieve_posting_detail_page(loc_tag.h2.a.get('href')))\r\n        \r\n        all_job_in_page.append(job_posting)\r\n    return all_job_in_page\r\n    \r\ndef retrieve_posting_detail_page(url):\r\n#    url = loc_tag.h2.a.get('href')\r\n    base_txt = \"https://www.indeed.com\"\r\n    li_list = list()\r\n    try:\r\n#        add timeout otherwise some website will keep the program hanging forever\r\n        posting_page = BeautifulSoup(requests.get(base_txt + url, timeout=5).content, 'lxml')\r\n        li_set = posting_page.find_all('li')\r\n#        make sure to not add blanks and li with specific type of fathers\r\n        for x in li_set:\r\n            if bool(x.text.strip()) & common_exclusion(x):\r\n                li_list.append(x.text.strip())\r\n#        li_list = [x.text.strip() for x in li_set]\r\n    except Exception as e:\r\n        print('failed to load job posting page:', str(e) )\r\n    return li_list\r\n\r\n# specifically call out some type of ui \r\n# for example, the embeded linkedin form comes with lot of noisy bullet points.\r\ndef common_exclusion(x):\r\n    if x.parent.parent.get('id') == 'eeoc_fields':\r\n        return False\r\n    else:\r\n        return True\r\n\r\n# create folder utility\r\ndef make_folder(directory):\r\n    if not os.path.exists(directory):\r\n        os.makedirs(directory)\r\n\r\n# save the final list(all_jobs_in_keyword) to individual files\r\ndef save_to_txt(scrape_result):\r\n    base_directory = r'.\\output'\r\n    make_folder(base_directory+ '\\\\' + job_keyword)\r\n    file_name_base = base_directory + '\\\\' + job_keyword+ '\\\\' + job_keyword\r\n    i = 0\r\n    for entry in scrape_result:\r\n        i += 1\r\n        thefile = open('{0}.{1}'.format(file_name_base, i), 'w', encoding='utf-8')\r\n        for item in entry:\r\n            if isinstance(item, str):\r\n                thefile.write(\"%s\\n\" % item)\r\n            else:\r\n                if isinstance(item, list):\r\n                    for li in item:\r\n                        thefile.write(\"%s\\n\" % li)\r\n        thefile.close()\r\n        \r\n#class job_scrapper:\r\n#    \r\n#    def __init__(self, job_keyword, **kwargs):\r\n#        self.job_keyword = job_keyword\r\n#        # pct of jobs need to be scraped\r\n#        self.scope = kwargs.get('scope', 0.05)\r\n#    \r\n#    # collect the job location statistics for setting up formal job searches\r\n#    def job_search_setup(self):\r\n        \r\nif __name__ == '__main__':\r\n\r\n    base_directory = r'.\\output'\r\n    job_keyword = argv[1]\r\n#    job_keyword = \"Project Manager\"\r\n    job_url = \"https://www.indeed.com/jobs?q={0}&l=\".format(job_keyword.replace(' ', '+'))\r\n    scope = 0.05\r\n    \r\n    location_set = job_search_setup(job_keyword)\r\n    scrape_result = get_result_by_location(location_set)\r\n    save_to_txt(scrape_result)","sub_path":"indeed-scrapping.py","file_name":"indeed-scrapping.py","file_ext":"py","file_size_in_byte":6822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"343267819","text":"def main():\n    N = int(input())\n    mousho, manatsu, natsu, nettai, huyu, mahuyu = 0, 0, 0, 0, 0, 0\n    for _ in range(N):\n        max_t, min_t = map(float, input().split())\n        if max_t >= 35.0:\n            mousho += 1\n        elif max_t >= 30.0:\n            manatsu += 1\n        elif max_t >= 25.0:\n            natsu += 1\n\n        if min_t >= 25.0:\n            nettai += 1\n\n        if min_t < 0 and max_t >= 0:\n            huyu += 1\n\n        if max_t < 0:\n            mahuyu += 1\n\n    print(mousho, manatsu, natsu, nettai, huyu, mahuyu)\n\n\nmain()\n","sub_path":"arc/arc15b.py","file_name":"arc15b.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"450039008","text":"group_live = ['Group live', 'Group-live', 'Group-Live', 'Live Presentation', 'Conferences', 'Group Lve', 'Virtual group live', 'GroupLive', 'Group/Live', 'Live', 'Live seminar']\n\ngroup_internet_based = [ 'Group Internet based', 'Group Internet-Based', 'Group-Intemet Based', 'Group - Internet-Based', 'Group Internet', 'Webcast', 'Group Intemet Based', 'Group - Internet Based', 'Internet Based', 'Group [nternet- Based', 'Group Program', 'Group Study', 'Internet-Based', 'Group ‘rternet basea', 'Group-Internet', 'Webinar', 'Group - Live', 'Virtual Instructor-Led', 'GroupInternet', 'Group intemet-based','Intermet', 'Group-Internel Based', 'Group-Interet Based', 'intemet', 'internel', 'Interet', 'Group-Infernel Based', 'Infernel', 'Groupod', 'Intemet-Bas', 'Internct-Based']\n\nqas_self_study = [ 'Internet Based Self-Study Program.', 'Interactive Self Study','QAS Self study', 'Self-Study', 'Self Study', 'QAS Self Study.', 'self', 'QAS Study']\n\nblendend_learning = ['Blended learning']\n\nnano_learning = ['Nano learning']\n\n\ndef map_with_given_list(delivery_method):\n    print(f\"MAPP_DELIVERY_METHOD---->{delivery_method}\")\n    for gl in group_live:\n        if gl.lower() in delivery_method.lower():\n            return 'Group live'\n    for qss in qas_self_study:\n        if qss.lower() in delivery_method.lower():\n            return 'QAS Self study'\n    for gib in group_internet_based:\n        if gib.lower() in delivery_method.lower():\n            return 'Group Internet based'\n    for bl in blendend_learning:\n        if bl.lower() in delivery_method.lower():\n            return 'Blended learning'\n    for nl in nano_learning:\n        if nl.lower() in delivery_method.lower():\n            return 'Nano learning'\n    return \"\"\n","sub_path":"test/lib/delivery_format_mapping.py","file_name":"delivery_format_mapping.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"425058873","text":"import sqlite3 as lite\nimport sys\nimport os\ncars = (\n    (1, 'NhungPro', 52642),\n    (2, 'Mercedes', 57127),\n    (3, 'Skoda', 9000),\n    (4, 'Volvo', 29000),\n    (5, 'Bentley', 350000),\n    (6, 'Hummer', 41400),\n    (7, 'Volkswagen', 21600)\n)\n \n \npath = os.path.dirname(__file__) + \"test.db\"\ncon = lite.connect(path)\n \nwith con:\n     \n    cur = con.cursor()    \n# Chúng ta xóa bảng và tạo lại bảng Cars.\n# chúng ta kiểm tra xem bảng Cars đã tồn tại chưa,\n    cur.execute(\"DROP TABLE IF EXISTS Cars\")\n# nếu rồi thì xóa bảng đó và tạo lại bảng mới.\n    cur.execute(\"CREATE TABLE Cars(Id INT, Name TEXT, Price INT)\")\n\n# Chúng ta insert 8 dòng dữ liệu vào bảng bằng một phương thức duy nhất là executemany(), \n# tham số đầu tiên là một câu lệnh SQL có tham số là các dấu ?, \n# tham số thứ 2 là một tuple chứa nhiều tuple khác là các dữ liệu cần truyền vào.\n    cur.executemany(\"INSERT INTO Cars VALUES(?, ?, ?)\", cars)\n\n\n# Phương thức executemany() tiện lợi hơn bằng cách thực thi nhiều câu lệnh cùng một lúc.\n","sub_path":"executemany.py","file_name":"executemany.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"40132063","text":"from django.db import models\nfrom mptt.models import MPTTModel, TreeForeignKey\nfrom userpage.models import Post\nfrom django.conf import settings\n\n\nclass Comment(MPTTModel):\n    post = models.ForeignKey(\n        Post, on_delete=models.CASCADE, related_name='comments')\n    parent = TreeForeignKey('self', on_delete=models.CASCADE,\n                            null=True, blank=True, related_name='children')\n    name = models.ForeignKey(\n        settings.AUTH_USER_MODEL, on_delete=models.CASCADE, related_name=\"user_commented\")\n    content = models.TextField()\n    publish = models.DateTimeField(auto_now_add=True)\n    status = models.BooleanField(default=True)\n\n    class MPTTMeta:\n        order_insertion_by = ['publish']\n\n    def __str__(self):\n        return f'Comment by {self.name}'\n","sub_path":"comment/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"299453515","text":"# Copyright (c) 2015. Mount Sinai School of Medicine\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import (\n    print_function,\n    division,\n    absolute_import,\n)\nfrom collections import namedtuple\n\nimport pandas as pd\nimport numpy as np\n\nfrom .common import normalize_allele_name\nfrom .amino_acid import amino_acid_letter_indices\n\nAlleleData = namedtuple(\"AlleleData\", \"X Y peptides ic50\")\n\n\ndef hotshot_encoding(peptides, peptide_length):\n    \"\"\"\n    Encode a set of equal length peptides as a binary matrix,\n    where each letter is transformed into a length 20 vector with a single\n    element that is 1 (and the others are 0).\n    \"\"\"\n    shape = (len(peptides), peptide_length, 20)\n    X = np.zeros(shape, dtype=bool)\n    for i, peptide in enumerate(peptides):\n        for j, amino_acid in enumerate(peptide):\n            k = amino_acid_letter_indices[amino_acid]\n            X[i, j, k] = 1\n    return X\n\n\ndef index_encoding(peptides, peptide_length):\n    \"\"\"\n    Encode a set of equal length peptides as a vector of their\n    amino acid indices.\n    \"\"\"\n    X = np.zeros((len(peptides), peptide_length), dtype=int)\n    for i, peptide in enumerate(peptides):\n        for j, amino_acid in enumerate(peptide):\n            X[i, j] = amino_acid_letter_indices[amino_acid]\n    return X\n\n\ndef indices_to_hotshot_encoding(X, n_indices=None, first_index_value=0):\n    \"\"\"\n    Given an (n_samples, peptide_length) integer matrix\n    convert it to a binary encoding of shape:\n        (n_samples, peptide_length * n_indices)\n    \"\"\"\n    (n_samples, peptide_length) = X.shape\n    if not n_indices:\n        n_indices = X.max() - first_index_value + 1\n\n    X_binary = np.zeros((n_samples, peptide_length * n_indices), dtype=bool)\n    for i, row in enumerate(X):\n        for j, xij in enumerate(row):\n            X_binary[i, n_indices * j + xij - first_index_value] = 1\n    return X_binary.astype(float)\n\n\ndef _infer_csv_separator(filename):\n    \"\"\"\n    Determine if file is separated by comma, tab, or whitespace.\n    Default to whitespace if the others are not detected.\n\n    Returns (sep, delim_whitespace)\n    \"\"\"\n    for candidate in [\",\", \"\\t\"]:\n        with open(filename, \"r\") as f:\n            for line in f:\n                if line.startswith(\"#\"):\n                    continue\n                if candidate in line:\n                    return candidate, False\n    return None, True\n\n\ndef load_dataframe(\n        filename,\n        peptide_length=None,\n        max_ic50=50000.0,\n        sep=None,\n        species_column_name=\"species\",\n        allele_column_name=\"mhc\",\n        peptide_column_name=None,\n        peptide_length_column_name=\"peptide_length\",\n        ic50_column_name=\"meas\",\n        only_human=True):\n    \"\"\"\n    Load a dataframe of peptide-MHC affinity measurements\n\n    filename : str\n        TSV filename with columns:\n            - 'species'\n            - 'mhc'\n            - 'peptide_length'\n            - 'sequence'\n            - 'meas'\n\n     peptide_length : int, optional\n        Which length peptides to use (default=load all lengths)\n\n    max_ic50 : float\n        Treat IC50 scores above this value as all equally bad\n        (transform them to 0.0 in the regression output)\n\n    sep : str, optional\n        Separator in CSV file, default is to let Pandas infer\n\n    peptide_column_name : str, optional\n        Default behavior is to try  {\"sequence\", \"peptide\", \"peptide_sequence\"}\n\n    only_human : bool\n        Only load entries from human MHC alleles\n\n    Returns DataFrame augmented with extra columns:\n        - \"log_ic50\" : log(ic50) / log(max_ic50)\n        - \"regression_output\" : 1.0 - log(ic50)/log(max_ic50), limited to [0,1]\n    \"\"\"\n    if sep is None:\n        sep, delim_whitespace = _infer_csv_separator(filename)\n    else:\n        delim_whitespace = False\n    df = pd.read_csv(\n        filename,\n        sep=sep,\n        delim_whitespace=delim_whitespace,\n        engine=\"c\")\n    # hack: get around variability of column naming by checking if\n    # the peptide_column_name is actually present and if not try \"peptide\"\n    if peptide_column_name is None:\n        columns = set(df.keys())\n        for candidate in [\"sequence\", \"peptide\", \"peptide_sequence\"]:\n            if candidate in columns:\n                peptide_column_name = candidate\n                break\n        if peptide_column_name is None:\n            raise ValueError(\n                \"Couldn't find peptide column name, candidates: %s\" % (\n                    columns))\n    if only_human:\n        human_mask = df[species_column_name] == \"human\"\n        df = df[human_mask]\n    if peptide_length is not None:\n        length_mask = df[peptide_length_column_name] == peptide_length\n        df = df[length_mask]\n\n    df[allele_column_name] = df[allele_column_name].map(normalize_allele_name)\n    ic50 = np.array(df[ic50_column_name])\n    log_ic50 = np.log(ic50) / np.log(max_ic50)\n    df[\"log_ic50\"] = log_ic50\n    regression_output = 1.0 - log_ic50\n    # clamp to values between 0, 1\n    regression_output = np.maximum(regression_output, 0.0)\n    regression_output = np.minimum(regression_output, 1.0)\n    df[\"regression_output\"] = regression_output\n    return df\n\n\ndef load_allele_datasets(\n        filename,\n        peptide_length=9,\n        max_ic50=5000.0,\n        binary_encoding=True,\n        flatten_binary_encoding=True,\n        sep=None,\n        species_column_name=\"species\",\n        allele_column_name=\"mhc\",\n        peptide_column_name=None,\n        peptide_length_column_name=\"peptide_length\",\n        ic50_column_name=\"meas\",\n        only_human=True):\n    \"\"\"\n    Loads an IEDB dataset, extracts \"hot-shot\" encoding of fixed length peptides\n    and log-transforms the IC50 measurement. Returns dictionary mapping allele\n    names to AlleleData objects (containing fields X, Y, ic50)\n\n    Parameters\n    ----------\n    filename : str\n        TSV filename with columns:\n            - 'species'\n            - 'mhc'\n            - 'peptide_length'\n            - 'sequence'\n            - 'meas'\n\n    peptide_length : int\n        Which length peptides to use (default=9)\n\n    max_ic50 : float\n        Treat IC50 scores above this value as all equally bad\n        (transform them to 0.0 in the rescaled output)\n\n    binary_encoding : bool\n        Encode amino acids of each peptide as indices or binary vectors\n\n    flatten_features : bool\n        If False, returns a (n_samples, peptide_length, 20) matrix, otherwise\n        returns the 2D flattened version of the same data.\n\n    sep : str, optional\n        Separator in CSV file, default is to let Pandas infer\n\n    peptide_column_name : str, optional\n        Default behavior is to try {\"sequence\", \"peptide\", \"peptide_sequence\"}\n\n    only_human : bool\n        Only load entries from human MHC alleles\n    \"\"\"\n    df = load_dataframe(\n        filename=filename,\n        max_ic50=max_ic50,\n        sep=sep,\n        peptide_length=peptide_length,\n        species_column_name=species_column_name,\n        allele_column_name=allele_column_name,\n        peptide_column_name=peptide_column_name,\n        peptide_length_column_name=peptide_length_column_name,\n        ic50_column_name=ic50_column_name,\n        only_human=only_human)\n\n    allele_groups = {}\n    for allele, group in df.groupby(allele_column_name):\n        ic50 = np.array(group[ic50_column_name])\n        Y = np.array(group[\"regression_output\"])\n        peptides = list(group[peptide_column_name])\n        if binary_encoding:\n            X = hotshot_encoding(peptides, peptide_length=peptide_length)\n            if flatten_binary_encoding:\n                # collapse 3D input into 2D matrix\n                X = X.reshape((X.shape[0], peptide_length * 20))\n        else:\n            X = index_encoding(peptides, peptide_length=peptide_length)\n        assert allele not in allele_groups, \\\n            \"Duplicate datasets for %s\" % allele\n        allele_groups[allele] = AlleleData(\n            X=X,\n            Y=Y,\n            ic50=ic50,\n            peptides=peptides)\n    return allele_groups\n","sub_path":"mhcflurry/data_helpers.py","file_name":"data_helpers.py","file_ext":"py","file_size_in_byte":8535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"589191127","text":"# coding:utf-8\n\nimport datetime\nfrom flask import Blueprint, render_template, request, jsonify\nfrom flask_security import login_required\nimport sqlalchemy as sqla\n\nfrom ..service import productOrderService, productService\nfrom ..model import ProductOrder, ProductOrderItem, Product\n\nbp = Blueprint(\"productOrders\", __name__, url_prefix=\"/productorders\")\n\n\n@bp.route(\"/\", methods=[\"GET\"])\n@login_required\ndef mgr():\n    products = productService.all()\n    return render_template(\"orderMgr.html\", products=products)\n\n\n@bp.route(\"/create\", methods=[\"GET\"])\n@login_required\ndef form():\n    return render_template(\"orderUpdate.html\")\n\n\n@bp.route(\"/create\", methods=[\"POST\"])\n@login_required\ndef create_order():\n    productOrderService.add_product_order(**request.form.to_dict())\n    return jsonify(data=dict(success=True))\n\n\n@bp.route(\"/<int:product_order_id>/set-status\", methods=[\"POST\"])\n@login_required\ndef set_order_status(product_order_id):\n    status = int(request.form.get(\"status\", \"1\"))\n    memo = request.form.get(\"memo\")\n    productOrderService.set_product_order_status(product_order_id, status, memo)\n    return jsonify(data=dict(success=True))\n\n\n@bp.route(\"/<int:product_order_id>/add\", methods=[\"POST\"])\n@login_required\ndef add_order_item(product_order_id):\n    productOrderService.add_product_order_item(product_order_id=product_order_id, **request.form.to_dict())\n    return jsonify(data=dict(success=True))\n\n\n@bp.route(\"/<int:product_order_id>/remove/<int:item_id>\", methods=[\"POST\"])\n@login_required\ndef remove_order_item(product_order_id, item_id):\n    productOrderService.remove_product_order_item(product_order_id, item_id)\n    return jsonify(data=dict(success=True))\n\n\n@bp.route(\"/<int:product_order_id>/items\", methods=[\"GET\"])\n@login_required\ndef list_order_items(product_order_id):\n    items = productOrderService.get_product_order_items(product_order_id)\n    if items:\n        items = [item.asdict(only=ProductOrderItem.__dictfields__) for item in items]\n    return jsonify(data=dict(success=True, items=items))\n\n\n@bp.route(\"/<int:product_order_id>/<int:item_id>/add_outstock\", methods=[\"POST\"])\n@login_required\ndef order_item_add_outstock(product_order_id, item_id):\n    date_outstock = datetime.datetime.strptime(request.form.get(\"date_outstock\"), '%Y-%m-%d')\n    serial_no_list = request.form.get(\"serial_no\").replace(u\"，\", \",\").split(\",\")\n    invalid_serial_no, duplicate_serial_no = productOrderService. \\\n        add_product_order_item_outstock(product_order_id, item_id, date_outstock, serial_no_list)\n    if invalid_serial_no or duplicate_serial_no:\n        data = dict(success=False, invalid_serial_no=invalid_serial_no, duplicate_serial_no=duplicate_serial_no)\n    else:\n        data = dict(success=True)\n    return jsonify(data=data)\n\n\n@bp.route(\"/<int:product_order_id>/<int:item_id>/remove_outstock\", methods=[\"POST\"])\n@login_required\ndef order_item_remove_outstock(product_order_id, item_id):\n    serial_no = request.form.get('serial_no')\n    productOrderService.remove_product_order_item_outstock(product_order_id, item_id, serial_no)\n    return jsonify(data=dict(success=True))\n\n\n@bp.route(\"/list\", methods=[\"GET\"])\n@login_required\ndef data():\n    limit = int(request.args.get(\"iDisplayLength\", \"10\"))\n    offset = int(request.args.get(\"iDisplayStart\", \"0\"))\n    sEcho = request.args.get(\"sEcho\")\n    content = request.args.get('content')\n    if content:\n        content = '%' + content + '%'\n        filters = [sqla.or_(ProductOrder.product.has(Product.name.like(content)), ProductOrder.order_no.like(content))]\n    else:\n        filters = []\n    count, data = productOrderService.paginate(filters=filters, offset=offset, limit=limit)\n    if data:\n        data = [product_order.asdict(only=ProductOrder.__dictfields__) for product_order in data]\n    return jsonify(data=dict(success=True, sEcho=sEcho, iTotalRecords=count, iTotalDisplayRecords=count, aaData=data))\n\n\n","sub_path":"program/frontend/product_orders.py","file_name":"product_orders.py","file_ext":"py","file_size_in_byte":3908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"33218651","text":"from world.utils import printer\n\n\nclass Monster(object):\n\n    def __init__(self, name, level, intro_message, body_message,\n                 defeated_message, attack_message, evaluate_function):\n        self.name = name\n        self.level = level\n        self.intro_message = intro_message\n        self.body_message = body_message\n        self.defeated_message = defeated_message\n        self.attack_message = attack_message\n        self.evaluate_function = evaluate_function\n\n    def introduction(self):\n        printer(self.intro_message)\n        printer(self.body_message)\n\n    def attack(self):\n        printer(self.attack_message)\n        printer('You received {0} damage!'.format(self.level))\n\n    def defeat(self):\n        printer(self.defeated_message)\n        printer('You defeated {}!'.format(self.name))\n\n    def evaluate(self, ans):\n        try:\n            return self.evaluate_function(ans)\n        except:\n            return False\n\n    def __eq__(self, monster):\n        return self.name == monster.name\n\n\n# Just place the evaluate functions of the monsters inside this function\n# so that the kids wouldn't be able to hack the answer\n# It would be advised that the monsters are in order of their level.\ndef create_the_monsters():\n    monsters = []\n\n    # -------------------------------------------\n    # ----------- BITTER WOMAN LVL. 1 -----------\n    # -------------------------------------------\n    name = 'Bitter Woman'\n    level = 1\n    intro = ('A wild Bitter Woman appeared!\\n'\n             'Bitter Woman: Walang forever!')\n    body = ('Provide a function that accepts a positive integer n, then '\n            'returns the string \\'#walangforever\\' (without quotes) n times '\n            'without being separated by a new line.\\n'\n            'For example:\\n'\n            '  your_function(3)\\n'\n            'should return:\\n'\n            '  #walangforever#walangforever#walangforever')\n    defeat = 'Bitter Woman died of loneliness.'\n    attack = 'Bitter Woman used flirt!'\n\n    def eval1(answer):\n        assert answer(10) == '#walangforever' * 10\n        assert answer(1) == '#walangforever'\n        assert answer(100) == '#walangforever' * 100\n        return True  # always remember to return True\n\n    monster1 = Monster(name, level, intro, body, defeat, attack, eval1)\n    monsters.append(monster1)\n\n    # -------------------------------------------------\n    # ------------ Hungry Mathematician LVL. 2 --------\n    # -------------------------------------------------\n    name = 'Hungry Mathematician'\n    level = 2\n    intro = ('A Hungry Mathematician appeared!\\n'\n             'Mathematician: I will eat your mother\\'s pi unless you solve '\n             'this problem!')\n    body = ('What is the sum of all even positive integers less than a given '\n            'positive integer n? Provide a function than accepts n and '\n            'returns the right answer.')\n    defeat = 'Mathematician died of hunger.'\n    attack = 'Mathematician used Number Theory!'\n\n    def eval2(answer):\n        def solution(n):\n            i = 2\n            s = 0\n            while i < n:\n                s += i\n                i += 2\n            return s\n\n        for number in [1, 100, 20]:\n            assert answer(number) == solution(number)\n        return True  # always remember to return True\n\n    monster2 = Monster(name, level, intro, body, defeat, attack, eval2)\n    monsters.append(monster2)\n\n    # ---------------------------------------------------------\n    # -------------- Indecisive Shoplifter LVL. 2 -------------\n    # ---------------------------------------------------------\n    name = 'Indecisive Shoplifter'\n    level = 2\n    intro = ('An Indecisive Shoplifter appeared!\\n'\n             'Indecisive Shoplifter: Should I steal this? or this? or that? '\n             'maybe all of them.')\n    body = ('A shoplifter would speak out her reaction aloud after seeing '\n            'the price of the dress she wanted to steal.\\n'\n            'If the price is 100 pesos or less, she would say, \"No one is '\n            'stealing this cheap sh*t!\".\\n'\n            'If the price is 500 pesos or less, but greater than 100 pesos, '\n            'she would say, \"This one is a class A replica!\".\\n'\n            'If the price is 1000 pesos or less, but greater than 500 pesos, '\n            'she would say, \"This is okay. Just going to fit it, not steal '\n            'it!\".\\n'\n            'If the price is greater than 1000 pesos, she would say, \"I have '\n            'to steal this!\"\\n'\n            'Provide a function that accepts the dress price, then returns '\n            'the appropriate reaction of the shoplifter (in string).')\n    defeat = 'Indecisive Shoplifter died of losing the dress she never owned.'\n    attack = 'Indecisive Shoplifter attacked with a purse she never owned.'\n\n    def eval3(answer):\n        def solution(n):\n            if n <= 100:\n                return 'No one is stealing this cheap sh*t!'\n            elif n <= 500:\n                return 'This one is a class A replica!'\n            elif n <= 1000:\n                return 'This is okay. Just going to fit it, not steal it!'\n            else:\n                return 'I have to steal this!'\n\n        for n in [0, 100, 500, 1000, 1001]:\n            assert answer(n) == solution(n)\n        return True  # always remember to return True\n\n    monster3 = Monster(name, level, intro, body, defeat, attack, eval3)\n    monsters.append(monster3)\n\n    # -------------------------------------------------------\n    # -------------- Scumbag Taxi Driver LVL. 2 -------------\n    # -------------------------------------------------------\n    name = 'Scumbag Taxi Driver'\n    level = 2\n    intro = ('A Scumbag Taxi Driver appeared!\\n'\n             'Taxi Driver: Asa ka dong? (giggles)')\n    body = ('The total taxi ride cost is divided into 4 parts:\\n'\n            '1) An initial of P40\\n'\n            '2) P3.50 for at least every  0.5 km travelled.\\n'\n            '3) P3.50 for at least every 30 seconds stationary.\\n'\n            '4) If you are new to cebu, the driver asks for an additional of '\n            'P100\\n'\n            'Provide a function that accepts 3 parameters: kms travelled, '\n            'number of seconds the taxi is stationary, and a boolean '\n            'signifying if the passenger is new to cebu (True) or not '\n            '(False).\\n'\n            'For example:\\n'\n            '   your_function(3.6, 60, True)\\n'\n            'should return:\\n'\n            '   171.5')\n    defeat = 'Taxi Driver died of being sued of illegal addition of fees.'\n    attack = 'Taxi Driver attacked with hit and run!'\n\n    def eval4(answer):\n        def solution(km, sec, new):\n            ans = 40 + (km // 0.5 * 3.5) + (sec // 30 * 3.5)\n            ans += (100 if new else 0)\n            return ans\n\n        for km, sec, new in [(3.6, 60, True), (5, 33, False)]:\n            assert answer(km, sec, new) == solution(km, sec, new)\n        return True  # always remember to return True\n\n    monster4 = Monster(name, level, intro, body, defeat, attack, eval4)\n    monsters.append(monster4)\n\n    # ---------------------------------------------------------\n    # --------- Cheating Presidential Candidate LVL. 3 --------\n    # ---------------------------------------------------------\n    name = 'Cheating Presidential Candidate'\n    level = 3\n    intro = ('A Cheating Presidential Candidate appeared!\\n'\n             'Cheating Presidential Candidate: Vote for me! Vote for me! '\n             '(throws 1000 peso bills to people)')\n    body = ('This cheating candidate stole (10%) from each of his opponents\\' '\n            'total votes by hacking the voting system. What\\'s bad about it '\n            'is that he used Python to do so. This person deserves no '\n            'respect, not because he gave free money to everyone, but '\n            'because he used Python as a tool for his evil deeds.\\n'\n            'Given a list of integers siginifying the votes of the '\n            'cheater\\'s opponents, return an array of integers signifying the '\n            'actual number of votes before they were altered.\\n'\n            'For example:\\n'\n            '   your_function([99, 9, 270, 111105])\\n'\n            'should return:\\n'\n            '   [110, 10, 300, 123450]')\n    defeat = ('Cheating Presidential Candidate died of assassination ordered '\n              'by another presidential candidate.')\n    attack = ('Cheating Presidential Candidate used lies, and money, and '\n              'more lies.')\n\n    def eval5(answer):\n        def solution(votes):\n            return [int(x/0.9) for x in votes]\n\n        cases = [[99, 10, 270, 111105], [0]]\n        for case in cases:\n            assert answer(case) == solution(case)\n\n        return True  # always remember to return True\n\n    monster5 = Monster(name, level, intro, body, defeat, attack, eval5)\n    monsters.append(monster5)\n\n    # ---------------------------------------------------------\n    # -------------- Super Addicted Gamer LVL. 3 --------------\n    # ---------------------------------------------------------\n    name = 'Super Addicted Gamer'\n    level = 3\n    intro = ('A Super Addicted Gamer appeared!\\n'\n             'Addicted Gamer: Stun oi! Bugo! Yawa!')\n    body = ('In a game called Dota2, every hero has at least 4 skills. '\n            'Some of them are burst damage while some are passive skills.\\n'\n            'Provide a function that returns the total burst damage when '\n            'given a dictionary composed of keys as their skills\\' names '\n            '(string) and values as the amount of damage the skill inflicts. '\n            'For passive skills, the value would not be the amount of damage, '\n            'but instead the string \\'passive\\'.\\n'\n            'For example:\\n'\n            '   your_function({\\'stun\\': 100, \\'2nd skill\\': \\'passive\\', '\n            '\\'skill that inflicts damage\\': 150, \\'ultimate skill\\': '\n            '10000})\\n'\n            'should return:\\n'\n            '   10250')\n    defeat = ('Addicted Gamer died of not enjoying life to the fullest.')\n    attack = ('Addicted Gamer used a speech about you being a stupid person '\n              'when you\\'re not good at gaming.')\n\n    def eval6(answer):\n        def solution(skills):\n            s = 0\n            for v in skills.values():\n                if type(v) == int:\n                    s += v\n            return s\n\n        cases = [\n            {'stun': 100, '2nd skill': 'passive',\n             'skill that inflicts damage': 150, 'ultimate skill': 10000},\n            {'skill': 'passive'},\n            {'a': 1, 'b': 1, 'c': 1, 'd': 1, 'e': 1}]\n\n        for case in cases:\n            assert answer(case) == solution(case)\n        return True  # always remember to return True\n\n    monster6 = Monster(name, level, intro, body, defeat, attack, eval6)\n    monsters.append(monster6)\n\n    # ---------------------------------------------------------\n    # -------------- Cat-calling Carpenter LVL. 3 -------------\n    # ---------------------------------------------------------\n    name = 'Cat-calling Carpenter'\n    level = 3\n    intro = ('A Cat-calling Carpenter appeared!\\n'\n             'Cat-calling Carpenter: Hi miga! Can i have your number miga? '\n             'Salig chicks kaayu, nagminaldita na ang akong miga. (grins)')\n    body = ('You have to show this guy that you deserve some respect. You\\'re '\n            'not a chick, you\\'re a Pythonista chick! You should show him a '\n            'good design for a fence. A fence is composed of pickets '\n            '(vertical bars) and rails (horizontal bars). For your design, '\n            'there are only two rails: for the top most part and the lowest '\n            'part of the fence. In between two pickets is a space. And the '\n            'function you need to provide returns a string composed of:\\n'\n            '\\'-\\' to signify part of a rail,\\n'\n            '\\'|\\' to signify one unit of a picket,\\n'\n            '\\' \\' to signify the space between two pickets, and\\n'\n            '\\'+\\' to signify the intersection between a rail and a picket\\n'\n            'You will be given 2 parameters: the height of the fence and the '\n            'number of pickets. Assume that the given height is at least 3 '\n            'units and the number of pickets is at least 3.'\n            'For example:\\n'\n            '   your_function(5, 7)\\n'\n            'should return:\\n'\n            '+-+-+-+-+-+-+\\n'\n            '| | | | | | |\\n'\n            '| | | | | | |\\n'\n            '| | | | | | |\\n'\n            '+-+-+-+-+-+-+')\n    defeat = ('Cat-calling Carpenter died of being stoned to death by '\n              'hardcore feminists.')\n    attack = ('Cat-calling Carpenter slapped your butt, then looks at you '\n              'while biting his lips.')\n\n    def eval7(answer):\n        def solution(height, pickets):\n            end = '+' + ('-+' * (pickets-1))\n            mid = '|' + (' |' * (pickets-1)) + '\\n'\n            return end + '\\n' + (mid * (height-2)) + end\n\n        for h, p in [(5, 7), (3, 3), (10, 10)]:\n            assert answer(h, p) == solution(h, p)\n\n        return True  # always remember to return True\n\n    monster7 = Monster(name, level, intro, body, defeat, attack, eval7)\n    monsters.append(monster7)\n\n    # ---------------------------------------------------------\n    # ----------------- Violent Drunk LVL. 4 ------------------\n    # ---------------------------------------------------------\n    name = 'Violent Drunk'\n    level = 4\n    intro = ('A Violent Drunk appeared!\\n'\n             'Violent Drunk: @ha% %h# f&ck a$# y(& l((k*ng a%')\n    body = ('Seriously, we need to understand this guy first. Fighting this '\n            'monster is like watching anime without subtitles. Here are the '\n            'equivalent letters to a symbol:\\n'\n            '! = q\\n'\n            '@ = w\\n'\n            '# = e\\n'\n            '$ = r\\n'\n            '% = t\\n'\n            '^ = y\\n'\n            '& = u\\n'\n            '* = i\\n'\n            '( = o\\n'\n            ') = p\\n'\n            'Provide a function that returns the deciphered message given a '\n            'string, which is what the drunkard said.\\n'\n            'For example:\\n'\n            '   your_function(\\'h* c&%*#\\')\\n'\n            'should return:\\n'\n            '   hi cutie')\n    defeat = 'Violent Drunk died of choking in his own vomit.'\n    attack = 'Violent Drunk used vomit on your face.'\n\n    def eval8(answer):\n        def solution(message):\n            d = {'!': 'q',\n                 '@': 'w',\n                 '#': 'e',\n                 '$': 'r',\n                 '%': 't',\n                 '^': 'y',\n                 '&': 'u',\n                 '*': 'i',\n                 '(': 'o',\n                 ')': 'p'}\n            deciphered = ''\n            for c in message:\n                if c in d.keys():\n                    deciphered += d[c]\n                else:\n                    deciphered += c\n            return deciphered\n\n        for m in ['@ha% %h# f&ck a$# y(& l((k*ng a%', 'h* c&%*#']:\n            assert answer(m) == solution(m)\n        return True  # always remember to return True\n\n    monster8 = Monster(name, level, intro, body, defeat, attack, eval8)\n    monsters.append(monster8)\n\n    # ---------------------------------------------------------\n    # ---------- Suspicious Drug Dealer LVL. 4 ----------------\n    # ---------------------------------------------------------\n    name = 'Suspicious Drug Dealer'\n    level = 4\n    intro = ('A Suspicious Drug Dealer appeared!\\n'\n             'Suspicious Drug Dealer: I tell you, this is a very, very '\n             'strong one! (shows packs of mik-mik)')\n    body = ('This Drug Dealer has three kinds of drugs. He gave you prices '\n            'for each kind but they are not the actual prices. The '\n            'first price is twice more expensive than its real price. The '\n            'second is 20 pesos more expensive than its real price. And the '\n            'last is cheaper than the real price by 1 peso.\\n'\n            'Provide a function that accepts a tuple of three integers (the '\n            'fake prices) and returns a tuple of integers of the real prices '\n            'in order from cheapest to most expensive.\\n'\n            'For example:\\n'\n            '   your_function((30, 30, 30))\\n'\n            'should return:\\n'\n            '   (10, 15, 31)')\n    defeat = 'Suspicious Drug Dealer died by the hands of Duterte.'\n    attack = ('Suspicious Drug Dealer injected you with something you don\\'t '\n              'want to know.')\n\n    def eval9(answer):\n        def solution(prices):\n            x, y, z = prices\n            a = [x / 2, y - 20, z + 1]\n            a.sort()\n            return tuple(a)\n\n        for t in [(30, 30, 30), (10, 24, 2), (10, 50, 10)]:\n            assert answer(t) == solution(t)\n        return True  # always remember to return True\n\n    monster9 = Monster(name, level, intro, body, defeat, attack, eval9)\n    monsters.append(monster9)\n\n    # ---------------------------------------------------------\n    # ------------- Emmanuel Lodovice LVL. 5 ------------------\n    # ---------------------------------------------------------\n    name = 'Emmanuel Lodovice'\n    level = 5\n    intro = ('Warning! Warning! Warning!\\n'\n             'Bost Fight!\\n'\n             'Are you sure you want to fight this last monster (y/n)?\\n'\n             'You are gonna fight him anyway.\\n'\n             'Emmanuel Lodovice appeared!\\n'\n             'Emmanuel Lodovice: ...')\n    body = ('Emmanuel Lodovice is one of the greatest legends of UP CEBU. '\n            'A monster is nothing compared to him. He is more like a GOD. '\n            'You shouldn\\'t disappoint him with your answers.\\n'\n            'You need to provide a function that returns a dictionary '\n            'composed of three keys:\\n'\n            'name => a function that accepts a firstname and a lastname, '\n            'then returns a string in the format of \\'lastname, firstname\\'\\n'\n            'organize => a function that accepts a list then returns a '\n            'dictionary with elements indexes\\' as keys and the elements as '\n            'values.\\n'\n            'encode => a function that accepts a string of at least '\n            'length=1, then returns a tuple composed of:\\n'\n            '   length of the string,\\n'\n            '   last character of the string,\\n'\n            '   the string without the last character\\n'\n            '   and a list of all its characters excluding whitespaces.')\n    defeat = ('Emmanuel Lodovice: ...\\n'\n              'Emmanuel Lodovice walked away, hiding the smile from his '\n              'face.')\n    attack = ('Emmanuel Lodovice turned his back.')\n\n    def eval10(answer):\n        ans = answer()\n        assert ans['name']('Eman', 'Lodovice') == 'Lodovice, Eman'\n        assert (\n            ans['organize'](['Eman', 3.2, True]) ==\n            {0: 'Eman', 1: 3.2, 2: True})\n        s = 'baboy ka'\n        t = (8, 'a', 'baboy k', ['b', 'a', 'b', 'o', 'y', 'k', 'a'])\n        assert ans['encode'](s) == t\n        return True\n\n    monster10 = Monster(name, level, intro, body, defeat, attack, eval10)\n    monsters.append(monster10)\n\n    return monsters\n","sub_path":"monsters.py","file_name":"monsters.py","file_ext":"py","file_size_in_byte":19178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"362500536","text":"import tensorflow as tf\nimport numpy as np\nimport time\n\ngsize = 1000\ngsparse = 1\nG_TEST_MODE = 'sparse'\nG_TEST_MODE = 'matmul'\n\ngssize = str(gsize)\ngssparse = str(gsparse)\ngfilename = '../text_matrix/matrix' + '_' + gssize + '_' + gssparse + '.txt' \ngbckname = 'trained_' + gssize + '_' + gssparse\ngpbname = '../data_' + G_TEST_MODE + '/'\n\ngtry = 500\ngcheck = 50\n\ngfile = np.loadtxt(gfilename, delimiter=' ', dtype=np.float32)\n\nxd = gfile[:, 0:-1]\nyd = gfile[:, [-1]]\n\nx = tf.placeholder(tf.float32, shape=[None, gsize])\ny = tf.placeholder(tf.float32, shape=[None, 1])\n\n\nif G_TEST_MODE == 'matmul':\n\n\n\tw1 = tf.Variable(tf.random_normal([gsize, gsize]), name = 'weight')\n\tlayer1 = tf.matmul(x, w1)\n\tw2 = tf.Variable(tf.random_normal([gsize, gsize]), name='weight')\n\tlayer2 = tf.matmul(x, w2)\n\tw3 = tf.Variable(tf.random_normal([gsize, gsize]), name='weight')\n\tlayer3 = tf.matmul(x, w3)\n\tw4 = tf.Variable(tf.random_normal([gsize, gsize]), name='weight')\n\tlayer4 = tf.matmul(x, w4)\n\n\nelse :\n\tw1 = tf.Variable(tf.random_normal([gsize, gsize]), name='weight')\n\tlayer1 = tf.sparse_matmul(x, w1)\n\tw2 = tf.Variable(tf.random_normal([gsize, gsize]), name='weight')\n\tlayer2 = tf.sparse_matmul(x, w2)\n\tw3 = tf.Variable(tf.random_normal([gsize, gsize]), name='weight')\n\tlayer3 = tf.sparse_matmul(x, w3)\n\tw4 = tf.Variable(tf.random_normal([gsize, gsize]), name='weight')\n\tlayer4 = tf.sparse_matmul(x, w4)\n\nhy = layer1 + layer2 + layer3 + layer4\ncost = tf.reduce_mean(tf.square(hy - y))\n\n\nwith tf.Session() as sess:\n\tsess.run(tf.global_variables_initializer())\n\tbeforetime = time.time()\n\tcnt_print = 0.0\n\tcnt_time = 0.0\n\tfor step in range(gtry + 1):\n\t\tcost_val = sess.run([cost], feed_dict={x:xd, y:yd})\n\t\tif step > 0 and step % gcheck == 0:\n\t\t\taftertime = time.time()\n\t\t\tprint(step, \" time : \", aftertime-beforetime)\n\t\t\tcnt_time += (aftertime - beforetime)\n\t\t\tbeforetime = time.time()\n\t\t\tcnt_print += 1.0\n\n\tgdirname = gpbname + G_TEST_MODE + '_' + gbckname + '/' + G_TEST_MODE + '_' + gbckname\n\tprint(\"DIRECTORY : \", gdirname)\n\ttf.train.Saver().save(sess, gdirname + '.ckpt')\n\n\ttf.train.write_graph(sess.graph_def, \".\", gdirname + '.pb', as_text=False)\n\ttf.train.write_graph(sess.graph_def, \".\", gdirname + '.txt', as_text=True)\n\t\n\n\tprint(\"\\nEntire time : \", cnt_time, \"\\nAverage time : \", cnt_time / cnt_print)\n\n\n","sub_path":"hiai/source/kyh.py","file_name":"kyh.py","file_ext":"py","file_size_in_byte":2301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"590295341","text":"# -*- coding: utf-8 -*-\n\"\"\"\n    https://www.acmicpc.net/problem/2292\n    https://elrion018.tistory.com/18\n\"\"\"\nN = int(input())\n\nfirst = 1\nplus = 6\nroom = 1\n\nif N == 1:\n    print(1)\nelse:\n    while True:\n        first = first + plus\n        room += 1\n        if N <= first:\n            print(room)\n            break\n        plus += 6\n","sub_path":"2292.py","file_name":"2292.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"102315409","text":"from project.demoHasCycleUF import writeResults\nimport unionfind.quickUnion\nimport project.GraphGenerator as GraphGenerator\nimport graph.Graph_AdjacencyList\nimport random\n\n\n@writeResults\ndef hasCycleDFS(G, rand=False):\n    \"\"\"\n    Esegue una visita DFS nel grafo G a partire dal primo nodo, o da un nodo random, aseconda del parametro rand.\n    Mantiene bool di marcatura per ogni nodo tramite indicizzazione diretta su una lista, verifica se ci sono archi\n    all'indietro, ovvero da un nodo ad uno già visitato che non ne sia il padre. Se non ci sono archi all'indietro\n    il grafo è aciclico.\n    :param G: Graph.\n    :param rand: bool.\n    :return: bool.\n    \"\"\"\n    if rand:\n        rootId = random.choice(list(G.nodes.keys()))  # inizia la visita da un nodo random\n    else:\n        rootId = list(G.nodes.keys())[0]\n    visited = [False for _ in G.nodes]  # inizializza la lista di marcatura\n    return hasCycleDFSRecursive(G, rootId, visited, rootId)  # inizia la visita vera e propria\n\n\ndef hasCycleDFSRecursive(G, v, visited, fatherId):\n    visited[v] = True  # marca il nodo v come visitato\n    for adjNode in G.getAdj(v):\n        if not visited[adjNode]:\n            if hasCycleDFSRecursive(G, adjNode, visited, v):  # se la chiamata ricorsiva individua un ciclo\n                return True  # ritorna True\n        elif adjNode != fatherId:\n            return True\n\n    return False\n\n\nclass EdgesIterator:\n    def __init__(self, G):\n        self.n = -1\n        self.edges = G.getEdges()\n        self.lastIndex = len(self.edges) - 1\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        self.n += 1\n        if self.n > self.lastIndex:\n            raise StopIteration\n        return self.edges[self.n]\n\n\n@writeResults\ndef hasCycleUF(G):\n    \"\"\"\n    Controlla se il grafo ha un ciclo sfruttando la struttura dati Union-Find.\n    :param G: Graph.\n    :return: bool.\n    \"\"\"\n    uf = unionfind.quickUnion.QuickUnionBalanced()\n    for n in G.nodes.keys():\n        uf.makeSet(n)\n    marked = set()\n    for edge in EdgesIterator(G):\n        if (edge.head, edge.tail) in marked:\n            continue\n        else:\n            # aggiungi solo l'arco (edge.tail, edge.head) a marked, dato che (edge.head, edge.tail)\n            # non verrà più generato dall'iterator\n            marked.add((edge.tail, edge.head))\n        if uf.find(uf.nodes[edge.head]) == uf.find(uf.nodes[edge.tail]):\n            return True\n\n        else:\n            uf.union(uf.findRoot(uf.nodes[edge.head]), uf.findRoot(uf.nodes[edge.tail]))\n\n    return False\n\n\ndef repeatedTest(func, typeOfGraph, maxN, steps, *otherParameters):\n    # per semplicita' si assuma maxN % steps = 0\n    assert typeOfGraph in range(4)\n    file = open(\"{}.txt\".format(func.__name__), \"a\")\n\n    delta = int(maxN / steps)\n\n    if typeOfGraph == 0:\n        file.write(\"Acyclic:\\n\")\n    elif typeOfGraph == 1:\n        file.write(\"Cyclic:\\n\")\n    elif typeOfGraph == 2:\n        file.write(\"Random:\\n\")\n    else:\n        file.write(\"Complete:\\n\")\n\n    file.close()\n\n    temp = delta\n    while temp <= maxN:\n        G = graph.Graph_AdjacencyList.GraphAdjacencyList()\n        if typeOfGraph == 0:\n            GraphGenerator.generateAcyclicGraph(G, temp)\n        elif typeOfGraph == 1:\n            GraphGenerator.generateCyclicGraphNaive(G, temp)\n        elif typeOfGraph == 2:\n            GraphGenerator.generateRandGraph(G, temp)\n        else:\n            GraphGenerator.generateCompleteGraph(G, temp)\n\n        func(G, *otherParameters)\n        temp += delta\n\n    print(\"Done!\")\n\n\nif __name__ == \"__main__\":\n    for i in range(4):\n        if i == 3:\n            repeatedTest(hasCycleUF, i, 2000, 10)  # build a smaller graph if complete\n        else:\n            repeatedTest(hasCycleUF, i, 5000, 10)\n    for i in range(4):\n        if i == 1:\n            repeatedTest(hasCycleDFS, i, 5000, 10, True)  # select a random rootId if graph is cyclic\n        elif i == 3:\n            repeatedTest(hasCycleDFS, i, 2000, 10)  # build a smaller graph if complete\n        else:\n            repeatedTest(hasCycleDFS, i, 5000, 10)\n","sub_path":"project/demoHasCycle.py","file_name":"demoHasCycle.py","file_ext":"py","file_size_in_byte":4092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"4404417","text":"# Exercicio 1\n# Crie um algoritmo que receba, como entrada, o valor de três notas de um aluno -\n# com valor entre 0 e 10, e, em seguida, informe a média entre elas.\n\ndef media_notas(nota1, nota2, nota3):\n    media_notas = (nota1+nota2+nota3)/3\n    media_notas = round(media_notas,2)\n    return media_notas\n\nnota1 = float(input('informe a primeira nota do aluno: '))\nnota2 = float(input('informe a segunda nota do aluno: '))\nnota3 = float(input('informe a receira nota do aluno: '))\n\nprint(f'A media de notas do aluno e: {media_notas(nota1, nota2,nota3)}')\n\n","sub_path":"Lista 01/01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"27929987","text":"import backtrader as bt\nimport os\nfrom datetime import datetime, date, time\n\nclass MyStrategy(bt.Strategy):\n    params = dict(maperiod=5)\n\n    def log(self, txt, dt=None):\n        \"\"\" Logging function for this strategy\"\"\"\n        dt = dt or self.datas[0].datetime.date(0)\n        print('%s, %s' % (dt.isoformat(), txt))\n\n    def __init__(self):\n        # Keep a reference to the \"close\" line in the data[0] dataseries\n        self.dataclose = self.datas[0].close\n\n        # To keep track of pending orders\n        self.order = None\n\n        # Add a MovingAverageSimple indicator\n        self.sma = bt.ind.SMA(period=self.params.maperiod)\n\n    def next(self):\n        # Simply log the closing price of the series from the reference\n        if len(self) > 2 and self.data0.low < self.data[-1].low and \\\n            self.data[-1].close < self.data[-1].open and \\\n            self.data[-2].close > self.data[-2].open and not self.position:\n            self.sell()\n            self.order = self.sell()\n\n        if self.position and self.data[-1].close > self.data[-1].open and \\\n            self.data[-1].close > self.sma(5) and self.data[0].high > self.data[-1].high:\n            self.buy()\n            self.order = self.buy()\n\n    def notify_trade(self, trade):\n        if not trade.isclosed:\n            return\n\n        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n                 (trade.pnl, trade.pnlcomm))\n\n    def notify_order(self, order):\n        if order.status in [order.Submitted, order.Accepted]:\n            # Buy/Sell order submitted/accepted to/by broker - Nothing to do\n            return\n\n        # Check if an order has been completed\n        # Attention: broker could reject order if not enough cash\n        if order.status in [order.Completed]:\n            if order.isbuy():\n                self.log('BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n                    (order.executed.price, order.executed.value, order.executed.comm))\n            elif order.issell():\n                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n                    (order.executed.price, order.executed.value, order.executed.comm))\n\n        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n            self.log('Order Canceled/Margin/Rejected')\n\n        # Write down: no pending order\n        self.order = None\n\n\n\n\nif __name__ == '__main__':\n    cerebro = bt.Cerebro()\n\n    # Add a strategy\n    cerebro.addstrategy(MyStrategy)\n\n    modpath = os.path.abspath('d:\\ib\\data')\n    #datapath = os.path.join(modpath, 'hsi_uptoday.csv')\n    datapath = os.path.join(modpath, '1.txt')\n\n    # Create a Data Feed\n    data = bt.feeds.CSVData(dataname=datapath)\n    \"\"\"\n    data = bt.feeds.GenericCSVData(\n        dataname=datapath,\n\n        fromdate=datetime(2018, 7, 1),\n        todate=datetime(2018, 12, 31),\n\n        nullvalue=0.0,\n\n        dtformat=('%Y-%m-%d'),\n\n        datetime=0,\n        time=1,\n        open=2,\n        high=3,\n        low=4,\n\n        close=5,\n        volume=6,\n        openinterest=-1\n    )\"\"\"\n\n    # Add the Data Feed to Cerebro\n    cerebro.adddata(data)\n\n    # Set our desired cash start\n    cerebro.broker.setcash(100000.0)\n\n    # Add a FixedSize sizer according to the stake\n    cerebro.addsizer(bt.sizers.FixedSize, stake=10)\n\n    # Set the commission - 0.1% ... divide by 100 to remove the %\n    cerebro.broker.setcommission(commission=0.001)\n\n    cerebro.run()\n\n    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())\n\n","sub_path":"main/ta/strategies/S1.py","file_name":"S1.py","file_ext":"py","file_size_in_byte":3503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"460839123","text":"import random\n\nclass Perceptron:\n  def __init__(self):  \n    self.bias = 0.0\n    self.weights = []    \n  \n  def __str__(self):\n    return '[Perceptron, bias: {}, weight count: {}, weights: {}]'.format(\n      self.bias, len(self.weights), self.weights)\n    \n  # count: number of weights (not including bias)\n  def assign_random_weights(self, count, low=-1.0, high=1.0):\n    self.bias = random.uniform(low, high)\n    self.weights = []   \n    for i in range(count):\n      self.weights.append(random.uniform(low, high))\n    \n  # return: True or False\n  def get_output_bool(self, input):    \n    if len(input) != len(self.weights):\n      raise Exception(\"Different lengths of 'input' and 'weights'\")    \n    sum = self.bias\n    for i in range(len(self.weights)):\n      sum += self.weights[i] * input[i]\n    return sum > 0.0\n  \n  # return: 1.0 or -1.0\n  @staticmethod\n  def out_bool_to_float(out_bool):\n    if out_bool:\n      return 1.0\n    else:\n      return -1.0\n  \n  # return: 1.0 or -1.0\n  def get_output_float(self, input):\n    return self.out_bool_to_float(self.get_output_bool(input))\n    \n  def train(self, train_data, learning_rate, max_cycles):\n    i = 0\n    mods = 0\n    while i < max_cycles:\n      mods = self.train_cycle(train_data, learning_rate)\n      i += 1\n      if mods == 0:\n        break      \n    print('Train cycles:', i)\n    if mods == 0:\n      print('Train result: success')\n    else:\n      print('Train result: FAILURE')\n\n  # return: count of steps that modified weights\n  def train_cycle(self, train_data, learning_rate):\n    ret = 0\n    print('Train cycle begin for', self)\n    for td in train_data:      \n      perc_result_bool = self.get_output_bool(td.input)\n      if perc_result_bool == td.result_bool:\n        continue\n      \n      ret += 1\n      direction = self.out_bool_to_float(td.result_bool)\n      self.bias += learning_rate * direction\n      for i in range(len(self.weights)):\n        self.weights[i] += learning_rate * direction * td.input[i]\n      print('Weights modified:', self)\n    \n    print('Train cycle finished:', self)\n    print('Steps with modifications:', ret)\n    return ret    \n  \nclass TrainSample:  \n  def __init__(self, input, result_bool):\n    self.input = input\n    self.result_bool = result_bool\n  \n  \np = Perceptron()\np.assign_random_weights(2)\ntd = (TrainSample((5, 1), False), TrainSample((9, -1), False), TrainSample((5, -5), False),\n      TrainSample((-6, 7), True), TrainSample((-2, 4), True), TrainSample((6, 4), True))      \np.train(td, 0.1, 10)\n","sub_path":"perc1.py","file_name":"perc1.py","file_ext":"py","file_size_in_byte":2507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"255072391","text":"import itertools\r\nfrom sklearn.base import TransformerMixin\r\nfrom pandas import DataFrame\r\n\r\n\r\n\r\nclass PreprocessGridSearch:\r\n    \"\"\"\r\n    definition for steps:\r\n    [ [{\"Pclass\":None,\"Sex\":None,\"\"}] ]\r\n    \"\"\"\r\n    def __init__(self, train_data, test_data=None, steps=[]):\r\n        \"\"\"\r\n\r\n        \"\"\"\r\n        if (type(steps) is list) and ( len(steps)!=0):\r\n            for i in steps:\r\n                if not(type(i) is list):\r\n                    raise RuntimeError(\"steps muste be a non-empty list of list \")\r\n            self.steps = steps\r\n            self.train_data = train_data\r\n            self.test_data = test_data\r\n            self.preprocessing_steps=None\r\n        else:\r\n            raise RuntimeError(\"steps muste be a non-empty list of list \")\r\n\r\n    def fit(self):\r\n        self.preprocessing_steps = list(itertools.product(*self.steps))\r\n        # print(self.preprocessing_steps)\r\n        return self\r\n\r\n    def transform(self):\r\n        self.transformed_data = []\r\n        for step in self.preprocessing_steps:\r\n            t_data = self.train_data.copy()\r\n            t_test_data = self.test_data.copy() if isinstance(self.test_data,DataFrame) else self.test_data\r\n\r\n            preprocess_title = \"\"\r\n            test_preprocess_title = \"\"\r\n            # print(\"transforming ....................\")\r\n            for transformer in step:\r\n                t_data = transformer.fit_transform(t_data)\r\n                if transformer.transformer_name != None:\r\n                    preprocess_title += \"\\n<{0}>\".format(transformer.transformer_name)\r\n                if isinstance(t_test_data,DataFrame):\r\n                    if not transformer.training_step_only:\r\n                        t_test_data = transformer.fit_transform(t_test_data)\r\n                        test_preprocess_title += \"\\n<{0}>\".format(transformer.transformer_name)\r\n            t_data.preprocess_title = preprocess_title\r\n            if isinstance(t_test_data, DataFrame):\r\n                t_test_data.preprocess_title = test_preprocess_title\r\n            self.transformed_data.append((t_data,t_test_data))\r\n\r\n    def fit_transform(self):\r\n        self.fit()\r\n        self.transform()\r\n        return self.transformed_data\r\n","sub_path":"sklearn_preprocessing_search/preprocess_grid_search.py","file_name":"preprocess_grid_search.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"352520705","text":"#!/usr/bin/env python3\n\nimport argparse\nimport collections\nimport datetime\nimport json\nimport operator\nimport subprocess\nimport sys\n\n\n# Renewal represents a renewal in ua-contracts.\nRenewal = collections.namedtuple(\n    \"Renewal\", \"id encoded_id status actionable start end contract_id sf_asset_id products account_id account_name sf_account_id\")\n\n\ndef run_contract(cmd, *args):\n    \"\"\"Run the contract CLI with the given subcommnd and args.\"\"\"\n    command = (\"contract\", cmd) + tuple(args)\n    return subprocess.check_output(command).decode(\"utf-8\")\n\n\ndef get_renewals(ids):\n    \"\"\"Generate renewal objects from the given decoded ids.\"\"\"\n    ids_len = len(ids)\n    for num, id in enumerate(ids):\n        print(f\"{num}/{ids_len}\", end=\"\\r\", file=sys.stderr)\n        encoded_id = run_contract(\"encode-id\", \"r\", id).strip()\n        out = run_contract(\"show-renewal\", encoded_id, \"--format\", \"json\")\n        info = json.loads(out)\n        contract_id = info[\"contractID\"]\n        out = run_contract(\"show-contract\", contract_id, \"--format\", \"json\")\n        account_contract_info = json.loads(out)\n        contract_info, account_info = account_contract_info[\"contractInfo\"], account_contract_info[\"accountInfo\"]\n        yield Renewal(\n            id=id,\n            encoded_id=info[\"id\"],\n            status=info[\"status\"],\n            actionable=info.get(\"actionable\", False),\n            start=_parse_datetime(info[\"start\"]),\n            end=_parse_datetime(info[\"end\"]),\n            contract_id=contract_id,\n            sf_asset_id=_parse_sf_ids(info[\"externalAssetIDs\"]),\n            products=contract_info['products'],\n            account_id=account_info[\"id\"],\n            account_name=account_info[\"name\"],\n            sf_account_id=_parse_sf_ids(account_info[\"externalAccountIDs\"][0]),\n        )\n\n\ndef _parse_datetime(value):\n    try:\n        return datetime.datetime.strptime(value, \"%Y-%m-%dT%H:%M:%S.%f%z\")\n    except ValueError:\n        return datetime.datetime.strptime(value, \"%Y-%m-%dT%H:%M:%S%z\")\n\n\ndef _parse_sf_ids(value):\n    if value is None or value[\"origin\"] != \"Salesforce\":\n        return \"\"\n    return \", \".join(value[\"IDs\"])\n\n\ndef setup():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"file\", type=argparse.FileType(\"r\"),\n        help=\"name of a file containing a list of renewal ids separated by newlines\")\n    return parser.parse_args()\n\n\ndef main(file):\n    counter = collections.Counter()\n    ids = set(line.strip() for line in file)\n    for r in get_renewals(ids):\n        verb = \"actionable\" if r.actionable else \"not actionable\"\n        products = \", \".join(sorted(r.products))\n\n        counter[\"total\"] += 1\n        counter[verb] += 1\n        counter[r.status] += 1\n        for product in r.products:\n            counter[product] += 1\n\n        print(f\"\\n# Delete {r.status} renewal {r.id}\")\n        print(f\"# (salesforce asset {r.sf_asset_id})\")\n        print(f\"# {verb} from {r.start} till {r.end}\")\n        print(f\"# for contract {r.contract_id} ({products})\")\n        print(f\"# owned by {r.account_name} ({r.account_id})\")\n        print(f\"# (salesforce account {r.sf_account_id})\")\n        print(f\"contract delete-renewal {r.encoded_id}\")\n\n    print(\"\\ncounts:\")\n    for key, value in counter.items():\n        print(f\"  {key}: {value}\")\n\n\nif __name__ == \"__main__\":\n    args = setup()\n    main(args.file)\n","sub_path":"contract-delete-renewals.py","file_name":"contract-delete-renewals.py","file_ext":"py","file_size_in_byte":3365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"636624305","text":"# coding=utf-8\n\n\"\"\"Find Bottom Left Tree Value.\"\"\"\n\nfrom __future__ import print_function\nfrom TreeNode import TreeNode\n\n\n# DFS 1\ndef _solve(root):\n    def _help(root, depth):\n        if not root.left and not root.right:\n            return root.val, depth\n        if root.left and root.right:\n            left_ans = _help(root.left, depth + 1)\n            right_ans = _help(root.right, depth + 1)\n            return right_ans if left_ans[1] < right_ans[1] else left_ans\n        return _help(root.left or root.right, depth + 1)\n\n    return _help(root, 0)[0] if root else 0\n\n\n# DFS2, fastest, 59 ms, beats 99.14%\ndef _solve1(root):\n    def _help(root, depth, result):\n        if result[1] < depth:\n            result[:] = [root.val, depth]\n        if root.left:\n            _help(root.left, depth + 1, result)\n        if root.right:\n            _help(root.right, depth + 1, result)\n        return result[0]\n    return _help(root, 1, [0, 0])\n\n\n# BFS with tricks\ndef _solve2(root):\n    queue = [root]\n    for node in queue:\n        queue += filter(None, (node.right, node.left))\n    return node.val\n\n\n# BFS in python\ndef _solve3(root):\n    queue, ans = [root], 0\n    while any(queue):\n        ans = queue[0].val\n        queue = [leaf for node in queue for leaf in (node.left, node.right) if leaf]\n    return ans\n\n\nif __name__ == '__main__':\n    print (_solve3(TreeNode.de_serialize('[2]')))\n    print (_solve3(TreeNode.de_serialize('[2,1,3]')))\n    print (_solve3(TreeNode.de_serialize('[1,2,3,4,null,5,6,null,null,7]')))\n","sub_path":"medium/513.py","file_name":"513.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"639568622","text":"import pygame\n\n# Initialize the pygame\npygame.init()\n\n\n# Create the screen\nscreen = pygame.display.set_mode((800, 600))\n\n\n# Title and Icon\npygame.display.set_caption('hackShip project')\nicon = pygame.image.load('src/scuba-diver.png')\npygame.display.set_icon(icon)\n\n\n# Game Loop\nRUNNING = True\nwhile RUNNING:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            RUNNING = False\n\n    pygame.display.update()\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"132619850","text":"import os\nimport cv2\nfrom collections import namedtuple\nimport imageio\nfrom PIL import Image\nfrom random import randrange\nimport numpy as np\nfrom sklearn.decomposition import PCA\nfrom scipy.spatial.distance import pdist, squareform\nimport torch\nimport matplotlib\nmatplotlib.use('Agg')   # Required for gif animations\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.image as image\nimport matplotlib.patches as patches\nfrom multimodal_affinities.visualization.vis_handler import VisHandler\nfrom multimodal_affinities.visualization.image_utils import resize_image\nfrom multimodal_affinities.visualization.colors_util import rgb_hex_to_tuple\n\nclass PlotsProducer:\n\n    def __init__(self, document, output_path):\n        # Load background image\n        self.image_path = document.image_path\n        self.img = plt.imread(self.image_path)\n        self.img_opencv = cv2.imread(self.image_path)\n\n        dpi = 120\n        mpl.rcParams['figure.dpi'] = dpi\n        height = self.img.shape[0]\n        width = self.img.shape[1]\n        self.figsize = width / float(dpi), height / float(dpi)   # Fig size in inches\n\n        self.document = document\n        self.output_path = output_path\n        if not os.path.exists(output_path):\n            os.makedirs(output_path)\n\n    def plot_word_boxes_on_image(self):\n        set_of_words = [[word] for word in self.document.get_words()]  # list of singleton word lists\n        fig, ax = plt.subplots(1, figsize=self.figsize)\n        monochrome_colors_list = ['#5a5d8f' for _ in self.document.get_words()]\n        self._draw_entity_bounding_boxes(fig=fig, ax=ax, bg_img=self.img,\n                                         title='',\n                                         entity_sets=set_of_words,\n                                         colors_list=monochrome_colors_list)\n        fig.savefig(os.path.join(self.output_path, self.document.basename + '_word_boxes.png'))\n        plt.close(fig)\n\n    def save_phrase_detection_results(self):\n        set_of_phrases = [[phrase] for phrase in self.document.get_phrases()]  # list of singleton phrase lists\n        fig, ax = plt.subplots(1, figsize=self.figsize)\n        self._draw_entity_bounding_boxes(fig=fig, ax=ax, bg_img=self.img,\n                                         title='Phrase Detection', entity_sets=set_of_phrases)\n        fig.savefig(os.path.join(self.output_path, self.document.basename + '_phrase_detection.png'))\n        plt.close(fig)\n\n    def save_clustering_results(self, with_title=True, colors_list=None):\n        set_of_clusters = [cluster.words for cluster in self.document.get_clusters()]  # list of list of words (clusters)\n        self._save_set_of_clusters(set_of_clusters, with_title, colors_list)\n\n    def save_clustering_labels(self, clustering_labels, colors_list=None):\n        cluster_ids = np.unique(np.array(clustering_labels))\n        cluster_id_to_cluster_idx = {cluster_id: idx for idx, cluster_id in enumerate(cluster_ids)}\n\n        # Converts from list of labels to list of list of words (clusters)\n        set_of_clusters = [[] for _ in range(len(cluster_ids))]\n        for word_idx, word in enumerate(self.document.get_words()):\n            cluster_id = clustering_labels[word_idx]\n            if cluster_id == -1:    # Ignore non-clustered words\n                continue\n            cluster_idx = cluster_id_to_cluster_idx[cluster_id]\n            set_of_clusters[cluster_idx].append(word)\n\n        self._save_set_of_clusters(set_of_clusters, colors_list)\n\n    def _save_set_of_clusters(self, set_of_clusters, with_title=True, colors_list=None):\n        \"\"\"\n        :param document:\n        :param set_of_clusters: list of list of words (clusters)\n        :return:\n        \"\"\"\n        output_img = self._draw_entity_bounding_boxes_opencv(bg_img=self.img_opencv,\n                                                             entity_sets=set_of_clusters,\n                                                             colors_list=colors_list)\n        cv2.imwrite(os.path.join(self.output_path, self.document.basename + '_clustering.png'), output_img)\n\n    @staticmethod\n    def _draw_entity_bounding_boxes_opencv(bg_img, entity_sets, colors_list=None):\n\n        img_height = bg_img.shape[0]\n        img_width = bg_img.shape[1]\n\n        if colors_list is None:\n            colors_list = VisHandler.generate_colors_list(amount=len(entity_sets))\n\n        face_colors = colors_list\n        edge_colors = VisHandler.generate_darker_palette(colors_list)\n        output_img = bg_img.copy()\n        alpha = 0.8\n        for set_idx, entities_set in enumerate(entity_sets):\n            face_color = face_colors[set_idx]\n            edge_color = edge_colors[set_idx]\n            for entity in entities_set:\n                x = entity.geometry.left * img_width\n                y = entity.geometry.top * img_height\n                width = entity.geometry.width * img_width\n                height = entity.geometry.height * img_height\n                # writing the text onto the image and returning it\n                rgb_color = rgb_hex_to_tuple(face_color)\n                cv2.rectangle(output_img, (int(x), int(y)), (int(x + width), int(y + height)),\n                              (rgb_color[2], rgb_color[1], rgb_color[0]), cv2.FILLED)\n\n        output_img = cv2.addWeighted(output_img, alpha, bg_img, 1 - alpha, 0)\n        return output_img\n\n\n    @staticmethod\n    def _draw_entity_bounding_boxes(fig, ax, bg_img, title, entity_sets, colors_list=None):\n        ax.set_title(title)\n\n        plt.tick_params(axis='both', which='both',\n                        bottom='off', top='off', labelbottom='off', right='off', left='off',\n                        labelleft='off')\n\n        plt.imshow(bg_img)\n        img_height = bg_img.shape[0]\n        img_width = bg_img.shape[1]\n\n        if colors_list is None:\n            colors_list = VisHandler.generate_colors_list(amount=len(entity_sets))\n\n        face_colors = colors_list\n        edge_colors = VisHandler.generate_darker_palette(colors_list)\n\n        for set_idx, entities_set in enumerate(entity_sets):\n            face_color = face_colors[set_idx]\n            edge_color = edge_colors[set_idx]\n            for entity in entities_set:\n                x = entity.geometry.left * img_width\n                y = entity.geometry.top * img_height\n                width = entity.geometry.width * img_width\n                height = entity.geometry.height * img_height\n                rect = patches.Rectangle((x, y), width, height,\n                                         linewidth=2,\n                                         edgecolor=edge_color,\n                                         facecolor=face_color,\n                                         alpha=0.4)\n                ax.add_patch(rect)\n\n    @staticmethod\n    def plot_pca_embedding_space_for_clusters(document, output_path,\n                                              embedding_property='embedding',\n                                              title=''):\n        \"\"\"\n        Plot 2d PCA visualization of the embedding space according to cluster colors.\n        :param document: Document with clustering results\n        :param embedding_property: Embedding property of words - normally 'embedding' or 'unprojected_embedding'\n        :return:\n        \"\"\"\n        if not os.path.exists(output_path):\n            os.makedirs(output_path)\n        words = document.get_words()\n        clusters = document.get_clusters()\n\n        if len(words) == 0 or getattr(words[0], embedding_property) is None:\n            return\n        if embedding_property == 'unprojected_embedding':\n            embeddings = []\n            for word in words:\n                unprojected_embedding = torch.cat(word.unprojected_embedding['embeddings'], dim=1)\n                unprojected_embedding = unprojected_embedding.detach().cpu().numpy()\n                embeddings.append(unprojected_embedding)\n        else:\n            embeddings = [getattr(word, embedding_property).detach().cpu().numpy() for word in words]\n        colors_palette = VisHandler.generate_colors_list(amount=len(clusters))\n        word_to_color = {word: colors_palette[cluster_idx]\n                         for cluster_idx, cluster in enumerate(clusters)\n                         for word in cluster.words}\n        colors = [word_to_color[word] for word in words]\n\n        embeddings_array = np.array(embeddings).squeeze()\n        num_pca_comp = 2\n        embeddings_2d = PCA(n_components=num_pca_comp).fit_transform(embeddings_array)\n        x_list = [embeddings_2d[i, 0] for i in range(embeddings_2d.shape[0])]\n        y_list = [embeddings_2d[i, 1] for i in range(embeddings_2d.shape[0])]\n\n        fig, ax = plt.subplots(1)\n        plot_title = embedding_property\n        if plot_title != '':\n            plot_title += ': ' + title\n        plt.title(plot_title)\n        plt.scatter(x_list, y_list, c=colors, s=1, alpha=0.8)\n\n        fig.tight_layout()\n        fig.savefig(os.path.join(output_path, document.basename + '_' + embedding_property + '_pca.png'))\n        plt.close(fig)\n\n    @staticmethod\n    def _find_k_furthest_words_per_cluster(document, embeddings_2d, k=3):\n        \"\"\" Greedy approximation algorithm for finding k furthest neighbour words per cluster.\n            k is expected to be relatively small (< 100)\n        \"\"\"\n        words = document.get_words()\n        word_to_embedding_2d_idx = {word: idx for idx, word in enumerate(words)}\n        clusters = document.get_clusters()\n        solution_per_cluster = {}\n        ClusterSolution = namedtuple('ClusterSolution', ['word_indices', 'words'])\n        for cluster in clusters:\n            # Generate cluster pairwise distances matrix\n            all_cluster_embeddings_indices = [word_to_embedding_2d_idx[word] for word in cluster.words]\n            all_cluster_embeddings = np.take(embeddings_2d, all_cluster_embeddings_indices, axis=0)\n            pairwise_distances = pdist(all_cluster_embeddings, metric='euclidean')\n            distances_matrix = squareform(pairwise_distances)\n\n            # Total distance from selected set so far\n            distances_accumulator = np.zeros(len(cluster.words))\n\n            # Sample first point\n            random_index = randrange(len(cluster.words))\n\n            # Indices of selected points\n            selected_points = [random_index]\n\n            # How many points we need to add\n            points_to_calc_count = min(k - 1, len(words) - 1)\n\n            for _ in range(points_to_calc_count):\n                last_point_selected = selected_points[-1]\n\n                # Update accumulator with distance collected from last point\n                distances_accumulator += distances_matrix[last_point_selected]\n\n                # Eliminate last point selected from distance matrix & accumulator\n                distances_matrix[:, random_index] = 0\n                distances_matrix[random_index, :] = 0\n\n                furthrest_point_from_set = np.argmax(distances_accumulator, axis=0)\n                selected_points.append(furthrest_point_from_set)\n\n            selected_words = [cluster.words[point] for point in selected_points]\n            selected_word_indices = [word_to_embedding_2d_idx[word] for word in selected_words]\n            solution_per_cluster[cluster] = ClusterSolution(word_indices=selected_word_indices, words=selected_words)\n\n        return solution_per_cluster\n\n    @staticmethod\n    def _extract_crops_per_cluster_solution(document, solution_per_cluster):\n        \"\"\"\n        Extracts crops for each selected word in k-furthest neighbours solution\n        :param document:\n        :param solution_per_cluster: Solution of k-furthest neighbours\n        :return:\n        \"\"\"\n        word_indices_to_crops = {}\n        for cluster, cluster_solution in solution_per_cluster.items():\n            for word_index, word in zip(cluster_solution.word_indices, cluster_solution.words):\n                bbox = word.get_bbox()  # left, top, width, height\n                y_min = int(round(bbox[1] * document.height))\n                y_max = int(round((bbox[1] + bbox[3]) * document.height))\n                x_min = int(round(bbox[0] * document.width))\n                x_max = int(round((bbox[0] + bbox[2]) * document.width))\n                image_of_crop = document.image[max(0, y_min):min(y_max, document.height),\n                                max(0, x_min):min(x_max, document.width), :]\n\n                pil_image = Image.fromarray(image_of_crop[...,::-1])    # BGR to RGB\n                pil_image = pil_image.convert('RGB')\n                word_indices_to_crops[word_index] = pil_image\n        return word_indices_to_crops\n\n    @staticmethod\n    def _space_out_crops(indices_to_crops, words, x_list, y_list, dist_from_pt=0.01, height=0.02):\n        \"\"\"\n        Calculates the positions and dimensions of crop images on the embedding space plot.\n        Makes sure crops don't overlay each other.\n        This method assumes a small number of crops (< 1000) and performs a naive linear comparison for each crop.\n        :param indices_to_crops: dict of word index (by order in doc) to PIL crop\n        :param words: List of words\n        :param x_list: List of corresponding pt x positions\n        :param y_list: List of corresponding pt y positions\n        :param dist_from_pt: How far in (x-y) coords the crop should be placed from the plot\n        :param height: Height of the crop, in figure axes dimensions (note: for normalized pca space: -1 to 1)\n        :return: indices_to_extents: dict of word index to extens describing position and dimensions of each crop.\n        Crops are shifted so they don't cover each other,\n        \"\"\"\n        indices_to_extents = {}\n        MatplotExtent = namedtuple('matplot_extent', ['left', 'right', 'bottom', 'top'])\n        is_extent_x_intersect = lambda e1, e2: not (e1.right < e2.left or e1.left > e2.right)\n        is_extent_y_intersect = lambda e1, e2: not (e1.top > e2.bottom or e1.bottom < e2.top)\n        is_extent_intersect = lambda e1, e2: is_extent_x_intersect(e1, e2) and is_extent_y_intersect(e1, e2)\n\n        min_x, max_x = min(x_list), max(x_list)\n        min_y, max_y = min(y_list), max(y_list)\n        height = (max_y - min_y) * height\n        dist_from_pt = min(max_y - min_y, max_x - min_x) * dist_from_pt\n\n        for point_index, crop in indices_to_crops.items():\n            word_aspect_ratio = words[point_index].geometry.width / words[point_index].geometry.height\n            axis_ratio = (max_x-min_x) / (max_y-min_y) / 2\n            width = height * word_aspect_ratio * axis_ratio\n            left, right = x_list[point_index] + dist_from_pt, x_list[point_index] + dist_from_pt + width\n            bottom, top = y_list[point_index] + dist_from_pt + height, y_list[point_index] + dist_from_pt\n\n            overlap = True\n            while overlap:\n                overlap = False\n                extent = MatplotExtent(left, right, bottom, top)\n                for other_crop_extent in indices_to_extents.values():\n                    other_left, other_right, other_bottom, other_top = other_crop_extent\n                    spaceout_margin = dist_from_pt / 2\n                    if is_extent_intersect(extent, other_crop_extent):\n                        overlap = True\n                        # shift below\n                        if other_bottom <= top <= other_top:\n                            top = other_bottom + spaceout_margin\n                            bottom = top + height\n                        else:   # shift above\n                            bottom = other_top - spaceout_margin\n                            top = bottom - height\n                        continue\n\n            indices_to_extents[point_index] = extent\n        return indices_to_extents\n\n    def plot_clusters_and_embedding_space_with_crops(self, document, output_path, crops_per_cluster=3,\n                                                     embedding_properties=['embedding', 'unprojected_embedding'],\n                                                     unprojected_caption=None):\n        \"\"\"\n        Plot 2d PCA visualization of the embedding space according to cluster colors.\n        :param document: Document with clustering results\n        :param embedding_property: Embedding property of words - normally 'embedding' or 'unprojected_embedding'\n        :return:\n        \"\"\"\n        if not os.path.exists(output_path):\n            os.makedirs(output_path)\n        words = document.get_words()\n        clusters = document.get_clusters()\n\n        if len(words) == 0 or \\\n                all([getattr(words[0], embedding_property) is None for embedding_property in embedding_properties]):\n                    return\n\n        colors_palette = VisHandler.generate_colors_list(amount=len(clusters))\n        word_to_color = {word: colors_palette[cluster_idx]\n                         for cluster_idx, cluster in enumerate(clusters)\n                         for word in cluster.words}\n        colors = [word_to_color[word] for word in words]\n\n        # Initially empty, the first embedding property we process will set those for all figures\n        selected_word_crops_per_cluster = None\n        indices_to_crops = None\n\n        for embedding_property in embedding_properties:\n            if embedding_property == 'unprojected_embedding':   # Can't handle tuples, concat them\n                embeddings = []\n                for word in words:\n                    unprojected_embedding = torch.cat(word.unprojected_embedding['embeddings'], dim=1)\n                    unprojected_embedding = unprojected_embedding.detach().cpu().numpy()\n                    embeddings.append(unprojected_embedding)\n            else:\n                embeddings = [getattr(word, embedding_property).detach().cpu().numpy() for word in words]\n\n            embeddings_array = np.array(embeddings).squeeze()\n            num_pca_comp = 2\n            embeddings_2d = PCA(n_components=num_pca_comp).fit_transform(embeddings_array)\n            x_list = [embeddings_2d[i, 0] for i in range(embeddings_2d.shape[0])]\n            y_list = [embeddings_2d[i, 1] for i in range(embeddings_2d.shape[0])]\n\n            fig, ax = plt.subplots(1)\n            if crops_per_cluster > 0:\n                if selected_word_crops_per_cluster is None and indices_to_crops is None:   # Calculate per first attribute\n                    selected_word_crops_per_cluster = PlotsProducer._find_k_furthest_words_per_cluster(document, embeddings_2d, k=crops_per_cluster)\n                    indices_to_crops = PlotsProducer._extract_crops_per_cluster_solution(document, selected_word_crops_per_cluster)\n                indices_to_extents = PlotsProducer._space_out_crops(indices_to_crops, words,\n                                                                    x_list, y_list, dist_from_pt=0.02, height=0.04)\n\n                # Plot crop images\n                for point_index, crop in indices_to_crops.items():\n                    extent = indices_to_extents[point_index]\n                    rect = patches.Rectangle((extent.left, extent.top), extent.right-extent.left, extent.bottom-extent.top,\n                                             linewidth=0.5,\n                                             edgecolor=\"black\",\n                                             facecolor=\"none\",\n                                             zorder=5)\n                    ax.imshow(crop, aspect='auto', alpha=0.65, extent=extent, zorder=4)\n                    ax.add_patch(rect)\n\n            # Plot points\n            if embedding_property == 'unprojected_embedding':\n                plot_title = 'Initial unprojected embeddings, pre training (PCA)'\n            else:\n                if unprojected_caption is None:\n                    plot_title = 'Projected embeddings, post training (PCA)'\n                else:\n                    plot_title = unprojected_caption\n            plt.title(plot_title)\n            plt.scatter(x_list, y_list, c=colors, s=18, alpha=1.0, edgecolors='black', linewidth=1.0, zorder=3)\n            plt.tick_params(axis='both', which='both',\n                            bottom='off', top='off', labelbottom='off', right='off', left='off',\n                            labelleft='off')\n            ax.get_xaxis().set_visible(False)\n            ax.get_yaxis().set_visible(False)\n\n            fig.tight_layout()\n            fig.savefig(os.path.join(output_path, document.basename + '_' + embedding_property + '_pca.png'))\n            plt.close(fig)\n\n        # Finally plot clusters on original image\n        self.save_clustering_results(with_title=False, colors_list=colors_palette)\n\n        return colors_palette\n\n    @staticmethod\n    def animate_pca_embedding_space_for_clusters(document, output_path, embeddings_history, colors_palette=None):\n        \"\"\"\n        Plot 2d PCA visualization of the embedding space according to cluster colors.\n        :param document: Document with clustering results\n        :param embedding_property: Embedding property of words - normally 'embedding' or 'unprojected_embedding'\n        :return:\n        \"\"\"\n        if not os.path.exists(output_path):\n            os.makedirs(output_path)\n        words = document.get_words()\n        clusters = document.get_clusters()\n\n        if len(words) == 0 or embeddings_history is None or len(embeddings_history) == 0:\n            return\n\n        if colors_palette is None:\n            colors_palette = VisHandler.generate_colors_list(amount=len(clusters))\n        word_to_color = {word: colors_palette[cluster_idx]\n                         for cluster_idx, cluster in enumerate(clusters)\n                         for word in cluster.words}\n        colors = [word_to_color[word] for word in words]\n        scatter_data = []\n\n        for state_idx, embeddings_state in enumerate(embeddings_history):\n            epoch = state_idx + 1\n            normalized_embeddings_dict = embeddings_state['normalized']\n            unnormalized_embeddings_dict = embeddings_state['unnormalized']\n            if len(normalized_embeddings_dict) > 0:\n                normalized_embeddings = [normalized_embeddings_dict[word].detach().cpu().numpy() for word in words]\n                chosen_embedding = normalized_embeddings\n            elif len(unnormalized_embeddings_dict) > 0:\n                unnormalized_embeddings = [unnormalized_embeddings_dict[word].detach().cpu().numpy() for word in words]\n                chosen_embedding = unnormalized_embeddings\n            else:\n                return\n\n            embeddings_array = np.array(chosen_embedding).squeeze()\n            num_pca_comp = 2\n            embeddings_2d = PCA(n_components=num_pca_comp).fit_transform(embeddings_array)\n            x_list = [embeddings_2d[i, 0] for i in range(embeddings_2d.shape[0])]\n            y_list = [embeddings_2d[i, 1] for i in range(embeddings_2d.shape[0])]\n            push_pull_ratio = embeddings_state['push_pull_ratio']\n            scatter_data.append((epoch, x_list, y_list, push_pull_ratio))\n\n        min_x = min(min(scatter_data, key=lambda entry: min(entry[1]))[1])\n        max_x = max(max(scatter_data, key=lambda entry: max(entry[1]))[1])\n        min_y = min(min(scatter_data, key=lambda entry: min(entry[2]))[2])\n        max_y = max(max(scatter_data, key=lambda entry: max(entry[2]))[2])\n        padding_factor = 0.1\n        min_x -= (max_x - min_x) * padding_factor\n        max_x += (max_x - min_x) * padding_factor\n        min_y -= (max_y - min_y) * padding_factor\n        max_y += (max_y - min_y) * padding_factor\n\n        frames = []\n        for epoch, x_list, y_list, push_pull_ratio in scatter_data:\n            fig, ax = plt.subplots(1)\n            ax.set_xlim(min_x, max_x)\n            ax.set_ylim(min_y, max_y)\n            plot_title = 'Projected embeddings at epoch #' + str(epoch) + ' (PCA)'\n            plt.title(plot_title)\n\n            plt.scatter(x_list, y_list, c=colors, s=18, alpha=1.0, edgecolors='black', linewidth=1.0, zorder=3)\n            plt.tick_params(axis='both', which='both',\n                            bottom='off', top='off', labelbottom='off', right='off', left='off',\n                            labelleft='off')\n            ax.get_xaxis().set_visible(False)\n            ax.get_yaxis().set_visible(False)\n\n            # Used to return the plot as an image rray\n            fig.tight_layout()\n            fig.canvas.draw()  # draw the canvas, cache the renderer\n            output_frame = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')\n            output_frame = output_frame.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n            frames.append(output_frame)\n\n        imageio.mimsave(os.path.join(output_path, document.basename + '_embeddings_history.gif'), frames, fps=2)\n\n","sub_path":"multimodal_affinities/evaluation/analysis/plots_producer.py","file_name":"plots_producer.py","file_ext":"py","file_size_in_byte":24713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"261761281","text":"from collections import namedtuple\nfrom itertools import count\nfrom PIL import Image\nimport re\nfrom sys import argv\n\n\nPoint = namedtuple('Point', ['x', 'y', 'vx', 'vy'])\n\n\ndef main():\n    points = loadpoints(argv[1])\n\n    minsize = size(points)\n    mint = 0\n\n    for t in count(start=1):\n        state = [simulate(p, t) for p in points]\n        n = size(state)\n        if n < minsize:\n            minsize = n\n            mint = t\n        elif n > minsize:\n            break\n    print(mint)\n    render([simulate(p, mint) for p in points])\n\n\ndef render(points):\n    minx, maxx, miny, maxy = bounds(points)\n    img = Image.new('1', (maxx - minx + 1, maxy - miny + 1))\n    for p in points:\n        img.putpixel((p.x - minx, p.y - miny), 1)\n    img.save('out.ppm')\n\n\ndef simulate(point, time):\n    return Point(point.x + point.vx * time, point.y + point.vy * time, point.vx, point.vy)\n\n\ndef size(points):\n    minx, maxx, miny, maxy = bounds(points)\n    return (maxx - minx) * (maxy - miny)\n\n\ndef bounds(points):\n    minx = min(map(lambda p: p.x, points))\n    maxx = max(map(lambda p: p.x, points))\n    miny = min(map(lambda p: p.y, points))\n    maxy = max(map(lambda p: p.y, points))\n    return minx, maxx, miny, maxy\n\n\ndef loadpoints(path):\n    pattern = re.compile(r'position=<([ -]?\\d+), ([ -]?\\d+)> velocity=<([ -]?\\d+), ([ -]?\\d+)>')\n    with open(path) as f:\n        points = []\n        for line in f:\n            match = pattern.match(line)\n            x, y, vx, vy = map(int, match.groups())\n            points.append(Point(x, y, vx, vy))\n        return points\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"2018/day10_part01/day10_part01.py","file_name":"day10_part01.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"467300153","text":"class GConfig:\n    class Cache:\n        # MT : memoized times\n        camp_userCount_MT = 0\n\n    class Topic:\n        options_per_page = 30\n        userTopic_MT = 0\n        popularTopic_MT = 0\n\n        # popular topic in the last ? days\n        popularTopicLast_Days = 30\n        recountTopicAfterModerated = 10 # minutes\n\n    class User:\n        markUserInactiveAfter = 15 # days\n        markUserInactiveTimeCycle = 5 # minutes\n\n        markUserOfflineAfter = 15\n        markUserOfflineTimeCycle = 10 # minutes\n\n","sub_path":"bgapp/GConfig.py","file_name":"GConfig.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"390206592","text":"import math;\nprint('등비수열은 인접한 항의 비가 일정한 수열로, 인접한 항의 비를 공비라고 합니다. \\n'\n      '등비수열의 특정 항을 구하는 공식은 첫쨰항을 a_1, 공비를 r이라고 할 때 \\n'\n      'a_n = a_1 * r ** (n - 1) 입니다. \\n'\n      '또한 등차수열 n번째 항까지의 합 S_n은 다음과 같습니다. \\n'\n      'S_n = a_1 * (r ** n - 1) / r - 1');\na=int(input('등비수열의 첫번째 항을 입력해주세요. '));\nr=int(input('등비수열의 공비를 입력해주세요. '));\nn=int(input('등비수열의 알고 싶은 항의 순서를 입력해주세요(예: 10번째 항=10) '))\na_n=a*(r ** (n-1))\nprint(a_n)\nS_n=a * ((r ** n) - 1) / (r - 1)\nprint(S_n)","sub_path":"python/multiple.py","file_name":"multiple.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"593835615","text":"'''\nExercise : Assignment-1\nimplement the function get_available_letters that takes in one parameter -\na list of letters, letters_guessed. This function returns a string\nthat is comprised of lowercase English letters - all lowercase English letters\nthat are not in letters_guessed\n'''\n\ndef get_available_letters(letters_guessed):\n    \"\"\"\n    :param letters_guessed: list, what letters have been guessed so far\n    returns: string, comprised of letters that represents what letters have not\n      yet been guessed.\n    Method 1:\n    B = set(string.ascii_lowercase).intersection(set(letters_guessed))\n    return(set(string.ascii_lowercase)-B)\n    Method 2:\n    A = list(string.ascii_lowercase)\n    for i in letters_guessed:\n        A.remove(i)\n    return \"\".join(A)\n    \"\"\"\n    import string\n    keys = list(string.ascii_lowercase)\n    ascii_val = []\n    for i in keys:\n        ascii_val.append(ord(i))\n    dictionary = dict(zip(keys, ascii_val))\n    for i in letters_guessed:\n        del dictionary[i]\n    list_1 = []\n    for i in dictionary.keys():\n        list_1.append(i)\n    return \"\".join(list_1)\ndef main():\n    '''\n    Main function for the given program\n    '''\n    user_input = input()\n    user_input = user_input.split()\n    data = []\n    for char in user_input:\n        data.append(char)\n    print(get_available_letters(data))\n\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"module 10/p1/assignment1.py","file_name":"assignment1.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"209509593","text":"import pandas as pd\nimport os\nfrom glob import glob\n\npath_to_neuron_stats = r'/media/ruairi/Ephys_back_up_1/SERT_DREADD/csvs'\nneuron_stats_csv_name = 'neuron_stats'\n\npath_to_ts = r'/media/ruairi/Ephys_back_up_1/SERT_DREADD/csvs/spikes_time_series'\n\nfile_out_dir = r'/media/ruairi/Ephys_back_up_1/SERT_DREADD/csvs'\nfile_out_name_ts = 'all_neurons_ts_with_clusters.csv'\n\n\ndef load_spikes_df(parent_dir):\n    sub_dirs = os.listdir(parent_dir)\n    df_list = []\n    for sub_dir in sub_dirs:\n        file_path = glob(os.path.join(parent_dir, sub_dir, '*.csv'))[0]  # should be only one csv file\n        df_list.append(pd.read_csv(file_path))\n    return pd.concat(df_list)\n\n\ndef load_neuron_stats(path, csv_name):\n    if not csv_name.endswith('.csv'):\n        csv_name = ''.join([csv_name, '.csv'])\n    return pd.read_csv(os.path.join(path, csv_name))\n\n\ndef neuron_category_mapper(row):\n    '''\n    This is the function we use to categorise neurons as either slow or fast, and as either regular or irregular\n    It categorises neurons firing slower than 4.5 Hz as slow and neurons with a CV ISI of less than 0.55 as regular\n    '''\n    if (row['Firing Rate'] <= 10) & (row['CV ISI'] <= 0.8):\n        rate = 'slow'\n        reg = 'regular'\n    elif (row['Firing Rate'] <= 10) & (row['CV ISI'] >= 0.8):\n        rate = 'slow'\n        reg = 'irregular'\n    elif (row['Firing Rate'] > 10) & (row['Firing Rate'] <25):\n        rate= 'fast'\n        reg = 'firing'\n    else:\n        rate= 'V.fast'\n        reg = 'firing'\n    return ' '.join([rate, reg])\n\ndf_ts = load_spikes_df(path_to_ts)\ndf_stats = load_neuron_stats(path=path_to_neuron_stats, csv_name=neuron_stats_csv_name)\ndf_stats['category'] = df_stats.apply(neuron_category_mapper, axis=1)\ndf_ts_merge = pd.merge(left=df_ts,\n                       right=df_stats[['recording', 'category', 'spike_cluster']],\n                       left_on=['recording', 'spike_cluster'],\n                       right_on=['recording', 'spike_cluster'],\n                       how='right')\n\ndf_ts_merge.to_csv(os.path.join(file_out_dir, file_out_name_ts), index=False)\ndf_stats.to_csv(os.path.join(file_out_dir, neuron_stats_csv_name) + '.csv',\n                index=False)\n","sub_path":"mua_preprocess/add_neuron_categories.py","file_name":"add_neuron_categories.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"336513350","text":"# fibonacci.py\n# This program computes the nth Fibonacci number where value n is a user input.\n\ndef fibonacci(f, s, c, n):\n    seq = []\n    for i in range(1, n+1):\n        seq.append(c)\n        f = s\n        s = c\n        c = f + s\n    fib = seq[n-1]\n    return fib\n\ndef main():\n    n = int(input(\"Enter the Fibonacci number: \"))\n    fir = 0\n    sec = 0\n    cur = 1\n\n    ans = fibonacci(fir, sec, cur, n)\n    \n    print(\"The Fibonnaci number at index {} is: {}\".format(n, ans))\n\nmain()\n    \n","sub_path":"python/python-programming-zelle/fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"78266089","text":"from eoflow.base.base_predict import BasePredict\nfrom eolearn.core import EOPatch, FeatureType\nimport tensorflow as tf\nfrom tqdm import tqdm\nimport numpy as np\nimport itertools\nimport os\n\n\nclass TFCNPredict(BasePredict):\n    def __init__(self, config, data, logger):\n        super(TFCNPredict, self).__init__(config, data, logger)\n\n    def _get_sampling_indices(self, height, width):\n        \"\"\" Compute indices of padded input image where patches are extracted\n\n        :param height: Height of input image to be classified\n        :type height: int\n        :param width: Width of input image to be classified\n        :type width: int\n        :return: sample indices along the height, sample indices along the width\n        :rtype: list of tuples, list of tuples\n        \"\"\"\n        hh, ww = np.arange(height), np.arange(width)\n        h_samples = [(h_min, h_max) for h_min, h_max in\n                     zip(hh[::self.config.im_size[1] - self.config.spatial_cropping - self.config.prediction_overlap],\n                         hh[self.config.im_size[1]::self.config.im_size[1] - self.config.spatial_cropping -\n                            self.config.prediction_overlap])]\n        h_tail = (height - self.config.im_size[1], height)\n        if h_samples[-1] != h_tail:\n            h_samples.append(h_tail)\n\n        w_samples = [(w_min, w_max) for w_min, w_max in\n                     zip(ww[::self.config.im_size[2] - self.config.spatial_cropping - self.config.prediction_overlap],\n                         ww[self.config.im_size[2]::self.config.im_size[2] - self.config.spatial_cropping -\n                            self.config.prediction_overlap])]\n        w_tail = (width - self.config.im_size[2], width)\n        if w_samples[-1] != w_tail:\n            w_samples.append(w_tail)\n\n        # if len(w_samples) != len(h_samples):\n        #     raise ValueError(\"Different number of sampling patches\")\n\n        return h_samples, w_samples\n\n    def _get_normalisation_mask(self, height, width, height_indices, width_indices):\n        \"\"\" Compute mask where output patches overlap\n\n        :param height: Height of input image to be classified\n        :type height: int\n        :param width: Width of input image to be classified\n        :type width: int\n        :param height_indices: List of patch indices along the image height\n        :type height_indices: list of tuples\n        :param width_indices: List of patch indices along the image width\n        :type width_indices: list of tuples\n        :return: Normalisation mask\n        :rtype: numpy array\n        \"\"\"\n        mask = np.zeros((height + self.config.spatial_cropping + 2*self.config.prediction_overlap,\n                         width + self.config.spatial_cropping + 2*self.config.prediction_overlap,\n                         self.config.n_classes), dtype=np.uint8)\n\n        for hi, wi in itertools.product(height_indices, width_indices):\n            mask[hi[0] + self.config.spatial_cropping // 2:hi[1] - self.config.spatial_cropping // 2,\n                 wi[0] + self.config.spatial_cropping // 2:wi[1] - self.config.spatial_cropping // 2, :] += \\\n                    np.ones((self.config.lb_size[0], self.config.lb_size[1], self.config.n_classes), np.uint8)\n\n        mask[mask == 0] = 1\n\n        return mask\n\n    def _merge_and_normalise(self, logits, height, width, height_indices, width_indices, norm_mask):\n        \"\"\" Merge the output patches back into one image\n\n        :param logits: Array storing the output of the U-net (output of softmax). Shape of array is\n                        N_PATCHESxOHxOWxN_CLASSES\n        :type logits: numpy array\n        :param height: Height of input image to be classified\n        :type height: int\n        :param width: Width of input image to be classified\n        :type width: int\n        :param height_indices: Indices defining the location of the patches to be extracted along the height of the\n                                input image\n        :type height_indices: list of tuples\n        :param width_indices: Indices defining the location of the patches to be extracted along the width of the\n                                input image\n        :type width_indices: list of tuples\n        :param norm_mask: Mask counting the overlap of hte extracted patches. It's used to normalise the combined prediction\n        :type norm_mask: numpy array\n        :return: Predicted mask, argmax along the class dimension\n        :rtype: numpy array\n        \"\"\"\n        pred_mask = np.zeros((height + self.config.spatial_cropping + 2*self.config.prediction_overlap,\n                              width + self.config.spatial_cropping + 2*self.config.prediction_overlap,\n                              self.config.n_classes), dtype=np.float32)\n\n        batch = 0\n        for hi, wi in itertools.product(height_indices, width_indices):\n            pred_mask[hi[0] + self.config.spatial_cropping // 2:hi[1] - self.config.spatial_cropping // 2,\n                      wi[0] + self.config.spatial_cropping // 2:wi[1] - self.config.spatial_cropping // 2,\n                      :] += logits[batch]\n            batch += 1\n        # Normalise prediction (average of overlapping values)\n        pred_mask /= norm_mask\n        # Find maximum of combined softmax (pseud-probability)\n        return pred_mask\n\n    def predict(self, predict_proba=True, log_accuracy=True):\n        loop = tqdm(range(len(self.data.cval_set)))\n        for _ in loop:\n            image, labels = next(self.data.next_batch(1, is_training=False, random=False))\n            eopatch_filename, = self.data.get_batch_filenames()\n            self.predict_step(image, labels, eopatch_filename, predict_proba=predict_proba, log_accuracy=log_accuracy)\n            self.counter += 1\n        return\n\n    def predict_step(self, image, labels, filename, predict_proba=True, log_accuracy=True):\n        \"\"\" Predict step\n\n        \"\"\"\n        # Load graph and get placeholders and tensors\n        images = self.graph.get_tensor_by_name('{:s}/images:0'.format(self.graph_name))\n        keep_prob = self.graph.get_tensor_by_name('{:s}/keep_prob:0'.format(self.graph_name))\n        softmax = self.graph.get_tensor_by_name('{:s}/{:s}:0'.format(self.graph_name, self.config.node_names))\n\n        batch_size, n_frames, height, width, depth = image.shape\n\n        # Check dimensions agree to specification\n        if self.config.im_size[-1] != depth or self.config.im_size[0] != n_frames:\n            raise ValueError(\"Wrong number of frames/bands provided as input to model\")\n\n        if height < self.config.im_size[1] or width < self.config.im_size[2]:\n            raise ValueError(\"Input tile is smaller than input patch. Increase spatial resolution\")\n\n        # Compute sampling indices to cover the entire input image\n        h_samples, w_samples = self._get_sampling_indices(height + self.config.spatial_cropping + 2*self.config.prediction_overlap,\n                                                          width + self.config.spatial_cropping + 2*self.config.prediction_overlap)\n        n_patches = len(h_samples) * len(w_samples)\n        # Clip image to [0,1] range and pad it using reflection\n        padding = ((0, 0),\n                   (0, 0),\n                   (self.config.spatial_cropping // 2 + self.config.prediction_overlap,\n                    self.config.spatial_cropping // 2 + self.config.prediction_overlap),\n                   (self.config.spatial_cropping // 2 + self.config.prediction_overlap,\n                    self.config.spatial_cropping // 2 + self.config.prediction_overlap),\n                   (0, 0))\n\n        image = np.clip(image, self.config.clip_low, self.config.clip_high)\n        image = np.pad(image, padding, mode='reflect')\n\n        # Break input image into patches of suitable input size for U-net architecture\n        img_batch = [image[:, :, hi[0]:hi[1], wi[0]:wi[1], :]\n                     for hi, wi in itertools.product(h_samples, w_samples)]\n        img_batch = np.concatenate(img_batch, axis=0)\n        del image\n\n        # Array holding the output of the U-net\n        logits = np.zeros(((n_patches,) + tuple(self.config.lb_size) + (self.config.n_classes,)), dtype=np.float32)\n\n        # Run prediction in mini-batches\n        with tf.Session(graph=self.graph) as sess:\n\n            mini_batches = np.split(np.arange(n_patches),\n                                    np.arange(n_patches)[self.config.batch_size::self.config.batch_size])\n\n            for mb in mini_batches:\n                tmp_logits = sess.run(softmax,\n                                      feed_dict={images: img_batch[mb],\n                                                 keep_prob: 1})\n                logits[mb] = tmp_logits.reshape((len(mb),) + tuple(self.config.lb_size) + (self.config.n_classes,))\n                del tmp_logits\n\n        del softmax, images, keep_prob, img_batch\n\n        # This normalisation mask handles overlaps between patches\n        normalisation_mask = self._get_normalisation_mask(height, width, h_samples, w_samples)\n\n        # Merge the patches back into an image of dimensions as input image\n        self.predicted_proba = self._merge_and_normalise(logits, height, width, h_samples, w_samples, normalisation_mask)\n\n        # Crop probabilities\n        self.predicted_proba = self.predicted_proba[\n                               self.config.spatial_cropping // 2 + self.config.prediction_overlap:self.config.spatial_cropping // 2 + self.config.prediction_overlap + height,\n                               self.config.spatial_cropping // 2 + self.config.prediction_overlap:self.config.spatial_cropping // 2 + self.config.prediction_overlap + width, :]\n        self.predicted_labels = np.argmax(self.predicted_proba, axis=-1).astype(np.uint8)\n\n        if labels is not None and log_accuracy:\n            accuracy = np.mean(np.equal(self.predicted_labels, np.argmax(labels, axis=-1).squeeze()).astype(np.float32))\n            self.logger.summarize(self.counter, summarizer=\"test\", summaries_dict={'loss': np.array(0.0),\n                                                                                   'acc': np.array(accuracy)})\n\n        # Save to new EOPatch predicted probs and labels\n        _, eopatch_name = os.path.split(filename)\n\n        eopatch = EOPatch.load(filename, features=[(FeatureType.BBOX, FeatureType.META_INFO)])\n        if predict_proba:\n            eopatch.data_timeless['PREDICTED_PROBA'] = self.predicted_proba\n        eopatch.mask_timeless['PREDICTED_LABELS'] = self.predicted_labels[..., np.newaxis]\n\n        eopatch.save(self.pred_dir + os.sep + eopatch_name)\n\n        return\n\n\n","sub_path":"eoflow/predictors/fcn_predictor.py","file_name":"fcn_predictor.py","file_ext":"py","file_size_in_byte":10603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"514057780","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Nov 10 14:48:54 2020\r\n\r\n@author: zswitaj\r\n\"\"\"\r\nimport pandas as pd\r\nimport YOY_searchTrends as trends\r\n\r\nkw_dict = {\r\n    \"Timberland\": \"/m/05kv16\",\r\n    \"Timberland Boots\": \"Timberland Boots\",\r\n    \"UGG\": \"/m/06wccjq\",\r\n    \"Dr Martens\": \"/m/01lsm6\",\r\n    \"Merrell\": \"/m/0kqrz3\",\r\n    \"The North Face\": \"/m/04n92b\",\r\n    \"Patagonia\": \"/m/0g152j\",\r\n    \"Allbirds\": \"/g/11g6j4k3hl\",\r\n    \"Vans\": \"/m/04kbwy\",\r\n    \"Amazon\": \"/m/0mgkg \",\r\n    \"Walmart\": \"/m/0841v\",\r\n    \"Target\": \"/m/01b39j\",\r\n    \"Zappos\": \"/m/02dfb9\",\r\n    \"Foot Locker\": \"/m/08fhy9\",\r\n    \"Journeys\": \"/m/03cbgd\",\r\n    \"DSW\": \"/m/0flp70\",\r\n    \"Nordstrom\": \"/m/01fc_q\",\r\n    \"Macy's\": \"/m/01pkxd \",\r\n    \"REI\": \"/m/02nx4d\",\r\n    \"Dick's Sports\": \"/m/06fgv_\",\r\n    \"Boot\": \"/m/01b638\",\r\n    \"Outerwear\": \"Outerwear\"\r\n    }\r\n\r\nlst_keywords = []\r\n\r\nprint(\"Adding keywords...\")\r\nfor topic in kw_dict:\r\n    lst_keywords.append(topic)\r\n\r\nlst_values = []\r\n\r\nfor topic in kw_dict:\r\n    print(\"Calculting {} results\".format(topic))\r\n    lst_values.append(round(trends.getTrendData(kw_dict[topic]),1))\r\n\r\nprint(\"Combining...\")\r\nres = dict(zip(lst_keywords, lst_values))\r\nres_df = pd.DataFrame.from_dict(res, orient = 'index', columns = ['YOY Change'])\r\nres_df = res_df.transpose()\r\n\r\n# res_df.to_csv(r'output.csv')\r\n","sub_path":"YOYTrends_SL/YOY_searchTrends2_SL.py","file_name":"YOY_searchTrends2_SL.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"609525222","text":"import base64\nimport json\nimport pathlib\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom channels.generic.websocket import WebsocketConsumer\nfrom .convert_img_base64 import image_to_byte_array\nimport torch.nn as nn\n\n\ndef denomralization(img_tensors):\n    return img_tensors * 0.5 + 0.5\n\n\ndef to_device(data, device):\n    \"\"\"Move tensors to chosen device\"\"\"\n    if isinstance(data, (list, tuple)):\n        return [to_device(x, device) for x in data]\n    return data.to(device, non_blocking=True)\n\n\ndef get_default_device():\n    \"\"\"Pick GPU if available, else CPU\"\"\"\n    if torch.cuda.is_available():\n        return torch.device('cuda')\n    return torch.device('cpu')\n\n\ndef build_generator_for_GAN_model(latent_size):\n    \"\"\"\n    Building generator for using model from .ckpt file\n    :param latent_size: size of latent vector\n    :return: model of generator\n    \"\"\"\n    model = nn.Sequential(\n\n        # input: latent_size x 1 x 1\n        nn.ConvTranspose2d(latent_size, 512, kernel_size=4, stride=1, padding=0, bias=False),\n        nn.BatchNorm2d(512),\n        nn.ReLU(),\n        # output: 512 x 4 x 4\n\n        nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=False),\n        nn.BatchNorm2d(256),\n        nn.ReLU(),\n        # output: 256 x 8 x 8\n\n        nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1, bias=False),\n        nn.BatchNorm2d(128),\n        nn.ReLU(),\n        # output: 128 x 16 x 16\n\n        nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, bias=False),\n        nn.BatchNorm2d(64),\n        nn.ReLU(),\n        # output: 64 x 32 x 32\n\n        nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1, bias=False),\n        nn.BatchNorm2d(32),\n        nn.ReLU(True),\n        # output: 32 x 64 x 64\n\n        nn.ConvTranspose2d(32, 3, kernel_size=4, stride=2, padding=1, bias=False),\n        nn.Tanh()\n        # out: 3 x 128 x 128\n    )\n    return model\n\n\nclass GANImageConsumer(WebsocketConsumer):\n    def __init__(self):\n        self.cancel = False\n        WebsocketConsumer.__init__(self)\n\n    def connect(self):\n        self.accept()\n        generator = build_generator_for_GAN_model(200)\n        path = pathlib.Path(__file__).parent.absolute()\n\n        generator.load_state_dict(\n            torch.load(f\"{path}/G_gan.ckpt\", map_location='cpu')\n        )\n        latent_tensor = torch.randn(1, 200, 1, 1)\n        fake_image = generator(latent_tensor)\n        fake_image = denomralization(fake_image).detach()[0]\n        fake_image = fake_image.permute(1, 2, 0).numpy()\n        np_arr = (np.asarray(fake_image) * 255).astype(np.uint8)\n\n        image_generated_bytes = image_to_byte_array(Image.fromarray(np_arr))\n        encoded_image_string = str(base64.b64encode(image_generated_bytes))\n        self.send(json.dumps({'iteration': 'Model is pretrained', 'message': encoded_image_string}))\n","sub_path":"mainApp/GANConsumer.py","file_name":"GANConsumer.py","file_ext":"py","file_size_in_byte":2870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"195576470","text":"from rest_framework import status\nfrom .test_setup import TestSetUp\n\n\nclass AgendaTestViews(TestSetUp):\n\n    def test_listar_consultas_com_autenticacao(self):\n        response = self.client.get(self.listar_consultas_url)\n        self.assertEquals(response.status_code, status.HTTP_200_OK)\n\n    def test_detalhes_agendas_sem_autenticacao(self):\n        self.client.force_authenticate(user=None)\n        response = self.client.get(self.listar_consultas_url)\n        self.assertEquals(response.status_code, status.HTTP_401_UNAUTHORIZED)\n","sub_path":"backend/apps/consultas/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"385086829","text":"from bs4 import BeautifulSoup\nimport requests\nimport sys\nimport io\n\nsys.stdout = io.TextIOWrapper(sys.stdout.detach(),encoding='utf-8')\nsys.stderr = io.TextIOWrapper(sys.stderr.detach(),encoding='utf-8')\n\nwith requests.session() as s :\n    login_req=s.post('https://movie.naver.com/movie/bi/mi/point.nhn?code=161967')\n    # print('login_header : ',login_req.headers)\n\n    # Response 정상 확인\n    if login_req.status_code==200 and login_req.ok :\n        # print(\"로그인 성공!\")\n\n        # 권한이 필요한 게시글 가져오기\n        post_one=s.get('https://movie.naver.com/movie/bi/mi/point.nhn?code=161967')\n        # print(post_one)\n\n        # 예외처리\n        # post_one.raise_for_status()\n        # print(post_one.text)\n\n        # BeautifulSoup 선언\n        soup=BeautifulSoup(post_one.text,\"html.parser\")\n        # print(soup.prettify())\n        article=soup.select('.score_reple>p')\n        # print(article)\n        for i,a in enumerate(article,1) :\n            print('{}번째 평론 : {}'.format(i,a.text))\n","sub_path":"section3/homework1.py","file_name":"homework1.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"355667697","text":"from rest_framework import serializers\nfrom api.models import Post, POST_STATUS_CHOICES\nfrom django.contrib.auth.models import User\n\nclass PostSerializer(serializers.Serializer):\n    id = serializers.IntegerField(read_only=True)\n    title = serializers.CharField(required=False, allow_blank=True, max_length=255)\n    content = serializers.CharField(required=False, allow_blank=True)\n    published_date = serializers.DateField(required=True)\n    user = serializers.PrimaryKeyRelatedField(queryset=User.objects.all())\n    status = serializers.ChoiceField(choices=POST_STATUS_CHOICES, default='DRAFT')\n\n\n    def create(self, validate_data):\n        \"\"\"\n        create and return new post, givin the validate datas\n        \"\"\"\n\n        return Post.objects.create(**validate_data)\n\n\n    def update(self, instance, validate_data):\n        \"\"\"\n        update Post\n        \"\"\"\n        instance.title = validate_data.get('title', instance.title)\n        instance.content = validate_data.get('content', instance.content)\n        instance.published_date = validate_data.get('published_date', instance.published_date)\n        instance.published_date = validate_data.get('published_date', instance.published_date)\n        instance.published_date = validate_data.get('published_date', instance.published_date)\n\n        instance.save()\n        return instance\n","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"448621093","text":"from os import error\nimport sys\nfrom collections import Counter\n\ndef readInput(inputfile):\n    input = {\n        \"dots\": [],\n        \"instructions\": [],\n        \"xmax\": 0,\n        \"ymax\": 0\n    }\n    with open(inputfile) as file:\n        lineFound = True\n\n        while lineFound:\n            line = file.readline()\n            if not line:\n                lineFound = False\n            else:\n                line = line.strip()\n                if line.startswith(\"fold\"):\n                    foldInst = line[11:].split(\"=\")\n                    input[\"instructions\"].append({\"axis\": foldInst[0], \"index\": int(foldInst[1])})\n            \n                else:\n                    if len(line) > 0:\n                        point = line.split(\",\")\n                        x = int(point[0])\n                        y = int(point[1])\n                        input[\"dots\"].append({\"x\": x, \"y\": y})\n                        if x > input[\"xmax\"]:\n                            input[\"xmax\"] = x\n                        \n                        if y > input[\"ymax\"]:\n                            input[\"ymax\"] = y\n    return input\n\ndef foldX(index, field):\n    foldedField = []\n\n    for row in field:\n        baseRow = row[:index]\n        mirrorRow = row[index+1:]\n        lastBaseIndex = len(baseRow) - 1\n\n        for x in range(len(mirrorRow)):\n            foldIndex = lastBaseIndex - x\n            if mirrorRow[x] == \"#\":\n                baseRow[foldIndex] = mirrorRow[x]\n        \n        foldedField.append(baseRow)\n\n    return foldedField\n\ndef foldY(index, field):\n    baseField = field[:index]\n    lastBaseIndex = len(baseField) - 1\n\n    foldedField = field[index+1:]\n    for y in range(len(foldedField)):\n        row = foldedField[y]\n        foldIndex = lastBaseIndex - y\n\n        for x in range(len(row)):\n            if row[x] == \"#\":\n                baseField[foldIndex][x] = row[x]\n\n    return baseField\n\ndef fold(axis, index, field):\n    print(\"Folding at {} axis on index {}\".format(axis, index))\n\n    if axis == \"x\":\n        return foldX(index, field)\n    \n    if axis == \"y\":\n        return foldY(index, field)\n    return field\n\ndef solutionPart1(input):\n    field = [['.' for _ in range(input[\"xmax\"] + 1)] for _ in range(input[\"ymax\"] + 1)]\n    for dot in input[\"dots\"]:\n        field[dot[\"y\"]][dot[\"x\"]] = \"#\"\n\n    instruction = input[\"instructions\"][0]\n    field = fold(instruction[\"axis\"], instruction[\"index\"], field)\n    \n    visibleDots = 0\n    for row in field:\n        print(row)\n        visibleDots += row.count(\"#\")\n    \n    print(\"There are {} visible dots in the folded sheet\".format(visibleDots))\n\ndef solutionPart2(input):\n    field = [['.' for _ in range(input[\"xmax\"] + 1)] for _ in range(input[\"ymax\"] + 1)]\n    for dot in input[\"dots\"]:\n        field[dot[\"y\"]][dot[\"x\"]] = \"#\"\n\n    instruction = input[\"instructions\"][0]\n    for instruction in input[\"instructions\"]:\n        field = fold(instruction[\"axis\"], instruction[\"index\"], field)\n        print(\"New Field:\")\n    \n    visibleDots = 0\n\n    print(\"Code:\")\n    for row in field:\n        minimalizedRow = \"\"\n        for dot in row:\n            minimalizedRow += dot\n            minimalizedRow += \" \"\n        print(minimalizedRow)\n        visibleDots += row.count(\"#\")\n\nif __name__ == \"__main__\":\n    part = int(sys.argv[1])\n    inputfile = sys.argv[2]\n    \n\n    print(\"Solution {} for file {}\".format(part, inputfile))\n\n    input = readInput(inputfile)\n    if part == 1:\n        solutionPart1(input)\n\n    else:\n        solutionPart2(input)\n\n","sub_path":"2021/Day 13/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":3516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"78239095","text":"# coding: utf-8\r\n# Your code here!\r\ndef euclid(a,b):\r\n    while b != 0:\r\n        a,b = b,a%b\r\n    return a\r\n\r\n\r\nmonsters = int(input())\r\narray = list(map(int,input().split()))\r\nfor i in range(monsters-1):\r\n    mod = euclid(array[i], array[i+1])\r\n    array[i+1] = mod\r\nprint(array[-1])","sub_path":"re118c.py","file_name":"re118c.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"638433316","text":"import unittest2 as unittest\n\n\n# ================ code ===================\nclass Solution:\n    def isSubsequence(self, s: str, t: str) -> bool:\n        b = iter(t)  # 把 t 转化成了一个迭代器\n        print(b)\n        gen = (i for i in s)  # 产生一个生成器， 这个生成器可以遍历对象 s\n        print(gen)\n        for i in gen:\n            print(i)\n        gen = ((i in b) for i in s)\n        print(gen)\n        for i in gen:\n            print(i)\n        return all(((i in b) for i in s))\n\n\n# ========================================\nTEST_DATA = [\n    {\n        \"input\": [\"abc\", \"ahbgdc\"],\n        \"target\": True\n    },\n    {\n        \"input\": [\"axc\", \"ahbgdc\"],\n        \"target\": False\n    }\n]\n\n\nclass Test(unittest.TestCase):\n    @unittest.skipIf(not isinstance(TEST_DATA[0][\"input\"], list), \"skip\")\n    def test_multi(self):\n        for t in TEST_DATA:\n            self.assertEqual(Solution.isSubsequence(self, *t[\"input\"]), t[\"target\"])\n\n    @unittest.skipIf(isinstance(TEST_DATA[0][\"input\"], list), \"skip\")\n    def test_single(self):\n        for t in TEST_DATA:\n            self.assertEqual(Solution.findTheDifference(self, t[\"input\"]), t[\"target\"])\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"647650839","text":"\n\nfrom xai.brain.wordbase.nouns._sweeper import _SWEEPER\n\n#calss header\nclass _SWEEPERS(_SWEEPER, ):\n\tdef __init__(self,): \n\t\t_SWEEPER.__init__(self)\n\t\tself.name = \"SWEEPERS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"sweeper\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_sweepers.py","file_name":"_sweepers.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"339814952","text":"import matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport numpy as np\n\nclass six_figures():\n\tdef __init__(self, show=True):\n\t\tprint ('Visualization module initializing ... ')\n\t\tself.show = show\n\t\tif show == True:\n\t\t\tplt.ion()\n\t\t\n\t\tplt.rcParams['image.cmap'] = 'gray'\n\t\tfig_size = (12,8) \n\t\tself.fig = plt.figure(figsize=fig_size)\n\t\tself.gs = gridspec.GridSpec(2,6, wspace=0.8, hspace=0.6)\n\n\t\tself.axs = [\n\t\t\t\t\tself.fig.add_subplot(self.gs[0,0:2]),\n\t\t\t\t\tself.fig.add_subplot(self.gs[0,2:4]),\n\t\t\t\t\tself.fig.add_subplot(self.gs[0,4:6]),\n\t\t\t\t\tself.fig.add_subplot(self.gs[1,0:2]),\n\t\t\t\t\tself.fig.add_subplot(self.gs[1,2:4]),\n\t\t\t\t\tself.fig.add_subplot(self.gs[1,4:6])\t\t\n\t\t\t\t\t]\n\t\tprint ('Complete!')\n\n\tdef draw(self, idx=1, mode='curve', data=[1,2,3,2,3], title='', data2=0):\n\t\tself.axs[idx].cla()\n\t\ttry:\n\t\t\tself.axs[idx].set_title(title)\n\t\t\tif mode == 'curve':\n\t\t\t\tself.axs[idx].grid()\n\t\t\t\tself.axs[idx].plot(data)\n\t\t\t\tself.axs[idx].set_xlabel('current: ' + str(data[-1]))\n\t\t\tif mode == 'imshow':\n\t\t\t\tself.axs[idx].imshow(data)\n\t\t\tif mode == 'text':\n\t\t\t\tself.axs[idx].text(0.1, 0.4, data, fontsize=15)\n\t\t\t\tself.axs[idx].xaxis.set_ticklabels([])\n\t\t\t\tself.axs[idx].yaxis.set_ticklabels([])\t\n\t\t\tif mode == 'twocurve':\n\t\t\t\tself.axs[idx].grid()\n\t\t\t\tself.axs[idx].plot(data, label='train')\n\t\t\t\tself.axs[idx].plot(data2, label='test')\n\t\t\t\tself.axs[idx].set_xlabel('\\ntrain:'+ str(data[-1]) +'\\nvali:'+ str(data2[-1]))\n\t\t\t\tself.axs[idx].set_ylim(0, 0.25)\n\t\t\t\tself.axs[idx].legend()\n\t\texcept:\n\t\t\tprint ('Warning: Figure drawing failure!')\n\t\t\tprint ('At: ' + str(idx) + ' with mode: ' + mode)\n\n\tdef display(self):\n\t\tif self.show == True:\n\t\t\tplt.draw()\n\t\t\tplt.pause(0.01)\n\t\telse:\n\t\t\tprint ('Figure display mode disabled.  No figure output.')\n\n\tdef save(self):\n\t\tplt.savefig('tfsave.pdf')\n\nif __name__ == '__main__':\n\t_show = six_figures()\n\t_show.draw()\n\t_show.display()","sub_path":"lib/visual.py","file_name":"visual.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"543865845","text":"from django.conf.urls import patterns, url\n\n\nurlpatterns = patterns('userlogin.views',\n                       url(r'^$', 'home', name='home'),\n                       url(r'^login$', 'login_handler', name='login_handler'),\n                       url(r'^logout$', 'logout_handler', name='logout_handler'),\n                       url(r'^users$', 'users', name='show_users'),\n                       url(r'^users/add$', 'add_user', name='add_user'),\n                       url(r'^users/upload_list$', 'upload_user_list', name='upload_user_list'),\n                       url(r'^user/set_branch$', 'set_branch', name='set_branch'),\n                       url(r'^user/get_message$', 'get_message', name='get_message'),\n)\n","sub_path":"userlogin/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"244585070","text":"import socket\nimport select\nimport time\nimport pickle\n\nfrom ConstantVariables import ConstantVariables\nfrom GameServer import GameServer\n\n\nclass ServerSocket:\n    def __init__(self):\n        self._header_length = ConstantVariables.NETWORK_HEADER_LENGTH\n        self._server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self._server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        self._server_socket.bind((ConstantVariables.NETWORK_IP, ConstantVariables.NETWORK_PORT))\n        self._server_socket.listen()\n        self._sockets_list = [self._server_socket]\n        self._clients = {}\n        self._game = GameServer()\n        self._client_count = 0\n        print(f'Listening for connections on {ConstantVariables.NETWORK_IP}:{ConstantVariables.NETWORK_PORT}...')\n\n    def start(self):\n\n        self._game.start()\n\n        while True:\n\n            read_sockets, _, exception_sockets = select.select(self._sockets_list, [], self._sockets_list)\n\n            for notified_socket in read_sockets:\n\n                # new connection\n                if notified_socket == self._server_socket:\n                    client_socket, client_address = self._server_socket.accept()\n                    client_message = self.receive_message(client_socket)\n\n                    if client_message is False:\n                        continue\n\n                    if client_message['data'].decode('utf-8') == \"ask_id\":\n                        self.create_client_and_send_id(client_socket)\n\n                    print('Accepted new connection from {}:{}, request: {}'.format(*client_address,\n                                                                                   client_message['data'].decode(\n                                                                                       'utf-8')))\n\n                # known connection\n                else:\n\n                    message = self.receive_message(notified_socket)\n\n                    if message is False:\n                        print('Closed connection from: {}'.format(self._clients[notified_socket]))\n\n                        # remove client\n                        for snake in self._game.snakes:\n                            if snake.id_client == self._clients[notified_socket]:\n                                self._game.snakes.remove(snake)\n\n                        self._sockets_list.remove(notified_socket)\n                        del self._clients[notified_socket]\n                        continue\n                        #\n\n                    client_message = self._clients[notified_socket]\n                    print(f\"Received message from {client_message}\")\n\n                    # update the snake of the client\n                    for snake in self._game.snakes:\n                        if snake.id_client == client_message:\n\n                            # change direction of the snake\n                            snake.direction_current = message['data'].decode('utf-8')\n\n                            # move the snake\n                            snake.move_head()\n                            if self._game.is_apple_caught(snake):\n                                self._game.create_new_apple()\n                                snake.growth()\n                            else:\n                                snake.move_body()\n\n                            # if the client has lost the game\n                            if self.is_client_lost(snake):\n                                # send to client\n                                message_game = \"\".encode('utf-8')\n                                message_game = bytes(f\"{len(message_game):<{self._header_length}}\",\n                                                     'utf-8') + message_game\n                                notified_socket.send(message_game)\n\n                                # remove client\n                                print('Closed connection from: {}'.format(self._clients[notified_socket]))\n                                self._game.snakes.remove(snake)\n                                self._sockets_list.remove(notified_socket)\n                                del self._clients[notified_socket]\n                                break\n\n                    # send game to the client\n                    message_game = pickle.dumps(self._game)\n                    message_game = bytes(f\"{len(message_game):<{self._header_length}}\", 'utf-8') + message_game\n                    notified_socket.send(message_game)\n\n            # remove clients of the notifications\n            for notified_socket in exception_sockets:\n                self._sockets_list.remove(notified_socket)\n                del self._clients[notified_socket]\n\n    def receive_message(self, client_socket):\n        try:\n            message_header = client_socket.recv(self._header_length)\n\n            if not len(message_header):\n                # if no data receive, client closed connection\n                return False\n\n            message_length = int(message_header.decode('utf-8').strip())\n\n            message = client_socket.recv(message_length)\n\n            return {'header': message_header, 'data': message}\n\n        except:\n            return False\n\n    def create_client_and_send_id(self, client_socket):\n        self._client_count += 1\n        client_id = str(self._client_count)  # the id must be a str for sending\n        self._clients[client_socket] = client_id\n\n        # creation of the snake of the client\n        self._game.create_snake(client_id)\n        # add the client id ine the list of clients\n        self._sockets_list.append(client_socket)\n\n        # send the id to the client\n        message = client_id.encode('utf-8')\n        message_header = f\"{len(message):<{self._header_length}}\".encode('utf-8')\n        client_socket.send(message_header + message)\n\n    def is_client_lost(self, snake_client):\n        for snake in self._game.snakes:\n            if snake != snake_client:\n                if snake.get_head_coordinate() == snake_client.get_head_coordinate():\n                    return True\n                elif snake_client.get_head_coordinate() in snake.body_coordinates:\n                    return True\n            else:\n                if snake.get_head_coordinate() in snake.body_coordinates:\n                    return True\n        return False\n","sub_path":"executables/graphic_with_network/ServerSocket.py","file_name":"ServerSocket.py","file_ext":"py","file_size_in_byte":6320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"472064276","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pylab as pl\nimport scipy as sp\nfrom scipy import stats\nimport sys\n\nroi = sys.argv[1]\nsubjs = ['MES_022817_0','MES_030217_0', 'MES_032117_1','MES_040217_0','MES_041117_0','MES_041217_0','MES_041317_0', 'MES_041417_0','MES_041517_0','MES_042017_0','MES_042317_0','MES_042717_0','MES_050317_0','MES_051317_0','MES_051917_0','MES_052017_0','MES_052017_1','MES_052317_0','MES_052517_0','MES_052617_0','MES_052817_0','MES_052817_1','MES_053117_0','MES_060117_0','MES_060117_1']\ndatadir = '/jukebox/norman/jamalw/MES/subjects/'\n\ncorrD3D = np.zeros((2511,2511,len(subjs)))\navgCorrD3D = np.zeros((16,16,len(subjs)))\n\nfor i in range(len(subjs)):\n    corrD3D[:,:,i]   = np.load(datadir+str(subjs[i]) + '/data/corrD' + roi + '.npy')\n    avgCorrD3D[:,:,i] = np.load(datadir+str(subjs[i]) + '/data/avgCorrD' + roi + '.npy')\n\nmeanCorrFull = np.mean(corrD3D,2)\nmeanCorrAvg  = np.mean(avgCorrD3D,2)\n\n# compute average section of avgCorrD\ncorr_eye = np.identity(8)\nclassical_within  = meanCorrAvg[0:8,0:8]\nclassical_within_off  = classical_within[corr_eye == 0]\njazz_within       = meanCorrAvg[8:16,8:16]\njazz_within_off       = jazz_within[corr_eye == 0]\nclassJazz_between = meanCorrAvg[8:16,0:8]\nclassJazz_between_off = classJazz_between[corr_eye == 0]\njazzClass_between = meanCorrAvg[0:8,8:16]\njazzClass_between_off = jazzClass_between[corr_eye == 0]\n\nlabels = [\"Classical\",\"Jazz\"]\n\nplt.figure(1,facecolor=\"1\")\nplt.imshow(meanCorrFull,interpolation='none')\nplt.colorbar()\nplt.axis('tight')\nax = plt.gca()\nplt.plot((ax.get_ylim()[0],ax.get_ylim()[1]),(ax.get_xlim()[1],ax.get_xlim()[0]),\"k-\")\npl.text(2800,2800,'N='+ str(len(subjs)),fontweight='bold')\nplt.plot((-.5, 2511.5), (1255.5, 1255.5), 'k-')\nplt.plot((1255.5, 1255.5), (-.5, 2511.5), 'k-')\npl.text(500,-70,labels[0])\npl.text(500,2800,labels[0])\npl.text(2550,500,labels[0],rotation='270')\npl.text(-300,500,labels[0],rotation='vertical')\npl.text(1800,-70,labels[1])\npl.text(-300,1800,labels[1],rotation='vertical')\npl.text(1800,2800,labels[1])\npl.text(2550,1800,labels[1],rotation='270')\npl.text(900.5, -200,'Full Correlation Matrix',fontweight='bold')\nplt.savefig('/jukebox/norman/jamalw/MES/data/plots/FullCorrMat_N' + str(len(subjs)) + roi)\n\nplt.figure(2,facecolor=\"1\")\nax = plt.gca()\nplt.imshow(meanCorrAvg,interpolation='none')\nplt.plot((-.5, 15.5), (7.5, 7.5), 'k-')\nplt.plot((7.5, 7.5), (-.5, 15.5), 'k-')\nplt.colorbar()\nplt.axis('tight')\nplt.plot((ax.get_ylim()[0],ax.get_ylim()[1]),(ax.get_xlim()[1],ax.get_xlim()[0]),\"k-\")\nplt.text(2.75,-1,labels[0],fontsize=15)\nplt.text(2.75,17,labels[0],fontsize=15)\nplt.text(15.65,2.75,labels[0],rotation='270',fontsize=15)\nplt.text(-2,2.75,labels[0],rotation='vertical',fontsize=15)\nplt.text(10.75,-1,labels[1],fontsize=15)\nplt.text(-2,10.75,labels[1],rotation='vertical',fontsize=15)\nplt.text(15.65,10.75,labels[1],rotation='270',fontsize=15)\nplt.text(10.75,17,labels[1],fontsize=15)\nplt.text(18,17,'N='+ str(len(subjs)),fontweight='bold',fontsize=15)\nplt.text(19.5,8,'r',fontweight='bold',fontsize=15)\nplt.text(1,-1.75,'Average Within-Song Correlation',fontweight='bold',fontsize=18)\nplt.savefig('/jukebox/norman/jamalw/MES/data/plots/AvgCorrMat_N' + str(len(subjs)) + roi)\n\nplt.figure(3,facecolor=\"1\")\nallComparisonsAvg = np.array([np.mean(classical_within_off),np.mean(jazz_within_off),np.mean(classJazz_between_off),np.mean(jazzClass_between_off)])\nallComparisonsSem = np.array([stats.sem(classical_within_off),stats.sem(jazz_within_off),stats.sem(classJazz_between_off),stats.sem(jazzClass_between_off)])\nN = 4\nind = np.arange(N)\nwidth = 0.35\nplt.bar(ind, allComparisonsAvg, width, color='k',yerr = allComparisonsSem,error_kw=dict(ecolor='lightseagreen',lw=3,capsize=0,capthick=0))\nplt.ylabel('Pattern Similarity (r)',fontsize=15)\nplt.title('Average Within and Between-Genre Similarity',fontweight='bold',fontsize=18)\nlabels = ['Classical vs Classical', 'Jazz vs Jazz', 'Jazz vs Classical', 'Classical vs Jazz']\nplt.xticks(ind + width / 2,labels,fontsize=12)\nplt.plot((-0.175,3.5),(0,0),'k-')\nplt.savefig('/jukebox/norman/jamalw/MES/data/plots/AvgGenreSim_N' + str(len(subjs)) + roi) \n\n# Plot average Within song and Between song comparison\nplt.figure(4,facecolor=\"1\")\ncorr_eye = np.identity(16)\nWithinBetwnSongAvgCorr = np.array([np.mean(meanCorrAvg[corr_eye == 1]),np.mean(meanCorrAvg[corr_eye == 0])])\nWithinBetwnSongSemCorr = np.array([stats.sem(meanCorrAvg[corr_eye == 1]),stats.sem(meanCorrAvg[corr_eye == 0])])\nN = 2\nind = np.arange(N)\nwidth = 0.35\nplt.bar(ind, WithinBetwnSongAvgCorr, width, color='k',yerr=WithinBetwnSongSemCorr,error_kw=dict(ecolor='lightseagreen',lw=3,capsize=0,capthick=0))\nplt.ylabel('Pattern Similarity (r)',fontsize=15)\nplt.title('Average Within- and Between-Song Similarity',fontweight='bold',fontsize=18)\nlabels = ['Same Song','Different Song']\nplt.xticks(ind + width / 2,labels,fontsize=15)\nplt.plot((-0.175,1.5),(0,0),'k-')\nplt.savefig('/jukebox/norman/jamalw/MES/data/plots/AvgSongSim_N' + str(len(subjs)) + roi)\n\n# Plot average Within song and Between song correlation for each genre\nplt.figure(5,facecolor=\"1\")\n#compute average of within song/within genre correlations \nWithinSongCorr = meanCorrAvg[corr_eye == 1]\nWithinSongAvgCorr = np.mean(meanCorrAvg[corr_eye == 1])\nClassicalWithinAvgOn = np.mean(WithinSongCorr[0:7])\nJazzWithinAvgOn = np.mean(WithinSongCorr[8:15])\nClassicalWithinSemOn = stats.sem(WithinSongCorr[0:7])\nJazzWithinSemOn = stats.sem(WithinSongCorr[8:15])\n\n#compute average of between song/within genre correlations\ncorrEye8 = np.identity(8)\nClassicalBtwnAvgOff = np.mean(classical_within[corrEye8 == 0])\nClassicalBtwnSemOff = stats.sem(classical_within[corrEye8 == 0])\nJazzBtwnAvgOff = np.mean(jazz_within[corrEye8 == 0])\nJazzBtwnSemOff = stats.sem(jazz_within[corrEye8 == 0])\n\nAvgAllGroups = np.array([ClassicalWithinAvgOn,ClassicalBtwnAvgOff,JazzWithinAvgOn,JazzBtwnAvgOff])\nSemAllGroups = np.array([ClassicalWithinSemOn,ClassicalBtwnSemOff,JazzWithinSemOn,JazzBtwnSemOff])\n\nN = 4\nind = np.arange(N)\nwidth = 0.35\nplt.bar(ind, AvgAllGroups, width, color='k',yerr=SemAllGroups,error_kw=dict(ecolor='lightseagreen',lw=3,capsize=0,capthick=0))\nplt.ylabel('Pattern Similarity (r)',fontsize=15)\nplt.title('Within- and Between-Song Similarity Within Genre',fontweight='bold',fontsize=18)\nlabels = ['Classical Within','Classical Between','Jazz Within', 'Jazz Between']\nplt.xticks(ind + width / 2,labels,fontsize=14)\nplt.plot((-0.175,1.5),(0,0),'k-')\npl.text(18,17,'N=2',fontweight='bold')\nplt.savefig('/jukebox/norman/jamalw/MES/data/plots/WithinGenreOnOffDiag_N' + str(len(subjs)) + roi)\n\n\n\nplt.show()\n\n\n","sub_path":"PlotAvgFuncXGenre.py","file_name":"PlotAvgFuncXGenre.py","file_ext":"py","file_size_in_byte":6609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"277505487","text":"from sklearn.preprocessing import MaxAbsScaler, RobustScaler, QuantileTransformer, PowerTransformer\n# 실습 MaxAbsScaler, RobustScaler, QuantileTransformer, PowerTransformer 각 결과 적어놓기\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.datasets import load_diabetes\nfrom icecream import ic\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import r2_score\n\n\n#1. 데이터\ndatasets = load_diabetes()\n\nx = datasets.data\ny = datasets.target\n\n# ic(x.shape, y.shape)  # (442, 10)  (442,)\n\n# ic(datasets.feature_names)   \n#['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n# ic(datasets.DESCR)\n\n# ic(x[:30])\n# ic(np.min(y), np.max(y))\n\nx_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, shuffle=True, random_state=9)\n\n# 데이터 전처리\n# scaler = MaxAbsScaler()\n# scaler = RobustScaler()\n# scaler = QuantileTransformer()\nscaler = PowerTransformer()\nscaler.fit(x_train)\nx_train = scaler.transform(x_train)\nx_test = scaler.transform(x_test)\n\n\n#2. 모델구성(validation)\nmodel = Sequential()\nmodel.add(Dense(100, input_shape=(10,), activation='relu'))  #relu : 음수값은 0, 양수만 제대로 잡음\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(8, activation='relu'))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(16, activation='relu'))\nmodel.add(Dense(1))\n\n\n#3. 컴파일, 훈련\nmodel.compile(loss='mse', optimizer='adam')\nmodel.fit(x_train, y_train, epochs=100, batch_size=10, validation_split=0.1, shuffle=True, verbose=1)\n\n#4. 평가, 예측(mse, r2)\nloss = model.evaluate(x_test, y_test)\nic(loss)\n\ny_predict = model.predict(x_test)\nr2 = r2_score(y_test, y_predict)\nic(r2)\n\n\n\n'''\n* MaxAbsScaler\nic| loss: 2240.12841796875\nic| r2: 0.588361118619634\n\n* RobustScaler\nic| loss: 3183.495361328125\nic| r2: 0.41501099016114074\n\n* QuantileTransformer\nic| loss: 2429.1533203125\nic| r2: 0.5536265040327801\n\n* PowerTransformer\nic| loss: 3609.73388671875\nic| r2: 0.33668676137425313\n'''","sub_path":"01_keras/keras22.diabets_Scaler.py","file_name":"keras22.diabets_Scaler.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"339594059","text":"import requests\nfrom uuid import uuid4\nfrom urllib.parse import quote\nimport os\nimport time\nfrom concurrent.futures import ThreadPoolExecutor\nheaders = {\n    \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.62 Safari/537.36\",\n    \"Referer\": \"https://image.baidu.com/search/index?tn=baiduimage&ipn=r&ct=201326592&cl=2&lm=-1&st=-1&fm=index&fr=&hs=0&xthttps=111111&sf=1&fmq=&pv=&ic=0&nc=1&z=&se=1&showtab=0&fb=0&width=&height=&face=0&istype=2&ie=utf-8&word=%E7%BE%8E%E5%A5%B3&oq=%E7%BE%8E%E5%A5%B3&rsp=-1\",\n    \"Cookie\": \"BDqhfp=%E7%BE%8E%E5%A5%B3%26%260-10-1undefined%26%261576%26%263; BIDUPSID=55B9F89BBD8CE76842ADD9DCE4E07064; PSTM=1588574309; BAIDUID=55B9F89BBD8CE76850CAB1E8E50293E6:FG=1; cflag=13%3A3; BDUSS=kxVN2lhTmEzVFVKcGU2UjMtfmhEV25ZVkRkazdRS2hhNHdQbUx4MmlVaVB2ZGxlSVFBQUFBJCQAAAAAAAAAAAEAAAAIy~xb09DDqMTl0f3f9wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI8wsl6PMLJea; BDRCVFR[X_XKQks0S63]=mk3SLVN4HKm; userFrom=www.google.com; BDRCVFR[-pGxjrCMryR]=mk3SLVN4HKm; firstShowTip=1; indexPageSugList=%5B%22%E7%BE%8E%E5%A5%B3%22%5D; cleanHistoryStatus=0; BDRCVFR[dG2JNJb_ajR]=mk3SLVN4HKm\"\n}\n#初始化一些参数，方便更好的调用\nsession = requests.session()\nsession.headers = headers\ndef get_img(url):\n    html = session.get(url)\n    #构造一个多线程池，就10个线程把\n    threadingPool = ThreadPoolExecutor(max_workers=10)\n    #为什么这里要用到防错机制，因为如果是用json的话，有一些图片可能会失败，要想全部成功获得，只有正则才行\n    try:\n        #定位它的内容\n        content = html.json()['data']\n        #用循环叠带把这些内容给打印出来\n        for c in content[:-1]:\n            #输出它的URL\n            print(c['middleURL'])\n            #用多线程来下载这些图片，从而提升我们的下载速度，达到我们的目的\n            threadingPool.submit(download,url)\n    except:\n        pass\n\ndef download(imgurl):\n    filename = '图片保存文件'\n    #创建一个文件夹，如果这个文件夹不存在，那么我们就创建这个文件夹，这个是一条死语句，必须得背下来\n    if not os.path.exists(filename):\n        os.makedirs(filename)\n    #获取这个图片的URL\n    image = session.get(imgurl)\n    #uuid4只是为了更好的命名而已，毕竟那么多图片，不可能一个个命名\n    with open(\"图片保存文件/{}.jpg\".format(uuid4()),\"wb\") as f:\n        f.write(image.content)\n\ndef main():\n    KEYWORD = input(\"请输入你要下载的图片内容：\")\n    for i in range(30,300,30):\n        url = \"https://image.baidu.com/search/acjson?tn=resultjson_com&ipn=rj&ct=201326592&is=&fp=result&queryWord={}&cl=2&lm=-1&ie=utf-8&oe=utf-8&adpicid=&st=-1&z=&ic=0&hd=&latest=&copyright=&word={}&s=&se=&tab=&width=&height=&face=0&istype=2&qc=&nc=1&fr=&expermode=&force=&cg=girl&pn={}&rn=30&gsm=96&1589535866384=\". \\\n            format(quote(KEYWORD), quote(KEYWORD), i)\n        get_img(url)\n\nif __name__ == '__main__':\n    start = time.time()\n    main()\n    print(time.time()-start)\n\n\n","sub_path":"爬虫之进阶篇/异步爬虫/baidutupian.py","file_name":"baidutupian.py","file_ext":"py","file_size_in_byte":3123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"634212758","text":"# -*- encoding: utf-8 -*-\n# pylint: disable=E0203,E1101,C0111\n\"\"\"\n@file\n@brief Runtime operator.\n\"\"\"\nimport numpy\nfrom numpy.fft import fft2\nfrom ..shape_object import ShapeObject\nfrom ._op import OpRun\nfrom ._new_ops import OperatorSchema\n\n\nclass FFT2D(OpRun):\n\n    atts = {'axes': [-2, -1]}\n\n    def __init__(self, onnx_node, desc=None, **options):\n        OpRun.__init__(self, onnx_node, desc=desc,\n                       expected_attributes=FFT2D.atts,\n                       **options)\n        if self.axes is not None:\n            self.axes = tuple(self.axes)\n            if len(self.axes) != 2:\n                raise ValueError(  # pragma: no cover\n                    \"axes must a set of 1 integers not %r.\" % self.axes)\n\n    def _find_custom_operator_schema(self, op_name):\n        if op_name == \"FFT2D\":\n            return FFT2DSchema()\n        raise RuntimeError(  # pragma: no cover\n            \"Unable to find a schema for operator '{}'.\".format(op_name))\n\n    def _run(self, a, fft_length=None):  # pylint: disable=W0221\n        if fft_length is None:\n            y = fft2(a, axes=self.axes)\n        else:\n            y = fft2(a, tuple(fft_length), axes=self.axes)\n        if a.dtype in (numpy.float32, numpy.complex64):\n            return (y.astype(numpy.complex64), )\n        if a.dtype in (numpy.float64, numpy.complex128):\n            return (y.astype(numpy.complex128), )\n        raise TypeError(  # pragma: no cover\n            \"Unexpected input type: %r.\" % a.dtype)\n\n    def _infer_shapes(self, a, b=None):  # pylint: disable=W0221,W0237\n        if a.dtype in (numpy.float32, numpy.complex64):\n            return (ShapeObject(a.shape, dtype=numpy.complex64), )\n        if a.dtype in (numpy.float64, numpy.complex128):\n            return (ShapeObject(a.shape, dtype=numpy.complex128), )\n        raise TypeError(  # pragma: no cover\n            \"Unexpected input type: %r.\" % a.dtype)\n\n    def _infer_types(self, a, b=None):  # pylint: disable=W0221,W0237\n        if a.dtype in (numpy.float32, numpy.complex64):\n            return (numpy.complex64, )\n        if a.dtype in (numpy.float64, numpy.complex128):\n            return (numpy.complex128, )\n        raise TypeError(  # pragma: no cover\n            \"Unexpected input type: %r.\" % a.dtype)\n\n    def to_python(self, inputs):\n        if self.axes is not None:\n            axes = tuple(self.axes)\n        else:\n            axes = None\n        if len(inputs) == 1:\n            return ('from numpy.fft import fft2',\n                    \"return fft2({}, axes={})\".format(\n                        inputs[0], axes))\n        return ('from numpy.fft import fft2',\n                \"return fft2({}, tuple({}), axes={})\".format(\n                    inputs[0], inputs[1], axes))\n\n\nclass FFT2DSchema(OperatorSchema):\n    \"\"\"\n    Defines a schema for operators added in this package\n    such as @see cl FFT.\n    \"\"\"\n\n    def __init__(self):\n        OperatorSchema.__init__(self, 'FFT2D')\n        self.attributes = FFT2D.atts\n","sub_path":"mlprodict/onnxrt/ops_cpu/op_fft2d.py","file_name":"op_fft2d.py","file_ext":"py","file_size_in_byte":2984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"630049488","text":"from flask import jsonify, request, Response\r\nfrom flask_restful import Resource\r\n\r\nfrom models.parts import Actions\r\nfrom etc.utcTimeStamp import get_utc_time_stamp\r\nfrom etc.errors import *\r\n\r\nnow = get_utc_time_stamp()\r\n\r\nclass ActionsApi(Resource):\r\n    def get(self, action_id) -> Response:\r\n        output = Actions.objects.get(id=action_id)\r\n        return jsonify({'result': output})\r\n\r\n    def put(self, action_id) -> Response:\r\n    \tdata = request.get_json()\r\n    \tif \"date\" in data:\r\n    \t\tif data[\"date\"] < now:\r\n    \t\t\treturn 401\r\n    \tput_data = Actions.objects(id=action_id).update(**data)\r\n    \treturn jsonify({\"result\": put_data})\r\n\r\n    def delete(self, action_id) -> Response:\r\n    \tout = Actions.objects(id=action_id).delete()\r\n    \treturn jsonify({\"result\": out})\r\n\r\nclass ActionApi(Resource):\r\n\tdef post(self) -> Response:\r\n\t\tdata = request.get_json()\r\n\t\tif \"date\" in data:\r\n\t\t\tif data[\"date\"] < now:\r\n\t\t\t\treturn 401\r\n\t\tpost_data = Actions(**data).save()\r\n\t\tout = {\"id\" : str(post_data.id)}\r\n\t\treturn jsonify({\"result\":out})","sub_path":"api/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"420657757","text":"from typing import List\n\n\nclass Solution:\n    def insert(self, intervals: List[List[int]], newInterval: List[int]) -> List[List[int]]:\n        intervals.append(newInterval)\n        intervals.sort()\n\n        i = 1\n        while i < len(intervals):\n            while i < len(intervals) and intervals[i - 1][1] >= intervals[i][0]:\n                intervals[i - 1][1] = max(intervals[i - 1][1], intervals[i][1])\n                intervals.pop(i)\n            i += 1\n        return intervals","sub_path":"leetcode/p0057_insert_interval/my_attempt.py","file_name":"my_attempt.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"361918187","text":"# -*- coding: utf-8 -*-\n\nfrom ...constants import ANNOTATIONS\n\n__all__ = [\n    'strip_annotations',\n]\n\n\ndef strip_annotations(graph):\n    \"\"\"Strips all the annotations from a BEL graph\n\n    :param pybel.BELGraph graph: A BEL graph\n    \"\"\"\n    for u, v, k in graph.edges_iter(keys=True):\n        if ANNOTATIONS in graph.edge[u][v][k]:\n            del graph.edge[u][v][k][ANNOTATIONS]\n","sub_path":"src/pybel/struct/mutation/metadata.py","file_name":"metadata.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"276379532","text":"import matplotlib.pyplot as plt\n\nclass Visualizer():\n    def __init__(self, array, taus, valids=None, stamps=None, source_pos=None):\n        self.array = array\n        self.num_mics = len(array.positions)\n\n        if stamps is None and not isinstance(taus[0][0], list):\n            self.taus = [taus]\n            self.stamps = [0]\n\n            if self.valids is None:\n                self.valids = None\n            else:\n                self.valids = [valids]\n        else:\n            self.taus = taus\n            self.valids = valids\n            self.stamps = stamps\n\n        self.source_pos = source_pos\n\n\n\n    def evaluate(self):\n        for i in range(0, len(self.stamps)):\n            fig = plt.figure()\n            self.create_plot(fig, i)\n\n        plt.show()\n\n    def create_plot(self, fig, iteration):\n        fig.suptitle(\"Tdoa at timestamp: \" + str(self.stamps[iteration]))\n\n        for mic in range(0, self.num_mics):\n            self.create_sub_plot(fig, mic, iteration)\n\n    \n    def create_sub_plot(self, fig, mic_nr, iteration):\n        #if mic_nr > 4:\n        #    mic_i = mic_nr - 4 + 1\n        #    upper_i = 2\n        #else:\n        #    mic_i = mic_nr + 1\n        #    upper_i = 1\n        \n        sub_p = fig.add_subplot(2, 4, mic_nr + 1)\n\n        self.add_array(sub_p)\n        self.draw_connections(sub_p, mic_nr, iteration)\n\n        if not self.source_pos is None:\n            self.add_source(sub_p)\n        \n        sub_p.set_title(\"Microphone: \" + str(mic_nr + 1))\n\n    \n    def add_array(self, sub_p):\n        x = []\n        y = []\n        for pos in self.array.positions:\n            x.append(pos[0])\n            y.append(pos[1])\n\n        sub_p.scatter(x, y, marker='o')\n\n\n    \n    def draw_connections(self, sub_p, mic_nr, iteration):\n        \n        for i in range(0, self.num_mics):\n            if not i == mic_nr:\n                tau = self.taus[iteration][mic_nr][i]\n                if not self.valids is None:\n                    valid = self.valids[iteration][mic_nr][i]\n                else:\n                    valid = True\n                    \n                self.draw_connection(sub_p, mic_nr, i, tau, valid)\n\n    #https://stackoverflow.com/questions/12864294/adding-an-arbitrary-line-to-a-matplotlib-plot-in-ipython-notebook\n    def draw_connection(self, sub_p, mic1, mic2, tau, valid):\n        pos1 = self.array.get_position(mic1)\n        pos2 = self.array.get_position(mic2)\n        pos3 = ((pos1[0] + pos2[0]) / 2, (pos1[1] + pos2[1]) / 2)\n\n        if not valid:\n            c = 'red'\n        else:\n            c = 'black'\n\n        sub_p.annotate(\"\",\n              xy=pos1, xycoords='data',\n              xytext=pos2, textcoords='data',\n              arrowprops=dict(color = c,\n                              arrowstyle=\"-\",\n                              connectionstyle=\"arc3,rad=0.\"), \n              )\n\n        sub_p.annotate(str(tau),\n              color = c,\n              xy=pos3, xycoords='data',\n              xytext=pos3, textcoords='data',\n               \n              )\n\n    def add_source(self, sub_p):\n        x = self.source_pos[0]\n        y = self.source_pos[1]\n        sub_p.scatter(x, y, marker='x')\n\n\n","sub_path":"tdoa/visualizer.py","file_name":"visualizer.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"440894021","text":"import os\nfrom pwn import *\nrem = remote('edu-ctf.csie.org', 10150)\nrem.recvuntil('shellcode >')\n\ncontext.clear(arch='x86_64')\nshellcode = asm(shellcraft.sh())\navoid = '\\x00\\x05\\x0f'\nencoded = pwnlib.encoders.encoder.encode(shellcode, avoid, force = 1)\nprint('shellcode is ', shellcode)\nprint('encoded is ', encoded)\nprint(disasm(encoded))\n\nrem.sendline(encoded)\nrem.interactive()\n","sub_path":"Hw0x00/send_ceiba/solve_shellc0de.py","file_name":"solve_shellc0de.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"217628187","text":"import time\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torchvision.models as models\nimport torchvision\nimport captcha_config as config\nimport numpy as np\nimport copy\n\nclass RES50(nn.Module):\n    def __init__(self):\n        super(RES50, self).__init__()\n        self.num_cls = config.MAX_CAPTCHA * config.ALL_CHAR_SET_LEN\n        self.base = torchvision.models.resnet50(pretrained=False)\n        self.base.fc = nn.Linear(self.base.fc.in_features, self.num_cls)\n    def forward(self, x):\n        out = self.base(x)\n        return out\n\nclass LinfPGDAttack(object):\n    def __init__(self, model, epsilon=0.1, k=7, a=0.03, random_start=True):\n        self.model = model\n        self.epsilon = epsilon\n        self.k = k\n        self.a = a\n        self.random_start = random_start\n        self.loss_fn = nn.MultiLabelSoftMarginLoss()\n\n    def perturb(self, x_natural, y):\n        if self.random_start:\n            x = x_natural + np.random.uniform(-self.epsilon, self.epsilon, x_natural.shape).astype('float32')\n        else:\n            x = np.copy(x_natural)\n        for i in range(self.k):\n            x_var = Variable(torch.from_numpy(x).to(device), requires_grad=True)\n            y_var = Variable(y, requires_grad=True)\n            scores = self.model(x_var)\n            loss = self.loss_fn(scores, y_var)\n            loss.backward()\n            grad = x_var.grad.data.cpu().numpy()\n            x += self.a * np.sign(grad)\n            x = np.clip(x, x_natural - self.epsilon, x_natural + self.epsilon)\n            x = np.clip(x, 0, 1)\n        return x\n\ndef attack(x, y, model, adversary):\n    model_copied = copy.deepcopy(model)\n    for parameter in model_copied.parameters():\n        parameter.requires_grad = False\n    model_copied.eval()\n    adversary.model = model_copied\n    x_adv = adversary.perturb(x.numpy(), y)\n    return torch.from_numpy(x_adv)\n\nuse_cuda = True\nif use_cuda and torch.cuda.is_available():\n    print('Cuda Available')\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nnum_epochs = 80\nlearning_rate = 0.0005\n\ndef test():\n    print(\"[ Test Mode ]\")\n    cnn = RES50()\n    cnn = torch.nn.DataParallel(cnn)\n    cnn.to(device)\n    cnn.eval()\n    cnn.load_state_dict(torch.load(\"model_pgd_delayed_weak_3.pkl\"))\n\n    print(\"[ Model Loaded ]\")\n    criterion = nn.MultiLabelSoftMarginLoss()\n    optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)\n    adversary = LinfPGDAttack(model=cnn)\n    test_data_loader = config.get_test_data_loader()\n\n    correct = 0\n    total = 0\n    t0 = time.time()\n    count = 0\n    total_loss = 0.0\n\n    for i, (images, labels) in enumerate(test_data_loader):\n        variable = Variable(images).to(device)\n        predict_labels = cnn(variable).to(device)\n\n        temp_labels = Variable(labels.float()).to(device)\n        loss = criterion(predict_labels, temp_labels)\n\n        total_loss += loss.item()\n        count += 1\n\n        current_batch_size = labels.size(0)\n\n        for k in range(current_batch_size):\n            predict_label = predict_labels[k]\n            c0 = config.ALL_CHAR_SET[np.argmax(predict_label[0:config.ALL_CHAR_SET_LEN].data.cpu().numpy())]\n            c1 = config.ALL_CHAR_SET[np.argmax(predict_label[config.ALL_CHAR_SET_LEN:2 * config.ALL_CHAR_SET_LEN].data.cpu().numpy())]\n            c2 = config.ALL_CHAR_SET[np.argmax(predict_label[2 * config.ALL_CHAR_SET_LEN:3 * config.ALL_CHAR_SET_LEN].data.cpu().numpy())]\n            c3 = config.ALL_CHAR_SET[np.argmax(predict_label[3 * config.ALL_CHAR_SET_LEN:4 * config.ALL_CHAR_SET_LEN].data.cpu().numpy())]\n\n            predict_label = '%s%s%s%s' % (c0, c1, c2, c3)\n            true_label = config.decode(labels.numpy()[k])\n            if predict_label == true_label:\n                correct += 1\n\n        total += current_batch_size\n\n    print(\"Total Test Accuracy of the model on the %d test images: %f %%\" % (total, 100 * correct / total))\n    print(\"Time Spent:\", time.time() - t0)\n    print(\"Average Loss:\", total_loss / count)\n\n    correct = 0\n    total = 0\n    t0 = time.time()\n    count = 0\n    total_loss = 0.0\n\n    for i, (images, labels) in enumerate(test_data_loader):\n        temp_labels = Variable(labels.float()).to(device)\n        images_adv = attack(images, temp_labels, cnn, adversary)\n        images_adv_var = Variable(images_adv).to(device)\n        predict_labels = cnn(images_adv_var).to(device)\n\n        temp_labels = Variable(labels.float()).to(device)\n        loss = criterion(predict_labels, temp_labels)\n\n        total_loss += loss.item()\n        count += 1\n\n        current_batch_size = labels.size(0)\n\n        for k in range(current_batch_size):\n            predict_label = predict_labels[k]\n            c0 = config.ALL_CHAR_SET[np.argmax(predict_label[0:config.ALL_CHAR_SET_LEN].data.cpu().numpy())]\n            c1 = config.ALL_CHAR_SET[np.argmax(predict_label[config.ALL_CHAR_SET_LEN:2 * config.ALL_CHAR_SET_LEN].data.cpu().numpy())]\n            c2 = config.ALL_CHAR_SET[np.argmax(predict_label[2 * config.ALL_CHAR_SET_LEN:3 * config.ALL_CHAR_SET_LEN].data.cpu().numpy())]\n            c3 = config.ALL_CHAR_SET[np.argmax(predict_label[3 * config.ALL_CHAR_SET_LEN:4 * config.ALL_CHAR_SET_LEN].data.cpu().numpy())]\n\n            predict_label = '%s%s%s%s' % (c0, c1, c2, c3)\n            true_label = config.decode(labels.numpy()[k])\n            if predict_label == true_label:\n                correct += 1\n\n        total += current_batch_size\n\n    print(\"Total Test Accuracy of the model on the %d test images: %f %%\" % (total, 100 * correct / total))\n    print(\"Time Spent:\", time.time() - t0)\n    print(\"Average Loss:\", total_loss / count)\n\ndef main():\n    cnn = RES50()\n    cnn = torch.nn.DataParallel(cnn)\n    cnn.cuda(device)\n    cnn.train()\n\n    torch.save(cnn.state_dict(), \"./model_pgd_delayed_weak_3.pkl\")\n    print(\"[ Model Saved ]\")\n    test()\n\n    print('[ Model Initialization ]')\n    criterion = nn.MultiLabelSoftMarginLoss()\n    optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)\n    adversary = LinfPGDAttack(model=cnn)\n\n    print('[ Time Checking Start ]')\n    start_time = time.time()\n    train_dataloader = config.get_train_data_loader()\n\n    for epoch in range(num_epochs):\n        epoch_start_time = time.time()\n        for i, (images, labels) in enumerate(train_dataloader):\n            labels = Variable(labels.float()).to(device)\n\n            images_natural = Variable(images).to(device)\n            predict_labels = cnn(images_natural).to(device)\n            loss = criterion(predict_labels, labels)\n\n            if epoch >= 5:\n                images_adv = attack(images, labels, cnn, adversary)\n                images_adv_var = Variable(images_adv).to(device)\n                predict_labels = cnn(images_adv_var).to(device)\n                loss = (loss + criterion(predict_labels, labels)) / 2\n\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n            if (i + 1) % 10 == 0:\n                print(\"epoch:\", epoch, \"step:\", i, \"loss:\", loss.item())\n\n        torch.save(cnn.state_dict(), \"./model_pgd_delayed_weak_3.pkl\")\n        print(\"[ Model Saved ]\")\n        print(\"epoch:\", epoch, \"step:\", i, \"loss:\", loss.item())\n        print(\"Time Spent:\", time.time() - epoch_start_time)\n        test()\n\n    torch.save(cnn.state_dict(), \"./model_pgd_delayed_weak_3.pkl\")\n    print(\"[ Last Model Saved ]\")\n    print(\"Total Time\", time.time() - start_time)\n\nmain()\n","sub_path":"main_pgd_captcha_solver_delayed_weak_3.py","file_name":"main_pgd_captcha_solver_delayed_weak_3.py","file_ext":"py","file_size_in_byte":7456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"652299608","text":"'''\np.110 상하좌우\nspace_size만큼의 크기를 같는 정방행렬\n첫 좌표가 1,1일 때 최종 좌표 구하기\n'''\nspace_size = int(input())\ndirection = input().split()\nx, y = 1, 1\n\ndx = [0,1,0,-1]\ndy = [1,0,-1,0]\ndirection_types = [\"R\", \"D\", \"L\", \"U\"]\n# 움직이는 방향 리스트와 동일한 인덱스로 x, y 변화하는 값 인덱스\n\nfor d in direction:\n    for i in range(len(direction_types)):\n        if direction_types[i] == d:\n            nx = x + dx[i]\n            ny = y + dy[i]\n\n    if nx < 1 or nx > space_size or ny < 1 or ny > space_size:\n        continue\n        # 범위를 벗어날 경우 다시\n\n    x, y = nx, ny\n\nprint(x,y)\n","sub_path":"implementation/UDLR.py","file_name":"UDLR.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"120250445","text":"from PYTHON_MODULES.PYQT.PROJECTS.LoginSystem.email_file_dialog import *\r\n\r\nclass Editor(QMainWindow):\r\n    def __init__(self, parent=None):\r\n        super(Editor, self).__init__(parent)\r\n        self.parent = parent\r\n        self.setGeometry(0, 0, 1000, 800)\r\n        self.setWindowTitle(\"Editor\")\r\n\r\n        '''Coding the main menu.'''\r\n        self.main_menu = self.menuBar()\r\n\r\n        '''Coding the options within the main menu.'''\r\n        self.file_main_menu_option = self.main_menu.addMenu(\"&File\")  # file\r\n        self.edit_main_menu_option = self.main_menu.addMenu(\"&Edit\")  # edit\r\n        self.view_main_menu_option = self.main_menu.addMenu(\"&View\")  # view\r\n\r\n        self.create_blank_text_editor()\r\n        self.add_file_sub_menus()\r\n        self.move_to_center(\"\")\r\n\r\n        self.show()\r\n\r\n    \"\"\"\r\n    Coding the options under the File secondary menu.\r\n    \"\"\"\r\n\r\n    def add_file_sub_menus(self):\r\n        self.file_actions = []\r\n\r\n        '''Actions to create an option for a new file.'''\r\n        self.new_file_action = QAction(\"&New...\", self)\r\n        self.new_file_action.setShortcut(\"Ctrl+N\")\r\n        self.new_file_action.setStatusTip(\"New File\")\r\n        self.new_file_action.triggered.connect(self.create_blank_text_editor)\r\n        self.file_actions.append(self.new_file_action)\r\n\r\n        '''Actions to open a file.'''\r\n        self.open_file_action = QAction(\"&Open...\", self)\r\n        self.open_file_action.setShortcut(\"Ctrl+O\")\r\n        self.open_file_action.setStatusTip(\"Open File\")\r\n        self.open_file_action.triggered.connect(self.open_file)\r\n        self.file_actions.append(self.open_file_action)\r\n\r\n        '''Actions to save a file'''\r\n        self.save_file_action = QAction(\"&Save...\", self)\r\n        self.save_file_action.setShortcut(\"Ctrl+S\")\r\n        self.save_file_action.setStatusTip(\"Save File\")\r\n        self.save_file_action.triggered.connect(self.save_file)\r\n        self.file_actions.append(self.save_file_action)\r\n\r\n        '''Add action to email file'''\r\n        self.email_file_action = QAction(\"&Share\", self)\r\n        self.email_file_action.setStatusTip(\"Email Your Current File\")\r\n        self.file_actions.append(self.email_file_action)\r\n        self.email_file_action.triggered.connect(self.email_file)\r\n\r\n        for action in self.file_actions:\r\n            self.file_main_menu_option.addAction(action)\r\n\r\n    def email_file(self):\r\n        self.hide()\r\n        self.email_dialog = Email(self)\r\n\r\n    \"\"\"\r\n    creates a new text editor with zero input\r\n    \"\"\"\r\n\r\n    def create_blank_text_editor(self):\r\n        self.text_editor = QTextEdit()\r\n        self.text_editor.clear()\r\n        self.setCentralWidget(self.text_editor)\r\n\r\n    \"\"\"\r\n    Moves the current window to the center of the screen.\r\n    \"\"\"\r\n\r\n    def move_to_center(self, screen):\r\n        if screen == \"\":\r\n            screen = self\r\n        resolution = QDesktopWidget().screenGeometry()\r\n        screen.move(resolution.width() / 2 - screen.frameSize().width() / 2,\r\n                    resolution.height() / 2 - screen.frameSize().height() / 2)\r\n\r\n    \"\"\"\r\n    Saves the current state of the text editor to a file and location of the user's choice.\r\n    \"\"\"\r\n\r\n    def save_file(self):\r\n        name = QFileDialog.getSaveFileName(self, \"Save File\")\r\n        try:\r\n            file = open(name, 'w')\r\n            text = self.text_editor.toPlainText()\r\n            file.write(text)\r\n            file.close()\r\n        except:\r\n            pass\r\n\r\n    \"\"\"\r\n    Open's a user-specified file from the computer.\r\n    \"\"\"\r\n\r\n    def open_file(self):\r\n        try:\r\n            name = QFileDialog.getOpenFileName(self, \"Open File\")\r\n            file = open(name, 'r')\r\n            self.create_blank_text_editor()\r\n            with file:\r\n                text = file.read()\r\n                self.text_editor.setText(text)\r\n        except:\r\n            pass\r\n\r\n    \"\"\"\r\n    Returns the current character length of the text editor.\r\n    \"\"\"\r\n\r\n    def get_editor_character_length(self):\r\n        my_text = self.text_editor.toPlainText()\r\n        return len(my_text)\r\n\r\n    def closeEvent(self, event):\r\n        event.accept()\r\n","sub_path":"LoginSystem/editor.py","file_name":"editor.py","file_ext":"py","file_size_in_byte":4152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"82716293","text":"from __future__ import division\n\nimport time\nfrom collections import Iterable\n\nfrom ignite._utils import _to_hours_mins_secs, to_variable\nfrom ignite.engine import Engine, Events\n\n__all__ = [\"Trainer\", \"create_supervised_trainer\"]\n\n\nclass Trainer(Engine):\n    \"\"\"\n    Generic trainer class.\n\n    Training update and validation functions receive batches of data and return values which will\n    be stored in the `training_history` and `validation_history`. The trainer defines multiple\n    events in `TrainingEvents` for which the user can attach event handlers to. The events get\n    passed the trainer, so they can access the training/validation history\n\n\n    Parameters\n    ----------\n    training_update_function : callable\n        Update function receiving the current training batch in each iteration\n    \"\"\"\n\n    def __init__(self, training_update_function):\n        super(Trainer, self).__init__(training_update_function)\n        self.current_epoch = 0\n        self.max_epochs = 0\n\n    def _train_one_epoch(self, training_data):\n        hours, mins, secs = self._run_once_on_dataset(training_data)\n        self._logger.info(\"Epoch[%s] Complete. Time taken: %02d:%02d:%02d\", self.current_epoch, hours,\n                          mins, secs)\n\n    def run(self, training_data, max_epochs=1):\n        \"\"\"\n        Train the model, evaluate the validation set and update best parameters if the validation loss\n        improves.\n        In the event that the validation set is not run (or doesn't exist), the training loss is used\n        to update the best parameters.\n\n        Parameters\n        ----------\n        training_data : Iterable\n            Collection of training batches allowing repeated iteration (e.g., list or DataLoader)\n        max_epochs: int, optional\n            max epochs to train for [default=1]\n\n        Returns\n        -------\n        None\n        \"\"\"\n        self.dataloader = training_data\n        self.current_iteration = 0\n        self.current_epoch = 0\n\n        try:\n            self._logger.info(\"Training starting with max_epochs={}\".format(max_epochs))\n\n            self.max_epochs = max_epochs\n\n            start_time = time.time()\n\n            self._fire_event(Events.STARTED)\n            while self.current_epoch < max_epochs and not self.should_terminate:\n                self.current_epoch += 1\n                self._fire_event(Events.EPOCH_STARTED)\n                self._train_one_epoch(training_data)\n                if self.should_terminate:\n                    break\n                self._fire_event(Events.EPOCH_COMPLETED)\n\n            self._fire_event(Events.COMPLETED)\n            time_taken = time.time() - start_time\n            hours, mins, secs = _to_hours_mins_secs(time_taken)\n            self._logger.info(\"Training complete. Time taken %02d:%02d:%02d\" % (hours, mins, secs))\n\n        except BaseException as e:\n            self._logger.error(\"Training is terminating due to exception: %s\", str(e))\n            self._handle_exception(e)\n\n\ndef create_supervised_trainer(model, optimizer, loss_fn, cuda=False):\n    \"\"\"\n    Factory function for creating a trainer for supervised models\n\n    Args:\n        model (torch.nn.Module): the model to train\n        optimizer (torch.optim.Optimizer): the optimizer to use\n        loss_fn (torch.nn loss function): the loss function to use\n        cuda (bool, optional): whether or not to transfer batch to GPU (default: False)\n\n    Returns:\n        Trainer: a trainer instance with supervised update function\n    \"\"\"\n\n    def _prepare_batch(batch):\n        x, y = batch\n        x = to_variable(x, cuda=cuda)\n        y = to_variable(y, cuda=cuda)\n        return x, y\n\n    def _update(batch):\n        model.train()\n        optimizer.zero_grad()\n        x, y = _prepare_batch(batch)\n        y_pred = model(x)\n        loss = loss_fn(y_pred, y)\n        loss.backward()\n        optimizer.step()\n        return loss.data.cpu()[0]\n\n    return Trainer(_update)\n","sub_path":"ignite/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":3944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"278453674","text":"import discord\nfrom discord.ext import commands\nimport random\nimport asyncio\nimport time\nfrom riotwatcher import RiotWatcher\nfrom riotwatcher import LoLException, error_404, error_429\nfrom returnStats import returnStats\n\nw = RiotWatcher('RIOT TOKENZ')\n\nprint(\"CAN MAKE RIOT API CALLS: \", end=\"\")\nprint(w.can_make_request())\n\nstarttime = time.time()\n\ndescription = '''I am Faker'''\nbot = commands.Bot(command_prefix=['faker ', 'Faker '], description=description)\n\n@bot.event\nasync def on_ready():\n    print('Logged in as')\n    print(bot.user.name)\n    print(bot.user.id)\n    print('------')\n    await bot.change_status(game=discord.Game(name=\"League of Stats\"))\n\n@bot.command()\nasync def player(username : str):\n    \"\"\"Get league info about player\"\"\"\n    try:\n        player = w.get_summoner(name=username)\n        imageUrl = (\"http://ddragon.leagueoflegends.com/cdn/6.9.1/img/profileicon/\" + str(player['profileIconId']) + \".png\")\n        name = player['name']\n        level = player['summonerLevel']\n        if(level == 30):\n\n            rank = w.get_league_entry([str(player['id'])])['5'][0]['tier']\n            await bot.say(\"Player Name: `\" + str(name) + \"` Level: `\" + str(level) + \"` Rank: `\" + rank + \"`\")\n        else:\n            await bot.say(\"Player Name: `\" + str(name) + \"` Level: `\" + str(level) + \"`\")\n        stats = w.get_stat_summary(player['id'])['playerStatSummaries']\n        await bot.say(returnStats(stats))\n\n    except LoLException as e:\n        if e == error_429:\n            await bot.say('Retry in {} seconds.'.format(e.headers['Retry-After']))\n        elif e == error_404:\n            await bot.say('Summoner not found.')\n\n@bot.command()\nasync def icon(username : str):\n    \"\"\"Get icon of league player by username\"\"\"\n    player = w.get_summoner(name=username)\n    imageUrl = (\"http://ddragon.leagueoflegends.com/cdn/6.9.1/img/profileicon/\" + str(player['profileIconId']) + \".png\")\n    await bot.say(\"Icon for `\" + player['name'] + '` ' + imageUrl)\n\n@bot.command()\nasync def uptime():\n    \"\"\"Gets current uptime of bot\"\"\"\n    seconds = time.time()-starttime\n    m, s = divmod(seconds, 60)\n    h, m = divmod(m, 60)\n    d, h = divmod(h, 24)\n    formatted = \"%d Days %02d Hours %02d Minutes %02d Seconds\" % (d, h, m, s)\n    await bot.say(\"`Uptime: \" + formatted + \"`\")\n\n@bot.command(pass_context=True, hidden=True)\nasync def say(ctx, phrase: str, chan : discord.Channel):\n    \"\"\"[ADMIN] ONLY\"\"\"\n    message = ctx.message\n    author = message.author\n    if any(role.name == \"ADMIN\" for role in author.roles):\n        #await bot.say(phrase)\n        await bot.send_message(chan, phrase)\n\n@bot.command()\nasync def ping():\n    \"\"\"ping pong ping pong\"\"\"\n    start = time.time()\n    message = await bot.say(\"pong\")\n    stop = time.time()\n    diff = (stop - start) * 1000\n    await bot.edit_message(message, (\"pong `{:.3f}ms`\".format(diff)))\n\n@bot.command()\nasync def joined(member : discord.Member):\n    \"\"\"Says when a member joined.\"\"\"\n    await bot.say('{0.name} joined on {0.joined_at}'.format(member))\n\nbot.run('DISCORD TOKENZ')\n","sub_path":"fakerbot.py","file_name":"fakerbot.py","file_ext":"py","file_size_in_byte":3051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"32097149","text":"#-*- coding:utf-8 -*-\n\n#定义一个借呗用户\nclass Worker:\n    def __init__(self, name, pay):\n        self.name = name\n        self.pay = pay\n\n    def lastName(self):\n        #按照空格进行切割，返回最后一位\n        return self.name.split()[-1]\n\n    def giveRaise(self, percent):\n        self.pay  *= (1 + percent)\n\n\njohn = Worker('john smith',600)\nkate = Worker('cary kate',300)\nprint(john.lastName())\njohn.giveRaise(.2)\nprint(john.pay)\n\nlist1 = [1,2,3,4]\nset1 = set(list1)\nset1[1] = 33\nprint(set1)\n\n","sub_path":"learn_python/chapter4/4.5_class.py","file_name":"4.5_class.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}
+{"seq_id":"526571047","text":"class Solution:\n    # @param A, a list of integer\n    # @return an integer\n    def singleNumber(self, A):\n        dups=dict()\n        n=len(A)\n        for i in range(n):\n            if A[i] not in dups:\n                dups[A[i]]=1\n            else:\n                dups[A[i]]=dups[A[i]]+1\n        for p in dups.keys():\n            if dups[p]<2:\n                return p\n\nclass Solution:\n    # @param A, a list of integer\n    # @return an integer\n    def singleNumber(self, A):\n        result=0\n        n=len(A)\n        for i in range(n):\n            result=result^A[i]\n        return result","sub_path":"singlenum.py","file_name":"singlenum.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"73"}